xvEPA
Emrironimnfal Ftoteciwi
Regulatory Impact Analysis for the Industrial
Boilers and Process Heaters NESHAP
Final Report
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EPA-452/R-04-002
February 2004
Regulatory Impact Analysis for the Industrial Boilers and Process Heaters NESHAP
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Strategies and Standards Division
Innovative Strategies and Economics Group
Research Triangle Park, NC
vin
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CONTENTS
Section Page
1 Introduction 1-1
1.1 Agency Requirements for an EIA 1-1
1.2 Scope and Purpose 1-2
1.3 Organization of the Report 1-3
2 Boiler and Process Heater Technologies 2-1
2.1 Characteristics of Steam 2-2
2.2 Fossil-Fuel Boiler Characterization 2-4
2.2.1 Industrial, Commercial, and Institutional Boilers 2-5
2.2.2 Heat Transfer Configurations 2-5
2.2.3 Major Design Types 2-6
2.2.3.1 Stoker-Fired Boilers (Coal) 2-6
2.2.3.2 Pulverized Coal Boilers (Coal) 2-6
2.2.3.3 Fluidized Bed Combustion (FBC) Boilers (Coal) 2-7
2.2.3.4 Tangentially Fired Boilers (Coal, Oil, Natural Gas) 2-7
2.2.3.5 Wall-fired Boilers (Coal, Oil, Natural Gas) 2-8
2.3 Process Heater Characterization 2-8
2.3.1 Classes of Process Heaters 2-8
2.3.2 Major Design Types 2-9
2.3.2.1 Combustion Chamber Set-Ups 2-10
2.3.2.2 Combustion Air Supply 2-10
2.3.2.3 Tube Configurations 2-11
2.3.2.4 Burners 2-12
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Profile of Affected Units and Facilities and Compliance Costs 3-1
3.1 Profile of Existing Boiler and Process Heaters Units 3-1
3.1.1 Distribution of Existing Boilers and Facilities by Industry 3-2
3.1.2 Technical Characteristics of Existing Boilers 3-2
3.1.2.1 Final Rule 3-2
3.2 Methodology for Estimating Cost Impacts 3-5
3.3 Projection of New Boilers and Process Heaters 3-14
3.4 National Engineering Population, Cost Estimates, and
Cost-Effectiveness Estimates 3-15
Profiles of Affected Industries 4-1
4.1 Textile Mill Products (SIC 22/NAICS 313) 4-1
4.2 Lumber and Wood Products (SIC 24/NAICS 321) 4-1
4.2.1 Supply Side of the Industry 4-2
4.2.1.1 Production Processes 4-2
4.2.1.2 Types of Output 4-4
4.2.1.3 Major By-Products and Co-Products 4-4
4.2.1.4 Costs of Production 4-4
4.2.1.5 Capacity Utilization 4-5
4.2.2 Demand Side of the Industry 4-5
4.2.3 Product Characteristics 4-6
4.2.4 Uses and Consumers of Outputs 4-6
4.2.5 Organization of the Industry 4-6
4.2.6 Markets and Trends 4-9
4.3 Furniture and Related Product Manufacturing (SIC 25/NAICS 337) 4-9
4.4 Paper and Allied Products (SIC 26/NAICS 322) 4-10
4.4.1 Supply Side of the Industry 4-11
4.4.1.1 Production Process 4-11
4.4.1.2 Types of Output 4-12
4.4.1.3 Major By-Products and Co-Products 4-12
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4.4.1.4 Costs of Production 4-13
4.4.1.5 Capacity Utilization 4-13
4.4.2 Demand Side of the Industry 4-14
4.4.2.1 Product Characteristics 4-14
4.4.2.2 Uses and Consumers of Products 4-14
4.4.3 Organization of the Industry 4-14
4.4.4 Markets and Trends 4-16
4.5 Medicinal Chemicals and Botanical Products and Pharmaceutical Preparations
(SICs 2833, 2834/NAICS 32451) 4-16
4.5.1 Supply Side of the Industry 4-17
4.5.1.1 Production Processes 4-17
4.5.1.2 Types of Output 4-18
4.5.1.3 Major By-Products and Co-Products 4-18
4.5.1.4 Costs of Production 4-18
4.5.1.5 Capacity Utilization 4-20
4.5.2 Demand Side of the Industry 4-20
4.5.3 Organization of the Industry 4-21
4.5.4 Markets and Trends 4-23
4.6 Industrial Organic Chemicals Industry (SIC 2869/NAICS 3251) 4-24
4.6.1 Supply Side of the Industry 4-24
4.6.1.1 Production Processes 4-24
4.6.1.2 Types of Output 4-25
4.6.1.3 Major By-Products and Co-Products 4-26
4.6.1.4 Costs of Production 4-26
4.6.1.5 Capacity Utilization 4-26
4.6.2 Demand Side of the Industry 4-28
4.6.3 Organization of the Industry 4-28
4.6.4 Markets and Trends 4-28
4.7 Electric Services (SIC 4911/NAICS 22111) 4-28
4.7.1 Electricity Production 4-29
4.7.1.1 Generation 4-31
4.7.1.2 Transmission 4-32
4.7.1.3 Distribution 4-32
4.7.2 Cost of Production 4-32
4.7.3 Organization of the Industry 4-33
XI
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4.7.3.1 Utilities 4-34
4.7.3.2 Nonutilities 4-36
4.7.4 Demand Side of the Industry 4-36
4.7.4.1 Electricity Consumption 4-36
4.7.4.2 Trends in the Electricity Market 4-38
Economic Analysis Methodology 5-1
5.1 Background on Economic Modeling Approaches 5-1
5.1.1 Modeling Dimension 1: Scope of Economic
Decisionmaking 5-2
5.1.2 Modeling Dimension 2: Interaction Between
Economic Sectors 5-3
5.2 Selected Modeling Approach for Boilers and Process
Heaters Analysis 5-4
5.2.1 Directly Affected Markets 5-5
5.2.1.1 Electricity Market 5-7
5.2.1.2 Petroleum Market 5-7
5.2.1.3 Goods and Services Markets: Agriculture, Manufacturing,
Mining, Commercial, and
Transportation 5-8
5.2.2 Indirectly Affected Markets 5-11
5.2.2.1 Market for Coal 5-11
5.2.2.2 Natural Gas Market 5-11
5.2.2.3 Goods and Services Markets 5-12
5.2.2.4 Impact on Residential Sector 5-12
5.3 Operationalizing the Economic Impact Model 5-12
5.3.1 Computer Model 5-14
5.3.2 Calculating Changes in Social Welfare 5-17
Economic Impact Analysis Results 6-1
6.1 Social Cost Estimates 6-1
6.2 National Market-Level Impacts 6-2
6.3 Executive Order 13211 (Energy Effects) 6-5
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6.4 Conclusions 6-6
7 Small Entity Impacts 7-1
7.1 Background on Small Entity Screenings 7-1
7.2 Identifying Small Entities 7-2
7.3 Analysis of Facility-Level and Parent-Level Data 7-3
7.4 Small Entity Impacts 7-7
7.5 Affected Government Entities 7-7
7.6 Assessment of SBREFA Screening 7-11
Emissions Inventories and Air Quality Changes 8-1
8.1 Results in Brief 8-1
8.2 Introduction 8-1
8.3 Baseline Emissions 8-2
8.3.1 EPA's Baseline Inventory 8-2
8.3.2 The MACT Floor 8-3
8.4 Air Quality Impacts 8-6
8.4.1 EPA's Baseline Inventory 8-6
8.4.2 EPA's Baseline Inventory 8-7
8.4.2.1 MACT Floor Option 8-7
8.4.3 Visibility Degradation Estimates 8-11
8.4.4 Residential Visibility Improvements 8-12
8.4.5 Recreational Visibility Improvements 8-13
9 Qualitative Assessment of Benefits of Emission Reductions 9-1
9.1 Identification of Potential Benefit Categories 9-1
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9.2 Qualitative Description of Air Related Benefits 9-1
9.2.1 Benefits of Reducing HAP Emissions 9-1
9.2.1.1 Health Benefits of HAP Reductions 9-2
9.2.1.2 Welfare Benefits of HAP Reductions 9-6
9.2.2 Benefits of Reducing Other Pollutants Due to HAP Controls 9-7
9.2.2.1 Benefits of Particulate Matter Reductions 9-7
9.2.2.2 Benefits of Sulfur Dioxide Reductions 9-8
9.3 Lack of Approved Methods to Quantify HAP Benefits 9-8
9.4 Summary 9-9
10 Quantified Benefits 10-1
10.1 Results in Brief 10-1
10.2 Introduction 10-2
10.3 Overview of Benefits Analysis Methodology 10-3
10.3.1 Methods for Estimating Benefits from Air Quality Improvements
10-6
10.3.2 Methods for Describing Uncertainty 10-8
10.4 Phase One Analysis: Modeled Air Quality Change and Health Effects Resulting
from a Portion of Emission Reductions at Boiler and Process Heater Sources ....
10-12
10.4.1 Quantifying Individual Health Effect Endpoints 10-15
10.4.1.1 Concentration-Response Functions for Premature Mortality 10-16
10.4.2 Valuing Individual Health Effect Endpoints 10-21
10.4.2.1 Valuation of Reductions in Premature Mortality
Risk 10-25
10.4.2.2 Valuation of Reductions in Chronic Bronchitis 10-28
10.4.3 Results of Phase One Analysis: Benefits Resulting from a Portion of
Emission Reductions at Boiler and Process Heater Sources 10-29
10.5 Phase Two Analysis: Benefit Transfer Valuation of Remaining
Emission Reductions 10-33
10.5.1 SO2 Benefits Transfer Values 10-34
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10.5.2 PM Benefits Transfer Values 10-36
10.5.3 Application of Benefits Transfer Values to Phase Two
Emission Reductions 10-40
10.6 Total Benefits of the Industrial Boilers/Process Heaters NESHAP 10-43
10.7 Limitations of the Analysis 10-47
10.7.1 Uncertainties and Assumptions 10-47
10.7.2 Unqualified Effects 10-48
10.8 Benefit-Cost Comparisons 10-50
References R-l
Appendix A Estimating Economic Impacts in Markets Affected by the
Boilers and Process Heaters MACT A-l
Appendix B Assumptions and Sensitivity Analysis B-l
Appendix C Air Quality Changes for the Above-the-Floor Option (Option
1A) C-l
Appendix D Derivation of Quantified Benefits D-l
Appendix E Impacts Based on Low-Risk Threshold Cutoffs for Hydrochloric Acid and
Manganese E-l
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LIST OF FIGURES
Number Page
2-1 Generating Electricity: Steam Turbines 2-4
3-1 Characteristics of Units Affected 3-5
4-1 Traditional Electric Power Industry Structure 4-30
4-2 Utility and Nonutility Generation and Shares by Class, 1985 and 1995 4-35
4-3 Annual Electricity Sales by Sector 4-38
5-1 Links Between Energy and Goods and Services Markets 5-6
5-2 Market Effects of Regulation-Induced Costs 5-8
5-3 Fuel Market Interactions with Facility-Level Production Decisions 5-10
5-4 Operationalizing the Estimation of Economic Impact 5-13
5-5 Changes in Economic Welfare with Regulation 5-18
7-1 Parent Size by Employment Range 7-6
7-2 Number of Parents by Sales Range 7-7
8-1 Recreational Visibility Regions for Continental U.S 8-15
10-1 Steps in Phase One of the Benefits Analysis for the Industrial Boilers/Process
Heaters NESHAP 10-13
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LIST OF TABLES
Number Page
3-1 Units and Facilities Affected by the Final Rule by Industry 3-3
3-2 Testing and Monitoring Costs for Units Covered 3-11
3-3 Cost Effectiveness (C/E) of Industrial Boiler and Process Heater MACT
on Existing Units and Subcategories 3-13
3-4 New Unit Projections by Industry, MACT Floor 3-16
3-5 Unit Cost and Population Estimates for the Final Rule by Industry, 2005 3-18
3-6 Unit Cost and Population Estimates for the Option 1A Above-the-Floor Alternative by
Industry, 2005 3-21
4-1 Lumber and Wood Products Markets Likely to Be Affected by the
Regulation 4-2
4-2 Value of Shipments for the Lumber and Wood Products Industry
(SIC 24/NAICS 321), 1987-1996 4-3
4-3 Inputs for the Lumber and Wood Products Industry (SIC 24/NAICS 321), 1987-1996 4-5
4-4 Capacity Utilization Ratios for Lumber and Wood Products Industry,
1991-1996 4-6
4-5 Size of Establishments and Value of Shipments for the Lumber and Wood Products
Industry (SIC 24/NAICS 321) 4-7
4-6 Measures of Market Concentration for Lumber and Wood Products
Markets 4-8
4-7 Paper and Allied Products Industry Markets Likely to Be Affected
by Regulation 4-10
4-8 Value of Shipments for the Paper and Allied Products Industry
(SIC 26/NAICS 322), 1987-1996 4-11
4-9 Inputs for the Paper and Allied Products Industry (SIC 26/NAICS 322), 1987-1996 4-13
4-10 Capacity Utilization Ratios for the Paper and Allied Products Industry, 1991-1996 4-14
4-11 Size of Establishments and Value of Shipments for the Paper and Allied
Products Industry (SIC 26/NAICS 322) 4-15
4-12 Measurements of Market Concentration for Paper and Allied Products
Markets 4-16
4-13 Value of Shipments for the Medicinals and Botanicals and Pharmaceutical Preparations
Industries, 1987-1996 4-17
4-14 Inputs for Medicinal Chemicals and Botanical Products Industry (SIC 2833/NAICS
32451), 1987-1996 4-19
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4-15 Inputs for the Pharmaceutical Preparations Industry
(SIC 2834/NAICS 32451), 1987-1996 4-20
4-16 Capacity Utilization Ratios for the Medicinal Chemicals and Botanical
Products (SIC 2833/NAICS 32451) and Pharmaceutical Preparations
(SIC 2834/NAICS 32451) Industries, 1991-1996 4-21
4-17 Size of Establishments and Value of Shipments for the Medicinal
Chemicals and Botanical Products (SIC 2833/NAICS 32451) and
Pharmaceutical Preparations (SIC 2834/NAICS 32451) Industries 4-22
4-18 Measures of Market Concentration for the Medicinal Chemicals and
Botanical Products (SIC 2833/NAICS 32451) and Pharmaceutical
Preparations (SIC 2834/NAICS 32451) Industries 4-23
4-19 Value of Shipments for the Industrial Organic Chemicals, N.E.C. Industry
(SIC 2869/NAICS 3251), 1987-1996 4-25
4-20 Inputs for the Industrial Organic Chemicals Industry
(SIC 2869/NAICS 3251), 1987-1996 4-27
4-21 Capacity Utilization Ratios for the Industrial Organic Chemicals
Industry (SIC 2869/NAICS 3251), 1991-1996 4-27
4-22 Size of Establishments and Value of Shipments for the Industrial Organic Chemicals
Industry (SIC 2869/NAICS 3251) 4-29
4-23 Net Generation by Energy Source, 1995 4-31
4-24 Total Expenditures in 1996 ($103) 4-33
4-25 Number of Electricity Suppliers in 1999 4-34
4-26 U.S. Electric Utility Retail Sales of Electricity by Sector, 1989 Through
1998 (106 kWh) 4-37
4-27 Key Parameters in the Cases 4-39
5-1 Comparison of Modeling Approaches 5-2
5-2 Supply and Demand Elasticities 5-15
5-3 Fuel Price Elasticities 5-17
6-1 Social Cost Estimates ($1998 106): Final Rule 6-1
6-2 Distribution of Social Costs by Sector/Market: Final Rule ($1998 106) 6-3
6-3 Market-Level Impacts 6-4
7-1 Summary of Small Entity Impacts 7-1
7-2 Facility-Level and Parent-Level Data by Industry 7-4
7-3 Small Parent Entities by Industry 7-8
7-4 Summary Statistics for SBREFA Screening Analysis 7-10
7-5 Regional Distribution of Municipal Systems 7-11
7-6 Selected Municipal Utilities' Capacity, Usage, and Consumer Types 7-12
7-7 Supplemental Screening Analysis for Small Governmental Jurisdictions 7-13
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7-8 Profit Margins for Industry Sectors with Affected Small Businesses 7-14
8-1 Summary of Nationwide Baseline Emissions and Emission Reductions for
the MACT Floor, Existing Units Only in 2005 8-4
8-2 HAP Emission Reductions for the MACT Floor Option, 2005 Existing Units
Only 8-6
8-3 Summary of 2005 Base Case PM Air Quality and Changes Due to MACT
Floor Option: Industrial Boiler/Process Heater Source Categories 8-8
8-4 Distribution of PM2.5 Air Quality Improvements Over 2005 Population Due to MACT
Floor Option: Industrial Boiler/Process Heater Source Categories 8-9
8-5 Summary of Absolute and Relative Changes in PM Air Quality Due to MACT
Floor Option: Industrial Boiler/Process Heater Source Categories 8-10
8-6 Distribution of Populations Experiencing Visibility Improvements in 2005 Due to MACT
Floor Option: Industrial Boiler/Process Heater Source Categories 8-12
8-7 Summary of 2005 Baseline Visibility and Changes by Region for to MACT
Floor Option: Residential 8-13
8-8 Summary of 2005 Baseline Visibility and Changes by Region for to MACT
Floor Option: Recreational 8-14
10-1 Summary of Results: Estimated PM-Related Benefits of the Industrial Boilers and
Process Heaters NESHAP 10-2
10-2 Estimate of Emission Reductions for Phases One and Two of the
Benefit Analysis 10-5
10-3 Primary Sources of Uncertainty in the Benefit Analysis 10-11
10-4 PM-Related Health Outcomes and Studies Included in the Base Analysis 10-18
10-5 Unit Values Used for Economic Valuation of Health Endpoints 10-22
10-6 Phase One Analysis: Base Estimate of Annual Benefits Associated with Approximately
50% of the Emission Reductions from the Industrial Boilers/Process Heaters NESHAP -
MACT Floor Regulatory Option in 2005, Using Air Quality Modeling & the CAPMS
Benefit Model 10-30
10-7 Phase One Analysis: Base Estimate for Annual Benefits Associated with Approximately
50% of the Emission Reductions from the Industrial Boilers/Process Heaters NESHAP -
Above the MACT Floor Regulatory Option in 2005, Using Air Quality Modeling & the
CAPMS Benefit Model 10-32
10-7 Boilers/Process Heaters NESHAP - Above the MACT Floor Regulatory Option in 2005,
Using Air Quality Modeling & the CAPMS Benefit Model 10-39
10-8 SO2 Benefit Transfer Values Based on Data From Phase One Analysis- MACT Floor
Regulatory Option 10-35
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10-9 SO2 Benefit Transfer Values Based on Data From Phase One Analysis- Above the MACT
Floor Regulatory Option 10-36
10-10 PM Benefit Transfer Values Based on Data From Phase One Analysis- MACT Floor
Regulatory Option 10-38
10-11 PM Benefit Transfer Values Based on Data From Phase One Analysis- MACT Floor
Regulatory Option 10-39
10-12 Phase Two Analysis: Base Estimate of Annual Health Benefits Associated with Non-
Inventory Emission Reductions of the Industrial Boilers/Process Heaters
NESHAP - MACT Floor Regulatory Option in 2005, Using Benefit Transfer
Values 10-41
10-13 Phase Two Analysis: Base Estimate of Annual Health Benefits Associated with Non-
Inventory Emission Reductions of the Industrial Boilers/Process Heaters
NESHAP - Above the Floor MACT Floor Regulatory Option in 2005, Using Benefit
Transfer Values 10-42
10-14 Total Annual Benefits of the Industrial Boilers/Process Heaters
NESHAP - MACT Floor Regulatory Option 10-45
10-15 Total Annual Benefits of the Industrial Boilers/Process Heaters
NESHAP - Above the MACT Floor Regulatory Option 10-46
10-16 Significant Uncertainties and Biases Associated with the Industrial Boilers/Process
Heaters Benefit Analysis 10-48
10-17 Unqualified Benefit Categories 10-49
10-18 Annual Net Benefits of the Industrial Boilers and Process Heaters NESHAP in 2005 . . .
10-53
C-l Summary of Nationwide Baseline Emissions and Emission Reductions3 for Option 1A
(in tons/year), Existing Units Onlyv in 2005 C-l
C-2 HAP Emission Reductions for Option 1A, 2005 Existing Units
Only C-2
C-3 Summary of 2005 Base Case PM Air Quality and Changes Due to MACT Above-the-
Floor Option: Industrial Boiler/Process Heater
Source Categories C-3
C-4 Distribution of PM2.5 Air Quality Improvements Over 2005 Population Due to MACT
Above-the-Floor Option: Industrial Boiler/Process Heater Source Categories
C-4
C-5 Summary of Absolute and Relative Changes in PM Air Quality Due to MACT
Above-the-Floor Option: Industrial Boiler/Process Heater Source Categories C-5
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C-6 Distribution of Populations Experiencing Visibility Improvements in 2005 Due to MACT
Above-the-Floor Option: Industrial Boiler/Process Heater Source Categories
C-6
C-7 Summary of 2005 Baseline Visibility and Changes by Region Due to MACT Above-the-
Floor Option: Residential (Average Annual Deciviews) C-7
C-8 Summary of 2005 Baseline Visibility and Changes by Region for MACT Above-the-
Floor Option: Recreational (Average Annual Deciviews) C-8
D-l(a) Results of Air Quality and Benefit Analyses for the Phase
One Analysis of the Industrial Boilers/Process Heaters NESHAP MACT Floor
in 2005 (SO2 Reductions Only) D-4
D-l(b) Results of Air Quality and Benefit Analyses for the Phase
Two Analysis of the Industrial Boilers/Process Heaters NESHAP MACT Floor
in 2005 (SO2 Reductions Only) D-5
D-2(a) Results of Air Quality and Benefit Analyses for the Phase
One Analysis of the Industrial Boilers/Process Heaters NESHAP MACT Floor
in 2005 (PM Reductions Only) D-6
D-2(b) Results of Air Quality and Benefit Analyses for the Phase
Two Analysis of the Industrial Boilers/Process Heaters NESHAP MACT Floor
in 2005 (PM Reductions Only) D-7
D-3(a) Results of Air Quality and Benefit Analyses for the Phase
One Analysis of the Industrial Boilers/Process Heaters NESHAP MACT Floor
in 2005 (PM and SO2 Reductions Only) D-8
D-3(b) Results of Air Quality and Benefit Analyses for the Phase
Two Analysis of the Industrial Boilers/Process Heaters NESHAP MACT Floor
in 2005 (PM and SO2 Reductions Only) D-9
D-4 Total Benefits of the Industrial Boilers/Process Heaters NESHAP - MACT Floor in 2005
(Combined Estimates of Reduced Incidences and Monetized Benefits from Phase One
and Two Analyses) D-10
D-5(a) Results of Air Quality and Benefit Analyses for the Phase
One Analysis of the Industrial Boilers/Process Heaters NESHAP Above the MACT Floor
in 2005 (SO2 Reductions Only) D-l 1
D-5(b) Results of Air Quality and Benefit Analyses for the Phase
Two Analysis of the Industrial Boilers/Process Heaters NESHAP Above the MACT
Floor in 2005 (SO2 Reductions Only) D-12
D-6(a) Results of Air Quality and Benefit Analyses for the Phase
One Analysis of the Industrial Boilers/Process Heaters NESHAP Above the MACT Floor
in 2005 (PM Reductions Only) D-13
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D-6(b) Results of Air Quality and Benefit Analyses for the Phase
Two Analysis of the Industrial Boilers/Process Heaters NESHAP Above the MACT
Floor in 2005 (PM Reductions Only) D-14
D-7(a) Results of Air Quality and Benefit Analyses for the Phase
One Analysis of the Industrial Boilers/Process Heaters NESHAP Above the MACT Floor
in 2005 (PM and SO2 Reductions Only) D-15
D-7(b) Results of Air Quality and Benefit Analyses for the Phase
Two Analysis of the Industrial Boilers/Process Heaters NESHAP Above the MACT
Floor in 2005 (PM and SO2 Reductions .Only) D-16
D-8 Total Benefits of the Industrial Boilers/Process Heaters NESHAP - Above the MACT
Floor in 2005 (Combined Estimates of Reduced Incidences and Monetized Benefits from
Phase One and Two Analyses) D-17
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Select List of Acronyms and Abbreviations
BOC - Bureau of Census
CAA - Clean Air Act
COPD - Chronic Obstructive Pulmonary Disease
dv - Deciview
DOC - Department of Commerce
DOE - Department of Energy
EIA - Energy Information Administration
EO - Executive Order
EPA - Environmental Protection Agency
FERC- Federal Energy Regulatory Commission
F£AP - Hazardous Air Pollutant
ICI - Industrial/Commercial/Institutional
ICR - Information Collection Request
Ib - Pound
LDs - Loss Days
LRS - Lower Respiratory Symptoms
MACT - Maximum Achievable Control Technology
mmBtu- million British Thermal Units
NAAQS - National Ambient Air Quality Standards
NAICS - North American Industrial Classification System
NESHAP - National Emission Standards for Hazardous Air Pollutants
NPR - Notice of Proposed Rulemaking
NSPS - New Source Performance Standards
NSR - New Source Review
OMB - Office of Management and Budget
O&M - Operation and Maintenance
PM - Particulate Matter
ppbdv - Parts Per Billion, dry volume
ppm - Parts Per Million
PRA - Paperwork Reduction Act of 1995
RIA - Regulatory Impact Analysis
RFA - Regulatory Flexibility Act
SAB - Science Advisory Board
SBA - Small Business Administration
SBREFA - Small Business Regulatory Enforcement Fairness Act of 1996
SIC - Standard Industrial Classification
SO2 - Sulfur Dioxide
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TAG - Total Annual Cost
tpd - Tons Per Day
tpy - Tons Per Year
UMRA - Unfunded Mandates Reform Act
URS - Upper Respiratory Symptoms
VSL - Value of Statistical Life
VOCs - Volatile Organic Compounds
WLDs - Work Loss Days
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EXECUTIVE SUMMARY
EPA is issuing a rule to reduce hazardous air pollutant (HAPs) emissions from existing and
new industrial boilers and process heaters that are major sources. This rule is a National Emission
Standards for Hazardous Air Pollutants (NESHAP), and will reduce HAP emissions by requiring
affected industrial boilers and process heaters to meet emissions limits in order to comply with the
Maximum Achievable Control Technology (MACT) floor for these sources. This MACT floor level
of control is the minimum level these sources must meet to comply with the rule. The major HAPs
whose emissions will be reduced are hydrochloric acid, hydrofluoric acid, arsenic, beryllium,
cadmium, and nickel. The rule will also lead to emission reductions of other pollutants such as
particulate matter (PM10 and PM25), sulfur dioxide (SO2), and mercury (Hg).
The rule requires emissions reductions necessary to meet the MACT by having affected
existing sources comply with emissions limits defined in terms of pound per mmBTU heat input of
emissions rate for each HAP. For new sources, the definition for emissions limits is based on the
source using the most stringent control technology for reduction of each HAP.
The rule is expected to reduce HAP emissions from existing sources by about 59,000 tons per
year by 2005. Of this amount, roughly 43,000 tons is hydrochloric acid, and there is 1,100 tons in
reductions of heavy metals such as arsenic, chromium, lead and nickel, among others. The rule is also
expected to reduce PM10 emissions from existing sources by 560,000 tons per year, and SO2 emissions
from existing sources by 113,000 tons per year by 2005. Hg emissions will be reduced by 1.7 tons
per year. The rule will reduce HAP emissions from new sources by about 73 tons in 2005 and PM10
emissions by 65 tons in 2005. The annual compliance costs to existing sources, which include the
costs of control and monitoring, recordkeeping and reporting requirements, are estimated at $863
million (1999 dollars). For new sources, the annual compliance costs are estimated at $19 million
(1999 dollars). The EPA is unable to monetize the benefits of the HAP emissions reductions due to
insufficient scientific data, but is able to monetize the benefits of the PM10 and SO2 emissions
reductions. The EPA's base estimate of the monetized benefits associated with the rule is $16.3
billion + B (1999 dollars). The estimated difference between monetized benefits and costs for the
proposed rule is $15.5 billion + B (1999 dollars). The value of B is the potential value of the large
number of unmonetized benefits associated with this rule, including health effects such as reductions
in cancer leading to mortality, genotoxicity, liver and kidney damage, and cardiovascular impairment,
and welfare effects such as corrosion of materials and crop yield reductions.
There are industries in 43 2-digit Standard Industrial Classification (SIC) codes and 3-digit
North American Industrial Classification System (NAICS) that are affected by the rule, but the
changes in product price and output are estimated to be no greater than 0.02 percent for any of these
affected industries. Effects on energy markets are expected to result in no more than a 0.05 percent
in electricity rates, and petroleum and natural gas prices. In addition, coal prices and output will
decline overall due to a reduction in coal demand. Based on the energy impacts analysis, the Agency
concluded that there is no significant adverse effect on the supply, distribution, and use of energy
associated with this rule. While the economic impacts of the above the floor option are also low, the
total costs to consumers and producers (the social costs) are more than double those for the final rule.
Of the 576 entities affected by this rule, 185 (or 31 percent) are identified as small entities.
Of these small entities, 31 of them have compliance costs of 1 percent of sales or greater, and 10 of
ES-1
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these 31 have compliance costs of 3 percent or greater. Based of the relatively low number of small
entities affected and the size of the price increases these entities will face, the Agency certifies that
there will not be significant impact on a substantial number of small entities (SISNOSE) associated
with this rule.
CHAPTER 1
INTRODUCTION AND REGULATORY ALTERNATIVES
ES-2
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The U.S. Environmental Protection Agency (referred to as EPA or the Agency) is developing
regulations under Section 112 of the Clean Air Act (CAA, referred to hereafter as the Act) for
industrial, commercial and institutional (ICI) boilers and process heaters. These combustion devices
are used in the production processes of numerous industries in the U.S. The hazardous air pollutants
(HAPs) are generated by the combustion of fossil fuels and biomass in boilers and process heaters.
The primary HAPs emitted by ICI boilers and process heaters include arsenic, beryllium, cadmium,
lead, hydrochloric acid, mercury, and other HAPs. In addition, ICI boilers and process heaters also
emit non-HAP pollutants such as sulfur dioxide and particulate matter. To inform this rulemaking, the
Innovative Strategies and Economics Group (ISEG) of EPA's Office of Air Quality Planning and
Standards (OAQPS) has developed a regulatory impact analysis (RIA) to estimate the potential
impacts of the regulation. This report presents the results of a set of analyses conducted by EPA in
order to assess the impacts of the regulation and other alternatives considered by the Agency.
Compliance costs, economic impacts, small entity impacts, energy effects impacts, air quality changes,
and benefits are included in this RIA.
1.1 Agency Requirements for an RIA
Congress and the Executive Office have imposed statutory and administrative requirements for
conducting various analyses to accompany regulatory actions. Section 317 of the CAA specifically
requires estimation of the cost and economic impacts for specific regulations and standards proposed
under the authority of the Act. In addition, Executive Order (EO) 12866 as amended by EO 13258
requires a more comprehensive analysis of benefits and costs for proposed significant regulatory
actions.1 The Executive Order defines "significant" regulatory action as one that is likely to result in a
rule that may:
1) Have an annual effect on the economy of $100 million or more or adversely affect in a material way
the economy, a sector of the economy, productivity, competition, jobs, the environment, public health
or safety, or state, local, or tribal governments or communities;
2) Create a serious inconsistency or otherwise interfere with an action taken or planned by another
agency;
3) Materially alter the budgetary impact of entitlements, grants, user fees, or loan programs, or the
rights and obligation of recipients thereof;
4) Raise novel legal or policy issues arising out of legal mandates, the President's priorities, or the
principles set forth in the Executive Order.
Pursuant to the terms of Executive Order 12866 as amended by EO 13258, it has been
determined that this rule is a "significant regulatory action" because the annual costs of complying
with the rule are expected to exceed $100 million. Consequently, this action was submitted to OMB
for review under Executive Order 12866 as amended by EO 13258.
'Office of Management and Budget (OMB) guidance under EO 12866 stipulates that a full benefit-cost analysis
is required only when the regulatory action has an annual effect on the economy of $100 million or more.
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1.1.1 Regulatory Flexibility Act and Small Business Regulatory Enforcement Fairness Act of
1996
The Regulatory Flexibility Act (RFA) of 1980 (PL 96-354) generally requires that agencies
conduct a screening analysis to determine whether a regulation adopted through notice-and-comment
rulemaking will have a significant impact on a substantial number of small entities (SISNOSE),
including small businesses, governments, and organizations. If a regulation will have such an impact,
agencies must prepare an Initial Regulatory Flexibility Analysis, and comply with a number of
procedural requirements to solicit and consider flexible regulatory options that minimize adverse
economic impacts on small entities. Agencies must then prepare a Final Regulatory Flexibility
Analysis that provides an analysis of the effect on small entities from consideration of flexible
regulatory options. The RFA's analytical and procedural requirements were strengthened by the Small
Business Regulatory Enforcement Fairness Act (SBREFA) of 1996 to include the formation of a panel
if a proposed rule was determined to have a SISNOSE. This panel would be made up of
representatives of the EPA, the Small Business Administration (SB A), and OMB.
For reasons explained more fully in Chapter 7 of this RIA and the economic impact analysis
for this proposed rule, EPA has determined that there is no SISNOSE for this rule. While there are
some impacts to some small firms as estimated in the economic impact analysis, these impacts are not
sufficient for a SISNOSE. Therefore, the EPA has not prepared a Regulatory Flexibility Analysis for
this rule.
The RFA and SBREFA require the use of definitions of "small entities," including small
businesses, governments, and organizations such as non-profits, published by the SBA.2 Screening
analyses of economic impacts presented in Chapter 7 of this RIA examine potential impacts on small
entities.
7.7.2 Unfunded Mandates Reform Act of 1995
The Unfunded Mandates Reform Act (UMRA) of 1995 (PL-4) was enacted to focus attention
on federal mandates that require other governments and private parties to expend resources without
federal funding, to ensure that Congress considers those costs before imposing mandates, and to
encourage federal financial assistance for intergovernmental mandates. The Act establishes a number
of procedural requirements. The Congressional Budget Office is required to inform Congressional
committees about the presence of federal mandates in legislation, and must estimate the total direct
costs of mandates in a bill in any of the first five years of a mandate, if the total exceeds $50 million
for intergovernmental mandates and $100 million for private-sector mandates.
Section 202 of UMRA directs agencies to provide a qualitative and quantitative assessment (or
a "written statement") of the anticipated costs and benefits of a Federal mandate that results in annual
expenditures of $100 million or more. The assessment should include costs and benefits to State,
local, and tribal governments and the private sector, and identify any disproportionate budgetary
impacts. Section 205 of the Act requires agencies to identify and consider alternatives, including the
least costly, most cost-effective, or least burdensome alternative that achieves the objectives of the
rule.
Since this rule may cause a mandate to the private sector of more than $100 million, EPA did
provide an analysis of the impacts of this rule on State and local governments to support compliance
with Section 202 of UMRA. A summary of this analysis is in Chapter 6 of this RIA. There are
government entities affected by this proposed regulation, and these are primarily municipalities that
own industrial boilers that may need to comply.
Where appropriate, agencies can propose and justify alternative definitions of "small entity." This RIA and the
screening analysis for small entities rely on the SBA definitions.
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1.1.3 Paperwork Reduction Act of 1995
The Paperwork Reduction Act of 1995 (PRA) requires Federal agencies to be responsible and
publicly accountable for reducing the burden of Federal paperwork on the public. EPA has submitted
an OMB-83I form, along with a supporting statement, to the OMB in compliance with the PRA. The
OMB-83I and the supporting statement explains the need for additional information collection
requirements and provides respondent burden estimates for additional paperwork requirements to State
and local governments associated with this proposed rule.
1.1.4 Executive Order 12898
Executive Order 12898, "Federal Actions to Address Environmental Justice in Minority
Populations and Low-Income Populations," requires Federal agencies to consider the impact of
programs, policies, and activities on minority populations and low-income populations.
Disproportionate adverse impacts on these populations should be avoided to the extent possible.
According to EPA guidance, agencies are to assess whether minority or low-income populations face
risk or exposure to hazards that is significant (as defined by the National Environmental Policy Act)
and that "appreciably exceeds or is likely to appreciably exceed the risk or rate to the general
population or other appropriate comparison group." (EPA, 1996). This guidance outlines EPA's
Environmental Justice Strategy and discusses environmental justice issues, concerns, and goals
identified by EPA and environmental justice advocates in relation to regulatory actions. The
industrial boilers and process heaters rule is expected to provide health and welfare benefits to
populations around the United States, regardless of race or income.
1.1.5 Executive Order 13045
Executive Order 13045, "Protection of Children from Environmental Health Risks and Safety
Risks," directs Federal agencies developing health and safety standards to include an evaluation of the
health and safety effects of the regulations on children. Regulatory actions covered under the
Executive Order include rulemakings that are economically significant under Executive Order 12866,
and that concern an environmental health risk or safety risk that the agency has reason to believe may
disproportionately affect children. EPA has developed internal guidelines for implementing E.O.
13045 (EPA, 1998).
The industrial boilers and process heaters rule is a "significant economic action," because the
annual costs are expected to exceed $100 million. Exposure to the F£APs whose emissions will be
reduced by this rule are known to affect the health of children and other sensitive populations.
However, this rule is not expected to have a disproportionate impact on children.
1.1.6 Executive Order 13211
Executive Order 13211, "Actions Concerning Regulations That Significantly Affect Energy
Supply, Distribution, or Use," was published in the Federal Register on May 22, 2001 (66 FR 28355).
This executive order requires Federal Agencies to weigh and consider the effect of regulations on
supply, distribution, and use of energy. To comply with this executive order, Federal Agencies are to
prepare and submit a "Statement of Energy Effects" for "significant energy actions." The executive
order defines "significant energy action" as the following:
1) an action that is a significant regulatory action under Executive Order 12866 or any successor order,
and
2) is likely to have a significant adverse effect on the supply, distribution, or use of energy; or
3) that is designated by the Administrator of the Office of Information and Regulatory Affairs as a
significant energy action.
An analysis of the effects of this rule on supply, distribution, and use of energy was conducted
as part of the economic impact analysis and is summarized in Chapter 7.
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1.2 Scope and Purpose of the Regulation
Section 112 of the CAA requires EPA to promulgate regulations for the control of HAP
emissions from each source category listed under section 112(c). The statute requires the regulations
to reflect the maximum degree of reductions in emissions of HAP that is achievable taking into
consideration the cost of achieving emissions reductions, any nonair quality health and environmental
impacts, and energy requirements. This level of control is commonly referred to as MACT. The
MACT regulation can be based on the emissions reductions achievable through application of
measures, processes, methods, systems, or techniques including, but not limited to: (1) reducing the
volume of, or eliminating emissions of, such pollutants through process changes, substitutions of
materials, or other modifications; (2) enclosing systems or processes to eliminate emissions; (3)
collecting, capturing, or treating such pollutants when released from a process, stack, storage or
fugitive emission point; (4) design, equipment, work practices, or operational standards as provided in
subsection 112(h); or (5) a combination of the above.
For new sources, MACT standards cannot be less stringent than the emission control achieved
in practice by the best-controlled similar source. The MACT standards for existing sources can be less
stringent than standards for new sources, but they cannot be less stringent than the average emission
limitation achieved by the best-performing 12 percent of existing sources for categories and
subcategories with 30 or more sources, or the best-performing 5 sources for categories or
subcategories with fewer than 30 sources.
In essence, these MACT standards would ensure that all major sources of air toxic emissions
achieve the level of control already being achieved by the better-controlled and lower-emitting sources
in each category. This approach provides assurance to citizens that each major source of toxic air
pollution will be required to effectively control its emissions. A major source of HAP emissions is any
stationary source or group of stationary sources located within a contiguous area and under common
control that emits or has the potential to emit any single HAP at a rate of 9.07 Mg (10 tons) or more
per year or any combination of HAPs at a rate of 22.68 Mg (25 tons) or more a year. At the same
time, this approach provides a level economic playing field, ensuring that facilities that employ cleaner
processes and good emission controls are not disadvantaged relative to competitors with poorer
controls.
1.2.1 Regulatory Backgroun d
In September 1996, the EPA chartered the Industrial Combustion Coordinated Rulemaking
(ICCR) advisory committee under the Federal Advisory Committee Act (FACA). The committee's
objective was to develop recommendations for regulations for several combustion source categories
under sections 112 and 129 of the CAA. The ICCR advisory committee, known as the Coordinating
Committee, formed Source Work Groups for the various combustion types covered under the ICCR.
One of the work groups was formed to research issues related to boilers. Another was formed to
research issues related to process heaters. The Boiler and Process Heater Work Groups submitted
recommendations, information, and data analysis results to the Coordinating Committee, which in turn
considered them and submitted recommendations and information to EPA. The Committee's
recommendations were considered by EPA in developing these proposed standards for boilers and
process heaters. The Committee's 2-year charter expired in September 1998.
Following the expiration of the ICCR FACA charter, EPA decided to combine boilers with
units in the process heater source category covering indirect fired units, and to regulate both under this
NESHAP. This was done because indirect fired process heaters and boilers are similar devices, burn
similar fuel, have similar emission characteristics, and emissions from each can be controlled using
similar control devices or techniques.
7.2.2 Regulatory Authority
Section 112 of the CAA requires that EPA promulgate regulations requiring the control of
HAP emissions from major sources and certain area sources. The control of HAP is achieved through
promulgation of emission standards under sections 112(d) and (f) and, in appropriate circumstances,
work practice standards under section 112(h) of the CAA.
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An initial list of categories of major and area sources of HAP selected for regulation in
accordance with section 112(c) of the CAA was published in the Federal Register on July 16, 1992 (57
FR 31576). Industrial boilers, commercial and institutional boilers, and process heaters are three of
the listed 174 categories of sources. The listing was based on the Administrator's determination that
they may reasonably be anticipated to emit several of the 188 listed F£AP in quantities sufficient to
designate them as major sources.
This rule affects industrial boilers, institutional and commercial boilers, and process heaters.
In this rule process heaters are defined as units in which the combustion gases do not directly come
into contact with process gases in the combustion chamber (e.g. indirect fired). Boiler means an
enclosed device using controlled flame combustion and having the primary purpose of recovering
thermal energy in the form of steam or hot water. A waste heat boiler (or heat recovery steam
generator) is a device that recovers normally unused energy and converts it to usable heat. Waste heat
boilers are excluded from this rule. A hot water heater is a closed vessel in which water is heated by
combustion of gaseous fuel and is withdrawn for use external to the vessel at pressures not exceeding
160 psig. Hot water heaters are excluded from this rule.
Boilers and process heaters emit particulate matter, volatile organic compounds, and
hazardous air pollutants, depending on the material burned. Solid and liquid fuel-fired units emit
metals, halogenated compounds and organic compounds. Gas fuel-fired units emit mostly organic
compounds.
The affected source is each individual industrial, commercial, or institutional boiler or process
heater located at a major facility. The affected source does not include units that are municipal waste
combustors (40 CFR part 60, subparts AAAA, BBBB or Cb), medical waste incinerators (40 CFR part
60, subpart Ce and EC), fossil fuel fired electric utility steam generating units, commercial and
industrial solid waste incineration units (40 CFR part 60 subparts CCCC or DDDD), recovery boilers
or furnaces (40 CFR part 63, subpart MM), or hazardous waste combustion units required to have a
permit under section 3005 of the Solid Waste Disposal Act or are subject to 40 CFR part 63, subpart
EEE.
The rule applies to an owner or operate a boiler or process heater at a major source meeting the
requirements in section II.C. A major source of HAP emissions is any stationary source or group of
stationary sources located within a contiguous area and under common control that emits or has the
potential to emit any single HAP at a rate of 9.07 Mg (10 tons) or more per year or any combination of
HAP at a rate of 22.68 Mg (25 tons) or more a year.
An affected operator must meet the emission limits for the subcategories in Table 1-1 of this
preamble for each of the pollutants listed. Emission limits were developed for new and existing
sources; and for large, small, and limited use solid, liquid, and gas fuel fired units. Large units are
those with heat input capacities greater than 10 MMBtu/hr. Small units are those with heat input
capacities less than or equal to 10 MMBtu/hr. Limited use units are those with capacity utilizations
less than or equal to 10 percent as required in a federally enforceable permit.
If your new or existing boiler or process heater is permitted to burn a solid fuel, or any
combination of solid fuel with liquid or gaseous fuel, the unit is in one of the solid subcategories. If
your new or reconstructed boiler or process heater burns a liquid fuel, or a liquid fuel in combination
with a gaseous fuel, the unit is in one of the liquid subcategories. If your new or existing boiler or
process heater burns a gaseous fuel only, the unit is in the gas subcategory and is not required to meet
any emission limit.
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Table 1-1. EMISSION LIMITS FOR BOILERS AND PROCESS HEATERS (Ib/MMBtu)
Source
New
Boiler or
Process
Heater
Existing
Boiler or
Process
Heater
Subcategory PM or
Solid Fuel, 0.04 or
Large Unit
Solid Fuel, 0.04 or
Small Unit
Solid Fuel, 0.04 or
Limited Use
Liquid Fuel, 0.068
Large Unit
Liquid Fuel, 0.068
Small Unit
Liquid Fuel, 0.068
Limited Use
Gaseous
Fuel, Large
Unit
Gaseous
Fuel, Small
Unit
Gaseous
Fuel,
Limited Use
Solid Fuel, 0.062 or
Large Unit
Solid Fuel,
Small Unit
Solid Fuel, 0.21 or
Limited Use
Liquid Fuel,
Large Unit
Liquid Fuel,
Small Unit
Liquid Fuel,
Limited Use
Gaseous
Fuel
Carbon
Monoxide
Total (CO - ppm
Selected Mercury @3%
Metals HC1 (Hg) oxygen)
0.00007 0.016 0.0000026 200
0.00007 0.032 0.0000026
0.00007 0.032 0.0000026 200
0.00045 200
0.0009
0.0009 - 200
200
~
200
0.001 0.048 0.000004
0.001
-
-
-
-
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For solid fuel-fired boilers or process heaters, we are allowing sources to choose one of two
emission limit options: (1) existing and new affected sources may choose to limit PM emissions to the
level listed in Table 1 of this preamble or (2) existing and new affected sources may choose to limit
total selected metals emissions to the level listed in Table 1 of the preamble.
If you do not use an add-on control or use an add-on control other than a wet scrubber, you
must maintain opacity level to less than or equal to the level established during the compliance test for
mercury and PM or total selected metals, and maintain the fuel chlorine content to less than or equal to
the operating level established during the HC1 compliance test.
If you use a wet scrubber, you must maintain the minimum pH, pressure drop and liquid
flowrate above the operating levels established during the performance tests.
If you use a dry scrubber, you must maintain opacity level and the minimum sorbent injection
rate established during the performance test.
If you use an ESP in combination with a wet scrubber and cannot monitor the opacity, you
must maintain the average secondary current and voltage or total power input established during the
performance test.
There is an alternative compliance procedure and operating limit for meeting the total selected
metals emission limit option. If you have no control or do not want to take credit of metals reductions
with your existing control device, and can show that total metals in the fuel would be less than the
metals emission level, then you can monitor the metals fuel analysis to meet the metals emissions
limitations. Similarly, if you have no control or do not want to take credit of mercury reduction with
your existing control device, and can show that mercury in the fuel would be less than the mercury
emission level, then you can monitor the mercury fuel analysis to meet the mercury emission
limitations.
1.2.3 Regulatory Alternatives and Control Technologies
1.2.3.1 MACTFloor Development
We considered several approaches to identifying MACT floor for existing industrial,
commercial, and institutional boilers and process heaters. First, we considered using emissions data on
boilers and process heaters to set the MACT floor. However, after review of the data available, we
determined that emissions information was inadequate to set MACT floors. We then considered using
State regulations and permits to set the MACT floors. However, we found no State regulations or
State permits which specifically limit HAP emissions from these sources.
Consequently, we concluded that the only reasonable approach for determining MACT floors
is to base it on control technology. Information was available on the control technologies employed
by the population of boilers identified by the EPA. We considered several possible control
technologies (i.e., factors that influence emissions), including fuel substitution, process changes and
work practices, and add-on control technologies.
We first considered whether fuel switching would be an appropriate control option for sources
in each subcategory. Both fuel switching to other fuels used in the subcategory and fuels from other
subcategories were considered. This consideration included determining whether switching fuels
would achieve lower HAP emissions. A second consideration was whether fuel switching could be
technically done on boilers and process heaters in the subcategory considering the existing design of
boilers and process heaters. We also considered the availability of the alternative fuel.
After considering these factors, we determined that fuel switching was not an appropriate
control technology to be included in determining the MACT floor level of control for any subcategory.
This decision was based on the overall effect of fuel switching on HAP emissions, technical and
design considerations discussed in section III.A of this preamble, and concerns about fuel availability.
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Based on the data available in the emissions database, we determined that while fuel switching
from solid fuels to gaseous or liquid fuels would decrease PM and some metals emissions, emissions
of some organic HAP would also increase, resulting in uncertain benefits. We determined that it
would be inappropriate in a MACT rulemaking, that is technology based, to consider a technology that
potentially will result in an increase in a HAP regardless of its potential to reduce other HAP without
determining the overall benefit. Determining the benefits of fuel switching would require an
assessment of the risk associated which each HAP emitted and a determination of which fuel results in
the overall lower risk taking into account the available control technology for each fuel. This
assessment will be performed in a future rulemaking.
A similar determination was made when considering fuel switching to "cleaner" fuels within a
subcategory. For example, the term "clean coal" refers to coal that is lower in sulfur content and not
necessarily lower in HAP content. Data gathered by EPA also indicates that within specific coal types
HAP content can vary significantly. Switching to a "clean coal" may increase emissions of some
HAP. Therefore, fuel switching to a "cleaner" coal would not be an appropriate option. Fuel
switching from coal to biomass would result in similar impacts on HAP emissions. While metallic
HAP emissions would be reduced, emissions of organics would increase based on information in the
emissions database.
Another factor considered was the availability of alternative fuels. Natural gas pipelines are
not available in all regions of the U.S., and natural gas is simply not available as a fuel for many
industrial, commercial, and institutional boilers and process heaters. Moreover, even where pipelines
provide access to natural gas, supplies of natural gas may not be adequate. For example, it is common
practice in cities during winter months (or periods of peak demand) to prioritize natural gas usage for
residential areas before industrial usage. Requiring EPA regulated combustion units to switch to
natural gas would place an even greater strain on natural gas resources. Consequently, even where
pipelines exist some units would not be able to run at normal of full capacity during these times if
shortages were to occur. Therefore, under any circumstances, there would be some units that could not
comply with a requirement to switch to natural gas.
Similar problems for fuel switching to biomass could arise. Existing sources burning biomass
generally are combusting a recovered material from the manufacturing or agriculture process.
Industrial, commercial, and institutional facilities that are not associated with the wood products
industry or agriculture may not have access to a sufficient supply of biomass materials to replace their
fossil fuel.
There are many concerns with switching fuels on sources designed and operated to burn
specific fuels. Changes to the fuel type (solid, liquid, or gas) will require extensive changes to the fuel
handling and feeding system (e.g., a stoker using wood as fuel would need to be redesigned to handle
fuel oil or gaseous fuel). Additionally, burners and combustion chamber designs are generally not
capable of handling different fuel types, and generally cannot accommodate increases or decreases in
the fuel volume and shape. Design changes to allow different fuel use, in some cases, may reduce the
capacity and efficiency of the boiler or process heater. Reduced efficiency may result in a greater
degree of incomplete combustion and, thus, an increase in organic HAP emissions. For the reasons
discussed above, we decided that fuel switching to "cleaner" solid fuels or to liquid or gaseous fuels
would not be appropriate or available as a MACT floor level.
We also determined that using process changes or work practices were not appropriate in
developing MACT floors. HAP emissions from boilers and process heaters are primarily dependent
upon the composition of the fuel. Fuel dependent HAP are metals, including mercury, and acid gases.
Fuel dependent HAP are typically controlled by removing them from the flue gas after combustion.
Therefore, they are not affected by the operation of the boiler or process heater. Consequently, process
changes would be ineffective in reducing these fuel-related HAP emissions.
On the other hand, organic HAP can be formed from incomplete combustion of the fuel. Data
are not available that definitively show that organic HAP emissions are related to the operation of the
boiler or process heater. Some studies indicate that organic HAP are greatly influence by time,
turbulence and temperature. Other studies indicate that organic HAP emissions are not affected by the
operation of the unit. The measurement of CO is generally an indicator of incomplete combustion
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since CO will burn to carbon dioxide if adequate oxygen is available. Correcting incomplete
combustion may be accomplished through providing more combustion air. Therefore, we consider
monitoring and maintaining CO emission levels to be associated with minimizing organic HAP
emission levels and, thus, CO monitoring would be a good indicator of combustion efficiency and
organic HAP emissions.
In summary, we determined that considering process changes and work practices would not be
appropriate in developing MACT floors for existing units. We are requesting comment, and
information on emission reductions, on whether there are other GCP practices that would be
appropriate for minimizing organic HAP emissions from industrial, commercial, and institutional
boilers and process heaters.
Consequently, we concluded that add-on control technology is the only factor that
significantly controls HAP emissions.
In order to determine the MACT floor based on add-on control technologies, we first
examined the population database of existing sources. Units not meeting the definition of an
industrial, commercial, or institutional boiler or process heater, and units located at area sources were
removed from the database. The remaining units were divided first into three subcategories based on
fuel state: gaseous fuel-fired, liquid fuel-fired, and solid fuel-fired units. Each of these three
subcategories was then further divided into subcategories based on capacity: (1) large boilers and
process heaters (units with heat inputs greater than 10 MMBtu/hr); (2) small units (with a maximum
rated heat input capacity of 10 MMBtu/hr or less); and (3) limited use units with capacity utilization
less than 10 percent.
We identified the types of air pollution control techniques currently used by existing boilers
and process heaters in each subcategory. We ranked those controls according to their effectiveness in
removing the different categories of pollutants; including metallic HAP and PM, inorganic HAP such
as acid gases, mercury, and organic HAP. The EPA ranked these existing control technologies by
incorporating recommendations made by the ICCR, and by reviewing emissions test data, previous
EPA studies, and other literature, as well as by using engineering judgement.
Based upon the emissions reduction potential of existing air pollution control techniques, we
listed all the boilers and process heaters in the population database in order of decreasing control
device effectiveness for each subcategory. Then the technology basis of the existing source MACT
floor was determined for each pollutant category by identifying the best-performing 12 percent of
units. We then selected the technology used by the median unit in the best performing 12 percent of
units (i.e., the boiler or process heater unit representing the 94th percentile) as the technology
associated with the MACT floor level of control for each subcategory. As previously described,
emissions data for this category is insufficient to identify the best-performing units. The most
appropriate way to identify the average emission limitation achieved by the best-performing 12 percent
of existing sources is to identify the technology used by the unit in the middle of the range of the best
performing 12 percent of units, i.e., the median unit).
After establishing the technology basis for the existing source MACT floor for each
subcategory and each type of pollutant, the EPA examined the emissions data available for boilers and
process heaters controlled by these technologies to determine achievable emission levels. The
resulting emission levels associated with the existing source MACT floors for each pollutant are based
on the average of the lowest three run average test data from units using the technology associated
with the MACT floor level of control, and by incorporating operational variability using results from
multiple tests on these best performing units. This approach reasonably ensures that the emission limit
selected as the MACT floor represents a level of control that can be consistently achieved by a unit in
the subcategory using the control technology associated with the MACT floor. This approach is
reasonable because the most informative way to predict the worst reasonably foreseeable performance
of the best-controlled units, with available data, is to examine the available long-term performance of
the best performing units that had multiple test results. In other words, the EPA considers all units
with the same control technology that is properly designed and operated to be equally well controlled,
even if the emission test results from such units vary considerably.
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The level of control "achieved" by the average of the top performing 12 percent of units is best
represented by the average emissions observed from all units using the same technology as that
employed by the unit representing the median of the top 12 percent.
The EPA's review of emissions data indicates that some boilers and process heaters within
each subcategory may be able to meet the floor emission levels without using the air pollution control
technology that is associated with the MACT floor. This is to be expected, given the variety of fuel
types, fuel input rates, and boiler designs included within each subcategory and the resulting
variability in emission rates. Thus, for instance, boilers or process heaters within the large unit solid
fuel subcategory that burn lower percentages of solid fuels may be able to achieve the emission levels
for the large unit solid fuel subcategory without the need for additional control devices.
Furthermore, solid fuels, especially coal, are very heterogeneous and can vary in composition
by location. Coal analysis data obtained from the electric utility industry in another rulemaking
contained information on the mercury, chlorine, and ash content of various coals. A preliminary
review of this data indicate that the composition can vary greatly from location to location, and also
within location. Based on the range of variation of mercury, chlorine, and ash content in coal, it is
possible for a unit with a lower performing control system to have emission levels lower than a unit
considered to be included in the best performing 12 percent of the units.
This situation is reflected in the emissions information used to set the MACT floor emission
limits. In some instances there are boilers with ESP's or other controls that achieve similar, or lower,
outlet emission levels of non-mercury metallic HAP, PM, or mercury to fabric filters. In most cases,
this is due to concentrations entering these other control devices being lower, even though the percent
reduction achieved is lower than fabric filters.
Additionally, the design of some control devices may have a substantial effect on the their
emission reduction capability. For example, fabric filters are largely insensitive to the physical
characteristics of the inlet gas stream. Thus, their design does not vary widely, and emissions
reductions are expected to be similar (e.g. 99 percent reduction of PM). However, ESP design can
vary significantly.
Consequently, since fuel substitution has been determined not to be an appropriate MACT
floor control technology, EPA still considers the fabric filter to be the best-performing control for non-
mercury metallic HAPs, PM, and mercury and only emissions information for fabric filters was used to
develop emission limits. A detailed discussion of the MACT floor methodology is presented in the
memorandum "MACT Floor Analysis for New and Existing Sources in the Industrial, Commercial,
and Institutional Boilers and Process Heaters Source Categories" in the docket.
Existing Solid Fuel Boilers and Process Heaters Large Units - Heat Inputs Greater than 10
MMBtu/hr.
The most effective control technologies identified for removing non-mercury metallic HAP
and PM are fabric filters. About 14 percent of solid fuel-fired boilers and process heater use fabric
filters. Because this is the technology used by the 94th percentile (the median of the best-performing
12 percent), the EPA considers a fabric filter to be the technology basis for the MACT floor for non-
mercury metallic HAP control for existing boilers and process heaters in this subcategory.
The most effective control technologies identified for removing inorganic HAP that are acid
gases, such as hydrogen chloride, are wet scrubbers and packed bed scrubbers. These technologies are
used by about 12 percent of the boilers and process heaters in the solid fuel subcategory. About 10
percent of solid-fired boilers and process heaters use wet scrubbers, and approximately 1 percent use
packed bed scrubbers. Because wet scrubbers are the technology used by the 94th percentile (median
of the best-performing 12 percent), the EPA considers a wet scrubber to be the technology basis for the
MACT floor for acid gas control for existing boilers and process heaters in the solid fuel subcategory.
The MACT floor emission level based on wet scrubbers and incorporating operational variability is
0.048 Ib HCl/MMBtu.
Based on test information on utility boilers, we have concluded that fabric filters are most
effective in controlling mercury, and units having them would constitute the best controlled mercury
sources. As discussed previously, more than 6 percent of sources in the subcategory have fabric
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filters. The MACT floor emission level based on fabric filters and incorporating operational
variability is 0.000004 Ib mercury/MMBtu.
For organic HAP, we assessed whether maintaining and monitoring CO levels would be part
of the MACT floor, and determined that less than 6 percent of the units in this subcategory do so.
Therefore, we concluded the MACT floor for existing sources in this subcategory is no emissions
reductions for organic HAP.
Therefore, the EPA determined that the combination of fabric filter and wet scrubber control
technologies forms the basis for the MACT floor level of control for existing solid fuel boilers or
process heaters in this subcategory. We recognize that some boilers and process heaters that use
technologies other than those used as the basis of the MACT floor can achieve the MACT floor
emission levels. For example, emission test data show that many boilers with well-designed and
operated ESP can meet the MACT floor emission levels for non-mercury metallic HAP and PM, even
though the floor emission level for these pollutants is based on a fabric filter (however, we would not
expect that all units using ESP would be able to meet the rule).
Small Units - Heat Inputs Less than or Equal to 10 MMBtu/hr.
Less than 6 percent of the units in this subcategory used control techniques that would reduce
non-mercury metallic HAP and PM, mercury, and inorganic HAP, such as HC1. Also, maintaining and
monitoring CO levels was used by less than 6 percent of the units in the subcategory.
Therefore, we determined that the MACT floor emission level for existing units for any of the
pollutant categories in this subcategory is no emissions reductions.
Limited Use Units - Capacity Utilizations Less than or Equal to 10 Percent.
The most effective control technologies identified for removing non-mercury metallic HAP
and PM are ESP and fabric filters. Less than 2 percent of solid fuel-fired boilers and process heater in
this subcategory use fabric filters, and 14 percent use ESP. Because ESP are the technology used by
the 94th percentile (the median of the best-performing 12 percent), the EPA considers an ESP to be the
technology basis for the MACT floor for non-mercury metallic HAP control for existing boilers and
process heaters in the solid fuel subcategory. A PM level is set as a surrogate for non-mercury
metallic HAP control. The MACT floor emission level based on ESPs, considering operational
variability, is 0.021 Ib PM/MMBtu. We are also providing an alternative metals limit of 0.001 Ib
metals/MMBtu which can be used to show compliance in cases where metal HAP emissions are low in
proportion to PM emissions.
Similar control technology analyses were done for the boilers and process heaters in this
subcategory for the other pollutant groups of interest, including inorganic HAP, organic HAP and
mercury. Less than 6 percent of the units in this subcategory have controls that would reduce
emissions of organic HAP, mercury, and inorganic HAP, so the existing source MACT floor for those
pollutants is no emissions reductions. Therefore, we determined that ESP control technology, which
achieves non-mercury metallic HAP and PM control forms the basis for the MACT floor level of
control for existing solid fuel boilers and process heaters in this subcategory.
Existing Liquid Fuel Boilers and Process Heaters
Emissions data for liquid subcategories was inadequate to identify the best-performing sources
for reasons described in section D of the preamble. We also found no State regulations or permits
which specifically limit HAP emissions from these sources. Therefore, we examined control
technology data to identify a MACT floor. We found that less than 6 percent of the units in each of
the liquid subcategories used control techniques that would reduce non-mercury metallic HAP and
PM, mercury, organic HAP, or inorganic HAP (such as HC1). Therefore, we determined that the
control technique associated with the 94th percentile (the median of the best-performing 12 percent)
could not be identified.
Therefore, we are unable to identify the best performing 12 percent of units in the
subcategories. In light of this analysis, we concluded the MACT floor for existing sources in these
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liquid subcategory is no emissions reductions for non-mercury metallic HAP, mercury, inorganic
HAP, and organic HAP.
Existing Gaseous Fuel Boilers and Process Heaters
Emissions data for gas subcategories was inadequate to identify the best-performing sources
for reasons described in section D of the preamble. We also found no State regulations or permits
which specifically limit HAP emissions from these sources. Therefore, we examined control
technology data to identify a MACT floor. We found that no existing units in the gaseous fuel-fired
subcategories were using control technologies that achieve consistently lower emission rates than
uncontrolled sources for any of the pollutant groups of interest. Therefore, we are unable to identify
the best performing 12 percent of units in the subcategories. Consequently, the EPA determined that
no existing source MACT floor based on control technologies could be identified for gaseous fuel-
fired units. Therefore, we concluded the MACT floor for existing sources in this subcategory is no
emissions reductions for non-mercury metallic HAP, mercury, inorganic HAP, and organic HAP.
1.2.3.2 Consideration of Options Beyond the Floor for Existing Units
Once the MACT floor determinations were done for each subcategory, the EPA considered
various regulatory options more stringent than the MACT floor level of control (i.e., technologies or
other work practices that could result in lower emissions) for the different subcategories.
Maintaining and monitoring CO levels was identified as a possible control for organic HAPs.
However, less than 6 percent of the sources in the existing source subcategories used this control
method and it was not considered the MACT floor control technology. We then looked at it as an
above-the-floor option. However, information was not available to estimate the HAP emissions
reductions that would be associated with CO monitoring and emission limits. This option would also
require a high cost to install and operate CO monitors. Given the cost and the uncertain emissions
reductions that might be achieved, we chose to not require CO monitoring and emission limits as
MACT.
The following sections discuss the above-the-floor options analyzed to control emissions of
metallic HAP, mercury, and inorganic HAP. Based on the analysis described in these sections, the
EPA decided to not go beyond the MACT floor level of control for the rule for any of the
subcategories of existing sources.
Existing Solid Fuel Units
Large Units - Heat Inputs Greater than 10 MMBtu/hr. Besides fuel switching (see section III.D
of this preamble), we identified a better designed and operated fabric filter (the MACT floor for new
units) as a control technology that could achieve greater emissions reductions of metallic HAP and PM
emissions than the MACT floor level of control (i.e., a typical existing fabric filter). Consequently, the
EPA analyzed the emissions reductions and additional cost of adopting an emission limit
representative of the performance of a unit with a better designed and operated fabric filter. The
additional annualized cost to comply with this emission limit was estimated to be approximately
500 million dollars with an additional emission reduction of approximately 100 tons of metallic HAP.
The results indicated that while additional emissions reductions would be realized, the costs would be
too high to consider it a feasible above the floor option. Non-air quality health, environmental
impacts, and energy effects were not significant factors, because there would be little difference in the
non-air quality health and environmental impacts of replacing existing fabric filters with improved
performance fabric filters. Therefore, we did not select these controls as MACT. Fuel switching was
not considered a feasible beyond-the-floor option for the same reasons described in section III.E of the
proposal preamble.
We identified packed bed scrubbers as a control technology that could achieve greater
emissions reductions of inorganic HAP, like HC1, than the MACT floor level of control (i.e., a wet
scrubber). Consequently, the EPA analyzed the emissions reductions and additional cost of adopting
an emission limit representative of the performance of a unit with a packed bed scrubber. The
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additional annualized cost to comply with this emission limit (using a packed bed scrubber) was
estimated to be approximately 900 million dollars with an additional emission reduction of
approximately 20,000 tons of HC1. The results indicated that while additional emissions reductions
would be realized, the costs would be too high to consider it a feasible above the floor option. Non-air
quality health, environmental impacts, and energy effects were not significant factors, because there
would be little difference in the non-air quality health and environmental impacts between packed bed
scrubbers and wet scrubbers. Therefore, we did not select these controls as MACT.
In reviewing potential regulatory options for existing sources, the EPA identified one existing
industrial boiler that was using a technology, carbon injection, used in other industries to achieve
greater control of mercury emissions than the MACT floor level of control. However, emission data
indicated that this unit was not achieving mercury emission reductions. The EPA does not have
information that would show carbon injection is effective for reducing mercury emissions from
industrial, commercial, and institutional boilers and process heaters. Therefore, carbon injection was
not evaluated as a regulatory option.
However, the EPA requests comments on whether carbon injection should be considered as a
beyond-the-floor option and whether existing industrial, commercial, or institutional boilers and
process heaters could use carbon injection technology, or other control techniques to consistently
achieve mercury emission levels that are lower than levels from similar sources with the MACT floor
level of control. The EPA is aware that research continues on ways to improve mercury capture by
PM controls, sorbent injection, and the development of novel techniques. The EPA requests comment
and information on the effectiveness of such control technologies in reducing mercury emissions.
Small Units - Heat Inputs Less than or Equal to 10 MMBtu/hr.
The EPA could not identify a technology-based level of control for the MACT floor for this
subcategory. To control non-mercury metallic HAP and mercury, we analyzed the above the floor
option of a fabric filter which was identified as the most effective control device for non-mercury
metallic HAP and mercury. To control inorganic HAP such as hydrogen chloride, we analyzed the
above the floor option of a wet scrubber since it was identified as the least cost option.
The total annualized cost of complying with the fabric filter option was estimated to be $10
million, with an estimated emission reduction of 1.9 tons per year of non-mercury metallic HAP and
0.003 tons of mercury. The annualized cost of complying with the wet scrubber option was estimated
to be $11 million, with an emission reduction of 48 per year of HC1. The results of this analysis
indicated that while additional emissions reductions could be realized, the costs would be too high to
consider them feasible options. Therefore, we did not select these controls as MACT. Non-air quality
health, environmental impacts, and energy effects were not significant factors.
Limited Use Units - Capacity Utilizations Less than or Equal to 10 Percent. The MACT floor
level of control for this subcategory for non-mercury metallic HAP control is an ESP. Although fabric
filters were identified as being more effective, many ESP can achieve similar levels. Any additional
emission reduction from using a fabric filter would be minimal and costly considering retrofit costs for
existing units that already have ESP. Therefore, an above-the-floor option for metallic HAP was not
analyzed in detail, and we did not select fabric filters as MACT. However, an above the floor option
of a fabric filter was analyzed for mercury control. The total annualized costs of the fabric filter option
was estimated to be an additional $21 million, with an estimated emission reduction of 0.04 tons of
mercury.
The EPA could not identify a technology-based level of control for the MACT floor for
inorganic HAP in this subcategory. To control inorganic HAP, we analyzed the above-the-floor option
of a wet scrubber since it was identified as the least cost option. The total annualized costs of the wet
scrubber option was estimated to be $49 million, with an estimated emission reduction of 463 tons per
yearofHCl.
The results of the above the floor options analyses indicated that while additional emissions
reductions could be realized, the costs would be too high to consider them feasible options. Therefore,
we did not select these controls as MACT. Non-air quality health, environmental impacts, and energy
effects were not significant factors.
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Existing Liquid Fuel Units
For the liquid fuel subcategories, the EPA could not identify a technology-based level of
control for the MACT floor. For beyond-the-floor options for the liquid subcategory, the EPA
identified several PM controls (e.g., fabric filters, electrostatic precipitators, and venturi scrubbers) that
would reduce non-mercury metallic F£AP emissions. For the above-the-floor analysis, we analyzed the
cost and emission reduction of applying a high efficiency PM control device, such as a fabric filter,
since these would be more likely to be installed for units firing liquid fuel. We identified wet
scrubbers as a technology option beyond the floor for reduction of inorganic HAP, such as HC1. We
identified fabric filters as a technology option beyond the floor for reduction of mercury.
Consequently, the EPA analyzed the emissions reductions and additional cost of applying high
efficiency PM controls and wet scrubbers on liquid fuel-fired units. The additional total annualized
cost of a high efficiency PM control device (such as a fabric filter) was estimated to be $460 million,
with an additional estimated emission reduction of 1,500 tons per year for non-mercury metallic F£AP
and 3 tons per year for mercury. The annualized cost of a wet scrubbers was estimated to be an
additional $480 million, with an additional HC1 reduction of 30 tons per year. The results indicated
that while additional emissions reductions would be realized, the costs would be too high to consider
them feasible options. Non-air quality health, environmental impacts, and energy effects were not
significant factors. Therefore, the EPA chose to not select these controls as MACT for existing liquid
units.
Existing Gas-fired Units
For the gaseous fuel subcategories, the EPA could not identify a technology-based level of
control for the MACT floor. The great majority, if not all, of the emissions from gas-fired units are
organic F£AP. As discussed in section III.E of the preamble, CO monitoring and emission limits were
considered as an above the floor option but was not selected as MACT given the costs and uncertain
reductions achieved. Therefore, no above the floor control technique was analyzed for organic F£APs,
and MACT is no emission reduction of non-mercury metallic F£AP and mercury, inorganic F£AP, and
organic F£AP.
Fuel Switching as a Bevond-the-floor Option
For the solid fuel and liquid fuel subcategories, fuel switching to natural gas is a regulatory
option more stringent than the MACT floor level of control that would reduce mercury, metallic HAP,
and inorganic HAP emissions. We determined that fuel switching was not an appropriate above-the-
floor option for the reasons discussed in sections III.A and III.D of this proposal preamble. In some
cases, organic HAP would be increased by fuel switching. Additionally, the estimated emissions
reductions that would be achieved if solid and liquid fuel units switched to natural gas were compared
with the estimated cost of converting existing solid fuel and liquid fuel units to fire natural gas. The
annualized cost of fuel switching was estimated to be $12 billion. The additional emission reduction
associated with it was estimated to be 1,500 tons per year for metallic HAP, 11 tons per year for
mercury, and 13,000 tons per year for inorganic HAP. Additional detail on the calculation procedures
is provided in the memorandum "Development of Fuel Switching Costs and Emissions reductions for
Industrial, Commercial, and Institutional Boilers and Process Heaters" in the docket.
1.2.3.3 EPA Response to Recent Court Decisions in Developing the Emission Limitations
In developing the emission limitations, we tried to be responsive to the recent court decisions
from National Lime Association v. EPA and Cement Kiln Recycling Coalition v. EPA, regarding the
methodology used for determining the MACT floor. In response, we determined that the most
acceptable and appropriate approach for determining the MACT floor appears to be using only
emission data. As discussed and explained in section II.E of the proposal preamble, we determined
that for these source categories and the subcategories established the use of only the available emission
data would be inappropriate for determining the MACT floor for existing and new units. If only the
available emission data (from a population of units that is deemed unrepresentative) is used, the
resulting MACT floor emission levels would be, in most many cases, unachievable. This is because
the concentration of HAP (metals, HC1, mercury) vary greatly within each fuel type. Some even have
fuel analysis levels below the detection limit. Therefore, some units without any add-on controls have
emission levels below those with add-on controls. Section III.E of the proposal preamble explains in
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more detail the approach used to develop the MACT floors for each subcategory and why the approach
is appropriate for the subcategories regulated by this rule and why the mandating of fuel choice (using
low HAP-containing fuel) is also inappropriate.
In terms of subcategorizing, the main difficulty of establishing a separate subcategory for each
specific fuel type is that many industrial boilers burn a combination of fuels. Determining which
subcategory applies if the mixture varies would be problematic. Would the applicable emission limits
change each time the fuel mixture changes? How would compliance be determine and how would
continuous compliance be monitored? Because of these concerns, EPA chose not to further
subcategorize sources by each specific fuel type.
