Document ID: EPA-HQ-OAR-2006-0790-2335
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2011-03-21T04:00Z

February 2011

Regulatory Impact Analysis:

National Emission Standards for Hazardous Air Pollutants for Industrial,
Commercial, and Institutional Boilers and Process Heaters

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards (OAQPS)

Air Economics Group

Risk and Benefits Group

Air Quality Modeling Group

Research Triangle Park, NC 27711

	

	

Regulatory Impact Analysis:

National Emission Standards for Hazardous Air Pollutants for Industrial,
Commercial, and Institutional Boilers and Process Heaters

February 2011

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards (OAQPS)

Air Economics Group

Risk and Benefits Group

Air Quality Modeling Group

Research Triangle Park, NC 27711

Contents

Section	Page

1	Introduction	1-1

1.1	Executive Summary	1-1

1.2	Organization of this Report	1-3

1.3	Section 1 References	1-6

2	Industry Profiles	2-1

2.1	Food Manufacturing	2-1

2.1.1	Introduction	2-1

2.1.2	Supply and Demand Characteristics	2-4

2.1.2.1	Goods and Services Used in Food Manufacturing	2-4

2.1.2.2	Energy	2-4

2.1.2.3	Uses and Consumers	2-7

2.1.3	Firm and Market Characteristics	2-7

2.1.3.1	Location	2-7

2.1.3.2	Production Capacity and Utilization	2-9

2.1.3.3	Employment	2-9

2.1.3.4	Plants and Capacity	2-10

2.1.3.5	Firm Characteristics	2-10

2.1.3.6	Size Distribution	2-12

2.1.3.7	Domestic Production	2-12

2.1.3.8	International Trade	2-12

2.1.3.9	Market Prices	2-16

2.2	Wood Product Manufacturing	2-16

2.2.1	Introduction	2-16

2.2.2	Supply and Demand Characteristics	2-20

2.2.2.1	Goods and Services Used in Wood Product Manufacturing	2-20

2.2.2.2	Uses and Consumers	2-22

2.2.3	Firm and Market Characteristics	2-24

2.2.3.1	Location	2-24

2.2.3.2	Production Capacity and Utilization	2-25

2.2.3.3	Employment	2-26

2.2.3.4	Plants and Capacity	2-26

2.2.3.5	Firm Characteristics	2-27

2.2.3.6	Size Distribution	2-27

2.2.3.7	Domestic Production	2-28

2.2.3.8	International Trade	2-28

2.2.3.9	Market Prices	2-31

2.3	Paper Manufacturing	2-33

2.3.1	Introduction	2-33

2.3.2	Supply and Demand Characteristics	2-37

2.3.2.1	Goods and Services Used in Paper Manufacturing	2-37

2.3.2.2	Uses and Consumers	2-42

2.3.3	Firm and Market Characteristics	2-43

2.3.3.1	Location	2-43

2.3.3.2	Production Capacity and Utilization	2-44

2.3.3.3	Employment	2-44

2.3.3.4	Plants and Capacity	2-45

2.3.3.5	Firm Characteristics	2-45

2.3.3.6	Size Distribution	2-47

2.3.3.7	Domestic Production	2-48

2.3.3.8	International Trade	2-49

2.3.3.9	Market Prices	2-49

2.4	Chemical Manufacturing	2-51

2.4.1	Introduction	2-51

2.4.2	Supply and Demand Characteristics	2-54

2.4.2.1	Goods and Services Used in Chemical Manufacturing	2-55

2.4.2.2	Uses and Consumers	2-55

2.4.3	Firm and Market Characteristics	2-58

2.4.3.1	Location	2-58

2.4.3.2	Production Capacity and Utilization	2-60

2.4.3.3	Employment	2-60

2.4.3.4	Plants and Capacity	2-60

2.4.3.5	Firm Characteristics	2-62

2.4.3.6	Size Distribution	2-64

2.4.3.7	Domestic Production	2-64

2.4.3.8	International Trade	2-64

2.4.3.9	Market Prices	2-64

2.5	Section 2 References	2-68

3	Engineering Cost Analysis	3-1

3.1	Major Sources	3-2

3.2	Area Sources	3-5

3.3	Section 3 References	3-6

4	Economic Impact Analysis	4-1

4.1	Partial Equilibrium Analysis (Multiple Markets)	4-1

4.1.1	Overview	4-1

4.1.2	Economic Impact Analysis Results	4-2

4.1.2.1	Market-Level Results	4-2

4.1.2.2	Social Cost Estimates Major Source Rule	4-3

4.1.2.3	Social Cost Estimates Area Source Rule	4-5

4.1.2.4	Job Effects	4-6

4.2	Section 4 References	4-14

5	Small Entity Analyses	5-1

5.1	Small Entity Screening Analysis	5-1

5.1.1	Small Businesses	5-1

5.1.1.1	Representative Small Business Analysis Using Census Statistics
of U.S. Businesses	5-1

5.1.1.2	Additional Small Business Analysis Using Sample of Small
Businesses Identified in Combustion Facility Survey	5-4

5.1.2	Small Governmental Jurisdictions and Not-for-Profit Enterprises
5-5

5.2	Final Regulatory Flexibility Analysis (FRFA): Major Sources	5-6

5.2.1	Need for Rule and Objectives	5-6

5.2.2	Summary of the Significant Issues raised by the Public Comments
and Agency Assessment	5-6

5.2.3	Description of and Estimate of the Number of Small entities to
Which the Rules Will Apply	5-7

5.2.4	Description of the Projected Reporting, Record keeping and Other
Compliance Requirements of the Rule	5-9

5.2.5	Description of the steps the Agency has Taken to Minimize the
Significant Economic Impact on Small Entities	5-11

5.3	Final Regulatory Flexibility Analysis (FRFA): Area Sources	5-12

5.3.1	Need for Rule and Objectives	5-12

5.3.2	Summary of the Significant Issues raised by the Public Comments
and Agency Assessment	5-12

5.3.3	Description of and Estimate of the Number of Small entities to
Which the Rules Will Apply	5-13

5.3.4	Description of the Projected Reporting, Record keeping and Other
Compliance Requirements of the Rule	5-15

5.3.5	Description of the steps the Agency has Taken to Minimize the
Significant Economic Impact on Small Entities	5-16

5.4	Section 5 References	5-16

6	Air Quality Modeling of Emissions Reductions	6-1

6.1	Synopsis	6-1

6.2	Photochemical Model Background	6-2

6.3	Model Domain and Grid Resolution	6-3

6.4	Emissions Input Data	6-4

6.4.1	2005 Baseline Emissions	6-4

6.4.2	Future Year Baseline Emissions	6-5

6.4.3	Future Year Sector Contribution Approach	6-7

6.5	Model Results	6-9

6.5.1	Impacts of Sector on Total Mercury Deposition	6-10

6.5.2	Impacts of Sector on Future Annual PM2.5 Levels	6-10

6.5.3	Impacts of Sector on Future 24-hour PM2.5 Levels	6-11

6.5.4	Impacts of Sector on Future Visibility Levels	6-11

6.5.5	Impacts of Sector on Future Ozone Levels	6-12

6.6	Section 6 References	6-12

7	Benefits of Emissions Reductions	7-1

7.1	Synopsis	7-1

7.2	Calculating Benefits	7-2

7.2.1	Methodology Improvements since Proposal	7-4

7.2.2	Benefits Analysis Approach for PM2.5 and Ozone	7-5

7.2.3	Health Impact Analysis (HIA)	7-6

7.2.4	Estimating PM2.5-related Premature Mortality	7-10

7.2.5	Economic Valuation of Health Impact	7-12

7.2.6	Calculating Boiler Sector-specific Benefit-per-ton Estimates	7-14

7.3	Health Benefits Results	7-20

7.4	Energy Disbenefits	7-34

7.4.1	Social Cost of Carbon and Greenhouse Gas Disbenefits	7-34

7.5	Unquantified or Nonmonetized Benefits	7-37

7.5.1	Other SO2 Benefits	7-37

7.5.2	Carbon Monoxide Benefits	7-39

7.5.3	Visibility Benefits	7-40

7.5.4	Ozone Vegetation Benefits	7-40

7.5.5	Direct HAP Benefits	7-40

7.5.5.1	Mercury	7-44

7.5.5.2	Hydrogen Chloride (HCl)	7-49

7.5.5.3	Chlorine Gas (Cl2)	7-50

7.5.5.4	Hydrogen Cyanide (HCN)	7-50

7.5.5.5	Hydrogen Fluoride (HF)	7-50

7.5.5.6	Toluene	7-50

7.5.5.7	Formaldehyde	7-51

7.5.5.8	Polycyclic Aromatic Hydrocarbons (PAHs)	7-53

7.5.5.9	Acetaldehyde	7-54

7.5.5.10	Nickel	7-55

7.5.5.11	Manganese	7-55

7.5.5.12	Dioxins (Chlorinated dibenzodioxins (CDDs)	7-56

7.5.5.13	Furans (Chlorinated dibenzofurans (CDFs))	7-57

7.5.5.14	Other Air Toxics	7-57

7.6	Limitations and Uncertainties	7-57

7.6.1	Monte Carlo Analysis	7-58

7.6.2	LML Assessment for PM2.5	7-59

7.6.3	Alternate Concentration-response Functions for PM Mortality	7-59

7.6.4	Alternate Concentration-response Functions for Ozone Mortality
7-60

7.6.5	Qualitative Assessment of Uncertainty and Other Analysis
Limitations	7-60

7.7	Section 7 References	7-62

8	Comparison of Monetized Benefits and Costs	8-1

8.1	Boiler MACT	8-1

8.2	Boiler Area Source Rule	8-1

8.3	Section 8 References	8-5

Appendixes

  TOC \t "AppHeading1,2"  A	OAQPS Multimarket Model to Assess the
Economic Impacts of Environmental Regulation	A-  PAGEREF _Toc285522848
\h  1 

B	Detailed Economic Model Results by Sector	B-  PAGEREF _Toc285522849 \h
 1 

 

List of Figures

Number	Page

2-1.	Distribution of Employment within Food Manufacturing (NAICS 311):
2006	2-3

2-2.	Distribution of Total Value of Shipments within Food Manufacturing
(NAICS 311): 2006	2-3

2-3.	Electric Power Use Trends in Food Manufacturing (NAICS 311):
1997–2005	2-7

2-4.	Establishment Concentration in Food Manufacturing (NAICS 311):
2002	2-8

2-5.	Capacity Utilization Trends in Food Manufacturing (NAICS 311)	2-9

2-6.	Employment Concentration in Food Manufacturing (NAICS 311): 2002
2-10

2-7.	Capacity Trends in Food Manufacturing (NAICS 311)	2-11

2-8.	Industrial Production Trends in Food Manufacturing (NAICS 311)
2-15

2-9.	International Trade Trends in Food Manufacturing (NAICS 311)	2-15

2-10.	Producer Price Trends in Food Manufacturing (NAICS 311)	2-16

2-11.	Distribution of Value of Shipments within Wood Product
Manufacturing (NAICS 322): 2007	2-19

2-12.	Distribution of Employment within Wood Product Manufacturing
(NAICS 322): 2007	2-20

2-13.	Electrical Power Use Trends in the Wood Product Manufacturing
Industry (NAICS 321): 1997–2005	2-24

2-14.	Establishment Concentration in the Wood Product Manufacturing
Industry (NAICS 321): 2002	2-26

2-15.	Capacity Utilization Trends in the Wood Product Manufacturing
Industry (NAICS 321)	2-27

2-16.	Employment Concentration in the Wood Product Manufacturing
Industry (NAICS 321): 2002	2-28

2-17.	Capacity Trends in the Wood Product Manufacturing Industry (NAICS
321)	2-29

2-18.	Industrial Production Trends in the Wood Product Manufacturing
Industry (NAICS 321): 1997–2009	2-32

2-19.	International Trade Trends in the Wood Product Manufacturing
Industry (NAICS 321)]	2-32

2-20.	Producer Price Trends in the Wood Product Manufacturing Industry
(NAICS 321)	2-33

2-21.	Distribution of Value of Shipments within Paper Manufacturing
(NAICS 322): 2007	2-36

2-22.	Distribution of Employment within Paper Manufacturing (NAICS 322):
2007	2-36

2-23.	Electrical Power Use Trends in the Paper Manufacturing Industry:
1997–2005	2-41

2-24.	Establishment Concentration in Paper Manufacturing Industry (NAICS
322): 2002	2-43

2-25.	Capacity Utilization Trends in the Paper Manufacturing Industry
(NAICS 322)	2-44

2-26.	Employment Concentration in the Paper Manufacturing Industry
(NAICS 322): 2002	2-45

2-27.	Capacity Trends in the Paper Manufacturing Industry (NAICS 322)
2-46

2-28.	Industrial Production Trends in the Paper Manufacturing Industry
(NAICS 322): 1997–2009	2-49

2-29.	International Trade Trends in the Paper Manufacturing Industry
(NAICS 322)	2-50

2-30.	Producer Price Trends in the Paper Manufacturing Industry (NAICS
222)	2-50

2-31.	Distribution of Employment within Chemical Manufacturing (NAICS
325): 2007	2-53

2-32.	Distribution of Total Value of Shipments within Chemical
Manufacturing (NAICS 325): 2007	2-53

2-33.	Electric Power Use Trends in Chemical Manufacturing (NAICS 325): 
1997–2005	2-58

2-34.	Establishment Concentration in Chemical Manufacturing (NAICS 325):
2002	2-60

2-35.	Capacity Utilization Trends in Chemical Manufacturing (NAICS 325)
2-61

2-36.	Employment Concentration in Chemical Manufacturing (NAICS 325):
2002	2-61

2-37.	Capacity Trends in Chemical Manufacturing (NAICS 325)	2-62

2-38.	Industrial Production Trends in Chemical Manufacturing (NAICS 325)
2-66

2-39.	International Trade Trends in Chemical Manufacturing (NAICS 325)
2-67

2-40.	Producer Price Trends in Chemical Manufacturing (NAICS 325)	2-67

4-1.	Market-Level Changes by Source and Option	4-3

4-2.	Distribution of U.S. Surplus Changes by Sector: Major Sources	4-4

4-3.	Distribution of Total Surplus Changes by Sector: Area Sources	4-6

4-4.	Employment Impacts Using Morgenstern, Pizer, Shih (2002) (1,000
FTEs)	4-10

5-1.	Share of NAICS/Enterprise Employment Categories (<500 employees)
with Sales Tests Exceeding 3%	5-4

6-1.	Map of the photochemical modeling domains. The black outer box
denotes the 36 km national modeling domain; the red inner box is the 12
km western U.S. grid; and the blue inner box is the 12 km eastern U.S.
grid.	6-3

6-2.	Locations of boilers in the NEI point inventory for the future
baseline (2016)	6-9

7-1.	Total Monetized PM2.5 and Ozone Benefits for the Final Boiler MACT
and Boiler Area Source Rule in 2014	7-2

7-2.	Illustration of BenMAP Approach	7-7

7-3.	Data Inputs and Outputs for the BenMAP Model for a PM2.5 Analysis
7-8

7-4.	Change in Ambient PM2.5 Levels from SO2 Emissions from Point Source
Boilers	7-17

7-5.	Change in Ambient PM2.5 Levels from PM Emissions from Point Source
Boilers	7-18

7-6.	Change in Ambient PM2.5 Levels from SO2 Emissions from Non-point
Source Boilers	7-18

7-7.	Change in Ambient PM2.5 Levels from PM Emissions from Non-point
Source Boilers	7-19

7-8.	Breakdown of Monetized PM2.5 Health Benefits Estimates using
Mortality Function from Pope et al. (2002)	7-28

7-9.	Breakdown of Monetized Ozone Health Benefits Estimates using
Mortality Function from Bell et al. (2004)	7-29

7-10.	Total Monetized PM2.5 and Ozone Benefits Estimates for the Final
Boiler MACT and Boiler Area Source Rule in 2014	7-30

7-11.	Breakdown of Monetized PM2.5 Benefits Estimates by Precursor for
the Final Boiler MACT in 2014	7-31

7-12.	Breakdown of Monetized PM2.5 Benefits Estimates by Precursor for
the Final Boiler Area Source Rule in 2014	7-31

7-13.	Breakdown of Monetized PM2.5 Benefits Estimates by Subcategory for
the Final Boiler MACT in 2014	7-32

7-14.	Breakdown of Monetized PM2.5 Benefits Estimates by Subcategory for
the Final Boiler Area Source Rule in 2014	7-32

7-15.	Percentage of Population Exposed to Baseline Air Quality Levels
for Final Boiler MACT and Boiler Area Source Rule	7-33

7-16.	Cumulative Percentage of Population Exposed to Baseline Air
Quality Levels for Final Boiler MACT and Boiler Area Source Rule	7-34

7-17.	Estimated County Level Carcinogenic Risk from HAP exposure from
outdoor sources (2002 NATA)	7-42

7-18.	Estimated County Level Noncancer (Respiratory) Risk from HAP
exposure from outdoor sources (2002 NATA)	7-42

7-19.	Percentage Reduction in Total Mercury Deposition in the Eastern
U.S.	7-48

7-20.	Percentage Reduction in Total Mercury Deposition in the Western
U.S.	7-49

8-1.	Net Benefits for the Final Major and Area Source Boiler Rules at 3%
Discount Rate	8-4

8-2.	Net Benefits for the Final Major and Area Source Boiler Rules at 7%
Discount Rate	8-5

List of Tables

Number	Page

1-1.	Summary of the Monetized Benefits, Social Costs, and Net Benefits
for the Boiler MACT (Major Sources) in 2014 (millions of 2008$)	1-4

1-2.	Summary of the Monetized Benefits, Social Costs, and Net Benefits
for the Boiler Area Source Rule in 2014 (millions of 2008$)	1-5

2-1.	Key Statistics: Food Manufacturing (North American Industry
Classification System [NAICS] 311)	2-2

2-2.	Industry Data: Food Manufacturing (NAICS 311)	2-2

2-3.	Costs of Goods and Services Used in Food Manufacturing (NAICS 311)
($2007)	2-5

2-4.	Key Goods and Services Used in Food Manufacturing (NAICS 311)
($2007, millions)	2-5

2-5.	Energy Used in Food Manufacturing (NAICS 311)	2-6

2-6.	Demand by Sector: Food Manufacturing (NAICS 311) ($2007, millions)
2-8

2-7.	Top Publicly Held U.S. Food Companies: 2007	2-11

2-8.	Corporate Income and Profitability for Food Manufacturing (NAICS
311)	2-12

2-9.	Small Business Size Standards: Food Manufacturing (NAICS 311)	2-13

2-10.	Distribution of Economic Data by Enterprise Size: Food
Manufacturing (NAICS 311)	2-14

2-11.	Key Statistics: Wood Product Manufacturing (NAICS 321)	2-17

2-12.	Industry Data: Wood Product Manufacturing (NAICS 321)	2-17

2-13.	Costs of Goods and Services in Wood Product Manufacturing (NAICS
321) ($2007)	2-21

2-14.	Key Goods and Services Used in Wood Product Manufacturing (NAICS
321) ($2007, millions)	2-22

2-15.	Energy Used in Wood Product Manufacturing (NAICS 321)	2-23

2-16.	Demand by Sector: Wood Product Manufacturing (NAICS 321) ($2007,
millions)	2-25

2-17.	Largest U.S. Paper and Forest Products Companies: 2006	2-29

2-18.	Distribution of Economic Data by Enterprise Size: Wood Product
Manufacturing (NAICS 321)	2-30

2-19.	Small Business Size Standards: Wood Product Manufacturing (NAICS
321)	2-31

2-20.	Key Statistics: Paper Manufacturing (NAICS 322)	2-34

2-21.	Industry Data: Paper Manufacturing (NAICS 322)	2-34

2-22.	Costs of Goods and Services Used in the Paper Manufacturing
Industry (NAICS 322)	2-38

2-23.	Key Goods and Services Used in the Paper Manufacturing Industry
(NAICS 322) ($millions, $2007)	2-39

2-24.	Energy Used in Paper Manufacturing (NAICS 322)	2-40

2-25.	Estimated Energy Sources for the U.S. Pulp and Paper Industry	2-41

2-26.	Demand by Sector: Paper Manufacturing Industry (NAICS 322)
($millions, $2007)	2-42

2-27.	Largest U.S. Paper and Forest Products Companies: 2006	2-46

2-28.	Distribution of Economic Data by Enterprise Size: Paper
Manufacturing (NAICS 322)	2-47

2-29.	Small Business Size Standards: Paper Manufacturing (NAICS 322)
2-48

2-30.	Key Statistics: Chemical Manufacturing (NAICS 325)	2-52

2-31.	Industry Data: Chemical Manufacturing (NAICS 325)	2-52

2-32.	Key Goods and Services Used in Chemical Manufacturing (NAICS 325)
($2007, millions)	2-54

2-33.	Costs of Goods and Services Used in Chemical Manufacturing (NAICS
325) ($2007)	2-56

2-34.	Energy Used in Chemical Manufacturing (NAICS 325)	2-57

2-35.	Demand by Sector: Chemical Manufacturing (NAICS 325) ($2007
millions)	2-59

2-36.	Top Chemical Producers: 2007	2-63

2-37.	2007 Corporate Income and Profitability (NAICS 325)	2-63

2-38.	Small Business Size Standards: Chemical Manufacturing (NAICS 325)
2-65

2-39.	Distribution of Economic Data by Enterprise Size: Chemical
Manufacturing (NAICS 325)	2-66

3-1.	Summary of Capital and Annual Costs for New and Existing Major
Sources	3-3

3-2.	Summary of Annual Costs for New and Existing Area Sources	3-6

4-1.	Distribution of Social Costs Major Sources (billion, 2008$): 2014
4-4

4-2.	Distribution of Social Costs Area Sources (billion, 2008$): 2014
4-5

4-3.	Employment Impacts Using Morgenstern, Pizer, Shih (2002) (FTE)	4-9

4-4.	Cost Approach Jobs Impacts in FTEs	4-12

4-5.	Employment Impacts	4-13

4-6.	Aggregate Employment in FTEs	4-13

4-7.	Aggregate Employment Distributed to Regulated Entities and Others
in FTEs	4-14

5-1.	Affected Sectors and Size Standards	5-2

5-2.	Major Sources: Sales Tests Using Small Companies Identified in the
Combustion Survey	5-3

5-3.	Information Collection Effort for Facilities with Combustion Units:
Major Sources	5-8

5-4.	Estimated Affected Facilities Using 13 State Boiler Inspector
Inventory: Area Sources	5-14

6-1.	Geographic Elements of Domains Used in Photochemical Modeling	6-4

6-2.	Control Strategies and/or Growth Assumptions Included in the 2016
Projection	6-6

6-3.	Estimated Future Year (2016) Controllable Boiler Sector Emissions
6-8

7-1.	Human Health and Welfare Effects of Air Pollutants Affected	7-9

7-2.	Summary of Monetized Benefits Estimates for Final Boiler MACT in
2014 (millions of 2008$)	7-21

7-3.	Summary of Monetized Benefits Estimates for Final Boiler Area
Source Rule in 2014 (millions of 2008$)	7-21

7-4.	Summary of Estimated Reductions in Health Incidences from PM2.5 for
the Final Boiler MACT in 2014 (95th percentile confidence interval) 
7-22

7-5.	Summary of Estimated Reductions in Health Incidences from PM2.5 for
the Final Boiler Area Source Rule in 2014 (95th percentile confidence
interval)	7-23

7-6.	Summary of Monetized Benefits Estimates from PM2.5 for the Final
Boiler MACT in 2014 (95th percentile confidence interval)	7-24

7-7.	Summary of Monetized Benefits Estimates from PM2.5 for the Final
Boiler Area Source Rule in 2014 (95th percentile confidence interval)
7-25

7-8.	Summary of Monetized Benefits Estimates from Ozone for the Final
Boiler MACT in 2014 (95th percentile confidence interval)	7-26

7-9.	Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for 2014
7-36

7-10. Monetized SCC-Derived Disbenefits of CO2 Emission Increases in
2014 (millions of 2008$)	7-37

7-11.	Top HAPs by Mass from Boilers by Fuel Type	7-44

8-1.	Summary of the Monetized Benefits, Social Costs, and Net Benefits
for the Boiler MACT in 2014 (millions of 2008$)	8-2

8-2	Summary of the Monetized Benefits, Social Costs, and Net Benefits
for the Boiler Area Source Rule in 2014 (millions of 2008$)	8-3



Introduction

The U.S. Environmental Protection Agency (EPA) is promulgating two rules
for national emission standards for hazardous air pollutants (NESHAP)
for new and existing industrial, commercial, and institutional boilers
and process heaters. One rule requires all major sources to meet
hazardous air pollutant (HAP) emissions standards reflecting the
application of the maximum achievable control technology (MACT). In the
other rule, EPA is promulgating a NESHAP for two area source categories:
industrial boilers and institutional and commercial boilers. The
emission standards for controlling mercury and polycyclic organic matter
(POM) emissions are based on the MACT. The emission standards for
controlling other HAPs are based on EPA’s determination as to what
constitutes the generally available control technology (GACT) or
management practices. As part of the regulatory process, EPA is required
to develop a regulatory impact analysis (RIA). The RIA includes an
economic impact analysis (EIA) and a small entity impacts analysis and
documents the RIA methods and results.

Executive Summary

The key results of the RIA are as follows:

Engineering Cost Analysis: EPA estimates the major source NESHAP’s
total annualized costs will be $1.4 billion (2008$). For the area source
NESHAP, EPA estimates the total annualized costs will be $0.5 billion.

Market Analysis: Under the major source NESHAP, the Agency’s economic
model suggests the average national prices for industrial sectors could
be small (less than 0.01% higher). Average annual domestic production
may also fall by less than 0.01%. Because of higher domestic prices,
imports rise by less than 0.01% per year. Market-level effects for the
area source NESHAP are smaller when compared to the major source rule;
average price, production, and import changes are less than 0.01%.

Social Cost Analysis: The estimated social cost of the major source rule
is just under $1.5 billion (2008$). The Agency’s economic model
suggests that industries are able to pass approximately $0.5 billion of
the rule’s costs to consumers (e.g., higher market prices). Domestic
industries’ surplus falls by $1.4 billion, while other countries on
net benefit from higher prices (a net increase in rest-of-the world
[ROW] surplus of less than $0.1 billion). Additional costs and fuel
savings for new and existing major sources that are not included in the
economic model represent a net benefit of $0.4 billion. The estimated
social cost of the area source rule is approximately $0.5 billion
(2008$). The Agency’s economic model suggests that industries are able
to pass approximately $0.2 billion of the rule’s costs to consumers.
Domestic industries’ surplus falls by $0.3 billion and the net
increase in ROW surplus is less than $0.1 billion. Additional costs and
fuel savings for unknown, existing, and new area sources not included in
the economic model results represent a net benefit of less than $0.1
billion.

−3100 to 6,500  employees, with a central estimate of +1,700 employees
for the major source NESHAP.   The estimated employment changes range
between −1,000 to 2,000  employees, with a central estimate of +500
employees for the area source NESHAP. 

Small Entity Analyses: EPA performed a screening analysis for impacts on
small entities by comparing compliance costs to sales/revenues (e.g.,
sales and revenue tests). EPA’s analysis found the tests were
typically higher than 3% for small entities included in the screening
analysis. Pursuant to section 603 of the RFA, EPA prepared an initial
regulatory flexibility analysis (IRFA) for the proposed rule and
convened a Small Business Advocacy Review Panel to obtain advice and
recommendations of representatives of the regulated small entities. A
detailed discussion of the Panel’s advice and recommendations is found
in the final Panel Report (Docket ID No. EPA-HQ-OAR-2002-0058-0797). A
summary of the Panel’s recommendations is also presented in the
preamble to the proposed rule at 75 FR 32044-32045 (June 4, 2010). In
the proposed rule, EPA included provisions consistent with four of the
Panel’s recommendations. As required by section 604 of the RFA, we
also prepared a final regulatory flexibility analysis (FRFA) the final
rule (see Section 5). 

Benefits Analysis: 

The benefits from reducing some air pollutants have not been monetized
in this analysis, including reducing a combined 113,000 tons of carbon
monoxide, 30,000 tons of HCl, 830 tons of HF, 2,900 pounds of mercury,
3,000 tons of other metals, and 23 grams of dioxins/furans (TEQ) each
year. We assess the benefits of these emission reductions qualitatively
in this analysis.

We have monetized the benefits from reducing PM (as a surrogate for
metal HAP), as well as the co-benefits that result from the HAP
emissions reductions (e.g., the pollution control equipment for HCl also
reduces sulfur dioxide, a pre-cursor to PM2.5). Thus all monetized
benefits reported reflect improvements in ambient PM2.5 and ozone
concentrations. As such, although the monetized benefits likely
underestimate the total benefits, the extent of the underestimate is
unclear. 

Using a 3% discount rate, we estimate the total monetized benefits of
the Boiler MACT to be $22 billion to $54 billion in the implementation
year (2014). Using a 7% discount rate, we estimate the total monetized
benefits of the Boiler MACT to be $20 billion to $49 billion in the
implementation year. Using alternate relationships between PM2.5 and
premature mortality supplied by experts, higher and lower benefits
estimates are plausible, but most of the expert-based estimates fall
between these estimates. 

Using a 3% discount rate, we estimate the total monetized benefits of
the Boiler Area Source Rule to be $210 million to $520 million in the
implementation year (2014). Using a 7% discount rate, we estimate the
total monetized of the Boiler Area Source Rule to be $190 million to
$470 million in the implementation year. 

Using a 3% discount rate, we estimate the total monetized benefits of
the combined Boiler MACT and Boiler Area Source Rule to be $22 billion
to $58 billion in the implementation year (2014). Using a 7% discount
rate, we estimate the total monetized benefits of the combined Boiler
MACT and Boiler Area Source Rule to be $20 billion to $50 billion in the
implementation year. All estimates are in 2008$. 

rate. For the Boiler Area Source Rule, the net benefits are −$280
million to $30 million at a 3% discount rate for the benefits and
−$300 million to −$20 million at a 7% discount rate. These results
are shown in Tables 1-1 and 1-2.

Organization of this Report

The remainder of this report supports and details the methodology and
the results of the EIA:

Section 2 presents the affected industry profiles.

Section 3 describes the engineering cost analysis.

Section 4 describes the economic impact analysis.

Section 5 describes the small entity analyses.

Section 6 describes the air quality modeling performed by EPA.

Section 7 presents the benefits estimates.

Section 8 presents the net benefits.

Appendix A describes the multimarket model used in the economic
analysis.

Appendix B provides additional economic model result tables by sector.

Table 1-1.	Summary of the Monetized Benefits, Social Costs, and Net
Benefits for the Boiler MACT (Major Sources) in 2014 (millions of
2008$)a

	3% Discount Rate	7% Discount Rate

Selected

Total Monetized Benefitsb	$22,000	to	$54,000	$20,000	to	$49,000

Total Social Costs3	$1,500	1,500

Net Benefits	$20,500	to	$52,500	$18,500	to	$47,500

Non-monetized Benefits	112,000 tons of carbon monoxide

	30,000 tons of HCl

	820 tons of HF 

	2,800 pounds of mercury 

	2,700 tons of other metals

	23 grams of dioxins/furans (TEQ)

	Health effects from SO2 exposure

	Ecosystem effects 

	Visibility impairment 

Alternative

Total Monetized Benefitsb	$18,000	to	$43,000	$16,000	to	$39,000

Total Social Costsb	$1,900	$1,900

Net Benefits	$16,100	to	$41,100	$14,100	to	$37,100

Non-monetized Benefits	112,000 tons of carbon monoxide

	22,000 tons of HCl

	620 tons of HF 

	2,400 pounds of mercury 

	2,600 tons of other metals

	23 grams of dioxins/furans (TEQ)

	Health effects from SO2 exposure

	Ecosystem effects 

 	Visibility impairment 

a	All estimates are for the implementation year (2014), and are rounded
to two significant figures. These results include units anticipated to
come online and the lowest cost disposal assumption. 

b	The total monetized benefits reflect the human health benefits
associated with reducing exposure to PM2.5 through reductions of
directly emitted PM2.5 and PM2.5 precursors such as SO2, as well as
reducing exposure to ozone through reductions of VOCs. It is important
to note that the monetized benefits include many but not all health
effects associated with PM2.5 exposure. Benefits are shown as a range
from Pope et al. (2002)  XE “Pope et al. (2002)”   to Laden et al.
(2006)  XE “Laden et al. (2006)”  . These models assume that all
fine particles, regardless of their chemical composition, are equally
potent in causing premature mortality because the scientific evidence is
not yet sufficient to allow differentiation of effect estimates by
particle type. These estimates include energy disbenefits associated
with the increased emissions from additional energy usage valued at $22
million for the selected option and $37 million for the alternative
option. Ozone benefits are valued at $3.6 to $15 million for both
options. 

c	The methodology used to estimate social costs for one year in the
multimarket model using surplus changes results in the same social costs
for both discount rates.

Table 1-2.	Summary of the Monetized Benefits, Social Costs, and Net
Benefits for the Boiler Area Source Rule in 2014 (millions of 2008$)a

−$280	to	$30	−$300	to	−$20

Non-monetized Benefits	1,100 tons of carbon monoxide

	340 tons of HCl

	8 tons of HF 

	90 pounds of mercury 

	320 tons of other metals

	<1 gram of dioxins/furans (TEQ)

	Health effects from SO2 exposure

	Ecosystem effects 

	Visibility impairment 

Proposed MACT Approach: Alternative

Total Monetized Benefitsb	$200	to	$490	$180	to	$440

Total Social Costsc	$850	$850

Net Benefits	−$650	to	−$360	−$670	to	−$410

Non-monetized Benefits	1,100 tons of carbon monoxide

	340 tons of HCl

	8 tons of HF 

	90 pounds of mercury 

	320 tons of other metals

	<1 gram of dioxins/furans (TEQ)

	Health effects from SO2 exposure

	Ecosystem effects 

 	Visibility impairment 

a All estimates are for the implementation year (2014), and are rounded
to two significant figures. These results include units anticipated to
come online and the lowest cost disposal assumption. 

b	The total monetized benefits reflect the human health benefits
associated with reducing exposure to PM2.5 through reductions of
directly emitted PM2.5 and PM2.5 precursors such as SO2. It is important
to note that the monetized benefits include many but not all health
effects associated with PM2.5 exposure. Benefits are shown as a range
from Pope et al. (2002)  XE “Pope et al. (2002)”   to Laden et al.
(2006)  XE “Laden et al. (2006)”  . These models assume that all
fine particles, regardless of their chemical composition, are equally
potent in causing premature mortality because the scientific evidence is
not yet sufficient to allow differentiation of effect estimates by
particle type. These estimates include energy disbenefits associated
with the increased emissions from additional energy usage valued at less
than $1 million.

c	The methodology used to estimate social costs for one year in the
multimarket model using surplus changes results in the same social costs
for both discount rates.

Section 1 References

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006.
“Reduction in Fine Particulate Air Pollution and Mortality.”
American Journal of Respiratory and Critical Care Medicine 173:667-672.

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K.
Ito, and G.D. Thurston. 2002. “Lung Cancer, Cardiopulmonary Mortality,
and Long-term Exposure to Fine Particulate Air Pollution.” Journal of
the American Medical Association 287:1132-1141.



Industry Profiles

In this section, we provide an introduction selected industries that are
affected by the rules. The industries were selected based on high
facility population counts within 3-digit NAICs industries reported in
the combustion facility survey. The purpose is to give the reader a
general understanding of economic aspects and industry trends to provide
additional context for the economic impact analysis.

Food Manufacturing

Introduction

Food manufacturing involves the transformation of raw agricultural and
livestock products into processed food. Between 1997 and 2002, shipment
values stagnated, falling 0.38%, while the number of employees and
payroll increased 2.71% and 7.76%, respectively (Table 2-1). This trend
reversed between 2002 and 2006, as shipment values rose 4.77 % and
number of employees and payroll fell 5.94% and 3.28% respectively (Table
2-1). Shipments, payroll, and employment continued to increase between
2006 and 2007, but there was a notable drop in the number of
establishments between 2002 and 2007 (Table 2-1). As Table 2-2 shows,
payroll per employee grew 4.91% from 1997 to 2002 and continued to
increase, albeit at a slower rate of 2.83%, from 2002 to 2006. Between
2006 and 2007, the payroll per employee declined as the growth in
employees outpaced the increase in the annual payroll (Table 2-2).

The food manufacturing industry consists of nine different industry
groups, each distinguished by the livestock or agricultural products
used as raw materials for the processed food products as follows:

Animal Food Manufacturing (North American Industry Classification System
[NAICS] 3111)

Grain and Oilseed Milling (NAICS 3112)

Sugar and Confectionery Product Manufacturing (NAICS 3113)

Fruit and Vegetable Preserving and Specialty Food Manufacturing (NAICS
3114)

Dairy Product Manufacturing (NAICS 3115)

Animal Slaughtering and Processing (NAICS 3116)

Seafood Product Preparation and Packaging (3117)

Bakeries and Tortilla Manufacturing (NAICS 3118)

Table 2-1.	Key Statistics: Food Manufacturing (North American Industry
Classification System [NAICS] 311)

	1997	2002	2006	2007

Shipments ($2007, millions)	$528,928	$526,939	$552,075	$589,550

Payroll ($2007, millions)	$48,118	$51,852	$50,151	$50,467

Employees	1,466,956	1,506,781	1,417,274	1,466,683

Establishments	26,302	27,899	NA	22,055

NA = Not available.

Sources:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Annual Survey of Manufactures:
General Statistics: Statistics for Industry Groups and Industries: 2006
and 2005.” <http://factfinder.census.gov>; (July 8, 2008) xe “U.S.
Census Bureau generated by RTI International using American FactFinder
Sector 31\: Annual Survey of Manufactures\: General Statistics\:
Statistics for Industry Groups and Industries\: 2006 and 2005.
<http\://factfinder.census.gov> (July 8, 2008)” .

	U.S. Census Bureau; generated by RTI International; using American
FactFinder; “Sector 00: All sectors: Core Business Statistics Series:
Comparative Statistics for the United States and the States (1997 NAICS
Basis): 2002 and 1997.” <http://factfinder.census.gov>; (July 8, 2008)
xe “U.S. Census Bureau generated by RTI International using American
FactFinder Sector 00\: All sectors\: Core Business Statistics Series\:
Comparative Statistics for the United States and the States (1997 NAICS
Basis)\: 2002 and 1997. <http\://factfinder.census.gov> (July 8,
2008)” .

	U.S. Census Bureau; generated by RTI International; using American
FactFinder; “Sector 00: All sectors: Core Business Statistics Series:
Comparative Statistics for the United States and the States (2007 NAICS
Basis): 2002 and 2007.” <http://factfinder.census.gov>; (January 4,
2010) xe “U.S. Census Bureau generated by RTI International using
American FactFinder Sector 00\: All sectors\: Core Business Statistics
Series\: Comparative Statistics for the United States and the States
(2007 NAICS Basis)\: 2002 and 2007. <http\://factfinder.census.gov>
(January 4, 2010)” .

Table 2-2.	Industry Data: Food Manufacturing (NAICS 311)

Industry Data	1997	2002	2006	2007

Total shipments ($2007, millions)	$528,928	$526,939	$552,075	589,550

Shipments per establishment ($2007, thousands)	$20,110	$18,887	NA
$26,731

Average Shipments per employee ($2007)	$360,561	$349,712	$389,533
$401,961

Average Shipments per $ of payroll ($2007)	$10.99	$10.16	$11.01	$11.68

Average Annual payroll per employee ($2007)	$32,800.97	$34,412.12
$35,385.46	$34,409.00

Average Employees per establishment	56	54	NA	67

NA = Not available.

Sources: U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Annual Survey of Manufactures:
General Statistics: Statistics for Industry Groups and Industries: 2006
and 2005.” <http://factfinder.census.gov>; (July 8, 2008) xe “U.S.
Census Bureau generated by RTI International using American FactFinder
Sector 31\: Annual Survey of Manufactures\: General Statistics\:
Statistics for Industry Groups and Industries\: 2006 and 2005.
<http\://factfinder.census.gov> (July 8, 2008)” .

	U.S. Census Bureau; generated by RTI International; using American
FactFinder; “Sector 00: All sectors: Core Business Statistics Series:
Comparative Statistics for the United States and the States (1997 NAICS
Basis): 2002 and 1997.” <http://factfinder.census.gov>; (July 8, 2008)
xe “U.S. Census Bureau generated by RTI International using American
FactFinder Sector 00\: All sectors\: Core Business Statistics Series\:
Comparative Statistics for the United States and the States (1997 NAICS
Basis)\: 2002 and 1997. <http\://factfinder.census.gov> (July 8,
2008)” .

	U.S. Census Bureau; generated by RTI International; using American
FactFinder; “Sector 00: All sectors: Core Business Statistics Series:
Comparative Statistics for the United States and the States (2007 NAICS
Basis): 2002 and 2007.” <http://factfinder.census.gov>; (January 4,
2010) xe “U.S. Census Bureau generated by RTI International using
American FactFinder Sector 00\: All sectors\: Core Business Statistics
Series\: Comparative Statistics for the United States and the States
(2007 NAICS Basis)\: 2002 and 2007. <http\://factfinder.census.gov>
(January 4, 2010)” .

In 2006, Animal Slaughtering and Processing made up the largest share of
both employment (33%) and the value of shipments (27%) in food
manufacturing (Figures 2-1 and 2-2).

Figure 2-1.	Distribution of Employment within Food Manufacturing
(NAICS 311): 2006

Source:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Annual Survey of Manufactures:
General Statistics: Statistics for Industry Groups and Industries: 2006
and 2005.” <http://factfinder.census.gov>; (July 8, 2008) xe “U.S.
Census Bureau generated by RTI International using American FactFinder
Sector 31\: Annual Survey of Manufactures\: General Statistics\:
Statistics for Industry Groups and Industries\: 2006 and 2005.
<http\://factfinder.census.gov> (July 8, 2008)” .

Figure 2-2.	Distribution of Total Value of Shipments within Food
Manufacturing (NAICS 311): 2006

Source:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Annual Survey of Manufactures:
General Statistics: Statistics for Industry Groups and Industries: 2006
and 2005.” <http://factfinder.census.gov>; (July 8, 2008) xe “U.S.
Census Bureau generated by RTI International using American FactFinder
Sector 31\: Annual Survey of Manufactures\: General Statistics\:
Statistics for Industry Groups and Industries\: 2006 and 2005.
<http\://factfinder.census.gov> (July 8, 2008)” .

Many major environmental regulations directly affect the food
manufacturing industry and/or other markets that provide key goods and
services to the industry (e.g., energy). RTI’s multimarket model is
specifically designed to analyze these types of regulations. The model
emphasizes the links among industrial sectors and provides policy makers
with new insights about the direct and indirect effects of a regulatory
program and the distribution of costs across the U.S. economy.

Supply and Demand Characteristics

Next, we provide a broad overview of the supply and demand sides of the
food manufacturing industry. We emphasize the economic interactions this
industry has with other industries and people, including identifying the
key goods and services used by the industry and the major uses and
consumers of food manufacturing products.

Goods and Services Used in Food Manufacturing

In 2006, the cost of materials made up 57% of the value of shipments in
food production. Total employee compensation accounted for 12% of this
value, with half of that coming from production workers’ wages (Table
2-3).

The top 10 industry groups supplying inputs to food production accounted
for 84% of the total intermediate inputs to the industry, with the top
three industry groups (food products, animal products, and crop
products) accounting for over half of the total intermediate inputs
(Table 2-4). Electric power generation, transmission, and distribution
accounted for 2% of the total intermediate inputs, whereas boilers,
tanks, and shipping containers accounted for 1%.

Energy

The Department of Energy (DOE) classifies the entire food products
industry as an energy-intensive industry to model within its Industrial
Demand Module (DOE, 2008  XE “DOE, 2008”  ). In 2002, food
manufacturing accounted for 6.86% of the total fuel consumption by all
manufacturing industries (NAICS 311–339) and 19.24% of the
conventional boiler use fuel consumption by all manufacturing industries
(DOE, Energy Information Administration, 2007a  XE “DOE, Energy
Information Administration, 2007a”  ).

Table 2-3.	Costs of Goods and Services Used in Food Manufacturing
(NAICS 311) ($2007)

Industry Ratios	2005	Share	2006	Share

Total shipments ($millions)	$563,797	100%	$552,075	100%

Total compensation ($millions)	$64,909	12%	$64,027	12%

Annual payroll	$50,650	9%	$50,151	9%

Fringe benefits	$14,259	3%	$13,877	3%

Total employees	1,440,283

1,417,274

	Average compensation per employee	$45,067

$45,176

	Total production workers’ wages ($millions)	$33,983	6%	$33,670	6%

Total production workers	1,099,530

1,090,081

	Total production hours (thousands)	2,242,558

2,198,396

	Average production wages per hour	$15

$15

	Total cost of materials ($thousands)	$315,993	56%	$312,847	57%

Materials, parts, packaging	$286,895	51%	$284,028	51%

Purchased electricity	$4,513	1%	$4,787	1%

Purchased fuel	$5,136	1%	$5,398	1%

Other	$19,449	3%	$18,634	3%

Source:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Annual Survey of Manufactures:
General Statistics: Statistics for Industry Groups and Industries: 2006
and 2005.” <http://factfinder.census.gov>; (July 8, 2008) xe “U.S.
Census Bureau generated by RTI International using American FactFinder
Sector 31\: Annual Survey of Manufactures\: General Statistics\:
Statistics for Industry Groups and Industries\: 2006 and 2005.
<http\://factfinder.census.gov> (July 8, 2008)” .

Table 2-4.	Key Goods and Services Used in Food Manufacturing
(NAICS 311) ($2007, millions)

Sector	BEA Code	Food Products

Food products	3110	$91,518

Animal products	1120	$85,785

Crop products	1110	$43,109

Management of companies and enterprises	5500	$34,235

Wholesale trade	4200	$27,849

Converted paper products	3222	$18,782

Truck transportation	4840	$12,943

Plastics and rubber products	3260	$9,641

Electric power generation, transmission, and distribution	2211	$6,004

Boilers, tanks, and shipping containers	3324	$4,564

Total intermediate inputs	T005	$400,067

Source:	U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark
Input-Output Accounts: 2002 Standard Make and Use Tables at the Summary
Level.” Table 2. Washington, DC: BEA  XE “U.S. Bureau of Economic
Analysis (BEA). 2008. \”2002 Benchmark Input-Output Accounts\: 2002
Standard Make and Use Tables at the Summary Level.\” Table 2.
Washington, DC\: BEA”  .

In both 2005 and 2006, purchased electricity and fuel each accounted for
1% of the total value of shipments in food manufacturing (Table 2-3). In
2002, total energy consumption totaled 1,116 TBTU, a 7% increase over
1998 (Table 2-5). Of this total fuel consumption, the largest share
(41.72%) was consumed for indirect uses including conventional boiler
use and combined heat and power (CHP) and/or cogeneration process (MECS
Table 5.2). Between 1997 and 2005, while the manufacturing sector as a
whole used less electricity, food manufacturing used more electricity
(Figure 2-3). From 2005 to 2006, the electricity consumption increased
by nearly 9% (Table 2-5).

Table 2-5.	Energy Used in Food Manufacturing (NAICS 311)

Fuel Type	1998	2002	2006

Net electricitya (million kWh)	62,457	67,521	73,440

Residual fuel oil (million bbl)	2	2	4

Distillate fuel oilb (million bbl)	3	3	3

Natural gasc (billion cu ft)	553	560	618

LPG and NGLd (million bbl)	1	1	1

Coal (million short tons)	6	8	7

Coke and breeze (million short tons)	*	*	*

Othere (trillion BTU)	97	90	107

Total (trillion BTU)	1,044	1,116	1,186

a	Net electricity is obtained by summing purchases, transfers in, and
generation from noncombustible renewable resources, minus quantities
sold and transferred out. It does not include electricity inputs from
on-site cogeneration or generation from combustible fuels because that
energy has already been included as generating fuel (for example, coal).

b	Distillate fuel oil includes Nos. 1, 2, and 4 fuel oils and Nos. 1, 2,
and 4 diesel fuels.

c	Natural gas includes natural gas obtained from utilities, local
distribution companies, and any other supplier(s), such as independent
gas producers, gas brokers, marketers, and any marketing subsidiaries of
utilities.

d	Examples of liquefied petroleum gases (LPGs) are ethane, ethylene,
propane, propylene, normal butane, butylene, ethane-propane mixtures,
propane-butane mixtures, and isobutene produced at refineries or natural
gas processing plants, including plants that fractionate raw natural gas
liquids (NGLs).

e	Other includes net steam (the sum of purchases, generation from
renewables, and net transfers), and other energy that respondents
indicated was used to produce heat and power.

*	Estimate less than 0.5.

Sources:	U.S. Department of Energy, Energy Information Administration.
2007a. “2002 Energy Consumption by Manufacturers—Data Tables.”
Tables 3.2 and N3.2. Washington, DC: DOE < HYPERLINK
"http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html"
http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html >  XE
“U.S. Department of Energy, Energy Information Administration. 2007a.
\”2002 Energy Consumption by Manufacturers—Data Tables.\” Tables
3.2 and N3.2. Washington, DC\: DOE
<http\://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html>“ 
.

	U.S. Department of Energy, Energy Information Administration. 2009a.
“2006 Energy Consumption by Manufacturers—Data Tables.” Table 3.1.
Washington, DC: DOE.
<http://www.eia.doe.gov/emeu/mecs/mecs2006/2006tables.html>  XE “U.S.
Department of Energy, Energy Information Administration. 2009a. \”2006
Energy Consumption by Manufacturers—Data Tables.\” Table 3.1.
Washington, DC\: DOE.
<http\://www.eia.doe.gov/emeu/mecs/mecs2006/2006tables.html>“  .

Figure 2-3.	Electric Power Use Trends in Food Manufacturing
(NAICS 311): 1997–2005

Source:	Federal Reserve Board. 2009. “Industrial Production and
Capacity Utilization: Electric Power Use: Manufacturing and Mining.”
<http://www.federalreserve.gov/datadownload/>  XE “Federal Reserve
Board. 2008. \”Industrial Production and Capacity Utilization\:
Electric Power Use\: Manufacturing and Mining.\” Series ID\:
G17/KW/KW.GMF.S & G17/KW/KW.G311.S.
<http\://www.federalreserve.gov/datadownload/>“  .

Uses and Consumers

The majority of food manufacturing’s total commodity output (58%) is
sold for personal consumption. Of the sales for intermediate use, 42%
are sold back into the food manufacturing industry (Table 2-6).

Firm and Market Characteristics

This remaining subsection describes geographic, production, and market
data. These data provide the basis for further analysis, including
regulatory flexibility analyses, and give a complete picture of the
recent historical trends of production and pricing.

Location

In 2002, California had the most food manufacturing establishments in
the United States, followed by New York and Texas (see Figure 2-4). In
addition, Pennsylvania, Illinois, Wisconsin, New Jersey, and Florida had
over 1,000 establishments in their states.

Table 2-6.	Demand by Sector: Food Manufacturing (NAICS 311) ($2007,
millions)

Sector	BEA Code	Food Products 

Food manufacturing	3110	$91,518

Food services and drinking places	7220	$37,291

Animal production	1120	$15,870

General state and local government services	S007	$15,170

Retail trade	4A00	$13,985

Beverage manufacturing	3121	$11,703

Hospitals	6220	$9,539

Educational services	6100	$4,485

Nursing and residential care facilities	6230	$4,187

Social assistance	6240	$2,277

Total intermediate use	T001	$217,570

Personal consumption expenditures	F010	$301,748

Exports of goods and services	F040	$28,151

Imports of goods and services	F050	−$33,119

Total final uses (GDP)	T004	$299,470

Total commodity output	T007	$517,040

Source:	U.S. Bureau of Economic Analysis (BEA). 2008  XE “U.S. Bureau
of Economic Analysis (BEA). 2008”  . “2002 Benchmark Input-Output
Accounts: 2002 Standard Make and Use Tables at the Summary Level.”
Table 2. Washington, DC: BEA.

Figure 2-4.	Establishment Concentration in Food Manufacturing
(NAICS 311): 2002

Source:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Manufacturing: Geographic Area
Series: Industry Statistics for the States, Metropolitan and
Micropolitan Statistical Areas, Counties, and Places: 2002.”
<http://factfinder.census.gov>; (July 23, 2008)  XE “U.S. Census
Bureau\; generated by RTI International\; using American FactFinder\;
\”Sector 31\: Manufacturing\: Geographic Area Series\: Industry
Statistics for the States, Metropolitan and Micropolitan Statistical
Areas, Counties, and Places\: 2002.\”
<http\://factfinder.census.gov>\; (July 23, 2008)”  .

Production Capacity and Utilization

Capacity utilization of the food manufacturing industry did not fall off
during the recession of 2001 as much as the manufacturing sector as a
whole (Figure 2-5). Food manufacturing’s capacity utilization has
remained higher than manufacturing as a whole and went above 85% in the
spring of 2008. The effects of the recent economic downturn have not
affected capacity utilization as sharply in the food industry relative
to the overall manufacturing sector (Figure 2-5).

Figure 2-5.	Capacity Utilization Trends in Food Manufacturing
(NAICS 311)

Source:	Federal Reserve Board. 2009. “Industrial Production and
Capacity Utilization: Capacity Utilization.”
<http://www.federalreserve.gov/datadownload/>  XE “Federal Reserve
Board. 2008. \”Industrial Production and Capacity Utilization\:
Capacity Utilization.\” Series ID\: G17/CAPUTL/CAPUTL.GMF.S &
G17/CAPUTL/CAPUTL.G311.S.
<http\://www.federalreserve.gov/datadownload/>“  .

Employment

The geographic distribution of employment in food manufacturing varies
substantially from the distribution of establishments. In 2002,
Arkansas, ranked thirty-first in number of establishments and had the
eighth most employees (53,844) because of its national high of 199
employees per establishment. New York, ranked second in number of
establishments, had only the tenth most employees (50,012). North
Carolina and Georgia also had greater than 50,000 employees, despite
having fewer than 600 establishments (Figure 2-6).

Figure 2-6.	Employment Concentration in Food Manufacturing (NAICS 311):
2002

Source:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Manufacturing: Geographic Area
Series: Industry Statistics for the States, Metropolitan and
Micropolitan Statistical Areas, Counties, and Places: 2002.”
<http://factfinder.census.gov>; (July 23, 2008)  XE “U.S. Census
Bureau\; generated by RTI International\; using American FactFinder\;
\”Sector 31\: Manufacturing\: Geographic Area Series\: Industry
Statistics for the States, Metropolitan and Micropolitan Statistical
Areas, Counties, and Places\: 2002.\”
<http\://factfinder.census.gov>\; (July 23, 2008)”  .

