i

Regulatory Impact Analysis (RIA)

for the final Transport Rule

Docket ID No. EPA-HQ-OAR-2009-0491

Regulatory Impact Analysis for the Federal Implementation Plans to 

Reduce Interstate Transport of Fine Particulate Matter and Ozone in 27 

States; Correction of SIP Approvals for 22 States

 

U.S. EPA

Office of Air and Radiation

June 2011
ii

TRANSPORT RULE RIA – TABLE OF CONTENTS

Section Page

1. EXECUTIVE SUMMARY .................................................................................................1

1.1 Key Findings

1.1.1 Health Benefits.............................................................................................2

1.1.2 Welfare Benefits ..........................................................................................7

1.1.3 Assessment of More and Less Stringent Scenarios ...................................10

1.1.3.1 Assessment of Other Alternatives.................................................10

1.2 Not All Benefits Quantified...................................................................................12

1.3 Costs and Economic Impacts.................................................................................14

1.4 Small Entity and Unfunded Mandates Impacts .....................................................17

1.5 Limitations and Uncertainties................................................................................18

1.6 References..............................................................................................................22

2. INTRODUCTION AND BACKGROUND ......................................................................23

2.1 Introduction............................................................................................................23

2.2 Background............................................................................................................23

2.2.1 Methodology for Identifying Needed Reductions .....................................24

2.2.2 How Reductions Will Be Achieved, and Different Options

To Do So....................................................................................................25

2.2.3 States Covered by the Final Rule...............................................................25

2.3 Regulated Entities..................................................................................................30

2.4 Baseline and Years of Analysis .............................................................................30

2.5 Control Scenarios...................................................................................................32

2.6 Benefits of Emission Controls...............................................................................33

2.7 Cost of Emission Controls.....................................................................................33

2.8 Organization of the Regulatory Impact Analysis...................................................33

3. EMISSIONS IMPACTS ....................................................................................................35

3.1 Overview of Modeling Platform and Emissions Processing Performed ...............35

3.2 Development of 2005 Base Year Emissions..........................................................36

3.3 Development of Future Year Base Case Emissions ..............................................45

3.4 Development of Future Year Control Case Emissions..........................................54

4. AIR QUALITY MODELING AND IMPACTS ...............................................................60

4.1 Air Quality Impacts................................................................................................60

4.1.1 Air Quality Modeling Platform ...........................................................60

4.1.1.1 Simulation Periods................................................................61

4.1.1.2 Air Quality Modeling Domain..............................................61

4.1.1.3 Air Quality Model Inputs......................................................64

4.2 Results for PM2.5 and Ozone .................................................................................64

4.2.1 Converting CAMx PM2.5 Outputs to Benefits Inputs .........................64

4.2.2 PM2.5 Air Quality Results.....................................................................65

4.2.3 Converting CAMx Outputs to Full-Season Profiles for

Benefits Analysis ................................................................................68

4.2.4 Ozone Air Quality Results...................................................................69

4.3 Visibility Degradation Estimates...........................................................................69
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4.4 References..............................................................................................................71

5. BENEFITS ANALYSIS AND RESULTS .......................................................................72

5.1 Overview................................................................................................................73

5.2 Benefits Analysis Methods....................................................................................79

5.2.1 Health Impact Assessment.............................................................80

5.2.2 Economic Valuation of Health Impacts.........................................82

5.2.3 Benefit per Ton Estimates..............................................................84

5.3 Uncertainty Characterization .................................................................................86

5.4 Benefits Analysis Data Inputs................................................................................90

5.4.1 Demographic Data .........................................................................90

5.4.2 Effect Coefficients.........................................................................91

5.4.2.1 PM2.5 Premature Mortality Effect Coefficients ..............97

5.4.2.2 Ozone Premature Mortality Effect Coefficients...........100

5.4.2.3 Chronic Bronchitis........................................................102

5.4.2.4 Nonfatal Myocardial Infarctions (Heart Attacks).........102

5.4.2.5 Hospital and Emergency Room Admissions................103

5.4.2.6 Acute Health Effects and School/Work Loss Days......106

5.4.2.7 School Absences...........................................................109

5.4.2.8 Outdoor Worker Productivity ......................................109

5.4.3 Baseline Incidence Estimates.......................................................110

5.4.4 Economic Valuation Estimates....................................................113

5.4.4.1 Mortality Valuation.......................................................114

5.4.4.2 Chronic Bronchitis Valuation .......................................119

5.4.4.3 Nonfatal Myocardial Infarctions Valuation..................120

5.4.4.4 Hospital Admissions valuation .....................................122

5.4.4.5 Asthma-Related Emergency Room Visits Valuation ...126

5.4.4.6 Minor Restricted Activity Days Valuation ...................126

5.4.4.7 School Absences valuation ...........................................127

5.4.4.8 Visibility valuation .......................................................128

5.4.4.9 Growth in WTP Reflecting National Income Growth

Over Time .....................................................................135

5.5 Unquantified Health and Welfare Benefits..........................................................139

5.5.1 Ecosystem Services......................................................................139

5.5.2 Ecosystem Benefits of Reduced Nitrogen and Sulfur Deposition

 .....................................................................................................142

5.5.2.1 Science of Deposition ........................................................142

5.5.2.2 Ecological Effects of Acidification....................................145

5.5.2.3 Aquatic Ecosystems...........................................................146

5.5.2.4 Terrestrial Ecosystems.......................................................150

 5.5.3 Ecological Effects Associated with Gaseous Sulfur Dioxide........ 155

5.5.4 Ecological Effects Associated with the Role of Sulfate in Mercury

 Methylation and Reduced Mercury Emissions...................................155

5.5.5 Nitrogen Enrichment................................................................... 163

5.5.5.1 Aquatic Enrichment ...........................................................163

5.5.5.2 Terrestrial Enrichment .......................................................165

5.5.6 Benefits of Reducing Ozone Effects on Vegetation
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and Ecosystems........................................................................... 168

5.5.6.1 Ozone Effects on Forests ...................................................170

5.5.6.2 Ozone Effects on Crops and Urban Ornamentals.............176

5.5.7 Unquantified SO2 and NO2-Related Human Health Benefits...... 177

5.6 Social Cost of Carbon and Greenhouse Gas Benefits..........................................178

5.7 Benefits Results ..................................................................................................181

5.8 Discussion............................................................................................................196

5.9 References............................................................................................................197

6. ELECTRIC POWER SECTOR PROFILE......................................................................222

6.1 Power-Sector Overview.......................................................................................222

6.1.1 Generation....................................................................................222

6.1.2 Transmission................................................................................226

6.1.3 Distribution ..................................................................................226

6.2 Deregulation and Restructuring ...........................................................................227

6.3 Pollution and EPA Regulation of Emissions.......................................................228

6.4 Pollution Control Technologies...........................................................................230

6.5 Air Regulation of the Power Sector.....................................................................231

6.6 Revenues, Expenses and Prices ...........................................................................234

6.6.1 Natural Gas Prices....................................................................................238

6.7 Electricity Demand, and Demand Response........................................................239

6.8 Reference .............................................................................................................242

7. COST, ECONOMIC, AND ENERGY IMPACTS..........................................................243

7.1 Background..............................................................................................243

7.2 Projected SO2 and NOx Emissions and Reductions.................................250

7.3 Overview of Costs and Other Impacts.....................................................253

7.4 Projected Compliance Costs....................................................................255

7.5 Projected Approaches to Emissions Reductions......................................255

7.6 Projected Allowance Prices .....................................................................259

7.7 Projected Generation Mix........................................................................260

7.8 Projected Capacity Additions ..................................................................263

7.9 Projected Coal Production for the Electric Power Sector........................263

7.10 Projected Retail Electricity Prices ...........................................................266

7.11 Projected Fuel Price Impacts ...................................................................267

7.12 Key Differences in EPA Model Runs for Transport Rule Modeling ......269

7.13 Projected Primary PM and Carbon Dioxide Emissions from

Power Plants.............................................................................................270

7.14 Limitations of Analysis............................................................................270

7.15 Significant Energy Impact ......................................................................274

7.16 References................................................................................................277

8. MACROECONOMIC AND EMPLOYMENT IMPACTS ...........................................279

8.1 Partial Equilibrium Analysis (Multiple Markets) ................................................279

8.1.1 Overview..................................................................................................279

8.1.2 Economic Impact Analysis Results .........................................................283

8.1.3 Alternative Approach to Estimating Social Cost.....................................285

8.2 Employment Impacts for the Transport Rule.......................................................286
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8.3 Employment Impacts primarily on the regulated industry: Morgenstern, 

Pizer, and Shih (2002)..........................................................................................287

8.3.1 Limitations...............................................................................................291

8.4 Employment Impacts of the Transport Rule-Environmental Protection 

Sector Approach by 2014 ....................................................................................291

8.4.1 Overall Approach and Methodology for Environmental Protection 

Sector Approach.......................................................................................293

8.4.2 Summary of Employment Estimates from Environmental 

Protection Sector Approach .....................................................................294

8.4.3 Other Employment Impacts of the Transport Rule..................................295

8.5 Summary..............................................................................................................296

8.6 References............................................................................................................298

9. STATUTORY AND EXECUTIVE ORDER IMPACT ANALYSES ...........................299

9.1 Small Entity Impacts ...........................................................................................299

9.1.1 Identification of Small Entities................................................................301

9.1.2 Overview of Analysis and Results...........................................................302

9.1.2.1 Methodology for Estimating Impacts of the Transport Rule on 

Small Entities.........................................................................302

9.1.2.2 Results....................................................................................305

9.1.3 Summary of Small Entity Impacts...........................................................310

9.2 Unfunded Mandates Reform Act (UMRA) Analysis .........................................310

9.2.1 Identification of Government-Owned Entities.........................................311

9.2.2 Overview of Analysis and Results...........................................................312

9.2.2.1 Methodology for Estimating Impacts of the Transport Rule on 

Government Entities ..............................................................312

9.2.2.2 Results....................................................................................315

9.2.3 Summary of Government Entity Impacts................................................319

9.3 Paperwork Reduction Act....................................................................................319

9.4 Protection of Children from Environmental Health and Safety Risks........320 

9.5 Executive Order 13132, Federalism..............................................................321

9.6 Executive Order 13175, Consultation and Coordination with Indian 

Tribal Governments.............................................................................................321

9.7 Environmental Justice ..................................................................................322

9.7.1 Consideration of Environmental Justice Issues in the Rule 

Development Process.......................................................................323

 9.7.2 Meaningful Public Participation ......................................................324

10. COMPARISON OF BENEFITS AND COSTS .............................................................325

10.1 Comparison of Benefits and Costs...........................................................325

10.2 References................................................................................................330

Appendix A: Distribution of the PM2.5-Related Benefits Among Vulnerable and Susceptible 

Populations ..........................................................................................................331
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Appendix B: OAQPS Multimarket Model to Assess the Economic Impacts of Environmental 

Regulation ...........................................................................................................342

Appendix C: Summary of State Level Mortality Impacts ........................................................366

Appendix D: Employment Estimates of Direct Labor In Response to the Final Transport Rule 

in 2014 ................................................................................................................371

Appendix E: Alternate Presentation of the Benefits of the Final Federal Transport Rule .......391

Appendix F: Alternate Tables Based on Remedy Sensitivity with Revised Variability Limits

Rule .....................................................................................................................399
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CHAPTER 1

EXECUTIVE SUMMARY

This Regulatory Impact Analysis (RIA) presents the health and welfare benefits, 

costs, and other impacts of the Transport Rule focusing primarily on 2014. 

1.1 Key Findings

The final Transport Rule will lower sulfur dioxide (SO2) and nitrogen oxide (NOx) 

emissions from the electric power industry in 28 eastern states starting in 2012. In 2014, this 

final rule will have annual benefits (in 2007$) between $120 to $280 billion using a 3% 

discount rate and $110 and $250 billion using a 7% discount rate. At these respective 

discount rates, the annual social costs are $0.8 billion and the annual quantified net benefits 

are $120 to $280 billion or $110 to $270 billion. The benefits outweigh social costs from 

150 up to 350 to 1, or from 110 up to 335 to 1. The benefits result primarily from 13,000 to 

34,000 fewer PM2.5 and ozone-related premature mortalities. There are some costs and 

important benefits that EPA could not monetize. Upon considering these limitations and 

uncertainties, it remains clear that the benefits of the Transport Rule are substantial and far 

outweigh the costs. The annualized private compliance costs to the power industry in 2014 

are $0.8 billion. Employment impacts associated with the final rule are estimated to be 

small. The benefits of the Transport Rule in 2012 are greater than in 2014 due, in part, to the 

final rule expediting emissions reductions that otherwise would have occurred in 2014. 

The benefits and costs in 2014 of the selected remedy (air quality-assured trading) in 

the final rule are in Table 1-1. This selected remedy covers the electric power industry and 

allows interstate emissions trading of sulfur dioxide (SO2) and nitrogen oxides (NOx) in 28

eastern states as listed in section 2.2 of this RIA. 
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Table 1-1. Summary of EPA’s Estimates of Benefits, Costs, and Net Benefits of the 

Selected Remedy in the Transport Rule in 2014a(billions of 2007$)

Description Estimate

(3% Discount Rate)

Estimate

(7% Discount Rate)

Social costsb$0.81 $0.81

Social benefitsc,d $120 to $280 + B $110 to $250 + B

Health-related benefits: $110 to $270 + B $100 to $250 + B

Visibility benefitse$4.1 $4.1

Net benefits (benefits-costs) $120 to $280 $110 to $250

a All estimates are rounded to two significant digits and represent annualized benefits and costs anticipated for 

the year 2014. For notational purposes, unquantified benefits are indicated with a “B” to represent the sum 

of additional monetary benefits and disbenefits. Data limitations prevented us from quantifying these 

endpoints, and as such, these benefits are inherently more uncertain than those benefits that we were able to 

quantify. A listing of health and welfare effects is provided in Table 1-5. Estimates here are subject to 

uncertainties discussed further in the body of the document.

b

Social costs are estimated using the MultiMarket model, the model employed by EPA in this RIA to estimate 

economic impacts of the industries outside the electric power sector. This model does not estimate indirect 

impacts associated with a regulation such as this one. Details on the social cost estimates can be found in 

Chapter 8 and Appendix B of this RIA.

c The reduction in premature mortalities account for over 90% of total monetized benefits. Benefit estimates 

are national except for visibility that covers Class I areas. Valuation assumes discounting over the SABrecommended 20-year segmented lag structure described in Chapter 5. Results reflect 3 percent and 7 

percent discount rates consistent with EPA and OMB guidelines for preparing economic analyses (U.S. 

EPA, 2010; OMB, 2003). The estimate of social benefits also includes CO2 related benefits calculated using 

the social cost of carbon, discussed further in Chapter 5. 

d

Potential benefit categories that have not been quantified and monetized are listed in Table 1-5.

e Over 99% of visibility-related benefits occur within Class-1 areas located in the Eastern U.S. 

1.1.1 Health Benefits

The final Transport Rule is expected to yield significant health benefits by reducing 

emissions of two key contributors to fine particle and ozone formation. Sulfur dioxide 

contributes to the formation of fine particle pollution (PM2.5), and nitrogen oxide contributes 

to the formation of both PM2.5 and ground-level ozone.

Our analyses suggest this would yield benefits in 2014 of $120 to $280 billion (based 

on a 3 percent discount rate) and $110 to $250 billion (based on a 7 percent discount rate). 

The estimated benefits of this rule are substantial, particularly when viewed within the 

context of the total public health burden of PM2.5 and ozone air pollution. A recent EPA 

analysis estimated that 2005 levels of PM2.5 and ozone were responsible for between 130,000 
3

and 320,000 PM2.5-related and 4,700 ozone-related premature deaths, or about 6.1% of total 

deaths (based on the lower end of the avoided mortality range) from all causes in the 

continental U.S. (Fann et al. 2011). This same analysis attributed almost 200,000 non-fatal 

heart attacks, 90,000 hospital admissions due to respiratory or cardiovascular illness and 2.5 

million cases of aggravated asthma among children--among many other impacts. We 

estimate the Transport Rule to reduce the number of PM2.5-related premature deaths in 2014 

by between 13,000 and 34,000, 15,000 non-fatal heart attacks, 8,700 fewer hospital 

admissions and 400,000 fewer cases of aggravated asthma. By 2014, in combination with 

other federal and state air quality actions, the Transport Rule will address a substantial 

fraction of the total public health burden of PM2.5 and ozone air pollution. However, the 

benefits and costs reported in this RIA reflect only the incremental costs and benefits of the 

Transport Rule.

We also estimate substantial additional health improvements for children from 

reductions in upper and lower respiratory illnesses, acute bronchitis, and asthma attacks. See 

Table 1-2 for a list of the annual reduction in health effects expected in 2014 and Table 1-3 

for the estimated value of those reductions. In these tables we summarize the benefits 

according to whether they accrue within or beyond the Transport region (Eastern part of the 

US covered by the final rule). While not analyzed here, we expect the benefits in 2012 (the 

first compliance year for this final rule) to be significantly larger than those modeled for 

2014 because of the much greater incremental SO2 reductions in 2012 compared to 2014 

from the base case. This occurs because the final rule expedites the adoption of SO2

emissions controls that are planned in the base case to occur after 2012 and be underway by

2014. 
4

Table 1-2: Estimated Reduction in Incidence of Adverse Health Effects of the Selected

remedy (95% confidence intervals)A

Health Effect Within transport region

Beyond transport 

region Total

PM-Related endpoints

Premature Mortality

Pope et al. (2002) (age >30) 13,000

(5,200—21,000)

33

(5—60)

13,000

(5,200—21,000)

Laden et al. (2006) (age 

>25)

34,000

(18,000—49,000)

84

(31—140)

34,000

(18,000—49,000)

Infant (< 1 year) 59

(-47—160)

0.15

(-0.2—0.5)

59

(-47—160)

Chronic Bronchitis 8,700

(1,600—16,000)

23

(-5—50)

8,700

(1,600—16,000)

Non-fatal heart attacks (age > 18) 15,000

(5,600—24,000)

40

(7—72)

15,000

(5,600—24,000)

Hospital admissions—respiratory 

(all ages)

2,700

(1,300—4,000)

5

(2—9)

2,700

(1,300—4,000)

Hospital admissions—cardiovascular 

(age > 18)

5,700

(4,200—6,600)

15

(10—19)

5,800

(4,200—6,600)

Emergency room visits for asthma 

(age < 18)

9,800

(5,800—14,000)

21

(7—36)

9,800

(5,800—14,000)

Acute bronchitis 

(age 8-12)

19,000

(-630—37,000)

50

(-29—130)

19,000

(-660—37,000)

Lower respiratory symptoms (age 7-14) 240,000

(120,000—360,000)

630

(130—1,100)

240,000

(120,000—360,000)

Upper respiratory symptoms 

(asthmatics age 9-18)

180,000

(57,000—310,000)

480

(-25—980)

180,000

(57,000—310,000)

Asthma exacerbation 

(asthmatics 6-18)

400,000

(45,000—1,100,000)

1,100

(-250—2,900)

400,000

(45,000—1,100,000)

Lost work days 

(ages 18-65)

1,700,000

(1,500,000—1,900,000)

4,300

(3,500—5,200)

1,700,000

(1,500,000—1,900,000)

Minor restricted-activity days 

(ages 18-65)

10,000,000

(8,400,000—11,000,000)

26,000

(20,000—32,000)

10,000,000

(8,400,000—12,000,000)
5

Ozone-related endpoints

Premature mortality Multi-city and NMMAPS

Bell et al. (2004) (all ages) 27

(11—42)

0.1

(0.01—0.3)

27

(11—42)

Schwartz et al. (2005) 

(all ages)

41

(17—64)

0.2

(0.1—0.4)

41

(17—65)

Huang et al. (2005) 

(all ages)

37

(17—57)

0.2

(0.1—0.4)

37

(17—57)

Meta-analyses

Ito et al. (2005) (all ages) 120

(78—160)

0.6

(0.3—0.9)

120

(79—160)

Bell et al. (2005) (all ages) 87

(48—130)

0.5

(0.2—0.8)

87

(48—130)

Levy et al. (2005) (all ages) 120

(89—150)

0.7

(0.4—0.9)

120

(90—160)

Hospital admissions—respiratory 

causes (ages > 65)

160

(21—280)

1.2

(0.1—2.3)

160

(21—290)

Hospital admissions—respiratory 

causes (ages <2)

83

(43—120)

0.5

(0.2—0.8)

84

(43—120)

Emergency room visits for asthma (all 

ages)

86

(-2—260)

0.4

(-0.2—1.4)

86

(-2—260)

Minor restricted-activity days (ages 18-

65)

160,000

(80,000—240,000)

910

(240—1,600)

160,000

(80,000—240,000)

School absence days 51,000

(22,000—73,000)

290

(59—490)

51,000

(22,000—74,000)

A Estimates rounded to two significant figures; column values will not sum to total value. 

B The negative estimates for certain endpoints are the result of the weak statistical power of the study used to calculate these health 

impacts and do not suggest that increases in air pollution exposure result in decreased health impacts.
6

Table 1-3: Estimated Economic Value of Health and Welfare Benefits (95% confidence 

intervals, billions of 2007$)A,B

Health Effect Pollutant

Within transport 

region

Beyond transport 

regionCTotal

Premature Mortality (Pope et al. 2002 PM mortality and Bell et al. 2004 ozone mortality 

estimates)

3% discount rate PM2.5 & O3

$100

($8.3—$320)

$0.3

($0.01—$0.9)

$100

($8.3—$320)

7% discount rate PM2.5 & O3

$94

($7.5—$280)

$0.2

($0.01—$0.8)

$94

($7.5—$290)

Premature Mortality (Laden et al. 2006 PM mortality and Levy et al. 2005 ozone mortality 

estimates)

3% discount rate PM2.5 & O3

$270

($23—$770)

$0.7

($0.05—$2)

$270

($23—$770)

7% discount rate PM2.5 & O3

$240

($21—$700)

$0.6

($0.05—$1.8)

$240

($21—$700)

Chronic Bronchitis PM2.5

$4.2

($0.2—$19)

$0.01

($-0.003--$0.06)

$4.2

($0.2—$19)

Non-fatal heart attacks 

3% discount rate PM2.5

$1.7

($0.3—$4.2)

$0.004

($0.003—$0.01)

$1.7

($0.3—$4.2)

7% discount rate PM2.5

$1.3

($0.3—$3.1)

$0.004

($0.002—$0.001)

$1.3

($0.3—$3.1)

Hospital admissions—

respiratory PM2.5 & O3

$0.04

($0.02—$0.06) ---

$0.04

($0.02—$0.06)

Hospital admissions—

cardiovascular PM2.5

$0.09

($0.01—$0.2) ---

$0.09

($0.01—$0.2)

Emergency room visits 

for asthma PM2.5 & O3

$0.003

($0.002—$0.006) ---

$0.003

($0.002—

$0.006)

Acute bronchitis PM2.5

$0.008

(<$-0.01—$0.02)D ---

$0.008

(<$-0.01—

$0.02)c

Lower respiratory 

symptoms PM2.5

$0.004

($0.002—$0.009) ---

$0.004

($0.002—

$0.009)

Upper respiratory 

symptoms PM2.5

$0.005

(<$0.01—$0.014) ---

$0.005

(<$0.01—

$0.014)

Asthma exacerbation PM2.5

$0.02

($0.002—$0.08) ---

$0.02

($0.002—

$0.08)

Lost work days PM2.5

$0.2

($0.17—$0.24) ---

$0.2

($0.17—$0.24)

School loss days O3

$0.01

($0.004—$0.013) ---

$0.01

($0.004—

$0.013)

Minor restricted-activity PM2.5 & O3 $0.7 --- $0.7
7

days ($0.3—$1) ($0.3—$1)

Recreational visibility, 

Class I areas PM2.5 $4.1

Social cost of carbon (3% 

discount rate, 2014 value) CO2 $0.6

Monetized total Benefits 

(Pope et al. 2002 PM2.5 mortality and Bell et al. 2004 ozone mortality estimates)

3% discount rate PM2.5, O3

$110

($8.8—$340)

$0.28

($0.01—$0.9)

$120

($14—$350)

7% discount rate PM2.5, O3 

$100

($8—$310)

$0.03

($0.01—$0.85)

$110

($13—$320)

Monetized total Benefits 

(Laden et al. 2006 PM2.5 mortality and Levy et al. 2005 ozone mortality estimates)

3% discount rate PM2.5, O3

$270

($24—$800)

$0.7

($0.05—$2.1)

$280

($29—$810)

7% discount rate PM2.5, O3 

$250

($22—$720)

$0.6

($0.04—$1.9)

$250

($26—$730)

A Estimates rounded to two significant figures. 

B States included in transport region may be found in chapter 2.

C Monetary value of endpoints marked with dashes are < $100,000. D The negative estimates for certain 

endpoints are the result of the weak statistical power of the study used to calculate these health impacts and do 

not suggest that increases in air pollution exposure result in decreased health impacts.

1.1.2 Welfare Benefits

The term welfare benefits covers both environmental and societal benefits of reducing 

pollution, such as reductions in damage to ecosystems, improved visibility and improvements 

in recreational and commercial fishing, agricultural yields, and forest productivity. Although 

we are unable to monetize all welfare benefits, EPA estimates the final Transport Rule will 

yield welfare benefits of $4.1 billion in 2014 (2007$) for visibility improvements in 

southeastern Class I (national park) areas for a total of $4.1 billion in benefits across 

southeastern, southwestern and California Class I areas. These benefits are included in the 

full suite of benefits categories that are accounted for in the monetized benefits for this final 

rule. 
8

Figure 1-1 summarizes an array of PM2.5-related monetized benefits estimates based 

on alternative epidemiology and expert-derived PM-mortality estimate as well as the sum of 

ozone-related benefits using the Bell et al. (2004) mortality estimate. 

Figure 1-2 summarizes the estimated net benefits for the selected remedy by

displaying all possible combinations of PM and ozone-related monetized benefits and costs. 

The graphic includes one estimate of ozone-related mortality and fourteen different PM2.5

related mortality and a single 3% or 7% discounted cost estimate.1 Each of the 14 bars in 

each graph represents a separate point estimate of net benefits under a certain combination of 

cost and benefit estimation methods. Because it is not a distribution, it is not possible to infer 

the likelihood of any single net benefit estimate. 

 

1 Versions of this figure found in previous EPA RIA’s have included the full suite of ozone mortality 

estimates. Because total benefits are relatively insensitive to the specification of ozone mortality estimate, for 

simplicity of presentation we have not included this full suite.
Figure 1-1 Estimated Monetized Value of 

Mortalities Avoided According to Epidemiology or Expert

Mortality Risk EstimateA

A Column total equals sum of PM2.5

mortality benefits using the Bell et al. (200

$0

$50

$100

$150

$200

$250

$300

$350

$400

Pope et al.

Billions (2007$)

PM

3% DR

7% DR

9

1 Estimated Monetized Value of Estimated PM2.5- Related Premature 

Mortalities Avoided According to Epidemiology or Expert-derived Derived PM 

2.5-related mortality and morbidity benefits and ozone-related morbidity and 

mortality benefits using the Bell et al. (2004) mortality estimate.

Pope et al.

Laden et al.

PM2.5 Benefits estimates derived from 2 epidemiology functions and 12 

expert functions

3% DR

7% DR

Related Premature 

Derived PM 

related morbidity and 

Laden et al.

Benefits estimates derived from 2 epidemiology functions and 12 
Figure 1-2: Net Benefits of the Transport Rule A

Expert-derived Mortality Risk Estimate

A Column total equals sum of PM2.5

mortality benefits using the Bell et al. (2004) mortality estimate.

1.1.3 Assessment of More and Less Stringent Scenarios

1.1.3.1 Alternatives that Are More or Less Stringent

In accordance with Circular A

Analyses, EPA also analyzed the costs and benefits of two options that differed in their 

stringency from the selected remedy option 

Both options have the same 2012 requirements and varied in the requirements for SO

$0

$50

$100

$150

$200

$250

$300

$350

$400

Pope et al.

Billions (2007$)

PM2.5 Benefits estimates derived from 2 epidemiology functions and 12 

3% DR

7% DR

10

Net Benefits of the Transport Rule According to PM2.5 Epidemiology or 

derived Mortality Risk EstimateA

2.5-related mortality and morbidity benefits and ozone-related morbidity and 

mortality benefits using the Bell et al. (2004) mortality estimate.

1.1.3 Assessment of More and Less Stringent Scenarios

Alternatives that Are More or Less Stringent

ith Circular A-4 and EPA’s for Guidelines for Preparing Economic 

, EPA also analyzed the costs and benefits of two options that differed in their 

remedy option – one less stringent, the other more stringent. 

s have the same 2012 requirements and varied in the requirements for SO

Laden et al.

Benefits estimates derived from 2 epidemiology functions and 12 

expert functions

Epidemiology or 

related morbidity and 

Preparing Economic 

, EPA also analyzed the costs and benefits of two options that differed in their 

one less stringent, the other more stringent. 

s have the same 2012 requirements and varied in the requirements for SO2 

Laden et al.

Benefits estimates derived from 2 epidemiology functions and 12 
11

emissions reductions in 2014. Both options only applied to the Group 1 states; requirements 

for SO2 reductions remain the same in Group 2 states in 2014 under each option. Annual and

ozone season NOx emissions requirements remain unchanged under these emission caps. 

Unlike the selected remedy, which requires greater SO2 reductions, reductions of up 

to $2,300/ton in marginal cost in 16 states (Group 1) beginning in 2014 from 2012 emissions 

levels, the less stringent option only requires SO2 reductions in 2014 of up to $1,600/ton in 

marginal cost in Group 1 states. The more stringent option requires SO2 reductions in 2014 of 

up to $10,000/ton in marginal cost in Group 1 states. 

Table 1-4 provides a summary of the benefits, costs, and net benefits for the two 

alternatives considered to the selected remedy along with those for the selected remedy. 

Table 1-4. Summary of Annual Benefits, Costs, and Net Benefits of Versions of the 

Selected Remedy Option in 2014a(billions of 2007 dollars)

Description Preferred Remedy Less Stringent 

Scenario

More Stringent 

Scenario

Social costsb

3 % discount rate $0.81 $0.43 $3.6

7 % discount rate $0.81 $0.43 $3.6

Health-related benefitsc,d

3 % discount rate

7 % discount rate

$110 to $270 + B

$100 to $250 + B

$98 to $240 + B

$89 to $220 + B

$130 to $320 + B

$120 to $290 + B

Net benefits (benefits-costs)

e

3 % discount rate

7 % discount rate

$110 to $270

$100 to $250

$98 to $240 + B

$88 to $220 + B

$130 to $320 + B

$120 to $290 + B

a When presenting benefits and net benefits, EPA traditionally rounds all estimates to two significant figures. In this case we have rounded 

to three significant digits to facilitate comparison of the benefits and costs among the preferred remedy and the less and more stringent 

scenarios. 

b The social costs are estimated using the MultiMarket model, the model employed by EPA in this RIA to estimate economic impacts of 

industries outside the electric power sector. This model does not estimate indirect impacts associated with a regulation such as this one. 

More information on the social costs can be found in Chapter 8 and Appendix B of this RIA. 

c Due to methodological limitations, the health benefits of the two A-4 alternative remedies include PM2.5 –related benefits but omit 

visibility, ozone, and CO2-related benefits. We present the PM2.5 –related benefits of the selected remedy, omitting these other important 

benefits, so that readers may compare directly the benefits of the selected and alternate remedies. Total benefits are primarily of the 

value of PM-related avoided premature mortalities. The reduction in these premature mortalities in each year account for over 90 percent

of total PM2.5 –related monetized benefits. Benefits in this table are nationwide and are associated with NOx and SO2 reductions. 

Visibility and ozone-related benefits not calculated for the more and less stringent scenarios because these impacts were estimated using 

PM2.5-related benefit per ton estimates. 

d Not all possible benefits or disbenefits are monetized in this analysis. These are listed in Table 1-5.

e Valuation assumes discounting over the SAB-recommended 20-year segmented lag structure. Results reflect the use of 3 % and 7 % 

discount rates consistent with EPA and OMB guidelines.
12

1.2 Not All Benefits Quantified

EPA was unable to quantify or monetize all of the health and environmental benefits 

associated with the Transport Rule. EPA believes these unquantified benefits are substantial, 

including the value of increased agricultural crop and commercial forest yields, visibility

improvements, reductions in nitrogen and acid deposition and the resulting changes in 

ecosystem functions, and health and welfare benefits associated with reduced mercury

emissions. Table 1-5 provides a list of these benefits. 

Table 1-5: Human Health and Welfare Effects of Pollutants Affected by the Transport Rule

Pollutant/ Effect Quantified and monetized in base estimate Unquantified

PM: healtha

Premature mortality based on cohort study 

estimatesb Low birth weight

Premature mortality based on expert 

elicitation estimates Pulmonary function

Hospital admissions: respiratory and 

cardiovascular

Chronic respiratory diseases other than chronic 

bronchitis

Emergency room visits for asthma Non-asthma respiratory emergency room visits

Nonfatal heart attacks (myocardial 

infarctions) UVb exposure (+/-)

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 impactsc

Ozone: health

Premature mortality based on short-term

study estimates Chronic respiratory damage

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

--Other commercial and noncommercial crops

Damage to urban ornamental plants

Recreational demand from damaged forest 
13

aesthetics

Ecosystem functions

UVb exposure (+/-)

c

NO2: health

Respiratory hospital admissions

Respiratory emergency department visits

Asthma exacerbation

Acute respiratory symptoms

Premature mortality

Pulmonary function

NO2: welfare

Commercial fishing and forestry from acidic 

deposition

Commercial fishing, agriculture and forestry 

from nutrient deposition

Recreation in terrestrial and estuarine 

ecosystems from nutrient deposition

Other ecosystem services and existence values 

for currently healthy ecosystems

Coastal eutrophication from nitrogen deposition

SO2: health

Respiratory hospital admissions

Asthma emergency room visits

Asthma exacerbation

Acute respiratory symptoms

Premature mortality

Pulmonary function

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

Mercury: 

welfare 

Impact on birds and mammals (e.g. reproductive 

effects)

Impacts to commercial., subsistence and 

recreational fishing

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 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.
14

1.3 Costs and Economic Impacts

For the affected region, the projected annual incremental private costs of the selected 

remedy option (air quality-assured trading) to the power industry are $1.4 billion in 2012 and 

$0.8 billion in 2014 (in 2007 dollars). Costs are lower in 2014 than in 2012 as the rule 

becomes more stringent because there are larger amounts of State and Federally enforceable 

controls that happen between 2012 and 2014 in the baseline. These costs represent the total 

cost to the electricity-generating industry of reducing NOx and SO2 emissions to meet the 

emissions caps set out in the rule. Estimates are in 2007 dollars. These costs of the rule are 

estimated using the Integrated Planning Model (IPM). It should be noted that the rule 

modeled for this analysis differs from the final rule in that it includes reductions that would 

be required by the supplemental proposal for six states (Iowa, Kansas, Michigan, Missouri, 

Oklahoma, and Wisconsin). These reductions are included in the cost and impacts estimates 

described in this RIA, and therefore are accounted for in the benefits estimates.

In estimating the net benefits of regulation presented above, the appropriate cost 

measure is “social costs.” Social costs represent the changes in social welfare from the rule 

measured as the change in total surplus (consumer and producer) in the macroeconomic 

analysis of this rule. 

There are several national changes in energy prices that result from the Transport 

Rule. Retail electricity prices are projected to increase nationally by an average of 1.3 % in 

2012 and 0.8 % in 2014 with the final Transport Rule. The average delivered coal price 

decreases by about 1.4 percent in 2012 and 0.5 percent in 2014 relative to the base case as a 

result of decreased coal demand and shifts in the type of coal demanded. EPA also projects 

that delivered natural gas prices for the electric power sector will increase by about 0.3% 

over the 2012-2030 timeframe and that natural gas use for electricity generation will increase 

by approximately 200 billion cubic feet (BCF) by 2014, or roughly 4%. This impact is well 

within the range of price variability that is regularly experienced in natural gas markets. 

Finally, under the Transport Rule, EPA projects coal production for use by the power sector 

will increase above 2009 levels by 40 million tons in 2012 and 54 million tons in 2014

(compared to roughly one billion tons of total coal produced for the power sector in 2009). 

This increase in production is 16% less in 2012 and 27% less in 2014 than the increase 
15

projected in the base case. The Transport Rule is not projected to impact production of coal 

for uses outside the power sector (e.g., export, industrial sources), which represent 

approximately 6% of total coal production in 2009. More detail and background for these 

results can be found in Chapter 7 of the RIA. 

There are several other types of energy impacts from the Transport Rule. A relatively 

small amount of coal-fired capacity, about 4.8 GW (1 percent of all coal-fired capacity and 

0.5% of total generating capacity), is projected to be uneconomic to maintain and EPA 

forecast that 1 GW of that capacity was likely to be unprofitable to operate in the 2020 in the 

base case. In practice units projected to be uneconomic to maintain may be “mothballed,” 

retired, or kept in service to ensure transmission reliability in certain parts of the grid. For 

the most part, these units are small and infrequently used generating units that are dispersed 

throughout the Transport Rule region. 

In addition to addressing the costs, benefits, and economic impacts of the Transport 

Rule, EPA has estimated a portion of the employment impacts of this rulemaking. We have 

estimated three types of impacts. One provides an estimate of the employment impacts on 

the regulated industry over time. The second covers the short-term employment impacts 

associated with the construction of needed pollution control equipment until the compliance 

date of the regulation. The third is to estimate short-term employment impacts extending 

outside of the power sector, as described in Appendix D. We expect that the rule’s impact on 

employment will be small.

In Table 1-6, we show the employment impacts of the Transport Rule as estimated by

the environmental protection sector approach and by the Morgenstern approach. The 

estimated employment changes due to changes in fuel use are reported in Chapter 8.
16

Table 1-6. Estimated Employment Impact Table

Annual (reoccurring) One time (construction 

during compliance period)

Environmental Protection 

Sector approach*

Not Applicable 2,230

Net Effect on Electric Utility

Sector Employment from 

Morgenstern et al. 

approach***

700**

-1, 000 to +3,000****

Not Applicable

*These one-time impacts on employment are estimated in terms of job-years.

**This estimate is not statistically different from zero.

**These annual or reoccurring employment impacts are estimated in terms of production workers as defined by 

the US Census Bureau’s Annual Survey of Manufacturers (ASM).

**** 95% confidence interval

Overall, the impacts of the final rule are modest, particularly in light of the large 

projected benefits mentioned earlier.
17

1.4 Small Entity and Unfunded Mandates Impacts

After preparing an analysis of small entity impacts, EPA has certified that this final 

rule will have no SISNOSE (significant economic impacts on a substantial number of small 

entities). First, of the small entities projected to have costs greater than 1 percent of revenues

(24 out of 108 affected), around 70 percent of them operate in cost of service regions and 

would generally be able to pass any increased costs along to rate-payers. In EPA’s modeling, 

most of the cost impacts for these small entities and their associated units are driven by lower 

electricity generation relative to the base case. Specifically, two units reduce their generation 

by significant amounts, driving the bulk of the costs for all small entities. Excluding these 

two units, another driver of small entity impacts for sub-divisions and private small entities is 

higher fuel costs, which the affected units would be expected to use irrespective of whether 

they had to comply with this rule. Further, increased fuel costs are often passed through to 

rate-payers as common practice in many areas of the U.S. due to fuel adder arrangements 

instituted by state public utility commissions. Finally, EPA’s decision to exclude units 

smaller than 25 Megawatt capacity (MW) has already significantly reduced the burden on 

small entities by reducing the number of affected small entity-owned units by about 390.

EPA examined the potential economic impacts on state and municipality-owned 

entities associated with this rulemaking based on assumptions of how the affected states will 

implement control measures to meet their emissions. These impacts have been calculated to

provide additional understanding of the nature of potential impacts and additional 

information. 

According to EPA’s analysis, of the 98 government entities considered in this 

analysis and the 365 government entities in the Transport Rule region that are included in 

EPA’s modeling, 26 may experience compliance costs in excess of 1 percent of revenues in 

2014, based on our assumptions of how the affected states implement control measures to 

meet their emissions budgets as set forth in this rulemaking.
18

Government entities projected to experience compliance costs in excess of 1 percent 

of revenues may have some potential for significant impact resulting from implementation of 

the Transport Rule. However, it is EPA’s position that because these government entities can 

pass on their costs of compliance to rate-payers, they will not be significantly affected. 

Furthermore, the decision to include only units greater than 25 MW in size exempts 354

government entities that would otherwise be potentially affected by the Transport Rule.

1.5 Limitations and Uncertainties

Every analysis examining the potential benefits and costs of a change in 

environmental protection requirements is limited to some extent by data gaps, limitations in 

model capabilities (such as geographic coverage), and variability or uncertainties in the 

underlying scientific and economic studies used to configure the benefit and cost models. 

Despite the uncertainties, we believe this benefit-cost analysis provides a reasonable 

indication of the expected economic benefits and costs of the final Transport Rule.

For this analysis, such uncertainties include possible errors in measurement and 

projection for variables such as population growth and baseline incidence rates; uncertainties 

associated with estimates of future-year emissions inventories and air quality; variability in 

the estimated relationships between changes in pollutant concentrations and the resulting 

changes in health and welfare effects; and uncertainties in exposure estimation. 

EPA’s cost estimates assume that all states in the final Transport Rule region 

participate in the programs that reduce SO2 and NOx emissions from the power industry, 

rather than complying with state-level requirements through other regulatory means. 

Below is a summary of the key uncertainties of the analysis:

Costs

 Analysis does not capture employment shifts as workers are retrained at the same 

company or re-employed elsewhere in the economy. 
19

 We do not include the costs of certain relatively small permitting costs associated with 

Title V that new program entrants face. 

 Technological innovation is not incorporated into these cost estimates.

 Economic impacts do not take into response of electric power consumers to changes in 

electricity prices. While this response is likely to be of small magnitude, it may have 

some impact on the final estimate of private compliance costs. 

Benefits

 Most of the estimated PM-related benefits in this rule accrue to populations exposed to 

higher levels of PM2.5. Of these estimated PM-related mortalities avoided, about 69%

occur among populations initially exposed to annual mean PM2.5 level of 10 μg/m3and 

about 96% occur among those initially exposed to annual mean PM2.5 level of 7.5 μg/m3 ; 

these are the lowest air quality levels considered in the Laden et al. (2006) and Pope et al. 

(2002) studies, respectively. This fact is important, because as we estimate PM-related 

mortality among populations exposed to levels of PM2.5 that are successively lower, our 

confidence in the results diminishes. However, our analysis shows that the great majority 

of the impacts occur at higher exposures.

 There are uncertainties related to the health impact functions used in the analysis. These 

include: within study variability; across study variation; the application of concentrationresponse (C-R) functions nationwide; extrapolation of impact functions across 

population; and various uncertainties in the C-R function, including causality and 

thresholds. Therefore, benefits may be under- or over-estimates. 

 Analysis is for 2014, and projecting key variables introduces uncertainty. Inherent in any

analysis of future regulatory programs are uncertainties in projecting atmospheric 

conditions and source level emissions, as well as population, health baselines, incomes, 

technology, and other factors. 

 This analysis omits certain unquantified effects due to lack of data, time and resources. 

These unquantified endpoints include other health and ecosystem effects. EPA will 

continue to evaluate new methods and models and select those most appropriate for 

estimating the benefits of reductions in air pollution. Enhanced collaboration between air 

quality modelers, epidemiologists, toxicologists, ecologists, and economists should result 
20

in a more tightly integrated analytical framework for measuring benefits of air pollution 

policies.

 PM2.5 mortality benefits represent a substantial proportion of total monetized benefits 

(over 90%), and these estimates have following key assumptions and uncertainties. 