However, if we were to further subcategorize solid-fuel units into separate fossil and non-
fossil subcategories, we would first determine if the MACT floor could be developed, for either
subcategory, based on emissions information. If not, then we would look at developing MACT floors
based on control technologies. First we would determine if fuel switching or work practices could be
used. Based on the MACT floor analysis for solid-fuel fired boilers, it is expected that emissions
information and fuel switching would not be appropriate to develop the MACT floors for a solid fossil
or solid non-fossil subcategory. Similarly, there would be an insufficient number of boilers or process
heaters that would be meeting CO limits to set a level for existing units. However, new units would
likely be subject to a CO limit and monitoring.
In order to determine the MACT floor based on add-on control technologies, we would follow
similar procedures described in section III.E of the preamble. We would examine the population
database of existing sources and subcategorize solid fossil and non-fossil fuel fired boilers into each of
the following three subcategories based on capacity: (1) large boilers and process heaters (units with
heat inputs greater than 10 MMBtu/hr); (2) small units (with a maximum rated heat input capacity of
10 MMBtu/hr or less); and (3) limited use units with capacity utilization less than 10 percent.
We would identify the types of air pollution control techniques currently used by existing
boilers and process heaters in each subcategory. Then we would rank those controls according to their
effectiveness in removing the different categories of pollutants; including metallic HAP and PM,
inorganic HAP such as acid gases, mercury, and organic HAP.
Based upon the emissions reduction potential of existing air pollution control techniques, we
would list all the boilers and process heaters in the population database in order of decreasing control
device effectiveness for each subcategory. Then the technology basis of the existing source MACT
floor would be determined for each pollutant category by identifying the best-performing 12 percent of
units. We would then selected the technology used by the median unit in the best performing 12
percent of units (i.e., the boiler or process heater unit representing the 94th percentile) as the
technology associated with the MACT floor level of control for each subcategory.
After establishing the technology basis for the existing source MACT floor for each
subcategory and each type of pollutant, we would examine the emissions data available for boilers and
process heaters controlled by these technologies to determine achievable emission levels. The
resulting emission levels associated with the existing source MACT floors for each pollutant would be
based on the average of the lowest three run average test data from units using the technology
associated with the MACT floor level of control, and by incorporating operational variability using
results from multiple tests on these best performing units.
The preliminary MACT floor control technology for solid fossil-fuel fired units would be a
combination of a fabric filter and a scrubber. The preliminary MACT floor control technology for
solid non-fossil-fuel fired units would be a combination of an ESP and a scrubber.
1.2.3.4 How did EPA Determine the Emission Limitations for New Units?
All standards established pursuant to section 112 of the CAA must reflect MACT, the
maximum degree of reduction in emissions of air pollutants that the Administrator, taking into
consideration the cost of achieving such emissions reductions, and any non-air quality health and
environmental impacts and energy requirements, determines is achievable for each category. The
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CAA specifies that the degree of reduction in emissions that is deemed achievable for new boilers and
process heaters must be at least as stringent as the emissions control that is achieved in practice by the
best-controlled similar unit. However, the EPA may not consider costs or other impacts in
determining the MACT floor. The EPA may require a control option that is more stringent than the
floor (bey ond-the-floor) if the Administrator considers the cost, environmental, and energy impacts to
be reasonable.
Determining the MACT floor for New Units
Similar to the MACT floor process used for existing units, we considered several approaches
to identifying MACT floors for new industrial, commercial, and institutional boilers and process
heaters. First, we considered using emissions data on boilers and process heaters to set the MACT
floor. However, after review of the data available, we determined that emissions information was
inadequate to set MACT floors. We also reviewed State regulations and permits for these sources, but
found no State regulations or State permits which specifically limit HAP emissions from industrial,
commercial, and institutional boilers and process heaters.
Consequently, we concluded that the only reasonable approach for determining MACT floors
is to base it on control technology. Data were available on the control technologies employed by the
population of boilers identified by the EPA. We considered several possible control technologies (i.e.,
factors that influence emissions), including fuel substitution, process changes and work practices, and
add-on control technologies.
We first considered whether fuel switching would be an appropriate control option for sources
in each subcategory. Both fuel switching to other fuels used in the subcategory and fuels from other
subcategories were considered. This consideration included determining whether switching fuels
would achieve lower HAP emissions. A second consideration was whether fuel switching could be
technically done on boilers and process heaters in the subcategory considering the existing design of
boilers and process heaters. We also considered the availability of the alternative fuel.
As discussed in section III.D of the proposal preamble, based on the data available in the
emissions database, we determined that while fuel switching would decrease some HAPs, emissions of
some organic HAPs would increase, resulting in uncertain benefits. We determined that it would be
inappropriate in a MACT rulemaking, that is technology based, to consider a technology that
potentially will result in an increase in a HAP regardless of its potential to reduce other HAP without
determining the overall benefit. A detailed discussion of the consideration of fuel switching is
discussed in proposal preamble section III.D.
We also determined that using process changes or work practices were not appropriate in most
cases for developing MACT floors. HAP emissions from boilers and process heaters are primarily
dependent upon the composition of the fuel. Fuel dependent HAP are metals, including mercury, and
acid gases. Fuel dependent HAP are typically controlled by removing them from the flue gas after
combustion. Therefore, they are not affected by the operation of the boiler or process heater.
Consequently, process changes would be ineffective in reducing their emissions. The exception to this
conclusion is monitoring and maintaining CO levels. The measurement of CO is generally an
indicator of incomplete combustion since CO will burn to carbon dioxide if adequate oxygen is
available. Correcting incomplete combustion may be accomplished through providing more
combustion air. Therefore, we consider monitoring and maintaining CO emission levels to be
associated with minimizing organic HAP emission levels and, thus, CO monitoring would be a good
indicator of combustion efficiency and organic HAP emissions. As discussed in the final preamble,
CO is considered a surrogate for organic HAP emissions in this rule.
To determine if CO monitoring would be the basis of the new source MACT floor for organic
emissions control, we examined available information. The population databases did not contain
information on existing units monitoring CO emissions. We reviewed State regulations applicable to
boilers and process heaters that required the use of CO monitoring to maintain a specific CO limit.
The analysis of the State regulations indicated that at least one of the boilers and process heaters in the
large and limited use subcategories for solid fuel, liquid fuel, and gaseous fuel were required to
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monitor CO emissions and meet a CO limit of 200 parts per million. Therefore, the new source
MACT floor level of control includes a CO emission limit of 200 parts per million for large and
limited use units.
We concluded that, except for CO monitoring for organic HAP, add-on control technology is
the only factor that significantly controls emissions. To determine the MACT floor for new sources,
the EPA reviewed the population database of existing major sources.
Based upon the emission reduction potential of existing air pollution control devices, the EPA
listed all the boilers and process heaters in the population database in order of decreasing control
device effectiveness for each subcategory and each type of pollutant. Once the ranking of all existing
boilers and process heaters was completed for each subcategory and type of pollutant, the EPA
determined the technology basis of the new source MACT floor by identifying the best-controlled
source using the air pollution control rankings.
After establishing the technology basis for the new source MACT floor for each subcategory
and each type of pollutant, the EPA examined the emissions data available for boilers and process
heaters controlled by these technologies to determine achievable emission levels for PM (as a
surrogate for non-mercury metallic HAP), total selected non-mercury metallic HAP, mercury, HC1 (as
a surrogate for inorganic HAP), and CO (as a surrogate for organic HAP). This approach is reasonable
because the most informative way to predict the worst reasonably foreseeable performance of the best-
controlled unit, with available data, is to examine the performance of other units that use the same
technology. In other words, the EPA considers all units with the same control technology to be
equally well controlled, and each unit with the best control technology is a "best controlled similar
unit" even if the emission test results from such units vary considerably.
Accordingly, we selected as the floor for new units the level of control that was being
achieved in practice by the best-controlled similar source, that is, the source with emissions
representing the performance of the most effective control technology under the worst reasonably
foreseeable circumstances. A detailed description of the MACT floor determination is in the
memorandum "MACT Floor Analysis for New and Existing Sources in the Industrial, Commercial,
and Institutional Boilers and Process Heaters Source Categories" in the docket.
New Solid Fuel-fired Units
Large Units - Heat Inputs Greater than 10 MMBtu/hr. The most effective control technology
identified for removing PM from boilers in this subcategory is fabric filters. Therefore, the EPA
considers a fabric filter to be the technology basis for the new source MACT floor for non-mercury
metallic HAP emissions. The MACT floor emission level based on fabric filters is 0.04 Ib
PM/MMBtu. This PM emission level was selected from a subset of fabric filters contained in the
database. This subset includes fabric filters assumed to be subject or achieving the NSPS for industrial
boilers. The NSPS (40 CFR 60.40b), which represent best demonstrated technology for criteria
pollutants, is based on the use of a fabric filter for PM and requires the use of a scrubber for sulfur
dioxide. Therefore, fabric filters subjected to the NSPS are assumed to be better designed, and
operated than those built prior to the NSPS.
We are also providing an alternative metals limit of 0.00007 Ib metals/MMBtu which can be
used to show compliance in cases where metal HAP emissions are low in proportion to PM emissions.
The emissions database indicates that some biomass units have low metals content but high PM
emissions. The emission level for metals was selected from metals test data associated with PM
emission tests from fabric filters that met the MACT floor PM emission level. The most effective
control technologies identified for removing inorganic HAP including acid gases, such as HC1, are wet
scrubbers and packed bed scrubbers. Wet scrubbers is a generic term that is most often used to
describe venturi scrubbers, but can include packed bed scrubbers, impingement scrubbers, etc. One
percent of boilers and process heaters in this subcategory reported using a packed bed scrubber.
Emission test data from other industries suggests that packed bed scrubbers achieve consistently lower
emission levels than wet scrubbers. Therefore, the EPA considers a packed bed scrubber to be the
technology basis for the new source MACT floor for acid gas control for boilers and process heaters in
the solid fuel subcategory. The MACT floor emission level based on packed scrubbers is 0.016 Ib
HCl/MMBtu.
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For mercury control, one technology, carbon injection, that has demonstrated mercury
reductions in other source categories (i.e., municipal waste combustors), was identified as being used
on one existing industrial boiler. However, test data on this carbon injection system indicated that this
unit was not achieving mercury emissions reductions. Therefore, we did not consider carbon injection
to be a MACT floor control technology for industrial, commercial, and institutional boilers and
process heaters. Data from electric utility boilers indicate that fabric filters can achieve mercury
emissions reductions. Therefore, the EPA considers a fabric filter to be the control technology basis for
controlling mercury in this subcategory. The MACT floor emission level based on fabric filters is
0.0000026 Ib mercury/MMBtu.
Similar control technology analysis was done for the boilers and process heaters in this
subcategory for organic HAP. One control technique, controlling inlet temperature to the PM control
device, that has demonstrated controlling downstream formation of dioxins in other source categories
(e.g., municipal waste combustors) was analyzed for industrial boilers. Inlet and outlet dioxins test
data were available on four boilers controlled with PM control devices. In all cases, no increase in
dioxins emissions were indicated across the PM control device even at high inlet temperatures.
However, we are requesting comment on controls that would achieve reductions of organic HAP,
including any additional data that might be available. The EPA did find that CO monitoring can
reduce organic HAP emissions, and has included it in the new source MACT floors as described under
section III.F. of this preamble.
In light of this analysis, the EPA determined that the combination of a fabric filter, a packed
bed scrubber, and CO monitoring forms the control technology basis for the new source MACT floor
for boilers and process heaters in this subcategory.
Small Units - Heat Inputs Less than or Equal to 10 MMBtu/hr. The most effective control
technologies identified for removing non-mercury metallic HAP used by units in this subcategory are
fabric filters. Therefore, the EPA considers fabric filters to be the technology basis for the new source
MACT floor for non-mercury metallic HAP control in this subcategory. The most effective control
technology identified for units in this subcategory for removing acid gases, such as HC1, are wet
scrubbers. The most effective control technologies identified for removing mercury used by units in
this subcategory are fabric filters.
The EPA identified no control technology being used in the existing population of boilers and
process heaters that consistently achieved lower emission rates than uncontrolled levels, such that a
best-controlled similar source for organic HAP could be identified. We concluded the MACT floor for
new sources in this subcategory is no emissions reductions for organic HAP. Furthermore, CO
monitoring is not required for small boilers and process heaters by any State rules.
Thus, the EPA determined that the combination of a fabric filter and a wet scrubber forms the
control technology basis for the new source MACT floor for boilers and process heaters in this
subcategory.
The emissions test database did not contain test data for boilers and process heaters less than
10 MMBtu/hr heat input. In order to develop emission levels for this subcategory, we decided to use
information from units in the large solid subcategory. We considered this to be an appropriate
methodology because although the units in this subcategory are different enough to warrant their own
subcategory (i.e., different designs and emissions), emissions of the specific HAP for which limits are
being proposed (HC1, PM and metals) are expected to be related more to the type of fuel burned and
the type of control used than to the unit design. Consequently, we determined that emissions
information from units greater than 10 MMBtu/hr heat input could be used to establish the MACT
floor levels for this subcategory for HC1, non-mercury metallic HAP (using PM as a surrogate), and
mercury because the fuels and controls are similar.
The MACT floor emission level based on emissions data for fabric filters on solid fuel-fired
boilers is 0.04 Ib PM/MMBtu or 0.00007 Ib selected non-mercury metals/MMBtu, and 0.0000026
mercury/MMBtu. The MACT floor emission level based on wet scrubbers is 0.032 Ib HCl/MMBtu. .
Limited Use Units - Capacity Utilizations Less than or Equal to 10 Percent. The most
effective control technologies identified for removing non-mercury metallic HAP and mercury used by
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units in this subcategory are fabric filters. Therefore, the EPA considers fabric filters to be the
technology basis for the new source MACT floor for non-mercury metallic HAP and mercury control
in this subcategory. The most effective control technology identified for units in this subcategory for
removing acid gases, such as hydrogen chloride, are wet scrubbers.
The EPA did find that monitoring CO is used by at least one unit and can reduce organic HAP
emissions, and has included it in the new source MACT floor for this subcategory as described under
section III.F of this preamble.
Therefore, based on this analysis, the EPA determined that the combination of a fabric filter, a
wet scrubber, and CO monitoring forms the control technology basis for the new source MACT floor
for boilers and process heaters in this subcategory.
Consequently, we determined that emissions information from units greater than 10
MMBtu/hr heat input could be used to establish MACT floor levels for this subcategory because the
fuels and controls are similar. The MACT floor emission level based on fabric filters is 0.04 Ib
PM/MMBtu or 0.00007 Ib metals/MMBtu, and 0.0000026 mercury/MMBtu. The MACT floor
emission level based on wet scrubbers is 0.032 Ib HCl/MMBtu.
New Liquid Fuel-fired Units
Large Units - Heat Inputs Greater than 10 MMBtu/hr. The most effective control technologies
identified for removing non-mercury metallic HAP and PM from units in this subcategory are fabric
filters. Therefore, the EPA considers a fabric filter to be the technology basis for the new source
MACT floor for non-mercury metallic HAP. A PM level is set as a surrogate for non-mercury metallic
HAP control. The MACT floor emission level based on emission data for fabric filters on liquid fuel
fired boilers is 0.068 Ib PM/MMBtu. Unlike for solid fuel subcategories, we are not aware of any
liquid fuels that are low in metals but would have high PM emissions. Therefore, we do not have an
alternative metals standard for the liquid subcategories.
The most effective control technologies identified for removing inorganic HAP that are acid
gases, such as HC1, are packed bed scrubbers. Therefore, the EPA considers a packed bed scrubber to
be the technology basis for the new source MACT floor for acid gas control for boilers and process
heaters in the liquid fuel subcategory. The MACT floor emission level based on packed scrubbers is
0.00045 Ib HCl/MMBtu.
Similar control technology analyses were done for the boilers and process heaters in this
subcategory for mercury and organic HAP.
Information in the emissions database or from other source categories does not show that
control technologies, such as fabric filters or wet scrubbers, achieve reductions in mercury emissions
from liquid fuel-fired industrial, commercial, and institutional boilers and process heaters. Therefore,
EPA identified no control technology being used in the existing population of boilers and process
heaters in these subcategories that consistently achieved lower emission rates than uncontrolled levels,
such that a best-controlled similar source for organic HAP could be identified. However, we did find
that monitoring CO is a good combustion practice that can reduce organic HAP emissions, and has
included it in the new source MACT floor as described under section III.D of this preamble. We
concluded the MACT floor for new sources in this subcategory is no emissions reductions for
mercury.
In light of this analysis, the EPA determined that the combination of a fabric filter, a packed
bed scrubber, and CO monitoring forms the control technology basis for the new source MACT floor
for boilers and process heaters in this subcategory.
Small Units - Heat Inputs Less than or Equal to 10 MMBtu/hr. The most effective control
technologies identified for removing non-mercury metallic HAP used by units in this subcategory are
fabric filters. Therefore, the EPA considers fabric filters to be the technology basis for the new source
MACT floor for non-mercury metallic HAP control in this subcategory. The most effective control
technology identified for units in this subcategory for removing acid gases, such as hydrogen chloride,
are wet scrubbers.
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Information in the emissions database or from other source categories does not show that other
control technologies, such as fabric filters or wet scrubbers, achieve reductions in mercury emissions
from liquid fuel-fired industrial, commercial, and institutional boilers and process heaters. Therefore,
EPA could not identify a control technology being used in the existing population of boilers and
process heaters that consistently achieved lower emission rates than uncontrolled levels, such that a
best-controlled similar source for mercury or organic HAP could be identified. We concluded the
MACT floor for new sources in this subcategory is no emissions reductions for mercury or organic
HAP.
Thus, the EPA determined that the combination of a fabric filter and a wet scrubber forms the
control technology basis for the new source MACT floor for boilers and process heaters in this
subcategory.
The emissions test database did not contain test data for boilers and process heaters less than
10 MMBtu/hr heat input. In order to develop emission levels for this subcategory, we decided to use
information from units in the large liquid subcategory. We considered this to be an appropriate
methodology because although the units in this subcategory are different enough to warrant their own
subcategory (i.e., different designs and emissions), emissions of the specific types of HAP for which
limits are being proposed (HC1 and metals) are expected to be more related to the type of fuel burned
and the type of control than to unit design. Consequently, we determined that emissions information
from units greater than 10 MMBtu/hr heat input could be used to establish MACT floor levels for this
subcategory because the fuels and controls are similar. The MACT floor emission level based on
fabric filters is 0.068 Ib PM/MMBtu. The MACT floor emission level based on wet scrubbers is
0.0009 Ib HCl/MMBtu.
Limited Use Units - Capacity Utilizations Less than or Equal to 10 Percent. The most
effective control technologies identified for removing non-mercury metallic HAP used by units in this
subcategory are fabric filters. Therefore, the EPA considers fabric filters to be the technology basis for
the new source MACT floor for non-mercury metallic HAP control in this subcategory. The most
effective control technology identified for units in this subcategory for removing acid gases, such as
hydrogen chloride, are wet scrubbers.
Information in the emissions database or from other source categories does not show that other
control technologies, such as fabric filters or wet scrubbers, achieve reductions in mercury emissions
from liquid fuel-fired industrial, commercial, and institutional boilers and process heaters. The EPA
identified no control technology being used in the existing population of boilers and process heaters
that consistently achieved lower emission rates than uncontrolled levels, such that a best-controlled
similar source for mercury could be identified. We concluded the MACT floor for new sources in this
subcategory is no emissions reductions for mercury.
We did find that monitoring CO can reduce organic HAP emissions and is used by at least one
unit in this subcategory, and have included it in the new source MACT floor as described under
section III.D of this preamble. Therefore, based on this analysis, the EPA determined that the
combination of a fabric filter, a wet scrubber, and CO monitoring forms the control technology basis
for the new source MACT floor for boilers and process heaters in this subcategory.
The emissions test database did not contain test data for limited use liquid-fired boilers and
process heaters. In order to develop emission levels for this subcategory, we decided to use
information from units in the large liquid subcategory. Consequently, we determined that emissions
information from units greater than 10 MMBtu/hr heat input could be used to establish MACT floor
levels for this subcategory because the fuels and controls are similar. The MACT floor emission level
based on fabric filters is 0.068 Ib PM/MMBtu. The MACT floor emission level based on wet
scrubbers is 0.0009 Ib HCl/MMBtu.
Gaseous Fuel Subcategories
No existing units were using control technologies that achieve consistently lower emission
rates than uncontrolled sources for any of the pollutant groups of interest, except organic HAP. At
least one unit in the population database in the large and limited use gaseous fuel subcategories is
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required to monitor CO. Therefore, the MACT floor for gaseous fuel-fired units includes a CO
monitoring requirement and emission limit, as described in section III.D of this preamble, but it does
not include any emission limits for PM, metallic HAP, mercury, or inorganic HAP based on the
utilization of add-on control technology.
How EPA Considered Beyond the Floor Options for New Units
The MACT floor level of control for new units is based on the emission control that is
achieved in practice by the best controlled similar source within each of the subcategories. No
technologies were identified that would achieve non-mercury metals reduction greater than the new
source floors (i.e., fabric filters) for the liquid and solid subcategories or CO monitoring for the solid,
liquid, and gaseous subcategories. For inorganic HAP control, we determined that packed bed
scrubbers achieve higher emissions reductions than MACT floors consisting of a wet scrubber.
Packed bed scrubbers are the technology basis of the MACT floor for the large unit subcategory, but
wet scrubbers were the technology basis of the floors for the small unit and limited unit subcategories.
Therefore, we examined the cost and emission reductions of applying a packed bed scrubber as a
beyond the floor option for new solid and liquid units within the small and limited use subcategories.
We determined that costs were excessive for the limited emission reduction that would be achieved.
Non-air quality health, environmental impacts, and energy effects were not significant factors, because
there would be little difference in the non-air quality health and environmental impacts between
packed bed scrubbers and wet scrubbers. Therefore, the EPA did not select this beyond-the-floor
option, and the proposed new source MACT level of control for PM, metallic HAP, and inorganic
HAP (HC1) is the same as the MACT floor level of control for all of the subcategories.
In reviewing potential regulatory options beyond the new source MACT floor level of control,
the EPA identified one existing solid fuel-fired industrial boiler that was using carbon injection
technology for mercury control. However, emission data obtained from this unit indicated that it was
not achieving mercury emission reductions from the uncontrolled levels. Moreover, we do not have
information to otherwise show that carbon injection is effective for reducing mercury emissions from
industrial, commercial, and institutional boilers and process heaters. Information in the emissions
database or from other source categories does not show that other control technologies, such as fabric
filters or wet scrubbers, achieve reductions in mercury emissions from liquid fuel-fired industrial,
commercial, and institutional boilers and process heaters. Therefore, carbon injection, for solid fuel
units, and other control techniques, for liquid fuel units, were not evaluated as regulatory options.
For the solid fuel and liquid fuel subcategories, fuel switching to natural gas is a potential
regulatory option beyond the new source floor level of control that would reduce mercury and metallic
HAP emissions. However, based on current trends within the industry, the EPA projects that the
majority of new boilers and process heaters will be built to fire natural gas as opposed to solid and
liquid fuels such that the overall emissions reductions associated with this option would be minimal.
Furthermore, organic HAP may be increased by fuel switching. Limited emissions reductions in
combination with the high cost of fuel switching and considerations about the availability and
technical feasibility of fuel switching makes this an unreasonable regulatory option that was not
considered further. Non-air quality health, environmental impacts, and energy effects were not
significant factors. No beyond-the-floor options for gas-fired boilers were identified.
Based on the analysis discussed above, the EPA decided to not go beyond the MACT floor
level of control for new sources for MACT in the rule.
1.2.4 Considerations of Possible Risk-Based Alternatives to Reduce Impacts to Sources
The Agency has made every effort in developing this rule to minimize the cost to the regulated
community and allow maximum flexibility in compliance options consistent with our statutory
obligations. However, we recognize that the rule may still require some facilities to take costly steps
to further control emissions even though their emissions may not result in exposures which could pose
an excess individual lifetime cancer risk greater than one in one million or which exceed thresholds
determined to provide an ample margin of safety for protecting public health and the environment
from the effects of hazardous air pollutants. We therefore solicited comment on whether there are
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further ways to structure the rule to focus on the facilities which pose significant risks and avoid the
imposition of high costs on facilities that pose little risk to public health and the environment.
Representatives of the plywood and composite wood products industry provided EPA with
descriptions of three mechanisms that they believed could be used to implement more cost-effective
reductions in risk. The docket for today's rule contains "white papers" prepared by industry that
outline their proposed approaches (see docket number A-98-44, Item # II-D-525). These approaches
could be effective in focusing regulatory controls on facilities that pose significant risks and avoiding
the imposition of high costs on facilities that pose little risk to public health or the environment, and
we sought public comment on the utility of each of these approaches with respect to this rule.
One of the approaches, an applicability cutoff for threshold pollutants, would be implemented
under the authority of CAA section 112(d)(4); the second approach, subcategorization and delisting,
would be implemented under the authority of CAA sections 112(c)(l) and 112(c)(9); and, the third
approach, would involve the use of a concentration-based applicability threshold. We sought
comments on whether these approaches are legally justified and asked for information that could be
used to support such approaches.
The approach the Agency has chosen to include in the final rule is the first approach - an
applicability cutoff for threshold pollutants. The threshold pollutants for which an applicability cutoff
is applied are hydrochloric acid (Hcl) and a series of eight metals known as the total selected metals
(TSM).
1.2.4.1 Applicability Cutoffs for Threshold Pollutants Under Section 112(d)(4) of the CAA
This approach is an "applicability cutoff for threshold pollutants that is based on EPA's
authority under CAA section 112(d)(4). A "threshold pollutant" is one for which there is a
concentration or dose below which adverse effects are not expected to occur over a lifetime of
exposure. For such pollutants, section 112(d)(4) allows EPA to consider the threshold level, with an
ample margin of safety, when establishing emissions standards. Specifically, section 112(d)(4) allows
EPA to establish emission standards that are not based upon the maximum achievable control
technology (MACT) specified under section 112(d)(2) for pollutants for which a health threshold has
been established. Such standards may be less stringent than MACT. Historically, EPA has interpreted
112(d)(4) to allow us to avoid further regulation of categories of sources that emit only threshold
pollutants, if those emissions result in ambient levels that do not exceed the threshold, with an ample
margin of safety.3
In the past, EPA routinely treated carcinogens as non-threshold pollutants. The EPA
recognizes that advances in risk assessment science and policy may affect the way EPA differentiates
between threshold and non-threshold HAP. The EPA's draft Guidelines for Carcinogen Risk
Assessment4 suggest that carcinogens be assigned non-linear dose-response relationships where data
warrant. Moreover, it is possible that dose-response curves for some pollutants may reach zero risk at
a dose greater than zero, creating a threshold for carcinogenic effects. It is possible that future
evaluations of the carcinogens emitted by this source category would determine that one or more of the
carcinogens in the category is a threshold carcinogen or is a carcinogen that exhibits a non-linear dose-
response relationship but does not have a threshold.
The dose-response assessments for formaldehyde and acetaldehyde are currently undergoing
revision by the EPA. As part of this revision effort, EPA is evaluating formaldehyde and acetaldehyde
as potential non-linear carcinogens. The revised dose-response assessments will be subject to review
by the EPA Science Advisory Board, followed by full consensus review, before adoption into the EPA
1 See 63 FR 18754, 18765-66 (April 15, 1998) (Pulp and Paper Combustion Sources Proposed
NESHAP)
' "Draft Revised Guidelines for Carcinogen Risk Assessment." NCEA-F-0644. USEPA, Risk
Assessment Forum, July 1999. pp 3-9ff http://www.epa.gov/ncea/raf/pdfs/cancer gls.pdf
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Integrated Risk Information System (IRIS). At this time, EPA estimates that the consensus review will
be completed sometime in 2004. The revision of the dose-response assessments could affect the
potency factors of these HAP, as well as their status as threshold or non-threshold pollutants. At this
time, the outcome is not known. In addition to the current reassessment by EPA, there have been
several reassessments of the toxicity of and carcinogenicity of formaldehyde in recent years, including
work by the World Health Organization and the Canadian Ministry of Health.
1.2.4.2 Applicability Cutoffs for Hydrogen Chloride Controls Under Section 112(d)(4)
of the CAA
HC1 Compliance Alternative.
As an alternative to the requirement for each large solid fuel-fired boiler to demonstrate
compliance with the HC1 emission limit in the final rule, you may demonstrate compliance with a
health-based facility-wide HC1 equivalent allowable emission limit.
The procedures for demonstrating eligibility for the HC1 compliance alternative (as outlined in
appendix A of the final rule) are:
(1) You must include in your demonstration every emission point within the facility that emits
a respiratory toxicant included on EPA's list of hazardous air pollutants.
(2) You must conduct HC1 and chlorine emissions tests for every emission point covered
under subpart DDDDD.
(3) You must obtain either through emission testing or through the development and
documentation of best engineering estimates of maximum emissions of respiratory toxicants from all
emission points at the facility not covered under subpart DDDDD of part 63 from which a respiratory
toxicant might reasonably be emitted.
(4) You must determine the total maximum hourly mass HCl-equivalent emission rate for your
facility by summing the maximum hourly toxicity-weighted emission rates of all appropriate
respiratory toxicants (calculated using the maximum rated capacities of the units) for each of the units
at your facility.
(5) Use the look-up table in the appendix A of subpart DDDDD to determine if your facility is
in compliance with health-based HCl-equivalent emission limit.
(6) Select the maximum allowable HCl-equivalent emission rate from the look-up table in
appendix A of subpart DDDDD of part 63 for your facility using the average stack height of your
subpart DDDDD emission units as your stack height and the minimum distance between any
respiratory toxicant emission point at the facility and the closest boundary of the nearest residential (or
residentially zoned) area as your fenceline distance.
(7) Your facility is in compliance if your maximum HCl-equivalent emission rate does not
exceed the value specified in the look-up table in appendix A of subpart DDDDD.
(8) As an alternative to using the look-up table, you may conduct a site-specific compliance
demonstration (as outlined in appendix A of subpart DDDDD of part 63) which demonstrate that your
facility cannot cause an individual chronic inhalation exposure from respiratory toxicants which can
exceed a Hazard Index (HI) value of 1.0.
1.2.4.3 Applicability Cutoffs for Total Selected Metals Controls Under Section 112(d)(4)
of the CAA
In lieu of complying with the emission standard for TSM in subpart DDDDD of part 63 based
on the sum of emissions for the eight selected metals (arsenic, cadmium, chromium, mercury,
manganese, nickel, lead, and ), you may demonstrate eligibility for complying with the TSM standard
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based on excluding manganese emissions from the summation of TSM emissions for the affected
source unit.
The procedures for demonstrating eligibility for the TSM compliance alternative (as outlined
in appendix A of the subpart DDDDD) are:
(1) You must include in your demonstration every emission point within the facility that emits
a CNS toxicant included on EPA's list of hazardous air pollutants.
(2) You must conduct manganese emissions tests for every emission point covered under
subpart DDDDD that emits manganese.
(3) You must obtain either through emission testing or through the development and
documentation of best engineering estimates of maximum emissions of CNS toxicants from all
emission points at the facility not covered under subpart DDDDD from which a CNS toxicant might
reasonably be emitted.
(4) You must determine the total maximum hourly manganese equivalent emission rate from
your facility by summing the maximum hourly toxicity-weighted emission rates of all appropriate
CNS toxicants (calculated using the maximum rated heat input capacities) for each of the units at your
facility.
(5) Use the look-up table in appendix A of subpart DDDDD to determine if your facility is
eligible for complying with the TSM limit based on the sum of emissions for seven metals (excluding
manganese) for the affected source units.
(6) Select the maximum allowable manganese-equivalent emission rate from the look-up table
in appendix A of subpart DDDDD for your facility using the average stack height of your subpart
DDDDD emission units as your stack height and the minimum distance between any CNS toxicant
emission point at the facility and the closest boundary of the nearest residential (or residentially zoned)
area as your fenceline distance.
(7) Your facility is eligible if your maximum manganese-equivalent emission rate does not
exceed the value specified in the look-up table in appendix A of subpart DDDDD.
(8) As an alternative to using look-up table to determine if your facility is eligible for the
TSM compliance alternative, you may conduct a site-specific compliance demonstration (as outlined
in appendix A of subpart DDDDD) which demonstrates that your facility cannot cause an individual
chronic inhalation exposure from CNS toxicants which can exceed a HI value of 1.0.
If you elect to demonstrate eligibility for either of the health-based compliance alternatives,
you must submit certified documentation supporting compliance with the procedures at least 1 year
before the compliance date.
You must submit supporting documentation including documentation of all maximum
capacities, existing control devices used to reduce emissions, stack parameters, and property boundary
distances to each on-site source of HCl-equivalent and/or manganese-equivalent emissions.
You must keep records of the information used in developing the eligibility demonstration for
your affected source.
To be eligible for either health-based compliance alternative, the parameters that defined your
affected source as eligible for the health-based compliance alternatives (including, but not limited to,
fuel type, type of control devices, process parameters documented as worst-case conditions during the
emissions testing used for your eligibility demonstration) must be incorporated as Federally
enforceable limits into your title V permit. If you do not meet these criteria, then your affected source
is subject to the applicable emission limits, operating limits, and work practice standards in Subpart
DDDDD.
If you intend to change key parameters (including distance of stack to the property boundary)
that may result in lower allowable health-based emission limits, you must recalculate the limits under
the provisions of this section, and submit documentation supporting the revised limits prior to
initiating the change to the key parameter.
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If you intend to install a new solid fuel-fired boiler or process heater or change any existing
emissions controls that may result in increasing HCl-equivalent and/or manganese-equivalent
emissions, you must recalculate the total maximum hourly HCl-equivalent and/or manganese-
equivalent emission rate from your affected source, and submit certified documentation supporting
continued eligibility under the revised information prior to initiating the new installation or change to
the emissions controls.
Facilities that could not demonstrate that they are eligible to be included in the low-risk
subcategory would be subject to MACT and possible future residual risk standards.
1.3 Other Federal Programs
There are a number of other federal programs that affect air pollutant emissions from these
sources. The effects of similar federal programs are the following:
• The commercial and industrial solid waste incinerators (CISWI) standards (in 40 CFR 60
subparts CCCC and DDDD) regulate commercial and industrial non-hazardous solid waste
incinerators. These standards are final as of Dec. 1, 2000. Sources subject to the CISWI rules
are exempt from the requirements of this NESHAP.
• The utility HAPs study Report to Congress provides information used to determine whether
fossil fuel fired utility boilers should be regulated in a future MACT standard. A fossil fuel-
fired utility boiler is a fossil fuel-fired combustion unit with a heat input greater than 25
megawatts that serves a generator producing electricity for sale. Fossil fuel-fired utility boilers
are exempt from this regulation. Non-fossil fuel-fired utility are, however, covered by this
proposed standard.
• EPA's Office of Solid Waste is in the process of developing MACT standards for hazardous
waste boilers. Boilers burning hazardous waste are not included in this regulation.
• Previously, EPA had codified new source performance standards (NSPS) for industrial boilers
in 1986 (in 40 CFR 60 subparts Db and DC) and revised portions of them in 1999. The NSPS
regulates emissions of particulate matter (PM), sulfur dioxide (SO2), and nitrogen oxides
(NOx) from boilers constructed after June 19, 1984. Source subject to the NSPS are still
subject to this NESHAP because the NESHAP regulates sources of hazardous air pollutants
while the NSPS does not. However, in developing the NESHAP for
industrial/commercial/institutional boilers and process heaters EPA minimized the monitoring,
recordkeeping requirements, and testing requirements so as not to duplicate requirements.
1.4 Scope of the Analyses in the RIA
The MACT floor will affect approximately 5,600 existing and new units. EPA developed
annual compliance costs for model units in each of 83 different model unit types. EPA then linked the
annualized compliance costs from the model units to the estimated existing population of boilers and
process heaters to obtain national impact estimates. In addition, the Agency projected entrance of new
boilers and process heaters through the year 2005, and linked the annualized compliance costs to these
projected new units.
The impacts of national compliance costs, including impacts to both existing and new units, on
affected markets was then estimated using a computerized market model. EPA used changes in prices
and quantities in energy markets and final product markets to estimate the firm-, industry-, market-,
and societal-level impacts associated with the regulation. EPA then estimated changes in particulate
matter (PM) concentrations associated with this regulation using an air quality model and then
estimated the benefits associated with these changes in PM concentrations. To estimate the benefits,
the Agency used an in-house model to calculate benefits and then monetize them for emission
reductions in areas where the assignment of controls to affected sources is well-defined. The Agency
then used a benefits transfer technique to apply the benefits estimates from reductions at sources with
well-defined control assignments to calculate benefits in areas where the assignment of controls is not
well-assigned. Finally, the Agency compared the benefits to the costs of the regulation.
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Results of these analyses are presented for the final rule (MACT floor) and Option 1A.
Results of the costs and some economic impact information are presented for Option IB. There is
insufficient information for estimating the economic impacts and small entity impacts associated with
Option IB, and the benefits estimate for this option is the same as that for Option 1A since there are no
additional emissions reductions expected.
1.5 Organization of the Report
The remainder of this report is divided into ten chapters that describe the analysis
methodologies and presents the analyses results:
• Chapter 2 provides background information on boiler and process heater technologies.
• Chapter 3 profiles existing boilers and process heaters by capacity, fuel type, and industry
and presents projections of the future population of units in 2005.
• Chapter 4 profiles the industries with the largest number of affected facilities. Included
are profiles of the lumber and wood products (SIC 24/NAICS 321), furniture and related
product manufacturing (SIC 25/NAICS 337), paper and allied products (SIC 26/NAICS
322), and electrical services (SIC 49/NAICS 221) industries.
• Chapter 5 describes the methodology for assessing the economic impacts of the National
Emission Standard for Hazardous Air Pollutants (NESHAP).
• Chapter 6 presents the results of the economic analysis, including market, industry, and
social cost impacts.
• Chapter 7 provides the Agency's analysis of the regulation's impact on small entities.
• Chapter 8 presents the Agency's analysis of the changes in air quality associated with
compliance with the regulation, and a description of the emissions inventories used in the
air quality analysis.
• Chapter 9 presents the results of the qualitative benefits associated with implementation of
this regulation.
• Chapter 10 presents the results of the quantitative and monetized benefits associated with
implementation of this regulation and a comparison of the benefits to the costs of the rule.
In addition to these chapters, there are five appendicies as well. Appendix A provides
information on the databases and equations used in the economic impact analysis, and Appendix B
provides details on assumptions behind the operation of the economic model, along with results of
sensitivity analyses. Appendix C provides some results from the air quality modeling conducted to
determine reductions in concentrations of PM associated with the emissions reductions expected to
take place. These results are for the above-the-floor option 1A only. Appendix D contains the results
of sensitivity analyses and alternative calculations for our benefits estimates. Finally, Appendix E
contains impact estimates associated with the health-based compliance alternatives for HC1 and Mn
sources.
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References
Federal Register, 1993. Executive Order 12866, Regulatory Planning and Review. Vol.58,
October 4, 1993, pg. 51735.
Federal Register, 2001. Executive Order 13211, Actions Concerning Regulations That
Significantly Affect Energy Supply, Distribution, or Use. Vol. 66, May 22, 2001, pg. 28355.
Federal Register, 2002. Executive Order 13258, Amending Executive Order 12866 -
Regulatory Planning and Review. Vol.67 , February 28, 2002, pg. 9385.
U.S. Environmental Protection Agency, 1996. Guidance for Providing Environmental Justice
Concerns in EPA 's NEPA Compliance Analyses (Review Draft). Office of Federal Activities,
Washington, D.C., July 12, 1996.
U.S. Environmental Protection Agency, 1996. Memorandum from Trovato and Kelly to
Assistant Administrators. Subject: "Implementation of Executive Order 13045, Protection of Children
from Environmental Health and Safety Risks." April 21, 1998.
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CHAPTER 2
BOILER AND PROCESS HEATER TECHNOLOGIES
The three categories of combustion devices affected under the regulations are industrial
boilers, commercial and institutional (ICI) boilers, and process heaters. Although their primary
function is to transfer heat generated from fuel combustion to materials used in the production process,
the applications for boilers and process heaters are somewhat different. As a result, the primary
industries using boilers may not be the same as those using process heaters. It is important to note that
throughout this report the terms "boilers and process heaters," and "units" are synonymous with "ICI
boilers and process heaters." Utility boilers primarily engaged in generating electricity are not covered
by the NESHAP under analysis and are therefore excluded from this analysis.
Boilers are combustion devices used to produce steam or heat water. Steam is produced in
boilers by heating water until it vaporizes. The steam is then channeled to applications within a
facility or group of facilities via pipes. Steam is an important power and heating source for the U.S.
economy. It is used in the preparation or manufacturing of many key products, such as paper,
petroleum products, furniture, and chemicals. Steam is also used to heat buildings and to generate the
majority of the electricity consumed in this country. There are literally thousands of boilers currently
being used in the United States throughout a wide variety of industries.
Process heaters are primarily used as heat transfer units in which heat from fuel combustion is
transferred to process fluids, although they may also be used to transfer heat to other nonfluid
materials or to heat transfer materials for use in a process unit (not including generation of steam).
Process heaters are generally used in heat transfer applications where boilers are inadequate. Often
these are uses in which heat must be transferred at temperatures in excess of 90° to 204°C (200° to
400°F). Process heaters are used in the petroleum refining and petrochemical industries, with minor
applications in the asphalt concrete, gypsum, iron and steel, and wood and forest products industries.
Since one of the main uses of boilers is to generate steam, some of the characteristics of steam
are discussed in this chapter. This chapter also provides an overview of the various types of boiler and
process heater characteristics and designs.
2.1 Characteristics of Steam
Steam, an odorless, invisible gas of vaporized water, may be interspersed with water droplets,
which gives it a cloudy appearance. It is produced naturally when underground water is heated by
volcanic processes and mechanically using boilers and other heating processes. When water is heated
at atmospheric pressure, it remains in liquid form until its temperature exceeds 212°F, the boiling point
of water. Additional heat does not raise the water's temperature but rather vaporizes the water,
converting it into steam. However, if water is heated under pressure, such as in a boiler, the boiling
point is higher than 212°F and more heat is required to generate steam. Once all the water has been
vaporized into steam, the addition of heat causes the temperature and volume to increase. Steam's
heating and work capabilities increase as it is produced under greater pressure coupled with higher
temperatures. As steam escapes from the boiler, it can be directed through pipes to drive mechanical
processes or to provide heat.
The steam used in most utility, industrial, and commercial applications is referred to as "clean
steam." Clean steam encompasses steam purities ranging from pure, solid-free steam used in critical
processes to filtered steam for less demanding applications. The various types of clean steam differ in
steam purity and steam quality. Steam purity is a quantitative measure of contamination of steam
caused by dissolved particles in the vapor or by tiny droplets of water that may remain in the steam.
Steam quality is a measure of how much liquid water is mixed in with the dry steam (Fleming, 1992).
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Firms select the levels of steam quality and steam purity for their applications based on the sensitivity
of their equipment to impurities, water droplet size, and condensation as well as the requirements for
their production process. Using clean steam minimizes the risk of product contamination and prolongs
equipment life. Although there are infinite possible levels of water purity and quality, the term "clean
steam" generally refers to three basic types of steam:
• filtered steam—produced by filtering plant steam using high-efficiency filters. Filtered
steam is generally of high steam quality because most large water droplets and other
contaminants will be filtered out.
• clean steam—steam that is frequently produced from deionized and distilled water.
Deionized and distilled water is free of dissolved solids and ions, which may corrode
pipework.
• pure steam—similar to clean steam except that it is always produced from deionized and
distilled water.
Steam applications can be categorized by the amount of pressure required: hot water, low
pressure, and high pressure. Low pressure is 0 to 15 pounds per square inch (psi) and high pressure
steam is above 15 psi (Plant Engineering, 1991). Hot water systems, which generate little steam, are
primarily used for comfort applications, such as hot water for a building. Low pressure applications
include process heat and space heating. High pressure steam applications are more frequently used in
industrial and utility applications. Some high pressure applications require that the steam be
superheated, a process which ensures that the steam is free of water droplets, to avoid damaging
sensitive equipment.
Electric cogenerators, such as large factories and processing facilities, use steam to drive
turbines to generate electricity. A conventional steam electric power plant burns fossil fuels (coal, gas,
or oil) in a boiler, releasing heat that boils water and converts it into high-pressure steam (see Figure 2-
1). The steam enters a turbine where it expands and pushes against blades to turn the generator shaft
and create electric current. In this way, the thermal energy of steam becomes mechanical energy,
which is converted into electricity. Steam used to drive turbines generates most of the electric power
in the United States (TXU, 2000).
Industrial operations use steam to perform work such as powering complex machinery
operations, in the same way that electric utilities use steam to rotate turbines. Textile mills, pulp and
paper mills, and other manufacturing outfits are examples of facilities that use steam to run machinery.
Steam also provides heat and pressure for manufacturing processes. Industrial establishments use
steam to provide heat for drying or to heat and separate materials. For example, the paper industry
uses steam to heat rollers that dry paper during the final stages of the production process. Petroleum
refineries and chemical producers use steam to heat petroleum, raw materials, and other inputs to
separate inputs into their constituent components or to facilitate chemical interactions. In addition to
these applications, steam is employed in many other industrial processes, including textile production,
wood working, furniture making, metal working, food preparation, and the manufacture of chemicals.
Substitutes for using steam as process heat include electrical heating equipment, infrared, and other
radiant drying techniques. Electricity may be used to power machinery, as well. However, switching
from steam-powered to electricity-powered machinery would require significant equipment retrofits or
replacement.
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Stack
Combustion Gases
Steam Turbine
Generator
Pulverized
Coal
Courtesy of TXU Boiler
Condenser
Electricity
Figure 2-1. Generating Electricity: Steam Turbines
Source: Texas Utilities (TXU). 2000. "Generating Electricity: Steam Turbines." As obtained in September
2000. .
Other steam applications include heating, sanitation, food processing and preparation, and
cleaning. In addition to using boilers to heat water, factories, hospitals, government buildings, schools
and other large buildings use boiler-generated steam to provide space heating. Substitutes for boilers
in heating air and water include electrical water and space heaters; furnaces; and other heating,
ventilation, and air conditioning equipment.
2.2 Fossil-Fuel Boiler Characterization
This section discusses the different classes of fossil-fuel boilers, the most common heat
transfer configurations, and the major design types. The discussion indicates the type(s) of fuel that
each design can use to operate.
2.2.7 Industrial, Commercial, and Institutional Boilers
Industrial, commercial, and institutional boilers are primarily used for process heating,
electrical or mechanical power generation, and/or space heating. Industrial boilers are used in all
major industrial sectors but primarily by the paper products, chemical, food, and petroleum industries.
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It is estimated that the heat input capacity for these boilers is typically between 10 and 250 MMBtu/hr;
however, larger industrial boilers do exist and are similar to utility boilers (EPA, 1997b).
Commercial/institutional boilers are generally smaller than the industrial units, with heat input
capacities generally below 10 MMBtu/hr. These units normally supply the steam and hot water for
space heating in a wide range of locations, including wholesale and retail trade, office buildings,
hotels, restaurants, hospitals, schools, museums, government buildings, and airports. Five hundred
ninety-three of the 3,615 units potentially affected by the floor alternative for the proposed regulation
are commercial/institutional units.
A boiler system includes the boiler itself, associated piping and valves, operation and safety
controls, water treatment system, and peripheral equipment such as pollution control devices,
economizers, or superheaters (Plant Engineering, 1991). Most boilers are made of steel, cast iron, or
copper. The primary fuels used by boilers are coal, oil, and natural gas, but some use electricity, waste
gases, orbiomass.
Boilers may either be erected onsite (field-erected boilers) or assembled at a factory (packaged
boilers). Packaged boilers are typically lower in initial cost and more simple to install. However,
field-erected boilers may have lower operating costs, less maintenance, and greater flexibility because
the furnace or convection pattern chosen to meet required steam pressure, capacity, and fuel
specifications is tailored to the boiler's potential use (Plant Engineering, 1991). Applications
requiring more than 100,000 pounds of steam per hour are usually equipped with a field-erected boiler.
2.2.2 Heat Transfer Configurations
The heat transfer configuration of a boiler refers to the method by which heat is transferred to
the water. The four primary boiler configurations are watertube, firetube, cast iron, and tubeless.
Most industrial users tend to rely on either watertube or firetube configurations.
In a watertube boiler, combustion heat is transferred to water flowing through tubes lining the
furnace walls and boiler passes. The furnace watertubes absorb primarily radiative heat, while the
watertubes in the boiler passes gain heat by convective heat transfer. These units have a wide range of
heat input capacities (ICI units range from 0.4 to 1,500 MMBtu/hr) and can be either field erected or
packaged.1 Watertube boilers with heat input capacities greater than 200 MMBtu/hr are typically field
erected.
Because firetube, cast iron, and tubeless heat transfer configurations typically have heat input
capacities below 10 MMBtu/hr, they will not generally be covered by theNESHAP. Therefore, this
profile focuses on those boiler types that use watertube heat transfer configurations.
2.2.5 Major Design Types
This section summarizes the five major design types for fossil fuel industrial boilers that will
be covered by the NESHAP. It also discusses, where possible, the fuels used, capacity, and assembly
method of each of these types of boilers.
2.2.3.1 Stoker-Fired Boilers (Coal)
These units use underfeed air to combust the coal char on a stationary grate, combined with
one or more levels of overfire air introduced above the grate. There are three types of stoker units:
• spreader stokers,
• underfeed stokers, and
• overfeed stokers.
Stokers generally burn all types of coal, with the exception of overfeed stokers, which do not burn
coking bituminous coals. Stokers can also burn other types of solid fuel, such as wood, wood waste,
and bagasse. Spreader stokers are the most common of these boiler types and have heat input
capacities that typically range from 5 to 550 MMBtu/hr. However, some of these boilers have
capacities as high as 1,500 MMBtu/hr. Smaller stoker units (i.e., those with heat input capacities less
than 100 MMBtu/hr) are generally packaged, while larger units are usually field erected.
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2.2.12 Pulverized Coal Boilers (Coal)
Combustion in pulverized coal-fired units takes place almost entirely while the coal is
suspended, unlike in stoker units in which the coal burns on a grate. Finely ground coal is typically
mixed with primary combustion air and fed to the burner or burners, where it is ignited and mixed with
secondary combustion air. Depending on the location of the burners and the direction of coal injection
into the furnace, pulverized coal-fired boilers can be classified into three different firing types:
• single and opposed wall,
• tangential, and
• cyclone.
Of these types, wall and tangential configurations are the most common. These firing methods are
described further in Sections 2.2.3.4 and 2.2.3.5.
2.2.13 Fluidized Bed Combustion (FBC) Boilers (Coal)
FBC is an integrated technology for reducing sulfur dioxide (SO2) and NOX emissions during
the combustion of coal. In a typical FBC boiler, crushed coal and inert material (sand, silica, alumina,
or ash) and/or a sorbent (limestone) are maintained in a highly turbulent suspended state by the upward
flow of primary air from the windbox located directly below the combustion floor. This fluidized state
provides a large amount of surface contact between the air and solid particles, which promotes uniform
and efficient combustion at lower furnace temperatures than conventional coal-fired boilers. Once the
hot gases leave the combustion chamber, they pass through the convective sections of the boiler, which
are similar or identical to components used in conventional boilers.
For the FBCs currently in use in all sectors, coal is the primary fuel source, followed in
descending order by biomass, coal waste, and municipal waste. The heat input capacities of all ICI
FBC units generally range from 1.4 to 1,075 MMBtu/hr.
2.2.3.4 Tangentially Fired Boilers (Coal, Oil, Natural Gas)
The tangentially fired boiler is based on the concept of a single flame zone within the furnace.
The fuel-air mixture projects from the four corners of the furnace along a line tangential to an
imaginary cylinder located along the furnace centerline. As fuel and air are fed to the burners and the
fuel is combusted, a rotating "fireball" is formed. Primarily because of their tangential firing pattern,
which leads to larger flame volumes and flame interaction, uncontrolled tangentially fired boilers
generally emit relatively lower NOX than other uncontrolled boiler designs.
Utilities primarily use this type of boiler. Coal is the most common fuel used by these units.
Tangentially fired boilers operated by utilities are typically larger than 400 MW, while industrial ones
almost always have heat input capacities over 100 MMBtu/hr. In general, most units with heat input
capacities over 100 MMBtu/hr are field erected.
2.2.3.5 Wall-fired Boilers (Coal, Oil, Natural Gas)
Wall-fired boilers are characterized by multiple individual burners located on a single wall or
on opposing walls of the furnace. In contrast to tangentially fired boilers, each of the burners in a
wall-fired boiler has a relatively distinct flame zone, and the burners in wall-fired boilers do not tilt.
Superheated steam temperatures are instead controlled by excess air levels, heat input, flue gas
recirculation, and/or steam attemperation (water spray). Depending on the design and location of the
burners, wall-fired boilers are referred to as single wall or opposed wall.
Wall-fired boilers are used to burn coal, oil, or natural gas, and some designs feature multifuel
capability. Almost all industrial wall-fired boilers have heat input capacities greater than 100
MMBtu/hr. Opposed-wall boilers in particular are usually much larger than 250 MMBtu/hr heat input
capacity and are much more common in utility rather than in industrial operations. Because of their
size, most wall-fired units are field erected. Field-erected watertube boilers strictly designed for oil
firing are more compact than coal-fired boilers with the same heat input, because of the more rapid
combustion characteristics of fuel oil. Field-erected watertube boilers fired by natural gas are even
more compact because of the rapid combustion rate of the gaseous fuel, the low flame luminosity, and
the ash-free content of natural gas.
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2.3 Process Heater Characterization
Process heaters are heat transfer units in which heat from fuel combustion is transferred to
materials used in a production process. The process fluid stream is heated primarily for one of two
reasons: to raise the temperature for additional processing or to make chemical reactions occur. This
section describes the different classes of process heaters and major design types.
2.3.1 Classes of Process Heaters
The universe of process heaters is divided into two categories:
• indirect-fired process heater—any process heater in which the combustion gases do not
mix with or exhaust to the atmosphere from the same stack(s) or vent(s) with any gases
emanating from the process or material being processed.
• direct-fired process heater—any process heater in which the combustion gases mix with
and exhaust to the atmosphere from the same stack(s) or vent(s) with gases originating
from the process or material being processed.
Indirect-fired units are used in situations where direct flame contact with the material being
processed is undesirable because of problems with contamination and ignition of the process material.
Direct-fired units are used where such problems are not an important factor. Emissions of indirect-
fired units consist solely of the products of combustion (including those of incomplete combustion).
On the other hand, direct-fired units will generate emissions consisting not only of the products of
combustion, but also the process material(s). This means that the emissions from indirect-fired process
heaters will be generic to the fuel in use and are common across industries while emissions from
direct-fired process heaters are unique to a given process and may vary widely depending on the
process material. Only indirect-fired process heaters are considered under this proposed regulation.
Many direct-fired process heaters are being considered under separate MACT-development projects.
In addition to the distinction between direct- and indirect-fired heaters, process heaters may
also be considered either heated feed or reaction feed. Heated feed process heaters are used to heat a
process fluid stream before additional processing. These types of process heaters are used as
preheaters for various operations in the petroleum refining industry such as distillation, catalytic
cracking, hydroprocessing, and hydroconversion. In addition, heated feed process heaters are used
widely in the chemical manufacturing industry as fired reactors (e.g., steam-hydrocarbon reformers
and olefins pyrolysis furnaces), feed preheaters for nonfired reactors, reboilers for distillation
operations, and heaters for heating transfer oils. Reaction feed process heaters are used to provide
enough heat to cause chemical reactions to occur inside the tubes being heated. Many chemical
reactions do not occur at room temperature and require the application of heat to the reactants to cause
the reaction to take place. Applications include steam-hydrocarbon reformers used in ammonia and
methanol manufacturing, pyrolysis furnaces used in ethylene manufacturing, and thermal cracking
units used in refining operations.
2.3.2 Major Design Types
Process heaters may be designed and constructed in a number of ways, but most process
heaters include burner(s), combustion chamber(s), and tubes that contain process fluids. Sections
2.3.2.1 through 2.3.2.4 describe combustion chambers setups, combustion air supply, tube
configurations, and burners, respectively.
2.3.2.1 Combustion Chamber Set-Ups
Process heaters contain a radiant heat transfer area in the combustion chamber. This area heats
the process fluid stream in the tubes by flame radiation. Equipment found in this area includes the
burner(s) and the combustion chamber(s). Most heat transfer to the process fluid stream occurs here,
but these tubes do not necessarily constitute a majority of the tubes in which the process fluid flows.
Most process heaters also use a convective heat transfer section to recover residual heat from
the hot combustion gases by convective heat transfer to the process fluid stream. This section is
located after the radiant heat transfer section and also contains tubes filled with process fluid. The first
few rows of tubes in this section are called shield tubes and are subject to some radiant heat transfer.
Typically, the process fluid flows through the convective section prior to entering the radiant section to
preheat the process fluid stream. The temperature of the flue gas upon entering the convective section
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usually ranges from 800°C to 1,000°C (1,500°F to 2,000°F). Preheating in the convective section
improves the efficiency of the process heater, particularly if the tube design includes fins or other
extended surface areas. An extended tube surface area can improve efficiency by 10 percent.
Extended tubes can reduce flue gas temperatures from 800°C to 1,000°C to (1,500°F to 2,000°F) to
120°C to 260°C (250°F to 500°F).
2.3.2.2 Combustion Air Supply
Air for combustion is supplied to the burners via either natural draft (ND) or mechanical draft
(MD) systems. Natural draft heaters use ductwork systems to route air, usually at ambient conditions,
to the burners. MD heaters use fans in the ductwork system to supply air, usually preheated, to the
burners. The combustion air supply must have sufficient pressure to overcome the burner system
pressure drops caused by ducting, burner registers, and dampers. The pressure inside the firebox is
generally a slightly negative draft of approximately 49.8 to 125 Pascals (Pa) at the radiant-to-
convective section transition point. The negative draft is achieved in ND systems via the stack effect
and in MD systems via fans or blowers.
ND combustion air supply uses the stack effect to induce the flow of combustion air in the
heater. The stack effect, or thermal buoyancy, is caused by the density difference between the hot flue
gas in the stack and the significantly cooler ambient air surrounding the stack. Approximately 90
percent of all gas-fired heaters and 76 percent of all oil-fired heaters use ND combustion air supply
(EPA, 1993).
There are three types of MD combustion air supply: forced draft, induced draft, and balanced
draft. The draft types are named according to the position, relative to the combustion chamber, of the
fans used to create the pressure difference in the process heater. All three types of MD heaters rely on
the fans to supply combustion air and remove flue gas. In forced draft combustion air supply systems,
the fan is located upstream from the combustion chamber, supplying combustion air to the burners.
The air pressure supplied to the burners in a forced draft heater is typically in the range of 0.747 to
2.49 kilopascals (kPa). Though combustion air is supplied to the burners under positive pressure, the
remainder of the process heater operates under negative pressure caused by the stack effect. In
induced draft combustion air systems, the fan is located downstream of the combustion chamber,
creating negative pressure inside the combustion chamber.
This negative pressure draws, or induces, combustion air into the burner registers. Balanced
draft combustion air systems use fans placed both upstream and downstream (forced and induced
draft) of the combustion chamber.
There are advantages and disadvantages for both ND and MD combustion air supply. One
advantage to natural draft heaters is that they do not require the fans and equipment associated with
MD combustion air supply. However, control over combustion air flow is not as precise in ND heaters
as in MD heaters. MD heaters, unlike ND heaters, provide the option of using alternate sources of
combustion oxygen, such as gas turbine exhaust. They also allow the use of combustion air preheat.
Combustion air preheat has limited application in ND heaters due to the pressure drops associated with
combustion air preheaters.
Combustion air preheaters are often used to increase the efficiency of MD process heaters.
The maximum thermal efficiency obtainable with current air preheat equipment is 92 percent.
Preheaters allow heat to be transferred to the combustion air from flue gas, steam, condensate,
hydrocarbon, or other hot streams. The preheater increases the efficiency of the process heater
because some of the thermal energy is reclaimed that would have been exhausted from the hot streams
via cooling towers. If the thermal energy is from a hot stream other than the flue gas, the entire plant's
efficiency is increased. The benefit of higher thermal efficiency is that less fuel is required to operate
the heater.
2.3.2.3 Tube Configurations
The orientation of the tubes through which a process fluid stream flows is also taken into
consideration when designing a process heater. The tubes in the convective section are oriented
horizontally in most process heaters to allow cross-flow convection. However, the tubes in the radiant
area may be oriented either horizontally or vertically. The orientation is chosen on a case-by-case
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basis according to the design specifications of the individual process heater. For example, the arbor, or
wicket, type of heater is a specialty design to minimize the pressure drop across the tubes.
2.3.2.4 Burners
Many different types of burners are used in process heaters. Burner selection depends on
several factors including process heat flux requirements, fuel type, and draft type. The burner chosen
must provide a radiant heat distribution that is consistent with the configuration of the tubes carrying
process fluid. Also, the number and location of the burner(s) depend on the process heater application.
Many burner flame shapes are possible, but the most common types are flat and conical. Flat
flames are generally used in applications that require high temperatures such as ethylene pyrolysis
furnaces, although some ethylene furnaces use conical flames to achieve uniform heat distribution.
Long conical flames are used in cases where a uniform heat distribution is needed in the radiant
section.
Fuel compatibility is also important in burner selection. Burners may be designed for
combustion of oil, gas, or a gas/oil mixture. Gas-fired burners are simpler in operation and design than
oil-fired burners and are classified as either premix or raw gas burners. In premix burners, 50 to 60
percent of the air necessary for combustion is mixed with the gas prior to combustion at the burner tip.
This air is induced into the gas stream as the gas expands through orifices in the burner. The
remainder of the air necessary for combustion is provided at the burner tip. Raw gas burners receive
fuel gas without any premixed combustion air. Mixing occurs in the combustion zone at the burner
tip.
Oil-fired burners are classified according to the method of fuel atomization used. Atomization
is needed to increase the mixing of fuel and combustion air. Three types of fuel atomization
commonly used are mechanical, air, and steam. Steam is the most widely used method because it is
the most economical, provides the best flame control, and can handle the largest turndown ratios.
Typical steam requirements are 0.07 to 0.16 kilogram (kg) steam/kg of oil.
Combination burners can burn 100 percent oil, 100 percent gas, or any combination of oil and
gas. A burner with this capability generally has a single oil nozzle in the center of a group of gas
nozzles. The air needed for combustion can be controlled separately in this type of burner. Another
option is to base load the burners with one fuel and to add the other fuel to meet increases in load
demand. Combination burners add flexibility to the process heater, especially when the composition
of the fuel is variable.
The location and number of burners needed for a process heater are also determined on an
individual basis. Burners can be located on the ceiling, walls, or floor of the combustion chamber.
Floor- and wall-fired units are the most common burner types found in process heaters because they
are both efficient and flexible. In particular, floor-mounted burners integrate well with the use of
combustion air preheat, liquid fuels, and alternate sources of combustion oxygen such as turbine
exhaust.
The number of burners in a heater can range from 1 to over 100. In the refinery industry, the
average number of burners is estimated at 24 in ND heaters with an average design heat release of 69.4
million Btu per hour (MMBtu/hr). The average number of burners is estimated at 20 in MD heaters
with ambient combustion air and an average design heat release of 103.6 MMBtu/hr. The average
number of burners is estimated at 14 in MD heaters with combustion air preheat and an average design
heat release of 135.4 MMBtu/hr. In general, the smaller the number of burners, the simpler the heater
will be. However, multiple burners provide a more uniform temperature distribution.
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References
U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, 1993.
Alternative Control Techniques Document - NOx Emissions from Process Heaters (Revised).
Research Triangle Park, NC.
U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards, 1997.
Regulatory Impact Analysis of Air Pollution Regulations: Utility and Industrial Boilers. Research
Triangle Park, NC.
Fleming, Ian. 1992. "Just How Clean is your Steam? Minimizing Risk of Product Contamination
and Prolonging Filter Life." Manufacturing Chemist 63(10): pp. 37-39.
Plant Engineering. 1991. "Boiler Systems." Plant Engineering 45(14): pp. 92-94.
Texas Utilities (TXU). 2000. "Generating Electricity: Steam Turbines." As obtained in September
2000. Found on the Internet at http://www.txu.com/knowledge/energy lib/generatingO 1 .html.
CHAPTER 3
PROFILE OF AFFECTED UNITS AND FACILITIES, AND COMPLIANCE COSTS
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The floor-level MACT, which is the final industrial boilers and process heaters rule will affect
existing and new ICI boilers and process heaters that have input capacity greater than 10 million Btus
and are fueled by fossil and nonfossil fuel solids and liquids. In addition, two above-the-floor
alternatives were investigated at proposal, Options 1A and IB. Option 1A broadens the scope of
affected units to include those fueled by residual fuel oil and units of covered fuel types with input
capacities less than 10 million Btus. Option IB further expands the affected population to include all
distillate fuel oil and natural gas-fueled units. Although descriptive statistics on the Option IB
population are included in this section, this alternative was not analyzed for this RIA. More
information on these options can be found in the preamble to the proposed regulation.
The economic impact estimates presented in Chapter 6 and the small entity screening analysis
presented in Chapter 7 are based on the estimated stock of existing units and the projection of new
units through the year 2005. They are also based on the compliance costs associated with the applying
a regulatory alternative to these units. This chapter begins with a review of the industry distribution
and technical characteristics of existing boilers and process heaters contained in the Agency's
Inventory Database. It also presents projected growth estimates for boilers and process heaters
through the year 2005, a description of how costs are estimated, and the national engineering cost
estimates and cost-effectiveness (cost/ton) estimates by pollutant controlled.
3.1 Profile of Existing Boiler and Process Heaters Units
This section profiles existing boilers and process heaters, collectively referred to as "units,"
with respect to business applications, industry of parent company, and fuel use. The unit population
database in combination with the model units that helped in preparing that database were used to
determine which types of boilers, fuel, and control devices were in the existing unit population so that
corresponding emission factors could be developed for all combinations. The development of the
population database and the model units are discussed in the memoranda, "Development of the
Population Database for the Industrial, Commercial, and Institutional Boiler and Process Heater
National Emission Standard for Hazardous Air Pollutants (NESHAP)" and "Development of the
Model Units for the Industrial, Commercial, and Institutional Boiler and Process Heater National
Emission Standard for Hazardous Air Pollutants (NESHAP)." The units contained in the Inventory
Database are based on information from the Aerometric Information Retrieval System (AIRS) and
Ozone Transport Assessment Group (OTAG) databases, state and local permit records, and the
combustion source Information Collection Request (ICR) conducted by the Agency in 1997. The list
of units contained in the Inventory Database was reviewed and updated by industry and environmental
stakeholders as part of the Industrial Combustion Coordinated Rulemaking (ICCR), chartered under
the Federal Advisory Committee Act (FACA).
The entire Inventory Database contains more than 58,000 ICI boilers and process heaters;
however, only about 4,000 are estimated to be affected by the floor alternative. Of these existing units,
a little over half had sufficient information on operating parameters to be included in the floor-level
EIA. The number of potentially affected units included in the profile for the floor alternative was
2,186. The number of units included in the profile was 3,580 for Option 1A and 22,117 for Option
IB.
3.1.1 Distribution of Existing Boilers and Facilities by Industry
Tables 3-1 through 3-3 present the number of existing boilers and process heaters and the
number of facilities owning units by two-digit SIC code and three-digit NAICS code that may be
affected by the floor or above-the-floor alternatives. For the floor alternative, the industries with the
largest number of potentially affected units are the furniture, paper, lumber, and electrical services
industries. These four industries alone account for nearly 60 percent of affected units. Almost all the
process heaters are in the lumber industry. (Chapter 4 presents industry profiles for the lumber and
wood products, electrical services, and paper industries, among others.) The remaining units are
primarily distributed across the manufacturing sector and service industries. The distribution of units
affected by the Option 1A alternative is similar to that for the floor alternative, although both the
number of units and the number of facilities is greater for the Option 1A alternative. For Option IB,
the industries with the greatest number of units shifts to oil and gas exploration, chemical and
transportation equipment manufacturing, and petroleum refining.
3-1
-------
3.1.2 Technical Characteristics of Existing Boilers
Figure 3-1 characterizes the population of 2,186 (3,580; 22,117) units identified in the
Inventory Database by capacity range, fuel type, and level of preexisting control for each alternative.
Throughout most of this section, the values in the text are for the MACT floor alternative. Those for
the above-the-floor alternatives follow in parentheses, first for Option 1A then for Option IB.
3-2
-------
Table 3-1. Units and Facilities Affected by the Floor Alternative by Industrya
SIC
Code
01
02
07
10
12
13
14
17
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
42
46
NAICS
Code
111
112
115
212
212
211
212
235
311
312
313
315
321
337
322
511
325
324
326
316
327
331
332
333
335
336
334
339
482
484
486
Description
Agriculture — Crops
Agriculture — Livestock
Agricultural Services
Metal Mining
Coal Mining
Oil and Gas Extraction
Mining/Quarrying — Nonmetallic Minerals
Construction — Special Trade Contractors
Food and Kindred Products
Tobacco Products
Textile Mill Products
Apparel and Other Products from Fabrics
Lumber and Wood Products
Furniture and Fixtures
Paper and Allied Products
Printing, Publishing, and Related Industries
Chemicals and Allied Products
Petroleum Refining and Related Industries
Rubber and Miscellaneous Plastics Products
Leather and Leather Products
Stone, Clay, Glass, and Concrete Products
Primary Metal Industries
Fabricated Metal Products
Industrial Machinery and Computer Equipment
Electronic and Electrical Equipment
Transportation Equipment
Scientific, Optical, and Photographic Equip.
Miscellaneous Manufacturing Industries
Railroad Transportation
Motor Freight and Warehousing
Pipelines, Except Natural Gas
Boilers
3
0
0
9
2
0
8
0
138
11
135
2
335
234
321
0
171
11
17
1
9
41
16
23
5
102
8
2
4
5
0
Heaters
0
0
0
0
0
0
0
0
0
0
0
0
25
0
0
0
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Total
Units
3
0
0
9
2
0
8
0
138
11
135
2
360
234
321
0
174
11
17
1
9
41
16
23
5
102
8
2
4
5
0
Facilities
3
0
0
4
1
0
4
0
60
7
71
2
262
154
194
0
70
8
13
1
7
16
10
12
5
41
4
2
1
1
0
(continued)
3-3
-------
Table 3-1. Units and Facilities Affected by the Floor Alternative by Industrya
(continued)
SIC
Code
49
50
51
55
58
60
59
70
72
76
80
81
82
83
86
87
89
91
92
94
96
97
NA
NAICS
Code
221
421
422
441
722
522
445-454
721
812
811
621
541
611
624
813
541
711/514
921
922
923
926
928
Description
Electric, Gas, and Sanitary Services
Wholesale Trade — Durable Goods
Wholesale Trade — Nondurable Goods
Automotive Dealers and Gasoline Service
Stations
Eating and Drinking Places
Depository Institutions
Miscellaneous Retail
Hotels and Other Lodging Places
Personal Services
Miscellaneous Repair Services
Health Services
Legal Services
Educational Services
Social Services
Membership Organizations
Engineering, Accounting, Research,
Management and Related Services
Services, N.E.C.
Executive, Legislative, and General
Administration
Justice, Public Order, and Safety
Administration of Human Resources
Administration of Economic Programs
National Security and International Affairs
SIC Information Not Available
Boilers
318
3
2
0
0
0
0
1
0
2
37
0
105
2
0
2
2
1
29
1
4
29
7
2,158
Heaters
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
28
Total
Units
318
3
2
0
0
0
0
1
0
2
37
0
105
2
0
2
2
1
29
1
4
29
7
2,186
Facilities
160
2
1
0
0
0
0
1
0
1
18
0
45
1
0
2
1
1
9
1
3
11
4
1,214
Based on the Inventory Database.
3-4
-------
Table 3-2. Units and Facilities Affected by the Option 1A Alternative by Industry"
SIC
Code
01
02
07
10
12
13
14
17
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
42
46
NAICS
Code
111
112
115
212
212
211
212
235
311
312
313
315
321
337
322
511
325
324
326
316
327
331
332
333
335
336
334
339
482
484
486
Description
Agriculture — Crops
Agriculture — Livestock
Agricultural Services
Metal Mining
Coal Mining
Oil and Gas Extraction
Mining/Quarrying — Nonmetallic Minerals
Construction — Special Trade Contractors
Food and Kindred Products
Tobacco Products
Textile Mill Products
Apparel and Other Products from Fabrics
Lumber and Wood Products
Furniture and Fixtures
Paper and Allied Products
Printing, Publishing, and Related Industries
Chemicals and Allied Products
Petroleum Refining and Related Industries
Rubber and Miscellaneous Plastics Products
Leather and Leather Products
Stone, Clay, Glass, and Concrete Products
Primary Metal Industries
Fabricated Metal Products
Industrial Machinery and Computer Equipment
Electronic and Electrical Equipment
Transportation Equipment
Scientific, Optical, and Photographic Equip.
Miscellaneous Manufacturing Industries
Railroad Transportation
Motor Freight and Warehousing
Pipelines, Except Natural Gas
Boilers
6
0
0
10
2
8
10
2
163
22
247
4
434
310
503
8
332
54
56
22
40
83
44
46
45
158
33
14
4
5
3
Heaters
0
0
0
1
0
10
0
0
0
0
3
0
28
0
0
0
101
108
0
0
2
2
0
0
0
0
0
0
0
2
3
Total
Units
6
0
0
11
2
18
10
2
163
22
250
4
462
310
503
8
433
162
56
22
42
85
44
46
45
158
33
14
4
7
6
Facilities
6
0
0
5
1
4
5
1
72
11
134
4
337
209
272
6
163
50
37
12
25
33
28
25
29
61
16
10
1
3
5
(continued)
3-5
-------
Table 3-2. Units and Facilities Affected by the Option 1A Alternative by Industry"
(continued)
SIC
Code
49
50
51
55
58
60
59
70
72
76
80
81
82
83
86
87
89
91
92
94
96
97
NA
NAICS
Code
221
421
422
441
722
522
445-454
721
812
811
621
541
611
624
813
541
711/514
921
922
923
926
928
Description
Electric, Gas, and Sanitary Services
Wholesale Trade — Durable Goods
Wholesale Trade — Nondurable Goods
Automotive Dealers and Gasoline Service
Stations
Eating and Drinking Places
Depository Institutions
Miscellaneous Retail
Hotels and Other Lodging Places
Personal Services
Miscellaneous Repair Services
Health Services
Legal Services
Educational Services
Social Services
Membership Organizations
Engineering, Accounting, Research,
Management and Related Services
Services, N.E.C.