Plants and Capacity

Production capacity in food manufacturing only grew 17.94% between 1997
and early 2008, a compound annual growth rate (CAGR) of 1.45%. This is
substantially less than the 42.50% growth for the manufacturing industry
as a whole (Figure 2-7).

Firm Characteristics

In fiscal year 2007, the top eight food manufacturing companies each had
greater than $10 billion in sales. These companies, however, are global,
many with a large portion of both sales and production coming from
operations outside of the United States (Table 2-7). The largest U.S.
food manufacturing company, Kraft Foods Inc., has 50.27% of its
long-lived assets located outside of the United States (Kraft Foods
Inc., 2008  XE “Kraft Foods Inc., 2008”  ).

Figure 2-7.	Capacity Trends in Food Manufacturing (NAICS 311)

Source:	Federal Reserve Board. 2009. “Industrial Production and
Capacity Utilization: Industrial Capacity.”
<http://www.federalreserve.gov/datadownload/>  XE “Federal Reserve
Board. 2008. \”Industrial Production and Capacity Utilization\:
Industrial Capacity.\” Series ID\: G17/CAP/CAP.GMF.S &
G17/CAP/CAP.G311.S. <http\://www.federalreserve.gov/datadownload/>“  .

Table 2-7.	Top Publicly Held U.S. Food Companies: 2007

	Sales ($millions)	% of Sales in North America

Kraft Foods Inc.	37,241	57.8%

Tyson Foods Inc.	26,900	90.0%

General Mills Inc.	12,442	82.9%

Sara Lee Corp.	12,278	53.8%

ConAgra Foods Inc.	12,028	89.2%

Smithfield Foods Inc.a	11,911	86.2%

Dean Foods Co.	11,822	>99.0%

Kellogg Co.a	11,776	66.1%

H.J. Heinz Co.	9,002	42.3%

Campbell Soup Co.	7,867	69.0%

a	Percentage of sales in the United States is actually percentage of
sales in North America.

Source:	Graves, T. 2008  XE “Graves, T. 2008”  . “Food and
Nonalcoholic Beverages.” Standard and Poor’s Industry Surveys.
176(25). 

For the industry as a whole, the number of corporations as well as the
number of corporations with net income in the food manufacturing
industry grew between 2004 and 2005. Although the overall number of
companies continued to grow in 2006, the number of those with a positive
net income declined along with profit margins and total receipts (Table
2-8).

Table 2-8.	Corporate Income and Profitability for Food Manufacturing
(NAICS 311)

	2004	2005	2006

Number of corporations	14,408	14,956	16,146

Number of corporations with net income	6,541	7,503	7,333

Total receipts (thousands)	$502,149,944	$504,944,378	$484,193,319

Business receipts (thousands)	$477,906,423	$465,369,666	$459,884,663

Before-tax profit margin	5.27%	10.09%	7.43%

After-tax profit margin	3.74%	7.62%	5.11%

Source:	Internal Revenue Service, U.S. Department of Treasury. 2008a  XE
“Internal Revenue Service, U.S. Department of Treasury. 2008”  .
“Corporation Source Book: Data File 2005.”
<http://www.irs.gov/taxstats/article/0,,id=167415,00.html>; (January 14,
2009).

Size Distribution

The primary criterion for categorizing a business as small is number of
employees, using definitions by the SBA for regulatory flexibility
analyses. The data describing size standards are provided in Table 2-9.
Over 80% of the NAICS industries within the food manufacturing industry
use a cutoff of 500 employees. In 2002, enterprises with fewer than 500
employees accounted for 32% of employment and 23% of receipts within
food manufacturing (Table 2-10).

Domestic Production

Between 1997 and early 2008, overall manufacturing production grew
faster (34.88%) than the food manufacturing component (26.18%)
(Figure 2-8). The food manufacturing industry has been less volatile,
particularly during the recession of 2001 and the current economic
downturn.

International Trade

In 2006, the United States regained a trade surplus in food
manufacturing it had briefly lost during 2004 to 2005 (see Figure 2-9).
The trade surplus in 2007 was over $4 billion. Both exports and imports
have declined since their 2008 peak as a result of the global economic
recession.

Table 2-9.	Small Business Size Standards: Food Manufacturing (NAICS
311)

NAICS	Description	Employees

311111	Dog and Cat Food Manufacturing	500

311119	Other Animal Food Manufacturing	500

311211	Flour Milling	500

311212	Rice Milling	500

311213	Malt Manufacturing	500

311221	Wet Corn Milling	750

311222	Soybean Processing	500

311223	Other Oilseed Processing	1,000

311225	Fats and Oils Refining and Blending	1,000

311230	Breakfast Cereal Manufacturing	1,000

311311	Sugarcane Mills	500

311312	Cane Sugar Refining	750

311313	Beet Sugar Manufacturing	750

311320	Chocolate and Confectionery Manufacturing from Cacao Beans	500

311330	Confectionery Manufacturing from Purchased Chocolate	500

311340	Non-Chocolate Confectionery Manufacturing	500

311411	Frozen Fruit, Juice and Vegetable Manufacturing	500

311412	Frozen Specialty Food Manufacturing	500

311421	Fruit and Vegetable Canning3	3,500

311422	Specialty Canning	1,000

311423	Dried and Dehydrated Food Manufacturing	500

311511	Fluid Milk Manufacturing	500

311512	Creamery Butter Manufacturing	500

311513	Cheese Manufacturing	500

311514	Dry, Condensed, and Evaporated Dairy Product Manufacturing	500

311520	Ice Cream and Frozen Dessert Manufacturing	500

311611	Animal (except Poultry) Slaughtering	500

311612	Meat Processed from Carcasses	500

311613	Rendering and Meat By-product Processing	500

311615	Poultry Processing	500

311711	Seafood Canning	500

311712	Fresh and Frozen Seafood Processing	500

311811	Retail Bakeries	500

311812	Commercial Bakeries	500

311813	Frozen Cakes, Pies, and Other Pastries Manufacturing	500

311821	Cookie and Cracker Manufacturing	750

311822	Flour Mixes and Dough Manufacturing from Purchased Flour	500

(continued)

Table 2-9.	Small Business Size Standards: Food Manufacturing (NAICS 311)
(continued)

NAICS	Description	Employees

311823	Dry Pasta Manufacturing	500

311830	Tortilla Manufacturing	500

311911	Roasted Nuts and Peanut Butter Manufacturing	500

311919	Other Snack Food Manufacturing	500

311920	Coffee and Tea Manufacturing	500

311930	Flavoring Syrup and Concentrate Manufacturing	500

311941	Mayonnaise, Dressing and Other Prepared Sauce Manufacturing	500

311942	Spice and Extract Manufacturing	500

311991	Perishable Prepared Food Manufacturing	500

311999	All Other Miscellaneous Food Manufacturing	500

Source:	U.S. Small Business Administration (SBA). 2008  XE “U.S. Small
Business Administration (SBA). 2008”  . “Table of Small Business
Size Standards Matched to North American Industry Classification System
Codes.” Effective August 22, 2008.
<http://www.sba.gov/services/contractingopportunities/sizestandardstopic
s/size/index.html>.

Table 2-10.	Distribution of Economic Data by Enterprise Size: Food
Manufacturing (NAICS 311) 

Enterprises with:

Variable	Total	1 to 20 Employeesa	20 to 99 Employees	100 to 499
Employees	500 to 749 Employees	750 to 999 Employees	1,000 to 1,499
Employees

Firms	21,384	13,645	3,935	1,247	147	63	96

Establishments	25,698	13,719	4,254	1,951	370	211	319

Employment	1,443,766	85,850	156,158	218,041	67,104	30,099	72,262

Receipts ($millions)	$457,521	$12,665	$32,274	$56,661	$23,103	$10,007
$21,878

Receipts/firm ($thousands)	$21,395	$928	$8,202	$45,438	$157,163	$158,835
$227,898

Receipts/establishment ($thousands)	$17,804	$923	$7,587	$29,042	$62,440
$47,425	$68,584

Receipts/employment ($)	$316,894	$147,523	$206,678	$259,862	$344,286
$332,457	$302,762

a	Excludes Statistics of U.S. Businesses (SUSB) employment category for
zero employees. These entities only operated for a fraction of the year.

Source:	U.S. Census Bureau. 2008  XE “U.S. Census Bureau. 2008”  .
“Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail
Employment Sizes: 2002.” < HYPERLINK
"http://www.census.gov/csd/susb/download_susb02.htm"
http://www.census.gov/csd/susb/download_susb02.htm >.

Figure 2-8.	Industrial Production Trends in Food Manufacturing
(NAICS 311)

Source:	Federal Reserve Board. 2008  XE “Federal Reserve Board.
2008”  . “Industrial Production and Capacity Utilization: Industrial
Production.” <http://www.federalreserve.gov/datadownload/>.

Figure 2-9.	International Trade Trends in Food Manufacturing
(NAICS 311)

Source:	U.S. International Trade Commission. 2008  XE “U.S.
International Trade Commission. 2009a”  . “U.S. Domestic Exports”
& “U.S. Imports for Consumption.”
<http://dataweb.usitc.gov/scripts/user_set.asp>.

Market Prices

Prices of goods in food manufacturing have moved generally in line with
prices in overall manufacturing (see Figure 2-10). Both indexes
increased over 31% since between early 2003 and early 2008, a CAGR of
5.13%. This rise was followed by a marked decline in recent years along
with the downward trend in prices throughout the economy.

Figure 2-10.	Producer Price Trends in Food Manufacturing (NAICS 311)

Source:	U.S. Department of Labor; Bureau of Labor Statistics. 2010  XE
“U.S. Department of Labor\; Bureau of Labor Statistics. 2010”  .
“Producer Price Indexes.” <http://www.bls.gov/pPI/Series Id:
PCU311—311—Food Manufacturing & PCUOMFG–OMFG–Total
Manufacturing>.

Wood Product Manufacturing

Introduction

According to a report by Standard & Poor’s (2008)  XE “Standard &
Poor’s (2008)”  , a number of factors are shaping the current
economic environment for wood products, including, but not limited to,
the housing slump, high input costs, low prices for lumber and other
building materials, and a weak dollar. Table 2-11 shows that revenues in
this industry are not entirely predictable, exhibiting a drop in
shipment revenue between 1997 and 2002 but a rise back to within $5
billion of the 1997 value in 2006 and a decline to within $14 billion of
the 2006 value in 2007.

Table 2-11.	Key Statistics: Wood Product Manufacturing (NAICS 321) 

 	1997	2002	2006	2007

Shipments ($2007, millions)	$110,956	$102,721	$115,390	$101,879

Payroll ($2007, millions)	$17,959	$18,528	$18,623	$17,439

Employees	570,034	543,459	536,094	519,651

Establishments	17,367	17,255	NA	14,862

NA = Not available.

Sources:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Annual Survey of Manufactures:
General Statistics: Statistics for Industry Groups and Industries: 2006
and 2005.” <http://factfinder.census.gov>; (July 8, 2008)  XE “U.S.
Census Bureau\; generated by RTI International\; using American
FactFinder\; \”Sector 31\: Annual Survey of Manufactures\: General
Statistics\: Statistics for Industry Groups and Industries\: 2006 and
2005.\” <http\://factfinder.census.gov>\; (July 8, 2008)”  .

	U.S. Census Bureau; generated by RTI International; using American
FactFinder; “Sector 00: All Sectors: Core Business Statistics Series:
Comparative Statistics for the United States and the States (1997 NAICS
Basis): 2002 and 1997.” <http://factfinder.census.gov>; (July 8, 2008)
 XE “U.S. Census Bureau\; generated by RTI International\; using
American FactFinder\; \”Sector 00\: All Sectors\: Core Business
Statistics Series\: Comparative Statistics for the United States and the
States (1997 NAICS Basis)\: 2002 and 1997.\”
<http\://factfinder.census.gov>\; (July 8, 2008)”  .

	U.S. Census Bureau; generated by Kapur Energy and Environment; using
American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic
Area Series: Economy-Wide Key Statistics: 2007.” Accessed on December
27, 2009  XE “U.S. Census Bureau\; generated by Kapur Energy and
Environment\; using American FactFinder\; \”Sector 00\: EC0700A1\: All
Sectors\: Geographic Area Series\: Economy-Wide Key Statistics\:
2007.\” Accessed on December 27, 2009”  .

While total payroll dropped 3% over from 1997 to 2007, annual payroll
per employee rose 6.5% because of the decline in the number of employees
(Table 2-12). Shipments per employee grew 10.6% from 1997 to 2006 and
dropped 8.9% from 2006 to 2007 (Table 2-12).

Table 2-12.	Industry Data: Wood Product Manufacturing (NAICS 321) 

Industry Data	1997	2002	2006	2007

Total shipments ($2007, millions)	$110,956	$102,721	$115,390	$101,879

Shipments per establishment ($thousands)	$25,613	$5,953	NA	$6,855

Average Shipments per employee ($2007)	$194,648	$189,014	$215,243
$196,053

Average Shipments per $ of payroll ($2007)	$6.18	$5.54	$6.20	$5.84

Average Annual payroll per employee ($2007)	$31,504	$34,093	$34,738
$33,558

Average Employees per establishment	33	31	NA	35

NA = Not available.

Sources:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Annual Survey of Manufactures:
General Statistics: Statistics for Industry Groups and Industries: 2006
and 2005.” <http://factfinder.census.gov>; (July 8, 2008)  XE “U.S.
Census Bureau\; generated by RTI International\; using American
FactFinder\; \”Sector 31\: Annual Survey of Manufactures\: General
Statistics\: Statistics for Industry Groups and Industries\: 2006 and
2005.\” <http\://factfinder.census.gov>\; (July 8, 2008)”  .

	U.S. Census Bureau; generated by RTI International; using American
FactFinder; “Sector 00: All Sectors: Core Business Statistics Series:
Comparative Statistics for the United States and the States (1997 NAICS
Basis): 2002 and 1997.” <http://factfinder.census.gov>; (July 8, 2008)
 XE “U.S. Census Bureau\; generated by RTI International\; using
American FactFinder\; \”Sector 00\: All Sectors\: Core Business
Statistics Series\: Comparative Statistics for the United States and the
States (1997 NAICS Basis)\: 2002 and 1997.\”
<http\://factfinder.census.gov>\; (July 8, 2008)”  .

	U.S. Census Bureau; generated by Kapur Energy and Environment; using
American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic
Area Series: Economy-Wide Key Statistics: 2007.” Accessed on December
27, 2009  XE “U.S. Census Bureau\; generated by Kapur Energy and
Environment\; using American FactFinder\; \”Sector 00\: EC0700A1\: All
Sectors\: Geographic Area Series\: Economy-Wide Key Statistics\:
2007.\” Accessed on December 27, 2009”  .

The U.S. Census Bureau categorizes this industry’s facilities into
three categories: “sawmills and wood preservation;” “veneer,
plywood, and engineered wood product manufacturing;” and “other wood
product manufacturing.” These are further divided into the following
types of facilities as defined by the Census Bureau:

Sawmills and Wood Preservation

Sawmills and Wood Preservation (NAICS 32111): This industry comprises
establishments primarily engaged in one or more of the following
manufacturing activities: (a) sawing dimension lumber, boards, beams,
timber, poles, ties, shingles, shakes, siding, and wood chips from logs
or bolts; (b) sawing round wood poles, pilings, and posts and treating
them with preservatives; and (c) treating wood sawed, planed, or shaped
in other establishments with creosote or other preservatives to prevent
decay and to protect against fire and insects. Sawmills may plane the
rough lumber that they make with a planning machine to achieve
smoothness and uniformity of size.

Veneer, Plywood, and Engineered Wood Product Manufacturing

Veneer, Plywood, and Engineered Wood Product Manufacturing (NAICS
32121): This industry comprises establishments primarily engaged in one
or more of the following manufacturing activities: (a) veneer and/or
plywood, (b) engineered wood members, and (c) reconstituted wood
products. This industry includes manufacturing plywood from veneer made
in the same establishment or from veneer made in other establishments,
and manufacturing plywood faced with non-wood materials, such as
plastics or metal.

Other Wood Product Manufacturing

Millwork (NAICS 32191): This industry comprises establishments primarily
engaged in manufacturing hardwood and softwood cut stock and dimension
stock (i.e., shapes); wood windows and wood doors; and other millwork
including wood flooring. Dimension stock or cut stock is defined as
lumber and worked wood products cut or shaped to specialized sizes.
These establishments generally use woodworking machinery, such as
jointers, planers, lathes, and routers to shape wood.

Wood Container and Pallet Manufacturing (NAICS 32192): This industry
comprises establishments primarily engaged in manufacturing wood
pallets, wood box shook, wood boxes, other wood containers, and wood
parts for pallets and containers.

All Other Wood Product Manufacturing (NAICS 32199): This industry
comprises establishments primarily engaged in manufacturing wood
products (except establishments operating sawmills and wood preservation
facilities; and establishments manufacturing veneer, plywood, engineered
wood products, millwork, wood containers, or pallets).

Figure 2-11 shows that the industry proportion of the value of shipments
for other wood product manufacturing (51%) was greater than the value of
shipments for sawmills and wood preservation (27%) and veneer, plywood,
and engineered wood products (22%). Figure 2-12 indicates that the
majority of employees in this industry fell under other wood products
(60%). Veneer, plywood, and engineered wood products had the same
percentage (20%) of employees as sawmills and wood preservation (20%),
even though it contributed to a lesser portion of the value of
shipments.

Figure 2-11.	Distribution of Value of Shipments within Wood Product
Manufacturing (NAICS 322): 2007

Source:	U.S. Census Bureau; generated by Kapur Energy and Environment;
“Sector 00: EC0700A1: All Sectors: Geographic Area Series:
Economy-Wide Key Statistics: 2007.” < HYPERLINK
"http://factfinder.census.gov" http://factfinder.census.gov >. Accessed
on December 27, 2009  XE “U.S. Census Bureau\; generated by Kapur
Energy and Environment\; \”Sector 00\: EC0700A1\: All Sectors\:
Geographic Area Series\: Economy-Wide Key Statistics\: 2007.\”
<http\://factfinder.census.gov>. Accessed on December 27, 2009”  .
[Source for 2007 numbers]

Figure 2-12.	Distribution of Employment within Wood Product
Manufacturing (NAICS 322): 2007

Source:	U.S. Census Bureau; generated by Kapur Energy and Environment;
“Sector 00: EC0700A1: All Sectors: Geographic Area Series:
Economy-Wide Key Statistics: 2007” Release Date: 12/22/09.
<http://factfinder.census.gov>. Accessed on December 27, 2009  XE
“U.S. Census Bureau\; generated by Kapur Energy and Environment\;
\”Sector 00\: EC0700A1\: All Sectors\: Geographic Area Series\:
Economy-Wide Key Statistics\: 2007\” Release Date\: 12/22/09.
<http\://factfinder.census.gov>. Accessed on December 27, 2009”  . 

Supply and Demand Characteristics

Next, we provide a broad overview of the supply and demand sides of the
wood product manufacturing industry. We emphasize the economic
interactions this industry has with other industries and people and
identify the key goods and services used by the industry and the major
uses and consumers wood products.

Goods and Services Used in Wood Product Manufacturing

In 2007, the cost of materials made up 59% of the total shipment value
of goods in the wood product manufacturing industry (Table 2-13). Total
compensation of employees represented 22% of the total value in 2007.
Both the number of total shipments and the number of employees in this
industry decreased between 2005 and 2007—the former by 14% and the
latter by 3%.

The top 10 industry groups supplying inputs to the wood product industry
accounted for 80% of the total intermediate inputs according to 2008
Bureau of Economic Analysis data (Table 2-14). The largest comes from
the wood product industry itself. This is quite understandable, since
the descriptions of the various industries within wood product
manufacturing imply that they supply each other with products in order
to add value and distribute their products to the broader market. The
top five inputs are rounded out by forestry and logging products,
wholesale trade, management of companies and enterprises, and truck
transportation, which together make up 70% of the total cost of input.

Table 2-13.	Costs of Goods and Services in Wood Product Manufacturing
(NAICS 321) ($2007)

Industry Ratios	2005	Share	2006	Share	2007	Share

Total shipments (millions) 	$118,705	100%	$115,390	100%	$102,002	100%

Total compensation (millions)	$23,327	20%	$23,306	20%	$22,513	22%

Annual payroll millions	$18,884	16%	$18,623	16%	$17,444	17%

Fringe benefits	$4,442	4%	$4,683	4%	$5,069	5%

Total employees	538,890

536,094

524,212

	Average compensation per employee 	$43,286

$43,473

$42,947

	Total production workers’ wages (millions)	$13,363	11%	$13,132	11%
$12,086	12%

Total production workers	431,569

432,315

417,471

	Total production hours (thousands)	911,332

887,613

837,074

	Average production wages per hour ($2007)	$15

$15

$14

	Total cost of materials (thousands)	$71,808	60%	$69,892	61%	$60,682	59%

Materials, parts, packaging	$65,319	55%	$63,499	55%	$54,462	53%

Purchased electricity	$1,530	1%	$1,625	1%	$1,446	1%

Purchased fuel 	$810	1%	$835	1%	$843	1%

Other 	$4,149	3%	$3,933	3%	$3,931	4%

Sources:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Annual Survey of Manufactures:
General Statistics: Statistics for Industry Groups and Industries: 2006
and 2005.” <http://factfinder.census.gov>; (July 8, 2008)  XE “U.S.
Census Bureau\; generated by RTI International\; using American
FactFinder\; \”Sector 31\: Annual Survey of Manufactures\: General
Statistics\: Statistics for Industry Groups and Industries\: 2006 and
2005.\” <http\://factfinder.census.gov>\; (July 8, 2008)”  .

	U.S. Census Bureau; generated by Kapur Energy and Environment: using
American FactFinder; “Sector 31: EC0731I1: Manufacturing: Industry
Series: Detailed Statistics by Industry for the United States: 2007.”
Accessed on December 27, 2009  XE “U.S. Census Bureau\; generated by
Kapur Energy and Environment\: using American FactFinder\; \”Sector
31\: EC0731I1\: Manufacturing\: Industry Series\: Detailed Statistics by
Industry for the United States\: 2007.\” Accessed on December 27,
2009”  .

2.2.2.1.1 Energy. The Department of Energy (DOE) categorizes wood
product manufacturing (NAICS 321) as a non-energy-intensive industry.
The 2008 Annual Energy Outlook predicts that the wood product industry
will be one of five (out of eight) non-energy-intensive industries
experiencing positive average growth of delivered energy consumption
between 2006 and 2030 (DOE, 2008 xe “DOE, 2008” ).

Table 2-14.	Key Goods and Services Used in Wood Product Manufacturing
(NAICS 321) ($2007, millions)

Description	BEA Commodity Code	Wood Products

Wood products	3210	$20,989

Forestry and logging products	1130	$18,914

Wholesale trade	4200	$5,417

Management of companies and enterprises	5500	$2,853

Truck transportation	4840	$2,542

Electric power generation, transmission, and distribution	2211	$1,388

Other fabricated metal products	332B	$1,310

Nonmetallic mineral products	3270	$1,110

Real estate	5310	$799

All other administrative and support services	561A	$748

Architectural and structural metal products	3323	$725

Rail transportation	4820	$723

Other inputs

$14,650

Total intermediate inputs	T005	$72,169

Source:	U.S. Bureau of Economic Analysis (BEA). 2008 xe “U.S. Bureau
of Economic Analysis (BEA). 2008” . “2002 Benchmark Input-Output
Accounts: 2002 Standard Make and Use Tables at the Summary Level.”
Table 2. Washington, DC: BEA.

Table 2-15 shows that total energy use between 1998 and 2002. Figure
2-13 shows that electrical power use decreased, since 2000.

Uses and Consumers

Table 2-16 shows that three of the top four consumers of wood products
are represented by the construction sector of the economy (NAICS 23).
New residential construction, new nonresidential construction, and
maintenance and repair construction consume 35% of the total commodity
output in this industry. The top 10 consumers of wood products make up
54% of the demand for wood products. Although many of the top consumers
deal with construction, repair, or real estate services, other types of
consumers, such as food services and drinking places, rail
transportation, plastics and rubber products manufacturing, and other,
use these products.

Table 2-15.	Energy Used in Wood Product Manufacturing (NAICS 321)

Fuel Type	1998	2002	2006

Net electricitya (million kWh)	21,170	20,985	26,723

Residual fuel oil (million bbl)	*	*	1

Distillate fuel oilb (million bbl)	2	2	3

Natural gasc (billion cu ft)	71	56	84

LPG and NGLd (million bbl)	1	1	1

Coal (million short tons)	*	*	Q

Coke and breeze (million short tons)	—	—	*

Othere (trillion BTU)	341	229	228

Total (trillion BTU)	504	375	445

a	Net electricity is obtained by summing purchases, transfers in, and
generation from noncombustible renewable resources, minus quantities
sold and transferred out. It does not include electricity inputs from
on-site cogeneration or generation from combustible fuels because that
energy has already been included as generating fuel (for example, coal).

b	Distillate fuel oil includes Nos. 1, 2, and 4 fuel oils and Nos. 1, 2,
and 4 diesel fuels.

c	Natural gas includes natural gas obtained from utilities, local
distribution companies, and any other supplier(s), such as independent
gas producers, gas brokers, marketers, and any marketing subsidiaries of
utilities.

d	Examples of liquefied petroleum gases (LPGs) are ethane, ethylene,
propane, propylene, normal butane, butylene, ethane-propane mixtures,
propane-butane mixtures, and isobutene produced at refineries or natural
gas processing plants, including plants that fractionate raw natural gas
liquids (NGLs).

e	Other includes net steam (the sum of purchases, generation from
renewables, and net transfers), and other energy that respondents
indicated was used to produce heat and power.

* 	Estimate less than 0.5.

Q = Withheld because relative standard error is greater than 50%.

Sources:	U.S. Department of Energy, Energy Information Administration.
2007a  XE “U.S. Department of Energy, Energy Information
Administration. 2007a”  . “2002 Energy Consumption by
Manufacturers—Data Tables.” Tables 3.2 and N3.2.
<http://www.eia.doe.gov/emeu/mecs/

mecs2002/data02/shelltables.html>. Washington, DC: DOE.

	U.S. Department of Energy, Energy Information Administration. 2007b  XE
“U.S. Department of Energy, Energy Information Administration.
2007b”  . “2006 Energy Consumption by Manufacturers—Data
Tables.” Tables 3.1. < HYPERLINK
"http://www.eia.doe.gov/emeu/mecs/mecs2006/2006tables.html"
http://www.eia.doe.gov/emeu/mecs/mecs2006/

2006tables.html >. [Source for 2006 numbers]

Figure 2-13.	Electrical Power Use Trends in the Wood Product
Manufacturing Industry (NAICS 321): 1997–2005

Source:	Federal Reserve Board. 2009  XE “Federal Reserve Board.
2009”  . “Industrial Production and Capacity Utilization: Electric
Power Use: Manufacturing and Mining.”
<http://www.federalreserve.gov/datadownload/>

Firm and Market Characteristics

This section describes geographic, production, and market data. These
data provide the basis for further analysis, including regulatory
flexibility analyses, as well as a complete picture of the recent
historical trends of production and pricing.

Location

As Figure 2-14 illustrates, the states with the largest number of wood
product manufacturing establishments are dispersed throughout the
country, with a significant concentration of establishments in the
northeastern states. Other states with many establishments include
California, Texas, and North Carolina.

Table 2-16.	Demand by Sector: Wood Product Manufacturing (NAICS 321)
($2007, millions)

Sector	BEA Code	3210 Wood Products 

New residential construction	2302	$19,997

New nonresidential construction	2301	$11,854

Furniture and related product manufacturing	3370	$8,197

Maintenance and repair construction	2303	$4,048

Motor vehicle body, trailer and parts manufacturing	336A	$2,516

Real estate	5310	$2,335

Food services and drinking places	7220	$2,307

Other miscellaneous manufacturing	3399	$1,311

Wholesale trade	4200	$1,284

Rail transportation	4820	$1,138

Retail trade	4A00	$1,047

Plastics and rubber products manufacturing	3260	$877

General state and local government use	S007	$3,116

Owner occupied dwelling	S008	$11,209

Private fixed investment	F020	$7,933

Exports of goods and services	F040	$3,978

Total final uses (gross domestic product [GDP])	T004	$3,719

Total commodity output	T007	$101,753

Source:	U.S. Bureau of Economic Analysis (BEA). 2008 xe “U.S. Bureau
of Economic Analysis (BEA). 2008” . “2002 Benchmark Input-Output
Accounts: 2002 Standard Make and Use Tables at the Summary Level.”
Table 2. Washington, DC: BEA.

Production Capacity and Utilization

Capacity utilization of the wood product manufacturing industry has been
experiencing capacity utilization increases and declines with more
extreme fluctuations than those of all manufacturing industries
combined. The decline in wood product manufacturing is similar to total
manufacturing between 1997 and 2002. However, capacity utilization in
total manufacturing, which peaked in 2006, started increasing at a
faster rate than wood product manufacturing, but decreased sharply after
its peak. Wood product manufacturing experienced its own rapid decrease
in capacity utilization between 2007 and 2009, though not at the same
rate as total manufacturing (Figure 2-15).

Figure 2-14.	Establishment Concentration in the Wood Product
Manufacturing Industry (NAICS 321): 2002

Source:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Manufacturing: Geographic Area
Series: Industry Statistics for the States, Metropolitan and
Micropolitan Statistical Areas, Counties, and Places: 2002.”
<http://factfinder.census.gov>; (July 23, 2008)  XE “U.S. Census
Bureau\; generated by RTI International\; using American FactFinder\;
\”Sector 31\: Manufacturing\: Geographic Area Series\: Industry
Statistics for the States, Metropolitan and Micropolitan Statistical
Areas, Counties, and Places\: 2002.\”
<http\://factfinder.census.gov>\; (July 23, 2008)”  .

Employment

California has the largest number of employees in the wood product
manufacturing industry with over 39,000 reported in the 2002 census
followed by over 32,000 in Oregon (Figure 2-16). The states with the
highest number of employees do not directly correlate with the states
with the highest number of establishments. States such as Indiana,
Georgia, Arkansas, and Oregon had fewer than 600 establishments, as
shown in Figure 2-14, but had more than 20,000 employees, whereas states
such as Ohio and New York had fewer than 20,000 employees but more than
600 establishments.

Plants and Capacity

While the capacity of the manufacturing sector has been growing
consistently since 1997, the wood product manufacturing industry has
experienced inconsistent growth. After a small amount of growth in
capacity between 1997 and 2001, the wood product manufacturing
industry’s capacity dipped between 2002 and 2005 but has been growing
at a slow rate since then though it started to dip again in 2008 and
2009 (Figure 2-17).

Figure 2-15.	Capacity Utilization Trends in the Wood Product
Manufacturing Industry (NAICS 321)

Source:	Federal Reserve Board. 2009  XE “Federal Reserve Board.
2009”  . “Industrial Production and Capacity Utilization: Capacity
Utilization.” <http://www.federalreserve.gov/datadownload/>

Firm Characteristics

In 2006, the top 10 paper and forest product companies produced over $75
billion in sales, with the top two companies—International Paper and
Weyerhaeuser—generating nearly $22 billion each (Table 2-17). The top
two companies’ revenue consists of 58% of the revenue of the top 10
companies in Standard & Poor’s (S&P’s) list (Benwart, 2006 xe
“Benwart, 2006” ). Although these numbers do not exclusively reflect
wood products, they do convey the market environment in which firms in
this sector compete.

Size Distribution

The primary criterion for categorizing a business as small is the number
of employees, using definitions by the SBA for regulatory flexibility
analyses. According to SUSB reports for 2002, small companies were the
recipients of the majority of receipts in 2002; 53% of receipts were
generated by companies with fewer than 500 employees (Table 2-18). The
number of employees in the small business cutoff is 500 employees for
all subindustries in the wood product manufacturing industry (Table
2-19).

Figure 2-16.	Employment Concentration in the Wood Product Manufacturing
Industry (NAICS 321): 2002

Source:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Manufacturing: Geographic Area
Series: Industry Statistics for the States, Metropolitan and
Micropolitan Statistical Areas, Counties, and Places: 2002.”
<http://factfinder.census.gov>; (July 23, 2008)  XE “U.S. Census
Bureau\; generated by RTI International\; using American FactFinder\;
\”Sector 31\: Manufacturing\: Geographic Area Series\: Industry
Statistics for the States, Metropolitan and Micropolitan Statistical
Areas, Counties, and Places\: 2002.\”
<http\://factfinder.census.gov>\; (July 23, 2008)”  .

Domestic Production

Similar to industry capacity rates, industry production rates for wood
product manufacturing have decreased since 2006 compared to the steady
increase in production for the manufacturing sector since 1997 (Figure
2-18). Similar to capacity utilization trends (Figure 2-16), the index
shows a faster rate of decline for wood products than the entire
manufacturing sector.

International Trade

Since 1997, the wood product manufacturing industry has contributed to
an increasing trade deficit (Figure 2-16). The value of imports has
fluctuated greatly since 1997; however, exports have remained fairly
constant, with seasonal changes, since 1997.

Figure 2-17.	Capacity Trends in the Wood Product Manufacturing Industry
(NAICS 321)

Source:	Federal Reserve Board. 2009. “Industrial Production and
Capacity Utilization: Industrial Capacity.”
<http://www.federalreserve.gov/datadownload/>. 

Table 2-17.	Largest U.S. Paper and Forest Products Companies: 2006

Company	Revenues ($millions)a

International Paper	21,995

Weyerhaeuser	21,896

Smurfit-Stone	7,157

MeadWestvaco	6,530

Temple-Inland	5,558

Bowater	3,530

Grief Inc.	2,628

Louisiana-Pacific	2,235

Packaging Corp.	2,187

Plum Creek	1,627

a	Includes revenues from operations other than paper and forest products
in certain cases.

Source:	Benwart, S.J. 2006 xe “Benwart, S.J. 2006” . “Paper &
Forest Products.” Standard and Poor’s Industry Surveys. 176(28).

Table 2-18.	Distribution of Economic Data by Enterprise Size: Wood
Product Manufacturing (NAICS 321)

Enterprises with

Variable	Total	1 to 20 Employeesa	20 to 99 Employees	100 to 499
Employees	500 to 749 Employees	750 to 999 Employees	1,000 to 1,499
Employees

Firms	15,198	9,740	3,280	791	63	27	30

Establishments	17,052	9,758	3,482	1,271	166	91	133

Employment	534,011	65,423	132,612	118,910	19,784	11,944	18,533

Receipts ($thousands )	$88,649	$8,204	$18,276	$19,717	$3,192	$1,902
$3,118

Receipts/firm ($thousands)	$5,833	$842	$5,572	$24,927	$50,673	$70,453
$103,927

Receipts/establishment ($thousands)	$5,199	$841	$5,249	$15,513	$19,231
$20,904	$23,442

Receipts/employment ($)	$166,006	$125,393	$137,818	$165,814	$161,363
$159,262	$168,231

a	Excludes Statistics of U.S. Businesses (SUSB) employment category for
zero employees. These entities only operated for a fraction of the year.

Source:	U.S. Census Bureau. 2008  XE “U.S. Census Bureau. 2008”  .
“Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail
Employment Sizes: 2002.”
<http://www.census.gov/csd/susb/download_susb02.htm>.

Table 2-19.	Small Business Size Standards: Wood Product Manufacturing
(NAICS 321)

NAICS	NAICS Description	Employees

321113	Sawmills	500

321114	Wood Preservation 	500

321211	Hardwood Veneer and Plywood Manufacturing	500

321212	Softwood Veneer and Plywood Manufacturing	500

321213	Engineered Wood Member (except Truss) Manufacturing	500

32121	Truss Manufacturing 	500

321219	Reconstituted Wood Product Manufacturing 	500

321911	Wood Window and Door Manufacturing	500

321912	Cut Stock, Resawing Lumber, and Planing	500

321918	Other Millwork (including Flooring) 	500

321920	Wood Container and Pallet Manufacturing	500

321991	Manufactured Home (Mobile Home) Manufacturing	500

321992	Prefabricated Wood Building Manufacturing	500

321999	All Other Miscellaneous Wood Product Manufacturing	500

Source:	U.S. Small Business Administration (SBA). 2008  XE “U.S. Small
Business Administration (SBA). 2008”  . “Table of Small Business
Size Standards Matched to North American Industry Classification System
Codes.” Effective August 22, 2008.
<http://www.sba.gov/services/contractingopportunities/sizestandardstopic
s/size/index.html>.

Market Prices

Prices of goods in the wood product manufacturing industry have remained
roughly the same since 2005. The prices for the entire manufacturing
sector increased between 2003 and 2008 but have decreased since August
2008. Producer price indices (PPIs) show that producer prices for wood
products increased by 6% from 2004 to 2007 (Figure 2-20).

Figure 2-18.	Industrial Production Trends in the Wood Product
Manufacturing Industry (NAICS 321): 1997–2009

Source:	Federal Reserve Board. 2009. “Industrial Production and
Capacity Utilization: Industrial Production.”
<http://www.federalreserve.gov/datadownload/>. 

Figure 2-19.	International Trade Trends in the Wood Product
Manufacturing Industry (NAICS 321)]

Source:	U.S. International Trade Commission. 2008  XE “U.S.
International Trade Commission. 2008”  . “U.S. Domestic Exports” &
“U.S. Imports for Consumption.”
<http://dataweb.usitc.gov/scripts/user_set.asp>.

Figure 2-20.	Producer Price Trends in the Wood Product Manufacturing
Industry (NAICS 321)

Source:	U.S. Bureau of Labor Statistics (BLS). 2009a  XE “U.S. Bureau
of Labor Statistics (BLS). 2009a”  . “Producer Price Index.”
Series ID: PCU321—321—& PCUOMFG—OMFG—.
<http://www.bls.gov/ppi/home.htm>. Accessed on January 8, 2010.

Paper Manufacturing

Introduction

The paper manufacturing subsector is an essential component of all
business operations worldwide. Broadly speaking, paper and paperboard
are manufactured by converting timber or other recycled material into
products such as printing and writing papers, newsprint, tissue, and
containerboard (Benwart, 2006 xe “Benwart, 2006” ). The subsector
has been experiencing a decline in shipments as of late. From 1997 to
2007, shipments in the industry declined 7%, and employment declined by
27% (Table 2-21). While total payroll dropped 26% over this time,
annual payroll per employee rose 2% from 1997 to 2007 because of the
decline in the number of employees (Table 2-20). Shipments per employee
grew 28% from 1997 to 2007, with much of that growth taking place
between 2002 and 2006 (Table 2-21).

Table 2-20.	Key Statistics: Paper Manufacturing (NAICS 322)

	1997	2002	2006	2007

Shipments ($2007, millions)	$188,496	$175,983	$174,887	$175,806

Payroll ($2007, millions)	$27,983	$24,561	$21,188	$20,804

Employees	574,274	489,367	414,049	416,886

Establishments	5,868	5,495	NA	4,803

NA = Not available.

Sources:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Annual Survey of Manufactures:
General Statistics: Statistics for Industry Groups and Industries: 2006
and 2005.” <http://factfinder.census.gov>; (July 8, 2008)  XE “U.S.
Census Bureau\; generated by RTI International\; using American
FactFinder\; \”Sector 31\: Annual Survey of Manufactures\: General
Statistics\: Statistics for Industry Groups and Industries\: 2006 and
2005.\” <http\://factfinder.census.gov>\; (July 8, 2008)”  .

	U.S. Census Bureau; generated by RTI International; using American
FactFinder; “Sector 00: All Sectors: Core Business Statistics Series:
Comparative Statistics for the United States and the States (1997 NAICS
Basis): 2002 and 1997.” <http://factfinder.census.gov>; (July 8, 2008)
 XE “U.S. Census Bureau\; generated by RTI International\; using
American FactFinder\; \”Sector 00\: All Sectors\: Core Business
Statistics Series\: Comparative Statistics for the United States and the
States (1997 NAICS Basis)\: 2002 and 1997.\”
<http\://factfinder.census.gov>\; (July 8, 2008)”  .

	U.S. Census Bureau; generated by Kapur Energy and Environment; using
American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic
Area Series: Economy-Wide Key Statistics: 2007.” Accessed on December
28, 2009  XE “U.S. Census Bureau\; generated by Kapur Energy and
Environment\; using American FactFinder\; \”Sector 00\: EC0700A1\: All
Sectors\: Geographic Area Series\: Economy-Wide Key Statistics\:
2007.\” Accessed on December 28, 2009”  . [Source for 2007 numbers]

Table 2-21.	Industry Data: Paper Manufacturing (NAICS 322)

Industry Data	1997	2002	2006	2007

Total shipments ($2007, millions)	$188,496	$175,983	$174,887	$175,806

Shipments per establishment ($2007, thousands)	$32,123	$32,026	NA
$36,603

Average Shipments per employee ($2007)	$328,233	$359,614	$422,381
$421,712

Average Shipments per $ of payroll ($2007)	$6.74	$7.17	$8.25	$8.45

Average Annual payroll per employee ($2007)	$48,727	$50,189	$51,174
$49,904

Average Employees per establishment	98	89	NA	87

NA = Not available.

Sources:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Annual Survey of Manufactures:
General Statistics: Statistics for Industry Groups and Industries: 2006
and 2005.” <http://factfinder.census.gov>; (July 8, 2008)  XE “U.S.
Census Bureau\; generated by RTI International\; using American
FactFinder\; \”Sector 31\: Annual Survey of Manufactures\: General
Statistics\: Statistics for Industry Groups and Industries\: 2006 and
2005.\” <http\://factfinder.census.gov>\; (July 8, 2008)”  .

	U.S. Census Bureau; generated by RTI International; using American
FactFinder; “Sector 00: All Sectors: Core Business Statistics Series:
Comparative Statistics for the United States and the States (1997 NAICS
Basis): 2002 and 1997.” <http://factfinder.census.gov>; (July 8, 2008)
 XE “U.S. Census Bureau\; generated by RTI International\; using
American FactFinder\; \”Sector 00\: All Sectors\: Core Business
Statistics Series\: Comparative Statistics for the United States and the
States (1997 NAICS Basis)\: 2002 and 1997.\”
<http\://factfinder.census.gov>\; (July 8, 2008)”  .

	U.S. Census Bureau; generated by Kapur Energy and Environment; using
American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic
Area Series: Economy-Wide Key Statistics: 2007.”
<http://factfinder.census.gov>. Accessed on December 28, 2009  XE
“U.S. Census Bureau\; generated by Kapur Energy and Environment\;
using American FactFinder\; \”Sector 00\: EC0700A1\: All Sectors\:
Geographic Area Series\: Economy-Wide Key Statistics\: 2007.\”
<http\://factfinder.census.gov>. Accessed on December 28, 2009”  .
[Source for 2007 numbers]

The U.S. Census Bureau categorizes this industry’s facilities into
two categories: pulp, paper, and paperboard manufacturing and converted
paper product manufacturing. These are further divided into the
following types of facilities as defined by the Census Bureau (2001)  XE
“Census Bureau (2001)”  :

Pulp, Paper, and Paperboard:

Pulp Mills (NAICS 32211): This industry comprises establishments
primarily engaged in manufacturing pulp without manufacturing paper or
paperboard. The pulp is made by separating the cellulose fibers from the
other impurities in wood or other materials, such as used or recycled
rags, linters, scrap paper, and straw.

Paper Mills (NAICS 32212): This industry comprises establishments
primarily engaged in manufacturing paper from pulp. These establishments
may manufacture or purchase pulp. In addition, the establishments may
convert the paper they make. The activity of making paper classifies an
establishment into this industry regardless of the output.

Paperboard Mills (NAICS 32213): This industry comprises establishments
primarily engaged in manufacturing paperboard from pulp. These
establishments may manufacture or purchase pulp. In addition, the
establishments may also convert the paperboard they make.

Converted Paper Products:

Paperboard Containers Manufacturing (NAICS 32221): This industry
comprises establishments primarily engaged in converting paperboard into
containers without manufacturing paperboard. These establishments use
corrugating, cutting, and shaping machinery to form paperboard into
containers. Products made by these establishments include boxes;
corrugated sheets, pads, and pallets; paper dishes; and fiber drums and
reels.

Paper Bag and Coated and Treated Paper Manufacturing (NAICS 32222): This
industry comprises establishments primarily engaged in one or more of
the following manufacturing activities: cutting and coating paper and
paperboard; cutting and laminating paper and paperboard and other
flexible materials (except plastics film to plastics film); bags or
multiwall bags or sacks of paper, metal foil, coated paper, or laminates
or coated combinations of paper and foil with plastics film; laminated
aluminum and other converted metal foils from purchased foils; and
surface coating paper or paperboard.

Stationary Product Manufacturing (NAICS 32223): This industry comprises
establishments primarily engaged in converting paper or paperboard into
products used for writing, filing, art work, and similar applications.

Other Converted Paper Products (NAICS 32229): This industry comprises
establishments primarily engaged in one of the following manufacturing
activities:

converting paper and paperboard into products (except containers, bags,
coated and treated paper and paperboard, and stationery products), or

converting pulp into pulp products, such as disposable diapers, or
molded pulp egg cartons, food trays, and dishes.

Figure 2-21 shows that the value of shipments for converted paper
products was 54% of the value of all paper products in 2007, while the
value of shipments for pulp, paper, and paperboard products was 46%.
Figure 2-22 indicates that 70% of industry employees worked in the
converted paper product category of the industry due to the labor
intensive aspects of those facilities.

Figure 2-21.	Distribution of Value of Shipments within Paper
Manufacturing (NAICS 322): 2007

Source:	U.S. Census Bureau; generated by Kapur Energy and Environment;
using American FactFinder: “Sector 31: EC0731I1: Manufacturing:
Industry Series: Detailed Statistics by Industry for the United States:
2007.” Accessed on December 28, 2009  XE “U.S. Census Bureau\;
generated by Kapur Energy and Environment\; using American FactFinder\:
\”Sector 31\: EC0731I1\: Manufacturing\: Industry Series\: Detailed
Statistics by Industry for the United States\: 2007.\” Accessed on
December 28, 2009”  .

Figure 2-22.	Distribution of Employment within Paper Manufacturing
(NAICS 322): 2007

Source:	U.S. Census Bureau; generated by Kapur Energy and Environment;
using American FactFinder; “Sector 31: EC0731I1: Manufacturing:
Industry Series: Detailed Statistics by Industry for the United States:
2007.” < HYPERLINK "http://factfinder.census.gov"
http://factfinder.census.gov >. Accessed on December 28, 2009  XE
“U.S. Census Bureau\; generated by Kapur Energy and Environment\;
using American FactFinder\; \”Sector 31\: EC0731I1\: Manufacturing\:
Industry Series\: Detailed Statistics by Industry for the United
States\: 2007.\” <http\://factfinder.census.gov>. Accessed on December
28, 2009”  .

Supply and Demand Characteristics

Next, we provide a broad overview of the supply and demand sides of the
paper manufacturing industry. We emphasize the economic interactions
this industry has with other industries and people and identify the key
goods and services used by the industry and the major uses and consumers
of paper manufacturing products.

Goods and Services Used in Paper Manufacturing

In 2007, the cost of materials made up 53% of the total shipment value
of goods in the paper manufacturing industry (Table 2-22). Total
compensation of employees represented 15% of the total value in 2007,
down from 17% in 2005. The total number of employees dropped by 2%,
between 2005 and 2007, while shipments increased by 3% in the same
period.

The top 10 industry groups supplying inputs to the paper manufacturing
subsector accounted for 70% of the total intermediate inputs according
to 2008 Bureau of Economic Analysis (BEA) data (Table 2-23). Inputs for
pulp, paper, and paperboard products are notably different from inputs
for converted paper products because the NAICS 3221 group represents the
initial step in the paper manufacturing process; thus, its inputs
include more raw resources such as wood products, forestry and logging
products, natural gas, and electricity. This becomes evident when
observing inputs for converted paper products: 49% of the cost of inputs
comes from pulp, paper, and paperboard products.

2.3.2.1.1 Energy. The Department of Energy (DOE) categorizes paper
manufacturing (NAICS 322) as an energy-intensive subsector. The 2008
Annual Energy Outlook predicts that the paper-producing subsector will
be one of four subsectors experiencing positive average growth of
delivered energy consumption between 2006 and 2030 (DOE, 2008  XE
“DOE, 2008”  ).

Energy generation from the recovery boiler is often insufficient for
total plant needs, so facilities augment recovery boilers with fossil
fuel–fired and wood waste–fired boilers (hogged fuel) to generate
steam and often electricity. Industry wide, the use of pulp wastes,
bark, and other papermaking residues supplies 58% of the energy
requirements of pulp and paper companies (EPA, 2002 xe “U.S. EPA,
2002” ).

Likewise, Table 2-24 shows that total energy use decreased between 1998
and 2006 by 14%. Figure 2-24 indicates that total electrical power use
changed sporadically between 2002 and 2004 but decreased consistently
and rapidly after 2004.

Table 2-22.	Costs of Goods and Services Used in the Paper Manufacturing
Industry (NAICS 322)

Variable	2005	Share	2006	Share	2007	Share

Total shipments ($2007, millions)	$171,477	100%	$174,887	100%	$176,018
100%

Total compensation ($2007, millions)	$28,846	17%	$27,791	16%	$27,150	15%

Annual payroll	$21,792	13%	$21,188	12%	$20,804	12%

Fringe benefits	$7,054	4%	$6,603	4%	$6,346	4%

Total employees	426,748

414,049

417,367

	Average compensation per employee	$67,596

$67,121

$65,051

	Total production workers wages ($2007, millions)	$14,965	9%	$14,689	8%
$14,190	8%

Total production workers	331,228

321,684

321,937

	Total production hours (thousands)	716,963

691,134

680,732

	Average production wages per hour	$21

$21

$21

	Total cost of materials ($2007, thousands)	$91,897	54%	$92,452	53%
$94,029	53%

Materials, parts, packaging	$77,494	45%	$78,202	45%	$79,984	45%

Purchase electricity	$3,788	2%	$3,841	2%	$3,780	2%

Purchased fuel ($2007)	$5,537	3%	$5,509	3%	$5,511	3%

Other	$5,078	3%	$4,901	3%	$4,755	3%

Sources:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Annual Survey of Manufactures:
General Statistics: Statistics for Industry Groups and Industries: 2006
and 2005.” <http://factfinder.census.gov>; (July 8, 2008)  XE “U.S.
Census Bureau\; generated by RTI International\; using American
FactFinder\; \”Sector 31\: Annual Survey of Manufactures\: General
Statistics\: Statistics for Industry Groups and Industries\: 2006 and
2005.\” <http\://factfinder.census.gov>\; (July 8, 2008)”  .

	U.S. Census Bureau; generated by Kapur Energy and Environment; using
American FactFinder; “Sector 31: EC0731I1: Manufacturing: Industry
Series: Detailed Statistics by Industry for the United States: 2007.”
<http://factfinder.census.gov>. Accessed on December 28, 2009  XE
“U.S. Census Bureau\; generated by Kapur Energy and Environment\;
using American FactFinder\; \”Sector 31\: EC0731I1\: Manufacturing\:
Industry Series\: Detailed Statistics by Industry for the United
States\: 2007.\” <http\://factfinder.census.gov>. Accessed on December
28, 2009”  . [Source for 2007 numbers]

Table 2-23.	Key Goods and Services Used in the Paper Manufacturing
Industry (NAICS 322) ($millions, $2007)

Description	BEA Code	NAICS 3221

Pulp, Paper, and Paperboard	NAICS 3222

Converted Paper Products	Total

Pulp, paper, and paperboard	3221	$4,155	$30,448	$34,603

Wholesale trade	4200	$3,916	$6,356	$10,273

Management of companies and enterprises	5500	$3,154	$3,838	$6,993

Forestry and logging products	1130	$5,389	$0	$5,389

Basic chemicals	3251	$3,734	$263	$3,997

Electric power generation, transmission, and distribution	2211	$2,690
$913	$3,603

Wood products	3210	$3,450	$33	$3,484

Converted paper products	3222	$1,415	$1,745	$3,159

Natural gas distribution	2212	$2,680	$345	$3,026

Truck transportation	4840	$1,428	$1,571	$2,999

Total intermediate inputs	T005	$47,835	$62,690	$110,525

Source:	U.S. Bureau of Economic Analysis (BEA). 2008  XE “U.S. Bureau
of Economic Analysis (BEA). 2008”  . “2002 Benchmark Input-Output
Accounts: 2002 Standard Make and Use Tables at the Summary Level.”
Table 2. Washington, DC: BEA.

Table 2-24.	Energy Used in Paper Manufacturing (NAICS 322)

Fuel Type	1998	2002	2006

Net electricitya (million kWh)	70,364	65,503	72,518

Residual fuel oil (million bbl)	24	16	15

Distillate fuel oilb (million bbl)	2	2	2

Natural gasc (billion cu ft)	570	490	461

LPG and NGLd (million bbl)	1	2	1

Coal (million short tons)	12	11	10

Coke and breeze (million short tons)	—	*	—

Othere (trillion BTU)	1,476	1,276	1,303

Total (trillion BTU)	2,744	2,361	2,354

a	Net electricity is obtained by summing purchases, transfers in, and
generation from noncombustible renewable resources, minus quantities
sold and transferred out. It does not include electricity inputs from
on-site cogeneration or generation from combustible fuels because that
energy has already been included as generating fuel (for example, coal).

b	Distillate fuel oil includes Nos. 1, 2, and 4 fuel oils and Nos. 1, 2,
and 4 diesel fuels.

c	Natural gas includes natural gas obtained from utilities, local
distribution companies, and any other supplier(s), such as independent
gas producers, gas brokers, marketers, and any marketing subsidiaries of
utilities.

d	Examples of liquefied petroleum gases (LPG) are ethane, ethylene,
propane, propylene, normal butane, butylene, ethane-propane mixtures,
propane-butane mixtures, and isobutene produced at refineries or natural
gas processing plants, including plants that fractionate raw natural gas
liquids (NGLs).

e	Other includes net steam (the sum of purchases, generation from
renewables, and net transfers), and other energy that respondents
indicated was used to produce heat and power.

*	Estimate less than 0.5.

Sources:	U.S. Department of Energy, Energy Information Administration.
2007a  XE “U.S. Department of Energy, Energy Information
Administration. 2007”  . “2002 Energy Consumption by
Manufacturers—Data Tables.” Tables 3.2 and N3.2.
<http://www.eia.doe.gov/emeu/mecs/mecs2002/

data02/shelltables.html>. Washington, DC: DOE.