1. The PM2.5 -related benefits of the alternative scenarios were derived through a 

benefit per-ton approach, which does not fully reflect local variability in 

population density, meteorology, exposure, baseline health incidence rates, or 

other local factors that might lead to an over-estimate or under-estimate of the 

actual benefits of controlling SO2.

2. 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 no clear scientific grounds exist for supporting differential effects 

estimates by particle type.

3. We assume that the health impact function for fine particles is linear within the 

range of ambient concentrations under consideration. Thus, the estimates include 

health benefits from reducing fine particles in areas with varied concentrations of 

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.

4. To characterize the uncertainty in the relationship between PM2.5 and premature 

mortality, 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.
21

These projected impacts of this final rule do not reflect minor technical corrections to 

SO2 budgets in three states (KY, MI, and NY). These projections also assumed preliminary

variability limits that were smaller than the variability limits finalized in this rule. EPA 

conducted sensitivity analysis confirming that these differences do not meaningfully alter any 

of the Agency's findings or conclusions based on the projected cost, benefit, and air quality 

impacts presented for the final Transport Rule. The results of this sensitivity analysis are 

presented in Appendix F in the final Transport Rule RIA.
22

1.6 References

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.

Fann N, Lamson A, Wesson K, Risley D, Anenberg S.C., Hubbell B.J. 2011. Estimating the 

National Public Health Burden Associated with Exposure to Ambient PM2.5 and Ozone. 

Risk Analysis. In Press. 

Ito, K., S.F. De Leon, and M. Lippmann. 2005. Associations between ozone and daily 

mortality: analysis and meta-analysis. Epidemiology. 16(4): p. 446-57.

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. Estimating the Public Health Benefits of Proposed Air 

Pollution Regulations. Washington, DC: The National Academies Press.

Levy JI, Baxter LK, Schwartz J. 2009. Uncertainty and variability in health-related damages 

from coal-fired power plants in the United States. Risk Anal. doi: 10.1111/j.1539-

6924.2009.01227.x [Online 9 Apr 2009]

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. 

U.S. Environmental Protection Agency (EPA). December 2010. Guidelines for Preparing 

Economic Analyses. EPA 240-R-10-001. 

U.S. Office of Management and Budget (OMB). 2003. Circular A-4 Guidance to Federal 

Agencies on Preparation of Regulatory Analysis.

Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected 

Causes of Postneonatal Infant Mortality and Particulate Infant Mortality and 

Particulate Air Pollution in the United States.” Environmental Health Perspectives

105(6):608-612.
23

CHAPTER 2

INTRODUCTION AND BACKGROUND

2.1 Introduction

EPA is addressing in this action the interstate transport of emissions of nitrogen 

oxides (NOx) and sulfur dioxide (SO2) that contribute significantly to nonattainment and 

maintenance problems with respect to the national ambient air quality standards (NAAQS) 

for fine particulate matter (PM2.5) that EPA promulgated in 1997 and 2006 and for 8-hour 

ozone that were promulgated in 1997. In this action, EPA will both identify and eliminate 

emissions within states in the eastern United States that significantly contribute to 

nonattainment and interfere with maintenance of the ozone and PM2.5 NAAQS in other

downwind states. This document presents the health and welfare benefits of the Transport 

Rule and compares the benefits of this rule to the estimated costs of implementing the rule in 

2012 and 2014. This chapter contains background information relative to the rule and an 

outline of the chapters of the report.

2.2 Background

Clean Air Act (CAA) section 110(a)(2)(D)(i)(I) requires states to prohibit emissions 

that contribute significantly to nonattainment in, or interfere with maintenance by, any other 

state with respect to the National Ambient Air Quality Standards (NAAQS). In this final 

rule, the Environmental Protection Agency (EPA) will partially or fully address the interstate 

transport of emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), and the fine particulate 

that they form in the atmosphere, that contribute significantly to nonattainment and interfere 

with maintenance with respect to the fine particulate matter (PM2.5) NAAQS promulgated in 

1997 and 2006. This final rule includes actions to partially or fully address the interstate 
24

transport of NOx and the ozone that it forms in the atmosphere that contribute significantly to 

nonattainment and interfere with maintenance with respect to the 8-hour ozone NAAQS 

promulgated in 1997. 

With this final rule, EPA is responding to the remand of the Clean Air Interstate Rule 

(CAIR) by the U.S. Court of Appeals for the D.C. Circuit in 2008. CAIR, promulgated May

12, 2005 (70 FR 25162) and the CAIR federal implementation plans (FIPs), promulgated

April 26, 2006 (71 FR 25328), aimed to address the interstate transport of pollutants that 

contributed significantly to downwind nonattainment of the PM2.5 and 8-hour ozone NAAQS 

promulgated in July 1997. In July 2008, the D.C. Circuit Court found CAIR and the CAIR 

FIPs unlawful. North Carolina v. EPA, 531 F.3d 896 (D.C. Cir. 2008). The Court’s original 

decision vacated CAIR. Id. at 929-30. However, the Court subsequently remanded CAIR to 

EPA without vacatur because it found that “allowing CAIR to remain in effect until it is 

replaced by a rule consistent with our opinion would at least temporarily preserve the 

environmental values covered by CAIR.” North Carolina v. EPA, 550 F.3d 1176, 1178 

(D.C. Cir. 2008). 

2.2.1 Methodology for Identifying Needed Reductions

As described in the preamble for this rule, EPA applies a state-specific methodology

to identify specific reductions that states in the eastern United States must make to satisfy the 

CAA section 110(a)(2)(D)(i)(I) prohibition on emissions that significantly contribute to 

nonattainment or interfere with maintenance in a downwind state. To facilitate 

implementation of the requirement that significant contribution and interference with 

maintenance be eliminated, EPA developed state emissions budgets. These are new 

emissions budgets which are based on the Agency’s state-by-state analysis of each upwind 

state’s significant contribution to nonattainment and interference with maintenance 

downwind. A state’s emissions budget is the quantity of emissions that would remain after 

elimination of significant contribution and interference with maintenance in an average year, 

assuming no abnormal meteorology or disruptions in electricity supply. EPA establishes SO2

and NOx budgets for each state covered for the 24-hour and/or annual PM2.5 NAAQS. EPA 
25

also establishes an ozone season2 NOx budget for each state covered for the 8-hour ozone 

NAAQS.

2.2.2 How Reductions Will Be Achieved, and Different Options to Do So

EPA is finalizing federal implementation plans (FIPs) to immediately implement the 

emissions reduction requirements. The FIPs would regulate electric generating units (EGUs) 

in the 28 covered states. EPA will regulate these sources through a program that uses statespecific budgets and allows interstate trading. 

2.2.3 States Covered by the Final Rule

In this action, EPA requires SO2 and NOx emissions controls in the following 22 

jurisdictions that contribute significantly to nonattainment in, or interfere with maintenance 

by, a downwind area with respect to the 24-hour PM2.5 NAAQS promulgated in September 

2006: Alabama, Georgia, Illinois, Indiana, Iowa, Kansas, Kentucky, Maryland, Michigan, 

Minnesota, Missouri, Nebraska, New Jersey, New York, North Carolina, Ohio, Pennsylvania, 

South Carolina, Tennessee, Virginia, West Virginia, and Wisconsin.

EPA requires SO2 and NOx emissions controls in the following 23 jurisdictions that 

contribute significantly to nonattainment in, or interfere with maintenance by, a downwind 

area with respect to the annual PM2.5 NAAQS promulgated in July 1997: Alabama, Georgia, 

Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Missouri, New Jersey, 

New York, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, Texas, Virginia, 

West Virginia, and Wisconsin.

EPA also requires with this action ozone season NOx emissions controls in the 

following 27 jurisdictions that contribute significantly to nonattainment in, or interfere with 

maintenance by, a downwind area with respect to the 8-hour ozone NAAQS promulgated in 

July 1997: Alabama, Arkansas, Florida, Georgia, Illinois, Indiana, Iowa, Kansas, Kentucky, 

Louisiana, Maryland, Michigan, Mississippi, Missouri, New Jersey, New York, North 

 

2

Consistent with the approach taken by the Ozone Transport Assessment Group (OTAG), the NOX SIP call, 

and the CAIR, we define the ozone season, for purposes of emissions reduction requirements in this rule, as 

May through September. We recognize that this ozone season for regulatory requirements will have differences 

from the official state-specific monitoring season.
26

Carolina, Ohio, Oklahoma, Pennsylvania, South Carolina, Tennessee, Texas, Virginia, West 

Virginia, and Wisconsin. This group of jurisdictions includes 21 states covered by the 

finalized rule and 6 proposed states (Iowa, Kansas, Michigan, Missouri, Oklahoma, and 

Wisconsin) in the supplemental rule. EPA is reporting on all 27 states in this RIA. 

As discussed above, EPA is finalizing FIPs to directly regulate EGU SO2 and/or NOx

emissions in the 28 covered states. The FIPs would require the 23 jurisdictions covered for 

purposes of the 24-hour and/or annual PM2.5 NAAQS to reduce SO2 and NOx emissions by

specified amounts. The FIPs would require the 27 states covered for purposes of the 8-hour 

ozone NAAQS to reduce ozone season NOx emissions by specified amounts. For the PM2.5

NAAQS, EPA would require two phases with an initial phase in 2012 and subsequent phase 

in 2014. For 8-hour ozone, EPA would require a single phase that would start in 2012. 

As discussed in detail in the preamble, the approach to significant contribution and 

interference with maintenance would group the 23 states covered for 24-hour and/or annual 

PM2.5 NAAQS in two tiers reflecting the stringency of SO2 reductions required to eliminate 

that state’s significant contribution and interference with maintenance. There would be a 

more stringent SO2 tier comprising 16 states (“group 1”) and a moderately stringent SO2 tier 

comprising 7 states (“group 2”) with uniform stringency within each tier.3 For these same 23 

states, there would be one annual NOx tier with uniform stringency of NOx reductions across 

all states. Similarly, for the 27 states covered for the 8-hour ozone NAAQS there would be 

one ozone season NOx tier with uniform stringency across all of these states.4

The more stringent SO2 tier (“group 1”) would include Illinois, Indiana, Iowa, 

Kentucky, Maryland, Michigan, Missouri, New Jersey, New York, North Carolina, Ohio, 

Pennsylvania, Tennessee, Virginia, West Virginia, and Wisconsin. The moderately 

stringent SO2 tier (“group 2”) would include Alabama, Georgia, Kansas, Minnesota, 

Nebraska, South Carolina, and Texas. 

For the 16 states in the more stringent SO2 tier (“group 1”), the 2014 phase would 

 

3 With regard to interstate trading, the two SO2 stringency tiers lead to two exclusive SO2 trading groups.

4

It should be noted that, as previously mentioned in this chapter, a supplemental proposal will be issued 

concurrently with the final Transport Rule to remove 6 of these 27 states from these ozone season NOx 

requirements. They are: Iowa, Kansas, Michigan, Missouri, Oklahoma, and Wisconsin. More information on 

this supplemental proposal can be found in the preamble for the Transport Rule.
27

increase the SO2 reduction requirements (i.e., these states would have smaller SO2 emissions 

budgets starting in 2014), reflecting the greater reductions needed to eliminate significant 

contribution and interference with maintenance from these states with respect to the 24-hour 

PM2.5 NAAQS. The 2014 annual NOx emissions budgets for all 23 states covered for the 24-

hour and/or annual PM2.5 NAAQS would remain the same as the 2012 annual NOx.

For the 7 states in the moderately stringent SO2 tier (“group 2”), the 2014 SO2

emission reduction requirements would remain the same as the 2012 SO2 emission reduction 

requirements for these states. See Table 2-1 for lists of covered states.

Table 2-1 -- Lists of Covered States for PM2.5 and 8-Hour Ozone NAAQS

State

Covered for 24-hour 

and/or annual PM2.5

Required to reduce SO2

and NOx

Covered for 8-hour 

ozone

Required to reduce 

ozone season NOx

Alabama X X

Arkansas X

Florida X

Georgia X X

Illinois X X

Indiana X X

Iowa X X

Kansas X X

Kentucky X X

Louisiana X

Maryland X X

Michigan X X

Minnesota X

Mississippi X

Missouri X X
28

Nebraska X X

New Jersey X X

New York X X

North Carolina X X

Ohio X X

Oklahoma X

Pennsylvania X X

South Carolina X X

Tennessee X X

Texas X X

Virginia X X

West Virginia X X

Wisconsin X X

TOTALS 23 27

The relevant regions for PM2.5 and ozone significant contribution are also depicted in 

the graphic in Figure 2-1. Maps are also available in Chapter 7 of this RIA. 
29

 Figure 2-1 – States Covered Under the Final Transport Rule 
30

2.3 Regulated Entities

This action will directly regulate emissions of NOx and SO2 from electric generating 

units (EGUs) with capacity greater than 25 MW in the covered states. 

2.4 Baseline and Years of Analysis

The rule on which this analysis is based sets forth the requirements for states to 

address their significant contribution to downwind nonattainment of ozone and PM2.5

NAAQS and interference with maintenance. To address this significant contribution and 

interference with maintenance, EPA requires that certain states reduce their emissions of SO2

and NOx. The Agency considered all promulgated CAA requirements and known state 

actions in the baseline used to develop the estimates of benefits and costs for this rule. This 

baseline analysis takes into account emissions reductions associated with the implementation 

of all federal rules promulgated by December 2010 and assumes that the CAIR is not in 

effect. However, this baseline presents a unique situation. EPA has been directed to replace 

the CAIR; yet the CAIR remains in place and has led to significant emissions reductions in 

many states. 

A key step in the process of developing a 110(a)(2)(D)(i)(I) rule involves analyzing 

existing (base case) emissions to determine which states significantly contribute to 

downwind nonattainment and maintenance areas. It should be noted that a state affected by 

CAIR may not be affected by the new rule and after the new rule goes into effect, the CAIR 

requirements will no longer apply. For a state covered by CAIR but not covered by the new 

rule, the CAIR requirements would not be replaced with new requirements, and therefore an 

increase in emissions relative to present levels could occur in that state. More fundamentally, 

the court has made clear that, due to legal flaws, the CAIR rule cannot remain in place and 

must be replaced. If EPA’s base case analysis were to ignore this fact and assume that 

reductions from CAIR would continue indefinitely, areas that are in attainment solely due to 

controls required by CAIR would again face nonattainment problems, because the existing 

protection from upwind pollution would not be replaced. For these reasons, EPA cannot 

assume in its base case analysis, that the reductions required by CAIR will continue to be 

achieved.
31

Following this logic, the 2012 base case shows emissions higher than current levels in 

some states. Because EPA has been directed to replace CAIR, EPA believes that for many

states, the absence of the CAIR NOx program will lead to the status quo of the NOx Budget 

Trading Program (NBP), which substantially limits ozone-season NOx emissions from 

electric power generation in a major part of the Eastern US and ensures the operation of NOx

controls in 20 covered states and the District of Columbia. The base case contains the NBP. 

Also, without the CAIR SO2 program, there would remain the broad federal SO2 emissions 

requirements for electric generation from fossil fuels in the lower 48 states for the 

comparatively less stringent CAA Title IV Acid Rain Program (ARP). As a result, SO2

emissions in many states would increase markedly in the 2012 base case relative to the 

present. Efforts to comply with ARP rules at the least-cost would occur in many cases solely 

through use of currently readily available, inexpensive Title IV allowances and without the 

operation of some existing scrubbers that do not have other binding enforceable 

requirements. Notably, all known controls for both SO2 and NOx that are required under 

state laws, NSPS, consent decrees, and other enforceable, binding commitments through 

2014 are accounted for in the base case. These requirements are quite substantial in 

maintaining the operation of much of the existing advanced controls in place. It is against 

this backdrop that the Transport Rule is analyzed and that significant contribution to 

nonattainment and interference with maintenance must be addressed.

The model’s base case features an updated Title IV SO2 allowance bank assumption 

and incorporates updates related to the Energy Independence and Security Act of 2007. 

Many key assumptions, notably demand for electricity, reflect the 2010 Annual Energy

Outlook from the Energy Information Administration (EIA). In addition, the model includes 

policies affecting the power sector: the Title IV of the Clean Air Act (the Acid Rain 

Program); the NOx SIP Call; various New Source Review (NSR) settlements5; and several 

 

5 The NSR settlements include agreements between EPA and Southern Indiana Gas and Electric Company 

(Vectren), Public Service Enterprise Group, Tampa Electric Company, We Energies (WEPCO), Virginia 

Electric & Power Company (Dominion), Santee Cooper, Minnkota Power Coop, American Electric Power 

(AEP), East Kentucky Power Cooperative (EKPC), Nevada Power Company, Illinois Power, Mirant, Ohio 

Edison, and Kentucky Utilities. These agreements lay out specific NOx, SO2, and other emissions controls 

for the fleets of these major Eastern companies by specified dates. Many of the pollution controls are required 

between 2010 and 2014.
32

state rules6affecting emissions of SO2 and NOx , that were finalized through December, 

2010. IPM includes state rules that have been finalized and/or approved by a state’s 

legislature or environmental agency. The IPM documentation TSD contains details on all of 

these other legally binding and enforceable commitments for installation and operation of 

advanced NOX and SO2 pollution controls.

The years 2012 and 2014 are the compliance years for the final rule, though as we 

explain in Chapters 5 and 7 we use 2015 (which covers 2014, 2015, 2016, and 2017) as a 

proxy for compliance in 2014 for our benefits and economic impact analysis due to 

availability of modeling impacts in that year. All estimates presented in this report 

represent annualized estimates of the benefits and costs of the final Transport Rule in 2012 

and 2015 rather than the net present value of a stream of benefits and costs in these particular 

years of analysis.

2.5 Control Scenarios

The Transport Rule includes FIPs that would utilize state-specific control budgets and

allow for interstate trading. This approach would assure environmental results while 

providing some limited flexibility for covered sources. The approach would also facilitate 

the transition from CAIR to the Transport Rule for implementing agencies and covered 

sources. The preferred remedy would use new allowance allocations developed on a different 

basis from CAIR. Fossil-fuel electric generating units (EGUs) over 25 megawatt (MW) 

capacity within the Transport Rule region would be covered by this action.

At proposal, EPA looked at two other alternatives of direct control and intrastate 

trading and fully analyzed their implications. The results of that analysis can be found in the 

proposal’s RIA. We also considered a more and less stringent option in accordance with 

OMB Circular A-4 in that RIA. We have re-done our A-4 analysis in this final RIA and the 

results are found in Chapter 10. 

 

6

These include current and future state programs in Alabama, Connecticut, Delaware, Georgia, Illinois, 

Kansas, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, New Hampshire, North 

Carolina, New Jersey, New York, Oregon, Pennsylvania, Tennessee, Texas, West Virginia, and Wisconsin.
33

2.6 Benefits of Emission Controls

The benefits of the Transport Rule are discussed in Chapter 5 of this report. Annual 

monetized benefits of $120 to 280 billion (3 percent discount rate) or $110 to $250 billion 

(7 percent discount rate) are expected for the final rule in 2014. 

2.7 Costs of Emission Controls 

EPA analyzed the costs to private industry of the Transport Rule using the Integrated 

Planning Model (IPM). EPA has used this model in the past to analyze the impacts of 

regulations on the power sector and used an earlier version of this model to analyze the 

impacts of the CAIR rule. The social cost is estimated using the Multimarket Model. IPM 

results are incorporated into the Multimarket Model when calculating the social cost of the 

Transport Rule. EPA estimates the private industry costs of the rule to the power sector to be 

$1.4 billion in 2012 and $0.8 billion in 2014 (2007 dollars). In estimating the net benefits 

(benefits – costs) of the rule, EPA uses social costs of the rule that represent the costs to 

society. These social costs include to the impact to industries affected by changes to 

electricity prices resulting from implementation of the Transport Rule. The social costs of 

the rule are estimated to be $0.8 billion in 2014. 

A description of the methodologies used to model the costs and economic impacts to 

the power sector is discussed in Chapter 7 of this report, and a description of the 

methodology used to estimate the social cost of the rule, the employment impacts of the rule, 

and model economic impacts outside of the power sector are discussed in Chapter 8 of this 

report.

2.8 Organization of the Regulatory Impact Analysis

This report presents EPA’s analysis of the benefits, costs, and other economic effects 

of the final Transport Rule to fulfill the requirements of a Regulatory Impact Analysis (RIA). 

This RIA includes the following chapters:
34

 Chapter 3, Emissions Impacts, describes the emission inventories and modeling 

that are essential inputs into the cost and benefit assessments.

 Chapter 4, Air Quality Impacts, describes the air quality data and modeling that 

are important for assessing the effect on contributions to air quality from the 

remedy option applied in this rule, and as inputs to the benefits assessment.

 Chapter 5, Benefits Analysis and Results, describes the methodology and results 

of the benefits analysis

 Chapter 6, Electric Power Sector Profile, describes the industry affected by the 

rule.

 Chapter 7, Cost, Economic, and Energy Impacts, describes the modeling 

conducted to estimate the cost, economic, and energy impacts to the power sector.

 Chapter 8, Macroeconomic and Employment Impacts, describes the describes the 

analysis to estimate the impacts on employment associated with the final rule, the 

modeling conducted to estimate the social cost of the rule and the economic 

impacts to industries outside of the power sector.

 Chapter 9, Statutory and Executive Order Impact Analyses, describes the small 

business, unfunded mandates, paperwork reduction act, environmental justice, and 

other analyses conducted for the rule to meet statutory and Executive Order 

requirements.

 Chapter 10, Comparison of Benefits and Costs, shows a comparison of the social 

benefits to social costs of the rule.

 Appendix A, Distribution of the PM2.5-Related Benefits Among Vulnerable and 

Susceptible Populations

 Appendix B, OAQPS Multimarket Model to Assess the Economic Impact of 

Environmental Regulation

 Appendix C, State Benefits Results
35

CHAPTER 3

EMISSIONS IMPACTS

This chapter summarizes the emissions inventories that are used to create emissions 

inputs to the air quality modeling that is described in Chapter 4. This chapter provides a 

summary of the baseline emissions inventories and the emissions reductions that were 

modeled for this rule. The emissions inventories are processed into a form that is required by 

the Comprehensive Air Quality Model with extensions (CAMx). CAMx simulates the 

numerous physical and chemical processes involved in the formation, transport, and 

destruction of ozone and particulate matter (PM). Separate runs of CAMx were performed 

for the base year, future baseline, and post-control scenarios. As part of the analysis for this 

rulemaking, CAMx outputs were used to calculate 8-hr maximum ozone concentrations; 

daily and annual concentrations of particulate matter less than 2.5 microns in diameter 

(PM2.5). In the remainder of this Chapter we provide an overview of (1) the emissions 

components of the modeling platform, (2) the development of the 2005 base-year emissions, 

(3) the development of the 2012 and 2014 future-year base case emissions, and (4) the 

development of the 2014 future year-control case (policy case) emissions. It should be noted 

that the projected future year inventory used for this analysis is generally representative of 

several years around 2014 such as 2015.

3.1 Overview of Modeling Platform and Emissions Processing Performed

The inputs to the air quality model; including emissions, meteorology, initial 

conditions, boundary conditions; along with the methods used to produce the inputs and the 

configuration of the air quality model are collectively known as a ‘modeling platform’. The 

2005-based air quality modeling platform used for the Transport Rule includes 2005 baseyear emissions and 2005 meteorology for modeling ozone and PM2.5 with CAMx (see 

http://www.camx.com/). Version 4.2 of the 2005-based platform (2005 v4.2 platform) was 
36

used for the Transport Rule, and it is described in the document: “Emissions Inventory

Technical Support Document (TSD) for the Transport Rule NFR”, posted at 

http://www.epa.gov/ttn/chief/emch/. The Emissions Inventory TSD provides more detail on 

(1) the development of the emissions inventories for all sectors and (2) the procedures 

followed to create emissions inputs to CAMx. For additional details on EPA’s projected 

emissions for the EGU sector, see Chapter 7 of this RIA. 

Emissions estimates were made for a 2005 base year and for 2012 and 2014 futureyear scenarios. All inventories include emissions from EGUs, non-EGU point sources, 

stationary nonpoint sources (previously referred to as stationary area sources), onroad mobile 

sources, nonroad mobile sources and natural, biogenic emissions. In support of this rule, 

EPA processed the emissions in support of air quality modeling for two domains, covering 

the East and the West (2 separate model runs) of the U.S. and parts of Canada and Mexico 

using a horizontal grid resolution of 12 x 12 kilometers (km). These 12 km modeling 

domains were “nested” within a modeling domain covering the lower 48 states using a grid 

resolution of 36 x 36 km7, therefore the tables of emissions in this section cover the 

contiguous 48 states. 

For each of the modeling scenarios conducted: 2005 base year, 2012 base case, 2014 

base case, and 2014 control case, the emissions inventory files were processed using the 

Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System version 2.6 to 

produce the gridded model-ready emissions for input to CAMx. SMOKE was used to create 

the hourly, gridded emissions data for the species required by CAMx species to perform air 

quality modeling for all sectors, including biogenic emissions. Details on the non-emissions 

portion of the modeling platform used for the RIA are provided in Chapter 4.

3.2 Development of 2005 Base Year Emissions

Emissions inventory inputs representing the year 2005 were developed to provide a 

base year for forecasting future air quality. These inventories include criteria air pollutants 

and some hazardous air pollutants. For some sectors, benzene, formaldehyde, acetaldehyde 

and methanol (sometimes abbreviated “BAFM”) are used from the inventory for chemical 

speciation of volatile organic compounds (VOC). The emission source sectors and the basis 

 

7

The air quality predictions from the 36 km Continental US (CONUS) domain were used to 

provide incoming “boundary” concentrations for the 12 km domains.
37

for current and future-year inventories are listed and defined in Table 3-1. These are the same 

sectors as were used in the 2005-based version 4 (v4) platform 

(www.epa.gov/ttn/chief/emch/index.html#2005), from which the v4.2 platform was derived. 

The starting point for both the v4 and v4.2 platforms was the 2005 National Emission 

Inventory (NEI), version 2 (v2) from October 6, 2008 

(http://www.epa.gov/ttn/chief/net/2005inventory.html). The v4.2 platform utilizes the same 

2006 Canadian inventory and a 1999 Mexican inventory as were used in the v4 platform; as 

these were the latest available data from these countries and were used for the portions of 

Canada and Mexico within the modeling domains.

Table 3-1. Emissions Source Sectors for Current and Future-Year Inventories, 2005-

based Platform, Version 4.2

Platform Sector, 

modeling abbrev. 

(and corresponding 

2005 NEI sector)

Description and resolution of the data input to SMOKE, 2005 v4.2 

platform

EGU sector: ptipm 

(point)

2005 NEI v2 point source EGUs mapped to the Integrated Planning Model (IPM) 

model using the National Electric Energy Database System (NEEDS) 2006 version 

4.10 database. Daily emissions input into SMOKE. Annual emissions allocated to 

months using 3 years of continuous emissions monitor (CEM) data, and allocated 

to days using month-to day allocations from the 2005 CEM data. Includes updates 

such as removing duplicates and reflecting comments received on the proposed 

Transport Rule.

Non-EGU sector: 

ptnonipm (point)

All 2005 NEI v2 point source records not matched to the ptipm sector. Includes all 

aircraft emissions. Includes updates to remove duplicates, improve estimates from 

ethanol plants, reflect new information collected from industry from the ICR for 

the Boiler MACT, and to reflect comments received on the proposed Transport 

Rule. Includes point source fugitive dust emissions for which county-specific PM 

transportable fractions were applied. Annual resolution. 

Average-fire sector: 

avefire 

Average-year wildfire and prescribed fire emissions; county and annual resolution.

Agricultural sector: 

ag (nonpoint)

NH3 emissions from 2002 NEI nonpoint livestock and fertilizer application, county 

and annual resolution. 

Area fugitive dust 

sector: afdust 

(nonpoint)

PM10 and PM2.5 from fugitive dust sources (e.g., building construction, road 

construction, paved roads, unpaved roads, agricultural dust) from the NEI nonpoint 

inventory (which used 2002 emissions for this sector) after application of countyspecific PM transportable fractions. Includes county and annual resolution. 

Remaining nonpoint 

sector: nonpt 

(nonpoint)

Primarily 2002 NEI nonpoint sources not otherwise included in other SMOKE 

sectors, county and annual resolution. Includes updated residential wood 

combustion emissions, year 2005 non-California WRAP oil and gas Phase II 

inventory, and year 2005 Texas and Oklahoma oil and gas emissions. Includes 

updates based on comments received on the proposed Transport Rule.

Nonroad sector: Monthly nonroad emissions from the National Mobile Inventory Model (NMIM) 
38

Platform Sector, 

modeling abbrev. 

(and corresponding 

2005 NEI sector)

Description and resolution of the data input to SMOKE, 2005 v4.2 

platform

nonroad (nonroad) using NONROAD2005 version nr05c-BondBase, which is equivalent to 

NONROAD2008a, since it incorporated Bond rule revisions to some of the base 

case inputs and the Bond rule controls did not take effect until later.

NMIM was used for all states except California. Monthly emissions for California 

created from annual emissions submitted by the California Air Resources Board 

(CARB) for the 2005v2 NEI.

Locomotive, and nonC3 commercial 

marine vessel (CMV): 

alm_no_c3 (nonroad)

2002 NEI non-rail maintenance locomotives, and category 1 and category 2 

commercial marine vessel (CMV) emissions sources, county and annual 

resolution. Aircraft emissions are included in the Non-EGU sector (as point 

sources) and category 3 CMV emissions are contained in the seca_c3 sector. 

C3 commercial 

marine: seca_c3 

(nonroad)

Annual point source-formatted, year 2005 category 3 (C3) CMV emissions, 

developed for the rule called “Control of Emissions from New Marine 

Compression-Ignition Engines at or Above 30 Liters per Cylinder”, usually 

described as the Emissions Control Area (ECA) study 

(http://www.epa.gov/otaq/oceanvessels.htm). Uses final projections from 2002, 

developed for the C3 ECA proposal to the International Maritime Organization 

(EPA-420-F-10-041, August 2010) and a state/federal water boundary of 3-10 

nautical miles. Includes other updates reflecting comments received on the 

proposed Transport Rule.

Onroad California, 

NMIM-based, and 

Motor Vehicle 

Emissions Simulator 

(MOVES) sources not 

subject to 

temperature 

adjustments: 

on_noadj (onroad)

Three, monthly, county-level components:

1) California onroad, created using annual emissions submitted by CARB for the 

2005 NEI version 2. NH3 (not submitted by CARB) from MOVES2010.

2) Onroad gasoline and diesel vehicle emissions from MOVES2010 not subject to 

temperature adjustments: exhaust carbon monoxide (CO), nitrogen oxides 

(NOX), sulfur dioxide (SO2), VOC, ammonia (NH3), benzene, formaldehyde, 

acetaldehyde, 1,3-butadiene, acrolein, naphthalene, brake and tire wear PM, and 

evaporative VOC, benzene, and naphthalene. 

Onroad cold-start 

gasoline exhaust mode 

vehicle from MOVES 

subject to 

temperature 

adjustments: 

on_moves_startpm 

(onroad)

2005 monthly, county-level MOVES2010 onroad gasoline vehicle emissions 

subject to temperature adjustments. Pollutants that are included are limited to PM 

species and Naphthalene for exhaust mode only. California emissions are not 

included (covered by on_noadj). This sector is limited to cold start mode 

emissions that contain different temperature adjustment curves from running 

exhaust (see on_moves_runpm sector).

Onroad running 

gasoline exhaust mode 

vehicle from MOVES 

subject to 

temperature 

adjustments: 

on_moves_runpm 

(onroad)

2005 monthly, county-level MOVES2010 onroad gasoline vehicle emissions 

subject to temperature adjustments. Pollutants that are included are limited to PM 

species and Naphthalene for exhaust mode only. California emissions are not 

included. This sector is limited to running mode emissions that contain different 

temperature adjustment curves from cold start exhaust (see on_moves_startpm 

sector).
39

Platform Sector, 

modeling abbrev. 

(and corresponding 

2005 NEI sector)

Description and resolution of the data input to SMOKE, 2005 v4.2 

platform

Biogenic: biog Hour-specific, grid cell-specific emissions generated from the BEIS3.14 model -

includes emissions in Canada and Mexico.

Other point sources 

not from the NEI: 

othpt

Point sources from Canada’s 2006 inventory and Mexico’s Phase III 1999 

inventory, annual resolution. Also includes annual U.S. offshore oil 2005 NEI v2 

point source emissions.

Other nonpoint and 

nonroad not from the 

NEI: othar

Year 2006 Canada (province resolution) and year 1999 Mexico Phase III 

(municipio resolution) nonpoint and nonroad mobile inventories, annual 

resolution.

Other onroad sources 

not from the NEI: 

othon

Year 2006 Canada (province resolution) and year 1999 Mexico Phase III 

(municipio resolution) onroad mobile inventories, annual resolution.

The onroad emissions were primarily based on the 12/21/2009 version of the Motor 

Vehicle Emissions Simulator (MOVES2010) (http://www.epa.gov/otaq/models/moves/) with 

database corrections for diesel toxics. MOVES was run with a State/month aggregation 

using average fuels for each state, state/month-average temperatures, and national default 

vehicle age distributions. The MOVES data were allocated to counties using state-county 

distributions from the 2005 National Mobile Inventory model (NMIM) results that are part of 

the 2005 NEI v2. MOVES2010 was used for onroad sources other than in California8for 

carbon monoxide (CO), nitrogen oxides (NOX), VOC, PM2.5, particulate matter less than ten 

microns (PM10), sulfur dioxide (SO2), ammonia (NH3), naphthalene,9and some VOC 

HAPs.10 To account for the temperature dependence of PM2.5, MOVES-based temperature 

adjustment factors were applied to gridded, hourly emissions using the same 2005 gridded, 

hourly 2 meter temperature data used in CAMx. Additional information on this approach is 

available in the Emissions Inventory TSD.

 

8 California onroad emissions were taken from the California Air Resources Board 

submission of 2005 data to the NEI. The inventory included all criteria air pollutants other 

than ammonia and hazardous air pollutants.

9 Naphthalene emissions were not used in the modeling.

10 1,3 Butadiene, Acrolein, Formaldehyde, Benzene and Acetaldehyde. Of these, the latter 3 

are used in the modeling.
40

The nonroad emissions utilized the NMIM model (other than California) to create 

county/month emissions, which are consistent with the annual emissions from the 2005 NEI 

v2. For California, state-submitted emissions were used.

Emissions from the point source NEI were primarily from the 2005 NEI v2 inventory, 

consisting primarily of 2005 values with some 2002 emissions values where 2005 data were 

not available. The point sources are split into “EGU” (aka “ptipm”) and “Non-EGU” (aka 

“ptnonipm”) sectors for modeling purposes, based on the matching of the unit level data in 

the NEI units in the National Electric Energy Database System (NEEDS) version 4.10 

database. All units that matched NEEDS were included in the EGU sector so that the future 

year emissions could easily be taken from the Integrated Planning Model (IPM) emissions 

that are based on the NEEDS units. Effort were made to ensure that there were not duplicate 

emissions in the 2005 data (e.g., from 2002 and 2005), to properly account for plants or units 

that shutdown prior to 2005, add estimates for ethanol plants, and to revise some of the 2002

data to reflect 2005 emissions based on controls put in place between 2002 and 2005.

The 2005 annual NOX and SO2 emissions for sources in the EGU sector as defined in 

Table 3-1 are based primarily on data from EPA’s Clean Air Markets Division’s Continuous

Emissions Monitoring (CEM) program, with other pollutants estimated using emission 

factors and the CEM annual heat input. For EGUs without CEMs, emissions were obtained 

from the state-submitted data in the NEI. Additional ORIS11 plant and unit code matches 

were implemented in version 4.2 of the platform, and for a subset of these units, annual 

emissions were recomputed12 to reflect the newly matched CEM data.

For the 2005 base year, the annual EGU NEI emissions were allocated to hourly

emissions values needed for modeling based on the 2004, 2005, and 2006 CEM data. The 

NOX CEM data were used to create NOX-specific profiles, the SO2 data were used to create 

SO2-specific profiles, and the heat input data were used to allocate all other pollutants. The 

three years of data were used to create monthly profiles by state, while the 2005 data were 

used to create state-averaged profiles for allocating monthly emissions to daily. These daily 

values were input into SMOKE, which utilized state-averaged 2005-based hourly profiles to 

 

11 An Oris code is a 4 digit number assigned by the Energy Information Administration (EIA) at the U.S. 

Department of Energy that is used to track emission generating units under numerous other data systems 

including the Clean Air Markets Divisions CEM data.

12 Net change was a decrease in NOX by 1700 tons and a decrease in SO2 by 600 tons.
41

allocate to hourly values. This approach to temporal allocation was used for all base and 

control cases modeled to provide a temporal consistency between the years modeled without 

tying the temporalization to the events of a single year.

The 2002 NEI v2 nonpoint inventory was augmented with updated oil and gas 

exploration emissions from Texas and Oklahoma (CO, NOX, PM, SO2, VOC). These oil and 

gas exploration emissions were in addition to oil and gas data previously available to the 

2005 v4 platform that includes emissions within the following states: Arizona, Colorado, 

Montana, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, and Wyoming.

The commercial marine category 3 (C3) vessel emissions (seca_c3 sector) used 

gridded 2005 emissions that reflect the final projections from 2002 developed for the 

category 3 commercial marine Emissions Control Area (ECA) proposal to the International 

Maritime Organization (EPA-420-F-10-041, August 2010). These emissions include Canada 

as part of the ECA, and were updated using region-specific growth rates; thus the v4.2 

seca_c3 sector inventories contain Canadian province codes. The state/federal water 

boundaries were based on a file available from the Mineral Management Service (MMS) that 

specify boundaries from three to ten nautical miles from the coast.

Other emissions inventories included average-year county-based inventories for 

emissions from wildfires and prescribed burning. These emissions are intended to be 

representative for both base and future years and are held constant for each, which minimizes 

their impact on the modeling results because of post-processing techniques. 

Once developed, the emissions inventories were processed to provide the hourly, 

gridded emissions for the model-species needed by CAMx. Details on this processing are 

further described in the Emissions Inventory TSD. Table 3-2 provides summaries of the 

2005 emissions inventories by sector for the final Transport Rule. Tables 3-3 through 3-4 

provide state-level summaries for SO2, and NOX. In these tables, “Nonpoint” represents the 

nonpt sector; “Area Fugitive Dust” (which contains only PM10 and PM2.5) represents the 

afdust sector; on_noadj, on_startpm and on_runpm sectors are summed into “Onroad”; 

nonroad, alm_no_c3 (locomotives and category 1 and 2 marine vessels) and seca_c3 

(category 3 marine vessels) sectors are summed into “Nonroad”; and “Fires” represent the 

average-year fire (avefire) emissions for wildfires and prescribed burning mentioned above.
42

Table 3-2. 2005 Emissions by Sector

 Sector Abbrev.