Executive, Legislative, and General
Administration
Justice, Public Order, and Safety
Administration of Human Resources
Administration of Economic Programs
National Security and International Affairs
SIC Information Not Available
Boilers
371
3
2
0
0
0
1
1
0
2
40
0
114
3
0
6
2
2
33
1
4
41
24
3,318
Heaters
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
262
Total
Units
372
3
2
1
0
0
1
1
0
2
40
0
114
3
0
6
2
2
33
1
4
41
24
3,580
Facilities
185
2
1
1
0
0
1
1
0
1
19
0
50
2
0
5
1
2
10
1
3
13
18
1,881
Based on the Inventory Database.
3-6
-------
Table 3-3. Units and Facilities Affected by the Option IB Alternative by Industrya
SIC
Code
01
02
07
10
12
13
14
17
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
42
46
NAICS
Code
111
112
115
212
212
211
212
235
311
312
313
315
321
337
322
511
325
324
326
316
327
331
332
333
335
336
334
339
482
484
486
Description
Agriculture — Crops
Agriculture — Livestock
Agricultural Services
Metal Mining
Coal Mining
Oil and Gas Extraction
Mining/Quarrying — Nonmetallic Minerals
Construction — Special Trade Contractors
Food and Kindred Products
Tobacco Products
Textile Mill Products
Apparel and Other Products from Fabrics
Lumber and Wood Products
Furniture and Fixtures
Paper and Allied Products
Printing, Publishing, and Related Industries
Chemicals and Allied Products
Petroleum Refining and Related Industries
Rubber and Miscellaneous Plastics Products
Leather and Leather Products
Stone, Clay, Glass, and Concrete Products
Primary Metal Industries
Fabricated Metal Products
Industrial Machinery and Computer Equipment
Electronic and Electrical Equipment
Transportation Equipment
Scientific, Optical, and Photographic Equip.
Miscellaneous Manufacturing Industries
Railroad Transportation
Motor Freight and Warehousing
Pipelines, Except Natural Gas
Boilers
7
6
3
55
20
497
48
2
441
69
755
4
561
499
981
333
2,265
322
508
91
423
754
771
402
430
803
180
123
4
5
8
Heaters
0
0
0
6
6
657
1
0
3
0
6
0
40
10
0
3
415
729
36
2
13
197
102
19
13
207
2
36
0
2
3
Total
Units
7
6
3
61
26
1,154
49
2
444
69
761
4
601
509
981
336
2,680
1,051
544
93
436
951
873
421
443
1,010
182
159
4
7
11
Facilities
6
1
1
20
5
371
19
1
145
30
347
4
412
297
493
134
913
184
268
44
184
314
388
191
203
291
71
65
1
3
7
(continued)
3-7
-------
Table 3-3. Units and Facilities Affected by the Option IB Alternative by Industrya
(continued)
SIC
Code
49
50
51
55
58
60
59
70
72
76
80
81
82
83
86
87
89
91
92
94
96
97
NA
NAICS
Code
221
421
422
441
722
522
445-454
721
812
811
621
541
611
624
813
541
711/514
921
922
923
926
928
Description
Electric, Gas, and Sanitary Services
Wholesale Trade — Durable Goods
Wholesale Trade — Nondurable Goods
Automotive Dealers and Gasoline Service
Stations
Eating and Drinking Places
Depository Institutions
Miscellaneous Retail
Hotels and Other Lodging Places
Personal Services
Miscellaneous Repair Services
Health Services
Legal Services
Educational Services
Social Services
Membership Organizations
Engineering, Accounting, Research,
Management and Related Services
Services, N.E.C.
Executive, Legislative, and General
Administration
Justice, Public Order, and Safety
Administration of Human Resources
Administration of Economic Programs
National Security and International Affairs
SIC Information Not Available
Boilers
1,227
4
2
0
0
3
1
3
2
58
27
2
144
4
1
6
2
7
36
2
11
51
6,163
19,126
Heaters
140
0
0
2
3
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
3
335
2,991
Total
Units
1,367
4
2
2
3
3
1
3
2
58
27
2
144
4
1
6
2
7
36
2
11
54
6,498
22,117
Facilities
615
2
1
2
1
1
1
2
1
28
25
0
57
2
1
5
1
5
10
2
5
15
2,378
8,573
Based on the Inventory Database.
3-8
-------
Floor Alternative (n=2,186)
Oto 10
Fabric Filter
16%
100 to 250
25%
10 to 100
52%
Wet
Scrubber
12%
No Control
13%
Input Capacity
(million Btu)
Cyclone
" 36%
Preexisting Control
Technology
Other
Biomass
14%
Other
4%
Wood
22%
Bagasse
" 4%
Coal
49%
Coal and
Wood
7%
Fuel Type
Option 1A Alternative (n=3,580)
>250
16%
Oto 10
13%
Fabric Filter
10%
10 to 100
51% Wet
Scrubber
8%
No Control
41%
100 to 250
20%
ESP
15%
Input Capacity
(million Btu)
Residual
Fuel Oil
32%
Cyclone
26%
Other
Biomass
9%
I
Preexisting Control
Technology
Other
3%
Bagasse
2%
Wood
18%
Fuel Type
Option 1B Alternative (n=22,117)
>250
100 to 250 5%
7%
10 to 100
36%
Oto 10
52%
Input Capacity
(million Btu)
Wet
Scrubber
2%
Wood,
Bagasse,
Residual and Other
Fuel Oil | 6o/0
5%
Distillate
Fuel Oil
6%
No Control
88%
Preexisting Control
Technology
• Natural Gas
78%
Fuel Type
Figure 3-1. Characteristics of Units Affected by Alternatives
3-9
-------
3.1.2.1 Floor Alternative
• Capacity Range: Unit input capacities in the population are expressed in four ranges:
0-10, 10-100, 100-250, and >250 MMBtu/hr. Fifty-two percent of the units affected for
this alternative have capacities between 10 and 100 MMBtu/hr. The two largest capacity
ranges each contain approximately one quarter of the population. Only 1 percent of units
have input capacities less than 10 MMBtu/hr.
• Fuel Type: About half of these units consume coal as their primary fuel (1,074 units).
After coal, the next most common fuel type is wood (479 units).
• Control Level: Eighty-three percent of units have some type of control device already
installed; 289 do not. Typical control devices include fabric filters, wet scrubbers, and
electrostatic precipitators.
3.1.2.2 Option 1A Alternative
• Capacity Range: About half of the 3,580 units affected by this alternative have input
capacities between 10 and 100 MMBtu/hr. Twenty percent have capacities between 100
and 250, 16 percent have capacities greater than 250, and 13 percent have capacities less
than 10 MMBtu/hr.
• Fuel Type: Coal and residual fuel oil are the primary fuel types each accounting for
slightly less than one-third of the units. The remaining third primarily consists of units
that consume wood or some other type of biomass fuel.
• Control Level: Forty-one percent have no existing pollution control equipment installed.
Typical control devices include fabric filters, wet scrubbers, and electrostatic precipitators.
3.1.2.3 Option IB Alternative
• Capacity Range: More than half of the 22,117 units affected by the Option IB alternative
have input capacities less than 10 MMBtu/hr. Thirty-six percent have input capacities
between 10 and 100 MMBtu/hr. The remaining 12 percent have input capacities in excess
of 100 MMBtu/hr.
• Fuel Type: This alternative includes those units affected under Option 1A, as well as a
large number of natural gas units that were not affected under Option 1A. The vast
majority of the 78 percent of the total number of potentially affected units are fueled by
natural gas.
• Control Level: Eighty-eight percent of the affected units have no preexisting control
equipment.
3.2 Methodology for Estimating Cost Impacts
The predominant type of control measure that is considered in the analysis of emission
reductions needed for sources to achieve the MACT floor, which is the proposed alternative, as well as
other alternatives, are add-on control technologies. Add-on control techniques are those technologies
that are applied to the vent gas stream of the boiler or process heater to reduce emissions. The boiler
and process heaters population database includes information on all control techniques that are applied
to industrial, commercial, institutional boilers and process heaters. Generally, they can be grouped
into PM control or acid gas control. The most common technologies, and the ones analyzed for the
impacts analysis, include fabric filters, ESP's, packed scrubbers, venturi scrubbers, and spray dryers.
In addition, when add-on technologies are used, the cost of ductwork and associated equipment also
needed to be considered.
3-10
-------
Components of capital cost include:
- purchased equipment cost of the primary device and auxiliary equipment,
- instrumentation,
- sales tax and freight, and
- installation costs. Installation costs include foundations and support, handling and erection,
electrical, piping, insulation, and painting, engineering, construction and field expenses,
contractor fees, start-up, performance tests, and contingencies.
Components of annual cost include:
- raw materials,
- utilities (electricity, fuel, steam, air, water),
- waste treatment and disposal,
- labor (operating, supervisory, maintenance),
- maintenance materials,
- replacement parts,
- overhead,
- property taxes,
- insurance,
- administration charges, and
- capital recovery costs.
For this analysis, costs were estimated in 1999 dollars. Capital recovery was calculated assuming 7
percent interest rate over the life of the equipment. The use of this interest rate is based on Office of
Management and Budget (OMB) guidance (Circular A-94, October 29, 1992).
The algorithms used to estimate these costs were obtained from previous EPA studies. These cost
algorithms are included as appendicies to the cost methodology memorandum in the public docket.
Inputs for the algorithms used in the impacts analysis are also presented in this memorandum.
Fabric filter
The algorithms used to estimate capital and annual costs of fabric filters were obtained from
EPA's EPA Air Pollution Control Cost Manual. Algorithms were provided for 4 types of fabric filters:
shaker, reversed air, pulse-jet modular, and pulse-jet common. The cost algorithms for estimating capital
costs reduced to basic equations for each are provided in Appendix A-l of the cost methodology
memorandum (henceforth called the "cost memo"). Capital costs are based on the gross cloth area of the
fabric filter, which is a function of the gas inlet flow rate. Algorithms for calculating annual costs are
provided in Appendix A-2 of the cost memo. Annual costs include dust disposal, electricity,
maintenance, labor, bag replacement, maintenance labor, compressed air, overhead, administrative,
property taxes, and insurance. Capital recovery is annualized over 20 years at 7 percent interest.
Appendix A-3 of the cost memo presents the values for the inputs used in this analysis and the reasons for
their use.
Electrostatic Precipitator
The algorithms used to estimate capital and annual costs of ESPs were obtained from EPA's Air
Pollution Control Cost Manual. Capital costs are based on the total collection plate area, which is
calculated from the gas inlet flow rate and the required removal efficiency. The cost algorithms for
3-11
-------
estimating capital costs of ESPs reduced to basic equations are provided in Appendix B-l of the cost
memo. Algorithms for calculating annual costs are provided in Appendix B-2 of the cost memo. Annual
costs include dust disposal, electricity, maintenance, labor, maintenance labor, overhead, administrative,
property taxes, and insurance. Capital recovery is annualized at 7 percent interest. Appendix B-3 of the
cost memo presents the values for the inputs used in this analysis and the reasons for their use.
Venturi Scrubber
The algorithms used to estimate capital and annual costs of venturi scrubbers were obtained from
EPA cost algorithms on EPA's website( http://www.epa.gov/ttn/catc/products.html#cccinfo.) Capital
costs include not only the cost of the venturi scrubber but also a pump to provide motive force for the
solvent. Capital costs are based on the gas flow rate and saturation temperature of the gas-solvent. The
cost algorithms for estimating capital costs of each piece of equipment were reduced to basic equations in
Appendix C-l of the cost memo. The cost algorithms for estimating annual costs were reduced to basic
equations in Appendix C-2 of the same memorandum. Annual costs include wastewater disposal, solvent,
electricity, maintenance, labor, maintenance labor overhead, administrative, property taxes, and
insurance. Capital recovery is an annualized cost estimated using a 7 percent interest rate. Appendix C-3
of the cost memo presents the values for the inputs used in this analysis and the reasons for their use.
Packed Bed Scrubber
The algorithms used to estimate capital and annual costs of packed bed scrubbers were obtained
from EPA's Air Pollution Control Cost Manual. The capital costs are comprised of the scrubber tower,
packing, pumps, and fans. Capital costs are based primarily on gas flow rate and removal efficiency. The
cost algorithms for estimating capital costs of packed scrubber equipment reduced to their basic equations
for each are provided in Appendix D-l of the cost memo. The cost algorithms for estimating annual costs
of packed scrubbers are provided in Appendix D-2 of the cost memo. Annual costs include caustic,
wastewater disposal, water, electricity, maintenance, labor, overhead, administrative, property taxes, and
insurance. Capital recovery is an annualized cost estimated using a 7 percent interest rate. Appendix D-3
of the cost memo presents the values for the inputs used in this analysis and the reasons for their use.
Spray Dryer
The algorithms used to estimate capital and annual costs of spray dryers were obtained from
previous EPA studies. Capital costs include the cost of the spray dryer and pumps. Capital costs are
based on the gas flow rate. The cost algorithms for estimating capital costs of spray dryer equipment
reduced to basic equations are provided in Appendix E-l of the cost memo. The cost algorithms for
estimating annual costs for spray dryers are provided in Appendix E-2 of the cost memo. Annual costs
include lime, water, electricity, maintenance, labor, maintenance labor, overhead, administrative, property
taxes, and insurance. Capital recovery is an annualized cost estimated using a 7 percent interest rate.
Appendix E-3 of the cost memo presents the values for the inputs used in this analysis and the reasons for
their use.
Ductwork
The algorithms used to estimate capital and annual costs of ductwork were obtained from EPA's
Air Pollution Control Cost Manual. Capital costs include 500 feet of ductwork, elbows, and fans. The
500 feet of ductwork was based on engineering judgement and previous experience on the distance
between emission points and control devices in chemical facilities and the availability of space for
retrofitting controls. Costs are based on ductwork diameter, which is calculated from the gas flow rate.
The cost algorithms for estimating capital costs and annual costs reduced to basic equations are provided
in Appendix F-l of the cost memo. Annual costs include electricity, maintenance, maintenance labor,
overhead, administrative, property taxes, and insurance. Capital recovery is an annualized cost estimated
using a 7 percent interest rate. Required inputs to the ductwork algorithms are provided in the input
tables provided in Appendices A-3, B-3, C-3, D-3, and E-3 of the cost memo.
3-12
-------
Good Combustion Practices
Few sources in the population database specifically reported using good combustion practices.
Boilers and process heaters within each subcategory might use any of a wide variety of different work
practices, depending on the characteristics of the individual unit.
Consequently, any uniform requirements or set of work practices that would meaningfully reflect
the use of good combustion practices, or that could be meaningfully implemented across any subcategory
of boilers and process heaters could not be identified.
Additionally, few of the GCP's have been documented to reduce organic HAP emissions, and
they could not be considered in the MACT analysis. One GCP that may effect organic HAP emissions is
maintaining CO emission levels. CO is generally an indicator of incomplete combustion because CO will
burn to carbon dioxide if adequate oxygen is available. Controlling CO emissions is a mechanism for
ensuring combustion efficiency, and therefore may be viewed as a kind of GCP.
Capital and annual costs for CO monitoring is presented in Appendix G of the cost memo. The
costing information was obtained from a previous EPA study. Capital costs are comprised of the initial
cost of the equipment. Annual costs include operating and maintenance costs, annual and quarterly
checks, recordkeeping and reporting, taxes, insurance, and administrative costs. Annualized costs such as
capital recovery costs are calculated assuming an equipment life of 20 years and an interest rate of 7
percent.
Testing and Monitoring Costs
The rule includes emission limits for HC1, PM, metallic HAP, and mercury. Additionally, as
mentioned in Chapter 1 of this RIA and the preamble, the rule allows sources to meet requirements by
monitoring fuel content instead of emissions. Consequently, testing and monitoring costs of meeting the
standards were incorporated into the cost estimates. Capital costs for testing include initial stack tests
for PM, HC1, and metals for fossil fuels, and materials and fuel analysis for biomass. Capital cost
components include operation and maintenance costs and capital recovery assuming the initial capital
investment is annualized over a 5 year period at 7 percent interest. Monitoring costs are included for
opacity monitoring, HC1 monitoring, and scrubber parametric monitoring.5 Monitoring costs include the
capital cost of monitoring equipment, and the annual costs of capital recovery assuming the initial capital
investment is annualized over a 20 year period at 7 percent interest. Annual monitoring costs also include
operation and maintenance as well as other additional costs. The testing and monitoring costs are shown
in Table 3-4. Appendix G of the cost memo includes further details on these costs. Information used to
estimate testing and monitoring costs were obtained from previous EPA studies.
Table 3-4. Testing and Monitoring Costs for Units Covered by the Proposed Rule
The monitoring costs reported for existing units are not the cost of continuous emission monitors
(CEM), but the costs associated with monitoring the process parameters of the control device.
Installation of these process monitors are integral to the control device and would be installed with
or without the monitoring requirements of the MACT. Therefore, even though we present these
monitoring costs separately, they are included in the overall reported control costs and should not
be considered as an additional cost for emission monitoring.
3-13
-------
Material or Fuel
Regular Use Units
Coal
Coal/Wood/NFFa
Liquid/NFF Solid
Gas
Gas/Wood/Other
Biomass/Liquid
FF
Distillate Liquid
FF
NFF Liquid/NFF
Solid/Gas
Wood
Wood/Other
Biomass/NFF
Liquid/NFF Solid
Residual Liquid
FF
Bagasse/Other
Total for Regular
Use Units
No. of
Industrial
Boilers
2,328
169
30,473
201
2,921
115
663
147
2,036
132
39,185
Limited Use Units ||
Coal
Coal/Wood/NFF
Liquid/NFF Solid
Gas
Gas/Wood/Other
Biomass/Liquid
FF
Distillate Liquid
FF
NFF Liquid/NFF
Solid/Gas
Wood
Wood/Other
Biomass/NFF
Liquid/NFF Solid
Residual Liquid
FF
198
4
2,314
8
672
4
28
6
533
No. of
Process
Heaters
0
0
13,481
0
353
11
42
0
674
0
14,561
0
0
624
0
31
1
0
0
31
Total
Capital
Investment
of Testing
and
Monitoring
(S)
151,169,238
8,847,579
0
9,831,749
0
7,452,131
26,446,200
8,180,852
0
5,821,106
217,748,855
6,427,715
119,600
0
290,366
0
156,800
1,074,549
194,000
0
Total
Annual
Costs of
Testing
(S)
63,608,655
2,444,456
0
2,909,994
0
3,074,918
5,268,614
3,003,146
0
490,000
80,799,783
1,584,000
32,000
0
64,000
0
40,000
224,000
48,000
0
Total
Annual
Costs of
Monitoring
(S)
59,828,340
1,302,784
0
2,327,840
0
2,930,348
6,392,240
2,001,492
0
2,891,728
77,674,772
1,716,416
29,772
0
105,020
0
39,696
331,200
49,620
0
Annual
Capital
Recovery -
Testing and
Monitoring
(1999$)
8,265,169
280,698
0
447,120
0
404,077
1,411,706
299,112
0
412,546
11,520,428
457,169
8,268
0
21,366
0
11,024
80,279
13,780
0
Total
Annual
Costs of
Testing and
Monitoring
(1999$)
123,436,995
3,747,240
0
5,237,834
0
6,005,266
11,660,854
5,004,638
0
3,381,728
158,114,555
3,330,416
61,772
0
169,020
0
79,696
555,200
97,620
0
3-14
-------
Material or Fuel
Total for Limited
Use Units
Grand Total
No. of
Industrial
Boilers
3,767
42,952
No. of
Process
Heaters
687
15,248
Total
Capital
Investment
of Testing
and
Monitoring
(S)
8,263,030
226,011,885
Total
Annual
Costs of
Testing
(S)
1,992,000
82,791,783
Total
Annual
Costs of
Monitoring
(S)
2,271,724
79,946,496
Annual
Capital
Recovery -
Testing and
Monitoring
(1999$)
591,886
12,112,314
Total
Annual
Costs of
Testing and
Monitoring
(1999$)
4,263,724
162,738,279
aNFF = costs for units that are not fossil fueled; FF = units that are fossil fueled.
Costs to Control Non-Air Effects Related to Rule Implementation
The EPA estimated the additional water usage that would result from the MACT floor level of
control to be 110 million gallons per year for existing sources and 0.6 million gallons per year for new
sources. In addition to the increased water usage, an additional 3.7 million gallons per year of wastewater
would be produced for existing sources and 0.6 million gallons per year for new sources. The EPA
estimated the additional solid waste that would result from the MACT floor level of control to be 102,000
tons per year for existing sources and 1 ton per year for new sources. The costs ($900,000) of handling
the additional solid waste generated from applying MACT floor technology are accounted for in the
control cost estimates for ESP and fabric filter applications. The costs ($20,000) of treating wastewater
from venturi and packed bed scrubber are also accounted for in the control cost estimates.
Cost Uncertainties
The primary limitation to the cost estimates developed for the proposed rule is that costs were
calculated for model units rather than each individual boiler or process heater. Consequently, the costs do
not characterize any "real" unit. This was done for practical reasons. Because there are over 60,000
units in the U.S., it would not be possible to gather unit-specific information for each unit necessary for
estimating costs, such as flue gas temperatures and flow rates. Additionally, emission information was
only available for less than 1 percent of the units. In order to estimate costs and emission reductions of
the proposed rule, model units were developed to represent the population of boilers and process heaters
in the U.S. While sufficient information was not available for characterizing each unit, sufficient
emissions and process information were available to develop model units. Each unit in the U.S. was then
assigned to a model based on their size and fuel burned. It also should be noted that the costing
methodology is the cost algorithms for the control devices provide a cost range of+/- 30 percent. This
aspect of the costing methodology reflects the degree of variability typically found in study-level cost
estimates. This is also the degree of variability found in the cost methodology employed in the EPA Air
Pollution Control Cost Manual, which is an important reference for the cost estimates supplied in the
RIA. Cost information available to owners and operators of boilers and process heaters will be more
specific and accurate. Consequently, the cost estimates may overestimate or underestimate costs.
3.3 Projection of New Boilers and Process Heaters
Energy Information Administration fuel consumption forecasts were used in conjunction with
existing model boiler population data to project the number and type of new boilers to be installed by
2005. EPA used the following steps to calculate new boiler population estimates:
1. Calculate the percentage change in industrial fuel consumption. Energy Information
Administration data were used to obtain industrial and commercial fuel use projections. The
percentage change in consumption (1998 to 2005) in the industrial and commercial sectors
was calculated for the following fuel categories using 1998 as the base year (the same year
that the model boiler algorithms are based on): steam coal (2.6%), natural gas (6.3%),
residual fuel oil (-7.4%), distillate fuel oil (12.0%), and biomass (11.5%). It should be noted
3-15
-------
that 1998 was a year of below average energy prices, and that current and potential future
energy prices are higher than the historical average. If real fuel prices increase faster than the
EIA's projections, then conservation measures may lead to fewer projected boilers and
process heaters. This trend would lead to an overestimate (upward bias) of the impact
estimates presented in this report.
2. Estimate the number of new boilers by model number-fuel type. To predict the number of
new boilers in operation by 2005, EPA applied the percentage difference for each fuel
category to the 1998 fuel consumption of boilers represented by the boiler models to calculate
total energy consumed by boilers in 2005 for each model number. The number of new
boilers per model was calculated by dividing the model fuel forecasts by the annual fuel
consumption of one unit and then subtracting the number of units present in 1998, as follows:
Number of _ ( Total energy consumed (2005) [MMBtu/yr] | Number of
New Units ~ [ Avg capacity [MMBtu/hr] x 8,760 [hr/yr] J ~ Units
Following these steps, EPA projects that 1,458 boilers and 374 process heaters to be installed
between 1998 and 2005 will be affected by the new source MACT floor and the Option 1A alternative.
The only new ICI boilers and process heaters that will be unaffected are those natural gas and distillate
fuel units that have input capacities less than 10 MMBtu/hr. These projections were developed by model
unit type, not by industry. To assess the distribution of the boilers and process heaters estimated to be
operating in 2005 across industries, EPA attached unit-level weights by model number to each unit in the
Inventory Database. These weights allow each unit in the Inventory Database to represent a number (or
fraction) of units that are predicted to be in use by the end of 2005. The weights were then summed by
two-digit SIC code to estimate the distribution of units by industry.
Table 3-6 presents the projected number of new boilers and process heaters for the MACT floor
and OptionlA above-the-floor alternatives. Industries with the estimated greatest concentrations of new
units include chemicals and allied products (295), petroleum refining (198), electric services (134), and
paper and allied products (96). New source estimates by industry were not developed for the Option IB
above-the-floor alternative.
3.4 National Engineering Population, Cost Estimates, and Cost-Effectiveness Estimates
The Agency estimates that in 2005 5,562 units (existing units and new units) may be affected by
the floor alternative and 9,163 units may be affected by the Option 1A above-the-floor alternative. These
populations were used to estimate national engineering costs. The population estimates were determined
by unit configuration, not by industry. Thus, the distribution of units by industry shown in Tables 3-6 and
3-7 was determined by weighting existing units by the estimates by unit configuration and tallying
weighted units by SIC code. The average cost of control by unit configuration was multiplied by the
weighted number of units to determine industry-level control cost estimates.
Table 3-8 presents industry-level population and cost estimates for boilers and process heaters
for both the floor and above-the-floor alternatives. The distribution of weighted units across industries
mirrors that of the analysis population even though it was determined by weighting units by
configuration, not industry-level growth estimates. The floor cost of control for the estimated 5,562
boilers and process heaters is $863.0 million, with an average per-unit additional control cost of
$155,157. The Option 1A cost of control for the 9,163 potentially affected units is $1,995.8 million, with
an average per-unit cost of $217,811.
The Agency estimates that Option IB will potentially affect 62,215 boilers and process heaters.
The Option IB cost of control for the 62,215 potentially affected units is $2,944.8 million. Option IB
costs are not presented by industry because approximately one-third of the units did not have SIC code
(and, hence, no NAICS code) information.
3-16
-------
To provide additional information on the magnitude of the cost estimates, Table 3-5 shows the
cost-effectiveness (cost/ton reduced estimates) for the HAP and non-HAP pollutants whose emissions are
reduced by this rule.
Table 3-5. Cost Effectiveness (C/E) of Industrial Boiler and Process Heater MACT on Existing
Units and Subcategories.
Control Costs
($)
PM Emissions
Reduction
(Tons/Year)
C/E
($/ton PM)
Metals
Emissions
Reduction
(Tons/Year)
C/E
($/ton metals)
HC1
Emissions
Reduction
(Tons/Year)
Total
Annualized
Costs
833,273,781b
565,900
l,472a
1,093
762,373a
46,515
Large Solid
fuel
Subcategory
810,422,230
563,060
1,439
1,087
745,558a
46,515
Large Solid
fuel
Subcategory -
Coal Only
669,353,690
359,920
1,860
591
l,132,578a
45,136
Large Solid
fuel
Subcategory -
Wood Only
141,068,540
203,140
694
496
284,412a
1,379
Limited Use
Solid fuel
Subcategory
22,851,551
2,840
8,046
6
3,808,592a
3-17
-------
C/E
($/ton HC1)
HAP
Emissions
Reduction
(Tons/Year)
C/E
($/ton HAP)
17,914a
47,608
17,502
17,422a
47,602
17,025
14,830a
45,727
14,638
102,298a
1,875
75,236
—
6
3,808,500
a The cost-effectiveness value is based on the total annualized cost of the rule and not on the cost for
controlling the specific pollutant, and, thus, overstates the cost/ton for the specific HAP or other pollutant.
b Costs are in 1999 dollars. Emission reductions are calculated for 2005.
3-18
-------
Table 3-6. New Unit Projections by Industry, MACT Floor and Option 1A Alternatives
SIC
Code
01
02
07
10
12
13
14
17
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
42
JNAICS
Code
111
112
115
212
212
211
212
235
311
312
313
315
321
337
322
511
325
324
326
316
327
331
332
333
335
336
334
339
482
484
Description
Agriculture — Crops
Agriculture — Livestock
Agricultural Services
Metal Mining
Coal Mining
Oil and Gas Extraction
Mining/Quarrying — Nonmetallic Minerals
Construction — Special Trade Contractors
Food and Kindred Products
Tobacco Products
Textile Mill Products
Apparel and Other Products from Fabrics
Lumber and Wood Products
Furniture and Fixtures
Paper and Allied Products
Printing, Publishing, and Related Industries
Chemicals and Allied Products
Petroleum Refining and Related Industries
Rubber and Miscellaneous Plastics Products
Leather and Leather Products
Stone, Clay, Glass, and Concrete Products
Primary Metal Industries
Fabricated Metal Products
Industrial Machinery and Computer
Equipment
Electronic and Electrical Equipment
Transportation Equipment
Scientific, Optical, and Photographic
Equipment
Miscellaneous Manufacturing Industries
Railroad Transportation
Motor Freight and Warehousing
Moor Alternative
New Units
—
—
—
6
1
89
6
—
63
7
73
—
61
47
96
19
295
198
44
5
37
80
53
35
40
80
11
9
—
1
Cost
—
—
—
$47,040
$7,840
$697,760
$87,740
—
$801,836
$54,880
$1,329,391
—
$1,748,655
$1,354,701
$1,526,704
$148,960
$3,793,738
$1,552,320
$385,660
$39,200
$549,975
$2,873,492
$496,920
$396,500
$313,600
$1,133,423
$86,240
$162,323
—
$48,540
Uption 1A
New Units
—
—
—
6
1
89
6
—
63
7
73
—
61
47
96
19
295
198
44
5
37
80
53
35
40
80
11
9
—
1
Alternative
Cost
—
—
—
$47,040
$7,840
$697,760
$87,740
—
$11,170,93
1
$54,880
$1,463,682
—
$10,621,23
2
$4,306,979
$15,984,33
2
$148,960
$3,883,243
$1,552,320
$385,660
$39,200
$549,975
$2,873,492
$496,920
$396,500
$313,600
$1,357,219
$86,240
$254,722
—
$48,540
(continued)
3-19
-------
Table 3-6. New Unit Projections by Industry, MACT Floor and Option 1A Alternatives
(continued)
SIC
Code
46
49
50
51
55
58
59
60
70
72
76
80
81
82
83
86
87
89
91
92
94
96
97
NA
State
NAICS
Code
486
221
421
422
441
722
445-454
522
721
812
811
621
541
611
624
813
541
711/514
921
922
923
926
928
Description
Pipelines, Except Natural Gas
Electric, Gas, and Sanitary Services
Wholesale Trade — Durable Goods
Wholesale Trade — Nondurable Goods
Automotive Dealers and Gasoline Service
Stations
Eating and Drinking Places
Miscellaneous Retail
Depository Institutions
Hotels and Other Lodging Places
Personal Services
Miscellaneous Repair Services
Health Services
Legal Services
Educational Services
Social Services
Membership Organizations
Engineering, Accounting, Research,
Management and Related Services
Services, N.E.C.
Executive, Legislative, and General
Administration
Justice, Public Order, and Safety
Administration of Human Resources
Administration of Economic Programs
National Security and International Affairs
SIC Information Not Available
Parent is a State Government
Floor Alternative
New Units
1
134
—
—
—
—
—
—
—
1
—
6
—
19
—
—
2
—
—
4
—
—
2
307
—
1,832
Cost
$7,840
$2,094,546
—
—
—
—
—
—
—
$7,840
—
$209,840
—
$815,855
—
—
$388,350
—
—
$153,460
—
—
$97,080
$2,497,327
—
$25,909,574
Option 1A
New Units
1
134
—
—
—
—
—
—
—
1
—
6
—
19
—
—
2
—
—
4
—
—
2
307
—
1,832
Alternative
Cost
$7,840
$10,490,757
—
—
—
—
—
—
—
$7,840
—
$209,840
—
$815,855
—
—
$388,350
—
—
$153,460
—
—
$97,080
$2,586,832
—
$71,586,861
3-20
-------
Table 3-7. Unit Cost and Population Estimates for the Floor Alternative by Industry, 2005
SIC
Code
01
02
07
10
12
13
14
17
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
42
NAICS
Code
111
112
115
212
212
211
212
235
311
312
313
315
321
337
322
511
325
324
326
316
327
331
332
333
335
336
334
339
482
484
Description
Agriculture — Crops
Agriculture — Livestock
Agricultural Services
Metal Mining
Coal Mining
Oil and Gas Extraction
Mining/Quarrying — Nonmetallic Minerals
Construction — Special Trade Contractors
Food and Kindred Products
Tobacco Products
Textile Mill Products
Apparel and Other Products from Fabrics
Lumber and Wood Products
Furniture and Fixtures
Paper and Allied Products
Printing, Publishing, and Related Industries
Chemicals and Allied Products
Petroleum Refining and Related Industries
Rubber and Miscellaneous Plastics Products
Leather and Leather Products
Stone, Clay, Glass, and Concrete Products
Primary Metal Industries
Fabricated Metal Products
Industrial Machinery and Computer Equipment
Electronic and Electrical Equipment
Transportation Equipment
Scientific, Optical, and Photographic
Equipment
Miscellaneous Manufacturing Industries
Railroad Transportation
Motor Freight and Warehousing
lotai
Floor
Units
5
—
—
27
6
89
25
—
312
28
360
4
483
311
565
19
644
217
73
7
57
159
87
84
52
300
26
12
9
12
units
Percent
0.08%
0.00%
0.00%
0.48%
0.10%
1.60%
0.46%
0.00%
5.60%
0.51%
6.47%
0.08%
8.68%
5.59%
10.15%
0.34%
11.58%
3.91%
1.32%
0.13%
1.02%
2.85%
1.56%
1.51%
0.93%
5.39%
0.46%
0.22%
0.16%
0.22%
lotai cost
Floor Costs
(by Unit)
$628,943
—
—
$6,651,678
$683,026
$697,760
$8,253,479
—
$37,774,020
$6,014,216
$74,152,804
$679,510
$48,896,055
$29,632,880
$123,008,263
$148,960
$116,236,183
$4,620,563
$6,356,835
$607,530
$6,253,678
$27,110,619
$10,042,680
$11,208,392
$3,744,828
$55,440,341
$3,511,206
$826,346
$1,251,062
$2,128,148
Percent
0.07%
0.00%
0.00%
0.77%
0.08%
0.08%
0.96%
0.00%
4.38%
0.70%
8.59%
0.08%
5.67%
3.43%
14.25%
0.02%
13.47%
0.54%
0.74%
0.07%
0.72%
3.14%
1.16%
1.30%
0.43%
6.42%
0.41%
0.10%
0.14%
0.25%
(continued)
3-21
-------
Table 3-7. Unit Cost and Population Estimates for the Floor Alternative by Industry, 2005
(continued)
SIC
Code
46
49
50
51
55
58
59
60
70
72
76
80
81
82
83
86
87
89
91
92
94
96
97
NA
State
NAICS
Code
486
221
421
422
441
722
445^54
522
721
812
811
621
541
611
624
813
541
711/514
921
922
923
926
928
Description
Pipelines, Except Natural Gas
Electric, Gas, and Sanitary Services
Wholesale Trade — Durable Goods
Wholesale Trade — Nondurable Goods
Automotive Dealers and Gasoline Service
Stations
Eating and Drinking Places
Miscellaneous Retail
Depository Institutions
Hotels and Other Lodging Places
Personal Services
Miscellaneous Repair Services
Health Services
Legal Services
Educational Services
Social Services
Membership Organizations
Engineering, Accounting, Research,
Management and Related Services
Services, N.E.C.
Executive, Legislative, and General
Administration
Justice, Public Order, and Safety
Administration of Human Resources
Administration of Economic Programs
National Security and International Affairs
SIC Information Not Available
Parent is a state government
Total
Floor
Units
1
718
6
4
—
—
—
—
2
1
4
86
—
251
5
—
38
2
2
69
2
8
64
326
—
Units
Percent
0.02%
12.91%
0.12%
0.07%
0.00%
0.00%
0.00%
0.00%
0.04%
0.02%
0.08%
1.55%
0.00%
4.52%
0.08%
0.00%
0.68%
0.04%
0.04%
1.23%
0.04%
0.15%
1.16%
5.86%
0.00%
5,562
Total Cost
Floor Costs
(by Unit)
$7,840
$150,341,645
$2,154,760
$1,673,511
—
—
—
—
$567,811
$7,840
$625,531
$15,172,212
—
$60,490,956
$820,191
—
$2,240,544
$918,360
$312,765
$13,707,649
$314,316
$2,300,308
$18,018,010
$6,747,652
—
Percent
0.00%
17.42%
0.25%
0.19%
0.00%
0.00%
0.00%
0.00%
0.07%
0.00%
0.07%
1.76%
0.00%
7.01%
0.10%
0.00%
0.26%
0.11%
0.04%
1.59%
0.04%
0.27%
2.09%
0.78%
0.00%
$862,981,906
3-22
-------
Table 3-8. Unit Cost and Population Estimates for the Option 1A Above-the-Floor
Alternative by Industry, 2005
SIC
Code
01
02
07
10
12
13
14
17
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
42
NAICS
Code
111
112
115
212
212
211
212
235
311
312
313
315
321
337
322
511
325
324
326
316
327
331
332
333
335
336
334
339
482
484
Description
Agriculture — Crops
Agriculture — Livestock
Agricultural Services
Metal Mining
Coal Mining
Oil and Gas Extraction
Mining/Quarrying — Nonmetallic Minerals
Construction — Special Trade Contractors
Food and Kindred Products
Tobacco Products
Textile Mill Products
Apparel and Other Products from Fabrics
Lumber and Wood Products
Furniture and Fixtures
Paper and Allied Products
Printing, Publishing, and Related Industries
Chemicals and Allied Products
Petroleum Refining and Related Industries
Rubber and Miscellaneous Plastics Products
Leather and Leather Products
Stone, Clay, Glass, and Concrete Products
Primary Metal Industries
Fabricated Metal Products
Industrial Machinery and Computer Equipment
Electronic and Electrical Equipment
Transportation Equipment
Scientific, Optical, and Photographic
Equipment
Miscellaneous Manufacturing Industries
Railroad Transportation
Motor Freight and Warehousing
lotai
Option
1A Units
11
—
—
34
6
137
31
2
376
56
673
10
620
421
1,050
37
1,359
677
178
66
154
271
165
151
167
453
104
37
9
19
units
Percent
0.12%
0.00%
0.00%
0.37%
0.06%
1.50%
0.34%
0.03%
4.10%
0.61%
7.34%
0.11%
6.77%
4.60%
11.46%
0.40%
14.83%
7.38%
1.94%
0.72%
1.68%
2.95%
1.80%
1.65%
1.82%
4.95%
1.13%
0.41%
0.10%
0.21%
lotai cost
Option 1A Costs
(by Unit)
$1,633,841
—
—
$8,952,098
$683,026
$6,070,001
$17,958,177
$230,525
$122,487,346
$13,685,614
$147,094,726
$1,213,586
$89,961,854
$50,045,573
$323,736,302
$1,824,933
$293,027,205
$73,172,001
$18,100,195
$6,924,480
$17,509,996
$65,174,064
$22,066,661
$26,418,385
$18,770,867
$107,402,909
$13,638,983
$4,222,427
$2,240,871
$3,475,610
Percent
0.08%
0.00%
0.00%
0.45%
0.03%
0.30%
0.90%
0.01%
6.14%
0.69%
7.37%
0.06%
4.51%
2.51%
16.22%
0.09%
14.68%
3.67%
0.91%
0.35%
0.88%
3.27%
1.11%
1.32%
0.94%
5.38%
0.68%
0.21%
0.11%
0.17%
(continued)
3-23
-------
Table 3-8. Unit Cost and Population Estimates for the Option 1A Above-the-Floor
Alternative by Industry, 2005 (continued)
SIC
Code
46
49
50
51
55
58
59
60
70
72
76
80
81
82
83
86
87
89
91
92
94
96
97
NA
State
NAICS
Code
486
221
421
422
441
722
445^54
522
721
812
811
621
541
611
624
813
541
711/514
921
922
923
926
928
Description
Pipelines, Except Natural Gas
Electric, Gas, and Sanitary Services
Wholesale Trade — Durable Goods
Wholesale Trade — Nondurable Goods
Automotive Dealers and Gasoline Service
Stations
Eating and Drinking Places
Miscellaneous Retail
Depository Institutions
Hotels and Other Lodging Places
Personal Services
Miscellaneous Repair Services
Health Services
Legal Services
Educational Services
Social Services
Membership Organizations
Engineering, Accounting, Research,
Management and Related Services
Services, N.E.C.
Executive, Legislative, and General
Administration
Justice, Public Order, and Safety
Administration of Human Resources
Administration of Economic Programs
National Security and International Affairs
SIC Information Not Available
Parent is a state government
Total
Option 1A
Units
19
865
6
4
2
—
3
—
2
1
4
93
—
273
8
—
49
2
5
77
2
8
96
368
—
Units
Percent
0.21%
9.44%
0.07%
0.04%
0.02%
0.00%
0.03%
0.00%
0.02%
0.01%
0.05%
1.01%
0.00%
2.98%
0.08%
0.00%
0.54%
0.02%
0.06%
0.85%
0.02%
0.09%
1.05%
4.01%
0.00%
9,163
Total Cost
Option 1A Costs
(by Unit)
$1,959,589
$331,479,389
$2,675,296
$2,693,380
$195,421
—
$259,585
—
$849,114
$7,840
$1,120,435
$22,545,605
—
$91,770,778
$1,448,405
—
$5,016,627
$1,211,582
$845,423
$21,308,885
$314,316
$4,200,975
$36,080,306
$12,099,975
—
Percent
0.10%
16.61%
0.13%
0.13%
0.01%
0.00%
0.01%
0.00%
0.04%
0.00%
0.06%
1.13%
0.00%
4.60%
0.07%
0.00%
0.25%
0.06%
0.04%
1.07%
0.02%
0.21%
1.81%
0.61%
0.00%
$1,995,805,181
3-24
-------
References
Eastern Research Group. Memorandum to Jim Eddinger, U.S. Environmental Protection Agency, Office
of Air Quality Planning and Standards. Development of the Population Database for the
Industrial/Commercial/Institutional Boiler and Indirect-Fired Process Heater National Emission Standard
for Hazardous Air Pollutants (NESHAP). May 18, 2000.
Eastern Research Group. Memorandum to Jim Eddinger, U.S. Environmental Protection Agency, Office
of Air Quality Planning and Standards. Development of Model Units for the
Industrial/Commercial/Institutional Boiler and Indirect-Fired Process Heater National Emission Standard
for Hazardous Air Pollutants (NESHAP). July, 2000.
U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards. Industrial
Combustion Coordinated Rulemaking, Inventory Database V4.1- Boilers. February 26, 1999.
U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards. Industrial
Combustion Coordinated Rulemaking, Inventory Database V4 - Process Heaters. November 13, 1998.
CHAPTER 4
PROFILES OF AFFECTED INDUSTRIES
4-25
-------
This chapter contains profiles of the major industries affected by the MACT for industrial boilers
and process heaters. Included are profiles of the following industries:
• Textile Mill Products (SIC 22/NAICS 313)
• Lumber and Wood Products (SIC 24/NAICS 321)
• Furniture and Related Product Manufacturing (SIC 25/NAICS 337)
• Paper and Allied Products (SIC 26/NAICS 322)
• Medicinal Chemicals and Botanical Products and Pharmaceutical Preparations (SICs 2833,
2834/NAICS 32451)
• Industrial Organic Chemicals (SIC 2869/NAICS 3251)
• Electric Services (SIC 4911/NAICS 22111)
4.1 Textile Mill Products (SIC 22/NAICS 313)
The textile industry is one of the few industries found throughout the world, from the most
industrialized countries to the poorest. This industry includes firms producing the following products:
broadwoven fabric; weft, lace, and warp knit fabrics; carpets and rugs; spun yarn products; and man-made
fibers. The United States has typically run a trade deficit in the textiles sector in recent years, importing
about $1.3 billion more than was exported in 1995. Although trade has become an increasingly important
part of this industry, trade in this segment is relatively small compared with trade in the downstream
apparel segment. In 1996, the total value of shipments for the textile industry was $80,242 million.
4.2 Lumber and Wood Products (SIC 24/NAICS 321)
The lumber and wood products industry comprises a large number of establishments engaged in
logging; operating sawmills and planing mills; and manufacturing structural wood panels, wooden
containers, and other wood products. Table 4-1 lists the lumber and wood products markets that are
likely to be affected by the regulation on boilers. Most products are produced for the domestic market,
but exports increasingly account for a larger proportion of sales (Haltmaier, 1998). The largest
consumers of lumber and wood products are the remodeling and construction industries.
Table 4-1. Lumber and Wood Products Markets Likely to Be Affected by the Regulation
SIC
2421
2434
2449
2491
2493
2499
NAICS
321113
33711
32192
32114
321219
321999
Description
Sawmills and Planing Mills, General
Wood Kitchen Cabinets
Wood Containers, N.E.C.
Wood Preserving
Reconstituted Wood Products
Wood Products, N.E.C.
Source: Industrial Combustion Coordinated Rulemaking (ICCR). 1998.
Data/Information Submitted to the Coordinating Committee at the Final Meeting
of the Industrial Combustion Coordinated Rulemaking Federal Advisory
Committee. EPA Docket Numbers A-94-63, II-K-4b2 through -4b5. Research
Triangle Park, North Carolina. September 16-17.
4-1
-------
In 1996, the lumber and wood products industry's total value of shipments was
$85,724.0 million. As seen in Table 4-2, shipment values increased steadily through the late 1980s before
declining slightly through the early 1990s as new construction starts and furniture purchases declined
(Haltmaier, 1998). Shipment values recovered, however, as the economy expanded in the mid-1990s.
4.2.1 Supply Side of the Industry
This section describes the lumber industry's production processes, output, costs of production, and
capacity utilization.
4.2.1.1 Production Processes
Sawn lumber. Sawn lumber is softwood or hardwood trimmed at a sawmill for future uses in
construction, flooring, furniture, or other markets. Softwoods, such as Douglas fir and spruce, are used for
framing in residential or light-commercial construction. Hardwoods, such as maple and oak, are used in
flooring, furniture, crating, and other applications.
Lumber is prepared at mills using a four-step process. First, logs are debarked and trimmed into
cants, or partially finished lumber. The cants are then cut to specific lengths. Logs are generally kept wet
during storage to prevent cracking and to keep them supple. However, after being cut, the boards undergo
a drying process, either in open air or in a kiln, to reduce the moisture content. The drying process may
take several months and varies according to the plant's climate and the process used. Finally, the lumber
may be treated with a surface protectant to prevent sap stains and prepare it for export (EPA, 1995a).
Reconstituted wood products. Reconstituted wood products, such as particleboard, medium
density fiberboard, hardboard, and oriented strandboard, are made from raw wood that is combined with
resins and other additives and processed into boards. The size of the wood particles used varies from
sawdust to strands of wood. Once combined, the ingredients are formed into a mat and then, at high
temperatures, pressed into a board. A final finishing process prepares the boards for delivery.
Wood preserving. Wood is treated with preservative to protect it from mechanical, physical, and
chemical influences (EPA, 1995a). Treatment agents are either water-based inorganics, such as copper
arsenate (78 percent), or oil-borne organics, such as creosote (21 percent) (EPA, 1995a). Wood
Table 4-2. Value of Shipments for the Lumber and Wood Products Industry
(SIC 24/NAICS 321), 1987-1996
Year Value of Shipments (1992 $10°)
1987 85,383.4
1988 85,381.2
1989 85,656.8
1990 86,203.0
1991 81,666.0
1992 81,564.8
1993 74,379.6
1994 79,602.0
1995 87,574.6
1996 85,724.0
Sources: U.S. Department of Commerce, Bureau of the Census. 1996. 1992 Census of
Manufactures, Subject Series: General Summary. Washington, DC: Government
Printing Office.
U.S. Department of Commerce, Bureau of the Census. 1990-1998. Annual Survey of
Manufactures [Multiple Years]. Washington, DC: Government Printing Office.
-------
preservatives are usually applied using a pressure treatment process or a dipping tank. Producers achieve
the best results when the lumber's moisture content is reduced to a point where the preservative can be
easily soaked into the wood. Treated wood is then placed in a kiln or stacked in a low-humidity climate to
dry.
4.2.1.2 Types of Output
The lumber and wood products industry produces essential inputs into the construction,
remodeling, and furniture sectors. Lumber and reconstituted wood products are produced in an array of
sizes and can be treated to enhance their value and shelf-life. These products are intermediate goods; they
are purchased by other industries and incorporated into higher value-added products. In addition to
sawmills, the lumber and wood products industry includes kitchen cabinets, wood containers, and other
wooden products used for fabricating finished goods for immediate consumption.
4.2.1.3 Major By-Products and Co-Products
Shavings, sawdust, and wood chips are the principal co-products of sawn lumber. Paper mills and
makers of reconstituted wood products frequently purchase this material as an input. By-products are
limited to emissions from the drying process and from use of preservatives.
Very little solid waste is generated by reconstituted wood products manufacturing. Because the
production process incorporates all parts of the sawn log, little is left over as waste. However, air
emissions from dryers are a source of emissions.
Wood preserving results in two types of by-products: air emissions and process debris. As
preservatives dry, either in a kiln or outside, they emit various chemicals into the air. At plants with
dipping processes, wood chips, stones, and other debris build up in the dipping tank. The debris is
routinely collected and disposed of.
4.2.1.4 Costs of Production
The costs of production for the wood products industry fluctuate with the demand for the
industry's products. Most notably, the costs of production steadily declined during the early 1990s as
recession stifled furniture purchases and new housing starts (see Table 4-3). Overall, employment in the
lumber and wood products industry increased approximately 6 percent from 1987 to 1996. During this
same period, payroll costs decreased 12 percent, indicating a decrease in average annual income per
employee. New capital investment and costs of materials generally moved in tandem over the 10-year
period, increasing from 1987 to 1990 and 1994 to 1996 and decreasing from 1991 to 1993.
4.2.1.5 Capacity Utilization
Full production capacity is broadly defined as the maximum level of production an establishment
can obtain under normal operating conditions. The capacity utilization ratio is the ratio of the actual
production level to the full production level. Table 4-4 presents the historical trends in capacity utilization
for the lumber and wood products industry. The varying capacity utilization ratios reflect adjusting
production levels and new production facilities going on- or off-line. The capacity utilization ratio for the
industry in 1996 was 78; the average over the last 6 years was 79 percent.
4.2.2 Demand Side of the Industry
This section describes the demand side of the market, including product characteristics, the uses
and consumers of the final products, organization of the industry, and markets and trends.
4.2.3 Product Characteristics
4-3
-------
Lumber and wood products are valued both for their physical attributes and their relative low cost.
Wood is available in varying degrees of durability, shades, and sizes and can be easily shaped. Lumber
and wood products have long been the principal raw materials for the residential and light commercial
construction industries, the remodeling industry, and the furniture industry.
Table 4-3. Inputs for the Lumber and Wood Products Industry (SIC 24/NAICS 321),
1987-1996
Labor
Year
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Quantity
(103)
698.4
702.4
684.2
677.7
623.6
655.8
685.4
718.5
740.2
738.7
Payroll
(1992 $106)
15,555.5
15,800.0
15,381.3
15,612.9
14,675.8
13,881.8
11,798.9
12,212.5
13,915.4
13,933.7
Materials
(1992 $106)
50,509.2
51,341.0
51,742.2
53,369.0
50,416.3
48,570.0
45,300.3
48,535.6
53,732.9
52,450.1
New Capital
Investment
(1992 $106)
2,234.3
2,099.4
2,329.9
2,315.3
2,006.5
1,760.1
1,538.1
1,956.8
2,553.1
2,659.9
Sources: U.S. Department of Commerce, Bureau of the Census. 1996. 1992 Census of
Manufactures, Subject Series: General Summary. Washington, DC: Government
Printing Office.
U.S. Department of Commerce, Bureau of the Census. 1990-1998. Annual Survey of
Manufactures [Multiple Years]. Washington, DC: Government Printing Office.
Table 4-4. Capacity Utilization Ratios for Lumber and Wood Products Industry, 1991-1996
1991
1992
1993
1994
1995
1996
78
80
81
4-4
80
77
78
Note: All values are percentages.
Source: U.S. Department of Commerce, Bureau of the Census. 1998. Survey of Plant
Capacity: 1996. Washington, DC: Government Printing Office.
-------
Wood is readily available because over one-third of the United States is forested. The ready supply of
wood reduces its costs.
4.2.4 Uses and Consumers of Outputs
Lumber and wood products are used in a wide range of applications, including residential and
noresidential construction; repair/remodeling and home improvement projects; manufactured housing;
millwork and wood products; pulp, paper, and paperboard mills; toys and sporting goods; kitchen cabinets;
crates and other wooden containers; office and household furniture; and motor homes and recreational
vehicles (Haltmaier, 1998).
4.2.5 Organ ization of th e In dustry
In 1992, 33,878 companies produced lumber and wood products and operated 35,807 facilities, as
shown in Table 4-5. By way of comparison, in 1987, 32,014 companies controlled 33,987 facilities.
About two-thirds of all establishments have nine or fewer employees. Between 1987 and 1992, the
number of facilities with nine or fewer employees increased more than 10 percent to 23,590. These
facilities' share of the value of shipments increased about 18.3 percent. Although the number of
establishments employing 100 to 249 people decreased during that time, that category's shipment value
jumped nearly 40 percent. The remaining facility categories lost both facilities and value of shipment.
Market structure can affect the size and distribution of regulatory impacts. Concentration ratios
are often used to evaluate the degree of competition in a market, with low concentration indicating the
presence of a competitive market, and higher concentration suggesting less-competitive markets. Firms in
less-concentrated industries are more likely to be price takers, while firms in more-concentrated industries
are more likely to influence market prices. Typical measures include four- and eight-firm concentration
ratios (CR4 and CR8) and Herfindahl-Hirschmann indices (HHI). The CR4 for lumber and wood products
subsectors represented in the boilers inventory database ranges between 13 and 50, meaning that, in each
subsector, the top firms' combined sales ranged from 13 to 50 percent of that respective subsector's total
sales. The CR8 ranges from 47 to 66 (U.S. Department of Commerce, 1995d).
Although there is no objective criterion for determining market structure based on the values of
concentration ratios, the 1992 Department of Justice's (DOJ's) Horizontal Merger Guidelines provide
4-5
-------
Table 4-5. Size of Establishments and Value of Shipments for the Lumber and Wood
Products Industry (SIC 24/NAICS 321)
1987
Average Number of
Employees in
Establishment
1 to 4 employees
5 to 9 employees
10 to 19 employees
20 to 49 employees
50 to 99 employees
100 to 249 employees
250 to 499 employees
500 to 999 employees
1,000 to 2,499 employees
2,500 or more employees
Total
Number of
Facilities
14,562
6,702
5,353
4,160
1,702
1,190
260
47
4
2
33,987
Value of
Shipments
(1992 $106)
2,769.7
4,264.4
6,982.3
28,551.3
(D)
24,583.3
12,093.4
3,907.9
2,231.3
(D)
85,383.4
1992
Number of
Facilities
15,921
7,669
5,331
3,924
1,615
1,082
219
39
4
3
35,807
Value of
Shipments
(1992 $106)
3,288.9
5,030.4
6,902.8
26,964.9
(D)
34,051.4
(D)
3,331.4
598.6
1,396.4
81,564.8
(D) = undisclosed
Sources:
U.S. Department of Commerce, Bureau of the Census. 1991. 7957 Census of
Manufactures, Subject Series: General Summary. Washington, DC: Government
Printing Office.
U.S. Department of Commerce, Bureau of the Census. 1996. 1992 Census of
Manufactures, Subject Series: General Summary. Washington, DC: Government
Printing Office.
criteria for doing so based on HHIs. According to these criteria, industries with HHIs below 1,000 are
4-6
-------
Table 4-6. Measures of Market Concentration for Lumber and Wood Products Markets
SIC
2421
2434
2449
2491
2493
2499
Description
Saw Mills and
Planing Mills
Wood Kitchen
Cabinets
Wood Containers,
N.E.C.
Wood Preserving
Reconstituted
Wood Products
Wood Products,
N.E.C.
CR4
14
19
34
17
50
13
CR8
20
25
47
28
66
19
HHI
78
156
414
152
765
70
Number
of
Companie
s
5,302
4,303
217
408
193
2,656
Number of
Facilities
6004
4323
225
486
288
2754
Sources: U.S. Department of Commerce, Bureau of the Census. 1995d. 1992
Concentration Ratios in Manufacturing. Washington, DC: Government Printing
Office.
U.S. Department of Commerce, Bureau of the Census. 1996. 1992 Census of
Manufactures, Subject Series: General Summary. Washington, DC: Government
Printing Office.
considered unconcentrated (i.e., more competitive), those with HHIs between 1,000 and 1,800 are
considered moderately concentrated (i.e., moderately competitive), and those with HHIs above 1,800 are
considered highly concentrated (i.e., less competitive) (DOJ, 1992). Firms in less-concentrated industries
are more likely to be price takers, while firms in more-concentrated industries are more likely to be able to
influence market prices. The unconcentrated nature of the markets is also indicated by HHIs of 1,000 or
less (DOJ, 1992). Table 4-6 presents various measures of market concentration for sectors within the
lumber and wood products industry. All lumber and wood products industries are considered
unconcentrated and competitive.
4.2.6 Markets and Trends
The U.S. market for lumber and wood products is maturing, and manufacturers are looking to
enter other markets. Although 91 percent of the industry's products are consumed by the U.S. domestic
market, the share of exports increases each year. Exports more than doubled in value from $3 billion in
1986 to $7.3 billion in 1996 (Haltmaier, 1998). The U.S. market grew only 2 percent between 1986 and
1996. American manufacturers are focusing on growing construction markets in Canada, Mexico, and the
Pacific Rim, with products such as durable hardwood veneer products and reconstituted wood boards
(EPA, 1995a).
4-7
-------
4.3 Furniture and Related Product Manufacturing (SIC 25/NAICS 337)
More than 20,000 establishments in the United States produce furniture and furniture-related
products. These establishments are located across the United States but are traditionally most concentrated
in southern states, such as North Carolina, Mississippi, Alabama, and Tennessee. According to the "1997
Economic Census," these establishments employed more than 600,000 people and paid annual wages of
nearly $15 billion. The overall industry-wide value of shipments was $63.9 billion that year (U.S.
Department of Commerce, 2001).
This industry is in a state of change: rapid U.S. economic growth translated into vigorous sales of
household and office funiture, but this trend is unlikely to continue as the U.S. economy cools after its
record run. Adding to industry fluctuation is the merger of two large firms, Lay-Z-Boy and LADD
Furniture. Although the industry includes a multitude of niche market players, it is really dominated by a
few large companies that operate several subsidiaries, each with its own brand identity. It is unclear
whether the merger between two key players in the market will compel other large manufacturers to pursue
mergers and acquisitions.
What is clear, however, is that large U.S. manufacturers will seek to leverage their brand identities
into wider profit margins by operating direct sales establishments and co-branding. Manufacturers that are
moving into retail and distribution include Bassett Furniture, Thomasville Furniture, Ethan Allen Interiors,
and Drexel. Co-branding efforts are aimed at capitalizing on the combined power of two identities, such
as the Thomas Kinkade Collection from Lay-Z-Boy and popular artist Thomas Kinkade and the Ernest
Hemingway Collection from Thomasville. The overarching goal is to enhance margins and ward off
invigorated competition from foreign companies that have used this strategy to capture U.S. market share,
such as the Swedish manufacturer Ikea (Lemm, 2000).
U.S. imports of household furniture totaled nearly $7 billion in 1998. Between 1992 and 1998,
furniture imports grew at an annualized rate of nearly 15 percent. Jamie Lemm, an analyst with the U.S.
Department of Commerce's Office of Consumer Goods attributes this growth to changes in U.S.
manufacturing and markets:
A portion of [the] increase can be attributed to the labor-intensive furniture parts imported
by U.S. manufacturers to enhance product lines, but the increase also signifies the growing
importance of the U.S. market to foreign firms. While some U.S. manufacturers operate
showrooms, galleries, and retail outlets in foreign markets, few sell internationally on a
large scale. In 1998, U.S. furniture exports totaled $1.6 billion, accounting for only 6
percent of all U.S. product shipments.
4.4 Paper and Allied Products (SIC 26/NAICS 322)
The paper and allied products industry is one of the largest manufacturing industries in the United
States. In 1996, the industry shipped nearly $150 billion in paper commodities. The industry produces a
wide range of wood pulp, primary paper products, and paperboard products such as printing and writing
papers, industrial papers, tissues, container board, and boxboard. The industry also includes manufacturers
that "convert" primary paper and paperboard into finished products like envelopes, packaging, and
shipping containers (EPA, 1995b). Paper and allied products industry subsectors that are likely to be
affected by the proposed regulation are listed in Table 4-7.
4-8
-------
Table 4-7. Paper and Allied Products Industry Markets Likely to Be Affected by
Regulation
SIC NAICS Industry Description
26H 32211 Pulp Mills
2621 32212 Paper Mills
2676 322291 Sanitary Paper Products
Source: Industrial Combustion Coordinated Rulemaking (ICCR). 1998. Data/Information
Submitted to the Coordinating Committee at the Final Meeting of the Industrial
Combustion Coordinated Rulemaking Federal Advisory Committee. EPA Docket
Numbers A-94-63, II-K-4b2 through -4b5. Research Triangle Park, North
Carolina. September 16-17.
Table 4-8 lists the paper and allied products industry's value of shipments from 1987 to 1996. The
industry's performance is tied to raw material prices, labor conditions, and worldwide inventories and
demand (EPA, 1995b). Performance over the 10-year period was typical of most manufacturing
industries. The industry expanded in the late 1980s, then contracted as demand tapered off as the industry
suffered recessionary effects. In the two years after 1994, the industry's value of shipments increased 9.3
percent to $149.5 billion.
4.4.1 Supply Side of the Industry
4.4.1.1 Production Process
The manufacturing paper and allied products industry is capital- and resource-intensive,
consuming large amounts of pulp wood and water in the manufacturing process. Approximately half of all
paper and allied products establishments are integrated facilities, meaning that they produce both pulp and
paper on-site. The remaining half produce only paper products; few facilities produce only pulp (EPA,
1995b).
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Table 4-8. Value of Shipments for the Paper and Allied Products Industry
(SIC 26/NAICS 322), 1987-1996
Year Value of Shipments (1992 $106)
1987 129,927.8
1988 136,829.4
1989 138,978.3
1990 136,175.7
1991 132,225.0
1992 133,200.7
1993 131,362.2
1994 136,879.9
1995 135,470.3
1996 149,517.1
Sources: U.S. Department of Commerce, Bureau of the Census. 1996. 1992 Census of
Manufactures, Subject Series: General Summary. Washington, DC: Government
Printing Office.
U.S. Department of Commerce, Bureau of the Census. 1990-1998. Annual Survey of
Manufactures, [Multiple Years]. Washington, DC: Government Printing Office.
The paper and paperboard manufacturing process can be divided into three general steps: pulp
making, pulp processing, and paper/paperboard production. Paper and paperboard are manufactured using
what is essentially the same process. The principal difference between the two products is that paperboard
is thicker than paper's 0.3 mm.
Producers manufacture pulp mixtures by using chemicals, machines, or both to reduce raw
material into small fibers. In the case of wood, the most common pulping material, chemical pulping
actions release cellulose fibers by selectively destroying the chemical bonds that bind the fibers together
(EPA, 1995b). Impurities are removed from the pulp, which then may be bleached to improve brightness.
Only about 20 percent of pulp and paper mills practice bleaching (EPA, 1995b). The pulp may also be
further processed to aid in the paper-making process.
During the paper-making stage, the pulp is strengthened and then converted into paper. Pulp can
be combined with dyes, resins, filler materials, or other additives to better fulfill specifications for the final
product. Next, the water is removed from the pulp, leaving the pulp on a wire or wire mesh conveyor.
The fibers bond together as they are carried through heated presses and rollers. The paper is stored on
large rolls before being shipped for conversion into another product, such as envelopes and boxes, or cut
into paper sheets for immediate consumption.
4.4.1.2 Types of Output
The paper and allied products industry's output ranges from writing papers to containers and
packaging. Paper products include printing and writing papers; paperboard boxes; corrugated and solid
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Table 4-9. Inputs for the Paper and Allied Products Industry (SIC 26/NAICS 322),
1987-1996
Labor
Year
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Quantity (103)
611.1
619.8
633.2
631.2
624.7
626.3
626.3
621.4
629.2
630.6
Payroll
(1992 $106)
20,098.6
19,659.0
19,493.1
19,605.2
19,856.3
20,491.9
20,602.6
20,429.7
18,784.3
19,750.0
Materials
(1992 $106)
70,040.6
73,447.4
75,132.5
74,568.8
72,602.5
73,188.0
73,062.6
76,461.6
79,968.6
75,805.9
New Capital
Investment
(1992 $106)
6,857.5
8,083.8
10,092.9
11,267.2
9,353.9
7,962.4
7,265.2
6,961.7
7,056.8
8,005.9
Sources: U.S. Department of Commerce, Bureau of the Census. 1996. 1992 Census of
Manufactures, Subject Series: General Summery. Washington, DC: Government
Printing Office.
U.S. Department of Commerce, Bureau of the Census. 1990-1998. Annual Survey of
Manufactures [Multiple Years]. Washington, DC: Government Printing Office.
fiber boxes; fiber cans, drums, and similar products; sanitary food containers; building paper; packaging;
bags; sanitary paper napkins; envelopes; stationary products; and other converted paper products.
4.4.1.3 Major By-Products and Co-Products
The paper and allied products industry is the largest user of industrial process water in the United
States. In 1988, a typical mill used between 16,000 and 17,000 gallons of water per ton of paper produced.
The equivalent amount of waste water discharged each day is about 16 million cubic meters (EPA, 1995b).
Most facilities operate waste water treatment facilities on site to remove biological oxygen demand (BOD),
total suspended solids (TSS), and other pollutants before discharging the water into a nearby waterway.
4.4.1.4 Costs of Production
Historical statistics for the costs of production for the paper and allied products industry are listed
in Table 4-9. From 1987 to 1996, industry payroll generally ranged from approximately $19 to 20 billion.
Employment peaked at 633,200 people in 1989 and declined slightly to 630,600 people by 1996.
Materials costs averaged $74.4 billion a year and new capital investment averaged $8.3 billion a year.
4.4.1.5 Capacity Utilization
Table 4-10 presents the trend in capacity utilization for the paper and allied products industry. The
varying capacities reflect adjusting production levels and new production facilities going on- or off-line.
The average capacity utilization ratio for the paper and allied products industry between 1991 and 1996
was approximately 80, with capacity declining slightly in recent years.
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Table 4-10. Capacity Utilization Ratios for the Paper and Allied Products Industry,
1991-1996
1991 1992 1993 1994 1995 1996
78 80 81 80 77 78~
Note: All values are percentages.
Source: U.S. Department of Commerce, Bureau of the Census. 1998. Survey of Plant
Capacity: 1996. Washington, DC: Government Printing Office.
4.4.2 Demand Side of the Industry
4.4.2.1 Product Characteristics
Paper is valued for its diversity in product types, applications, and low cost due to ready access to
raw materials. Manufacturers produce papers of varying durabilities, textures, and colors. Consumers
purchasing large quantities of papers may have papers tailored to their specification. Papers may be simple
writing papers or newsprint for personal consumption and for the printing and publishing industry or
durable for conversion into shipping cartons, drums, or sanitary boxes. Inputs in the paper production
process are readily available in the United States because one-third of the country is forested, and facilities
generally have ready access to waterways.
4.4.2.2 Uses and Consumers of Products
The paper and allied products industry is an integral part of the U.S. economy; nearly every
industry and service sector relies on paper products for its personal, education, and business needs.
Among a myriad of uses, papers are used for correspondence, printing and publishing, packing and
storage, and sanitary purposes. Common applications are all manners of reading material,
correspondence, sanitary containers, shipping cartons and drums, and miscellaneous packing materials.
4.4.3 Organ ization of th e In dustry
In 1992, 4,264 companies produced paper and allied products and operated 6,416 facilities. By
way of comparison, 4,215 companies controlled 1,732 facilities in 1987. Although the number of small
firms and facilities increased during those 5 years, the industry is dominated by high-volume, low-cost
producers (Haltmaier, 1998). Even though they account for only 45 percent of all facilities, those with 50
or more employees contribute more than 93 percent of the industry's total value of shipments (see Table 4-
11). (According to the Small Business Administration, those companies employing fewer than 500
employees are "small.")
For paper and allied products markets likely to be affected by the proposed boilers regulation, the
CR4 ranged between 29 and 68 in 1992 (see Table 4-12). This means that, in each subsector, the top
firms' combined sales ranged from 29 to 68 percent of their respective industry's total sales. For example,
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Table 4-11. Size of Establishments and Value of Shipments for the Paper and Allied
Products Industry (SIC 26/NAICS 322)
1987
Number of Employees in
Establishment
1 to 4 employees
4 to 9 employees
10 to 19 employees
20 to 49 employees
50 to 99 employees
100 to 249 employees
250 to 499 employees
500 to 999 employees
1,000 to 2,499 employees
2,500 or more employees
Total
Number of
Facilities
729
531
888
1,433
1,018
1,176
308
145
63
1
1,732
Value of
Shipments
($106)
640.6
(D)
1,563.4
18,328.6
(D)
32,141.7
24,221.1
28,129.1
24,903.1
(D)
129,927.8
1992
Number of
Facilities
786
565
816
1,389
1,088
1,253
298
159
62
6,416
Value of
Shipments
($106)
216
483
1,456.5
6,366.6
12,811.5
35,114.0
22,281.2
31,356.5
23,115.4
133,200.7
(D) = undisclosed
Sources:
U.S. Department of Commerce, Bureau of the Census. 1990c. 7957 Census of
Manufactures, Industry Series: Pulp, Paper, and Board Mills. Washington, DC:
Government Printing Office.
U.S. Department of Commerce, Bureau of the Census. 1995c. 1992 Census of
Manufactures, Industry Series: Pulp, Paper, and Board Mills. Washington, DC:
Government Printing Office.
in the sanitary paper products industry, the CR4 ratios indicate that a few firms control 68 percent of the
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Table 4-12. Measurements of Market Concentration for Paper and Allied Products
Markets
SIC
2611
2621
2676
Description
Pulp Mills
Paper Mills
Sanitary Paper Products
CR4
48
29
68
CR8
75
49
82
HHI
858
392
1,451
Number of
Companies
29
127
80
Number of
Facilities
45
280
150
Sources: U.S. Department of Commerce, Bureau of the Census. 1995d. 1992
Concentration Ratios in Manufacturing. Washington, DC: Government Printing
Office.
U.S. Department of Commerce, Bureau of the Census. 1995c. 1992 Census of
Manufactures, Industry Series: Pulp, Paper, and Board Mills. Washington, DC:
Government Printing Office.
market. This sector's moderately concentrated nature is
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also indicated by its HHI of 1,451 (DOJ, 1992). The remaining two sectors' HHIs indicate that their
respective markets are unconcentrated (i.e., competitive).
4.4.4 Markets and Trends
The Department of Commerce projects that shipments of paper and allied products will increase
through 2002 by an annual average of 2.5 percent (Haltmaier, 1998). Because nearly all of the industry's
products are consumer related, shipments will be most affected by the health of the U.S. and global
economy. The United States is a key competitor in the international market for paper products and, after
Canada, is the largest exporter of paper products. According to Haltmaier (1998), the largest paper and
allied products exporters in the world are Canada (with 23 percent of the market), the United States (10 to
15 percent), Finland (8 percent), and Sweden (7 percent).
4.5 Medicinal Chemicals and Botanical Products and Pharmaceutical Preparations (SICs 2833,
2834/NAICS 32451)
The pharmaceutical preparations industry (SIC 2834/NAICS 32451) and the medicinal chemicals
and botanical products industry (SIC 2833/NAICS 32451) are both primarily engaged in the research,
development, manufacture, and/or processing of medicinal chemicals and pharmaceutical products. Apart
from manufacturing drugs for human and veterinary consumption, the industries grind, grade, and mill
botanical products that are inputs for other industries. Typically, most facilities cross over into both
industries (EPA, 1997a). Products include drugs, vitamins, herbal remedies, and production inputs, such
as alkaloids and other active medicinal principals.
Table 4-13 presents both industries' value of shipments from 1987 to 1996. Medicinals and
botanicals' performance during the late 1980s and early 1990s was mixed. However, shipments increased
steadily from 1994 to 1996, increasing 37.7 percent as natural products such as herbs and vitamins became
more popular (EPA, 1997a). Pharmaceutical preparations' shipments increased steadily over the 10-year
period. From 1987 to 1996, the industry's shipments increased 24.3 percent to $55.1 billion in 1996.
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Table 4-13. Value of Shipments for the Medicinals and Botanicals and Pharmaceutical
Preparations Industries, 1987-1996
Year
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
SIC 2833 Medicinals &
Botanicals ($106)
4,629.1
5,375.4
5,708.9
5,535.8
6,637.7
6,438.5
5,669.2
5,774.7
6,404.1
7,952.8
SIC 2834 Pharmaceutical
Preparations ($106)
44,345.7
46,399.1
48,083.6
49,718.0
49,866.3
50,417.9
50,973.5
53,144.7
53,225.9
55,103.6
Sources: U.S. Department of Commerce, Bureau of the Census. 1995a. 1992 Census of
Manufactures, Industry Series: Drug Industry. Washington, DC: Government
Printing Office.
U.S. Department of Commerce, Bureau of the Census. 1990-1998. Annual Survey of
Manufactures [Multiple Years]. Washington, DC: Government Printing Office.
4.5.1 Supply Side of the Industry
4.5.1.1 Production Processes
The medicinals and botanical products industry and the pharmaceutical preparations industry share
similar production processes. Many products of the former are inputs in the latter's production process.