	U.S. Department of Energy, Energy Information Administration. 2007b  XE
“U.S. Department of Energy, Energy Information Administration.
2007b”  . “2006 Energy Consumption by Manufacturers—Data
Tables.” Table 3.1. < HYPERLINK
"http://www.eia.doe.gov/emeu/mecs/mecs2006/2006tables.html"
http://www.eia.doe.gov/emeu/mecs/mecs2006/

2006tables.html >. Accessed on December 27, 2009. [Source for 2006
numbers]

Over the last 25 years, the pulp and paper subsector has changed its
energy generation methods from fossil fuels to a greater use of
processes such as increases in the use of wood wastes in place of fuel
(Table 2-25). During the 1972–1999 period, the proportion of total
industry power generated from the combination of woodroom wastes, spent
liquor solids, and other self-generated methods increased from about 41%
to about 56%, while coal, fuel oil, and natural gas use decreased from
about 54% to about 36% (EPA, 2002 xe “U.S. EPA, 2002” ).

Figure 2-23.	Electrical Power Use Trends in the Paper Manufacturing
Industry: 1997–2005

Source:	Federal Reserve Board. 2009. “Industrial Production and
Capacity Utilization: Electric Power Use: Manufacturing and Mining.”
<http://www.federalreserve.gov/datadownload/> xe “Federal Reserve
Board. 2009b. Industrial Production and Capacity Utilization\: Electric
Power Use\: Manufacturing and Mining.
<http\://www.federalreserve.gov/datadownload/>“ .

Table 2-25.	Estimated Energy Sources for the U.S. Pulp and Paper
Industry

Energy Source	1972	1979	1990	1999

Purchased steam	5.4%	6.7%	7.3%	1.5%

Coal	9.8%	9.1%	13.7%	12.5%

Fuel oil	22.3%	19.1%	6.4%	6.3%

Natural gas	21.5%	17.8%	16.4%	17.6%

Other purchased energy	—	—	—	6.7%

Waste wood and wood chips (hogged fuel) and bark	6.6%	9.2%	15.4%	13.5%

Spent liquor solids	33.7%	37.3%	39.4%	40.3%

Other self-generated power	0.6%	0.8%	1.2%	1.6%

Source: 	U.S. Environmental Protection Agency. 2002  XE “U.S.
Environmental Protection Agency. 2002”  . “Profile of the Pulp and
Paper Industry.” Sector Notebook Project.
<http://www.epa.gov/Compliance/resources/publications/assistance/sectors
/notebooks/

index.html>.

Uses and Consumers

Products manufactured in the NAICS groups 3221 and 3222 have different,
but complementary, consumer profiles. NAICS 3221 supplies a significant
portion of NAICS 3222 demand (37% of total commodity output). Both
industries specialize in products with intermediate uses, with an
average of 92% of sales between the two going toward this purpose. NAICS
3222 has a very diverse assortment of subsector groups from which it
receives demand. Food manufacturing makes up 21% of the demand, making
members of this industry the largest consumer of converted paper
products (Table 2-26). Pulp, paper, and paperboard products have a
large trade deficit, while converted paper products have a very small
trade surplus.

Table 2-26.	Demand by Sector: Paper Manufacturing Industry (NAICS 322)
($millions, $2007)

Sector	BEA Code	3221

Pulp, Paper, and Paperboard	3222

−$15,284	−$5,720	−$21,005

Total final uses (GDP)	T004	$4,996	$9,607	$14,604

Total commodity output	T007	$81,725	$90,469	$172,195

Source:	U.S. Bureau of Economic Analysis (BEA). 2008  XE “U.S. Bureau
of Economic Analysis (BEA). 2008”  . “2002 Benchmark Input-Output
Accounts: 2002 Standard Make and Use Tables at the Summary Level.”
Table 2. Washington, DC: BEA.

Firm and Market Characteristics

This section describes geographic, production, and market data. These
data provide the basis for further analysis, including regulatory
flexibility analyses, and give a complete picture of the recent
historical trends of production and pricing.

Location

As Figure 2-24 illustrates, California is home to the most paper
manufacturing establishments in the United States, followed by Illinois
and some bordering northeastern states. The location of establishments
in the paper manufacturing industry varies a great deal by subsector.
Wisconsin and New York have the most pulp, paper, and paperboard
establishments, while California dominates with over 500 converted paper
product establishments. Overall, the United States has 561 pulp, paper,
and paperboard establishments and 4,956 converted paper product
establishments.

Figure 2-24.	Establishment Concentration in Paper Manufacturing Industry
(NAICS 322): 2002

Source:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Manufacturing: Geographic Area
Series: Industry Statistics for the States, Metropolitan and
Micropolitan Statistical Areas, Counties, and Places: 2002.”
<http://factfinder.census.gov>; (July 23, 2008)  XE “.S. Census
Bureau\; generated by RTI International\; using American FactFinder\;
\”Sector 31\: Manufacturing\: Geographic Area Series\: Industry
Statistics for the States, Metropolitan and Micropolitan Statistical
Areas, Counties, and Places\: 2002.\”
<http\://factfinder.census.gov>\; (July 23, 2008)”  .

Production Capacity and Utilization

Capacity utilization of the paper manufacturing subsector has been
experiencing a steady decline, similar to the decline of the total
manufacturing sector. However, paper manufacturing has managed to use
its capacity at a consistently higher rate than the average for
manufacturing industries (Figure 2-25).

Figure 2-25.	Capacity Utilization Trends in the Paper Manufacturing
Industry (NAICS 322)

Source:	Federal Reserve Board. 2009  XE “Federal Reserve Board.
2009”  . “Industrial Production and Capacity Utilization: Capacity
Utilization.” <http://www.federalreserve.gov/datadownload/>.

Employment

Wisconsin has the largest number of employees in the paper manufacturing
subsector with over 38,008 reported in the 2002 census followed by
29,379 in California (Figure 2-26). The converted paper products group
has more employees per establishment, 283, than the pulp, paper, and
paperboard group, 67.

Figure 2-26.	Employment Concentration in the Paper Manufacturing
Industry (NAICS 322): 2002

Source:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Manufacturing: Geographic Area
Series: Industry Statistics for the States, Metropolitan and
Micropolitan Statistical Areas, Counties, and Places: 2002.”
<http://factfinder.census.gov>; (July 23, 2008)  XE “U.S. Census
Bureau\; generated by RTI International\; using American FactFinder\;
\”Sector 31\: Manufacturing\: Geographic Area Series\: Industry
Statistics for the States, Metropolitan and Micropolitan Statistical
Areas, Counties, and Places\: 2002.\”
<http\://factfinder.census.gov>\; (July 23, 2008)”  .

Plants and Capacity

While the manufacturing sector has been growing consistently since 1997,
the paper manufacturing sector has not experienced the same amount of
success in the same period. Despite a small amount of growth in capacity
between 1997 and 2001, the paper manufacturing subsector’s capacity
has declined to as much as 7% below 1997 capacity levels (Figure 2-27).

Firm Characteristics

In 2006, the top 10 paper and forest product companies produced over $75
billion in sales, with the top two companies—International Paper and
Weyerhaeuser—generating nearly $22 billion each (Table 2-27). The top
two companies’ revenue consists of 58% of the revenue of the top 10
companies in Standard & Poor’s (S&P’s) list (Benwart, 2006 xe
“Benwart, 2006” ). Although these numbers do not exclusively reflect
paper products, they do convey the market environment in which firms in
this sector compete.

Figure 2-27.	Capacity Trends in the Paper Manufacturing Industry (NAICS
322)

Source:	 Federal Reserve Board. 2009  XE “Federal Reserve Board.
2009”  . “Industrial Production and Capacity Utilization: Industrial
Capacity.” <http://www.federalreserve.gov/datadownload/>.

Table 2-27.	Largest U.S. Paper and Forest Products Companies: 2006

Company	Revenues ($millions)a

International Paper	21,995

Weyerhaeuser	21,896

Smurfit-Stone	7,157

MeadWestvaco	6,530

Temple-Inland	5,558

Bowater	3,530

Grief Inc.	2,628

Louisiana-Pacific	2,235

Packaging Corp.	2,187

Plum Creek	1,627

a	Includes revenues from operations other than paper and forest products
in certain cases.

Sources:	Benwart, S.J. 2006 xe “Benwart, S.J. 2006” . “Paper &
Forest Products. Standard and Poor’s Industry Surveys.” 176(28).

	U.S. and international sales data from company reports.

Size Distribution

The primary criterion for categorizing a business as small is the number
of employees, using definitions by the SBA for regulatory flexibility
analyses. According to SUSB reports for 2002, large companies dominated
revenue-generating transactions in the paper manufacturing subsector;
80% of receipts were generated by companies with 500 employees or more
(Table 2-28). This was especially true in the pulp, paper, and
paperboard group, in which large companies generated 92% of receipts.
The number of employees in the small business cutoff varies according to
six-digit NAICS codes (Table 2-29). The cutoff for all subsectors in
the pulp, paper, and paperboard group is 750 employees, while the cutoff
for most converted paper product groups is 500 employees.

Table 2-28.	Distribution of Economic Data by Enterprise Size: Paper
Manufacturing (NAICS 322)

Enterprises with

Variable	Total	1 to 20 Employeesa	20 to 99 Employees	100 to 499
Employees	500 to 749 Employees	750 to 999 Employees	1,000 to 1,499
Employees

Firms	3,538	1,482	1,200	476	43	22	33

Establishments	5,546	1,488	1,271	755	83	69	138

Employment	495,990	11,325	52,334	78,402	13,293	12,496	23,283

Receipts ($millions )	$154,746	$2,218	$9,483	$17,620	$3,034	$3,951
$6,798

Receipts/firm ($thousands)	$43,738	$1,497	$7,903	$37,017	$70,561
$179,577	$206,001

Receipts/establishment ($thousands)	$27,902	$1,491	$7,461	$23,338
$36,556	$57,256	$49,261

Receipts/employment ($)	$311,994	$195,850	$181,203	$224,742	$228,250
$316,157	$291,974

a	Excludes SUSB employment category for zero employees. These entities
only operated for a fraction of the year.

Source:	U.S. Census Bureau. 2008  XE “U.S. Census Bureau. 2008”  .
“Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail
Employment Sizes: 2002.”
<http://www.census.gov/csd/susb/download_susb02.htm>.

Table 2-29.	Small Business Size Standards: Paper Manufacturing (NAICS
322)

NAICS	NAICS Description	Employees

322110	Pulp Mills	750

322121	Paper (except Newsprint) Mills	750

322122	Newsprint Mills	750

322130	Paperboard Mills	750

322211	Corrugated and Solid Fiber Box Manufacturing	500

322212	Folding Paperboard Box Manufacturing	750

322213	Setup Paperboard Box Manufacturing	500

322214	Fiber Can, Tube, Drum, and Similar Products Manufacturing	500

322215	Non-Folding Sanitary Food Container Manufacturing	750

322221	Coated and Laminated Packaging Paper Manufacturing	500

322222	Coated and Laminated Paper Manufacturing	500

322223	Coated Paper Bag and Pouch Manufacturing	500

322224	Uncoated Paper and Multiwall Bag Manufacturing	500

322225	Laminated Aluminum Foil Manufacturing for Flexible, Packaging
Uses	500

322226	Surface-Coated Paperboard Manufacturing	500

322231	Die-Cut Paper and Paperboard Office Supplies, Manufacturing	500

322232	Envelope Manufacturing	500

322233	Stationery, Tablet, and Related Product Manufacturing	500

322291	Sanitary Paper Product Manufacturing	500

322299	All Other Converted Paper Product Manufacturing	500

Source:	U.S. Small Business Administration (SBA). 2008  XE “U.S. Small
Business Administration (SBA). 2008”  . “Table of Small Business
Size Standards Matched to North American Industry Classification System
Codes.” Effective August 22, 2008.
<http://www.sba.gov/services/contractingopportunities/sizestandardstopic
s/size/index.html>.

Domestic Production

Similar to industry capacity rates, subsector production rates for paper
manufacturing have witnessed a decreasing rate of production compared to
the steady increase in production for the manufacturing sector since
1997 (Figure 2-28). It seems that the paper manufacturing sector was
not able to return to its former levels of growth following the 2001
recession; it has experienced a downward production trend since then.

Figure 2-28.	Industrial Production Trends in the Paper Manufacturing
Industry (NAICS 322): 1997–2009

Source:	Federal Reserve Board. 2009  XE “Federal Reserve Board.
2009”  . “Industrial Production and Capacity Utilization: Industrial
Production.” <http://www.federalreserve.gov/datadownload/>.

International Trade

Since 1997, paper manufacturing products, both pulp, paper, and
paperboard products and converted paper products, have contributed to an
increasing trade surplus in this sector (Figure 2-29). Imports and
exports have been changing at similar rates since 1999.

Market Prices

Prices of goods in paper manufacturing have been increasing at a rate
consistent with all manufacturing products (Figure 2-30). Producer
price indices (PPIs) show that producer prices for paper in 2007
increased by 20% since 1997, while producer prices for all manufacturing
goods increased by roughly 27%.

Figure 2-29.	International Trade Trends in the Paper Manufacturing
Industry (NAICS 322)

Source:	U.S. International Trade Commission. 2008  XE “U.S.
International Trade Commission. 2008b”  . “U.S. Domestic Exports”
& “U.S. Imports for Consumption.”
<http://dataweb.usitc.gov/scripts/user_set.asp>.

Figure 2-30.	Producer Price Trends in the Paper Manufacturing Industry
(NAICS 222)

Source:	U.S. Bureau of Labor Statistics (BLS). 2009b  XE “U.S. Bureau
of Labor Statistics (BLS). 2009”  . “Producer Price Index.” Series
ID: PCU322–322– & PCUOMFG–OMFG–.
<http://www.bls.gov/ppi/home.htm>.

Chemical Manufacturing

Introduction

The chemical manufacturing industry produces over 70,000 chemical
substances, many of which are ubiquitous in American life. Broadly
speaking, chemical manufacturing operates by converting feedstocks into
chemical products that can serve as intermediate goods or final products
such as medicine, soap, and printer ink. From 1997 to 2007, shipments in
the industry grew 42%, while employment declined by 8% (Table 2-30).
While total payroll dropped 0.6% over this time, annual payroll per
employee rose 7.8% from 1997 to 2007 because of the decline in the
number of employees (Table 2-31). Shipments per employee grew 54% from
1997 to 2007, with much of that growth taking place between 2002 and
2006 (Table 2-31).

Chemical manufacturing (NAICS 325) covers a diverse set of industry
groups, which we have aggregated into the following three groups:

Bulk Chemicals—Includes the most energy-intensive industry groups as
aggregated by the Department of Energy (DOE). Basic Chemical
Manufacturing (NAICS 3251); Resin, Rubber, and Artificial Fibers
Manufacturing (NAICS 3252); and Agricultural Chemical Manufacturing
(NAICS 3253).

Pharmaceutical and Medicine Manufacturing (NAICS 3254)—Consists
primarily of pharmaceutical preparation manufacturing. This industry
group is the largest importer of goods within chemical manufacturing.

Other Chemical Manufacturing: Consists of Paint, Coating, and Adhesive
Manufacturing (NAICS 3255); Soap, Cleaning Compound, and Toiletry
Manufacturing (NAICS 3256); and Other Chemical Product and Preparation
Manufacturing (NAICS 3259).

In 2007, each of these groups generated approximately one-third of the
total employment in chemical manufacturing (Figure 2-31). The bulk
chemicals group accounted for the biggest share of chemical
manufacturing’s total value of shipments (Figure 2-32).

Table 2-30.	Key Statistics: Chemical Manufacturing (NAICS 325)

 	1997	2002	2006	2007

Shipments ($2007, millions)	$521,251	$531,173	$675,223	$738,303

Payroll ($2007, millions)	$49,961	$51,317	$46,981	$49,648

Employees	882,645	853,224	747,134	814,024

Establishments	13474	13,475	NA	12,937

NA = Not available.

Sources:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Annual Survey of Manufactures:
General Statistics: Statistics for Industry Groups and Industries: 2006
and 2005.” <http://factfinder.census.gov>; (July 8, 2008)  XE “U.S.
Census Bureau\; generated by RTI International\; using American
FactFinder\; \”Sector 31\: Annual Survey of Manufactures\: General
Statistics\: Statistics for Industry Groups and Industries\: 2006 and
2005.\” <http\://factfinder.census.gov>\; (July 8, 2008)”  .

	U.S. Census Bureau; generated by RTI International; using American
FactFinder; “Sector 00: All Sectors: Core Business Statistics Series:
Comparative Statistics for the United States and the States (1997 NAICS
Basis): 2002 and 1997.” <http://factfinder.census.gov>; (July 8, 2008)
 XE “U.S. Census Bureau\; generated by RTI International\; using
American FactFinder\; \”Sector 00\: All Sectors\: Core Business
Statistics Series\: Comparative Statistics for the United States and the
States (1997 NAICS Basis)\: 2002 and 1997.\”
<http\://factfinder.census.gov>\; (July 8, 2008)”  .

	U.S. Census Bureau; generated by Kapur Energy and Environment; using
American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic
Area Series: Economy-Wide Key Statistics: 2007.”
<http://factfinder.census.gov>. Accessed on December 27, 2009  XE
“U.S. Census Bureau\; generated by Kapur Energy and Environment\;
using American FactFinder\; \”Sector 00\: EC0700A1\: All Sectors\:
Geographic Area Series\: Economy-Wide Key Statistics\: 2007.\”
<http\://factfinder.census.gov>. Accessed on December 27, 2009”  .
[Source for 2007 numbers]

Table 2-31.	Industry Data: Chemical Manufacturing (NAICS 325)

Industry Data	1997	2002	2006	2007

Total shipments ($2007, millions)	$521,251	$531,173	$675,223	$738,303

Shipments per establishment ($thousands)	$38,686	$39,419	NA	$57,069

Shipments per employee ($2007)	$590,556	$622,548	$903,750	$906,979

Shipments per $ of payroll ($2007)	$10.43	$10.35	$14.37	$14.87

Annual payroll per employee ($2007)	$56,603	$60,145	$62,882	$60,991

Employees per establishment	66	63	NA	63

NA = Not available.

Sources:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Annual Survey of Manufactures:
General Statistics: Statistics for Industry Groups and Industries: 2006
and 2005.” <http://factfinder.census.gov>; (July 8, 2008)  XE “U.S.
Census Bureau\; generated by RTI International\; using American
FactFinder\; \”Sector 31\: Annual Survey of Manufactures\: General
Statistics\: Statistics for Industry Groups and Industries\: 2006 and
2005.\” <http\://factfinder.census.gov>\; (July 8, 2008)”  .

	U.S. Census Bureau; generated by RTI International; using American
FactFinder; “Sector 00: All Sectors: Core Business Statistics Series:
Comparative Statistics for the United States and the States (1997 NAICS
Basis): 2002 and 1997.” <http://factfinder.census.gov>; (July 8, 2008)
 XE “U.S. Census Bureau\; generated by RTI International\; using
American FactFinder\; \”Sector 00\: All Sectors\: Core Business
Statistics Series\: Comparative Statistics for the United States and the
States (1997 NAICS Basis)\: 2002 and 1997.\”
<http\://factfinder.census.gov>\; (July 8, 2008)”  .

	U.S. Census Bureau; generated by Kapur Energy and Environment; using
American FactFinder; “Sector 00: EC0700A1: All Sectors: Geographic
Area Series: Economy-Wide Key Statistics: 2007.”
<http://factfinder.census.gov>. Accessed on December, 27, 2009  XE
“U.S. Census Bureau\; generated by Kapur Energy and Environment\;
using American FactFinder\; \”Sector 00\: EC0700A1\: All Sectors\:
Geographic Area Series\: Economy-Wide Key Statistics\: 2007.\”
<http\://factfinder.census.gov>. Accessed on December, 27, 2009”  .
[Source for 2007 numbers]

Figure 2-31.	Distribution of Employment within Chemical Manufacturing
(NAICS 325): 2007

Source:	U.S. Census Bureau; generated by Kapur Energy and Environment;
using American FactFinder; “Sector 31: EC0731I1: Manufacturing:
Industry Series: Detailed Statistics by Industry for the U.S.: 2007.”
Release date: October 30, 2009. Accessed on December 27, 2009  XE
“U.S. Census Bureau\; generated by Kapur Energy and Environment\;
using American FactFinder\; \”Sector 31\: EC0731I1\: Manufacturing\:
Industry Series\: Detailed Statistics by Industry for the U.S.\:
2007.\” Release date\: October 30, 2009. Accessed on December 27,
2009”  .

Figure 2-32.	Distribution of Total Value of Shipments within Chemical
Manufacturing (NAICS 325): 2007

Source:	U.S. Census Bureau; generated by Kapur Energy and Environment;
using American FactFinder; “Sector 31:EC0731I1: Manufacturing Industry
Series: Detailed Statistics by Industry for U.S.: 2007.”
<http://factfinder.census.gov>. Accessed on December 27, 2009  XE
“U.S. Census Bureau\; generated by Kapur Energy and Environment\;
using American FactFinder\; \”Sector 31\:EC0731I1\: Manufacturing
Industry Series\: Detailed Statistics by Industry for U.S.\: 2007.\”
<http\://factfinder.census.gov>. Accessed on December 27, 2009”  .

Supply and Demand Characteristics

Next, we provide a broad overview of the supply and demand side of the
chemical manufacturing industry. We emphasize the economic interactions
this industry has with other industries and people, including
identifying the key goods and services used by the industry and the
major uses and consumers of chemical manufacturing products.

The top 10 industry groups supplying inputs to the chemical
manufacturing industry in 2002 accounted for 71% of the total
intermediate inputs (Table 2-32). Bulk chemicals’ production was the
most energy intensive, using 79% of the chemical manufacturing inputs
from petroleum and coal products, electric power generation,
transmission and distribution, and natural gas distribution.

Table 2-32.	Key Goods and Services Used in Chemical Manufacturing (NAICS
325) ($2007, millions)

Good or Service	BEA Code	Bulk Chemicals	Pharmaceuticals	Other Chemicals
Total

Basic chemicals	3251	$59,495	$4,772	$14,021	$78,288

Management of companies and enterprises	5500	$15,071	$19,380	$16,396
$50,846

Pharmaceuticals and medicines	3254	$0	$25,125	$0	$25,125

Wholesale trade	4200	$9,428	$8,367	$6,077	$23,872

Scientific research and development services	5417	$6,172	$6,139	$5,554
$17,865

Petroleum and coal products	3240	$10,066	$398	$3,432	$13,896

Plastics and rubber products	3260	$2,675	$1,132	$5,556	$9,363

Resins, rubber, and artificial fibers	3252	$4,048	$0	$4,949	$8,996

Electric power generation, transmission, and distribution	2211	$6,025
$716	$807	$7,548

Natural gas distribution	2212	$6,390	$154	$390	$6,934

Total intermediate use	T005	$167,699	$82,403	$91,833	$341,935

Source:	U.S. Bureau of Economic Analysis (BEA). 2008  XE “U.S. Bureau
of Economic Analysis (BEA). 2008”  . “2002 Benchmark Input-Output
Accounts: 2002 Standard Make and Use Tables at the Summary Level.”
Table 2. Washington, DC: BEA.

Goods and Services Used in Chemical Manufacturing

In 2007, the cost of materials made up 49% of chemical manufacturing’s
total shipment value (Table 2-32). Total compensation to employees
represented 9% of total shipment value, down from 10% in 2005.

2.4.2.1.1 Energy. The Department of Energy (DOE) classifies bulk
chemical manufacturing as an energy-intensive industry. Pharmaceuticals
and other chemical manufacturing are categorized as non-energy-intensive
industries, grouped together with other industry groups under the
“Balance of Manufacturing” category (DOE, 2008 xe “U.S. DOE,
2008” ).

Fuel used in chemical production can either facilitate chemical
processes or provide the feedstock to derive value-added chemicals. In
2007, 70% of chemical manufacturing’s energy bill was spent on fuel
used as feedstocks (O’Reilly, 2008 xe “O’Reilly, 2008” ). These
fuel costs represented 2% of chemical manufacturing’s total value of
shipments (Table 2-33).

As a whole, chemical manufacturing use less energy over the last 10
years. According to DOE, natural gas use by the chemical manufacturing
industry dropped 30% from 1998 to 2006, and electricity use fell 10%
(Table 2-34). From 1997 to 2005, when data ceased to be available,
chemical manufacturing used less electricity relative to the
manufacturing sector as a whole (Figure 2-33).

Uses and Consumers

Products manufactured in the groups bulk chemicals, pharmaceuticals, and
other chemicals have very different consumer profiles. Bulk chemicals is
dominated by intermediate use, representing 93% of its total commodity
output and 56% of the total intermediate use of chemical manufacturing
products. Pharmaceuticals has both a high level of demand from personal
consumption, accounting for 67% of the total personal consumption of
chemical manufacturing products, and a large trade deficit (Table 2-35).

Table 2-33.	Costs of Goods and Services Used in Chemical Manufacturing
(NAICS 325) ($2007)

Variable	2005	Share	2006	Share	2007	Share

Total shipments 	$646,895	100%	$675,223	100%	$722,494	100%

Total compensation (millions)	$62,669	10%	$61,683	9%	$63,591	9%

Annual payroll	$48,159	7%	$46,981	7%	$48,780	7%

Fringe benefits	$14,510	2%	$14,702	2%	$14,811	2%

Total employees	756,078

747,134

801,567

	Average compensation per employee 	$82,887

$82,559

$79,333

	Total production workers’ wages (millions)	$22,643	4%	$22,231	3%
$23,157	3%

Total production workers	431,502

430,880

463,802

	Total production hours (thousands)	899,499

885,993

948,244

	Average production wages per hour	$25

$25

$24

	Total cost of materials ($thousands)	$299,859	46%	$318,945	47%	$357,055
49%

Materials, parts, packaging	$247,851	38%	$260,934	39%	$291,656	40%

Purchase electricity	$8,291	1%	$8,490	1%	$8,936	1%

Purchased fuel	$14,568	2%	$13,667	2%	$14,227	2%

Other	$29,148	5%	$35,855	5%	$42,236	6%

Sources:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Annual Survey of Manufactures:
General Statistics: Statistics for Industry Groups and Industries: 2006
and 2005.” <http://factfinder.census.gov>; (July 8, 2008)  XE “U.S.
Census Bureau\; generated by RTI International\; using American
FactFinder\; \”Sector 31\: Annual Survey of Manufactures\: General
Statistics\: Statistics for Industry Groups and Industries\: 2006 and
2005.\” <http\://factfinder.census.gov>\; (July 8, 2008)”  .

	U.S. Census Bureau; generated by Kapur Energy and Environment; using
American FactFinder; “Sector 31: EC0731I1: Manufacturing: Industry
Series: Detailed Statistics by Industry for the United States: 2007.”
Accessed on December, 27, 2009  XE “U.S. Census Bureau\; generated by
Kapur Energy and Environment\; using American FactFinder\; \”Sector
31\: EC0731I1\: Manufacturing\: Industry Series\: Detailed Statistics by
Industry for the United States\: 2007.\” Accessed on December, 27,
2009”  .

Table 2-34.	Energy Used in Chemical Manufacturing (NAICS 325)

Fuel Type	1998	2002	2006

Total (trillion BTU)	3,704	3,769	3,159

Net electricitya (million kWh)	169,233	153,104	151,646

Residual fuel oil (million bbl)	8	7	4

Distillate fuel oilb (million bbl)	2	2	2

Natural gasc (billion cu ft)	1,931	1,634	1,349

LPG and NGLd (million bbl)	15	9	2

Coal (million short tons)	13	14	8

Coke and breeze (million short tons)	*	*	*

Othere (trillion BTU)	748	1,158	1,045

Total (trillion BTU)	3,704	3,769	3,159

a	Net electricity is obtained by summing purchases, transfers in, and
generation from noncombustible renewable resources, minus quantities
sold and transferred out. It does not include electricity inputs from
on-site cogeneration or generation from combustible fuels because that
energy has already been included as generating fuel (for example, coal).

b	Distillate fuel oil includes Nos. 1, 2, and 4 fuel oils and Nos. 1, 2,
and 4 diesel fuels.

c	Natural gas includes natural gas obtained from utilities, local
distribution companies, and any other supplier(s), such as independent
gas producers, gas brokers, marketers, and any marketing subsidiaries of
utilities.

d	Examples of liquefied petroleum gases (LPGs) are ethane, ethylene,
propane, propylene, normal butane, butylene, ethane-propane mixtures,
propane-butane mixtures, and isobutene produced at refineries or natural
gas processing plants, including plants that fractionate raw natural gas
liquids (NGLs).

e	Other includes net steam (the sum of purchases, generation from
renewables, and net transfers), and other energy that respondents
indicated was used to produce heat and power.

*	Estimate less than 0.5.

Sources:	U.S. Department of Energy, Energy Information Administration.
2007b  XE “U.S. Department of Energy, Energy Information
Administration. 2007b”  . “2006 Energy Consumption by
Manufacturers—Data Tables.” Table 3.1. Washington, DC: DOE.
<http://www.eia.doe.gov/emeu/mecs/

mecs2006/2006tables.html>. [Source for 2006 numbers]

	U.S. Department of Energy, Energy Information Administration. 2007a  XE
“U.S. Department of Energy, Energy Information Administration. 2007”
 . “2002 Energy Consumption by Manufacturers—Data Tables.” Tables
3.2 and N3.2. Washington, DC: DOE.
<http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html>.

Figure 2-33.	Electric Power Use Trends in Chemical Manufacturing (NAICS
325): 

1997–2005

Source:	Federal Reserve Board. 2009  XE “Federal Reserve Board.
2009”  . “Industrial Production and Capacity Utilization: Electric
Power Use: Manufacturing and Mining.”
<http://www.federalreserve.gov/datadownload/>.

Firm and Market Characteristics

This remaining subsection describes geographic, production, and market
data. These data provide the basis for further analysis, including
regulatory flexibility analyses, and give a complete picture of the
recent historical trends of production and pricing.

Location

In 2002, California had the most chemical manufacturing establishments
in the United States, followed by Texas and New Jersey (Figure 2-34).
The composition of establishments in these states differs among the
different industry groups. Despite the fact that each group employed an
approximately equal share of people in 2002, 54% of the total
establishments were other chemicals establishments, and only 13% were
pharmaceutical establishments.

Table 2-35.	Demand by Sector: Chemical Manufacturing (NAICS 325) ($2007
millions)

Sector	BEA Code	Bulk Chemicals	Pharmaceuticals	Other Chemicals	Total

Plastics and rubber products manufacturing	3260	$39,353	$0	$3,057
$42,410

Basic chemical manufacturing	3251	$33,972	$0	$1,675	$35,647

Pharmaceutical and medicine manufacturing	3254	$4,778	$25,125	$462
$30,365

Resin, rubber, and artificial fibers manufacturing	3252	$28,249	$0
$1,076	$29,325

Ambulatory health care services	6210	$2,716	$22,900	$934	$26,550

General state and local government services	S007	$7,150	$10,586	$8,807
$26,543

Hospitals	6220	$2,936	$15,390	$394	$18,720

Other chemical product and preparation manufacturing	3259	$8,021	$0
$2,680	$10,701

Textile mills	3130	$9,568	$0	$930	$10,498

Soap, cleaning compound, and toiletry manufacturing	3256	$3,886	$0
$6,289	$10,176

Total intermediate use	T001	$212,996	$83,279	$82,107	$378,382

Personal consumption expenditures	F010	$4,449	$123,746	$55,882	$184,077

Exports of goods and services	F040	$47,121	$15,683	$13,136	$75,940

Imports of goods and services	F050	−$38,732	−$67,950	−$10,906
−$117,588

Total final uses (GDP)	T004	$15,733	$73,485	$58,023	$147,241

Total commodity output	T007	$228,729	$156,765	$140,129	$525,623

Source:	U.S. Bureau of Economic Analysis (BEA). 2008  XE “U.S. Bureau
of Economic Analysis (BEA). 2008”  . “2002 Benchmark Input-Output
Accounts: 2002 Standard Make and Use Tables at the Summary Level.”
Table 2. Washington, DC: BEA.

Figure 2-34.	Establishment Concentration in Chemical Manufacturing
(NAICS 325): 2002

Source:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Manufacturing: Geographic Area
Series: Industry Statistics for the States, Metropolitan and
Micropolitan Statistical Areas, Counties, and Places: 2002.”
<http://factfinder.census.gov>; (July 23, 2008)  XE “U.S. Census
Bureau\; generated by RTI International\; using American FactFinder\;
\”Sector 31\: Manufacturing\: Geographic Area Series\: Industry
Statistics for the States, Metropolitan and Micropolitan Statistical
Areas, Counties, and Places\: 2002.\”
<http\://factfinder.census.gov>\; (July 23, 2008)”  .

Production Capacity and Utilization

Capacity utilization of the chemical manufacturing industry has been
broadly in line with the manufacturing sector (Figure 2-35). In the
second half of 2005, the chemical manufacturing industry’s capacity
utilization fell dramatically because of the multiple hurricanes
affecting the Gulf Coast states. The impact of the economic downturn in
2001 can be seen in the capacity utilization of both manufacturing and
chemical manufacturing.

Employment

The geographic distribution of employment in chemical manufacturing
differs largely among the different groups. In California, 52% of the
chemical manufacturing employment comes from the pharmaceutical
industry, while 60% of the chemical manufacturing employment in the Gulf
Coast states comes from bulk chemicals manufacturing (Figure 2-36).

Plants and Capacity

Production capacity in chemical manufacturing has grown 33% since 1997.
This growth, however, is 9% less than the growth rate for the
manufacturing industry as a whole (Figure 2-37).

Figure 2-35.	Capacity Utilization Trends in Chemical Manufacturing
(NAICS 325)

Source:	Federal Reserve Board. 2009  XE “Federal Reserve Board.
2009”  . “Industrial Production and Capacity Utilization: Capacity
Utilization.” <http://www.federalreserve.gov/datadownload/>.

Figure 2-36.	Employment Concentration in Chemical Manufacturing (NAICS
325): 2002

Source:	U.S. Census Bureau; generated by RTI International; using
American FactFinder; “Sector 31: Manufacturing: Geographic Area
Series: Industry Statistics for the States, Metropolitan and
Micropolitan Statistical Areas, Counties, and Places: 2002.”
<http://factfinder.census.gov>; (July 23, 2008)  XE “U.S. Census
Bureau\; generated by RTI International\; using American FactFinder\;
\”Sector 31\: Manufacturing\: Geographic Area Series\: Industry
Statistics for the States, Metropolitan and Micropolitan Statistical
Areas, Counties, and Places\: 2002.\”
<http\://factfinder.census.gov>\; (July 23, 2008)”  .

Figure 2-37.	Capacity Trends in Chemical Manufacturing (NAICS 325)

Source:	Federal Reserve Board. 2009  XE “Federal Reserve Board.
2009”  . “Industrial Production and Capacity Utilization: Industrial
Capacity.” <http://www.federalreserve.gov/datadownload/>.

Firm Characteristics

In 2007, the top six companies by chemical sales had greater than $10
billion in sales. Together, their sales are greater than the next 44
highest chemical companies combined. These, however, are global
companies, with a large portion of both sales and production coming from
operations outside of the United States (Table 2-36). The largest
chemical manufacturing company, Dow Chemicals, has 108 out of 150
manufacturing sites located outside of the United States (Dow Chemical
Company, 2008  XE “Dow Chemical Company, 2008”  ).

In 2007, 58% of U.S. chemical manufacturing corporations generated net
income. Including those with and without net income, chemical
manufacturers had an average before-tax profit margin of 10.24%.
Profitability is highest for pharmaceutical and medicine corporations
(Table 2-37).

Table 2-36.	Top Chemical Producers: 2007

	Chemical Sales ($millions)	% of Total Sales	% of Sales in United States

Dow Chemical	53,513	100%	35%

ExxonMobil	36,826	9%	38%

DuPont	29,218	100%	38%

Lyondella	16,165	57%	80%

Chevron Phillips	12,534	100%	86%

PPG Industriesa	10,025	90%	56%

Huntsman Chemical	9,651	100%	50%

Praxair	9,402	100%	43.5%

Air Productsa	8,820	88%	51%

Rohm & Haasb	7,837	88%	49%

a	Percentage of sales in the United States calculated from total sales,
not chemical sales.

b	Percentage of sales in the United States is actually percentage of
sales in North America.

Source:	O’Reilly, R. 2008  XE “O’Reilly, R. 2008”  .
“Chemicals.” Standard and Poor’s Industry Surveys. 176(28). 

Table 2-37.	2007 Corporate Income and Profitability (NAICS 325)

Industry	Number of Corporations	Number of Corporations with Net Income
Total Receipts 

($thousands)	Business Receipts 

($thousands)	Before-Tax Profit Margin	After-Tax Profit Margin

Basic chemical 	1,244	757	$195,022,700	$178,019,490	5.07%	4.10%

Resin, synthetic rubber, and artificial synthetic fibers and filaments
1,067	648	$44,692,366	$40,078,009	8.06%	6.33%

Pharmaceutical and medicine 	1,034	611	$381,339,258	$317,414,432	15.63%
11.66%

Paint, coating, and adhesive 	1,411	1,260	$51,778,868	$49,486,744	5.39%
4.02%

Soap, cleaning compound, and toilet preparation 	1,862	463	$150,506,485
$139,836,602	9.07%	7.51%

Other chemical product and preparation 	2,946	1,773	$89,014,032
$84,062,534	6.71%	5.27%

Chemical manufacturing 	9,564	5,512	$912,353,710	$808,897,810	10.24%
7.89%

Source:	Internal Revenue Service, U.S. Department of Treasury. 2008b  XE
“Internal Revenue Service, U.S. Department of Treasury. 2008”  .
“Corporation Source Book: Data File 2007.”
<http://www.irs.gov/taxstats/article/0,,id=167415,00.html>; (January,
15, 2010).

Size Distribution

The primary criterion for categorizing a business as small is number of
employees, using definitions by the SBA for regulatory flexibility
analyses. The data describing size standards are provided in Table 2-38
and Table 2-39. In 2002, enterprises with fewer than 500 employees
accounted for 27% of employment and 15% of receipts within the chemical
manufacturing industry).

Domestic Production

In the late 1990s, overall manufacturing production was growing much
faster than the chemical manufacturing component (Figure 2-38).
Following the recession of 2001, however, the components have moved
broadly in line with one another, except for the drop in chemical
manufacturing production caused by the hurricane season of 2005.

International Trade

In the year 2000, the United States moved from having a trade surplus to
a trade deficit in chemical manufacturing products (Figure 2-39). This
change occurred because the trade deficit in pharmaceutical
manufacturing, currently at $35 billion, overwhelmed the trade surplus
of bulk chemicals and other chemical manufacturing combined, currently
at $22 billion.

Market Prices

Prices of goods in chemical manufacturing have accelerated rapidly in
the last 2 years, having outpaced overall manufacturing since 2002
(Figure 2-40). Much of this recent acceleration seen in the industry
PPI is due to the bulk chemicals segment, largely reflecting the rapid
increase in fertilizer prices.

Table 2-38.	Small Business Size Standards: Chemical Manufacturing (NAICS
325)

NAICS	Description	Employees

325110	Petrochemical Manufacturing	1,000

325120	Industrial Gas Manufacturing	1,000

325131	Inorganic Dye and Pigment Manufacturing	1,000

325132	Synthetic Organic Dye and Pigment Manufacturing	750

325181	Alkalies and Chlorine Manufacturing	1,000

325182	Carbon Black Manufacturing	500

325188	All Other Basic Inorganic Chemical Manufacturing	1,000

325191	Gum and Wood Chemical Manufacturing	500

325192	Cyclic Crude and Intermediate Manufacturing	750

325193	Ethyl Alcohol Manufacturing	1,000

325199	All Other Basic Organic Chemical Manufacturing	1,000

325211	Plastics Material and Resin Manufacturing	750

325212	Synthetic Rubber Manufacturing	1,000

325221	Cellulosic Organic Fiber Manufacturing	1,000

325222	Noncellulosic Organic Fiber Manufacturing	1,000

325311	Nitrogenous Fertilizer Manufacturing	1,000

325312	Phosphatic Fertilizer Manufacturing	500

325314	Fertilizer (Mixing Only) Manufacturing	500

325320	Pesticide and Other Agricultural Chemical Manufacturing	500

325411	Medicinal and Botanical Manufacturing	750

325412	Pharmaceutical Preparation Manufacturing	750

325413	In-Vitro Diagnostic Substance Manufacturing	500

325414	Biological Product (except Diagnostic) Manufacturing	500

325510	Paint and Coating Manufacturing	500

325520	Adhesive Manufacturing	500

325611	Soap and Other Detergent Manufacturing	750

325612	Polish and Other Sanitation Good Manufacturing	500

325613	Surface Active Agent Manufacturing	500

325620	Toilet Preparation Manufacturing	500

325910	Printing Ink Manufacturing	500

325920	Explosives Manufacturing	750

325991	Custom Compounding of Purchased Resins	500

325992	Photographic Film, Paper, Plate and Chemical Manufacturing	500

325998	All Other Miscellaneous Chemical Product and Preparation
Manufacturing	500

Source:	U. S. Small Business Administration (SBA). 2008  XE “U. S.
Small Business Administration (SBA). 2008”  . “Table of Small
Business Size Standards Matched to North American Industry
Classification System Codes.” Effective August 22, 2008.
<http://www.sba.gov/services/contractingopportunities/sizestandardstopic
s/size/index.html>.

Table 2-39.	Distribution of Economic Data by Enterprise Size: Chemical
Manufacturing (NAICS 325) 

Enterprises with

Variable	Total	1 to 20 Employeesa	20 to 99 Employees	100 to 499
Employees	500 to 749 Employees	750 to 999 Employees	1,000 to 1,499
Employees

Firms	9,341	5,413	1,974	790	95	56	71

Establishments	13,096	5,433	2,208	1,352	250	185	276

Employment	827,430	34,838	78,090	113,326	28,025	18,119	28,338

Receipts ($millions)	$468,211	$9,631	$21,394	$39,111	$12,217	$7,324
$14,762

Receipts/firm ($thousands)	$50,124	$1,779	$10,838	$49,507	$128,603
$130,779	$207,913

Receipts/establishment ($thousands)	$35,752	$1,773	$9,689	$28,928
$48,869	$39,587	$53,485

Receipts/employment ($)	$565,862	$276,464	$273,971	$345,117	$435,942
$404,195	$520,920

a	Excludes SUSB employment category for zero employees. These entities
only operated for a fraction of the year.

Source:	U.S. Census Bureau. 2008  XE “U.S. Census Bureau. 2008”  .
“Firm Size Data from the Statistics of U.S. Businesses: U.S. Detail
Employment Sizes: 2002.”
<http://www.census.gov/csd/susb/download_susb02.htm>.

Figure 2-38.	Industrial Production Trends in Chemical Manufacturing
(NAICS 325)

Source:	Federal Reserve Board. 2009  XE “Federal Reserve Board.
2009”  . “Industrial Production and Capacity Utilization: Industrial
Production.” <http://www.federalreserve.gov/datadownload/>.

Figure 2-39.	International Trade Trends in Chemical Manufacturing (NAICS
325)

Source:	U.S. International Trade Commission. 2008  XE “U.S.
International Trade Commission. 2008a”  . “U.S. Domestic Exports”
& “U.S. Imports for Consumption.”
<http://dataweb.usitc.gov/scripts/user_set.asp>.

Figure 2-40.	Producer Price Trends in Chemical Manufacturing (NAICS 325)

Source:	U.S. Bureau of Labor Statistics (BLS). 2009c  XE “U.S. Bureau
of Labor Statistics (BLS). 2009”  . Producer Price Index. Series ID:
PCU325—325—&PCUOMFG—OMFG—. <http://www.bls.gov/ppi/home.htm>.

Section 2 References

Benwart, S.J. 2006. “Paper & Forest Products.” Standard and Poor’s
Industry Surveys 176(28).

Dow Chemical Company. 2008. “Transforming: The Dow Chemical Company
2007 10-K and Stockholder Summary.”
<http://www.dow.com/financial/2007ann/>.

Federal Reserve Board. 2009. “Industrial Production and Capacity
Utilization.” <http://www.federalreserve.gov/datadownload/>.

Graves, T. 2008. “Food and Nonalcoholic Beverages.” Standard and
Poor’s Industry Surveys 176(25).

Internal Revenue Service, U.S. Department of Treasury. 2008a.
“Corporation Source Book: Data File 2005.”
<http://www.irs.gov/taxstats/article/0,,id=167415,00.html>.

Internal Revenue Service, U.S. Department of Treasury. 2008b.
“Corporation Source Book: Data File 2007.”
<http://www.irs.gov/taxstats/article/0,,id=167415,00.html>.

Kraft Foods Inc. 2008. “Form 10-K, Period ending December 31, 2007.”
<http://www.kraft.com/Investor/sec-filings-annual-report/>.

O’Reilly, R. 2008. “Chemicals.” Standard and Poor’s Industry
Surveys. 176(28).

Standard & Poors, 2008. Standard & Poor’s netAdvantage [electronic
resource]. New York: Standard & Poors. 

U.S. Bureau of Economic Analysis (BEA). 2008. “2002 Benchmark
Input-Output Accounts: 2002 Standard Make and Use Tables at the Summary
Level.” Table 2. Washington, DC: BEA. U.S. Bureau of Labor Statistics.
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PCUOMFG—OMFG—. <http://www.bls.gov/ppi/home.htm>.

U.S. Bureau of Labor Statistics. 2009b. “Producer Price Index.”
Series ID: PCU322–322– & PCUOMFG–OMFG–.
<http://www.bls.gov/ppi/home.htm>.

U.S. Bureau of Labor Statistics. 2009c. “Producer Price Index.”
Series ID: PCU325—325—&PCUOMFG—OMFG—.
<http://www.bls.gov/ppi/home.htm>.

U.S. Census Bureau. 2001. “1997 Census NAICS Definitions.”
<http://www.census.gov/epcd/ec97/industry/E322.HTM#def>.

U.S. Census Bureau. 2008. “Firm Size Data from the Statistics of U.S.
Businesses: U.S. Detail Employment Sizes: 2002.” < HYPERLINK
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http://www.census.gov/csd/susb/download_susb02.htm >.

U.S. Census Bureau. “Sector 00: All Sectors: Core Business Statistics
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(1997 NAICS Basis): 2002 and 1997” (generated by RTI International
using American FactFinder). <http://factfinder.census.gov>. Accessed
July 8, 2008.

U.S. Census Bureau. “Sector 00: All Sectors: Core Business Statistics
Series: Comparative Statistics for the United States and the States
(2007 NAICS Basis): 2002 and 2007” (generated by RTI International
using American FactFinder). <http://factfinder.census.gov>. Accessed
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U.S. Census Bureau. “Sector 00: EC0700A1: All Sectors: Geographic Area
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<http://factfinder.census.gov>. Accessed July 8, 2008.

U.S. Census Bureau. “Sector 31: EC0731I1: Manufacturing: Industry
Series: Detailed Statistics by Industry for the United States: 2007”
(generated by Kapur Energy and Environment using American FactFinder).
<http://factfinder.census.gov>. Accessed December 27, 2009. 

U.S. Census Bureau. “Sector 31: Manufacturing: Geographic Area Series:
Industry Statistics for the States, Metropolitan and Micropolitan
Statistical Areas, Counties, and Places: 2002” (generated by RTI
International using American FactFinder).
<http://factfinder.census.gov>. Accessed July 23, 2008.

U.S. Department of Energy, Energy Information Administration. 2007a.
“2002 Energy Consumption by Manufacturers—Data Tables” (Tables 3.2
and N3.2). < HYPERLINK
"http://www.eia.doe.gov/emeu/mecs/mecs2002/data02/shelltables.html"
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U.S. Department of Energy, Energy Information Administration. 2007b.
“2006 Energy Consumption by Manufacturers—Data Tables” (Table
3.1). < HYPERLINK
"http://www.eia.doe.gov/emeu/mecs/mecs2006/2006tables.html"
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2006tables.html >. 

U.S. Department of Energy, Energy Information Administration. 2008.
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U.S. Environmental Protection Agency. 2002. “Profile of the Pulp and
Paper Industry. Sector Notebook Project.”
<http://www.epa.gov/Compliance/resources/publications/assistance/

sectors/notebooks/index.html>.

U.S. International Trade Commission. 2008. “U.S. Domestic Exports”
and “U.S. Imports for Consumption.”
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Size Standards Matched to North American Industry Classification System
Codes.” <http://www.sba.gov/services/

contractingopportunities/sizestandardstopics/size/index.html>.



Engineering Cost Analysis

We provide an overview of the engineering cost analysis used to estimate
the additional private expenditures industry may make in order to comply
with the rule. A detailed discussion of the methodology used to estimate
cost impacts is presented in Appendices C and D.

Several provisions in the rule and the data used to generate regulatory
impacts analysis between proposal and the final rule. As a result, the
costs of the RIA analysis decreased from the proposal to final analysis.

The major regulatory provisions resulting in a reduction in the cost
estimates were:

Consolidating the biomass and coal subcategories into a single solid
fuel subcategory for fuel-based HAP and HAP surrogates (Hg, HCl, PM).
This provision has reduced the number of biomass units expected to
require HCl control in order to meet the emission limits.

Creating a hybrid grate/suspension category for combustion based HAP and
HAP surrogates to address boilers that express unique design features to
handle the combustion of fuels with a very high moisture content. This
increased the CO emission level for this subcategory which resulting in
less units needing CO catalyst or advanced combustion controls in order
to meet the limit.

Creating a limited use subcategory. This subcategory removes the
control, testing and monitoring costs for 134 large boilers firing solid
and liquid fuels that operate less than 876 hours per year.

Creating a fuel gas specification to allow additional gaseous fuels to
qualify for the gas 1 subcategory. Based on the data in the record, most
fuels are expected to meet the mercury specification and will qualify
for a work practice standard instead of meeting emission limits. The
number of gas 2 units estimated to meet emission limits at proposal was
and this has been reduced to an estimated 71 units in the final rule,
compared with an estimated 199 gas 2 units in the proposed analysis.

Removing the CO CEMS monitoring requirement for units with a heat input
capacity. Although this change in monitoring is offset in part by O2
parameter monitoring requirement. 

Removing duplicative monitoring requirements for PM CEMS, opacity
monitoring and bag leak detection monitors. The proposal had included
requirements to measure more than one of these parameters and this
duplication has been removed from the final rule.

Reducing testing frequency for dioxin/furan emissions. The final rule
requires a one-time initial test instead of annual testing.

The major data analysis elements resulting in a reduction in cost were:

As discussed in detail in the “Handling and Processing of Corrections
and New Data in the EPA ICR Databases  XE “Gibson, 2011”  ”
memorandum, several new data submissions and corrections to existing
entries were received from the public comment process. These new data
incorporated into the revised MACT floor, baseline emission, and
emission reduction analysis.

The criteria for considering data in the MACT floor analysis were
revised to reduce the low bias impact from tests conducted during
co-firing configurations. In the final rule, the test had to be fired
with at least 90 percent of its heat input belonging to the same
subcategory for existing sources and 100 percent of its heat input
belonging to the same subcategory for new sources. At proposal, data
were considered relevant for consideration in the MACT floor analysis if
at least 10 percent of its heat input belonged to the same subcategory.
This modified the make-up of the top performing units and the resulting.

The CO emission data analysis was revised to incorporate measurement
error resulting from calibration error, system bias, and drift
requirements using the reported instrument calibration span as the basis
for estimating the error. This increased CO emission levels in several
cases in order to better incorporate this error in the final standard.

Several elements of the MACT floor analysis statistical approach were
incorporated from public commenters, which resulted in increased
emission limits in some cases. The memorandum “Revised MACT Floor
Analysis (2011) for the Industrial, Commercial, and Institutional
Boilers and Process Heaters National Emission Standards for Hazardous
Air Pollutants—Major Source” details the specific statistical
modifications that were made in the final analysis.

Major Sources

To estimate the national cost impacts of the proposed rule for existing
sources, EPA developed average baseline emission factors for each fuel
type/control device combination based on the emission data obtained and
contained in the Boiler MACT emission database. If a unit reported
emission data, we assigned its unit-specific emission data as its
baseline emissions. For units that did not report emission data, but
similar units at the same facility reported data, we assigned the
average emission factors from similar units at the same facility to the
baseline emissions of the unit. For all other units that did not report
emission data, we assigned the appropriate emission factors to each
existing unit in the inventory database, based on the average emission
factors for boilers with similar fuel, design, control devices. We then
compared each unit’s baseline emission factors to the proposed MACT
floor emission limit to determine if control devices were needed to meet
the emission limits. The control analysis considered fabric filters and
activated carbon injection to be the primary control devices for mercury
control, electrostatic precipitators for units meeting mercury limits
but requiring additional control to meet the PM limits, wet scrubbers
and dry injection fabric filter combinations to meet the HCl limits,
tune-ups, replacement burners, combustion controls, and CO oxidation
catalyst for CO and organic HAP control. We identified where one control
device could achieve reductions in multiple pollutants, for example a
fabric filter was expected to achieve both PM and mercury control in
order to avoid overestimating the costs. We also included costs for
testing and monitoring requirements contained in the proposed rule. The
resulting total national cost impact of the proposed rule is 5.1 billion
dollars in capital expenditures and 1.8 billion dollars per year in
total annual costs. Considering estimated fuel savings resulting from
work practice standards and combustion controls, the total annualized
costs are reduced to 1.4 billion. The total capital and annual costs
include costs for control devices, work practices, testing and
monitoring. Table 3-1 of this shows the capital and annual cost impacts
for each subcategory. Costs include testing and monitoring costs, but
not recordkeeping and reporting costs.

Table 3-1.	Summary of Capital and Annual Costs for New and Existing
Major Sourcesa

Source	Subcategory	Estimated/ Projected No. of Affected Units	Capital
Costs

(million)	Annualized Cost

(million per yr)a	Annualized Cost

(million per yr)b

Existing Units	Solid units	1,014	$2,182	$873	$846

	Liquid units	713	$2,656	$833	$828

	Non-Continental Liquid Units	27	$86	$24	$21

	Gas 1 units	10,797	$70	$31	$(325)

	Gas 1 Metallurgical Furnaces	694	$4.5	$2.0	$(6)

	Gas (other) units	118	$79	$39	$37

	Limited Use	477	$3.1	$1.3	$(25)

Energy Audit

New Units	ALL

	27	$27

	Total Existing Costs	13,840	$5,081	$1,803	$1,403

	Solid units	0	0	0	0

	Liquid units	13	$21	$6.1	$6.1

	Gas 1 units	34	$0.2	$0.1	0

	Total New Costs	47	$21.2	$6.1	$6.1

a	Does not include fuel savings.

b	Includes Fuel Savings.