2005 NOX 

[tons/yr]

2005 SO2 

[tons/yr] 

2005 

PM2_5 

[tons/yr]

2005 

PM10 

[tons/yr]

2005 

NH3 

[tons/yr]

2005 CO 

[tons/yr] 

2005 VOC 

[tons/yr]

Area fugitive dust 0 0 1,030,391 8,858,992 0 0 0

Agriculture

0 0 0 0

3,251,99

0 0 0

Locomotive/marine 1,922,723 153,068 56,666 59,342 773 270,007 67,690

Commercial marine 

Category 3 (US 

only) 130,164 97,485 10,673 11,628 0 11,862 4,570

Nonpoint 1,696,902 1,216,362 1,079,906 1,349,639 133,962 7,410,946 7,530,578

Nonroad 2,115,408 197,341 201,138 211,807 1,972 19,502,718 2,691,844

Onroad running PM 0 0 49,789 54,071 0 0 0

Onroad start PM 0 0 20,929 22,729 0 0 0

Onroad other 9,142,274 177,977 236,927 308,497 156,528 43,356,130 3,949,362

EGU 3,729,161 10,380,883 496,877 602,236 21,995 603,788 41,089

Non-EGU 2,226,250 2,082,159 433,381 646,373 158,524 3,214,833 1,309,895

Average fires 189,428 49,094 684,035 796,229 36,777 8,554,551 1,958,992

Commercial marine 

Category 3 (non-US)

1,801,699 1,085,894 134,604 146,312 0 146,027 62,132

Canada area1

734,587 95,086 432,402 1,666,188 546,034 3,789,362 1,281,095

Canada onroad 524,837 5,309 10,395 14,665 21,312 4,403,745 270,872

Canada point 857,977 1,664,040 68,689 117,669 21,268 1,270,438 447,313

Mexico area 249,045 101,047 92,861 143,816 486,484 644,733 586,842

Mexico onroad 147,419 8,270 6,372 6,955 2,547 1,455,121 183,429

Mexico point 258,510 980,359 88,132 125,385 0 88,957 113,044

Off-shore point 82,581 1,961 837 839 0 89,812 51,240

Table 3-3. 2005 Base Year SO2 Emissions (tons/year) for States by Sector 

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total

Alabama 460,123 66,373 52,325 5,622 3,983 983 589,408

Arizona 52,733 23,966 2,571 6,154 3,919 2,888 92,231

Arkansas 66,384 13,039 27,260 5,678 1,998 728 115,087

California 601 33,136 77,672 41,140 4,935 6,735 164,217

Colorado 64,174 1,549 6,810 4,897 3,064 1,719 82,213

Connecticut 10,356 1,831 18,455 2,556 1,375 4 34,576

Delaware 32,378 34,859 1,030 2,657 519 6 71,449

District of Columbia 1,082 686 1,559 414 218 0 3,961

Florida 417,321 57,429 70,490 31,191 13,280 7,018 596,729

Georgia 616,063 52,830 56,829 9,223 7,163 2,010 744,119
43

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total

Idaho 0 17,151 2,915 2,304 951 3,845 27,166

Illinois 330,382 156,154 5,395 19,303 7,279 20 518,531

Indiana 878,979 87,795 59,775 9,437 4,937 24 1,040,947

Iowa 130,264 64,448 19,832 8,838 2,045 25 225,451

Kansas 136,520 13,234 36,381 8,035 2,241 103 196,515

Kentucky 502,731 25,962 34,229 6,942 3,377 364 573,604

Louisiana 109,875 165,705 2,378 25,447 3,043 892 307,340

Maine 3,887 18,512 9,969 1,625 986 150 35,129

Maryland 283,205 34,988 40,864 9,372 2,706 32 371,166

Massachusetts 84,234 19,620 25,261 6,524 2,819 93 138,551

Michigan 349,877 76,509 42,066 14,597 8,966 91 492,106

Minnesota 101,678 25,158 14,747 10,412 3,111 631 155,736

Mississippi 75,047 29,892 6,796 5,930 2,681 1,051 121,397

Missouri 284,384 78,307 44,573 10,464 5,339 186 423,253

Montana 19,715 11,056 2,600 3,813 912 1,422 39,518

Nebraska 74,955 6,469 7,659 9,199 1,640 105 100,026

Nevada 53,363 2,253 12,477 2,877 702 1,346 73,018

New Hampshire 51,445 3,155 7,408 789 780 38 63,614

New Jersey 57,044 7,639 10,726 13,315 3,112 61 91,898

New Mexico 30,628 8,062 3,193 3,541 1,879 3,450 50,755

New York 180,847 58,426 125,158 15,663 6,500 113 386,707

North Carolina 512,231 59,433 22,020 8,766 6,506 696 609,652

North Dakota 137,371 9,678 6,455 5,986 525 66 160,082

Ohio 1,116,095 115,154 19,810 15,630 7,715 22 1,274,427

Oklahoma 110,081 40,482 8,556 5,015 3,316 469 167,918

Oregon 12,304 9,825 9,845 5,697 1,872 4,896 44,438

Pennsylvania 1,002,203 83,375 68,349 11,999 6,597 32 1,172,555

Rhode Island 176 2,743 3,365 816 265 1 7,366

South Carolina 218,781 31,495 13,489 7,719 3,741 646 275,871

South Dakota 12,215 1,999 10,347 3,412 612 498 29,083

Tennessee 266,148 67,160 32,714 6,288 6,088 277 378,676

Texas 534,949 223,625 115,192 34,943 17,970 1,178 927,857

Tribal 3 1,511 0 0 0 0 1,515

Utah 34,813 9,132 3,577 2,439 1,999 1,934 53,893

Vermont 9 902 5,385 385 346 49 7,078

Virginia 220,287 69,401 32,923 10,094 4,647 399 337,752

Washington 3,409 24,211 7,254 18,810 3,490 407 57,580

West Virginia 469,456 46,710 14,589 2,133 1,289 215 534,392

Wisconsin 180,200 66,807 6,369 7,134 3,735 70 264,315

Wyoming 89,874 22,321 6,721 2,674 807 1,106 123,503

Total 10,380,883 2,082,159 1,216,362 447,895 177,977 49,094 14,354,370
44

Table 3-4. 2005 Base Year NOX Emissions (tons/year) for States by Sector

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total

Alabama 133,051 72,795 32,024 60,373 182,224 3,814 484,282

Arizona 79,776 15,975 8,650 62,711 223,130 10,532 400,774

Arkansas 35,407 35,846 21,453 63,493 106,127 2,654 264,979

California 6,925 90,708 121,882 424,268 665,225 24,563 1,333,571

Colorado 73,909 20,971 43,652 50,856 138,976 6,271 334,635

Connecticut 6,865 5,824 12,554 21,802 82,677 14 129,736

Delaware 11,917 5,567 2,274 10,617 28,088 23 58,486

District of Columbia 492 501 1,740 3,494 10,575 0 16,802

Florida 217,282 53,757 29,533 176,468 553,534 25,600 1,056,174

Georgia 111,281 50,434 38,919 89,708 364,376 7,955 662,673

Idaho 19 10,354 30,317 22,087 45,427 14,024 122,228

Illinois 127,940 97,409 47,645 223,696 368,378 71 865,139

Indiana 213,588 67,479 30,185 110,100 252,229 88 673,669

Iowa 72,806 41,818 15,150 92,965 108,205 90 331,034

Kansas 90,220 70,858 42,286 86,553 97,259 378 387,554

Kentucky 164,783 35,425 17,557 90,669 172,502 1,326 482,262

Louisiana 64,987 162,770 27,559 222,575 145,398 3,254 626,542

Maine 1,100 17,949 7,423 9,928 49,128 566 86,094

Maryland 62,574 24,621 21,715 41,889 161,294 137 312,230

Massachusetts 25,134 18,429 34,373 43,494 161,867 341 283,638

Michigan 120,026 94,118 43,499 101,060 359,421 330 718,454

Minnesota 84,304 63,971 56,700 115,872 183,758 2,300 506,905

Mississippi 45,166 53,985 12,212 79,288 130,111 3,833 324,595

Missouri 127,431 38,604 32,910 123,228 240,506 678 563,356

Montana 39,858 5,356 14,415 40,687 43,926 5,187 149,429

Nebraska 52,426 12,187 14,749 107,180 76,791 381 263,714

Nevada 47,297 17,191 5,379 27,747 49,381 4,910 151,905

New Hampshire 8,827 2,805 11,235 9,220 41,101 137 73,325

New Jersey 30,142 20,570 26,393 71,738 192,310 223 341,376

New Mexico 75,483 44,107 69,175 45,552 96,239 12,582 343,139

New York 63,315 55,122 87,608 112,441 369,210 412 688,109

North Carolina 111,576 44,502 18,869 79,893 287,918 11,424 554,183

North Dakota 76,381 7,672 10,046 59,635 28,313 240 182,289

Ohio 258,944 71,090 41,466 173,981 360,765 81 906,327

Oklahoma 86,204 73,465 67,762 55,424 149,720 1,709 434,284

Oregon 9,383 22,927 17,059 65,058 107,934 17,857 240,218

Pennsylvania 176,891 89,287 53,435 118,796 343,120 117 781,647

Rhode Island 545 2,164 2,964 5,028 17,676 4 28,381

South Carolina 52,657 29,069 17,706 49,026 163,461 2,357 314,276

South Dakota 15,650 5,271 5,766 30,324 32,508 1,817 91,336

Tennessee 102,934 54,255 18,676 82,331 267,818 1,012 527,027

Texas 176,170 292,806 317,192 348,014 867,843 4,890 2,006,916

Tribal 78 13,322 0 0 0 0 13,400
45

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total

Utah 65,261 19,466 13,844 26,985 84,202 7,052 216,810

Vermont 297 799 3,438 3,951 17,032 179 25,696

Virginia 62,793 59,820 53,605 77,661 232,928 1,456 488,263

Washington 17,634 25,427 16,911 106,226 189,992 1,484 357,674

West Virginia 159,947 36,196 14,519 32,739 64,469 785 308,655

Wisconsin 72,170 40,688 21,994 75,979 190,138 256 401,226

Wyoming 89,315 30,516 40,480 35,482 37,065 4,035 236,894

Total 3,729,161 2,226,250 1,696,902 4,168,294 9,142,274 189,428 21,152,309

3.3 Development of Future Year Base Case Emissions

The 2012 and 2014 base case scenarios represent predicted emissions including 

known federal measures for all sectors. They reflect projected economic changes and fuel 

usage for the EGU and mobile sectors. Emissions from non-EGU stationary sectors have 

previously been shown to not be well correlated with economic forecasts, and therefore 

economic impacts were not included for non-EGU stationary sources. Like the 2005 base 

case, these emissions cases include criteria pollutants and for some sectors, benzene, 

formaldehyde, acetaldehyde and methanol from the inventory is used in VOC speciation. 

The 2012 and 2014 base case EGU emissions projections of SO2, and NOX were 

obtained using Version 4.10 Final of the Integrated Planning Model (IPM) 

(http://www.epa.gov/airmarkt/progsregs/epa-ipm/index.html). The IPM is a multiregional, 

dynamic, deterministic linear programming model of the U.S. electric power sector. Version 

4.10 Final reflects state rules and consent decrees through December 1, 2010, information 

obtained from the 2010 Information Collection Request (ICR), and information from 

comments received on the IPM-related Notice of Data Availability (NODA) published on 

September 1, 2010. Notably, IPM 4.1 Final included the addition of over 20 GW of existing 

Activated Carbon Injection (ACI) for coal-fired EGUs reported to EPA via the ICR. 

Additional unit-level updates that identified existing pollution controls (such as scrubbers) 

were also made based on the ICR and on comments from the IPM NODA. Units with SO2 or 

NOX advanced controls (e.g., scrubber, SCR) that were not required to run for compliance 

with Title IV, New Source Review (NSR), state settlements, or state-specific rules were 

modeled by IPM to either operate those controls or not based on economic efficiency

parameters. 

Additionally, IPM v4.1 Final corrected a natural gas emission factor responsible for 

an over-prediction in PM2.5 emissions of 85 thousand tons from the EGU sector. Other 
46

updates includes adjustments to assumptions regarding the performance of acid gas control 

technologies, new costs imposed on fuel-switching (e.g., bituminous to sub-bituminous), 

correction of lignite availability to some plants, incorporation of additional planned 

retirements, a more inclusive implementation of the scrubber upgrade option, and the 

availability of a scrubber retrofit to waste-coal fired fluidized bed combustion units without 

an existing scrubber. Further details on the future year EGU emissions inventory used for 

this rule can be found in the IPM v.4.10 Documentation, available at 

http://www.epa.gov/airmarkt/progsregs/epa-ipm/index.html. Note that the Transport Rule 

future year base cases do not include the Toxics Rule, which was proposed on March 16, 

2011. In addition, the Boiler MACT was not represented in the final Transport Rule 

modeling because the rule was not final at the time the modeling was performed.

 Mobile source inventories of onroad and nonroad mobile emissions were created 

for 2012 and 2014 using a combination of the NMIM and MOVES models in a consistent 

approach with the 2005 base year. As with the 2005 emissions, the 2012 and 2014 onroad 

emissions were based on MOVES2010 emissions provided at the state-month resolution that 

were then allocated to counties using NMIM emissions values. Future-year vehicle miles 

travelled (VMT) were projected from the 2005 NEI v2 VMT using growth rates from the 

2009 Annual Energy Outlook (AEO) data. The same MOVES-based PM2.5 temperature 

adjustment factors were applied as in 2005 for running mode emissions because these are not 

dependent on year; however, cold start emissions used year-specific temperature adjustment 

factors. The 2012 and 2014 onroad emissions reflect control program implementation 

through 2012 and 2014 and include the Light-Duty Vehicle Tier 2 Rule, the Onroad HeavyDuty Rule, and the Mobile Source Air Toxics (MSAT) final rule. Emission reductions and 

increases from the Renewable Fuel Standard version 2 (RFS2) are not reflected because they

do not take effect until later years.

Nonroad mobile emissions were created only with NMIM using a consistent approach 

as was used for 2005, but using NMIM future-year equipment population estimates and 

control programs for 2015 and 2012 using national level inputs. Year 2014 emissions were 

created by interpolating 2012 and 2015 emissions. Emissions for locomotives and category 1 

and 2 (C1 and C2) commercial marine vessels were derived for 2012 and 2014 based on 

emissions published in the Final Locomotive Marine Rule, Regulatory Impact Assessment, 

Chapter 3 (see http://www.epa.gov/otaq/locomotives.htm#2008final).
47

The future baseline nonroad mobile emissions reductions include reductions to 

locomotives, various nonroad engines including diesel engines and various marine engine 

types, fuel sulfur content, and evaporative emissions standards, including the category 3 

marine diesel engines and International Maritime Organization standards which include the 

establishment of emission control areas for these ships. A summary of the mobile source 

control programs included in the projected future year baseline is shown in Table 3-5.

Table 3-5. Summary of Mobile Source Control Programs Included in 2014 Baseline

National Onroad Rules:

Tier 2 Rule (Signature date: February 28, 2000)

Onroad Heavy-Duty Rule (February 24, 2009)

Final Mobile Source Air Toxics Rule (MSAT2) (February 9, 2007)

Renewable Fuel Standard (March 26, 2010)

Local Onroad Programs:

National Low Emission Vehicle Program (NLEV) (March 2, 1998)

Ozone Transport Commission (OTC) LEV Program (January, 1995)

National Nonroad Controls:

Tier 1 nonroad diesel rule (June 17, 2004)

Phase 1 nonroad SI rule (July 3, 1995)

Marine SI rule (October 4, 1996)

Nonroad diesel rule (October 23, 1998)

Phase 2 nonroad nonhandheld SI rule (March 30, 1999)

Phase 2 nonroad handheld SI rule (April 25, 2000)

Nonroad large SI and recreational engine rule (November 8, 2002)

Clean Air Nonroad Diesel Rule - Tier 4 (June 29, 2004)

Locomotive and marine rule (May 6, 2008)

Nonroad SI rule (October 8, 2008)

Aircraft:

Itinerant (ITN) operations at airports adjusted to years 2012 and 2014

Locomotives:

Clean Air Nonroad Diesel Final Rule – Tier 4 (June 29, 2004)

Locomotive rule (April 16, 2008)

Locomotive and marine rule (May 6, 2008)

Commercial Marine:

Locomotive and marine rule (May 6, 2008)

Category 3 marine diesel engines Clean Air Act and International Maritime 

Organization standards (April, 30, 2010)
48

For non-EGU point sources, emissions were projected by including emissions 

reductions and increases from a variety of source data.13 For non-EGU point sources, other 

than for certain large municipal waste combustors and airports, emissions were not grown 

using economic growth projections, but rather were held constant at the emissions levels in 

2005. Emissions reductions were applied to non-EGU point source to reflect final federal 

measures, known plant closures, refinery and other consent decrees. The starting point was 

the emission projections done for the 2005v4 platform for the proposed Transport Rule. The 

2012 and 2014 projection factors developed for the Transport Rule proposal (see 

http://www.epa.gov/ttn/chief/emch/index.html#transport) were updated for these 2012 and 

2014 baseline projections. Several additional NESHAP were promulgated since emission 

projections were done for the proposed Transport Rule, and these were included for the 2012 

and 2014 base cases. Emission reductions were also applied to include local controls for 

NOX and VOC from the New York State Implementation Plan (SIP) as part of another effort; 

we do not anticipate that this change significantly impacts the results of this RIA, which are 

primarily resulting from changes to SO2 and PM2.5.

Since aircraft at airports were treated as point emissions sources in the 2005 NEI v2, 

we applied projection factors based on activity growth projected by the Federal Aviation 

Administration Terminal Area Forecast (TAF) system, published December 2008 for these 

sources.

Emissions from stationary nonpoint sources were projected using procedures specific 

to individual source categories. Refueling emissions were projected using the refueling 

results from the NMIM runs performed for the onroad mobile sector. Portable fuel container 

emissions were projected using estimates from previous rulemaking inventories compiled by

the Office of Transportation and Air Quality (OTAQ). Emissions of ammonia and dust from 

animal operations were projected based on animal population data from the Department of 

Agriculture and EPA. Residential wood combustion was projected by replacement of 

obsolete woodstoves with new woodstoves and a 1 percent annual increase in fireplaces. 

Landfill emissions were projected using MACT controls. In addition, many of the NY SIP 

controls applied to nonpoint categories and were included in the projection. All other 

nonpoint sources were held constant between 2005 and the 2014 future year scenarios.

 

13 Controls from the NOX SIP call were assumed to have been in place by 2005 and captured in the 2005 NEI 

v2.
49

A summary of all rules and growth assumptions impacting non-EGU stationary

sources is provided in Table 3-6. The table is broken out into two sections: (1) the 

approaches used to project emissions for the proposed Transport Rule that were carried 

forward for the final Transport Rule and (2) the added controls/reductions used for the final 

Transport rule that had not been used for the proposed Transport rule.
50

Table 3-6. Control Strategies and/or Growth Assumptions Included in the 2012 and 

2014 Projection for Non EGU Stationary Sources

Control Strategies and/or Growth Assumptions Applied to 2005 emissions for the 2014 

projection 

Projection Approaches Carried Forward from the Proposed Transport Rule

MACT rules, national, VOC: national applied by SCC, MACT VOC

Consent Decrees and Settlements, including refinery consent decrees, and settlements 

for: Alcoa, TX and Premcor (formerly MOTIVA), DE 

All

Municipal Waste Combustor Reductions –plant level PM 

Hazardous Waste Combustion PM

Hospital/Medical/Infectious Waste Incinerator Regulations 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 years 2012 and 2014 NH3, PM

Residential Wood Combustion Growth and Change-outs from years 2005 to years 2012 

and 2014

All

Gasoline Stage II growth and control from year 2005 to year 2014 VOC

Portable Fuel Container Mobile Source Air Toxics Rule 2: inventory growth and control 

from year 2005 to years 2012 and 2014

VOC

Additional Projection Approaches For the Final Transport Rule 

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 and include closures. For year 2012, 

only known closures and new units through year 2009 were included; ISIS-based future year 

projections were not included.

Hg, NOX, 

SO2, PM, 

HCL 

NESHAP: Industrial, Commercial, Institutional (ICI) Boilers, aka “Boiler MACT” (signed 

02/21/2011) 

Hg, SO2, 

HCL, PM

New York ozone SIP standards VOC, HAP 

VOC, NOX

Additional Plant and Unit closures provided by state, regional, and EPA agencies All

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: Reciprocating Internal Combustion Engines (RICE). SO2 controls for RICE were 

not effective until after 2012, but are applied in 2014.

NOX, CO, 

PM, SO2

Use Phase II WRAP 2018 Oil and Gas, and apply RICE controls to these emissions VOC, SO2, 

NOX, CO

Use 2008 Oklahoma and Texas Oil and Gas, and apply RICE controls to these emissions VOC, SO2, 

NOX, CO, 

PM

State fuel sulfur content rules for fuel oil –effective in 2014 only in Maine and New York SO2
51

In all future year cases, we used the 2005 base year emissions for Canada and Mexico 

because appropriate future-year emissions for sources in these countries were not available. 

The future-year emissions need to reflect expected percent reductions or increases between 

the base year and the future year to be considered appropriate for this type of modeling.

Table 3-7 shows a summary of the 2005 and 2014 modeled base case emissions for 

the sum of the lower 48 states. Tables 3-8 and 3-9 below provide summaries of SO2 and 

NOX in the 2014 base case for each sector by state. The 2012 emissions are not presented 

here because they were not considered as part of the RIA. For details on the 2012 emissions, 

see the Emissions Inventory TSD. 

Table 3-7. Summary of Modeled Base Case Annual Emissions (tons/year) for 48 States 

by Sector: SO2 and NOX

Source Sector SO2 Emissions 2005 2014

EGU Point 10,380,883 7,159,569

Non-EGU Point 2,082,159 1,643,061

Nonpoint 1,216,362 1,126,861

Nonroad 447,895 74,180

On-road 177,977 26,022

Average Fire 49,094 49,094

Total SO2, All Sources 14,354,370 10,078,786

Source Sector NOX Emissions 2005 2014

EGU Point 3,729,161 2,089,422

Non-EGU Point 2,226,250 2,021,334

Nonpoint 1,696,902 1,628,712

Nonroad 4,168,294 3,049,397

On-road 9,142,274 4,946,217

Average Fire 189,428 189,428

 Total NOX, All Sources 21,152,309 13,924,510
52

Table 3-8. 2014 Base Case SO2 Emissions (tons/year) for States by Sector

State EGU* NonEGU Nonpoint Nonroad Onroad Fires Total

Alabama 417,340 63,152 52,200 523 502 983 534,700

Arizona 35,601 24,180 2,478 51 593 2,888 65,792

Arkansas 99,411 12,163 26,723 296 279 728 139,599

California 7,350 21,979 67,943 13,111 2,150 6,735 119,268

Colorado 62,105 1,403 6,446 47 507 1,719 72,227

Connecticut 3,774 1,831 18,200 538 271 4 24,618

Delaware 2,172 4,917 1,024 1,116 77 6 9,311

District of Columbia 0 686 1,505 3 35 0 2,230

Florida 143,601 49,057 69,968 8,754 1,835 7,018 280,233

Georgia 170,288 44,228 55,848 886 1,071 2,010 274,332

Idaho 182 17,132 2,906 21 162 3,845 24,248

Illinois 141,606 111,567 5,231 600 1,008 20 260,031

Indiana 727,786 75,715 59,453 288 656 24 863,923

Iowa 133,083 45,804 19,437 115 283 25 198,747

Kansas 69,819 10,749 36,072 56 251 103 117,050

Kentucky 488,005 23,838 33,873 580 426 364 547,085

Louisiana 118,230 131,620 2,372 8,073 391 892 261,579

Maine 2,355 14,345 3,234 429 128 150 20,642

Maryland 42,926 33,557 40,688 2,386 500 32 120,089

Massachusetts 13,364 17,350 24,990 1,631 485 93 57,914

Michigan 269,434 48,697 42,028 2,887 899 91 364,035

Minnesota 70,937 25,048 14,433 563 488 631 112,099

Mississippi 30,972 24,405 6,593 812 323 1,051 64,156

Missouri 390,287 75,587 44,543 425 636 186 511,664

Montana 15,447 7,505 2,179 24 102 1,422 26,678

Nebraska 73,073 4,776 7,615 55 176 105 85,799

Nevada 14,416 2,120 12,027 25 177 1,346 30,112

New Hampshire 6,453 2,470 7,282 32 117 38 16,391

New Jersey 38,856 6,740 10,574 4,576 648 61 61,455

New Mexico 11,857 8,042 2,905 23 229 3,450 26,507

New York 42,887 45,206 71,141 2,677 1,277 113 163,302

North Carolina 126,048 58,499 21,675 948 786 696 208,652

North Dakota 103,633 9,678 5,912 34 61 66 119,385

Ohio 851,199 93,846 19,591 1,334 946 22 966,938

Oklahoma 137,981 29,033 7,503 50 423 469 175,459

Oregon 11,336 9,792 9,710 1,081 359 4,896 37,175

Pennsylvania 509,649 64,926 67,696 2,013 961 32 645,278

Rhode Island 0 2,743 3,340 230 70 1 6,385

South Carolina 213,281 28,534 13,275 2,044 451 646 258,231

South Dakota 29,711 1,947 10,201 22 75 498 42,453

Tennessee 284,468 60,476 32,634 349 674 277 378,878

Texas 453,332 133,219 108,505 6,061 2,016 1,178 704,311

Tribal 0 677 0 0 0 0 677

Utah 33,498 6,757 3,449 25 285 1,934 45,947

Vermont 263 901 5,307 7 86 49 6,614

Virginia 77,256 50,378 32,434 1,407 737 399 162,611

Washington 3,430 20,157 6,976 6,463 630 407 38,062

West Virginia 498,507 33,356 14,294 169 159 215 546,702
53

State EGU* NonEGU Nonpoint Nonroad Onroad Fires Total

Wisconsin 130,538 61,097 6,226 327 537 70 198,795

Wyoming 51,817 21,174 6,221 17 85 1,106 80,419

Total 7,159,569 1,643,061 1,126,861 74,180 26,022 49,094 10,078,786

* Emission estimates apply to all fossil Electric Generating Units, including those with capacity < 25MW

Table 3-9. 2014 Base Case NOX Emissions (tons/year) for States by Sector

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total

Alabama 76,012 72,271 31,836 44,834 93,207 3,814 321,975

Arizona 35,616 13,283 8,476 43,525 137,141 10,532 248,574

Arkansas 36,347 32,253 20,464 45,075 56,878 2,654 193,670

California 26,874 84,169 116,422 303,640 386,587 24,563 942,254

Colorado 49,381 19,256 41,872 35,819 84,697 6,271 237,296

Connecticut 2,854 5,823 12,264 14,257 45,574 14 80,787

Delaware 1,701 3,675 2,194 10,177 13,959 23 31,729

District of Columbia 0 501 1,687 2,402 5,183 0 9,773

Florida 100,581 50,044 28,826 143,368 289,809 25,600 638,227

Georgia 49,411 48,274 37,840 63,607 196,604 7,955 403,691

Idaho 608 9,687 30,152 15,964 31,275 14,024 101,710

Illinois 55,269 79,963 46,835 153,821 210,508 71 546,467

Indiana 117,832 63,444 29,359 76,941 143,678 88 431,342

Iowa 48,400 35,331 14,679 66,475 58,415 90 223,390

Kansas 32,637 62,469 41,859 62,453 48,897 378 248,692

Kentucky 83,544 33,996 17,084 67,238 91,074 1,326 294,262

Louisiana 31,573 150,460 26,054 179,194 75,553 3,254 466,089

Maine 5,402 15,199 7,236 8,117 25,137 566 61,657

Maryland 17,566 23,288 21,396 32,179 87,342 137 181,909

Massachusetts 6,992 17,789 33,333 31,258 85,562 341 175,275

Michigan 67,705 78,719 42,524 73,231 186,832 330 449,343

Minnesota 41,474 63,653 56,230 85,182 96,643 2,300 345,483

Mississippi 26,294 49,191 11,818 59,663 65,639 3,833 216,438

Missouri 57,318 43,918 32,195 89,795 133,942 678 357,846

Montana 19,399 5,253 13,619 28,847 20,418 5,187 92,723

Nebraska 45,047 10,719 14,117 76,764 39,379 381 186,408

Nevada 14,074 15,149 5,295 19,478 22,135 4,910 81,041

New Hampshire 5,126 1,939 11,072 6,587 22,776 137 47,637

New Jersey 8,006 18,996 25,749 54,659 102,495 223 210,127

New Mexico 64,745 40,746 63,420 32,645 50,276 12,582 264,414

New York 21,689 52,969 86,472 82,154 215,392 412 459,087

North Carolina 49,322 44,759 18,341 53,665 144,033 11,424 321,544

North Dakota 53,265 7,328 9,237 43,482 13,573 240 127,125

Ohio 104,149 62,913 40,188 122,508 192,610 81 522,450

Oklahoma 66,966 68,904 70,100 40,079 80,924 1,709 328,683

Oregon 9,584 22,516 16,786 49,681 62,899 17,857 179,324

Pennsylvania 134,092 76,430 52,492 84,800 181,742 117 529,673

Rhode Island 442 2,179 2,923 3,408 9,853 4 18,808
54

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total

South Carolina 39,018 26,467 17,433 35,050 84,063 2,357 204,389

South Dakota 14,270 4,862 5,623 22,105 16,820 1,817 65,498

Tennessee 29,276 49,126 18,184 60,111 144,394 1,012 302,103

Texas 142,087 249,562 286,315 256,684 433,197 4,890 1,372,735

Tribal 11 13,126 0 0 0 0 13,137

Utah 67,434 18,314 13,321 18,845 45,873 7,052 170,840

Vermont 455 802 3,383 2,771 15,234 179 22,824

Virginia 40,469 50,895 52,600 56,304 132,997 1,456 334,720

Washington 13,322 22,726 16,303 86,529 108,957 1,484 249,322

West Virginia 64,824 30,195 13,847 24,369 32,074 785 166,094

Wisconsin 40,750 39,141 21,569 54,245 106,241 256 262,201

Wyoming 70,207 28,659 37,687 25,412 17,725 4,035 183,726

Total 2,089,422 2,021,334 1,628,712 3,049,397 4,946,217 189,428 13,924,510

3.4 Development of Future Year Control Case Emissions for Air Quality Modeling

For the future year control case (policy case) air quality modeling, the emissions for 

all sectors were unchanged from the base case modeling except for those from EGUs. The 

IPM model was used to prepare the 2014 policy case (i.e., the final Transport Rule) for EGU 

emissions as described in the IPM v.4.10 Documentation, available at 

http://www.epa.gov/airmarkt/progsregs/epa-ipm/index.html. As with the base case 

projections, photochemical modeling of the policy case is based on IPM v.4.10 Final. The 

changes in EGU SO2, and NOx emissions as a result of the policy case for the lower 48 states 

are summarized in Table 3-10. State-specific summaries of EGU SO2 and NOX for the sum 

of the lower 48 states are shown in Tables 3-11 and 3-12, respectively.
55

Table 3-10. Summary of Emissions Changes for the Transport Rule in the Lower 48 

States

2014 EGU Emissions SO2 NOX

Base Case EGU Emissions (tons) 7,159,569 2,089,422

Control EGU Emissions (tons) 3,356,577 1,890,590

Reductions to Base Case in 

Control Case (tons)

3,802,991 198,832

Percentage Reduction of Base 

EGU Emissions

53% 10%

Total 2014 Man-made Emissions*

Total Base Case Emissions (tons) 10,078,786 13,924,510

Total Control Case Emissions 

(tons)

6,275,795 13,725,678

Percentage Reduction of All 

Man-made Emissions

37.7% 1.4%

* In this table, man-made emissions includes average fires.
56

Table 3-11. State Specific Changes in Annual EGU SO2 for the Lower 48 States

State 

2014 Base Case 

SO2 (tons)

2014 Policy Case 

SO2 (tons)

EGU SO2 

reduction 

(tons)

EGU SO2 

reduction 

(%)

Alabama 417,340 173,566 243,775 58%

Arizona 35,601 35,601 0 0%

Arkansas 99,411 106,685 -7,274 -7%

California 7,350 7,350 0 0%

Colorado 62,105 73,858 -11,753 -19%

Connecticut 3,774 3,883 -109 -3%

Delaware 2,172 2,172 0 0%

District of Columbia 0 0 0 -

Florida 143,601 148,069 -4,468 -3%

Georgia 170,288 93,208 77,080 45%

Idaho 182 182 0 0%

Illinois 141,606 132,647 8,959 6%

Indiana 727,786 195,046 532,740 73%

Iowa 133,083 83,827 49,256 37%

Kansas 69,819 45,740 24,078 34%

Kentucky 488,005 116,927 371,078 76%

Louisiana 118,230 139,204 -20,973 -18%

Maine 2,355 2,355 0 0%

Maryland 42,926 30,368 12,558 29%

Massachusetts 13,364 13,363 1 0%

Michigan 269,434 162,632 106,802 40%

Minnesota 70,937 49,622 21,315 30%

Mississippi 30,972 32,109 -1,137 -4%

Missouri 390,287 186,899 203,388 52%

Montana 15,447 22,826 -7,379 -48%

Nebraska 73,073 71,339 1,734 2%

Nevada 14,416 14,416 0 0%

New Hampshire 6,453 6,742 -289 -4%

New Jersey 38,856 6,243 32,614 84%

New Mexico 11,857 13,926 -2,068 -17%

New York 42,887 15,160 27,727 65%
57

State 

2014 Base Case 

SO2 (tons)

2014 Policy Case 

SO2 (tons)

EGU SO2 

reduction 

(tons)

EGU SO2 

reduction 

(%)

North Carolina 126,048 69,377 56,670 45%

North Dakota 103,633 103,624 9 0%

Ohio 851,199 178,975 672,224 79%

Oklahoma 137,981 138,072 -91 0%

Oregon 11,336 11,336 0 0%

Pennsylvania 509,649 125,545 384,105 75%

Rhode Island 0 0 0 -

South Carolina 213,281 100,788 112,494 53%

South Dakota 29,711 29,711 0 0%

Tennessee 284,468 64,721 219,747 77%

Texas 453,332 266,648 186,685 41%

Tribal 0 0 0 -

Utah 33,498 33,968 -469 -1%

Vermont 263 263 0 0%

Virginia 77,256 51,144 26,112 34%

Washington 3,430 3,430 0 0%

West Virginia 498,507 84,344 414,163 83%

Wisconsin 130,538 50,137 80,401 62%

Wyoming 51,817 58,530 -6,714 -13%

Total 7,159,569 3,356,577 3,802,991 53%

Table 3-12. State Specific Changes in Annual EGU NOX for the Lower 48 States

State 

2014 Base Case 

NOX (tons)

2014 Policy Case 

NOX (tons)

EGU NOX

reduction 

(tons)

EGU NOX

reduction 

(%)

Alabama 76,012 69,192 6,820 9%

Arizona 35,616 35,613 4 0%

Arkansas 36,347 37,640 -1,293 -4%

California 26,874 26,776 97 0%

Colorado 49,381 49,331 50 0%

Connecticut 2,854 2,860 -6 0%

Delaware 1,701 1,717 -15 -1%

District of Columbia 0 0 0 -

Florida 100,581 78,508 22,073 22%

Georgia 49,411 41,484 7,927 16%

Idaho 608 608 0 0%

Illinois 55,269 49,162 6,107 11%

Indiana 117,832 110,740 7,092 6%

Iowa 48,400 42,231 6,169 13%
58

State 

2014 Base Case 

NOX (tons)

2014 Policy Case 

NOX (tons)

EGU NOX

reduction 

(tons)

EGU NOX

reduction 

(%)

Kansas 32,637 24,328 8,308 25%

Kentucky 83,544 76,088 7,456 9%

Louisiana 31,573 31,582 -9 0%

Maine 5,402 5,402 0 0%

Maryland 17,566 17,190 375 2%

Massachusetts 6,992 7,033 -41 -1%

Michigan 67,705 60,907 6,798 10%

Minnesota 41,474 34,429 7,045 17%

Mississippi 26,294 26,080 214 1%

Missouri 57,318 52,103 5,216 9%

Montana 19,399 19,303 96 0%

Nebraska 45,047 28,211 16,836 37%

Nevada 14,074 14,050 24 0%

New Hampshire 5,126 4,971 156 3%

New Jersey 8,006 7,720 286 4%

New Mexico 64,745 64,833 -88 0%

New York 21,689 20,528 1,160 5%

North Carolina 49,322 45,008 4,314 9%

North Dakota 53,265 53,267 -2 0%

Ohio 104,149 89,753 14,396 14%

Oklahoma 66,966 44,143 22,823 34%

Oregon 9,584 9,632 -48 0%

Pennsylvania 134,092 118,981 15,110 11%

Rhode Island 442 442 0 0%

South Carolina 39,018 36,747 2,271 6%

South Dakota 14,270 14,273 -3 0%

Tennessee 29,276 20,512 8,764 30%

Texas 142,087 137,964 4,123 3%

Tribal 11 11 0 0%

Utah 67,434 67,434 0 0%

Vermont 455 455 0 0%

Virginia 40,469 39,734 735 2%

Washington 13,322 13,322 0 0%

West Virginia 64,824 53,975 10,849 17%

Wisconsin 40,750 33,537 7,212 18%

Wyoming 70,207 70,778 -571 -1%

Total 2,089,422 1,890,590 198,832 10%
59
60

CHAPTER 4

 AIR QUALITY MODELING AND IMPACTS

4.1 Air Quality Impacts

This section summarizes the methods for and results of estimating air quality for the 

2014 base case and control scenario for the purposes of the benefit analysis. EPA has 

focused on the health, welfare, and ecological effects that have been linked to air quality

changes. These air quality changes include the following:

1. Ambient fine particulate matter (PM2.5) and ground-level ozone (O3)–as 

estimated using a national-scale applications of the Comprehensive Air 

Quality Model with Extensions (CAMx; Environ, 2010); 

2. Visibility degradation (i.e., regional haze), as developed using empirical 

estimates of light extinction coefficients and efficiencies in combination with 

CAMx modeled reductions in pollutant concentrations.

The air quality estimates in this section are based on the emission changes 

summarized in the preceding section. These air quality results are in turn associated with 

human populations and ecosystems to estimate changes in health and welfare effects. In 

Section 4.1.1, we describe the air quality modeling platform and in Section 4.2, we cover the 

impacts on PM2.5 and ozone. Lastly, in Section 4.3, we discuss the estimation of visibility

degradation.

4.1.1 Air Quality Modeling Platform

We use the emissions inputs summarized above with national scale and regional scale 

application of the CAMx modeling system to estimate PM2.5 and ozone air quality in the 

contiguous U.S. CAMx is a three-dimensional grid-based Eulerian photochemical model 
61

designed to estimate PM2.5 and ozone concentrations over annual time periods. 

Consideration of the different processes that affect primary (directly emitted) and secondary

(formed by atmospheric processes) PM2.5 in different locations is fundamental to 

understanding and assessing the effects of pollution control measures that affect PM2.5 and 

ozone concentrations at the surface.14 Because it accounts for spatial and temporal variations 

as well as differences in the reactivity of emissions, CAMx is useful for evaluating the 

impacts of the rule on PM2.5 and ozone concentrations. Version 5.3 of CAMx was employed 

for this Transport Rule modeling, as described in the Air Quality Modeling Technical 

Support Document (EPA, 2011). 

For this analysis we used CAMx to simulate air quality for every hour of every day of 

the year. These model applications required a variety of input files that contain information 

pertaining to the modeling domain and simulation period. In addition to the CAMx model, 

our modeling system includes (1) emissions for a 2005 base year and emissions for the 2014 

base case and control scenario, (2) meteorology for the year 2005, and (3) estimates of 

intercontinental transport (i.e., boundary concentrations) from a global photochemical model. 

Using these data, CAMx generates hourly predictions of ozone and PM2.5 component species 

concentrations. As discussed in the Air Quality Modeling TSD, we use the relative 

predictions from the model by combining the 2005 base-year and each future-year scenario 

with speciated ambient air quality observations to determine the expected change in 2014 

concentrations due to the rule. After completing this process, we then calculated annual 

mean PM2.5 and seasonal mean ozone air quality metrics as inputs to the health and welfare 

C-R functions of the benefits analysis. 

4.1.1.1 Simulation Periods

For use in this benefits analysis, the simulation period modeled by CAMx included 

separate full-year application for each of the three emissions scenarios (i.e., 2005 base year 

and the 2014 base case and 2014 control scenario).

4.1.1.2 Air Quality Modeling Domain

 

14Given the focus of this rule on secondarily formed particles it is important to employ a Eulerian model such as 

CAMx. The impact of secondarily formed pollutants typically involves primary precursor emissions from a 

multitude of widely dispersed sources, and chemical and physical processes of pollutants are best addressed 

using an air quality model that employs an Eulerian grid model design.
62

Although air quality estimate are provided for the entire U.S., the focus of our 

analysis is on the Eastern U.S. since this is the geographic area of importance for this rule. 

The areas modeled (i.e., modeling domains) are segmented into rectangular blocks referred to 

as grid cells. The model actually predicts pollutant concentrations for each of these grid 

cells. Our modeling for the East (referred to as the Eastern regional scale domain) was 

performed at a horizontal resolution of 12 x 12 km. Modeling for the remainder of the U.S. 

(referred to as the national scale domain) was performed at a resolution of 36 x 36 km. The 

national and regional scale modeling domains contain 14 vertical layers with the top of the 

modeling domain at about 16,200 meters, or approximately 100 mb. The Eastern domain is 

nested within the National domain, as shown in Figure 4-1. 
63

Figure 4-1. National and Eastern U.S. air quality modeling domains.

Nationwide 36 km 

domain

Eastern 12 km 

domain
64

4.1.1.3 Air Quality Model Inputs

CAMx requires a variety of input files that contain information pertaining to the 

modeling domain and simulation period. These include gridded, hourly emissions estimates 

and meteorological data, and initial and boundary conditions. Separate emissions inventories 

were prepared for the 2005 base year and each future-year scenario. All other inputs were 

specified for the 2005 base year model application and remained unchanged for each futureyear modeling scenario.

CAMx requires detailed emissions inventories containing temporally allocated 

emissions for each grid-cell in the modeling domain for each species being simulated. The 

previously described annual emission inventories were preprocessed into model-ready inputs 

through the SMOKE emissions preprocessing system. Meteorological inputs reflecting 2005 

conditions across the contiguous U.S. were derived from Version 5 of the Mesoscale Model 

(MM5). These inputs included horizontal wind components (i.e., speed and direction), 

temperature, moisture, vertical diffusion rates, and rainfall rates for each grid cell in each 

vertical layer. Details of the annual 2005 MM5 modeling are provided in the Air Quality 

Modeling TSD.

The lateral boundary and initial species concentrations are provided by a threedimensional global atmospheric chemistry and transport model (GEOS-CHEM). The lateral 

boundary species concentrations varied with height and time (every 3 hours). Terrain 

elevations and land use information were obtained from the U.S. Geological Survey database 

at 10 km resolution and aggregated to the roughly 36 km horizontal resolution used for this 

CAMx application. The development of model inputs is discussed in greater detail in the Air 

Quality Modeling TSD, which is available in the docket for this rule.

4.2 Results for PM2.5 and Ozone

4.2.1 Converting CAMx PM2.5 Outputs to Benefits Inputs

CAMx generates predictions of hourly PM2.5 species concentrations for every grid. 

The species include a primary fraction and several secondary particles (e.g., sulfates, nitrates, 

and organics). PM2.5 is calculated as the sum of the primary and the secondary formed 

particles. Future-year estimates of PM2.5 were calculated using relative reduction factors 

(RRFs) applied to 2005 ambient PM2.5 species concentrations. Gridded fields of species 
65

concentrations were created by interpolating ambient data from the PM2.5 speciation network 

and IMPROVE data. The ambient data were interpolated to the 36 km and 12 km grid 

resolutions.

The procedures for determining the RRFs are similar to those in EPA guidance for 

modeling the PM2.5 standard (EPA, 2007). This guidance recommends that model 

predictions be used in a relative sense to estimate changes expected to occur in each PM2.5

species. The procedure for calculating future year PM2.5 values is called the Modeled 

Attainment Test Software (MATS). EPA used this procedure to estimate the ambient 

impacts of the Transport Rule emissions controls. For the purposes of projecting future 

PM2.5 concentrations for input to the benefits calculations, we applied the MATS procedure 

using the base year 2005 modeling results and each of the results from each of the 2014 base 

case and 2014 control scenario. In our application of MATS for PM2.5 we used temporally 

scaled speciated PM2.5 monitoring data from 2005 as the set of base-year measured 

concentrations. Temporal scaling is based on the ratios of model-predicted future case PM2.5 

species concentrations to the corresponding model-predicted 2005 concentrations. Output 

files from this process include both quarterly and annual mean PM2.5 mass concentrations 

which are then manipulated within SAS to produce a BenMAP input file containing 364 

daily values (created by replicating the quarterly mean values for each day of the appropriate 

season).

The MATS procedures documented in the Air Quality Modeling TSD are applicable 

for projecting future nonattainment and maintenance sites and downwind receptor areas for 

the transport analysis. Those procedures are similar as those performed for the PM benefits 

analysis in Chapter 5 with the following exceptions:

1) The benefits analysis uses interpolated PM2.5 data that cover all of the grid cells in the 

modeling domain, whereas the nonattainment analysis is performed at each ambient 

monitoring site using measured PM2.5 data (only the species data are interpolated).

2) The benefits analysis is anchored by the interpolated PM2.5 data from the single year of 

2005, whereas the nonattainment analysis uses design values from three, 3-year periods (i.e., 

2003-2005, 2004-2006, and 2005-2007) at individual monitoring sites.

4.2.2 PM2.5 Air Quality Results
66

Table 4-1 summaries the projected ambient PM2.5 concentrations for the 2014 base 

case and 2014 impacts associated with rule. This table includes the annual mean 

concentrations averaged across all model grid cells in the East along with the average change 

between the 2014 base and control concentrations. We also provide the population-weighted 

average that better reflects the baseline levels and predicted changes for more populated 

areas of the East. This measure, therefore better reflects the potential benefits of these 

predicted changes through exposure changes to the affected populations. As shown, the 

average annual mean concentrations of PM2.5 across populated areas of the East declines by 

roughly 9 percent (or 0.6 μg/m3) in 2014. The population-weighted average mean 

concentration declined by 9 percent (or 0.87 μg/m3) in 2014. This indicates the rule generates 

greater absolute air quality improvements in more populated, urban areas. 

Table 4-1. Summary of Base Case PM2.5 Air Quality and Changes Due to the Transport 

Rule.

2014

Statistic Base Case Control 

Case

Percent 

Changea

PM2.5 (μg/m3)

Minimum Annual Mean 1.84 1.84 0%

Maximum Annual Mean 19 19 0%

Average Annual Mean 7.3 6.7 9%

Pop-Weighted Average Annual Mean b

9.95 9.08 9%

a The percent change is defined as the control case value minus the base case value multiplied by 100. A 

negative value denotes an increase in PM2.5 concentration.

b Calculated by summing the product of the projected CAMx grid-cell population and the estimated 

concentration, for that grid-cell and then dividing by the total population.

Table 4-2 provides information on the populations in 2014 that will experience 

improved PM air quality. Significant populations that live in areas with meaningful 

reductions in annual mean PM2.5 concentrations resulting from the rule. As shown, in 2014, 
67

about 12percent of the U.S. population located in the modeling domain are predicted to 

experience reductions of greater than 1.75 μg/m3. Furthermore, nearly 30 percent of this 

population will benefit from reductions in annual mean PM2.5 concentrations of greater than 

1.25 μg/m3and 68 percent will live in areas with reductions of greater than 0.5μg/m3.

Table 4-2. Distribution of PM2.5 Air Quality Improvements Over Population in 2014 

Due to the Transport Rule for the Eastern U.S.