There are three manufacturing stages: research and development, preparation of bulk ingredients, and
formulation of the final product.
The research and development stage is a long process both to ensure the validity and benefit of the
end product and to satisfy the requirements of stringent federal regulatory committees. (The
pharmaceutical industry operates under strict oversight of the Food and Drug Administration [FDA].)
Therefore, every stage in the development of new drugs is thoroughly documented and studied. After a
new compound is discovered, it is subjected to numerous laboratory and animal tests. Results are
presented to the FDA via applications that present and fully disclose all findings to date. As research and
development proceeds, studies are gradually expanded to involve human trials of the new compound.
Should FDA approve the compound, the new product is readied for mass production.
To ensure a uniform product, all ingredients are prepared in bulk using batch processes.
Companies produce enough of each ingredient to satisfy projected sales demand (EPA, 1997a). Prior to
production, all equipment is thoroughly cleaned, prepared, and validated to prevent any contaminants from
entering the production cycle. Most ingredients are prepared by chemical synthesis, a method whereby
primary ingredients undergo a complex series of processes, including many intermediate stages and
chemical reactions in a step-by-step fashion (EPA, 1997a).
After the bulk materials are prepared, they are converted into a final usable form. Common forms
include tablets, pills, liquids, creams, and ointments. Equipment used in this final stage is prepared in the
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same manner as that involved in the bulk preparation process. Clean and validated machinery is used to
process and package the pharmaceuticals for shipment and consumption.
4.5.1.2 Types of Output
Both industries produce pharmaceutical and botanical products for end consumption and
intermediate products for the industries' own applications. Products include vitamins, herbal remedies,
and alkaloids. Prescription and over-the-counter drugs are produced in liquid, tablet, cream, and other
forms.
4.5.1.3 Major By-Products and Co-Products
Both industries produce many by-products because of the large number of primary inputs and the
extensive chemical processes involved. Wastes and emissions vary by the process employed, raw
materials consumed, and equipment used. In general, emissions originate during drying and heating stages
and during process water discharge. Emissions controls are in place pursuant to environmental
regulations. Other wastes include used filters, spent raw materials, rejected product, and reaction residues
(EPA, 1997a).
4.5.1.4 Costs of Production
Table 4-14 presents SIC 2833 industry's costs of production and employment statistics from 1987
to 1996. Employment was stable during the late 1980s before steadily growing in the 1990s. In 1987,
medicinals and botanicals employed 11,600 people. By 1996, the industry employed 16,800, an increase
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Table 4-14. Inputs for Medicinal Chemicals and Botanical Products Industry
(SIC 2833/NAICS 32451), 1987-1996
Labor
Year
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Quantity (103)
11.6
11.3
11.4
10.9
12.5
13.0
13.0
13.9
14.1
16.8
Payroll
($106)
520.2
494.4
504.9
476.4
568.6
587.1
584.3
572.6
625.0
752.1
Materials
($106)
2,229.3
2,658.8
3,118.4
2,902.4
3,368.2
3,245.9
2,638.4
2,755.2
3,006.0
3,793.9
New Capital
Investment
($106)
158.2
194.9
263.4
218.9
512.9
550.5
470.0
480.3
356.2
752.1
Sources: U.S. Department of Commerce, Bureau of the Census. 1995a. 1992 Census of
Manufactures, Industry Series: Drug Industry. Washington, DC: Government
Printing Office.
U.S. Department of Commerce, Bureau of the Census. 1990-1998. Annual Survey of
Manufactures, [Multiple Years]. Washington, DC: Government Printing Office.
of nearly 45 percent. Materials costs matched the increase in shipments over this same period. Industry
growth also fed new capital investments, which averaged $191.2 million a year in the late 1980s and
$515.6 million a year in the early to mid-1990s.
SIC 2834's costs of production and employment for 1987 to 1996 are presented in Table 4-15.
The number of people employed by the industry ranged between 123,000 and 144,000; employment
peaked in 1990 before declining by 21,000 jobs by the end of 1992. During this 10-year period, the cost of
materials rose 42.1 percent. The increase is associated with increased product shipments and the
development of new, more expensive medications (Haltmaier, 1998). New capital investment averaged
$2.3 billion a year.
4.5.1.5 Capacity Utilization
Table 4-16 presents the trend in these ratios from 1991 to 1996 for both industries. The varying
capacity ratios reflect adjusting production volumes and new production facilities and capacity going both
on- and off-line. In 1996, the capacity utilization ratios for SICs 2833 and 2834 were 84 and 67,
respectively.
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Table 4-15. Inputs for the Pharmaceutical Preparations Industry (SIC 2834/NAICS 32451),
1987-1996
Labor
Year
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Quantity
(103)
131.6
133.4
141.8
143.8
129.1
122.8
128.2
134.2
143.0
136.9
Payroll
($106)
5,759.2
5,447.2
6,177.5
6,223.9
5,275.8
4,949.4
5,184.2
5,368.4
5,712.4
5,547.3
Materials
($106)
11,693.7
12,634.8
12,874.2
13,237.6
13,546.6
13,542.5
13,508.7
13,526.1
15,333.6
16,611.1
New Capital
Investment
($106)
2,032.7
2,234.0
2,321.4
2,035.3
1,864.7
2,450.0
2,385.2
2,531.9
2,856.1
2,317.0
Sources: U.S. Department of Commerce, Bureau of the Census. 1995a. 1992 Census of
Manufactures, Industry Series: Drug Industry. Washington, DC: Government
Printing Office.
U.S. Department of Commerce, Bureau of the Census. 1990-1998. Annual Survey of
Manufactures, [Multiple Years]. Washington, DC: Government Printing Office.
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Table 4-16. Capacity Utilization Ratios for the Medicinal Chemicals and Botanical
Products (SIC 2833/NAICS 32451) and Pharmaceutical Preparations
(SIC 2834/NAICS 32451) Industries, 1991-1996
1991 1992 1993 1994 1995 1996
SIC 2833/NAICS 84 86 89 80 90 84
32451
SIC 2834/NAICS 76 74 70 67 63 67
32451
Note: Capacity utilization ratio is the ratio of the actual production level to the full production
level. All values are percentages.
Source: U.S. Department of Commerce, Bureau of the Census. 1998. Survey of Plant
Capacity: 1996. Washington, DC: Government Printing Office.
4.5.2 Demand Side of the Industry
New product introductions and improvements on older medications by the drug industry have
greatly improved the health and well-being of the U.S. population (Haltmaier, 1998). Products help
alleviate or reduce physical, mental, and emotional ailments or reduce the severity of symptoms associated
with disease, age, and degenerative conditions. Dietary supplements, such as vitamins and herbal
remedies, ensure that consumers receive nutrients of which they may not ordinarily consume enough.
Products are available in a range of dosage types, such as tablets and liquids.
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Although prescription medications are increasingly distributed through third parties, such as
hospitals and health maintenance organizations, the general population remains the end user of
pharmaceutical products. As the average age of the U.S. population adjusts to reflect large numbers of
older people, the variety and number of drugs consumed increases. An older population will generally
consume more medications to maintain and improve quality of life (Haltmaier, 1998).
4.5.3 Organ ization of th e In dustry
In 1992, 208 companies produced medicinal chemicals and botanical products and operated 225
facilities (see Table 4-17). The number of companies and facilities in 1992 was the same as that of 1987,
although shipment values increased almost 40 percent. The average facility employed more people in
1992 than in 1987. In fact, the number of facilities employing 50 or more people grew from 37 to 45.
These facilities accounted for the lion's share of the industry's shipments. According to the Small
Business Administration, companies in this SIC code are considered small if they employ fewer than 750
employees. It is unclear what percentage of the facilities listed in Table 4-17 are small companies.
In 1992, 585 companies manufactured pharmaceutical preparations and operated 691 facilities. By
way of comparison, 640 companies operated 732 facilities in 1987. Although the number of facilities
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H^
-------
4-23
-------
with more than 50 employees accounted for at least 95 percent of the industry's shipments.
Table 4-18 presents the measures of market concentration for both industries. For the medicinals
and botanicals industry, the CR4 was 76 in 1992, and the CR8 was 84 (U.S. Department of Commerce,
1995b). The highly concentrated nature of the market is further indicated by an HHI of 2,999 (DOJ,
1992). According to the Department of Justice's Horizontal Merger Guidelines, industries with HHIs
above 1,800 are less competitive.
Table 4-18. Measures of Market Concentration for the Medicinal Chemicals and Botanical
Products (SIC 2833/NAICS 32451) and Pharmaceutical Preparations (SIC 2834/NAICS
32451) Industries
Number
of Number
Companie of
SIC NAICS Industry CR4 CR8 HHI s Facilities
2833 32451 Medicinal Chemicals 76 84 2,999 208 225
and Botanical Products
2834 32451 Pharmaceutical 26 42 341 585 691
Preparations
Sources: U.S. Department of Commerce, Bureau of the Census. 1995b. 1992
Concentration Ratios in Manufacturing. Washington, DC: Government Printing
Office.
U.S. Department of Commerce, Bureau of the Census. 1995a. 1992 Census of
Manufactures, Industry Series: Drug Industry. Washington, DC: Government Printing
Office.
The pharmaceuticals preparations industry is less concentrated than the medicinal chemicals and
botanical products industry. For SIC 2834, the CR4 and CR8 were 26 and 42, respectively, in 1992. The
industry's FIHI was 341, indicating a competitive market.
4.5.4 Markets and Trends
According to the Department of Commerce, global growth in the consumption of pharmaceuticals
is projected to accelerate over the coming decade as populations in developed countries age and those in
developing nations gain wider access to health care. Currently, the United States remains the largest
market for drugs, medicinals, and botanicals and produces more new products than any other country
(Haltmaier, 1998). But, nearly two-fifths of American producers' sales are generated abroad. Top markets
for American exports are China, Canada, Mexico, Australia, and Japan. Most imports originate in Canada,
Russia, Mexico, Trinidad and Tobago, and Norway.
4.6 Industrial Organic Chemicals Industry (SIC 2869/NAICS 3251)
The industrial organic chemicals (not elsewhere classified) industry (SIC 2869/NAICS 3251)
produces organic chemicals for end-use applications and for inputs into numerous other chemical
manufacturing industries. In nominal terms, it was the single largest segment of the $367 billion chemical
and allied products industry (SIC 28) in 1996, accounting for approximately 17 percent of the industry's
shipments.
All organic chemicals are, by definition, carbon-based and are divided into two general categories:
commodity and specialty. Commodity chemical manufacturers compete on price and produce large
4-24
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volumes of staple chemicals using continuous manufacturing processes. Specialty chemicals cater to
custom markets, using batch processes to produce a diverse range of chemicals. Specialty chemicals
generally require more technical expertise and research and development than the more standardized
commodity chemicals industry (EPA, 1995c). Consequently, specialty chemical manufacturers have a
greater value added to their products. End products for all industrial organic chemical producers are as
varied as synthetic perfumes, flavoring chemicals, glycerin, and plasticizers.
Table 4-19 presents the shipments of industrial organic chemicals from 1987 to 1996. In real
terms, the industry's shipments rose in the late 1980s to a high of $54.9 billion before declining in the
early 1990s as the U.S. economy went into recession. By the mid-1990s, the industry recovered, as
product values reached record highs (Haltmaier, 1998). Between 1993 and 1996, the industry's shipments
grew 7.3 percent to $57.7 billion.
Table 4-19. Value of Shipments for the Industrial Organic Chemicals, N.E.C. Industry (SIC
2869/NAICS 3251), 1987-1996
Year Value of Shipments (1992 $10°)
1987 48,581.7
1988 53,434.7
1989 54,962.9
1990 53,238.8
1991 51,795.6
1992 54,254.2
1993 53,805.2
1994 57,357.1
1995 59,484.3
1996 57,743.3
Sources: U.S. Department of Commerce, Bureau of the Census. 1995b. 1992 Census of
Manufactures, Industry Series: Industrial Organic Chemicals. Washington, DC:
Government Printing Office.
U.S. Department of Commerce, Bureau of the Census. 1990-1998. Annual Survey of
Manufactures, Multiple Years. Washington, DC: Government Printing Office.
4.6.1 Supply Side of the Industry
4.6.1.1 Production Processes
Processes used to manufacture industrial organic chemicals are as varied as the end-products
themselves. There are thousands of possible ingredients and hundreds of processes. Therefore, the
discussion that follows is a general description of the ingredients and stages involved in a typical
manufacturing process.
Essentially a set of ingredients (feedstocks) is combined in a series of reactions to produce end
products and intermediates (EPA, 1995c). The typical chemical synthesis processes incorporate multiple
feedstocks in a series of chemical reactions. Commodity chemicals are produced in a continuous reactor,
and specialty chemicals are produced in batches. Specialty chemicals may undergo a series of reaction
steps, as opposed to commodity chemicals' one continuous reaction because a finite amount of ingredients
are prepared and used in the production process. Reactions usually take place at high temperatures, with
one or two additional components being intermittently added. As the production advances, by-products
4-25
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are removed using separation, distillation, or refrigeration techniques. The final product may undergo a
drying or pelletizing stage to form a more manageable substance.
4.6.1.2 Types of Output
Miscellaneous industrial organic chemicals comprise nine general categories of products:
• aliphitic and other acyclic organic chemicals (ethylene); acetic, chloroaceptic, adipic, formic,
oxalic, and tartaric acids and their metallic salts; chloral, formaldehyde, and methylamine;
• solvents (ethyl alcohol etc.); methanol; amyl, butyl, and ethyl acetates; ethers; acetone, carbon
disulfide and chlorinated solvents;
• polyhydric alcohols (synthetic glycerin, etc.);
• synthetic perfume and flavoring materials (citral, methyl, oinone, etc.);
• rubber processing chemicals, both accelerators and antioxidants (cyclic and acyclic);
• cyclic and acyclic plasticizers (phosphoric acid, etc.);
• synthetic tanning agents;
• chemical warfare gases; and
• esters, amines, etc., of polyhydric alcohols and fatty and other acids.
4.6.1.3 Major By-Products and Co-Products
Co-products, by-products, and emissions vary according to the ingredients, processes, maintenance
practices, and equipment used (EPA, 1997b). Frequently, residuals from the reaction process that are
separated from the end product are resold or possibly reused in the manufacturing process. A by-product
from one process may be another's input. The industry is strictly regulated because it emits chemicals
through many types of media, including discharges to air, land, and water, and because of the volume and
composition of these emissions.
4.6.1.4 Costs of Production
Of all the factors of production, employment in industrial organic chemicals fluctuated most often
between 1987 and 1996 (see Table 4-20). During that time, employment fell 8.18 percent to 92,100, after
a high of 101,000 in 1991. Most jobs lost were at the production level (Haltmaier, 1998). Facilities
became far more computerized, incorporating advanced technologies into the production process. Even
with the drop in employment, payroll was $200 million more in 1995 than in 1987. The cost of materials
fluctuated between $29 and $36 billion for these years, and new capital investment averaged $3,646
million a year.
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Table 4-20. Inputs for the Industrial Organic Chemicals Industry (SIC 2869/NAICS 3251),
1987-1996
Labor
Year
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Quantity (103)
100.3
97.1
97.9
100.3
101.0
100.1
97.8
89.8
92.1
100.3
Payroll
(1992 $106)
4,295.8
4,045.1
3,977.4
4,144.6
4,297.3
4,504.2
4,540.2
4,476.5
4,510.4
5,144.8
Materials
(1992 $106)
28,147.7
29,492.8
29,676.4
29,579.2
29,335.2
31,860.6
30,920.1
33,267.4
33,163.9
36,068.9
New Capital
Investment
(1992 $106)
2,307.4
2,996.5
3,513.0
4,085.5
4,428.7
4,216.6
3,386.1
2,942.8
3,791.0
4,794.7
Sources: U.S. Department of Commerce, Bureau of the Census. 1995b. 1992 Census of
Manufactures. Washington, DC: Government Printing Office.
U.S. Department of Commerce, Bureau of the Census. 1990-1998. Annual Survey of
Manufactures. Washington, DC: Government Printing Office.
4.6.1.5 Capacity Utilization
Table 4-21 presents the trend in capacity utilization ratios from 1991 to 1996 for the industrial
organic chemicals industry. The varying capacity utilization ratios reflect changes in production volumes
and new production facilities and capacities going on- and off-line. The capacity utilization ratio for the
industry averaged 85.3 over the 6-year period presented.
4.6.2 Demand Side of the Industry
Industrial organic chemicals are components of many chemical products. Most of the chemical
sectors (classified under SIC 28) are downstream users of organic chemicals. These sectors either
purchase commodity chemicals or enter into contracts with industrial organic chemical producers to obtain
specialty chemicals. Consumers include inorganic chemicals (SIC 281), plastics and synthetics (SIC 282),
drugs (283), soaps and cleaners (SIC 284), paints and allied products (SIC 286), and miscellaneous
chemical products (SIC 289).
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Table 4-21. Capacity Utilization Ratios for the Industrial Organic Chemicals Industry (SIC
2869/NAICS 3251), 1991-1996
1991 1992 1993 1994 1995 1996
SIC 2869/NAICS 86 81 91 89 84 84
3251
Note: The capacity utilization ratio is the ratio of the actual production level to the full
production level.
All values are percentages.
Source: U.S. Department of Commerce, Bureau of the Census. 1998. Survey of Plant
Capacity: 1996. Washington, DC: Government Printing Office.
4.6.3 Organization of the Industry
Although the industry's value of shipments increased nearly 12 percent between 1987 and 1992,
the number of facilities producing industrial organic chemicals only increased by 6 percent. Facilities with
100 or more employees continued to account for the majority of the industry's shipment values. For
example, in 1992, 28 percent of all facilities had 100 or more employees (see Table 4-22), and these
facilities produced 89 percent of the industry's shipment values. The average number of facilities per firm
was 1.4 in both years. According to the Small Business Administration, an industrial organic chemicals
company is considered small if the total number of employees does not exceed 500. It is unclear what
percentage of facilities are owned by small businesses.
The industrial organic chemicals (not elsewhere classified) industry is unconcentrated and
competitive. The CR4 was 29 and the CR8 43; the industry's HHI was 336.
4.6.4 Markets and Trends
The U.S. industrial organic chemical industry is expected to expand through 2002 at an annual rate
of 1.4 percent (Haltmaier, 1998). U.S. producers face increasing competition domestically and abroad as
chemical industries in developing nations gain market share and increase exports to the United States.
American producers will, however, benefit from decreasing costs for raw materials and energy and
productivity gains.
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Table 4-22. Size of Establishments and Value of Shipments for the Industrial Organic
Chemicals Industry (SIC 2869/NAICS 3251)
1987
Number of Employees in
Establishment
1 to 4 employees
5 to 9 employees
10 to 19 employees
20 to 49 employees
50 to 99 employees
100 to 249 employees
250 to 499 employees
500 to 999 employees
1,000 to 2,499 employees
2,500 or more employees
Number of
Facilities
97
80
91
137
99
110
41
27
11
6
Value of
Shipments
(1992 $106)
552.8
200.9
484.7
1,749.9
2556.3
10,361.2
17,156.9
9,615.5
9,184.6
7,156.9
1992
Number of
Facilities
100
80
97
125
106
111
41
30
10
5
Value of
Shipments
(1992 $106)
102.6
208.7
533.9
1,701.5
3,460.9
8,855.9
9,971.1
13,755.0
9,051.0
6,613.5
Sources: U.S. Department of Commerce, Bureau of the Census. 1995b. 1992 Census of
Manufactures, Industry Series: Industrial Organic Chemicals. Washington, DC:
Government Printing Office.
U.S. Department of Commerce, Bureau of the Census. 1990b. 7957 Census of
Manufactures, Industry Series, Paints and Allied Products. Washington, DC:
Government Printing Office.
4.7 Electric Services (SIC 4911/NAICS 22111)
The ongoing process of deregulation of wholesale and retail electric markets is changing the
structure of the electric power industry. Deregulation is leading to the functional unbundling of
generation, transmission, and distribution and to competition in the generation segment of the industry.
This profile provides background information on the U.S. electric power industry and discusses current
industry characteristics and trends that will influence the future generation and consumption of electricity.
It is important to note that through out this report the terms "boilers," "process heaters," and "units" are
synonymous with "ICI boilers" and "process heaters." Boilers primarily engaged in the generation of
electricity are not covered by the NESHAP under analysis and are therefore excluded from this analysis.
Utility sources are not affected by this NESHAP except for a small number of nonfossil fuel units within
this industry. Those units in this industry that are affected may be engaged in activities such as heating
and mechanized work.
4.7.1 Electricity Production
Figure 4-1 illustrates the typical structure of the electric utility market. Even with the
technological and regulatory changes in the 1970s and 1980s, at the beginning of the 1990s the structure of
4-29
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the electric utility industry could still be characterized in terms of generation, transmission, and
distribution. Commercial and retail customers were in essence "captive," and rates and service quality
were primarily determined by public utility commissions.
The majority of utilities are interconnected and belong to a regional power pool. Pooling
arrangements enable facilities to coordinate the economic dispatch of generation facilities and manage
transmission congestion. In addition, pooling diverse loads can increase load factors and decrease costs by
sharing reserve capacity.
4.7.1.1 Generation
As shown in Table 4-23, coal-fired plants have historically accounted for the bulk of electricity
generation in the United States. With abundant national coal reserves and advances in pollution abatement
technology, such as advanced scrubbers for pulverized coal and flue gas-desulfurization systems, coal will
likely remain the fuel of choice for most existing generating facilities over the near term.
Natural gas accounts for approximately 10 percent of current generation capacity but is expected
to grow; advances in natural gas exploration and extraction technologies and new coal gasification have
contributed to the use of natural gas for power generation.
Nuclear plants and renewable energy sources (e.g., hydroelectric, solar, wind) provide
approximately 20 percent and 10 percent of current generating capacity, respectively. However, there are
no plans for new nuclear facilities to be constructed, and there is little additional growth forecasted in
renewable energy.
Table 4-23. Net Generation by Energy Source, 1995
Utility Generators Nonutility
Energy Source
Fossil fuels
Coal
Natural gas
Petroleum
Nuclear
Hydroelectric
Renewable/other
Total
(MWh)
2,021,064
1,652,914
307,306
60,844
673,402
293,653
6,409
2,994,582
Generators (MWh)
287,696
63,440
213,437
3,957
—
14,515
98,295
400,505
Total (MWh)
2,308,760
673,402
308,168
104,704
3,395,033
Sources: U.S. Department of Energy, Energy Information Administration. 1996. Electric
Power Annual, 1995. Vol. 1. DOE/EIA-0348(95/1). Washington, DC: U.S.
Department of Energy.
U.S. Department of Energy, Energy Information Administration. 1999b. The Changing
Structure of the Electric Power Industry 1999: Mergers and Other Corporate
Combinations. Washington, DC: U.S. Department of Energy.
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Electricity
Generation
Power Plants
Trans-
mission
High Voltage Lines
Transformer
Distribution
Residential
Customers
Small C/l
Customers
Large C/l
Customers
Figure 4-1. Traditional Electric Power Industry Structure
4.7.1.2 Transmission
Transmission refers to high voltage lines used to link generators to substations where power is
stepped down for local distribution. Transmission systems have been traditionally characterized as a
collection of independently operated networks or grids interconnected by bulk transmission interfaces.
Within a well-defined service territory, the regulated utility has historically had responsibility for
all aspects of developing, maintaining, and operating transmissions. These responsibilities included
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• system planning and expanding,
• maintaining power quality and stability, and
• responding to failures.
Isolated systems were connected primarily to increase (and lower the cost of) power reliability. Most
utilities maintained sufficient generating capacity to meet customer needs, and bulk transactions were
initially used only to support extreme demands or equipment outages.
4.7.1.3 Distribution
Low-voltage distribution systems that deliver electricity to customers comprise integrated
networks of smaller wires and substations that take the higher voltage and step it down to lower levels to
match customers' needs.
The distribution system is the classic example of a natural monopoly because it is not practical to
have more than one set of lines running through neighborhoods or from the curb to the house.
4.7.2 Cost of Production
Table 4-24 shows total industry expenditures by production activities. Generation accounts for
approximately 75.6 percent of the cost of delivered electric power in 1996. Transmission and distribution
accounted for 2.5 percent and 5.6 percent, respectively. Customer accounts and sales and administrative
costs accounted for the remaining 16.3 percent of the cost of delivered power.
4.7.3 Organ ization of th e In dustry
Because the restructuring plans and time tables are made at the state level, the issues of asset
ownership and control throughout the current supply chain in the electric power industry vary from state to
state. However, the activities conducted throughout the supply chain are generally the same. This section
focuses on the generation segment of the market because all the boilers affected by the regulation are
involved in generation.
As part of deregulation, the transmission and distribution of electricity are being separated from
the business of generating electricity, and a new competitive market in electricity generation is evolving.
As power generators prepare for the competitive market, the share of electricity generation attributed to
nonutilities and utilities is shifting.
More than 7,000 electricity suppliers currently operate in the U.S. market. As shown in Table
4-25, approximately 42 percent of suppliers are utilities and 58 percent are nonutilities. Utilities include
investor-owned, cooperatives, and municipal systems. Of the approximately 3,100
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Table 4-24. Total Expenditures in 1996 ($103)
Utility
Ownership
Investor-
owned
Publicly
owned
Federal
Cooperatives
Generation Transmission
80,891,644
12,495,324
3,685,719
15,105,404
112,178,091
75.6%
148,370,552
2,216,113
840,931
327,443
338,625
3,723,112
2.5%
Distributio
n
6,124,443
1,017,646
1,435
1,133,984
8,277,508
5.6%
Customer Administration
Accounts and General
and Sales Expenses
6,204,229
486,195
55,536
564,887
7,310,847
4.9%
13,820,059
1,360,111
443,809
1,257,015
16,880,994
11.4%
Sources: U.S. Department of Energy, Energy Information Administration (EIA). 1998b.
Financial Statistics of Major Publicly Owned Electric Utilities, 1997.
Washington, DC: U.S. Department of Energy.
U.S. Department of Energy, Energy Information Administration (EIA). 1997. Financial
Statistics of Major U.S. Investor-Owned Electric Utilities, 1996. Washington, DC: U.S.
Department of Energy.
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utilities operating in the United States, only about 700 generate electric power. The majority of utilities
distribute electricity that they have purchased from power generators via their own distribution systems.
Utility and nonutility generators produced atotal of 3,369 billion kWh in 1995. Although utilities
generate the vast majority of electricity produced in the United States, nonutility generators are quickly
eroding utilities' shares of the market. Nonutility generators include private entities that generate power
for their own use or to sell to utilities or other end users. Between 1985 and 1995, nonutility generation
increased from 98 billion kWh (3.8 percent of total generation) to 374 billion kWh (11.1 percent).
Figure 4-2 illustrates this shift in the share of utility and nonutility generation.
4.7.3.1 Utilities
There are four categories of utilities: investor-owned utilities (lOUs), publicly owned utilities,
cooperative utilities, and federal utilities. Of the four, only lOUs always generate electricity.
lOUs are increasingly selling off generation assets to nonutilities or converting those assets into
nonutilities (Haltmaier, 1998). To prepare for the competitive market, lOUs have been lowering their
operating costs, merging, and diversifying into nonutility businesses.
In 1995, utilities generated 89 percent of electricity, a decrease from 96 percent in 1985. lOUs
generate the majority of the electricity produced in the United States. lOUs are either individual
corporations or a holding company, in which a parent company operates one or more utilities integrated
with one another. lOUs account for approximately three-quarters of utility generation, a percentage that
held constant between 1985 and 1995.
Many states, municipalities, and other government organizations also own and operate utilities,
although the majority do not generate electricity. Those that do generate electricity operate capacity to
supply some or all of their customers' needs. They tend to be small, localized outfits and can be found in
47 states. These publicly owned utilities accounted for about one-tenth of utility generation in 1985 and
1995. In a deregulated market, these generators may be in direct competition with other utilities to service
their market.
Table 4-25. Number of Electricity Suppliers in 1999
Electricity Suppliers
Number
Percent
Utilities
Investor-owned utilities
Cooperatives
Municipal systems
Public power districts
State projects
Federal agencies
Nonutilities
Nonutilities (excluding EWGs)
Exempt wholesale generators
Total
3,124
222
875
1,885
73
55
14
4,247
4,103
144
7,371
42°A
58
-------
Utilities
Shares of Total
Utility Generation
Shares of Total
Utility Generation
1988 Generation
Utility 93%
Nonutility 7%
Nonutilities
1998 Generation
Utility 89%
NonutiliLy 1 1%
Shares of Total
Nonutility Generation
1998 Nonutility Total
406 Billion kWh
Shares of Total
Nonutility Generation
a Includes facilities classified in more than one of the following FERC designated categories: cogenerator QF, small power
producer QF, or exempt wholesale generator.
Cogen = Cogenerator.
EWG = Exempt wholesale generator.
Other Non-QF = NocogeneratorNon-QF.
QF = Qualifying facility.
SPP = Small power producer.
Note: Sum of components may not equal total due to independent rounding. Classes for nonutility generation are determined
by the class of each generating unit.
Sources: Utility data: U.S. Department of Energy, Energy Information Administration (EIA). 1996. Electric Power Annual
1995. Volumes I and II. DOE/EIA-0348(95)/1. Washington, DC: U.S. Department of Energy; Table 8 (and previous
issues); 1985 nonutility data: Shares of generation estimated by EIA; total generation from Edison Electric Institute
(EEI). 1998. Statistical Yearbook of the Electric Utility Industry 1998. November. Washington, DC; 1995 nonutility
data: U.S. Department of Energy, Energy Information Administration (EIA). 1996. Electric Power Annual 1995.
Volumes I and II. DOE/EIA-0348(95)/1. Washington, DC: U.S. Department of Energy.
Figure 4-2. Utility and Nonutility Generation and Shares by Class, 1988 and 1998
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Rural electric cooperatives are formed and owned by groups of residents in rural areas to supply
power to those areas. Cooperatives generally purchase from other utilities the energy that they sell to
customers, but some generate their own power. Cooperatives only produced 5 percent of utility generation
in 1985 and only 6 percent in 1995.
Utilities owned by the federal government accounted for about one-tenth of generation in both
1985 and 1995. The federal government operated a small number of large utilities in 1995 that supplied
power to large industrial consumers or federal installations. The Tennessee Valley Authority is an
example of a federal utility.
4.7.3.2 Nonutilities
Nonutilities are private entities that generate power for their own use or to sell to utilities or other
establishments. Nonutilities are usually operated at mines and manufacturing facilities, such as chemical
plants and paper mills, or are operated by electric and gas service companies (DOE, EIA, 1998a). More
than 4,200 nonutilities operate in the United States.
4.7.4 Demand Side of the Industry
4.7.4.1 Electricity Consumption
This section analyzes the growth projections for electricity consumption as well as the price
elasticity of demand for electricity. Growth in electricity consumption has traditionally paralleled gross
domestic product growth. However, improved energy efficiency of electrical equipment, such as high-
efficiency motors, has slowed demand growth over the past few decades. The magnitude of the
relationship has been decreasing over time, from growth of 7 percent per year in the 1960s down to 1
percent in the 1980s. As a result, determining what the future growth will be is difficult, although it is
expected to be positive (DOE, EIA, 1999a). Table 4-26 shows consumption by sector of the economy
over the past 10 years. The table shows that since 1989 electricity sales have increased at least 10 percent
in all four sectors. The commercial sector has experienced the largest increase, followed by residential
consumption.
In the future, residential demand is expected to be at the forefront of increased electricity
consumption. Between 1997 and 2020, residential demand is expected to increase at 1.6 percent annually.
Commercial growth in demand is expected to be approximately 1.4 percent, while
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Table 4-26. U.S. Electric Utility Retail Sales of Electricity by Sector, 1989 Through 1998
(106 kWh)
Period
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
Percentage
change
1989-1998
Residential
905,525
924,019
955,417
935,939
994,781
1,008,482
1,042,501
1,082,491
1,075,767
1,124,004
19%
Commercial
725,861
751,027
765,664
761,271
794,573
820,269
862,685
887,425
928,440
948,904
24%
Industrial
925,659
945,522
946,583
972,714
977,164
1,007,981
1,012,693
1,030,356
1,032,653
1,047,346
12%
Other"
89,765
91,988
94,339
93,442
94,944
97,830
95,407
97,539
102,901
99,868
10%
All Sectors
2,646,809
2,712,555
2,762,003
2,763,365
2,861,462
2,934,563
3,013,287
3,097,810
3,139,761
3,220,121
18%
Includes public street and highway lighting, other sales to public authorities, sales to railroads
and railways, and interdepartmental sales.
Sources: U.S. Department of Energy, Energy Information Administration (EIA). 1999d.
Electric Power Annual 1998. Volumes I and II. Washington, DC: U.S.
Department of Energy.
U.S. Department of Energy, Energy Information Administration (EIA). 1996. Electric
Power Annual 1995. Volumes I and II. Washington, DC: U.S. Department of Energy.
industry is expected to increase demand by 1.1 percent (DOE, EIA, 1999a). Figure 4-3 shows the annual
electricity sales by sector from 1970 with projections through 2020.
The literature suggests that electricity consumption is relatively price inelastic. Consumers are
generally unable or unwilling to forego a large amount of consumption as the price increases. Numerous
studies have investigated the short-run elasticity of demand for electricity. Overall, the studies suggest
that, for a 1 percent increase in the price of electricity, demand will decrease by 0.15 percent. However, as
Table 4-27 shows, elasticities vary greatly, depending on the demand characteristics of end users and the
price structure. Demand elasticities are estimated to range from a -0.05 percent elasticity of demand for a
"flat rates" case (i.e., no time-of-use assumption) up to a -0.50 percent demand elasticity for a "high
consumer response" case (DOE, EIA, 1999c).
4-37
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1970
1980
1990
3000
$010 SOSO
Figure 4-3. Annual Electricity Sales by Sector
4.7.4.2 Trends in the Electricity Market
Beginning in the latter part of the 19th century and continuing for about 100 years, the prevailing
view of policymakers and the public was that the government should use its power to require or prescribe
the economic behavior of "natural monopolies" such as electric utilities. The traditional argument is that it
does not make economic sense for there to be more than one supplier—running two sets of wires from
generating facilities to end users is more costly than one set. However, since monopoly supply is not
generally regarded as likely to provide a socially optimal allocation of resources, regulation of rates and
other economic variables was seen as a necessary feature of the system.
Beginning in the 1970s, the public policy view shifted against traditional regulatory approaches
and in favor of deregulation for many important industries including transportation, communications,
finance, and energy. The major drivers for deregulation of electric power included the following:
• existence of rate differentials across regions offering the promise of benefits from more
efficient use of existing generation resources if the power can be transmitted across larger
geographic areas than was typical in the era of industry regulation;
the erosion of economies of scale in generation with advances in combustion turbine technology;
• complexity of providing a regulated industry with the incentives to make socially efficient
investment choices;
• difficulty of providing a responsive regulatory process that can quickly adjust rates and
conditions of service in response to changing technological and market conditions; and
• complexity of monitoring utilities' cost of service and establishing cost-based rates for various
customer classes that promote economic efficiency while at the same time addressing equity
concerns of regulatory commissions.
Viewed from one perspective, not much changes in the electric industry with restructuring. The
same functions are being performed, essentially the same resources are being used, and in a broad sense
the same reliability criteria are being met. In other ways, the very nature of restructuring, the harnessing of
competitive forces to perform a previously regulated function, changes almost everything. Each provider
and each function become separate competitive entities that must be judged on their own.
This move to market-based provision of generation services is not matched on the transmission
and distribution side. Network interactions on AC transmission systems have made it impossible to have
separate transmission paths compete. Hence, transmission and distribution remain regulated.
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Transmission and generation heavily interact, however, and transmission congestion can prevent specific
generation from getting to market. Transmission expansion planning becomes an open process with many
interested parties. This open process, coupled with frequent public opposition to transmission expansion,
slows transmission enhancement. The net result is greatly increased pressure on the transmission system.
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Table 4-27. Key Parameters in the Cases
Key Assumptions
Case Name
AEO97 Reference
Case
No Competition
Flat Rates
(no time-of-use rates)
Moderate Consumer
Response
High Consumer
Response
High Efficiency
No Capacity Additions
High Gas Price
Low Gas Price
High Value of
Reliability
HalfO&M
Intense Competition
Cost Reduction
and Efficiency
Improvements
AEO97 Reference
Case
No change from
1995
AEO97 Reference
Case
AEO97 Reference
Case
AEO97 Reference
Case
Increased cost
savings and
efficiencies
AEO97 Reference
Case
AEO97 Reference
Case
AEO97 Reference
Case
AEO97 Reference
Case
AEO97 Reference
Case
AEO97 Reference
Case
Short-Run
Elasticity
of
Demand
(Percent)
—
—
-0.05
-0.15
-0.50
-0.15
-0.15
-0.15
-0.15
-0.15
-0.15
-0.15
Natural Gas
Prices
AEO97
Reference Case
AEO97
Reference Case
AEO97
Reference Case
AEO97
Reference Case
AEO97
Reference Case
AEO97
Reference Case
AEO97 Low Oil
and Gas Supply
Technology Case
AEO97 High Oil
and Gas Supply
Technology Case
AEO97
Reference Case
AEO97
Reference Case
AEO97
Reference Case
AEO97
Reference Case
Capacity
Additions
As needed
to meet demand
As needed
to meet demand
As needed
to meet demand
As needed
to meet demand
As needed
to meet demand
As needed
to meet demand
Not allowed
As needed
to meet demand
As needed
to meet demand
As needed
to meet demand
As needed
to meet demand
As needed to
meet demand
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Restructuring of the electric power industry could result in any one of several possible market
structures. In fact, different parts of the country will probably use different structures, as the current trend
indicates. The eventual structure may be dominated by a power exchange, bilateral contracts, or a
combination. A strong Regional Transmission Organization (RTO) may operate in the area, or a vertically
integrated utility may continue to operate a control area. In any case, several important characteristics will
change:
• Commercial provision of generation-based services (e.g., energy, regulation, load following,
voltage control, contingency reserves, backup supply) will replace regulated service provision.
This drastically changes how the service provider is assessed.
• Individual transactions will replace aggregated supply meeting aggregated demand. It will be
necessary to continuously assess each individual's performance.
• Transaction sizes will shrink. Instead of dealing only in hundreds and thousands of MW, it
will be necessary to accommodate transactions of a few MW and less.
• Supply flexibility will greatly increase. Instead of services coming from a fixed fleet of
generators, service provision will change dynamically among many potential suppliers as
market conditions change.
References
Haltmaier, Susan. 1998. "Electricity Production and Sales." In U.S. Industry & Trade Outlook '98,
DRI/McGraw-Hill, Standard & Poor's, and U.S. Department of Commerce/international Trade
Administration. New York: McGraw-Hill, pp. 5-1 to 5-9.
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Lemm, Jamie. 2000. "Household Furniture." In U.S. Industry & Trade Outlook 2000. New York:
DRI/McGraw-Hill, Standard & Poor's, and U.S. Department of Commerce/International Trade
Administration.
U.S. Department of Commerce, Bureau of the Census. 1995a. 7992 Census of Manufactures, Industry
Series: Industrial Organic Chemicals. Washington, DC: Government Printing Office.
U.S. Department of Commerce, Bureau of the Census. 1995b. 7992 Concentration Ratios in
Manufacturing. Washington, DC: Government Printing Office.
U.S. Department of Commerce, Bureau of the Census. 2001. "1997 Economic Census—United States."
As obtained on March 13, 2001. .
U.S. Department of Energy, Energy Information Administration (EIA). 1998. The Changing Structure of
the Electric Power Industry: Selected Issues, 1998. DOE/EIA-0562(98). Washington, DC: U.S.
Department of Energy.
U.S. Department of Energy, Energy Information Administration (EIA). 1999a. "Annual Energy Outlook
1999—Market Trend—Electricity." http://www.eia.doe.gov/oiaf/aeo99/electricity.html. As accessed
November 15, 1999.
U.S. Department of Energy, Energy Information Administration (EIA). 1999b. The Changing Structure
of the Electric Power Industry 1999: Mergers and Other Corporate Combinations. Washington, DC:
U.S. Department of Energy.
U.S. Department of Energy, Energy Information Administration (EIA), Office of Integrated Analysis and
Forecasting. 1999c. "Competitive Electricity Price Projections." http://www.eia.doe.gov/oia/
elepri97/chap3.html. As obtained on November 15, 1999.
U.S. Department of Justice. 1992. Horizontal Merger Guidelines. Washington, DC: U.S. Department of
Justice.
U.S. Environmental Protection Agency (EPA), Office of Compliance Sector Notebook Project. 1995a.
Profile of the Lumber and Wood Products Industry. Washington, DC.
U.S. Environmental Protection Agency (EPA), Office of Compliance Sector Notebook Project. 1995b.
Profile of the Pulp and Paper Industry. Washington, DC: U.S. Environmental Protection Agency.
U.S. Environmental Protection Agency (EPA), Office of Compliance Sector Notebook Project. 1995c.
Profile of the Organic Chemical Industry. Washington, DC: U.S. Environmental Protection Agency.
U.S. Environmental Protection Agency (EPA). 1997a. EPA Office of Compliance Sector Notebook
Project: Profile of the Pharmaceutical Manufacturing Industry. Washington, DC: U.S. Environmental
Protection Agency.
4-42
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U.S. Environmental Protection Agency (EPA). 1997b. Regulatory Impact Analysis of Air Pollution
Regulations: Utility and Industrial Boilers. Research Triangle Park, NC: U.S. Environmental Protection
Agency.
CHAPTER 5
ECONOMIC ANALYSIS METHODOLOGY
The rule to control emissions of HAPs from industrial, commercial, and institutional boilers and
process heaters will affect almost all sectors of the U.S. economy. Several markets will bear the direct
compliance costs. In addition, sectors that consume energy will also bear indirect costs through higher
prices for energy. Finally, consumers of goods and services will experience impacts from higher market
prices.
This chapter presents the methodology for analyzing the economic impacts of the NESHAP. This
economic analysis provides the economic data and supporting information needed by EPA to support its
regulatory determination. The methodology to operationalize this theory is based on microeconomic
theory and the methods developed for earlier EPA studies. These methods are tailored to and extended for
this analysis, as appropriate, to meet EPA's requirements for an EIA of controls placed on boilers and
process heaters.
This methodology chapter includes background information on typical economic modeling
approaches, the conceptual approach selected for this EIA, and an overview of the computerized market
model used in the analysis with emphasis on the links between energy markets and the markets for goods
and services. Appendix A of this RIA includes a description of the model's baseline data set and
specifications.
5.1 Background on Economic Modeling Approaches
5-43
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In general, the EIA methodology needs to allow EPA to consider the effects of the different
regulatory alternatives. Several types of economic impact modeling approaches have been developed to
support regulatory development. These approaches can be viewed as varying along two modeling
dimensions:
• the scope of economic decisionmaking accounted for in the model and
• the scope of interaction between different segments of the economy.
Each of these dimensions was considered in determining the approach for this study. The advantages and
disadvantages of different modeling approaches are discussed below.
5.7.7 Modeling Dimension 1: Scope of Economic Decisionmaking
Models incorporating different levels of economic decisionmaking can generally be categorized as
with behavior responses and without behavior responses (accounting approach). Table 5-1 provides a brief
comparison of the two approaches. The nonbehavioral approach essentially holds fixed all interactions
between facility production and market forces. It assumes that firms absorb all control costs and
consumers do not face any of the costs of regulation. Typically, engineering control costs are weighted by
the number of affected units to develop "engineering" estimates of the total annualized costs. These costs
are then compared to company or industry sales to determine the regulation's impact.
Table 5-1. Comparison of Modeling Approaches
EIA With Behavioral Responses
• Incorporates control costs into production function
• Includes change in quantity produced
• Includes change in market price
• Estimates impacts for
/^ affected producers
/ unaffected producers
/^ consumers
/ foreign trade
EIA Without Behavioral Responses
• Assumes firm absorbs all control costs
• Typically uses discounted cash flow analysis to evaluate burden of control costs
• Includes depreciation schedules and corporate tax implications
• Does not adjust for changes in market price
• Does not adjust for changes in plant production
In contrast, the behavioral approach is grounded in economic theory related to producer and
consumer behavior in response to changes in market conditions. Owners of affected facilities are
economic agents that can, and presumably will, make adjustments such as changing production rates or
altering input mixes that will generally affect the market environment in which they operate. As producers
change their behavior in response to regulation, consumers are typically faced with changes in prices that
cause them to alter the quantity that they are willing to purchase. In essence, this approach models the
expected reallocation of society's resources in response to a regulation. The changes in price and
production from the market-level impacts are used to estimate the distribution of social costs between
consumers and producers.
5-1
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5.7.2 Modeling Dimension 2: Interaction Between Economic Sectors
Because of the large number of markets potentially affected by the regulation on boilers and
process heaters, an issue arises concerning the level of sectoral interaction to model. In the broadest sense,
all markets are directly or indirectly linked in the economy; thus, the regulation affects all commodities
and markets to some extent. For example, controls on boilers and process heaters may indirectly affect
almost all markets for goods and services to some extent because the cost of fuel (an input in the provision
of most goods and services) is likely to increase with the regulation in effect. However, the impact of
rising fuel prices will differ greatly between different markets depending on how important fuel is as an
input in that market.
The appropriate level of market interactions to be included in the EIA is determined by the scope
of the regulation across industries and the ability of affected firms to pass along the regulatory costs in the
form of higher prices. Alternative approaches for modeling interactions between economic sectors can
generally be divided into three groups:
• Partial equilibrium model: Individual markets are modeled in isolation. The only factor
affecting the market is the cost of the regulation on facilities in the industry being modeled.
• General equilibrium model: All sectors of the economy are modeled together. General
equilibrium models operationalize neoclassical microeconomic theory by modeling not only
the direct effects of control costs, but also potential input substitution effects, changes in
production levels associated with changes in market prices across all sectors, and the
associated changes in welfare economywide. A disadvantage of general equilibrium modeling
is that substantial time and resources are required to develop a new model or tailor an existing
model for analyzing regulatory alternatives.
• Multiple-market partial equilibrium model: A subset of related markets are modeled together,
with intersectoral linkages explicitly specified. To account for the relationships and links
between different markets without employing a full general equilibrium model, analysts can
use an integrated partial equilibrium model. The multiple-market partial equilibrium approach
represents an intermediate step between a simple, single-market partial equilibrium approach
and a full general equilibrium approach. This approach involves identifying and modeling the
most significant subset of market interactions using an integrated partial equilibrium
framework. In effect, the modeling technique is to link a series of standard partial equilibrium
models by specifying the interactions between supply functions and then solving for prices and
quantities across all markets simultaneously. In instances where separate markets are closely
related and there are strong interconnections, there are significant advantages to estimating
market adjustments in different markets simultaneously using an integrated market modeling
approach.
5.2 Selected Modeling Approach for Boilers and Process Heaters Analysis
To conduct the analysis for the boilers and process heaters MACT, the Agency used a market
modeling approach that incorporates behavioral responses in a multiple-market partial equilibrium model
as described above. This approach allows for a more realistic assessment of the distribution of impacts
across different groups than the nonbehavioral approach, which may be especially important in accurately
assessing the impacts of a significant rule affecting numerous industries. Because of the size and
complexity of this regulation, it is important to use a behavioral model to examine the distribution of costs
across society. Because the regulations on boilers and process heaters primarily affect energy costs, an
input into many production processes, complex market interactions need to be captured to provide an
accurate picture of the distribution of regulatory costs. Because of the large number of affected industries
under this MACT, an appropriate model should include multiple markets and the interactions between
them. Multiple-market partial equilibrium analysis provides a manageable approach to incorporate
interactions between energy markets and final product markets into the EIA to accurately estimate the
regulation's impact.
5-2
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The model used for this analysis includes energy, agriculture, manufacturing, mining, commercial,
and transportation markets affected by the controls placed on boilers and process heaters.6 The energy
markets are divided into natural gas, petroleum products, coal, and electricity. The residential sector is
treated as a single representative demander in the energy markets.
Figure 5-1 presents an overview of the key market linkages included in the economic impact
model used for analyzing the boilers and process heaters MACT. The analysis' emphasis is on the energy
supply chain and the consumption of energy by producers of goods and services. The industries most
directly affected by the boilers and process heaters MACT are the electricity industry, chemical industry
and pulp and paper industry. However, changes in the equilibrium prices and quantities of energy and
goods and services affect all sectors of the economy. (See Figure 5-1.) This analysis explicitly models the
linkages between these market segments to capture both the direct costs of compliance and the indirect
costs due to changes in prices. For example, production costs will increase for chemical companies using
boilers and process heaters as a result of the capital investments and monitoring costs, as well as the
resulting increase in the price of electricity used as an energy input in the production process.
The economic model also captures behavioral changes of producers of goods and services that
feedback into the energy markets. Changes in production levels and fuel switching in the manufacturing
process affect the demand for Btus in fuel markets. The change in output is determined by the size of the
cost increase per Btu (typically variable cost per output), the facility's production function (slope of supply
curve), and the demand characteristics of the facility's downstream market (other market suppliers and
market demanders). For example, if consumers' demand for a product is not very sensitive to price, then
producers can pass the majority of the cost of the regulation through to consumers and output may not
change appreciably. However, if only a small proportion of market output is produced by producers
affected by the regulation, then competition will prevent the affected producers from raising their prices
significantly.
One possible feedback pathway that this analysis does not model is technical changes in the
manufacturing process. For example, if the cost of Btus increases, a facility may use measures to increase
manufacturing efficiency or capture waste heat. Facilities could also possibly change the
6These markets are defined at the two- and three-digit NAICS code level. This allows for a fairly disaggregated
examination of the regulation's impact on producers. However, if the costs of the regulation are
concentrated on a particular subset of one of these markets, then treating the cost as if it fell on the entire
NAICS code may still underestimate the impacts on the subset of producers affected by the regulation.
5-3
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5-4
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input mix that they use, substituting other inputs for fuel. These facility-level responses will also act to
reduce pollution, but including these responses is beyond the scope of this analysis.
5.2.1 Directly Affected Markets
Markets where boilers and process heaters are used as an input to production are considered to be
directly affected. As outlined in Chapter 3, facilities using several types of boilers or process heaters will
be required to add controls. In addition, a larger population of boilers and process heaters will incur
monitoring costs to comply with the regulation. Therefore, the regulation will increase their production
costs and cause these directly affected firms to reduce the quantity that they are willing to supply at any
given price.
5.2.1.1 Electricity Market
Boilers are used to generate power throughout the electricity industry. Even though utility boilers
are not covered under this regulation, the Agency estimates over 300 industrial, commercial, and
institutional boilers involved in providing electric services (SIC 4911/NAICS22111) will be affected.
Most of these are owned by municipal electric service providers.
For this study, the electricity market was modeled as a nationally competitive market. The
electricity market is modeled this ways primarily due to tractability concerns. Given the difficulty in
ascertaining how many States would decide to deregulate their electricity markets, a competitive electricity
market was the most reasonable approach for this modeling exercise. The direct costs of compliance on
affected boilers lead to an upward shift in the total market supply for electricity. Figure 5-2 illustrates the
shifts in the supply curve for a representative energy market. In addition to the direct costs, the market for
electricity will also be indirectly affected through changes in fuel prices. Electricity generators are
extremely large consumers of coal, natural gas, and petroleum products. For example, some of the impact
of control costs on the petroleum industry will be on the electricity industry in the form of higher prices.
Indirect costs will also lead to an upward shift in the supply curve.
The demand for electricity is derived by aggregating across the goods and services markets and the
residential sector. Because of direct compliance costs on the goods and services markets, the demand
curve for electricity will shift downward. Therefore, it is ambiguous whether equilibrium quantity will rise
or fall. The changes in the price and quantity are determined by the relative magnitude of the shifts in the
price elasticities of the supply and demand curves.
5-5
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Qu Q10 Quantity
(a) Producers bearing control
costs (affected)
Quantity
(b) Producers bearing no control
costs (unaffected)
QT1 QTO Quantity
(c) Total Market
P0 = market price without regulation
Pj = market price with regulation
S10 = supply function for affected firms without regulation
Sj j = supply function for affected firms with regulation
Q10 = quantity sold for affected firms without regulation
Qj j = quantity sold for affected firms with regulation
S20 = supply function for unaffected firms both with and without regulation
Q20 = quantity sold for unaffected firms without regulation
Q21 = quantity sold for unaffected firms with regulation
STO = total market supply function without regulation
ST1 = total market supply function with regulation
QTO = total market quantity sold without regulation
QT1 = total market quantity sold with regulation
Figure 5-2. Market Effects of Regulation-Induced Costs
5.2.1.2 Petroleum Market
Control costs associated with boilers and process heaters will increase the cost of refining
petroleum products. The supply curve for petroleum products will shift upward by the proportional
increase in total production costs caused by the control costs on boilers and process heaters. For petroleum
products, a single composite product was used to model market adjustment because boilers and process
heaters are used throughout the refinement process, from distillation to reformulation. In addition,
examining the full heterogeneity of petroleum products and the impacts to all specific end products would
require a model of much greater complexity than this one. As a result, assigning costs to specific end
products and estimating economic impacts to them, such as fuel oil #2 or reformulated gasoline, is
difficult. The use of a composite product tends to understate the impacts for petroleum products where
compliance costs as a percentage of production costs are greater than average and overstate impacts for
products where compliance costs as a percentage of production costs are less than average.
5-6
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5.2.1.3 Goods and Services Markets: Agriculture, Manufacturing, Mining, Commercial, and
Transportation
Many manufacturing facilities use boilers and process heaters in their production processes to
generate steam and process heat. Commercial entities use boilers for space heating and to generate
supplementary electricity. In addition to the direct costs of the regulation, goods and services markets are
indirectly affected through price increases in the energy markets.
Directly affected producers are segmented into sectors defined at the two- or three-digit NAICS
code level. A partial equilibrium analysis was conducted for each sector to model the supply and demand.
Changes in production levels and fuel switching due to the regulation's impact on the price of Btus were
then linked back into the energy markets.
The impact of the regulation on producers in these sectors was modeled as an increase in the cost
of Btus used in the production process. In this context, Btus refer to the generic energy requirements used
to generate process heat, process steam, or shaft power. Compliance costs associated with the regulation
will increase the cost of Btu production in the manufacturing sectors. The cost of Btu production for
industry increases because of both direct control costs on boilers and process heaters owned by
manufacturers, and increases in the price of fuels. Because Btus are an input into the production process,
these price increases lead to an upward shift in the facility (and industry) supply curves as shown in Figure
5-2, leading to a change in the equilibrium market price and quantity.
The changes in equilibrium supply and demand in each market are modeled to estimate the
regulation's impact on each sector. In a perfectly competitive market, the point where supply equals
demand determines the market price and quantity, so market price and quantity are determined by solving
the model for the price where the quantity supplied and the quantity demanded are equal. The size of the
regulation-induced shifts in the supply curve is a function of the total direct control costs associated with
boilers and process heaters and the indirect fuel costs (determined by the change in fuel price and intensity
of use) in each goods and services market. The proportional shift in the supply curve is determined by the
ratio of total control costs (both direct and indirect) to total revenue.
Compliance
Costs
A $/Btu
Fuel
Markets
A
$/Btu
Btu
Production
Decision
A$/Btu
Production
Decision
Output
Market
A Fuel Use
A Output
Figure 5-3. Fuel Market Interactions with Facility-Level Production Decisions
This impact on the price of Btus facing industrial users feeds back to the fuel market in two ways
(see Figure 5-3). The first is through the company's input decision concerning the fuel(s) that will be used
for its manufacturing process. As the cost of Btus increases, firms may switch fuels and/or change
production processes to increase energy efficiency and reduce the number of Btus required per unit of
output. Fuel switching impacts were modeled using cross-price elasticities of demand between energy
sources. For example, a cross-price elasticity of demand between natural gas and electricity of 0.5 implies
that a 1 percent increase in the price of electricity will lead to a 0.5 percent increase in the demand for
5-7
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natural gas. Own-price elasticities of demand are used to estimate the change in the use of fuel by
demanders. For example, a demand elasticity of-0.175 for electricity implies that a 1 percent increase in
the price of electricity will lead to a 0.175 percent decrease in the quantity of electricity demanded.
The second feedback pathway to the energy markets is through the facility's change in output.
Because Btus are an input into the production process, energy price increases lead to an upward shift in the
facility supply curves (not modeled individually). This leads to an upward shift in the industry supply
curve when the shifts at the facility level are aggregated across facilities. A shift in the industry supply
curve leads to a change in the equilibrium market price and quantity. In a perfectly competitive market,
the point where supply equals demand determines the new market price and quantity. The Agency
modeled the feedback into the energy market by assuming that the percentage change in output in the
manufacturing sectors translates into a equivalent percentage change in the demand for energy (Btus).
This implies that there are constant returns to scale from energy inputs in the manufacturing process over
the relevant range of output and time period of analysis. This is an appropriate assumption for this
analysis because the output changes in these sectors being modeled are relatively small (always less than 1
percent) and reflect short-run production decisions.7
The Agency assumed that the demand curves for goods and services in all sectors are unchanged
by the regulation. However, because the demand function quantifies the change in quantity demanded in
response to a change in price, the baseline demand conditions are important in determining the regulation's
impact. The key demand parameters are the elasticities of demand with respect to changes in the price of
goods and services. For these markets, a "reasonable" range of elasticity values is assigned based on
estimates from similar commodities. Because price changes are anticipated to be small, the point
elasticities at the original price and quantity should be applicable throughout the relevant range of prices
and quantities examined in this model.
For more information on how these energy markets are modeled in this analysis, please refer to
Appendix B of the RIA.
5.2.2 In directly Affected Markets
In addition to the many markets that are directly affected by the regulation on boilers and process
heaters, some markets feel the regulation's impacts despite having no direct costs resulting from the
regulation. Firms in these markets generally face changes in the price of energy that affect their
production decisions.
5.2.2.7 Market for Coal
The coal market is not directly affected by the regulation, but it has the potential to be significantly
affected through indirect costs. Although boilers and process heaters are not commonly used in the
production or transportation of coal, the supply of coal will be affected by the price of energy used in coal
production. However, the indirect impacts on coal production costs are relatively small compared to the
direct impacts on the production costs in the electricity and petroleum markets; thus, the "relative" price of
coal (per Btu) will decrease compared with other energy sources.
The demand for coal from the industrial sectors will be affected by differences in compliance costs
by fuel type applied to boilers and process heaters in the industrial sectors. Because compliance costs are
high for coal-fired units, manufacturers will switch away from coal units toward natural gas units with
lower compliance costs. However, the overall impact on the demand for coal is ambiguous because the
relative increase in the cost of producing Btus by burning coal will be offset by the relative decrease in the
price of coal. Similarly, the demand for coal by utility generators will be affected through changes in the
relative price of alternative (noncoal) energy sources and direct costs on coal boilers.
5.2.2.2 Natural Gas Marke t
The natural gas market is included in the economic model to complete coverage of the energy
markets. EPA projects that there are no direct and minimal indirect impacts on the production costs of
natural gas. However, the demand for natural gas will increase because of the relative decrease in the price
of natural gas and the lower relative compliance costs for gas-fired boilers and process heaters.
7Long-run production decisions of fuel switching and increased energy efficiency are captured by the cross- and
own-price elasticities in the energy markets.
5-8
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5.2.2.3 Goods and Services Marke ts
Some goods and services markets do not include any boilers or process heaters and are therefore
not directly affected by the regulation. However, these markets will still be affected indirectly because of
the changes in energy prices that they will face following the regulation. There will be a tendency for
these users to shift away from electricity and petroleum products and towards natural gas and coal.
5.2.2.4 Impact on Residential Sector
The residential sector does not bear any direct costs associated with the regulation because this
sector does not own boilers or process heaters. However, they bear indirect costs due to price increases.
The residential sector is a significant consumer of electricity, natural gas, and petroleum products used for
heating, cooling, and lighting, as well as many other end uses. The change in the quantity of energy
demanded by these consumers in response to changes in energy prices is modeled as a single demand
curve parameterized by demand elasticities for residential consumers obtained from the literature.
5.3 Operationalizing the Economic Impact Model
Figure 5-4 illustrates the linkages used to operationalize the estimation of economic impacts
associated with the compliance costs. Compliance costs placed on boilers and process heaters shift the
supply curve for electricity and petroleum products. Adjustments in the electricity and petroleum energy
markets determine the share of the cost increases that producers (electric service providers and petroleum
companies) and consumers (product manufacturers, commercial business, and residential households) bear.
The supply and demand relationships between the energy markets are fully modeled. For
example, changes in electricity production feed back and affect the demand for coal, natural gas and
petroleum products. Similar changes in refinery production affect the petroleum industry's demand for
electricity.
Manufacturers experience supply curve shifts due to control costs on affected boilers and process
heaters they operate and changes in prices for natural gas, petroleum, electricity, and coal. The share of
these costs borne by producers and consumers is determined by the new equilibrium price and quantity in
the goods and services markets. Changes in manufacturers' Btu demands due to fuel switching and
changes in production levels feed back into the energy markets.
Adjustments in price and quantity in all markets occur simultaneously. A computer model was
used to numerically simulate market adjustments by iterating over commodity prices until equilibrium is
reached (i.e., until the quantity supplied equals the quantity demanded in all markets being modeled).
Using the results provided by the model, economic impacts of the regulation (changes in consumer and
producer surplus) were estimated for all sectors of the economy being modeled.
5-9
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5-10
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5.3.1 Computer Model
The computer model comprises a series of computer spreadsheet modules. The modules integrate
the engineering cost inputs and the market-level adjustment parameters to estimate the regulation's impact
on the price and quantity in each market being analyzed. At the heart of the model is a market-clearing
algorithm that compares the total quantity supplied to the total quantity demanded for each market
commodity.
Current prices and production levels are used to calibrate the baseline scenario (without
regulation) for the model. Then, the compliance costs associated with the regulation are introduced as a
"shock" to the system, and the supply and demand for market commodities are allowed to adjust to
account for the increased production costs resulting from the regulation. Using an iterative process, if the
supply does not equal demand in all markets, a new set of prices is "called out" and sent back to producers
and consumers to "ask" what quantities they would supply and demand based on these new prices. This
technique is referred to as an auctioneer approach because new prices are continually called out until an
equilibrium set of prices is determined (i.e., where supply equals demand for all markets).
Supply and demand quantities are computed at each price iteration. The market supply for each
market is obtained by using a mathematical specification of the supply function, and the key parameter is
the point elasticity of supply at the baseline condition. Supply elasticities are traditionally the most
difficult to obtain from prior sources and analyses. As a result, EPA used an assumed value of 0.75 for 21
of the 25 manufacturing, agriculture, other mining, transportation, and commercial industries. The
remaining 4 supply elasticities (for the textile mills, textile products, primary metals, and other mining
industries) were obtained from a previous report conducted for EPA by E.H. Pechan and Associates, Inc
(1997), and studies by Warfield, et al (2001) and the U.S. International Trade Commission (2001)8. EPA
is currently using the last two studies to study the economic impacts of MACT standards for the Fabric
Coatings, Taconite, and Steel Industries. Table 5-2 lists the supply elasticities for the markets used in the
model.
The demand curves for the energy markets are the sum of demand responses across all markets.
The demand for energy in the manufacturing sectors is a derived demand calculated using baseline energy
usage and changes associated with fuel switching and changes in output levels. Similarly, the energy
demand in residential sectors is obtained through mathematical specification of a demand function (see
Appendix A).
The demand for goods and service in the two- and three-digit NAICS code manufacturing sectors
is obtained by using a mathematical specification of the demand function. Demand elasticity estimates are
more readily available from literature searches. The majority of demand elasticities for the manufacturing
sectors were obtained from the E.H. Pechan and Associates, Inc. report (1997) prepared for the RIA of the
PM NAAQS in 1997. This document reports results of a substantive literature search for elasticity
estimates for use in conducting an analysis of the NAAQS. Point estimates are reported for 22 of the 25
and are derived from previous EPA analyses and selected working papers. Absent information for the
remaining 3 industries (the transportation, construction, and commercial sectors), we have assumed a
demand elasticity value of -1.0. Table 5-2 lists the demand elasticities for the markets used in the model.
EPA modeled fuel switching using secondary data developed by the U.S. Department of Energy
for the National Energy Modeling System (NEMS). Table 5-3 contains fuel price elasticities of demand
for electricity, natural gas, petroleum products, and coal. The diagonal elements in the table represent
own-price elasticities. For example, the table indicates that for steam coal, a 1 percent change in the price
of coal will lead to a 0.499 percent decrease in the use of coal. The off diagonal elements are cross-price
elasticities and indicate fuel switching propensities. For example, for steam coal, the second column
indicates that a 1 percent increase in the price of coal will lead to a 0.061 percent increase in the use of
natural gas.
5.3.2 Calculating Changes in Social Welfare
8Pechan reports the results of their literature review in Appendix B. Point estimates are provided by SIC code.
5-11
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The boilers and process heaters MACT will impact almost every sector of the economy, either
directly through control costs or indirectly through changes in the price of energy and final products. For
example, a share of control costs that originate in the energy markets is passed through the goods and
services markets and borne by both the producers and consumers of their products.
5-12
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Table 5-2. Supply and Demand Elasticities
Petroleum
Natural Gas
Electricity
Coal
NAICS
311
312
313
314
315
316
321
322
323
325
326
327
331
332
333
334
335
336
337
339
11
Supply Elasticities
0.58b
0.41b
0.75C
1.00b
Demand Elasticities
Industrial Residential3
Derived -0.28
Derived -0.26
Derived -0.23
Derived -0.26
Description
Food
Beverage and Tobacco Products
Textile Mills
Textile Product Mills
Apparel
Leather and Allied Products
Wood Products
Paper
Printing and Related Support
Chemicals
Plastics and Rubber Products
Nonmetallic Mineral Products
Primary Metals
Fabricated Metal Products
Machinery
Computer and Electronic Products
Electrical Equipment, Appliances, and Components
Transportation Equipment
Furniture and Related Products
Miscellaneous
Agricultural Sector
Transportation
Derived
Derived
Derived
Derived
Supply"
0.75C
0.75C
0.37e
0.37e
0.75C
0.75C
0.75d
1.20C
0.75C
0.75C
0.75C
0.75C
3.50f
0.75C
0.75C
0.75C
0.75C
0.75C
0.75C
0.75C
0.75C
Commercial
Derived
Derived
Derived
Derived
Demand11
-0.30
-1.30
-0.85e
-0.85e
-1.80
-1.20
-0.20
-1.09
-1.80
-1.50
-1.80
-0.90
-0.80
-0.20
-0.50
-0.30
-0.50
-1.00C
-3.40
-0.60
-1.80
(continued)
5-13
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Table 5-2. Supply and Demand Elasticities (continued)
INA1CS
23
21
48
Commercial
Description
Construction Sector
Other Mining Sector
Transportation
Commercial
Supply"1
0.75C
0.43
0.75C
0.75C
i»emanaj
-1.00C
-0.30
-0.70
-1.00C
a U.S. Department of Energy, Energy Information Administration (EIA). "Issues in Midterm Analysis and
Forecasting 1999—Table 1." . As obtained on May 8, 2000a.
b Dahl, Carol A., and Thomas E. Duggan. 1996. "U.S. Energy Product Supply Elasticities: A Survey and
Application to the U.S. Oil Market." Resource and Energy Economicsl$:243-263.
0 Assumed value.
d E.H. Pechan & Associates, Inc. 1997. Qualitative Market Impact Analysis for Implementation of the Selected
Ozone and PM NAAQS. Appendix B. Prepared for the U.S. Environmental Protection Agency.
e Warfield, et al. 2001. "Multifiber Arrangement Phaseout: Implications for the U.S. Fibers/Textiles/Fabricated
Products Complex." www.fibronet.com.tw/mirron/ncs/9312/mar.html> As obtained September 19, 2001.
f U.S. International Trade Commission (USITC). November 21, 2001. Memorandum to the Commission from Craig
Thomsen, John Giamalua, John Benedetto, Joshua Levy, International Economists. Investigation No. TA-201-73:
STEEL-Remedy Memorandum.
To estimate the total change in social welfare without double-counting impacts across the linked
partial equilibrium markets being modeled, EPA quantified social welfare changes for the following
categories:
• change in producer surplus in the energy markets;
• change in producer surplus in the goods and services markets;
• change in consumer surplus in the goods and services markets; and
• change in consumer surplus in the residential sector.
Figure 5-5 illustrates the change in producer and consumer surplus in the intermediate energy market and
the goods and services markets. For example, assume a simple world with only one energy market,
wholesale electricity, and one product market, pulp and paper. If the regulation increases the cost of
generating wholesale electricity, then part of the cost of the regulation will be borne by the electricity
producers as decreased producer surplus, and part of the costs will be passed on to the pulp and paper
manufacturers. In Figure 5-5(a), the pulp and paper manufacturers are the consumers of electricity, so the
change in consumer surplus is displayed. This change in consumer surplus in the energy market is
captured by the product market (because the consumer is the pulp and paper industry in this case), where it
is split between consumer surplus and producer surplus in those markets. Figure 5-5(b) shows the change
in producer surplus in the energy market, where B represents an increase in producer surplus and C
represents a decrease.
5-14
-------
Table 5-3. Fuel Price Elasticities
Own and Cross Elasticities
Inputs
Electricity Natural Gas Coal
Residual
Distillate
Electricity
Natural Gas
Steam Coal
Residual
Distillate
-0.074
0.496
0.021
0.236
0.247
0.092
-0.229
0.061
0.036
0.002
0.605
1.087
-0.499
0.650
0.578
0.080
0.346
0.151
-0.587
0.044
0.017
0.014
0.023
0.012
-0.055
Source: U.S. Department of Energy, Energy Information Administration (EIA). January 2000b. Model
Documentation Report: Industrial Sector Demand Module of the National Energy Modeling System.
DOE/EIA-M064(2000). Washington, DC: U.S. Department of Energy.
As shown in Figures 5-5(c) and 5-5(d), the cost affects the pulp and paper industry by shifting up
the supply curve in the pulp and paper market. These higher electricity prices therefore lead to costs in the
pulp and paper industry that are distributed between producers and consumers of paper products in the
form of lower producer surplus and lower consumer surplus. Note that the change in consumer surplus in
the intermediate energy market must equal the total change in consumer and producer surplus in the
product market. Thus, to avoid double-counting, the change in consumer surplus in the intermediate
energy market was not quantified; instead the total change in social welfare was calculated as
Change in Social Welfare = £APSE + £APSF + £ACSF + £ACSR (5.1)
5-15
-------
(a) Change in Consumer Surplus
in the Energy Market
(b) Change in Producer Surplus in
the Energy Market
(c) Change in Consumer Surplus
in Goods and Services Markets
(d) Change in Producer Surplus in
Goods and Services Markets
Figure 5-5. Changes in Economic Welfare with Regulation
where
APSE = change in producer surplus in the energy markets;
APSF = change in producer surplus in the goods and services markets;
ACSF = change in consumer surplus in the goods and services markets; and
ACSR = change in consumer surplus in the commercial, residential, and transportation energy
markets.
Appendix A contains the mathematical algorithms used to calculate the change in producer and consumer
surplus in the appropriate markets. The market analysis is conducted for the year 2005 and incorporates
both growth in supply and demand. The data for 2005 are based on projections of Department of Energy
data and Census data, as well as projections based on the engineering data used in preparing the profile
data that is an input to this analysis. Appendix A contains more information on the specific data sets and
5-16
-------
how they are used to construct a baseline data set for 2005 for use in this analysis. Both new and existing
sources are evaluated using the same analysis approach.
Appendix B contains a list of key assumptions that underlie the model used to calculate economic
impacts in this report, and also the results of sensitivity analyses conducted which reflect the outcomes
from varying key parameters such as demand and supply elasticities.
The engineering control costs presented in Chapter 3 are inputs (regulatory "shocks") in the
market model approach. The magnitude and distribution of the regulatory costs' impact on the economy
depend on the relative size of the impact on individual markets (relative shift of the market supply curves)
and the behavioral responses of producers and consumers in each market (measured by the price
elasticities of supply and demand).
CHAPTER 6
ECONOMIC IMPACT ANALYSIS RESULTS
The underlying objective of the EIA is to evaluate the effect of the regulation on the welfare of
affected stakeholders and society in general. Although the engineering cost analysis presented in Chapter
3 does represent an estimate of the resources required to comply with the rule under baseline economic
conditions, the analysis does not account for the fact that the regulations may cause the economic
conditions to change. For instance, producers may reduce production in the face of higher production
costs, thereby reducing market supply. Moreover, the control costs may be passed along to other parties
through various economic exchanges. Therefore, EPA developed an analytical structure and economic
model to measure and track these effects (described in detail in Chapter 5 and the economic impact
6-17
-------
analysis). In this section, we report quantitative estimates of these welfare impacts and their distribution
across stakeholders. This includes the impact on energy markets as well.
6.1 Results in Brief
The economic impacts associated with the rule are relatively low. Price increases of less than
0.02 percent are expected to occur across the many products, both energy and manufacturing, that will be
affected by this rule. Reductions in output are expected to be about 0.02 percent, also. Manufacturing
industries such as paper, wood products, and textiles are expected to be the most impacted. Energy prices
and outputs will also experience small changes, with the largest change in energy price being a 0.05
percent increase in electricity rates. While the price and output changes associated with Option 1A are
also low, the social costs increase by over $1 billion.
6.2 Social Cost Estimates
Table 6-1 summarizes the economic impact estimates for existing and new source units. Under the
MACT floor alternative, EPA estimates the total change in social welfare is estimated to be $862.9 million.
Under the Option 1 A, welfare impacts are over twice as high as the MACT floor alternative with social
welfare changes estimated to equal $1,995.5 million. Both of these estimates are slightly smaller (less than
$0.3 million) than the estimated baseline engineering costs as a result of behavior changes by producers
and consumers that reflect lower cost alternatives. Possible behavior responses include changes in
consumption and production patterns and fuel switching.