Using Department of Energy (DOE) projections on fuel expenditures, the
number of additional boilers that could be potentially constructed was
estimated. A detailed description of how the DOE data was used to
project new units expected to be constructed is discussed in the
memorandum, “Revised New Unit Analysis Industrial, Commercial, and
Institutional Boilers and Process Heaters National Emission Standards
for Hazardous Air Pollutants—Major Source (Gibson et al., 2011  XE
“Gibson et al., 2011”  ).” This analysis consisted of two steps,
(1) calculating the predicted percent change in fuel consumption in the
industrial and commercial/institutional sectors between the baseline
year (2008) of the major source boiler and process heater inventory and
the target years of the projection (2013 and 2015); and (2) calculating
the expected fuel consumption in future in boilers and heaters and
predicting the number of new units required to fire a particular fuel.
The resulting total national cost impact of the proposed rule in the 3rd
year is 21 million dollars in capital expenditures and 6 million dollars
per year in total annual costs, when considering a 1 percent fuel
savings.

The fuel savings are based on an estimated savings of 1 percent from
tuning the boiler to adjust oxygen levels and inspecting the flame
pattern, consistent with manufacturer’s specifications. For units with
an automatic air to fuel ratio control, it also includes inspection and
calibration of that system. It is primarily the tune-ups that are
expected to obtain these fuel savings, but approximately 440 units are
estimated to retrofit their units with linkageless boiler management
systems in order to reduce CO emissions and improve boiler efficiency.
Fuel savings of one percent was selected for the basis of the fuel
savings analysis. The fuel savings was estimated for units firing
gaseous, liquid, and coal fuels only. Biomass fuels were not considered
in the fuel savings analysis since it is difficult to establish a market
value for the various biomass fuel streams

The estimated fuel savings between proposal and promulgation are
similar. At proposal, fuel savings from existing units was estimated to
be $376 million, this assumed that 12,887 units would install combustion
controls or conduct a tune-up in order to meet the CO emission limit or
comply with a work practice standard. At the final rule, the fuel
savings was estimated to be $428 million, based on 13,279 units
installing combustion controls or conducting a work practice standard.
The difference resulted from two main changes: 

First, more units were added to the inventory, based on comments
received from additional facilities that were left out of the analysis
at the proposal. Many of these additional units will conduct a tune-up
or install combustion controls in order to meet the work practice or CO
emission limit.

Second, the baseline emission and emission limits have been modified,
which have in turn increased some of the limits for CO. As a result, the
assumptions associated with how many units will conduct a tune-up or
install combustion controls in order to meet the emission limits have
changed. At proposal, 458 units were estimated to install a linkageless
boiler management system, one boiler was expected to install a
replacement burner, and 12,428 boilers were estimated to conduct a
tune-up. In the final analysis, 440 units were estimated to install a
linkageless boiler management system, 1 unit was expected to install a
replacement LNB, and 12,838 boilers were expected to conduct a tune-up.

Third, the treatment of gas 2 units has been modified to use a fuel gas
specification. This is expected to increase the number of gas units
eligible for a work practice standard instead of an emission limit. As a
result of this change more of these units are expected to conduct a
tune-up to fulfill the requirements of the work practice standard.

Although the age of the boiler can affect combustion efficiency,
periodic tune-ups are expected to improve the combustion units of both
new and existing units. Even new units are susceptible of being operated
at air-to-fuel ratios that are not optimal. EPA adopted the same fuel
savings assumptions for both new and existing units. The analysis
estimates that if the annual tune-up provisions are incorporated, a
savings of one percent would be achieved on an annual basis when
compared to a new boiler installation that does not conduct routine
tune-ups. The analysis projects the construction of 6,424 new liquid
fired boilers at area sources, as opposed to 155 new coal units and 200
biomass units, respectively. All of these boilers are expected to
conduct a tune-up in order to meet the requirements of the final rule.
Liquid-fired units have the greatest energy cost on a per mmBtu basis of
any of the subcategories, on which the fuel savings are based. For
residual fuels, the energy cost was calculated to be $12.96/mmBtu.
Distillate fuels were found to have an energy cost of either $22.66 for
commercial units, units or $23.23 for industrial units. The combination
of the large number of projected units in the liquid subcategory and the
high energy cost resulted in the fuel savings shown.

A discussion of the methodology used to estimate cost impacts is
presented in “Revised Methodology for Estimating Cost and Emission
Impacts for Industrial, Commercial, and Industrial Boilers and Process
Heaters National Emission Standards for Hazardous Air Pollutants—Major
Source (2011)” in an Appendix.

Area Sources

To estimate the national cost impacts of the proposed rule for existing
sources, EPA developed several model boilers and determined the cost of
control for these model boilers. The EPA assigned a model boiler to each
existing unit based on the fuel, size, and current controls. The
analysis considered all air pollution control equipment currently in
operation at existing boilers. Model costs were then assigned to all
existing units that could not otherwise meet the proposed standards. The
resulting total national cost impact of the final rule for existing
units is $487 million dollars in total annualized costs after
considering fuel savings from efficiency improvements. The total
annualized costs for installing controls, conducting an annual tune-up
and an energy assessment, and implementing testing and monitoring
requirements, is $535 million. Table 3-2 of this preamble shows the
total annualized cost impacts for each subcategory.

Table 3-2.	Summary of Annual Costs for New and Existing Area Sources

Source	Subcategory	Estimated/

Projected No. of Affected Units	Total Annualized Cost (million per yr)a

Existing Units	Coal	3,710	$37

	Biomass	10,958	$24

	Oil	168,003	$374

Facility Energy Audit	All

$52

New Unitsb 	Coal	155	$0.4

	Biomass	200	$2.6

	Oil	6,424	$45

a	TAC does not include fuel savings from improving combustion
efficiency.

b	Impacts for new units assume the number of units online in the first 3
years of this rule (2010 to 2013).

EPA also estimated the number of additional boilers that could be
potentially constructed. The resulting total national cost impact of the
final rule on new sources by the 3rd year, 2013, is $48 million dollars
in total annualized costs. When accounting for a 1 percent fuel savings
resulting from improvements to combustion efficiency, the total national
cost impact on new sources is ($3.6 million). The size of the fuel
savings estimate resulted primarily from the large number of new liquid
fired boilers, for which the cost of energy is the highest on a per
mmBtu basis. This large energy cost created to a correspondingly high
cost savings with the increased combustion efficiency. 

Section 3 References

Gibson et al., 2011. Revised New Unit Analysis Industrial, Commercial,
and Institutional Boilers and Process Heaters National Emission
Standards for Hazardous Air Pollutants—Major Source. 

Economic Impact Analysis

EPA prepares an RIA to provide decision makers with a measure of the
social costs of using resources to comply with a program (EPA, 2000  XE
“U.S. EPA, 2000”  ). The social costs can then be compared with
estimated social benefits (as presented in Section 6). As noted in
EPA’s (2010)  XE “U.S. EPA (2010)”   Guidelines for Preparing
Economic Analyses, several tools are available to estimate social costs
and range from simple direct compliance cost methods to the development
of a more complex market analysis that estimates market changes (e.g.,
price and consumption) and economic welfare changes (e.g., changes in
consumer and producer surplus).

The Office of Air Quality Planning and Standards (OAQPS) adopted a
standard market analysis as described in the Office’s resource manual
(EPA, 1999  XE “U.S. EPA, 1999”  ). The approach uses a
single-period multimarket partial equilibrium model to compare
pre-policy market baselines with expected post-policy market outcomes.
The analysis’ time horizon is the short run; some production factors
are fixed and some are variable and is distinguished from the very short
run where all factors are fixed and producers cannot adjust inputs or
outputs (EPA, 1999  XE “U.S. EPA, 1999”  , 5-6). The time horizon
allows us to capture important transitory stakeholder outcomes. Key
measures in this analysis include industry-level changes in price
levels, production and consumption, jobs, international trade, and
social costs (changes in producer and consumer surplus).

Partial Equilibrium Analysis (Multiple Markets)

The partial equilibrium analysis develops a market model that simulates
how stakeholders (consumers and industries) might respond to the
additional regulatory program costs. In this section, we provide an
overview of the economic model. Appendix A provides additional details
on the behavioral assumptions, data, parameters, and model equations.

Overview

Although several tools are available to estimate social costs, current
EPA guidelines suggest that multimarket models “…are best used when
potential impacts on related markets might be considerable” and
modeling using a computable general equilibrium model is not available
or practical (EPA, 2010  XE “U.S. EPA, 2010”  , p. 9-21). Other
guides for environmental economists offer similar advice (Berck and
Hoffmann, 2002  XE “Berck and Hoffmann, 2002”  ; Just, Hueth, and
Schmitz, 2004  XE “Just, Hueth, and Schmitz, 2004”  ). Multimarket
models focus on “short-run” time horizons and measure a policy’s
near-term or transition costs (EPA, 1999  XE “U.S. EPA, 1999”  ).
Note that the multimarket model is not designed to directly estimate
employment impacts. Job effects are discussed later, in section 4.1.2.4.
The multimarket model contains the following features:

Industry sectors and benchmark data set

100 industry sectors

a single benchmark year (2010)

industry employment data

Economic behavior

industries respond to regulatory costs by changing production rates

market prices rise and fall to reflect higher energy and other
non-energy material costs and changes in demand

customers respond to these price increases and consumption falls

Model scope

100 sectors are linked with each other based on their use of energy and
other non-energy materials. For example, the construction industry is
linked with the petroleum, cement, and steel industries and is
influenced by price changes that occur in each sector. The links allow
EPA to account for indirect effects the regulation has on related
markets.

production adjustments influence employment levels

international trade (imports/exports) responds to domestic price changes

Model time horizon (“short run”) for a single period (2014)

fixed production resources (e.g., capital) lead to an upward-sloping
industry supply function

firms cannot alter input mixes; there is no substitution among
intermediate production inputs

price of labor (i.e., wage) is fixed

investment and government expenditures are fixed

Economic Impact Analysis Results

Market-Level Results

Market-level impacts include price and quantity adjustments including
the changes in international trade (Figure 4-1). Under the major source
NESHAP, the Agency’s economic model suggests the average national
price increases for industrial sectors are less than 0.01%, while
average annual domestic production may fall by less than 0.01%. Because
of higher domestic prices, imports slightly rise. Market-level effects
for the area source NESHAP are smaller when compared to the major source
rule; average price, production, and import changes are less than 0.01%.
Industrial sector details are provided in Appendix B.

Figure 4-1.	Market-Level Changes by Source and Option

Social Cost Estimates Major Source Rule

In the near term, the Agency’s economic model suggests that industries
are able to pass on $0.5 billion (2008$) of the major source rule’s
costs to U.S. households in the form of higher prices (Table 4-1).
Existing U.S. industries’ surplus falls by $1.4 billion and the net
U.S. loss in aggregate is $1.9 billion. As U.S. prices rise, other
countries are affected through international trade relationships. The
price of goods produced in the United States increase slightly and
domestic production declines, replaced to a certain degree by imports;
the model estimates a net gain of less than $0.1 billion to foreign
companies. As shown in Figure 4-2, the U.S. surplus losses are
concentrated in other services (23 percent), lumber, paper, and printing
(16 percent), energy industries (15 percent), and chemicals (13
percent).

Table 4-1.	Distribution of Social Costs Major Sources (billion, 2008$):
2014 

Method

Selected Option	Alternative Option

Partial Equilibrium Model (Multiple Markets)

	Change in U.S. consumer surplus

−$0.530	−$0.600

Change in U.S. producer surplus

−$1.360	−$1.730

Change in U.S. surplus

−$1.890	−$2.330

Direct Compliance Costs Method (Not Modeled)

	Total annualized costs, new major sourcesa

−$0.006	−$0.920

Fuel savings, existing major sources 

$0.430	$0.400

Fuel savings, new major sourcesa 

Less than $0.001	Less than $0.001

Net Change in U.S. Surplusb

−$1.470	−$1.940

Net change in rest of world surplus

$0.060	$0.090

Net change in total surplus

−$1.410	−$1.850

a	Estimates for the Alternative option are assumed to be the same as the
Selected option.

b	U.S. surplus changes add the partial equilibrium model estimates and
the direct compliance estimates not included in the partial equilibrium
model. For example, the selected option’s net change in U.S. surplus
is $1.890 + (0.430 – 0.006) = –$1.470 billion.

Figure 4-2.	Distribution of U.S. Surplus Changes by Sector: Major
Sources

The Agency also considered other elements of the engineering cost
analysis that could not be modeled within the multimarket model (e.g.,
fuel savings benefits [existing and new major sources] and total
annualized compliance costs [new major sources]). The net effect of the
adjustments is a U.S. surplus loss estimate of $1.5 billion.

Social Cost Estimates Area Source Rule

In the near term, the Agency’s economic model suggests that industries
are able to pass on $0.2 billion (2008$) of the area source rule’s
costs to U.S. households in the form of higher prices (Table 4-2).
Existing U.S. industries’ surplus falls by $0.3 billion and the net
loss for U.S. stakeholders is $0.5 billion. As U.S. prices rise, other
countries are affected through international trade relationships.
Households that buy U.S. exports pay higher prices and purchase fewer
U.S. produced goods. Other countries that that sell goods to the United
States benefit; the model estimates a net rest of the world gain of less
than $0.01 billion. As shown in Figure 4-3, the U.S. surplus losses are
concentrated in the other services (82 percent ). Other services include
information, finance and insurance, real estate, professional services,
management, administrative services, education, health care, arts,
accommodations, and public services.

Table 4-2.	Distribution of Social Costs Area Sources (billion, 2008$):
2014

Method

Final MACT/GACT Approach	Proposed MACT Approach

Partial Equilibrium Model (Multiple Markets)

	Change in U.S. consumer surplus

−$0.240	−$0.300

Change in U.S. producer surplus

−$0.250	−$0.330

Change in U.S. surplus

−$0.490	−$0.630

Direct Compliance Costs Method (Not Modeled)

	Total annualized costs, unknown existing area sources 

−$0.003	−$0.008

Total annualized costs, new area sources

−$0.050	−$0.270

Fuel savings, existing and new area sources)

$0.050	$0.050

Net Change in U.S. Surplus a

−$0.490	−$0.850

Net change in rest of world surplus

$0.004	$0.005

Net change in total surplus

−$0.480	−$0.850

a	U.S. surplus changes add the partial equilibrium model estimates and
the direct compliance estimates not included in the partial equilibrium
model. For example, the Final MACT/GACT net change in U.S. surplus is
$0.490 + (0.050 – 0.050–0.003) = –$0.490 billion.



Figure 4-3.	Distribution of Total Surplus Changes by Sector: Area
Sources

a	Other services include information, finance and insurance, real
estate, professional services, management, administrative services,
education, health care, arts, accommodations, and public services.

The Agency also considered other elements of the engineering cost
analysis that could not be modeled within the multimarket model (e.g.,
fuel-savings benefits [existing and new area sources] and total
annualized compliance costs [unknown existing and new area sources]).
The net effect of the adjustments is a total surplus loss estimate of
$0.5 billion.

Job Effects

4.1.2.4.1 Background

In addition to estimating this rule’s social costs and benefits, EPA
has estimated the employment impacts of the final rule based on
Morgenstern, Pizer and Shih (2002).  A stand-alone analysis of jobs is
not included in a standard cost-benefit analysis.   Executive Order
13563 however, states, “Our regulatory system must protect public
health, welfare, safety, and our environment while promoting economic
growth, innovation, competitiveness, and job creation” (emphasis
added).  Therefore, we have provided this analysis to inform the
discussion of job impacts.   EPA continues to explore the relevant
theoretical and empirical literature and to seek public comments in
order to ensure that such estimates are as accurate as possible. 

 From an economic perspective labor is an input into producing goods and
services; if regulation requires that more labor be used to produce a
given amount of output, that additional labor is reflected in an
increase in the cost of production.  Moreover, when the economy is near
full employment, jobs created in one industry as a result of regulation
displace jobs in other industries. On the other hand, in periods of high
unemployment, an increase in labor demand due to regulation may have a
stimulative effect that results in a net increase in overall employment.
With significant numbers of workers unemployed, the opportunity costs
associated with displacing jobs in other sectors are likely to be much
smaller. 

For this reason, this RIA looks carefully at a subset of the employment
consequences of this final rule. It is important to note that EPA has
estimated only a portion of the employment effects -- namely, those
associated with the direct impacts on employment in the regulated
industry. A full analysis would include estimates of the direct impacts
on other industries (e.g. suppliers of pollution control equipment) as
well as the indirect and induced effects on employment throughout the
economy as a whole in response to changes in output and factor prices.

We expect that the rule’s direct impact on employment will be small.
The Agency’s analysis does not include all the direct effects of this
regulation.  For example, EPA is currently exploring ways to quantify
the job impacts in the pollution control sector that result from these
and future regulations.  Furthermore, we have not quantified the
rule’s indirect or induced impacts. What follows is an overview of the
various ways that environmental regulation can affect employment,
followed by a discussion of the estimated impacts of this rule. An
environmental regulation can affect the demand for labor in several
ways:

Direct Effects:

Increased prices for industry output may reduce the demand for labor:
Environmental regulations increase production costs causing firms to
increase prices; higher prices reduce consumption (and production), thus
reducing demand for labor within the regulated industry.  The extent of
this effect will depend on the extent of the price increase and the
elasticity of the demand curve.

Regulated firms demand labor workers to operate and maintain pollution
controls within those firms.  Once pollution control equipment is
installed, regulated firms may hire workers to operate and maintain it,
just as they would hire workers to produce more output. The extent of
this effect will depend in part on whether the operation and maintenance
of pollution controls are labor intensive

Increased demand for pollution control equipment and services: When a
regulation requiring emission reductions is promulgated, affected
sources must immediately place orders for pollution control equipment
and services. Filling these orders will require a scale-up in
manufacturing of pollution control equipment, performance of engineering
analyses and significant expenditures for assembly and installation of
such equipment. These activities will be job-creating during the period
before firms must comply with the rule, at which point all pollution
control equipment must be installed and operating.

 

.

 Indirect and Induced Effects:

Environmental regulations create employment in many basic industries. 
In addition to the increase in employment in the environmental
protection industry (increased orders for pollution control equipment),
environmental regulations also create employment in industries that
provide intermediate goods to the environmental protection industry. 
For example, capital expenditures to reduce air pollution involve the
purchase of abatement equipment. The equipment manufacturers, in turn,
order steel, tanks, vessels, blowers, pumps, and chemicals to
manufacture and install the equipment. On the other hand, demand for
labor will decrease in  sectors that supply inputs for, or demand the
outputs of the regulated industry. None of these impacts is accounted
for in the current analysis. We also do not estimate employment impacts
“induced” by increased output of the environmental protection
sector, or decreased output of the regulated sectors. 

4.1.2.4.2 Methodology and Results

The estimated impacts of the final rule on employment in affected
sources are based on an empirically derived relationship reported in
Morgenstern, Pizer and Shih (2002), a peer-reviewed, published study.
Estimates of the employment impacts of the capital investments and other
non-recurring requirements of the rule are derived from the cost
analysis developed for the regulation.

Morgenstern, Pizer and Shih (2002): Overview of Conceptual Approach

The fundamental insight of Morgenstern, Pizer and Shih (2002) is that
environmental regulations can be understood as requiring regulated firms
to add a new output (environmental quality) to their product mixes.
Although legally compelled to satisfy this new demand, regulated firms
have to finance this additional production with the proceeds of sales of
their other (market) products. Satisfying this new demand requires
additional inputs, including labor, and may alter the relative
proportions of labor and capital used by regulated firms in their
production processes.

Thus, Morgenstern et al., decompose the overall effect of a regulation
on employment into the following three subcomponents:

The “Demand Effect”: higher production costs raise market
prices,reducing consumption (and production), thereby reducing demand
for labor within the regulated industry ;

The “Cost Effect”: As production costs increase, plants use more of
all inputs, including labor, to maintain a given level of output.  For
example, in order to reduce pollutant emissions while holding output
levels constant, regulated firms may require additional labor; 

The “Factor-Shift Effect”: Regulated firms’ production
technologies may be more or less labor intensive after complying with a
regulation (i.e., more/less labor is required per dollar of output).

Decomposing the overall employment impact of environmental regulation
into three subcomponents clarifies the conceptual relationship between
environmental regulation and employment in regulated sectors, and
permitted Morgenstern, et al. to provide an empirical estimate of the
net impact. For present purposes, the net effect is of particular
interest, and is the focus of our analysis.

Morgenstern, Pizer and Shih (2002): Empirical Results

Morgenstern et al. empirically estimate a model for four highly
polluting, regulated industries (pulp and paper, plastics, petroleum
refining and steel) to examine the effect of higher abatement costs from
regulation on employment.  They conclude that increased abatement
expenditures generally do not cause a significant change in employment.
More specifically, their results show that, on average across the four
industries, each additional $1 million spending on pollution abatement
results in a (statistically insignificant) net increase of 1.55 (+/-
2.24) jobs. “In plastics and petroleum, [Morgenstern et al] find that
increased regulation raises employment by a small but statistically
significant amount:  6.9 and 2.2 jobs per million dollars of regulatory
expense, respectively.  In pulp and paper and steel, the estimates are
even smaller and insignificantly different from zero.”  By applying
these estimates to pollution abatement costs, we estimated the net
employment effect for major and areas sources to range from -4,100 to
+8,500 jobs in the directly affected sectors with a central estimate of
+2,200 (Table 4-3). ,  



Table 4-3.	Employment Impacts Using Morgenstern, Pizer, Shih (2002)
(FTE)

	Demand Effect	Cost Effect	Factor Shift Effect	Net

−3.56	2.42	2.68	1.55

Standard Error	2.03	1.35	0.83	2.24

EPA estimate for Major Sources Rule 	−3,900

−8,200 to +500	2,600

+900 to +4,400	2,900

 0 to +5,800	1,700

−3,100 to +6,500

EPA estimate for Area Source Rule 	−1,200

−2,600 to +100	800

+300 to +1,400	900

 0 to +1,800	500

−1,000 to +2,000

EPA estimate for both Rules b	−5,100

−10,800 to +600	3,500

+1,100 to +5,800	3,900

 0 to 7,700	2,200

−4,100 to +8,500

a Estimates from Morgenstern, Pizer, and Shih (2002) expressed in 2010
dollars using the GDP price deflator (see footnote 7).

bTotals may not add due to independent rounding.



Figure 4-3.  Employment Impacts Using Morgenstern, Pizer, Shih (2002)
(1,000 FTEs)

Limitations of the Analysis

Although the Morgenstern et al. paper provides information about the
potential job effects of environmental protection programs, there are
several caveats associated with using those estimates to analyze the
final rule. First, the Morgenstern et al. estimates presented in Table
4-3 and used in EPA’s analysis represent the weighted average
parameter estimates for a set of manufacturing industries (pulp and
paper, plastics, petroleum, and steel).  This set of industries only
partially overlaps with the sectors affected by this rule. Second,
relying on Morgenstern et al. implicitly assumes that estimates derived
from 1979–1991 data are still applicable.  Third, the methodology used
in Morgenstern et al. assumes that regulations affect plants in
proportion to their total costs. In other words, each additional dollar
of regulatory burden affects a plant by an amount equal to that plant's
total costs relative to the aggregate industry costs. By transferring
the estimates, EPA assumes a similar distribution of regulatory costs by
plant size and that the regulatory burden does not disproportionately
fall on smaller or larger plants.  Further, Morgenstern et al. does not
include most indirect effects and all induced effects. 



Small Entity Analyses

The RFA as amended by SBREFA 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 (SISNOSE). Small entities include small businesses, small
governmental jurisdictions, and small not-for-profit enterprises. EPA
assessed the potential small entity economic impacts using a screening
analysis. After reviewing screening analysis results, EPA has determined
it cannot certify that the rules will not have a SISNOSE and presumes
that both rules are not eligible for certification under the RFA as
amended by SBREFA. 

Small Entity Screening Analysis

Small Businesses

The sectors covered by the rule were identified through lists of small
entities at major and area sources provided by the engineering analysis.
Table 5-1 provides a list of the sectors affected (3-digit NAICS) and
the range of SBA size definitions.

Representative Small Business Analysis Using Census Statistics of U.S.
Businesses

For each 3-digit NAICS code, the SUSB provides national information on
the distribution of economic variables by industry and enterprise size
(U.S. Census Bureau, 2008  XE “U.S. Census Bureau, 2008”  ). The
Census Bureau and the Office of Advocacy of the SBA supported and
developed these files for use in a broad range of economic analyses.
Statistics include the total number of establishments and receipts for
all entities within an industry; however, only a subset of entities will
be covered by the rule. SUSB also provides statistics by enterprise
employment and receipt size.

The Census Bureau’s definitions used in the SUSB are as follows:

Establishment: An establishment is a single physical location where
business is conducted or where services or industrial operations are
performed.

Receipts: Receipts (net of taxes) are defined as the revenue for goods
produced, distributed, or services provided, including revenue earned
from premiums, commissions and fees, rents, interest, dividends, and
royalties. Receipts exclude all revenue collected for local, state, and
federal taxes.

Table 5-1.	Affected Sectors and Size Standards

2007 NAICS	Description	Size Standard

(Effective August 22, 2008)

211	Oil and Gas Extraction	500 employees

212	Mining (except Oil and Gas)	500 employees

221	Utilities	a

311	Food Manufacturing	500 to 1,000 employees

312	Beverage and Tobacco Product Manufacturing	500 to 1,000 employees

313	Textile Mills	500 to 1,000 employees

321	Wood Product Manufacturing	500 employees

322	Paper Manufacturing	500 to 750 employees

323	Printing and Related Support Activities	500 employees

324	Petroleum and Coal Products Manufacturing	Typically 500 to 1,500
employees

325	Chemical Manufacturing	500 to 1,000 employees

326	Plastics and Rubber Products Manufacturing	Typically 500 to 1,000
employees

327	Nonmetallic Mineral Product Manufacturing	500 to 1,000 employees

331	Primary Metal Manufacturing	500 to 1,000 employees

332	Fabricated Metal Product Manufacturing	500 to 1,500 employees

335	Electrical Equipment Manufacturing	500 to 1,000 employees

336	Transportation Equipment Manufacturing	500 to 1,000 employees

337	Furniture and Related Product Manufacturing	500 employees

339	Miscellaneous Manufacturing	500 employees

423	Merchant Wholesalers, Durable Goods	100 employees

493	Warehousing and Storage	$25.5 million in annual receipts

562	Waste Management and Remediation Services	Typically $7 to $14
million in annual receipts

611	Educational Services	Typically $7 to $35.5 million in annual
receipts

a	NAICS codes 221111, 221112, 221113, 221119, 221121, 221122: A firm is
small if, including its affiliates, it is primarily engaged in the
generation, transmission, and/or distribution of electric energy for
sale and its total electric output for the preceding fiscal year did not
exceed 4 million megawatt hours.

Enterprise: An enterprise is a business organization consisting of one
or more domestic establishments that were specified under common
ownership or control. The enterprise and the establishment are the same
for single-establishment firms. Each multi-establishment company forms
one enterprise—the enterprise employment and annual payroll are summed
from the associated establishments. Enterprise size designations are
determined by the total employment of all associated establishments.

Because the SBA’s business size definitions (SBA, 2008  XE “SBA,
2008”  ) apply to an establishment’s “ultimate parent company,”
we assumed in this analysis that the “enterprise” definition above
is consistent with the concept of ultimate parent company that is
typically used for SBREFA screening analyses, and the terms are used
interchangeably.

The analysis generated a set of establishment sales tests (represented
as cost-to-receipt ratios) for NAICS codes associated with sectors
listed in Table 5-2. Although the appropriate SBA size definition
should be applied at the parent company (enterprise) level, we can only
compute and compare ratios for a model establishment owned by an
enterprise within an SUSB size range (employment or receipts). Using the
SUSB size range helps us account for receipt differences between
establishments owned by large and small enterprises and also allows us
to consider the variation in small business definitions across affected
industries. Using establishment receipts is also a conservative
approach, because an establishment’s parent company (the
“enterprise”) may have other economic resources that could be used
to cover the costs of the regulatory program.

Table 5-2.	Major Sources: Sales Tests Using Small Companies Identified
in the Combustion Survey

Sample Statistic	Proposal	Selected Option	Alternative Option

Mean	4.9%	4.0%	3.8%

Median	0.4%	0.2%	0.4%

Maximum	72.9%	59.8%	31.4%

Minimum	<0.01%	<0.01%	<0.01%

Ultimate parent company observations	50	50	50

Ultimate parent companies with sale tests exceeding 3%	14	8	13

For each representative establishment in the SUSB data, we developed a
range of facility-level cost numerators based on the engineering cost
analysis. For major sources, we used the maximum and minimum small
entity facility-level costs observed within each 3-digit NAICS code. For
area sources, we were limited to two representative small entity
facility-level costs (approximately $27,000,000 to $43,000). Using these
cost data and the Census estimates of average establishment receipts, a
substantial number of SUSB NAICS/enterprise categories have ratios over
3% (Figure 5-1).

Figure 5-1.	Share of NAICS/Enterprise Employment Categories (<500
employees) with Sales Tests Exceeding 3%

Additional Small Business Analysis Using Sample of Small Businesses
Identified in Combustion Facility Survey

Next, we performed a more detailed analysis that compares the Census
SUSB representative small entity results with a firm-specific sample of
major small private enterprises. In this approach, we identified a
sample of survey facility names listed as small, traced the ultimate
parent company name to verify the facility was owned by a small
business, and collected the most recent parent company sales and
employment figures. As Table 5-2 shows, the average cost-to-sales ratios
for small major source companies are above 3%. The median ratios are
below one percent.

Small Governmental Jurisdictions and Not-for-Profit Enterprises 

In addition to the private sector, this rule also covers sectors that
include entities owned by small and large governments and not-for-profit
enterprises. Given the uncertainty and data limitations associated with
identifying and appropriately classifying these entities, we computed a
“revenue” test, where the annualized compliance cost is a percentage
of annual revenues (U.S. Census, 2005a and b  XE “U.S. Census, 2005a
and b”  ).

Compliance costs were estimated for model facilities for major and area
sources for multiple options. A summary of the compliance costs used for
the small entity analysis follows:

Major Sources:

Selected option: $1.1 million (median cost small public facility)

Alternative Option: $0.9 million (median cost small public facility)

Area Sources:

Final MACT/GACT Approach and Proposed MACT Approach:

Other Public: $45,000

Hospital: $11,300

Schools: $4,500

Churches: $2,200

From the 2002 Census (in 2008 dollars), the average revenue for small
governments (counties and municipalities with populations fewer than
10,000) are $3 million per entity, and the average revenue for local
governments with populations fewer than 50,000 is $7 million per entity.
Churches are assumed to have an operation budget of $150,000.

Representative small major public entities would have cost-to-revenue
ratios above 10 percent. The following types of representative small
area source facilities would have cost-to-revenue ratios exceeding 1
percent but below 3 percent:

Final MACT/GACT Approach: other public (ratio > 1.7 percent) and
churches (ratio = 1.5 percent)

Proposed MACT Approach: other public (ratio > 1.7 percent) and churches
(ratio = 1.5 percent)

Final Regulatory Flexibility Analysis (FRFA): Major Sources

Pursuant to section 603 of the RFA, EPA prepared an initial regulatory
flexibility analysis (IRFA) for the proposed rule and convened a Small
Business Advocacy Review Panel to obtain advice and recommendations of
representatives of the regulated small entities. A detailed discussion
of the Panel’s advice and recommendations is found in the final Panel
Report (Docket ID No. EPA-HQ-OAR-2002-0058-0797). A summary of the
Panel’s recommendations is also presented in the preamble to the
proposed rule at 75 FR 32044-32045 (June 4, 2010). In the proposed rule,
EPA included provisions consistent with four of the Panel’s
recommendations. As required by section 604 of the RFA, we also prepared
a final regulatory flexibility analysis (FRFA) the final rule. 

Need for Rule and Objectives 

The rule is intended to reduce emissions of HAP as required under
section 112 of the CAA. Section II.A of the final rule’s preamble
describes the reasons that EPA is finalizing this action. 

Summary of the Significant Issues raised by the Public Comments and
Agency Assessment

Many significant issues were raised during the public comment period,
and EPA’s responses to those comments are presented in section V of
this preamble or in the response to comments document contained in the
docket. Significant changes to the rule that resulted from the public
comments are described in section IV of the final rule’s preamble.

The primary comments on the IRFA were provided by SBA, with the
remainder of the comments generally supporting SBA’s comments. Those
comments included the following: EPA should have adopted a health-based
compliance alternative (HBCA) which provides alternative emission limits
for threshold chemicals; EPA should have adopted additional
subcategories, including the following: subcategories based on fuel type
(including coal rank, bagasse, biomass by type, and oil by type), unit
design type (e.g., process heater, fluidized bed, stoker, fuel cell,
suspension burner), duty cycle, geographic location, boiler size, burner
type (with and without low-NOx burners), and hours of use (limited use);
EPA should have minimized facility monitoring and reporting
requirements; EPA should not have proposed the energy audit requirement;
EPA’s proposed emissions standards are too stringent; and, EPA should
provide more flexibility for emissions averaging.

In response to the comments on the IRFA and other public comments, EPA
made the following changes to the final rule. EPA adopted additional
subcategories, including a limited-use subcategory for units that
operate less than 10 percent of the operating hours in a year, a
non-continental liquid unit subcategory for units with the unique
challenges faced by remote island locations, and a combination
suspension/grate boiler subcategory. EPA also consolidated the
subcategories for units combusting various types of solid fuels, which
will simplify compliance and will allow units to combust varying
percentages of different solid fuels without triggering subcategory
changes. EPA also decreased monitoring and testing costs by eliminating
the CO CEMS requirement for units greater than 100 mmBtu/hr and changing
the dioxin testing requirement to a one-time test. The final rule also
includes work practice standards for additional subcategories, including
limited-use units, new small units, and units combusting gaseous fuels
that are demonstrated to have similar contaminant levels to natural gas.
Finally, EPA is finalizing emission limits that are less stringent than
the proposed limits for most of the category/pollutant combinations. The
emission limit changes are largely due to the changes in subcategories,
data corrections, and incorporation of new data into the floor
calculations. Additional details on the changes discussed in this
paragraph are included in sections IV and V of the final rule’s
preamble.

Description of and Estimate of the Number of Small entities to Which the
Rules Will Apply

Based on the distribution of major source facilities with affected
boilers or process heaters reported in the 2008 survey entitled
“Information Collection Effort for Facilities with Combustion Units.
(ICR No. 2286.01),” there are 1,639 existing facilities with affected
boilers or process heaters. Of these, 94 percent are located in the
private sector and the remaining 6 percent are located in the public
sector. Table 5-3 summarizes the types of small entities expected to be
affected by the major source rule. The number of small entities is based
on the facilities which responded to the ICR by confirming small entity
status. If that question was not answered, or if the facility answered
that their small entity status was unknown, it was assumed that those
facilities were not small entities. The summary in Table 5-3 is for
major source facilities which have at least one boiler or process heater
expected to be covered by the rule. Table 5-4 summarizes the EPA
estimates of the number of area source facilities expected to be
affected by the major source rule (84,700 total). EPA does not have
sufficient information to estimate the number of small entities expected
to be covered by the area source rule.

Table 5-3.	Information Collection Effort for Facilities with Combustion
Units: Major Sources

NAICS	NAICS Description	Total Number of Facilities	Total Self-Reported
Facilities Owned by Small Businesses

111	Crop Production	1	0

113	Forestry and Logging	1	0

115	Support Activities for Agriculture and Forestry	1	0

211	Oil and Gas Extraction	24	3

212	Mining (Except Oil and Gas)	14	1

221	Utilities	183	23

311	Food Manufacturing	110	7

312	Beverage and Tobacco Product Manufacturing	5	0

313	Textile Mills	14	1

314	Textile Product Mills	1	0

316	Leather and Allied Product Manufacturing	3	1

321	Wood Product Manufacturing	183	18

322	Paper Manufacturing	186	14

323	Printing and Related Support Activities	33	5

324	Petroleum and Coal Products Manufacturing	84	8

325	Chemical Manufacturing	220	17

326	Plastics and Rubber Products Manufacturing	89	11

327	Nonmetallic Mineral Product Manufacturing	41	2

331	Primary Metal Manufacturing	57	6

332	Fabricated Metal Product Manufacturing	46	8

333	Machinery Manufacturing	13	0

334	Computer and Electronic Product Manufacturing	2	0

335	Electrical Equipment, Appliance, and Component Manufacturing	12	0

336	Transportation Equipment Manufacturing	100	7

337	Furniture and Related Product Manufacturing	45	8

339	Miscellaneous Manufacturing	15	1

423	Durable Goods Merchant Wholesalers	1	1

424	Nondurable Goods Merchant Wholesalers	1	0

441	Motor Vehicle and Parts Dealers	1	0

481	Air Transportation	7	0

482	Rail Transportation	1	0

486	Pipeline Transportation	60	0

488	Support Activities for Transportation	3	0

493	Warehousing and Storage	5	1

(continued)

Table 5-3.	Information Collection Effort for Facilities with Combustion
Units: Major Sources (continued)

NAICS	NAICS Description	Total Number of Facilities	Total Self-Reported
Facilities Owned by Small Businesses

531	Real Estate	1	0

541	Professional, Scientific, and Technical Services	8	0

561	Administrative and Support Services	1	0

562	Waste Management and Remediation Services	7	2

611	Educational Services	29	2

622	Hospitals	4	0

623	Nursing and Residential Care Facilities	1	0

811	Repair and Maintenance	1	0

921	Executive, Legislative, and Other General Government Support	2	0

928	National Security and International Affairs	23	0

As discussed in Section 5.1 of this RIA, using these cost data and the
Census estimates of average establishment receipts, a substantial number
of SUSB NAICS/enterprise categories have ratios over 3%. EPA determined
8 of 50 small ultimate parent companies owning major source facilities
listed in Table 5-3 will experience an impact of over 3 percent of
revenues. Representative small major public entities would have
cost-to-revenue ratios above 10 percent. 

Description of the Projected Reporting, Record keeping and Other
Compliance Requirements of the Rule

The information collection activities in this ICR include initial and
annual stack tests, fuel analyses, operating parameter monitoring,
continuous O2 monitoring for all units greater than 10 mmBtu/hr,
continuous emission monitoring for PM at units greater than 250
mmBtu/hr, certified energy audits, annual or biennial tune-ups
(depending on the size of the combustion equipment), preparation of a
startup, shutdown, malfunction plan (SSMP), preparation of a
site-specific monitoring plan and a site-specific fuel monitoring plan,
one-time and periodic reports, and the maintenance of records.

For sources that can demonstrate compliance through fuel analysis, the
regulation requires an initial fuel analysis and monthly fuel analyses.
Sources must conduct additional fuel analyses if they burn a new type of
fuel. For sources that are demonstrating that their gaseous fuels other
than natural gas and refinery gas meet the specifications for H2S and Hg
contained in the final rule, they must conduct either an initial or
monthly fuel analysis to remain in the gas 1 subcategory. If the content
of these constituents are not going to exceed the specifications, these
units may conduct an initial testing and include a statement that the
gas will not exceed the specification in the initial Notification of
Compliance Status. If the gaseous fuel constituents will vary, the unit
is required to conduct monthly testing and maintain records to
demonstrate that the gaseous fuels meet the specifications. 

An initial performance test must be completed for particulate matter,
mercury, hydrogen chloride, carbon monoxide, and dioxin and furans for
affected sources with applicable emission limits. During the initial
performance test, the owner or operator must establish maximum or
minimum values for each operating parameter. Thereafter, the owner or
operator must, in some cases, conduct annual stack tests for particulate
matter, mercury, hydrogen chloride, carbon monoxide, and dioxin and
furans and must continuously monitor the operating parameters. If a
source is required to use PM CEMS, performance testing is not required
for particulate matter.

Following the initial performance test, the owner or operator must
submit a report that documents the performance test results and the
values for their required operating parameters.

All existing units will be required to conduct an initial certified
energy audit by qualified personnel which includes a visual inspection
of the boiler system, establishing operating characteristics,
identifying major energy consuming systems and energy savings potential,
reviewing available engineering plans, and listing major energy
conservation measures. A signed certification that an audit has been
completed should be submitted to the Agency for each energy audit.

All new and existing small and limited use units, and all large units
firing natural gas, refinery gas, or other gas 1 fuels meeting the fuel
spec can demonstrate compliance by conducting a tune-up of the boiler.
Small and limited use units are requested to conduct a tune-up
biennially and large natural gas, refinery gas, or other gas 1 units
will conduct a tune-up annually. Any large natural gas, refinery gas, or
other gas 1 unit will also submit a notification of alternative fuel use
if the unit fires alternative fuels during periods of gas curtailment or
gas supply emergencies. 

For all units other than small and limited use boilers and process
heaters and units firing natural gas a semiannual report is required
that documents the values for the operating parameters; any deviation;
the results of any annual stack tests; the results of any fuel analysis
and emissions calculations; fuel usage, and if no deviation occurred, a
statement that no deviations occurred.

As specified in the ICI Boiler and Process Heater NESHAP, owners or
operators of boilers and process heaters must keep records of certain
parameters and information for a period of five years. Owners or
operators must maintain records of the initial performance test, annual
stack tests, fuel analyses, and any subsequent stack tests or fuel
analyses. Owners or operators must also maintain records of the
monitoring data for the operating parameters and daily fuel usage.

Owners or operators must also maintain records for boiler or process
heater malfunctions and any deviations from the operating parameters.
Records must also be maintained of all monitoring device calibration
data.

The Agency expects that persons with knowledge of .pdf software,
spreadsheet and relational database programs will be necessary in order
to prepare the report or record. Based on experience with previous
emission stack testing, we expect most facilities to contract out
preparation of the reports associated with emission stack testing,
including creation of the Electronic Reporting Tool submittal, which
will minimize the need for in depth knowledge of databases or
spreadsheet software at the source. We also expect affected sources will
need to work with web-based applicability tools and flowcharts to
determine the requirements applicable to them, knowledge of the heat
input capacity and fuel use of the combustion units at each facility
will be necessary in order to develop the reports and determine initial
applicability to the rule. Affected facilities will also need skills
associated with vendor selection in order to identify service providers
that can help them complete their compliance requirements, as necessary.

Description of the steps the Agency has Taken to Minimize the
Significant Economic Impact on Small Entities

While EPA did make significant changes based on public comment, EPA did
not finalize a HBCA or HBELs and is maintaining, but clarifying, the
energy assessment requirement. The discussion of the HBCA decision is
included in section V of this preamble. Some changes to the energy
assessment requirement that will reduce costs for small entities include
a the following provisions: the energy assessment for facilities with
affected boilers and process heaters using less than 0.3 trillion Btu
per year heat input will be one day in length maximum. The boiler system
and energy use system accounting for at least 50 percent of the energy
output will be evaluated to identify energy savings opportunities,
within the limit of performing a one-day energy assessment; and the
energy assessment for facilities with affected boilers and process
heaters using 0.3 to 1.0 trillion Btu per year will be 3 days in length
maximum. The boiler system and any energy use system accounting for at
least 33 percent of the energy output will be evaluated to identify
energy savings opportunities, within the limit of performing a 3-day
energy assessment. While EPA did not make major adjustments to the
emissions averaging provisions, the change to a solid fuel subcategory
will enable all solid fuel-fired units at a facility to use the
emissions averaging provision for Hg, PM, and HCl.

As required by section 212 of SBREFA, EPA also is preparing a Small
Entity Compliance Guide to help small entities comply with this rule.
Small entities will be able to obtain a copy of the Small Entity
Compliance guide at the following web site:
http://www.epa.gov/ttn/atw/boiler/boilerpg.html.

Final Regulatory Flexibility Analysis (FRFA): Area Sources

Pursuant to section 603 of the RFA, EPA prepared an initial regulatory
flexibility analysis (IRFA) for the proposed rule and convened a Small
Business Advocacy Review Panel to obtain advice and recommendations of
representatives of the regulated small entities. A detailed discussion
of the Panel’s advice and recommendations is found in the final Panel
Report (Docket ID No. EPA-HQ-OAR-2002-0058-0797). A summary of the
Panel’s recommendations is also presented in the preamble to the
proposed rule at 75 FR 32044-32045 (June 4, 2010). In the proposed rule,
EPA included provisions consistent with four of the Panel’s
recommendations. As required by section 604 of the RFA, we also prepared
a final regulatory flexibility analysis (FRFA) the final rule. 

Need for Rule and Objectives

The rule is intended to reduce emissions of HAP as required under
section 112 of the CAA. Section II.A of the final rule’s preamble
describes the reasons that EPA is finalizing this action. 

Summary of the Significant Issues raised by the Public Comments and
Agency Assessment

Many significant issues were raised during the public comment period,
and EPA’s responses to those comments are presented in section V of
this preamble or in the response to comments document contained in the
docket. Significant changes to the rule that resulted from the public
comments are described in section IV of the final rule’s preamble.

The primary comments on the IRFA were provided by SBA, with the
remainder of the comments generally supporting SBA’s comments. Those
comments applicable to the proposal regarding area source boilers
included the following: EPA should have adopted additional
subcategories, including the following: unit design type (e.g.,
fluidized bed, stoker, fuel cell, suspension burner), duty cycle,
geographic location, boiler size, burner type (with and without low-NOx
burners), and hours of use (limited use); EPA should have minimized
facility monitoring and reporting requirements; EPA should not have
proposed the energy audit requirement; and EPA’s proposed emissions
standards are too stringent.

In response to the comments on the IRFA and other public comments, EPA
made the following changes to the final rule. EPA is promulgating
management practice standards requiring the implementation of a boiler
tune-up program for area source boilers in the biomass and oil
subcategories instead of the proposed CO emission limits. This change
will significantly reduce the monitoring and testing costs for existing
and new biomass-fired and oil-fired area source boilers. EPA also
decreased monitoring and testing costs for coal-fired area source
boilers by eliminating the CO CEMS requirement for boilers greater than
100 MMBtu/h. The final rule also includes work practice standards or
management practice standards, instead of emission limits, for new area
source boilers less than 10 MMBtu/h. Finally, EPA is finalizing emission
limits that are less stringent than the proposed limits. The emission
limit changes are largely due to the changes in data corrections and
incorporation of new data into the floor calculations. Additional
details on the changes discussed in this paragraph are included in
sections IV and V of the final rule’s preamble.

Description of and Estimate of the Number of Small entities to Which the
Rules Will Apply

Table 5-4 summarizes the EPA estimates of the number of area source
facilities expected to be affected by the major source rule (84,700
total). EPA does not have sufficient information to estimate the number
of small entities expected to be covered by the area source rule.

As discussed in Section 5.1 of this RIA, using these cost data and the
Census estimates of average establishment receipts, a substantial number
of SUSB NAICS/enterprise categories have ratios over 3%. The following
types of representative small area source public facilities would have
cost-to-revenue ratios exceeding 1 percent but below 3 percent: other
public facilities (ratio > 1.7 percent) and churches (ratio = 1.5
percent).

Table 5-4.	Estimated Affected Facilities Using 13 State Boiler
Inspector Inventory: Area Sources

SIC	Total Number of Affected Facilities in SIC Code

01	0

02	247

07	0

09	0

14	83

16	0

17	247

20	5,733

23	83

24	2,676

26	0

40	329

41	0

42	83

43	0

44	0

45	0

47	0

48	741

50	165

51	247

52	0

53	494

54	0

55	801

56	0

57	0

58	905

59	288

60	329

64	0

(continued)

Table 5-4.	Estimated Affected Facilities Using 13 State Boiler
Inspector Inventory: Area Sources (continued)

SIC	Total Number of Affected Facilities in SIC Code

65	2,878

70	4,893

72	2,138

73	165

75	1,606

76	0

79	1,151

80	15,293

81	0

82	33,303

83	0

84	165

86	3,330

87	666

91 to 98	5,098

Unknown	576

Description of the Projected Reporting, Record keeping and Other
Compliance Requirements of the Rule

The information collection activities in this ICR include initial and
annual stack tests, fuel analyses, operating parameter monitoring,
continuous O2 monitoring for all coal-fired area source boilers greater
than 10 MMBtu/h, certified energy assessments for area source facilities
having a boiler greater than 10 MMBtu/h, biennial tune-ups, preparation
of a startup, shutdown, malfunction plan (SSMP), preparation of a
site-specific monitoring plan and a site-specific fuel monitoring plan,
one-time and periodic reports, and the maintenance of records. Based on
13 states’ inventories of boilers, there are an estimated 92,000
existing facilities with affected boilers. It is estimated that 53
percent are located in the private sector and the remaining 47 percent
are located in the public sector. Of these, only about 0.3 percent of
the area source facilities are subject to emission limits and the
testing and monitoring requirements in the final rule. A table included
in the FRFA summarizes the types and number of each type of small
entities expected to be affected by the area source rule.

The Agency expects that persons with knowledge of .pdf software,
spreadsheet and relational database programs will be necessary in order
to prepare the report or record. Based on experience with previous
emission stack testing, we expect most facilities to contract out
preparation of the reports associated with emission stack testing,
including creation of the Electronic Reporting Tool submittal which will
minimize the need for in depth knowledge of databases or spreadsheet
software at the source. We also expect affected sources will need to
work with web-based applicability tools and flowcharts to determine the
requirements applicable to them, knowledge of the heat input capacity
and fuel use of the combustion units at each facility will be necessary
in order to develop the reports and determine initial applicability to
the rule. Affected facilities will also need skills associated with
vendor selection in order to identify service providers that can help
them complete their compliance requirements, as necessary.

Description of the steps the Agency has Taken to Minimize the
Significant Economic Impact on Small Entities

While EPA did make significant changes based on public comment, EPA is
maintaining, but clarifying, the energy assessment requirement. Some
changes to the energy assessment requirement that will reduce costs for
small entities include a the following provisions: the energy assessment
for facilities with affected boilers using less than 0.3 trillion Btu
per year heat input will be one day in length maximum. The boiler system
and energy use system accounting for at least 50 percent of the energy
output will be evaluated to identify energy savings opportunities,
within the limit of performing a one-day energy assessment; and the
energy assessment for facilities with affected boilers using 0.3 to 1.0
trillion Btu per year will be 3 days in length maximum. The boiler
system and any energy use system accounting for at least 33 percent of
the energy output will be evaluated to identify energy savings
opportunities, within the limit of performing a 3-day energy assessment.
In addition, the final rule allows facilities to use a previously
completed energy assessment to satisfy the energy assessment
requirement.

As required by section 212 of SBREFA, EPA also is preparing a Small
Entity Compliance Guide to help small entities comply with this rule.
Small entities will be able to obtain a copy of the Small Entity
Compliance guide at the following web site:
http://www.epa.gov/ttn/atw/boiler/boilerpg.html.

Section 5 References

U.S. Bureau of Economic Analysis. 2010. “Implicit Price Deflators for
Gross Domestic Product” (Table 1.1.9).
<http://www.bea.gov/national/nipaweb/SelectTable.asp?Selected=N>.

U.S. Census Bureau. 2005a. 2002 Census of Governments, Volume 4, Number
3, Finances of County Governments: 2002 GC02(4)-3 (Table 12).Washington,
DC: U.S. Government Printing Office. 

U.S. Census Bureau. 2005b. 2002 Census of Governments, Volume 4, Number
4, Finances of Municipal and Township Governments: 2002 GC02(4)-4 (Table
13). Washington, DC: U.S. Government Printing Office. 

U.S. Census Bureau. 2008. “Firm Size Data from the Statistics of U.S.
Businesses: U.S. Detail Employment Sizes: 2002.”
<http://www.census.gov/csd/susb/download_susb02.htm>.

U.S. Small Business Administration. 2008. “Table of Small Business
Size Standards Matched to North American Industry Classification System
Codes.”
<http://www.sba.gov/services/contractingopportunities/sizestandardstopic
s/

size/index.html>. 



Air Quality Modeling of Emissions Reductions

Synopsis

This section describes the air quality modeling performed by EPA in
support of the final boiler MACT rule. A national scale air quality
modeling analysis was performed to estimate the impact of the sector
emissions changes on future year: annual and 24-hour PM2.5
concentrations, 8-hr maximum ozone, total mercury deposition, as well as
visibility impairment. Air quality benefits are estimated with the
Comprehensive Air Quality Model with Extensions (CAMx) model. CAMx
simulates the numerous physical and chemical processes involved in the
formation, transport, and destruction of ozone, particulate matter and
air toxics. In addition to the CAMx model, the modeling platform
includes the emissions, meteorology, and initial and boundary condition
data which are inputs to this model.

Emissions and air quality modeling decisions are made early in the
analytical process. For this reason, it is important to note that the
inventories used in the air quality modeling and the benefits modeling
are different than the final boiler sector inventories presented in the
RIA. At the time of proposal, we did not have the results of the ICR;
those results have been incorporated into this final analysis as
explained below. The boiler ICR does not have the emissions release
point information such as stack height, exit velocity, exit temperature,
etc. necessary for photochemical modeling so the ICR emissions data was
matched with the National Emissions Inventory (NEI). Since States and
local agencies have different criteria for emissions that get
inventoried as a point source with stack information and area sources
(no specific stack information) the ability to match ICR emissions to
NEI varies from State to State depending on the level of information
provided in the ICR and NEI. 

Photochemical grid models use state of the science numerical algorithms
to estimate pollutant formation, transport, and deposition over a
variety of spatial scales that range from urban to continental.
Emissions of precursor species are injected into the model where they
react to form secondary species such as ozone and then transport around
the modeling domain before ultimately being removed by deposition or
chemical reaction. Photochemical model source apportionment tracks the
formation and transport of primarily and secondarily formed pollutants
from emissions sources and allows the estimation of contributions at
receptors. This type of emissions apportionment is useful to understand
what types of sources or regions are contributing to pollutants
estimated by photochemical grid models.

The 2005-based CAMx modeling platform was used for the air quality
modeling for this rule. This platform represents a structured system of
connected modeling-related tools and data that provide a consistent and
transparent basis for assessing the air quality response to projected
changes in emissions. The base year of data used to construct this
platform includes emissions and meteorology for 2005. The platform is
intended to support a variety of regulatory and research model
applications and analyses. This modeling platform and analysis is
described below. Additional information about the photochemical modeling
is available as part of the modeling technical assessment document to
support this source sector rule (USEPA, 2010  XE “U.S. EPA, 2010” 
). 