Change in Annual Mean PM2.5

Concentrations (μg/m3) 2014 Populationb

Number 

(millions) Percent (%)

-0.25 > Δ PM2.5 Conc < 0 6,594,134 2%

0 = Δ PM2.5 Conc 34,315,378 11%

0 > Δ PM2.5 Conc  0.25

36,670,522 11%

0.25 > Δ PM2.5 Conc  0.5

27,463,668 9%

0.5 > Δ PM2.5 Conc  0.75

43,099,722 13%

0.75 > Δ PM2.5 Conc  1.0

53,403,287 17%

1.0 > Δ PM2.5 Conc  1.25

28,161,111 9%

1.25 > Δ PM2.5 Conc  1.5

30,602,368 10%

1.5 > Δ PM2.5 Conc  1.75

21,163,369 7%

Δ PM2.5 Conc > 1.75

39,290,133 12%

a The change is defined as the control case value minus the base case value.

b

Population counts and percentages are for the fraction of the national population located in the eastern 37 

state modeling domain (as shown in Figure 4-1) considered in modeling health benefits for the rule.
68

4.2.3 Converting CAMx Outputs to Full-Season Profiles for Benefits Analysis

This study extracted hourly, surface-layer ozone concentrations for each grid-cell 

from the standard CAMx output file containing hourly average ozone values. These model 

predictions are used in conjunction with the observed concentrations obtained from the 

Aerometric Information Retrieval System (AIRS) to generate ozone concentrations for the 

entire ozone season.15,16 The predicted changes in ozone concentrations from the future-year 

base case to future-year control scenario serve as inputs to the health and welfare C-R 

functions of the benefits analysis (i.e., BenMAP). 

To estimate ozone-related health and welfare effects, full-season ozone data are 

required for every grid cell. Given available ozone monitoring data, we generated fullseason ozone profiles for each location in the contiguous 48 States in two steps: (1) we 

combine monitored observations and modeled ozone predictions to interpolate hourly ozone 

concentrations to a grid of 8 km by 8 km population grid-cells, and (2) we converted these 

full-season hourly ozone profiles to an ozone measure of interest, such as the daily

average.17,18

 

15The ozone season for this analysis is defined as the 5-month period from May to September; however, to 

estimate certain crop yield benefits, the modeling results were extended to include months outside the 5-

month ozone season.

16 Based on AIRS, there were 961 ozone monitors with sufficient data, i.e., 50 percent or more days reporting at 

least 9 hourly observations per day (8 am to 8 pm) during the ozone season.

17 The 12 km grid squares contain the population data used in the health benefits analysis model, BenMAP. See 

Chapter 5 for a discussion of this model.

18This approach is a generalization of planar interpolation that is technically referred to as enhanced Voronoi 

Neighbor Averaging (EVNA) spatial interpolation.
69

4.2.4 Ozone Air Quality Results

This section provides a summary of the predicted ambient ozone concentrations from 

the CAMx model for the 2014 base case and changes associated with the rule. Table 4-3 

provides those ozone metrics for grid cells in the Eastern U.S. that enter the C-R functions 

for health benefits endpoints. The population-weighted average reflects the baseline levels 

and predicted changes for more populated areas of the nation. This measure, therefore, will 

better reflect the potential benefits of these predicted changes through exposure changes to 

these populations.

Table 4-3. Summary of CAMx Derived Population-Weighted Ozone Season Air 

Quality Metrics for Health Benefits Endpoints Due to the Transport Rule for the 

Eastern U.S.

2014

Statistica Base Case Change b Percent 

Change b

Population-Weighted Average (ppb) c

Daily 8-Hour Average Concentration 44.85 0.09 0.2%

a This ozone metric is calculated at the CAMx grid-cell level for use in health effects estimates based on the results of spatial 

and temporal Voronoi Neighbor Averaging. 

b The change is defined as the control case value minus the base case value. The percent change is the “Change” divided by 

the “Base Case,” and then multiplied by 100 to convert the value to a percentage. 

c Calculated by summing the product of the projected CAMx grid-cell population and the estimated CAMx grid-cell 

seasonal ozone concentration, and then dividing by the total population.

4.3 Visibility Degradation Estimates

Visibility degradation is often directly proportional to decreases in light transmittal in 

the atmosphere. Scattering and absorption by both gases and particles decrease light 

transmittance. To quantify changes in visibility, our analysis computes a light-extinction 

coefficient, based on the work of Sisler (1996), which shows the total fraction of light that is 
70

decreased per unit distance. This coefficient accounts for the scattering and absorption of 

light by both particles and gases, and accounts for the higher extinction efficiency of fine 

particles compared to coarse particles. Fine particles with significant light-extinction 

efficiencies include sulfates, nitrates, organic carbon, elemental carbon (soot), and soil 

(Sisler, 1996).

Based upon the light-extinction coefficient, we also calculated a unitless visibility

index, called a “deciview,” which is used in the valuation of visibility. The deciview metric 

provides a scale for perceived visual changes over the entire range of conditions, from clear 

to hazy. Under many scenic conditions, the average person can generally perceive a change 

of one deciview. The higher the deciview value, the worse the visibility. Thus, an 

improvement in visibility is a decrease in deciview value. 

Table 4-4 provides the visibility improvements, measured in annual average 

deciviews, expected to occur in the Eastern and Western U.S. As shown, Class I visibility

regions in the Eastern U.S., including such regions as the Great Smoky Mountains and 

Shenandoah, are expected to see significant improvements in visibility. By 2014, such 

regions in the Eastern U.S. are expected to see improvements of over 1.5 deciview (11

percent), and such regions in the Western U.S. are expected to see improvements of over 

0.02 deciviews (or less than 1 percent). 

Table 4-4. Summary of Basecase Recreational Visibility and Changes by Region: 

(annual average deciviews)

2014

Class I Visibility Regions Base Case 

(Deciviews)

Change in 

Annual 

Average 

(Deciviews)

Percent 

Change in 

Annual 

Average (%)

Eastern US 14.84 1.57 11.0

Western US 9.09 0.02 <1
71

4.4 References

Environ, 2010. Comprehensive Air Quality Model with Extensions Version 5.3 User’s 

Guide. Environ International Corporation. Novato, CA. December 2010.

Sisler, J.F. July 1996. Spatial and Seasonal Patterns and Long Term Variability of the 

Composition of the Haze in the United States: An Analysis of Data from the IMPROVE 

Network. Fort Collins, CO: Cooperative Institute for Research in the Atmosphere, Colorado 

State University.

U.S. Environmental Protection Agency (EPA). 2007. Guidance on the Use of Models and 

Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and 

Regional Haze. Office of Air Quality Planning and Standards, Research Triangle Park, NC.

U.S. Environmental Protection Agency (EPA). 2011. Air Quality Modeling Technical 

Support Document for the Transport Rule. Office of Air Quality Planning and Standards, 

Research Triangle Park, NC.
72

 Chapter 5

Benefits Analysis and Results

Synopsis

This chapter contains a subset of the estimated health and welfare benefits of the 

Transport Rule remedy in 2014. This rule is expected to yield significant reductions in SO2 

and NOx from EGUs, which in turn would lower overall ambient levels of PM2.5 and ozone 

across much of the eastern U.S. In this chapter we quantify the health and welfare benefits 

resulting from these air quality improvements. 

We estimate the monetized benefits of the selected remedy to be $120 billion to $280 

billion at a 3% discount rate and $110 billion to $250 billion at a 7% discount rate in 2014. 

The benefits of the more and less stringent alternatives may be found in the benefit-cost 

comparison chapter. All estimates are in 2007$. We estimate the benefits of the selected 

remedy using modeled changes in ambient pollution concentrations while the benefits of the 

more and less stringent remedies are based on a benefit per ton approach described below. 

This benefits analysis accounts for both decreases and increases in emissions across the 

country. These estimates omit the benefits from several important categories, including 

ecosystem benefits, mercury benefits, and the direct health benefits from reducing exposure 

to NO2 and SO2 due to time constraints. 

The estimated benefits of this rule are substantial, particularly when viewed within 

the context of the total public health burden of PM2.5 and ozone air pollution. A recent EPA 

analysis estimated that 2005 levels of PM2.5 and ozone were responsible for between 130,000 

and 320,000 PM2.5-related and 4,700 ozone-related premature deaths, or about 6.1% of total 
73

deaths from all causes in the continental U.S. (Fann et al. 2011). This same analysis 

attributed almost 200,000 non-fatal heart attacks, 90,000 hospital admissions due to 

respiratory or cardiovascular illness and 2.5 million cases of aggravated asthma among 

children--among many other impacts. We estimate the Transport Rule to reduce the number 

of PM2.5-related premature deaths in 2014 by between 13,000 and 34,000, 15,000 non-fatal 

heart attacks, 8,700 fewer hospital admissions and 400,000 fewer cases of aggravated 

asthma. By 2014, in combination with other federal and state air quality actions, the 

Transport Rule will address a substantial fraction of the total public health burden of PM2.5

and ozone air pollution.

EPA expects greater emission reductions due to this rule in 2012 than in 2014, due to 

substantial emission reductions expected to occur in the baseline (i.e., unrelated to the 

Transport Rule) between those years. As a result, we anticipate that the avoided health 

impacts and monetized benefits would also be greater in 2012, though we have not calculated 

these estimates for this analysis.

Appendix A to this RIA contains an assessment of the distribution of health benefits 

among different populations. In this analysis, we considered the level of PM2.5 mortality risk 

according to the race, income and educational attainment of the population before and after 

the implementation of the Transport Rule. We found those populations whose PM2.5

mortality risk was before the implementation of the rule received the greatest risk reduction 

from the Transport Rule—irrespective of the race of the population. We also found that 

populations with lower levels of educational attainment, an attribute that may be associated 

with increased vulnerability to PM2.5 mortality risk, also received a significant reduction in 

risk. 

Finally, Appendix E provides an alternate presentation of the benefits as an attempt to 

incorporate the recommendations from EPA’s recently published Guidelines for Preparing 

Economic Analyses (U.S. EPA, 2010). 

5.1 Overview

This chapter contains a subset of the estimated health and welfare benefits of the selected 
74

and alternate rule remedies for the Transport Rule in 2014. The Transport Rule is expected to 

yield significant aggregate reductions in SO2 and NOx from EGUs, which in turn would 

lower overall ambient levels of PM2.5 and ozone across much of the eastern U.S. To perform 

this analysis, EPA followed an approach that is generally consistent with the proposal 

Transport Rule analysis, with the exception of the baseline incidence rates that are an input 

into the health impact calculation for PM2.5 and ozone health outcomes. These updated rates 

are both more current and provide better spatial resolution in many areas of the U.S. As we 

describe in section 5.4 below, these updated data are likely to yield a better overall estimate 

of PM and ozone-related health impacts. 

The analysis in this chapter aims to characterize the benefits of the selected remedy by

answering two key questions: 

1. What are the health and welfare effects of changes in ambient particulate matter 

(PM2.5) and ozone air quality resulting from reductions in precursors including NOx

and SO2?

2. What is the economic value of these effects?

In this analysis we consider an array of health and welfare impacts attributable to 

changes in PM2.5 and ozone air quality. The 2009 PM2.5 Integrated Science Assessment (U.S. 

EPA, 2009d) and the 2006 ozone criteria document (U.S. EPA, 2006a) identify the human 

health effects associated with these ambient pollutants, which include premature mortality 

and a variety of morbidity effects associated with acute and chronic exposures. PM welfare 

effects include visibility impairment and materials damage. Ozone welfare effects include 

damages to agricultural and forestry sectors. NOx welfare effects include aquatic and 

terrestrial acidification and nutrient enrichment (U.S. EPA, 2008f). SO2 welfare effects 

include aquatic and terrestrial acidification and increased mercury methylation (U.S. EPA, 

2008f). Though models exist for quantifying these ecosystem impacts, time and resource 

constraints precluded us from quantifying most of those effects in this analysis. 

Table 5-1 summarizes the total monetized benefits of the final Transport Rule remedy 

in 2014. This table reflects the economic value of the change in PM2.5 and ozone-related 

human health impacts occurring as a result of the Transport Rule. 

Table 5-2 summarizes the human health and welfare benefits categories contained 
75

within the primary benefits estimate, those categories that were unquantified due to limited 

data or time.

Table 5-1: Estimated monetized benefits of the final Transport Rule remedy (billions of 

2007$)A

Benefits Estimate

Within Transport 

RegionB

Outside 

Transport 

Region Total

Pope et al. (2002) PM2.5 mortality and Bell et al. (2004) ozone mortality estimates

Using a 3% discount 

rate

$110 +B

($8.8—$340)

$0.28 +B

($0.01—$0.9)

$120 +B

($14—$350)

Using a 7% discount 

rate

$100 +B

($8—$310)

$0.25 +B

($0.01—$0.85)

$110 +B

($13—$320)

Laden et al. (2006) PM2.5 mortality and Levy et al. (2005) ozone mortality estimates

Using a 3% discount 

rate

$270 +B

($24—$800)

$0.7 +B

($0.05—$0.21)

$280 +B

($29—$810)

Using a 7% discount 

rate

$250 +B

($22—$720)

$0.6 +B

($0.04—$1.9)

$250 +B

($26—$730)

A For notational purposes, unquantified benefits are indicated with a “B” to represent the sum of 

additional monetary benefits and disbenefits. Data limitations prevented us from quantifying these 

endpoints, and as such, these benefits are inherently more uncertain than those benefits that we were 

able to quantify. A detailed listing of unquantified health and welfare effects is provided in Table 5-2. 

Estimates here are subject to uncertainties discussed further in the body of the document. Estimates 

include the value of CO2-related benefits and the monetized benefits of visibility improvements in 

Class I areas.

B Rounded to two significant figures. 

The benefits analysis in this chapter relies on an array of data inputs—including air 

quality modeling, health impact functions and valuation estimates among others—which are 

themselves subject to uncertainty and may also in turn contribute to the overall uncertainty in 

this analysis. As a means of characterizing this uncertainty we employ two primary 

techniques. First, we use Monte Carlo methods for characterizing random sampling error 

associated with the concentration response functions from epidemiological studies and 

economic valuation functions. Second, because this characterization of random statistical 
76

error may omit important sources of uncertainty we also employ the results of an expert 

elicitation on the relationship between premature mortality and ambient PM2.5 concentration 

(Roman et al., 2008); this provides additional insight into the likelihood of different 

outcomes and about the state of knowledge regarding the benefits estimates. Both approaches 

have different strengths and weaknesses, which are fully described in Chapter 5 of the PM 

NAAQS RIA (U.S. EPA, 2006).

Given that reductions in premature mortality dominate the size of the overall 

monetized benefits, more focus on uncertainty in mortality-related benefits gives us greater 

confidence in our uncertainty characterization surrounding total benefits. Certain EPA RIA’s 

including the 2008 Ozone NAAQS RIA (U.S. EPA, 2008a) contained a suite of sensitivity

analyses, only some of which we include here due in part to time constraints. In particular, 

these analyses characterized the sensitivity of the monetized benefits to the specification of 

alternate cessation lags and income growth adjustment factors. The estimated benefits 

increased or decreased in proportion to the specification of alternate income growth 

adjustments and cessation lags, making it possible for readers to infer the sensitivity of the 

results in this RIA to these parameters by referring to the PM NAAQS RIA (2006d) and 

Ozone NAAQS RIA (2008a). 

For example, the use of an alternate lag structure would change the PM2.5-related 

mortality benefits discounted at 3% discounted by between 10.4% and –27%; when 

discounted at 7%, these benefits change by between 31% and -49%. When applying higher 

and lower income growth adjustments, the monetary value of PM2.5 and ozone-related 

premature changes between 30% and -10%; the value of chronic endpoints change between 

5% and -2% and the value of acute endpoints change between 6% and -7%. 

Below we include a new analysis (Figures 5-19 and 5-20) in which we bin the 

estimated number of avoided PM2.5-related premature mortalities resulting from the 

implementation of the Transport Rule according to the projected 2014 baseline PM2.5 air 

quality levels. This presentation is consistent with our approach to applying PM2.5 mortality

risk coefficients that have not been adjusted to incorporate an assumed threshold. The very

large proportion of the avoided PM-related impacts we estimate in this analysis occur among 

populations exposed at or above the LML of each study, increasing our confidence in the PM 

mortality analysis. Approximately 69% of the avoided impacts occur at or above an annual 
77

mean PM2.5 level of 10 μg/m3(the LML of the Laden et al. 2006 study); about 96% occur at 

or above an annual mean PM2.5 level of 7.5 μg/m3 (the LML of the Pope et al. 2002 study). 

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. 

However, the analysis below confirms that the great majority of the impacts occur at or 

above each study’s LML.
78

Table 5-2: Human Health and Welfare Effects of Pollutants Affected by the Transport 

Rule

Pollutant/ 

Effect Quantified and monetized in base estimate Unquantified

PM: 

healtha

Premature mortality based on cohort study 

estimatesb and expert elicitation estimates

Low birth weight, pre-term birth and other reproductive 

outcomes

Hospital admissions: respiratory and 

cardiovascular Pulmonary function

Emergency room visits for asthma Chronic respiratory diseases other than chronic 

bronchitis

Nonfatal heart attacks (myocardial 

infarctions) Non-asthma respiratory emergency room visits

Lower and upper respiratory illness UVb exposure (+/-)

c

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 in SE, SW, and CA 

regions

Household soiling

Visibility in residential areas

Visibility in non-class I areas and class 1 areas in NW, 

NE, and Central regions

UVb exposure (+/-)

c

Global climate impactsc

Ozone: 

health

Premature mortality based on short-term

study estimates Chronic respiratory damage

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

--Other commercial and noncommercial crops

Damage to urban ornamental plants

Recreational demand from damaged forest aesthetics

Ecosystem functions

UVb exposure (+/-)

c

Climate impacts

NO2: 

health

Respiratory hospital admissions

Respiratory emergency department visits

Asthma exacerbation

Acute respiratory symptoms

Premature mortality

Pulmonary function

NOX: 

welfare

Commercial fishing and forestry from acidic deposition 

effects

Commercial fishing, agriculture and forestry from 

nutrient deposition effects

Recreation in terrestrial and estuarine ecosystems from 

nutrient deposition effects 

Other ecosystem services and existence values for 
79

currently healthy ecosystems

Coastal eutrophication from nitrogen deposition effects

SO2: 

health

Respiratory hospital admissions

Asthma emergency room visits

Asthma exacerbation

Acute respiratory symptoms

Premature mortality

Pulmonary function

SOX: 

welfare

Commercial fishing and forestry from acidic deposition 

effects

Recreation in terrestrial and aquatic ecosystems from 

acid deposition effects

Increased mercury methylation

Mercury: 

health

Incidence of neurological disorders

Incidence of learning disabilities

Incidences in developmental delays

Mercury: 

welfare

Impact on birds and mammals (e.g. reproductive 

effects)

Impacts to commercial, subsistence and recreational 

fishing

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 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.

5.2 Benefits Analysis Methods 

We follow a “damage-function” approach in calculating total benefits of the modeled 

changes in environmental quality. This approach estimates changes in individual health and 

welfare endpoints (specific effects that can be associated with changes in air quality) and assigns 

values to those changes assuming independence of the individual values. Total benefits are 

calculated simply as the sum of the values for all non-overlapping health and welfare endpoints. 

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; Hubbell et al., 2009; 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. In some 

cases, the changes in environmental quality can be directly valued, as is the case for changes in 
80

visibility. In other cases, such as for changes in ozone and PM, a health and welfare impact 

analysis must first be conducted to convert air quality changes into effects that can be assigned 

dollar values.

For the purposes of this RIA, the health impacts analysis (HIA) is limited to those health 

effects that are directly linked to ambient levels of air pollution and specifically to those linked to 

ozone and PM. There may be other, indirect health impacts associated with implementing 

emissions controls, such as occupational health impacts for coal miners. 

The welfare impacts analysis is limited to changes in the environment that have a direct 

impact on human welfare. For this analysis, we are limited by the available data to examine 

impacts of changes in visibility in Class 1 areas. We also provide qualitative discussions of the 

impact of changes in other environmental and ecological effects, for example, changes in 

deposition of nitrogen and sulfur to terrestrial and aquatic ecosystems, but we are unable to place 

an economic value on these changes due to time and resource limitations.

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. (2000) 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.

5.2.1 Health Impact Assessment 

The Health Impact Assessment (HIA) quantifies the changes in the incidence of adverse 

health impacts resulting from changes in human exposure to PM2.5 and ozone air quality. HIAs 

are a well-established approach for estimating the retrospective or prospective change in adverse 

health impacts expected to result from population-level changes in exposure to pollutants (Levy 

et al. 2009). PC-based tools such as the environmental Benefits Mapping and Analysis Program 

(BenMAP) can systematize health impact analyses by applying a database of key input 

parameters, including health impact functions and population projections. Analysts have applied 

the HIA approach to estimate human health impacts resulting from hypothetical changes in 

pollutant levels (Hubbell et al. 2005; Davidson et al. 2007, Tagaris et al. 2009). EPA and others 
81

have relied upon this method to predict future changes in health impacts expected to result from 

the implementation of regulations affecting air quality (U.S. EPA, 2008a).

The HIA approach used in this analysis involves three basic steps: (1) utilizing CAMxgenerated 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) to this change in population exposure. 

A typical health impact function might look as follows:

∆ =  ∙ 

∙∆ − 1 ∙ 

where y0 is the baseline incidence rate for the health endpoint being quantified (for 

example, a health impact function quantifying changes in mortality would use the baseline, or 

background, mortality rate for the given population of interest); Pop is the population affected by

the change in air quality; x is the change in air quality; and β is the effect coefficient drawn 

from the epidemiological study. Tools such as BenMAP can systematize the HIA calculation 

process, allowing users to draw upon a library of existing air quality monitoring data, population 

data and health impact functions. 

Figure 5-1 provides a simplified overview of this approach.
82

Figure 5-1: Illustration of BenMAP Approach

5.2.2 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. The appropriate economic value for a change in a 

health effect depends on whether the health effect is viewed ex ante (before the effect has 

occurred) or ex post (after the effect has occurred). Reductions in ambient concentrations of air 

pollution generally lower the risk of future adverse health effects by a small amount for a large 

population. The appropriate economic measure is therefore ex ante Willingness to Pay (WTP) for 

changes in risk. However, epidemiological studies generally provide estimates of the relative 

risks of a particular health effect avoided due to a reduction in air pollution. A convenient way to 

use this data in a consistent framework is to convert probabilities to units of avoided statistical 

incidences. This measure is calculated by dividing individual WTP for a risk reduction by the 

related observed change in risk. For example, suppose a measure is able to reduce the risk of 

premature mortality from 2 in 10,000 to 1 in 10,000 (a reduction of 1 in 10,000). If individual 

WTP for this risk reduction is $100, then the WTP for an avoided statistical premature mortality 

amounts to $1 million ($100/0.0001 change in risk). Using this approach, the size of the affected 
83

population is automatically taken into account by the number of incidences predicted by 

epidemiological studies applied to the relevant population. The same type of calculation can 

produce values for statistical incidences of other health endpoints. 

For some health effects, such as hospital admissions, WTP estimates are generally not 

available. In these cases, we use the cost of treating or mitigating the effect as a primary

estimate. For example, for the valuation of hospital admissions we use the avoided medical costs 

as an estimate of the value of avoiding the health effects causing the admission. These cost of 

illness (COI) estimates generally (although not in every case) understate the true value of 

reductions in risk of a health effect. They tend to reflect the direct expenditures related to 

treatment but not the value of avoided pain and suffering from the health effect.

We use the BenMAP model (Abt Associates, 2008) to estimate the health impacts and 

monetized health benefits for the preferred remedy. Figure 5-2 below shows the data inputs and 

outputs for the BenMAP model. 
84

Figure 5-2: Data inputs and outputs for the BenMAP model

5.2.3 Benefit Per-Ton Estimates 

Benefit per-ton (BPT) estimates 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 perform air quality modeling because of resource or time constraints, this approach can 

provide a reliable estimate of the benefits of emission reduction scenarios. EPA has used the 

benefit per-ton technique in previous RIAs, including the recent Ozone NAAQS RIA (U.S. EPA, 

2008) and NO2 NAAQS RIA (U.S. EPA, 2010b). Time constraints prevented the Agency from 

modeling the air quality changes resulting from e the more and less stringent SO2 caps and so we 

estimate a subset of these health benefits using PM2.5 benefit per-ton estimates. The assessment 

of the alternate scenarios omits ozone-related benefits for two reasons. First, the overall level of 

ozone-related benefits in the modeled case is relatively small compared to those associated with 

PM2.5 reductions (see table 5-17 below), due in part to the fairly modest summer time NOx

Census

Population 

Data

Modeled 

Baseline and 

Post-Control 

2014 Ambient 

PM2.5 and O3

Concentrations

PM2.5 & O3 Health 

Functions

Economic 

Valuation 

Functions

2014

Population 

Projections

PM2.5 & O3

Incremental Air 

Quality Change

PM2.5 & O3-

Related Health 

mpacts

Woods & 

Poole 

Population 

Projections

Background 

Incidence and 

Prevalence Rates

Monetized PM2.5

and O3-related 

Benefits

Blue identifies a user-selected input within the BenMAP model

Green identifies a data input generated outside of the BenMAP 

model
85

emission reductions under this scenario. The level of summertime NOx emission reductions of 

the alternate scenarios are very similar to the modeled scenario, suggesting that the omission of 

ozone-related impacts would not greatly influence the overall level of benefits. Second, the 

complex non-linear chemistry of ozone formation introduces uncertainty to the development and 

application of a benefit per ton estimate. Taken together, these factors argued against developing 

an ozone benefit per ton estimate for this RIA. 

For this analysis, EPA applies PM2.5 BPT estimates that are methodologically consistent 

with those reported in Fann et al. (2009), but have been adjusted for this analysis to better match 

the spatial distribution of air quality changes projected for the Transport Rule. To derive the BPT 

estimates for this analysis, we:

1. Quantified the PM2.5 -related human and monetized health benefits of the SO2 emission 

reductions of the proposed remedy. We first quantified the health impacts and monetized 

benefits of total PM2.5 mass formed from the SO2 reductions of the proposed remedy, 

allowing us to isolate the PM air quality impacts from SO2 reductions alone.19 This 

procedure allowed us to develop PM2.5 BPT estimates that quantified the PM2.5-related 

benefits of incremental changes in SO2 emissions. Because reductions in NOx emissions 

are relatively small in each scenario, and previous EPA modeling indicates that PM2.5

formation is less sensitive to NOx emission reductions on a per-μg/m3basis (Fann et al, 

2009), we did not quantify the NOx-related PM2.5 changes.

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 

for the more and less stringent scenarios to produce an estimate of the PM- and ozone-related 

health impacts and monetized benefits. There is no analogous approach for estimating a BPT for 

 

19 The Transport Rule includes both SO2 and NOx emissions reductions. In general SO2 is a precursor to particulate 

sulfate and NOx is a precursor to particulate nitrate. However, there are also several interactions between the PM2.5

precursors which cannot be easily quantified. For example, under conditions in which SO2 levels are reduced by a 

substantial margin, "nitrate replacement" may occur. This occurs when particulate ammonium sulfate 

concentrations are reduced, thereby freeing up excess gaseous ammonia. The excess ammonia is then available to 

react with gaseous nitric acid to form particulate nitrate. The impact of nitrate replacement is also affected by 

concurrent NOx reductions. NOx reductions can lead to decreases in nitrate, which competes with the process of 

nitrate replacement. NOx reductions can also lead to reductions in photochemical by-products which can reduce 

both particulate sulfate and secondary organic carbon PM concentrations. Due to the complex nature of these 

interactions, EPA performed a sensitivity modeling analysis in which only SO2 emissions were reduced at levels that 

approximated those of the selected remedy. We calculated benefits from this air quality modeling run to generate an 

SO2-only benefit per ton estimate. The results of the SO2-only sensitivity run may be found in the EPA Benefits 

TSD [Docket No. EPA-HQ-OAR-2010-0491]
86

visibility, and so the benefits of the alternative remedies omit this important monetized benefit. 

5.3 Uncertainty Characterization

In any complex analysis using estimated parameters and inputs from numerous models, 

there are likely to be many sources of uncertainty and this analysis is no exception. As outlined 

both in this and preceding chapters, many inputs were used to derive the estimate of benefits for 

the proposed remedy, including emission inventories, air quality models (with their associated 

parameters and inputs), epidemiological health effect estimates, estimates of values (both from 

WTP and COI studies), population estimates, income estimates, and estimates of the future state 

of the world (i.e., regulations, technology, and human behavior). Each of these inputs may be 

uncertain and, depending on its role in the benefits analysis, may have a disproportionately large 

impact on estimates of total benefits. For example, emissions estimates are used in the first stage 

of the analysis. As such, any uncertainty in emissions estimates will be propagated through the 

entire analysis. When compounded with uncertainty in later stages, small uncertainties in 

emission levels can lead to large impacts on total benefits.

The National Research Council (NRC) (2002, 2008) highlighted the need for EPA to 

conduct rigorous quantitative analysis of uncertainty in its benefits estimates and to present these 

estimates to decision makers in ways that foster an appropriate appreciation of their inherent 

uncertainty. In general, the NRC concluded that EPA’s general methodology for calculating the 

benefits of reducing air pollution is reasonable and informative in spite of inherent uncertainties. 

Since the publication of these reports, EPA’s Office of Air and Radiation (OAR) continues to 

make progress toward the goal of characterizing the aggregate impact of uncertainty in key 

modeling elements on both health incidence and benefits estimates in two key ways: Monte 

Carlo analysis and expert-derived concentration-response functions. In this analysis, we use both 

of these two methods to assess uncertainty quantitatively, as well as provide a qualitative 

assessment for those aspects that we are unable to address quantitatively. 

First, we used Monte Carlo methods for characterizing random sampling error associated 

with the concentration response functions from epidemiological studies and random effects 

modeling to characterize both sampling error and variability across the 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 premature 

mortality. Specifically, we used Monte Carlo methods to generate confidence intervals around 

the estimated health impact and dollar benefits. The reported standard errors in the 
87

epidemiological studies determined the distributions for individual effect estimates.

Second, because characterization of random statistical error omits important sources of 

uncertainty (e.g., in the functional form of the model—e.g., whether or not a threshold may

exist), we also incorporate the results of an expert elicitation on the relationship between 

premature mortality and ambient PM2.5 concentration (Roman et al., 2008). Use of the expert 

elicitation and incorporation of the standard errors approaches provide insights into the 

likelihood of different outcomes and about the state of knowledge regarding the benefits 

estimates. However, there are significant unquantified uncertainties present in upstream inputs 

including emission and air quality. Both approaches have different strengths and weaknesses, 

which are fully described in Chapter 5 of the PM NAAQS RIA (U.S. EPA, 2006). 

In benefit analyses of air pollution regulations conducted to date, the estimated impact of 

reductions in premature mortality has accounted for 85% to 95% of total monetized benefits. 

Therefore, it is particularly important to attempt to characterize the uncertainties associated with 

reductions in premature mortality. The health impact functions used to estimate avoided 

premature deaths associated with reductions in ozone have associated standard errors that 

represent the statistical errors around the effect estimates in the underlying epidemiological 

studies. In our results, we report credible intervals based on these standard errors, reflecting the 

uncertainty in the estimated change in incidence of avoided premature deaths. We also provide 

multiple estimates, to reflect model uncertainty between alternative study designs. 

For premature mortality associated with exposure to PM, we follow the same approach 

used in the RIA for 2006 PM NAAQS (U.S. EPA, 2006), presenting two empirical estimates of 

premature deaths avoided, and a set of twelve estimates based on results of the expert elicitation 

study. Even these multiple characterizations, including confidence intervals, omit the 

contribution to overall uncertainty of uncertainty in air quality changes, baseline incidence rates, 

populations exposed and transferability of the effect estimate to diverse locations. Furthermore, 

the approach presented here does not yet include methods for addressing correlation between 

input parameters and the identification of reasonable upper and lower bounds for input 

distributions characterizing uncertainty in additional model elements. As a result, the reported 

confidence intervals and range of estimates give an incomplete picture about the overall 

uncertainty in the estimates. This information should be interpreted within the context of the 

larger uncertainty surrounding the entire analysis.

In 2006 the EPA requested an NAS study to evaluate the extent to which the epidemiological 

literature to that point improved the understanding of ozone-related mortality. The NAS found 
88

that short-term ozone exposure was likely to contribute to ozone-related mortality (NRC, 2008) 

and issued a series of recommendations to EPA, including that the Agency should:

1. Present multiple short-term ozone mortality estimates, including those based on multicity analyses such as the National Morbidity, Mortality and Air Pollution Study

(NMMAPS) as well as meta-analytic studies.

2. Report additional risk metrics, including the percentage of baseline mortality attributable 

to short-term exposure.

3. Remove reference to a no-causal relationship between ozone exposure and premature 

mortality.

The quantification and presentation of ozone-related premature mortality in this chapter 

is responsive to these NRC recommendations and generally consistent with EPA’s recent ozone 

reconsideration analysis (U.S. EPA, 2010a). 

Some key sources of uncertainty in each stage of both the PM and ozone health impact 

assessment are the following:

 gaps in scientific data and inquiry;

 variability in estimated relationships, such as epidemiological effect estimates, introduced 

through differences in study design and statistical modeling;

 errors in measurement and projection for variables such as population growth rates;

 errors due to misspecification of model structures, including the use of surrogate 

variables, such as using PM10 when PM2.5 is not available, excluded variables, and 

simplification of complex functions; and

 biases due to omissions or other research limitations.

In Table 5-3 we summarize some of the key uncertainties in the benefits analysis. 
89

Table 5-3. Primary Sources of Uncertainty in the Benefits Analysis

1. Uncertainties Associated with Impact Functions

- The value of the ozone or PM effect estimate in each impact function.

- Application of a single impact function to pollutant changes and populations in all locations.

- Similarity of future-year impact functions to current impact functions. 

- Correct functional form of each impact function. 

- Extrapolation of effect estimates beyond the range of ozone or PM concentrations observed in the 

source epidemiological study. 

- Application of impact functions only to those subpopulations matching the original study population.

2. Uncertainties Associated with CAMx-Modeled Ozone and PM Concentrations 

- Responsiveness of the models to changes in precursor emissions from the control policy.

- Projections of future levels of precursor emissions, especially ammonia and crustal materials.

- Lack of ozone and PM2.5 monitors in all rural areas requires extrapolation of observed ozone data from 

urban to rural areas.

3. Uncertainties Associated with PM Mortality Risk

- Limited scientific literature supporting a direct biological mechanism for observed epidemiological 

evidence.

- Direct causal agents within the complex mixture of PM have not been identified.

- The extent to which adverse health effects are associated with low-level exposures that occur many 

times in the year versus peak exposures.

- The extent to which effects reported in the long-term exposure studies are associated with historically 

higher levels of PM rather than the levels occurring during the period of study.

- Reliability of the PM2.5 monitoring data in reflecting actual PM2.5 exposures.

4. Uncertainties Associated with Possible Lagged Effects

- The portion of the PM-related long-term exposure mortality effects associated with changes in annual 

PM levels that would occur in a single year is uncertain as well as the portion that might occur in 

subsequent years.

5. Uncertainties Associated with Baseline Incidence Rates

- Some baseline incidence rates are not location specific (e.g., those taken from studies) and therefore 

may not accurately represent the actual location-specific rates.

- Current baseline incidence rates may not approximate well baseline incidence rates in 2014.

- Projected population and demographics may not represent well future-year population and 

demographics.

6. Uncertainties Associated with Economic Valuation

- Unit dollar values associated with health and welfare endpoints are only estimates of mean WTP and 

therefore have uncertainty surrounding them.

- Mean WTP (in constant dollars) for each type of risk reduction may differ from current estimates 

because of differences in income or other factors.

7. Uncertainties Associated with Aggregation of Monetized Benefits

- Health and welfare benefits estimates are limited to the available impact functions. Thus, 

unquantified or unmonetized benefits are not included.
90

5.4 Benefits Analysis Data Inputs

In Figure 5-2, we summarized the key data inputs to the health impact and economic 

valuation estimate. Below we summarize the data sources for each of these inputs, including 

demographic projections, effect coefficients, incidence rates and economic valuation. We 

indicate where we have updated key data inputs since the 2005 CAIR benefits analysis.

5.4.1 Demographic Data

Quantified and monetized human health impacts depend on the demographic characteristics 

of the population, including age, location, and income. We use projections based on economic 

forecasting models developed by Woods and Poole, Inc (Woods and Poole, 2008). The Woods 

and Poole (WP) database contains county-level projections of population by age, sex, and race 

out to 2030. Projections in each county are determined simultaneously with every other county 

in the United States to take into account patterns of economic growth and migration. The sum of 

growth in county-level populations is constrained to equal a previously determined national 

population growth, based on Bureau of Census estimates (Hollman et al., 2000). According to 

WP, linking county-level growth projections together and constraining to a national-level total 

growth avoids potential errors introduced by forecasting each county independently. County 

projections are developed in a four-stage process: 

1. First, national-level variables such as income, employment, and populations are 

forecasted. 

2. Second, employment projections are made for 172 economic areas defined by the Bureau 

of Economic Analysis, using an “export-base” approach, which relies on linking 

industrial-sector production of non-locally consumed production items, such as outputs 

from mining, agriculture, and manufacturing with the national economy. The exportbased approach requires estimation of demand equations or calculation of historical 

growth rates for output and employment by sector. 

3. Third, population is projected for each economic area based on net migration rates 

derived from employment opportunities and following a cohort-component method based 

on fertility and mortality in each area. 

4. Fourth, employment and population projections are repeated for counties, using the 

economic region totals as bounds. The age, sex, and race distributions for each region or 
91

county are determined by aging the population by single year of age by sex and race for 

each year through 2014 based on historical rates of mortality, fertility, and migration.

5.4.2 Effect Coefficients

The first step in selecting effect coefficients is to identify the health endpoints to be 

quantified. We base our selection of health endpoints on consistency with EPA’s Integrated 

Science Assessments (which replace the Criteria Document), with input and advice from the 

EPA Science Advisory Board - Health Effects Subcommittee (SAB-HES), a scientific review 

panel specifically established to provide advice on the use of the scientific literature in 

developing benefits analyses for air pollution regulations (http://www.epa.gov/sab/). In general, 

we follow a weight of evidence approach, based on the biological plausibility of effects, 

availability of concentration-response functions from well conducted peer-reviewed 

epidemiological studies, cohesiveness of results across studies, and a focus on endpoints 

reflecting public health impacts (like hospital admissions) rather than physiological responses 

(such as changes in clinical measures like Forced Expiratory Volume (FEV1)). 

There are several types of data that can support the determination of types and magnitude 

of health effects associated with air pollution exposures. These sources of data include 

toxicological studies (including animal and cellular studies), human clinical trials, and 

observational epidemiology studies. All of these data sources provide important contributions to 

the weight of evidence surrounding a particular health impact. However, only epidemiology

studies provide direct concentration-response relationships which can be used to evaluate 

population-level impacts of reductions in ambient pollution levels in a health impact assessment.

For the data-derived estimates, we relied on the published scientific literature to ascertain 

the relationship between PM and adverse human health effects. We evaluated epidemiological 

studies using the selection criteria summarized in Table 5-4. These criteria include consideration 

of whether the study was peer-reviewed, the match between the pollutant studied and the 

pollutant of interest, the study design and location, and characteristics of the study population, 

among other considerations. The selection of C-R functions for the benefits analysis is guided by

the goal of achieving a balance between comprehensiveness and scientific defensibility. In 

general, the use of results from more than a single study can provide a more robust estimate of 

the relationship between a pollutant and a given health effect. However, there are often 

differences between studies examining the same endpoint, making it difficult to pool the results 

in a consistent manner. For example, studies may examine different pollutants or different age 

groups. For this reason, we consider very carefully the set of studies available examining each 
92

endpoint and select a consistent subset that provides a good balance of population coverage and 

match with the pollutant of interest. In many cases, either because of a lack of multiple studies, 

consistency problems, or clear superiority in the quality or comprehensiveness of one study over 

others, a single published study is selected as the basis of the effect estimate.
93

Table 5-4. Criteria Used when Selecting C-R functions

Consideration Comments

Peer-Reviewed 

Research

Peer-reviewed research is preferred to research that has not undergone the peer-review 

process.

Study Type

Among studies that consider chronic exposure (e.g., over a year or longer), prospective 

cohort studies are preferred over ecological studies because they control for important 

individual-level confounding variables that cannot be controlled for in ecological studies. 

Study Period

Studies examining a relatively longer period of time (and therefore having more data) are 

preferred, because they have greater statistical power to detect effects. More recent 

studies are also preferred because of possible changes in pollution mixes, medical care, 

and lifestyle over time. However, when there are only a few studies available, studies 

from all years will be included.

Population Attributes

The most technically appropriate measures of benefits would be based on impact functions 

that cover the entire sensitive population but allow for heterogeneity across age or other 

relevant demographic factors. In the absence of effect estimates specific to age, sex, 

preexisting condition status, or other relevant factors, it may be appropriate to select effect 

estimates that cover the broadest population to match with the desired outcome of the 

analysis, which is total national-level health impacts. When available, multi-city studies 

are preferred to single city studies because they provide a more generalizable 

representation of the C-R function.

Study Size

Studies examining a relatively large sample are preferred because they generally have 

more power to detect small magnitude effects. A large sample can be obtained in several 

ways, either through a large population or through repeated observations on a smaller 

population (e.g., through a symptom diary recorded for a panel of asthmatic children).

Study Location

U.S. studies are more desirable than non-U.S. studies because of potential differences in 

pollution characteristics, exposure patterns, medical care system, population behavior, and 

lifestyle.

Pollutants Included in 

Model

When modeling the effects of ozone and PM (or other pollutant combinations) jointly, it is 

important to use properly specified impact functions that include both pollutants. Using 

single-pollutant models in cases where both pollutants are expected to affect a health 

outcome can lead to double-counting when pollutants are correlated.

Measure of PM

For this analysis, impact functions based on PM2.5 are preferred to PM10 because of the 

focus on reducing emissions of PM2.5 precursors, and because air quality modeling was 

conducted for this size fraction of PM. Where PM2.5 functions are not available, PM10

functions are used as surrogates, recognizing that there will be potential downward 

(upward) biases if the fine fraction of PM10 is more (less) toxic than the coarse fraction. 

Economically 

Valuable Health 

Effects

Some health effects, such as forced expiratory volume and other technical measurements 

of lung function, are difficult to value in monetary terms. These health effects are not 

quantified in this analysis.

Non-overlapping 

Endpoints

Although the benefits associated with each individual health endpoint may be analyzed 

separately, care must be exercised in selecting health endpoints to include in the overall 

benefits analysis because of the possibility of double-counting of benefits. 
94

When several effect estimates for a pollutant and a given health endpoint have been 

selected, they are quantitatively combined or pooled to derive a more robust estimate of the 

relationship. The BenMAP Technical Appendices provides details of the procedures used to 

combine multiple impact functions (Abt Associates, 2008). In general, we used fixed or random 

effects models to pool estimates from different studies of the same endpoint. Fixed effects 

pooling simply weights each study’s estimate by the inverse variance, giving more weight to 

studies with greater statistical power (lower variance). Random effects pooling accounts for both 

within-study variance and between-study variability, due, for example, to differences in 

population susceptibility. We used the fixed effects model as our null hypothesis and then 

determined whether the data suggest that we should reject this null hypothesis, in which case we 

would use the random effects model. Pooled impact functions are used to estimate hospital 

admissions and asthma exacerbations. For more details on methods used to pool incidence 

estimates, see the BenMAP Manual Appendices (Abt Associates, 2008), which are available 

with the BenMAP software at http://www.epa.gov/benmap.html.

Effect estimates selected for a given health endpoint were applied consistently across all 

locations nationwide. This applies to both impact functions defined by a single effect estimate 

and those defined by a pooling of multiple effect estimates. Although the effect estimate may, in 

fact, vary from one location to another (e.g., because of differences in population susceptibilities 

or differences in the composition of PM), location-specific effect estimates are generally not 

available.