EPA also estimated the distribution of social costs between producers and consumers and report
the distribution of impacts across sectors/markets in Tables 6-2 and 6-3. Values in the text are impacts
from the floor alternative; those in parentheses are impacts from the Option 1A alternative. The market
analysis estimates that consumers will bear $414.3 million ($955.3 million), or 48 (48) percent of the total
social cost, because of the increased prices and lower consumption levels in these markets. Producer
surplus is projected to decrease by $448.7 million ($1,040.2 million), or 52 (52) percent of the total social
cost as result of direct control costs, higher energy costs, and reductions in output.
Table 6-1. Social Cost Estimates ($1998 106)
Change in Social
Welfare, MACT Change in Social
Floor Welfare, Option 1A
Baseline engineering costs $863.0 $1,995.8
Social costs with market adjustments $862.9 $1,995.5
Difference between engineering and social costs $0.1 $0.3
With exception of the natural gas market, energy producers are expected to experience producer
surplus losses. Under the MACT floor, electricity, petroleum, and coal producer surplus is projected to
decline by approximately $35 million. This value increases to $113 million under Option 1A. In contrast,
natural gas producer surplus is projected to increase by $2 to $4 million as they benefit from increased
demand from industries switching from petroleum and electricity.
The majority welfare impacts fall on the agriculture, manufacturing, and mining industries. EPA
estimates total welfare losses of $609.8 million ($1,444.3 million) for these sectors. Manufacturing
6-1
-------
industries with large number of boilers and process heaters and industries that consume electricity
experience the majority these losses (e.g., chemicals and allied products, paper, textile mill products, and
food). Consumers in these industries experience losses of $295.2 million ($709.9 million) and producers
bear $314.6 million ($734.4 million). The cost of this rule to producers as a percentage of baseline 2005
shipments is 0.011 (0.026) percent.
EPA also examined the impact on the commercial, transportation and residential sectors. The total
welfare loss for the commercial sector is estimated to be $167.1 million ($301.8 million). Therefore, the
regulatory burden associated with the MACT is estimated as 0.001 (0.002) total 2005 commercial sector
revenues. Consumers in this sector bear approximately $71.6 million ($129.3 million) and producers bear
$95.5 million ($172.5 million) of these impacts. In contrast, the total welfare loss forthe transportation
sector is estimated to be $9.0 million ($46.5 million). The regulatory burden associated with the rule is
estimated as 0.003 (0.015) percent of total 2005
6-2
-------
Table 6-2. Distribution of Social Costs by Sector/Market: Floor Alternative ($1998 106)
Cnange in:
Sectors/Marke
ts
Energy
Markets
Petroleum
Natural gas
Electricity
Coal
Subtotal
NAICS Code
311
312
313
314
315
316
321
322
323
325
326
327
331
332
333
334
335
336
337
339
11
23
21
48
42; 44-45; 49;
51-56; 61-62;
71-72; 81
Grand Total
Producer Consumer Social
Surplus Surplus Welfare
SIC Code
20 (pt)
20 (pt); 21
22 (pt)
22 (pt)
23
31
24
26
27
28
30
32
33
34
35
36 (pt)
36 (pt)
37
25
39
01-08
15-17
10; 14
40-47 (pt)
40-48 (pt);
50-99
Description
Food
Beverage and Tobacco
Products
Textile Mills
Textile Product Mills
Apparel
Leather and Allied Products
Wood Products
Paper
Printing and Related Support
Chemicals
Plastics and Rubber Products
Nonmetallic Mineral Products
Primary Metals
Fabricated Metal Products
Machinery
Computer and Electronic
Products
Electrical Equipment,
Appliances, and Components
Transportation Equipment
Furniture and Related
Products
Miscellaneous
Agricultural Sector
Construction Sector
Other Mining Sector
Transportation
Commercial
Residential
-$1.9
$4.1
-$33.7
-$2.7
-$34.2
-$28.2
-$2.4
-$22.7
-$0.1
-$0.4
-$0.3
-$39.1
-$66.1
-$0.2
-$40.9
-$2.2
-$3.4
-$25.2
-$8.5
-$7.3
-$3.6
-$2.5
-$24.6
-$5.4
-$0.8
-$0.6
-$0.8
-$10.1
-$4.7
-$71.6
NA
-$414.3
-$11.3
-$4.1
-$52.0
-$0.1
-$1.1
-$0.4
-$10.4
-$60.0
-$0.4
-$81.8
-$5.4
-$4.0
-$5.7
-$2.3
-$4.9
-$1.4
-$1.6
-$32.8
-$24.6
-$0.7
-$1.3
-$1.1
-$7.0
-$4.3
-$95.5
-$42.7
-$448.7
-$39.4
-$6.5
-$74.7
-$0.2
-$1.5
-$0.7
-$49.5
-$126.1
-$0.6
-$122.8
-$7.6
-$7.4
-$30.9
-$10.8
-$12.2
-$5.0
-$4.1
-$57.3
-$30.1
-$1.5
-$1.9
-$1.9
-$17.2
-$9.0
-$167.1
-$42.7
-$862.9
6-3
-------
Table 6-3. Distribution of Social Costs by Sector/Market: Option 1A Alternative
($1998 106)
Sectors/Marke
ts
Energy
Markets
Petroleum
Natural gas
Electricity
Coal
Subtotal
NAICS Code
311
312
313
314
315
316
321
322
323
325
326
327
331
332
333
334
335
336
337
339
11
23
21
48
42; 44-45; 49;
51-56; 61-62;
71-72; 81
SIC Code
20 (pt)
20 (pt); 21
22 (pt)
22 (pt)
23
31
24
26
27
28
30
32
33
34
35
36 (pt)
36 (pt)
37
25
39
01-08
15-17
10; 14
40-47 (pt)
40-48 (pt);
50-99
Description
Food
Beverage and Tobacco
Products
Textile Mills
Textile Product Mills
Apparell
Leather and Allied Products
Wood Products
Paper
Printing and Related Support
Chemicals
Plastics and Rubber Products
Nonmetallic Mineral Products
Primary Metals
Fabricated Metal Products
Machinery
Computer and Electronic
Products
Electrical Equipment,
Appliances, and Components
Transportation Equipment
Furniture and Related
Products
Miscellaneous
Agricultural Sector
Construction Sector
Other Mining Sector
Transportation
Commercial
Residential
Producer
Surplus
-$27.3
$2.4
-$79.5
-$6.4
-$110.8
-$90.0
-$5.4
-$45.0
-$0.1
-$0.9
-$2.7
-$72.0
-$173.1
-$0.4
-$102.4
-$6.1
-$9.1
-$59.5
-$18.6
-$17.1
-$12.0
-$11.7
-$47.8
-$9.2
-$3.2
-$1.5
-$3.2
-$18.9
-$24.1
-$129.3
NA
Cnange in:
Consumer
Surplus
-$36.0
-$9.3
-$103.2
-$0.3
-$2.1
-$4.3
-$19.2
-$157.2
-$1.0
-$204.7
-$14.6
-$10.9
-$13.6
-$5.0
-$11.4
-$4.8
-$7.8
-$63.7
-$41.8
-$2.5
-$3.6
-$4.3
-$13.1
-$22.5
-$172.5
-$92.0
Social
Welfare
-$126.0
-$14.7
-$148.2
-$0.4
-$3.0
-$7.1
-$91.2
-$330.3
-$1.4
-$307.1
-$20.7
-$20.0
-$73.1
-$23.6
-$28.5
-$16.8
-$19.6
-$111.4
-$51.0
-$5.7
-$5.1
-$7.5
-$32.0
-$46.5
-$301.8
-$92.0
6-4
-------
transportation sector revenues. Transportation consumers bear approximately $4.7 million ($24.1 million)
and producers bear $4.3 million ($22.5 million) of these impacts. Finally, the social cost burden to
residential consumers of energy, $42.7 million ($92.0 million), is 0.037 (0.078) percent of annual
residential energy expenditures in 2005.
Sensitivity analyses of how social costs behave with changes in the demand and supply elasticities
are available in Appendix B.
6.3 National Market-Level Impacts
Increases in the costs of production in the energy and final product markets due to the regulation
are expected to result in changes in prices, production, and consumption from baseline levels. As shown in
Table 6-4, the electricity market price increases by 0.050 (0.108) percent, while production/consumption
decreases by 0.011 (0.026) percent as a result of additional control costs. A significant share of electricity
is produced in the United States using coal as a primary input. Therefore, projected reductions in
electricity production also lead to a decrease in demand for coal. As a result, the price and quantities of
coal are projected to fall by 0.007 (0.020) percent and 0.010 (0.024) percent, respectively. In the
petroleum market, the model projects small price and quantity effects (i.e., less than 0.01 percent). In the
natural gas market, the model projects the market price will rise in response to increased demand (0.005
percent under both alternatives). The price increase is the result of additional control costs and increased
demand. Production and consumption quantities also increase in this market (0.002 percent under the floor
alternative and 0.001 percent under Option 1A) as a result of increased demand.
Additional control costs and higher energy costs associated with the regulation lead to higher
goods and services prices in all markets and a decline in output. However, the changes are generally very
small. Under the MACT Floor, three markets have price increases greater than or equal to 0.02
percent—Wood Product(NAICS 321), Paper (NAICS 322), and Textile Mills (NAICS 313). Under Option
1A, these three markets have price increases greater than or equal to 0.05 percent. The producers in these
sectors are expected to face higher per-unit control costs relative to other industries. In addition, these
industries are also electricity-intensive; therefore, costs of production also increase as a result of higher
electricity prices.
Although the impacts on price and quantity in the goods and services markets are estimated to be
small, one possible effect of modeling market impacts at the two and three digit NAICS code level is that
fuel-intensive industries within the larger NAICS code definition may be affected more significantly than
the average industry for that NAICS code. Thus, the changes in price and
6-5
-------
Table 6-4. Market-Level Impacts
Sectors/Markets
Energy Markets
Petroleum
Natural gas
Electricity
Coal
JNAICS Code
311
312
313
314
315
316
321
322
323
325
326
327
331
332
333
334
335
336
337
339
11
23
21
48
42; 44-45; 49; 51-
56; 61-62; 71-72;
81
SIC Code
20 (pt)
20 (pt); 21
22 (pt)
22 (pt)
23
31
24
26
27
28
30
32
33
34
35
36 (pt)
36 (pt)
37
25
39
01-08
15-17
10; 14
40-47 (pt)
40-48 (pt);
50-99
Description
Food
Beverage and Tobacco Products
Textile Mills
Textile Product Mills
Apparel
Leather and Allied Products
Wood Products
Paper
Printing and Related Support
Chemicals
Plastics and Rubber Products
Nonmetallic Mineral Products
Primary Metals
Fabricated Metal Products
Machinery
Computer and Electronic Products
Electrical Equipment, Appliances, and
Components
Transportation Equipment
Furniture and Related Products
Miscellaneous
Agricultural Sector
Construction Sector
Other Mining Sector
Transportation
Commercial
JVIA^T
Floor
Percent Change
Price
0.002%
0.005%
0.050%
-0.007%
0.006%
0.003%
0.025%
0.000%
0.000%
0.002%
0.041%
0.026%
0.000%
0.009%
0.001%
0.003%
0.011%
0.003%
0.002%
0.001%
0.002%
0.004%
0.008%
0.001%
0.000%
0.000%
0.012%
0.001%
0.000%
Quantity
0.000%
0.002%
-0.011%
-0.010%
-0.002%
-0.004%
-0.021%
0.000%
-0.001%
-0.003%
-0.008%
-0.028%
0.000%
-0.013%
-0.002%
-0.003%
-0.009%
-0.001%
-0.001%
0.000%
-0.001%
-0.004%
-0.026%
0.000%
0.000%
0.000%
-0.004%
-0.001%
0.000%
Option 1A
Percent
Price
0.019%
0.005%
0.108%
-0.020%
Change
Quantity
-0.005%
0.001%
-0.026%
-0.024%
0.019%
0.007%
0.050%
0.000%
0.001%
0.025%
0.075%
0.068%
0.000%
0.021%
0.003%
0.009%
0.026%
0.007%
0.005%
0.002%
0.009%
0.007%
0.013%
0.003%
0.001%
0.000%
0.023%
0.007%
0.001%
-0.006%
-0.009%
-0.043%
0.000%
-0.001%
-0.030%
-0.015%
-0.074%
-0.001%
-0.032%
-0.005%
-0.008%
-0.021%
-0.001%
-0.002%
-0.001%
-0.004%
-0.007%
-0.044%
-0.002%
-0.001%
0.000%
-0.007%
-0.005%
-0.001%
pt = Part.
-------
quantity should be interpreted as an average for the whole NAICS code, not necessarily for each
disaggregated industry within that NAICS code.
6.4 Executive Order 13211 (Energy Effects)
Executive Order 13211, "Actions Concerning Regulations That Significantly Affect Energy
Supply, Distribution, or Use" (66 Fed. Reg. 28355 [May 22, 2001]), requires EPA to prepare and submit a
Statement of Energy Effects to the Administrator of the Office of Information and Regulatory Affairs,
Office of Management and Budget, for certain actions identified as "significant energy actions." Section
4(b) of Executive Order 13211 defines "significant energy actions" as "any action by an agency (normally
published in the Federal Register) that promulgates or is expected to lead to the promulgation of a final
rule or regulation, including notices of inquiry, advance notices of proposed rulemaking, and notices of
proposed rulemaking:
• that is a significant regulatory action under Executive Order 12866 or any successor order, and
is likely to have a significant adverse effect on the supply, distribution, or use of energy; or
• that is designated by the Administrator of the Office of Information and Regulatory Affairs as
a significant energy action."
EPA has provided additional information on the impacts of the rule on affected energy markets
below.9
Energy Price Effects. As described in the market-level results section, electricity prices are
projected to increase by less than 1 percent. Petroleum and natural gas prices are all projected to increase
by less than 0.1 percent. The price of coal is projected to decrease slightly.
Impacts on Electricity Supply, Distribution, and Use. We project the increased compliance costs
for the electricity market will result in an annual production decline of approximately 415 million kWh
under the MACT floor and 980 million kWh under Option 1 A.
Impacts on Petroleum, Natural Gas, and Coal Supply, Distribution, and Use. The model projects
decreases in petroleum production/consumption of approximately 68 barrels per day under the MACT
floor and 975 barrels per day under Option 1A. In contrast, natural gas production/consumption is
projected to increase by 1.1 million cubic feet per day under the MACT floor and 600,000 cubic feet per
day under Option 1A This is the result of fuel switching in response to relative price changes. Finally, the
model also projects less than a 1,000 tons per day decrease in coal production/consumption under both
scenarios in response to reduced output from the electricity sector (a significant consumer of coal). Based
on these results, the Agency concludes that the industrial boiler and process heater NESHAP will not have
a significant adverse effect on the supply, distribution, or use of energy.
6.5 Conclusions
The decrease in social surplus estimated using the market analysis is $862.9 million ($1,955.5
million). This estimate is slightly smaller than the estimated baseline engineering costs because the market
model accounts for behavioral changes of producers and consumers. Although the rule affects boilers and
process heaters used in energy industries, energy producers only incur less than 6 percent of the total social
cost of the regulation. This burden is spread across numerous markets because the price of energy
increases slightly as a result of the regulation, which increases the cost of production for all markets that
use energy as part of their production process.
The remaining share of the social cost is mostly borne by the manufacturing sectors which operate
the majority of the boilers and process heaters affected by the regulation. Manufacturing industries
bearing the largest social costs include percent—Wood Products (NAICS 321), Paper (NAICS 322), and
Textile Mills (NAICS 313). However, the market model predicts that changes in these industries' price
and quantity do not exceed 0.02 percent under the floor alternative and 0.05 percent under Option 1A..
'Conversion factors for heat rates were obtained from AEO 2002, Appendix H. These factors vary by year to
year; 2010 values are reported in this Appendix.
6-7
-------
Because of the minimal changes in price and quantity estimated for most of the affected markets,
EPA expects that there would be no discernable impact on international trade. Although an increase in the
price of U.S. products relative to those of foreign producers is expected to decrease exports and increase
imports, the changes in price due to the industrial boilers and process heaters MACT are generally too
small to significantly influence trade patterns. There may also be a small decrease in employment, but
because the impact of the regulation is spread across so many industries and the decreases in market
quantities are so small, it is unlikely that any particular industry will face a significant decrease in
employment.
CHAPTER 7
SMALL BUSINESS IMPACTS
This chapter investigates the potential impact the regulation will have on small entities. The
Agency has identified 185 small entities that will be affected by the MACT floor alternative for the
industrial boilers and process heaters NESHAP. For these entities, the average cost-to-sales ratio (CSR) is
0.78 percent and the average annual control cost (in 1999 dollars) is $198,675.
7.1 Results in Brief
-------
As listed in Table 7-1, 34 of the 185 affected entities will incur annual compliance costs that are
greater than or equal to 1 percent of their annual sales or revenues, and 10 of these 34 are expected to incur
annual compliance costs of 3 percent or greater of annual sales or revenues. As explained later in this
chapter, the Agency has certified that this rule will not impose a significant impact on a substantial number
of small entities. This certification is based on the results shown for the MACT floor alternative and on
the results of the economic impact analysis shown in Chapter 6. For Option 1A, as listed in Table 7-1,
there are almost twice as many small entities affected (369), and 148 (or 40 percent) of these incur annual
compliance costs of greater than or equal to 1 percent of their annual sales or revenues, and 45 (or 12
percent) of the total incur annual compliance costs of 3 percent or greater of annual sales or revenues.
Table 7-1. Summary of Small Entity Impacts
MACT Floor
Alternative Option 1A Alternative
Number of small entities 185 369
Total number of entities 576 970
Average annual control cost per small entity $198,675 $269,842
Average control cost/sales ratio 0.78% 1.65%
Number of small entities with cost-to-sales 34 148
ratios > 1 percent
Number of small entities with cost-to-sales 10 45
ratios >3 percent
7.2 Background on Small Business Screenings
The regulatory costs imposed on domestic producers and government entities to reduce air
emissions from boilers and process heaters will have a direct impact on owners of the affected facilities.
Firms or individuals that own the facilities with boilers and process heaters are typically business entities
that have the capacity to conduct business transactions and make business decisions that affect the facility.
The legal and financial responsibility for compliance with a regulatory action ultimately rests with these
owners, who must bear the financial consequences of their decisions. Environmental regulations
potentially affect all sizes of businesses, but small businesses may have special problems relative to large
businesses in complying with such regulations.
The Regulatory Flexibility Act (RFA) generally requires an agency to prepare a regulatory
flexibility analysis of any rule subject to notice and comment rulemaking requirements under the
Administrative Procedure Act or any other statute unless the agency certifies that the rule will not have a
significant economic impact on a substantial number of small entities. Small entities include small
businesses, small organizations, and small governmental jurisdictions.
For purposes of assessing the impacts of today's rule on small entities, small entity is defined as:
(1) a small business according to Small Business Administration (SBA) size standards by the North
American Industry Classification System (NAICS) category of the owning entity. The range of small
business size standards for the 40 affected industries ranges from 500 to 1,000 employees, except for
petroleum refining and electric utilities. In these latter two industries, the size standard is 1,500 employees
and a mass throughput of 75,000 barrels/day or less, and 4 million kilowatt-hours of production or less,
respectively. (2) a small governmental jurisdiction that is a government of a city, county, town, school
7-1
-------
district or special district with a population of less than 50,000; and (3) a small organization that is any
not-for-profit enterprise which is independently owned and operated and is not dominant in its field.
This section investigates characteristics of businesses and government entities that own existing
boilers and process heaters affected by this rule and provides a preliminary screening-level analysis to
assist in determining whether this rule is likely to impose a significant impact on a substantial number of
the small businesses within this industry. The screening-level analysis employed here is a "sales test,"
which computes the annualized compliance costs as a share of sales/revenue for existing
companies/government entities.
7.3 Identifying Small Businesses
To support the economic impact analysis of the regulation, EPA identified 2,186 (3,580) boilers
and process heaters located at commercial, industrial, and government facilities that would be affected by
the regulation. The population of boilers and process heaters was developed from the EPA ICCR
Inventory Database version 4.1.10 The list of boilers and process heaters contained in these databases was
developed from information in the AIRS and OTAG databases, state and local permit records, and the
combustion source ICR conducted by the Agency. Industry and environmental stakeholders reviewed the
units contained in these databases as part of the ICCR FACA process. In addition, stakeholders
contributed to the databases by identifying and including omitted units. Information was extracted from
the ICCR databases to support the ICI boilers and process heaters NESHAP. This modified database
containing information on only boilers and process heaters is referred to as the Inventory Database.
The small entities screening analysis for the regulation is based on the evaluation of existing
owners of boilers and process heaters for which information was available. It is assumed that the size and
ownership distribution of units in the Inventory Database is representative of the entire estimated
population of existing boilers and process heaters. In addition, it is assumed that new sources included in
the 2005 population will also be representative of the Inventory Database. However, because our analysis
is based on a subset of the total population of boilers and process heaters, the number of entities identified
as highly affected in this analysis may not be identical to the actual impact of the regulation on small
entities.
The remainder of this section presents cost and sales information on small companies and
government organizations that own existing boilers and process heaters. Also, in this section, as in
previous sections, the values from the Inventory Database in the text are for the floor alternative.
Following in parentheses are those for the Option 1A alternative.
7.4 Analysis of Facility-Level and Parent-Level Data
The 2,186 (3,580) units in the Inventory Database with full information were linked to 1,214
(1,881) existing facilities. As shown in Table 7-2, these 1,186 (1,521) facilities are owned by 576 (970)
parent companies. The average number of facilities per company is approximately 2.0 (2.2); however, as
is also illustrated in Table 7-2, several large entities in the health services industry and government sectors
own many facilities with boilers and process heaters.
10The ICCR Inventory Database contains data for boilers, process heaters, incinerators, landfill gas flares,
turbines, and internal combustion engines.
7-2
-------
Table 7-2. Facility-Level and Parent-Level Data by Industry
jioor Alternative
SIC
Code
01
02
07
10
12
13
14
17
20
21
22
23
24
25
26
27
28
29
30
NAICS
Code
111
112
115
212
212
211
212
235
311
312
313
315
321
337
322
511
325
324
326
Description
Agriculture — Crops
Agriculture — Livestock
Agricultural Services
Metal Mining
Coal Mining
Oil and Gas Extraction
Mining/Quarrying — Nonmetallic
Minerals
Construction — Special Trade
Food and Kindred Products
Tobacco Products
Textile Mill Products
Apparel & Other Products from
Fabrics
Lumber and Wood Products
Furniture and Fixtures
Paper and Allied Products
Printing, Publishing, and Related
Industries
Chemicals and Allied Products
Petroleum Refining and Related
Industries
Rubber and Misc. Plastics
Products
Number
of
Units
3
—
—
9
2
—
8
—
138
11
135
2
360
234
321
—
174
11
17
Number
of
Facilities
3
—
—
4
1
—
4
—
60
7
71
2
262
154
194
—
70
8
13
Number
of
Parent
Companies
3
—
—
2
—
—
3
—
32
4
33
1
122
67
68
—
41
9
9
Avg.
Number of
Facilities
Per Parent
Entity
1.0
—
—
2.0
—
—
1.3
—
1.9
1.8
2.2
2.0
2.1
2.3
2.9
—
1.7
0.9
1.4
wpiion IA Alternative
Number
of
Units
6
—
—
11
2
18
10
2
163
22
250
4
462
310
503
8
433
162
56
Number
of
Facilities
6
—
—
5
1
4
5
1
72
11
134
4
337
209
272
6
163
50
37
Number
of
Parent
Companies
6
—
—
2
—
1
4
1
38
6
73
3
175
100
100
3
91
31
24
Avg.
Number of
Facilities
Per Parent
Entity
1.0
—
—
2.5
—
4.0
1.3
1.0
1.9
1.8
1.8
1.3
1.9
2.1
2.7
2.0
1.8
1.6
1.5
(continued)
-------
Table 7-2. Facility-Level and Parent-Level Data by Industry (continued)
*ioor Alternative
SIC
Code
31
32
33
34
35
36
37
38
39
40
42
46
49
50
51
55
58
59
60
NAICS
Code
316
327
331
332
333
335
336
334
339
482
484
486
221
421
422
441
722
445^54
522
Description
Leather and Leather Products
Stone, Clay, Glass, and Concrete
Products
Primary Metal Industries
Fabricated Metal Products
Industrial Machinery and
Computer Equip.
Electronic and Electrical
Equipment
Transportation Equipment
Scientific, Optical, and
Photographic Equipment
Misc. Manufacturing Industries
Railroad Transportation
Motor Freight and Warehousing
Pipelines, Except Natural Gas
Electric, Gas, and Sanitary
Services
Wholesale Trade — Durable Goods
Wholesale Trade — Nondurable
Goods
Automotive Dealers and Gasoline
Service Stations
Eating and Drinking Places
Miscellaneous Retail
Depository Institutions
Number
of
Units
1
9
41
16
23
5
102
8
2
4
5
—
318
3
2
—
—
—
Number
of
Facilities
1
7
16
10
12
5
41
4
2
1
1
—
160
2
1
—
—
—
Number
of
Parent
Companies
1
4
10
7
9
3
12
3
2
1
1
—
80
1
1
—
—
—
Avg.
Number of
Facilities
Per Parent
Entity
1.0
1.8
1.6
1.4
1.3
1.7
3.4
1.3
1.0
1.0
1.0
—
2.0
2.0
1.0
—
—
—
option IA Alternative
Number
of
Units
22
42
85
44
46
45
158
33
14
4
7
6
372
3
2
1
—
1
—
Number
of
Facilities
12
25
33
28
25
29
61
16
10
1
3
5
185
2
1
1
—
1
—
Number
of
Parent
Companies
8
15
22
18
20
19
26
9
9
1
3
1
98
1
1
1
—
1
—
Avg.
Number of
Facilities
Per Parent
Entity
1.5
1.7
1.5
1.6
1.3
1.5
2.3
1.8
1.1
1.0
1.0
5.0
1.9
2.0
1.0
1.0
—
1.0
—
(continued)
-------
Table 7-2. Facility-Level and Parent-Level Data by Industry (continued)
Floor Alternative
SIC
Code
70
72
76
80
81
82
83
86
87
89
91
92
94
96
97
NA
State
NAICS
Code
721
812
811
621
541
611
624
813
541
711/514
921
922
923
926
928
Description
Hotels and Other Lodging Places
Personal Services
Misc. Repair Services
Health Services
Legal Services
Educational Services
Social Services
Membership Organizations
Engineering, Accounting, Research,
Management and Related Services
Services, N.E.C.
Executive, Legislative, and General
Administration
Justice, Public Order, and Safety
Administration of Human
Resources
Administration of Economic
Programs
National Security and International
Affairs
SIC Information Not Available
Parent is a state government
Total
Number
of
Units
1
—
2
37
—
105
2
—
2
2
1
29
1
4
29
7
—
2,186
Number
of
Facilities
1
—
1
18
—
45
1
—
2
1
1
9
1
3
11
4
—
1,214
Number
of
Parent
Companies
1
—
—
2
—
30
—
—
1
—
—
—
—
1
2
—
10
576
Avg.
Number of
Facilities
Per Parent
Entity
1.0
—
—
9.0
—
1.5
—
—
2.0
—
—
—
—
3.0
5.5
—
—
2.0
option IA Alternative
Number
of
Units
1
—
2
40
—
114
3
—
6
2
2
33
1
4
41
24
—
3,580
Number
of
Facilities
1
—
1
19
—
50
2
—
5
1
2
10
1
3
13
18
—
1,881
Number
of
Parent
Companies
1
—
—
2
—
35
2
—
2
—
1
—
—
1
2
2
11
970
Avg.
Number of
Facilities
Per Parent
Entity
1.0
—
—
9.5
—
1.4
1.0
—
2.5
—
2.0
—
—
3.0
6.5
9.0
—
2.2
Source: Industrial Combustion Coordinated Rulemaking (ICCR). 1998. Data/Information Submitted to the Coordinating Committee at the Final Meeting of the Industrial
Combustion Coordinated Rulemaking Federal Advisory Committee. EPA Docket Numbers A-94-63, II-K-4b2 through -4b5. Research Triangle Park, North
Carolina. September 16-17.
-------
Employment and sales are typically used as measures of business size. Employment, sales,
population, and tax revenue data (when applicable) were collected for the 576 (970) parent companies and
government entities.11 Figure 7-1 shows the distribution of employees by parent company for the floor
alternative. Employment for parent companies ranges from 5 to 608,000 employees. One hundred
seventy-eight or more of the firms have fewer than 500 employees, and 55 companies have more than
25,000 employees. The distribution of parents by employment range for the above-the-floor alternative is
similar to the floor alternative.
1± 1^0 -
re
0.
•£ 100 -
s_
1 50
•2.
n -
141
10 12
I 1 I 1
31
125
52
121
55
<25
25 to 49 50 to 99
100 to
499
500 to
999
1,000 to 5,000 to >25,000
4,999 24,999
Parent Employment
Figure 7-1. Parent Size by Employment Range, Floor Alternative
*Excludes 29 parent entities for which employment information was unavailable.
Sales provide another measure of business size. Figure 7-2 presents the sales distribution for
affected parent companies for the floor alternative. The median sales figure for affected companies is $300
million ($200 million), and the average sales figure is $4.1 billion ($3.5 billion) (excluding the federal
government). As shown in Figure 7-2, revenue and sales figures vary greatly across the population: 209
firms and governments affected by the floor alternative have annual revenues less than $100 million per
year. These figures include all sales associated with the parent company, not just facilities affected by the
"Total annualized cost is compared to tax revenue to assess the relative impact on local governments.
7-6
-------
200
re
Q_
t 100
^
0)
1 50
z
0 -
18
128
56
110
51
133
~n 33
ZU -| /
| |
<5 5to9 10to49 50to99 100 to 500 to 1,000 to 5,000to 10,000to >25,000
499 999 4,999 9,999 24,999
Parent Sales ($10s)
Figure 7-2. Number of Parents by Sales Range, Floor Alternative
*Excludes 3 parent entities for which sales or revenue information was unavailable.
regulation (i.e., facilities with boilers or process heaters). The distribution for the Option 1A above-the-
floor alternative is similar to that for the floor alternative.
Based on SBA guidelines, 185 (369) of the companies were identified as small businesses.12 Small
businesses by business type are presented in Table 7-3. The lumber and wood products industry contains
the largest number of the small businesses with 84 (134), followed by furniture and fixtures with 28 (55),
electric services with 26 (30), and paper and allied products with 13 (30). The remaining small businesses
are distributed across 40 different two-digit SIC code groupings.
12Small business guidelines typically define small businesses based on employment, and the threshold varies
from industry to industry. For example, in the paints and allied products industry, a business with fewer
than 500 employees is considered a small business; whereas in the industrial gases industry, a business with
fewer than 1,000 employees is considered small. However, for a few industries, usually services, sales are
used as the criterion. For example, in the veterinary hospital industry, companies with less than $5 million
in annual sales are defined as small businesses.
7-7
-------
Table 7-3. Small Parent Companies by Industry
SIC
Code
01
02
07
10
12
13
14
17
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
NAICS
Code
111
112
115
212
212
211
212
235
311
312
313
315
321
337
322
511
325
324
326
316
327
331
332
Description
Agriculture — Crops
Agriculture — Livestock
Agricultural Services
Metal Mining
Coal Mining
Oil and Gas Extraction
Mining/Quarrying —
Nonmetallic Minerals
Construction — Special
Trade Contractors
Food and Kindred Products
Tobacco Products
Textile Mill Products
Apparel and Other Products
from Fabrics
Lumber and Wood Products
Furniture and Fixtures
Paper and Allied Products
Printing, Publishing, and
Related Industries
Chemicals and Allied
Products
Petroleum Refining and
Related Industries
Rubber and Misc. Plastics
Products
Leather and Leather
Products
Stone, Clay, Glass, and
Concrete Products
Primary Metal Industries
Fabricated Metal Products
Floor Alternative
Number of
Parent
Companies
3
—
—
2
—
—
3
—
32
4
33
1
122
67
68
—
41
9
9
1
4
10
7
Number of
Small Parent
Companies
—
—
—
2
—
—
—
—
12
—
5
—
84
28
13
—
4
2
1
1
—
1
3
Option 1A
Number of
Parent
Companies
6
—
—
2
—
1
4
1
38
6
73
3
175
100
100
3
91
31
24
8
15
22
18
Alternative
Number of
Small Parent
Companies
1
—
—
2
—
1
—
1
15
—
27
2
134
55
30
2
19
9
4
4
3
3
5
(continued)
7-S
-------
Table 7-3. Small Parent Companies by Industry (continued)
SIC
Code
35
36
37
38
39
40
42
46
49
50
51
55
58
59
60
70
72
76
80
81
82
83
NAICS
Code
333
335
336
334
339
482
484
486
221
421
422
441
722
445-454
522
721
812
811
621
541
611
624
Description
Industrial Machinery and
Computer Equip.
Electronic and Electrical
Equipment
Transportation Equipment
Scientific, Optical, and
Photographic Equip.
Miscellaneous
Manufacturing Industries
Railroad Transportation
Motor Freight and
Warehousing
Pipelines, Except Natural
Gas
Electric, Gas, and Sanitary
Services
Wholesale Trade — Durable
Goods
Wholesale
Trade — Nondurable Goods
Automotive Dealers and
Gasoline Service Stations
Eating and Drinking Places
Miscellaneous Retail
Depository Institutions
Hotels and Other Lodging
Places
Personal Services
Misc. Repair Services
Health Services
Legal Services
Educational Services
Social Services
Floor Alternative
Number of Number of
Parent Small Parent
Companies Companies
9 1
3 —
12 1
3 —
2 —
1 —
1 —
— —
80 26
1 —
1 —
— —
— —
— —
— —
1 —
— —
— —
2 1
— —
30 —
— —
Option 1A
Number of
Parent
Companies
20
19
26
9
9
1
3
1
98
1
1
1
—
1
—
1
—
—
2
—
35
2
Alternative
Number of
Small Parent
Companies
5
—
5
1
1
—
1
—
30
—
—
1
—
1
—
—
—
—
1
—
3
1
(continued)
7-9
-------
Table 7-3. Small Parent Companies by Industry (continued)
SIC
Code
86
87
89
91
92
94
96
97
NA
State
NAICS
Code
813
541
711/514
921
922
923
926
928
Description
Membership Organizations
Engineering, Accounting,
Research, Management and
Related Services
Services, N.E.C.
Executive, Legislative, and
General Administration
Justice, Public Order, and
Safety
Administration of Human
Resources
Administration of Economic
Programs
National Security and
International Affairs
SIC Information Not
Available
Parent is a State Government
Total
Floor Alternative
Number of Number of
Parent Small Parent
Companies Companies
— —
1 —
— —
— —
— —
— —
1 —
2 —
10 —
576 185
Option 1A
Number of
Parent
Companies
—
2
—
1
—
—
1
2
2
11
970
Alternative
Number of
Small Parent
Companies
—
—
—
—
—
—
—
2
—
369
Source: Industrial Combustion Coordinated Rulemaking (ICCR). 1998. Data/Information Submitted to the
Coordinating Committee at the Final Meeting of the Industrial Combustion Coordinated Rulemaking Federal
Advisory Committee. EPA Docket Numbers A-94-63, II-K-4b2 through -4b5. Research Triangle Park,
North Carolina. September 16-17.
Fifty-nine governmental jurisdictions are affected by the final rule. The entities operate 290 units
located at 121 facilities. Thirteen of these jurisdictions are classified as small because they serve a
population of 50,000 or fewer. The affected small governments operate 13 units at 13 facilities. More
information on impacts to these entities can be found in Section 7.6.
7.5 Small Business Impacts
Table 7-4 presents a summary of the ratio of floor and above-the-floor control costs to sales for
affected large and small entities. The average CSR is 0.14 (0.23) percent for large entities
7-10
-------
Table 7-4. Summary Statistics for SBREFA Screening Analysis: Floor and Above-the-Floor Cost-to-Sales Ratios
Total Number of Small Entities
Average Annual Compliance Cost per Small Entity
Entities with Sales/Revenue Data
Compliance costs are <1% of sales
Compliance costs are > 1 to 3% of sales
Compliance costs are >3% of sales
Compliance Cost-to-Sales/Revenue Ratios
Average
Median
Maximum
Minimum
Floor
185
$198,675
141
34
10
0.78
0.50
7.83
0.011
Option 1A
369
$269,842
176
148
45
1.65
0.77
38.83
0.009
-------
(excluding the federal government) and 0.78 (1.65) percent for small entities. Forty-four (193) small
parents had floor CSRs greater than 1 percent, assuming add-on control is employed to meet the standard.
For these 44 (193) parent companies, the CSRs ranged from 1.00 (1.00) percent to 7.83 (38.83) percent.
Ten (45) entities out of these 44 (193) had CSRs ratios greater than 3 percent.
7.6 Affected Government Entities
The RFA as amended by SBREFA provides the following standard definition of "small
governmental jurisdiction": a city, county, town, township, village, school district, or special district
with a population of less than fifty thousand. Using this definition, EPA identified thirteen small
governmental jurisdictions that own and operate "public power" producers with affected boilers. For
this part of the small entity analysis, which focuses on affected government entities, public power
producers are defined as nonprofit publicly owned electrical utilities operated by municipalities,
counties, and states or other publicly owned bodies such as public utility districts. This excludes rural
electric cooperatives.
As illustrated in Table 7-5, the vast majority of small municipal systems with affected boilers
are located in the Midwest (11 systems or 85 percent). Four of the eleven municipal systems are
located in Minnesota, with two in Indiana and two in Michigan.
Table 7-5. Regional Distribution of Municipal Systems
Regional
Distribution
East
Vermont
Midwest
Indiana
Iowa
Michigan
Minnesota
Ohio
Wisconsin
West
California
Total
# of Facilities
1
2
1
2
4
1
1
1
13
Historically municipal utilities were set up to provide residents of a community with reliable
energy. For example, the residential sector accounts more than two thirds of total consumers in all
cases (see Table 7-6). However the residential sector generally represents smallest group in terms of
total energy consumption. The industrial and commercial sectors consume approximately 70 percent
of total energy supplied. Power not consumed by the residential, commercial or industrial sectors is
sold into wholesale energy market.
Table 7-6. Selected Municipal Utilities' Capacity, Usage and Consumer Types
7-12
-------
Distribution of Energy Usage by
Customer Type
Distribution of Customers
RO
WID
1
2
3
4
5
6
7
8
9
10
11
12
Capacit
y(MW)
50.5
115
24.3
22.2
34.5
23
35
46
103.1
32
26
34
Energy
Usage
332,524,000
371,823,000
388,066,000
185,191,000
147,335,000
573,003,000
338,903,000
194,753,000
837,175,000
218,208,000
267,201,000
95,642,000
Residentia
1
27%
36%
19%
26%
26%
8%
38%
22%
NA
40%
16%
33%
Commerc
ial
NA
28%
10%
14%
27%
NA
8%
NA
NA
3%
NA
67%
Industri
al
NA
16%
70%
58%
44%
NA
51%
NA
NA
55%
NA
NA
Total
Consumer
19,313
15,615
9,082
6,235
5,955
7,207
13,247
6,890
NA
10,829
9,471
5,747
Resident!
al
82%
87%
84%
86%
86%
90%
87%
85%
NA
88%
75%
83%
Commercia
1
15%
11%
14%
13%
14%
7%
11%
13%
NA
3%
24%
17%
Industri
al
3.7%
0.3%
1.0%
1.6%
0.3%
1.0%
1.3%
0.1%
NA
8.4%
0.3%
0.3%
Source: Giles, Ellen F. 2000. platts Directory of Electric Power Producers and Distributors 109th Edition of the Electrical World Directory. New York: McGraw Hill.
Public power producers do not pay state or local taxes. However, they typically are under agreement to make annual contributions to
state and local government operating funds. In addition, they are not guaranteed at rate of return (as regulated public utilities are), however,
their rates are set by agreement with local councils and these rates are typically adjusted to reflect changes in operating costs.
7-13
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Municipal utilities have the ability to generate capital through the issuance of tax exempt
municipal bonds. These municipal bonds are exempt from federal income tax which allows the
publicly owned utilities to finance capital projects at a more affordable rate. Additionally the local
governments investing in municipal utilities generally issue revenue bonds rather than general
obligation bonds. This ensures that the debit can be paid back through revenues from the generation
of electricity and does not obligate the local government or community tax base.
As shown in Table 7-7, the average total annual compliance costs per entity are $223
thousand under the floor alternative and increase to $548 thousand for the above -the- floor
alternative (Option 1A). For the floor alternative, the median cost-to-revenue ratio is 0.94 percent
and ratios range from less than 0.5 percent to 8 percent. Three of the affected small governments
have cost-to-revenue ratios at or above 3 percent. Similar analysis for the above the MACT floor
alternative shows the median cost-to-revenue ratio is 2.2 percent and ratios range from less than 0.5
percent to 16 percent. Five of the thirteen affected small governments have cost-to-revenue ratios at
or above 3 percent.
Table 7-7. Summary of Impacts to Small Government Entities
Total Number of Small Entities
Average Total Annual Compliance Cost (TACC) per Small Entity ($)
Compliance Costs are <1% of Revenue
Compliance Costs are 1 to 3% of Revenue
Compliance Costs are >=3% of Revenue
Average Compliance Cost as a % of Revenue
Median
Maximum
Minimum
Floor
13
$ 223
7
3
3
1.67
0.94
7.83
0.02
Option 1A
13
$ 548
2
6
5
4.18
2.21
16.30
0.02
Source: American Public Power Association (APPA). 2002. Straight Answers to False
Charges about Public Power. Washington D.C.: APPA. As obtained on November 13, 2003
at http://www.appanet.org/about/publicpower/index.cfm .
7.6 Assessment of SBREFA Screening
This analysis indicates that over two-thirds of the parent companies affected by the industrial
boilers and process heaters standard are large companies. The relatively small proportion of small
businesses affected by the regulation at the floor level is due in part to the exclusion of ICI boilers
and process heaters with less than 10 MMBtu input capacity that also use a fossil fuel liquid or gas as
primary fuel. As a result, a large share of small boilers and process heaters, which are presumably
owned disproportionately by smaller entities, will not incur compliance costs. The Agency estimates
that approximately 57 percent of the U.S. population are less than 10 MMBtus or are emergency units
and, hence, are excluded from the proposed regulation for the floor alternative. These units are
included, however, in the Option 1A above-the-floor alternative, except where they consume a fossil
fuel liquid or gas other than residual fuel oil.
13Based on SBA guidelines for determining small businesses.
7-14
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Of the small businesses affected by the regulation, the majority are in the lumber and wood
products, furniture and fixtures, paper and allied products, and electric, gas and sanitary services
sectors. As shown in Table 7-5, the median profit margin for these four sectors is approximately
3 percent. Table 7-5 also shows the profit margins for the other industry sectors with affected small
businesses. All profit margins of industry sectors with affected small businesses are above 2 percent.
After considering the economic impact of today's rule on small entities, EPA certifies that
Table 7-5. Profit Margins for Industry Sectors with Affected Small Businesses
SIC
Code
20
22
24
25
26
28
49
JNA1CS
Code
311
313
321
337
322
325
221
Description
Food and Kindred Products
Textile Mill Products
Lumber and Wood Products
Furniture and Fixtures
Paper and Allied Products
Chemicals and Allied Products
Electric, Gas, and Sanitary Services
Median Profit Margin
3.6%
2.1%
3.0%
3.0%
3.3%
2.7%
7.5%
Source: Dun & Bradstreet. 1997. Industry Norms & Key Business Ratios. Desktop Edition 1996-97. Murray
Hill, NJ: Dun & Bradstreet, Inc.
this action will not have a significant impact on a substantial number of small entities. In accordance
with the RFA, as amended by the SBREFA, 5 U.S.C. 601, et. seq., EPA conducted an assessment of
the standard on small businesses within the industries affected by the rule. Based on SBA size
definitions for the affected industries and reported sales and employment data, the Agency identified
185 of the 576 companies, or 32 percent, owning affected facilities as small businesses. Although
small businesses represent 32 percent of the companies within the SBREFA screening population,
they are expected to incur only 8 percent of the total compliance costs of $445.6 million (1998$) for
the evaluated 576 firms. Only ten small firms have compliance costs equal to or greater than 3
percent of their sales. In addition, only 24 small firms have CSRs between 1 and 3 percent.
An EIA was performed to estimate the changes in product price and production quantities for
this rule. As mentioned in the summary of economic impacts earlier in this report, the estimated
changes in prices and output for affected firms are no more than 0.04 percent.
This analysis indicates that the rule should not generate a significant impact on a substantial
number of small entities for following reasons. First, only 31 small firms (or 17 percent of all
affected small firms) have compliance costs equal to or greater than 1 percent of their sales. Of these,
only ten small firms (or 5 percent of all affected small firms) have compliance costs equal to or
greater than 3 percent of their sales. Second, the EIA results show minimal impacts on prices and
output from affected firms, including small entities, due to implementing this rule. This analysis
therefore allows us to certify that there will not be a significant impact on a substantial number of
small entities from the implementing this rule.
This rule will not have a significant economic impact on a substantial number of small
entities as a result of several decisions EPA made regarding the development of this rulemaking
which resulted in limiting the impact of this rule on small entities. First, as mentioned earlier, EPA
identified small units (heat input of 10 MMBtu/hr or less) and limited-use boilers (operate less than
10 percent of the time) as separate subcategories from large units. Many small and limited-use units
are located at small entities. As also discussed earlier, the result of the MACT floor analysis for these
7-15
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subcategories of existing sources was that no MACT floor could be identified except for the limited-
use solid fuel subcategory, which is less stringent than the MACT floor for large units. Furthermore,
the results of the above-the-floor analysis for these subcategories indicated that the costs would be too
high to be considered feasible. Consequently, this rule contains no emission limitations for any of the
existing small and limited-use subcategories except the existing limited-use solid fuel subcategory. In
addition, the alternative metals emission limit resulted in minimizing the impacts on small entities
because some of the potential entities burning a fuel containing very little metals are small entities.
Finally, the risk-based alternative compliance options for HC1 and manganese sources may also serve
to mitigate impacts to small entities.
References
U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards. Industrial
Combustion Coordinated Rulemaking, Inventory Database V4.1- Boilers. February 26, 1999.
U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards. Industrial
Combustion Coordinated Rulemaking, Inventory Database V4 - Process Heaters. November 13,
1998.
U.S. Small Business Administration. Small Business Size Standards by NAICS Codes. February 22,
2002. Found on the Internet at
http://www.sba.gov/size/Table-of-Small-Business-Size-Standards-from-fmal-rule.html.
References
7-16
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Federal Register, 2001. Executive Order 13211, Actions Concerning Regulations That Significantly
Affect Energy Supply, Distribution, or Use. Vol. 66, May 22, 2001, pg. 28355.
CHAPTER 8
EMISSIONS INVENTORIES AND AIR QUALITY CHANGES
8.1 Results in Brief
8-17
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An analysis of changes in air quality associated with implementation of the industrial boilers
and process heaters MACT rule shows that the majority of the U.S. population in 2005 will live in
areas with predicted improvement in annual average visibility of between 0.4 to 0.6 deciviews
resulting from the rule. Almost 4 percent of the projected 2005 U.S. population are predicted to
experience improved annual average visibility of greater than 0.25 deciviews. Furthermore, roughly
10 percent of the projected 2005 U.S. population will benefit from reductions in annual average
visibility of greater than 0.1 deciviews. The mean improvement across all U.S. counties is 0.05
deciviews, or almost 2 percent from baseline visibility levels. In urban areas (i.e., areas with a
population of 250,000 or more), the mean improvement in annual visibility was 0.06 deciviews. In
rural areas (i.e. all non-urban areas), the mean improvement in visibility was 0.04 deciviews in 2005.
On average, the Eastern U.S. experienced slightly larger absolute but smaller relative
improvements in visibility than the Western U.S. from the emission reductions associated with this
rule.
8.2 Introduction
Executive Order 12866 as amended by E.O. 13258 contains as one its requirements the
assessment of benefits for any major rule, where a major rule is one that meets one or more of the 4
criteria listed in Chapter 1 of this RIA. Since this regulation is a major rule according to the
Executive Order, we have undertaken to estimate the benefits associated with implementation of this
regulation. Assessing the benefits requires knowledge of the emission reductions resulting from
application of this rule, the change in air quality due to the emission reductions, and the locations
where these emission reductions and air quality changes take place. This chapter of the RIA
presents the baseline emissions upon which the emission reductions are calculated and the changes in
air quality resulting from the emission reductions.
While this regulation is intended to reduce HAP emissions, including mercury, from
industrial boilers and process heaters, it also provides reductions in non-HAP species such as
particulate matter (PM) and sulfur dioxide (SO2). Reductions in PM and SO2 are those that are the
focus of the benefits assessment, for we currently have sufficient information to monetize the benefits
from reductions of these pollutants. We currently lack sufficient information to monetize the
benefits from the HAP and mercury reductions from this regulation. It is quite possible that the
benefits from the 58,575 tons of HAP reductions and the 1.7 tons of mercury emission reductions
may be substantial.
8.3 Baseline Emissions
We measure air quality impact as a change in concentration in PM in the counties affected by
the emission reductions taking place due to implementation of this regulation. In this case, changes in
particulate matter less than 10 microns (PM10) and changes in the particulate matter fraction of less
than 2.5 microns (PM25) are calculated in this analysis. Calculations of changes in both PM fractions
are necessary in order to provide a more complete assessment of benefits. In addition, changes in
visibility are also estimated in order to calculate the benefits associated with this category of effects.
In order to determine the air quality impact of the emission reductions, we first calculated a baseline,
then took the PM and SO2 emission reductions prepared in the engineering analysis, estimated the
PM25 reductions from the PM10 reductions, and then entered the emission reductions into an air
quality model. This section describes how the baseline inventories were determined.
8.3.1 EPA's Baseline Inventory
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Initially, our plan was to utilize the same baseline and control scenarios being analyzed to
estimate the control costs. The baseline inventory for the control costs is the Industrial Combustion
Coordinated Rulemaking (ICCR) inventory database, which was developed to support the
rulemakings for the Combustion Turbines and Reciprocating Internal Combustion Engine MACTs as
well as this MACT. However, we were unable to use this baseline inventory because it did not
contain a number of data fields necessary for air quality modeling and possessed incomplete data at
the unit level necessary for such modeling. Instead, we included 1996 National Emission Trends
(NET) inventory data for these sources to augment the ICCR data in order to prepare an inventory
with sufficient data for the air quality modeling. The NET inventory provides baseline emissions data
of criteria pollutants from point, area, and mobile sources. Version 3.12 of the NET is being used to
prepare the baseline inventory for this air quality analysis. The ICCR inventory provides the PM and
SO2 emissions. All other pollutant emissions used to establish the baseline inventory are taken from
the NET. Readers desiring more information about the inventory methodologies or results should
consult those documents for details.
The baseline reflects air quality and emissions present in 1996, therefore, it reflects controls
from various air pollution programs that are implemented by 1996. To the extent that additional
controls are implemented before 2005, the year of analysis in this report, the air quality results would
differ but the extent of the difference cannot be determined. To our knowledge, only phase II of the
the Acid Rain Program which was implemented at utility sources nationwide in 2000 could influence
baseline emission inventories. For more details see Pechan, 2001.
The analysis uses a baseline inventory with a base year of 1996 to estimate the benefits of the
regulation in 2005. We determined that minimal changes in unit population and baseline emissions
would occur between the current time and 2005, so that the use of this inventory without imposition
of growth factors was deemed adequate.
8.3.2 The MACT floor and Other Emissions Reduction Scenarios
Table 8-1 summarizes the baseline PM10, PM25, and SO2 emissions and emission reductions
nationwide for the MACT floor option. Baseline emission and emission reductions nationwide for
Option 1A, an above-the-MACT floor option, are presented in Appendix C of the RIA. These
regulatory options are described in Chapter 1 of the RIA. The air quality analysis presumes no
change in volatile organic compound (VOC), nitrogen oxides (NOx), carbon monoxide (CO), and
ammonia (NH3) emissions. Hence, the baseline emissions for these pollutants are not shown in this
table. For these baseline emissions, refer to Pechan, 2001.
The split of emission reductions shown in the latter two columns results from the assignment
of specific control devices to only a portion of the affected units. The emissions reductions
associated with this portion, which is slightly more than half of the known affected units, can be
included in the benefits model (described in Chapter 10 of the RIA) for calculation of the benefits
from these reductions. This is true since these emission reductions can be linked to decreased
exposures to affected populations. For the emission reductions from the other affected others, we
employ a benefits transfer method that takes the benefits values estimated for the units with assigned
control devices and transfers them to these remaining emission reductions to estimate the resulting
monetized benefits. For more information on the benefits transfer method, refer to Chapter 10.
As mentioned earlier in this chapter, we conducted no air quality modeling for the HAP or
the mercury emission reductions that occur from implementation of this regulation. These emission
8-1
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reductions are listed in Table 8-2. For a description of how HAP emissions and emission factors are
estimated for this rule, refer to the emission factors/emissions estimates memo in the public docket
(ERG, 2002).
Table 8-1. Summary of Nationwide Baseline Emissions and Emission Reductions" for the
MACT floor, Existing Units Onlyb'c in 2005
Pollutant
SO2
PM10
Source
Type
Point
Area
Motor
Vehicle
Nonroad
Point
Area
Motor
Vehicle
Nonroad
1996
Baseline
Emissions
(tons/year)
3,745,790
1,397,425
302,938
840,167
1,167,995
30,771,607
294,764
463,579
MACT
Floor
Option
Emission
Known
Affected
Units
82,542
-
_
-
266,491
-
_
-
Unknown
Affected
Units
30,394
298,109
Total
Emission
Reductions
for MACT
floor option
112,936
564,600
Option 1A Emission
Reductions
Known Unknown Total
Affected Affected
Units Units
95,361 41,372 136,733
313,947 255,282 569,229
8-2
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PM25
Point
Area
Motor
Vehicle
Nonroad
576,022
6,675,777
230,684
410,334
75,095
-
-
-
84,125
159,220
94,565 76,894 171,459
a Reductions are Baseline Emissions - Control Scenario Emissions. All emissions estimates are in tons.
b The totals reflect emissions for the 48 contiguous States, excluding Alaska and Hawaii.
°The totals do not reflect new source emissions and emission reductions. These emission reductions were not
considered in the air quality modeling since they were far smaller that those for existing units (484 tons for PM10
from new units, versus 564,600 tons from existing units). The differences between such emission reductions for
PM2 5 are identical, since PM? 5 emissions are derived from PM10 emissions. Also, the differences between SO2
emission reductions for existing and new units are just as great.
Table 8-2. HAP Emission Reductions for the MACT floor option, 2005
Existing Sources Only
Pollutant
HC1
Pb
Hg
Non-mercury metals3
Selected inorganics'3
Total HAP reductions
Emission Reductions (tons/year)
MACT floor
42,100
105
1.7
1,080
18,000
58,350
aNon-mercury metals include: arsenic, beryllium, cadmium, chromium, manganese, and nickel.
bSelected inorganics include: chlorine, hydrofluoric acid, and phosphorus.
8.4 Air Quality Impacts
This section summarizes the methods for and results of estimating air quality for the baseline
and control scenarios. Based on the emissions inventories described above, ambient particulate
matter (PM10 and PM25) concentrations are projected from the S-R Matrix developed from the
Climatological Regional Dispersion Model (CRDM). In Section 8.3.1, we provide brief background
on the S-R Matrix model. In Section 8.3.2, we estimate PM air quality, and in Section 8.3.3, we
estimate visibility degradation. Visibility degradation (i.e., regional haze), is developed using
empirical estimates of light extinction coefficients and efficiencies in combination with modeled
reductions in pollutant concentrations.
-------
8.4.1. PM Air Quality Modeling
EPA used the emissions inputs described above with a national-scale source-receptor (S-R)
Matrix to evaluate the effects of the milestone reductions on ambient concentrations of both PM10 and
PM25. Ambient concentrations of PM are composed of directly emitted particles and of secondary
aerosols of sulfate, nitrate, ammonium, and organics.
The S-R Matrix was developed from multiple simulations of the CRDM using meteorological
data for 1990 coupled with emissions data from version 2.0 of the 1990 National Particulate
Inventory (NPI). Relative to more sophisticated and resource-intensive three-dimensional modeling
approaches, the CRDM and its associated S-R Matrix do not fully account for all the complex
chemical interactions that take place in the atmosphere in the secondary formation of PM. Instead it
relies on more simplistic species dispersion-transport mechanisms supplemented with chemical
conversion at the receptor location.
The S-R Matrix consists of fixed-coefficients that reflect the relationship between annual
average PM concentration values at a single receptor in each county (i.e., a hypothetical monitor sited
at the county population centroid) and the contribution by PM species to this concentration from each
emission source (E.H. Pechan, 1996). The modeled receptors include all U.S. county centroids as
well as receptors in 10 Canadian provinces and 29 Mexican cities/states. The methodology used here
for estimating PM air quality concentrations is detailed in Pechan-Avanti (2000) and is similar to the
method used in the July 1997 PM and Ozone NAAQS RIA (U.S. EPA, 1997e) and the RIA for the
final Regional Haze Rule (U.S. EPA, 1999a), and the Tier 2/Gasoline Sulfur Rule (US EPA, 1999c).
8.4.2 PM Air Quality Results
This section presents the projected reductions in particulate matter concentrations resulting
from reductions in SO, and PM10, with PM25 emissions being derived from the PM10 emissions using
the PM Calculator tool for the final rule (MACT floor). The results for the above-the-floor option,
Option 1A, are presented in Appendix C of the RIA.
8.4.2.1 MACT Floor Option
Table 8-3 provides a summary of the predicted ambient PM10 and PM25 concentrations from
the S-R matrix for the 2005 baseline and changes associated with the rule. The results indicate that
the predicted change in PM concentrations is composed almost entirely of reductions in fine
particulates (PM25) with little or no reduction in coarse particles (PM10less PM25). Therefore, the
observed changes in PM10are composed primarily of changes in PM25. In addition to the standard
frequency statistics (e.g., minimum, maximum, average, median), Table 8-3 provides the population-
weighted average which belter reflects the baseline levels and predicted changes for more populated
areas of the nation. This measure, therefore, will better reflect the potential benefits of these
predicted changes through exposure changes to these populations. As shown, the average annual
mean concentrations of PM25 across all U.S. grid-cells declines by roughly 0.8 percent, or 0.09 (ig/m3.
The population-weighted average annual mean PM25 concentration declined by 0.7 percent, or 0.10
14 The PM Calculator Tool can be found on the Internet at www.epa.gov/chief/software/pmcalc/index.html.
8-4
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(ig/m3, which is roughly similar in absolute terms to the spatial average. This indicates the rule
generates roughly equivalent absolute air quality improvements in less populated, rural areas as in
more populated, urban areas.
Table 8-3.
Summary of 2005 Base Case PM Air Quality and Changes Due to MACT Floor Option:
Industrial Boiler/Process Heater Source Categories
Statistic
2005 Baseline
Change"
Percent Change
PMIO
Minimum Annual Mean (ug/m3) b
Maximum Annual Mean (ug/m3) b
Average Annual Mean (ug/m3)
Median Annual Mean (ug/m3)
Population- Weighted Average Annual Mean (ug/m3) c
6.09
69.30
22.68
21.84
28.79
-0.07
-0.03
-0.32
-0.36
-0.33
-1.2%
-0.1%
-1.4%
-1.6%
-1.1%
PM25
Minimum Annual Mean (ug/m3) b
Maximum Annual Mean (ug/m3) b
Average Annual Mean (ug/m3)
Median Annual Mean (ug/m3)
Population- Weighted Average Annual Mean (ug/m3) c
0.74
30.35
11.15
11.11
13.50
-0.01
-0.71
-0.09
-0.11
-0.10
0.0%
-2.3%
-0.8%
-1.1%
-0.7%
* The change is defined as the control case value minus the baseline value.
b The baseline minimum (maximum) is the value for the populated county with the lowest (highest) annual average. The
change relative to the baseline is the observed change for the populated county with the lowest (highest) annual average in
the baseline.
c Calculated by summing the product of the projected 2005 county population and the estimated 2005 PM concentration for
that county, and then dividing by the total population in the 48 contiguous States.
Table 8-4 provides information on the 2005 populations that will experience improved PM
air quality. There are significant populations that live in areas with meaningful reductions in annual
mean PM25 concentrations resulting from the rule. As shown, just over 2 percent of the 2005 U.S.
population are predicted to experience reductions of greater than 0.5 (ig/m . Furthermore, almost 8
percent of the 2005 U.S. population will benefit from reductions in annual mean PM25 concentrations
of greater than 0.2 (ig/m3 and slightly over 28 percent will live in areas with reductions of greater than
0.1 (ig/m3. This information indicates how widespread the improvements in PM air quality are
expected to be and the large populations that will benefit from these improvements.
Table 8-4.
Distribution of PM2.5 Air Quality Improvements Over 2005 Population Due to MACT Floor
Option: Industrial Boiler/Process Heater Source Categories
$-5
-------
Change in Annual Mean PM2 s Concentrations
(ftg/m3)
Q> A PM25 Cone < 0.05
0.05>APM25Conc < 0.1
0.1 >APM25Conc < 0.25
0.25>APM25Conc < 0.5
0.5>APM25Conc < 1.0
l.O>APM25Conc <2.0
APM25Conc>2.0
2005 Population
Number (millions) Percent (°/o)
105.0
56.3
57.2
17.1
4.5
1.3
0.2
37.1%
19.9%
20.2%
6.1%
1.6%
0.5%
0.1%
1 The change is defined as the control case value minus the baseline value.
Table 8-5 provides additional insights on the changes in PM air quality resulting from the
final rule. The information presented previously in Table 8-3 illustrated the absolute and relative
changes for different points along the distribution of baseline 2005 PM concentration levels, e.g., the
change reflects the lowering of the minimum predicted baseline concentration rather than the
minimum predicted change for 2005. The latter is the focus of Table 8-5 as it presents the
distribution of predicted changes in both absolute terms (i.e., (ig/m3) and relative terms (i.e., percent)
across individual grid-cells. Therefore, it provides more information on the range of predicted
changes that as shown, the absolute reduction in annual mean PM10 concentration ranged from a low
of 0.00 (ig/m3 to a high of 16.89 (ig/m3, while the relative (or percent) reduction ranged from a low of
0.0 percent to a high of 50.5 percent. Alternatively, for mean PM2 5, the absolute reduction ranged
from 0.00 to 4.65 (ig/m3, while the relative reduction ranged from 0.0 to 29.4 percent.
Table 8-5.
Summary of Absolute and Relative Changes in PM Air Quality Due to MACT Floor Option:
Industrial Boiler/Process Heater Source Categories
Statistic
PM10 Annual Mean
PM2 s Annual Mean
Absolute Change from 2005 Baseline (jug/m3)"
Minimum
Maximum
Average
Median
0.00
-16.89
-0.32
-0.16
0.00
-4.65
-0.09
-0.05
8-6
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Population- Weighted Average c
-0.33
-0.10
Relative Change from 2005 Baseline (%)b
Minimum
Maximum
Average
Median
Population- Weighted Average c
0.00%
-50.52%
-1.32%
-0.78%
-1.26%
0.00%
-29.37%
-0.70%
-0.50%
-0.71%
* The absolute change is defined as the control case value minus the baseline value for each county.
b The relative change is defined as the absolute change divided by the baseline value, or the percentage change, for each
county. The information reported in this section does not necessarily reflect the same county as is portrayed in the absolute
change section.
c Calculated by summing the product of the projected 2005 county population and the estimated 2005 county PM
absolute/relative measure of change, and then dividing by the total population in the 48 contiguous states.
For this standard, the MACT floor was chosen as the final alternative. For more information on the
choice of this option as the alternative, please refer to Chapter 1 of this RIA and the preamble.
It should be noted that air quality modeling runs using the S-R matrix are available for cases
in which only PM emission reductions occur and only SO2 reductions occur. These runs are
necessary as inputs to the benefits transfer method that estimates monetized benefits for emissions
from sources that are not linked to a specific control device. Results from these pollutant-specific
runs are presented in the technical support document (Pechan, 2001). The benefits transfer method is
explained in Chapter 10, and results from the use of that method are also shown in that chapter.
8.4.3. Visibility Degradation Estimates
Visibility degradation is often directly proportional to decreases in light transmittal in the
atmosphere. Scattering and absorption by both gases and particles decrease light transmittance. To
quantify changes in visibility, our analysis computes a light-extinction coefficient, based on the work
of Sisler (1996), which shows the total fraction of light that is decreased per unit distance. This
coefficient accounts for the scattering and absorption of light by both particles and gases, and
accounts for the higher extinction efficiency of fine particles compared to coarse particles. Fine
particles with significant light-extinction efficiencies include sulfates, nitrates, organic carbon,
elemental carbon (soot), and soil (Sisler, 1996).
Based upon the light-extinction coefficient, we also calculated a unitless visibility index,
called a "deciview," which is used in the valuation of visibility. The deciview metric provides a
linear scale for perceived visual changes over the entire range of conditions, from clear to hazy.
Under many scenic conditions, the average person can generally perceive a change of one deciview.
The higher the deciview value, the worse the visibility. Thus, an improvement in visibility is a
decrease in deciview value.
Table 8-6 provides the distribution of visibility improvements across the 2005 U.S.
population resulting from the industrial boilers and process heaters rule. The majority of the 2005
8-7
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U.S. population live in areas with predicted improvement in annual average visibility of between 0.4
to 0.6 deciviews resulting from the rule. As shown, almost 4 percent of the 2005 U.S. population are
predicted to experience improved annual average visibility of greater than 0.25 deciviews.
Furthermore, roughly 10 percent of the 2005 U.S. population will benefit from reductions in annual
average visibility of greater than 0.1 deciviews. The information provided in Table 8-6 indicates how
widespread the improvements in visibility are expected to be and the share of populations that will
benefit from these improvements.
Because the visibility benefits analysis distinguishes between general regional visibility
degradation and that particular to Federally-designated Class I areas (i.e., national parks, forests,
recreation areas, wilderness areas, etc.), we separated estimates of visibility degradation into
"residential" and "recreational" categories. The estimates of visibility degradation for the
"recreational" category apply to Federally-designated Class I areas, while estimates for the
"residential" category apply to non-Class I areas. Deciview estimates are estimated using outputs
from the S-R matrix for the 2005 baseline and the MACT floor, which are the same scenarios for
which changes in PM10 and PM25 concentrations are estimated and shown earlier in this chapter.
Deciview estimates for Option 1A are presented in Appendix C of this RIA
Table 8-6.
Distribution of Populations Experiencing Visibility Improvements in 2005 Due to MACT Floor
Option: Industrial Boiler/Process Heater Source Categories
Improvements in Visibility a
(annual average deciviews)
A Deciview = 0
Q> ADeciview < 0.05
0.05 > A Deciview < 0.1
0.1 > A Deciview < 0.15
0. 15 > A Deciview < 0.25
0.25 > A Deciview < 0.5
A Deciview > 0.5
2005 Population
Number (millions) Percent (%)
46.0
168.5
41.1
11.5
5.9
3.7
1.1
16.3%
59.5%
14.5%
4.1%
2.1%
3.1%
0.4%
1 The change is defined as the MACT Floor control case deciview level minus the baseline deciview level.
8.4.4 Residential Visibility Improvements
Air quality modeling results predict that the rule will create improvements in visibility
through the country. In Table 8-7, we summarize residential visibility improvements across the
Eastern and Western U.S. in 2005. The baseline annual average visibility for all U.S. counties is 21.2
deciviews. The mean improvement across all U.S. counties is 0.05 deciviews, or almost 2 percent. In
urban areas (i.e., areas with a population of 250,000 or more), the mean improvement in annual
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visibility was 0.06 deciviews. In rural areas (i.e. all non-urban areas), the mean improvement in
visibility was 0.04 deciviews in 2005.
On average, the Eastern U.S. experienced slightly larger absolute but smaller relative
improvements in visibility than the Western U.S. from the industrial boilers and process heaters
emission reductions. In Eastern U.S., the mean improvement was 0.05 deciviews from an average
baseline of 22.00 deciviews. Western counties experienced a mean improvement of 0.01 deciviews
from an average baseline of 17.82 deciviews projected in 2005. Overall, the data suggest that the rule
has the potential to provide some improvements in visibility across the U.S. in 2005.
Table 8-7.
Summary of 2005 Baseline Visibility and Changes by Region for the MACT Floor Option:
Residential
(Annual Average Deciviews)
Regions*
Eastern U.S.
Urban
Rural
Western U.S.
Urban
Rural
National, all counties
Urban
Rural
2005 Baseline
22.00
22.95
21.62
17.82
19.19
17.55
21.19
22.49
20.72
Change11
-0.05
-0.06
-0.05
-0.01
-0.01
-0.01
-0.05
-0.06
-0.04
Percent Change
-0.2%
-0.3%
-0.2%
-0.1%
-0.1%
-0.1%
-0.2%
-0.3%
-0.2%
* Eastern and Western regions are separated by 100 degrees West longitude. Background visibility conditions differ by
region.
b An improvement in visibility is a decrease in deciview value. The change is defined as the MACT Floor control case
deciview level minus the baseline deciview level.
8.4.5. Recreational Visibility Improvements
In Table 8-8, we summarize recreational visibility improvements by region in 2005 in Federal
Class I areas. These recreational visibility regions are shown in Figure 8-1. As shown, the national
improvement in visibility for these areas is 0.1 percent, or 0.02 deciviews. Predicted relative
visibility improvements are the largest in the Eastern U.S. as shown for the Southeast (0.4%), and the
Northeast/Midwest (2.3%). The Southwest and California regions are predicted to have the smallest
relative visibility improvement at 0.0 percent, or 0.00 deciview decline from the baseline.
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Table 8-8.
Summary of 2005 Baseline Visibility and Changes by Region for the MACT Floor Option:
Recreational
(Annual Average Deciviews)
National Average (unweigmbQ
1 Regions are pictured in Figure 8-1 and
TSD, U.S. EPA, 2001.
/ Duty Vehicle/Diesel Fuel
.Floor control case
Note: Study regions were represented in the Chestnut and Rowe (1990a, 1990b) studies used
in evaluating the benefits of visibility improvements, while transfer regions used extrapolated
study results. These are referred to in the Heavy Duty Vehicle/Diesel Fuel Benefits TSD
(U.S. EPA, 2000).
Figure 8-1. Recreational Visibility Regions for Continental U.S.
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References
Eastern Research Group. Memorandum to Jim Eddinger, U.S. Environmental Protection Agency,
Office of Air Quality Planning and Standards. "Development of Average Emission Factors and
Baseline Emissions Estimates for the Industrial, Commercial, and Institutional Boiler and Process
Heater NESHAP." Draft Memorandum. May 23, 2002.
E.H. Pechan & Associates, Inc. "Emissions and Air Quality Impacts of NESHAP for Industrial,
Commercial, and Institutional Boilers and Process Heaters." Revised Final Report. March, 2001.
Sisler, J. "Spatial and Seasonal Patterns and Long Term Variability of the Composition of Haze in
the United States (An Analysis of Data from the IMPROVE Network)," report prepared for the
Cooperative Institute for Research in the Atmosphere, Colorado State University, 1996.
U.S. Environmental Protection Agency, Regulatory Impact Analysis: Heavy-Duty Engine and Vehicle
Standards and Highway Diesel Fuel Sulfur Control Requirements. Prepared by: Office of Air and
Radiation. Available at http://www.epa.gov/otaq/diesel.htm. December, 2000.
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CHAPTER 9
QUALITATIVE ASSESSMENT OF BENEFITS
OF EMISSION REDUCTIONS
The emission reductions achieved by this environmental regulation will provide benefits to
society by improving environmental quality. In this chapter, and the following chapter, information
is provided on the types and levels of social benefits anticipated from the Industrial and Commercial
Boilers and Process Heaters NESHAP. This chapter discusses the health and welfare effects
associated with the HAPs and other pollutants emitted by affected boilers and process heaters. The
following chapter places a monetary value on a portion of the benefits that are described here.
In general, the reduction of HAP emissions, including mercury, resulting from the regulation
will reduce human and environmental exposure to these pollutants and thus, reduce potential adverse
health and welfare effects. This chapter provides a general discussion of the various components of
total benefits that may be gained from a reduction in HAPs and mercury through this NESHAP. The
rule will also achieve reductions of particulate matter (PM), both coarse (PM10) and fine (PM25)
particle fractions, and sulfur dioxide (SO2), which results in additional health and welfare benefits
above those achieved by the HAP reductions. HAP benefits are presented separately from the
benefits associated with other pollutant reductions.
9.1 Identification of Potential Benefit Categories
The benefit categories associated with the emission reductions predicted for this regulation
can be broadly categorized as those benefits which are attributable to reduced exposure to HAPs, and
those attributable to reduced exposure to other pollutants. Several of the HAPs associated with this
regulation have been classified as known or probable human carcinogens. As a result, one of the
benefits of the proposed regulation is a reduction in the risk of cancer. Other benefit categories
include: reduced incidence of neurological effects and irritations of the lungs and skin, reduced
mortality and other morbidity effects associated with PM and SO2 (as it transforms into PM). In
addition to health impacts occurring as a result of reductions in HAPs and other pollutant emissions,
there are welfare impacts which can also be identified. In general, welfare impacts include effects on
crops and other plant life, materials damage, soiling, visibility impairment, and acidification of water
bodies. Each category is discussed separately in the following section.
9.2 Qualitative Description of Air Related Benefits
The health and welfare benefits of HAPs, including mercury, PM, and SO2 reductions are
summarized separately in the discussions below.
9.2.1 Benefits of Reducing HAP Emissions
According to baseline emission estimates, the source categories affected by this currently
emits approximately 102,927 tons per year of HAPs at existing sources including about 11 tons of
mercury and it is estimated that by the year 2005, new boilers and process heaters will emit 1,548
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tons per year of HAPs and 0.4 tons of mercury. This totals 104,474 tons of HAPs and 11.4 tons of
mercury annually at all boiler and process heater sources. The regulation will reduce approximately
58,575 tons of emissions of HAPs and 1.9 tons of mercury at new and existing sources by 2005. For
more information on these HAP emissions and emission reductions, please refer to Chapter 8 of this
RIA and the docket for this rule.