Photochemical Model Background

CAMx version 5.20 is a freely available computer model that simulates
the formation and fate of photochemical oxidants, ozone, primary and
secondary PM concentrations, and air toxics, over regional and urban
spatial scales for given input sets of meteorological conditions and
emissions. CAMx includes numerous science modules that simulate the
emission, production, decay, deposition and transport of organic and
inorganic gas-phase and particle-phase pollutants in the atmosphere
(Baker and Scheff, 2007  XE “Baker and Scheff, 2007”  ; Nobel et
al., 2001  XE “Nobel et al., 2001”  ; Russell, 2008  XE “Russell,
2008”  ). CAMx is applied with ISORROPIA inorganic chemistry (Nenes et
al., 1999  XE “Nenes et al., 1999”  ), a semi-volatile equilibrium
scheme to partition condensable organic gases between gas and particle
phase (Strader et al., 1999  XE “Strader et al., 1999”  ), Regional
Acid Deposition Model (RADM) aqueous phase chemistry (Chang et al., 1987
 XE “Chang et al., 1987”  ), and Carbon Bond 05 (CB05) gas-phase
chemistry module (ENVIRON, 2008  XE “ENVIRON, 2008”  ; Gery et al.,
1989  XE “Gery et al., 1989”  ). Mercury oxidation pathways are
represented for both the gas and aqueous phases in addition to aqueous
phase reduction reactions (ENVIRON, 2008).

CAMx contains a variety of ozone source apportionment tools, including
the original ozone source apportionment tool (OSAT) and the
anthropogenic pre-cursor culpability assessment (APCA) tool (ENVIRON,
2008  XE “ENVIRON, 2008”  ). Ozone source apportionment tracers are
treated using the standard model algorithms for vertical advection,
vertical diffusion, and horizontal diffusion. Horizontal advective
fluxes for each of the regular model species that make up nitrogen
oxides (NOx) and volatile organic compounds (VOC) are combined and
normalized by a concentration based weighted mean. Separate ozone
tracers are used in CAMx to track ozone formation that happens under NOx
and VOC limited conditions. 

Particulate matter source apportionment technology (PSAT) implemented in
CAMx estimates the contribution from specific emissions source groups to
PM2.5 and all forms of mercury using reactive tracers (ENVIRON, 2008  XE
“ENVIRON, 2008”  ; Wagstrom et al., 2008  XE “Wagstrom et al.,
2008”  ). The tracer species are estimated with source apportionment
algorithms rather than by the host model routines. PSAT tracks
contribution to PM2.5 sulfate, nitrate, ammonium, secondary organic
aerosol, and inert primarily emitted species. Non-linear processes like
gas and aqueous phase chemistry are solved for bulk species and then
apportioned to the tagged species. Emissions of nitrogen oxides are
tracked through all intermediate nitrogen species to particulate nitrate
ion. Ammonia emissions are tracked to particulate ammonium ion. This
modeling assessment used the PSAT approach to estimate source
contribution to PM2.5 species and mercury and the APCA method to
estimate source contribution to modeled ozone. 

Model Domain and Grid Resolution

The modeling analyses were performed for a domain covering the
continental United States, as shown in Figure 6-1. This domain has a
parent horizontal grid of 36 km with two finer-scale 12 km grids over
portions of the eastern and western U.S. The model extends vertically
from the surface to 100 millibars (approximately 15 km) using a
sigma-pressure coordinate system. Air quality conditions at the outer
boundary of the 36 km domain were taken from a global model and vary in
time and space. The 36 km grid was only used to establish the incoming
air quality concentrations along the boundaries of the 12 km grids. Only
the finer grid data were used in determining the impacts of the emission
standard program changes. Table 6-1 provides some basic geographic
information regarding the photochemical model domains.

Figure 6-1.	Map of the photochemical modeling domains. The black outer
box denotes the 36 km national modeling domain; the red inner box is the
12 km western U.S. grid; and the blue inner box is the 12 km eastern
U.S. grid. 

Table 6-1.	Geographic Elements of Domains Used in Photochemical Modeling

	Photochemical Modeling Configuration

	National Grid	Western U.S. Fine Grid	Eastern U.S. Fine Grid

Map Projection	Lambert Conformal Projection

Grid Resolution	36 km	12 km	12 km

Coordinate Center	97 deg W, 40 deg N

True Latitudes	33 deg N and 45 deg N

Dimensions	148 x 112 x 14	213 x 192 x 14	279 x 240 x 14

Vertical extent	14 Layers: Surface to 100 millibar level (see Table
II-3)

Emissions Input Data

2005 Baseline Emissions

The emissions data used in the 2005 base year are from the EPA’s
2005-based v4.1 modeling platform. This platform is based on the 2005
National Emissions Inventory (NEI), version 2. Emissions were processed
to photochemical model inputs with the Sparse Matrix Operator Kernel
Emissions (SMOKE) modeling system (Houyoux et al., 2000  XE “Houyoux
et al., 2000”  ). 

This platform includes criteria pollutants and precursors: particulate
matter less than 10 microns (PM10), PM2.5, nitrogen oxides (NOX), sulfur
dioxide (SO2), carbon monoxide (CO), volatile organic compounds (VOC),
ammonia (NH3) and hazardous air pollutants (HAP): hydrogen chloride,
chlorine and mercury. Additionally, for some sectors, HAP emissions of
benzene, formaldehyde, acetaldehyde and methanol are used from the
inventory for chemical speciation VOC. For this rule, mercury emissions
were added to the v4.1 platform to reflect the needs for the rule
development will not be used for other rules. The mercury emissions
included in this platform are primarily from the 2005 National Air
Toxics Assessment (NATA) inventory, which was updated from the 2005 NEI
v2 in order to incorporate updated data for particular source categories
such as cement and hazardous waste incineration, and also revised from
comments from state and local inventory providers as a result of NATA
review.

This inventory was further modified to remove sources that were found to
have shut down prior to 2005 and to update the gold mine emissions per
information collected during the Gold Mine Ore and Production NESHAP. In
addition, mercury emissions were revised for the boiler sector to allow
for greater consistency with the Information Collection Request (ICR)
data collected for this rule. In particular, we used the unit-specific
ICR mercury emissions for all ICR facilities that could be mapped to the
NATA Inventory. We used the NEI to add important emissions release point
information necessary for photochemical grid modeling such as geographic
coordinates, stack coordinates, stack release height, exit temperature,
exit velocity because the ICR lacks this information. 

The replacement of the NATA mercury with the ICR mercury is described in
more detail in the air quality modeling TSD. ICR emissions were not used
directly for any other pollutants used in the v4.1 platform; however
they informed numerous corrections and updates to the inventory
including the removal of duplicates and facilities that had shut down
prior to 2005, and the inclusion of control information.

The stationary inventory used in the v4.1 platform is separated into
modeling sectors such as EGU point (ptipm), non-EGU point (ptnonipm),
and stationary emissions not included in the point source inventories
(nonpt). This nonpoint category is generally referred to as the
“area” source inventory, and this category is not a direct
representation of sources classified as area sources for the purposes of
National Emissions Standards for Hazardous Air Pollutants (NESHAP) and
Maximum Available Control Technology (MACT) standards.

Future Year Baseline Emissions

The 2016 baseline emissions are intended to represent the emissions
associated with growth and controls in 2016. The projections used for
this effort are unique to this project and are not associated with a
particular modeling platform.

The EGU point source (ptipm) emissions estimates for the future year
reference were created by the Integrated Planning Model (IPM) version
3.02 for criteria pollutants, hydrochloric acid, and mercury in 2015.
For the non-EGU point (ptnonipm) and nonpoint (nonpt) sectors, both
control and growth factors were applied to a subset of the 2005 v4.1
platform data to create the 2016 reference case. The 2014 projection
factors developed for the Transport Rule proposal (see  HYPERLINK
"http://www.epa.gov/ttn/chief/emch/index.html" \l "transport"
http://www.epa.gov/ttn/chief/emch/index.html#transport ) were further
enhanced and updated for these 2016 baseline projections.

The projected inventory incorporates emissions projections for the
proposed Transport Rule, cement kiln NESHAP, RICE NESHAP, gold mine
NESHAP, changes to boiler emissions based on the ICR database developed
for this rule, and known consent decrees. A complete list of rules
included in the future point source baseline inventory is shown in Table
6-2.

Table 6-2.	Control Strategies and/or Growth Assumptions Included in the
2016 Projection

Projections Carried Forward from the proposed Transport Rulea,b

Description	Pollutants

MACT rules, national, VOC: national applied by SCC, MACT	VOC

Consent Decrees and Settlements, including refinery consent decrees, and
settlements for: (1) Alcoa, TX and (2) Premcor (formerly MOTIVA), DE 
All

Municipal Waste Combustor Reductions—plant level	PM

Hazardous Waste Combustion	PM

Hospital/Medical/Infectious Waste Incinerator Regulations under Section
129d/11 1d	NOX, PM, SO2

Large Municipal Waste Combustors—growth applied to specific plants	All

MACT rules, plant-level, VOC: Auto Plants	VOC

MACT rules, plant-level, PM & SO2: Lime Manufacturing	PM, SO2

MACT rules, plant-level, PM: Taconite Ore	PM

Municipal Waste Landfills: project factor of 0.25 applied	All

Livestock Emissions Growth from year 2002 to 2016	NH3, PM

Residential Wood Combustion Growth and Changeouts from year 2005 to year
2016	All

Gasoline Stage II growth and control from year 2005 to year 2016	VOC

Portable Fuel Container MSAT2 inventory growth and control from year
2005 to year 2016	VOC

Additional Projections Used In Boiler MACT modelingc

	Emission Reductions resulting from controls put on specific boiler
units (not due to MACT) after 2005, identified through analysis of the
control data gathered from the ICR from the ICI Boiler NESHAP.	NOX, SO2,
HCL

NESHAP: Portland Cement (09/09/10)—plant level based on Industrial
Sector Integrated Solutions (ISIS) policy emissions in 2013. The ISIS
results are from the ISIS-Cement model runs for the NESHAP and NSPS
analysis of July 28, 2010.	HG, NOX, SO2, PM, HCL 

NESHAP: Gold Mine Ore Processing and Production Area Source Category
(based on proposed rule 04-15-10)—finalized 12/2010	HG

(continued)

Table 6-2.	Control Strategies and/or Growth Assumptions Included in the
2016 Projection (continued)

Projections Carried Forward from the proposed Transport Rulea,b

New York SIP reductions	VOC, NOX

Additional plant and unit closures	All

NESHAP: Reciprocating Internal Combustion Enginesd	NOX, CO, PM

a	They were only changed in that the projection year was 2015 or 2016,
rather than 2012 / 2014.

b	We inadvertently did not apply closures that had been applied for the
Transport Rule proposal; emissions from these plants sum to 3300 tons
VOC, 178 tons PM2.5, 1982 tons SO2, 1639 tons NOX, 6 tons NH3 and 379
tons CO. At the state level, the largest impact is in West Virginia (717
tons NOX, which is 2% of emissions in ptnonipm) and 1604 tons SO2, which
is 7% of the ptnonipm sector. When considering emissions from other
sectors, the percentages will be much smaller. All other errors are
under 500 tons (less than 1% of the ptnonipm sector).

c	We inadvertently did not apply LaFarge and SaintGobain consent
decrees, since one of the LaFarge facilities was already covered in the
cement ISIS projections, the reductions missed were lower than estimated
by the consent decree were on the order of 20,000 tons SO2, 15,000 tons
NOX, 400 tons HCL, 200 tons PM2.5.

d	Note that SO2 reductions are expected to occur to due fuel sulfur
limits but were excluded from the projection. They were expected to
reduce SO2 by 27,000 tons, nationwide.

The 2016 onroad emissions reflect control program implementation through
2016 and include the Light-Duty Vehicle Tier 2 Rule, the Onroad
Heavy-Duty Rule, and the Mobile Source Air Toxics (MSAT) final rule and
the category 3 marine diesel engines Clean Air Act and International
Maritime Organization standards which includes the establishment of
emission control areas for these ships. Emission reductions and
increases from the Renewable Fuel Standard version 2 (RFS2) are not
included. The future baseline case nonroad mobile emissions reductions
for these years include reductions to locomotives, various nonroad
engines including diesel engines and various marine engine types, fuel
sulfur content, and evaporative emissions standards. 

Future Year Sector Contribution Approach

The 2016 reference scenario for the boilers affected by the rule
includes 2005 emissions estimates with some emissions removed from the
inventory because of shut downs. The length of time required to conduct
emissions and photochemical modeling precluded the use of the final
facility-specific emissions estimates based on controls implemented for
this rule. A 2016 “control” or emissions adjustment scenario was
developed by tracking the total contribution from potentially
controllable boiler sector emissions from the 2016 baseline inventory.
This total contribution estimate, essentially a “zero-out,” of the
sector creates a policy space where potential control impacts would be
maximized at all locations. Since emissions reductions at controllable
sources are not 100% (100% ~ total contribution), the boiler sector air
quality contribution estimates from the 2016 source apportionment model
simulation are adjusted based on nation-wide estimates of control
percentages by pollutant to create a final 2016 “control” emissions
scenario. 

The 2016 estimated controllable emissions for the boiler sector are
shown in Table 6-3. Boiler sector emissions are contained in several
different general classes of emissions used for emissions modeling:
non-point or area (nonpt), point EGU (ptipm), and point non-EGU
(ptnonipm). These totals are the sum of emissions in the eastern and
western U.S. modeling domains so the non-point (area) category contains
some double-counting of emissions where the model domains overlap in the
central United States (see Figure 6-2). 

Table 6-3.	Estimated Future Year (2016) Controllable Boiler Sector
Emissions

	Sector	VOC	NOX	SO2	PM2.5	NH3	HG0	HG2	PM2.5 HG

Total (TPY)	Non-point—Boiler (nonpt)	14,107	394,459	1,149,402	108,386
8,489	0	0	0

Total (TPY)	EGU Point—Boiler (ptipm)	57	0	54,337	826	0	0	0	0

Total (TPY)	Non-EGU Point—Boiler (ptnonipm)	15,451	215,809	492,676
28,838	706	1	2	1

Pct of Sector	Non-point—Boiler (nonpt)	0	19	82	9	5

	Pct of Sector	EGU Point—Boiler (ptipm)	0	0	1	0	0

	Pct of Sector	Non-EGU Point—Boiler (ptnonipm)	1	9	26	6	0	10	7	12

Figure 6-2 shows the locations of boilers in the NEI point source
inventory used in the modeling analysis. Non-point boilers in the NEI do
not have stack location information and are not shown. These boilers are
spatially distributed in the modeling domain using spatial surrogates
appropriate for this sector.

Boiler emissions in the non-point/area (nonpt) modeling sector are used
as the basis of estimating air quality impacts and health benefits for
the area source rule. These emissions are based on SCC codes shown in
Table II-5. Boiler emissions in the point non-EGU (ptnonipm) and point
EGU (ptipm) are used to estimate impacts of the major source rule.
Facilities were identified for the major source rule based on meeting
several criteria: (1) NEI facility ID and process level fuel type
matched to a facility in the boiler ICR database, (2) NEI source had a
boiler SCC code, and (3) unit design capacity was less than 25 MW. 

Figure 6-2.	Locations of boilers in the NEI point inventory for the
future baseline (2016)

Major source mercury emissions were identified as the units added from
the boiler ICR database plus units in the NATA Hg inventory that were
not in the ICR but were considered part of the Boiler MACT Universe. A
percent emissions reduction was estimated based on the rule proposal
unit-specific facility-fuel combination in the ICR database. Each
facility/process matching the facility-fuel combinations that had
emission reductions larger than 1% based on the proposed rule were
tracked for source contribution.

Model Results

As part of the analysis for this rulemaking, the modeling system was
used to calculate daily and annual PM2.5 concentrations, 8-hr maximum
ozone, annual total mercury deposition levels and visibility impairment.
Model predictions are used to estimate future-year design values of
PM2.5 and ozone. The annual PM2.5 design value determines whether a
monitor location is attaining the annual PM2.5 NAAQS. Specifically, we
compare a 2016 reference scenario, a scenario without the boiler sector
controls, to a 2016 control scenario which includes the adjustments to
the boiler sector. This is done by calculating the simulated air quality
ratios between any particular future year simulation and the 2005 base.
These predicted ratios are then applied to ambient base year design
values. The design value projection methodology used here followed EPA
guidance for such analyses (USEPA, 2007  XE “U.S. EPA, 2007”  ).
Additionally, the raw model outputs are also used in a relative sense as
inputs to the health and welfare impact functions of the benefits
analysis. Only model predictions for mercury deposition were analyzed
using absolute model changes, although percent changes between the
control case and two future baselines are also estimated.

The 36 km and both 12 km modeling domains were modeled for the entire
year of 2005 and projected year 2016. Data from the entire year were
utilized when looking at the estimation of PM2.5, total mercury
deposition, and visibility impacts from the regulation. Data from April
through October is used to estimate ozone impacts. All air quality
impacts are based on improvements in future year pollution based on
emissions changes from this source sector.

Impacts of Sector on Total Mercury Deposition

This section summarizes the results of our modeling of total mercury
deposition impacts in the future based on changes to boiler emissions.
Available data indicate that the mercury emissions from these sources
are a mixture of gaseous elemental mercury (25%), inorganic divalent
mercury (reactive gas phase mercury) (50%), and particulate bound
mercury (25%). Model results for the eastern and central United States
indicate that estimated total mercury deposition (wet and dry forms)
reductions from this sector would be 1,393 µg/m2 (1.5% of total mercury
deposition from all sources); approximately 51% in dry form and 49% in
wet form. The chemical composition of this estimated reduction is
approximately 60% reactive gas phase (HG2) and 40% particulate. A
reduction of 75 µg/m2 (0.5% of total mercury deposition from all
sources) is estimated for the western United States; approximately 56%
in dry form and 44% in wet form. The chemical composition of the
reductions in the western model domain is approximately 63% reactive gas
phase (HG2) and 37% particulate. These reductions are related to changes
in emissions at major boiler sources using the ptnonipm sector as a
surrogate. 

Impacts of Sector on Future Annual PM2.5 Levels

This section summarizes the results of our modeling of annual average
PM2.5 air quality impacts in the future due to estimated reductions in
emissions from this sector. Specifically, we compare a 2016 reference
scenario to a 2016 control scenario. The modeling assessment indicates a
decrease up to 0.81 µg/m3 in annual PM2.5 design values is possible
given an area’s proximity to controlled sources and the amount of
reduced sulfur dioxide and primary PM2.5 emissions from major sources
under the recommended solid fuel category option. The estimated mean
reduction over all monitor locations is 0.15 µg/m3 for major source
reductions under the recommended solid fuel category. A decrease up to
0.66 µg/m3 in annual PM2.5 design values is possible given an area’s
proximity to controlled sources and the amount of reduced sulfur dioxide
and primary PM2.5 emissions from major sources under the alternative
option. The estimated mean reduction over all monitor locations is 0.12
µg/m3 for major source reductions under the alternative option. Area
source reductions show a decrease up to 0.01 µg/m3 in annual PM2.5
design values is possible given an area’s proximity to controlled area
sources and the amount of reduced sulfur dioxide and primary PM2.5
emissions.

Impacts of Sector on Future 24-hour PM2.5 Levels

This section summarizes the results of our modeling of 24-hr average
PM2.5 air quality impacts in the future due to reductions in emissions
from this sector. Specifically, we compare a 2016 reference scenario to
a 2016 control scenario. The modeling assessment indicates an estimated
average decrease of 0.50 µg/m3 in 24-hr average PM2.5 design values
over all monitor locations in the United States is possible given the
amount of reduced sulfur dioxide and primary PM2.5 emissions from major
sources under the recommended solid fuel category option. An estimated
average decrease of 0.39 µg/m3 in 24-hr average PM2.5 design values
over all monitor locations in the United States is possible given the
amount of reduced sulfur dioxide and primary PM2.5 emissions from major
sources under the alternative option. An estimated decrease up to 0.03
µg/m3 in 24-hr average PM2.5 design values at monitor locations in the
United States is possible given an area’s proximity to controlled
sources and the amount of reduced sulfur dioxide and primary PM2.5
emissions from area sources.

Impacts of Sector on Future Visibility Levels

Air quality modeling conducted for this final rule was used to project
visibility conditions in 138 mandatory Class I federal areas across the
U.S. in 2016 (USEPA, 2007). The level of visibility impairment in an
area is based on the light-extinction coefficient and a unitless
visibility index, called a “deciview,” which is used in the
valuation of visibility. The deciview metric provides a 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. Higher deciview values are indicative
of worse visibility. Thus, an improvement in visibility is a decrease in
deciview value. 

The modeling assessment indicates an estimated average visibility
improvement of 0.51 deciviews in annual 20% worst visibility days over
all Class I area monitors based on controls for major sources under the
recommended solid fuel category option. An estimated average visibility
improvement of 0.42 deciviews in annual 20% worst visibility days over
all Class I area monitors based on controls for major sources under the
alternative option. An improvement in visibility up to 0.03 deciviews at
Class I monitor locations in the United States is possible given an
area’s proximity to controlled sources and the amount of reduced
sulfur dioxide and primary PM2.5 emissions from area sources.

Impacts of Sector on Future Ozone Levels

This section summarizes the results of our modeling of 8-hr maximum
ozone air quality impacts in the future due to reductions in emissions
from this sector. Specifically, we compare a 2016 reference scenario to
a 2016 control scenario. The modeling assessment indicates a decrease of
less than 0.01 ppb in ozone design values is possible given an area’s
proximity to controlled sources and the amount of reduced VOC emissions
under all major and area source options. The full details involved in
calculating design value are given in appendix P of 40 CFR part 50.
Projected air quality benefits are estimated using procedures outlined
by United States Environmental Protection Agency modeling guidance
(USEPA, 2007).

Section 6 References

Baker, K., Scheff, P., 2007. Photochemical model performance for PM2.5
sulfate, nitrate, ammonium, and precursor species SO2, HNO3, and NH3 at
background monitor locations in the central and eastern United States.
Atmospheric Environment 41, 6185-6195.

Chang, J.S., Brost, R.A., Isaksen, I.S.A., Madronich, S., Middleton, P.,
Stockwell, W.R., Walcek, C.J. 1987. A 3-dimensional eulerian acid
deposition model—physical concepts and formulation. J. Geophys.
Res.-Atmos. 92, 14681-14700.

ENVIRON, 2008. User’s Guide Comprehensive Air Quality Model with
Extensions. ENVIRON International Corporation, Novato.

Gery, M.W., Whitten, G.Z., Killus, J.P., Dodge, M.C., 1989. A
photochemical kinetics mechanism for urban and regional scale computer
modeling. J. Geophys. Res.-Atmos. 94, 12925-12956.

Houyoux, M.R., Vukovich, J.M., Coats, C.J., Wheeler, N.J.M., Kasibhatla,
P.S., 2000. Emission inventory development and processing for the
Seasonal Model for Regional Air Quality (SMRAQ) project. Journal of
Geophysical Research-Atmospheres 105, 9079-9090.

Nenes, A., Pandis, S.N., Pilinis, C., 1999. Continued development and
testing of a new thermodynamic aerosol module for urban and regional air
quality models. Atmospheric Environment 33, 1553-1560.

Nobel, C.E., McDonald-Buller, E.C., Kimura, Y., Allen, D.T., 2001.
Accounting for spatial variation of ozone productivity in NOx emission
trading. Environmental Science & Technology 35, 4397-4407.

Russell, A.G., 2008. EPA Supersites Program-related emissions-based
particulate matter modeling: Initial applications and advances. J. Air
Waste Manage. Assoc. 58, 289-302.

Strader, R., Lurmann, F., Pandis, S.N., 1999. Evaluation of secondary
organic aerosol formation in winter. Atmospheric Environment 33,
4849-4863.USEPA, 2007. Guidance on the Use of Models and Other Analyses
for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and
Regional Haze, RTP.

USEPA, 2010. Air Quality Modeling Technical Support Document: Boiler
Source Sector Rules (EPA-454/R-10-006), Research Triangle Park, North
Carolina.

Wagstrom, K.M., Pandis, S.N., Yarwood, G., Wilson, G.M., Morris, R.E.,
2008. Development and application of a computationally efficient
particulate matter apportionment algorithm in a three-dimensional
chemical transport model. Atmospheric Environment 42, 5650-5659.



Benefits of Emissions Reductions

Synopsis

In this section, we provide an estimate of the monetized benefits
associated with reducing exposure to particulate matter (PM) and ozone
for the final Boiler MACT and Boiler Area Source Rule. The PM2.5
reductions are the result of emission limits on PM as well as emission
limits on other pollutants, including hazardous air pollutants (HAPs).
The total PM2.5 reductions are the consequence of the technologies
installed to meet these multiple limits. The latter are often referred
to as “co-benefits.” Ozone reductions are the result of reductions
in emissions of VOCs, which are precursors to ozone formation. These
benefit estimates include the number of cases of avoided morbidity and
premature mortality among populations exposed to PM2.5 and ozone, as
well as the monetized value of those avoided cases. Because we were
unable to monetize the direct benefits associated with reducing HAPs,
the monetized benefits estimate is an underestimate of the total
benefits. The extent of this underestimate, whether small or large, is
unknown. Using a 3% discount rate, we estimate the total combined
monetized benefits of the final Boiler MACT and Boiler Area Source Rule
to be $22 billion to $54 billion in the implementation year (2014).
Using a 7% discount rate, we estimate the total combined monetized
benefits of the final Boiler MACT and Boiler Area Source Rule to be $20
billion to $49 billion in the implementation year. Higher or lower
estimates of benefits are possible using other assumptions; examples of
this are provided in Figure 7-1. All estimates are in 2008$ and include
any energy disbenefits associated with the increased emissions from
additional energy usage. 

These monetized estimates reflect EPA’s current interpretation of the
scientific literature (U.S. EPA, 2009c  XE “U.S. EPA, 2009c”  ). In
addition, these estimates incorporate an array of improvements
introduced since the proposal, including boiler sector-specific air
quality modeling data, lowest measured level (LML) assessment, mercury
deposition maps, ozone benefits, and energy disbenefits. Methodological
and time limitations under the court-ordered schedule prevented EPA from
monetizing the benefits from several important benefit categories,
including direct benefits from reducing hazardous air pollutants,
ecosystem effects, and visibility impairment. The direct benefits from
reducing other air pollutants have not been monetized in this analysis,
including reducing a combined 113,000 tons of carbon monoxide, 30,000
tons of HCl, 830 tons of HF, 2,900 pounds of mercury, 3,000 tons of
other metals, and 23 grams of dioxins/furans (TEQ) each year. We assess
the direct benefits of these emission reductions qualitatively in this
analysis.

Figure 7-1.	Total Monetized PM2.5 and Ozone Benefits for the Final
Boiler MACT and Boiler Area Source Rule in 2014a

a	This graph shows the estimated benefits at discount rates of 3% and 7%
using effect coefficients derived from the Pope et al. (2002)  XE
“Pope et al. (2002)”   study and the Laden et al. (2006)  XE
“Laden et al. (2006)”   study, as well as 12 effect coefficients
derived from EPA’s expert elicitation on PM mortality. The results
shown are not the direct results from the studies or expert elicitation;
rather, the estimates are based in part on the concentration-response
function provided in those studies. These estimates do not include the
direct benefits from reducing HAP emissions, but they do include the
energy disbenefits associated with the increased emissions from
additional energy usage. Due to methodology and time limitations under
the court-ordered schedule, we were unable to monetize the benefits
associated with several categories of benefits, including direct
exposure to HAPs and SO2, as well as ecosystem effects, and visibility
effects. These benefits reflect existing boilers and new boilers
anticipated to come online by 2014.

Calculating Benefits

The benefit categories associated with the emission reduction
anticipated for these rules can be broadly categorized as those benefits
directly attributable to reduced exposure to HAPs, the PM co-benefits
associated with reducing HAPs (i.e., PM reduced by introducing
technology designed to reduce HAPs), and those attributable to exposure
to other pollutants. Several of the HAPs that would be reduced by these
rules have been classified as known or probable human carcinogens. As a
result, one benefit of the proposed regulation is a reduction in the
risk of cancer. Other benefit categories include potential reduced
incidence of neurological effects and irritations of the lungs and skin,
reduced mortality and other morbidity effects associated with PM and
SO2. 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
vegetation, visibility impairment, and acidification of water bodies. We
were unable to monetize the direct benefits associated with reducing
HAPs in this analysis. In Section 7.5.5 of this RIA, we provide a full
qualitative discussion of the direct health benefits associated with the
reductions in emissions of HAPs anticipated by these rules, including a
full discussion of the complexity associated with monetizing HAP
benefits. We also provide maps of reduced mercury deposition in that
section. Therefore, all monetized benefits provided in this analysis
only reflect improvements in ambient PM2.5 and ozone concentrations.
Thus, the monetized benefits estimate is an underestimate of the total
benefits. The extent of this underestimate, whether small or large, is
unknown. In addition to pollutants we do not directly monetize, these
rulemakings are expected to reduce emissions of PM2.5, SO2, and VOCs.
Because SO2 is also a precursor to PM2.5, reducing SO2 emissions will
also reduce PM2.5 formation, human exposure, and therefore reduce
estimated incidence of PM2.5-related health effects. The estimated PM
reductions are the result of emission limits on PM as well as emission
limits on other pollutants, including hazardous air pollutants for these
rules. The total PM2.5 reductions are the consequence of the
technologies installed to meet these multiple limits. In addition, these
rules are expected to result in reductions in VOCs, which will result in
changes in ambient ozone concentrations.

In implementing these rules, emission controls may lead to reductions in
ambient PM2.5 below the National Ambient Air Quality Standards (NAAQS)
for PM in some areas and assist other areas with attaining the PM NAAQS.
Because the PM NAAQS RIAs also calculate PM benefits, there are
important differences worth noting in the design and analytical
objectives of each RIA. The NAAQS RIAs illustrate the potential costs
and benefits of attaining a new air quality standard nationwide based on
an array of emission control strategies for different sources. In short,
NAAQS RIAs hypothesize, but do not predict, the control strategies that
States may choose to enact when implementing a NAAQS. The setting of a
NAAQS does not directly result in costs or benefits, and as such, the
NAAQS RIAs are merely illustrative and are not intended to be added to
the costs and benefits of other regulations that result in specific
costs of control and emission reductions. However, some costs and
benefits estimated in this RIA account for the same air quality
improvements as estimated in the illustrative PM2.5 NAAQS RIA. 

By contrast, the emission reductions for this rule are from a specific
class of well-characterized sources. In general, EPA is more confident
in the magnitude and location of the emission reductions for these
rules. It is important to note that emission reductions anticipated from
these rules do not result in emission increases elsewhere (other than
potential energy disbenefits). Emission reductions achieved under these
and other promulgated rules will ultimately be reflected in the baseline
of future NAAQS analyses, which would reduce the incremental costs and
benefits associated with attaining the NAAQS. EPA remains forward
looking towards the next iteration of the 5-year review cycle for the
NAAQS, and as a result does not issue updated RIAs for existing NAAQS
that retroactively update the baseline for NAAQS implementation. For
more information on the relationship between the NAAQS and rules such as
analyzed here, please see Section 1.2.4 of the SO2 NAAQS RIA (U.S. EPA,
2010h  XE “U.S. EPA, 2010h”  ).

Methodology Improvements since Proposal

This benefits analysis for the final Boiler MACT and Area Source Rule
incorporates an array of policy and technical improvements since the
proposal analysis in 2010 (U.S. EPA, 2010c  XE “U.S. EPA, 2010c”  ),
including:

Boiler sector-specific air quality modeling data. The benefits estimates
for this final analysis are derived from air quality data modeled using
CAMx that reflect the emissions from the boiler sector and their
contribution to ambient PM2.5 and ozone levels. These data provide a
different and likely superior representation of the geographic
distribution of emissions and associated ambient concentrations
specifically from boilers than the generic benefit-per-ton estimates
used in the proposal that reflected emissions for the entire non-EGU
(non-electrical generating unit) category. For more information
regarding the modeling inputs and assumptions, please see Section 6 of
this RIA. For more information regarding the derivation of boiler
sector-specific benefit-per-ton estimates and how they are superior to
the estimates used in the proposal, please see Section 7.2.6 of this
RIA. 

Lowest Measured Level (LML) assessment. Consistent with the rationale
outlined in the Cement NESHAP proposal RIA (U.S. EPA, 2009a), EPA has
recently changed its approach to characterizing the uncertainty
associated with benefits estimated at lower air quality levels.
Specifically, EPA now estimates PM-related mortality without assuming an
threshold at 10 µg/m3 in the concentration-response function.
Consistent with recent scientific advice, we are replacing the previous
threshold sensitivity analysis with a new LML assessment. We discuss
this assessment in more detail in Section 7.2.4 and provide the results
of this LML assessment in Section 7.3. 

Mercury deposition. The air quality modeling data provide an estimate of
the reduction in mercury deposition associated with the mercury emission
reductions anticipated as a result of these rules. We provide maps of
the reduced mercury deposition in Section 7.5.4.1. EPA did not model
mercury methylation, bioaccumulation in fish tissue, and human
consumption of mercury-contaminated fish that would be needed in order
to estimate the human health benefits from reducing mercury emissions.

Ozone benefits. The air quality modeling data provide an estimate of the
change in ambient ozone concentrations associated with the VOC emission
reductions anticipated as a result of the Boiler MACT. We provide a
table of the ozone-related health benefits in Section 7.3.

Energy disbenefits. For this final analysis, we include an estimate of
the increased CO2 emissions associated with the additional electricity
required to operate control devices. We provide the results of this
analysis in Section 7.4. 

Improved characterization of uncertainty. For this final analysis, we
characterize uncertainty using four methods (Monte Carlo methods, LML
assessment, alternate concentration-response functions, and qualitative
descriptions). While the proposal incorporated some of these approaches,
the Monte Carlo methods and LML assessment are an improvement for the
final analysis. Confidence intervals reflecting the standard errors in
the underlying epidemiology and economics literature are provided for
incidence and valuation results in Tables 7-4 to 7-8.

Benefits Analysis Approach for PM2.5 and Ozone

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 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. The “damage-function”
approach is the standard method for assessing costs and benefits of
environmental quality programs and has been used in several recent
published analyses (Levy et al., 2009  XE “Levy et al., 2009”  ;
Hubbell et al., 2009  XE “Hubbell et al., 2009”  ; Tagaris et al.,
2009  XE “Tagaris et al., 2009”  ).

To assess economic value in a damage-function framework, the changes in
environmental quality must be translated into effects on people or on
the things that people value. For changes in PM2.5 or ozone, a health
impact analysis (HIA) must first be conducted to convert air quality
changes into effects that can be assigned dollar values. For this RIA,
the health impacts analysis is limited to those health effects that are
directly linked to ambient levels of air pollution and specifically to
those linked to PM2.5 and ozone. We also provide qualitative discussions
of the impact of changes in other environmental and ecological effects,
including the benefits associated with decreasing deposition of sulfur
to terrestrial and aquatic ecosystems, but we are unable to place an
economic value on these changes due to time limitations under the
court-ordered schedule.

We note at the outset that EPA rarely has the time or resources to
perform extensive new research to measure directly either the health
outcomes or their values for regulatory analyses. Thus, similar to
Kunzli et al. (2001)  XE “Kunzli et al. (2001)”   and other recent
health impact analyses, 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 for the environmental quality change under
analysis. Adjustments are made for the level of environmental quality
change, the socio-demographic and economic characteristics of the
affected population, and other factors to improve the accuracy and
robustness of benefits estimates.

Health Impact Analysis (HIA)

The HIA quantifies the potential changes in the incidence of adverse
health impacts resulting from estimated changes in human exposure to air
pollution. HIAs are a well-established approach for estimating the
retrospective or prospective change in adverse health impacts resulting
from population-level changes in exposure to pollutants (Levy et al.,
2009  XE “Levy et al., 2009”  ). Analysts have applied the HIA
approach to estimate human health impacts resulting from hypothetical
changes in pollutant levels (Hubbell et al., 2005  XE “Hubbell et al.,
2005”  ; Davidson et al., 2007  XE “Davidson et al., 2007”  ;
Tagaris et al., 2009  XE “Tagaris et al., 2009”  ). For this
analysis, we used the environmental Benefits Mapping and Analysis
Program (BenMAP), which is a PC-based tool that can systematize health
impact analyses by applying a database of key input parameters,
including health impact functions and population projections.

The HIA approach used in this analysis involves three basic steps: (1)
utilizing CAMx-generated projections of PM2.5 and ozone air quality and
estimating the change in the spatial distribution of the ambient air
quality; (2) determining the subsequent change in population-level
exposure; (3) calculating health impacts by applying
concentration-response relationships drawn from the epidemiological
literature (Hubbell et al., 2009  XE “Hubbell et al., 2009”  ) to
this change in population exposure. 

A typical health impact function might look as follows:

 

is the change in air quality; and β is the effect coefficient drawn
from the epidemiological study. For this analysis, we systematize the
HIA calculation process using BenMAP’s library of existing air quality
monitoring data, population data and health impact functions. Figure 7-2
provides a simplified overview of this approach, and Figure 7-3
identifies the data inputs and outputs for the BenMAP model for a PM2.5
analysis. 

Figure 7-2.	Illustration of BenMAP Approach

 

Figure 7-3.	Data Inputs and Outputs for the BenMAP Model for a PM2.5
Analysis

The benefits estimates in this analysis were derived using modified
versions of the health impact functions used in the PM NAAQS Regulatory
Impact Analysis (RIA) (U.S. EPA, 2006  XE “U.S. EPA, 2006”  ) and
the Ozone NAAQS RIA (U.S. EPA, 2010a  XE “U.S. EPA, 2010a”  ). While
many of the functions are identical to those used in the PM NAAQS RIA,
we have updated a few of the underlying assumptions over the last few
years. For a detailed description of the underlying functions, studies,
baseline incidence rates, and population data used in this analysis,
please refer to Chapter 5 of the recently proposed Transport Rule (U.S.
EPA, 2010e  XE “U.S. EPA, 2010e”  ). Table 7-1 identifies which
human health and welfare endpoints are included in the monetized
benefits and which endpoints remain unquantified. In summary, the
estimate of monetized PM benefits includes premature mortality and
eleven morbidity endpoints, and the estimate of monetized ozone benefits
includes premature mortality and five morbidity endpoints. 

Table 7-1.	Human Health and Welfare Effects of Air Pollutants Affected 

Pollutant/Effect	Quantified and Monetized 	Unquantified

PM: health a	Premature mortality based on cohort study estimates b	Low
birth weight

Pre-term births

	Premature mortality based on expert elicitation estimates	Pulmonary
function

Nonfatal cardiovascular outcomes other than myocardial infarctions

	Chronic bronchitis

−)c

	Lower and upper respiratory illness

Minor restricted activity days

Work loss days

Asthma exacerbations (among asthmatic populations

Respiratory symptoms (among asthmatic populations)

Infant mortality

	PM: welfare

Visibility in Class I areas

Household soiling

Visibility in residential and non-class I areas

UVb exposure (+/−)c

Global climate impacts c

Ozone: health	Premature mortality based on short-term study estimates
Chronic respiratory damage

Premature mortality due to long-term exposures

	Hospital admissions: respiratory	Premature aging of the lungs

	Emergency room visits for asthma	Non-asthma respiratory emergency room
visits

	Minor restricted activity days	UVb exposure (+/−)c

	School loss days

	

Ozone: welfare	Decreased outdoor worker productivity	Yields for:

– Commercial forests

– Fruits and vegetables, and

−)c

SO2: health

Respiratory hospital admissions

Asthma emergency room visits

Asthma exacerbation

Acute respiratory symptoms

Premature mortality

Pulmonary function

(continued)

Table 7-1.	Human Health and Welfare Effects of Air Pollutants Affected
(continued)

Pollutant/Effect	Quantified and Monetized 	Unquantified

SO2: welfare

Commercial fishing and forestry from acidic deposition

Recreation in terrestrial and aquatic ecosystems from acid deposition

Increased mercury methylation

Mercury: health 

Incidence of neurological disorders

Incidence of learning disabilities

Incidences in developmental delays

Potential cardiovascular effects including:

– Altered blood pressure regulation

– Increased heart rate variability

– Incidences of heart attack

Potential reproductive effects

Mercury: environment

Impact on birds and mammals (e.g., reproductive effects, cardiovascular
effects)

Mercury: welfare 

Impacts to commercial., subsistence and recreational fishing

HAPs

Health effects associated with HAP exposure

a	In addition to primary economic endpoints, there are a number of
biological responses that have been associated with PM health effects
including morphological changes and altered host defense mechanisms. The
public health impact of these biological responses may be partly
represented by our quantified endpoints.

b	Cohort estimates are designed to examine the effects of long-term
exposures to ambient pollution, but relative risk estimates may also
incorporate some effects due to shorter term exposures (see Kunzli et
al., 2001  XE “Kunzli et al., 2001”   for a discussion of this
issue). While some of the effects of short-term exposure are likely to
be captured by the cohort estimates, there may be additional premature
mortality from short term PM exposure not captured in the cohort
estimates included in the primary analysis.

c	May result in benefits or disbenefits.

Estimating PM2.5-related Premature Mortality

Consistent with all RIAs since the proposal RIA for the Portland Cement
NESHAP (U.S. EPA, 2009a  XE “U.S. EPA, 2009a”  ), the PM2.5 benefits
estimates utilize the concentration-response functions as reported in
the epidemiology literature, as well as the 12 functions obtained in
EPA’s expert elicitation study as a characterization of uncertainty. 

One estimate is based on the concentration-response (C-R) function
developed from the extended analysis of American Cancer Society (ACS)
cohort, as reported in Pope et al. (2002) xe “Pope et al. (2002)” ,
a study that EPA has previously used to generate its primary benefits
estimate. When calculating the estimate, EPA applied the effect
coefficient as reported in the study without an adjustment for assumed
concentration threshold of 10 µg/m3 as was done in recent (2006–2009)
Office of Air and Radiation RIAs.

EPA strives to use the best available science to support our benefits
analyses, and we recognize that interpretation of the science regarding
air pollution and health is dynamic and evolving. Based on our review of
the current body of scientific literature, EPA now estimates PM-related
mortality without applying an assumed concentration threshold. EPA’s
Integrated Science Assessment for Particulate Matter (U.S. EPA, 2009c 
XE “U.S. EPA, 2009c”  ), which was recently reviewed by EPA’s
Clean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009a  XE “U.S.
EPA-SAB, 2009a”  ; U.S. EPA-SAB, 2009b  XE “U.S. EPA-SAB, 2009b” 
), concluded that the scientific literature consistently finds that a
no-threshold log-linear model most adequately portrays the PM-mortality
concentration-response relationship while recognizing potential
uncertainty about the exact shape of the concentration-response
function. Since then, the Health Effects Subcommittee (U.S. EPA-SAB,
2010  XE “U.S. EPA-SAB, 2010”  ) of EPA’s Council concluded,
“The HES fully supports EPA’s decision to use a no-threshold model
to estimate mortality reductions. This decision is supported by the
data, which are quite consistent in showing effects down to the lowest
measured levels. Analyses of cohorts using data from more recent years,
during which time PM concentrations have fallen, continue to report
strong associations with mortality. One estimate is based on the C-R
function developed from the extended analysis of the Harvard Six Cities
cohort, as reported by Laden et al. (2006)  XE “Laden et al. (2006)”
 . This study, published after the completion of the Staff Paper for the
2006 PM2.5 NAAQS, has been used as an alternative estimate in the PM2.5
NAAQS RIA and PM2.5 benefits estimates in RIAs completed since the PM2.5
NAAQS. When calculating the estimate, EPA applied the effect coefficient
as reported in the study without an adjustment for assumed concentration
threshold of 10 µg/m3 as was done in recent (2006–2009) RIAs. 

Twelve estimates are based on the C-R functions from EPA’s expert
elicitation study (IEc, 2006  XE “IEc, 2006”  ; Roman et al., 2008 
XE “Roman et al., 2008”  ) on the PM2.5-mortality relationship and
interpreted for benefits analysis in EPA’s final RIA for the PM2.5
NAAQS. For that study, twelve experts (labeled A through L) provided
independent estimates of the PM2.5 -mortality concentration-response
function. EPA practice has been to develop independent estimates of
PM2.5-mortality estimates corresponding to the concentration-response
function provided by each of the twelve experts, to better characterize
the degree of variability in the expert responses.

Therefore, there is no evidence to support a truncation of the CRF
[concentration-response function].” In conjunction with the underlying
scientific literature, this document provided a basis for reconsidering
the application of thresholds in PM2.5 concentration-response functions
used in EPA’s RIAs. For a summary of these scientific review
statements and the panel members commenting on thresholds since 2002,
please consult the Technical Support Document (TSD) Summary of Expert
Opinions on the Existence of a Threshold (U.S. EPA, 2010d  XE “U.S.
EPA, 2010d”  ).

Consistent with the recent scientific advice summarized above, we are
replacing the previous threshold sensitivity analysis with a new
“Lowest Measured Level” (LML) assessment. This approach summarizes
the distribution of avoided PM mortality impacts according to the
baseline PM2.5 levels experienced by the population receiving the PM2.5
mortality benefit. In the results section, we identify on the figures
the lowest air quality levels measured in each of the primary cohort
studies that estimate PM-related mortality. This information allows
readers to determine the portion of PM-related mortality benefits
occurring above or below the LML of each study; in general, our
confidence in the estimated PM mortality decreases as we consider air
quality levels further below the LML in the two epidemiological studies.
While an LML assessment provides some insight into the level of
uncertainty in the estimated PM mortality benefits, EPA does not view
the LML as a threshold and continues to quantify PM-related mortality
impacts using a full range of modeled air quality concentrations. Unlike
an assumed threshold, which is a modeling assumption that reduces the
magnitude of the estimated health impacts, the LML is a characterization
of the fraction of benefits that are more uncertain. It is important to
emphasize that just because we have greater confidence in the benefits
above the LML, this does not mean that we have no confidence that
benefits occur below the LML. 

Analyses of these cohorts using data from more recent years, during
which time PM concentrations have fallen, continue to report strong
associations with mortality. As we model mortality impacts among
populations exposed to levels of PM2.5 that are successively lower than
the LML of each study, our confidence in the results diminishes. As air
pollution emissions continue to decrease over time, there will be more
people in areas where we do not have published epidemiology studies.
However, each successive cohort study has shown evidence of effects at
successively lower levels of PM2.5. As more cohort studies follow large
populations over time, we will likely have more studies with lower LML
as air quality levels continue to improve. Even in the absence of a
definable threshold, we have more confidence in the benefits estimates
above the LML of the large cohort studies. To account for the
uncertainty in each of the studies that we base our mortality estimates
on, we provide the LML for each of the cohort studies. However, the
finding of effects at the lowest LML from recent studies indicates that
confidence in PM2.5-related mortality effects down to at least 7.5
µg/m3 is high. 

In implementing these rules, emission controls may lead to reductions in
ambient PM2.5 below the PM NAAQS in some areas. While benefits occurring
below the standard may be somewhat more uncertain than those occurring
above the standard, EPA considers them to be legitimate components of
the total benefits estimate. Furthermore, given that the epidemiological
literature in most cases has not provided estimates based on threshold
models, there would be additional uncertainties imposed by assuming
thresholds or other non-linear concentration-functions for the purposes
of benefits analysis.

Economic Valuation of Health Impacts

These models assume that all fine particles, regardless of their
chemical composition, are equally potent in causing premature mortality
because the scientific evidence is not yet sufficient to allow
differentiation of effect estimates by particle type (U.S. EPA, 2009c).
Directly emitted PM, SO2, and VOC are the dominant PM2.5 precursors
affected by this rule. Even though we assume that all fine particles
have equivalent health effects, the benefit-per-ton estimates (described
in detail in Section 7.2.6) vary between precursors because each ton of
precursor reduced has a different propensity to form PM2.5 and a
different pattern of transport, resulting geographic distribution of
exposure. When more people are exposed, the benefits per ton are
greater. For example, SO2 tends to have a lower benefit-per-ton estimate
than direct PM2.5 because sulfate particles formed from SO2 emissions
can transport many miles, meaning that higher exposures may occur over
areas with low populations. On the other hand, to the extent that direct
PM2.5 emissions occur in high density population areas, exposures will
tend to be higher there, leading to higher monetized health benefits for
direct PM2.5 than for SO2 emissions. Economic Valuation of Health
Impacts

After quantifying the change in adverse health impacts, the final step
is to estimate the economic value of these avoided impacts. Please refer
to Table 5-11 in the recently proposed Transport Rule (U.S. EPA, 2010e 
XE “U.S. EPA, 2010e”  ) for a detailed description of the underlying
valuation functions and the monetized unit values for each endpoint
incorporated into this analysis. The monetized mortality benefits
dominate the total benefits estimates. 

As is the nature of RIAs, the assumptions and methods used to estimate
air quality benefits evolve over time to reflect the Agency’s most
current interpretation of the scientific and economic literature. For a
period of time (2004–2006), the Office of Air and Radiation (OAR)
valued mortality risk reductions using a value-of-a-statistical-life
(VSL) estimate derived from a limited analysis of some of the available
studies. OAR arrived at a VSL using a range of $1 million to $10 million
(2000$) consistent with two meta-analyses of the wage-risk literature.
The $1 million value represented the lower end of the interquartile
range from the Mrozek and Taylor (2002)  XE “Mrozek and Taylor
(2002)”   meta-analysis of 33 studies. The $10 million value
represented the upper end of the interquartile range from the Viscusi
and Aldy (2003)  XE “Viscusi and Aldy (2003)”   meta-analysis of 43
studies. The mean estimate of $5.5 million (2000$) was also consistent
with the mean VSL of $5.4 million estimated in the Kochi et al. (2006) 
XE “Kochi et al. (2006)”   meta-analysis. However, the Agency
neither changed its official guidance on the use of VSL in rule-makings
nor subjected the interim estimate to a scientific peer-review process
through the Science Advisory Board (SAB) or other peer-review group. 

During this time, the Agency continued work to update its guidance on
valuing mortality risk reductions, including commissioning a report from
meta-analytic experts to evaluate methodological questions raised by EPA
and the SAB on combining estimates from the various data sources. In
addition, the Agency consulted several times with the Science Advisory
Board Environmental Economics Advisory Committee (SAB-EEAC) on the
issue. With input from the meta-analytic experts, the SAB-EEAC advised
the Agency to update its guidance using specific, appropriate
meta-analytic techniques to combine estimates from unique data sources
and different studies, including those using different methodologies
(i.e., wage-risk and stated preference) (U.S. EPA-SAB, 2007  XE “U.S.
EPA-SAB, 2007”  ). 

Until updated guidance is available, the Agency determined that a
single, peer-reviewed estimate applied consistently best reflects the
SAB-EEAC advice it has received. Therefore, the Agency has decided to
apply the VSL that was vetted and endorsed by the SAB in the Guidelines
for Preparing Economic Analyses (U.S. EPA, 2000  XE “U.S. EPA, 2000”
 ) while the Agency continues its efforts to update its guidance on this
issue. This approach calculates a mean value across VSL estimates
derived from 26 labor market and contingent valuation studies published
between 1974 and 1991. The mean VSL across these studies is $6.3 million
(2000$). The Agency is committed to using scientifically sound,
appropriately reviewed evidence in valuing mortality risk reductions and
has made significant progress in responding to the SAB-EEAC’s specific
recommendations. 

Calculating Boiler Sector-specific Benefit-per-ton Estimates

Benefit per-ton (BPT) estimates can be used to quantify the health
impacts and monetized human health benefits of an incremental change in
air pollution precursor emissions. In situations when we are unable to
specifically model a regulatory option because of data or resource
constraints, this approach can provide a reliable estimate of the
benefits of emission reduction scenarios. EPA has used the BPT technique
in previous RIAs, including the recent Ozone NAAQS RIA (U.S. EPA, 2010a 
XE “U.S. EPA, 2010a”  ) and Federal Transport Rule RIA (U.S. EPA,
2010e  XE “U.S. EPA, 2010e”  ). For this analysis, the emissions
inventories used in the air quality modeling and benefits modeling are
slightly different from the final boiler sector emission inventories
reflected in the rules’ emission memos.  These discrepancies exist
because emissions and air quality modeling decisions are made early in
the analytical process, and the sheer number of boilers (over 180,000
area boilers and 13,000 major boilers) made it impossible to reconcile
the data completely. Part of the discrepancy is because the emissions
inventory categorizes boilers as “point” and “non-point” instead
of “major source” or “area source.” We assume that the boilers
in the point source inventory are appropriate surrogates for the major
source boilers and that the boilers in the non-point source inventory
are appropriate surrogates for the area source boilers. Furthermore, the
emission inventories used for air quality modeling do not reflect
emissions from new sources anticipated to come online by the analysis
year. To address concerns about the discrepancies between these
inventories, we utilized the air quality modeling to derive boiler
sector-specific BPT estimates, which are then multiplied by the tons of
emissions reduced for each regulatory option. 

To derive the BPT estimates for this analysis, we:

1.	Quantified the PM2.5 -related human health and monetized benefits of
the SO2 emission reductions for major source boilers. We first
quantified the health impacts and monetized benefits of total PM2.5 mass
formed from the SO2 reductions, allowing us to isolate the PM air
quality impacts from SO2 reductions alone. This procedure allowed us to
develop PM2.5 BPT estimates that quantified the PM2.5-related benefits
of incremental changes in SO2 emissions. 

2.	Divided the health impacts and monetized benefits by the emission
reduction. This calculation yields BPT estimates for PM-related SO2. The
resulting BPT estimates were then multiplied by the projected SO2
emission reductions to produce an estimate of the PM-related health
impacts and monetized benefits. 

This process was repeated for directly emitted PM2.5 from major sources
and for PM2.5 and SO2 emissions from area source boilers. This process
was also repeated using the 2.5th percentile and 97.5th percentile
benefits to provide confidence intervals for the BPT estimates. To
calculate the total benefits, we simply multiply the BPT estimate by the
emission reductions of directly emitted PM2.5 and SO2 for each
regulatory option. Deriving boiler sector-specific BPT estimates
provides equivalent benefits results as scaling the air quality data
directly to correspond to the inventory differences, but it provides
greater flexibility to examine alternate regulatory scenarios. 

The BPT estimates used in this analysis are different from the BPT
estimates used in the proposal analysis. While these boiler
sector-specific BPT estimates are methodologically consistent with the
BPT estimates in Fann, Fulcher, and Hubbell (2009)  XE “Fann, Fulcher,
and Hubbell (2009)”   that were used in the proposal analysis, the BPT
estimates applied in this final analysis provide a different and likely
superior representation of the health impacts and monetized benefits
from boilers compared to the proposal. This is because the BPT used in
the proposal lumped emissions from major boilers together with other
emission sources in the non-EGU category and area boilers together with
other emission sources in the area category. For example, the non-EGU
category includes emissions from many different large stationary
sources, including boilers, metal production, mineral products, chemical
manufacturing, petroleum industry, pulp and paper production, and oil
and gas production, and waste disposal, which all have different
geographic distributions across the country. Because the geographic
distribution of boilers is not likely to exactly match the average
distribution of all of these sources, ambient air quality and the
associated benefits for reducing boiler emissions are going to be
different than for the entire non-EGU category. In addition to the
location of the emission sources, the geographic differences also affect
the benefits to due to geographic differences in local air chemistry,
meteorology, population density, and baseline health incidence. 