The specific studies from which effect estimates for the primary analysis are drawn are 

included in Table 5-5. We highlight in blue those studies that have been added since the 2005 

CAIR benefits analysis and incorporated into the central benefits estimate. In all cases where 

effect estimates are drawn directly from epidemiological studies, standard errors are used as a 

partial representation of the uncertainty in the size of the effect estimate. Below we provide the 

basis for selecting these studies.
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Table 5-5. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts

Endpoint Pollutant Study

Study 

Population

Premature Mortality

Premature mortality—daily 

time series

O3 

(8-hour 

max)

Bell et al.(2004) (NMMAPS study)

Huang et al. (2004) (multi-city)

Schwartz (2005) (multi-city)

Meta-analyses:

Bell et al. (2005)

Ito et al. (2005)

Levy et al. (2005)

All ages

Premature mortality—cohort 

study, all-cause

PM2.5

(annual 

avg)

Pope et al. (2002)

Laden et al. (2006)

>29 years

>25 years

Premature mortality, total 

exposures

PM2.5

(annual 

avg)

Expert Elicitation (Roman et al., 2008) >24 years

Premature mortality—allcause

PM2.5

(annual 

avg)

Woodruff et al. (2006) Infant (<1 year)

Chronic Illness

Chronic bronchitis

PM2.5

(annual 

avg)

Abbey et al. (1995) >26 years

Nonfatal heart attacks

PM2.5

(24-hour 

avg)

Peters et al. (2001) Adults (>18 

years)

Hospital Admissions 

Respiratory

O3

(8-hour 

max)

Pooled estimate:

Schwartz (1995)—ICD 460–519 (all resp)

Schwartz (1994a; 1994b)—ICD 480–486 

(pneumonia)

Moolgavkar et al. (1997)—ICD 480–487 

(pneumonia)

Schwartz (1994b)—ICD 491–492, 494–496 

(COPD)

Moolgavkar et al. (1997)—ICD 490–496 

(COPD)

>64 years

Burnett et al. (2001) <2 years

PM2.5

(24-hour 

avg)

Pooled estimate:

Moolgavkar (2003)—ICD 490–496 (COPD)

Ito (2003)—ICD 490–496 (COPD)

>64 years

PM2.5

(24-hour 

avg)

Moolgavkar (2000)—ICD 490–496 (COPD) 20–64 years

PM2.5

(24-hour 

avg)

Ito (2003)—ICD 480–486 (pneumonia) >64 years

PM2.5

(24-hour 

avg)

Sheppard (2003)—ICD 493 (asthma) <65 years
96

Endpoint Pollutant Study

Study 

Population

Cardiovascular

PM2.5

(24-hour 

avg)

Pooled estimate:

Moolgavkar (2003)—ICD 390–429 (all 

cardiovascular)

Ito (2003)—ICD 410–414, 427–428 (ischemic 

heart disease, dysrhythmia, heart failure)

>64 years

PM2.5

(24-hour 

avg)

Moolgavkar (2000)—ICD 390–429 (all 

cardiovascular)

20–64 years

Asthma-related ER visits

O3

(8-hour 

max)

Pooled estimate:

Peel et al. (2005)

Wilson et al.(2005)

All ages

All ages

PM2.5

(24-hour 

avg)

Norris et al. (1999) 0–18 years

Other Health Endpoints

Acute bronchitis

PM2.5

(annual 

avg)

Dockery et al. (1996)

8–12 years

Upper respiratory symptoms

PM10 

(24-hour 

avg)

Pope et al. (1991)

Asthmatics, 9–

11 years

Lower respiratory symptoms

PM2.5

(24-hour 

avg)

Schwartz and Neas (2000)

7–14 years

Asthma exacerbations

PM2.5

(24-hour 

avg)

Pooled estimate:

Ostro et al. (2001) (cough, wheeze and shortness 

of breath)

Vedal et al. (1998) (cough)

6–18 yearsa

Work loss days

PM2.5

(24-hour 

avg)

Ostro (1987)

18–65 years

School absence days

O3

(8-hour 

max)

Pooled estimate:

Gilliland et al. (2001)

Chen et al. (2000)

5–17 yearsb

Minor Restricted Activity 

Days (MRADs)

O3 

(8-hour 

max)

Ostro and Rothschild (1989)

18–65 years

PM2.5

(24-hour 

avg)

Ostro and Rothschild (1989)

18–65 years

a The original study populations were 8 to 13 for the Ostro et al. (2001) study and 6 to 13 for the Vedal et al. (1998) 

study. Based on advice from the Science Advisory Board Health Effects Subcommittee (SAB-HES), we extended 

the applied population to 6 to 18, reflecting the common biological basis for the effect in children in the broader 

age group. See: U.S. Science Advisory Board. 2004. Advisory Plans for Health Effects Analysis in the Analytical 

Plan for EPA’s Second Prospective Analysis –Benefits and Costs of the Clean Air Act, 1990—2020. EPA-SABCOUNCIL-ADV-04-004. See also National Research Council (NRC). 2002. Estimating the Public Health 

Benefits of Proposed Air Pollution Regulations. Washington, DC: The National Academies Press.

b Gilliland et al. (2001) studied children aged 9 and 10. Chen et al. (2000) studied children 6 to 11. Based on recent 

advice from the National Research Council and the EPA SAB-HES, we have calculated reductions in school 

absences for all school-aged children based on the biological similarity between children aged 5 to 17.
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5.4.2.1 PM2.5 Premature Mortality Effect Coefficients

Both long- and short-term exposures to ambient levels of PM2.5 air pollution have been 

associated with increased risk of premature mortality. The size of the mortality risk estimates 

from epidemiological studies, the serious nature of the effect itself, and the high monetary value 

ascribed to prolonging life make mortality risk reduction the most significant health endpoint 

quantified in this analysis.

Although a number of uncertainties remain to be addressed by continued research (NRC, 

2002), a substantial body of published scientific literature documents the correlation between 

elevated PM2.5 concentrations and increased mortality rates (U.S. EPA, 2009d). Time-series 

methods have been used to relate short-term (often day-to-day) changes in PM2.5 concentrations 

and changes in daily mortality rates up to several days after a period of elevated PM2.5 

concentrations. Cohort methods have been used to examine the potential relationship between 

community-level PM exposures over multiple years (i.e., long-term exposures) and communitylevel annual mortality rates. Researchers have found statistically significant associations between 

PM2.5 and premature mortality using both types of studies. In general, the risk estimates based on 

the cohort studies are larger than those derived from time-series studies. Cohort analyses are 

thought to better capture the full public health impact of exposure to air pollution over time, 

because they account for the effects of long-term exposures and possibly some component of 

short-term exposures (Kunzli et al., 2001; NRC, 2002). This section discusses some of the issues 

surrounding the estimation of PM2.5-related premature mortality. To demonstrate the sensitivity

of the benefits estimates to the specific sources of information regarding the impact of PM2.5

exposures on the risk of premature death, we are providing estimates in our results tables based 

on studies derived from the epidemiological literature and from the EPA sponsored expert 

elicitation. The epidemiological studies from which these estimates are drawn are described 

below. The expert elicitation project and the derivation of effect estimates from the expert 

elicitation results are described in the 2006 PM2.5 NAAQS RIA and Roman et al. (2008). In the 

interest of brevity we do not repeat those details here. However, Figure 5-18 summarizes the 

estimated PM2.5-related premature mortalities avoided using risk estimates drawn from the expert 

elicitation. 

Over a dozen epidemiological studies have found significant associations between 

various measures of long-term exposure to PM and elevated rates of annual mortality, beginning 

with Lave and Seskin (1977). Most of the published studies found positive (but not always 

statistically significant) associations with available PM indices such as total suspended particles 

(TSP). However, exploration of alternative model specifications sometimes raised questions 
98

about causal relationships (e.g., Lipfert et al., 1989). These early “ecological cross-sectional” 

studies (Lave and Seskin, 1977; Ozkaynak and Thurston, 1987) were criticized for a number of 

methodological limitations, particularly for inadequate control at the individual level for 

variables that are potentially important in causing mortality, such as wealth, smoking, and diet.

Over the last 17 years, several studies using “prospective cohort” designs have been 

published that appear to be consistent with the earlier body of literature. These new “prospective 

cohort” studies reflect a significant improvement over the earlier work because they include 

individual level information with respect to health status and residence. The most extensive 

analyses have been based on data from two prospective cohort groups, often referred to as the 

Harvard “Six-Cities Study” (Dockery et al., 1993; Laden et al., 2006) and the “American Cancer 

Society or ACS study” (Pope et al., 1995; Pope et al., 2002; Pope et al., 2004, Krewski et al. 

2009); these studies have found consistent relationships between fine particle indicators and 

premature mortality across multiple locations in the United States. A third major data set comes 

from the California-based 7th Day Adventist Study (e.g., Abbey et al., 1999), which reported 

associations between long-term PM exposure and mortality in men. Results from this cohort, 

however, have been inconsistent, and the air quality results are not geographically representative 

of most of the United States, and the lifestyle of the population is not reflective of much of the 

U.S. population. Analysis is also available for a cohort of adult male veterans diagnosed with 

hypertension has been examined (Lipfert et al., 2000; Lipfert et al., 2003, 2006). The 

characteristics of this group differ from the cohorts in the Six-Cities, ACS, and 7th Day

Adventist studies with respect to income, race, health status, and smoking status. Unlike previous 

long-term analyses, this study found some associations between mortality and ozone but found 

inconsistent results for PM indicators. Because of the selective nature of the population in the 

veteran’s cohort, we have chosen not to include any effect estimates from the Lipfert et al. 

(2000) study in our benefits assessment. 

Given their consistent results and broad geographic coverage, and importance in 

informing the NAAQS development process, the Six-Cities and ACS data have been particularly

important in benefits analyses. The credibility of these two studies is further enhanced by the fact 

that the initial published studies (Pope et al., 1995 and Dockery et al., 1993) were subject to 

extensive reexamination and reanalysis by an independent team of scientific experts 

commissioned by the Health Effect Institute (HEI) (Krewski et al., 2000). The final results of the 

reanalysis were then independently peer reviewed by a Special Panel of the HEI Health Review 

Committee. The results of these reanalyses confirmed and expanded the conclusions of the 

original investigators. While the HEI reexamination lends credibility to the original studies, it 
99

also highlights sensitivities concerning the relative impact of various pollutants, such as SO2, the 

potential role of education in mediating the association between pollution and mortality, and the 

influence of spatial correlation modeling. Further confirmation and extension of the findings of 

the 1993 Six City Study and the 1995 ACS study were recently completed using more recent air 

quality and a longer follow-up period for the ACS cohort was published over the past several 

years (Pope et al., 2002, 2004; Laden et al., 2006, Krewski et al. 2009). The follow up to the 

Harvard Six City Study both confirmed the effect size from the first analysis and provided 

additional confirmation that reductions in PM2.5 are likely to result in reductions in the risk of 

premature death. This additional evidence stems from the observed reductions in PM2.5 in each 

city during the extended follow-up period. Laden et al. (2006) found that mortality rates 

consistently went down at a rate proportionate to the observed reductions in PM2.5. 

A number of additional analyses have been conducted on the ACS cohort data (Jarrett et 

al., 2009; Pope et al., 2009). These studies have continued to find a strong significant 

relationship between PM2.5 and mortality outcomes and life expectancy. Specifically, much of 

the recent research has suggested a stronger relationship between cardiovascular mortality and 

lung cancer mortality with PM2.5, and a less significant relationship between respiratory-related 

mortality and PM2.5. The extended analyses of the ACS cohort data (Krewski et al. 2009) 

provides additional refinements to the analysis of PM-related mortality by (a) extend the followup period by 2 years to the year 2000, for a total of 18 years; (b) incorporate ecological., or 

neighborhood-level co-variates so as to better estimate personal exposure; (c) perform an 

extensive spatial analysis using land use regression modeling. These additional refinements may

make this analysis well-suited for the assessment of PM-related mortality for EPA benefits 

analyses. 

In developing and improving the methods for estimating and valuing the potential 

reductions in mortality risk over the years, EPA consulted with the SAB-HES. That panel 

recommended using long-term prospective cohort studies in estimating mortality risk reduction 

(U.S. EPA-SAB, 1999b). This recommendation has been confirmed by a report from the 

National Research Council, which stated that “it is essential to use the cohort studies in benefits 

analysis to capture all important effects from air pollution exposure” (NRC, 2002, p. 108). More 

specifically, the SAB recommended emphasis on the ACS study because it includes a much 

larger sample size and longer exposure interval and covers more locations (e.g., 50 cities 

compared to the Six Cities Study) than other studies of its kind. Because of the refinements in the 

extended follow-up analysis, the SAB-HES recommended using the Pope et al. (2002) study as 

the basis for the primary mortality estimate for adults and suggests that alternate estimates of 
100

mortality generated using other cohort and time-series studies could be included as part of the 

sensitivity analysis (U.S. EPA-SAB, 2004b). The PM NAAQS Risk and Exposure Assessment 

(U.S EPA, 2010) utilized risk coefficients drawn from the Krewski et al. (2009) study. In a 

December of 2009 consultation with the SAB-HES, the Agency proposed utilizing the Krewski 

et al. (2009) extended analysis of the ACS cohort data. The panel was supportive of this 

approach.

As noted above, since 2004 SAB review, an extended follow-up of the Harvard Six cities 

study has been published (Laden et al., 2006) and in recent RIAs (see for example the SO2

NAAQS, PM NAAQS, CAIR and Nonroad Diesel RIAs), we have included this estimate of 

mortality impacts based on application of the C-R function derived from this study. We use this 

specific estimate to represent the Six Cities study because it both reflects among the most up-todate science and was cited by many of the experts in their elicitation responses. It is clear from 

the expert elicitation that the results published in Laden et al. (2006) are potentially influential, 

and in fact the expert elicitation results encompass within their range the estimates from both the 

Pope et al. (2002) and Laden et al. (2006) studies (see Figure 5-18 below). These are logical 

choices for anchor points in our presentation because, while both studies are well designed and 

peer reviewed, there are strengths and weaknesses inherent in each, which we believe argues for 

using both studies to generate benefits estimates.

5.4.2.2 Ozone Premature Mortality Effect Coefficients

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. The 2006 Ozone Criteria Document found that “[c]onsistent 

with observed ozone-related increases in respiratory- and cardiovascular-related morbidity, 

several newer multi-city studies, single-city studies, and several meta-analyses of these studies 

have provided relatively strong epidemiologic evidence for associations between short-term 

ozone exposure and all-cause mortality, even after adjustment for the influence of season and 

PM” (U.S. EPA, 2006). The epidemiologic data are also supported by recent experimental data 

from both animal and human studies, which provide evidence suggestive of plausible pathways 

by which risk of respiratory or cardiovascular morbidity and mortality could be increased by 

ambient ozone. With respect to short-term exposure, the Ozone Criteria Document concluded, 

“This overall body of evidence is highly suggestive that ozone directly or indirectly contributes 

to non-accidental and cardiopulmonary-related mortality, but additional research is needed to 

more fully establish underlying mechanisms by which such effects occur” (U.S. EPA, 2006).
101

With respect to the time-series studies, the conclusion regarding the relationship between 

short-term ozone exposure and premature mortality is based, in part, upon recent city-specific 

time-series studies such as the Schwartz (2004) analysis in Houston and the Huang et al. (2004) 

analysis in Los Angeles.20 This conclusion is also based on recent meta-analyses by Bell et al. 

(2005), Ito et al. (2005), and Levy et al. (2005), and a new analysis of the National Morbidity, 

Mortality, and Air Pollution Study (NMMAPS) data set by Bell et al. (2004), which specifically 

sought to disentangle the roles of ozone, PM, weather-related variables, and seasonality. The 

2006 Criteria Document states that “the results from these meta-analyses, as well as several 

single- and multiple-city studies, indicate that co-pollutants generally do not appear to 

substantially confound the association between ozone and mortality” (p. 7-103). However, 

CASAC raised questions about the implications of these time-series results in a policy context. 

Specifically, CASAC emphasized that “...while the time-series study design is a powerful tool to 

detect very small effects that could not be detected using other designs, it is also a blunt tool” 

(U.S. EPA-SAB, 2006). They point to findings (e.g., Stieb et al., 2002, 2003) that indicated 

associations between premature mortality and all of the criteria pollutants, indicating that 

“findings of time-series studies do not seem to allow us to confidently attribute observed effects 

to individual pollutants” (id.). They note that “not only is the interpretation of these associations 

complicated by the fact that the day-to-day variation in concentrations of these pollutants is, to a 

varying degree, determined by meteorology, the pollutants are often part of a large and highly 

correlated mix of pollutants, only a very few of which are measured” (id.). Even with these

uncertainties, the CASAC Ozone Panel, in its review of EPA’s Staff Paper, found “...premature 

total non-accidental and cardiorespiratory mortality for inclusion in the quantitative risk 

assessment to be appropriate.”

In 2006 the EPA requested an NAS study to answer four key questions regarding ozone 

mortality: (1) how did the epidemiological literature to that point improve our understanding of 

the size of the ozone mortality effect? (2) How best can EPA quantify the level of ozone 

mortality impacts from short-term exposure? (3) How might EPA estimate the change in life 

expectancy? (4) What methods should EPA use to estimate the monetary value of changes in 

ozone-related mortality risk and life expectancy?

 

20 For an exhaustive review of the city-specific time-series studies considered in the ozone staff paper, see: U.S. 

Environmental Protection Agency, 2007. Review of the National Ambient Air Quality Standards for Ozone: 

Policy Assessment of Scientific and Technical Information. Prepared by the Office of Air and Radiation. 

Available at http://www.epa.gov/ttn/naaqs/standards/ozone/data/2007_01_ozone_staff_paper.pdf. pp. 5-36.
102

In 2008 the NAS (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 metaanalyses” (NRC, 2008). 

Recent evidence also suggests a relationship between long-term exposure to ozone and 

premature respiratory mortality in the ACS cohort (Jerrett et al. 2009). Jerrett and colleagues find 

that long-term exposure to ozone is linked to respiratory premature mortality in a two-pollutant 

model that controls for PM2.5. This is the first long-term cohort study to have observed such a 

relationship. In a December of 2009 consultation with the SAB-HES, the Agency proposed 

utilizing the Jerrett et al. (2009) analysis of the ACS cohort data. In its advisory, the panel 

recommended quantifying ozone-related mortality using risk coefficients drawn from the shortterm time-series studies.

In view of the findings of the Criteria document and the NAS panel, we include used 

estimates of short-term ozone mortality from the Bell et al. (2004) NMMAPS analysis, the 

Schwartz (2005) multi-city study, the Huang et al. (2005) multi-city study as well as effect 

estimates from the three meta-analyses (Bell et al. 2005, Levy et al. 2005 and Ito et al. 2005). 

5.4.2.3 Chronic Bronchitis

CB is characterized by mucus in the lungs and a persistent wet cough for at least 3 

months a year for several years in a row. CB affects an estimated 5 percent of the U.S. 

population (American Lung Association, 1999). A limited number of studies have estimated the 

impact of air pollution on new incidences of CB. Schwartz (1993) and Abbey et al. (1995) 

provide evidence that long-term PM exposure gives rise to the development of CB in the United 

States. Because the Transport Rule is expected to reduce primarily PM2.5, this analysis uses only 

the Abbey et al. (1995) study, because it is the only study focusing on the relationship between 

PM2.5 and new incidences of CB. 

5.4.2.4 Nonfatal Myocardial Infarctions (Heart Attacks)
103

Nonfatal heart attacks have been linked with short-term exposures to PM2.5 in the United 

States (Peters et al., 2001) and other countries (Poloniecki et al., 1997). We used a recent study 

by Peters et al. (2001) as the basis for the impact function estimating the relationship between 

PM2.5 and nonfatal heart attacks. Peters et al. is the only available U.S. study to provide a 

specific estimate for heart attacks. Other studies, such as Samet et al. (2000) and Moolgavkar 

(2000), show a consistent relationship between all cardiovascular hospital admissions, including 

those for nonfatal heart attacks, and PM. Given the lasting impact of a heart attack on long-term 

health costs and earnings, we provide a separate estimate for nonfatal heart attacks. The estimate 

used in the Transport Rule analysis is based on the single available U.S. effect estimate. The 

finding of a specific impact on heart attacks is consistent with hospital admission and other 

studies showing relationships between fine particles and cardiovascular effects both within and 

outside the United States. Several epidemiologic studies (Liao et al., 1999; Gold et al., 2000; 

Magari et al., 2001) have shown that heart rate variability (an indicator of how much the heart is 

able to speed up or slow down in response to momentary stresses) is negatively related to PM 

levels. Heart rate variability is a risk factor for heart attacks and other coronary heart diseases 

(Carthenon et al., 2002; Dekker et al., 2000; Liao et al., 1997; Tsuji et al., 1996). As such, 

significant impacts of PM on heart rate variability are consistent with an increased risk of heart 

attacks.

5.4.2.5 Hospital and Emergency Room Admissions

Because of the availability of detailed hospital admission and discharge records, there is 

an extensive body of literature examining the relationship between hospital admissions and air 

pollution. Because of this, many of the hospital admission endpoints use pooled impact 

functions based on the results of a number of studies. In addition, some studies have examined 

the relationship between air pollution and emergency room visits. Since most emergency room 

visits do not result in an admission to the hospital (the majority of people going to the emergency 

room are treated and return home), we treat hospital admissions and emergency room visits 

separately, taking account of the fraction of emergency room visits that are admitted to the 

hospital.

The two main groups of hospital admissions estimated in this analysis are respiratory

admissions and cardiovascular admissions. There is not much evidence linking ozone or PM 

with other types of hospital admissions. The only type of emergency room visits that have been 

consistently linked to ozone and PM in the United States are asthma-related visits. 
104

To estimate avoided incidences of cardiovascular hospital admissions associated with 

PM2.5, we used studies by Moolgavkar (2003) and Ito (2003). Additional published studies show 

a statistically significant relationship between PM10 and cardiovascular hospital admissions. 

However, given that the control options we are analyzing are expected to reduce primarily PM2.5, 

we focus on the two studies that examine PM2.5. Both of these studies provide an effect estimate 

for populations over 65, allowing us to pool the impact functions for this age group. Only 

Moolgavkar (2000) provided a separate effect estimate for populations 20 to 64.21 Total 

cardiovascular hospital admissions are thus the sum of the pooled estimate for populations over 

65 and the single study estimate for populations 20 to 64. Cardiovascular hospital admissions 

include admissions for myocardial infarctions. To avoid double-counting benefits from 

reductions in myocardial infarctions when applying the impact function for cardiovascular 

hospital admissions, we first adjusted the baseline cardiovascular hospital admissions to remove 

admissions for myocardial infarctions. 

To estimate total avoided incidences of respiratory hospital admissions, we used impact 

functions for several respiratory causes, including chronic obstructive pulmonary disease 

(COPD), pneumonia, and asthma. As with cardiovascular admissions, additional published 

studies show a statistically significant relationship between PM10 and respiratory hospital 

admissions. We used only those focusing on PM2.5. Both Moolgavkar (2000) and Ito (2003) 

provide effect estimates for COPD in populations over 65, allowing us to pool the impact 

functions for this group. Only Moolgavkar (2000) provides a separate effect estimate for 

populations 20 to 64. Total COPD hospital admissions are thus the sum of the pooled estimate 

for populations over 65 and the single study estimate for populations 20 to 64. Only Ito (2003) 

estimated pneumonia and only for the population 65 and older. In addition, Sheppard (2003) 

provided an effect estimate for asthma hospital admissions for populations under age 65. Total 

avoided incidences of PM-related respiratory-related hospital admissions are the sum of COPD, 

pneumonia, and asthma admissions.

To estimate the effects of PM air pollution reductions on asthma-related ER visits, we use 

the effect estimate from a study of children 18 and under by Norris et al. (1999). As noted 

earlier, there is another study by Schwartz examining a broader age group (less than 65), but the 

Schwartz study focused on PM10 rather than PM2.5. We selected the Norris et al. (1999) effect 

 

21Note that the Moolgavkar (2000) study has not been updated to reflect the more stringent GAM convergence 

criteria. However, given that no other estimates are available for this age group, we chose to use the existing 

study. Given the very small (<5 percent) difference in the effect estimates for people 65 and older with

cardiovascular hospital admissions between the original and reanalyzed results, we do not expect this choice to 

introduce much bias.
105

estimate because it better matched the pollutant of interest. Because children tend to have higher 

rates of hospitalization for asthma relative to adults under 65, we will likely capture the majority 

of the impact of PM2.5 on asthma emergency room visits in populations under 65, although there 

may still be significant impacts in the adult population under 65. 

To estimate avoided incidences of respiratory hospital admissions associated with ozone, 

we used a number of studies examining hospital admissions for a range of respiratory illnesses, 

including pneumonia and COPD. Two age groups, adults over 65 and children under 2, were 

examined. For adults over 65, Schwartz (1995) provides effect estimates for two different cities 

relating ozone and hospital admissions for all respiratory causes (defined as ICD codes 460–

519). Impact functions based on these studies were pooled first before being pooled with other 

studies. Two studies (Moolgavkar et al., 1997; Schwartz, 1994a) examine ozone and pneumonia 

hospital admissions in Minneapolis. One additional study (Schwartz, 1994b) examines ozone 

and pneumonia hospital admissions in Detroit. The impact functions for Minneapolis were 

pooled together first, and the resulting impact function was then pooled with the impact function 

for Detroit. This avoids assigning too much weight to the information coming from one city. 

For COPD hospital admissions, two studies are available: Moolgavkar et al. (1997), conducted 

in Minneapolis, and Schwartz (1994b), conducted in Detroit. These two studies were pooled 

together. To estimate total respiratory hospital admissions for adults over 65, COPD admissions 

were added to pneumonia admissions, and the result was pooled with the Schwartz (1995) 

estimate of total respiratory admissions. Burnett et al. (2001) is the only study providing an 

effect estimate for respiratory hospital admissions in children under 2.

We used two studies as the source of the concentration-response functions we used to 

estimate the effects of ozone exposure on asthma-related emergency room (ER) visits: Peel et al. 

(2005) and Wilson et al. (2005). We estimated the change in ER visits using the effect 

estimate(s) from each study and then pooled the results using the random effects pooling 

technique (see Abt, 2005). The Peel et al. study (2005) estimated asthma-related ER visits for all 

ages in Atlanta, using air quality data from 1993 to 2000. Using Poisson generalized estimating 

equations, the authors found a marginal association between the maximum daily 8-hour average 

ozone level and ER visits for asthma over a 3-day moving average (lags of 0, 1, and 2 days) in a 

single pollutant model. Wilson et al. (2005) examined the relationship between ER visits for 

respiratory illnesses and asthma and air pollution for all people residing in Portland, Maine from 

1998–2000 and Manchester, New Hampshire from 1996–2000. For all models used in the 

analysis, the authors restricted the ozone data incorporated into the model to the months ozone 

levels are usually measured, the spring-summer months (April through September). Using the 
106

generalized additive model, Wilson et al. (2005) found a significant association between the 

maximum daily 8-hour average ozone level and ER visits for asthma in Portland, but found no 

significant association for Manchester. Similar to the approach used to generate effect estimates 

for hospital admissions, we used random effects pooling to combine the results across the 

individual study estimates for ER visits for asthma. The Peel et al. (2005) and Wilson et al. 

(2005) Manchester estimates were not significant at the 95 percent level, and thus, the 

confidence interval for the pooled incidence estimate based on these studies includes negative 

values. This is an artifact of the statistical power of the studies, and the negative values in the 

tails of the estimated effect distributions do not represent improvements in health as ozone 

concentrations are increased. Instead, these should be viewed as a measure of uncertainty due to 

limitations in the statistical power of the study. We included both hospital admissions and ER 

visits as separate endpoints associated with ozone exposure because our estimates of hospital 

admission costs do not include the costs of ER visits and most asthma ER visits do not result in a 

hospital admission.

5.4.2.6 Acute Health Events and School/Work Loss Days

In addition to mortality, chronic illness, and hospital admissions, a number of acute 

health effects not requiring hospitalization are associated with exposure to ambient levels of 

ozone and PM. The sources for the effect estimates used to quantify these effects are described 

below. 

Around 4 percent of U.S. children between the ages of 5 and 17 experience episodes of 

acute bronchitis annually (American Lung Association, 2002c). Acute bronchitis is 

characterized by coughing, chest discomfort, slight fever, and extreme tiredness, lasting for a 

number of days. According to the MedlinePlus medical encyclopedia,22 with the exception of 

cough, most acute bronchitis symptoms abate within 7 to 10 days. Incidence of episodes of acute 

bronchitis in children between the ages of 5 and 17 were estimated using an effect estimate 

developed from Dockery et al. (1996). 

Incidences of lower respiratory symptoms (e.g., wheezing, deep cough) in children aged 

7 to 14 were estimated using an effect estimate from Schwartz and Neas (2000). 

Because asthmatics have greater sensitivity to stimuli (including air pollution), children 

with asthma can be more susceptible to a variety of upper respiratory symptoms (e.g., runny or 

stuffy nose; wet cough; and burning, aching, or red eyes). Research on the effects of air 

 

22See http://www.nlm.nih.gov/medlineplus/ency/article/000124.htm, accessed January 2002. 
107

pollution on upper respiratory symptoms has thus focused on effects in asthmatics. Incidences of 

upper respiratory symptoms in asthmatic children aged 9 to 11 are estimated using an effect 

estimate developed from Pope et al. (1991). 

Health effects from air pollution can also result in missed days of work (either from 

personal symptoms or from caring for a sick family member). Days of work lost due to PM2.5

were estimated using an effect estimate developed from Ostro (1987). Children may also be 

absent from school because of respiratory or other diseases caused by exposure to air pollution. 

Most studies examining school absence rates have found little or no association with PM2.5, but 

several studies have found a significant association between ozone levels and school absence 

rates. We used two recent studies, Gilliland et al. (2001) and Chen et al. (2000), to estimate 

changes in absences (school loss days) due to changes in ozone levels. The Gilliland et al. study

estimated the incidence of new periods of absence, while the Chen et al. study examined absence 

on a given day. We converted the Gilliland estimate to days of absence by multiplying the 

absence periods by the average duration of an absence. We estimated an average duration of 

school absence of 1.6 days by dividing the average daily school absence rate from Chen et al. 

(2000) and Ransom and Pope (1992) by the episodic absence rate from Gilliland et al. (2001). 

This provides estimates from Chen et al. (2000) and Gilliland et al. (2001), which can be pooled 

to provide an overall estimate.

MRAD result when individuals reduce most usual daily activities and replace them with 

less strenuous activities or rest, yet not to the point of missing work or school. For example, a 

mechanic who would usually be doing physical work most of the day will instead spend the day

at a desk doing paper and phone work because of difficulty breathing or chest pain. The effect of 

PM2.5 and ozone on MRAD was estimated using an effect estimate derived from Ostro and 

Rothschild (1989).

For the Transport Rule, we have followed the SAB-HES recommendations regarding 

asthma exacerbations in developing the primary estimate. To prevent double-counting, we 

focused the estimation on asthma exacerbations occurring in children and excluded adults from 

the calculation.23 Asthma exacerbations occurring in adults are assumed to be captured in the 

 

23Estimating asthma exacerbations associated with air pollution exposures is difficult, due to concerns about double 

counting of benefits. Concerns over double counting stem from the fact that studies of the general population also 

include asthmatics, so estimates based solely on the asthmatic population cannot be directly added to the general 

population numbers without double counting. In one specific case (upper respiratory symptoms in children), the 

only study available is limited to asthmatic children, so this endpoint can be readily included in the calculation of 

total benefits. However, other endpoints, such as lower respiratory symptoms and MRADs, are estimated for the 

total population that includes asthmatics. Therefore, to simply add predictions of asthma-related symptoms 
108

general population endpoints such as work loss days and MRADs. Consequently, if we had 

included an adult-specific asthma exacerbation estimate, we would likely double-count incidence 

for this endpoint. However, because the general population endpoints do not cover children 

(with regard to asthmatic effects), an analysis focused specifically on asthma exacerbations for 

children (6 to 18 years of age) could be conducted without concern for double-counting. 

To characterize asthma exacerbations in children, we selected two studies (Ostro et al., 

2001; Vedal et al., 1998) that followed panels of asthmatic children. Ostro et al. (2001) followed 

a group of 138 African-American children in Los Angeles for 13 weeks, recording daily

occurrences of respiratory symptoms associated with asthma exacerbations (e.g., shortness of 

breath, wheeze, and cough). This study found a statistically significant association between 

PM2.5, measured as a 12-hour average, and the daily prevalence of shortness of breath and 

wheeze endpoints. Although the association was not statistically significant for cough, the 

results were still positive and close to significance; consequently, we decided to include this 

endpoint, along with shortness of breath and wheeze, in generating incidence estimates (see 

below). Vedal et al. (1998) followed a group of elementary school children, including 74 

asthmatics, located on the west coast of Vancouver Island for 18 months including measurements 

of daily peak expiratory flow (PEF) and the tracking of respiratory symptoms (e.g., cough, 

phlegm, wheeze, chest tightness) through the use of daily diaries. Association between PM10 and 

respiratory symptoms for the asthmatic population was only reported for two endpoints: cough 

and PEF. Because it is difficult to translate PEF measures into clearly defined health endpoints 

that can be monetized, we only included the cough-related effect estimate from this study in 

quantifying asthma exacerbations. We employed the following pooling approach in combining 

estimates generated using effect estimates from the two studies to produce a single asthma 

exacerbation incidence estimate. First, we pooled the separate incidence estimates for shortness 

of breath, wheeze, and cough generated using effect estimates from the Ostro et al. study, 

because each of these endpoints is aimed at capturing the same overall endpoint (asthma 

exacerbations) and there could be overlap in their predictions. The pooled estimate from the 

Ostro et al. study is then pooled with the cough-related estimate generated using the Vedal study. 

 

generated for the population of asthmatics to these total population-based estimates could result in double 

counting, especially if they evaluate similar endpoints. The SAB-HES, in commenting on the analytical blueprint 

for 812, acknowledged these challenges in evaluating asthmatic symptoms and appropriately adding them into the 

primary analysis (SAB-HES, 2004). However, despite these challenges, the SAB-HES recommends the addition 

of asthma-related symptoms (i.e., asthma exacerbations) to the primary analysis, provided that the studies use the 

panel study approach and that they have comparable design and baseline frequencies in both asthma prevalence 

and exacerbation rates. Note also, that the SAB-HES, while supporting the incorporation of asthma exacerbation 

estimates, does not believe that the association between ambient air pollution, including ozone and PM, and the 

new onset of asthma is sufficiently strong to support inclusion of this asthma-related endpoint in the primary 

estimate. 
109

The rationale for this second pooling step is similar to the first; both studies are attempting to 

quantify the same overall endpoint (asthma exacerbations). 

5.4.2.7 School Absences

Children may be absent from school due to respiratory or other acute diseases caused, or 

aggravated by, exposure to air pollution. Several studies have found a significant association 

between ozone levels and school absence rates. We use two studies (Gilliland et al., 2001; Chen 

et al., 2000) to estimate changes in school absences resulting from changes in ozone levels. The 

Gilliland et al. study estimated the incidence of new periods of absence, while the Chen et al. 

study examined daily absence rates. We converted the Gilliland et al. estimate to days of absence 

by multiplying the absence periods by the average duration of an absence. We estimated 1.6 days 

as the average duration of a school absence, the result of dividing the average daily school 

absence rate from Chen et al. (2000) and Ransom and Pope (1992) by the episodic absence 

duration from Gilliland et al. (2001). Thus, each Gilliland et al. period of absence is converted 

into 1.6 absence days.

Following advice from the National Research Council (2002), we calculated reductions in 

school absences for the full population of school age children, ages five to 17. This is consistent 

with recent peer-reviewed literature on estimating the impact of ozone exposure on school 

absences (Hall et al. 2003). We estimated the change in school absences using both Chen et al. 

(2000) and Gilliland et al. (2001) and then, similar to hospital admissions and ER visits, pooled 

the results using the random effects pooling procedure.

5.4.2.8 Outdoor Worker Productivity

To monetize benefits associated with increased worker productivity resulting from 

improved ozone air quality, we used information reported in Crocker and Horst (1981). Crocker 

and Horst examined the impacts of ozone exposure on the productivity of outdoor citrus workers. 

The study measured productivity impacts. Worker productivity is measuring the value of the loss 

in productivity for a worker who is at work on a particular day, but due to ozone, cannot work as 

hard. It only applies to outdoor workers, like fruit and vegetable pickers, or construction workers. 

Here, productivity impacts are measured as the change in income associated with a change in 

ozone exposure, given as the elasticity of income with respect to ozone concentration. The 

reported elasticity translates a ten percent reduction in ozone to a 1.4 percent increase in income. 

Given the national median daily income for outdoor workers engaged in strenuous activity

reported by the U.S. Census Bureau (2002), $81 per day (2006$), a ten percent reduction in 
110

ozone yields about $0.97 in increased daily wages. We adjust the national median daily income 

estimate to reflect regional variations in income using a factor based on the ratio of county 

median household income to national median household income. No information was available 

for quantifying the uncertainty associated with the central valuation estimate. Therefore, no 

uncertainty analysis was conducted for this endpoint.

5.4.3 Baseline Incidence Estimates

Epidemiological studies of the association between pollution levels and adverse health 

effects generally provide a direct estimate of the relationship of air quality changes to the relative 

risk of a health effect, rather than estimating the absolute number of avoided cases. For example, 

a typical result might be that a 10 ppb decrease in daily ozone levels might, in turn, decrease 

hospital admissions by 3 percent. The baseline incidence of the health effect is necessary to 

convert this relative change into a number of cases. A baseline incidence rate is the estimate of 

the number of cases of the health effect per year in the assessment location, as it corresponds to 

baseline pollutant levels in that location. To derive the total baseline incidence per year, this rate 

must be multiplied by the corresponding population number. For example, if the baseline 

incidence rate is the number of cases per year per million people, that number must be multiplied 

by the millions of people in the total population.

Table 5-6 summarizes the sources of baseline incidence rates and provides average 

incidence rates for the endpoints included in the analysis. For both baseline incidence and 

prevalence data, we used age-specific rates where available. We applied concentration-response 

functions to individual age groups and then summed over the relevant age range to provide an 

estimate of total population benefits. We highlight in blue those rates that have changed since the 

proposal RIA. 

The baseline incidence rates for hospital and emergency department visits that we applied 

in this analysis are an improvement over the rates we used in the proposal analysis in two ways. 

First, these data are newer, and so are a more recent representation of the rates at which 

populations of different ages, and in different locations, visit the hospital and emergency

department for illnesses that may be air pollution related. Second, these newer data are also more 

spatially refined. For many locations within the U.S., these data are resolved at the county- or 

state-level, providing a better characterization of the geographic distribution of hospital and 

emergency department visits. Newer and more spatially resolved incidence rates are likely to 

yield a more reliable estimate of air pollution-related hospitalizations and emergency department 

visits. Consistent with the proposal RIA, we continue to use county-level mortality rates. We 
111

have projected mortality rates such that future mortality rates are consistent with our projections 

of population growth (Abt Associates, 2008).

For the set of endpoints affecting the asthmatic population, in addition to baseline 

incidence rates, prevalence rates of asthma in the population are needed to define the applicable 

population. Table 5-7 lists the prevalence rates used to determine the applicable population for 

asthma symptom endpoints. Note that these reflect current asthma prevalence and assume no 

change in prevalence rates in future years. We again highlight in blue those rates that have been 

updated since the publication of the 2014 proposed Transport Rule RIA.
112

Table 5-6: Baseline Incidence Rates and Population Prevalence Rates for Use in Impact 

Functions, General Population

Endpoint Parameter

Rates

Value Source

Mortality Daily or annual mortality rate 

projected to 2020

Age-, cause-, and 

county-specific 

rate

CDC Wonder (2006–2008)

U.S. Census bureau

Hospitalizations Daily hospitalization rate

Age-, region-, 

state-, county- and 

cause- specific 

rate

2007 HCUP data filesa

Asthma ER Visits Daily asthma ER visit rate

Age-, region-, 

state-, county- and 

cause- specific 

rate

2007 HCUP data filesa

Chronic Bronchitis

Annual prevalence rate per 

person

 Aged 18–44

 Aged 45–64

 Aged 65 and older

0.0367

0.0505

0.0587

1999 NHIS (American Lung 

Association, 2002b, Table 4) 

Annual incidence rate per 

person

0.00378 Abbey et al. (1993, Table 3)

Nonfatal Myocardial 

Infarction (heart 

attacks)

Daily nonfatal myocardial 

infarction incidence rate per 

person, 18+

Age-, region-, 

state-, and countyspecific rate

2007 HCUP data filesa; adjusted 

by 0.93 for probability of 

surviving after 28 days 

(Rosamond et al., 1999)

Asthma Exacerbations

Incidence among asthmatic 

African-American children

 daily wheeze

 daily cough

 daily dyspnea

0.076

0.067

0.037 

Ostro et al. (2001)

Acute Bronchitis Annual bronchitis incidence 

rate, children 0.043 American Lung Association (2002c, Table 11)

Lower Respiratory 

Symptoms

Daily lower respiratory 

symptom incidence among 

childrenb

0.0012 Schwartz et al. (1994, Table 2)

Upper Respiratory 

Symptoms

Daily upper respiratory 

symptom incidence among 

asthmatic children

0.3419 Pope et al. (1991, Table 2)

Work Loss Days Daily WLD incidence rate per 

person (18–65)

 Aged 18–24

 Aged 25–44

 Aged 45–64

0.00540

0.00678

0.00492

1996 HIS (Adams, Hendershot, 

and Marano, 1999, Table 41); 

U.S. Bureau of the Census (2000)

School Loss Days Rate per person per year, 

assuming 180 school days per 

year

9.9

National Center for Education 

Statistics (1996) and 1996 HIS 

(Adams et al., 1999, Table 47); 

Minor RestrictedActivity DaysDaily MRAD incidence rate 

per person

0.02137 Ostro and Rothschild (1989, p. 

243)

a Healthcare Cost and Utilization Program (HCUP) database contains individual level, state and regional-level 

hospital and emergency department discharges for a variety of ICD codes.
113

b Lower respiratory symptoms are defined as two or more of the following: cough, chest pain, phlegm, and 

wheeze.

Table 5-7. Asthma Prevalence Rates Used for this Analysis

Population Group

Asthma Prevalence Rates

Value Source

All Ages 0.0780

American Lung Association (2010b, Table 7)

< 18 0.0941

5–17 0.1070

18–44 0.0719

45–64 0.0745

65+ 0.0716

African American, 5 to 17 0.1776 American Lung Association (2010b, Table 9)

African American, <18 0.1553 American Lung Associationb

A

See ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/NHIS/2000/.

B Calculated by ALA for U.S. EPA, based on NHIS data (CDC, 2008)

5.4.4 Economic Valuation Estimates

Reductions in ambient concentrations of air pollution generally lower the risk of future 

adverse health effects for a large population. Therefore, the appropriate economic measure is 

WTP for changes in risk of a health effect rather than WTP for a health effect that would occur 

with certainty (Freeman, 1993). Epidemiological studies generally provide estimates of the 

relative risks of a particular health effect that is avoided because of a reduction in air pollution. 

We converted those to units of avoided statistical incidence for ease of presentation. We 

calculated the value of avoided statistical incidences by dividing individual WTP for a risk 

reduction by the related observed change in risk. 

WTP estimates generally are not available for some health effects, such as hospital 

admissions. In these cases, we used the cost of treating or mitigating the effect as a primary

estimate. These cost-of-illness (COI) estimates generally understate the true value of reducing 

the risk of a health effect, because they reflect the direct expenditures related to treatment, but 

not the value of avoided pain and suffering (Harrington and Portney, 1987; Berger, 1987). We 

provide unit values for health endpoints (along with information on the distribution of the unit 
114

value) in Table 5-8. All values are in constant year 2006 dollars, adjusted for growth in real 

income out to 2014 using projections provided by Standard and Poor’s. Economic theory argues 

that WTP for most goods (such as environmental protection) will increase if real income 

increases. Many of the valuation studies used in this analysis were conducted in the late 1980s 

and early 1990s. Because real income has grown since the studies were conducted, people’s 

willingness to pay for reductions in the risk of premature death and disease likely has grown as 

well. We did not adjust cost of illness-based values because they are based on current costs. 