Human exposure to these HAPs may occur directly through inhalation or indirectly through
ingestion of food or water contaminated by HAPs or through exposure to the skin. HAPs may also
enter terrestrial and aquatic ecosystems through atmospheric deposition. HAPs can be deposited on
vegetation and soil through wet or dry deposition. HAPs may also enter the aquatic environment
from the atmosphere via gas exchange between surface water and the ambient air, wet or dry
deposition of particulate HAPs and particles to which HAPs adsorb, and wet or dry deposition to
watersheds with subsequent leaching or runoff to bodies of water (EPA,1992a). This analysis is
focused only on the air quality benefits of HAP reduction.
9.2.1.1 Health Benefits of HAP and Mercury Reductions.
The HAP emission reductions achieved by this rule are expected to reduce exposure to
ambient concentrations of arsenic, cadmium, chromium, hydrogen chloride, hydrogen flouride, lead,
manganese, mercury, and nickel, which will reduce a variety of adverse health effects considering
both cancer and noncancer endpoints. Information for each pollutant to be reduced by this rule is
obtained from the Integrated Risk Information System (IRIS), an EPA system for disseminating
information about the effects of several chemicals emitted to the air and /or water, and classifying
these chemicals by cancer risk (IRIS, 2000). These adverse health effects include chronic health
disorders (e.g., irritation of the lung, skin, and mucus membranes and effects on the blood, digestive
tract, kidneys, and central nervous system), and acute health disorders (e.g., lung irritation and
congestion, alimentary effects such as nausea and vomiting, and effects on the central nervous
system). EPA has classified several of these HAPs as known or probable human carcinogens.
The EPA does not have the type of current detailed data on each of the facilities covered by
the emissions standards for this source category, and the people living around the facilities, that
would be necessary to conduct an analysis to determine the actual population exposures to the HAP
emitted from these facilities and potential for resultant health effects. Therefore, the EPA does not
know the extent to which the adverse health effects described above occur in the populations
surrounding these facilities. However, to the extent the adverse effects do occur, the rule will reduce
emissions and subsequent exposures. Health effects associated with the significant HAPs emitted
from boilers and process heaters are discussed below.
Arsenic
Acute (short term) high-level inhalation exposure to arsenic dust or fumes has resulted in
gastrointestinal effects (nausea, diarrhea, abdominal pain), and central and peripheral nervous system
disorders. Chronic (long-term) inhalation exposure to inorganic arsenic in humans is associated with
irritation of the skin and mucous membranes. Human data suggest a relationship between inhalation
exposure of women working at or living near metal smelters and an increased risk of reproductive
effects, such as spontaneous abortions. Inorganic arsenic exposure in humans by the inhalation route
has been shown to be strongly associated with lung cancer, while ingestion of inorganic arsenic in
humans has been linked to a form of skin cancer and also to bladder, liver, and lung cancer. EPA has
classified inorganic arsenic as a Group A, known human carcinogen.
Cadmium
The acute (short-term) effects of cadmium inhalation in humans consist mainly of effects on
the lung, such as pulmonary irritation. Chronic (long-term) inhalation or oral exposure to cadmium
leads to a build-up of cadmium in the kidneys that can cause kidney disease. Cadmium has been
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shown to be a developmental toxicant in animals, resulting in fetal malformations and other effects,
but no conclusive evidence exists in humans. An association between cadmium exposure and an
increased risk of lung cancer has been reported from human studies, but these studies are inconclusive
due to confounding factors. Animal studies have demonstrated an increase in lung cancer from long-
term inhalation exposure to cadmium. EPA has classified cadmium as a Group Bl, probable
carcinogen.
Chromium
Chromium may be emitted in two forms, trivalent chromium (chromium III) or hexavalent
chromium (chromium VI). The respiratory tract is the major target organ for chromium VI toxicity,
for acute (short-term) and chronic (long-term) inhalation exposures. Shortness of breath, coughing,
and wheezing have been reported from acute exposure to chromium VI, while perforations and
ulcerations of the septum, bronchitis, decreased pulmonary function, pneumonia, and other
respiratory effects have been noted from chronic exposure. Limited human studies suggest that
chromium VI inhalation exposure may be associated with complications during pregnancy and
childbirth, while animal studies have not reported reproductive effects from inhalation exposure to
chromium VI. Human and animal studies have clearly established that inhaled chromium VI is a
carcinogen, resulting in an increased risk of lung cancer. EPA has classified chromium VI as a Group
A, human carcinogen.
Chromium III is less toxic than chromium VI. The respiratory tract is also the major target
organ for chromium III toxicity, similar to chromium VI. Chromium III is an essential element in
humans, with a daily intake of 50 to 200 micrograms per day recommended for an adult. The body
can detoxify some amount of chromium VI to chromium III. EPA has not classified chromium III
with respect to carcinogenicity. For this rule, EPA has not determined the species of chromium
emitted at industrial boilers and process heaters.
Hydrogen chloride
Hydrogen chloride, also called hydrochloric acid, is corrosive to the eyes, skin, and mucous
membranes. Acute (short-term) inhalation exposure may cause eye, nose, and respiratory tract
irritation and inflammation and pulmonary edema in humans. Chronic (long-term) occupational
exposure to hydrochloric acid has been reported to cause gastritis, bronchitis, and dermatitis in
workers. Prolonged exposure to low concentrations may also cause dental discoloration and erosion.
No information is available on the reproductive or developmental effects of hydrochloric acid in
humans. In rats exposed to hydrochloric acid by inhalation, altered estrus cycles have been reported
in females and increased fetal mortality and decreased fetal weight have been reported in offspring.
EPA has not classified hydrochloric acid for carcinogenicity.
Hydrogen fluoride
Acute (short term) inhalation exposure to gaseous hydrogen fluoride can cause severe
respiratory damage in humans, including severe irritation and pulmonary edema.
Lead
Lead is a very toxic element, causing a variety of effects at low dose levels. Brain damage,
kidney damage, and gastrointestinal distress may occur from acute (short-term) exposure to high
levels of lead in humans. Chronic (long-term) exposure to lead in humans results in effects on the
blood, central nervous system (CNS), blood pressure, and kidneys. Children are particularly sensitive
to the chronic effects of lead, with slowed cognitive development, reduced growth and other effects
reported. Reproductive effects, such as decreased sperm count in men and spontaneous abortions in
women, have been associated with lead exposure. The developing fetus is at particular risk from
maternal lead exposure, with low birth weight and slowed postnatal neurobehavioral development
noted. Human studies are inconclusive regarding lead exposure and cancer, while animal studies
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have reported an increase in kidney cancer from lead exposure by the oral route. EPA has classified
lead as a Group B2 pollutant, probable human carcinogen15.
Manganese
Health effects in humans have been associated with both deficiencies and excess intakes of
manganese. Chronic (long-term) exposure to low levels of manganese in the diet is considered to be
nutritionally essential in humans, with a recommended daily allowance of 2 to 5 milligrams per day
(mg/d). Chronic exposure to high levels of manganese by inhalation in humans results primarily in
central nervous system (CNS) effects. Visual reaction time, hand steadiness, and eye-hand
coordination were affected in chronically-exposed workers. Manganism, characterized by feelings of
weakness and lethargy, tremors, a mask-like face, and psychological disturbances, may result from
chronic exposure to higher levels. Impotence and loss of libido have been noted in male workers
afflicted with manganism attributed to inhalation exposures. EPA has classified manganese in Group
D, not classifiable as to carcinogenicity in humans.
Nickel
Nickel is an essential element in some animal species, and it has been suggested it may be
essential for human nutrition. Nickel dermatitis, consisting of itching of the fingers, hand and
forearms, is the most common effect in humans from chronic (long-term) skin contact with nickel.
Respiratory effects have also been reported in humans from inhalation exposure to nickel. No
information is available regarding the reproductive or developmental effects of nickel in humans, but
animal studies have reported such effects. Human and animal studies have reported an increased risk
of lung and nasal cancers from exposure to nickel refinery dusts and nickel subsulfide. Animal
studies of soluble nickel compounds (i.e., nickel carbonyl) have reported lung tumors. EPA has
classified nickel refinery subsulfide as Group A, human carcinogens and nickel carbonyl as a Group
B2, probable human carcinogen.
Mercury
Mercury emitted from industrial boiles and other natural and man-made sources is carried by
winds through the air and eventually is deposited to water and land. Recent estimates (which are
highly uncertain) of annual total global mercury emissions from all sources (natural and
anthropogenic) are about 5,000 to 5,500 tons per year (tpy). Of this total, about 1,000 tpy are
estimated to be natural emissions and about 2,000 tpy are estimated to be contributions through the
natural global cycle of re-emissions of mercury associated with past anthropogenic activity. Current
anthropogenic emissions account for the remaining 2,000 tpy. Point sources such as fuel combustion;
waste incineration; industrial processes; and metal ore roasting, refining, and processing are the
largest point source categories on a world-wide basis. Given the global estimates noted above, U.S.
anthropogenic mercury emissions are estimated to account for roughly 3 percent of the global total,
and U.S. utilities are estimated to account for about 1 percent of total global emissions. Mercury
exists in three forms: elemental mercury, inorganic mercury compounds (primarily mercuric
chloride), and organic mercury compounds (primarily methylmercury). Mercury is usually released
in an elemental form and later converted into methylmercury by bacteria. Methylmercury is more
toxic to humans than other forms of mercury, in part because it is more easily absorbed in the body
(EPA, 1996).
If the deposition is directly to a water body, then the processes of aqueous fate, transport, and
transformation begin. If deposition is to land, then terrestrial fate and transport processes occur first
and then aqueous fate and transport processes occur once the mercury has cycled into a water body.
In both cases, mercury may be returned to the atmosphere through resuspension. In water, mercury is
transformed to methylmercury through biological processes and for exposures affected by this
rulemaking, methylmercury is considered to be the form of greatest concern. Once mercury has been
15 In addition to the information provided in IRIS, another detailed discussion of the benefits of
reducing lead emissions can be found in the Final Report to Congress on Benefits and Costs of the
Clean Air Act, 1970 to 1990 (EPA 410-R-97-002).
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transformed into methylmercury, it can be ingested by the lower trophic level organisms where it can
bioaccumulate in fish tissue (i.e., concentrations of mercury remain in the fish's system for a long
period of time and accumulates in the fish tissue as predatory fish consume other species in the food
chain). Fish and wildlife at the top of the food chain can, therefore, have mercury concentrations that
are higher than the lower species, and they can have concentrations of mercury that are higher than
the concentration found in the water body itself. In addition, when humans consume fish
contaminated with methylmercury, the ingested methymercury is almost completely absorbed into the
blood and distributed to all tissues (including the brain); it also readily passes through the placenta to
the fetus and fetal brain (EPA, 200la).
Based on the findings of the National Research Council, EPA has concluded that benefits of
Hg reductions would be most apparent at the human consumption stage, as consumption offish is the
major source of exposure to methylmercury. At lower levels, documented Hg exposure effects may
include more subtle, yet potentially important, neurodevelopmental effects. Some
subpopulations in the U.S., such as: Native Americans, Southeast Asian Americans, and lower
income subsistence fishers, may rely on fish as a primary source of nutrition and/or for cultural
practices. Therefore, they consume larger amounts offish than the general population and may be at
a greater risk to the adverse health effects from Hg due to increased exposure. In pregnant women,
methylmercury can be passed on to the developing fetus, and at sufficient exposure may lead to a
number of neurological disorders in children. Thus, children who are exposed to low concentrations
of methylmercury prenatally may be at increased risk of poor performance on neurobehavioral tests,
such as those measuring attention, fine motor function, language skills, visual-spatial abilities (like
drawing), and verbal memory. The effects from prenatal exposure can occur even at doses that do not
result in effects in the mother. Mercury may also affect young children who consume fish
contaminated with Hg. Consumption by children may lead to neurological disorders and
developmental problems, which may lead to later economic consequences.
In response to potential risks of mercury-contaminated fish consumption, EPA and FDA have
issued fish consumption advisories which provide recommended limits on consumption of certain fish
species for different populations. EPA and FDA are currently developing a joint advisory that has
been released in draft form. This newest draft FDA-EPA fish advisory recommends that women and
young children reduce the risks of Hg consumption in their diet by moderating their fish
consumption, diversifying the types offish they consume, and by checking any local advisories that
may exist for local rivers and streams. This collaborative FDA-EPA effort will greatly assist in
educating the most susceptible populations. Additionally, the reductions of Hg from this regulation
may potentially lead to fewer fish consumption advisories (both from federal or state agencies), which
will benefit the fishing community. Currently 44 states have issued fish consumption advisories for
non-commercial fish for some or all of their waters due to contamination of mercury. The scope of
FCA issued by states varies considerably, with some warnings applying to all water bodies in a state
and others applying only to individual lakes and streams. Note that the absence of a state advisory
does not necessarily indicate that there is no risk of exposure to unsafe levels of mercury in
recreationally caught fish. Likewise, the presence of a state advisory does not indicate that there is a
risk of exposure to unsafe levels of mercury in recreationally caught fish, unless people consume
these fish at levels greater than those recommended by the fish advisory.
Reductions in methylmercury concentrations in fish should reduce exposure, subsequently
reducing the risks of mercury-related health effects in the general population, to children, and to
certain subpopulations. Fish consumption advisories (FCA) issued by the States may also help to
reduce exposures to potential harmful levels of methylmercury in fish (although some studies have
shown limited knowledge of and compliance with advisories by at risk populations (May and Burger,
1996; Burger, 2000)). To the extent that reductions in mercury emissions reduces the probability that
a water body will have a FCA issued, there are a number of benefits that will result from fewer
advisories, including increased fish consumption, increased fishing choices for recreational fishers,
increased producer and consumer surplus for the commercial fish market, and increased welfare for
subsistence fishing populations.
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There is a great deal of variability among individuals in fish consumption rates; however,
critical elements in estimating methylmercury exposure and risk from fish consumption include the
species offish consumed, the concentrations of methylmercury in the fish, the quantity offish
consumed, and how frequently the fish is consumed. The typical U.S. consumer eating a wide variety
offish from restaurants and grocery stores is not in danger of consuming harmful levels of
methylmercury from fish and is not advised to limit fish consumption. Those who regularly and
frequently consume large amounts offish, either marine or freshwater, are more exposed. Because
the developing fetus may be the most sensitive to the effects from methylmercury, women of child-
bearing age are regarded as the population of greatest interest. The EPA, Food and Drug
Administration, and many States have issued fish consumption advisories to inform this population of
protective consumption levels.
The EPA's 1997 Mercury Study RTC supports a plausible link between anthropogenic
releases of Hg from industrial and combustion sources in the U.S. and methylmercury in fish.
However, these fish methylmercury concentrations also result from existing background
concentrations of Hg (which may consist of Hg from natural sources, as well as Hg which has been
re-emitted from the oceans or soils) and deposition from the global reservoir (which includes Hg
emitted by other countries). Given the current scientific understanding of the environmental fate and
transport of this element, it is not possible to quantify how much of the methylmercury in locally-
caught fish consumed by the U.S. population is contributed by U.S. emissions relative to other
sources of Hg (such as natural sources and re-emissions from the global pool). As a result, the
relationship between Hg emission reductions from Utility Units and methylmercury concentrations in
fish cannot be calculated in a quantitative manner with confidence. In addition, there is uncertainty
regarding over what time period these changes would occur. This is an area of ongoing study.
Given the present understanding of the Hg cycle, the flux of Hg from the atmosphere to land
or water at one location is comprised of contributions from: the natural global cycle; the cycle
perturbed by human activities; regional sources; and local sources. Recent advances allow for a
general understanding of the global Hg cycle and the impact of the anthropogenic sources. It is more
difficult to make accurate generalizations of the fluxes on a regional or local scale due to the site-
specific nature of emission and deposition processes. Similarly, it is difficult to quantify how the
water deposition of Hg leads to an increase in fish tissue levels. This will vary based on the specific
characteristics of the individual lake, stream, or ocean.
9.2.1.2 Welfare Benefits of HAP Reductions.
The welfare effects of exposure to HAPs have received less attention from analysts than the
health effects. However, this situation is changing, especially with respect to the effects of toxic
substances on ecosystems. Over the past ten years, ecotoxicologists have started to build models of
ecological systems which focus on interrelationships in function, the dynamics of stress, and the
adaptive potential for recovery. Chronic sub-lethal exposures may affect the normal functioning of
individual species in ways that make it less than competitive and therefore more susceptible to a
variety of factors including disease, insect attack, and decreases in habitat quality (EPA, 1991). All
of these factors may contribute to an overall change in the structure (i.e., composition) and function
of the ecosystem.
The adverse, non-human biological effects of HAP emissions include ecosystem and
recreational and commercial fishery impacts. Atmospheric deposition of HAPs directly to land may
affect terrestrial ecosystems. Atmospheric deposition of HAPs also contributes to adverse aquatic
ecosystem effects. This not only has adverse implications for individual wildlife species and
ecosystems as a whole, but also the humans who may ingest contaminated fish and waterfowl.
A number of wildlife species are a risk from consuming mercury-contaminated fish (Duvall
and Baron, 2000). Mercury can affect reproductive success in birds and mammals which may affect
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population levels (Peakall, 1996). This can affect human welfare in several ways. If changes in
populations reduces biological diversity in an area this may impact the total ecological system. To
the extent that people value biological diversity (existence value), there may be benefits to preventing
this loss. Also, hunters may experience direct losses if populations of game birds or animals are
reduced. Hunters may also experience welfare losses if game birds or animals are not fit for
consumption. Hunters may also be affected if predator populations are reduced from reduced
availability of prey species. In addition to hunting, other non-consumptive uses of wildlife including
bird or wildlife viewing may be impacted by reductions in bird and animal populations. In one
special case, that of the endangered Florida panther, there may be special value placed on reducing
the risks of species loss.
In general, HAP emission reductions achieved through the Industrial Boilers and Process
Heaters NESHAP should reduce the associated adverse environmental impacts.
9.2.2 Benefits of Reducing Other Pollutants Due to HAP Controls
As is mentioned above, controls that will be required on boilers and process heaters to reduce
HAPs will also reduce emissions of other pollutants, namely: PM10, PM25, and SO2. According to
baseline emission estimates, the source categories affected by this proposal currently emit
approximately 766,000 tons per year of PM10, 217,000 tons per year of PM25, and 3,405,000 tons per
year of SO2 at existing sources. It is estimated that by the year 2005, new boilers and process
heaters will emit 3,600 tons per year of PM10, 1,000 tons of PM25, and 38,200 tons of SO2 This
totals 769,600 tons of PM10, 218,000 tons of PM25, and 3,443,200 tons of SO2 annually at all boiler
and process heater sources. The regulation will reduce approximately 562,500 tons of PM10
emissions, 159,000 tons of PM25, and 113,000 tons of SO2 at new and existing sources by 2005. For
more information on these HAP emissions and emission reductions, please refer to Chapter 8 of this
RIA and the docket for this rule. The adverse effects from PM (both coarse and fine) and SO2
emissions are presented below.
9.2.2.1 Benefits of Paniculate Matter Reductions. Scientific studies have linked PM (alone
or in combination with other air pollutants) with a series of health effects (EPA, 1996). Coarse
(PM10) particles can accumulate in the respiratory system and aggravate health problems such as
asthma. Fine (PM2 5) particles can penetrate deep into the lungs to contribute to a number of the
health effects. These health effects include decreased lung function and alterations in lung tissue and
structure and in respiratory tract defense mechanisms which may be manifest in increased respiratory
symptoms and disease or in more severe cases, increased hospital admissions and emergency room
visits or premature death. Children, the elderly, and people with cardiopulmonary disease, such as
asthma, are most at risk from these health effects.
PM also causes a number of adverse effects on the environment. Fine PM is the major cause
of reduced visibility in parts of the U.S., including many of our national parks and wilderness areas.
Other environmental impacts occur when particles deposit onto soil, plants, water, or materials. For
example, particles containing nitrogen and sulfur that deposit onto land or water bodies may change
the nutrient balance and acidity of those environments, leading to changes in species composition and
buffering capacity.
Particles that are deposited directly onto leaves of plants can, depending on their chemical
composition, corrode leaf surfaces or interfere with plant metabolism. Finally, PM causes soiling and
erosion damage to materials.
Thus, reducing the emissions of PM and PM precursors from boilers and process heater
sources can help to improve some of the effects mentioned above - either those related to primary PM
emissions, or the effects of secondary PM generated by the combination of SO2 with other pollutants
in the atmosphere.
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9.2.2.2 Benefits of Sulfur Dioxide Reductions. Very high concentrations of sulfur dioxide
(SO2) affect breathing and ambient levels have been hypothesized to aggravate existing respiratory
and cardiovascular disease. Potentially sensitive populations include asthmatics, individuals with
bronchitis or emphysema, children and the elderly. SO2 is also a primary contributor to acid
deposition, or acid rain, which causes acidification of lakes and streams and can damage trees, crops,
historic buildings and statues. In addition, sulfur compounds in the air contribute to visibility
impairment in large parts of the country. This is especially noticeable in national parks.
PM can also be formed from SO2 emissions. Secondary PM is formed in the atmosphere
through a number of physical and chemical processes that transform gases, such as SO2, into particles.
Overall, emissions of SO2 can lead to some of the effects discussed in this section - either those
directly related to SO2 emissions, or the effects of ozone and PM resulting from the combination of
SO2 with other pollutants.
9.3 Lack of Approved Methods to Quantify HAP Benefits
The most significant effect associated with the HAPs that are controlled with the rule is the
potential incidence of cancer. In previous analyses of the benefits of reductions in HAPs, EPA has
quantified and monetized the benefits of potential reductions in the incidences of cancer (EPA,
1992b, 1995). In some cases, EPA has also quantified (but not monetized) reductions in the number
of people exposed to non-cancer HAP risks above no-effect levels (EPA, 1995).
Monetization of the benefits of reductions in cancer incidences requires several important
inputs, including central estimates of cancer risks, estimates of exposure to carcinogenic HAPs, and
estimates of the value of an avoided case of cancer (fatal and non-fatal). In the above referenced
analyses, EPA relied on unit risk factors (URF) developed through risk assessment procedures. The
unit risk factor is a quantitative estimate of the carcinogenic potency of a pollutant, often expressed as
the probability of contracting cancer from a 70 year lifetime continuous exposure to a concentration
of one i-ig/m3 of a pollutant. These URFs are designed to be conservative, and as such, are more
likely to represent the high end of the distribution of risk rather than a best or most likely estimate of
risk.
In a typical analysis of the expected health benefits of a regulation (see for example the
benefit analysis of the Interstate Air Quality Rule), health effects are estimated by applying changes
in pollutant concentrations to best estimates of risk obtained from epidemiological studies. As the
purpose of a benefit analysis is to describe the benefits most likely to occur from a reduction in
pollution, use of high-end, conservative risk estimates over-estimate of the expected benefits of the
regulation. For this reason, we will not attempt to quantify the health benefits of reductions in HAPs
unless best estimates of risks are available. While we used high-end risk estimates in past analyses,
recent advice from the EPA Science Advisory Board (SAB) and internal methods reviews have
suggested that we avoid using high-end estimates in current analyses. EPA is working with the SAB
to develop better methods for analyzing the benefits of reductions in HAPs.
While not appropriate as part of a primary estimate of benefits, to estimate the potential
baseline risks posed by the industrial boiler and process heater source categories and the potential
impact of applicability cutoffs discussed in Chapter 3 of this RIA, EPA performed a "rough" risk
assessment, described below. There are large uncertainties regarding all components of the risk
quantification step, including location of emission reductions, emission estimates, air concentrations,
exposure levels and dose-response relationships. However, if these uncertainties are properly
identified and characterized, it is possible to provide upper-bound estimates of the potential reduction
in inhalation cancer incidence associated with this rule. It is important to keep in mind that these
estimates will not cover non-inhalation based cancer risks and non-cancer health effects.
-------
To estimate the potential baseline risks posed by the industrial boiler and process heater
source categories, EPA performed a crude risk analysis of the industrial boiler and process heater
source categories that focused only on cancer risks. The results of the analysis are based on
approaches for estimating cancer incidence that carry significant assumptions, uncertainties, and
limitations. Based on the assessment, if this proposed rule is implemented at all affected facilities,
annual cancer incidence is estimated to be reduced on the order of tens of cases/year. Due to the
uncertainties associated with the analysis, annual cancer incidence could be higher or lower than these
estimates. (Details of this assessment are available in the docket.)
For non-cancer health effects, previous analyses have estimated changes in populations
exposed above the reference concentration level (RfC). However, this requires estimates of
populations exposed to HAPs from controlled sources. Due to data limitations, we do not have
sufficient information on emissions from specific sources and thus are unable to model changes in
population exposures to ambient concentrations of HAPs above the RfC. As a result, we are unable
to place a monetary value of the HAP benefits associated with this rule.
9.4 Summary
The HAPs that are reduced as a result of implementing the Industrial Boilers and Process
Heaters NESHAP will produce a variety of benefits, some of which include: the reduction in the
incidence of cancer to exposed populations, neurotoxicity, irritation, and crop or plant damage. The
rule will also produce benefits associated with reductions in fine and coarse PM and SO2 emissions.
Exposure to PM (either directly or through secondary formation from SO2) can lead to several health
effects, including premature death and increased hospital admissions and emergency room visits,
increased respiratory symptoms and disease, decreased lung function, and alterations in lung tissue
and structure and in respiratory tract defense mechanisms. Children, the elderly, and people with
cardiopulmonary disease, such as asthma, are most at risk from these health effects. It can also form
a haze that reduces the visibility of scenic areas, can cause acidification of water bodies, and have
other impacts on soil, plants, and materials. High concentrations of SO2 affect breathing and may
aggravate existing respiratory and cardiovascular disease, which is more likely to affect asthmatics,
individuals with bronchitis or emphysema, children and the elderly. SO2 is also a primary contributor
to acid deposition, or acid rain, which causes acidification of lakes and streams and can damage trees,
crops, historic buildings and statues. In addition, sulfur compounds in the air contribute to visibility
impairment in large parts of the country. This is especially noticeable in national parks.
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REFERENCES:
EPA, 2000. U.S. Environmental Protection Agency. Integrated Risk Information System; website
access available at www.epa.gov/ngispgm3/iris. Data as of December 2000.
EPA, 1991. U.S. Environmental Protection Agency. Ecological Exposure and Effects of Airborne
Toxic Chemicals: An Overview. EPA/6003-91/001. Environmental Research Laboratory.
Corvallis, OR. 1991.
EPA, 1992a. U.S. Environmental Protection Agency. Regulatory Impact Analysis for the National
Emissions Standards for Hazardous Air Pollutants for Source Categories: Organic Hazardous Air
Pollutants from the Synthetic Organic Chemical Manufacturing Industry and Seven Other Processes.
Draft Report. Office of Air Quality Planning and Standards. Research Triangle Park, NC. EPA-
450/3-92-009. December 1992.
EPA, 1992b. U.S. Environmental Protection Agency. Draft Regulatory Impact Analysis of National
Emissions Standards for Hazardous Air Pollutants for By Product Coke Oven Charging, Door Leaks,
and Topside Leaks. Office of Air Quality Planning and Standards, Research Triangle Park, NC.
1992.
EPA, 1995. U.S. Environmental Protection Agency. Regulatory Impact Analysis for the Petroleum
Refinery NESHAP. Revised Draft for Promulgation. Office of Air Quality Planning and Standards,
Research Triangle Park, N.C. 1995.
EPA, 1996. Review of the National Ambient Air Quality Standards for Particulate Matter:
Assessment of Scientific and Technical Information. Office of Air Quality Planning and Standards,
Research Triangle Park, N.C.; EPA report no. EPA/4521R-96-013.
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10.0 QUANTIFIED BENEFITS
10.1 Results in Brief
In this section, we calculate monetary benefits for the reductions in ambient PM
concentrations resulting from the emission reductions described in Chapters 3 and 9. Benefits related
to PMJO and PM25 reductions are calculated using a combination of two approaches: (1) a direct
valuation based on air quality analysis of modeled PM and SO2 reductions at specific industrial
boilers/process heaters, and (2) a benefits transfer approach which uses dollar per ton values
generated from the air quality analysis completed in the first approach to value reductions from non-
specific sources. Incremental benefits (in 1999 dollars) from boilers and process heater PM and
SO2 emission reductions are approximately $16 billion for the MACT floor. We also evaluated an
above the floor regulatory option that is more stringent than final rule's MACT floor. Total annual
benefits of the above the floor option are $17 billion. Although the benefits of the more stringent
option are greater than the MACT floor, there are other costs and economic impacts that deem it an
inferior regulatory option. Thus, the final rule is based on the selection of the MACT floor.
This benefits analysis does not quantify all potential benefits or disbenefits associated with
PM and SO2 reductions. This analysis also does not quantify the benefits associated with reductions
in hazardous air pollutants (HAP). The magnitude of the unquantified benefits associated with
omitted categories and pollutants, such as avoided cancer cases, damage to ecosystems, or materials
damage to industrial equipment and national monuments, is not known. However, to the extent that
unquantified benefits exceed unquantified disbenefits, the estimated benefits presented above will be
an underestimate of actual benefits. There are many other sources of uncertainty in the estimates of
quantified benefits. These sources of uncertainty, along with the methods for estimating monetized
benefits for this NESHAP and a more detailed analysis of the results are presented below.
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Table 10-1. Summary of Results: Estimated PM-Related Benefits
of the Industrial Boilers and Process Heaters NESHAP
Estimation Method
Total BenefitsA'B
(millions 1999$)
MACT Floor:
Using a 3% discount rate
Using a 7% discount rate
Above the MACT Floor:
Using a 3% discount rate
Using a 7% discount rate
$16H
$15 H
$17H
$16H
-B
-B
-B
-B
A Benefits of HAP emission reductions are not quantified in this analysis and, therefore, are not presented in this table. The
quantifiable benefits are from emission reductions of SO2 and PM only. For notational purposes, unquantified benefits are
indicated with a "B" to represent additional monetary benefits. A detailed listing of unquantified SO2, PM , and HAP related
health effects is provided in Table 10-13.
B Results reflect the use of two different discount rates; a 3% rate which is recommended by EPA's Guidelines for Preparing
Economic Analyses (US EPA, 2000a), and 7% which is recommended by OMB Circular A-94 (OMB, 1992).
10.2 Introduction
This chapter presents the methods used to estimate the monetary benefits of the
reductions in PM and SO2 emissions associated with control requirements resulting from the
Industrial Boilers/Process Heaters NESHAP. Results are presented for the emission controls
described in Chapter 3. The benefits that result from the rule include both the primary
impacts from application of control technologies or changes in operations and processes, and
the secondary effects of the controls. The regulation induced reductions in PM and SO2
emissions also described in Chapter 3 will result in changes in the physical damages
associated with exposure to elevated ambient concentrations of PM. These damages include
changes in both human health and welfare effects categories. Benefits are calculated for the
nation as a whole, assuming that controls are implemented at major sources (sources emitting
> 10 tons of a HAP annual, or >25 tons of two or more HAPs annually).
The remainder of this chapter provides the following:
Subsection 3 provides an overview of the benefits methodology.
Subsection 4 discusses Phase One of the analysis: modeled air quality change and
health effects resulting from a portion of emission reductions at a subset of boiler and
process heaters sources
Subsection 5 discusses Phase Two of the analysis: Benefit transfer valuation of
remaining emission reductions
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Subsection 6 discusses total benefit estimated by combining the results of Phases 1
and 2.
Subsection 7 discusses potential benefit categories that are not quantified due to data
and/or methodological limitations, and provides a list of analytical uncertainties,
limitations, and biases.
10.3 Overview of Benefits Analysis Methodology
This section documents the general approach used to estimate benefits resulting from
emissions reductions from boiler and process heater sources. We follow the basic
methodology described in the Regulatory Impact Analysis of the Heavy Duty Engine/Diesel
Fuel rule [hereafter referred to as the HDD RIA] (US EPA, 2000), as well as discussions
provided in the Proposed Non-Road Diesel Engines rule (NRD rule) and the Integrated Air
Quality Rule (IAQR).
Since proposal of the Industrial Boilers and Process Heaters NESHAP, the benefit
methodology utilized by EPA has been updated to reflect the current science in air quality
modeling and benefits modeling. EPA has carefully considered the differences in
methodology from proposal. Based on the IAQR benefit analysis document, we determined
that the NESHAP's analysis from proposal does not include additional benefit endpoints
(i.e., infant mortality, heart attacks, and asthma exacerbation), which would increase the total
benefit estimate from proposal. The IAQR also uses a newer study of premature mortality
due to PM, which would increase the benefit estimate from proposal. The VSL estimate for
premature mortality has been lowered slightly from $6 million to $5.5 million in the IAQR,
which would decrease the benefit estimate from proposal. Finally, an updated air quality
model (i.e., REMSAD) would also increase our total benefit estimate in this analysis.
Although the overall impact on total benefits is not determinable without a full reassessement
of benefits, it is unlikely that our comparison of benefits to costs would not reveal a
substantially different conclusion (e.g., we still expect benefits to exceed costs by a
substantial amount). Therefore, we did not update the benefit analysis from proposal as it
would not impact the benefit-cost comparison for this rule.
On September 26, 2002, the National Academy of Sciences (NAS) released a report
on its review of the Agency's methodology for analyzing the health benefits of measures
taken to reduce air pollution. The report focused on EPA's approach for estimating the
health benefits of regulations designed to reduce concentrations of airborne particulate matter
(PM).
In its report, the NAS said that EPA has generally used a reasonable framework for
analyzing the health benefits of PM-control measures. It recommended, however, that the
Agency take a number of steps to improve its benefits analysis. In particular, the NAS stated
that the Agency should:
• include benefits estimates for a range of regulatory options;
• estimate benefits for intervals, such as every five years, rather than a single
year;
• clearly state the project baseline statistics used in estimating health benefits,
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including those for air emissions, air quality, and health outcomes;
• examine whether implementation of proposed regulations might cause
unintended impacts on human health or the environment;
• when appropriate, use data from non-US studies to broaden age ranges to
which current estimates apply and to include more types of relevant health
outcomes;
• begin to move the assessment of uncertainties from its ancillary analyses into
its primary analyses by conducting probabilistic, multiple-source uncertainty
analyses. This assessment should be based on available data and expert
judgment.
Although the NAS made a number of recommendations for improvement in EPA's
approach, it found that the studies selected by EPA for use in its benefits analysis were
generally reasonable choices. In particular, the NAS agreed with EPA's decision to use
cohort studies to derive benefits estimates. It also concluded that the Agency's selection of
the American Cancer Society (ACS) study for the evaluation of PM-related premature
mortality was reasonable, although it noted the publication of new cohort studies that should
be evaluated by the Agency.
Several of the NAS recommendations addressed the issue of uncertainty and how the
Agency can better analyze and communicate the uncertainties associated with its benefits
assessments. In particular, the Committee expressed concern about the Agency's reliance on
a single value from its analysis and suggested that EPA develop a probabilistic approach for
analyzing the health benefits of proposed regulatory actions. The Agency agrees with this
suggestion and is working to develop such an approach for use in future rulemakings. In
particular, the EPA is currently in the process of developing a comprehensive integrated
strategy for characterizing the impact of uncertainty in key elements of the benefits modeling
process (e.g., emissions modeling, air quality modeling, health effects incidence estimation,
valuation) on the results that are generated. A subset of this effort, which is currently
underway, involves an expert elicitation designed to characterize uncertainty in the
estimation of PM-related mortality resulting from both short-term and longer-term exposure.
The EPA will be evaluating the results of this elicitation to determine its usefulness in
characterizing uncertainty in our estimates of PM-related mortality benefits. As elements of
this uncertainty analysis strategy are finalized, it may be possible to integrate them into later
iterations of regulatory analyses.
In this RIA at proposal, the Agency used an interim approach for characterizing
uncertainty that showed the impact of several important alternative assumptions about the
estimation and valuation of reductions in premature mortality and chronic bronchitis. This
approach provided an alternative estimate of health benefits using the time series studies in
place of cohort studies, as well as alternative valuation methods for mortality and chronic
bronchitis risk reductions. However, reflecting comments from the SAB-HES as well as the
NAS panel, rather than including an alternative estimate in the final rule, the EPA will
continue to investigate the impact of key assumptions on mortality and morbidity estimates.
The analysis of benefits of this NESHAP is conducted in two phases. For a portion
of the emission reductions expected from this rule, the first phase of analysis models the
change in air quality and health effects around specific boiler and process heater sources.
The benefits resulting from the changes in air quality are then quantified and monetized. For
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the remaining set of emission reductions, the specific location of the emission reduction is
unknown due to limitations in the data. Therefore, the second phase of our benefits analysis
is based on benefits transfer of the modeled changes in air quality and health effects from the
location specific emissions reductions achieved in phase one of the analysis. More
specifically, the benefit value per ton of emission reduction estimated in phase one is
transferred and applied to the emission reductions in phase two of the analysis. Table 10-2
summarizes the emissions reductions associated with the phase one and phase two analyses.
This table shows the emission reduction expected from two regulatory options considered for
this rulemaking: the MACT floor, and an above the floor regulatory option. Although the
NESHAP is expected to result in reductions in emissions of many HAPs as well as PM and
SO2, benefits transfer values are generated for only PM and SO2 due to limitations in
availability of transfer values, concentration-response functions, or air quality and exposure
models for HAPs. For this analysis, we focus on directly emitted PM, and SO2 in its role as a
precursor in the formation of ambient particulate matter. Other potential impacts of PM and
SO2 reductions not quantified in this analysis, as well as potential impacts of HAPs
reductions are described in Chapter 9.
Table 10-2.
Estimate of Emission Reductions for Phases One and Two of the Benefit Analysis
Regulatory Option
MACT Floor:
SO2
PM10
PM25
Above MACT Floor:
SO2
PM10
PM25
Total Emission
Reductions
(tons/yr)
112,936
562,110
159,196
136,733
569,229
171,459
Phase One: Modeled
Emission Reductions
(tons/yr)
82,542
265,115
75,095
95,361
313,947
94,565
Phase Two:
Reductions Applied
to Benefit Transfer
Values
30,394
296,955
84,101
41,372
255,282
76,894
The general term "benefits" refers to any and all outcomes of the regulation that
contribute to an enhanced level of social welfare. In this case, the term "benefits" refers to
the dollar value associated with all the expected positive impacts of the regulation, that is, all
regulatory outcomes that lead to higher social welfare. If the benefits are associated with
market goods and services, the monetary value of the benefits is approximated by the sum of
the predicted changes in consumer (and producer) "surplus." These "surplus" measures are
standard and widely accepted measures in the field of applied welfare economics, and reflect
the degree of well-being enjoyed by people given different levels of goods and prices. If the
benefits are non-market benefits (such as the risk reductions associated with environmental
quality improvements), however, other methods of measuring benefits must be used. In
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contrast to market goods, non-market goods such as environmental quality improvements are
public goods, whose benefits are shared by many people. The total value of such a good is
the sum of the dollar amounts that all those who benefit are willing to pay.
We follow a "damage-function" approach in calculating total benefits of the modeled
changes in environmental quality. This approach estimates changes in individual health and
welfare endpoints (specific effects that can be associated with changes in air quality) and
assigns values to those changes assuming independence of the individual values. Total
benefits are calculated simply as the sum of the values for all non-overlapping health and
welfare endpoints. This imposes no overall preference structure, and does not account for
potential income or substitution effects, i.e. adding a new endpoint will not reduce the value
of changes in other endpoints. The "damage-function" approach is the standard approach for
most cost-benefit analyses of regulations affecting environmental quality, and it has been
used in several recent published analyses (Banzhaf et al., 2002; Levy et al, 2001; Kunzli et
al, 2000; Levy et al, 1999; Ostro and Chestnut, 1998). Time and resource constraints
prevented us from performing extensive new research to measure either the health outcomes
or their values for this analysis. Thus, similar to these studies, our estimates are based on the
best available methods of benefits transfer. Benefits transfer is the science and art of
adapting primary research from similar contexts to obtain the most accurate measure of
benefits available for the environmental quality change under analysis.
10.3.1 Methods for Estimating Benefits from Air Quality Improvements
Environmental and health economists have a number of methods for estimating the
economic value of improvements in (or deterioration of) environmental quality. The method
used in any given situation depends on the nature of the effect and the kinds of data, time,
and resources that are available for investigation and analysis. This section provides an
overview of the methods we selected to monetize the benefits included in this RIA.
We note at the outset that EPA rarely has the time or resources to perform extensive
new research in the form of evaluating the response in human health effects from specific
changes in the concentration of pollutants, or by issuing surveys to collect data of
individual's willingness to pay for a particular rule's given change in air quality, which is
needed to fully measure the economic benefits of individual rulemakings. As a result, our
estimates are based on the best available methods of benefit transfer from epidemiological
studies and studies of the economic value of reducing certain health and welfare effects.
Benefit transfer is the science and art of adapting primary benefits research on concentration-
response functions and measures of the value individuals place on an improvement in a given
health effect to the scenarios evaluated for a particular regulation. Thus, we strive to obtain
the most accurate measure of benefits for the environmental quality change under analysis
given availability of current, peer reviewed research and literature.
In general, economists tend to view an individual's willingness-to-pay (WTP) for an
improvement in environmental quality as the most complete and appropriate measure of the
value of an environmental or health risk reduction. An individual's willingness-to-accept
(WTA) compensation for not receiving the improvement is also a valid measure. Willingness
to pay and Willingness to accept are comparable measures when the change in environmental
quality is small and there are reasonably close substitutes available. However, WTP is
generally considered to be a more readily available and conservative measure of benefits.
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Adoption of WTP as the measure of value implies that the value of environmental quality
improvements is dependent on the individual preferences of the affected population and that
the existing distribution of income (ability to pay) is appropriate.
For many goods, WTP can be observed by examining actual market transactions. For
example, if a gallon of bottled drinking water sells for one dollar, it can be observed that at
least some persons are willing to pay one dollar for such water. For goods not exchanged in
the market, such as most environmental "goods," valuation is not as straightforward.
Nevertheless, a value may be inferred from observed behavior, such as sales and prices of
products that result in similar effects or risk reductions, (e.g., non-toxic cleaners or bike
helmets). Alternatively, surveys may be used in an attempt to directly elicit WTP for an
environmental improvement.
One distinction in environmental benefits estimation is between "use values"and
"non-use values." Although no general agreement exists among economists on a precise
distinction between the two, the general nature of the difference is clear. Use values are
those aspects of environmental quality that affect an individual's welfare more or less
directly. These effects include changes in product prices, quality, and availability, changes
in the quality of outdoor recreation and outdoor aesthetics, changes in health or life
expectancy, and the costs of actions taken to avoid negative effects of environmental quality
changes.
Non-use values are those for which an individual is willing to pay for reasons that do
not relate to the direct use or enjoyment of any environmental benefit, but might relate to
existence values and bequest values. Non-use values are not traded, directly or indirectly, in
markets. For this reason, the measurement of non-use values has proved to be significantly
more difficult than the measurement of use values. The air quality changes produced by this
NESHAP cause changes in both use and non-use values, but the monetary benefit estimates
are almost exclusively for use values.
More frequently than not, the economic benefits from environmental quality changes
are not traded in markets, so direct measurement techniques can not be used. Avoided cost
methods are ways to estimate the costs of pollution by using the expenditures made
necessary by pollution damage. For example, if buildings must be cleaned or painted more
frequently as levels of PM increase, then the appropriately calculated increment of these
costs is a reasonable lower bound estimate (under most conditions) of true economic benefits
when PM levels are reduced. Avoided costs methods are used to estimate some of the
health-related benefits related to morbidity, such as hospital admissions (see the NRD rule
and the IAQR for a detailed discussion of methods to value benefit categories).
Indirect market methods can also be used to infer the benefits of pollution reduction.
The most important application of this technique for our analysis is the calculation of the
value of a statistical life for use in the estimate of benefits from mortality reductions. There
exists no market where changes in the probability of death are directly exchanged. However,
people make decisions about occupation, precautionary behavior, and other activities
associated with changes in the risk of death. By examining these risk changes and the other
characteristics of people's choices, it is possible to infer information about the monetary
values associated with changes in mortality risk (see Section 10.4). For measurement of
health benefits, this analysis captures the WTP for most use and non-use values, with the
exception of the value of avoided hospital admissions, which only captures the avoided cost
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of illness because no WTP values were available in the published literature.
10.3.2 Methods for Describing Uncertainty
In any complex analysis using estimated parameters and inputs from numerous
models, there are likely to be many sources of uncertainty.16 This analysis is no exception.
As outlined both in this and preceding chapters, there are many inputs used to derive the final
estimate of benefits, including emission inventories, air quality models (with their associated
parameters and inputs), epidemiological estimates of concentration-response (C-R)
functions, estimates of values (both from WTP and cost-of-illness studies), population
estimates, income estimates, and estimates of the future state of the world (i.e., regulations,
technology, and human behavior). Each of these inputs may be uncertain, and depending on
their location in the benefits analysis, may have a disproportionately large impact on final
estimates of total benefits. For example, emissions estimates are used in the first stage of the
analysis. As such, any uncertainty in emissions estimates will be propagated through the
entire analysis. When compounded with uncertainty in later stages, small uncertainties in
emission levels can lead to much larger impacts on total benefits.
Some key sources of uncertainty in each stage of the benefits analysis are:
• Gaps in scientific data and inquiry;
Variability in estimated relationships, such as C-R functions, introduced
through differences in study design and statistical modeling;
• Errors in measurement and projection for variables such as population growth
rates;
Errors due to mis-specification of model structures, including the use of
surrogate variables, such as using PM10 when PM2 5 is not available, excluded
variables, and simplification of complex functions; and
• Biases due to omissions or other research limitations.
Some of the key uncertainties in the benefits analysis are presented in Table 10-3.
Several of the methods employed in this analysis are similar to the methods employed in the
Heavy Duty Diesel and Fuel Standard (HDD TSD). Information on the uncertainty
surrounding particular C-R and valuation functions is provided in the HDD TSD, and have
been updated in the TSD for the benefits of the Proposed Non-Road Diesel Engines rule
(NRD rule) (EPA, 2003a), and in the documentation for the Integrated Air Quality Rule
(IAQR) (EPA, 2003b).
16 It should be recognized that in addition to uncertainty, the annual benefit estimates for the Industrial
Boilers/Process Heaters NESHAP presented in this analysis are also inherently variable, due to the truly
random processes that govern pollutant emissions and ambient air quality in a given year. Factors such as
electricity demand and weather display constant variability regardless of our ability to accurately measure
them. As such, the estimates of annual benefits should be viewed as representative of the types of benefits
that will be realized, rather than the actual benefits that would occur every year.
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Our estimated range of total benefits should be viewed as an approximate result
because of the sources of uncertainty discussed above (see Table 10-3). The total benefits
estimate may understate or overstate actual benefits of the rule.
In considering the monetized benefits estimates, the reader should remain aware of
the many limitations of conducting these analyses mentioned throughout this RIA. One
significant limitation of both the health and welfare benefits analyses is the inability to
quantify many of the serious effects discussed in Chapter 9.
In particular, there are significant categories of PM-related benefits that cannot be
monetized (or in many cases even quantified), and thus they are not included in our
accounting of health and welfare benefits. These unqualified effects include low birth
weight, changes in pulmonary function, chronic respiratory diseases other than chronic
bronchitis, morphological changes, altered host defense mechanisms, non-fatal cancers, and
non-asthma respiratory emergency room visits. A complete discussion of PM related health
effects can be found in the PM Criteria Document (U.S. EPA, 1996). In general, if it were
possible to monetize these benefits categories, the benefits estimates presented in this
analysis would increase. Unquantified benefits are qualitatively discussed in the in Chapter
9 and presented in Table 10-16. The net effect of excluding benefit and disbenefit categories
from the estimate of total benefits depends on the relative magnitude of the effects.
In addition, when we proposed the Industrial Boilers and Process Heaters NESHAP
in 2003, we also included an alternative estimate of benefits in addition to a base estimate
that was intended to evaluate the impact of several key assumptions on the estimated
reductions in premature mortality and CB. However, reflecting comments from the SAB-
HES as well as the NAS panel, we do not present an alternative estimate to reflect
uncertainty in our benefit estimate. To better understand the scope of potential uncertainties,
in several upcoming analyses EPA will investigate the impact of key assumptions on
mortality and morbidity estimates through a series of sensitivity analyses.
The benefits estimates generated for the final rule are subject to a number of
assumptions and uncertainties, which are discussed throughout the document. For example,
key assumptions underlying the primary estimate for the mortality category include the
following:
(1) Inhalation of fine particles is causally associated with premature death at
concentrations near those experienced by most Americans on a daily basis.
Although biological mechanisms for this effect have not yet been definitively
established, the weight of the available epidemiological evidence supports an
assumption of causality.
(2) All fine particles, regardless of their chemical composition, are equally potent
in causing premature mortality. This is an important assumption, because PM
produced via transported precursors emitted from EGUs may differ
significantly from direct PM released from automotive engines and other
industrial sources, but no clear scientific grounds exist for supporting
differential effects estimates by particle type.
(3) The C-R function for fine particles is approximately linear within the range of
ambient concentrations under consideration. Thus, the estimates include
health benefits from reducing fine particles in areas with varied
concentrations of PM, including both regions that are in attainment with fine
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particle standard and those that do not meet the standard.
(4) The forecasts for future emissions and associated air quality modeling are
valid. Although recognizing the difficulties, assumptions, and inherent
uncertainties in the overall enterprise, these analyses are based on
peer-reviewed scientific literature and up-to-date assessment tools, and we
believe the results are highly useful in assessing this proposal.
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Table 10-3. Primary Sources of Uncertainty in the Source Benefit Analyses
1. Uncertainties Associated With Health Impact Functions
The value of the PM effect estimate in each impact function.
Application of a single effect estimate to pollutant changes and populations in all locations.
Similarity of future year effect estimates to current effect estimates.
Correct functional form of each impact function.
Application of effect estimates to changes in PM outside the range of PM concentrations observed in the
study.
Application of effect estimates only to those subpopulations matching the original study population.
2. Uncertainties Associated With PM Concentrations
— Responsiveness of the models to changes in precursor emissions.
— Projections of future levels of precursor emissions, especially ammonia and crustal materials.
— Model chemistry for the formation of ambient nitrate concentrations.
— Use of separate air quality models for ozone and PM does not allow for a fully integrated analysis of pollutants
and their interactions.
3. Uncertainties Associated with PM Mortality Risk
Limited scientific literature supporting a direct biological mechanism for observed epidemiological evidence.
Direct causal agents within the complex mixture of PM have not been identified.
The extent to which adverse health effects are associated with low level exposures that occur many times in the
year versus peak exposures.
The extent to which effects reported in the long-term exposure studies are associated with historically higher
levels of PM rather than the levels occurring during the period of study.
— Reliability of the limited ambient PM2 5 monitoring data in reflecting actual PM2 5 exposures.
4. Uncertainties Associated With Possible Lagged Effects
— The portion of the PM-related long-term exposure mortality effects associated with changes in annual PM
levels would occur in a single year is uncertain as well as the portion that might occur in subsequent years.
5. Uncertainties Associated With Baseline Incidence Rates
— Some baseline incidence rates are not location-specific (e.g., those taken from studies) and may therefore not
accurately represent the actual location-specific rates.
— Current baseline incidence rates may not approximate well baseline incidence rates in 2010.
— Projected population and demographics may not represent well future-year population and demographics.
6. Uncertainties Associated With Economic Valuation
— Unit dollar values associated with health endpoints are only estimates of mean WTP and therefore have
uncertainty surrounding them.
— Mean WTP (in constant dollars) for each type of risk reduction may differ from current estimates due to
differences in income or other factors.
7. Uncertainties Associated With Aggregation of Monetized Benefits
— Health benefits estimates are limited to the available effect estimates. Thus, unquantified or unmonetized
benefits are not included.
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10.4 Phase One Analysis: Modeled Air Quality Change and Health Effects Resulting
from a Portion of Emission Reductions at Boiler and Process Heaters Sources
In phase one of the benefit analysis, we are able to link approximately 50 percent of
the emission reductions from this regulation to specific locations of boilers/process heaters.
This allows us to evaluate the change in air quality around these sources and the resulting
effect on the health of the surrounding population. The analysis performed for the emission
reductions evaluated in phase one can be thought of as having three parts, including:
1. Calculation of the impact that our standards will have on the nationwide
inventories for PM and SO2 emissions;
2. Air quality modeling to determine the changes in ambient concentrations of
PM that will result from the changes in nationwide inventories of directly
emitted PM and precursor pollutants; and
3. A benefits analysis to determine the changes in human health, both in terms of
physical effects and monetary value, that result from the changes in ambient
concentrations of PM.
Steps 1 and 2 are discussed in previous chapters of this RIA. For step 3, we follow
the same general methodology used in the benefits analysis of the HDD rulemaking, as well
as the proposed NRD rule and the IAQR. EPA also relies heavily on the advice of its
independent Science Advisory Board (SAB) in determining the health and welfare effects
considered in the benefits analysis and in establishing the most scientifically valid
measurement and valuation techniques.
Figure 10-1 illustrates the steps necessary to link the emission reductions included in
the phase one analysis with economic measures of benefits. The first two steps involve the
specification and implementation of the regulation. First, the specific regulatory options for
reducing air pollution from industrial boilers/process heaters are established. In this chapter,
we evaluate the benefits of two regulatory options: the MACT floor and an above the floor
option. Next, we determine the changes in boiler and process heater control technology that
can be used to meet the level of emissions reductions specified by the regulatory options (see
Chapter 2). The changes in pollutant emissions resulting from the required changes in
control technology at boilers/process heaters are then calculated, along with predictions of
emissions for other industrial sectors in the baseline. The predicted emissions reductions
described in Chapter 3 are then used as inputs to air quality models that predict ambient
concentrations of pollutants over time and space. These concentrations depend on climatic
conditions and complex chemical interactions.
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Figure 10-1. Steps in Phase One of the Benefits Analysis for the Industrial
Boilers/Process Heaters NESHAP
NESHAP Regulatory Options
Apply Control Technology to Affected
Sources
Estimate Expected Reductions in SO2
and PM Emissions
Model Changes in Ambient
Concentrations of PM25 and PM10
Estimate Expected Changes in Human
Health Outcomes
istimate Monetary Value of Changes ii
Human Health Outcomes
Account for Income Growth
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and Calculate Total Benefits
Changes in ambient concentrations will lead to new levels of environmental quality
in the U.S., reflected both in human health and in non-health welfare effects. For this
analysis, however, we do not evaluate and monetize changes in non-health welfare effects,
such as visibility and agricultural yields. To generate estimated health outcomes, projected
changes in ambient PM concentrations were input to a benefits model, known as the Criteria
Air Pollutant Modeling System (CAPMS), a customized GIS-based program. CAPMS
assigns pollutant concentrations to population grid cells for input into concentration-response
functions. CAPMS uses census block population data and changes in pollutant
concentrations to estimate changes in health outcomes for each grid cell. For purposes of
this analysis, we assume a constant proportion of baseline incidence of the various health
effects to the future incidence of health effects.
Our analysis also accounts for expected growth in real income over time. Economic
theory argues that WTP for most goods (such as environmental protection) will increase if
real incomes increase. The economics literature suggests that the severity of a health effect
is a primary determinant of the strength of the relationship between changes in real income
and WTP (Alberini, 1997; Miller, 2000; Viscusi, 1993). As such, we use different factors to
adjust the WTP for minor health effects, severe and chronic health effects, and premature
mortality. Adjustment factors used to account for projected growth in real income from 1990
to 2005 are 1.03 for minor health effects, 1.09 for severe and chronic health effects, and 1.08
for premature mortality17.
It should be noted that since proposal of the Industrial Boilers and Process Heaters
NESHAP, the benefit methodology utilized by EPA has been updated to reflect the current
science in air quality modeling and benefits modeling. Due to time and resource constraints,
EPA was unable to complete a full reassessment of the benefits analysis from proposal.
However, EPA has carefully considered the differences in methodology from proposal.
Based on the IAQR benefit analysis document, we determined that the NESHAP's analysis
from proposal does not include additional benefit endpoints (i.e., infant mortality, heart
attacks, and asthma exacerbation), which would increase the total benefit estimate from
proposal. The IAQR also uses a newer study of premature mortality due to PM, which
would increase the benefit estimate from proposal. The VSL estimate for premature
mortality has been lowered slightly from $6 million to $5.5 million in the IAQR, which
would decrease the benefit estimate from proposal. Finally, an updated air quality model
(i.e., REMSAD) would also increase our total benefit estimate in this analysis. Although the
overall impact on total benefits is not determinable without a full reassessement of benefits,
it is unlikely that our comparison of benefits to costs would reveal a substantially different
conclusion (e.g., we still expect benefits to exceed costs by a substantial amount).
Based on the structure of analysis presented above, Section 10.4.1 provides a
description of how we quantify and value changes in individual health effects. Then, in
Section 10.4.2 we present quantified estimates of the reductions in health effects resulting
17Detailsof the calculation of the income adjustment factors are provided in the IAQR RIA (U.S. EPA, 2003b).
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from phase one of the benefit analysis.
10.4.1 Quantifying Individual Health Effect Endpoints
We use the term "endpoints" to refer to specific effects that can be associated with
changes in air quality. To estimate these endpoints, EPA combines changes in ambient air
quality levels with epidemiological evidence about population health response to pollution
exposure. The most significant monetized benefits of reducing ambient concentrations of
PM are attributable to reductions in human health risks. EPA's Criteria Document for PM
lists numerous health effects known to be linked to ambient concentrations of the pollutant
(US EPA, 1996a). The previous chapter described some of these effects. This section
describes methods used to quantify and monetize changes in the expected number of
incidences of various health effects. For further detail on the methodology used to assess
human health benefits such as those included in phase one of this analysis, refer to the HDD
RIA and TSD, and the IAQR benefit analysis.
The specific PM endpoints that are evaluated in this analysis include:
• Premature mortality
• Bronchitis - chronic and acute
• Hospital admissions - respiratory and cardiovascular
• Emergency room visits for asthma
• Asthma attacks
• Lower and upper respiratory illness
• Minor restricted activity days
• Work loss days
As is discussed previously, this analysis relies on concentration-response (C-R)
functions estimated in published epidemiological studies relating health effects to ambient
air quality. The specific studies from which C-R functions are drawn are included in Table
10-4. Because we rely on methodology used in prior benefit analyses, a complete discussion
of the C-R functions used for this analysis and information about each endpoint are
contained in the IAQR RIA .
While a broad range of serious health effects have been associated with exposure to
elevated PM levels (described more fully in the EPA's PM Criteria Document (US EPA,
1996a), we include only a subset of health effects in this quantified benefit analysis. Health
effects are excluded from this analysis for four reasons: (i) the possibility of double counting
(such as hospital admissions for specific respiratory diseases); (ii) uncertainties in applying
effect relationships based on clinical studies to the affected population; (iii) a lack of an
established C-R relationship; or (iv) lack of resources to estimate some endpoints.
Using the C-R functions derived from the studies cited in this table, we apply that
same C-R relationship to all locations in the U.S. Although the C-R relationship may in fact
vary somewhat from one location to another (for example, due to differences in population
susceptibilities or differences in the composition of PM), location-specific C-R functions are
generally not available. A single function applied everywhere may result in overestimates of
incidence changes in some locations and underestimates in other locations, but these
location-specific biases will, to some extent, cancel each other out when the total incidence
change is calculated. It is not possible to know the extent or direction of the bias in the total
incidence change based on the general application of a single C-R function everywhere.
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Recently, the Health Effects Institute (HEI) reported findings by investigators at
Johns Hopkins University and others that have raised concerns about aspects of the statistical
methodology used in a number of recent time-series studies of short-term exposures to air
pollution and health effects (Greenbaum, 2002a). Some of the concentration-response
functions used in this benefits analysis were derived from such short-term studies. The
estimates derived from the long-term mortality studies, which account for a major share of
the benefits in theanalysis, are not affected. As discussed in HEI materials provided to
sponsors and to the Clean Air Scientific Advisory Committee (Greenbaum, 2002a, 2002b),
these investigators found problems in the default "convergence criteria" used in Generalized
Additive Models (GAM) and a separate issue first identified by Canadian investigators about
the potential to underestimate standard errors in the same statistical package.1 These and
other investigators have begun to reanalyze the results of several important time series
studies with alternative approaches that address these issues and have found a downward
revision of some results. For example, the mortality risk estimates for short-term exposure to
PM10 from NMMAPS were overestimated (the C-R function based on the NMMAPS results
used in this benefits analysis uses the revised NMMAPS results).2 However, both the
relative magnitude and the direction of bias introduced by the convergence issue is case-
specific. In most cases, the concentration-response relationship may be overestimated; in
other cases, it may be underestimated. The preliminary renalyses of the mortality and
morbidity components of NMMAPS suggest that analyses reporting the lowest relative risks
appear to be affected more greatly by this error than studies reporting higher relative risks
(Dominici et al., 2002; Schwartz and Zanobetti, 2002).
Our examination of the original studies used in this analysis finds that the health
endpoints that are potentially affected by the GAM issues include: reduced hospital
admissions and reduced lower respiratory symptoms; reduced lower respiratory symptoms;
and reduced premature mortality due to short-term PM10 exposures and reduced premature
mortality due to short-term PM25 exposures. While resolution of these issues is likely to
take some time, the preliminary results from ongoing reanalyses of some of the studies used
in our analyses (Dominici et al, 2002; Schwartz and Zanobetti, 2002; Schwartz, personal
communication 2002) suggest a more modest effect of the S-plus error than reported for the
NMMAPS PM10 mortality study. While we wait for further clarification from the scientific
community, we have chosen not to remove these results from the Industrial Boilers and
Process Heaters NESHAP benefits estimates, nor have we elected to apply any interim
adjustment factor based on the preliminary reanalyses. EPA will continue to monitor the
progress of this concern, and make appropriate adjustments as further information is made
available.
10.4.1.1 Concentration-Response Functions for Premature Mortality
Both long and short-term exposures to ambient levels of air pollution have been
associated with increased risk of premature mortality. The size of the mortality risk
estimates from these epidemiological studies, the serious nature of the effect itself, and the
high monetary value ascribed to prolonging life make mortality risk reduction the most
important health endpoint quantified in this analysis. Because of the importance of this
endpoint and the considerable uncertainty among economists and policymakers as to the
appropriate way to value reductions in mortality risks, this section discusses some of the
issues surrounding the estimation of premature mortality. For additional discussion on
mortality and issues related to estimating risk for other health effects categories, we refer
readers to the discussions presented in the IAQR.
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Epidemiological analyses have consistently linked air pollution, especially PM, with
excess mortality. Although a number of uncertainties remain to be addressed by continued
research (NRC, 1998), a substantial body of published scientific literature documents the
correlation between elevated PM concentrations and increased mortality rates. Community
epidemiological studies that have used both short-term and long-term exposures and
response have been used to estimate PM/ mortality relationships. Short-term studies use a
time-series approach to relate short-term (often day-to-day) changes in PM concentrations
and changes in daily mortality rates up to several days after a period of elevated PM
concentrations. Long-term studies examine the potential relationship between community-
level PM exposures over multiple years and community-level annual mortality rates.
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Table 10-4. PM-related Health Outcomes
and Studies Included in the Analysis
Health Outcome
Premature Mortality
All-cause premature
mortality from long-term
exposure
Chronic Illness
Chronic Bronchitis
(pooled estimate)
Hospital Admissions
COPD
Pneumonia
Asthma
Total Cardiovascular
Asthma-Related ER
Visits
Other Effects
Asthma Attacks
Acute Bronchitis
Upper Respiratory
Symptoms
Lower Respiratory
Symptoms
Work Loss Days
Minor Restricted Activity
Days (minus asthma
attacks)
Pollutant
PM25
PM25
PM10
PM10
PM10
PM25
PM10
PM10
PM10
PM25
PM10
PM25
PM25
PM25
Applied
Population
> 29 years
> 26 years
> 29 years
> 64 years
> 64 years
< 65 years
> 64 years
All ages
Asthmatics, all
ages
Children, 8-12
years
Asthmatic children,
9-11
Children, 7-14
years
Adults, 18-65 years
Adults, 18-65 years
Source of Effect
Estimate
Krewski et al., 2000
Abbey etal., 1995
Schwartz etal., 1993
Samet et al., 2000
Samet et al., 2000
Sheppard et al., 1999
Samet et al., 2000
Schwartz etal., 1993
Whittemore and Korn,
1980
Dockery et al., 1996
Pope etal., 1991
Schwartz etal., 1994
Ostro, 1987
Ostro and Rothschild.,
1989
Source of Baseline
Incidence
U.S. Centers for
Disease Control, 1999
Abbey etal., 1993
Abbey etal., 1993
Adams and Marano,
1995
Graves and Gillum,
1997
Graves and Gillum,
1997
Graves and Gillum,
1997
Graves and Gillum,
1997
Smith etal., 1997
Graves and Gillum,
1997
Krupnick, 1988
Adams and Marano,
1995
Adams and Marano,
1995
Pope etal., 1991
Schwartz et al., 1994
Adams and Marano,
1995
Ostro and Rothschild,
1989
Researchers have found statistically significant associations between PM and
premature mortality using both types of studies. In general, the risk estimates based on the
long-term exposure studies are larger than those derived from short-term studies. Cohort
analyses are better able to capture the full public health impact of exposure to air pollution
over time (Kunzli, 2001; NRC, 2002). This section discusses some of the issues surrounding
the estimation of premature mortality.
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Over a dozen studies have found significant associations between various measures
of long-term exposure to PM and elevated rates of annual mortality, beginning with Lave and
Seskin (1977). Most of the published studies found positive (but not always statistically
significant) associations with available PM indices such as total suspended particles (TSP).
Particles of different fine particles components (i.e., sulfates), and fine particles, as well as
exploration of alternative model specifications sometimes found inconsistencies (e.g.,
Lipfert, [1989]). These early "cross-sectional" studies (e.g., Lave and Seskin [1977];
Ozkaynak and Thurston [1987]) were criticized for a number of methodological limitations,
particularly for inadequate control at the individual level for variables that are potentially
important in causing mortality, such as wealth, smoking, and diet. More recently, several
long-term studies have been published that use improved approaches and appear to be
consistent with the earlier body of literature. These new "prospective cohort" studies reflect
a significant improvement over the earlier work because they include individual-level
information with respect to health status and residence. The most extensive study and
analyses has been based on data from two prospective cohort groups, often referred to as the
Harvard "Six-City Study" (Dockery et al., 1993) and the "American Cancer Society or ACS
study" (Pope et al., 1995); these studies have found consistent relationships between fine
particle indicators and premature mortality across multiple locations in the United States. A
third major data set comes from the California based 7th Day Adventist Study (e.g., Abbey et
al, 1999), which reported associations between long-term PM exposure and mortality in men.
Results from this cohort, however, have been inconsistent and the air quality results are not
geographically representative of most of the United States. More recently, a cohort of adult
male veterans diagnosed with hypertension has been examined (Lipfert et al., 2000). The
characteristics of this group differ from the cohorts in the ACS, Six-Cities, and 7th Day
Adventist studies with respect to income, race, health status, and smoking status. Unlike
previous long-term analyses, this study found some associations between mortality and
ozone but found inconsistent results for PM indicators. Because of the selective nature of the
population in the veteran's cohort, which may have resulted in estimates of relative risk that
are biased relative to a relative risk for the general population, we have chosen not to include
any effect estimates from the Lipfert et al. (2000) study in our benefits assessment.18
Given their consistent results and broad geographic coverage, the Six-City and ACS data
have been particularly important in benefits analyses. The credibility of these two studies is
18The EPA recognizes that the ACS cohort also is not completely representative of the demographic mix in the
general population. The ACS cohort is almost entirely white, and has higher income and education levels
relative to the general population. The EPA's approach to this problem is to match populations based on the
potential for demographic characteristics to modify the effect of air pollution on mortality risk. Thus, for the
various ACS-based models, we are careful to apply the effect estimate only to ages matching those in the
original studies, because age has a potentially large modifying impact on the effect estimate, especially when
younger individuals are excluded from the study population. For the Lipfert analysis, the applied population
should be limited to that matching the sample used in the analysis. This sample was all male, veterans, and
diagnosed hypertensive. There are also a number of differences between the composition of the sample and
the general population, including a higher percentage of African Americans (35 percent), and a much higher
percentage of smokers (81 percent former smokers, 57 percent current smokers) than the general population
(12 percent African American, 24 percent current smokers). These composition differences cannot be
controlled for, but should be recognized as adding to the potential extrapolation bias. The EPA recognizes
the difficulty in controlling for composition of income and education levels. However, in or out criterion
such as age, veteran status, hypertension, race and sex are all controllable by applying filters to the
population data. The EPA has traditionally only controlled for age, because the ACS study used only age as
a screen.
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further enhanced by the fact that they were subject to extensive reexamination and reanalysis
by an independent team of scientific experts commissioned by HEI (Krewski et al., 2000). The
final results of the reanalysis were then independently peer reviewed by a Special Panel of the
HEI Health Review Committee. The results of these reanalyses confirmed and expanded those
of the original investigators. This intensive independent reanalysis effort was occasioned both
by the importance of the original findings as well as concerns that the underlying individual
health effects information has never been made publicly available.
The HEI re-examination lends credibility to the original studies and highlights
sensitivities concerning the relative impact of various pollutants, the potential role of education
in mediating the association between pollution and mortality, and the influence of spatial
correlation modeling. Further confirmation and extension of the overall findings using more
recent air quality and a longer follow-up period for the ACS cohort was recently published in
the Journal of the American Medical Association (Pope et al., 2002).
In developing and improving the methods for estimating and valuing the potential
reductions in mortality risk over the years, the EPA has consulted with the SAB-HES. That
panel recommended use of long-term prospective cohort studies in estimating mortality risk
reduction (EPA-SAB-COlMCIL-ADV-99-005, 1999). This recommendation has been
confirmed by a recent report from the National Research Council, which stated that "it is
essential to use the cohort studies in benefits analysis to capture all important effects from air
pollution exposure" (NAS, 2002, p. 108). More specifically, the SAB recommended emphasis
on the ACS study because it includes a much larger sample size and longer exposure interval and
covers more locations (e.g., 50 cities compared to the Six Cities Study) than other studies of its
kind. As explained in the regulatory impact analysis for the Heavy-Duty Engine/Diesel Fuel
rule (EPA, 2000a), more recent EPA benefits analyses have relied on an improved specification
of the ACS cohort data that was developed in the HEI reanalysis (Krewski et al., 2000). The
latest reanalysis of the ACS cohort data (Pope et al., 2002), provides additional refinements to
the analysis of PM-related mortality by (a) extending the follow-up period for the ACS study
subjects to 16 years, which triples the size of the mortality data set; (b) substantially increasing
exposure data, including consideration for cohort exposure to PM2.5 following implementation
of PM2.5 standard in 1999; (c) controlling for a variety of personal risk factors including
occupational exposure and diet; and (d) using advanced statistical methods to evaluate specific
issues that can adversely affect risk estimates including the possibility of spatial autocorrelation
of survival times in communities located near each other. Because of these refinements, the
SAB- HES recommends using the Pope et al. (2002) study as the basis for the primary mortality
estimate for adults and suggests that alternate estimates of mortality generated using other cohort
and time series studies could be included as part of the sensitivity analysis (SAB-HES, 2003).
However, as is discussed above EPA did not reassess the benefit analysis presented at proposal
of this rule to account for the new data of the Pope et al. (2002) study.
This analysis also accounts for a lag between reductions in PM 2.5 concentrations and
reductions in mortality incidence. It is currently unknown whether there is a time lag (a delay
between changes in PM exposures and changes in mortality rates) in the long-term
PM2.5/premature mortality relationship. The existence of such a lag is important for the
valuation of premature mortality incidences because economic theory suggests that benefits
occurring in the future should be discounted. Although there is no specific scientific evidence
of the existence or structure of a PM effects lag, current scientific literature on adverse health
effects, such as those associated with PM (e.g., smoking-related disease) and the difference in
the effect size between chronic exposure studies and daily mortality studies suggest that all
incidences of premature mortality reduction associated with a given incremental change in PM
exposure probably would not occur in the same year as the exposure reduction. This same
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smoking-related literature implies that lags of up to a few years are plausible. Adopting the lag
structure endorsed by the SAB (EPA-SAB-COUNCIL-ADV-00-001, 1999), we assume a five-
year lag structure, with 25 percent of premature deaths occurring in the first year (in 2005),
another 25 percent in the second year, and 16.7 percent in each of the remaining three years.
The mortality incidences across the 5-year period is then discounted back to our year of analysis,
2005.
For reductions in direct emissions of PM10, we use a different C-R function, based on the
studies of mortality and shorter term exposures to PM. Long-term studies of the relationship
between chronic exposure and mortality have not found significant associations with coarse
particles or total PM10. As discussed earlier in this chapter, concerns have recently been raised
about aspects of the statistical methodology used in a number of recent time-series studies of
short-term exposures to air pollution and health effects. Due to the "S-Plus" issue identified by
the Health Effects Institute, we use as the basis for the our primary estimate the revised relative
risk from the NMMAPS study, reported on the website of the Johns Hopkins School of Public
Health19. Similar to the PM2 5 lag adjustment discussed above, we also include an adjustment
for PM10 to account for recent evidence that daily mortality is associated with particle levels
from a number of previous days. We use the overall pooled NMMAPS estimate of a 0.224
percent increase in mortality for a 10 |ig/m3 increase in PM10 as the starting point in developing
our C-R function. In a recent analysis, Schwartz (2000) found that elevated levels of PM10 on
a given day can elevate mortality on a number of following days. This type of multi-day model
is often referred to as a "distributed lag" model because it assumes that mortality following a PM
event will be distributed over a number of days following or "lagging" the PM event5. Because
the NMMAPS study reflects much broader geographic coverage (90 cities) than the Schwartz
study (10 cities), and the Schwartz study has not been reanalyzed to account for the "S-Plus"
issue, we choose to apply an adjustment based on the Schwartz study to the NMMAPS study to
reflect the effect of a distributed lag model.
The distributed lag adjustment factor is constructed as the ratio of the estimated
coefficient from the unconstrained distributed lag model to the estimated coefficient from the
single-lag model reported in Schwartz (2000) The unconstrained distributed lag model
coefficient estimate is 0.0012818 and the single-lag model coefficient estimate is 0.0006479.