In addition, the air quality modeling for these rules reflected higher
spatial resolution (12 km by 12 km nationally) than the previous BPT
estimates and reflects an updated baseline emissions inventory. We
believe that these updates result in benefits estimates that are more
accurate than the proposal. For comparison, the BPT for directly emitted
PM2.5 in this analysis is lower for major and area sources than the
proposal, whereas the BPT for SO2 is higher in this analysis than the
proposal. In addition, we have updated the analysis year used in this
final analysis to be consistent with the implementation timeline, but
this revision would not have a significant impact on the benefits
results. In the proposal, the BPT estimates reflected emissions
inventories, income growth, and population growth for 2015 because we
did not have BPT estimates available for the proposal analysis year
(2013). For this final analysis, the emissions inventories are for 2016,
but income growth and population growth are for the final analysis year
(2014). Because the projected emissions inventory does not assume growth
in the boiler sector, the boiler benefits are not likely to change due
to a 2016 inventory instead of a 2014 inventory. We provide the
boiler-specific BPT estimates in Section 7.3. We also provide maps of
the total ambient PM2.5 impacts from the modeled point and non-point
boilers in Figures 7-4 through 7-7. 

Figure 7-4.	Change in Ambient PM2.5 Levels from SO2 Emissions from Point
Source Boilers

Figure 7-5.	Change in Ambient PM2.5 Levels from PM Emissions from Point
Source Boilers

Figure 7-6.	Change in Ambient PM2.5 Levels from SO2 Emissions from
Non-point Source Boilers

Figure 7-7.	Change in Ambient PM2.5 Levels from PM Emissions from
Non-point Source Boilers

Although VOCs are also precursors to PM2.5, we have not monetized their
contribution to the PM2.5 benefits in this analysis for several reasons.
Analysis of organic carbon measurements suggest only a fraction of
secondarily formed organic carbon aerosols are of anthropogenic origin.
The current state of the science of secondary organic carbon formation
indicates that anthropogenic VOC contribution to secondary organic
carbon aerosols is often lower than the biogenic (natural) contribution.
Given that a fraction of secondarily formed organic carbon aerosols from
anthropogenic VOC emissions and the extremely small amount of VOC
emissions from this sector relative to the entire VOC inventory, it is
unlikely this sector has a large contribution to ambient secondary
organic carbon aerosols. Photochemical models typically estimate
secondary organic carbon aerosols from anthropogenic VOC emissions to be
less than 0.1 µg/m3. Given the resource requirements to apply source
apportionment technology in the photochemical model for secondary
organic carbon aerosols and that only a very small portion of secondary
organic carbon aerosols (i.e., from anthropogenic sources) would be
apportioned, this option was not employed to estimate the impact of this
sector’s VOC emissions on secondary organic carbon aerosols. Lastly,
the contribution of VOC reductions to total monetized PM2.5 benefits
would likely be very small for these rules. For example, while not an
appropriate source for this application, if the BPT for VOC emissions
from industrial point sources and EGUs from Fann, Fulcher, and Hubbell
(2009)  XE “Fann, Fulcher, and Hubbell (2009)”   were applied to the
boiler rule, the VOC-related benefits would be less than $6 million for
the Boiler MACT.

The differences between the VOC emissions inventories are comparatively
small, and VOCs are only reduced by 20% for both of the Boiler MACT
options. In addition, the complex non-linear chemistry of ozone
formation introduces uncertainty to the development and application of a
BPT estimate. Therefore, we used the scaled air quality modeling results
directly to estimate the ozone benefits associated with VOC reductions.
As the ozone-related benefits are very small, we do not believe that the
differences in the VOC emissions inventory contribute much uncertainty
to the overall benefits results. Because the VOC emission reductions for
the Boiler Area Source Rule are less than 1%, we do not include the
ozone-related benefits associated with that rule, as the benefits would
be very small. 

Health Benefits Results

The health benefits have increased since the proposal analysis for two
reasons: (1) emission reductions of SO2 have increased, and (2) the BPT
for SO2 has increased. The decreases in directly emitted PM2.5 emissions
and lower BPT for PM2.5 since the proposal do not offset the increase in
benefits from SO2 emission reductions.

Tables 7-2 and 7-3 provide a summary of the monetized PM2.5 benefits
(including the indirect PM co-benefits) using the anchor points of Pope
et al. (2002)  XE “Pope et al. (2002)”   and Laden et al. (2006)  XE
“Laden et al. (2006)”   at discount rates of 3% and 7% for the final
Boiler MACT and Boiler Area Source Rule, respectively. Tables 7-4 and
7-5 provide the reductions in health incidences as a result of the
reduction in ambient PM2.5 levels for the final Boiler MACT and Boiler
Area Source Rule, respectively. Tables 7-6 and 7-7 provide the total
monetized PM2.5 benefits derived from Pope et al. (2002)  XE “Pope et
al. (2002)”   and Laden et al. (2006)  XE “Laden et al. (2006)”  
as well as the expert elicitation for the final Boiler MACT and Boiler
Area Source Rule, respectively. Table 7-8 provides the estimated
reductions in health incidences and the monetized benefits associated
with estimated reductions in ambient ozone concentrations. 

Table 7-2.	Summary of Monetized Benefits Estimates for Final Boiler MACT
in 2014 (millions of 2008$)a

 	Pollutant	Emissions Reductions (tons)	Benefit per ton (Pope, 3%)
Benefit per ton (Laden, 3%)	Benefit per ton (Pope, 7%)	Benefit per ton
(Laden, 7%)	Total Monetized Benefits (millions of 2008$ at 3%)	Total
Monetized Benefits (millions of 2008$ at 7%)

Selected Option 	Direct PM2.5 	29,007	$72,000	$180,000	$65,000	$160,000
$2,100	to	$5,100	$1,900	to	$4,600

	SO2	439,901	$46,000	$110,000	$42,000	$100,000	$20,000	to	$49,000
$18,000	to	$45,000

	 	 	 	 	 	Total	$22,000	to	$54,000	$20,000	to	$49,000

Alternative Option	Direct PM2.5 	28,139	$72,000	$180,000	$65,000
$160,000	$2,000	to	$5,000	$1,800	to	$4,500

	SO2	337,514	$46,000	$110,000	$42,000	$100,000	$15,000	to	$38,000
$14,000	to	$34,000

	 	 	 	 	 	Total	$17,000	to	$43,000	$16,000	to	$39,000

 a	All estimates are for the implementation year (2014), and are rounded
to two significant figures so numbers may not sum across columns. These
models assume that all fine particles, regardless of their chemical
composition, are equally potent in causing premature mortality because
the scientific evidence is not yet sufficient to allow differentiation
of effect estimates by particle type. The benefit per ton estimates vary
because each ton of precursor reduced has a different propensity to
become PM2.5. The monetized benefits incorporate the conversion from
precursor emissions to ambient fine particles. These estimates do not
include benefits from reducing HAP emissions, VOC emissions and ozone
exposure, nor energy disbenefits associated with the increased emissions
from additional energy usage described in the next section. These
benefits reflect existing boilers and 47 new boilers anticipated to come
online by 2014.

Table 7-3.	Summary of Monetized Benefits Estimates for Final Boiler Area
Source Rule in 2014 (millions of 2008$)a 

 	Pollutant	Emissions Reductions (tons)	Benefit per ton (Pope, 3%)
Benefit per ton (Laden, 3%)	Benefit per ton (Pope, 7%)	Benefit per ton
(Laden, 7%)	Total Monetized Benefits (millions of 2008$ at 3%)	Total
Monetized Benefits (millions of 2008$ at 7%)

Proposed MACT Approach 	Direct PM2.5 	590	$120,000	$290,000	$110,000
$260,000	$69	to	$170	$63	to	$150

	SO2	3,197	$41,000	$100,000	$37,000	$91,000	$130	to	$320	$120	to	$290

	 	 	 	 	 	Total	$200	to	$490	$180	to	$440

Final MACT/ GACT Approach	Direct PM2.5 	678	$120,000	$290,000	$110,000
$260,000	$79	to	$190	$72	to	$180

	SO2	3,197	$41,000	$100,000	$37,000	$91,000	$130	to	$320	$120	to	$290

	 	 	 	 	 	Total	$210	to	$520	$190	to	$470

 a	All estimates are for the implementation year (2014), and are rounded
to two significant figures so numbers may not sum across columns. These
models assume that all fine particles, regardless of their chemical
composition, are equally potent in causing premature mortality because
the scientific evidence is not yet sufficient to allow differentiation
of effect estimates by particle type. The benefit per ton estimates vary
because each ton of precursor reduced has a different propensity to
become PM2.5. The monetized benefits incorporate the conversion from
precursor emissions to ambient fine particles. These estimates do not
include benefits from reducing HAP emissions, nor energy disbenefits
associated with the increased emissions from additional energy usage
described in the next section.

Table 7-4.	Summary of Estimated Reductions in Health Incidences from
PM2.5 for the Final Boiler MACT in 2014 (95th percentile confidence
interval) a

 	Selected Option	Alternative Option

Avoided Premature Mortality	 

	Pope et al. (2002)	2,500	2,000

	(1,600 – 8,100)	(1,400 – 7,100)

Laden et al. (2006)	6,500	5,100

	(6,200 – 19,000)	(5,500 – 16,000)

Woodruff (Infant mortality)	10	8

	(0 – 58)	(0 – 51)

Avoided Morbidity	 	 

Chronic Bronchitis	1,600	1,300

	(340 – 6,000)	(300 – 5,200)

Acute Myocardial Infarction	4,000	3,100

	(2,400 – 13,000)	(2,100 – 11,000)

Hospital Admissions, Respiratory	610	480

	(520 – 1,800)	(460 – 1,600)

Hospital Admissions, Cardiovascular	1,300	1,000

	(1,800 – 3,000)	(1,600 – 2,700)

Emergency Room Visits, Respiratory	2,400	1,900

	(2,600 – 6,800)	(2,300 – 6,000)

Acute Bronchitis	3,700	2,900

	(0 – 15,000)	(0 – 13,000)

Work Loss Days	310,000	250,000

	(520,000 – 690,000)	(460,000 – 600,000)

Asthma Exacerbation	41,000	32,000

	(5,800 – 250,000)	(5,000 – 220,000)

Minor Restricted Activity Days	1,900,000	1,500,000

	(3,000,000 – 4,200,000)	(2,600,000 – 3,700,000)

Lower Respiratory Symptoms	44,000	35,000

	(37,000 – 130,000)	(32,000 – 120,000)

Upper Respiratory Symptoms	34,000	26,000

	(16,000 – 110,000)	(14,000 – 99,000)

a	All estimates are for the analysis year (2014) and are rounded to
whole numbers with two significant figures. These models assume that all
fine particles, regardless of their chemical composition, are equally
potent in causing premature mortality because the scientific evidence is
not yet sufficient to allow differentiation of effect estimates by
particle type. These estimates do not include benefits from reducing HAP
emissions, VOC emissions and ozone exposure, nor energy disbenefits
associated with the increased emissions from additional energy usage
described in the next section. These confidence intervals only reflect
the standard errors within the epidemiology studies, but they do not
reflect other sources of uncertainty inherent within the boiler-specific
BPT estimates. These benefits reflect existing boilers and 47 new
boilers anticipated to come online by 2014.

Table 7-5.	Summary of Estimated Reductions in Health Incidences from
PM2.5 for the Final Boiler Area Source Rule in 2014 (95th percentile
confidence interval)a

	Proposed MACT Approach	Final MACT/GACT Approach

Avoided Premature Mortality	 

	Pope et al. (2002)	23	24

	(8 – 38)	(8 – 40)

Laden et al. (2006)	58	61

	(29 – 87)	(31 – 91)

Woodruff (Infant mortality)	0	0

	(0 – 0)	(0 – 0)

Avoided Morbidity	 	 

Chronic Bronchitis	16	17

	(2 – 30)	(2 – 31)

Acute Myocardial Infarction	38	40

	(12 – 64)	(12 – 67)

Hospital Admissions, Respiratory	6	6

	(3 – 9)	(3 – 9)

Hospital Admissions, Cardiovascular	12	13

	(8 – 14)	(9 – 15)

Emergency Room Visits, Respiratory	20	21

	(11 – 28)	(11 – 30)

Acute Bronchitis	37	38

	(0 – 77)	(0 – 81)

Work Loss Days	3,100	3,200

	(2,600 – 3,500)	(2,800 – 3,700)

Asthma Exacerbation	400	420

	(29 – 1,200)	(31 – 1,300)

Minor Restricted Activity Days	18,000	19,000

	(15,000 – 21,000)	(16,000 – 22,000)

Lower Respiratory Symptoms	430	460

	(190 – 680)	(200 – 710)

Upper Respiratory Symptoms	330	350

	(83 – 580)	(87 – 610)

a	All estimates are for the analysis year (2014) and are rounded to
whole numbers with two significant figures. These models assume that all
fine particles, regardless of their chemical composition, are equally
potent in causing premature mortality because the scientific evidence is
not yet sufficient to allow differentiation of effect estimates by
particle type. These estimates do not include benefits from reducing HAP
emissions, nor energy disbenefits associated with the increased
emissions from additional energy usage described in the next section.
These confidence intervals only reflect the standard errors within the
epidemiology studies, but they do not reflect other sources of
uncertainty inherent within the boiler-specific BPT estimates. These
benefits reflect existing boilers and 6,779 new boilers anticipated to
come online by 2014.

Table 7-6.	Summary of Monetized Benefits Estimates from PM2.5 for the
Final Boiler MACT in 2014 (95th percentile confidence interval)a

 	Selected Option	Alternative Option

 	3%	7%	3%	7%

Based on Epidemiology Literature

	Pope et al. (2002)	$22,000	$20,000	$17,000	$16,000

	($1,900 – $68,000)	($1,600 – $62,000)	($1,500 – $53,000)	($1,300
– $49,000)

Laden et al. (2006)	$54,000	$49,000	$43,000	$39,000

	($4,900 – $160,000)	($4,300 – $140,000)	($3,800 – $120,000)
($3,400 – $110,000)

Based on Expert Elicitation

	Expert A	$58,000	$52,000	$45,000	$41,000

	($3,400 – $190,000)	($3,000 – $170,000)	($2,700 – $150,000)
($2,400 – $140,000)

Expert B	$44,000	$39,000	$34,000	$31,000

	($1,700 – $180,000)	($1,500 – $160,000)	($1,300 – $140,000)
($1,200 – $130,000)

Expert C	$44,000	$40,000	$35,000	$31,000

	($2,600 – $170,000)	($2,300 – $150,000)	($2,100 – $130,000)
($1,800 – $120,000)

Expert D	$31,000	$28,000	$24,000	$22,000

	($2,100 – $100,000)	($1,800 – $91,000)	($1,600 – $78,000)	($1,400
– $71,000)

Expert E	$72,000	$65,000	$56,000	$51,000

	($6,200 – $210,000)	($5,500 – $190,000)	($4,900 – $170,000)
($4,400 – $150,000)

Expert F	$40,000	$36,000	$31,000	$28,000

	($3,900 – $120,000)	($3,500 – $110,000)	($3,100 – $92,000)
($2,700 – $84,000)

Expert G	$26,000	$24,000	$21,000	$19,000

	($250 – $95,000)	($160 – $86,000)	($190 – $74,000)	($120 –
$68,000)

Expert H	$33,000	$30,000	$26,000	$24,000

	($300 – $130,000)	($210 – $120,000)	($240 – $100,000)	($160 –
$92,000)

Expert I	$44,000	$39,000	$34,000	$31,000

	($2,500 – $140,000)	($2,200 – $130,000)	($1,900 – $110,000)
($1,700 – $100,000)

Expert J	$36,000	$32,000	$28,000	$25,000

	($2,700 – $140,000)	($2,400 – $120,000)	($2,100 – $110,000)
($1,900 – $97,000)

Expert K	$8,400	$7,800	$6,600	$6,100

	($250 – $54,000)	($160 – $50,000)	($190 – $43,000)	($120 –
$39,000)

Expert L	$29,000	$27,000	$23,000	$21,000

	($1,200 – $110,000)	($1,000 – $100,000)	($930 – $89,000)	($790
– $81,000)

a	All estimates are for the analysis year (2014) and are rounded to
whole numbers with two significant figures. These models assume that all
fine particles, regardless of their chemical composition, are equally
potent in causing premature mortality because the scientific evidence is
not yet sufficient to allow differentiation of effect estimates by
particle type. These estimates do not include benefits from reducing HAP
emissions, nor energy disbenefits associated with the increased
emissions from additional energy usage described in the next section.
These confidence intervals only reflect the standard errors within the
epidemiology studies and valuation functions, but they do not reflect
other sources of uncertainty inherent within the boiler-specific BPT
estimates. These benefits reflect existing boilers and 47 new boilers
anticipated to come online by 2014.

Table 7-7.	Summary of Monetized Benefits Estimates from PM2.5 for the
Final Boiler Area Source Rule in 2014 (95th percentile confidence
interval)a

 	Proposed MACT Approach	Final MACT/GACT Approach

 	3%	7%	3%	7%

Based on Epidemiology Literature

	Pope et al. (2002)	$200	$180	$210	$190

	($17 – $610)	($15 – $560)	($18 – $650)	($16 – $590)

Laden et al. (2006)	$490	$440	$520	$470

	($44 – $1,400)	($39 – $1,300)	($46 – $1,500)	($41 – $1,400)

Based on Expert Elicitation

	Expert A	$520	$470	$550	$490

	($30 – $1,700)	($27 – $1,500)	($32 – $1,800)	($28 – $1,600)

Expert B	$390	$360	$410	$370

	($15 – $1,600)	($14 – $1,500)	($16 – $1,700)	($14 – $1,500)

Expert C	$400	$360	$420	$380

	($24 – $1,500)	($21 – $1,400)	($25 – $1,600)	($22 – $1,400)

Expert D	$280	$250	$300	$270

	($19 – $900)	($17 – $820)	($20 – $950)	($17 – $860)

Expert E	$640	$580	$680	$610

	($56 – $1,900)	($50 – $1,700)	($59 – $2,000)	($52 – $1,800)

Expert F	$360	$330	$380	$340

	($35 – $1,100)	($31 – $960)	($37 – $1,100)	($33 – $1,000)

Expert G	$240	$220	$250	$230

	($2.1 – $850)	($1.6 – $780)	($2.2 – $900)	($1.6 – $820)

Expert H	$300	$270	$310	$280

	($2.6 – $1,200)	($2.0 – $1,000)	($2.7 – $1,200)	($2.1 – $1,100)

Expert I	$390	$350	$410	$370

	($22 – $1,300)	($20 – $1,200)	($23 – $1,400)	($21 – $1,200)

Expert J	$320	$290	$340	$300

	($24 – $1,200)	($22 – $1,100)	($25 – $1,300)	($23 – $1,200)

Expert K	$77	$71	$81	$75

	($2.1 – $490)	($1.6 – $450)	($2.2 – $520)	($1.6 – $480)

Expert L	$270	$240	$280	$250

	($11 – $1,000)	($10 – $930)	($12 – $1,100)	($10 – $970)

a	All estimates are for the analysis year (2014) and are rounded to
whole numbers with two significant figures. These models assume that all
fine particles, regardless of their chemical composition, are equally
potent in causing premature mortality because the scientific evidence is
not yet sufficient to allow differentiation of effect estimates by
particle type. These estimates do not include benefits from reducing HAP
emissions, nor energy disbenefits associated with the increased
emissions from additional energy usage described in the next section.
These confidence intervals only reflect the standard errors within the
epidemiology studies and valuation functions, but they do not reflect
other sources of uncertainty inherent within the boiler-specific BPT
estimates. These benefits reflect existing boilers and 6,779 new boilers
anticipated to come online by 2014.

Table 7-8.	Summary of Monetized Benefits Estimates from Ozone for the
Final Boiler MACT in 2014 (95th percentile confidence interval)a

Avoided Premature Mortality	Incidence	Valuation 

(millions of 2008$)

NMMAPS	Bell et al. (2004)	0	$3.3

(0 – 1)	($0.27 – $9.8)

	Schwartz (2005)	1	$5.0

(0 – 1)	($0.41 – $15)

	Huang and Bell (2005)	1	$5.5

(0 – 1)	($0.46 – $16)

Meta-analyses	Bell et al. (2005)	1	$11

(1 – 2)	($0.94 – $31)

	Ito et al. (2005)	2	$15

(1 – 2)	($1.4 – $41)

	Levy et al. (2005)	2	$15

(1 – 2)	($1.4 – $41)

Avoided Morbidity	 	 

Adult Hospital Admissions, Respiratory	3	$0.07

	(0 – 5)	($0.01 – $0.12)

Infant Hospital Admissions, Respiratory	2	$0.03

	(1 – 4)	($0.01 – $0.04)

Emergency Room Visits, Respiratory	2	< $0.01

	(0 – 5)	($0.00 – $0.01)

School Loss Days	810	$0.08

	(330 – 1,200)	($0.03 – $0.11)

Worker Productivity	N/A	$0.02

($0.02 – $0.02)

Minor Restricted Activity Days	2,300	$0.15

	(1,100 – 3,600)	($0.06 – $0.28)

a	All estimates are for the analysis year (2014) and are rounded to two
significant figures. Health effects associated with ozone exposure are
assumed to occur within the analysis year. Therefore, the monetized
benefits are the same for any discount rate. These confidence intervals
reflect the standard errors within the epidemiology studies and
valuation functions. These benefits reflect existing boilers and 47 new
boilers anticipated to come online by 2014.

Figures 7-8 and 7-9 illustrate the relative breakdown of the estimated
monetized health benefits associated with changes in PM2.5 and ozone,
respectively. Figure 7-10 shows the total combined monetized benefits
for the final Boiler MACT and Area Source Rule at discount rates of 3%
and 7%. Figures 7-11 and 7-12 provide a breakdown of the estimated
monetized PM2.5 benefits by precursor pollutant for the final Boiler
MACT and Boiler Area Source Rule, respectively. Figures 7-13 and 7-14
provide a breakdown of the monetized PM2.5 benefits by subcategory for
the final Boiler MACT and Boiler Area Source Rule, respectively. 

This analysis shows that the majority of the population is exposed to PM
levels at or above the lowest LML of the cohort studies (Figures 7-15
and 7-16), increasing our confidence in the PM mortality analysis.
Because we used BPT estimates, we are unable to provide an estimate of
the mortality impacts that occur at various PM levels. Figure 7-15 shows
a bar chart of the percentage of the adult population exposed to each
PM2.5 level in the baseline. Figure 7-16 shows a cumulative distribution
function of the same data. Both figures identify the LML for each of the
major cohort studies. 

Using the Pope et al. (2002)  XE “Pope et al. (2002)”   study,
approximately 79% of the population is exposed to annual mean PM2.5
levels at or above the LML of 7.5 µg/m3. Using the Laden et al. (2006) 
XE “Laden et al. (2006)”   study, 34% of the population is exposed
to annual mean PM2.5 levels at or above the LML of 10 µg/m3. As we
model mortality impacts among populations exposed to levels of PM2.5
that are successively lower than the LML of the lowest cohort study, our
confidence in the results diminishes. However, the analysis above
confirms that the great majority of the impacts occur at or above the
lowest cohort study’s LML. It is important to emphasize that we have
high confidence in PM2.5-related effects down to the lowest LML of the
major cohort studies. 



Figure 7-8.	Breakdown of Monetized PM2.5 Health Benefits Estimates using
Mortality Function from Pope et al. (2002)a

a	This pie chart breakdown is illustrative, using the results based on
Pope et al. (2002)  XE “Pope et al. (2002)”   as an example. Using
the Laden et al. (2006)  XE “Laden et al. (2006)”   function for
premature mortality, the percentage of total monetized benefits due to
adult mortality would be 97%. This chart shows the breakdown using a 3%
discount rate, and the results would be similar if a 7% discount rate
was used. 

Figure 7-9.	Breakdown of Monetized Ozone Health Benefits Estimates using
Mortality Function from Bell et al. (2004)a

a	This pie chart breakdown is illustrative, using the results based on
Bell et al. (2004)  XE “Bell et al. (2004)”   as an example. Using
the Levy et al. (2005)  XE “Levy et al. (2005)”   function for
premature mortality, the percentage of total monetized benefits due to
mortality would be 98%. 

Figure 7-10.	Total Monetized PM2.5 and Ozone Benefits Estimates for the
Final Boiler MACT and Boiler Area Source Rule in 2014a

a	This graph shows the estimated benefits at discount rates of 3% and 7%
using effect coefficients derived from the Pope et al. (2002)  XE
“Pope et al. (2002)”   study and the Laden et al. (2006)  XE
“Laden et al. (2006)”   study, as well as 12 effect coefficients
derived from EPA’s expert elicitation on PM mortality. The results
shown are not the direct results from the studies or expert elicitation;
rather, the estimates are based in part on the concentration-response
function provided in those studies. These estimates do not include
benefits from reducing HAP emissions, but they do include the energy
disbenefits associated with the increased emissions from additional
energy usage. Due to methodology and time limitations under the
court-ordered schedule, we were unable to monetize the benefits
associated with several categories of benefits, including direct
exposure to HAPs and SO2, as well as ecosystem effects, and visibility
effects. These benefits reflect existing boilers and new boilers
anticipated to come online by 2014.

Figure 7-11.	Breakdown of Monetized PM2.5 Benefits Estimates by
Precursor for the Final Boiler MACT in 2014a

Figure 7-12.	Breakdown of Monetized PM2.5 Benefits Estimates by
Precursor for the Final Boiler Area Source Rule in 2014a

Figure 7-13.	Breakdown of Monetized PM2.5 Benefits Estimates by
Subcategory for the Final Boiler MACT in 2014a

Figure 7-14.	Breakdown of Monetized PM2.5 Benefits Estimates by
Subcategory for the Final Boiler Area Source Rule in 2014a

Figure 7-15.	Percentage of Population Exposed to Baseline Air Quality
Levels for Final Boiler MACT and Boiler Area Source Rulea

a	Approximately 79% of the population is exposed to baseline exposure to
annual mean PM2.5 levels at or above 7.5 µg/m3, which is the lowest air
quality level considered in the ACS cohort study by Pope et al. (2002) 
XE “Pope et al. (2002)”  .

Figure 7-16.	Cumulative Percentage of Population Exposed to Baseline Air
Quality Levels for Final Boiler MACT and Boiler Area Source Rulea

a	Approximately 79% of the population is exposed baseline exposure to
annual mean PM2.5 levels at or above 7.5 µg/m3, which is the lowest air
quality level considered in the ACS cohort study by Pope et al. (2002) 
XE “Pope et al. (2002)”  .

Energy Disbenefits

In this section, we provide an estimate of the energy disbenefits
associated with the increased emissions from additional energy usage.
Electricity usage associated with the operation of control devices is
anticipated to increase emissions of pollutants from electric utility
boilers (EGU boilers) that supply electricity to the non-EGU boiler
facilities. We estimate emission increases of 910,000 tpy CO2 for major
boilers and 22,000 tpy CO2 for area boilers. 

Social Cost of Carbon and Greenhouse Gas Disbenefits

EPA has assigned a dollar value to reductions in carbon dioxide (CO2)
emissions using recent estimates of the “social cost of carbon”
(SCC). The SCC is an estimate of the monetized damages associated with
an incremental increase in carbon emissions in a given year. It is
intended to include (but is not limited to) changes in net agricultural
productivity, human health, property damages from increased flood risk,
and the value of ecosystem services due to climate change. The SCC
estimates used in this analysis were developed through an interagency
process that included EPA and other executive branch entities, and
concluded in February, 2010. EPA first used these SCC estimates in the
benefits analysis for the final joint EPA/DOT Rulemaking to establish
Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate
Average Fuel Economy Standards; see the rule’s preamble for discussion
about application of SCC (75 FR 25324; 5/7/10). The SCC Technical
Support Document (SCC TSD) provides a complete discussion of the methods
used to develop these SCC estimates. 

The interagency group selected four SCC values for use in regulatory
analyses, which we have applied in this analysis: $5, $21, $35, and $65
per metric ton of CO2 emissions in 2010, in 2007 dollars. The first
three values are based on the average SCC from three integrated
assessment models, at discount rates of 2.5, 3, and 5 percent,
respectively. SCCs at several discount rates are included because the
literature shows that the SCC is quite sensitive to assumptions about
the discount rate, and because no consensus exists on the appropriate
rate to use in an intergenerational context. The fourth value is the
95th percentile of the SCC from all three models at a 3 percent discount
rate. It is included to represent higher-than-expected impacts from
temperature change further out in the tails of the SCC distribution. Low
probability, high impact events are incorporated into all of the SCC
values through explicit consideration of their effects in two of the
three models as well as the use of a probability density function for
equilibrium climate sensitivity. Treating climate sensitivity
probabilistically results in more high temperature outcomes, which in
turn lead to higher projections of damages.

The SCC increases over time because future emissions are expected to
produce larger incremental damages as physical and economic systems
become more stressed in response to greater climatic change. Note that
the interagency group estimated the growth rate of the SCC directly
using the three integrated assessment models rather than assuming a
constant annual growth rate. This helps to ensure that the estimates are
internally consistent with other modeling assumptions. The SCC estimates
for the analysis years of 2014, in 2005 dollars are provided in Table
7-9.

Table 7-9.	Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for
2014a

Discount Rate and Statistic

SCC estimate (2008$)

5% (Average)

$5.7

3% (Average)

$24.2

2.5% (Average) 

$39.1

3% (95th percentile)

$73.9

a	The SCC values are dollar-year and emissions-year specific. SCC values
represent only a partial accounting of climate impacts.

When attempting to assess the incremental economic impacts of carbon
dioxide emissions, the analyst faces a number of serious challenges. A
recent report from the National Academies of Science (NRC, 2009  XE
“NRC, 2009”  ) points out that any assessment will suffer from
uncertainty, speculation, and lack of information about (1) future
emissions of greenhouse gases, (2) the effects of past and future
emissions on the climate system, (3) the impact of changes in climate on
the physical and biological environment, and (4) the translation of
these environmental impacts into economic damages. As a result, any
effort to quantify and monetize the harms associated with climate change
will raise serious questions of science, economics, and ethics and
should be viewed as provisional. 

The interagency group noted a number of limitations to the SCC analysis,
including the incomplete way in which the integrated assessment models
capture catastrophic and non-catastrophic impacts, their incomplete
treatment of adaptation and technological change, uncertainty in the
extrapolation of damages to high temperatures, and assumptions regarding
risk aversion. The limited amount of research linking climate impacts to
economic damages makes the interagency modeling exercise even more
difficult. The interagency group hopes that over time researchers and
modelers will work to fill these gaps and that the SCC estimates used
for regulatory analysis by the Federal government will continue to
evolve with improvements in modeling. Additional details on these
limitations are discussed in the SCC TSD.

In light of these limitations, the interagency group has committed to
updating the current estimates as the science and economic understanding
of climate change and its impacts on society improves over time.
Specifically, the interagency group has set a preliminary goal of
revisiting the SCC values within two years or at such time as
substantially updated models become available, and to continue to
support research in this area. 

Applying the global SCC estimates to the estimated increases in CO2
emissions for the range of policy scenarios, we estimate the dollar
value of the climate-related disbenefits captured by the models for each
analysis year. For internal consistency, the annual disbenefits are
discounted back to NPV terms using the same discount rate as each SCC
estimate (i.e., 5%, 3%, and 2.5%) rather than 3% and 7%. These estimates
are provided in Table 7-10.

Table 7-10. Monetized SCC-Derived Disbenefits of CO2 Emission Increases
in 2014 (millions of 2008$)a

Discount Rate and Statistic	Selected Option (Major)	Alternative Option
(Major)	Proposed MACT Approach (Area)	Final MACT/GACT Approach (Area)

Tons of CO2	911,048	1,524,572	24,936	22,191

5% (Average)	$5.2	$8.7	$0.1	$0.1

3% (Average)	$22	$37	$0.6	$0.5

2.5% (Average)	$36	$60	$1.0	$0.9

3% (95th percentile)	$67	$113	$1.8	$1.6

a	The SCC values are dollar-year and emissions-year specific. SCC values
represent only a partial accounting of climate impacts.

Unquantified or Nonmonetized Benefits

The monetized benefits estimated in this RIA only reflect the portion of
benefits attributable to the health impacts associated with exposure to
ambient fine particles. Data, resource, and methodological limitations
prevented EPA from quantifying or monetizing the benefits from several
important benefit categories, including benefits from reducing toxic
emissions, ecosystem effects, and visibility impairment. The direct
health benefits from reducing HAPs have not been monetized in this
analysis. In addition to being a PM2.5 precursor, SO2 emissions also
contribute to adverse effects from acidic deposition in aquatic and
terrestrial ecosystems, increase mercury methylation, as well as
visibility impairment. In addition to health effects, ozone is
associated with adverse vegetation effects to forests and crops.

Other SO2 Benefits

In addition to being a precursor to PM2.5, SO2 emissions are also
associated with a variety of respiratory health effects. Unfortunately,
we were unable to estimate the health benefits associated with reduced
SO2 exposure in this analysis because we do not have air quality
modeling data available. Without knowing the location of the emission
reductions and the resulting ambient concentrations, we were unable to
estimate the exposure to SO2 for nearby populations. Therefore, this
analysis only quantifies and monetizes the PM2.5 benefits associated
with the reductions in SO2 emissions.

Following an extensive evaluation of health evidence from epidemiologic
and laboratory studies, the Integrated Science Assessment (ISA) for
Sulfur Dioxide concluded that there is a causal relationship between
respiratory health effects and short-term exposure to SO2 (U.S. EPA,
2008a  XE “U.S. EPA, 2008a”  ). According to summary of the ISA in
EPA’s risk and exposure assessment (REA) for the SO2 NAAQS “the
immediate effect of SO2 on the respiratory system in humans is
bronchoconstriction” (U.S. EPA, 2009b  XE “U.S. EPA, 2009b”  ). In
addition, the REA summarized from the ISA that “asthmatics are more
sensitive to the effects of SO2 likely resulting from preexisting
inflammation associated with this disease.” A clear
concentration-response relationship has been demonstrated in laboratory
studies following exposures to SO2 at concentrations between 20 and 100
ppb, both in terms of increasing severity of effect and percentage of
asthmatics adversely affected (U.S. EPA, 2009b  XE “U.S. EPA, 2009b”
 ). Based on our review of this information, we identified four
short-term morbidity endpoints that the SO2 ISA identified as a
“causal relationship”: asthma exacerbation, respiratory-related
emergency department visits, and respiratory-related hospitalizations.
The differing evidence and associated strength of the evidence for these
different effects is described in detail in the SO2 ISA. The SO2 ISA
also concluded that the relationship between short-term SO2 exposure and
premature mortality was “suggestive of a causal relationship”
because it is difficult to attribute the mortality risk effects to SO2
alone. Although the SO2 ISA stated that studies are generally consistent
in reporting a relationship between SO2 exposure and mortality, there
was a lack of robustness of the observed associations to adjustment for
pollutants.

SO2 emissions also contribute to adverse welfare effects from acidic
deposition, visibility impairment, and enhanced mercury methylation
(U.S. EPA, 2008c  XE “U.S. EPA, 2008c”  ). Deposition of sulfur
causes acidification, which can cause a loss of biodiversity of fishes,
zooplankton, and macro invertebrates in aquatic ecosystems, as well as a
decline in sensitive tree species, such as red spruce (Picea rubens) and
sugar maple (Acer saccharum) in terrestrial ecosystems. In the
northeastern United States, the surface waters affected by acidification
are a source of food for some recreational and subsistence fishermen and
for other consumers and support several cultural services, including
aesthetic and educational services and recreational fishing. Biological
effects of acidification in terrestrial ecosystems are generally linked
to aluminum toxicity, which can cause reduced root growth, which
restricts the ability of the plant to take up water and nutrients. These
direct effects can, in turn, increase the sensitivity of these plants to
stresses, such as droughts, cold temperatures, insect pests, and disease
leading to increased mortality of canopy trees. Terrestrial
acidification affects several important ecological services, including
declines in habitat for threatened and endangered species (cultural),
declines in forest aesthetics (cultural), declines in forest
productivity (provisioning), and increases in forest soil erosion and
reductions in water retention (cultural and regulating) 

Carbon Monoxide Benefits

Carbon monoxide (CO) exposure is associated with a variety of health
effects. Without knowing the location of the emission reductions and the
resulting ambient concentrations using fine-scale air quality modeling,
we were unable to estimate the exposure to CO for nearby populations.
Due to methodological and time limitations under the court-ordered
schedule, we were unable to estimate the benefits associated with the
reductions in CO emissions that would occur as a result of this rule. 

Carbon monoxide in ambient air is formed primarily by the incomplete
combustion of carbon-containing fuels and photochemical reactions in the
atmosphere. The amount of CO emitted from these reactions, relative to
carbon dioxide (CO2), is sensitive to conditions in the combustion zone,
such as fuel oxygen content, burn temperature, or mixing time. Upon
inhalation, CO diffuses through the respiratory system to the blood,
which can cause hypoxia (reduced oxygen availability). Carbon monoxide
can elicit a broad range of effects in multiple tissues and organ
systems that are dependent upon concentration and duration of exposure. 

The Integrated Science Assessment for Carbon Monoxide (U.S. EPA, 2010b 
XE “U.S. EPA, 2010b”  ) concluded that short-term exposure to CO is
“likely to have a causal relationship” with cardiovascular
morbidity, particularly in individuals with coronary heart disease.
Epidemiologic studies associate short-term CO exposure with increased
risk of emergency department visits and hospital admissions. Coronary
heart disease includes those who have angina pectoris (cardiac chest
pain), as well as those who have experienced a heart attack. Other
subpopulations potentially at risk include individuals with diseases
such as chronic obstructive pulmonary disease (COPD), anemia, or
diabetes, and individuals in very early or late life stages, such as
older adults or the developing young. The evidence is suggestive of a
causal relationship between short-term exposure to CO and respiratory
morbidity and mortality. The evidence is also suggestive of a causal
relationship for birth outcomes and developmental effects following
long-term exposure to CO, and for central nervous system effects linked
to short- and long-term exposure to CO. 

Visibility Benefits

Reducing SO2 and PM emissions would improve the level of visibility
throughout the United States (U.S. EPA, 2009c  XE “U.S. EPA, 2009c” 
). Fine particles with significant light-extinction efficiencies include
sulfates, nitrates, organic carbon, elemental carbon, and soil (Sisler,
1996  XE “Sisler, 1996”  ). These suspended particles and gases
degrade visibility by scattering and absorbing light. Higher visibility
impairment levels in the East are due to generally higher concentrations
of fine particles, particularly sulfates, and higher average relative
humidity levels. In fact, particulate sulfate is the largest contributor
to regional haze in the eastern U.S. (i.e., 40% or more annually and 75%
during summer). In the western U.S., particulate sulfate contributes to
20–50% of regional haze (U.S. EPA, 2009c  XE “U.S. EPA, 2009c”  ).
Visibility has direct significance to people’s enjoyment of daily
activities and their overall sense of wellbeing. Good visibility
increases the quality of life where individuals live and work, and where
they engage in recreational activities. Due to time limitations under
the court-ordered schedule, we were unable to estimate the monetized
benefits associated with visibility improvements. Previous analyses
(U.S. EPA, 2006  XE “U.S. EPA, 2006”  ; U.S. EPA, 2010e  XE “U.S.
EPA, 2010e”  ) show that visibility benefits are a significant welfare
benefit category. As shown in Section 6.5.4 of this RIA, an average
visibility improvement of 0.51 deciviews in annual 20% worst visibility
days over all Class I area monitors is anticipated as a result of these
rules. 

Ozone Vegetation Benefits

Exposure to ozone has been associated with a wide array of vegetation
and ecosystem effects in the published literature (U.S. EPA, 2010a  XE
“U.S. EPA, 2010a”  ). Sensitivity to ozone is highly variable across
species, with over 65 plan species identified as “ozone-sensitive,”
many of which occur in state and national parks and forests. These
effects include those that damage or impair the intended use of the
plant or ecosystem. Such effects are considered adverse to the public
welfare and can include reduced growth and/or biomass production in
sensitive plant species, including forest trees, reduced crop yields,
visible foliar injury, reduced plant vigor (e.g., increased
susceptibility to harsh weather, disease, insect pest infestation, and
competition), species composition shift, and changes in ecosystems and
associated ecosystem services. 

Direct HAP Benefits

Americans are exposed to ambient concentrations of air toxics at levels
which have the potential to cause adverse health effects. The levels of
air toxics to which people are exposed vary depending on where people
live and work and the kinds of activities in which they engage. In order
to identify and prioritize air toxics, emission source types and
locations that are of greatest potential concern, U.S. EPA conducts the
National-Scale Air Toxics Assessment (NATA). The most recent NATA was
conducted for calendar year 2002, and was released in June 2009. NATA
for 2002 includes four steps:

1.	Compiling a national emissions inventory of air toxics emissions from
outdoor sources

2.	Estimating ambient concentrations of air toxics across the United
States

3.	Estimating population exposures across the United States

4.	Characterizing potential public health risk due to inhalation of air
toxics including both cancer and noncancer effects

Noncancer health effects can result from chronic, subchronic, or acute
inhalation exposures to air toxics, and include neurological,
cardiovascular, liver, kidney, and respiratory effects as well as
effects on the immune and reproductive systems. According to the 2002
NATA, nearly the entire U.S. population was exposed to an average
concentration of air toxics that has the potential for adverse noncancer
respiratory health effects. Figures 7-17 and 7-18 depict estimated
county-level carcinogenic risk and noncancer respiratory hazard from the
assessment. Results from the 2002 NATA suggest that acrolein is the
primary driver for noncancer respiratory risk. Large reductions in HAP
emissions may not necessarily translate into significant reductions in
health risk because toxicity varies by pollutant and whether or not
there are exposures at or above levels of concern is not known. For
example, acetaldehyde mass emissions are more than double acrolein
emissions on a national basis, according to EPA’s 2005 National
Emissions Inventory (NEI). However, the Integrated Risk Information
System (IRIS) reference concentration (RfC) for acrolein is considerably
lower than that for acetaldehyde, suggesting that acrolein could be
potentially more toxic than acetaldehyde. Thus, it is important to 

Figure 7-17.	Estimated County Level Carcinogenic Risk from HAP exposure
from outdoor sources (2002 NATA  XE “NATA, 2002”  )

Figure 7-18.	Estimated County Level Noncancer (Respiratory) Risk from
HAP exposure from outdoor sources (2002 NATA  XE “NATA, 2002”  )

account for the toxicity and exposure, as well as the mass of the
targeted emissions when designing reduction strategies to maximize
health benefits.

Due to methodology and time limitations under the court-ordered
schedule, we were unable to estimate the benefits associated with the
hazardous air pollutants that would be reduced as a result of these
rules. In a few previous analyses of the benefits of reductions in HAPs,
EPA has quantified the benefits of potential reductions in the
incidences of cancer and non-cancer risk (e.g., U.S. EPA, 1995  XE
“U.S. EPA, 1995”  ). In those analyses, EPA relied on unit risk
factors (URF) developed through risk assessment procedures. 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. 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 would
overestimate the benefits of the regulation. While we used high-end risk
estimates in past analyses, advice from the EPA’s Science Advisory
Board (SAB) recommended that we avoid using high-end estimates in
benefit analyses (U.S. EPA, 2002  XE “U.S. EPA, 2002”  ). Since this
time, EPA has continued to develop better methods for analyzing the
benefits of reductions in HAPs.

In the Second Prospective 812 Analysis, EPA conducted a case study
analysis of the health effects associated with reducing exposure to
benzene in Houston from implementation of the Clean Air Act (IEc, 2009 
XE “IEc, 2009”  ). While reviewing the draft report, EPA’s
Advisory Council on Clean Air Compliance Analysis concluded that “the
challenges for assessing progress in health improvement as a result of
reductions in emissions of hazardous air pollutants (HAPs) are
daunting...due to a lack of exposure-response functions, uncertainties
in emissions inventories and background levels, the difficulty of
extrapolating risk estimates to low doses and the challenges of tracking
health progress for diseases, such as cancer, that have long latency
periods” (U.S. EPA-SAB, 2008  XE “U.S. EPA-SAB, 2008”  ).

In 2009, EPA convened a workshop to address the inherent complexities,
limitations, and uncertainties in current methods to quantify the
benefits of reducing HAPs. Recommendations from this workshop included
identifying research priorities, focusing on susceptible and vulnerable
populations, and improving dose-response relationships (Gwinn et al.,
2011  XE “Gwinn et al., 2011”  ). 

In summary, 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). Due to methodology and time limitations under the
court-ordered schedule, 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. For this reason,
we did not attempt to quantify or monetized the health benefits of
reductions in HAPs in this analysis. Instead, we provide a qualitative
analysis of the health effects associated with the HAPs anticipated to
be reduced by these rules. EPA remains committed to improving methods
for estimating HAP benefits by continuing to explore additional concepts
of benefits, including changes in the distribution of risk. 

Available emissions data show that boilers emit several different HAPs,
either contained within the fuel burned or formed during the combustion
process. Although numerous HAPs may be emitted from boilers, a few HAPs
account for the majority of the total mass of HAPs emissions. See Table
7-11 for the list of the major HAPs for each fuel type. These rules are
anticipated to reduce a combined 113,000 tons of carbon monoxide, 30,000
tons of HCl, 830 tons of HF, 2,900 pounds of mercury, 3,000 tons of
other metals, and 23 grams of dioxins/furans (TEQ) each year. We discuss
the health effects associated with these top HAPs as well as the HAPs
for which we have emission reduction estimates.

Table 7-11.	Top HAPs by Mass from Boilers by Fuel Type

Coal	Gas	Biomass	Oil

68% HCl	44% Formaldehyde	32% Acetaldehyde	28% Nickel

5% HF	25% PAH	28% HCl	19% Manganese

	3% Toluene	25% Formaldehyde

	

Mercury

Mercury is a highly neurotoxic contaminant that enters the food web as a
methylated compound, methylmercury (U.S. EPA, 2008c  XE “U.S. EPA,
2008c”  ). The contaminant is concentrated in higher trophic levels,
including fish eaten by humans. Experimental evidence has established
that only inconsequential amounts of methylmercury can be produced in
the absence of sulfate (U.S. EPA, 2008c  XE “U.S. EPA, 2008c”  ).
Current evidence indicates that in watersheds where mercury is present,
increased sulfate deposition very likely results in methylmercury
accumulation in fish (Drevnick et al., 2007  XE “Drevnick et al.,
2007”  ; Munthe et al., 2007  XE “Munthe et al., 2007”  ). The NOx
SOX Ecological ISA concluded that evidence is sufficient to infer a
casual relationship between sulfur deposition and increased mercury
methylation in wetlands and aquatic environments (U.S. EPA, 2008c  XE
“U.S. EPA, 2008c”  ).

In addition to the role of sulfate deposition on methylation, these
rules would also reduce mercury emissions. Mercury is emitted to the air
from various man-made and natural sources. These emissions transport
through the atmosphere and eventually deposit to land or water bodies.
This deposition can occur locally, regionally, or globally, depending on
the form of mercury emitted and other factors such as the weather. The
form of mercury emitted varies depending on the source type and other
factors. Available data indicate that the mercury emissions from these
sources are a mixture of gaseous elemental mercury (25%), inorganic
divalent mercury (reactive gas phase mercury) (50%), and particulate
bound mercury (25%) (U.S. EPA, 2010f  XE “U.S. EPA, 2010f”  ).
Gaseous elemental mercury can be transported very long distances, even
globally, to regions far from the emissions source (becoming part of the
global “pool”) before deposition occurs. Inorganic divalent and
particulate bound mercury have a shorter atmospheric lifetime and can
deposit to land or water bodies closer to the emissions source.
Furthermore, elemental mercury in the atmosphere can undergo
transformation into divalent mercury, providing a significant pathway
for deposition of emitted elemental mercury.

Potential exposure routes to mercury emissions include both direct
inhalation and consumption of fish containing methylmercury. The primary
route of human exposure to mercury emissions from industrial sources is
generally indirectly through the consumption of fish containing
methylmercury. As described above, mercury that has been emitted to the
air eventually settles into water bodies or onto land where it can
either move directly or be leached into waterbodies. Once deposited,
certain microorganisms can change it into methylmercury, a highly toxic
form that builds up in fish, shellfish and animals that eat fish.
Consumption of fish and shellfish are the main sources of methylmercury
exposure to humans. Methylmercury builds up more in some types of fish
and shellfish than in others. The levels of methylmercury in fish and
shellfish vary widely depending on what they eat, how long they live,
and how high they are in the food chain. Most fish, including ocean
species and local freshwater fish, contain some methylmercury. For
example, in recent studies by EPA and the U.S. Geological Survey (USGS)
of fish tissues, every fish sampled from 291 streams across the country
contained some methylmercury (Scudder, 2009  XE “Scudder, 2009”  ).

The majority of fish consumed in the U.S. are ocean species. The
methylmercury concentrations in ocean fish species are primarily
influenced by the global mercury pool. However, the methylmercury found
in local fish can be due, at least partly, to mercury emissions from
local sources. Research shows that most people’s fish consumption does
not cause a mercury-related health concern. However, certain people may
be at higher risk because of their routinely high consumption of fish
(e.g., tribal and other subsistence fishers and their families who rely
heavily on fish for a substantial part of their diet). It has been
demonstrated that high levels of methylmercury in the bloodstream of
unborn babies and young children may harm the developing nervous system,
making the child less able to think and learn. Moreover, mercury
exposure at high levels can harm the brain, heart, kidneys, lungs, and
immune system of people of all ages.

Several studies suggest that the methylmercury content of fish may
reduce cardio-protective effects of fish consumption. Some of these
studies also suggest that methylmercury may cause adverse effects to the
cardiovascular system. For example, the NRC (2000)  XE “NRC (2000)” 
 review of the literature concerning methylmercury health effects took
note of two epidemiological studies that found an association between
dietary exposure to methylmercury and cardiovascular effects. In a study
of 1,833 males in Finland aged 42 to 60 years, Solonen et al. (1995)  XE
“Solonen et al. (1995)”   observed a relationship between
methylmercury exposure via fish consumption and acute myocardial
infarction (AMI or heart attacks), death from coronary heart disease or
cardiovascular disease. The NRC also noted a study of 917 seven-year old
children in the Faroe Islands, whose initial exposure to methylmercury
was in utero although postnatal exposures may have occurred as well. At
seven years of age, these children exhibited an increase in blood
pressure and a decrease in heart rate variability. Based on these and
other studies, NRC concluded in 2000 that, “Although the data base is
not as extensive as it is for other end points (i.e., neurologic
effects) the cardiovascular system appears to be a target for
methylmercury toxicity in humans and animals.” NRC also stated that
“additional studies are needed to better characterize the effect of
methylmercury exposure on blood pressure and cardiovascular function at
various stages of life.”

Since publication of the NRC report there have been some 30 published
papers presenting the findings of studies that have examined the
possible cardiovascular effects of methylmercury exposure. These studies
include epidemiological, toxicological, and toxicokinetic
investigations. Over a dozen review papers have also been published. If
there were a causal relationship between methylmercury exposure and
adverse cardiovascular effects, then reducing exposure to methylmercury
would result in public health benefits from reduced cardiovascular
effects.

In early 2010, EPA sponsored a workshop in which a group of experts were
asked to assess the plausibility of a causal relationship between
methylmercury exposure and cardiovascular health effects and to advise
EPA on methodologies for estimating population level cardiovascular
health impacts of reduced methylmercury exposure. The report from that
workshop is in preparation.

Baseline emissions for major and area source boilers in the U.S. are
about 11 tons of mercury in the air. Based on the EPA’s National
Emission Inventory, about 103 tons of mercury were emitted from all
anthropogenic sources in the U.S. in 2005. Moreover, the United Nations
has estimated that about 2,100 tons of mercury were emitted worldwide by
anthropogenic sources in 2005. We believe that total mercury emissions
in the U.S. and globally in 2008 were about the same magnitude in 2005.
Therefore, we estimate that in 2008, these sources emitted about 11% of
the total anthropogenic mercury emissions in the U.S. and about 0.5% of
the global emissions. 

Overall, the major and area source rules would reduce mercury emissions
by about 1.5 tons (2,900 pounds, or a 13% reduction) per year from
current levels, and therefore, contribute to reductions in mercury
exposures and health effects. Due to time limitations under the
court-ordered schedule for this rule, we were unable to model mercury
methylation, bioaccumulation in fish tissue, and human consumption of
mercury-contaminated fish that would be needed in order to estimate the
human health benefits from reducing mercury emissions. However, we were
able to model the change in mercury deposition using CAMx for the final
Boiler MACT and Boiler Area Source Rule. These modeling results indicate
significantly reduced total mercury deposition (wet and dry forms) in
some areas, including reducing total deposition from all sources by an
average of 1.4% in the East and 0.5% in the West in the analysis year.
This modeling indicates that mercury deposition reductions tend to be
greatest nearest the sources. Figure 7-19 shows the percentage reduction
in total mercury deposition as a result of the final Boiler MACT and
Boiler Area Source Rule in the Eastern U.S., and Figure 7-20 shows the
percentage reduction in mercury deposition in the Western U.S. based on
the air quality modeling conducted for these rules.

Figure 7-19.	Percentage Reduction in Total Mercury Deposition in the
Eastern U.S.

Figure 7-20.	Percentage Reduction in Total Mercury Deposition in the
Western U.S.

Hydrogen Chloride (HCl) 

Hydrogen chloride gas is intensely irritating to the mucous membranes of
the nose, throat, and respiratory tract. Brief exposure to 35 ppm causes
throat irritation, and levels of 50 to 100 ppm are barely tolerable for
1 hour. The greatest impact is on the upper respiratory tract; exposure
to high concentrations can rapidly lead to swelling and spasm of the
throat and suffocation. Most seriously exposed persons have immediate
onset of rapid breathing, blue coloring of the skin, and narrowing of
the bronchioles. Patients who have massive exposures may develop an
accumulation of fluid in the lungs. Exposure to hydrogen chloride can
lead to Reactive Airway Dysfunction Syndrome (RADS), a chemically- or
irritant-induced type of asthma. Children may be more vulnerable to
corrosive agents than adults because of the relatively smaller diameter
of their airways. Children may also be more vulnerable to gas exposure
because of increased minute ventilation per kg and failure to evacuate
an area promptly when exposed. Hydrogen chloride has not been classified
for carcinogenic effects. 

Chlorine Gas (Cl2) 

Chlorine gas is irritating and corrosive to the eyes, skin, and
respiratory tract. Exposure to chlorine may cause burning of the eyes,
nose, and throat; cough as well as constriction and edema of the airway
and lungs can occur. 