Similarly, we did not adjust the value of school absences, because that value is based on current 

wage rates. For these two reasons, these cost of illness estimates may underestimate the 

economic value of avoided health impacts in 2014. The discussion below provides additional 

details on ozone and PM2.5-related related endpoints.

5.4.4.1 Mortality Valuation

Following the advice of the EEAC of the SAB, EPA currently uses the VSL approach in 

calculating the primary estimate of mortality benefits, because we believe this calculation 

provides the most reasonable single estimate of an individual’s willingness to trade off money

for reductions in mortality risk (U.S. EPA-SAB, 2000). The VSL approach is a summary

measure for the value of small changes in mortality risk experienced by a large number of 

people. For a period of time (2004-2008), the Office of Air and Radiation (OAR) valued 

mortality risk reductions using a value of 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) meta-analysis of 33 studies. The $10 million value represented the upper end of the 

interquartile range from the 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) 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 
115

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). 

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)24 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 $6.3 million (2000$).25 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 In the process, the Agency has identified a number of important issues to be 

considered in updating its mortality risk valuation estimates. These are detailed in a white paper 

on “Valuing Mortality Risk Reductions in Environmental Policy,” submitted to the EPA’s 

independent Science Advisory Board (SAB) for review. A meeting with the SAB on this paper 

was held on March 14, 2011 and formal recommendations are anticipated in Summer 2011. 

Draft guidance responding to SAB recommendations will be developed shortly thereafter.

As indicated in the previous section on quantification of premature mortality benefits, we 

assumed for this analysis that some of the incidences of premature mortality related to PM 

exposures occur in a distributed fashion over the 20 years following exposure. To take this into 

account in the valuation of reductions in premature mortality, we applied an annual 3% discount 

rate to the value of premature mortality occurring in future years.26

 

24 In the (draft) update of the Economic Guidelines (U.S. EPA, 2008d), 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. 

25 In this analysis, we adjust the VSL to account for a different currency year (2006$) and to account for income 

growth to 2014. After applying these adjustments to the $6.3 million value, the VSL is $7.8M. 

26 The choice of a discount rate, and its associated conceptual basis, is a topic of ongoing discussion within the 

federal government. EPA adopted a 3% discount rate for its base estimate in this case to reflect reliance on a 

“social rate of time preference” discounting concept. We have also calculated benefits and costs using a 7% rate 

consistent with an “opportunity cost of capital” concept to reflect the time value of resources directed to meet 

regulatory requirements. In this case, the benefit and cost estimates were not significantly affected by the choice 

of discount rate. Further discussion of this topic appears in EPA’s Guidelines for Preparing Economic Analyses

(EPA, 2000b).
116

The economics literature concerning the appropriate method for valuing reductions in 

premature mortality risk is still developing. The adoption of a value for the projected reduction 

in the risk of premature mortality is the subject of continuing discussion within the economics 

and public policy analysis community. EPA strives to use the best economic science in its 

analyses. Given the mixed theoretical finding and empirical evidence regarding adjustments to 

VSL for risk and population characteristics, we use a single VSL for all reductions in mortality

risk.

Although there are several differences between the labor market studies EPA uses to 

derive a VSL estimate and the PM air pollution context addressed here, those differences in the 

affected populations and the nature of the risks imply both upward and downward adjustments. 

Table 5-11 lists some of these differences and the expected effect on the VSL estimate for air 

pollution-related mortality. In the absence of a comprehensive and balanced set of adjustment 

factors, EPA believes it is reasonable to continue to use the $6.3 million value (2000$) while 

acknowledging the significant limitations and uncertainties in the available literature.

Table 5-8: Expected Impact on Estimated Benefits of Premature Mortality Reductions of 

Differences Between Factors Used in Developing Applied VSL and Theoretically 

Appropriate VSL

Attribute Expected Direction of Bias

Age Uncertain, perhaps overestimate

Life Expectancy/Health Status Uncertain, perhaps overestimate

Attitudes Toward Risk Underestimate

Income Uncertain

Voluntary vs. Involuntary Uncertain, perhaps underestimate

Catastrophic vs. Protracted Death Uncertain, perhaps underestimate

The SAB-EEAC has reviewed many potential VSL adjustments and the state of the 

economics literature. The SAB-EEAC advised EPA to “continue to use a wage-risk-based VSL

as its primary estimate, including appropriate sensitivity analyses to reflect the uncertainty of 

these estimates,” and that “the only risk characteristic for which adjustments to the VSL can be 

made is the timing of the risk” (U.S. EPA, 2000a). In developing our primary estimate of the 

benefits of premature mortality reductions, we have followed this advice and discounted over the 

lag period between exposure and premature mortality.
117

Uncertainties Specific to Premature Mortality Valuation. The economic benefits 

associated with reductions in the risk of premature mortality are the largest category of 

monetized benefits of the Transport Rule. In addition, in prior analyses, EPA has identified 

valuation of mortality-related benefits as the largest contributor to the range of uncertainty in 

monetized benefits (U.S. EPA, 1999b).27 Because of the uncertainty in estimates of the value of 

reducing premature mortality risk, it is important to adequately characterize and understand the 

various types of economic approaches available for valuing reductions in mortality risk. Such an 

assessment also requires an understanding of how alternative valuation approaches reflect that 

some individuals may be more susceptible to air pollution-induced mortality or reflect 

differences in the nature of the risk presented by air pollution relative to the risks studied in the 

relevant economics literature.

The health science literature on air pollution indicates that several human characteristics 

affect the degree to which mortality risk affects an individual. For example, some age groups 

appear to be more susceptible to air pollution than others (e.g., the elderly and children). Health 

status prior to exposure also affects susceptibility. An ideal benefits estimate of mortality risk 

reduction would reflect these human characteristics, in addition to an individual’s WTP to 

improve one’s own chances of survival plus WTP to improve other individuals’ survival rates. 

The ideal measure would also take into account the specific nature of the risk reduction 

commodity that is provided to individuals, as well as the context in which risk is reduced. To 

measure this value, it is important to assess how reductions in air pollution reduce the risk of 

dying from the time that reductions take effect onward and how individuals value these changes. 

Each individual’s survival curve, or the probability of surviving beyond a given age, should shift 

as a result of an environmental quality improvement. For example, changing the current 

probability of survival for an individual also shifts future probabilities of that individual’s 

survival. This probability shift will differ across individuals because survival curves depend on 

such characteristics as age, health state, and the current age to which the individual is likely to 

survive.

Although a survival curve approach provides a theoretically preferred method for valuing 

the benefits of reduced risk of premature mortality associated with reducing air pollution, the 

approach requires a great deal of data to implement. The economic valuation literature does not 

 

27 This conclusion was based on an assessment of uncertainty based on statistical error in epidemiological effect 

estimates and economic valuation estimates. Additional sources of model error such as those examined in the PM 

mortality expert elicitation may result in different conclusions about the relative contribution of sources of 

uncertainty.
118

yet include good estimates of the value of this risk reduction commodity. As a result, in this 

study we value reductions in premature mortality risk using the VSL approach.

Other uncertainties specific to premature mortality valuation include the following:

 Across-study variation: There is considerable uncertainty as to whether the available 

literature on VSL provides adequate estimates of the VSL for risk reductions from air 

pollution reduction. Although there is considerable variation in the analytical designs 

and data used in the existing literature, the majority of the studies involve the value of 

risks to a middle-aged working population. Most of the studies examine differences in 

wages of risky occupations, using a hedonic wage approach. Certain characteristics of 

both the population affected and the mortality risk facing that population are believed to 

affect the average WTP to reduce the risk. The appropriateness of a distribution of WTP 

based on the current VSL literature for valuing the mortality-related benefits of 

reductions in air pollution concentrations therefore depends not only on the quality of the 

studies (i.e., how well they measure what they are trying to measure), but also on the 

extent to which the risks being valued are similar and the extent to which the subjects in 

the studies are similar to the population affected by changes in pollution concentrations.

 Level of risk reduction: The transferability of estimates of the VSL from the wage-risk 

studies to the context of the PM NAAQS analysis rests on the assumption that, within a 

reasonable range, WTP for reductions in mortality risk is linear in risk reduction. For 

example, suppose a study provides a result that the average WTP for a reduction in 

mortality risk of 1/100,000 is $50, but that the actual mortality risk reduction resulting 

from a given pollutant reduction is 1/10,000. If WTP for reductions in mortality risk is 

linear in risk reduction, then a WTP of $50 for a reduction of 1/100,000 implies a WTP 

of $500 for a risk reduction of 1/10,000 (which is 10 times the risk reduction valued in 

the study). Under the assumption of linearity, the estimate of the VSL does not depend 

on the particular amount of risk reduction being valued. This assumption has been shown 

to be reasonable provided the change in the risk being valued is within the range of risks 

evaluated in the underlying studies (Rowlatt et al., 1998).

 Voluntariness of risks evaluated: Although job-related mortality risks may differ in 

several ways from air pollution-related mortality risks, the most important difference may

be that job-related risks are incurred voluntarily, or generally assumed to be, whereas air 

pollution-related risks are incurred involuntarily. Some evidence suggests that people 

will pay more to reduce involuntarily incurred risks than risks incurred voluntarily. If 

this is the case, WTP estimates based on wage-risk studies may understate WTP to 

reduce involuntarily incurred air pollution-related mortality risks.
119

 Sudden versus protracted death: A final important difference related to the nature of the 

risk may be that some workplace mortality risks tend to involve sudden, catastrophic 

events, whereas air pollution-related risks tend to involve longer periods of disease and 

suffering prior to death. Some evidence suggests that WTP to avoid a risk of a protracted 

death involving prolonged suffering and loss of dignity and personal control is greater 

than the WTP to avoid a risk (of identical magnitude) of sudden death. To the extent that 

the mortality risks addressed in this assessment are associated with longer periods of 

illness or greater pain and suffering than are the risks addressed in the valuation 

literature, the WTP measurements employed in the present analysis would reflect a 

downward bias.

 Self-selection and skill in avoiding risk: Recent research (Shogren and Stamland, 2002) 

suggests that VSL estimates based on hedonic wage studies may overstate the average 

value of a risk reduction. This is based on the fact that the risk-wage trade-off revealed in 

hedonic studies reflects the preferences of the marginal worker (i.e., that worker who 

demands the highest compensation for his risk reduction). This worker must have either 

a higher workplace risk than the average worker, a lower risk tolerance than the average 

worker, or both. However, the risk estimate used in hedonic studies is generally based on 

average risk, so the VSL may be upwardly biased because the wage differential and risk 

measures do not match.

 Baseline risk and age: Recent research (Smith, Pattanayak, and Van Houtven, 2006) 

finds that because individuals reevaluate their baseline risk of death as they age, the 

marginal value of risk reductions does not decline with age as predicted by some lifetime 

consumption models. This research supports findings in recent stated preference studies 

that suggest only small reductions in the value of mortality risk reductions with 

increasing age.

5.4.4.2 Chronic Bronchitis Valuation

The best available estimate of WTP to avoid a case of CB comes from Viscusi, Magat, 

and Huber (1991). The Viscusi, Magat, and Huber study, however, describes a severe case of 

CB to the survey respondents. We therefore employ an estimate of WTP to avoid a pollutionrelated case of CB, based on adjusting the Viscusi, Magat, and Huber (1991) estimate of the 

WTP to avoid a severe case. This is done to account for the likelihood that an average case of 

pollution-related CB is not as severe. The adjustment is made by applying the elasticity of WTP 

with respect to severity reported in the Krupnick and Cropper (1992) study. Details of this 

adjustment procedure are provided in the Benefits Technical Support Document (TSD) for the 

Nonroad Diesel rulemaking (Abt Associates, 2003).
120

We use the mean of a distribution of WTP estimates as the central tendency estimate of 

WTP to avoid a pollution-related case of CB in this analysis. The distribution incorporates 

uncertainty from three sources: the WTP to avoid a case of severe CB, as described by Viscusi, 

Magat, and Huber; the severity level of an average pollution-related case of CB (relative to that 

of the case described by Viscusi, Magat, and Huber); and the elasticity of WTP with respect to 

severity of the illness. Based on assumptions about the distributions of each of these three 

uncertain components, we derive a distribution of WTP to avoid a pollution-related case of CB 

by statistical uncertainty analysis techniques. The expected value (i.e., mean) of this distribution, 

which is about $410,000 (2007$), is taken as the central tendency estimate of WTP to avoid a 

PM-related case of CB.

5.4.4.3 Nonfatal Myocardial Infarctions Valuation

The Agency has recently incorporated into its analyses the impact of air pollution on the 

expected number of nonfatal heart attacks, although it has examined the impact of reductions in 

other related cardiovascular endpoints. We were not able to identify a suitable WTP value for 

reductions in the risk of nonfatal heart attacks. Instead, we use a COI unit value with two 

components: the direct medical costs and the opportunity cost (lost earnings) associated with the 

illness event. Because the costs associated with a myocardial infarction extend beyond the initial 

event itself, we consider costs incurred over several years. Using age-specific annual lost 

earnings estimated by Cropper and Krupnick (1990) and a 3% discount rate, we estimated a 

present discounted value in lost earnings (in 2007$) over 5 years due to a myocardial infarction 

of $11,080 for someone between the ages of 25 and 44, $16,331 for someone between the ages 

of 45 and 54, and $94,396 for someone between the ages of 55 and 65. The corresponding agespecific estimates of lost earnings (in 2007$) using a 7% discount rate are $9,920, $14,621, and 

$84,513, respectively. Cropper and Krupnick (1990) do not provide lost earnings estimates for 

populations under 25 or over 65. As such, we do not include lost earnings in the cost estimates 

for these age groups.

We found three possible sources in the literature of estimates of the direct medical costs 

of myocardial infarction:

 Wittels et al. (1990) estimated expected total medical costs of myocardial infarction over 

5 years to be $51,211 (in 1986$) for people who were admitted to the hospital and 

survived hospitalization. (There does not appear to be any discounting used.) Wittels et 

al. was used to value coronary heart disease in the 812 Retrospective Analysis of the 

Clean Air Act. Using the CPI-U for medical care, the Wittels estimate is $147,359 in 

year 2007$. This estimated cost is based on a medical cost model, which incorporated 
121

therapeutic options, projected outcomes, and prices (using “knowledgeable cardiologists” 

as consultants). The model used medical data and medical decision algorithms to 

estimate the probabilities of certain events and/or medical procedures being used. The 

authors note that the average length of hospitalization for acute myocardial infarction has 

decreased over time (from an average of 12.9 days in 1980 to an average of 11 days in 

1983). Wittels et al. used 10 days as the average in their study. It is unclear how much 

further the length of stay for myocardial infarction may have decreased from 1983 to the 

present. The average length of stay for ICD code 410 (myocardial infarction) in the year2000 Agency for Healthcare Research and Quality (AHRQ) HCUP database is 5.5 days. 

However, this may include patients who died in the hospital (not included among our 

nonfatal myocardial infarction cases), whose length of stay was therefore substantially

shorter than it would be if they had not died.

 Eisenstein et al. (2001) estimated 10-year costs of $44,663 in 1997$, or $66,833 in 2007$ 

for myocardial infarction patients, using statistical prediction (regression) models to 

estimate inpatient costs. Only inpatient costs (physician fees and hospital costs) were 

included.

 Russell et al. (1998) estimated first-year direct medical costs of treating nonfatal 

myocardial infarction of $15,540 (in 1995$) and $1,051 annually thereafter. Converting 

to year 2007$, that would be $30,058 for a 5-year period (without discounting) or 

$39,256 for a 10-year period.

In summary, the three different studies provided significantly different values (see Table 

5-9).

Table 5-9: Alternative Direct Medical Cost of Illness Estimates for Nonfatal Heart Attacks

Study Direct Medical Costs (2007$) Over an x-Year Period, for x =

Wittels et al. (1990) $147,359a5

Russell et al. (1998) $30,058b5

Eisenstein et al. (2001) $66,833b10

Russell et al. (1998) $39,256b10

a Wittels et al. (1990) did not appear to discount costs incurred in future years.

b Using a 3% discount rate. Discounted values as reported in the study.

As noted above, the estimates from these three studies are substantially different, and we 

have not adequately resolved the sources of differences in the estimates. Because the wagerelated opportunity cost estimates from Cropper and Krupnick (1990) cover a 5-year period, we 

used estimates for medical costs that similarly cover a 5-year period (i.e., estimates from Wittels 
122

et al. (1990) and Russell et al. (1998). We used a simple average of the two 5-year estimates, or 

$88,708, and added it to the 5-year opportunity cost estimate. The resulting estimates are given 

in Table 5-10.

Table 5-10: Estimated Costs Over a 5-Year Period (in 2007$) of a Nonfatal Myocardial 

Infarction

Age Group Opportunity Cost Medical CostaTotal Cost

0–24 $0 $88,709 $88,709

25–44 $11,080b$88,709 $99,789

45–54 $16,331b$88,709 $105,040

55–65 $94,396b$88,709 $183,105

> 65 $0 $88,709 $88,709

a An average of the 5-year costs estimated by Wittels et al. (1990) and Russell et al. (1998).

b

From Cropper and Krupnick (1990), using a 3% discount rate.

5.4.4. Hospital Admissions Valuation

In the absence of estimates of societal WTP to avoid hospital visits/admissions for 

specific illnesses, estimates of total cost of illness (total medical costs plus the value of lost 

productivity) typically are used as conservative, or lower bound, estimates. These estimates are 

biased downward, because they do not include the willingness-to-pay value of avoiding pain and 

suffering. 

The International Classification of Diseases (ICD-9, 1979) code-specific COI estimates 

used in this analysis consist of estimated hospital charges and the estimated opportunity cost of 

time spent in the hospital (based on the average length of a hospital stay for the illness). We 

based all estimates of hospital charges and length of stays on statistics provided by the Agency

for Healthcare Research and Quality (AHRQ 2000). We estimated the opportunity cost of a day

spent in the hospital as the value of the lost daily wage, regardless of whether the hospitalized 

individual is in the workforce. To estimate the lost daily wage, we divided the 1990 median 

weekly wage by five and inflated the result to year 2007$ using the CPI-U “all items.” The 

resulting estimate is $127.93. The total cost-of-illness estimate for an ICD code-specific hospital 

stay lasting n days, then, was the mean hospital charge plus $127.93 multiplied by n.
123

Table 5-11: Unit Values for Economic Valuation of Health Endpoints (2007$)

Health Endpoint Central Estimate of Value 

Per Statistical Incidence

2000

Income Level

2014 Income 

Level

Derivation of Distributions of Estimates

Premature Mortality 

(Value of a 

Statistical Life)

$7,900,000 $8,700,000 EPA currently recommends a central VSL of $6.3m 

(2000$) based on a Weibull distribution fitted to 26 

published VSL estimates (5 contingent valuation and 

21 labor market studies). The underlying studies, the 

distribution parameters, and other useful information 

are available in Appendix B of EPA's current 

Guidelines for Preparing Economic Analyses (U.S. 

EPA, 2000). 

Chronic Bronchitis 

(CB)

$430,000 $480,000 The WTP to avoid a case of pollution-related CB is 

calculated as where x is the severity of an average CB 

case, WTP13 is the WTP for a severe case of CB, and 

$ is the parameter relating WTP to severity, based on 

the regression results reported in Krupnick and 

Cropper (1992). The distribution of WTP for an 

average severity-level case of CB was generated by 

Monte Carlo methods, drawing from each of three 

distributions: (1) WTP to avoid a severe case of CB is 

assigned a 1/9 probability of being each of the first 

nine deciles of the distribution of WTP responses in 

Viscusi et al. (1991); (2) the severity of a pollutionrelated case of CB (relative to the case described in 

the Viscusi study) is assumed to have a triangular 

distribution, with the most likely value at severity 

level 6.5 and endpoints at 1.0 and 12.0; and (3) the 

constant in the elasticity of WTP with respect to 

severity is normally distributed with mean = 0.18 and 

standard deviation = 0.0669 (from Krupnick and 

Cropper [1992]). This process and the rationale for 

choosing it is described in detail in the Costs and 

Benefits of the Clean Air Act, 1990 to 2010 (U.S. 

EPA, 1999b). 
124

Nonfatal 

Myocardial 

Infarction (heart 

attack)

3% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–65

Age 66 and over

7% discount rate

Age 0–24

Age 25–44

Age 45–54

Age 55–65

Age 66 and over

$88,709

$99,789

$105,040

$183,105

$88,709

$87,889

$98,970

$104,220

$182,285

$87,889

$88,709

$99,789

$105,040

$183,105

$88,709 

$87,889

$98,970

$104,220

$182,285

$87,889

No distributional information available. Age-specific 

cost-of-illness values reflect lost earnings and direct 

medical costs over a 5-year period following a 

nonfatal MI. Lost earnings estimates are based on 

Cropper and Krupnick (1990). Direct medical costs 

are based on simple average of estimates from Russell 

et al. (1998) and Wittels et al. (1990).

Lost earnings:

Cropper and Krupnick (1990). Present discounted 

value of 5 years of lost earnings:

age of onset: at 3% at 7%

25–44 $8,774 $7,855

45–54 $12,932 11,578

55–65 $74,746 66,920

Direct medical expenses: An average of:

1. Wittels et al. (1990) ($102,658—no discounting)

2. Russell et al. (1998), 5-year period ($22,331 at 

3% discount rate; $21,113 at 7% discount rate)

Hospital 

Admissions

Chronic 

Obstructive 

Pulmonary 

Disease (COPD)

$17,106 $17,106 No distributional information available. The COI 

estimates (lost earnings plus direct medical costs) are 

based on ICD-9 code-level information (e.g., average 

hospital care costs, average length of hospital stay, 

and weighted share of total COPD category illnesses) 

reported in Agency for Healthcare Research and 

Quality (2000) (www.ahrq.gov). 

Asthma 

Admissions

$11,366 $11,366 No distributional information available. The COI 

estimates (lost earnings plus direct medical costs) are 

based on ICD-9 code-level information (e.g., average 

hospital care costs, average length of hospital stay, 

and weighted share of total asthma category illnesses) 

reported in Agency for Healthcare Research and 

Quality (2000) (www.ahrq.gov). 

All 

Cardiovascular

$28,760 $28,760 No distributional information available. The COI 

estimates (lost earnings plus direct medical costs) are 

based on ICD-9 code-level information (e.g., average 

hospital care costs, average length of hospital stay, 

and weighted share of total cardiovascular category 

illnesses) reported in Agency for Healthcare Research 

and Quality (2000) (www.ahrq.gov). 

All respiratory 

(ages 65+)

$24,157 $24,157 No distributions available. The COI point estimates 

(lost earnings plus direct medical costs) are based on 

ICD-9 code level information (e.g., average hospital 

care costs, average length of hospital stay, and 

weighted share of total COPD category illnesses) 

reported in Agency for Healthcare Research and 

Quality, 2000 (www.ahrq.gov).
125

All respiratory 

(ages 0–2)

$10,402 $10,402 No distributions available. The COI point estimates 

(lost earnings plus direct medical costs) are based on 

ICD-9 code level information (e.g., average hospital 

care costs, average length of hospital stay, and 

weighted share of total COPD category illnesses) 

reported in Agency for Healthcare Research and 

Quality, 2000 (www.ahrq.gov).

Emergency Room 

Visits for Asthma

$385 $385 No distributional information available. Simple 

average of two unit COI values:

(1) $311.55, from Smith et al. (1997) and

(2) $260.67, from Stanford et al. (1999).

Respiratory Ailments Not Requiring Hospitalization

Upper Respiratory 

Symptoms (URS)

$30 $31 Combinations of the three symptoms for which WTP 

estimates are available that closely match those listed 

by Pope et al. result in seven different “symptom 

clusters,” each describing a “type” of URS. A dollar 

value was derived for each type of URS, using midrange estimates of WTP (IEc, 1994) to avoid each 

symptom in the cluster and assuming additivity of 

WTPs. In the absence of information surrounding the 

frequency with which each of the seven types of URS 

occurs within the URS symptom complex, we 

assumed a uniform distribution between $9.2 and 

$43.1.

Lower 

Respiratory 

Symptoms (LRS)

$19 $20 Combinations of the four symptoms for which WTP 

estimates are available that closely match those listed 

by Schwartz et al. result in 11 different “symptom 

clusters,” each describing a “type” of LRS. A dollar 

value was derived for each type of LRS, using midrange estimates of WTP (IEc, 1994) to avoid each 

symptom in the cluster and assuming additivity of 

WTPs. The dollar value for LRS is the average of the 

dollar values for the 11 different types of LRS. In the 

absence of information surrounding the frequency 

with which each of the 11 types of LRS occurs within 

the LRS symptom complex, we assumed a uniform 

distribution between $6.9 and $24.46.

Asthma 

Exacerbations

$52 $54 Asthma exacerbations are valued at $45 per incidence, 

based on the mean of average WTP estimates for the 

four severity definitions of a “bad asthma day,” 

described in Rowe and Chestnut (1986). This study 

surveyed asthmatics to estimate WTP for avoidance of 

a “bad asthma day,” as defined by the subjects. For 

purposes of valuation, an asthma exacerbation is 

assumed to be equivalent to a day in which asthma is 

moderate or worse as reported in the Rowe and 

Chestnut (1986) study. The value is assumed have a 

uniform distribution between $15.6 and $70.8.
126

Acute Bronchitis $430 $450 Assumes a 6-day episode, with the distribution of the 

daily value specified as uniform with the low and high 

values based on those recommended for related 

respiratory symptoms in Neumann et al. (1994). The 

low daily estimate of $10 is the sum of the mid-range 

values recommended by IEc (1994) for two symptoms 

believed to be associated with acute bronchitis: 

coughing and chest tightness. The high daily estimate 

was taken to be twice the value of a minor respiratory 

restricted-activity day, or $110. 

Work Loss Days 

(WLDs)

Variable 

(U.S. median 

= $130)

Variable 

(U.S. median 

= $130)

No distribution available. Point estimate is based on 

county-specific median annual wages divided by 50 

(assuming 2 weeks of vacation) and then by 5—to get 

median daily wage. U.S. Year 2000 Census, compiled 

by Geolytics, Inc.

Minor Restricted 

Activity Days 

(MRADs)

$61 $64 Median WTP estimate to avoid one MRAD from 

Tolley et al. (1986). Distribution is assumed to be 

triangular with a minimum of $22 and a maximum of 

$83, with a most likely value of $52. Range is based 

on assumption that value should exceed WTP for a 

single mild symptom (the highest estimate for a single 

symptom—for eye irritation—is $16.00) and be less 

than that for a WLD. The triangular distribution 

acknowledges that the actual value is likely to be 

closer to the point estimate than either extreme.

School Absence 

Days

$90 $90 No distribution available

A Due to a clerical error, the VSL estimates summarized in the proposal RIA were incorrectly reported; this 

error was not present in the calculation of mortality impacts.

5.4.4.5 Asthma-Related Emergency Room Visits Valuation

To value asthma emergency room visits, we used a simple average of two estimates 

from the health economics literature. The first estimate comes from Smith et al. (1997), who 

reported approximately 1.2 million asthma-related emergency room visits in 1987, at a total 

cost of $186.5 million (1987$). The average cost per visit that year was $155; in 2007$, that 

cost was $419.37 (using the CPI-U for medical care to adjust to 2007$). The second estimate 

comes from Stanford et al. (1999), who reported the cost of an average asthma-related 

emergency room visit at $350.87, based on 1996–1997 data. A simple average of the two 

estimates yields a (rounded) unit value of $385.

5.4.4.6 Minor Restricted Activity Days Valuation 

No studies are reported to have estimated WTP to avoid a minor restricted activity

day. However, one of EPA’s contractors, IEc (1994) has derived an estimate of willingness 
127

to pay to avoid a minor respiratory restricted activity day, using estimates from Tolley et al. 

(1986) of WTP for avoiding a combination of coughing, throat congestion and sinusitis. The 

IEc estimate of WTP to avoid a minor respiratory restricted activity day is $38.37 (1990$), or 

about $63.09 (2007$).

Although Ostro and Rothschild (1989) statistically linked ozone and minor restricted 

activity days, it is likely that most MRADs associated with ozone exposure are, in fact, minor 

respiratory restricted activity days. However, for the purpose of valuing this health endpoint, 

we used the estimate of mean WTP to avoid a minor respiratory restricted activity day.

5.4.4.7 School Absences Valuation

To value a school absence, we: (1) estimated the probability that if a school child 

stays home from school, a parent will have to stay home from work to care for the child; and 

(2) valued the lost productivity at the parent’s wage. To do this, we estimated the number of 

families with school-age children in which both parents work, and we valued a school-loss 

day as the probability that such a day also would result in a work-loss day. We calculated this 

value by multiplying the proportion of households with school-age children by a measure of 

lost wages.

We used this method in the absence of a preferable WTP method. However, this 

approach suffers from several uncertainties. First, it omits willingness to pay to avoid the 

symptoms/illness that resulted in the school absence; second, it effectively gives zero value 

to school absences that do not result in work-loss days; and third, it uses conservative 

assumptions about the wages of the parent staying home with the child. Finally, this method 

assumes that parents are unable to work from home. If this is not a valid assumption, then 

there would be no lost wages. 

For this valuation approach, we assumed that in a household with two working 

parents, the female parent will stay home with a sick child. From the Statistical Abstract of 

the United States (U.S. Census Bureau, 2001), we obtained: (1) the numbers of single,

married and “other” (widowed, divorced or separated) working women with children; and (2) 

the rates of participation in the workforce of single, married and “other” women with 

children. From these two sets of statistics, we calculated a weighted average participation 
128

rate of 72.85 percent.

Our estimate of daily lost wage (wages lost if a mother must stay at home with a sick 

child) is based on the year 2007 median weekly wage among women ages 25 and older (U.S. 

Census Bureau, 2001). This median weekly wage is $675. Dividing by five gives an 

estimated median daily wage of $140. To estimate the expected lost wages on a day when a 

mother has to stay home with a school-age child, we first estimated the probability that the 

mother is in the workforce then multiplied that estimate by the daily wage she would lose by 

missing a workday: 72.85 percent times $140, for a total loss of $98.48. This valuation 

approach is similar to that used by Hall et al. (2003).

5.4.4.8 Visibility Valuation

Reductions in NO2 and SO2 emissions along with the secondary formation of PM2.5 

would improve the level of visibility throughout the United States because these suspended 

particles and gases degrade visibility by scattering and absorbing light (U.S. EPA, 2009d). 

Visibility is also referred to as visual air quality (VAQ), and it directly affects people’s 

enjoyment of a variety of daily activities (U.S. EPA, 2009d). Good visibility increases 

quality of life where individuals live and work, and where they travel for recreational 

activities, including sites of unique public value, such as the Great Smoky Mountains 

National Park. This section discusses the measurement of the economic benefits of improved 

visibility. Visibility benefit estimates are generated for all Class I areas in the U.S., though 

the majority of the air quality improvements occur among eastern states.

Visual air quality (VAQ) is commonly measured as either light extinction, which is 

defined as the loss of light per unit of distance in terms of inverse megameters (Mm-1) or the 

deciview (dv) metric (Pitchford and Malm, 1994), which is a logarithmic function of 

extinction. Extinction and deciviews are physical measures of the amount of visibility

impairment (e.g., the amount of “haze”), with both extinction and deciview increasing as the 

amount of haze increases. Pitchford and Malm (1994) characterize a change of one deciview 

as “a small but perceptible scenic change under many circumstances.” Light extinction is the 

optical characteristic of the atmosphere that occurs when light is either scattered or absorbed, 

which converts the light to heat. Particulate matter and gases can both scatter and absorb 

light. Fine particles with significant light-extinction efficiencies include sulfates, nitrates, 

organic carbon, elemental carbon, and soil (Sisler, 1996). The extent to which any amount of 
129

light extinction affects a person’s ability to view a scene depends on both scene and light 

characteristics. For example, the appearance of a nearby object (i.e. a building) is generally 

less sensitive to a change in light extinction than the appearance of a similar object at a 

greater distance. See Figure 5-3 for an illustration of the important factors affecting 

visibility. 

Figure 5-3: Important factors involved in seeing a scenic vista (Malm, 1999)

In conjunction with the U.S. National Park Service, the U.S. Forest Service, other 

Federal land managers, and State organizations in the U.S., the U.S. EPA has supported 

visibility monitoring in national parks and wilderness areas since 1988. The monitoring 

network known as IMPROVE (Interagency Monitoring of Protected Visual Environments) 

includes 156 sites that represent almost all of the Class I areas across the country (see Figure 

5-4) (U.S. EPA, 2009d). 
130

Figure 5-4: Mandatory Class I Areas in the U.S. 

Annual average visibility conditions (reflecting light extinction due to both 

anthropogenic and non-anthropogenic sources) vary regionally across the U.S. (U.S. EPA, 

2009d). Particulate sulfate is the dominant source of regional haze in the eastern U.S (>50% 

of the particulate light extinction) and an important contributor to haze elsewhere in the 

country (>20% of particulate light extinction) (U.S. EPA, 2009d). Higher visibility

impairment levels in the East are due to generally higher concentrations of fine particles, 

particularly sulfates, and higher average relative humidity levels. (U.S. EPA, 2009d). 

Humidity increases visibility impairment because some particles such as ammonium sulfate 

and ammonium nitrate absorb water and form droplets that become larger when relative 

humidity increases, thus resulting in increased light scattering (U.S. EPA, 2009d). 

While visibility trends have improved in most Class I areas, the recent data show that 

these areas continue to suffer from visibility impairment. In eastern parks, average visual 
131

range has decreased from 90 miles to 15-25 miles, and in the West, visual range has 

decreased from 140 miles to 35-90 miles (U.S. EPA, 2004; U.S. EPA, 1999b). 

 EPA distinguishes benefits from two categories of visibility changes: residential 

visibility and recreational visibility. In both cases economic benefits are believed to consist 

of use values and nonuse values. Use values include the aesthetic benefits of better visibility, 

improved road and air safety, and enhanced recreation in activities like hunting and 

birdwatching. Nonuse values are based on people’s beliefs that the environment ought to 

exist free of human-induced haze. Nonuse values may be more important for recreational 

areas, particularly national parks and monuments.

Residential visibility benefits are those that occur from visibility changes in urban, 

suburban, and rural areas. In previous assessments, EPA used a study on residential visibility

valuation conducted in 1990 (McClelland et al., 1993). Subsequently, EPA designated the 

McClelland et al. study as significantly less reliable for regulatory benefit-cost analysis 

consistent with SAB advice (U.S. EPA-SAB, 1999). Although a wide range of published, 

peer-review literature supports a non-zero value for residential visibility (Brookshire et al., 

1982; Rae, 1983; Tolley et al., 1986; Chestnut and Rowe, 1990c; McClleand et al., 1993; 

Loehman et al., 1994), the residential visibility benefits have not been calculated in this 

analysis.

For recreational visibility, EPA has determined that only one existing study provides 

monetary estimates of the value of changes in recreational visibility; a contingent valuation 

(CV) survey conducted by Chestnut and Rowe in 1988 (Chestnut and Rowe, 1990a; 1990b). 

Although there are several other studies in the literature on recreational visibility valuation, 

they are older and use less robust methods. In EPA’s judgment, the Chestnut and Rowe 

study contains many of the elements of a valid CV study and is sufficiently reliable to serve 

as the basis for monetary estimates of the benefits of visibility changes in recreational areas. 

There has been a great deal of controversy and significant development of both theoretical 

and empirical knowledge about how to conduct CV surveys in the past couple of decades. In 

EPA’s judgment, the Chestnut and Rowe study contains many of the elements of a valid CV 

study and is sufficiently reliable to serve as the basis for monetary estimates of the benefits of 
132

visibility changes in recreational areas.28 This study serves as an essential input to our 

estimates of the benefits of recreational visibility improvements in the primary benefits 

estimates. 

For the purposes of this analysis, recreational visibility improvements are defined as 

those that occur specifically in federal Class I areas.29 A key distinction between recreational 

and residential benefits is that only those people living in residential areas are assumed to 

receive benefits from residential visibility, while all households in the United States are 

assumed to derive some benefit from improvements in Class I areas. Values are assumed to 

be higher if the Class I area is located close to their home.30 The Chestnut and Rowe study

measured the demand for visibility in Class I areas managed by the National Park Service 

(NPS) in three broad regions of the country: California, the Southwest, and the Southeast. 

Respondents in five states were asked about their WTP to protect national parks or NPSmanaged wilderness areas within a particular region. The survey used photographs reflecting 

different visibility levels in the specified recreational areas. The visibility levels in these 

photographs were later converted to deciviews for the current analysis. The survey data 

collected were used to estimate a WTP equation for improved visibility. In addition to the 

visibility change variable, the estimating equation also included household income as an 

explanatory variable.

The Chestnut and Rowe survey measured the demand for visibility in Class I areas 

managed by the NPS in three broad regions of the country that included 86 of the 156 Class I 

areas; California, the Colorado Plateau (Southwest), and the Southeast areas. We can infer 

the value of visibility changes in the other Class I areas by transferring values of visibility

changes at Class I areas in the study regions. A complete description of the benefits transfer 

 

28 An SAB advisory letter indicates that “many members of the Council believe that the Chestnut and Rowe 

study is the best available” (EPA-SAB-COUNCIL-ADV-00-002, 1999, p. 13). However, the committee did 

not formally approve use of these estimates because of concerns about the peer-reviewed status of the study. 

EPA believes the study has received adequate review and has been cited in numerous peer-reviewed 

publications (Chestnut and Dennis, 1997).

29 EPA has designated 156 areas as mandatory Class I federal areas for visibility protection, including national 

parks that exceed 6,000 acres and wilderness areas that exceed 5,000 acres (40 CFR §81.400).

30 For details of the visibility estimates discussed in this chapter, please refer to the Benefits TSD for the 

Nonroad Diesel rulemaking (Abt Associates, 2003).
133

method used to infer values for visibility changes in Class I areas outside the study regions 

is provided in the Appendix I of the PM NAAQS RIA (U.S. EPA, 2006).

The Chestnut and Rowe study (Chestnut and Rowe, 1990a; 1990b), although 

representing the best available estimates, has a number of limitations. These include the 

following:

 The vintage of the survey (late 1980s) invites questions whether the values would still 

be valid for current populations, or more importantly for this analysis, future 

populations in 2014.

The survey focused only on populations in five states, so the application of the estimated 

values to populations outside those states requires that preferences of populations in 

the five surveyed states be similar to those of non-surveyed states.

 There is an inherent difficulty in separating values expressed for visibility

improvements from an overall value for improved air quality. The survey attempted 

to control for this by informing respondents that “other households are being asked 

about visibility, human health, and vegetation protections in urban areas and at 

national parks in other regions.” However, most of the respondents did not feel that 

they were able to segregate visibility at national parks entirely from residential 

visibility and health effects.

 It is not clear exactly what visibility improvements the respondents to the Chestnut 

and Rowe survey were valuing. The WTP question asked about changes in average 

visibility, but the survey respondents were shown photographs of only summertime 

conditions, when visibility is generally at its worst. It is possible that the respondents 

believed those visibility conditions held year-round, in which case they would have 

been valuing much larger overall improvements in visibility than what otherwise 

would be the case. For the purpose of the benefits analysis for this rule, EPA assumed 

that respondents provided values for changes in annual average visibility. Because 

most policies will result in a shift in the distribution of visibility (usually affecting the 

worst days more than the best days), the annual average may not be the most relevant 

metric for policy analysis.
134

 The survey did not include reminders of possible substitutes (e.g., visibility at other 

parks) or budget constraints. These reminders are considered to be best practice for 

stated preference surveys.

 The Chestnut and Rowe survey focused on visibility improvements in and around 

national parks and wilderness areas in three regions of the United States: Southwest, 

Southeast and California. Given that national parks and wilderness areas exhibit 

unique characteristics, it is not clear whether the WTP estimate obtained from 

Chestnut and Rowe can be transferred to other national parks and wilderness areas, 

without introducing additional uncertainty.

In general, the survey design and implementation reflect the period in which the 

survey was conducted. Since that time, many improvements to the stated preference 

methodology have been developed. As future survey efforts are completed, EPA will 

incorporate values for visibility improvements reflecting the improved survey designs.

The estimated relationship from the Chestnut and Rowe study is only directly 

applicable to the populations represented by survey respondents. EPA used benefits transfer 

methodology to extrapolate these results to the population affected by the reductions in 

precursor emissions associated with this rule. A general WTP equation for improved 

visibility (measured in deciviews) was developed as a function of the baseline level of 

visibility, the magnitude of the visibility improvement, and household income. The 

behavioral parameters of this equation were taken from analysis of the Chestnut and Rowe

data. These parameters were used to calibrate WTP for the visibility changes resulting from 

this rule. The method for developing calibrated WTP functions is based on the approach 

developed by Smith et al. (2002). Available evidence indicates that households are willing to 

pay more for a given visibility improvement as their income increases (Chestnut, 1997). The 

benefits estimates here incorporate Chestnut’s estimate that a 1% increase in income is 

associated with a 0.9% increase in WTP for a given change in visibility. A more detailed 

explanation of the visibility benefits methodology is provided in Appendix I of the PM 

NAAQS RIA (U.S. EPA, 2006).
135

One major source of uncertainty for the visibility benefits estimate is the benefits 

transfer process used. Judgments used to choose the functional form and key parameters of 

the estimating equation for WTP for the affected population could have significant effects on 

the size of the estimates. Assumptions about how individuals respond to changes in visibility

that are either very small or outside the range covered in the Chestnut and Rowe study could 

also affect the results.

In addition, our estimate of visibility benefits is incomplete. For example, we 

anticipate improvement in visibility in residential areas within the Transport Rule region for 

which we are currently unable to monetize benefits as well as improvements in recreational 

visibility in the Northeastern and Central regions of the U.S. The value of visibility benefits 

in areas where we were unable to monetize benefits could also be substantial.

5.4.4.9 Growth in WTP Reflecting National Income Growth Over Time

Our analysis accounts for expected growth in real income over time. Economic 

theory argues that WTP for most goods (such as environmental protection) will increase if 

real incomes increase. There is substantial empirical evidence that the income elasticity31 of 

WTP for health risk reductions is positive, although there is uncertainty about its exact value. 

Thus, as real income increases, the WTP for environmental improvements also increases. 

Although many analyses assume that the income elasticity of WTP is unit elastic (i.e., a 10% 

higher real income level implies a 10% higher WTP to reduce risk changes), empirical 

evidence suggests that income elasticity is substantially less than one and thus relatively 

inelastic. As real income rises, the WTP value also rises but at a slower rate than real 

income.

The effects of real income changes on WTP estimates can influence benefits 

estimates in two different ways: through real income growth between the year a WTP study 

was conducted and the year for which benefits are estimated, and through differences in 

income between study populations and the affected populations at a particular time. 

Empirical evidence of the effect of real income on WTP gathered to date is based on studies 

examining the former. The Environmental Economics Advisory Committee (EEAC) of the 

 

31 Income elasticity is a common economic measure equal to the percentage change in WTP for a 1% change in 

income.
136

Science Advisory Board (SAB) advised EPA to adjust WTP for increases in real income 

over time but not to adjust WTP to account for cross-sectional income differences “because 

of the sensitivity of making such distinctions, and because of insufficient evidence available 

at present” (U.S. EPA-SAB, 2000a). A recent advisory by another committee associated 

with the SAB, the Advisory Council on Clean Air Compliance Analysis, has provided 

conflicting advice. While agreeing with “the general principle that the willingness to pay to 

reduce mortality risks is likely to increase with growth in real income (U.S. EPA-SAB, 

2004a, p. 52)” and that “The same increase should be assumed for the WTP for serious 

nonfatal health effects (U.S. EPA-SAB, 2004a, p. 52),” they note that “given the limitations 

and uncertainties in the available empirical evidence, the Council does not support the use of 

the proposed adjustments for aggregate income growth as part of the primary analysis (U.S. 