The ratio of these estimates is 1.9784. This adjustment factor is then multiplied by the revised
estimated coefficients from the NMMAPS study. The NMMAPS coefficient corresponding to
the 0.224 percent increase in mortality risk is 0.000224. The adjusted NMMAPS coefficent is
then 0.000224*1.9784 = 0.000444.
10.4.2 Valuing Individual Health Effect Endpoints
The appropriate economic value of a change in a health effect depends on whether the
health effect is viewed ex ante (before the effect has occurred) or ex post (after the effect has
occurred). Reductions in ambient concentrations of air pollution generally lower the risk of
future adverse health affects by a fairly small amount for a large population. The appropriate
economic measure is therefore ex ante WTP for changes in risk. However, epidemiological
studies generally provide
19http://www.biostat.jhsph.edi^iostat/research/update.main.htm
20Both the single day and distributed lag models are likely to be affected to the same degree by the S-Plus
convergence issue. As such, the ratio of the coefficients from the models should not be affected as much by
any changes in the coefficient
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estimates of the relative risks of a particular health effect avoided due to a reduction in air
pollution. A convenient way to use this data in a consistent framework is to convert
probabilities to units of avoided statistical incidences. This measure is calculated by dividing
individual WTP for a risk reduction by the related observed change in risk. For example,
suppose a measure is able to reduce the risk of premature mortality from 2 in 10,000 to 1 in
10,000 (a reduction of 1 in 10,000). If individual WTP for this risk reduction is $100, then the
WTP for an avoided statistical premature mortality amounts to $1 million ($100/0.0001 change
in risk). Using this approach, the size of the affected population is automatically taken into
account by the number of incidences predicted by epidemiological studies applied to the relevant
population. The same type of calculation can produce values for statistical incidences of other
health endpoints.
For some health effects, such as hospital admissions, WTP estimates are generally not
available. In these cases, we use the cost of treating or mitigating the effect as a primary
estimate. For example, for the valuation of hospital admissions we use the avoided medical
costs as an estimate of the value of avoiding the health effects causing the admission. These
costs of illness (COI) estimates generally understate the true value of reductions in risk of a
health effect. They tend to reflect the direct expenditures related to treatment but not the value
of avoided pain and suffering from the health effect. In the NRD rule RIA and TSD, and the
IAQR, we describe how the changes in health effects should be valued and indicate the value
functions selected to provide monetized estimates of the value of changes in health effects.
Table 10-5 below summarizes the value estimates per health effect that we used in this analysis.
Note that the unit values for hospital admissions are the weighted averages of the ICD-9 code-
specific values for the group of ICD-9 codes included in the hospital admission categories.
Table 10-5. Unit Values Used for Economic Valuation of Health Endpoints
Health or Welfare
End point
Premature Mortality (long-
term exposure) )
Chronic Bronchitis
Estimated
Value Per
Incidence
(1999$)
Central
Estimate
$6 million per
statistical life
$331,000
Derivation of Estimates
Value is the mean of value-of-statistical-life estimates from
26 studies (5 contingent valuation and 21 labor market
studies) reviewed for the Section 812 Costs and Benefits of
the Clean Air Act, 1990-2010 (US EPA, 1999).
Value is the mean of a generated distribution of WTP to
avoid a case of pollution-related CB. WTP to avoid a case
of pollution-related CB is derived by adjusting WTP (as
described in Viscusi et al, 1991) to avoid a severe case of
CB for the difference in severity and taking into account the
elasticity of WTP with respect to severity of CB.
Hospital Admissions
Chronic Obstructive
Pulmonary Disease (COPD)
(ICD codes 490-492, 494-
496)
$12,378
The COI estimates are based on ICD-9 code level
information (e.g., average hospital care costs, average
length of hospital stay, and weighted share of total COPD
category illnesses) reported in Elixhauser (1993).
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Table 10-5. Unit Values Used for Economic Valuation of Health Endpoints
Health or Welfare
End point
Pneumonia
(ICD codes 480-487)
Asthma admissions
All Cardiovascular
(ICD codes 390-429)
Emergency room visits for
asthma
Estimated
Value Per
Incidence
(1999$)
Central
Estimate
$14,693
$6,634
$18,387
$299
Derivation of Estimates
The COI estimates are based on ICD-9 code level
information (e.g., average hospital care costs, average
length of hospital stay, and weighted share of total
pneumonia category illnesses) reported in Elixhauser
(1993).
The COI estimates are based on ICD-9 code level
information (e.g., average hospital care costs, average
length of hospital stay, and weighted share of total asthma
category illnesses) reported in Elixhauser (1993).
The COI estimates are based on ICD-9 code level
information (e.g., average hospital care costs, average
length of hospital stay, and weighted share of total
cardiovascular illnesses) reported in Elixhauser (1993).
COI estimate based on data reported by Smith, et al. (1997).
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Table 10-5. Unit Values Used for Economic Valuation of Health Endpoints
Health or Welfare
End point
Estimated
Value Per
Incidence
(1999$)
Central
Estimate
Derivation of Estimates
Respiratory Ailments Not Requiring Hospitalization
Upper Respiratory
Symptoms (URS)
Lower Respiratory
Symptoms (LRS)
Acute Bronchitis
$24
$15
$57
Combinations of the 3 symptoms for which WTP estimates
are available that closely match those listed by Pope, et al.
result in 7 different "symptom clusters," each describing a
"type" of URS. A dollar value was derived for each type of
URS, using mid-range estimates of WTP (lEc, 1994) to
avoid each symptom in the cluster and assuming additivity
of WTPs. The dollar value for URS is the average of the
dollar values for the 7 different types of URS.
Combinations of the 4 symptoms for which WTP estimates
are available that closely match those listed by Schwartz, et
al. result in 1 1 different "symptom clusters," each
describing a "type" of LRS. A dollar value was derived for
each type of LRS, using mid-range estimates of WTP (lEc,
1994) to avoid each symptom in the cluster and assuming
additivity of WTPs. The dollar value for LRS is the average
of the dollar values for the 1 1 different types of LRS.
Average of low and high values recommended for use in
Section 812 analysis (Neumann, et al. 1994)
Restricted Activity and Work Loss Days
Work Loss Days (WLDs)
Minor Restricted Activity
Days (MRADs)
Variable
$48
Regionally adjusted median weekly wage for 1990 divided
by 5 (adjusted to 1999$) (US Bureau of the Census, 1992).
Median WTP estimate to avoid one MRAD from Tolley, et
al. (1986) .
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Adjustments for Growth in Real Income
Our analysis also accounts for expected growth in real income over time. Economic
theory argues that WTP for most goods (such as environmental protection) will increase if real
incomes increase. The economics literature suggests that the severity of a health effect is a
primary determinant of the strength of the relationship between changes in real income and WTP
(Alberini, 1997; Miller, 2000; Viscusi, 1993). As such, we use different factors to adjust the
WTP for minor health effects, severe and chronic health effects, and premature mortality.
Adjustment factors used to account for projected growth in real income from 1990 to 2005 are
1.03 for minor health effects, 1.09 for severe and chronic health effects, and 1.08 for premature
mortality.
10.4.2.1 Valuation of Reductions in Premature Mortality Risk
We estimate the monetary benefit of reducing premature mortality risk using the "value
of statistical lives saved" (VSL) approach, which is a summary measure for the value of small
changes in mortality risk experienced by a large number of people. The VSL approach applies
information from several published value-of-life studies to determine a reasonable benefit of
preventing premature mortality. The mean value of avoiding one statistical death is estimated
to be $6 million in 1999 dollars. This represents an intermediate value from a range of estimates
that appear in the economics literature, and it is a value the EPA has used in rulemaking support
analyses and in the Section 812 Reports to Congress. This estimate is the mean of a distribution
fitted to the estimates from 26 value-of-life studies identified in the Section 812 reports as
"applicable to policy analysis." The approach and set of selected studies mirrors that of Viscusi
(1992) (with the addition of two studies), and uses the same criteria as Viscusi in his review of
value-of-life studies. The $6 million estimate is consistent with Viscusi's conclusion (updated
to 1999$) that "most of the reasonable estimates of the value of life are clustered in the $3.7 to
$8.6 million range." Five of the 26 studies are contingent valuation (CV) studies, which
directly solicit WTP information from subjects; the rest are wage-risk studies, which base WTP
estimates on estimates of the additional compensation demanded in the labor market for riskier
jobs, controlling for other job and employee characteristics such as education and experience.
As indicated in the previous section on quantification of premature mortality benefits,
we assume for this analysis that some of the incidences of premature mortality related to PM
exposures occur in a distributed fashion over the five years following exposure. To take this into
account in the valuation of reductions in premature mortality, we apply an annual three percent
discount rate to the value of premature mortality occurring in future years21.
The economics literature concerning the appropriate method for valuing reductions in
premature mortality risk is still developing. The adoption of a value for the projected reduction
in the risk of premature mortality is the subject of continuing discussion within the economic
and public policy analysis community. Regardless of the theoretical economic considerations,
the EPA prefers not to draw distinctions in the monetary value assigned to the lives saved even
if they differ in age, health status, socioeconomic status, gender, or other characteristic of the
adult population.
21The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the
federal government. The EPA adopted a 3 percent discount rate for its primary estimate in this case to
reflect reliance on a "social rate of time preference" discounting concept. We have also calculated benefits
and costs using a 7 percent rate consistent with an "opportunity cost of capital" concept to reflect the time
value of resources directed to meet regulatory requirements. In this case, the benefit and cost estimates were
not significantly affected by the choice of discount rate. Further discussion of this topic appears in the
EPA's Guidelines for Preparing Economic Analyses (in press).
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Following the advice of the EEAC of the SAB, the EPA currently uses the VSL approach
in calculating the primary estimate of mortality benefits, because we believe this calculation
provides the most reasonable single estimate of an individual's willingness to trade off money
for reductions in mortality risk (EPA-SAB-EEAC-00-013). Although there are several
differences between the labor market studies we use to derive a VSL estimate and the
particulate matter air pollution context addressed here, those differences in the affected
populations and the nature of the risks imply both upward and downward adjustments. In the
absence of a comprehensive and balanced set of adjustment factors, the EPA believes it is
reasonable to continue to use the $6 million value while acknowledging the significant
limitations and uncertainties in the available literature.
Some economists emphasize that the value of a statistical life is not a single number
relevant for all situations. Indeed, the VSL estimate of $6 million (1999 dollars) is itself the
central tendency of a number of estimates of the VSL for some rather narrowly defined
populations. When there are significant differences between the population affected by a
particular health risk and the populations used in the labor market studies, as is the case here,
some economists prefer to adjust the VSL estimate to reflect those differences.
There is general agreement that the value to an individual of a reduction in mortality risk
can vary based on several factors, including the age of the individual, the type of risk, the level
of control the individual has over the risk, the individual's attitudes towards risk, and the health
status of the individual. While the empirical basis for adjusting the $6 million VSL for many
of these factors does not yet exist, a thorough discussion of these factors is contained in the
benefits TSD for the nonroad diesel rulemaking (Abt Associates, 2003). The EPA recognizes
the need for investigation by the scientific community to develop additional empirical support
for adjustments to VSL for the factors mentioned above.
The SAB-EEAC advised in their recent report that the EPA "continue to use a wage-
risk-based VSL as its primary estimate, including appropriate sensitivity analyses to reflect the
uncertainty of these estimates," and that "the only risk characteristic for which adjustments to
the VSL can be made is the timing of the risk" (EPA-SAB-EEAC-00-013). In developing our
primary estimate of the benefits of premature mortality reductions, we have followed this advice
and discounted over the lag period between exposure and premature mortality.
Uncertainties Specific to Premature Mortality Valuation. The economic benefits
associated with premature mortality are the largest category of monetized benefits of the
NESHAP. In addition, in prior analyses, the EPA has identified valuation of mortality benefits
as the largest contributor to the range of uncertainty in monetized benefits (see EPA [1999]).
Because of the uncertainty in estimates of the value of premature mortality avoidance, it is
important to adequately characterize and understand the various types of economic approaches
available for mortality valuation. Such an assessment also requires an understanding of how
alternative valuation approaches reflect that some individuals may be more susceptible to air
pollution-induced mortality or reflect differences in the nature of the risk presented by air
pollution relative to the risks studied in the relevant economics literature.
The health science literature on air pollution indicates that several human characteristics
affect the degree to which mortality risk affects an individual. For example, some age groups
appear to be more susceptible to air pollution than others (e.g., the elderly and children). Health
status prior to exposure also affects susceptibility. An ideal benefits estimate of mortality risk
reduction would reflect these human characteristics, in addition to an individual's WTP to
improve one's own chances of survival plus WTP to improve other individuals' survival rates.
The ideal measure would also take into account the specific nature of the risk reduction
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commodity that is provided to individuals, as well as the context in which risk is reduced. To
measure this value, it is important to assess how reductions in air pollution reduce the risk of
dying from the time that reductions take effect onward, and how individuals value these changes.
Each individual's survival curve, or the probability of surviving beyond a given age, should shift
as a result of an environmental quality improvement. For example, changing the current
probability of survival for an individual also shifts future probabilities of that individual's
survival. This probability shift will differ across individuals because survival curves depend on
such characteristics as age, health state, and the current age to which the individual is likely to
survive.
Although a survival curve approach provides a theoretically preferred method for valuing
the benefits of reduced risk of premature mortality associated with reducing air pollution, the
approach requires a great deal of data to implement. The economic valuation literature does not
yet include good estimates of the value of this risk reduction commodity. As a result, in this
study we value avoided premature mortality risk using the VSL approach.
Other uncertainties specific to premature mortality valuation include the following:
• Across-study variation: There is considerable uncertainty as to whether the available
literature on VSL provides adequate estimates of the VSL saved by air pollution
reduction. Although there is considerable variation in the analytical designs and data
used in the existing literature, the majority of the studies involve the value of risks
to a middle-aged working population. Most of the studies examine differences in
wages of risky occupations, using a wage-hedonic approach. Certain characteristics
of both the population affected and the mortality risk facing that population are
believed to affect the average WTP to reduce the risk. The appropriateness of a
distribution of WTP based on the current VSL literature for valuing the
mortality-related benefits of reductions in air pollution concentrations therefore
depends not only on the quality of the studies (i.e., how well they measure what they
are trying to measure), but also on the extent to which the risks being valued are
similar and the extent to which the subjects in the studies are similar to the
population affected by changes in pollution concentrations.
• Level of risk reduction: The transferability of estimates of the VSL from the wage-
risk studies to the context of the Interstate Air Quality Rulemaking analysis rests on
the assumption that, within a reasonable range, WTP for reductions in mortality risk
is linear in risk reduction. For example, suppose a study estimates that the average
WTP for a reduction in mortality risk of 1/100,000 is $50, but that the actual
mortality risk reduction resulting from a given pollutant reduction is 1/10,000. If
WTP for reductions in mortality risk is linear in risk reduction, then a WTP of $50
for a reduction of 1/100,000 implies a WTP of $500 for a risk reduction of 1/10,000
(which is 10 times the risk reduction valued in the study). Under the assumption of
linearity, the estimate of the VSL does not depend on the particular amount of risk
reduction being valued. This assumption has been shown to be reasonable provided
the change in the risk being valued is within the range of risks evaluated in the
underlying studies (Rowlatt et al., 1998).
• Voluntariness of risks evaluated: Although job-related mortality risks may differ in
several ways from air pollution-related mortality risks, the most important difference
may be that job-related risks are incurred voluntarily, or generally assumed to be,
whereas air pollution-related risks are incurred involuntarily. Some evidence
suggests that people will pay more to reduce involuntarily incurred risks than risks
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incurred voluntarily. If this is the case, WTP estimates based on wage-risk studies
may understate WTP to reduce involuntarily incurred air pollution-related mortality
risks.
Sudden versus protracted death: A final important difference related to the nature
of the risk may be that some workplace mortality risks tend to involve sudden,
catastrophic events, whereas air pollution-related risks tend to involve longer periods
of disease and suffering prior to death. Some evidence suggests that WTP to avoid
a risk of a protracted death involving prolonged suffering and loss of dignity and
personal control is greater than the WTP to avoid a risk (of identical magnitude) of
sudden death. To the extent that the mortality risks addressed in this assessment are
associated with longer periods of illness or greater pain and suffering than are the
risks addressed in the valuation literature, the WTP measurements employed in the
present analysis would reflect a downward bias.
Self-selection and skill in avoiding risk. Recent research (Shogren et al., 2002)
suggests that VSL estimates based on hedonic wage studies may overstate the
average value of a risk reduction. This is based on the fact that the risk-wage
tradeoff revealed in hedonic studies reflects the preferences of the marginal worker
(i.e., that worker who demands the highest compensation for his risk reduction).
This worker must have either higher risk, lower risk tolerance, or both. However,
the risk estimate used in hedonic studies is generally based on average risk, so the
VSL may be upwardly biased because the wage differential and risk measures do not
match.
10.4.2.2 Valuation of Reductions in Chronic Bronchitis
The best available estimate of WTP to avoid a case of chronic bronchitis (CB) comes
from Viscusi, et al. (1991). The Viscusi, et al. study, however, describes a severe case of CB to
the survey respondents. We therefore employ an estimate of WTP to avoid a pollution-related
case of CB, based on adjusting the Viscusi, et al. (1991) estimate of the WTP to avoid a severe
case. This is done to account for the likelihood that an average case of pollution-related CB is
not as severe. The adjustment is made by applying the elasticity of WTP with respect to severity
reported in the Krupnick and Cropper (1992) study. Details of this adjustment procedure can
be found in the IAQR and its supporting documentation, and in the most recent Section 812
study (EPA 1999).
We use the mean of a distribution of WTP estimates as the central tendency estimate of
WTP to avoid a pollution-related case of CB in this analysis. The distribution incorporates
uncertainty from three sources: (1) the WTP to avoid a case of severe CB, as described by
Viscusi, et al.; (2) the severity level of an average pollution-related case of CB (relative to that
of the case described by Viscusi, et al.); and (3) the elasticity of WTP with respect to severity
of the illness. Based on assumptions about the distributions of each of these three uncertain
components, we derive a distribution of WTP to avoid a pollution-related case of CB by
statistical uncertainty analysis techniques. The expected value (i.e., mean) of this distribution,
which is about $331,000 (1999$), is taken as the central tendency estimate of WTP to avoid a
PM-related case of CB.
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10.4.3 Results of Phase One Analysis: Benefits Resulting from a Portion of Emission
Reductions at a Subset of Boiler and Process Heater Sources
Applying the C-R and valuation functions described above to the estimated changes in
PM from phase one of our analysis yields estimates of the number of avoided incidences (i.e.
premature mortalities, cases, admissions, etc.) and the associated monetary values for those
avoided incidences. In Table 10-6, we provide the results for the MACT floor option resulting
from the phase one analysis. Tables 10-7 present the results for the above the MACT floor
option resulting from the phase one analysis. To obtain a total benefit estimate, we aggregate
dollar benefits associated with each of the health effects examined, such as hospital admissions,
assuming that none of the included health and welfare effects overlap. All of the monetary
benefits are in constant 1999 dollars.
As we have discussed, not all known PM-related health and welfare effects could be
quantified or monetized. These unmonetized benefits are indicated in Tables 10-6 and 10-7 by
place holders, labeled Bx and B2. In addition, unmonetized benefits associated with HAP
reductions are indicated by the placeholder B3. Unquantified reduce incidences of physical
effects are indicated by Uj and U2. The estimate of total monetized health benefits is thus equal
to the subset of monetized PM-related health benefits plus BH, the sum of the unmonetized
health benefits.
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Table 10-6. Phase One Analysis: Estimate of Annual Benefits
Associated with Approximately 50% of the Emission Reductions
from the Industrial Boilers/Process Heaters NESHAP
(MACT Floor Regulatory Option in 2005)
Using Air Quality Modeling & the CAPMS Benefit ModelA B
Endpoint
Premature mortalityE>F (long-term exposure, adults 30 and over)
-Using a 3% discount rate
-Using a 7% discount rate
Chronic bronchitis (adults, 26 and over, WTP valuation)
Hospital Admissions - Pneumonia (adults, over 64)
Hospital Admissions - COPD (adults, 64 and over)
Hospital Admissions -Asthma (65 and younger)
Hospital Admissions - Cardiovascular (adults, over 64)
Emergency Room Visits for Asthma (65 and younger)
Asthma Attacks (asthmatics, all ages)
Acute bronchitis (children, 8-12)
Lower respiratory symptoms (children, 7-14)
Upper respiratory symptoms (asthmatic children, 9-11)
Work loss days (adults, 18-65)
Minor restricted activity days (adults, age 18-65)
Other PM-related health effects0
HAP health effects0
Total Monetized Health-Related BenefitsF
-Using a 3% discount rate
-Using a 7% discount rate
Avoided
Incidence0
(cases/year)
1,170
1,170
2,340
500
420
120
1,230
930
79,020
2,430
26,470
89,480
205,400
1,011,200
Uj
U2
—
—
Monetary Benefits"
(millions 1999$,
adjusted for growth in
real income)
$7,325
$6,880
$845
$5
$5
$1
$25
<$1
B!
<$1
<$1
$5
$20
$50
B2
B3
$8,280+BH
$7,835+BH
AThe results presented in this table are based on those SO2 and PM emission reductions identified for specific sources included in the Inventory
Database. This includes approximately 50% of all emission reductions estimated by the rule. The location of all other emission reductions (i.e.
non-inventory reductions) cannot be determined specifically and hence cannot be modeled in an air quality model. See Section 10.5 and
Appendix D for benefit estimation of non-inventory emission reductions.
BThe results presented in this table reflect the outcome of the combination of PM and SO2 model runs. See Appendix D for a presentation of
results for each pollutant independently.
c Incidences are rounded to the nearest 10 and may not add due to rounding. Incidences of unquantified endpoints are indicated with a U.
D Dollar values are rounded to the nearest 5 million and may not add due to rounding. The value of unquantified endpoints are indicated with
aB.
E Note that the estimated value for PM-related premature mortality assumes the 5 year distributed lag structure described in detail in the
Regulatory Impact Analysis of Heavy Duty Engine/Diesel Fuel rule.
F Monetized benefits are presented using two different discount rates. Results calculated using 3 percent discount rate are recommended by
EPA's Guidelines for Preparing Economic Analyses (U.S. EPA, 2000a). Results calculated using 7 percent discount rate are recommended by
OMB Circular A-94 (OMB, 1992).
G A detailed listing of unquantified PM and HAP related health effects is provided in Table 10-17.
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Thus, the estimate of total monetized benefits for phase one of the Industrial
Boilers/Process Heaters NESHAP benefit analysis associated with the MACT floor is
approximately $8 billion + BH (using either a 3% or 7% discount rate). The benefits of phase
one in combination with the phase two estimate of benefits will serve as the basis for our
estimate of the total benefits of the regulation.
For the Above the MACT floor option of this NESHAP, Table 10-7 indicates that the
estimate of total monetized benefits for phase one of the analysis is approximately $10 billion
+ BH using a 3% discount rate (or approximately $9.5 billion using a 7% discount rate).
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Table 10-7. Phase One Analysis: Estimate of Annual Benefits
Associated with Approximately 50% of the Emission Reductions
from the Industrial Boilers/Process Heaters NESHAP
(Above the MACT Floor Regulatory Option in 2005)
Using Air Quality Modeling & the CAPMS Benefit ModelA B
Endpoint
Premature mortalityE>F (long-term exposure, adults, 30 and over)
-Using a 3% discount rate
-Using a 7% discount rate
Chronic bronchitis (adults, 26 and over, WTP valuation)
Hospital Admissions - Pneumonia (adults, over 64)
Hospital Admissions - COPD (adults, 64 and over)
Hospital Admissions -Asthma (65 and younger)
Hospital Admissions - Cardiovascular (adults, over 64)
Emergency Room Visits for Asthma (65 and younger)
Asthma Attacks (asthmatics, all ages)
Acute bronchitis (children, 8-12)
Lower respiratory symptoms (children, 7-14)
Upper respiratory symptoms (asthmatic children, 9-11)
Work loss days (adults, 18-65)
Minor restricted activity days (adults, age 18-65)
Other PM-related health effectsF
HAP health effects0
Total Monetized Health-Related BenefitsF
-Using a 3% discount rate
-Using a 7% discount rate
Avoided
Incidence0
(cases/year)
1,390
1,390
2,860
610
500
140
1,480
1,140
97,060
2,870
31,290
110,370
243,870
1,196,500
Uj
U2
—
—
Monetary Benefits"
(millions 1999$,
adjusted for growth in
real income)
$8,740
$8,210
$1,030
$10
$5
$1
$25
<$1
B!
<$1
<$1
$5
$25
$60
B2
B3
$9,905+BH
$9,375+BH
The results presented in this table are based on those SO2 and PM emission reductions identified for specific sources included in the Inventory
Database. This includes approximately 50% of all emission reductions estimated by the rule. The location of all other emission reductions (i.e.
non-inventory reductions) cannot be determined specifically and hence cannot be modeled in an air quality model. See Section 10.5 and
Appendix D for benefit estimation of non-inventory emission reductions.
B The results presented in this table reflect the outcome of the combination of PM and SO2 model runs. See Appendix D for a presentation of
results for each pollutant independently.
c Incidences are rounded to the nearest 10 and may not add due to rounding. Incidences of unquantified endpoints are indicated with a U.
D Dollar values are rounded to the nearest 5 million and may not add due to rounding. The value of unquantified endpoints are indicated with
aB.
E Note that the estimated value for PM-related premature mortality assumes the 5 year distributed lag structure described in detail in the
Regulatory Impact Analysis of Heavy Duty Engine/Diesel Fuel rule.
E Monetized benefits are presented using two different discount rates. Results calculated using 3 percent discount rate are recommended by
EPA's Guidelines for Preparing Economic Analyses (U.S. EPA, 2000a). Results calculated using 7 percent discount rate are recommended by
OMB Circular A-94 (OMB, 1992).
F A detailed listing of unquantified PM and HAP related health effects is provided in Table 10-17.
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10.5 Phase Two Analysis: Benefit Transfer Valuation of Remaining Emission Reductions
As is mentioned previously, only a portion of the expected emission reductions of the
rule can be mapped to specific locations and hence modeled to determine the change in air
quality (e.g., change in ambient PM concentrations). For approximately 50% of the PM
reductions and approximately 30% of the SO2 reductions, the lack of location-specific data
prevents us from utilizing the S-R Matrix to determine air quality changes and the CAPMS
model to estimate total benefits. We can assume, however, that these reductions are achieved
uniformly throughout the country because the location of boilers/process heaters in the U.S. is
spread fairly evenly across all states. To estimate benefits for these reductions, we use the
results of the air quality and benefit analysis provided in phase one to infer the average benefit
value per ton of emission reduction for each pollutant - PM and SO2. The benefit transfer values
for PM and SO2 are then applied to all remaining emission reductions to approximate total
benefits of phase two of this analysis.
Before determining the benefit value to transfer to these reductions, one consideration
must first be made. The total benefits that result from the air quality analysis of phase one is due
to the combination of both direct PM reductions and SO2 reductions that transform into
secondary PM. Without knowledge of the percent of the total benefits in phase one that can be
attributed to direct PM versus the percent of phase one benefits attributed to SO2, we cannot
accurately assign the monetized benefits to the tons reduced of each pollutant. To correctly
apportion the total benefit value from phase one to the respective PM and SO2 reductions, we
performed two additional S-R Matrix model runs of the reductions valued in phase one; one
evaluation of the benefits of the PM reductions alone (holding SO2 unchanged), and one run of
the benefits of the SO2 reductions alone (holding PM reductions unchanged). This allows us to
determine the appropriate benefit transfer value for each individual pollutant. Because the
combined effect of reducing both PM and SO2 simultaneously at one location would result in
a larger change in the concentration of PM, it can be expected that the air quality analyses of
each pollutant alone will result in lower changes in concentrations and hence lower calculated
benefits. The air quality and benefit assessment of the individual pollutants are again performed
for each regulatory option: the MACT floor, and the above the MACT floor option. The detailed
results of the additional air quality and benefit model runs are reported in Appendix D.
These data, along with the set of C-R and valuation functions contained in CAPMS,
constitute the input set for the benefits transfer value function. The benefits transfer function
for each pollutant is specified as:
Transfer Value =
Emission Reductions
The numerator in the transfer value formula is total monetary benefits, which is
determined by applying the same economic valuation functions specified in Table 10-5 to
changes in incidences of human health endpoints resulting from the air quality modeling of each
pollutant separately. In Appendix D, we show the calculated benefit transfer value of the total
monetized benefits of PM alone and SO2 alone and also for each individual endpoint included
in this analysis.
A similar calculation is also done for the number of incidences associated with each
endpoint. From the air quality assessments of PM and SO2 alone, we divide total incidences of
an endpoint by the total emission reductions included in the air quality scenario. Therefore, we
determine a measure of the number of incidences of each health effect that can result from a ton
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of pollutant reductions (for example, 0.10 fewer asthma cases per ton reduced). This allows us
to transfer the incidence per ton reduced to the remaining set of emission reductions of the phase
two analysis.
Note that for both dollar and incidence per ton estimates, we assume that each ton of
pollutant has the same impact, so that subnational applications are inappropriate as the national
application requires assuming populations are uniformly distributed throughout the U.S.
Once all transfer values are determined for each endpoint and total benefits, we apply
them to the set of phase two emission reductions. Finally, we combine our phase two estimates
of benefits with the phase one calculated benefits to provide an estimate of total benefits for each
endpoint and determine the total monetized benefits associated with the rule.
Sections 10.5.1 and 10.5.2 provide further detail on the transfer values obtained for SO2
and PM in this analysis.
10.5.1 SO2 Benefits Transfer Values
Using the results of the air quality analysis of SO2 reductions alone (holding PM
unchanged) from phase one, we can extract information on the total number of incidences and
total benefit value of each endpoint to estimate the SO2 benefit transfer values. As an example
of the calculation consider the following: the total SO2 emission reductions applied in the S-R
matrix analysis for phase one of this analysis are 82,542 tons. Under the MACT floor, the
analysis yields approximately 240 fewer premature deaths at a total value of $1.5 billion (see
Appendix D for details). Therefore, the benefit transfer value to apply to SO2 emission
reductions in the phase two analysis associated with the mortality endpoint would on average
be $18,385 per ton reduced. This procedure is repeated for each endpoint and for the total
benefits estimate associated with SO2 reductions alone. Further, based on these results it can be
estimated that SO2 reductions from the MACT floor on average result in 0.003 fewer incidences
of mortality per ton reduced (240 incidences/82,542 tons).
The following tables present the incidence and benefits data necessary to calculate the
benefits transfer values for SO2. Table 10-8 present the benefit transfer values for the MACT
floor option, while Table 10-9 presents benefit transfer values associated with the Above the
MACT floor option. The benefits transfer values for SO2 emission reductions are reported in
1999 dollars. Differences in benefit/ton estimates between the MACT floor option and the above
the floor option may be due to differences in the location of emission reductions and other
factors. In particular, while PM reductions from process heaters are not expected to accrue at
the MACT floor level of control, approximately 18,300 tons are estimate for the above the floor
option. The Inventory Database provides information on the location of the majority of process
heaters and thus we can apply a large percentage of these reductions directly into the air quality
and benefit analysis. In addition, the process heaters affected by this proposal are largely found
at large facilities located near large cities, thus the changes in air quality are applied to the
populated areas around the cities.
10-34
-------
Table 10-8. SO2 Benefit Transfer Values
Based on Data From Phase One Analysis
MACTFloor Regulatory Option^
Endpoint
Premature mortalityE (long-term exposure, adults
30 and over)
-Using a 3% discount rate
-Using a 7% discount rate
Chronic bronchitis (adults, 26 and over, WTP
valuation)
Hospital Admissions - Pneumonia (adults, over
64)
Hospital Admissions - COPD (adults, 64 and over)
Hospital Admissions - Asthma (65 and younger)
Hospital Admissions - Cardiovascular (adults,
over 64)
Emergency Room Visits for Asthma (65 and
younger)
Asthma Attacks (asthmatics, all ages)
Acute bronchitis (children, 8-12)
Lower respiratory symptoms (children, 7-14)
Upper respiratory symptoms (asthmatic children,
9-11)
Work loss days (adults, 18-65)
Minor restricted activity days (adults, age 18-65)
Other PM-related health effectsF
HAP-related health effectsF
Total Benefits of SO2-Related Reductions"
-Using a 3% discount rate
-Using a 7% discount rate
Avoided
Incidence8
(cases/year)
240
240
320
60
50
20
150
130
11,120
490
5,330
12,980
42,611
214,592
u,
U2
—
—
Incidence Per
Ton Reduced0
0.0029
0.0029
0.0039
0.0008
0.0006
0.0003
0.0018
0.0016
0.1347
0.0059
0.0645
0.1572
0.5162
2.5998
Monetary
Benefits6
(millions 1999$,
adjusted for
growth in real
income)
$1,520
$1,425
$115
$1
$1
<$1
$5
<$1
B,
<$1
<$1
<$1
$5
$10
B2
B3
$1,650
$1,560
Total
Benefit Per
Ton
Reduced0
($/ton)
$18,385
$17,270
$1,400
$10
$5
<$5
$30
<$1
B,
<$1
$1
$5
$55
$130
B2
B3
$20,030+B
H
$18,910+B
H
1 Results of the phase one benefit analysis of SO2 emission reductions are presented in Appendix D, and replicated in columns 2 and 4 of this
table.
B Incidences are rounded to the nearest 10 and may not add due to rounding. Incidences of unquantified endpoints are indicated with a U.
c Total SO2 emission reductions included in the phase one analysis and applied to derive the benefit transfer values of this table are 82,542 tons.
D Dollar values are rounded to the nearest 5 million and may not add due to rounding. The value of unquantified endpoints are indicated with
aB.
E Monetized benefits are presented using two different discount rates. Results calculated using 3 percent discount rate are recommended by
EPA's Guidelines for Preparing Economic Analyses (U.S. EPA, 2000a). Results calculated using 7 percent discount rate are recommended by
OMB Circular A-94 (OMB, 1992).
10-35
-------
Table 10-9. SO2 Benefit Transfer Values
Based on Data From Phase One Analysis
Above the MACTFloor Regulatory Option^
Endpoint
Premature mortality (long-term exposure, adults,
30 and over)
-Using a 3% discount rate
-Using a 7% discount rate
Chronic bronchitis (adults, 26 and over, WTP
valuation)
Hospital Admissions - Pneumonia (adults, over
64)
Hospital Admissions - COPD (adults, 64 and over)
Hospital Admissions - Asthma (65 and younger)
Hospital Admissions - Cardiovascular (adults,
over 64)
Emergency Room Visits for Asthma (65 and
younger)
Asthma Attacks (asthmatics, all ages)
Acute bronchitis (children, 8-12)
Lower respiratory symptoms (children, 7-14)
Upper respiratory symptoms (asthmatic children,
9-11)
Work loss days (adults, 18-65)
Minor restricted activity days (adults, age 18-65)
Other PM-related health effects
HAP-related health effects
Total Benefits of SO2-Related Reductions
-Using a 3% discount rate
-Using a 7% discount rate
Avoided
Incidence8
(cases/year)
310
310
400
70
60
30
170
150
12,250
660
7,170
14,160
54,980
279,760
u,
U2
—
—
Incidence Per
Ton Reduced0
0.0032
0.0032
0.0042
0.0007
0.0006
0.0003
0.0018
0.0015
0.1284
0.0069
0.0752
0.1485
0.5765
2.9337
Monetary
Benefits6
(millions 1999$,
adjusted for
growth in real
income)
$1,935
$1,820
$145
$1
$1
<$1
$5
<$1
B,
<$1
<$1
<$1
$5
$15
B2
B3
$2,105
$1,990
Total
Benefit Per
Ton
Reduced0
($/ton)
$20,305
$19,070
$1,500
$10
$10
<$5
$35
<$1
B,
<$1
$1
$5
$60
$145
B2
B3
$22,070+^
$20,840+B
H
1 Results of the phase one benefit analysis of SO2 emission reductions are presented in Appendix D, and replicated in columns 2 and 4 of this
table.
B Incidences are rounded to the nearest 10 and may not add due to rounding. Incidences of unquantified endpoints are indicated with a U.
c Total SO2 emission reductions included in the phase one analysis and applied to derive the benefit transfer values of this table are 95,361 tons.
D Dollar values are rounded to the nearest 5 million and may not add due to rounding. The value of unquantified endpoints are indicated with
aB.
10.5.2 PMBenefits Transfer Values
The transfer values for PM are developed using the same basic approach as for the SO2
reductions. However, the PM benefits analysis conducted for this RIA includes health benefits
associated with reductions in both PM2 5 and PM1(,. Therefore, the benefit transfer values for
endpoints associated with PM2 5 alone will be established using an estimate of the portion of total
PM reductions that are likely to be PM2 5. Likewise the benefit endpoints associated with PM10
alone require an estimate of PM10 emission reductions to derive the benefit transfer value for
10-36
-------
such endpoints. Fortunately, the S-RMatrix model has a component that can approximate PM2 5
emissions from a total change in PM. Based on this approximation, of the 265,155 tons of PM10
emission reductions included in the air quality analysis of the MACT floor from phase one,
approximately 75,095 tons are PM2 5.22
The endpoints associated with PM2 5 are long-term mortality, minor restricted activity
days (MRAD), and acute respiratory symptoms. All other endpoints are associated with PM10
reductions. For the MACT floor option, Tables 10-9 present the total incidence and benefits
data for each endpoint from the phase one analysis , and the calculated the benefits transfer
values for PM that are to be applied for the phase two analysis. Table 10-10 present similar data
for the above the MACT floor regulatory option.
Reductions in PM2 s are derived as a function of the estimated PM10 reductions. The S-R matrix model
contains coefficients that relate reductions in both directly emitted PM10 and directly emitted PM2 5. At the
time the S-R matrix was being developed in the early 1990s, a nationwide inventory of directly emitted
PM2 5 emissions was not available, so the author developed a method for crudely estimating PM2 5 emissions
from PM10 emissions. The air quality changes predicted by the model for direct PM2 5 were then developed
from these crude emissions estimates. A full discussion of the derivation of PM25 estimates is provided in
E.H. Pechan (1994 and 1996), and Latimer and Associates(1996). The PM Calculator Tool can also be
found on the Internet at www.epa.gov/chief/software/pmcalc/index.html.
10-37
-------
Table 10-10. PM Benefit Transfer Values
Based on Data From Phase One Analysis
MACTFloor Regulatory Option^
End point
Premature mortality (long-term, adults, 30 and
over)
-Using a 3% discount rate
-Using a 7% discount rate
Chronic bronchitis (adults, 26 and over, WTP
valuation)
Hospital Admissions - Pneumonia (adults, over
64)
Hospital Admissions - COPD (adults, 64 and
over)
Hospital Admissions - Asthma (65 and younger)
Hospital Admissions - Cardiovascular (adults,
over 64)
Emergency Room Visits for Asthma (65 and
younger)
Asthma Attacks (asthmatics, all ages)
Acute bronchitis (children, 8-12)
Lower respiratory symptoms (children, 7-14)
Upper respiratory symptoms (asthmatic children,
9-11)
Work loss days (adults, 18-65)
Minor restricted activity days (adults, age 18-65)
Other PM-related health effects
HAP-related health effects
Total Benefits of PM-Related Reductions
-Using a 3% discount rate)
-Using a 7% discount rate
Avoided
Incidence8
(cases/year)
900
900
2,360
510
420
90
1,230
950
80,700
1,870
20,370
91,620
158,560
760,870
u,
U2
—
—
Incidence Per
Ton Reduced0
0.01202
0.01202
0.0089
0.0019
0.0016
0.0012
0.0046
0.0036
0.3043
0.0248
0.2712
0.3455
2.1115
10.132
Monetary
Benefits6
(millions 1999$,
adjusted for
growth in real
income)
$5,675
$5,330
$850
$10
$5
$1
$25
<$1
B,
<$1
<$1
$5
$20
$40
B2
B3
$6,620
$6,275
Total
Benefit Per
Ton
Reduced0
($/ton)
$75,595
$71,005
$3,195
$30
$20
$10
$85
$1
B,
$1
$5
$10
$225
$500
B2
B3
$88,120+B
H
$83,530+B
H
1 Results of the phase one benefit analysis of PM emission reductions are presented in Appendix D, and replicated in columns 2 and 4 of this
table.
B Incidences are rounded to the nearest 10 and may not add due to rounding. Incidences of unquantified endpoints are indicated with a U.
c Total PM10 and PM2 5 emission reductions included in the phase one analysis and applied to derive the benefit transfer values of this table are
265,155 tons and 75,095 tons, respectively.
D Dollar values are rounded to the nearest 5 million and may not add due to rounding. The value of unquantified endpoints are indicated with
aB.
10-38
-------
Table 10-11. PM Benefit Transfer Values
Based on Data From Phase One Analysis
Above the MACTFloor Regulatory Option^
Endpoint
Premature mortality (long-term exposure, adults,
30 and over)
-Using a 3% discount rate
-Using a 7% discount rate
Chronic bronchitis (adults, 26 and over, WTP
valuation)
Hospital Admissions - Pneumonia (adults, over
64)
Hospital Admissions - COPD (adults, 64 and over)
Hospital Admissions - Asthma (65 and younger)
Hospital Admissions - Cardiovascular (adults,
over 64)
Emergency Room Visits for Asthma (65 and
younger)
Asthma Attacks (asthmatics, all ages)
Acute bronchitis (children, 8-12)
Lower respiratory symptoms (children, 7-14)
Upper respiratory symptoms (asthmatic children,
9-11)
Work loss days (adults, 18-65)
Minor restricted activity days (adults, age 18-65)
Other PM-related health effects
HAP-related health effects
Total Benefits of PM-Related Reductions
-Using a 3% discount rate
-Using a 7% discount rate
Avoided
Incidence8
(cases/year)
1,090
1,090
2,680
570
470
110
1,390
1,070
90,940
2,230
24,330
103,400
190,370
918,650
u,
U2
—
—
Incidence Per
Ton Reduced0
0.0115
0.0115
0.0085
0.0018
0.0015
0.0012
0.0044
0.0034
0.2897
0.0236
0.2572
0.3294
2.0131
9.7144
Monetary
Benefits6
(millions 1999$,
adjusted for
growth in real
income)
$6,835
$6,420
$965
$10
$5
$1
$25
<$1
B,
<$1
<$1
$5
$20
$45
B2
B3
$7,910
$7,495
Total
Benefit Per
Ton
Reduced0
$72,290
$67,900
$3,070
$30
$20
$10
$80
$1
B,
$1
$5
$10
$215
$485
B2
B3
$83,645+^
$79,255+B
H
A Results of the phase one benefit analysis of PM emission reductions are presented in Appendix D, and replicated in columns 2 and 4 of this
table.
B Incidences are rounded to the nearest 10 and may not add due to rounding. Incidences of unquantified endpoints are indicated with a U.
c Total PM10 and PM2 5 emission reductions included in the phase one analysis and applied to derive the benefit transfer values of this table are
313,947 tons and 94,565 tons, respectively.
D Dollar values are rounded to the nearest 5 million and may not add due to rounding. The value of unquantified endpoints are indicated with
aB.
10-39
-------
10.5.3 Application of Benefits Transfer Values to Phase Two Emission Reductions
Emission reductions included in phase two of our benefit analysis are summarized in
Table 10-2. These reductions will be applied to the benefit transfer values developed in the
previous section. These emission reductions are derived by simply subtracting the emission
reductions including in the phase one analysis from the total emission reductions anticipated
from this NESHAP.
Thus, in the final step of the phase two analysis, the transfer values calculated in section
10.5.3 are multiplied by the emission reductions associated with the phase two analysis.
Appendix D provides tables showing the benefit estimation for each pollutant (PM and SO2)
separately. In the tables below, we combine the total SO2 benefits of phase two with the total
PM benefits of phase two from Appendix D to provide a summary of total benefits associated
with phase two of this analysis for each regulatory option analyzed.
10-40
-------
Table 10-12. Phase Two Analysis:
Annual Health Benefits
Associated with Non-Inventory Emission Reductions
of the Industrial Boilers/Process Heaters NESHAP -
MACT Floor Regulatory Option in 2005,
Using Benefit Transfer ValuesA
Endpoint
Premature mortality13 (long-term exposure, adults, 30 and over)
-Using a 3% discount rate
-Using a 7% discount rate
Chronic bronchitis (adults, 26 and over, WTP valuation)
Hospital Admissions - Pneumonia (adults, over 64)
Hospital Admissions - COPD (adults, 64 and over)
Hospital Admissions - Asthma (65 and younger)
Hospital Admissions - Cardiovascular (adults, over 64)
Emergency Room Visits for Asthma (65 and younger)
Asthma Attacks (asthmatics, all ages)
Acute bronchitis (children, 8-12)
Lower respiratory symptoms (children, 7-14)
Upper respiratory symptoms (asthmatic children, 10-11)
Work loss days (adults, 18-65)
Minor restricted activity days (adults, age 18-65)
Other PM-related health effects13
HAP-related health effects13
Total Monetized Health-Related Benefits
-Using a 3% discount rate
-Using a 7% discount rate
Avoided
Incidence8
(cases/year)
1,100
1,110
2,760
590
490
110
1,430
1,110
94,470
2,270
24,770
107,380
193,270
931,140
u,
U2
—
—
Monetary Benefitsc
(millions 1999$,
adjusted for growth in
real income)
$6,920
$6,495
$990
$10
$5
$1
$25
<$1
B!
<$1
<$1
<$5
$20
$45
B2
B3
$8,020+BH
$7,600+BH
A The results presented in this table reflect the outcome of the combination of PM and SO2 benefit estimates from the application of benefit
transfer values applied in the phase two analysis. See Appendix D for a presentation of results for each pollutant independently.
B Incidences are rounded to the nearest 10 and may not add due to rounding. Incidences of unquantified endpoints are indicated with a U.
c Dollar values are rounded to the nearest 5 million and may not add due to rounding. The value of unquantified endpoints are indicated with
aB.
D Note that the estimated value for PM-related premature mortality assumes the 5 year distributed lag structure described in detail in the
Regulatory Impact Analysis of Heavy Duty Engine/Diesel Fuel rule.
E A detailed listing of unquantified PM and HAP related health effects is provided in Table 10-16.
10-41
-------
Table 10-13. Phase Two Analysis:
Annual Health Benefits Associated with Non-Inventory
Emission Reductions of the Industrial Boilers/Process Heaters NESHAP -
Above the MACT Floor Regulatory Option in 2005,
Using Benefit Transfer ValuesA
Endpoint
Premature mortality13 (long-term exposure, adults, 30 and over)
-Using a 3% discount rate
-Using a 7% discount rate
Chronic bronchitis (adults, 26 and over, WTP valuation)
Hospital Admissions - Pneumonia (adults, over 64)
Hospital Admissions - COPD (adults, 64 and over)
Hospital Admissions - Asthma (65 and younger)
Hospital Admissions - Cardiovascular (adults, over 64)
Emergency Room Visits for Asthma (65 and younger)
Asthma Attacks (asthmatics, all ages)
Acute bronchitis (children, 8-12)
Lower respiratory symptoms (children, 7-14)
Upper respiratory symptoms (asthmatic children, 10-11)
Work loss days (adults, 18-65)
Minor restricted activity days (adults, age 18-65)
Other PM-related health effects13
HAP-related health effects13
Total Monetized Health-Related Benefits
-Using a 3% discount rate
-Using a 7% discount rate
Avoided
Incidence8
(cases/year)
1,020
1,020
2,350
500
410
100
1,200
930
79,260
2,100
22,890
90,220
178,650
868,360
u,
U2
—
—
Monetary Benefitsc
(millions 1999$,
adjusted for growth in
real income)
$6,400
$6,010
$850
$10
$5
$1
$20
<$1
B!
<$1
<$1
<$5
$20
$45
B2
B3
$7,350+BH
$6,960+BH
A The results presented in this table reflect the outcome of the combination of PM and SO2 benefit estimates from the application of benefit
transfer values applied in the phase two analysis. See Appendix D for a presentation of results for each pollutant independently.
B Incidences are rounded to the nearest 10 and may not add due to rounding. Incidences of unquantified endpoints are indicated with a U.
c Dollar values are rounded to the nearest 5 million and may not add due to rounding. The value of unquantified endpoints are indicated with
aB.
D Note that the estimated value for PM-related premature mortality assumes the 5 year distributed lag structure described in detail in the
Regulatory Impact Analysis of Heavy Duty Engine/Diesel Fuel rule.
E A detailed listing of unquantified PM and HAP related health effects is provided in Table 10-16.
10-42
-------
10.6 Total Benefits of the Industrial Boilers/Process Heaters NESHAP
Given the estimates of benefits from phases one and two of this analysis, this section
combines those results to present the estimate of total benefits of the NESHAP. To obtain this
estimate, we aggregate dollar benefits associated with each of the effects examined, such as
hospital admissions, into a total benefits estimate assuming that none of the included health and
welfare effects overlap. The benefits associated with the health and welfare effects is the sum
of the separate effects estimates. Total monetized benefits associated with the MACT floor
regulatory option of the Industrial Boilers/Process Heaters NESHAP are listed in Table 10-14,
along with a breakdown of benefits by endpoint. Table 10-15 provides total annual benefits of
the above the MACT floor option.
Again, note that the value of endpoints known to be affected by PM that we are not able
to monetize are assigned a placeholder value (e.g., Bl3 B2, etc.). Unquantified physical effects
are indicated by a U. The estimate of total benefits is thus the sum of the monetized benefits and
a constant, B, equal to the sum of the unmonetized benefits, B1+B2+...+Bn.
A comparison of the incidence column to the monetary benefits column reveals that there
is not always a close correspondence between the number of incidences avoided for a given
endpoint and the monetary value associated with that endpoint. For example, under the MACT
floor option there are over 75 times more asthma attacks than premature mortalities, yet these
asthma attacks account for only a very small fraction of total monetized benefits. This reflects
the fact that many of the less severe health effects, while more common, are valued at a lower
level than the more severe health effects. Also, some effects, such as asthma attacks, are valued
using a proxy measure of WTP. As such the true value of these effects may be higher than that
reported in Table 10-14 and Table 10-15.
Theestimate of total monetized benefits for the MACT floor is $16.3 billion when using
a 3 percent discount rate (or $15.4 billion when using a 7 percent discount rate). Of this total,
$14.2 billion (or $13.4 billion) are the benefits of reduced premature mortality risk from PM
exposure. Total monetized benefits are dominated by the benefits of reduced mortality risk,
accounting for 87 percent of total monetized benefits, followed by chronic bronchitis totaling
$1.8 billion, which represents 11 percent of the total. Following chronic bronchitis, minor
restricted activity days (MRADs) is the next largest quantified benefit category totaling $100
million, and it also presents the category with the largest number of incidences at 1,942,340 per
year. MRADs in combination with lost work days and avoided hospital admissions from
cardiovascular-related illness account for $140 million of total benefits. For the asthma-related
endpoints, we note that the MACT floor will result in approximately 173,000 fewer asthma
attacks, more than 2,000 fewer visits to the emergency room of hospitals for asthma, and 200
fewer hospital admissions for asthma-related effects.
Total annual benefits of the above the MACT floor regulatory option are $17.2 billion
under when using a 3 percent discount rate (or $16.3 billion when using a 7 percent discount
rate). Similar to the MACT floor results, the mortality endpoint accounts for the majority of
benefits at $15.1 billion (or $14.2 billion), followed by chronic bronchitis at $1.9 billion.
MRADs account for $100 million in benefits and 2,064,854 fewer incidences. The monetized
benefits of MRADs combined with lost work days and cardiovascular-related hospital
admissions account for $180 million of benefits. For the asthma-related endpoints, we note that
the above the MACT floor option will result in approximately 82,000 fewer asthma attacks,
more than 2,000 fewer visits to the emergency room of hospitals for asthma, and about 240
fewer hospital admissions for asthma-related effects.
10-43
-------
10-44
-------
Table 10-14. Total Annual Benefits of the
Industrial Boilers/Process Heaters NESHAP A
MACTFloor Regulatory Option
Endpoint
Premature mortality13 (long-term exposure, adults, 30 and over)
-Using a 3% discount rate
-Using a 7% discount rate
Chronic bronchitis (adults, 26 and over, WTP valuation)
Hospital Admissions - Pneumonia (adults, over 64)
Hospital Admissions - COPD (adults, 64 and over)
Hospital Admissions -Asthma (65 and younger)
Hospital Admissions - Cardiovascular (adults, over 64)
Emergency Room Visits for Asthma (65 and younger)
Asthma Attacks (asthmatics, all ages)
Acute bronchitis (children, 8-12)
Lower respiratory symptoms (children, 7-14)
Upper respiratory symptoms (asthmatic children, 10-11)
Work loss days (adults, 18-65)
Minor restricted activity days (adults, age 18-65)
Other PM-related health effects15
HAP-related health effects15
Total Monetized Health-Related Benefits1
-Using a 3% discount rate
-Using a 7% discount rate
Avoided
Incidence8
(cases/year)
2,270
2,270
5,100
1,100
900
230
2,660
2,040
173,490
4,700
51,240
196,860
398,670
1,942,340
Uj
U2
—
—
Monetary Benefitsc
(millions 1999$,
adjusted for growth in
real income)
$14,240
$13,375
$1,835
$15
$10
<$5
$50
<$1
B!
<$1
$1
$5
$40
$100
B2
B3
$16,300+BH
$15,430+^
The results presented in this table include all emission reductions including those identified for specific sources included in the Inventory
Database included in the Phase One analysis and the remaining reductions not included in the Inventory Database included in the Phase Two
analysis
B Incidences are rounded to the nearest 10 and may not add due to rounding. Incidences of unquantified endpoints are indicated with a U.
c Dollar values are rounded to the nearest 5 million and may not add due to rounding. The value of unquantified endpoints are indicated with
aB.
D The estimated value for PM-related premature mortality assumes a 5-year distributed lag structure and discounted at a 3% rate, which is
described in the IAQR benefit anlaysis.
E A detailed listing of unquantified PM and HAP related health effects is provided in Table 10-16.
10-45
-------
Table 10-15. Total Annual Benefits of the
Industrial Boilers/Process Heaters NESHAP A
Above the MACTFloor Regulatory Option
Endpoint
Premature mortality13 (long-term exposure, adults, 30 and over)
-Using a 3% discount rate
-Using a 7% discount rate
Chronic bronchitis (adults, 26 and over, WTP valuation)
Hospital Admissions - Pneumonia (adults, over 64)
Hospital Admissions - COPD (adults, 64 and over)
Hospital Admissions - Asthma (65 and younger)
Hospital Admissions - Cardiovascular (adults, over 64)
Emergency Room Visits for Asthma (65 and younger)
Asthma Attacks (asthmatics, all ages)
Acute bronchitis (children, 8-12)
Lower respiratory symptoms (children, 7-14)
Upper respiratory symptoms (asthmatic children, 10-11)
Work loss days (adults, 18-65)
Minor restricted activity days (adults, age 18-65)
Other PM-related health effects13
HAP-related health effects13
Total Monetized Health-Related Benefits
-Using a 3% discount rate
-Using a 7% discount rate
Avoided
Incidence8
(cases/year)
2,410
2,410
5,220
1,110
910
240
2,680
2,080
82,130
4,970
54,190
200,590
275,710
2,064,850
u,
U2
—
—
Monetary Benefitsc
(millions 1999$,
adjusted for growth in
real income)
$15,135
$14,220
$1,875
$15
$10
<$5
$50
<$1
B!
<$1
$1
$5
$30
$100
B2
B3
$17,230+BH
$16,310+BH
The results presented in this table include all emission reductions including those identified for specific sources included in the Inventory
Database and the remaining reductions not included in the Inventory Database.
B Incidences are rounded to the nearest 10 and may not add due to rounding. Incidences of unquantified endpoints are indicated with a U.
c Dollar values are rounded to the nearest 5 million and may not add due to rounding. The value of unquantified endpoints are indicated with
aB.
D The estimated value for PM-related premature mortality assumes a 5-year distributed lag structure and discounted at a 3% rate, which is
described in the IAQR benefit anlaysis.
E A detailed listing of unquantified PM and HAP related health effects is provided in Table 10-16.
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10.7 Limitations of the Analysis
10.7.1 Uncertainties and Assumptions
Significant uncertainties and potential biases are inherent in any benefits analysis based
on benefits transfer techniques. This analysis uses two forms of benefit transfer, (1) the transfer
of dose-response functions and valuation estimates from published articles, and (2) the transfer
of value per ton reduced from the monetized estimate in the phase one analysis. The degree of
uncertainty and bias depends on how divergent the reality of the policy situation is from the state
of the world assumed in the benefits transfer approaches.
For this analysis, several key assumptions may lead to over or underestimation of
benefits. Table 10-8 lists these assumptions, and where possible indicate the expected direction
of the bias. This is by no means an exhaustive list, but captures what we have identified as key
assumptions. In addition to these uncertainties and biases, there are uncertainties and biases
embedded in the original benefits analyses from which the transfer values were generated. Some
of these potential biases and assumptions are discussed in the preceding sections. For a full
discussion of these uncertainties, see the RIA for the Heavy Duty Engine/Diesel Fuel rule, as
well as the Section 812 report to congress on the Benefits and Costs of the Clean Air Act 1999
to 2010.
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Table 10-16.
Significant Uncertainties and Biases Associated with the
Industrial Boilers/Process Heaters Benefit Analysis
Assumption
Omission of HAP effects, and PM effects
associated with visibility and materials
damage benefit categories
Estimated emission reductions accurately
reflect conditions in 2005
Future meteorology well-represented by
modeled meteorology
Benefits from source studies do not include
all benefits and disbenefits
Population demographics, exposures, and air
quality included in phase one analysis is
representative for the transfer to the phase
two analysis
Linear extrapolation of future populations
Accuracy of S-R Matrix representativeness of
secondary PM formation chemistry
Direction of BiasA
Downward
Unknown
Unknown
Unknown
Unknown
Unknown
Unknown
A downward bias is an indicator that total benefits are underestimated. An upward bias is an
indicator that total benefits are overestimated. In several cases, the direction of the bias is
unknown and can potential be an underestimate or an overestimate of total benefits.
10.7.2 Unqualified Effects
In addition to the monetized benefits presented in the above tables, it is important to
recognize that many benefit categories associated with HAP, SO2, and PM reductions are not
quantified or monetized for this analysis. With respect to the benefits of reducing exposure to
HAPs, EPA has developed a rudimentary risk analysis focusing only on cancer risks. As
discussed above, this analysis suggests that the rule would reduce cancer incidence by roughly
tens of cases per year if it were implemented at all affected boiler and process heater facilities.
Placing a value on these impacts would increase the economic benefits of the rule. This analysis
carries significant assumptions, uncertainties, and limitations. EPA is working with the SAB to
develop better methods for analyzing the cancer and non-cancer benefits of HAP reductions.
EPA will include a monetized estimate of the benefits of reducing HAP emissions with the
analysis for the final rule if it is able to develop better methods before promulgation of this rule.
Other potentially important unquantified benefit categories are listed in Table 10-17. For a more
complete discussion of unquantified benefits and disbenefits, see the RIA for the Heavy Duty
Engine/Diesel Fuel rule.
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Table 10-17. Unquantified Benefit Categories
Unquantified Benefit Categories
Associated with HAPs
Unquantified Benefit Categories
Associated with PM
Health
Categories
Airway responsiveness
Pulmonary inflammation
Increased susceptibility to respiratory
infection
Acute inflammation and respiratory
cell damage
Chronic respiratory damage/
Premature aging of lungs
Emergency room visits for asthma
Changes in pulmonary function
Morphological changes
Altered host defense mechanisms
Other chronic respiratory disease
Emergency room visits for asthma
Emergency room visits for non-
asthma respiratory and
cardiovascular causes
Lower and upper respiratory
symptoms
Acute bronchitis
Shortness of breath
Increased school absence rates
Welfare
Categories
Ecosystem and vegetation effects
Damage to urban ornamentals
(e.g.,grass, flowers, shrubs, and
trees in urban areas)
Commercial field crops
Fruit and vegetable crops
Reduced yields of tree seedlings,
commercial and non-commercial
forests
Damage to ecosystems
Materials damage
Materials damage
Damage to ecosystems (e.g., acid
sulfate deposition)
Nitrates in drinking water
Visibility in recreational and
residential areas
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10.8 Benefit-Cost Comparison
This Regulatory Impact Analysis (RIA) provides cost, economic impact, and benefit
estimates that are potentially useful for evaluating regulatory alternatives for the industrial
boilers and process heaters rule. Benefit-cost analysis provides a systematic framework for
assessing and comparing such alternatives. According to economic theory, the efficient
alternative maximizes net benefits to society (i.e., social benefits minus social costs).
However, there are practical limitations for the comparison of benefits to costs in this analysis.
In particular, the inability to quantify the primary HAP related benefits of the rule, as well as the
inability to quantify the disbenefits of increased electricity generation related emissions
introduces biases into our estimate of benefits that make comparison with costs less meaningful.
Executive Order 12866 clearly indicates that unquantifiable or nonmonetizable categories of
both costs and benefits should not be ignored. There are many important unquantified and
unmonetized costs and benefits associated with reductions in PM10 and PM25 emissions,
including many health and welfare effects. Potential PM benefit categories that have not been
quantified and monetized are listed in Table 10-18 of this chapter. It is also important to recall
that this analysis is only of the monetizable benefits associated with PM10 and PM2 5 reductions.
The rule is designed to reduce HAP emissions. By achieving these HAP reductions, the rule
reduces the risks associated with exposures to those chemicals, including the risk of fatal
cancers. It is likely the monetized benefit estimates presented in this chapter are expected to
underestimate total benefits of the rule.
In addition to categories that cannot be included in the calculated net benefits, there are
also practical limitations for the comparison of benefits to costs in this analysis, which have been
discussed throughout this chapter. Several specific limitations deserve to be mentioned again
here:
• The state of atmospheric modeling is not sufficiently advanced to provide a workable
"one atmosphere" model capable of characterizing ground-level pollutant exposure for
all pollutants of interest (e.g., ozone, particulate matter, carbon monoxide, nitrogen
deposition, etc). Therefore, the EPA must employ several different pollutant models to
characterize the effects of alternative policies on relevant pollutants. Also, not all
atmospheric models have been widely validated against actual ambient data. In
particular, since a broad-scale monitoring network is in the early stages of development
for fine particulate matter (PM2 5), atmospheric models designed to capture the effects
of alternative policies on PM25 are not fully validated. Additionally, significant
shortcomings exist in the data that are available to perform these analyses. While
containing identifiable shortcomings and uncertainties, EPA believes the models and
assumptions used in the analysis are reasonable based on the available data and evidence.
• Qualitative and more detailed discussions of the above and other uncertainties and
limitations are included in detail in earlier sections. In particular, the fact that only half
of the sources expected to be affected by this rule are actually covered in these analysis
contributes to the uncertainty of the benefits estimates (as well those of the costs and
economic impacts, as well). Data limitations prevent an overall quantitative estimate
of the uncertainty associated with final estimates. Nevertheless, the reader should keep
all of these uncertainties and limitations in mind when reviewing and interpreting the
results.
• The PM benefit estimates do not include the monetary value of several known PM-
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related welfare effects, including recreational and residential visibility, household
soiling, and materials damage.
Nonetheless, if one is mindful of these limitations, the relative magnitude of the benefit-
cost comparison presented here can be useful information. Thus, this section summarizes the
benefit and cost estimates that are potentially useful for evaluating the efficiency of the
Industrial Boilers and Process Heaters rule.
The estimated social cost of implementing the NESHAP at the MACT floor is
approximately $837 million (1999$) in third year after issuance of this rule. The monetized
benefits of the MACT floor are $16.3 billion when using a 3 percent discount rate (or
approximately $15.4 billion when using a 7 percent discount rate). Keeping in mind that no
primary HAP-related benefits are quantified, comparison with costs indicates that our estimate
of monetized benefits of ancillary PM10 and SO2 reductions alone exceed the compliance costs
by nearly a factor of 20.
For the above the floor option (also called "Option 1 A" in this RIA), the estimated social
cost is $1.9 billion (1999$) in third year after issuance of this rule. The monetized benefits of
the above the floor option are $17.2 billion when using a 3 percent discount rate (or
approximately $16.3 billion when using a 7 percent discount rate). Thus, our estimate of
benefits of the above the floor option exceed the costs by a factor of 8.
It is also useful to consider the incremental costs and benefits of moving from the MACT
floor to the above the floor option. The incremental net benefits of going to the above the floor
option from the NESHAP (the MACT floor alternative) is -$160 million (using a 3 percent
discount rate). Hence, the final rule can be considered a more efficient alternative to society
than the above the floor option from the standpoint of maximizing net benefits. Note that while
monetized benefits of PM10 and SO2 reductions are large in this instance, they account for only
a portion of the benefits of this rule. Notable omissions include all benefits of HAPs and VOC
reductions, including reduced cancer incidences, central nervous system and cardiovascular
system effects, and ozone related benefits. It is also important to note that not all benefits of
PM10 reductions have been monetized. Categories which have contributed significantly to
monetized benefits in past analyses (see the Heavy Duty Engine/Diesel Fuel RIA) include
recreational and residential visibility and household soiling. Table 10-17 lists known
unquantified benefits associated with PM and HAP reductions. Table 10-18 summarizes the
costs, benefits, and net benefits for the rule and the above the floor option, and shows a
comparison of the two options.
We did not attempt to estimate welfare benefits associated with PM reductions for this
rule because of the difficulty in developing acceptable benefit transfer values for these effects.
The SAB has recently reviewed existing studies valuing improvements in residential visibility
and reductions in household soiling and advised that these studies do not provide an adequate
basis for valuing these effects in cost-benefit analyses (EPA-SAB-COUNCIL-ADV-00-002,
1999; EPA-SAB-Council-ADV-003, 1998). Reliable methods do exist for valuing visibility
improvements in Federal Class I areas, however, the benefits transfer method outlined above
does not allow for predictions of changes in visibility at specific Class I areas. These predictions
are necessary to estimate Class I area visibility benefits. As such we have left this potentially
important endpoint unquantified for this analysis. Given the proximity of some sources to
national parks in the Northwest (Mt. Ranier, Olympic, and Crater Lake), Northern Rockies
(Glacier), and Maine (Acadia), these omitted benefits may be significant.
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As we characterize the comparison of benefits to costs, it should be recognized that the
Agency believes its risk-based approach to regulating HC1 and Mn emissions from industrial
boilers will reduce the cost impact of this final MACT standard while still achieving substantial
reduction in HC1 and Mn exposure by affected populations. In offering this approach, the
Agency recognizes that there may be foregone benefits associated in excess of the resulting
reduction in costs. As is discussed in earlier in the RIA, the Agency is not able to quantify the
benefits of HAP reductions. However, the reduction in HC1 and Mn benefits are not anticipated
to be substantial based on the description of potential effects described in Chapter 9 of this RIA.
The acid gas scrubbers installed by industrial boilers not only reduce HC1 emissions but also
sulfur dioxide (SO2) emissions simultaneously. Reduction of SO2 emissions can provide large
monetized benefits since it is a precursor of fine parti culate matter (PM2 5), a pollutant associated
with a high degree of premature mortality in exposed populations. The fabric filters installed
by industrial boilers not only reduce Mn emissions but also PM emissions simultaneously.
Reduction of directly emitted coarse parti culate matter (PM10) emissions can also provide large
monetized benefits as well. While there may be foregone benefits in excess of the cost reduction,
it should be recognized that the estimated monetized benefits from implementation of the final
rule including this risk-based approach are still much larger than the costs ($14.5 billion versus
$690 million, or greater by a factor of 21). In addition, it should be recognized that the
reductions not achieved due to industrial boilers taking advantage of the risk-based approach
could be obtained in a more efficient manner through other regulatory programs to reduce PM.
More information on comparing the benefits of this rule to its costs can be found earlier in this
RIA chapter.
The Agency recognizes that many States will want to reduce SO2 and PM emissions from
current levels in order to meet requirements associated with the proposed Interstate Air Quality
Rule (IAQR) and PM National Ambient Air Quality Standards (NAAQS). It may be necessary
for States to require reductions of SO2 in higher amounts than can be obtained from the venturi
scrubbers or PM reductions from fabric filters that would be required to meet this final MACT
standard. The Agency understands that it would be difficult for States to justify requiring
industrial boilers to dismantle scrubbers and fabric filters installed to comply with the MACT
standard in order to install more expensive ones that meet potentially more stringent SO2 and
PM control requirements associated with implementation of the IAQR and PM NAAQS. The
Agency will work carefully with States to help them minimize the potential "stranded
investment" by industrial boiler owners in venturi scrubbers that may result as State agencies
develop SIPs to meet the IAQR and PM NAAQS.
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Table 10-18. Annual Net Benefits of the
Industrial Boilers and Process Heaters NESHAP in 2005
Social CostsB
MACT Floor
(Million 1999$)
$837
Above the
MACT Floor
(Million 1999$)
$1,923
Social Benefits8 CD:
HAP-related health and welfare benefits
PM-related welfare benefits
SO2- and PM-related health benefits:
-Using 3% Discount Rate
-Using 7% Discount Rate
Net Benefits (Benefits - Costs)c D:
-Using 3% Discount Rate
-Using 7% Discount Rate
Not monetized
Not monetized
$16,300 + B
$15,430 + B
$15,465
$14,595
Not monetized
Not monetized
$17,230 + B
$16,310 + B
$15,305 +B
$14,385 +B
' All costs and benefits are rounded to the nearest $5 million. Thus, figures presented in this table may not exactly equal benefit and cost
numbers presented in earlier sections of the chapter.
B Note that costs are the total costs of reducing all pollutants, including HAPs as well as SO2 and PM10. Benefits in this table are associated only
with PM and SO2 reductions.
c Not all possible benefits or disbenefits are quantified and monetized in this analysis. Potential benefit categories that have not been quantified
and monetized are listed in Table 8-13. B is the sum of all unquantified benefits and disbenefits.
D Monetized benefits are presented using two different discount rates. Results calculated using 3 percent discount rate are recommended by
EPA's Guidelines for Preparing Economic Analyses (U.S. EPA, 2000a). Results calculated using 7 percent discount rate are recommended by
OMB Circular A-94 (OMB, 1992).
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APPENDIX A:
ECONOMIC MODEL OF MARKETS AFFECTED BY THE BOILERS AND PROCESS
HEATERS MACT
The primary purpose of the EIA for the final rule is to describe and quantify the
economic impacts associated with the rule. The Agency used a basic framework that is
consistent with economic theory and the analyses performed for other rules to develop estimates
of these impacts. This approach employs standard microeconomic concepts to model behavioral
responses expected to occur with regulation. This appendix describes the spreadsheet model in
more detail and discusses how the Agency
• collected the baseline data set from the Annual Energy Outlook 2002 (DOE, EIA,
2002), U.S. Census Bureau (U.S. Department of Commerce, 2001), and U.S.
Department of Agriculture (USDA, 2002).
• characterized market supply and demand for each market and specified links
between the energy and agricultural, manufacturing, mining, and commercial
markets.
• introduced a policy "shock" into the model by using control cost-induced shifts in
the supply functions, and
• used a solution algorithm to determine a new with-regulation equilibrium for each
market.
A.I Baseline Data Set
EPA collected the following data to characterize the baseline year, 2005:
• Energy Market Data—The Department of Energy's Supplemental Tables to the
Annual Energy Outlook 2002 report forecasts of price, quantity, and fuel intensities
used to calibrate the model.
• Agriculture, Mining, Manufacturing, Commercial Sectors—EPA obtained shipment
data from the 1997 Economic Census and 1997 Agriculture Census. We then used
annual growth rates reported by the Bureau of Economic Analysis (BEA, 1997) to
estimate baseline shipment data for 2005. The Agency selected units for output such
that the price in each market equals one. We computed energy demand using fuel
intensity data reported in the AEO 2002.
• Supply and Demand Elasticities—The supply and demand elasticity values used in
the market model are reported in Table 5-2 of this report. Given the uncertainties
regarding these parameters, EPA also conducted several sensitivity analyses and
report these results in Appendix B.
A.2 Multi-Market Model
The model includes four energy markets (coal, electricity, natural gas, and petroleum)
and 24 goods and service markets. The following sections describe model equations the Agency
developed to characterize these markets and estimate welfare changes resulting from the rule.
A. 1.1 Supply Side Modeling
EPA estimated the change in quantity supplied as follows:
A-58
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n
Ap-c - £ ,_
i 1 J J (A.I)
Aqs = %s • es • ^
Po
where qs is the baseline quantity, gs is the domestic supply elasticity, the term
n
An- c - y a Ap IB the change in the producer's net price, and p0 is the baseline price. The
change in net price is composed of the change in baseline price resulting from the regulation,
the direct shift in the supply function resulting from compliance costs, and the indirect shift in
the supply function resulting from changes in input prices in energy market (j). The fuel share
is allowed to vary using a fuel switching rule relying on cross-price elasticities of demand
between energy sources.
A. 1.1.2 Producer Welfare Measurement
EPA approximated the change in producer surplus with the following equation:
Increased control costs, higher energy input costs, and output declines have a negative
n n
APS = qj-(Ap-c- £ OjAp.) - 0.5-Aq-(Ap-c- £ c^Ap.) (A.2)
effect on domestic producer surplus. However, these losses are mitigated to some degree as a
result of higher market prices.
A. 1.2 Energy Demand Side Modeling
Market demand in the energy markets is expressed as the sum of the energy, residential,
agriculture, manufacturing, mining, commercial, and transportation sectors:
QDJ = I %ji • (A-3)
i= 1
where j indexes the energy market and i indexes the consuming sector. The change in residential
quantity demanded of energy market] can be approximated as follows:
AqDj = q0Dj • nDj • —- (A.4)
Pjo
where Dj is baseline consumption, r|Dj is the residential demand elasticity and (Ap) is the change in
4o
the market price.
In contrast, energy demand from energy, agricultural, manufacturing, mining, commercial, and
transportation sectors is modeled as a derived demand resulting from the production and consumption
choices in these industries. Energy demand responds to changes in sector output and fuel switching that
occurs in response to changes in relative energy prices. For each of these sectors, energy demand is
expressed as follows:
BILL
BTUjii = * • FSW • qu (A.5)
where BTU is demand for energy market j from sector i, q is sector i's output, and FSW is a factor
A-l
-------
generated by the fuel switching algorithm. The subscripts 0 and 1 represent baseline and with regulation
conditions, respectively.