Hydrogen Cyanide (HCN) 

Hydrogen cyanide is highly toxic by all routes of exposure and may cause
abrupt onset of profound central nervous system, cardiovascular, and
respiratory effects, leading to death within minutes. Exposure to lower
concentrations of hydrogen cyanide may produce eye irritation, headache,
confusion, nausea, and vomiting followed in some cases by coma and
death. Hydrogen cyanide acts as a cellular asphyxiant. By binding to
mitochondrial cytochrome oxidase, it prevents the utilization of oxygen
in cellular metabolism. The central nervous system and myocardium are
particularly sensitive to the toxic effects of cyanide.

Hydrogen Fluoride (HF) 

Acute (short-term) inhalation exposure to gaseous hydrogen fluoride can
cause severe respiratory damage in humans, including severe irritation
and pulmonary edema. Chronic (long-term) exposure to fluoride at low
levels has a beneficial effect of dental cavity prevention and may also
be useful for the treatment of osteoporosis. Exposure to higher levels
of fluoride may cause dental fluorosis. One study reported menstrual
irregularities in women occupationally exposed to fluoride. The EPA has
not classified hydrogen fluoride for carcinogenicity.

Toluene

Toluene is found in evaporative as well as exhaust emissions from motor
vehicles. Under the 2005 Guidelines for Carcinogen Risk Assessment,
there is inadequate information to assess the carcinogenic potential of
toluene because studies of humans chronically exposed to toluene are
inconclusive, toluene was not carcinogenic in adequate inhalation cancer
bioassays of rats and mice exposed for life, and increased incidences of
mammary cancer and leukemia were reported in a lifetime rat oral
bioassay.

The central nervous system (CNS) is the primary target for toluene
toxicity in both humans and animals for acute and chronic exposures. CNS
dysfunction (which is often reversible) and narcosis have been
frequently observed in humans acutely exposed to low or moderate levels
of toluene by inhalation; symptoms include fatigue, sleepiness,
headaches, and nausea. Central nervous system depression has been
reported to occur in chronic abusers exposed to high levels of toluene.
Symptoms include ataxia, tremors, cerebral atrophy, nystagmus
(involuntary eye movements), and impaired speech, hearing, and vision.
Chronic inhalation exposure of humans to toluene also causes irritation
of the upper respiratory tract, eye irritation, dizziness, headaches,
and difficulty with sleep.

Human studies have also reported developmental effects, such as CNS
dysfunction, attention deficits, and minor craniofacial and limb
anomalies, in the children of women who abused toluene during pregnancy.
A substantial database examining the effects of toluene in subchronic
and chronic occupationally exposed humans exists. The weight of evidence
from these studies indicates neurological effects (i.e., impaired color
vision, impaired hearing, decreased performance in neurobehavioral
analysis, changes in motor and sensory nerve conduction velocity,
headache, and dizziness) as the most sensitive endpoint.

Formaldehyde

Since 1987, EPA has classified formaldehyde as a probable human
carcinogen based on evidence in humans and in rats, mice, hamsters, and
monkeys. EPA is currently reviewing recently published epidemiological
data. For instance, research conducted by the National Cancer Institute
(NCI) found an increased risk of nasopharyngeal cancer and
lymphohematopoietic malignancies such as leukemia among workers exposed
to formaldehyde., In an analysis of the lymphohematopoietic cancer
mortality from an extended follow-up of these workers, NCI confirmed an
association between lymphohematopoietic cancer risk and peak exposures.
A recent National Institute of Occupational Safety and Health (NIOSH)
study of garment workers also found increased risk of death due to
leukemia among workers exposed to formaldehyde. Extended follow-up of a
cohort of British chemical workers did not find evidence of an increase
in nasopharyngeal or lymphohematopoietic cancers, but a continuing
statistically significant excess in lung cancers was reported.

In the past 15 years there has been substantial research on the
inhalation dosimetry for formaldehyde in rodents and primates by the
CIIT Centers for Health Research (formerly the Chemical Industry
Institute of Toxicology), with a focus on use of rodent data for
refinement of the quantitative cancer dose-response assessment.,,
CIIT’s risk assessment of formaldehyde incorporated mechanistic and
dosimetric information on formaldehyde. However, it should be noted that
recent research published by EPA indicates that when two-stage modeling
assumptions are varied, resulting dose-response estimates can vary by
several orders of magnitude.,,, These findings are not supportive of
interpreting the CIIT model results as providing a conservative (health
protective) estimate of human risk. EPA research also examined the
contribution of the two-stage modeling for formaldehyde towards
characterizing the relative weights of key events in the mode-of-action
of a carcinogen. For example, the model-based inference in the published
CIIT study that formaldehyde’s direct mutagenic action is not relevant
to the compound’s tumorigenicity was found not to hold under
variations of modeling assumptions.

Based on the developments of the last decade, in 2004, the working group
of the IARC concluded that formaldehyde is carcinogenic to humans (Group
1), on the basis of sufficient evidence in humans and sufficient
evidence in experimental animals—a higher classification than previous
IARC evaluations. After reviewing the currently available
epidemiological evidence, the IARC (2006)  XE “IARC (2006)”  
characterized the human evidence for formaldehyde carcinogenicity as
“sufficient,” based upon the data on nasopharyngeal cancers; the
epidemiologic evidence on leukemia was characterized as “strong.”
EPA is reviewing the recent work cited above from the NCI and NIOSH, as
well as the analysis by the CIIT Centers for Health Research and other
studies, as part of a reassessment of the human hazard and dose-response
associated with formaldehyde.

Formaldehyde exposure also causes a range of noncancer health effects,
including irritation of the eyes (burning and watering of the eyes),
nose and throat. Effects from repeated exposure in humans include
respiratory tract irritation, chronic bronchitis and nasal epithelial
lesions such as metaplasia and loss of cilia. Animal studies suggest
that formaldehyde may also cause airway inflammation—including
eosinophil infiltration into the airways. There are several studies that
suggest that formaldehyde may increase the risk of asthma—particularly
in the young., 

Polycyclic Aromatic Hydrocarbons (PAHs)

At least seven PAH compounds are classified by EPA as probable human
carcinogens based on animal data, including benzo(a)anthracene,
benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(a)pyrene, chrysene,
dibenz(a,h)anthracene, and indeno(1,2,3-cd)pyrene. Recent studies have
found that maternal exposures to PAHs in a population of pregnant women
were associated with several adverse birth outcomes, including low birth
weight and reduced length at birth, as well as impaired cognitive
development at age three.,  EPA has not yet evaluated these recent
studies.

Acetaldehyde

Acetaldehyde is classified in EPA’s IRIS database as a probable human
carcinogen, based on nasal tumors in rats, and is considered toxic by
the inhalation, oral, and intravenous routes. Acetaldehyde is reasonably
anticipated to be a human carcinogen by the U.S. DHHS in the 11th Report
on Carcinogens and is classified as possibly carcinogenic to humans
(Group 2B) by the IARC.,  EPA is currently conducting a reassessment of
cancer risk from inhalation exposure to acetaldehyde.

The primary noncancer effects of exposure to acetaldehyde vapors include
irritation of the eyes, skin, and respiratory tract. In short-term (4
week) rat studies, degeneration of olfactory epithelium was observed at
various concentration levels of acetaldehyde exposure. Data from these
studies were used by EPA to develop an inhalation reference
concentration. Some asthmatics have been shown to be a sensitive
subpopulation to decrements in functional expiratory volume (FEV1 test)
and bronchoconstriction upon acetaldehyde inhalation. The agency is
currently conducting a reassessment of the health hazards from
inhalation exposure to acetaldehyde.

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. The
EPA has classified nickel refinery subsulfide as Group A, human
carcinogens and nickel carbonyl as a Group B2, probable human
carcinogen.

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.
Chronic exposure to high levels of manganese by inhalation in humans
results primarily in 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 masklike 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. The EPA has classified manganese in Group D, not classifiable
as to carcinogenicity in humans.

Dioxins (Chlorinated dibenzodioxins (CDDs) 

A number of effects have been observed in people exposed to 2,3,7,8-TCDD
levels that are at least 10 times higher than background levels. The
most obvious health effect in people exposure to relatively large
amounts of 2,3,7,8-TCDD is chloracne. Chloracne is a severe skin disease
with acne-like lesions that occur mainly on the face and upper body.
Other skin effects noted in people exposed to high doses of 2,3,7,8-TCDD
include skin rashes, discoloration, and excessive body hair. Changes in
blood and urine that may indicate liver damage also are seen in people.
Alterations in the ability of the liver to metabolize (or breakdown)
hemoglobin, lipids, sugar, and protein have been reported in people
exposed to relatively high concentrations of 2,3,7,8-TCDD. Most of the
effects are considered mild and were reversible. However, in some people
these effects may last for many years. Slight increases in the risk of
diabetes and abnormal glucose tolerance have been observed in some
studies of people exposed to 2,3,7,8-TCDD. We do not have enough
information to know if exposure to 2,3,7,8-TCDD would result in
reproductive or developmental effects in people, but animal studies
suggest that this is a potential health concern. 

In certain animal species, 2,3,7,8-TCDD is especially harmful and can
cause death after a single exposure. Exposure to lower levels can cause
a variety of effects in animals, such as weight loss, liver damage, and
disruption of the endocrine system. In many species of animals,
2,3,7,8-TCDD weakens the immune system and causes a decrease in the
system’s ability to fight bacteria and viruses at relatively low
levels (approximately 10 times higher than human background body
burdens). In other animal studies, exposure to 2,3,7,8-TCDD has caused
reproductive damage and birth defects. Some animal species exposed to
CDDs during pregnancy had miscarriages and the offspring of animals
exposed to 2,3,7,8-TCDD during pregnancy often had severe birth defects
including skeletal deformities, kidney defects, and weakened immune
responses. In some studies, effects were observed at body burdens 10
times higher than human background levels. 

Furans (Chlorinated dibenzofurans (CDFs)) 

Most of the information on the adverse health effects comes from studies
in people who were accidentally exposed to food contaminated with CDFs.
The amounts that these people were exposed to were much higher than are
likely from environmental exposures or from a normal diet. Skin and eye
irritations, especially severe acne, darkened skin color, and swollen
eyelids with discharge, were the most obvious health effects of the CDF
poisoning. CDF poisoning also caused vomiting and diarrhea, anemia, more
frequent lung infections, numbness, effects on the nervous system, and
mild changes in the liver. Children born to exposed mothers had skin
irritation and more difficulty learning, but it is unknown if this
effect was permanent or caused by CDFs alone or CDFs and polychlorinated
biphenyls in combination.

Many of the same effects that occurred in people accidentally exposed
also occurred in laboratory animals that ate CDFs. Animals also had
severe weight loss, and their stomachs, livers, kidneys, and immune
systems were seriously injured. Some animals had birth defects and
testicular damage, and in severe cases, some animals died. These effects
in animals were seen when they were fed large amounts of CDFs over a
short time, or small amounts over several weeks or months. Nothing is
known about the possible health effects in animals from eating CDFs over
a lifetime.

Other Air Toxics

In addition to the compounds described above, other compounds in gaseous
hydrocarbon and PM emissions would be affected by these rules, including
metal and organic HAPs. Information regarding the health effects of
these compounds can be found in EPA’s IRIS database.

Limitations and Uncertainties

The National Research Council (NRC) (2002)  XE “NRC (2002)”  
concluded that EPA’s general methodology for calculating the benefits
of reducing air pollution is reasonable and informative in spite of
inherent uncertainties. To address these inherent uncertainties, NRC
highlighted the need to conduct rigorous quantitative analysis of
uncertainty and to present benefits estimates to decisionmakers in ways
that foster an appropriate appreciation of their inherent uncertainty.
In response to these comments, EPA’s Office of Air and Radiation (OAR)
is developing a comprehensive strategy for characterizing the aggregate
impact of uncertainty in key modeling elements on both health incidence
and benefits estimates. Components of that strategy include emissions
modeling, air quality modeling, health effects incidence estimation, and
valuation. 

In this analysis, we use three methods to assess uncertainty
quantitatively: Monte Carlo analysis, LML assessment, and alternate
concentration-response functions for PM- and ozone-related mortality. We
also provide a qualitative assessment for those aspects that we are
unable to address quantitatively in this analysis. Each of these
analyses is described in the following sections. 

This analysis includes many data sources as inputs, including emission
inventories, air quality data from models (with their associated
parameters and inputs), population data, health effect estimates from
epidemiology studies, and economic data for monetizing benefits. Each of
these inputs may be uncertain and would affect the benefits estimate.
When the uncertainties from each stage of the analysis are compounded,
small uncertainties can have large effects on the total quantified
benefits. In this analysis, we are unable to quantify the cumulative
effect of all of these uncertainties, but we provide the following
analyses to characterize many of the largest sources of uncertainty. 

Monte Carlo Analysis

Similar to other recent RIAs, we used Monte Carlo methods for
characterizing random sampling error associated with the concentration
response functions and economic valuation functions. Monte Carlo
simulation uses random sampling from distributions of parameters to
characterize the effects of uncertainty on output variables, such as
incidence of morbidity. Specifically, we used Monte Carlo methods to
generate confidence intervals around the estimated health impact and
monetized benefits. The reported standard errors in the epidemiological
studies determined the distributions for individual effect estimates, as
shown in Tables 7-4 and 7-5. The confidence intervals around the
monetized benefits in Tables 7-6 and 7-7 incorporate the epidemiology
standard errors as well as the distribution of the valuation function.
These confidence intervals do not reflect other sources of uncertainty
inherent within the boiler-specific BPT estimates, nor do they reflect
baseline incidence rates, populations exposed and transferability of the
effect estimate to diverse locations. As a result, the reported
confidence intervals and range of estimates give an incomplete picture
about the overall uncertainty in the benefits estimates.

LML Assessment for PM2.5

PM2.5 mortality benefits are the largest benefit category that we
monetized in this analysis. To better characterize the uncertainty
associated with mortality impacts that are estimated to occur in areas
with low baseline levels of PM2.5, we included the LML assessment. We
have more confidence in the mortality impacts among populations exposed
to levels of PM2.5 above the lowest LML of the large cohort studies, and
our confidence in the results diminish as we model that are lower than
the LML. While an LML assessment provides some insight into the level of
uncertainty in the estimated PM mortality benefits, EPA does not view
the LML as a threshold and continues to quantify PM-related mortality
impacts using a full range of modeled air quality concentrations. It is
important to emphasize that just because we have greater confidence in
the benefits above the LML, this does not mean that we have no
confidence that benefits occur below the LML. In Section 7.3, we provide
the results of the LML assessment in Figures 7-15 and 7-16.

Alternate Concentration-response Functions for PM Mortality

PM2.5 mortality benefits are the largest benefit category that we
monetized in this analysis. To better understand the
concentration-response relationship between PM2.5 exposure and premature
mortality, EPA conducted an expert elicitation in 2006 (Roman et al.,
2008  XE “Roman et al., 2008”  ; IEc, 2006  XE “IEc, 2006”  ).
In general, the results of the expert elicitation support the conclusion
that the benefits of PM2.5 control are very likely to be substantial. In
previous RIAs, EPA presented benefits estimates using concentration
response functions derived from the PM2.5 Expert Elicitation as a range
from the lowest expert value (Expert K) to the highest expert value
(Expert E). However, this approach did not indicate the agency’s
judgment on what the best estimate of PM benefits may be, and EPA’s
Science Advisory Board described this presentation as misleading.
Therefore, we began to present the cohort-based studies (Pope et al.,
2002  XE “Pope et al., 2002”  ; and Laden et al., 2006  XE “Laden
et al., 2006”  ) as our core estimates in the proposal RIA for the
Portland Cement NESHAP (U.S. EPA, 2009a  XE “U.S. EPA, 2009a”  ).
Using alternate relationships between PM2.5 and premature mortality
supplied by experts, higher and lower benefits estimates are plausible,
but most of the expert-based estimates fall between the two
epidemiology-based estimates (Roman et al., 2008 xe “Roman et al.,
2008” ). 

In this analysis, we present the results derived from the expert
elicitation as indicative of the uncertainty associated with a major
component of the health impact functions, and we provide the independent
estimates derived from each of the twelve experts to better characterize
the degree of variability in the expert responses. In this section, we
provide the results using the concentration-response functions derived
from the expert elicitation in both tabular (Tables 7-7 and 7-8) and
graphical form (Figure 7-10). Please note that these results are not the
direct results from the studies or expert elicitation; rather, the
estimates are based in part on the concentration-response function
provided in those studies. 

Alternate Concentration-response Functions for Ozone Mortality

While particulate matter is the criteria pollutant most clearly
associated with premature mortality, recent research suggests that
short-term repeated ozone exposure also likely contributes to premature
death. In 2008 the National Research Council (NRC) of the National
Academy of Science (NAS) (NRC, 2008  XE “NRC, 2008”  ) issued a
series of recommendations to the EPA regarding the quantification and
valuation of ozone-related short-term mortality. Chief among these was
that “…short-term exposure to ambient ozone is likely to contribute
to premature deaths” and the committee recommended that
“ozone-related mortality be included in future estimates of the health
benefits of reducing ozone exposures…” The NAS also recommended that
“…the greatest emphasis be placed on the multicity and NMMAPS
studies without exclusion of the meta-analyses” (NRC, 2008  XE “NRC,
2008”  ). 

In view of the findings of the NAS panel, we include used estimates of
short-term ozone mortality from the Bell et al. (2004)  XE “Bell et
al. (2004)”   NMMAPS analysis, the Schwartz (2005)  XE “Schwartz
(2005)”   multi-city study, the Huang and Bell (2005)  XE “Huang and
Bell (2005)”   multi-city study as well as effect estimates from the
three meta-analyses (Bell et al., 2005  XE “Bell et al., 2005”  ;
Levy et al., 2005  XE “Levy et al., 2005”  ; Ito et al., 2005  XE
“Ito et al., 2005”  ). 

Qualitative Assessment of Uncertainty and Other Analysis Limitations

Although we strive to incorporate as many quantitative assessments of
uncertainty, there are several aspects for which we are only able to
address qualitatively. These aspects are important factors to consider
when evaluating the relative benefits of the attainment strategies for
each of the alternative standards: 

Above we present the estimates of the total monetized benefits, based on
our interpretation of the best available scientific literature and
methods and supported by the SAB-HES and the NAS (NRC, 2002  XE “NRC,
2002”  ). The benefits estimates are subject to a number of
assumptions and uncertainties. For example, the key assumptions
underlying the estimates for premature mortality, which typically
account for at least 90% of the total monetized benefits, include the
following: 

We assume that all fine particles, regardless of their chemical
composition, are equally potent in causing premature mortality. This is
an important assumption, because PM2.5 produced via transported
precursors emitted from EGUs may differ significantly from direct PM2.5
released from diesel engines and other industrial sources, but the
scientific evidence is not yet sufficient to allow differentiation of
effect estimates by particle type. 

We assume that the health impact function for fine particles is linear
down to the lowest air quality levels modeled in this analysis. Thus,
the estimates include health benefits from reducing fine particles in
areas with varied concentrations of PM2.5, including both regions that
are in attainment with fine particle standard and those that do not meet
the standard down to the lowest modeled concentrations. 

To characterize the uncertainty in the relationship between PM2.5 and
premature mortality (which typically accounts for 85% to 95% of total
monetized benefits), we include a set of twelve estimates based on
results of the expert elicitation study in addition to our core
estimates. Even these multiple characterizations omit the uncertainty in
air quality estimates, baseline incidence rates, populations exposed and
transferability of the effect estimate to diverse locations. As a
result, the reported confidence intervals and range of estimates give an
incomplete picture about the overall uncertainty in the PM2.5 estimates.
This information should be interpreted within the context of the larger
uncertainty surrounding the entire analysis. For more information on the
uncertainties associated with PM2.5 benefits, please consult the PM2.5
NAAQS RIA (Table 5.5).

It is important to note that the monetized benefit-per-ton estimates
used here reflect specific geographic patterns of emissions reductions
and specific air quality and benefits modeling assumptions. For example,
these estimates assume that emissions are reduced equally across all
boilers and that the geographic distribution of boiler emissions will
remain constant through the analysis year. As we did not model fuel
subcategories, we assume that each fuel type is equally represented in
the emissions and geographic distribution. Use of these $/ton values to
estimate benefits associated with a specific fuel type (e.g., for
reducing emissions from liquid fuel) may lead to higher or lower benefit
estimates than if benefits were calculated based on direct air quality
modeling of that fuel subcategory. Great care should be taken in
applying these estimates to emission reductions occurring in any
specific location, as these are all based on national emission reduction
assumptions and therefore represent an average benefits-per-ton over the
entire United States. The benefits- per-ton for variable emission
reductions in specific locations may be very different from the
estimates presented here. Despite our inability to fully characterize
and quantify these relatively small uncertainties, we believe that, on
net, the air quality impacts and associated monetized benefits are
representative of the magnitude of benefits anticipated from these
rules. 

As previously described, we strive to monetize as many of the benefits
anticipated from these rules as possible, but the monetized benefits
estimated in this RIA inevitably only reflect the portion of benefits.
Specifically, only the benefits attributable to the health impacts
associated with exposure to ambient fine particles and ozone have been
monetized in this analysis. Methodological and time limitations under
the court-ordered schedule for this rule prevented EPA from quantifying
or monetizing the benefits from several important benefit categories,
including benefits from reducing toxic emissions, ecosystem effects, and
visibility impairment. Data limitations include limited monitoring for
HAPs, incomplete emissions inventories for HAPs, and limited
photochemical air quality modeling for non-mercury HAPs. Resource
limitations include limited staff and extramural funding in conjunction
with a heavy regulatory workload, which led EPA to not model these
endpoints. Methodological limitations include an absence of
concentration-response functions for many HAP health effects, with
issues such as exposure misclassification, small number of cases,
confounding, and extrapolation of toxicological effects down to ambient
levels  XE “IEc, 2008”  . Despite our inability to monetize all of
the benefit categories, the total combined monetized benefits still
exceed the costs by a substantial margin. 

This RIA does not include the type of detailed uncertainty assessment
found in the PM NAAQS RIA (U.S. EPA, 2006  XE “U.S. EPA, 2006”  ).
However, the results of the Monte Carlo analyses of the health and
welfare benefits presented in Chapter 5 of the PM RIA can provide some
evidence of the uncertainty surrounding the benefits results presented
in this analysis.

Section 7 References

Abt Associates, Inc. 2008. Environmental Benefits and Mapping Program
(Version 3.0). Bethesda, MD. Prepared for U.S. Environmental Protection
Agency—Office of Air Quality Planning and Standards. Research Triangle
Park, NC. Available on the Internet at <http://www.epa.gov/air/benmap>.

Bell, M.L., et al. 2004. “Ozone and short-term mortality in 95 US
urban communities, 1987–2000.” Journal of the American Medical
Association. 292(19): p. 2372-8.

Bell, M.L., F. Dominici, and J.M. Samet. 2005. “A meta-analysis of
time-series studies of ozone and mortality with comparison to the
national morbidity, mortality, and air pollution study.” Epidemiology.
16(4): p. 436-45.

Davidson, K., A. Hallberg, D. McCubbin, and B.J. Hubbell. 2007.
“Analysis of PM2.5 Using the Environmental Benefits Mapping and
Analysis Program (BenMAP).” J Toxicol Environ Health. 70: p. 332-46. 

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Comparison of Monetized Benefits and Costs

Boiler MACT

Because we were unable to monetize the benefits associated with reducing
HAPs, all monetized benefits reflect improvements in ambient PM2.5 and
ozone concentrations. This results in an underestimate of the monetized
benefits. Using a 3% discount rate, we estimate the total monetized
benefits of the major source rule to be $22 billion to $54 billion in
the implementation year (2014) (Table 8-1). Using a 7% discount rate, we
estimate the total monetized benefits to be $20 billion to $49 billion.
The annualized costs are $1.5 billion at a 7% interest rate. The net
benefits are $21 billion to $53 billion at a 3% discount rate for the
benefits and $19 million to $48 billion at a 7% discount rate. All
estimates are in 2008$. The benefits from reducing other air pollutants
have not been monetized in this analysis, including reducing 112,000
tons of carbon monoxide, 30,000 tons of HCl, 820 tons of HF, 2,800
pounds of mercury, 2,700 tons of other metals, and 23 grams of
dioxins/furans (TEQ) each year.

Boiler Area Source Rule

Because we were unable to monetize the benefits associated with reducing
HAPs, all monetized benefits reflect improvements in ambient PM2.5 and
ozone concentrations. This results in an underestimate of the monetized
benefits. Using a 3% discount rate, we estimate the total monetized
benefits of the area source rule to be $210 million to $520 million in
the implementation year (2014) (Table 8-2). Using a 7% discount rate, we
estimate the total monetized benefits to be $190 million to $470
million. The annualized costs are $490 million at a 7% interest rate.
The net benefits are −$280 million to $30 million at a 3% discount
rate for the benefits and −$300 million to −$20 million at a 7%
discount rate. All estimates are in 2008$. The benefits from reducing
other air pollutants have not been monetized in this analysis, including
reducing 1,100 tons of carbon monoxide, 340 tons of HCl, 8 tons of HF,
90 pounds of mercury, 320 tons of other metals, and less than 1 gram of
dioxins/furans (TEQ) each year.

Table 8-1.	Summary of the Monetized Benefits, Social Costs, and Net
Benefits for the Boiler MACT in 2014 (millions of 2008$)a

 	3% Discount Rate	7% Discount Rate

Selected

Total Monetized Benefitsb	$22,000	to	$54,000	$20,000	to	$49,000

Total Social Costsc	$1,500	$1,500

Net Benefits	$20,500	to	$52,500	$18,500	to	$47,500

Non-monetized Benefits	112,000 tons of carbon monoxide

	30,000 tons of HCl

	820 tons of HF 

	2,800 pounds of mercury 

	2,700 tons of other metals

	23 grams of dioxins/furans (TEQ)

	Health effects from SO2 exposure

	Ecosystem effects 

	Visibility impairment 

Alternative

Total Monetized Benefitsb	$18,000	to	$43,000	$16,000	to	$39,000

Total Social Costsb	$1,900	$1,900

Net Benefits	$16,100	to	$41,100	$14,100	to	$37,100

Non-monetized Benefits	112,000 tons of carbon monoxide

	22,000 tons of HCl

	620 tons of HF 

	2,400 pounds of mercury 

	2,600 tons of other metals

	23 grams of dioxins/furans (TEQ)

	Health effects from SO2 exposure

	Ecosystem effects 

 	Visibility impairment 

a	All estimates are for the implementation year (2014), and are rounded
to two significant figures. These results include units anticipated to
come online and the lowest cost disposal assumption. 

b	The total monetized benefits reflect the human health benefits
associated with reducing exposure to PM2.5 through reductions of
directly emitted PM2.5 and PM2.5 precursors such as SO2, as well as
reducing exposure to ozone through reductions of VOCs. It is important
to note that the monetized benefits include many but not all health
effects associated with PM2.5 exposure. Benefits are shown as a range
from Pope et al. (2002)  XE “Pope et al. (2002)”   to Laden et al.
(2006)  XE “Laden et al. (2006)”  . These models assume that all
fine particles, regardless of their chemical composition, are equally
potent in causing premature mortality because the scientific evidence is
not yet sufficient to allow differentiation of effect estimates by
particle type. These estimates include energy disbenefits associated
with the increased emissions from additional energy usage valued at $22
million for the selected option and $37 million for the alternative
option. Ozone benefits are valued at $3.6 to $15 million for both
options. 

c	The methodology used to estimate social costs for one year in the
multimarket model using surplus changes results in the same social costs
for both discount rates.

Table 8-2	Summary of the Monetized Benefits, Social Costs, and Net
Benefits for the Boiler Area Source Rule in 2014 (millions of 2008$)a

−$280	to	$30	−$300	to	−$20

Non-monetized Benefits	1,100 tons of carbon monoxide

	340 tons of HCl

	8 tons of HF 

	90 pounds of mercury 

	320 tons of other metals

	<1 gram of dioxins/furans (TEQ)

	Health effects from SO2 exposure

	Ecosystem effects 

	Visibility impairment 

Proposed MACT Approach

Total Monetized Benefitsb	$200	to	$490	$180	to	$440

Total Social Costsc	$850	$850

Net Benefits	−$650	to	−$360	−$670	to	−$410

Non-monetized Benefits	1,100 tons of carbon monoxide

	340 tons of HCl

	8 tons of HF 

	90 pounds of mercury 

	320 tons of other metals

	<1 gram of dioxins/furans (TEQ)

	Health effects from SO2 exposure

	Ecosystem effects 

 	Visibility impairment 

a	All estimates are for the implementation year (2014), and are rounded
to two significant figures. These results include units anticipated to
come online and the lowest cost disposal assumption. 

b	The total monetized benefits reflect the human health benefits
associated with reducing exposure to PM2.5 through reductions of
directly emitted PM2.5 and PM2.5 precursors such as SO2. It is important
to note that the monetized benefits include many but not all health
effects associated with PM2.5 exposure. Benefits are shown as a range
from Pope et al. (2002)  XE “Pope et al. (2002)”   to Laden et al.
(2006)  XE “Laden et al. (2006)”  . These models assume that all
fine particles, regardless of their chemical composition, are equally
potent in causing premature mortality because the scientific evidence is
not yet sufficient to allow differentiation of effect estimates by
particle type. These estimates include energy disbenefits associated
with the increased emissions from additional energy usage valued at less
than $1 million.

c	The methodology used to estimate social costs for one year in the
multimarket model using surplus changes results in the same social costs
for both discount rates.

Figures 8-1 and 8-2 show the full range of net benefits estimates (i.e.,
annual benefits minus annualized costs) quantified in terms of PM2.5
benefits for the implementation year (2014). 

Figure 8-1.	Net Benefits for the Final Major and Area Source Boiler
Rules at 3% Discount Ratea

a	Net benefits are quantified in terms of PM2.5 benefits for the
implementation year (2014). This graph shows 14 benefits estimates
combined with the cost estimate. These models assume that all fine
particles, regardless of their chemical composition, are equally potent
in causing premature mortality because the scientific evidence is not
yet sufficient to allow differentiation of effect estimates by particle
type. The monetized benefits incorporate the conversion from precursor
emissions to ambient fine particles.

Figure 8-2.	Net Benefits for the Final Major and Area Source Boiler
Rules at 7% Discount Ratea

a	Net benefits are quantified in terms of PM2.5 benefits for the
implementation year (2014). This graph shows 14 benefits estimates
combined with the cost estimate. These models assume that all fine
particles, regardless of their chemical composition, are equally potent
in causing premature mortality because the scientific evidence is not
yet sufficient to allow differentiation of effect estimates by particle
type. The monetized benefits incorporate the conversion from precursor
emissions to ambient fine particles.

Section 8 References

Laden, F., J. Schwartz, F.E. Speizer, and D.W. Dockery. 2006.
“Reduction in Fine Particulate Air Pollution and Mortality.”
American Journal of Respiratory and Critical Care Medicine 173:667-672.

Pope, C.A., III, R.T. Burnett, M.J. Thun, E.E. Calle, D. Krewski, K.
Ito, and G.D. Thurston. 2002. “Lung Cancer, Cardiopulmonary Mortality,
and Long-term Exposure to Fine Particulate Air Pollution.” Journal of
the American Medical Association 287:1132-1141.

Appendix A

OAQPS Multimarket Model to Assess the Economic 

Impacts of Environmental 

Regulation

A.1	Introduction

An economic impact analysis (EIA) provides information about a
policy’s effects (i.e., social costs); emphasis is also placed on how
the costs are distributed among stakeholders (EPA, 2000 xe “U.S. EPA,
2000” ). In addition, large-scale policies require additional analysis
to better understand how costs are passed across the economy. Although
several tools are available to estimate social costs, current EPA
guidelines suggest that multimarket models “…are best used when
potential economic impacts and equity effects on related markets might
be considerable” and modeling using a computable general equilibrium
model is not available or practical (EPA, 2000 xe “U.S. EPA, 2000” ,
p. 146). Other guides for environmental economists offer similar advice
(Berck and Hoffmann, 2002 xe “Berck and Hoffmann, 2002” ; Just,
Hueth, and Schmitz, 2004 xe “Just, Hueth, and Schmitz, 2004” ).

Multimarket models focus on “short-run” time horizons and measure a
policy’s near term or transition costs (EPA, 1999  XE “U.S. EPA,
1999”  ). Recent studies suggest short-run analyses can complement
full dynamic general equilibrium analysis.

The multimarket model described in this appendix is a new addition to
the Office of Air Quality Planning and Standards’ (OAQPS’s) economic
model tool kit; it is designed to be used as a transparent tool that can
respond quickly to requests about how stakeholders in 100 U.S.
industries might respond to new environmental policy. Next, we provide
an overview of the model, data, and parameters.

A.2	Multimarket Model

The multimarket model contains the following features:

Industry sectors and benchmark data set

100 industry sectors

a single benchmark year (2010)

estimates of industry carbon dioxide (CO2) emissions

estimates of industry employment

Economic behavior

industries respond to regulatory costs by changing production rates

market prices rise to reflect higher energy and other nonenergy material
costs

customers respond to these price increases and consumption falls

Model scope

100 sectors are linked with each other based on their use of energy and
other nonenergy materials. For example, the construction industry is
linked with the petroleum, cement, and steel industries and is
influenced by price changes that occur in each sector. The links allow
EPA to account for indirect effects the regulation has on related
markets.

 Links come from input-output information used from OAQPS’s computable
general equilibrium model (EMPAX)

production adjustments influence employment levels

international trade (imports/exports) behavior considered

Model time horizon (“short-run”)

fixed production resources (e.g., capital) leads to an upward-sloping
industry supply function

firms cannot alter input mixes; there is no substitution among
intermediate production inputs 

price of labor (i.e., wage) is fixed

investment and government expenditures are fixed

A.2.1	Industry Sectors and Benchmark Data Set

The multimarket model includes 100 industries. For the benchmark year,
the model uses information from OAQPS’s computable general equilibrium
model’s balanced social accounting matrix (SAM) and the following
accounting identity holds:

	Output + Imports = Consumption + Investment + Government + Exports
(A.1)

If we abstract and treat each industry as a national market, the
identity represents the prepolicy market-clearing condition, or
benchmark “equilibrium”; supply equals demand in each market. In
Table A-1, we identify the 100 industries for the multimarket model;
Table A-2 provides the 2010 benchmark data set. Since the benchmark data
are reported in value terms, we also use the common “Harberger
convention” and choose units where are all prices are one in the
benchmark equilibrium (Shoven and Whalley, 1995 xe “Shoven and
Whalley, 1995” ).

Table A-1.	Industry Sectors Included in Multimarket Model

Industry Label	Description	Representative NAICSa

Energy Industries

COL	Coal	2121

CRU	Crude Oil Extraction	211111 (exc. nat gas)

ELE	Electric Generation	2211

GAS	Natural Gas	211112 2212 4862

OIL	Refined Petroleum	324

Nonmanufacturing

AGR	Agricultural	11

MIN	Mining	21 less others

CNS	Construction	23

Manufactured Goods

Food, beverages, and textiles

	ANM	Animal Foods	3111

GRN	Grain Milling	3112

SGR	Sugar	3113

FRU	Fruits and Vegetables	3114

MIL	Dairy Products	3115

MEA	Meat Products	3116

SEA	Seafood	3117

BAK	Baked Goods	3118

OFD	Other Food Products	3119

BEV	Beverages and Tobacco	312

TEX	Textile Mills	313

TPM	Textile Product Mills	314

WAP	Wearing Apparel	315

LEA	Leather	316

Lumber, paper, and printing

	SAW	Sawmills	3211

PLY	Plywood and Veneer	3212

LUM	Other Lumber	3219

PAP	Pulp and Paper Mills	3221

CPP	Converted Paper Products	3222

PRN	Printing	323

Chemicals

CHM	Chemicals and Gases	3251

RSN	Resins	3252

FRT	Fertilizer	3253

MED	Drugs and Medicine	3254

PAI	Paints and Adhesives	3255

SOP	Soap	3256

OCM	Other Chemicals	3259

(continued)

Table A-1.	Industry Sectors Included in Multimarket Model (continued)

Industry Label	Description	Representative NAICSa

Plastics and Rubber

PLS	Plastic	3261

RUB	Rubber	3262

Nonmetallic Minerals

CLY	Clay	3271

GLS	Glass	3272

CEM	Cement	3273

LIM	Lime and Gypsum	3274

ONM	Other Non-Metallic Minerals	3279

Primary Metals

I_S	Iron and Steel	3311 3312 33151

ALU	Aluminum	3313 331521 331524

OPM	Other Primary Metals	3314 331522 331525 331528

Fabricated Metals

FRG	Forging and Stamping	3321

CUT	Cutlery	3322

FMP	Fabricated Metals	3323

BOI	Boilers and Tanks	3324

HRD	Hardware	3325

WIR	Springs and Wires	3326

MSP	Machine Shops	3327

EGV	Engraving	3328

OFM	Other Fabricated Metals	3329

Machinery and Equipment

	CEQ	Construction and Agricultural Equipment	3331

IEQ	Industrial Equipment	3332

SEQ	Service Industry Equipment	3333

HVC	HVAC Equipment	3334

MEQ	Metalworking Equipment	3335

EEQ	Engines	3336

GEQ	General Equipment	3339

Electronic Equipment

CPU	Computers	3341

CMQ	Communication Equipment	3342

TVQ	TV Equipment	3343

SMI	Semiconductor Equipment	3344

INS	Instruments	3345

MGT	Magnetic Recording Equipment	3346

LGT	Lighting	3351

APP	Appliances	3352

(continued)

Table A-1.	Industry Sectors Included in Multimarket Model (continued)

Industry Label	Description	Representative NAICSa

Electronic Equipment (continued)

ELQ	Electric Equipment	3353

OEQ	Other Electric Equipment	3359

Transportation Equipment

	M_V	Motor Vehicles	3361

TKB	Truck Bodies	3362

MVP	Motor Vehicle Parts	3363

ARC	Aircraft	3364

R_R	Rail Cars	3365

SHP	Ships	3366

OTQ	Other Transport Equipment	3369

Other

FUR	Furniture	337

MSC	Miscellaneous Manufacturing	339

Services

Wholesale and Retail Trade

	WHL	Wholesale Trade	42

RTL	Retail Trade	44–45

Transportation Services

	ATP	Air Transportation	481

RTP	Railroad Transportation	482

WTP	Water Transportation	483

TTP	Freight Truck Transportation	484

PIP	Pipeline Transport	486

OTP	Other Transportation Services	485 487 488

Other Services

INF	Information	51

FIN	Finance and Insurance	52

REL	Real Estate	53

PFS	Professional Services	54

MNG	Management	55

ADM	Administrative Services	56

EDU	Education	61

HLT	Health Care	62

ART	Arts	71

ACM	Accommodations	72

OSV	Other Services	81

PUB	Public Services	92

a	NAICS = North American Industry Classification System. Industry
assignments are based on data used in the EMPAX-modeling system, which
relies on the commodity code system used in IMPLAN.

Table A-2.	2010 Benchmark Data Set (billion 2006$)

Industry Label	Industry Description	Output	Imports	Consumption
Investment and Government	Exports

ACM	Accommodations	$828	$6	$816	$17	$1

ADM	Administrative Services	$795	$37	$771	$61	Less than $1

AGR	Agricultural	$314	$53	$333	$5	$29

ALU	Aluminum	$65	$17	$70	$4	$8

ANM	Animal Foods	$45	Less than $1	$36	Less than $1	$9

APP	Appliances	$25	$19	$35	$6	$3

ARC	Aircraft	$217	$60	$58	$120	$98

ART	Arts	$252	Less than $1	$246	$3	$3

ATP	Air Transportation	$154	$28	$91	$32	$59

BAK	Baked Goods	$61	$3	$61	$2	Less than $1

BEV	Beverages and Tobacco	$133	$54	$186	Less than $1	$1

BOI	Boilers and Tanks	$27	$2	$19	$9	$2

CEM	Cement	$52	Less than $1	$47	$3	$2

CEQ	Construction and Agricultural Equipment	$70	$24	$47	$33	$14

CHM	Chemicals and Gases	$284	$75	$300	$10	$49

CLY	Clay	$8	$4	$10	$1	$2

CMQ	Communication Equipment	$73	$40	$47	$56	$11

CNS	Construction	$983	$77	$594	$465	Less than $1

COL	Coal	$44	$2	$42	Less than $1	$4

CPP	Converted Paper Products	$52	$2	$43	$6	$6

CPU	Computers	$145	$76	$132	$52	$37

CRU	Crude Oil Extraction	$67	$189	$255	Less than $1	Less than $1

CUT	Cutlery	$11	$5	$9	$5	$2

EDU	Education	$970	Less than $1	$257	$701	$13

EEQ	Engines	$36	$14	$30	$6	$13

EGV	Engraving	$21	Less than $1	$9	$5	$7

ELE	Electric Generation	$317	Less than $1	$287	$31	Less than $1

ELQ	Electric Equipment	$33	$16	$23	$17	$10

FIN	Finance and Insurance	$2,015	$106	$1,972	$43	$106

FMP	Fabricated Metals	$66	$1	$58	$7	$2

FRG	Forging and Stamping	$20	Less than $1	$17	$1	$2

FRT	Fertilizer	$42	$5	$33	$4	$10

(continued)

Table A-2.	2010 Benchmark Data Set (billion 2006$) (continued)

Industry Label	Industry Description	Output	Imports	Consumption
Investment and Government	Exports

FRU	Fruits and Vegetables	$74	$12	$76	$4	$5

FUR	Furniture	$66	$38	$84	$17	$2

GAS	Natural Gas	$139	$32	$160	$6	$6

GEQ	General Equipment	$54	$32	$47	$25	$14

GLS	Glass	$30	Less than $1	$18	$2	$10

GRN	Grain Milling	$77	$9	$74	$2	$10

HLT	Health Care	$1,863	Less than $1	$1,823	$20	$20

HRD	Hardware	$8	$4	$5	$4	$3

HVC	HVAC Equipment	$34	$9	$26	$10	$6

I_S	Iron and Steel	$125	$42	$143	$10	$13

IEQ	Industrial Equipment	$26	$14	$16	$14	$11

INF	Information	$1,305	$77	$1,217	$154	$11

INS	Instruments	$112	$44	$71	$65	$20

LEA	Leather	$4	$26	$29	Less than $1	$1

LGT	Lighting	$12	$11	$16	$5	$1

LIM	Lime and Gypsum	$7	Less than $1	$1	Less than $1	$5

LUM	Other Lumber	$41	$2	$32	$9	$2

M_V	Motor Vehicles	$272	$190	$313	$106	$43

MEA	Meat Products	$174	$9	$169	$5	$10

MED	Drugs and Medicine	$258	$102	$316	$18	$27

MEQ	Metalworking Equipment	$24	$11	$16	$14	$4

MGT	Magnetic Recording Equipment	$15	$2	$13	$2	$2

MIL	Dairy Products	$87	$3	$84	$4	$2

MIN	Mining	$53	$2	$30	$15	$11

MNG	Management	$469	Less than $1	$378	Less than $1	$92

MSC	Miscellaneous Manufacturing	$178	$129	$189	$73	$46

MSP	Machine Shops	$38	$2	$32	$5	$2

MVP	Motor Vehicle Parts	$220	$75	$226	$17	$52

OCM	Other Chemicals	$45	$2	$23	$9	$15

OEQ	Other Electric Equipment	$31	$16	$28	$7	$11

OFD	Other Food Products	$92	$7	$90	$2	$7

OFM	Other Fabricated Metals	$56	$28	$50	$22	$12

(continued)

Table A-2.	2010 Benchmark Data Set (billion 2006$) (continued)

Industry Label	Industry Description	Output	Imports	Consumption
Investment and Government	Exports

OIL	Refined Petroleum	$415	$106	$462	$12	$47

ONM	Other Non-Metallic Minerals	$13	$5	$15	$1	$2

OPM	Other Primary Metals	$40	$27	$52	$2	$12

OSV	Other Services	$2,321	Less than $1	$1,479	$598	$244

OTP	Other Transportation Services	$245	Less than $1	$202	$22	$22

OTQ	Other Transport Equip	$23	$10	$14	$13	$5

PAI	Paints and Adhesives	$36	$1	$28	$3	$6

PAP	Pulp and Paper Mills	$131	$21	$133	$5	$14

PFS	Professional Services	$2,103	$84	$1,715	$461	$10

PIP	Pipeline Transport	$37	$83	$20	$98	$1

PLS	Plastic	$145	$14	$139	$4	$15

PLY	Plywood and Veneer	$19	$8	$25	$1	$1

PRN	Printing	$51	$1	$34	$11	$6

PUB	Public Services	$1,099	$22	$355	$766	Less than $1

R_R	Rail Cars	$11	$2	$6	$6	$2

REL	Real Estate	$2,719	$2	$2,559	$131	$31

RSN	Resins	$107	$23	$98	$6	$26

RTL	Retail Trade	$1,440	$53	$1,412	$82	Less than $1

RTP	Railroad Transportation	$70	Less than $1	$42	$18	$11

RUB	Rubber	$38	$20	$36	$15	$6

SAW	Sawmills	$29	$9	$36	$1	$1

SEA	Seafood	$13	$3	$14	$1	$1

SEQ	Service Industry Equipment	$29	$23	$22	$24	$6

SGR	Sugar	$34	$6	$36	$2	$3

SHP	Ships	$36	$6	$13	$29	Less than $1

SMI	Semiconductor Equipment	$141	$69	$157	$12	$41

SOP	Soap	$82	$5	$74	$3	$9

TEX	Textile Mills	$29	$9	$31	$1	$6

TKB	Truck Bodies	$58	$12	$34	$32	$5

TPM	Textile Product Mills	$27	$19	$37	$7	$2

TTP	Freight Truck Transportation	$301	Less than $1	$211	$39	$51

(continued)

Table A-2.	2010 Benchmark Data Set (billion 2006$) (continued)

Industry Label	Industry Description	Output	Imports	Consumption
Investment and Government	Exports

TVQ	TV Equipment	$19	$37	$50	$3	$3

WAP	Wearing Apparel	$25	$94	$117	$1	Less than $1

WHL	Wholesale Trade	$1,309	$22	$1,021	$172	$138

WIR	Springs and Wires	$5

$2	$1	$3

WTP	Water Transportation	$45

$14	$12	$19

A.2.1.2	Employment Data

The model includes employment forecasts for each of the 100 sectors.
Employment estimates are based on data from three sources: the AEO 2009
estimates of employment (AEO supplemental Table 126 and indicators of
Macroeconomic Activity), and Global Insights, Inc., and the Bureau of
Labor Statistics (BLS) 2008 end-of year-employment (Current Employment
Statistics—CES [National]). Typically, 3-digit NAICS sectors’
employment estimates are either directly reported in the updated AEO
2009 release or Global Insights For multimarket industries with finer
NAICs detail, estimates were calculated by selecting a primary NAICS
supersector estimate (AEO or Global Insights) and distributing total
employment from the larger NAICS supersectors across more detailed NAICS
sectors within the super-sector. The distributions were determined using
observed 2008 BLS employment data. In the last step, In order to match
aggregate U.S. employment numbers reported in the AEO 2009 release
(140.1 million), a single adjustment factor was applied to all sectors
that use Global Insights’ supersector data. Table A-4 reports the
baseline employment for each of the 100 sectors.

A.2.2	Economic Behavior

A.2.2.1	U.S. Supply

In a postpolicy scenario, industry responds to changes in the new
market-clearing “net” price
f牯琠敨朠潯⁤牯猠牥楶散猠汯㩤

	%Δ”net” price = %Δ market price − %Δ direct costs − %Δ
indirect costs	(A.2)

The %Δ direct costs are approximated using the engineering cost
analysis and baseline value of output. For example, a $1 billion
increase in compliance costs for the electricity sector (ELE) would be
represented in the model as follows:

	%Δ direct costs = $1/$317= 0.03%	(A.3)

As shown in Figure A-1, the cost change provides the industry with
incentives to alter production rates at current market prices; market
prices must rise to maintain the original prepolicy production levels
(Q).

Table A-4.	2010 U.S. Employment Projections

Industry Label	Industry Description	Projected Employment (1,000)

ACM	Accommodations	11,239

ADM	Administrative Services	9,274

AGR	Agricultural	1,607

ALU	Aluminum	87

ANM	Animal Foods	45

APP	Appliances	33

ARC	Aircraft	449

ART	Arts	1,939

ATP	Air Transportation	506

BAK	Baked Goods	247

BEV	Beverages and Tobacco	92

BOI	Boilers and Tanks	67

CEM	Cement	164

CEQ	Construction and Agricultural Equipment	176

CHM	Chemicals and Gases	147

CLY	Clay	38

CMQ	Communication Equipment	73

CNS	Construction	7,446

COL	Coal	79

CPP	Converted Paper Products	306

CPU	Computers	104

CRU	Crude Oil Extraction	384

CUT	Cutlery	34

EDU	Education	2,892

EEQ	Engines	75

EGV	Engraving	100

ELE	Electric Generation	219

ELQ	Electric Equipment	72

FIN	Finance and Insurance	6,051

FMP	Fabricated Metals	285

FRG	Forging and Stamping	75

FRT	Fertilizer	35

FRU	Fruits and Vegetables	153

FUR	Furniture	327

(continued)

Table A-4.	2010 U.S. Employment Projections (continued)

Industry Label	Industry Description	Projected Employment (1,000)

GAS	Natural Gas	98

GEQ	General Equipment	198

GLS	Glass	71

GRN	Grain Milling	55

HLT	Health Care	15,190

HRD	Hardware	20

HVC	HVAC Equipment	109

I_S	Iron and Steel	205

IEQ	Industrial Equipment	88

INF	Information	2,939

INS	Instruments	250

LEA	Leather	3

LGT	Lighting	26

LIM	Lime and Gypsum	10

LUM	Other Lumber	216

M_V	Motor Vehicles	170

MEA	Meat Products	450

MED	Drugs and Medicine	279

MEQ	Metalworking Equipment	139

MGT	Magnetic Recording Equipment	20

MIL	Dairy Products	113

MIN	Mining	599

MNG	Management	1,732

MSC	Miscellaneous Manufacturing	180

MSP	Machine Shops	251

MVP	Motor Vehicle Parts	485

OCM	Other Chemicals	92

OEQ	Other Electric Equipment	63

OFD	Other Food Products	144

OFM	Other Fabricated Metals	196

OIL	Refined Petroleum	70

ONM	Other Non-metallic Minerals	61

OPM	Other Primary Metals	87

OSV	Other Services	5,271

OTP	Other Transportation Services	1,064

(continued)

Table A-4.	2010 U.S. Employment Projections (continued)

Industry Label	Industry Description	Projected Employment (1,000)

OTQ	Other Transport Equipment	36

PAI	Paints and Adhesives	60

PAP	Pulp and Paper Mills	121

PFS	Professional Services	18,989

PIP	Pipeline Transport	43

PLS	Plastic	473

PLY	Plywood and Veneer	74

PRN	Printing	248

PUB	Public Services	21,787

R_R	Rail Cars	25

REL	Real Estate	2,158

RSN	Resins	102

RTL	Retail Trade	15,283

RTP	Railroad Transportation	236

RUB	Rubber	117

SAW	Sawmills	84

SEA	Seafood	36

SEQ	Service Industry Equipment	77

SGR	Sugar	62

SHP	Ships	140

SMI	Semiconductor Equipment	245

SOP	Soap	104

TEX	Textile Mills	110

TKB	Truck Bodies	126

TPM	Textile Product Mills	32

TTP	Freight Truck Transportation	1,429

TVQ	TV Equipment	15

WAP	Wearing Apparel	153

WHL	Wholesale Trade	5,869

WIR	Springs and Wires	36

WTP	Water Transportation	67

Total

144,100

The multimarket model also simultaneously considers how the policy
influences other important production costs (via changes in energy and
other intermediate material prices). As a result, the multimarket model
can provide additional information about how policy costs are
transmitted through the economy. As shown in Figure A-2, the indirect
cost change provides the industry with additional incentives to alter
production rates at current market prices.

The %Δ indirect effects associated with each input are approximated
using an input “use” ratio and the price change that occurs in the
input market.

	%Δ indirect costs = input use ratio x %Δ input price	(A.4)

The social accounting matrix provides an internally consistent estimate
of the use ratio and describes the dollar amount of an input that is
required to produce a dollar of output. Higher ratios suggest strong
links between industries, while lower ratios suggest weaker links. Given
the short time horizon, we assume the input use ratio is fixed and
cannot adjust their input mix; this is a standard assumption in public
and commercial input-output (IO) and SAM multiplier models (Berck and
Hoffmann, 2002 xe “Berck and Hoffmann, 2002” ). Morgenstern and
colleagues (2004 xe “Morgenstern and colleagues (2004” ) and Ho and
colleagues (2008)  XE “Ho and colleagues (2008)”   also use this
assumption when examining near-term effects of environmental policy.

Figure A-1.	Direct Compliance Costs Reduce Production Rates at Benchmark
Prices

Figure A-2.	Indirect Costs Further Reduce Production Rates at Benchmark
Prices

Following guidance in the OAQPS economic resource manual (EPA, 1999  XE
“U.S. EPA, OAQPS, 1999”  ), we use a general form for the U.S.
industry supply function:

 	(A.5)

where

 	=	with-policy supply quantity (g)

 	=	calibrated scale parameter for the supply price relationship

 	=	with-policy price for output (g)

 	=	direct compliance costs per unit of supply

 	=	input use ratio (g using input i)

 	=	with-policy input (i) price

 	=	benchmark input (i) price

 	=	price elasticity of supply for output (g)

The key supply parameter that controls the industry production
adjustments is the price elasticity of supply (εg). To our knowledge,
there is no existing empirical work that estimates short-run supply
elasticities for all industry groups used in the multimarket model. As a
result, we assume the U.S. supply elasticities are a function of
econometrically estimated rest-of-world (ROW) export supply elasticities
(see discussion in the next section). We report the values currently
available in the model in Table A-5.

A.2.2.2	International Competition

International competition is captured by a single ROW supply function:

 	(A.6)

where

 	=	with-policy supply quantity (g)

c	=	calibrated scale parameter for the supply and price relationship

 	=	with-policy U.S. price for output (g)

 	=	price elasticity of supply of goods from the ROW to the United
States (imports) (g)

 ). We obtained these estimates for a variety of industry groups from a
recently published article by Broda and colleagues (2008b)  XE “Broda
and colleagues (2008b)”  .