EPA-SAB, 2004a, p. 53).” Until these conflicting advisories have been reconciled, EPA will 

continue to adjust valuation estimates to reflect income growth using the methods described 

below, while providing sensitivity analyses for alternative income growth adjustment factors.

Based on a review of the available income elasticity literature, we adjusted the 

valuation of human health benefits upward to account for projected growth in real U.S. 

income. Faced with a dearth of estimates of income elasticities derived from time-series 

studies, we applied estimates derived from cross-sectional studies in our analysis. Details of 

the procedure can be found in Kleckner and Neumann (1999). An abbreviated description of 

the procedure we used to account for WTP for real income growth between 1990 and 2014 is 

presented below.

Reported income elasticities suggest that the severity of a health effect is a primary

determinant of the strength of the relationship between changes in real income and WTP. As 

such, we use different elasticity estimates to adjust the WTP for minor health effects, severe 

and chronic health effects, and premature mortality. Note that because of the variety of 

empirical sources used in deriving the income elasticities, there may appear to be 

inconsistencies in the magnitudes of the income elasticities relative to the severity of the 

effects (a priori one might expect that more severe outcomes would show less income 

elasticity of WTP). We have not imposed any additional restrictions on the empirical 

estimates of income elasticity. One explanation for the seeming inconsistency is the 

difference in timing of conditions. WTP for minor illnesses is often expressed as a short 

term payment to avoid a single episode. WTP for major illnesses and mortality risk 
137

reductions are based on longer term measures of payment (such as wages or annual 

income). Economic theory suggests that relationships become more elastic as the length of 

time grows, reflecting the ability to adjust spending over a longer time period. Based on this 

theory, it would be expected that WTP for reducing long term risks would be more elastic 

than WTP for reducing short term risks. We also expect that the WTP for improved visibility

in Class I areas would increase with growth in real income. The relative magnitude of the 

income elasticity of WTP for visibility compared with those for health effects suggests that 

visibility is not as much of a necessity as health, thus, WTP is more elastic with respect to 

income. The elasticity values used to adjust estimates of benefits in 2014 are presented in 

Table 5-12.

Table 5-12: Elasticity Values Used to Account for Projected Real Income Growtha

Benefit Category Central Elasticity Estimate

Minor Health Effect 0.14

Severe and Chronic Health Effects 0.45

Premature Mortality 0.40

Visibility 0.90

a Derivation of estimates can be found in Kleckner and Neumann (1999) and Chestnut (1997). COI 

estimates are assigned an adjustment factor of 1.0.

In addition to elasticity estimates, projections of real gross domestic product (GDP) 

and populations from 1990 to 2020 are needed to adjust benefits to reflect real per capita 

income growth. For consistency with the emissions and benefits modeling, we used national 

population estimates for the years 1990 to 1999 based on U.S. Census Bureau estimates 

(Hollman, Mulder, and Kallan, 2000). These population estimates are based on application 

of a cohort-component model applied to 1990 U.S. Census data projections (U.S. Bureau of 

Census, 2000). For the years between 2000 and 2014, we applied growth rates based on the 

U.S. Census Bureau projections to the U.S. Census estimate of national population in 2000. 

We used projections of real GDP provided in Kleckner and Neumann (1999) for the years 
138

1990 to 2010.32 We used projections of real GDP (in chained 1996 dollars) provided by 

Standard and Poor’s (2000) for the years 2010 to 2014.33

Using the method outlined in Kleckner and Neumann (1999) and the population and 

income data described above, we calculated WTP adjustment factors for each of the elasticity 

estimates listed in Table 5-13. Benefits for each of the categories (minor health effects, 

severe and chronic health effects, premature mortality, and visibility) are adjusted by

multiplying the unadjusted benefits by the appropriate adjustment factor. Note that, for 

premature mortality, we applied the income adjustment factor to the present discounted value 

of the stream of avoided mortalities occurring over the lag period. Also note that because of 

a lack of data on the dependence of COI and income, and a lack of data on projected growth 

in average wages, no adjustments are made to benefits based on the COI approach or to work 

loss days and worker productivity. This assumption leads us to underpredict benefits in 

future years because it is likely that increases in real U.S. income would also result in 

increased COI (due, for example, to increases in wages paid to medical workers) and 

increased cost of work loss days and lost worker productivity (reflecting that if worker 

incomes are higher, the losses resulting from reduced worker production would also be 

higher).

Table 5-13: Adjustment Factors Used to Account for Projected Real Income Growtha

Benefit Category 2014

Minor Health Effect 1.04

Severe and Chronic Health Effects 1.16

Premature Mortality 1.14

Visibility 1.35

a

 Based on elasticity values reported in Table 5-3, U.S. Census population projections, and projections of 

real GDP per capita.

 

32 U.S. Bureau of Economic Analysis, Table 2A (1992$) (available at 

http://www.bea.doc.gov/bea/dn/0897nip2/ tab2a.htm.) and U.S. Bureau of Economic Analysis, Economics 

and Budget Outlook. Note that projections for 2007 to 2010 are based on average GDP growth rates between 

1999 and 2007.

33 In previous analyses, we used the Standard and Poor’s projections of GDP directly. This led to an apparent 

discontinuity in the adjustment factors between 2010 and 2011. We refined the method by applying the 

relative growth rates for GDP derived from the Standard and Poor’s projections to the 2010 projected GDP 

based on the Bureau of Economic Analysis projections.
139

5.5 Unquantified Health and Welfare Benefits

This analysis is limited by the available data and resources. As such, we are not able 

to quantify several welfare benefit categories, as shown in Table 5-2. In this section, we 

provide a qualitative assessment of some of the major welfare benefit categories associated 

with reducing NO2 and SO2 emissions: health and ecosystem benefits of reducing nitrogen 

and sulfur emissions and deposition and vegetation benefits from reducing ozone.34 In

addition, there are mercury benefits associated with reducing mercury emissions and the role 

of sulfate deposition in mercury methylation. While we were unable to quantify how large 

these benefits might be as a result of the emission reductions achieved by this rule, previous 

EPA assessments show that these benefits could be substantial (U.S. EPA, 2008f; U.S. EPA, 

2009c; U.S. EPA, 2007b; U.S. EPA, 1999b). The omission of these endpoints from the 

monetized results should not imply that the impacts are small or unimportant. 

5.5.1 Ecosystem Services

Ecosystem services can be generally defined as the benefits that individuals and 

organizations obtain from ecosystems. EPA has defined ecological goods and services as the 

“outputs of ecological functions or processes that directly or indirectly contribute to social 

welfare or have the potential to do so in the future. Some outputs may be bought and sold, 

but most are not marketed” (U.S. EPA, 2006b). Figure 5-5 provides the Millennium 

Ecosystem Assessment’s schematic demonstrating the connections between the categories of 

ecosystem services and human well-being. The interrelatedness of these categories means 

that any one ecosystem may provide multiple services. Changes in these services can affect 

human well-being by affecting security, health, social relationships, and access to basic 

material goods (MEA, 2005). 

In the Millennium Ecosystem Assessment (MEA, 2005), ecosystem services are 

classified into four main categories:

 

34 Some quantitative estimates of the total value of certain recreational and environmental goods given current 

and historic emission levels are provided below. They do not reflect benefits that would accrue as a result of this 

result. However, these values would be expected to increase as emissions are decreased a result of this rule. 
140

1. Provisioning: Products obtained from ecosystems, such as the production of food and 

water

2. Regulating: Benefits obtained from the regulation of ecosystem processes, such as the 

control of climate and disease

3. Cultural: Nonmaterial benefits that people obtain from ecosystems through spiritual 

enrichment, cognitive development, reflection, recreation, and aesthetic experiences

4. Supporting: Services necessary for the production of all other ecosystem services, such as 

nutrient cycles and crop pollination
141

Figure 5-5: Linkages between categories of ecosystem services and components of 

human well-being from Millennium Ecosystem Assessment (MEA, 2005)

The monetization of ecosystem services generally involves estimating the value of 

ecological goods and services based on what people are willing to pay (WTP) to increase 

ecological services or by what people are willing to accept (WTA) in compensation for 

reductions in them (U.S. EPA, 2006b). There are three primary approaches for estimating 

the monetary value of ecosystem services: market-based approaches, revealed preference 

methods, and stated preference methods (U.S. EPA, 2006b). Because economic valuation of 

ecosystem services can be difficult, nonmonetary valuation using biophysical measurements 

and concepts also can be used. An example of a nonmonetary valuation method is the use of 

relative-value indicators (e.g., a flow chart indicating uses of a water body, such as boatable, 

fishable, swimmable, etc.). It is necessary to recognize that in the analysis of the 

environmental responses associated with any particular policy or environmental management 

action, only a subset of the ecosystem services likely to be affected are readily identified. Of 

those ecosystem services that are identified, only a subset of the changes can be quantified. 

Within those services whose changes can be quantified, only a few will likely be monetized, 

and many will remain nonmonetized. The stepwise concept leading up to the valuation of 

ecosystems services is graphically depicted in Figure 5-6. 
142

Figure 5-6: Schematic of the benefits assessment process (U.S. EPA, 2006b)

5.5.2 Ecosystem Benefits of Reduced Nitrogen and Sulfur Deposition

5.5.2.1 Science of Deposition

Nitrogen and sulfur emissions occur over large regions of North America. Once these 

pollutants are lofted to the middle and upper troposphere, they typically have a much longer 

lifetime and, with the generally stronger winds at these altitudes, can be transported long 

distances from their source regions. The length scale of this transport is highly variable 

owing to differing chemical and meteorological conditions encountered along the transport 

path (U.S. EPA, 2008f).. Sulfur is primarily emitted as SO2, and nitrogen can be emitted as 

NO, NO2, or NH3. Secondary particles are formed from NOx and SOX gaseous emissions and 

associated chemical reactions in the atmosphere. Deposition can occur in either a wet (i.e., 

rain, snow, sleet, hail, clouds, or fog) or dry form (i.e., gases or particles). Together these 

emissions are deposited onto terrestrial and aquatic ecosystems across the U.S., contributing 

to the problems of acidification, nutrient enrichment, and methylmercury production as 

represented in Figures 5-7 and 5-8. Although there is some evidence that nitrogen deposition 
143

may have positive effects on agricultural and forest output through passive fertilization, it is 

likely that the overall value is very small relative to other health and welfare effects. In 

addition to deposition effects, SO2 can affect vegetation at ambient levels near pollution 

sources.
Figure 5-7: Schematics of Ecological Effects of Nitrogen and Sulfur Deposition

Acidification

Aquatic 

Ecosystems

Acidification

Aquatic 

Ecosystems

144

7: Schematics of Ecological Effects of Nitrogen and Sulfur Deposition

NOX 

Atmospheric 

Fate and 

Transport

Deposition 

Process

Acidification

Terrestrial 

Ecosystems

Nutrient 

Enrichment

Aquatic 

Ecosystems

Terrestrial 

Ecosystems

SO2 

Atmospheric 

Fate and 

Transport

Deposition 

Process

Acidification

Terrestrial 

Ecosystems

Methylmercur

y Production

7: Schematics of Ecological Effects of Nitrogen and Sulfur Deposition
145

Figure 5-8. Nitrogen and sulfur cycling, and interactions in the environment (U.S. 

EPA, 2008f)

The atmospheric lifetimes of particles vary with particle size. Accumulation-mode particles 

such as sulfates are kept in suspension by normal air motions and have a lower deposition 

velocity than coarse-mode particles; they can be transported thousands of kilometers and 

remain in the atmosphere for a number of days. They are removed from the atmosphere 

primarily by cloud processes. Particulates affect acid deposition by serving as cloud 

condensation nuclei and contribute directly to the acidification of rain. In addition, the gasphase species that lead to the dry deposition of acidity are also precursors of particles. 

Therefore, reductions in NO2 and SO2 emissions will decrease both acid deposition and PM 

concentrations, but not necessarily in a linear fashion. (U.S. EPA, 2008f). Sulfuric acid is 

also deposited on surfaces by dry deposition and can contribute to environmental effects 

(U.S. EPA, 2008f). 

5.5.2.2 Ecological Effects of Acidification 

Deposition of nitrogen and sulfur can cause acidification, which alters 

biogeochemistry and affects animal and plant life in terrestrial and aquatic ecosystems across 
146

the U.S. Soil acidification is a natural process, but is often accelerated by acidifying 

deposition, which can decrease concentrations of exchangeable base cations in soils (U.S. 

EPA, 2008f). Major terrestrial effects include a decline in sensitive tree species, such as red 

spruce (Picea rubens) and sugar maple (Acer saccharum) (U.S. EPA, 2008f). Biological 

effects of acidification in terrestrial ecosystems are generally linked to aluminum toxicity and 

decreased ability of plant roots to take up base cations (U.S. EPA, 2008f). Decreases in the 

acid neutralizing capacity and increases in inorganic aluminum concentration contribute to 

declines in zooplankton, macro invertebrates, and fish species richness in aquatic ecosystems 

(U.S. EPA, 2008f). 

Geology (particularly surficial geology) is the principal factor governing the 

sensitivity of terrestrial and aquatic ecosystems to acidification from nitrogen and sulfur 

deposition (U.S. EPA, 2008f). Geologic formations having low base cation supply generally 

underlie the watersheds of acid-sensitive lakes and streams. Other factors contribute to the 

sensitivity of soils and surface waters to acidifying deposition, including topography, soil 

chemistry, land use, and hydrologic flow path (U.S. EPA, 2008f).

5.5.2.3 Aquatic Ecosystems

Aquatic effects of acidification have been well studied in the U.S. and elsewhere at various 

trophic levels. These studies indicate that aquatic biota have been affected by acidification at 

virtually all levels of the food web in acid sensitive aquatic ecosystems. . The ISA for 

NOx/SOx – Ecological Criteria concluded that the evidence is sufficient to infer a causal 

relationship between acidifying deposition and effects on biogeochemistry related to aquatic 

ecosystems and biota in aquatic ecosystems (U.S. EPA, 2008f). Effects have been most 

clearly documented for fish, aquatic insects, other invertebrates, and algae. Biological effects 

are primarily attributable to a combination of low pH and high inorganic aluminum 

concentrations. Such conditions occur more frequently during rainfall and snowmelt that 

cause high flows of water and less commonly during low-flow conditions, except where 

chronic acidity conditions are severe. Biological effects of episodes include reduced fish 

condition factor35, changes in species composition and declines in aquatic species richness 

 

35 Condition factor is an index that describes the relationship between fish weight and length, and is one 

measure of sublethal acidification stress that has been used to quantify effects of acidification on an individual 

fish (U.S.EPA, 2008f).
147

across multiple taxa, ecosystems and regions. These conditions may also result in direct 

fish mortality (Van Sickle et al., 1996). Biological effects in aquatic ecosystems can be 

divided into two major categories: effects on health, vigor, and reproductive success; and 

effects on biodiversity. Surface water with ANC values greater than 50 μeq/L generally 

provides moderate protection for most fish (i.e., brook trout, others) and other aquatic 

organisms (U.S. EPA, 2009c). Table 5-14 provides a summary of the biological effects 

experienced at various ANC levels.

Table 5-14: Aquatic Status Categories

Category Label ANC Levels Expected Ecological Effects

Acute 

Concern

<0 micro 

equivalent per 

Liter (μeq/L)

Near complete loss of fish populations is expected. Planktonic communities 

have extremely low diversity and are dominated by acidophilic forms. The 

number of individuals in plankton species that are present is greatly reduced.

Severe 

Concern

0–20 μeq/L

Highly sensitive to episodic acidification. During episodes of high acidifying 

deposition, brook trout populations may experience lethal effects. Diversity 

and distribution of zooplankton communities decline sharply. 

Elevated 

Concern 20–50 μeq/L

Fish species richness is greatly reduced (i.e., more than half of expected 

species can be missing). On average, brook trout populations experience 

sublethal effects, including loss of health, reproduction capacity, and fitness. 

Diversity and distribution of zooplankton communities decline.

Moderate

Concern

50–100 μeq/L

Fish species richness begins to decline (i.e., sensitive species are lost from 

lakes). Brook trout populations are sensitive and variable, with possible 

sublethal effects. Diversity and distribution of zooplankton communities also 

begin to decline as species that are sensitive to acidifying deposition are 

affected.

Low 

Concern >100 μeq/L

Fish species richness may be unaffected. Reproducing brook trout 

populations are expected where habitat is suitable. Zooplankton communities 

are unaffected and exhibit expected diversity and distribution.

A number of national and regional assessments have been conducted to estimate the 

distribution and extent of surface water acidity in the U.S (U.S. EPA, 2008f). As a result, 

several regions of the U.S. have been identified as containing a large number of lakes and 

streams that are seriously impacted by acidification. Figure 5-9 illustrates those areas of the 

U.S. where aquatic ecosystems are at risk from acidification. 
148

Figure 5-9: Areas Potentially Sensitive to Aquatic Acidification (U.S. EPA, 2008f)

Because acidification primarily affects the diversity and abundance of aquatic biota, it 

also affects the ecosystem services that are derived from the fish and other aquatic life found 

in these surface waters.

While acidification is unlikely to have serious negative effects on, for example, water 

supplies, it can limit the productivity of surface waters as a source of food (i.e., fish). In the 

northeastern United States, the surface waters affected by acidification are not a major source 

of commercially raised or caught fish; however, they are a source of food for some 
149

recreational and subsistence fishermen and for other consumers. For example, there is 

evidence that certain population subgroups in the northeastern United States, such as the 

Hmong and Chippewa ethnic groups, have particularly high rates of self-caught fish 

consumption (Hutchison and Kraft, 1994; Peterson et al., 1994). However, it is not known if 

and how their consumption patterns are affected by the reductions in available fish 

populations caused by surface water acidification.

Inland surface waters support several cultural services, including aesthetic and 

educational services and recreational fishing. Recreational fishing in lakes and streams is 

among the most popular outdoor recreational activities in the northeastern United States. 

Based on studies conducted in the northeastern United States, Kaval and Loomis (2003)

estimated average consumer surplus values per day of $36 for recreational fishing (in 2007 

dollars); therefore, the implied total annual value of freshwater fishing in the northeastern 

United States was $5.1 billion in 2006.36 For recreation days, consumer surplus value is most 

commonly measured using recreation demand, travel cost models. 

Another estimate of the overarching ecological benefits associated with reducing lake 

acidification levels in Adirondacks Park can be derived from the contingent valuation (CV) 

survey (Banzhaf et al., 2006), which elicited values for specific improvements in 

acidification-related water quality and ecological conditions in Adirondack lakes. The 

survey described a base version with minor improvements said to result from the program, 

and a scope version with large improvements due to the program and a gradually worsening 

status quo. After adapting and transferring the results of this study and converting the 10-

year annual payments to permanent annual payments using discount rates of 3% and 5%, the 

WTP estimates ranged from $48 to $107 per year per household (in 2004 dollars) for the base 

version and $54 to $154 for the scope version. Using these estimates, the aggregate annual 

benefits of eliminating all anthropogenic sources of NOx and SOx emissions were estimated 

to range from $291 million to $829 million (U.S. EPA, 2009c).37

 

36 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule. 

37 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule. 
150

In addition, inland surface waters provide a number of regulating services associated 

with hydrological and climate regulation by providing environments that sustain aquatic food 

webs. These services are disrupted by the toxic effects of acidification on fish and other 

aquatic life. Although it is difficult to quantify these services and how they are affected by

acidification, some of these services may be captured through measures of provisioning and 

cultural services. 

5.5.2.4 Terrestrial Ecosystems

Acidifying deposition has altered major biogeochemical processes in the U.S. by

increasing the nitrogen and sulfur content of soils, accelerating nitrate and sulfate leaching 

from soil to drainage waters, depleting base cations (especially calcium and magnesium) 

from soils, and increasing the mobility of aluminum. Inorganic aluminum is toxic to some 

tree roots. Plants affected by high levels of aluminum from the soil often have reduced root 

growth, which restricts the ability of the plant to take up water and nutrients, especially 

calcium (U. S. EPA, 2008f). These direct effects can, in turn, influence the response of these 

plants to climatic stresses such as droughts and cold temperatures. They can also influence 

the sensitivity of plants to other stresses, including insect pests and disease (Joslin et al., 

1992) leading to increased mortality of canopy trees. In the U.S., terrestrial effects of 

acidification are best described for forested ecosystems (especially red spruce and sugar 

maple ecosystems) with additional information on other plant communities, including shrubs 

and lichen (U.S. EPA, 2008f). The ISA for NOx/SOx – Ecological Criteria concluded that 

the evidence is sufficient to infer a causal relationship between acidifying deposition and 

effects on biogeochemistry related to terrestrial ecosystems and biota in terrestrial 

ecosystems (U.S. EPA, 2008f).

Certain ecosystems in the continental U.S. are potentially sensitive to terrestrial 

acidification, which is the greatest concern regarding nitrogen and sulfur deposition U.S. 

EPA (2008f). Figure 5-10 depicts the areas across the U.S. that are potentially sensitive to 

terrestrial acidification.
151

Figure 5-10: Areas Potentially Sensitive to Terrestrial Acidification (U.S. EPA, 2008f)

Both coniferous and deciduous forests throughout the eastern U.S. are experiencing 

gradual losses of base cation nutrients from the soil due to accelerated leaching from 

acidifying deposition. This change in nutrient availability may reduce the quality of forest 

nutrition over the long term. Evidence suggests that red spruce and sugar maple in some 

areas in the eastern U.S. have experienced declining health because of this deposition. For 

red spruce, (Picea rubens) dieback or decline has been observed across high elevation 

landscapes of the northeastern U.S., and to a lesser extent, the southeastern U.S., and 

acidifying deposition has been implicated as a causal factor (DeHayes et al., 1999). Figure 5-

11 shows the distribution of red spruce (brown) and sugar maple (green) in the eastern U.S. 
152

Figure 5-11: Distribution of Red Spruce (pink) and Sugar Maple (green) in the Eastern 

U.S. (U.S. EPA, 2008f)

Terrestrial acidification affects several important ecological endpoints, 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).

Forests in the northeastern United States provide several important and valuable 

provisioning services in the form of tree products. Sugar maples are a particularly important 

commercial hardwood tree species, providing timber and maple syrup. In the United States, 

sugar maple saw timber was nearly 900 million board feet in 2006 (USFS, 2006), and annual 

production of maple syrup was nearly 1.4 million gallons, accounting for approximately 19% 

of worldwide production. The total annual value of U.S. production in these years was 

approximately $160 million (NASS, 2008). Red spruce is also used in a variety of products 

including lumber, pulpwood, poles, plywood, and musical instruments. The total removal of 

red spruce saw timber from timberland in the United States was over 300 million board feet 

in 2006 (USFS, 2006). 

Forests in the northeastern United States are also an important source of cultural 
153

ecosystem services—nonuse (i.e., existence value for threatened and endangered species), 

recreational, and aesthetic services. Red spruce forests are home to two federally listed 

species and one delisted species:

1. Spruce-fir moss spider (Microhexura montivaga)—endangered

2. Rock gnome lichen (Gymnoderma lineare)—endangered

3. Virginia northern flying squirrel (Glaucomys sabrinus fuscus)—delisted, but 

important

Forestlands support a wide variety of outdoor recreational activities, including 

fishing, hiking, camping, off-road driving, hunting, and wildlife viewing. Regional statistics 

on recreational activities that are specifically forest based are not available; however, more 

general data on outdoor recreation provide some insights into the overall level of recreational 

services provided by forests. More than 30% of the U.S. adult population visited a 

wilderness or primitive area during the previous year and engaged in day hiking (Cordell et 

al., 2008). From 1999 to 2004, 16% of adults in the northeastern United States participated 

in off-road vehicle recreation, for an average of 27 days per year (Cordell et al., 2005). The 

average consumer surplus value per day of off-road driving in the United States was $25 (in 

2007 dollars), and the implied total annual value of off-road driving recreation in the 

northeastern United States was more than $9 billion (Kaval and Loomis, 2003). More than 

5% of adults in the northeastern United States participated in nearly 84 million hunting days 

(U.S. FWS and U.S. Census Bureau, 2007). Ten percent of adults in northeastern states 

participated in wildlife viewing away from home on 122 million days in 2006. For these 

recreational activities in the northeastern United States, Kaval and Loomis (2003) estimated 

average consumer surplus values per day of $52 for hunting and $34 for wildlife viewing (in 

2007 dollars). The implied total annual value of hunting and wildlife viewing in the 

northeastern United States was, therefore, $4.4 billion and $4.2 billion, respectively, in 2006.

As previously mentioned, it is difficult to estimate the portion of these recreational 

services that are specifically attributable to forests and to the health of specific tree species. 

However, one recreational activity that is directly dependent on forest conditions is fall color 

viewing. Sugar maple trees, in particular, are known for their bright colors and are, 

therefore, an essential aesthetic component of most fall color landscapes. A survey of 
154

residents in the Great Lakes area found that roughly 30% of residents reported at least one 

trip in the previous year involving fall color viewing (Spencer and Holecek, 2007). In a 

separate study conducted in Vermont, Brown (2002) reported that more than 22% of 

households visiting Vermont in 2001 made the trip primarily for viewing fall colors. 

Two studies estimated values for protecting high-elevation spruce forests in the 

southern Appalachian Mountains. Kramer et al. (2003) conducted a contingent valuation 

study estimating households’ WTP for programs to protect remaining high-elevation spruce 

forests from damages associated with air pollution and insect infestation. Median household 

WTP was estimated to be roughly $29 (in 2007 dollars) for a smaller program, and $44 for 

the more extensive program. Jenkins et al. (2002) conducted a very similar study in seven 

Southern Appalachian states on a potential program to maintain forest conditions at status 

quo levels. The overall mean annual WTP for the forest protection programs was $208 (in 

2007 dollars). Multiplying the average WTP estimate from these studies by the total number 

of households in the seven-state Appalachian region results in an aggregate annual range of 

$470 million to $3.4 billion for avoiding a significant decline in the health of high-elevation 

spruce forests in the Southern Appalachian region(U.S. EPA, 2009c).

38

Forests in the northeastern United States also support and provide a wide variety of 

valuable regulating services, including soil stabilization and erosion control, water 

regulation, and climate regulation. The total value of these ecosystem services is very

difficult to quantify in a meaningful way, as is the reduction in the value of these services 

associated with total nitrogen and sulfur deposition. As terrestrial acidification contributes to 

root damages, reduced biomass growth, and tree mortality, all of these services are likely to 

be affected; however, the magnitude of these impacts is currently very uncertain.

 

38 These estimates reflect the marginal value of the service for the hypothetical program described in the 

survey, not the marginal change in the value of the service as a result of the emission reductions achieved by 

this rule.
155

5.5.3 Ecological Effects Associated with Gaseous Sulfur Dioxide 

Uptake of gaseous sulfur dioxide in a plant canopy is a complex process involving 

adsorption to surfaces (leaves, stems, and soil) and absorption into leaves. SO2 penetrates 

into leaves through to the stomata, although there is evidence for limited pathways via the 

cuticle. Pollutants must be transported from the bulk air to the leaf boundary layer in order to 

get to the stomata. When the stomata are closed, as occurs under dark or drought conditions, 

resistance to gas uptake is very high and the plant has a very low degree of susceptibility to 

injury. In contrast, mosses and lichens do not have a protective cuticle barrier to gaseous 

pollutants or stomates and are generally more sensitive to gaseous sulfur and nitrogen than 

vascular plants (U.S. EPA, 2008f). Acute foliar injury usually happens within hours of 

exposure, involves a rapid absorption of a toxic dose, and involves collapse or necrosis of 

plant tissues. Another type of visible injury is termed chronic injury and is usually a result of 

variable SO2 exposures over the growing season. Besides foliar injury, chronic exposure to 

low SO2 concentrations can result in reduced photosynthesis, growth, and yield of plants 

(U.S. EPA, 2008f). These effects are cumulative over the season and are often not associated 

with visible foliar injury. As with foliar injury, these effects vary among species and 

growing environment. SO2 is also considered the primary factor causing the death of lichens 

in many urban and industrial areas (Hutchinson et al., 1996). 

5.5.4 Ecological Effects Associated with the Role of Sulfate in Mercury Methylation and 

Reduced Mercury Emissions 

Mercury is a persistent, bioaccumulative toxic metal that is emitted from power plants 

in three forms: gaseous elemental Hg (Hg0), oxidized Hg compounds (Hg+2), and particlebound Hg (HgP). Elemental Hg does not quickly deposit or chemically react in the 

atmosphere, resulting in residence times that are long enough to contribute to global scale 

deposition. Oxidized Hg and HgP deposit quickly from the atmosphere impacting local and 

regional areas in proximity to sources. Methylmercury (MeHg) is formed by microbial 

action in the top layers of sediment and soils, after Hg has precipitated from the air and 

deposited into waterbodies or land. Once formed, MeHg is taken up by aquatic organisms 

and bioaccumulates up the aquatic food web. Larger predatory fish may have MeHg 

concentrations many times, typically on the order of one million times, that of the 
156

concentrations in the freshwater body in which they live. 

EPA’s 2008 Integrated Science Assessment (ISA) for Oxides of Nitrogen and Sulfur–

Ecological Criteria (Final Report)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, 2008f). Specifically, there appears to be a relationship 

between SO4

2-

deposition and mercury methylation; however, the rate of mercury

methylation varies according to several spatial and biogeochemical factors whose influence 

has not been fully quantified (see Figure 5-11). Therefore, the correlation between SO4

2-

deposition and MeHg could not be quantified for the purpose of interpolating the association 

across waterbodies or regions. Nevertheless, because changes in MeHg in ecosystems 

represent changes in significant human and ecological health risks, the association between 

sulfur and mercury cannot be neglected (U.S. EPA, 2008f).
157

Figure 5-11. Spatial and Biogeochemical Factors Influencing MeHg Production

As research evolves and the computational capacity of models expands to meet the 

complexity of mercury methylation processes in ecosystems, the role of interacting factors 

may be better parsed out to identify ecosystems or regions that are more likely to generate 

higher concentrations of MeHg. Figure 5-12 illustrates the type of current and forwardlooking research being developed by the U.S. Geological Survey (USGS) to synthesize the 

contributing factors of mercury and to develop a map of sensitive watersheds. The mercury

score referenced in Figure 5-12 is based on SO4

2-

concentrations, acid neutralizing capacity 

(ANC), levels of dissolved organic carbon and pH, mercury species concentrations, and soil 

types to gauge the methylation sensitivity (Myers et al., 2007).
158

Figure 5.12: Preliminary USGS Map of Mercury Methylation–Sensitive Watersheds

Derived from More than 55,000 Water Quality Sites and 2,500 Watersheds

(Myers et al., 2007)

Interdependent biogeochemical factors preclude the existence of simple sulfaterelated mercury methylation models. It is clear that decreasing sulfate deposition is likely to 

result in decreased MeHg concentrations. Future research may allow for the characterization 

of a usable sulfate-MeHg response curve; however, no regional or classification calculation 

scale can be created at this time because of the number of confounding factors.

Decreases in SO4

2-

deposition have already shown promising reductions in MeHg. 
159

Observed decreases in MeHg fish tissue concentrations have been linked to decreased 

acidification and declining SO4

2-

and mercury deposition in Little Rock Lake, WI (Hrabik 

and Watras, 2002); and to decreased SO4

2-

deposition in Isle Royale in Lake Superior, MI 

(Drevnick et al., 2007). Although the possibility exists that reductions in SO4

2-

emissions 

could generate a pulse in MeHg production because of decreased sulfide inhibition in sulfatesaturated waters, this effect would likely involve a limited number of U.S. waters (Harmon et 

al., 2007). Also, because of the diffusion and outward flow of both mercurysulfide 

complexes and SO4

2-

, increased mercury methylation downstream may still occur in sulfateenriched ecosystems with increased organic matter and/or downstream transport capabilities.

Remediation of sediments heavily contaminated with mercury has yielded significant 

reductions of MeHg in biotic tissues. Establishing quantitative relations in biotic responses to 

MeHg levels as a result of changes in atmospheric mercury deposition, however, presents 

difficulties because direct associations can be confounded by all of the factors discussed in 

this section. Current research does suggest that the levels of MeHg and total mercury in 

ecosystems are positively correlated, so that reductions in mercury deposited into ecosystems 

would also eventually lead to reductions in MeHg in biotic tissues. Ultimately, an integrated 

approach that involves the reduction of both sulfur and mercury emissions may be most 

efficient because of the variability in ecosystem responses. Reducing SOX emissions could 

have a beneficial effect on levels of MeHg in many waters of the United States.

In addition to the role of sulfate deposition on methylation, the technologies installed 

to reduce emissions of NOx and SO2 associated with this final rule would also reduce 

mercury emissions. Thus, the total mercury benefits would reflect both the reduction in 

methylation from decreased sulfate deposition as well as the reduction in mercury emissions. 

Due to time and resource limitations, we were unable in any event to model mercury

dispersion, deposition, 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 these mercury emissions. Instead, we provide the following 

qualitative assessment of the human health and ecosystem effects associated with reducing 

exposure to methylmercury.

In the United States, humans are exposed to MeHg mainly by consuming fish that 

contain MeHg.. MeHg is the only form of mercury that biomagnifies in the food web. 
160

Concentrations of MeHg in fish are generally on the order of a million times the MeHg 

concentration in water. In addition to mercury deposition, key factors affecting MeHg 

production and accumulation in fish include the amount and forms of sulfur and carbon 

species present in a given waterbody. Thus, two adjoining water bodies receiving the same 

deposition can have significantly different fish mercury concentrations. 

 Methylmercury builds up more in some types of fish and shellfish than in others. 

The levels of methylmercury in high 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. In general, higher mercury

concentrations are expected in top predators, which are often large fish relative to other 

species in a waterbody.

Research shows that most people’s fish consumption does not cause a mercuryrelated health concern (NRC, 2000). 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). The majority of fish 

consumed in the U.S. are ocean species. The methylmercury concentrations in ocean fish 

species are primarily influences by the global mercury pool. However, the methylmercury

found in local fish can be due, at least partly, to mercury emissions from local sources. 

The ecosystem service most directly affected by sulfate-mediated mercury

methylation is the provision of fish for consumption as a food source. This service is of 

particular importance to groups engaged in subsistence fishing, pregnant women and young 

children. State-level fish consumption advisories for mercury are based on state criteria, 

many of which are based on EPA’s fish tissue criterion for methylmercury (U.S. EPA, 2001) 

or on U.S. Food and Drug Administration’s action levels (U.S. FDA, 2001). In 2008, there 

were 3,361 fish advisories issued at least in part for mercury contamination (80% of all fish 

advisories), covering 16.8 million lake acres (40% of total lake acreage) and 1.3 million river 

miles (35% of total river miles) over all 50 states, one U.S. territory, and 3 tribes (U.S. EPA, 

2009f). Recently, the U.S. Geological Survey (USGS) examined mercury levels in toppredator fish, bed sediment, and water from 291 streams across the U.S. (Scudder et al., 

2009). USGS detected mercury contamination in every fish sampled, and the concentration 

of mercury in fish exceeded EPA’s criterion in 27% of the sites sampled. 
161

In 2000, the National Research Council (NRC) of the NAS issued the NAS Study

(NRC, 2000), which provides a thorough review of the effects of MeHg on human health. 

There are numerous studies that have been published more recently that report effects on 

neurologic and other endpoints. In its review of the literature, the NAS found 

neurodevelopmental effects to be the most sensitive and best documented endpoints and 

appropriate for establishing an RfD (NRC, 2000); in particular NAS supported the use of 

results from neurobehavioral or neuropsychological tests. The NAS report (NRC, 2000) 

noted that studies in animals reported sensory effects as well as effects on brain development 

and memory functions and support the conclusions based on epidemiology studies. The NAS 

noted that their recommended endpoints for an RfD are associated with the ability of children 

to learn and to succeed in school. They concluded the following: “The population at highest 

risk is the children of women who consumed large amounts of fish and seafood during 

pregnancy. The committee concludes that the risk to that population is likely to be sufficient 

to result in an increase in the number of children who have to struggle to keep up in school.”

The NAS summarized data on cardiovascular effects available up to 2000. Based on 

these studies, the NRC (2000) concluded that “Although the data base is not as extensive for 

cardiovascular effects as it is for other end points (i.e. neurologic effects) the cardiovascular 

system appears to be a target for MeHg toxicity in humans and animals.”39 The 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.” Additional 

cardiovascular studies have been published since 2000. EPA did not to develop a 

quantitative dose-response assessment for cardiovascular effects associated with MeHg 

exposures, as there is no consensus among scientists on the dose-response functions for these 

effects. In addition, there is inconsistency among available studies as to the association 

between MeHg exposure and various cardiovascular system effects. The pharmacokinetics 

of some of the exposure measures (such as toenail Hg levels) are not well understood. The 

studies have not yet received the review and scrutiny of the more well-established 

neurotoxicity data base.

 

39National 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.
162

The Mercury Study noted that MeHg is not a potent mutagen but is capable of 

causing chromosomal damage in a number of experimental systems (U.S. EPA, 1997). The 

NAS concluded that evidence that human exposure to MeHg caused genetic damage is 

inconclusive; they note that some earlier studies showing chromosomal damage in 

lymphocytes may not have controlled sufficiently for potential confounders. One study of 

adults living in the Tapajós River region in Brazil (Amorim et al., 2000) reported a direct 

relationship between MeHg concentration in hair and DNA damage in lymphocytes; as well 

as effects on chromosomes. Long-term MeHg exposures in this population were believed to 

occur through consumption of fish, suggesting that genotoxic effects (largely chromosomal 

aberrations) may result from dietary, chronic MeHg exposures similar to and above those 

seen in the Faroes and Seychelles populations.

Although exposure to some forms of Hg can result in a decrease in immune activity

or an autoimmune response (ATSDR, 1999), evidence for immunotoxic effects of MeHg is 

limited (NRC, 2000). 

Based on limited human and animal data, MeHg is classified as a “possible” human 

carcinogen by the International Agency for Research on Cancer (IARC, 1994) and in IRIS 

(U.S. EPA, 2002). The existing evidence supporting the possibility of carcinogenic effects in 

humans from low-dose chronic exposures is tenuous. Multiple human epidemiological 

studies have found no significant association between Hg exposure and overall cancer 

incidence, although a few studies have shown an association between Hg exposure and 

specific types of cancer incidence (e.g., acute leukemia and liver cancer) (NAS, 2000).

There is also some evidence of reproductive and renal toxicity in humans from MeHg 

exposure. However, overall, human data regarding reproductive, renal, and hematological 

toxicity from MeHg are very limited and are based on either studies of the two high-dose 

poisoning episodes in Iraq and Japan or animal data, rather than epidemiological studies of 

chronic exposures at the levels of interest in this analysis.

Deposition of mercury to waterbodies can also have an impact on ecosystems and 

wildlife. Mercury contamination is present in all environmental media with aquatic systems 

experiencing the greatest exposures due to bioaccumulation. Bioaccumulation refers to the 

net uptake of a contaminant from all possible pathways and includes the accumulation that 

may occur by direct exposure to contaminated media as well as uptake from food. Wet and 
163

dry deposition of oxidized mercury is a dominant pathway for bringing mercury to 

terrestrial surfaces. In forest ecosystems, elemental mercury may also be absorbed by plants 

stomatally, incorporated by foliar tissues and released in litterfall (Ericksen et al., 2003). 

Mercury in throughfall, direct deposition in precipitation, and uptake of dissolved mercury by

roots (Rea et al., 2002) are also important in mercury accumulation in terrestrial ecosystems. 

Soils have significant capacity to store large quantities of atmospherically deposited mercury

where it can leach into groundwater and surface waters. Numerous studies have generated 

field data on the levels of mercury in a variety of wild species. The risk of mercury exposure 

extends to insectivorous terrestrial species such as songbirds, bats, spiders, and amphibians 

that receive mercury deposition or from aquatic systems near the forest areas they inhabit 

(Bergeron et al., 2010a, b; Cristol et al., 2008; Rimmer et al., 2005; Wada et al., 2009 & 

2010). The body of work examining the effects of these exposures is growing but still 

incomplete given the complexities of the natural world. 

The studies cited here provide a glimpse of the scope of mercury effects on wildlife 

particularly reproductive and survival effects at current exposure levels. These effects range 

across species from fish to mammals and spatially across a wide area of the United States. 

The literature is far from complete however. Much more research is required to establish a 

link between the ecological effects on wildlife and the effect on ecosystem services (services 

that the environment provides to people) for example recreational fishing, bird watching, and 

wildlife viewing. EPA is not, however, currently able to quantify or monetize the benefits of 

reducing mercury exposures affecting provision of ecosystem services.

5.5.5 Nitrogen Enrichment

5.5.5.1 Aquatic Enrichment

The ISA for NOx/SOx – Ecological Criteria concluded that the evidence is sufficient 

to infer a causal relationship between nitrogen deposition and the alteration of species 

richness, species composition, and biodiversity in wetland, freshwater aquatic and coastal 

marine ecosystems (U.S. EPA, 2008f). 

One of the main adverse ecological effects resulting from N deposition, particularly 
164

in the Mid-Atlantic region of the United States, is the effect associated with nutrient 

enrichment in estuarine waters. A recent assessment of 141 estuaries nationwide by the 

National Oceanic and Atmospheric Administration (NOAA) concluded that 19 estuaries 

(13%) suffered from moderately high or high levels of eutrophication due to excessive inputs 

of both N and phosphorus, and a majority of these estuaries are located in the coastal area 

from North Carolina to Massachusetts (NOAA, 2007). For estuaries in the Mid-Atlantic 

region, the contribution of atmospheric distribution to total N loads is estimated to range 

between 10% and 58% (Valigura et al., 2001). 

Eutrophication in estuaries is associated with a range of adverse ecological effects. 

The conceptual framework developed by NOAA emphasizes four main types of 

eutrophication effects—low dissolved oxygen (DO), harmful algal blooms (HABs), loss of 

submerged aquatic vegetation (SAV), and low water clarity. Low DO disrupts aquatic 

habitats, causing stress to fish and shellfish, which, in the short-term, can lead to episodic 

fish kills and, in the long-term, can damage overall growth in fish and shellfish populations. 

Low DO also degrades the aesthetic qualities of surface water. In addition to often being 

toxic to fish and shellfish, and leading to fish kills and aesthetic impairments of estuaries, 

HABs can, in some instances, also be harmful to human health. SAV provides critical 

habitat for many aquatic species in estuaries and, in some instances, can also protect 

shorelines by reducing wave strength; therefore, declines in SAV due to nutrient enrichment 

are an important source of concern. Low water clarity is in part the result of accumulations 

of both algae and sediments in estuarine waters. In addition to contributing to declines in 

SAV, high levels of turbidity also degrade the aesthetic qualities of the estuarine 

environment. 

Estuaries in the eastern United States are an important source of food production, in 

particular fish and shellfish production. The estuaries are capable of supporting large stocks 

of resident commercial species, and they serve as the breeding grounds and interim habitat 

for several migratory species. To provide an indication of the magnitude of provisioning 

services associated with coastal fisheries, from 2005 to 2007, the average value of total catch

was $1.5 billion per year. It is not known, however, what percentage of this value is directly 

attributable to or dependent upon the estuaries in these states. 

In addition to affecting provisioning services through commercial fish harvests, 
165

eutrophication in estuaries may also affect the demand for seafood. For example, a wellpublicized toxic pfiesteria bloom in the Maryland Eastern Shore in 1997, which involved 

thousands of dead and lesioned fish, led to an estimated $56 million (in 2007 dollars) in lost 

seafood sales for 360 seafood firms in Maryland in the months following the outbreak 

(Lipton, 1999). 