A. 1.3 Agriculture, Manufacturing, Mining, Commercial, and Transportation Demand Side
Modeling
The change in quantity demanded in these markets can be approximated as follows:
(A.6)
PIC
where Di is baseline output, r|D is the demand elasticity of the respective market (i) and (A p:) is the
4o
change in the market price.
The change in consumer surplus in markets is approximated as follows:
As shown, higher market prices and reduced consumption lead to welfare losses for consumers.
ACS = - qj-Ap + 0.5-Aq-Ap (A.7)
A.2 With-Regulation Market Equilibrium Determination
Market adjustments can be conceptualized as an interactive feedback process. Supply segments
face increased production costs as a result of the rule and are willing to supply smaller quantities at the
baseline price. This reduction in market supply leads to an increase in the market price that all producers
and consumers face, which leads to further responses by producers and consumers and thus new market
prices. The new with-regulation equilibrium is the result of a series of iterations in which price is
adjusted and producers and consumers respond, until a set of stable market prices arises where total
market supply equals market demand (i.e., Qs = QD) in each market. Market price adjustment takes place
based on a price revision rule that adjusts price upward (downward) by a given percentage in response
to excess demand (excess supply).
The algorithm for determining with-regulation equilibria can be summarized by seven recursive
steps:
1. Impose the control costs on affected supply segments, thereby affecting their supply
decisions.
2. Recalculate the market supply in each market. Excess demand currently exists.
3. Determine the new prices via a price revision rule.
4. Recalculate market supply with new prices, accounting for fuel switching choices associated
with new energy prices.
5 . Compute market demand in each market.
6. Compare supply and demand in each markets. If equilibrium conditions are not satisfied,
go to Step 3, resulting in a new set of market prices. Repeat until equilibrium conditions are
satisfied (i.e., the ratio of supply to demand is arbitrarily close to one).
A-2
-------
APPENDIX B
ASSUMPTIONS AND SENSITIVITY ANALYSIS
In developing the economic model to estimate the impacts of the industrial/
commercial/institutional boilers and process heaters NESHAP, several assumptions were necessary to
make the model operational. This appendix lists and explains the major model assumptions and describes
their potential impact on the analysis results. Sensitivity analyses are presented for numeric assumptions.
Assumption: The domestic markets for goods and services are all perfectly competitive.
Explanation: Assuming that these markets are perfectly competitive implies that the producers of these
products are unable to unilaterally affect the prices they receive for their products. Because the industries
used in this analysis are aggregated across a large number of individual producers, it is a reasonable
assumption that the individual producers have a very small share of industry sales and cannot individually
influence the price of output from that industry.
Possible Impact: If these product markets were in fact imperfectly competitive, implying that individual
producers can exercise market power and thus affect the prices they receive for their products, then the
economic model would understate possible increases in the price of final products due to the regulation
as well as the social costs of the regulation. Under imperfect competition, producers would be able to
pass along more of the costs of the regulation to consumers; thus, consumer surplus losses would be
greater, and producer surplus losses would be smaller in the final product markets.
Assumption: Market Supply and Demand Elasticity Uncertainty
Explanation: The goods and service markets are modeled at the two or three-digit NAICS code level
to operationalize the economic model. Because of the high level of aggregation, only limited data on
elasticities of supply and demand estimates are available. However, these elasticities strongly influence
the distribution of economic impacts between producers and consumers.
B-3
-------
Sensitivity Analysis: Tables B- la and Table B-lb show how the economic impact estimates vary as the
supply and demand elasticities for goods and services change by 25 percent.
Table B-la. Sensitivity Analysis: Supply and Demand Elasticities in the Goods and
Services Markets
Change Supply „, ,. ... „ , ,
" rr J Elasticities Reported
Demand Constant 25% Decrease in Section 6 25% Increase
Change in consumer surplus
Change in producer surplus
Change in social welfare
-367.8
-495.2
-862.9
-414.3
-448.7
-862.9
-450.5
-412.4
-862.9
Assumption: Cross-price elasticities of demand for fuels are based on 2015 NEMS projections.
Explanation: Cross- and own-price elasticities of demand from NEMS were used to capture fuel
switching in the manufacturing sectors in the economic model. As shown in Table 5-2, allowing
manufacturers to switch fuels in response to changes in relative energy prices decreases the change in
Table B-lb. Sensitivity Analysis: Supply and Demand Elasticities in the Goods and
Services Markets
Supply Constant _. x. .,.-., x ,
rr J Elasticities Reported
Demand Change 25% Decrease in Section 6 25% Increase
Change in consumer surplus
Change in producer surplus
Change in social welfare
-462.7
-400.2
-862.9
-414.3
-448.7
-862.9
-364.4
-498.5
-862.9
social welfare by approximately 10 percent. However, the NEMS projection reflects aggregate
behavioral responses in the year 2015. Because this is a longer window of analysis compared to the
baseline year 2005, this analysis may overestimate firms' ability to switch fuels in the short run.
Sensitivity Analysis: Table B-2 shows how the economic impact estimates vary as the own- and cross-
price elasticities used in the EIA are reduced by 50 percent and 75 percent.
B-4
-------
Table B-2. Sensitivity Analysis: Own- and Cross-Price Elasticities Used to Model Fuel
Switching
Fuel Price Elasticities Reduced by Reduced by 75
Presented in Table 5-2 50 Percent Percent
Change in consumer surplus
Change in producer surplus
Change in social welfare
-414.3
-448.7
-862.9
-414.6
-448.4
-862.9
-414.9
-448.0
-862.9
Assumption: The domestic markets for energy are perfectly competitive.
Explanation: Assuming that the markets for energy are perfectly competitive implies that individual
producers are not capable of unilaterally affecting the prices they receive for their products. Under
perfect competition, firms that raise their price above the competitive price are unable to sell at that
higher price because they are a small share of the market and consumers can easily buy from one of a
multitude of other firms that are selling at the competitive price level. Given the relatively homogeneous
nature of individual energy products (petroleum, coal, natural gas, electricity), the assumption of perfect
competition at the national level seems to be appropriate.
Possible Impact: If energy markets were in fact imperfectly competitive, implying that individual
producers can exercise market power and thus affect the prices they receive for their products, then the
economic model would understate possible increases in the price of energy due to the regulation as well
as the social costs of the regulation. Under imperfect competition, energy producers would be able to
pass along more of the costs of the regulation to consumers; thus, consumer surplus losses would be
greater, and producer surplus losses would be smaller in the energy markets.
Assumption: The elasticity of supply in the electricity market for existing sources is approximately
0.75.
Explanation: The price elasticity of supply in the electricity markets represents the behavioral responses
from existing sources to changes in the price of electricity. However, there is no consensus on estimates
of the price elasticity of supply for electricity. This is in part because, under traditional regulation, the
electric utility industry had a mandate to serve all its customers and utilities were compensated on a
rate-based rate of return. As a result, the market concept of supply elasticity was not the driving force
in utilities' capital investment decisions. This has changed under deregulation. The market price for
electricity has become the determining factor in decisions to retire older units or to make higher cost units
available to the market.
Sensitivity Analysis: Table B-3 shows how the economic impact estimates vary as the elasticity of
supply in the electricity markets varies.
Table B-3. Sensitivity Analysis: Elasticity of Supply in the Electricity Markets
ES = 0.5
ES = 0.75
ES =
Change in consumer surplus -405.0
Change in producer surplus -457.9
Change in social welfare -862.9
-414.3
-448.7
-862.9
-419.6
-443.4
-862.9
B-l
-------
Appendix C
Air Quality Changes for the Above-the-Floor Option (Option 1A)
Table C-l summarizes the baseline PM10,PM25, and SO2 emissions and emission
reductions nationwide for the MACT floor option. The air quality analysis presumes no change
in volatile organic compound (VOC), nitrogen oxides (NOx), carbon monoxide (CO), and
ammonia (NH3) emissions. Hence, the baseline emissions for these pollutants are not shown
in this table. For these baseline emissions, refer to Pechan, 2001.
Table C-l. Summary of Nationwide Baseline Emissions and Emission Reductions"
for the MACT floor (in tons/year), Existing Units Onlyb c in 2005
Pollutant
SO2
PM10
Source Type
Point
Area
Motor Vehicle
Nonroad
Point
1996
Baseline
Emissions
(tons/year)
3,745,790
1,397,425
302,938
840,167
1,167,995
Unknow
n
Affected
Units
30,394
298,109
Option 1A Emission
Reductions
Known Unknown
Total Affected Affected
Affected
Units Units Units
95,361 41,372 136,733
313,947 255,282
569,229
C-2
-------
PM25
Area
Motor Vehicle
Nonroad
Point
Area
Motor Vehicle
Nonroad
30,771,607
294,764
463,579
576,022
6,675,777
230,684
410,334
84,125
94,565 76,894
171,459
As mentioned in Chapter 8 of this RIA, we conducted no air quality modeling for the
HAP or the mercury emission reductions that occur from the potential implementation of Option
1A. These emission reductions are listed in Table C-2. For a description of how HAP
emissions and emission factors are estimated for this rule, refer to the emission factors/emissions
estimates memo in the public docket (ERG, 2002).
Table C-2. HAP Emission Reductions for Option 1A, 2005
Existing Sources Only
Pollutant
HC1
Pb
Hg
Non-mercury metal sa
Selected inorganics'3
Total HAP reductions
Emission Reductions (tons/year)
Option 1A
40,406
105
2.2
1,135
18,250
59,190
aNon-mercury metals include: arsenic, beryllium, cadmium, chromium, manganese, and nickel.
bSelected inorganics include: chlorine, hydrofluoric acid, and phosphorus.
Table C-3 provides a summary of the predicted ambient PM10 and PM25 concentrations
from the S-R matrix for the 2005 baseline and changes associated with Option 1 A, the above-
the-MACT floor examined in this RIA. The results indicate that the predicted change in PM
concentrations is composed almost entirely of reductions in fine particulates (PM25) with little
or no reduction in coarse particles (PM10less PM2 5). Therefore, the observed changes in PM10
are composed primarily of changes in PM2 5. These results are quite similar to those for the final
-------
rule (MACT floor option). In addition to the standard frequency statistics (e.g., minimum,
maximum, average, median), Table C-3 provides the population-weighted average which better
reflects the baseline levels and predicted changes for more populated areas of the nation. This
measure, therefore, will better reflect the potential benefits of these predicted changes through
exposure changes to these populations. As shown, the average annual mean concentrations of
PM25across all U.S. grid-cells declines by roughly 0.9 percent, or 0.10 |ig/m3. The population-
weighted average mean concentration declined by 0.9 percent, or 0.12 |ig/m3, which is slightly
larger in absolute terms than the spatial average. This indicates that the above-the-floor option
generates slightly greater absolute air quality improvements in more populated, urban areas than
in less populated, rural areas.
Table C-3.
Summary of 2005 Base Case PM Air Quality and Changes Due to MACT Above-the-
Floor Option: Industrial Boiler/Process Heater Source Categories
Statistic
2005 Baseline
Change*
Percent
Change
PM10
Minimum Annual Mean (ug/m3) b
Maximum Annual Mean (ug/m3) b
Average Annual Mean (ug/m3)
Median Annual Mean (ug/m3)
Population- Weighted Average Annual Mean (ug/m3) °
6.09
69.30
22.68
21.84
28.79
-0.08
-0.03
-0.36
-0.43
-0.38
-1.3%
-0.1%
-1.6%
-1.9%
-1.3%
PM25
Minimum Annual Mean (ug/m3) b
Maximum Annual Mean (ug/m3) b
Average Annual Mean (ug/m3)
Median Annual Mean (ug/m3)
Population- Weighted Average Annual Mean (ug/m3) °
0.74
30.35
11.15
11.11
13.50
-0.01
-0.77
-0.10
-0.13
-0.12
0.0%
-2.5%
-0.9%
-1.2%
-0.9%
aThe change is defined as the control case value minus the baseline value.
b The baseline minimum (maximum) is the value for the populated county with the lowest
(highest) annual average. The change relative to the baseline is the observed change for the
populated county with the lowest (highest) annual average in the baseline.
c Calculated by summing the product of the projected 2005 county population and the estimated
2005 PM concentration for that county, and then dividing by the total population in the 48
contiguous States.
Table C-4 provides information on the 2005 populations that will experience improved
PM air quality under the above-the-floor option. There are also fairly significant populations
that live in areas with meaningful reductions in annual mean PM2 5 concentrations resulting from
the above-the-floor option, though the increment of reduction between the above-the-floor
option and the MACT floor option is quite small. As shown, about 1 percent of the 2005
continental U.S. population are predicted to experience reductions of greater than 1 |ig/m3.
Furthermore, about 4 percent of the 2005 U.S. population will benefit from reductions in annual
C-l
-------
mean PM2 5 concentrations of greater than 0.5 |ig/m3 and about 3 8 percent will live in areas with
reductions of greater than 0.1 |ig/m3.
Table C-4.
Distribution of PM2.5 Air Quality Improvements Over 2005 Population Due to
MACT Above-the-Floor Option: Industrial Boiler/Process Heater Source Categories
Change in Annual Mean PM25 Concentrations
fag/m3)
A PM25 Cone = 0
0 > A PM25 Cone < 0.05
0.05 > A PM25 Cone < 0.1
0.\ > A PM25 Cone <0.25
0.25>APM25Conc < 0.5
0.5 > A PM25 Cone < 1.0
\.0>APM25Conc <2.0
A PM25 Cone > 2.0
2005 Population
Number (millions) Percent (%)
34.3
86.4
56.5
77.2
18.1
8.6
2.0
0.2
12.1%
30.5%
19.9%
27.3%
6.4%
3.0%
0.7%
0.1%
The change is defined as the control case value minus the baseline value.
C-2
-------
Table C-5.
Summary of Absolute and Relative Changes in PM Air Quality Due to MACT
Above-the-Floor Option: Industrial Boiler/Process Heater Source Categories
Statistic
PMW Annual Mean
PM2 5 Annual Mean
Absolute Change from 2005 Baseline (fig/m3)0
Minimum
Maximum
Average
Median
Population- Weighted Average °
0.00
-19.20
-0.36
-0.20
-0.38
0.00
-6.09
-0.10
-0.07
-0.12
Relative Change from 2005 Baseline (%)b
Minimum
Maximum
Average
Median
Population- Weighted Average °
0.00%
-58.34%
-1.52%
-0.94%
-1.46%
0.00%
-38.47%
-0.85%
-0.65%
-0.87%
a The absolute change is defined as the control case value minus the baseline value for each county.
b The relative change is defined as the absolute change divided by the baseline value, or the percentage change, for
each county. The information reported in this section does not necessarily reflect the same county as is portrayed
in the absolute change section.
0 Calculated by summing the product of the projected 2005 county population and the estimated 2005 county PM
absolute/relative measure of change, and then dividing by the total population in the 48 contiguous states.
Table C-5 provides additional insights on the changes in PM air quality resulting from
the above-the-floor option. The information presented previously in Table 8-6 illustrated the
absolute and relative changes for different points along the distribution of baseline 2005 PM
concentration levels, e.g., the change reflects the lowering of the minimum predicted baseline
concentration rather than the minimum predicted change for 2005. The latter is the focus of
Table C-5 as it presents the distribution of predicted changes in both absolute terms (i.e., |ig/m3)
and relative terms (i.e., percent) across individual grid-cells. Therefore, it provide more
information on the range of predicted changes that as shown, the absolute reduction in annual
mean PM10 concentration ranged from a low of 0.00 |ig/m3 to a high of 19.20 |ig/m3, while the
relative reduction ranged from a low of 0.0 percent to a high of 58.5 percent. Alternatively, for
mean PM2 5, the absolute reduction ranged from 0.00 to 6.09 |ig/m3, while the relative reduction
ranged from 0.0 to 38.5 percent.
-------
Comparison of Air Quality Changes for the MACT Floor and Above The Floor Options
The increment in air quality improvements between the above the floor option and the
MACT floor option (the final rule) in 2005 is quite small as seen in a comparison between the
results for each option. There is only a 0.01 |ig/m3 decrease in nationwide average annual mean
PM2 5 concentration for the above-the-floor option compared to the MACT floor option, and a
0.04 |ig/m3 decrease in average annual mean PM10 concentration. In addition, the differences
in the nationwide population-weighted average annual mean are 0.02 |ig/m3 for PM2 5 and 0.05
|ig/m3 for PM10 concentrations. Hence, the difference in air quality improvement between the
options is small. The improvements in air quality is one possible component of choosing
between a MACT floor option and an above the floor option.
Visibility Improvements
Table C-6 provides the distribution of visibility improvements across the 2005 U.S.
population resulting from the above-the-floor MACT option. The majority of the 2005 U.S.
population live in areas with predicted improvement in annual average visibility of between 0
to 0.1 deciviews. As shown, 5 percent of the 2005 U.S. population are predicted to experience
improved annual average visibility of greater than 0.25 deciviews. Furthermore, just over 80
percent of the 2005 U.S. population will benefit from an improvement in visibility, i.e., change
in deciview greater than zero.
Table C-6.
Distribution of Populations Experiencing Visibility Improvements in 2005 Due to MACT
Above-the-Floor Option: Industrial Boiler/Process Heater Source Categories
Improvements in Visibility "
(annual average deciviews)
A Deciview = 0
0 > A Deciview < 0.05
0.05 > A Deciview < 0.1
0.1 > A Deciview < 0.15
0. 15 > A Deciview < 0.25
0.25 > A Deciview < 0.5
A Deciview > 0.5
2005 Population
Number (millions) Percent (%)
50.2
152.5
55.8
10.5
10.2
2.8
1.1
17.7%
53.9%
19.7%
3.7%
3.6%
1.0%
0.4%
aThe change is defined as the MACT Above-the-Floor control case deciview level minus the
base case deciview level.
Residential Visibility
For the above-the-floor option, the air quality modeling results predict slightly greater
improvements in visibility through the country than for the MACT floor option. In Table C-7, we
summarize residential visibility improvements across the Eastern and Western U.S. in 2005 that result
C-4
-------
from the above-the-floor MACT option. The baseline annual average visibility for all U.S. counties in
the contiguous 48 States is 14.8 deciviews. The mean improvement across these U.S. counties is 0.05
deciviews, or almost 0.2 percent. In urban areas with a population of 250,000 or more (i.e., 819 out of
3,080 counties), the mean improvement in annual visibility was 0.06 deciviews and ranged from 0.01 to
0.98 deciviews. In rural areas (i.e., 2,261 counties), the mean improvement in visibility was 0.05
deciviews in 2005 and ranged from 0.01 to 0.52 deciviews.
On average, the Eastern U.S. experienced larger absolute and relative improvements in visibility
than the Western U.S. from the industrial boilers and process heaters reductions. In Eastern U.S., the
mean improvement was 0.06 deciviews from an average baseline of 22 deciviews. Western counties
experienced a mean improvement of 0.01 deciviews from an average baseline of 17.82 deciviews
projected in 2005. Overall, the data suggest that the rule provides slight improvements in visibility for
2005.
Table C-7.
Summary of 2005 Baseline Visibility and Changes by Region Due to MACT Above-the-Floor
Option: Residential(Annual Average Deciviews)
Regions"
Eastern U.S.
Urban
Rural
Western U.S.
Urban
Rural
National, all counties
Urban
Rural
2005 Baseline
22.00
22.95
21.62
17.82
19.19
17.55
21.19
22.49
20.72
Change*
-0.06
-0.07
-0.06
-0.01
-0.01
-0.01
-0.05
-0.06
-0.04
Percent Change
-0.2%
-0.3%
-0.2%
-0.1%
-0.1%
-0.1%
-0.2%
-0.3%
-0.2%
a Eastern and Western regions are separatedby 100 degrees West longitude. Background visibility conditions differ
by region.
b An improvement in visibility is a decrease in deciview value. The change is defined as the MACT Above-the-
Floor control case deciview level minus the baseline deciview level
Recreational Visibility
In Table C-8, we summarize recreational visibility improvements resulting from the Above-the-
Floor MACT option in 2005 for Federal Class I areas by region. These recreational visibility regions are
the same ones as those in Figure 8-1 in Chapter 8 of the RIA. As shown, the national improvement in
visibility for these areas is 0.3 percent, or 0.05 deciviews. Predicted relative visibility improvements are
the largest in the Southeast (0.3%) and Northeast/Midwest (0.2%). These improvements are only slightly
greater than those estimated for the MACT floor option. California was predicted to have no visibility
improvements in Class I areas within that state.
C-5
-------
Table C-8.
Summary of 2005 Baseline Visibility and Changes by Region Due to MACT Above-the-
Floor Option: Recreational (Annual Average Deciviews)
Class I Visibility Regions'1
Southeast
Southwest
California
Northeast/Midwest
Rocky Mountain
Northwest
National Average (unweighted)
2005 Baseline
21.49
17.18
19.86
20.64
17.29
20.62
19.17
Change*
-0.07
-0.01
0.00
-0.06
-0.02
-0.03
-0.05
Percent Change
-0.3%
-0.1%
0.0%
-0.2%
-0.1%
-0.1%
-0.3%
* Regions are pictured in Figure 8-1 and are defined in the technical support document for the air quality analysis.
b An improvement in visibility is a decrease in deciview value. The change is defined as the MACT Above-the-Floor control
case deciview level minus the baseline deciview level.
C-6
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APPENDIX D:
Derivation of Quantified Benefits
C-7
-------
Appendix D: Derivation of Quantified Benefits
As Chapter 10 of this RIA explains, the benefit analysis of the Industrial
Boilers/Process Heaters NESHAP entails two phases of analysis. These results reflect the
use of two different discount rates to value reduced incidences of mortality; a 3% rate which
is recommended by EPA's Guidelines for Preparing Economic Analyses (US EPA, 2000a),
and 7% which is recommended by OMB Circular A-94 (OMB, 1992). In phase one, we
modeled approximately 50 percent of the estimated emission reductions of SO2 and PM in
an air quality model (the SR Matrix) and a benefit valuation model (the CAPMS model).
This appendix provides tables that detail the steps necessary to derive the total benefits of the
NESHAP.
Tables D-l to D-4 show the benefits estimation for the MACT floor. Table D-l(a)
shows the results of the phase one analysis when we modeled SO2 emission reductions
alone. Given a total benefit estimate of $1.7 billion from the assessment of benefits for
85,542 tons of SO2 reduced out of a total estimated reduction of 112,936 tons, we then
calculate a coefficient for each benefit endpoint to derive benefit transfer values for (1)
incidence per ton reduced, and (2) benefit per ton reduced.
Table D-l(b) shows the results of phase two of the analysis associated with SO2
reductions. Using the benefit transfer values for incidence and value, we calculate the
approximate benefits of the remaining 30,394 tons of SO2 out of the total 112,936 tons.
Multiplying the total benefit per ton from Table D-l(a) of $20,028 to the 30,394 tons SO2
yields total benefits of the phase two analysis for SO2 of $609 million.
Tables D-2(a) and D-2(b) present results of the phase one and phase two analysis for
the expected 562,110 tons of PM reduced due to the MACT Floor regulatory option of the
NESHAP. The phase one analysis of PM reductions (Table D-2(a)) results in total benefits
of $6.6 billion for 265,155 tons of PM10 and 75,095 tons of PM2.5. The resulting total
benefit transfer value is $88,118 per ton of PM. Applying the benefit transfer values to the
remaining 296,955 tons of PM results in total phase two benefits of approximately $7.4
billion.
Tables D-3(a) and D-3(b) show the summary of results of the phase one and phase
two analysis for the combination of SO2 and PM reductions. Then Table D-4 aggregates the
results of the two phases for all pollutant reductions to provided an estimate of the total
benefits of the Industrial Boilers/Process Heaters NESHAP under the MACT Floor
regulatory option in 2005 equal to $16.3 billion.
Tables D-5 to D-8 show the estimate of benefits for the above the MACT floor
regulatory option. Table D-5(a) shows the results of the phase one analysis when we
modeled SO2 emission reductions alone. Given a total benefit estimate of $2.1 billion from
the assessment of benefits of 95,361 tons of SO2 reduced out of a total estimated reduction
of 136,733 tons, we then calculate a coefficient for each benefit endpoint to derive benefit
transfer values for (1) incidence per ton reduced, and (2) benefit per ton reduced.
Table D-5(b) shows the results of phase two of the analysis associated with SO2
reductions. Using the benefit transfer values for incidence and value, we calculate the
approximate benefits of the remaining 41,372 tons of SO2 out of the total 136,733 tons.
Multiplying the total benefit per ton from Table D-5(a) of $22,071 to the 41,372 tons SO2
yields total benefits of the phase two analysis for SO2 of $913 million.
-------
Tables D-6(a) and D-6(b) present results of the phase one and phase two analysis for
the expected 569,229 tons of PM reduced due to the above the MACT floor regulatory option
of the NESHAP. The phase one analysis of PM reductions (Table D-6(a)) results in total
benefits of $7.9 billion for 313,947 tons of PM10 and 94,565 tons of PM2.5. The resulting
total benefit transfer value is $83,647 per ton of PM. Applying the benefit transfer values to
the remaining 255,282 tons of PM results in total phase two benefits of approximately $6.4
billion.
Tables D-7(a) and D-7(b) show the summary of results of the phase one and phase
two analysis for the combination of SO2 and PM reductions. Then Table D-8 aggregates the
results of the two phases for all pollutant reductions to provided an estimate of the total
benefits of the Industrial Boilers/Process Heaters NESHAP under the above MACT floor
regulatory option in 2005 equal to $17.2 billion.
C-9
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Table D-l(a). Base Estimate: Results of Air Quality and Benefit Analyses for the Phase One Analysis
of the Industrial Boilers/Process Heaters NESHAP
MACT Floor in 2005 (SO2 reductions only)
National Benefit-
Transfer Values
Endpoint Reference
MORTALITY
Ages 30+, Mean, Discount Rate = 3% Krewski et al. (2000)
Ages 30+, Mean, Discount Rate = 7% Krewski et al. (2000)
CHRONIC ILLNESS
Chronic Bronchitis Schwartz, 1993
HOSPITALIZATION
COPD-Related Samet et al. (2000)
Pneumonia-Related Samet et al. (2000)
Asthma-Related Sheppard et al. (1999)
Cardiovascular-Related Samet et al. (2000)
Asthma-Related ER Visits Schwartz et al. (1993)
MINOR ILLNESS
Acute Bronchitis Dockery et al. (1996)
Upper Respiratory Symptoms Pope et al. (1991)
Lower Respiratory Symptoms Schwartz et al. (1994)
Asthma Attacks Whittemore and Korn (1980)
Work Loss Days Ostro (1987)
MRAD - Adjusted Ostro and Rothschild (1989)
WELFARE EFFECTS
Visibility
Recreational
Avoided Incidence (cases/year)
Mean
241
241
321
51
62
24
149
134
490
12,976
5,327
11,120
42,611
214,592
Direct Economic Valuation
Total Base PM-Related Benefits, Discount Rate = 3%
Total Base PM-Related Benefits, Discount Rate = 7%
Monetary Benefits (millions 1999$)
Simple Mean
$1,405
$1,319
$106
$1
$1
$0
$3
$0.0
$0.0
$0.3
$0
B
$5
$10
$0
$1,530
$1,445
Income Adjustment
Factor
1.0805
1.0805
1.0911
1.0000
1.0000
1.0000
1.0000
1.0000
1.0275
1.0275
1.0275
1.0275
1.0000
1.0275
1.1908
Adjusted Benefits
(millions 1999$)
$1,518
$1,425
$115
$1
$1
$0
$3
$0.0
$0.0
$0.3
$0
B
$5
$11
$0
$1,653
$1,561
Incidence/ton
0.00292461
0.00292461
0.00388893
0.00061787
0.00075113
0.00029076
0.00180514
0.00162342
0.00593637
0.15720022
0.06453591
0.13471911
0.51623645
2.59979181
$/ton
(1999$)
$18,385.89
$17,269.44
$1,397.96
$7.65
$11.04
$1.99
$33.19
$0.48
$0.35
$3.91
$1.01
B
$54.72
$129.42
$0.00
$20,027.62
$18,911.17
NOTE: Emission Reduction Summary (Converted from Ma to Tons)
SO2 Emission Reductions modeled in SR Matrix & CAPMS
82542
Total SO2 Emission Reductions from all sources (MACT floor)
SO2 reductions applied to benefit transfer values
112936
30394
C-10
-------
Table D-l(b). Base Estimate: Results of Benefit Transfer Application for the Phase Two Analysis
of the Industrial Boilers/Process Heaters NESHAP
MACT Floor in 2005 (SO2 reductions only)
Endpoint Reference
MORTALITY
Ages 30+, Mean, Discount Rate = 3% Krewski et al. (2000)
Ages 30+, Mean, Discount Rate = 7% Krewski et al. (2000)
CHRONIC ILLNESS
Chronic Bronchitis Schwartz, 1993
HOSPITALIZATION
COPD-Related Samet et al. (2000)
Pneumonia-Related Samet et al. (2000)
Asthma-Related Sheppard et al. (1999)
Cardiovascular-Related Samet et al. (2000)
Asthma-Related ER Visits Schwartz et al. (1993)
MINOR ILLNESS
Acute Bronchitis Dockery et al. (1996)
Upper Respiratory Symptoms Pope et al. (1991)
Lower Respiratory Symptoms Schwartz et al. (1 994)
Asthma Attacks Whittemore and Korn (1 980)
Work Loss Days Ostro (1987)
MRAD - Adjusted Ostro and Rothschild (1989)
WELFARE EFFECTS
Visibility
Recreational
Avoided Incidence (cases/year)
Mean
89
89
118
19
23
9
55
49
180
4,778
1,962
4,095
15,690
79,018
Direct Economic Valuation
Total Base PM-Related Benefits, Discount Rate = 3%
Total Base PM-Related Benefits, Discount Rate = 7%
Monetary Benefits (millions 1999$)
Simple Mean
$517
$486
$39
$0
$0
$0
$1
$0.0
$0.0
$0.1
$0
B
$2
$4
$0
$563
$532
Income Adjustment
Factor
1.0805
1.0805
1.0911
1.0000
1.0000
1.0000
1.0000
1.0000
1.0275
1.0275
1.0275
1.0275
1.0000
1.0275
1.1908
Adjusted Benefits
(millions 1999$)
$559
$525
$42
$0
$0
$0
$1
$0.0
$0.0
$0.1
$0
B
$2
$4
$0
$609
$575
D-l
-------
Table D-2(a). Base Estimate: Results of Air Quality and Benefit Analyses for the Phase One Analysis
of the Industrial Boilers/Process Heaters NESHAP
MACT Floor in 2005 (PM reductions only)
National Benefit-
Transfer Values
Endpoint Reference
MORTALITY
Ages 30+, Mean, Discount Rate = 3% Krewski et al. (2000)
Ages 30+, Mean, Discount Rate = 7% Krewski et al. (2000)
CHRONIC ILLNESS
Chronic Bronchitis Schwartz, 1993
HOSPITALIZATION
COPD-Related Samet et al. (2000)
Pneumonia-Related Samet et al. (2000)
Asthma-Related Sheppard et al. (1999)
Cardiovascular-Related Samet et al. (2000)
Asthma-Related ER Visits Schwartz et al. (1993)
MINOR ILLNESS
Acute Bronchitis Dockery et al. (1996)
Upper Respiratory Symptoms Pope et al. (1991)
Lower Respiratory Symptoms Schwartz et al. (1994)
Asthma Attacks Whittemore and Korn (1980)
WorkLossDays Ostro (1987)
MRAD - Adjusted Ostro and Rothschild (1989)
WELFARE EFFECTS
Visibility
Recreational
Avoided Incidence (cases/year)
Mean
903
903
2,356
417
509
90
1,229
949
1,866
91,618
20,369
80,696
158,563
760,866
Direct Economic Valuation
Total Base PM-Related Benefits, Discount Rate = 3%
Total Base PM-Related Benefits, Discount Rate = 7%
Monetary Benefits (millions 1999$)
Simple Mean
$5,254
$4,935
$776
$5
$7
$1
$23
$0.3
$0.1
$2.2
$0
B
$17
$37
$0
$6,123
$5,803
Income Adjustment
Factor
1.0805
1.0805
1.0911
1.0000
1.0000
1.0000
1.0000
1.0000
1.0275
1.0275
1.0275
1.0275
1.0000
1.0275
1.1908
Adjusted Benefits
$5,677
$5,332
$847
$5
$7
$1
$23
$0.3
$0.1
$2.3
$0
B
$17
$38
$0
$6,617
$6,273
Incidence/ton
0.01202477
0.01202477
0.00888537
0.00157267
0.00191963
0.00119848
0.00463502
0.00357904
0.02484853
0.34552721
0.27124181
0.30433468
2.11150235
10.13204793
$/ton
(1999$)
$75,594.95
$71,004.58
$3,194.03
$19.47
$28.21
$8.21
$85.22
$1.07
$1.46
$8.60
$4.26
B
$223.82
$504.40
$0.00
$88,118.38
$83,528.02
NOTE: Emission Reduction Summary (Converted from Mg to Tons)
Industrial Boiler PM Reductions modeled in SR Matrix & CAPMS
Process Heater PM Reductions modeled in SR Matrix & CAPMS
Total PM10 Reductions modeled in Phase One
Total PM2.5 Reductions modeled in Phase One
265155
0
265155
75095
Total PM Reductions from All Sources (MACT floor)
PM10 reductions applied to benefit transfer values
Non-Inventory PM2.5 reductions applied to benefit transfer values
562110
296955
84101
D-2
-------
Table D-2(b). Base Estimate: Results of Benefit Transfer Application for the Phase Two Analysis
of the Industrial Boilers/Process Heaters NESHAP
MACT Floor in 2005 (PM reductions only)
Endpoint Reference
MORTALITY
Ages 30+, Mean, Discount Rate = 3% Krewski et al. (2000)
Ages 30+, Mean, Discount Rate = 7% Krewski et al. (2000)
CHRONIC ILLNESS
Chronic Bronchitis Schwartz, 1993
HOSPITALIZATION
COPD-Related Samet et al. (2000)
Pneumonia-Related Samet et al. (2000)
Asthma-Related Sheppard et al. (1999)
Cardiovascular-Related Samet et al. (2000)
Asthma-Related ER Visits Schwartz et al. (1993)
MINOR ILLNESS
Acute Bronchitis Dockery et al. (1996)
Upper Respiratory Symptoms Pope et al. (1991)
Lower Respiratory Symptoms Schwartz et al. (1994)
Asthma Attacks Whittemore and Korn (1 980)
Work Loss Days Ostro (1987)
MRAD - Adjusted Ostro and Rothschild (1989)
WELFARE EFFECTS
Visibility
Recreational
Avoided Incidence (cases/year)
Mean
1,011
1,011
2,639
467
570
101
1,376
1,063
2,090
102,606
22,812
90,374
177,580
852,117
Direct Economic Valuation
Total Base PM-Related Benefits, Discount Rate = 3%
Total Base PM-Related Benefits, Discount Rate = 7%
Monetary Benefits (millions 1999$)
$5,884
$5,527
$869
$6
$8
$1
$25
$0.3
$0.1
$2.5
$0
B
$19
$41
$0
$6,857
$6,499
Income Adjustment
Factor
1.0805
1.0805
1.0911
1.0000
1.0000
1.0000
1.0000
1.0000
1.0275
1.0275
1.0275
1.0275
1.0000
1.0275
1.1908
Adjusted Benefits
$6,358
$5,972
$948
$6
$8
$1
$25
$0.3
$0.1
$2.6
$0
B
$19
$42
$0
$7,411
$7,025
D-3
-------
Table D-3(a). Base Estimate: Results of Air Quality and Benefit Analyses for the Phase One Analysis
of the Industrial Boilers/Process Heaters NESHAP
MACT Floor in 2005 (PM and SO2 reductions modeled together)
Endpoint Reference
MORTALITY
Ages 30+, Mean, Discount Rate = 3% Krewski et al. (2000)
Ages 30+, Mean, Discount Rate = 7% Krewski et al. (2000)
CHRONIC ILLNESS
Chronic Bronchitis Schwartz, 1993
HOSPITALIZATION
COPD-Related Samet et al. (2000)
Pneumonia-Related Samet et al. (2000)
Asthma-Related Sheppard et al. (1999)
Cardiovascular-Related Samet et al. (2000)
Asthma-Related ER Visits Schwartz et al. (1 993)
MINOR ILLNESS
Acute Bronchitis Dockery et al. (1 996)
Upper Respiratory Symptoms Pope et al. (1991)
Lower Respiratory Symptoms Schwartz et al. (1994)
Asthma Attacks Whittemore and Korn (1 980)
Work Loss Days Ostro (1 987)
MRAD - Adjusted Ostro and Rothschild (1989)
WELFARE EFFECTS
Visibility
Recreational
Avoided Incidence (cases/year)
Mean
1,165
1,165
2,344
415
507
117
1,225
925
2,425
89,477
26,465
79,018
205,400
1,011,204
Direct Economic Valuation
Total Base PM-Related Benefits, Discount Rate = 3%
Total Base PM-Related Benefits, Discount Rate = 7%
Monetary Benefits (millions 1999$)
Simple Mean
$6,778
$6,367
$772
$5
$7
$1
$23
$0.3
$0.1
$2.2
$0
B
$22
$49
$0
$7,660
$7,249
Income Adjustment
Factor
1.0805
1.0805
1.0911
1.0000
1.0000
1.0000
1.0000
1.0000
1.0275
1.0275
1.0275
1.0275
1.0000
1.0275
1.1908
Adjusted Benefits
$7,324
$6,879
$843
$5
$7
$1
$23
$0.3
$0.1
$2.2
$0
B
$22
$50
$0
$8,278
$7,833
D-4
-------
Table D-3(b). Base Estimate: Results of Benefit Transfer Application for the Phase Two Analysis
of the Industrial Boilers/Process Heaters NESHAP
MACT Floor in 2005 (PM and SO2 reductions)
Endpoint Reference
MORTALITY
Ages 30+, Mean, Discount Rate = 3% Krewski et al. (2000)
Ages 30+, Mean, Discount Rate = 7% Krewski et al. (2000)
CHRONIC ILLNESS
Chronic Bronchitis Schwartz, 1993
HOSPITALIZATION
COPD-Related Samet et al. (2000)
Pneumonia-Related Samet et al. (2000)
Asthma-Related Sheppard et al. (1999)
Cardiovascular-Related Samet et al. (2000)
Asthma-Related ER Visits Schwartz et al. (1993)
MINOR ILLNESS
Acute Bronchitis Dockery et al. (1996)
Upper Respiratory Symptoms Pope et al. (1991)
Lower Respiratory Symptoms Schwartz et al. (1994)
Asthma Attacks Whittemore and Korn (1980)
Work Loss Days Ostro (1987)
MRAD - Adjusted Ostro and Rothschild (1989)
WELFARE EFFECTS
Visibility
Recreational
Avoided Incidence (cases/year)
Mean
1,100
1,100
2,757
486
593
110
1,431
1,112
2,270
107,384
24,773
94,468
193,270
931,135
Direct Economic Valuation
Total Base PM-Related Benefits, Discount Rate = 3%
Total Base PM-Related Benefits, Discount Rate = 7%
Monetary Benefits (millions 1999$)
$6,401
$6,012
$908
$6
$9
$1
$26
$0.3
$0.1
$2.6
$0
B
$20
$45
$0
$7,420
$7,032
Income Adjustment
Factor
1.0805
1.0805
1.0911
1.0000
1.0000
1.0000
1.0000
1.0000
1.0275
1.0275
1.0275
1.0275
1.0000
1.0275
1.1908
Adjusted Benefits
(millions 1999$)
$6,916
$6,496
$991
$6
$9
$1
$26
$0.3
$0.1
$2.7
$0
B
$20
$46
$0
$8,020
$7,600
NOTE: Results of this table are based on the addition of incidences and monetary values from Tables D-l(b) and D-2(b).
D-5
-------
Table D-4. Base Estimate: Total Benefits of the Industrial Boilers/Process Heaters NESHAP - MACT Floor in 2005
(Combined Estimates of Reduced Incidences and Monetized Benefits from Phase One and Two Analyses)
Endpoint Reference
MORTALITY
Ages 30+, Mean, Discount Rate = 3% Krewski et al. (2000)
Ages 30+, Mean, Discount Rate = 7% Krewski et al. (2000)
CHRONIC ILLNESS
Chronic Bronchitis Schwartz, 1993
HOSPITALIZATION
COPD-Related Samet et al. (2000)
Pneumonia-Related Samet et al. (2000)
Asthma-Related Sheppard et al. (1999)
Cardiovascular-Related Samet et al. (2000)
Asthma-Related ER Visits Schwartz et al. (1993)
MINOR ILLNESS
Acute Bronchitis Dockery et al. (1996)
Upper Respiratory Symptoms Pope et al. (1991)
Lower Respiratory Symptoms Schwartz et al. (1994)
Asthma Attacks Whittemore and Korn (1980)
Work Loss Days Ostro (1987)
MRAD - Adjusted Ostro and Rothschild (1989)
WELFARE EFFECTS
Visibility
Recreational
Avoided Incidence (cases/year)
Mean
2,265
2,265
5,101
901
1,100
227
2,656
2,037
4,695
196,861
51,238
173,486
398,671
1,942,339
Direct Economic Valuation
Total Base PM-Related Benefits, Discount Rate = 3%
Total Base PM-Related Benefits, Discount Rate = 7%
Monetary Benefits (millions 1999$)
$13,179
$12,379
$1,680
$11
$16
$2
$49
$0.6
$0.3
$4.8
$1
B
$42
$94
$0
$15,080
$14,280
Income Adjustment
Factor
1.0805
1.0805
1.0911
1.0000
1.0000
1.0000
1.0000
1.0000
1.0275
1.0275
1.0275
1.0275
1.0000
1.0275
1.1908
Adjusted Benefits
(millions 1999$)
$14,240
$13,376
$1,834
$11
$16
$2
$49
$0.6
$0.3
$4.9
$1
B
$42
$97
$0
$16,297
$15,432
NOTE: Results of this table are based on the addition of results from Tables D-3(a) and D-3(b).
D-6
-------
Table D-5(a). Base Estimate: Results of Air Quality and Benefit Analyses for the Phase One Analysis
of the Industrial Boilers/Process Heaters NESHAP
Above the MACT Floor in 2005 (SO2 reductions only)
National Benefit-
Transfer Values
Endpoint Reference
MORTALITY
Ages 30+, Mean, Discount Rate = 3% Krewski et al. (2000)
Ages 30+, Mean, Discount Rate = 7% Krewski et al. (2000)
CHRONIC ILLNESS
Chronic Bronchitis Schwartz, 1993
HOSPITALIZATION
COPD-Related Samet et al. (2000)
Pneumonia-Related Samet et al. (2000)
Asthma-Related Sheppard et al. (1999)
Cardiovascular-Related Samet et al. (2000)
Asthma-Related ER Visits Schwartz et al. (1993)
MINOR ILLNESS
Acute Bronchitis Dockery et al. (1996)
Upper Respiratory Symptoms Pope et al. (1991)
Lower Respiratory Symptoms Schwartz et al. (1994)
Asthma Attacks Whittemore and Korn (1980)
Work Loss Days Ostro (1987)
MRAD - Adjusted Ostro and Rothschild (1989)
WELFARE EFFECTS
Visibility
Recreational
Avoided Incidence (cases/year)
Mean
308
308
398
58
71
31
170
147
657
14,162
7,174
12,248
54,979
279,759
Direct Economic Valuation
Total Base PM-Related Benefits, Discount Rate = 3%
Total Base PM-Related Benefits, Discount Rate = 7%
Monetary Benefits (millions 1999$)
Simple Mean
$1,792
$1,683
$131
$1
$1
$0
$3
$0.0
$0.0
$0.3
$0
B
$6
$14
$0
$1,948
$1,839
Income Adjustment
Factor
1.0805
1.0805
1.0911
1.0000
1.0000
1.0000
1.0000
1.0000
1.0275
1.0275
1.0275
1.0275
1.0000
1.0275
1.1908
Adjusted Benefits
(millions 1999$)
$1,936
$1,819
$143
$1
$1
$0
$3
$0.0
$0.0
$0.4
$0
B
$6
$14
$0
$2,105
$1,987
Incidence /ton
0.00322983
0.00322983
0.00417361
0.00060822
0.00074454
0.00032508
0.00178270
0.00154151
0.00688919
0.14851322
0.07523289
0.12844191
0.57653799
2.93367993
$/ton
(1999$)
$20,304.67
$19,071.71
$1,500.29
$7.53
$10.94
$2.23
$32.78
$0.46
$0.41
$3.70
$1.18
B
$61.11
$146.05
$0.00
$22,071.34
$20,838.38
NOTE: Emission Reduction Summary (Converted from Mq to Tons)
SO2 Emission Reductions modeled in SR Matrix & CAPMS
Total SO2 Reductions from all sources (Above MACT Floor)
SO2 reductions applied to benefit transfer values
95361
136733.3
41372.3
D-7
-------
Table D-5(b). Base Estimate: Results of Benefit Transfer Application for the Phase Two Analysis
of the Industrial Boilers/Process Heaters NESHAP
Above the MACT Floor in 2005 (SO2 reductions only)
Endpoint Reference
MORTALITY
Ages 30+, Mean, Discount Rate = 3% Krewski et al. (2000)
Ages 30+, Mean, Discount Rate = 7% Krewski et al. (2000)
CHRONIC ILLNESS
Chronic Bronchitis Schwartz, 1993
HOSPITALIZATION
COPD-Related Samet et al. (2000)
Pneumonia-Related Samet et al. (2000)
Asthma-Related Sheppard et al. (1999)
Cardiovascular-Related Samet et al. (2000)
Asthma-Related ER Visits Schwartz et al. (1 993)
MINOR ILLNESS
Acute Bronchitis Dockery et al. (1996)
Upper Respiratory Symptoms Pope et al. (1991)
Lower Respiratory Symptoms Schwartz et al. (1994)
Asthma Attacks Whittemore and Korn (1980)
Work Loss Days Ostro (1987)
MRAD - Adjusted Ostro and Rothschild (1989)
WELFARE EFFECTS
Visibility
Recreational
Avoided Incidence (cases/year)
Mean
134
134
173
25
31
13
74
64
285
6,144
3,113
5,314
23,853
121,373
Direct Economic Valuation
Total Base PM-Related Benefits, Discount Rate = 3%
Total Base PM-Related Benefits, Discount Rate = 7%
Monetary Benefits (millions 1999$)
Simple Mean
$777
$730
$57
$0
$0
$0
$1
$0.0
$0.0
$0.1
$0
B
$3
$6
$0
$845
$798
Income Adjustment
Factor
1.0805
1.0805
1.0911
1.0000
1.0000
1.0000
1.0000
1.0000
1.0275
1.0275
1.0275
1.0275
1.0000
1.0275
1.1908
Adjusted Benefits
(millions 1999$)
$840
$789
$62
$0
$0
$0
$1
$0.0
$0.0
$0.2
$0
B
$3
$6
$0
$913
$862
D-8
-------
Table D-6(a). Base Estimate: Results of Air Quality and Benefit Analyses for the Phase One Analysis
of the Industrial Boilers/Process Heaters NESHAP
Above the MACT Floor in 2005 (PM reductions only)
National Benefit-
Transfer Values
Endpoint Reference
MORTALITY
Ages 30+, Mean, Discount Rate = 3% Krewski et al. (2000)
Ages 30+, Mean, Discount Rate = 7% Krewski et al. (2000)
CHRONIC ILLNESS
Chronic Bronchitis Schwartz, 1993
HOSPITALIZATION
COPD-Related Samet et al. (2000)
Pneumonia-Related Samet et al. (2000)
Asthma-Related Sheppard et al. (1999)
Cardiovascular-Related Samet et al. (2000)
Asthma-Related ER Visits Schwartz et al. (1993)
MINOR ILLNESS
Acute Bronchitis Dockery et al. (1996)
Upper Respiratory Symptoms Pope et al. (1991)
Lower Respiratory Symptoms Schwartz et al. (1994)
Asthma Attacks Whittemore and Korn (1980)
Work Loss Days Ostro (1987)
MRAD - Adjusted Ostro and Rothschild (1989)
WELFARE EFFECTS
Visibility
Recreational
Avoided Incidence (cases/year)
Mean
1,087
1,087
2,683
470
573
109
1,385
1070
2,230
103,400
24,325
90,940
190,370
918,645
Direct Economic Valuation
Total Base PM-Related Benefits, Discount Rate = 3%
Total Base PM-Related Benefits, Discount Rate = 7%
Monetary Benefits (millions 1999$)
Simple Mean
$6,327
$5,942
$884
$6
$8
$1
$25
$0.3
$0.1
$2.5
$0
B
$20
$45
$0
$7,319
$6,935
Income Adjustment
Factor
1.0805
1.0805
1.0911
1.0000
1.0000
1.0000
1.0000
1.0000
1.0275
1.0275
1.0275
1.0275
1.0000
1.0275
1.1908
Adjusted Benefits
$6,836
$6,421
$964
$6
$8
$1
$25
$0.3
$0.1
$2.6
$0
B
$20
$46
$0
$7,910
$7,495
Incidence/ton
0.01149862
0.01149862
0.00854575
0.00149707
0.00182515
0.00115265
0.00441157
0.00340822
0.02358633
0.32935392
0.25722847
0.28966831
2.01311570
9.71442399
$/ton
(1999$)
$72,287.27
$67,897.76
$3,071.95
$18.53
$26.82
$7.89
$81.12
$1.02
$1.39
$8.20
$4.04
B
$213.39
$483.61
$0.00
$83,646.62
$79,257.11
NOTE: Emission Reduction Summary (Converted from Ma to Tons)
Industrial Boiler PM Reductions modeled in SR Matrix & CAPMS
Process Heater PM Reductions modeled in SR Matrix & CAPMS
Total PM10 Reductions modeled
Total PM2.5 Reductions modeled
Total PM Reductions from All Sources (Above MACT Floor)
PM10 reductions applied to benefit transfer values
PM2.5 reductions applied to benefit transfer values
295645
18302
313947
94565
569229.1
255282.1
76894
D-9
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Table D-6(b). Base Estimate: Results of Benefit Transfer Application for the Phase Two Analysis
of the Industrial Boilers/Process Heaters NESHAP
Above the MACT Floor in 2005 (PM reductions only)
Endpoint Reference
MORTALITY
Ages 30+, Mean, Discount Rate = 3% Krewski et al. (2000)
Ages 30+, Mean, Discount Rate = 7% Krewski et al. (2000)
CHRONIC ILLNESS
Chronic Bronchitis Schwartz, 1993
HOSPITALIZATION
COPD-Related Samet et al. (2000)
Pneumonia-Related Samet et al. (2000)
Asthma-Related Sheppard et al. (1999)
Cardiovascular-Related Samet et al. (2000)
Asthma-Related ER Visits Schwartz et al. (1993)
MINOR ILLNESS
Acute Bronchitis Dockery et al. (1996)
Upper Respiratory Symptoms Pope et al. (1 991 )
Lower Respiratory Symptoms Schwartz et al. (1994)
Asthma Attacks Whittemore and Korn (1980)
Work Loss Days Ostro (1987)
MRAD - Adjusted Ostro and Rothschild (1989)
WELFARE EFFECTS
Visibility
Recreational
Avoided Incidence (cases/year)
Mean
884
884
2,182
382
466
89
1,126
870
1,814
84,078
19,779
73,947
154,797
746,984
Direct Economic Valuation
Total Base PM-Related Benefits, Discount Rate = 3%
Total Base PM-Related Benefits, Discount Rate = 7%
Monetary Benefits (millions 1999$)
$5,144
$4,832
$719
$5
$7
$1
$21
$0.3
$0.1
$2.0
$0
B
$16
$36
$0
$5,951
$5,639
Income Adjustment
Factor
1.0805
1.0805
1.0911
1.0000
1.0000
1.0000
1.0000
1.0000
1.0275
1.0275
1.0275
1.0275
1.0000
1.0275
1.1908
Adjusted Benefits
$5,558
$5,221
$784
$5
$7
$1
$21
$0.3
$0.1
$2.1
$0
B
$16
$37
$0
$6,432
$6,094
D-10
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Table D-7(a). Base Estimate: Results of Air Quality and Benefit Analyses for the Phase One Analysis
of the Industrial Boilers/Process Heaters NESHAP
Above the MACT Floor in 2005 (PM and SO2 reductions modeled together)
Endpoint Reference
MORTALITY
Ages 30+, Mean, Discount Rate = 3% Krewski et al. (2000)
Ages 30+, Mean, Discount Rate = 7% Krewski et al. (2000)
CHRONIC ILLNESS
Chronic Bronchitis Schwartz, 1993
HOSPITALIZATION
COPD-Related Samet et al. (2000)
Pneumonia-Related Samet et al. (2000)
Asthma-Related Sheppard et al. (1999)
Cardiovascular-Related Samet et al. (2000)
Asthma-Related ER Visits Schwartz et al. (1993)
MINOR ILLNESS
Acute Bronchitis Dockery et al. (1996)
Upper Respiratory Symptoms Pope et al. (1991)
Lower Respiratory Symptoms Schwartz et al. (1994)
Asthma Attacks Whittemore and Korn (1 980)
Work Loss Days Ostro (1987)
MRAD - Adjusted Ostro and Rothschild (1 989)
WELFARE EFFECTS
Visibility
Recreational
Avoided Incidence (cases/year)
Mean
1,390
1,390
2,864
502
613
139
1,480
1142
2,869
110,367
31,293
97,058
243,866
1,196,497
Direct Economic Valuation
Total Base PM-Related Benefits, Discount Rate = 3%
Total Base PM-Related Benefits, Discount Rate = 7%
Monetary Benefits (millions 1999$)
Simple Mean
$8,086
$7,595
$944
$6
$9
$1
$27
$0.3
$0.2
$2.7
$0
$4
$26
$58
$0
$9,165
$8,674
Income Adjustment
Factor
1.0805
1.0805
1.0911
1.0000
1.0000
1.0000
1.0000
1.0000
1.0275
1.0275
1.0275
1.0275
1.0000
1.0275
1.1908
Adjusted Benefits
$8,737
$8,207
$1,029
$6
$9
$1
$27
$0.3
$0.2
$2.7
$0
$4
$26
$60
$0
$9,904
$9,373
D-ll
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Table D-7(b). Base Estimate: Results of Benefit Transfer Application for the Phase Two Analysis
of the Industrial Boilers/Process Heaters NESHAP
Above the MACT Floor in 2005 (PM and SO2 reductions)
Endpoint Reference
MORTALITY
Ages 30+, Mean, Discount Rate = 3% Krewski et al. (2000)
Ages 30+, Mean, Discount Rate = 7% Krewski et al. (2000)
CHRONIC ILLNESS
Chronic Bronchitis Schwartz, 1993
HOSPITALIZATION
COPD-Related Samet et al. (2000)
Pneumonia-Related Samet et al. (2000)
Asthma-Related Sheppard et al. (1999)
Cardiovascular-Related Samet et al. (2000)
Asthma-Related ER Visits Schwartz et al. (1993)
MINOR ILLNESS
Acute Bronchitis Dockery et al. (1996)
Upper Respiratory Symptoms Pope et al. (1991)
Lower Respiratory Symptoms Schwartz et al. (1994)
Asthma Attacks Whittemore and Korn (1980)
Work Loss Days Ostro (1987)
MRAD - Adjusted Ostro and Rothschild (1989)
WELFARE EFFECTS
Visibility
Recreational
Avoided Incidence (cases/year)
Mean
1,018
1,018
2,354
407
497
102
1,200
934
2,099
90,222
22,892
79,261
178,650
868,357
Direct Economic Valuation
Total Base PM-Related Benefits, Discount Rate = 3%
Total Base PM-Related Benefits, Discount Rate = 7%
Monetary Benefits (millions 1999$)
$5,922
$5,562
$776
$5
$7
$1
$22
$0.3
$0.1
$2.2
$0
$3
$19
$42
$0
$6,800
$6,440
Income Adjustment
Factor
1.0805
1.0805
1.0911
1.0000
1.0000
1.0000
1.0000
1.0000
1.0275
1.0275
1.0275
1.0275
1.0000
1.0275
1.1908
Adjusted Benefits
(millions 1999$)
$6,399
$6,010
$846
$5
$7
$1
$22
$0.3
$0.1
$2.2
$0
$3
$19
$43
$0
$7,348
$6,960
NOTE: Results of this table are based on the addition of incidences and monetary values from Tables D-5(b) and D-6(b).
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Table D-8. Base Estimate: Total Benefits of the Industrial Boilers/Process Heaters NESHAP - Above the MACT Floor in 2005
(Combined Estimates of Reduced Incidences and Monetized Benefits from Phase One and Two Analyses)
Endpoint Reference
MORTALITY
Ages 30+, Mean, Discount Rate = 3% Krewski et al. (2000)
Ages 30+, Mean, Discount Rate = 7% Krewski et al. (2000)
CHRONIC ILLNESS
Chronic Bronchitis Schwartz, 1993
HOSPITALIZATION
COPD-Related Samet et al. (2000)
Pneumonia-Related Samet et al. (2000)
Asthma-Related Sheppard et al. (1999)
Cardiovascular-Related Samet et al. (2000)
Asthma-Related ER Visits Schwartz et al. (1993)
MINOR ILLNESS
Acute Bronchitis Dockery et al. (1996)
Upper Respiratory Symptoms Pope et al. (1991)
Lower Respiratory Symptoms Schwartz et al. (1994)
Asthma Attacks Whittemore and Korn (1980)
Work Loss Days Ostro (1987)
MRAD - Adjusted Ostro and Rothschild (1989)
WELFARE EFFECTS
Visibility
Recreational
Avoided Incidence (cases/year)
Mean
2,408
2,408
5,218
909
1,110
241
2,680
2,076
4,968
200,589
54,185
82,130
275,708
2,064,854
Direct Economic Valuation
Total Base PM-Related Benefits, Discount Rate = 3%
Total Base PM-Related Benefits, Discount Rate = 7%
Monetary Benefits (millions 1999$)
$14,008
$13,158
$1,719
$11
$16
$2
$49
$0.6
$0.3
$4.9
$1
B
$29
$100
$0
$15,942
$15,091
Income Adjustment
Factor
1.0805
1.0805
1.0911
1.0000
1.0000
1.0000
1.0000
1.0000
1.0275
1.0275
1.0275
1.0275
1.0000
1.0275
1.1908
Adjusted Benefits
(millions 1999$)
$15,136
$14,217
$1,876
$11
$16
$2
$49
$0.6
$0.3
$5.0
$1
B
$29
$103
$0
$17,229
$16,310
NOTE: Results of this table are based on the addition of results from Tables D-7(a) and D-7(b).
D-13
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Appendix E. Impacts Based on Low-Risk Threshold Cutoffs for
Hydrochloric Acid (HC1) and Manganese (Mn)
Background
Among the alternatives to compliance with the final rule are health-based threshold cutoffs for different pollutants. As
an alternative to the requirement for each large solid fuel-fired boiler to demonstrate compliance with the HC1 emission limit in
the final rule, you may demonstrate compliance with a health-based HC1 equivalent allowable emission limit. In lieu of
complying with the emission standard for total selected metals (TSM) in the final rule based on the sum of emissions for the
eight selected metals, you may demonstrate eligibility for complying with the TSM standard based on excluding manganese
emissions from the summation of TSM emissions for the affected source unit(s).
Emission Reductions
Nationwide emissions of selected HAP (i.e., HC1, hydrogen fluoride, lead, and nickel) will be reduced by 58,500 tpy for
existing units and 73 tpy for new units. Depending on the number of facilities demonstrating eligibility for the health-based
compliance alternatives, the total HAP reduction for existing units could be 50,600 tpy. Emissions of HC1 will be reduced by
42,000 tpy for existing units and 72 tpy for new units. Depending on the number of facilities demonstrating eligibility for the
health-based compliance alternatives, the total HC1 emissions reduction for existing units could be 36,400 tpy. Emissions of
mercury will be reduced by 1.9 tpy for existing units and 0.006 tpy for new units. Emissions of PM will be reduced by 565,000
tpy for existing units and 480 tpy for new units. Depending on the number of facilities demonstrating eligibility for the health-
based compliance alternatives, the total PM emissions reduction for existing units could be 547,000 tpy. Emissions of total
selected nonmercury metals (i.e., arsenic, beryllium, cadmium, chromium, lead, manganese, nickel, and selenium) will be
reduced by 1,100 tpy for existing units and will be reduced by 1.4 tpy for new units. Depending on the number of facilities
demonstrating eligibility for the health-based compliance alternatives, the total nonmercury metals emissions reduction for
existing units could fall to be 950 tpy. In addition, emissions of sulfur dioxide are established to be reduced by 113,000 tpy for
existing sources and 110 tpy for new sources. Depending on the number of facilities demonstrating eligibility for the health-
based compliance alternatives, the total sulfur dioxide emissions reduction for existing units could fall to be 49,000 tpy.
A discussion of the methodology used to estimate emissions and emissions reductions is presented in "Estimation of
Baseline Emissions and Emissions Reductions for Industrial, Commercial, and Institutional Boilers and Process Heaters" in the
docket. To estimate the potential impacts of the health-based compliance alternatives, we performed a preliminary "rough"
assessment of the large solid fuel subcategory to determine the extent to which facilities might become eligible for the health-
based compliance alternatives. Based on the results of this rough assessment, 448 coal-fired boilers could potentially be eligible
for the HC1 compliance alternative and 386 biomass-fired boilers could be potentially eligible for the TSM compliance
alternative.
Wastewater and Solid Waste impacts
The EPA estimates the additional water usage that would result from the MACT floor level of control to be 110 million
gallons per year for existing sources and 0.6 million gallons per year for new sources. In addition to the increased water usage,
an additional 3.7 million gallons per year of wastewater will be produced for existing sources and 0.6 million gallons per year
for new sources. The costs of treating the additional wastewater are $18,000 for existing sources and $2,300 for new sources, in
advance of any facility demonstrating eligibility for the health-based compliance alternatives. These costs are accounted for in
the control costs estimates.
The EPA estimates the additional solid waste that would result from the MACT floor level of control to be 102,000 tpy
for existing sources and 1 tpy for new sources. The estimated costs of handling the additional solid waste generated are $1.5
million for existing sources and $17,000 for new sources, in advance of any facility demonstrating eligibility for the health-
based compliance alternatives. These costs are also accounted for in the control costs estimates.
A discussion of the methodology used to estimate impacts is presented in "Estimation of Impacts for Industrial,
Commercial, and Institutional Boilers and Process Heaters NESHAP" in the docket.
Energy Impact from Additional Control Equipment
E-l
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The EPA expects an increase of approximately 1,130 million kilowatt hours (kWh) in national annual energy usage as a
result of the final rule, in advance of any facility demonstrating eligibility for the health-based compliance alternatives. Of this
amount, 1,120 million kWh is estimated from existing sources and 13 million kWh is estimated from new sources. The increase
results from the electricity required to operate control devices installed to meet the final rule, such as wet scrubbers and fabric
filters.
Compliance Costs
To estimate the national cost impacts of the final rule for existing sources, EPA developed several model boilers and
process heaters and determined the cost of control equipment for these model boilers. The EPA assigned a model boiler or
heater to each existing unit in the database based on the fuel, size, design, and current controls. The analysis considered all air
pollution control equipment currently in operation at existing boilers and process heaters. Model costs were then assigned to all
existing units that could not otherwise meet the proposed emission limits. The resulting total national cost impact of the final
rule is $1,790 million in capital expenditures and $860 million per year in total annual costs. Depending on the number of
facilities demonstrating eligibility for the health-based compliance alternatives, these costs could fall to be $1,440 million in
capital expenditures and $690 million per year in total annual costs. The total capital and annual costs include costs for testing,
monitoring, and recordkeeping and reporting.- Costs include testing and monitoring costs, but not recordkeeping and reporting
costs.Using Department of Energy projections on fuel expenditures, EPA estimated the number of additional boilers that could
be potentially constructed. The resulting total national cost impact of the final rule in the 5th year is $58 million in capital
expenditures and $18.6 million per year in total annual costs, in advance of any facility demonstrating eligibility for the health-
based provisions. Costs are mainly for testing and monitoring.
A discussion of the methodology used to estimate cost impacts is presented in "Methodology and Results of Estimating
the Cost of Complying with the Industrial, Commercial, and Institutional Boiler and Process Heater NESHAP" in the docket.
Economic Impacts
The economic impact analysis shows that the expected price increase for output in the 40 affected industries would be
no more than 0.04 percent as a result of the final rule for industrial boilers and process heaters. The expected change in
production of affected output is a reduction of only 0.03 percent or less in the same industries. In addition, impacts to affected
energy markets show that prices of petroleum, natural gas, electricity and coal should increase by no more than 0.05 percent as a
result of implementation of the final rule, and output of these types of energy should decrease by no more than 0.01 percent.
These impacts are generated in advance of any facility demonstrating eligibility for the health-based compliance alternatives.
Depending on the number of affected facilities demonstrating eligibility for the health-based compliance alternatives, these
impacts on product prices could fall to a 0.03 percent increase, and a decrease in output of the energy types mentioned
previously of less than 0.01 percent. Therefore, it is likely that there is no adverse impact expected to occur for those industries
that produce output affected by the final rule, such as lumber and wood products, chemical manufacturers, petroleum refining,
and furniture manufacturing.
Small Entity Impacts
After considering the economic impact of the final rule on small entities, we have determined that the final rule will not
have a significant economic impact on a substantial number of small entities. Based on SBA size definitions for the affected
industries and reported sales and employment data, EPA identified 185 of the 576 entities, or 32 percent, owning affected
facilities as small entities. Although small entities represent 32 percent of the entities within the source category, they are
expected to incur only 4 percent of the total compliance costs of $862.7 million (1999 dollars). There are only ten small entities
with compliance costs equal to or greater than 3 percent of their sales. In addition, there are only 24 small entities with cost-to-
sales ratios between 1 and 3 percent.
An economic impact analysis was performed to estimate the changes in product price and production quantities for the
final rule. As mentioned in the summary of economic impacts earlier in this preamble, the estimated changes in prices and
output for affected entities is no more than 0.05 percent.
For more information, consult the docket for the final rule.
It should be noted that these small entity impacts are in advance of any facility demonstrating eligibility for the health-
based compliance alternatives. Depending on the number of affected facilities demonstrating eligibility for the health-based
compliance alternatives, the estimated small entity impacts fall to eight small entities with compliance costs equal to or greater
than 3 percent of their sales, and 14 small entities with compliance costs between 1 and 3 percent of their sales.
E-2
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The final rule will not have a significant economic impact on a substantial number of small entities as a result of several
decisions EPA made regarding the development of the rule, which resulted in limiting the impact of the rule on small entities.
First, as mentioned earlier in this preamble, EPA identified small units (heat input of 10 MMBtu/hr or less) and limited use
boilers (operate less than 10 percent of the time) as separate subcategories different from large units. Many small and limited
use units are located at small entities. As also discussed earlier, the results of the MACT floor analysis for these subcategories of
existing sources was that no MACT floor could be identified except for the limited use solid fuel subcategory, which is less
stringent than the MACT floor for large units. Furthermore, the results of the beyond-the-floor analysis for these subcategories
indicated that the costs would be too high to consider them feasible options. Consequently, the final rule contains no emission
limitations for any of the existing small and limited use subcategories except the existing limited use solid fuel subcategory. In
addition, the alternative metals emission limit resulted in minimizing the impacts on small entities since some of the potential
entities burning a fuel containing very little metals are small entities.
Social Costs and Benefits
The regulatory impact analysis prepared for the final rule including the EPA's assessment of costs and benefits, is
detailed in the "Regulatory Impact Analysis for the Industrial Boilers and Process Heaters MACT" in the docket. Based on
estimated compliance costs associated with the final rule and the predicted change in prices and production in the affected
industries, the estimated social costs of the final rule are $863 million (1999 dollars). Depending on the number of affected
facilities demonstrating eligibility for the health-based compliance alternatives, these annualized social costs could fall to $746
million.
It is estimated that 5 years after implementation of the final rule, HAP will be reduced by 58,500 tpy due to reductions in
arsenic, beryllium, dioxin, hydrochloric acid, and several other HAP from industrial boilers and process heaters. Studies have
determined a relationship between exposure to these HAP and the onset of cancer, however, there are some questions remaining
on how cancers that may result from exposure to these HAP can be quantified in terms of dollars. Therefore, the EPA is unable
to provide a monetized estimate of the benefits of the HAP reduced by the final rule at this time. However, there are significant
reductions in PM and in SC>2 that occur. Reductions of 560,000 tons of PM with a diameter of less than or equal to 10
micrometers (PM^g), 159,000 tons of PM with a diameter of less than or equal to 2.5 micrometers (PM2 5), and 112,000 tons of
SC>2 are expected to occur. These reductions occur from existing sources in operation 5 years after the implementation of the
regulation and are expected to continue throughout the life of the affected sources. The major health effect that results from
these PM and SC>2 emissions reductions is a reduction in premature mortality. Other health effects that occur are reductions in
chronic bronchitis, asthma attacks, and work-lost days (i.e., days when employees are unable to work).
While we are unable to monetize the benefits associated with the HAP emissions reductions, we are able to monetize the
benefits associated with the PM and SC>2 emissions reductions. For SC>2 and PM, we estimated the benefits associated with
health effects of PM, but were unable to quantify all categories of benefits (particularly those associated with ecosystem and
environmental effects). Unqualified benefits are noted with "B" in the estimates presented below. Our primary estimate of the
monetized benefits in 2005 associated with the implementation of the proposed alternative is $16.3 billion + B (1999 dollars).
This estimate is about $15.3 billion + B (1999 dollars) higher than the estimated social costs shown earlier in this section. These
benefit estimates are in advance of any facility demonstrating eligibility for the health-based compliance alternatives. Depending
on the number of affected facilities demonstrating eligibility for the health-based compliance alternatives, the benefit estimate
presuming the health-based compliance alternatives is $14.5 billion + B, which is $1.7 billion lower than the estimate for the
final rule. This estimate is $13.8 billion + B higher than the estimated social costs presuming the health-based compliance
alternatives. The general approach to calculating monetized benefits is discussed in more detail earlier in this preamble. For
more detailed information on the benefits estimated for the final rule, refer to the RIA in the docket.
Energy Impact Analysis
As mentioned in the economic impact analysis, the reduction in petroleum product output, which includes reductions in
fuel production, is estimated at only 0.001 percent, or about 68 barrels per day based on 2000 U.S. fuel production nationwide.
That is a minimal reduction in nationwide petroleum product output. The reduction in coal production is estimated at only 0.014
percent, or about 3.5 million tpy (or less than 1,000 tons per day) based on 2000 U.S. coal production nationwide. The
combination of the increase in electricity usage estimated with the effect of the increased price of affected output yields an
increase in electricity output estimated at only 0.012 percent, or about 0.72 billion kilowatt-hours per year based on 2000 U.S.
electricity production nationwide. All energy price changes estimated show no increase in price more than 0.05 percent
nationwide, and a similar result occurs for energy distribution costs. We also expect that there will be no discernable impact on
the import of foreign energy supplies, and no other adverse outcomes are expected to occur with regards to energy supplies. All
of the results presented above account for the pass through of costs to consumers, as well as the cost impact to producers. For
more information on the estimated energy effects, please refer to the economic impact analysis for the final rule.
Depending on the number of affected facilities demonstrating eligibility for the health-based compliance alternatives, the
E-3
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reduction in petroleum product output, which includes reductions in fuel production, could fall to 65 barrels per day, or only
0.001 percent.The reduction in coal production could fall to only 0.010 percent, or about 2.5 million tpy based on 2000 U.S.
coal production nationwide. The combination of the increase in electricity usage estimated with the effect of the increased price
of affected output could yield an increase in electricity output that could be only 0.0067 percent, or about 0.40 billion kilowatt-
hours per year based on 2000 U.S. electricity production nationwide. All energy price changes estimated could now fall to
increases of no more than 0.04 percent nationwide, and a similar result occurs for energy distribution costs. There should be no
discernable impact on import of foreign energy supplies, and no other adverse outcomes are expected to occur with regards to
energy supplies. All of the results presented-with presumption of the health-based compliance alternatives also account for the
pass through of costs to consumers as well as the cost impact to producers.
E-4
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TECHNICAL REPORT DATA
(Please read Instructions on reverse before completing)
1. REPORT NO.
EPA-452-R-04-002
3. RECIPIENT'S ACCESSION NO.
4. TITLE AND SUBTITLE
Regulatory Impact Analysis for the Industrial Boilers and Process
Heaters NESHAP
5. REPORT DATE
February 2004
6. PERFORMING ORGANIZATION CODE
7. AUTHOR(S)
8. PERFORMING ORGANIZATION REPORT NO.
9. PERFORMING ORGANIZATION NAME AND ADDRESS
10. PROGRAM ELEMENT NO.
U.S. OAQPS
11. CONTRACT/GRANT NO.
12. SPONSORING AGENCY NAME AND ADDRESS
13. TYPE OF REPORT AND PERIOD COVERED
Office of Air Quality Planning and Standards
Air Quality Strategies and Standards Division
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
14. SPONSORING AGENCY CODE
EPA/200/04
15. SUPPLEMENTARY NOTES
16. ABSTRACT
This report includes benefits, costs, and economic impacts for the final Industrial Boilers and Process
Heaters NESHAP, which is a MACT standard. The benefits of the rule are well in excess of the social
costs ($16.3 billion compared to $863 million).
17.
KEY WORDS AND DOCUMENT ANALYSIS
DESCRIPTORS
b. IDENTIFIERS/OPEN ENDED TERMS
c. COSATI Field/Group
Air Pollution control, Economic
Impacts, Benefits, Costs
18. DISTRIBUTION STATEMENT
Release Unlimited
19. SECURITY CLASS (Report)
Unclassified
21. NO. OF PAGES
310
20. SECURITY CLASS (Page)
Unclassified
22. PRICE
EPA Form 2220-1 (Rev. 4-77) PREVIOUS EDITION IS OBSOLETE
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United States
Environmental Protection
Agency
Office of Air Quality Planning and Standards
Air Quality Strategies and Standards Division
Research Triangle Park, NC
Publication No. EPA-452/R-04-002
February 2004
Postal information in this section where appropriate.
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