A.2.2.3	Price Elasticity of Supply: Rest of World (ROW)

Broda and colleagues (2008a and 2008b)  XE “Broda and colleagues
(2008a and 2008b)”   provide an empirical basis for the multimarket
model supply elasticities. Broda et al. provide over 1,000 inverse
elasticities that RTI organized to be comparable with the 100-sector
model. The first step was to match the Harmonized Trade System (HS)
elasticities estimated in the article to the appropriate NAICS codes.
Many of the HS codes correspond with a detailed NAICS codes (5- and
6-digit level), while the multimarket sector industries typically
correspond with more aggregated sectors (NAICS 2-, 3-, or 4-digit
levels). To adapt these labels to our model, we combined the 5- and
6-digit NAICS under their 3- and 4-digit codes and calculated an average
inverse elasticity value for codes that fell within the multimarket
model’s aggregate industrial sectors. This gives a crude way to
account for the variety of products detailed in the original data set.
We also restricted the elasticity sample to those that Broda et al.
classify as “medium” and “low” categories; these categories tend
to have lower elasticity values that are consistent with the multimarket
model’s modeling horizon (i.e., in the short run importers are likely
to have less flexibility to respond to price changes and elasticities
are low).

Table A-5.	Supply Elasticities

Industry Label	Industry Description	Rest of World (ROW)	U.S.

ACM	Accommodations	0.7	0.7

ADM	Administrative Services	0.7	0.7

AGR	Agricultural	1.0	1.0

ALU	Aluminum	0.8	0.5

ANM	Animal Foods	1.1	0.8

APP	Appliances	0.9	0.8

ARC	Aircraft	0.9	0.6

ART	Arts	0.7	0.7

ATP	Air Transportation	0.7	0.7

BAK	Baked Goods	0.8	0.7

BEV	Beverages and Tobacco	2.9	2.9

BOI	Boilers and Tanks	1.1	0.8

CEM	Cement	0.9	0.7

CEQ	Construction and Agricultural Equipment	0.8	0.6

CHM	Chemicals and Gases	1.1	0.8

CLY	Clay	0.8	0.6

CMQ	Communication Equipment	2.5	1.0

CNS	Construction	0.7	0.7

COL	Coal	2.2	2.2

CPP	Converted Paper Products	0.9	0.7

CPU	Computers	1.0	0.7

CRU	Crude Oil Extraction	3.7	3.7

CUT	Cutlery	1.4	1.1

EDU	Education	0.7	0.7

EEQ	Engines	1.2	1.0

EGV	Engraving	1.1	0.8

ELE	Electric Generation	2.0	2.0

ELQ	Electric Equipment	0.8	0.6

FIN	Finance and Insurance	0.7	0.7

FMP	Fabricated Metals	1.2	1.1

FRG	Forging and Stamping	1.6	1.5

(continued)

Table A-5.	Supply Elasticities (continued)

Industry Label	Industry Description	Rest of World (ROW)	U.S.

FRT	Fertilizer	1.0	0.7

FRU	Fruits and Vegetables	1.0	0.7

FUR	Furniture	1.9	1.9

GAS	Natural Gas	12.2	12.2

GEQ	General Equipment	1.0	0.7

GLS	Glass	0.8	0.6

GRN	Grain Milling	1.7	1.5

HLT	Health Care	0.7	0.7

HRD	Hardware	1.1	0.8

HVC	HVAC Equipment	0.9	0.6

I_S	Iron and Steel	1.0	0.6

IEQ	Industrial Equipment	0.9	0.6

INF	Information	0.7	0.7

INS	Instruments	0.9	0.6

LEA	Leather	0.9	0.7

LGT	Lighting	1.1	0.7

LIM	Lime and Gypsum	0.9	0.7

LUM	Other Lumber	0.9	0.7

M_V	Motor Vehicles	1.3	0.7

MEA	Meat Products	1.2	3.9

MED	Drugs and Medicine	1.3	1.0

MEQ	Metalworking Equipment	0.7	0.5

MGT	Magnetic Recording Equipment	1.0	0.7

MIL	Dairy Products	1.1	0.9

MIN	Mining	2.2	2.2

MNG	Management	0.7	0.7

MSC	Miscellaneous Manufacturing	1.0	0.8

MSP	Machine Shops	1.1	0.8

MVP	Motor Vehicle Parts	0.9	0.6

OCM	Other Chemicals	1.1	0.6

OEQ	Other Electric Equipment	1.0	0.7

OFD	Other Food Products	1.1	0.7

(continued)

Table A-5.	Supply Elasticities (continued)

Industry Label	Industry Description	Rest of World (ROW)	U.S.

OFM	Other Fabricated Metals	0.9	0.6

OIL	Refined Petroleum	1.0	0.7

ONM	Other Non-metallic Minerals	1.5	0.7

OPM	Other Primary Metals	0.7	0.5

OSV	Other Services	0.7	0.7

OTP	Other Transportation Services	0.7	0.7

OTQ	Other Transport Equipment	1.0	0.7

PAI	Paints and Adhesives	1.0	0.7

PAP	Pulp and Paper Mills	1.1	0.7

PFS	Professional Services	0.7	0.7

PIP	Pipeline Transport	2.0	2.0

PLS	Plastic	1.0	0.7

PLY	Plywood and Veneer	1.3	1.3

PRN	Printing	1.0	0.7

PUB	Public Services	0.7	0.7

R_R	Rail Cars	1.8	0.7

REL	Real Estate	0.7	0.7

RSN	Resins	1.0	0.7

RTL	Retail Trade	0.7	0.7

RTP	Railroad Transportation	0.7	0.7

RUB	Rubber	1.3	1.1

SAW	Sawmills	0.8	0.6

SEA	Seafood	1.1	0.8

SEQ	Service Industry Equipment	0.8	0.6

SGR	Sugar	1.1	0.8

SHP	Ships	1.0	0.7

SMI	Semiconductor Equipment	1.2	1.0

SOP	Soap	0.8	0.6

TEX	Textile Mills	1.0	0.7

TKB	Truck Bodies	3.2	3.1

TPM	Textile Product Mills	0.8	0.6

TTP	Freight Truck Transportation	0.7	0.7

TVQ	TV Equipment	5.8	5.4

(continued)

Table A-5.	Supply Elasticities (continued)

Industry Label	Industry Description	Rest of World (ROW)	U.S.

WAP	Wearing Apparel	1.2	0.8

WHL	Wholesale Trade	0.7	0.7

WIR	Springs and Wires	1.9	0.8

WTP	Water Transportation	0.7	0.7

Note:	RTI mapped Broda et al. data for their industry aggregation to the
multimarket model’s 100 industries. Domestic supply elasticities are
typically assumed to be within one standard deviation of the sample of
supply elasticities used for the ROW. In selected cases where this
information is not available, the U.S. supply elasticity is set equal to
the ROW.

Source:	Broda, C., N. Limao, and D. Weinstein. 2008a  XE “Broda, C.,
N. Limao, and D. Weinstein. 2008a”  . “Export Supply
Elasticities.”  HYPERLINK
"http://faculty.chicagobooth.edu/christian.broda/website/research/unrest
ricted/TradeElasticities/TradeElasticities.html"
http://faculty.chicagobooth.edu/christian.broda/website/research/unrestr
icted/TradeElasticities/TradeElasticities.html . Accessed September
2009.

Our ideal preference was to use an exact 3- or 4-digit match from the
medium category if one was available. If the multimarket model had a
4-digit code for which there was no direct match, we aggregated up a
level and applied the relevant 3-digit elasticity. If a multimarket code
was not covered in the medium set of elasticities, we used the low
elasticity category. This method was sufficient for mapping the majority
of the sectors in the model. After applying our inverse elasticity
values to the multimarket sectors, we calculated the inverse of the
value to arrive at the actual supply elasticity. Since Broda et al.’s
article focused on industrial production goods, some of the multimarket
sectors were not covered in the elasticity data. These sectors included
mainly service industries, transportation, and energy sources.

In order to fill these gaps, we turned to the source substitution
elasticities from Purdue University’s Global Trade Analysis Project
(GTAP). Although the elasticities in the GTAP model are a different type
of international trade elasticity and cannot be directly applied in the
multimarket model (e.g., they are based on the Armington structure), the
parameters provide us with some additional information about the
relative trade elasticity differences between industry sectors. To use
the GTAP information to develop assumptions about the multimarket model
sectors with missing elasticities, we chose a base industrial sector
(iron and steel) for which we had parameter value from Broda et al.
Next, we developed industry-specific ratios for missing industries using
the corresponding GTAP sector trade elasticities and the GTAP iron and
steel sector. We multiplied the resulting ratio by the Broda et al. iron
and steel parameter (1.0). For example, the GTAP trade elasticity for
coal (6.10) is approximately 2.2 times the trade elasticity for iron and
steel (2.95). As a result, the multimarket import supply elasticity for
coal is computed as 2.2 (2.2 x 1.0).

A.2.2.4	Price Elasticity of Supply: United States

We also used Broda et al.’s elasticities to derive a set of domestic
supply elasticities for the model. We have assumed that a product’s
domestic supply would be equal to or less elastic than other
countries’ supply of imports. When we aggregated and averaged the
original elasticities to the 3- and 4-digit NAICS level for our foreign
supply elasticities, we also calculated the standard deviation of each
3- and 4-digit NAICS sample. By adding the standard deviation to the
corresponding foreign supply and then taking the inverse, we were able
to calculate a domestic supply elasticity for each sector that was lower
than its foreign counterpart while maintaining the structure of the
original elasticities. For sectors in which no standard deviation was
available, we used professional judgment to apply the closest available
substitute from a similar industry. Without a comparable way of scaling
our foreign elasticities for the sectors in which we used the GTAP
elasticities, we elected to keep the domestic and foreign supply
elasticities the same.

A.2.2.5	Demand

Uses for industry output are divided into three groups:
investment/government use, domestic intermediate uses, and other final
use (domestic and exports). Given the short time horizon,
investment/government does not change. Intermediate use is determined by
the input use ratios and the industry output decisions.

 	(A.7)

 	=	with-policy input demand quantity (i)

 	=	input use ratio (g using input i)

 	=	with-policy output quantity (g)

Other final use does respond to market price changes. Following guidance
in the OAQPS economic resource manual (EPA, 1999  XE “U.S. EPA, OAQPS,
1999”  ), we use a general form for the U.S. industry demand function:

 	(A.8)

where

 	=	with-policy demand quantity (g)

 	=	calibrated scale parameter for the demand and price relationship

 	=	with-policy price for output (g)

 	=	price elasticity of demand (g)

The key parameter that controls consumption adjustments is the price
elasticity of demand (ηg). To approximate the response, we use demand
elasticities that were simulated with a general equilibrium model (Ho,
Morgenstern, and Shih, 2008 xe “Ho, Morgenstern, and Shih, 2008” ).
Table A-6 reports the values currently available in the model.

A.2.2.6	Model Scope

The multimarket model includes 100 sectors covering energy,
manufacturing, and service applications. Each sector’s production
technology requires the purchase of energy and other intermediate goods
made by other sectors included in the model. Linking the sectors in this
manner allows the model to trace direct and indirect policy effects
across different sectors. Therefore, it is best used when potential
economic impacts and equity effects on related markets might be
important to stakeholders not directly affected by an environmental
policy. However, the model can also be run in single-market partial
equilibrium mode to support and provide insights for other types of
environmental policies.

A.2.2.7	Model Time Horizon

The model is designed to address short-run and transitional effects
associated with environmental policy. Production technologies are fixed;
the model does not assess substitution among production inputs (labor,
energy intermediates, and other intermediates) and assumes each
investment cannot be changed during the time frame of the analysis.
These issues are better addressed using other frameworks such as
computable general equilibrium modeling. Similarly, government purchases
from each sector do not adjust in response to changes in goods/service
prices. Although, employment levels (number of jobs) adjust as
production levels change, wages are assumed to be fixed.

Table A-6.	U.S. Demand Elasticities

Demand Elasticity

ηg

ACM	Accommodations	−0.7

ADM	Administrative Services	−0.7

AGR	Agricultural	−0.8

ALU	Aluminum	−1.0

ANM	Animal Foods	−0.6

APP	Appliances	−2.6

ARC	Aircraft	−2.5

ART	Arts	−0.7

ATP	Air Transportation	−0.8

BAK	Baked Goods	−0.6

BEV	Beverages and Tobacco	−0.6

BOI	Boilers and Tanks	−0.5

CEM	Cement	−0.8

CEQ	Construction and Agricultural Equipment	−1.7

CHM	Chemicals and Gases	−1.0

CLY	Clay	−0.8

CMQ	Communication Equipment	−2.6

CNS	Construction	−0.8

COL	Coal	−0.1

CPP	Converted Paper Products	−0.7

CPU	Computers	−2.6

CRU	Crude Oil Extraction	−0.3

CUT	Cutlery	−0.5

EDU	Education	−0.7

EEQ	Engines	−1.7

EGV	Engraving	−0.5

ELE	Electric Generation	−0.2

ELQ	Electric Equipment	−2.6

FIN	Finance and Insurance	−0.7

FMP	Fabricated Metals	−0.5

FRG	Forging and Stamping	−0.5

FRT	Fertilizer	−1.0

FRU	Fruits and Vegetables	−0.6

FUR	Furniture	−0.7

(continued)

Table A-6.	U.S. Demand Elasticities (continued)

Industry Label	Industry Description	Demand Elasticity

ηg

GAS	Natural Gas	−0.3

GEQ	General Equipment	−1.7

GLS	Glass	−0.8

GRN	Grain Milling	−0.6

HLT	Health Care	−0.7

HRD	Hardware	−0.5

HVC	HVAC Equipment	−1.7

I_S	Iron and Steel	−1.0

IEQ	Industrial Equipment	−1.7

INF	Information	−0.7

INS	Instruments	−2.6

LEA	Leather	−1.1

LGT	Lighting	−2.6

LIM	Lime and Gypsum	−0.8

LUM	Other Lumber	−0.7

M_V	Motor Vehicles	−2.5

MEA	Meat Products	−0.6

MED	Drugs and Medicine	−1.0

MEQ	Metalworking Equipment	−1.7

MGT	Magnetic Recording Equipment	−2.6

MIL	Dairy Products	−0.6

MIN	Mining	−0.6

MNG	Management	−0.7

MSC	Miscellaneous Manufacturing	−1.7

MSP	Machine Shops	−0.5

MVP	Motor Vehicle Parts	−2.5

OCM	Other Chemicals	−1.0

OEQ	Other Electric Equipment	−2.6

OFD	Other Food Products	−0.6

OFM	Other Fabricated Metals	−0.5

OIL	Refined Petroleum	−0.1

ONM	Other Non-metallic Minerals	−0.8

OPM	Other Primary Metals	−1.0

OSV	Other Services	−0.7

OTP	Other Transportation Services	−0.8

(continued)

Table A-6.	U.S. Demand Elasticities (continued)

Industry Label	Industry Description	Demand Elasticity

ηg

OTQ	Other Transport Equip	−2.5

PAI	Paints and Adhesives	−1.0

PAP	Pulp and Paper Mills	−0.7

PFS	Professional Services	−0.7

PIP	Pipeline Transport	−0.8

PLS	Plastic	−1.0

PLY	Plywood and Veneer	−0.7

PRN	Printing	−0.7

PUB	Public Services	−0.7

R_R	Rail Cars	−2.5

REL	Real Estate	−0.7

RSN	Resins	−1.0

RTL	Retail Trade	−0.7

RTP	Railroad Transportation	−0.8

RUB	Rubber	−1.0

SAW	Sawmills	−0.7

SEA	Seafood	−0.6

SEQ	Service Industry Equipment	−1.7

SGR	Sugar	−0.6

SHP	Ships	−2.5

SMI	Semiconductor Equipment	−2.6

SOP	Soap	−1.0

TEX	Textile Mills	−1.1

TKB	Truck Bodies	−2.5

TPM	Textile Product Mills	−1.1

TTP	Freight Truck Transportation	−0.8

TVQ	TV Equipment	−2.6

WAP	Wearing Apparel	−2.4

WHL	Wholesale Trade	−0.7

WIR	Springs and Wires	−0.5

WTP	Water Transportation	−0.8

Note: RTI assigned an elasticity using the most similar industry from Ho
and colleagues’ industry aggregation.

Source:	Ho, M. S, R. Morgenstern, and J. S. Shih. 2008  XE “Ho, M. S,
R. Morgenstern, and J. S. Shih. 2008”  . “Impact of Carbon Price
Policies on US Industry.” RFF Discussion Paper 08-37.
Http://Www.Rff.Org/Publications/Pages/Publicationdetails.Aspx?.
Publicationid=20680. Accessed August 2009. Table B.6.

A.3	Appendix A References

Berck, P., and S. Hoffmann. 2002. “Assessing the Employment Impacts of
Environmental and Natural Resource Policy.” Environmental and Resource
Economics 22(1):133-156.

Broda, C., N. Limao, and D.E. Weinstein. 2008a. “Optimal Tariffs and
Market Power: The Evidence.” American Economic Review 98(5):2032-2065.

Broda, C., N. Limao, and D. Weinstein. 2008b. “Export Supply
Elasticities.” <http://faculty.

chicagobooth.edu/christian.broda/website/research/unrestricted/TradeElas
ticities/

TradeElasticities.html.> 

Ho, M.S., R. Morgenstern, and J.S. Shih. 2008. “Impact of Carbon Price
Policies on US Industry” (RFF Discussion Paper 08-37). < HYPERLINK
"http://Www.Rff.Org/Publications/Pages/Publicationdetails.Aspx?.%20Publi
cationid=20680"
http://www.Rff.Org/Publications/Pages/Publicationdetails.Aspx?.
Publicationid=20680 >. 

Just, R.E., D.L. Hueth, and A. Schmitz. 2004. The Welfare Economics of
Public Policy. Northampton, MA: Edward Elgar.

Morgenstern, R.D., M. Ho, J.S. Shih, and X. Zhang. 2004. “The
Near-Term Impacts of Carbon Mitigation Policies on Manufacturing
Industries.” Energy Policy 32(16): 1825-1841. 

Shoven, J.B., and J. Whalley. 1995. Applying General Equilibrium. New
York: Cambridge University Press.

U.S. Environmental Protection Agency, Office of Air Quality Planning and
Standards. 1999. OAQPS Economic Analysis Resource Document.  HYPERLINK
"http://www.epa.gov/ttn/ecas/analguid.html"
http://www.epa.gov/ttn/ecas/analguid.html .

U.S. Environmental Protection Agency. 2000. “Guidelines for Preparing
Economic Analyses” (EPA 240-R-00-003). < HYPERLINK
"http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/c
over.pdf%20"
http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/co
ver.pdf >.

Appendix B

Detailed Economic Model Results by Sector

Table B-1.	Prices (Percentage Change from Benchmark): Industry Detail

 	Major

Area

 	Selected Option	Alternative Option

Selected Option	Alternative Option

Energy 	0.0279%	0.0228%

−0.0001%	−0.0001%

Nonmanufacturing	0.0007%	0.0054%

0.0013%	0.0016%

Manufacturing	 	 

 	 

Food, beverages, and textiles	0.0106%	0.0118%

0.0016%	0.0030%

Lumber, paper, and printing	0.1006%	0.2427%

0.0133%	0.0162%

Chemicals	0.0082%	0.0038%

−0.0003%	−0.0005%

Plastics and rubber	0.0183%	0.0147%

−0.0006%	−0.0008%

Nonmetallic minerals	0.0033%	0.0041%

−0.0004%	−0.0005%

Primary metals	0.0221%	0.0139%

−0.0002%	−0.0002%

Fabricated metals	0.0036%	0.0018%

−0.0003%	−0.0004%

Machinery and equipment	0.0051%	0.0004%

−0.0002%	−0.0003%

Electronic equipment	−0.0004%	−0.0006%

−0.0001%	−0.0001%

Transportation equipment	0.0025%	0.0020%

−0.0001%	−0.0001%

Other	0.0035%	0.0079%

0.0005%	0.0006%

Wholesale and retail trade	−0.0018%	−0.0024%

0.0000%	0.0001%

Transportation services	−0.0010%	−0.0022%

−0.0001%	−0.0002%

Other services	0.0001%	0.0000%

0.0012%	0.0014%

Table B-2.	Production (Percentage Change from Benchmark): Industry
Detail

 	Major

Area

 	Selected Option	Alternative Option

Selected Option	Alternative Option

Energy 	−0.0087%	−0.0084%	 	−0.0002%	−0.0002%

Nonmanufacturing	−0.0029%	−0.0046%	 	−0.0007%	−0.0011%

Manufacturing	 	 	 	 	 

Food, beverages, and textiles	−0.0121%	−0.0136%	 	−0.0013%
−0.0022%

Lumber, paper, and printing	−0.0442%	−0.0999%	 	−0.0057%
−0.0071%

Chemicals	−0.0168%	−0.0150%	 	−0.0001%	−0.0002%

Plastics and rubber	−0.0153%	−0.0155%	 	−0.0003%	−0.0004%

Nonmetallic minerals	−0.0038%	−0.0054%	 	−0.0003%	−0.0004%

Primary metals	−0.0233%	−0.0167%	 	−0.0001%	−0.0001%

Fabricated metals	−0.0047%	−0.0047%	 	−0.0002%	−0.0003%

Machinery and equipment	−0.0071%	−0.0029%	 	−0.0001%	−0.0001%

Electronic equipment	−0.0026%	−0.0034%	 	−0.0001%	−0.0001%

Transportation equipment	−0.0059%	−0.0049%	 	0.0000%	0.0000%

Other	−0.0077%	−0.0162%	 	−0.0013%	−0.0016%

Wholesale and retail trade	−0.0012%	−0.0015%	 	−0.0002%
−0.0004%

Transportation services	−0.0030%	−0.0035%	 	−0.0002%	−0.0003%

Other services	−0.0011%	−0.0014%	 	−0.0006%	−0.0008%

Table B-3.	Consumption (Percentage Change from Benchmark): Industry
Detail

 	Major

Area

 	Selected Option	Alternative Option

Selected Option	Alternative Option

Energy 	−0.0057%	−0.0059%	 	−0.0002%	−0.0002%

Nonmanufacturing	−0.0026%	−0.0039%	 	−0.0006%	−0.0009%

Manufacturing	 	 	 	 	 

Food, beverages, and textiles	−0.0063%	−0.0074%	 	−0.0009%
−0.0016%

Lumber, paper, and printing	−0.0273%	−0.0566%	 	−0.0018%
−0.0024%

Chemicals	−0.0114%	−0.0109%	 	−0.0002%	−0.0003%

Plastics and rubber	−0.0094%	−0.0104%	 	−0.0003%	−0.0005%

Nonmetallic minerals	−0.0032%	−0.0045%	 	−0.0003%	−0.0004%

Primary metals	−0.0117%	−0.0088%	 	−0.0001%	−0.0002%

Fabricated metals	−0.0036%	−0.0036%	 	−0.0002%	−0.0003%

Machinery and equipment	−0.0034%	−0.0018%	 	−0.0001%	−0.0001%

Electronic equipment	−0.0014%	−0.0016%	 	−0.0001%	−0.0001%

Transportation equipment	−0.0033%	−0.0027%	 	0.0000%	0.0000%

Other	−0.0025%	−0.0050%	 	−0.0004%	−0.0006%

Wholesale and retail trade	−0.0012%	−0.0015%	 	−0.0002%
−0.0003%

Transportation services	−0.0026%	−0.0031%	 	−0.0002%	−0.0003%

Other Services	−0.0011%	−0.0014%	 	−0.0006%	−0.0008%

Table B-4.	Imports (Percentage Change from Benchmark): Industry Detail

 	Major

Area

 	Selected Option	Alternative Option

Selected Option	Alternative Option

Energy 	0.0509%	0.0413%	 	−0.0001%	−0.0002%

Nonmanufacturing	0.0010%	0.0037%	 	0.0008%	0.0010%

Manufacturing	 	 	 	 	 

Food, beverages, and textiles	0.0134%	0.0155%	 	0.0018%	0.0032%

Lumber, paper, and printing	0.1034%	0.2467%	 	0.0136%	0.0166%

Chemicals	0.0093%	0.0045%	 	−0.0004%	−0.0006%

Plastics and rubber	0.0197%	0.0158%	 	−0.0006%	−0.0009%

Nonmetallic minerals	0.0012%	0.0022%	 	−0.0001%	−0.0001%

Primary metals	0.0208%	0.0132%	 	−0.0002%	−0.0002%

Fabricated metals	0.0042%	0.0023%	 	−0.0003%	−0.0004%

Machinery and equipment	0.0045%	0.0004%	 	−0.0002%	−0.0003%

Electronic equipment	−0.0003%	−0.0003%	 	−0.0001%	−0.0002%

Transportation equipment	0.0036%	0.0029%	 	0.0000%	−0.0001%

Other	0.0059%	0.0137%	 	0.0011%	0.0013%

Wholesale and retail trade	−0.0012%	−0.0016%	 	0.0000%	0.0001%

Transportation services	−0.0007%	−0.0014%	 	0.0000%	−0.0001%

Other services	−0.0002%	−0.0004%	 	0.0001%	0.0001%

Table B-5.	Exports (Percentage Change from Benchmark): Industry Detail

 	Major

Area

 	Selected Option	Alternative Option

Selected Option	Alternative Option

Energy 	−0.0041%	−0.0033%	 	0.0000%	0.0000%

Nonmanufacturing	−0.0004%	−0.0040%	 	−0.0010%	−0.0013%

Manufacturing	 	 	 	 	 

Food, beverages, and textiles	−0.0069%	−0.0076%	 	−0.0010%
−0.0019%

Lumber, paper, and printing	−0.0702%	−0.1690%	 	−0.0093%
−0.0113%

Chemicals	−0.0081%	−0.0038%	 	0.0003%	0.0005%

Plastics and rubber	−0.0181%	−0.0145%	 	0.0006%	0.0008%

Nonmetallic minerals	−0.0027%	−0.0034%	 	0.0003%	0.0004%

Primary metals	−0.0211%	−0.0133%	 	0.0002%	0.0002%

Fabricated metals	−0.0018%	−0.0009%	 	0.0002%	0.0002%

Machinery and equipment	−0.0084%	−0.0006%	 	0.0004%	0.0005%

Electronic equipment	0.0010%	0.0015%	 	0.0003%	0.0004%

Transportation equipment	−0.0062%	−0.0049%	 	0.0001%	0.0002%

Other	−0.0034%	−0.0069%	 	−0.0002%	−0.0002%

Wholesale and retail trade	0.0013%	0.0018%	 	0.0000%	−0.0001%

Transportation services	0.0008%	0.0018%	 	0.0001%	0.0002%

Other services	−0.0001%	−0.0001%	 	−0.0009%	−0.0011%

 On June 19, 2007, the U.S. Court of Appeals for the District of
Columbia Circuit (DC Circuit) vacated the NESHAP for
industrial/commercial/institutional boilers and process heaters. This
action provides EPA’s rule in response to the court’s vacatur.

 Gas-fired boilers are not part of the area source categories of
industrial boilers and institutional/commercial boilers.

 See additional details in Chapter 3 and Cost Appendices.

 Richard D. Morgenstern, William A. Pizer, and Jhih-Shyang Shih, Journal
of Environmental Economics and Management | May 2002 | Vol. 43, no. 3 |
pp. 412-436.

 The Morgenstern et al. results rely on industry demand and supply
elasticities to determine cost pass-through and reductions in output.

 These results are similar to Berman and Bui, who find that while
sharply increased air quality regulation in Los Angeles to reduce NOx
emissions resulted in large abatement costs they did not result in
substantially reduced employment. "Environmental regulation and labor
demand: evidence from the South Coast Air Basin." Journal of Public
Economics 79(2): 265-295.

 Morgenstern, Pizer and Shih, p. 413.

 Since Morgenstern’s analysis reports environmental expenditures in
$1987, we make an inflation adjustment the engineering cost analysis
using GDP implicit price deflator (64.76/108.48) = 0.60) 

 Net employment effect = 1.55× $2,400 million × 0.60

 See  HYPERLINK "http://www.census.gov/csd/susb/"
http://www.census.gov/csd/susb/  and  HYPERLINK
"http://www.sba.gov/advo/research/data.html"
http://www.sba.gov/advo/research/data.html  for additional details.

 Prior to computing the cost-to-receipt ratios, we adjusted the
engineering compliance costs to reflect 2002 dollars using the implicit
price deflators for gross domestic product (GDP). The values used are
2002 = 92.118 and 2008 = 108.483 (U.S. BEA, 2010 xe “U.S. BEA, 2010”
). 

 Graham Gibson, Susan McClutchey, and Amanda Singleton, ERG.  (January,
2011).  Methodology for Estimating Impacts from Industrial,  Commercial,
Institutional Boilers at Area Sources of Hazardous Air Pollutant
Emissions.

 The “NEI_UNIQUE_ID” field was used to map the ICR facilities to the
NATA inventory. We used 7032 units’ Hg emissions out of 7,738 total
units in the ICR. These 7032 units from the ICR sum to 4.66 tons. ICR
emissions that were not included sum to 0.177 tons. 

 The 2015 IPM run represents the average of 2014 to 2016.

 The full details involved in calculating the annual PM2.5 design value
are provided in appendix N of 40 CFR part 50.

 As described in Section 7.5, formaldehyde, several PAHs, and
acetaldehyde are classified as probable human carcinogens. Different
nickel compounds are classified as human carcinogens and probable human
carcinogens.

 For this analysis, we used BenMAP version 3.0 (Abt Associates, 2008  XE
“Abt Associates, 2008”  ). This model is available for free download
on the Internet at <http://www.epa.gov/air/benmap>.

 All of the RIAs cited in this analysis are available on EPA’s website
at  HYPERLINK "http://www.epa.gov/ttn/ecas/ria.html"
http://www.epa.gov/ttn/ecas/ria.html . 

To comply with Circular A-4, EPA provides monetized benefits using
discount rates of 3% and 7% (OMB, 2003  XE “OMB, 2003”  ). These
benefits are estimated for a specific analysis year (i.e., 2014), and
most of the PM benefits occur within that year with two exceptions:
acute myocardial infarctions (AMIs) and premature mortality. For AMIs,
we assume 5 years of follow-up medical costs and lost wages. For
premature mortality, we assume that there is a “cessation” lag
between PM exposures and the total realization of changes in health
effects. Although the structure of the lag is uncertain, EPA follows the
advice of the SAB-HES to assume a segmented lag structure characterized
by 30% of mortality reductions in the first year, 50% over years 2 to 5,
and 20% over the years 6 to 20 after the reduction in PM2.5 (U.S.
EPA-SAB, 2004  XE “U.S. EPA-SAB, 2004”  ). Changes in the lag
assumptions do not change the total number of estimated deaths but
rather the timing of those deaths. Therefore, discounting only affects
the AMI costs after the analysis year and the valuation of premature
mortalities that occur after the analysis year. As such, the monetized
benefits using a 7% discount rate are only approximately 10% less than
the monetized benefits using a 3% discount rate. 

After adjusting the VSL to account for a different currency year (2008$)
and to account for income growth to 2015, the $5.5 million VSL is $7.9
million.

In the (draft) update of the Economic Guidelines (U.S. EPA, 2008b  XE
“U.S. EPA, 2008b”  ), EPA retained the VSL endorsed by the SAB with
the understanding that further updates to the mortality risk valuation
guidance would be forthcoming in the near future. Therefore, this report
does not represent final agency policy.

In this analysis, we adjust the VSL to account for a different currency
year (2008$) and to account for income growth to 2015. After applying
these adjustments to the $6.3 million value, the VSL is $9.1 million.

Technical details regarding the emissions inventory used in the air
quality modeling are available in the Air Quality Modeling TSD (U.S.
EPA, 2010f  XE “U.S. EPA, 2010f”  ). Technical details regarding the
emission reductions estimated for this rule are available in these
docket memos: “Revised Development of Baseline Emission Factors for
Boilers and Process Heaters at Commercial, Industrial, and Institutional
Facilities” and “Revised Methodology for Estimating Cost and
Emissions Impacts for Industrial, Commercial, Institutional Boilers and
Process Heaters National Emission Standards for Hazardous Air
Pollutants.”

These confidence intervals only reflect the standard errors within the
epidemiology studies and valuation functions, but they do not reflect
other sources of uncertainty inherent within the boiler-specific BPT
estimates. 

For more information on the changes in the emissions inventory, please
see Table 2-2 of U.S. EPA, (2010g)  XE “U.S. EPA, (2010g)”  , which
provides a list of the differences between the 2002 and 2005 base
inventory. In addition to including consent decrees and several recent
mobile source rules, the emissions platform used for this analysis also
removed duplicates and plant closures and an updated version of IPM to
project EGU emissions.

As we use the term “energy disbenefits” in this analysis, we are not
referring to the cost of purchasing additional electricity or fuel. 

Docket ID EPA-HQ-OAR-2009-0472-114577, Technical Support Document:
Social Cost of Carbon for Regulatory Impact Analysis Under Executive
Order 12866, Interagency Working Group on Social Cost of Carbon, with
participation by Council of Economic Advisers, Council on Environmental
Quality, Department of Agriculture, Department of Commerce, Department
of Energy, Department of Transportation, Environmental Protection
Agency, National Economic Council, Office of Energy and Climate Change,
Office of Management and Budget, Office of Science and Technology
Policy, and Department of Treasury (February 2010)  XE “Interagency
Working Group on Social Cost of Carbon, with participation by Council of
Economic Advisers, Council on Environmental Quality, Department of
Agriculture, Department of Commerce, Department of Energy, Department of
Transportation, Environmental Protection Agency, National Economic
Council, Office of Energy and Climate Change, Office of Management and
Budget, Office of Science and Technology Policy, and Department of
Treasury (February 2010)”  . Also available at  HYPERLINK
"http://www.epa.gov/otaq/climate/regulations.htm"
http://www.epa.gov/otaq/climate/regulations.htm 

The interagency group decided that these estimates apply only to CO2
emissions. Given that warming profiles and impacts other than
temperature change (e.g., ocean acidification) vary across GHGs, the
group concluded “transforming gases into CO2-equivalents using GWP,
and then multiplying the carbon-equivalents by the SCC, would not result
in accurate estimates of the social costs of non-CO2 gases” (SCC TSD,
pg 13). 

It is possible that other benefits or costs of proposed regulations
unrelated to CO2 emissions will be discounted at rates that differ from
those used to develop the SCC estimates.

U.S. EPA. (2009  XE “U.S. EPA. (2009”  ) 2002 National-Scale Air
Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/

U.S. EPA. (2009  XE “U.S. EPA. (2009”  ) 2002 National-Scale Air
Toxics Assessment. http://www.epa.gov/ttn/atw/nata2002/

Chronic exposure is defined in the glossary of the Integrated Risk
Information (IRIS) database (http://www.epa.gov/iris) as repeated
exposure by the oral, dermal, or inhalation route for more than
approximately 10% of the life span in humans (more than approximately 90
days to 2 years in typically used laboratory animal species).

Defined in the IRIS database as exposure to a substance spanning
approximately 10% of the lifetime of an organism.

Defined in the IRIS database as exposure by the oral, dermal, or
inhalation route for 24 hours or less.

The NATA modeling framework has a number of limitations which prevent
its use as the sole basis for setting regulatory standards. These
limitations and uncertainties are discussed on the 2002 NATA website.
Even so, this modeling framework is very useful in identifying air toxic
pollutants and sources of greatest concern, setting regulatory
priorities, and informing the decision making process. U.S. EPA. (2009 
XE “U.S. EPA. (2009”  ) 2002 National-Scale Air Toxics Assessment.
http://www.epa.gov/ttn/atw/nata2002/

 Details about the overall confidence of certainty ranking of the
individual pieces of NATA assessments including both quantitative (e.g.,
model-to-monitor ratios) and qualitative (e.g., quality of data, review
of emission inventories) judgments can be found at
http://www.epa.gov/ttn/atw/nata/roy/page16.html.

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 µg/m3 of a pollutant.

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ᔀ驨Yᘀ᭨硂　ᩊ唀Ĉᔌ驨Yᘀ᭨硂㘀 the human health benefits
from reducing mercury emissions. However, it is important to emphasize,
that we generally have more data and accepted methods to estimate
mercury benefits than we have for other HAPs.

National Research Council (NRC). 2000  XE “National Research Council
(NRC). 2000”  . Toxicological Effects of Methylmercury. Committee on
the Toxicological Effects of Methylmercury, Board on Environmental
Studies and Toxicology. National Academies Press. Washington, DC.
pp.168-173.

Salonen, J.T., Seppanen, K. Nyyssonen et al. 1995  XE “Salonen, J.T.,
Seppanen, K. Nyyssonen et al. 1995”  . “Intake of mercury from fish
lipid peroxidation, and the risk of myocardial infarction and coronary,
cardiovascular and any death in Eastern Finnish men.” Circulation, 91
(3):645-655.

Sorensen, N, K. Murata, E. Budtz-Jorgensen, P. Weihe, and Grandjean, P.,
1999  XE “Sorensen, N, K. Murata, E. Budtz-Jorgensen, P. Weihe, and
Grandjean, P., 1999”  . “Prenatal Methylmercury Exposure As A
Cardiovascular Risk Factor At Seven Years of Age,” Epidemiology,
pp370-375.

National Research Council (NRC). 2000  XE “National Research Council
(NRC). 2000”  . Toxicological Effects of Methylmercury. Committee on
the Toxicological Effects of Methylmercury, Board on Environmental
Studies and Toxicology. National Academies Press. Washington, DC. p.
229.

See “Revised Development of Baseline Emission Factors for Boilers and
Process Heaters at Commercial, Industrial, and Institutional
Facilities,” which available in the docket. 

See Section 6 of this RIA and U.S. EPA (2010f)  XE “U.S. EPA
(2010f)”   for more information on the air quality modeling conducted
for these rules. 

All health effects language for this section came from: Agency for Toxic
Substances and Disease Registry (ATSDR). Medical Management Guidelines
for Hydrogen Chloride (HCl). CAS#: 7647-01-0. Atlanta, GA: U.S.
Department of Health and Human Services, Public Health Service.
Available on the Internet at < HYPERLINK
"http://www.atsdr.cdc.gov/Mhmi/mmg173.html"
http://www.atsdr.cdc.gov/Mhmi/mmg173.html >.

All health effects language for this section came from: Agency for Toxic
Substances and Disease Registry (ATSDR). 2007  XE “Agency for Toxic
Substances and Disease Registry (ATSDR). 2007”  . Medical Management
Guidelines for Chlorine (CAS 7782-50-5; UN 1017). Atlanta, GA: U.S.
Department of Health and Human Services, Public Health Service.
Available on the Internet at  HYPERLINK
"http://www.atsdr.cdc.gov/MHMI/mmg172.html" \l "bookmark02"
http://www.atsdr.cdc.gov/MHMI/mmg172.html#bookmark02 .

All health effects language for this section came from: Agency for Toxic
Substances and Disease Registry (ATSDR). 2007. Medical Management
Guidelines for Hydrogen Cyanide (HCN) (CAS#: 7782-50-5). Atlanta, GA:
U.S. Department of Health and Human Services, Public Health Service.
Available on the Internet at  HYPERLINK
"http://www.atsdr.cdc.gov/Mhmi/mmg8.html" \l "bookmark02"
http://www.atsdr.cdc.gov/Mhmi/mmg8.html#bookmark02 . 

All health effects language for this section came from: U.S. EPA,
“National Emission Standards for Hazardous Air Pollutants for
Industrial/Commercial/Institutional Boilers and Process Heaters;
Proposed Rule,” 68 Federal Register 8 (January 13, 2003  XE “U.S.
EPA, \”National Emission Standards for Hazardous Air Pollutants for
Industrial/Commercial/Institutional Boilers and Process Heaters\;
Proposed Rule,\” 68 Federal Register 8 (January 13, 2003”  ). pp.
1664-1665. Available on the internet at  HYPERLINK
"http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf"
http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf 

All health effects language for this section came from: U.S. EPA. 2005 
XE “U.S. EPA. 2005”  . “Full IRIS Summary for Toluene (CASRN
108-88-3)” Environmental Protection Agency, Integrated Risk
Information System (IRIS), Office of Health and Environmental
Assessment, Environmental Criteria and Assessment Office, Cincinnati,
OH. Available on the Internet at < HYPERLINK
"http://www.epa.gov/iris/subst/0118.htm"
http://www.epa.gov/iris/subst/0118.htm >.

U.S. EPA. 1987  XE “U.S. EPA. 1987”  . Assessment of Health Risks to
Garment Workers and Certain Home Residents from Exposure to
Formaldehyde, Office of Pesticides and Toxic Substances, April 1987.

Hauptmann, M.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A.
2003  XE “Hauptmann, M.\; Lubin, J. H.\; Stewart, P. A.\; Hayes, R.
B.\; Blair, A. 2003”  . Mortality from lymphohematopoetic malignancies
among workers in formaldehyde industries. Journal of the National Cancer
Institute 95: 1615-1623.

Hauptmann, M.; Lubin, J. H.; Stewart, P. A.; Hayes, R. B.; Blair, A.
2004  XE “Hauptmann, M.\; Lubin, J. H.\; Stewart, P. A.\; Hayes, R.
B.\; Blair, A. 2004”  . Mortality from solid cancers among workers in
formaldehyde industries. American Journal of Epidemiology 159:
1117-1130.

Beane Freeman, L. E.; Blair, A.; Lubin, J. H.; Stewart, P. A.; Hayes, R.
B.; Hoover, R. N.; Hauptmann, M. 2009  XE “Beane Freeman, L. E.\;
Blair, A.\; Lubin, J. H.\; Stewart, P. A.\; Hayes, R. B.\; Hoover, R.
N.\; Hauptmann, M. 2009”  . Mortality from lymphohematopoietic
malignancies among workers in formaldehyde industries: The National
Cancer Institute cohort. J. National Cancer Inst. 101: 751-761.

Pinkerton, L. E. 2004  XE “Pinkerton, L. E. 2004”  . Mortality among
a cohort of garment workers exposed to formaldehyde: an update. Occup.
Environ. Med. 61: 193-200.

Coggon, D, EC Harris, J Poole, KT Palmer. 2003  XE “Coggon, D, EC
Harris, J Poole, KT Palmer. 2003”  . Extended follow-up of a cohort of
British chemical workers exposed to formaldehyde. J National Cancer
Inst. 95:1608-1615.

Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston,
and FJ Miller. 2003  XE “Conolly, RB, JS Kimbell, D Janszen, PM
Schlosser, D Kalisak, J Preston, and FJ Miller. 2003”  . Biologically
motivated computational modeling of formaldehyde carcinogenicity in the
F344 rat. Tox Sci 75: 432-447.

Conolly, RB, JS Kimbell, D Janszen, PM Schlosser, D Kalisak, J Preston,
and FJ Miller. 2004  XE “Conolly, RB, JS Kimbell, D Janszen, PM
Schlosser, D Kalisak, J Preston, and FJ Miller. 2004”  . Human
respiratory tract cancer risks of inhaled formaldehyde: Dose-response
predictions derived from biologically-motivated computational modeling
of a combined rodent and human dataset. Tox Sci 82: 279-296.

Chemical Industry Institute of Toxicology (CIIT). 1999  XE “Chemical
Industry Institute of Toxicology (CIIT). 1999”  . Formaldehyde: Hazard
characterization and dose-response assessment for carcinogenicity by the
route of inhalation. CIIT, September 28, 1999. Research Triangle Park,
NC.

U.S. EPA. Analysis of the Sensitivity and Uncertainty in 2-Stage Clonal
Growth Models for Formaldehyde with Relevance to Other
Biologically-Based Dose Response (BBDR) Models. U.S. Environmental
Protection Agency, Washington, D.C., EPA/600/R-08/103, 2006  XE “U.S.
Environmental Protection Agency, Washington, D.C., EPA/600/R-08/103,
2006”  

Subramaniam, R; Chen, C; Crump, K; et al. (2006  XE “Subramaniam, R\;
Chen, C\; Crump, K\; et al. (2006”  ) Uncertainties in
biologically-based modeling of formaldehyde-induced cancer risk:
identification of key issues. Risk Anal 28(4):907-923.

Subramaniam, R; Chen, C; Crump, K; et al. (2007  XE “Subramaniam, R\;
Chen, C\; Crump, K\; et al. (2007”  ). Uncertainties in the CIIT
2-stage model for formaldehyde-induced nasal cancer in the F344 rat: a
limited sensitivity analysis-I. Risk Anal 27:1237

Crump, K; Chen, C; Fox, J; et al. (2006  XE “Crump, K\; Chen, C\; Fox,
J\; et al. (2006”  ) Sensitivity analysis of biologically motivated
model for formaldehyde-induced respiratory cancer in humans. Ann Occup
Hyg 52:481-495.

Crump, K; Chen, C; Fox, J; et al. (2006  XE “Crump, K\; Chen, C\; Fox,
J\; et al. (2006”  ) Sensitivity analysis of biologically motivated
model for formaldehyde-induced respiratory cancer in humans. Ann Occup
Hyg 52:481-495.

Subramaniam, R; Chen, C; Crump, K; et al. (2007  XE “Subramaniam, R\;
Chen, C\; Crump, K\; et al. (2007”  ). Uncertainties in the CIIT
2-stage model for formaldehyde-induced nasal cancer in the F344 rat: a
limited sensitivity analysis-I. Risk Anal 27:1237

International Agency for Research on Cancer (2006  XE “International
Agency for Research on Cancer (2006”  ) Formaldehyde, 2-Butoxyethanol
and 1-tert-Butoxypropan-2-ol. Monographs Volume 88. World Health
Organization, Lyon, France.

Agency for Toxic Substances and Disease Registry (ATSDR). 1999  XE
“Agency for Toxic Substances and Disease Registry (ATSDR). 1999”  .
Toxicological profile for Formaldehyde. Atlanta, GA: U.S. Department of
Health and Human Services, Public Health Service.
http://www.atsdr.cdc.gov/toxprofiles/tp111.html

WHO (2002  XE “WHO (2002”  ) Concise International Chemical
Assessment Document 40: Formaldehyde. Published under the joint
sponsorship of the United Nations Environment Programme, the
International Labour Organization, and the World Health Organization,
and produced within the framework of the Inter-Organization Programme
for the Sound Management of Chemicals. Geneva.

U.S. EPA (1997)  XE “U.S. EPA (1997)”  . Integrated Risk Information
System File of benzo(a)anthracene  XE “U.S. EPA (1997). Integrated
Risk Information System File of benzo(a)anthracene”  . Research and
Development, National Center for Environmental Assessment, Washington,
DC. This material is available electronically at
http://www.epa.gov/ncea/iris/subst/0454.htm.

U.S. EPA (1997). Integrated Risk Information System File of
benzo(b)fluoranthene.  XE “U.S. EPA (1997). Integrated Risk
Information System File of benzo(b)fluoranthene.”   Research and
Development, National Center for Environmental Assessment, Washington,
DC. This material is available electronically at
http://www.epa.gov/ncea/iris/subst/0453.htm.

U.S. EPA (1997). Integrated Risk Information System File of
benzo(k)fluoranthene.  XE “U.S. EPA (1997). Integrated Risk
Information System File of benzo(k)fluoranthene.”   Research and
Development, National Center for Environmental Assessment, Washington,
DC. This material is available electronically at
http://www.epa.gov/ncea/iris/subst/0452.htm.

U.S. EPA (1998)  XE “U.S. EPA (1998)”  . Integrated Risk Information
System File of benzo(a)pyrene. Research and Development, National Center
for Environmental Assessment, Washington, DC. This material is available
electronically at http://www.epa.gov/ncea/iris/subst/0136.htm.

U.S. EPA (1997). Integrated Risk Information System File of chrysene  XE
“U.S. EPA (1997). Integrated Risk Information System File of
chrysene”  . Research and Development, National Center for
Environmental Assessment, Washington, DC. This material is available
electronically at http://www.epa.gov/ncea/iris/subst/0455.htm

U.S. EPA (1997). Integrated Risk Information System File of
dibenz(a,h)anthracene  XE “U.S. EPA (1997). Integrated Risk
Information System File of dibenz(a,h)anthracene”  . Research and
Development, National Center for Environmental Assessment, Washington,
DC. This material is available electronically at
http://www.epa.gov/ncea/iris/subst/0456.htm.

U.S. EPA (1997). Integrated Risk Information System File of
indeno(1,2,3-cd)pyrene.  XE “U.S. EPA (1997). Integrated Risk
Information System File of indeno(1,2,3-cd)pyrene.”   Research and
Development, National Center for Environmental Assessment, Washington,
DC. This material is available electronically at
http://www.epa.gov/ncea/iris/subst/0457.htm.

Perera, F.P.; Rauh, V.; Tsai, W-Y.; et al. (2002  XE “Perera, F.P.\;
Rauh, V.\; Tsai, W-Y.\; et al. (2002”  ) Effect of transplacental
exposure to environmental pollutants on birth outcomes in a multiethnic
population. Environ Health Perspect. 111: 201-205.

Perera, F.P.; Rauh, V.; Whyatt, R.M.; Tsai, W.Y.; Tang, D.; Diaz, D.;
Hoepner, L.; Barr, D.; Tu, Y.H.; Camann, D.; Kinney, P. (2006  XE
“Perera, F.P.\; Rauh, V.\; Whyatt, R.M.\; Tsai, W.Y.\; Tang, D.\;
Diaz, D.\; Hoepner, L.\; Barr, D.\; Tu, Y.H.\; Camann, D.\; Kinney, P.
(2006”  ) Effect of prenatal exposure to airborne polycyclic aromatic
hydrocarbons on neurodevelopment in the first 3 years of life among
inner-city children. Environ Health Perspect 114: 1287-1292.

U.S. EPA (1988)  XE “U.S. EPA (1988)”  . Integrated Risk Information
System File of Acetaldehyde. Research and Development, National Center
for Environmental Assessment, Washington, DC. This material is available
electronically at http://www.epa.gov/iris/subst/0290.htm.

U.S. Department of Health and Human Services National Toxicology Program
11th Report on Carcinogens available at:
http://ntp.niehs.nih.gov/go/16183  XE “U.S. Department of Health and
Human Services National Toxicology Program 11th Report on Carcinogens
available at\: http\://ntp.niehs.nih.gov/go/16183”  .

International Agency for Research on Cancer (IARC). 1999  XE
“International Agency for Research on Cancer (IARC). 1999”  .
Re-evaluation of some organic chemicals, hydrazine, and hydrogen
peroxide. IARC Monographs on the Evaluation of Carcinogenic Risk of
Chemical to Humans, Vol 71. Lyon, France.

U.S. EPA (1988). Integrated Risk Information System File of Acetaldehyde
 XE “U.S. EPA (1988). Integrated Risk Information System File of
Acetaldehyde”  . This material is available electronically at
http://www.epa.gov/iris/subst/0290.htm.

Appleman, L.M., R.A. Woutersen, and V.J. Feron. (1982  XE “Appleman,
L.M., R.A. Woutersen, and V.J. Feron. (1982”  ). Inhalation toxicity
of acetaldehyde in rats. I. Acute and subacute studies. Toxicology. 23:
293-297.

Myou, S.; Fujimura, M.; Nishi K.; Ohka, T.; and Matsuda, T. (1993  XE
“Myou, S.\; Fujimura, M.\; Nishi K.\; Ohka, T.\; and Matsuda, T.
(1993”  ) Aerosolized acetaldehyde induces histamine-mediated
bronchoconstriction in asthmatics. Am. Rev. Respir.Dis.148(4 Pt 1):
940-943.

All health effects language for this section came from: U.S. EPA,
“National Emission Standards for Hazardous Air Pollutants for
Industrial/Commercial/Institutional Boilers and Process Heaters;
Proposed Rule,” 68 Federal Register 8 (January 13, 2003  XE “U.S.
EPA, \”National Emission Standards for Hazardous Air Pollutants for
Industrial/Commercial/Institutional Boilers and Process Heaters\;
Proposed Rule,\” 68 Federal Register 8 (January 13, 2003”  ). pp.
1664-1665. Available on the internet at  HYPERLINK
"http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf"
http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf 

All health effects language for this section came from: U.S. EPA,
“National Emission Standards for Hazardous Air Pollutants for
Industrial/Commercial/Institutional Boilers and Process Heaters;
Proposed Rule,” 68 Federal Register 8 (January 13, 2003). pp.
1664-1665. Available on the internet at  HYPERLINK
"http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf"
http://www.epa.gov/ttn/atw/boiler/fr13ja03.pdf 

All health effects language for this section came from: Agency for Toxic
Substances and Disease Registry (ATSDR). 1999  XE “Agency for Toxic
Substances and Disease Registry (ATSDR). 1999”  . ToxFAQs for
Chlorinated Dibenzo-p-dioxins (CDDs) (CAS#: 2,3,7,8-TCDD 1746-01-6).
Atlanta, GA: U.S. Department of Health and Human Services, Public Health
Service. Available on the Internet at  HYPERLINK
"http://www.atsdr.cdc.gov/tfacts104.html"
http://www.atsdr.cdc.gov/tfacts104.html .

All health effects language for this section came from: Agency for Toxic
Substances and Disease Registry (ATSDR). 1995  XE “Agency for Toxic
Substances and Disease Registry (ATSDR). 1995”  . ToxFAQs™ for
Chlorodibenzofurans (CDFs). Atlanta, GA: U.S. Department of Health and
Human Services, Public Health Service. Available on the Internet at
<http://www.atsdr.cdc.gov/tfacts32.html>.

U.S. EPA Integrated Risk Information System (IRIS) database is available
at: www.epa.gov/iris  XE “U.S. EPA Integrated Risk Information System
(IRIS) database is available at\: www.epa.gov/iris”  

This step is required because Global Insight’s data used by EPA are an
older vintage than the forecasts used in the AEO.

Broda et al.’s intent was to use these categories to describe or proxy
for domestic market power.

See Chapter 14 of the GTAP 7 Database Documentation for the full
description of the parameters at  HYPERLINK
"https://www.gtap.agecon.purdue.edu/resources/download/4184.pdf"
https://www.gtap.agecon.purdue.edu/resources/download/4184.pdf ; see
Table 14.2 for elasticities. 

Detailed documentation of the entire GTAP 7 Database is available at 
HYPERLINK "https://www.gtap.agecon.purdue.edu/databases/v7/v7_doco.asp"
https://www.gtap.agecon.purdue.edu/databases/v7/v7_doco.asp . The GTAP
also uses a unique system of categorizing commodities that does not
match the NAICS or HS system exactly.

No standard deviations were calculated for the 3- and 4-digit codes that
had only one observation (i.e., Broda et al.’s model used the exact
3- or 4-digit code).

_________________________________

RTI International is a trade name of Research Triangle Institute.

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Census Population Data

Baseline and Post-Control PM2.5 Concentrations

PM2.5 Health Functions

Economic Valuation Functions

Population Projections

PM2.5 Incremental Air Quality Change

PM2.5 Related Health Impacts

Woods & Poole Population Projections

Background Incidence and Prevalence Rates

kd

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B*

B*

B*

B*

 -related Benefits

Blue identifies a user-selected input within the BenMAP model

Green identifies a data input generated outside of the BenMAP model