Estuaries in the United States also provide an important and substantial variety of 

cultural ecosystem services, including water-based recreational and aesthetic services. The 

water quality in the estuary directly affects the quality of these experiences. For example, 

there were 26 million days of saltwater fishing coastal states from North Carolina to 

Massachusetts in 2006 (FWA and Census, 2007). Assuming an average consumer surplus 

value for a fishing day at $36 (in 2007 dollars) in the Northeast and $87 in the Southeast 

(Kaval and Loomis, 2003), the aggregate value was approximately $1.3 billion (in 2007 

dollars). 40 In addition, almost 6 million adults participated in motorboating in coastal states 

from North Carolina to Massachusetts, for a total of nearly 63 million days annually during 

1999–2000 (Leeworthy and Wiley, 2001). Using a national daily value estimate of $32 (in 

2007 dollars) for motorboating (Kaval and Loomis (2003), the aggregate value of these 

coastal motorboating outings was $2 billion per year. 41 Almost 7 million participated in 

birdwatching for 175 million days per year, and more than 3 million participated in visits to 

non-beach coastal waterside areas. 

Estuaries and marshes have the potential to support a wide range of regulating 

services, including climate, biological, and water regulation; pollution detoxification; erosion 

prevention; and protection against natural hazards from declines in SAV (MEA, 2005). SAV 

can help reduce wave energy levels and thus protect shorelines against excessive erosion, 

which increases the risks of episodic flooding and associated damages to near-shore 

properties or public infrastructure or even contribute to shoreline retreat. 

5.5.5.2 Terrestrial Enrichment

Terrestrial enrichment occurs when terrestrial ecosystems receive N loadings in 

 

40 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule. 

41 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule. 
166

excess of natural background levels, either through atmospheric deposition or direct 

application. Evidence presented in the Integrated Science Assessment (U.S. EPA, 2008f) 

supports a causal relationship between atmospheric N deposition and biogeochemical cycling 

and fluxes of N and carbon in terrestrial systems. Furthermore, evidence summarized in the 

report supports a causal link between atmospheric N deposition and changes in the types and

number of species and biodiversity in terrestrial systems. Nitrogen enrichment occurs over a 

long time period; as a result, it may take as much as 50 years or more to see changes in 

ecosystem conditions and indicators. This long time scale also affects the timing of the 

ecosystem service changes. The ISA for NOx/SOx – Ecological Criteria concluded that the 

evidence is sufficient to infer a causal relationship between nitrogen deposition and the 

alteration of species richness, species composition, and biodiversity in terrestrial ecosystems 

(U.S. EPA, 2008f). 

One of the main provisioning services potentially affected by N deposition is grazing 

opportunities offered by grasslands for livestock production in the Central U.S. Although N 

deposition on these grasslands can offer supplementary nutritive value and promote overall 

grass production, there are concerns that fertilization may favor invasive grasses and shift the 

species composition away from native grasses. This process may ultimately reduce the 

productivity of grasslands for livestock production. Losses due to invasive grasses can be 

significant; for example, based on a bioeconomic model of cattle grazing in the upper Great 

Plains, Leitch, Leistritz, and Bangsund (1996) and Leistritz, Bangsund, and Hodur (2004) 

estimated $130 million in losses due to a leafy spurge infestation in the Dakotas, Montana, 

and Wyoming. 42 However, the contribution of N deposition to these losses is still uncertain. 

Terrestrial nutrient enrichment also affects cultural and regulating services. For 

example, in California, Coastal Sage Scrub (CSS) habitat concerns focus on a decline in CSS 

and an increase in nonnative grasses and other species, impacts on the viability of threatened 

and endangered species associated with CSS, and an increase in fire frequency. Changes in 

Mixed Conifer Forest (MCF) include changes in habitat suitability and increased tree 

mortality, increased fire intensity, and a change in the forest’s nutrient cycling that may

affect surface water quality through nitrate leaching (U.S. EPA, 2008f). CSS and MCF are an 

integral part of the California landscape, and together the ranges of these habitats include the 

 
167

densely populated and valuable coastline and the mountain areas. Numerous threatened and 

endangered species at both the state and federal levels reside in CSS and MCF. The value 

that California residents and the U.S. population as a whole place on CSS and MCF habitats 

is reflected in the various federal, state, and local government measures that have been put in 

place to protect these habitats, including the Endangered Species Act, conservation planning 

programs, and private and local land trusts. CSS and MCF habitat are showcased in many

popular recreation areas in California, including several national parks and monuments. In 

addition, millions of individuals are involved in fishing, hunting, and wildlife viewing in 

California every year (DOI, 2007). The quality of these trips depends in part on the health of 

the ecosystems and their ability to support the diversity of plants and animals found in 

important habitats found in CSS or MCF ecosystems and the parks associated with those 

ecosystems. Based on analyses in the NOx SOx REA average values of the total benefits in 

2006 from fishing, hunting, and wildlife viewing away from home in California were 

approximately $950 million, $170 million, and $3.6 billion, respectively (U.S.EPA, 2009c).43

In addition, data from California State Parks (2003) indicate that in 2002, 69% of adult 

residents participated in trail hiking for an average of 24 days per year. The aggregate annual 

benefit for California residents from trail hiking in 2007 was $11 billion (U.S.EPA, 2009c).44

It is not currently possible to quantify the loss in value of services due to nitrogen deposition 

as those losses are already reflected in the estimates of the contemporaneous total value of 

these recreational activities. Restoration of services through decreases in nitrogen deposition 

would likely increase the total value of recreational services.

Fire regulation is also an important regulating service that could be affected by

nutrient enrichment of the CSS and MCF ecosystems by encouraging growth of more 

flammable grasses, increasing fuel loads, and altering the fire cycle. Over the 5-year period 

from 2004 to 2008, Southern California experienced, on average, over 4,000 fires per year 

burning, on average, over 400,000 acres per year (National Association of State Foresters 

[NASF], 2009). It is not possible at this time to quantify the contribution of nitrogen 

deposition, among many other factors, to increased fire risk.

 

43 These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule. 

44These estimates reflect the total value of the service, not the marginal change in the value of the service as a 

result of the emission reductions achieved by this rule. 
168

5.5.6 Benefits of Reducing Ozone Effects on Vegetation and Ecosystems

Ozone causes discernible injury to a wide array of vegetation (U.S. EPA, 2006a; Fox 

and Mickler, 1996). Sensitivity to ozone is highly variable across plant species, with over 

65 plant species identified as “ozone-sensitive”, many of which occur in state and national 

parks and forests. In terms of forest productivity and ecosystem diversity, ozone may be the 

pollutant with the greatest potential for regional-scale forest impacts (U.S. EPA, 2006a). 

Studies have demonstrated repeatedly that ozone concentrations commonly observed in 

polluted areas can have substantial impacts on plant function (De Steiguer et al., 1990; Pye, 

1988).

When ozone is present in the air, it can enter the leaves of plants, where it can cause 

significant cellular damage. Like carbon dioxide (CO2) and other gaseous substances, ozone 

enters plant tissues primarily through the stomata in leaves in a process called “uptake” 

(Winner and Atkinson, 1986). Once sufficient levels of ozone (a highly reactive substance), 

or its reaction products, reaches the interior of plant cells, it can inhibit or damage essential 

cellular components and functions, including enzyme activities, lipids, and cellular 

membranes, disrupting the plant's osmotic (i.e., water) balance and energy utilization patterns 

(U.S. EPA, 2006a; Tingey and Taylor, 1982). With fewer resources available, the plant 

reallocates existing resources away from root growth and storage, above ground growth or 

yield, and reproductive processes, toward leaf repair and maintenance, leading to reduced 

growth and/or reproduction. Studies have shown that plants stressed in these ways may

exhibit a general loss of vigor, which can lead to secondary impacts that modify plants' 

responses to other environmental factors. Specifically, plants may become more sensitive to 

other air pollutants, or more susceptible to disease, pest infestation, harsh weather (e.g., 

drought, frost) and other environmental stresses, which can all produce a loss in plant vigor 

in ozone-sensitive species that over time may lead to premature plant death. Furthermore, 

there is evidence that ozone can interfere with the formation of mycorrhiza, essential 

symbiotic fungi associated with the roots of most terrestrial plants, by reducing the amount of 

carbon available for transfer from the host to the symbiont (U.S. EPA, 2006a).

This ozone damage may or may not be accompanied by visible injury on leaves, and 

likewise, visible foliar injury may or may not be a symptom of the other types of plant 

damage described above. Foliar injury is usually the first visible sign of injury to plants from 
169

ozone exposure and indicates impaired physiological processes in the leaves (Grulke, 2003). 

When visible injury is present, it is commonly manifested as chlorotic or necrotic spots, 

and/or increased leaf senescence (accelerated leaf aging). Because ozone damage can consist

of visible injury to leaves, it can also reduce the aesthetic value of ornamental vegetation and 

trees in urban landscapes, and negatively affects scenic vistas in protected natural areas. 

Ozone can produce both acute and chronic injury in sensitive species depending on 

the concentration level and the duration of the exposure. Ozone effects also tend to 

accumulate over the growing season of the plant, so that even lower concentrations 

experienced for a longer duration have the potential to create chronic stress on sensitive 

vegetation. Not all plants, however, are equally sensitive to ozone. Much of the variation in 

sensitivity between individual plants or whole species is related to the plant’s ability to 

regulate the extent of gas exchange via leaf stomata (e.g., avoidance of ozone uptake through 

closure of stomata) (U.S. EPA, 2006a; Winner, 1994). After injuries have occurred, plants 

may be capable of repairing the damage to a limited extent (U.S. EPA, 2006a). Because of 

the differing sensitivities among plants to ozone, ozone pollution can also exert a selective 

pressure that leads to changes in plant community composition. Given the range of plant 

sensitivities and the fact that numerous other environmental factors modify plant uptake and 

response to ozone, it is not possible to identify threshold values above which ozone is 

consistently toxic for all plants. 

Because plants are at the base of the food web in many ecosystems, changes to the 

plant community can affect associated organisms and ecosystems (including the suitability of 

habitats that support threatened or endangered species and below ground organisms living in 

the root zone). Ozone impacts at the community and ecosystem level vary widely depending 

upon numerous factors, including concentration and temporal variation of tropospheric 

ozone, species composition, soil properties and climatic factors (U.S. EPA, 2006a). In most 

instances, responses to chronic or recurrent exposure in forested ecosystems are subtle and 

not observable for many years. These injuries can cause stand-level forest decline in 

sensitive ecosystems (U.S. EPA, 2006a, McBride et al., 1985; Miller et al., 1982). It is not 

yet possible to predict ecosystem responses to ozone with much certainty; however, 

considerable knowledge of potential ecosystem responses has been acquired through longterm observations in highly damaged forests in the United States (U.S EPA, 2006a).
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5.5.6.1 Ozone Effects on Forests 

Air pollution can affect the environment and affect ecological systems, leading to 

changes in the ecological community and influencing the diversity, health, and vigor of 

individual species (U.S. EPA, 2006a). Ozone has been shown in numerous studies to have a 

strong effect on the health of many plants, including a variety of commercial and 

ecologically important forest tree species throughout the United States (U.S. EPA, 2007b).

In the U.S., this data comes from the U.S. Department of Agriculture (USDA) Forest 

Service Forest Inventory and Analysis (FIA) program. As part of its Phase 3 program, 

formerly known as Forest Health Monitoring, FIA examines ozone injury to ozone-sensitive 

plant species at ground monitoring sites in forestland across the country (excluding woodlots 

and urban trees). FIA looks for damage on the foliage of ozone-sensitive forest plant species 

at each site that meets certain minimum criteria. Because ozone injury is cumulative over the 

course of the growing season, examinations are conducted in July and August, when ozone 

injury is typically highest. 

Monitoring of ozone injury to plants by the USDA Forest Service has expanded over 

the last 15 years from monitoring sites in 10 states in 1994 to nearly 1,000 monitoring sites in 

41 states in 2002. Since 2002, the monitoring program has further expanded to 1,130 

monitoring sites in 45 states. Figure 5-13 shows the results of this monitoring program for 

the year 2002 broken down by U.S. EPA Regions.45 Figure 5-14 identifies the counties that 

were included in Figure 5-13, and provides the county-level data regarding the presence or 

absence of ozone-related injury. As shown in Figure 5-14 large geographic areas of EPA 

Regions 6, 8, and 10 were not included in the assessment. Ozone damage to forest plants is 

classified using a subjective five-category biosite index based on expert opinion, but 

designed to be equivalent from site to site. Ranges of biosite values translate to no injury, 

low or moderate foliar injury (visible foliar injury to highly sensitive or moderately sensitive 

plants, respectively), and high or severe foliar injury, which would be expected to result in 

tree-level or ecosystem-level responses, respectively (U.S. EPA, 2006a; Coulston, 2004). 

The highest percentages of observed high and severe foliar injury, which are most likely to 

be associated with tree or ecosystem-level responses, are primarily found in the Mid-Atlantic 

 

45The data are based on averages of all observations collected in 2002, which is the last year for which data are 

publicly available. For more information, please consult EPA’s 2008 Report on the Environment (U.S. EPA, 

2008b).
171

and Southeast regions. While the assessment showed considerable regional variation in 

ozone injury, this assessment targeted different ozone-sensitive species in different parts of 

the country with varying ozone sensitivity, which contributes to the apparent regional 

differences. It is important to note that ozone can have other, more significant impacts on 

forest plants (e.g. reduced biomass growth in trees) prior to showing signs of visible foliar 

injury (U.S. EPA, 2006a). 
172

Figure 5-13: Visible Foliar Injury to Forest Plants from Ozone in U.S. by EPA Regions, 

a, b, c

c Degree of Injury: These categories reflect a subjective 

index based on expert opinion. Ozone can have other, more 

significant impacts on forest plants (e.g. reduced biomass 

growth in trees) prior to showing signs of visible foliar 

injury.
173

Figure 5-14. Presence and Absence of Visible Foliar Injury, as measured by 

U.S. Forest Service, 2002 (U.S. EPA, 2007)

Assessing the impact of ground-level ozone on forests in the eastern United States involves 

understanding the risks to sensitive tree species from ambient ozone concentrations and 

accounting for the prevalence of those species within the forest. As a way to quantify the 

risks to particular plants from ground-level ozone, scientists have developed ozoneexposure/tree-response functions by exposing tree seedlings to different ozone levels and 

measuring reductions in growth as “biomass loss.” Typically, seedlings are used because 

they are easy to manipulate and measure their growth loss from ozone pollution. The 

mechanisms of susceptibility to ozone within the leaves of seedlings and mature trees are 

identical, and the decreases predicted using the seedlings should be related to the decrease in 

overall plant fitness for mature trees, but the magnitude of the effect may be higher or lower 

depending on the tree species (Chappelka and Samuelson, 1998). In areas where certain 

ozone-sensitive species dominate the forest community, the biomass loss from ozone can be 

significant. Experts have identified 2% annual biomass loss as a level of concern, which 

would cause long term ecological harm as the short-term negative effects on seedlings 
174

compound to affect long-term forest health (Heck and Cowling, 1997).

Some of the common tree species in the United States that are sensitive to ozone are 

black cherry (Prunus serotina), tulip-poplar (Liriodendron tulipifera), and eastern white pine 

(Pinus strobus). Ozone-exposure/tree-response functions have been developed for each of 

these tree species, as well as for aspen (Populus tremuliodes), and ponderosa pine (Pinus 

ponderosa) (U.S. EPA, 2007b). Other common tree species, such as oak (Quercus spp.) and 

hickory (Carya spp.), are not as sensitive to ozone. Consequently, with knowledge of the 

distribution of sensitive species and the level of ozone at particular locations, it is possible to 

estimate a “biomass loss” for each species across their range. As shown in Figure 5-15, 

current ambient levels of ozone are associated with significant biomass loss across large 

geographic areas (U.S. EPA, 2009b). However, this information is unavailable for this final 

rule. 

To estimate the biomass loss for forest ecosystems across the eastern United States, 

the biomass loss for each of the seven tree species was calculated using the three-month, 12-

hour W126 exposure metric at each location, along with each tree’s individual C-R functions. 

The W126 exposure metric was calculated using monitored ozone data from CASTNET and 

AQS sites, and a three-year average was used to mitigate the effect of variations in 

meteorological and soil moisture conditions. The biomass loss estimate for each species was 

then multiplied by its prevalence in the forest community using the U.S. Department of 

Agriculture (USDA) Forest Service IV index of tree abundance calculated from Forest 

Inventory and Analysis (FIA) measurements (Prasad, 2003). Sources of uncertainty include 

the ozone-exposure/plant-response functions, the tree abundance index, and other factors 

(e.g., soil moisture). Although these factors were not considered, they can affect ozone 

damage (Chappelka, 1998).
175

Figure 5-15: Estimated Black Cherry, Yellow Poplar, Sugar Maple, Eastern White 

Pine, Virginia Pine, Red Maple, and Quaking Aspen Biomass Loss due to Current 

Ozone Exposure, 2006-2008 (U.S. EPA, 2009b)

Ozone damage to the plants including the trees and understory in a forest can affect 

the ability of the forest to sustain suitable habitat for associated species particularly

threatened and endangered species that have existence value – a nonuse ecosystem service -

for the public. Similarly, damage to trees and the loss of biomass can affect the forest’s 

provisioning services in the form of timber for various commercial uses. In addition, ozone 

can cause discoloration of leaves and more rapid senescence (early shedding of leaves), 

which could negatively affect fall-color tourism because the fall foliage would be less 

available or less attractive. Beyond the aesthetic damage to fall color vistas, forests provide 

the public with many other recreational and educational services that may be affected by

reduced forest health including hiking, wildlife viewing (including bird watching), camping, 

picnicking, and hunting. Another potential effect of biomass loss in forests is the subsequent 

loss of climate regulation service in the form of reduced ability to sequester carbon (Felzer et 

al., 2005). 
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5.5.6.2 Ozone Effects on Crops and Urban Ornamentals

Laboratory and field experiments have also shown reductions in yields for agronomic 

crops exposed to ozone, including vegetables (e.g., lettuce) and field crops (e.g., cotton and 

wheat). Damage to crops from ozone exposures includes yield losses (i.e., in terms of 

weight, number, or size of the plant part that is harvested), as well as changes in crop quality 

(i.e., physical appearance, chemical composition, or the ability to withstand storage) (U.S. 

EPA, 2007b). The most extensive field experiments, conducted under the National Crop 

Loss Assessment Network (NCLAN) examined 15 species and numerous cultivars. The 

NCLAN results show that “several economically important crop species are sensitive to 

ozone levels typical of those found in the United States” (U.S. EPA, 2006a). In addition, 

economic studies have shown reduced economic benefits as a result of predicted reductions 

in crop yields, directly affecting the amount and quality of the provisioning service provided 

by these crops, associated with observed ozone levels (Kopp et al., 1985; Adams et al., 1986; 

Adams et al., 1989). In addition, visible foliar injury by itself can reduce the market value of 

certain leafy crops (such as spinach, lettuce). According to the Ozone Staff Paper, there has 

been no evidence that crops are becoming more tolerant of ozone (U.S. EPA, 2007b). Using 

the Agriculture Simulation Model (AGSIM) (Taylor, 1994) to calculate the agricultural 

benefits of reductions in ozone exposure, U.S. EPA estimated that attaining a W126 standard 

of 13 ppm-hr would produce monetized benefits of approximately $400 million to $620 

million in 2006 (inflated to 2006 dollars) (U.S. EPA, 2007b). 46

Urban ornamental plants are an additional vegetation category likely to experience 

some degree of negative effects associated with exposure to ambient ozone levels. Several 

ornamental species have been listed as sensitive to ozone (Abt Associates, 1995).Because 

ozone causes visible foliar injury, the aesthetic value of ornamentals (such as petunia, 

geranium, and poinsettia) in urban landscapes would be reduced (U.S. EPA, 2007b). 

Sensitive ornamental species would require more frequent replacement and/or increased 

maintenance (fertilizer or pesticide application) to maintain the desired appearance because 

of exposure to ambient ozone (U.S. EPA, 2007b). In addition, many businesses rely on 

healthy-looking vegetation for their livelihoods (e.g., horticulturalists, landscapers, Christmas 

 

46 These estimates illustrate the value of vegetation effects from a substantial reduction of ozone 

concentrations, not the marginal change in ozone concentrations anticipated a result of the emission reductions 

achieved by this rule. 
177

tree growers, farmers of leafy crops, etc.) and a variety of ornamental species have been 

listed as sensitive to ozone (Abt Associates, 1995). The ornamental landscaping industry is 

multi-billion industry that affected both private property owners/tenants and by governmental 

units responsible for public areas (Abt Associates, 1995). Preliminary data from the 2007 

Economic Census indicate that the landscaping services industry, which is primarily engaged 

in providing landscape care and maintenance services and installing trees, shrubs, plants, 

lawns, or gardens, was valued at $53 billion (U.S. Census Bureau, 2010). Therefore, urban 

ornamentals represent a potentially large unquantified benefit category. This aesthetic 

damage may affect the enjoyment of urban parks by the public and homeowners’ enjoyment 

of their landscaping and gardening activities. In addition, homeowners may experience a 

reduction in home value or a home may linger on the market longer due to decreased 

aesthetic appeal. In the absence of adequate exposure-response functions and economic 

damage functions for the potential range of effects relevant to these types of vegetation, we 

cannot conduct a quantitative analysis to estimate these effects.

5.5.7 Unquantified SO2 and NO2 -Related Human Health Benefits

Following an extensive evaluation of health evidence from epidemiologic and 

laboratory studies, the Integrated Science Assessment for Sulfur Dioxide concluded that there 

is a causal relationship between respiratory health effects and short-term exposure to SO2 

(U.S. EPA, 2008). The immediate effect of SO2 on the respiratory system in humans is 

bronchoconstriction. 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. 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 
178

of the observed associations to adjustment for pollutants. We did not quantify these benefits 

due to time constraints.

Epidemiological researchers have associated NO2 exposure with adverse health 

effects in numerous toxicological, clinical and epidemiological studies, as described in the 

Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (Final Report) (U.S. 

EPA, 2008c). The NO2 ISA provides a comprehensive review of the current evidence of 

health and environmental effects of NO2. The NO2 ISA concluded that the evidence “is 

sufficient to infer a likely causal relationship between short-term NO2 exposure and adverse 

effects on the respiratory system” (ISA, section 5.3.2.1). These epidemiologic and 

experimental studies encompass a number of endpoints including [Emergency Department 

(ED)] visits and hospitalizations, respiratory symptoms, airway hyperresponsiveness, airway

inflammation, and lung function. Effect estimates from epidemiologic studies conducted in 

the United States and Canada generally indicate a 2-20% increase in risks for ED visits and 

hospital admissions and higher risks for respiratory symptoms (ISA, section 5.4). The NO2

ISA concluded that the relationship between short-term NO2 exposure and premature 

mortality was “suggestive but not sufficient to infer a causal relationship” because it is 

difficult to attribute the mortality risk effects to NO2 alone. Although the NO2 ISA stated 

that studies consistently reported a relationship between NO2 exposure and mortality, the 

effect was generally smaller than that for other pollutants such as PM. We did not quantify

these benefits due to time constraints.

5.6 Social Cost of Carbon and Greenhouse Gas Benefits

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 
179

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.47 

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 emissions48 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 

 

47 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). Also 

available at http://www.epa.gov/otaq/climate/regulations.htm

48 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). 
180

modeling assumptions. The SCC estimates for the analysis years of 2014, in 2007 dollars 

are provided in Table 5-15.

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) 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 noncatastrophic impacts, their incomplete treatment of adaptation and technological change, 

uncertainty in the extrapolation of damages to high temperatures, and assumptions regarding 

risk aversion. The limited amount of research linking climate impacts to economic damages 

makes the interagency modeling exercise even more difficult. 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 reductions in CO2 emissions for 

the range of policy scenarios, we estimate the dollar value of the climate related benefits 

captured by the models for each analysis year. For internal consistency, the annual benefits 

are discounted back to NPV terms using the same discount rate as each SCC estimate (i.e. 
181

5%, 3%, and 2.5%) rather than 3% and 7%.49 These estimates are provided in Table 5-16.

Table 5-15. Social Cost of Carbon (SCC) Estimates (per tonne of CO2) for 2014 (in 

2007$)a

Discount Rate and Statistic SCC estimate

5% Average $5.5

3% Average $23.4

2.5% Average $37.7

3% 95%ile $71.2

a The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts.

Table 5-16. Monetized SCC-Derived Benefits of CO2 Emissions Reductions in 2014 (in 

millions of 2007$)a

Discount Rate and Statistic SCC-derived benefits

Tons of CO2 reduced (millions) 25

5% Average $140

3% Average $590

2.5% Average $950

3% 95%ile $1,800

a The SCC values are dollar-year and emissions-year specific. SCC values represent only a partial accounting of 

climate impacts.

5.7 Benefits Results

Applying the impact and valuation functions described previously in this chapter to 

the estimated changes in ozone and PM yields estimates of the changes in physical damages 

(e.g., premature mortalities, cases, admissions, and change in light extinction) and the 

associated monetary values for those changes. Estimates of physical health impacts among 

those states in either the ozone or PM2.5 trading region, or outside the trading region, are 

presented in Table 5-17. Monetized values for both health and welfare endpoints within the 

trading region are presented in Table 5-18, along with total aggregate monetized benefits. All 

of the monetary benefits are in constant-year 2007 dollars. The PM2.5-related benefits of the 

more and less stringent scenarios were within about 15% of the selected remedy. The results 

 

49 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.
182

of that analysis may be found in the cost-benefit comparison chapter (Chapter 10 of this 

RIA). 
183

Table 5-17: Estimated Reduction in Incidence of Adverse Health Effects of the 

Selected remedy (95% confidence intervals)A

Health Effect Within transport region

Beyond transport 

region Total

PM-Related endpoints

Premature Mortality

Pope et al. (2002) (age >30) 13,000

(5,200—21,000)

33

(5—60)

13,000

(5,200—21,000)

Laden et al. (2006) (age 

>25)

34,000

(18,000—49,000)

84

(31—140)

34,000

(18,000—49,000)

Infant (< 1 year) 59

(-47—160)

0.15

(-0.2—0.5)

59

(-47—160)

Chronic Bronchitis 8,700

(1,600—16,000)

23

(-5—50)

8,700

(1,600—16,000)

Non-fatal heart attacks (age > 18) 15,000

(5,600—24,000)

40

(7—72)

15,000

(5,600—24,000)

Hospital admissions—respiratory 

(all ages)

2,700

(1,300—4,000)

5

(2—9)

2,700

(1,300—4,000)

Hospital admissions—cardiovascular 

(age > 18)

5,700

(4,200—6,600)

15

(10—19)

5,800

(4,200—6,600)

Emergency room visits for asthma 

(age < 18)

9,800

(5,800—14,000)

21

(7—36)

9,800

(5,800—14,000)

Acute bronchitis 

(age 8-12)

19,000

(-630—37,000)

50

(-29—130)

19,000

(-660—37,000)

Lower respiratory symptoms (age 7-14) 240,000

(120,000—360,000)

630

(130—1,100)

240,000

(120,000—360,000)

Upper respiratory symptoms 

(asthmatics age 9-18)

180,000

(57,000—310,000)

480

(-25—980)

180,000

(57,000—310,000)

Asthma exacerbation 

(asthmatics 6-18)

400,000

(45,000—1,100,000)

1,100

(-250—2,900)

400,000

(45,000—1,100,000)

Lost work days 

(ages 18-65)

1,700,000

(1,500,000—1,900,000)

4,300

(3,500—5,200)

1,700,000

(1,500,000—1,900,000)

Minor restricted-activity days 

(ages 18-65)

10,000,000

(8,400,000—11,000,000)

26,000

(20,000—32,000)

10,000,000

(8,400,000—12,000,000)

Ozone-related endpoints

Premature mortality

Bell et al. (2004) (all ages) 27

(11—42)

0.1

(0.01—0.3)

27

(11—42)
184

Schwartz et al. (2005) 

(all ages)

41

(17—64)

0.2

(0.1—0.4)

41

(17—65)

Huang et al. (2005) 

(all ages)

37

(17—57)

0.2

(0.1—0.4)

37

(17—57)

Meta-analyses

Ito et al. (2005) (all ages) 120

(78—160)

0.6

(0.3—0.9)

120

(79—160)

Bell et al. (2005) (all ages) 87

(48—130)

0.5

(0.2—0.8)

87

(48—130)

Levy et al. (2005) (all ages) 120

(89—150)

0.7

(0.4—0.9)

120

(90—160)

Hospital admissions—respiratory 

causes (ages > 65)

160

(21—280)

1.2

(0.1—2.3)

160

(21—290)

Hospital admissions—respiratory 

causes (ages <2)

83

(43—120)

0.5

(0.2—0.8)

84

(43—120)

Emergency room visits for asthma (all 

ages)

86

(-2—260)

0.4

(-0.2—1.4)

86

(-2—260)

Minor restricted-activity days (ages 18-

65)

160,000

(80,000—240,000)

910

(240—1,600)

160,000

(80,000—240,000)

School absence days 51,000

(22,000—73,000)

290

(59—490)

51,000

(22,000—74,000)

A Estimates rounded to two significant figures; column values will not sum to total value. 

B The negative estimates for certain endpoints are the result of the weak statistical power of the study used to calculate these health 

impacts and do not suggest that increases in air pollution exposure result in decreased health impacts.
185

Table 5-18: Estimated Economic Value of Health and Welfare Benefits (95% 

confidence intervals, billions of 2007$)A

Health Effect Pollutant

Within transport 

region

Beyond transport 

regionBTotal

Premature Mortality (Pope et al. 2002 PM mortality and Bell et al. 2004 ozone mortality 

estimates)

3% discount rate PM2.5 & O3

$100

($8.3—$320)

$0.3

($0.01—$0.9)

$100

($8.3—$320)

7% discount rate PM2.5 & O3

$94

($7.5—$280)

$0.2

($0.01—$0.8)

$94

($7.5—$290)

Premature Mortality (Laden et al. 2006 PM mortality and Levy et al. 2005 ozone mortality 

estimates)

3% discount rate PM2.5 & O3

$270

($23—$770)

$0.7

($0.05—$2)

$270

($23—$770)

7% discount rate PM2.5 & O3

$240

($21—$700)

$0.6

($0.05—$1.8)

$240

($21—$700)

Chronic Bronchitis PM2.5

$4.2

($0.2—$19)

$0.01

($-0.003--$0.06)

$4.2

($0.2—$19)

Non-fatal heart attacks 

3% discount rate PM2.5

$1.7

($0.3—$4.2)

$0.004

($0.003—$0.01)

$1.7

($0.3—$4.2)

7% discount rate PM2.5

$1.3

($0.3—$3.1)

$0.004

($0.002—$0.001)

$1.3

($0.3—$3.1)

Hospital admissions—

respiratory PM2.5 & O3

$0.04

($0.02—$0.06) ---

$0.04

($0.02—$0.06)

Hospital admissions—

cardiovascular PM2.5

$0.09

($0.01—$0.2) ---

$0.09

($0.01—$0.2)

Emergency room visits 

for asthma PM2.5 & O3

$0.003

($0.002—$0.006) ---

$0.003

($0.002—

$0.006)

Acute bronchitis PM2.5

$0.008

(<$-0.01—$0.02)c ---

$0.008

(<$-0.01—

$0.02)c

Lower respiratory 

symptoms PM2.5

$0.004

($0.002—$0.009) ---

$0.004

($0.002—

$0.009)

Upper respiratory 

symptoms PM2.5

$0.005

(<$0.01—$0.014) ---

$0.005

(<$0.01—

$0.014)

Asthma exacerbation PM2.5

$0.02

($0.002—$0.08) ---

$0.02

($0.002—

$0.08)

Lost work days PM2.5

$0.2

($0.17—$0.24) ---

$0.2

($0.17—$0.24)

School loss days O3

$0.01

($0.004—$0.013) ---

$0.01

($0.004—

$0.013)

Minor restricted-activity 

days PM2.5 & O3

$0.7

($0.3—$1) ---

$0.7

($0.3—$1)
186

Recreational visibility, 

Class I areas PM2.5 $4.1

Social cost of carbon (3% 

discount rate, 2014 value) CO2 $0.6

Monetized total Benefits 

(Pope et al. 2002 PM2.5 mortality and Bell et al. 2004 ozone mortality estimates)

3% discount rate PM2.5, O3

$110

($8.8—$340)

$0.28

($0.01—$0.9)

$120

($14—$350)

7% discount rate PM2.5, O3 

$100

($8—$310)

$0.25

($0.01—$0.85)

$110

($13—$320)

Monetized total Benefits 

(Laden et al. 2006 PM2.5 mortality and Levy et al. 2005 ozone mortality estimates)

3% discount rate PM2.5, O3

$270

($24—$800)

$0.7

($0.05—$2.1)

$280

($29—$810)

7% discount rate PM2.5, O3 

$250

($22—$720)

$0.6

($0.04—$1.9)

$250

($26—$730)

A Estimates rounded to two significant figures. 

B Monetary value of endpoints marked with dashes are < $100,000. States included in transport region may be 

found in chapter 2.

C The negative estimates for certain endpoints are the result of the weak statistical power of the study used to 

calculate these health impacts and do not suggest that increases in air pollution exposure result in decreased 

health impacts.

Not all known PM- and ozone-related health and welfare effects could be quantified or 

monetized. The monetized value of these unquantified effects is represented by adding an 

unknown “B” to the aggregate total. The estimate of total monetized health benefits is thus 

equal to the subset of monetized PM- and ozone-related health and welfare benefits plus B, 

the sum of the nonmonetized health and welfare benefits; this B represents both uncertainty 

and a bias in this analysis, as it reflects those benefits categories that we are unable quantify

in this analysis. 

Total monetized benefits are dominated by benefits of mortality risk reductions. The 

primary analysis projects that the preferred remedy will result in between 13,000 and 34,000 

PM2.5 and ozone-related avoided premature deaths annually in 2014. Our estimate of total 

monetized benefits in 2014 for the selected remedy is between $120 billion and $280 billion 

using a 3 percent discount rate and between $110 billion and $250 using a 7 percent discount 

rate. Health benefits account for between 97 and 99 percent of total benefits depending on 

the PM2.5 and ozone mortality estimates used, in part because we are unable to quantify most 

of the non-health benefits. The monetized benefit associated with reductions in the risk of 
187

premature mortality, which accounts for between $100 and $270 billion in 2014, depending 

again on the PM and ozone mortality risk estimates used, is between 93 and 97 percent of 

total monetized health benefits. The next largest benefit is for reductions in chronic illness 

(CB and nonfatal heart attacks), although this value is more than an order of magnitude lower 

than for premature mortality. Hospital admissions for respiratory and cardiovascular causes, 

visibility, MRADs, work loss days, school absence days, and worker productivity account for 

the majority of the remaining benefits. The remaining categories each account for a small 

percentage of total benefit; however, they represent a large number of avoided incidences 

affecting many individuals. A comparison of the incidence table to the monetary benefits 

table reveals that there is not always a close correspondence between the number of 

incidences avoided for a given endpoint and the monetary value associated with that 

endpoint. For example, there are almost 100 times more work loss days than premature 

mortalities, yet work loss days account for only a very small fraction of total monetized 

benefits. This reflects the fact that many of the less severe health effects, while more

common, are valued at a lower level than the more severe health effects. Also, some effects, 

such as hospital admissions, are valued using a proxy measure of WTP. As such, the true 

value of these effects may be higher than that reported in Table 5-18. 

Figures 5-15 and 5-16 illustrates the geographic distribution of avoided PM2.5 and 

ozone-related mortalities estimated to result from the selected remedy. Figure 5-17 plots the 

cumulative distribution of the percentage of deaths due to PM2.5 and ozone in 2014, prior to 

and after the implementation of the Transport Rule remedy. As illustrated in this figure, once 

implemented the Transport Rule is estimated to reduce by a substantial fraction the 

percentage of total deaths due to PM2.5 and ozone. While not quantified in this RIA, we 

expect the Transport Rule to produce important public health benefits for populations living 

in Canada. Approximately 90% of the Canadian population lives within 100 miles of the U.S. 

border, suggesting that some of the air quality improvements projected in areas near the U.S.-

Canada border would be enjoyed by Canadian populations as well. A recent analysis 

(Chestnut and Mills, 2005) of the U.S. Acid Rain Program estimates annual benefits of the 

program in 2010 to both Canada and the United States at $122 billion and costs for that year 

at $3 billion (2000$)—a 40-to-1 benefit/cost ratio. These quantified benefits in the United 

States and Canada are the result of improved air quality prolonging lives, reducing heart 

attacks and other cardiovascular and respiratory problems, and improving visibility. The 

complete report is available in volume 77, issue 3, of the Journal of Environmental 
188

Management.

These figures show that while there are very large health benefits throughout most of 

the East, there could be several areas where a very small disbenefit could result if further 

governmental actions do not occur to address them in the future. There are several upcoming 

planned federal actions that could lead to further large reductions throughout the US of 

ambient levels of fine particles and ozone. Additionally, state actions to address regional 

haze in the near future and the existing NAAQS for fine particles and ozone could address 

these situations. There are also other state actions, such as the recent Colorado Clean Air –

Clean Jobs Act of April 2010 that is likely to convert much of the Front Range coal-fired 

generation in Colorado to natural gas in the near future.
189

Figure 5-15: Estimated reduction in excess PM2.5-related premature mortalities 

estimated to occur in each county in 2014 as a result of the selected remedy.
190

Figure 5-16: Estimated reduction in excess ozone-related premature mortalities 

estimated to occur in each county in 2014 as a result of the selected remedy
Figure 5-17: Cumulative percentage of the reduction in all

to reductions in PM2.5 and Ozone resulting from the 

ABell et al. 2005 ozone mortality estimate and Pope et al. 2002 PM

191

17: Cumulative percentage of the reduction in all-cause mortality 

and Ozone resulting from the selected remedy by county in 2014

Bell et al. 2005 ozone mortality estimate and Pope et al. 2002 PM2.5 mortality estimates.

cause mortality attributable 

remedy by county in 2014A

mortality estimates.
192

Figure 5-18 summarizes an array of PM2.5-related monetized benefits estimates based 

on alternative epidemiology and expert-derived PM-mortality estimate as well as the sum of 

ozone-related benefits using the Bell et al. (2004) mortality estimate. 

Based on our review of the current body of scientific literature, EPA estimated PMrelated mortality without applying an assumed concentration threshold. EPA’s Integrated 

Science Assessment for Particulate Matter (U.S. EPA, 2009b), which was recently reviewed 

by EPA’s Clean Air Scientific Advisory Committee (U.S. EPA-SAB, 2009a; U.S. EPA-SAB, 

2009b), concluded that the scientific literature consistently finds that a no-threshold loglinear model most adequately portrays the PM-mortality concentration-response relationship 

while recognizing potential uncertainty about the exact shape of the concentration-response 

function. Consistent with this finding, we have conformed the threshold sensitivity analysis 

to the current state of the PM science improved upon our previous approach for estimating 

the sensitivity of the benefits estimates to the presence of an assumed threshold by

incorporating a new “Lowest Measured Level” (LML) assessment.

This approach summarizes the distribution of avoided PM mortality impacts 

according to the baseline (i.e. pre-Transport Rule) PM2.5 levels experienced by the population 

receiving the PM2.5 mortality benefit (Figure 5-19). We identify on this figure the lowest air 

quality levels measured in each of the two primary epidemiological studies EPA uses to 

quantify 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 the LML analysis 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.

The very large proportion of the avoided PM-related impacts we estimate in this 

analysis occur among populations exposed at or above the LML of each study (Figures 5-19) 

increasing our confidence in the PM mortality analysis. Approximately 69% of the avoided 

impacts occur at or above an annual mean PM2.5 level of 10 μg/m3(the LML of the Laden et 

al. 2006 study); about 96% occur at or above an annual mean PM2.5 level of 7.5 μg/m3 (the 
193

LML of the Pope et al. 2002 study). 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. However, the analysis above confirms that the great 

majority of the impacts occur at or above each study’s LML.

As an example, when considering mortality impacts among populations living in 

areas with an annual mean PM level of 8 μg/m3, we would place greater confidence in 

estimates drawn from the Pope et al. 2002 study, as this air quality level is above the LML of 

this study. Conversely, we would place equal confidence when estimating mortality impacts 

among populations living in locations where the annual mean PM levels are above 10 μg/m3 

because this value is at or above the LML of each study. 

Finally, Figure 5-20 illustrates the percentage of population exposed to different 

levels of annual mean PM2.5 levels in the baseline and after the implementation of the 

Transport Rule in 2014. The Transport Rule reduces overall PM2.5 levels substantially, 

particularly among highly exposed populations located within the states covered by the rule. 

Locations of the U.S. where annual mean PM levels are below the lowest measured level of 

the Pope study--western states in particular--are generally unaffected by the rule. However, 

for populations in the far western portion of the Transport Rule region, where annual mean 

PM2.5 concentrations are below 7.5 μg /m3, there are benefits of the rule, although the 

relative magnitude of those benefits compared to benefits in the majority of the areas covered 

by the Transport Rule is small. In these areas there is lower confidence in the magnitude of 

the benefits associated with reductions in long-term PM2.5. However, in these same areas, 

there may be additional benefits associated with short term elevated levels of PM, and there 

is relatively greater confidence in those benefits. In addition, we note that prior to the 

implementation of the Transport Rule, 85% of the population live in areas where PM2.5 levels 

are projected to be above the lowest measured levels of the Pope study. Taken together, this 

information increases our confidence in the estimated mortality reductions for this rule.

While the LML of each study is important to consider when characterizing and 

interpreting the overall level PM-related benefits, as discussed earlier in this chapter, EPA 

believes that both cohort-based mortality estimates are suitable for use in air pollution health 

impact analyses. When estimating PM mortality impacts using risk coefficients drawn from 

the Laden et al. analysis of the Harvard Six Cities and the Pope et al. analysis of the 
American Cancer Society cohorts there are innumerable other attribu

size of the reported risk estimates

size of the cohort, activity patterns and particle composition among others. The LML

assessment presented here provides a limited representati

the two studies.

Figure 5-18: Estimated PM

epidemiology or expert-derived PM mortality risk estimate

A

Column total equals sum of PM2.5

mortality benefits using the Bell et al. (2004) mortality estimate.

$0

$50

$100

$150

$200

$250

$300

$350

$400

Pope et al.

Billions (2006$)

PM2.5 Benefits estimates derived from 2 epidemiology functions and 12 expert 

3% DR

7% DR

194

American Cancer Society cohorts there are innumerable other attributes that may affect the 

size of the reported risk estimates—including differences in population demographics, the 

size of the cohort, activity patterns and particle composition among others. The LML

assessment presented here provides a limited representation of one key difference between 

18: Estimated PM2.5- related premature mortalities avoided according to 

derived PM mortality risk estimateA

2.5-related mortality and morbidity benefits and ozone-related morbidity and 

mortality benefits using the Bell et al. (2004) mortality estimate.

Laden et al.

Benefits estimates derived from 2 epidemiology functions and 12 expert 

functions

tes that may affect the 

including differences in population demographics, the 

size of the cohort, activity patterns and particle composition among others. The LML

on of one key difference between 

related premature mortalities avoided according to 

related morbidity and 

Laden et al.

Benefits estimates derived from 2 epidemiology functions and 12 expert 
Figure 5-19: Distribution of PM

PM2.5 epidemiology study and lowest measured l

195

2.5-related mortality impacts by baseline PM

epidemiology study and lowest measured level (LML) of each study

2.5 levels, 
196

Figure 5-20: Cumulative percentage of adult population at annual mean PM2.5 levels 

(pre- and post-2014 Transport Rule) 

5.8 Discussion

This analysis demonstrates the significant health and welfare benefits of the Transport 

Rule. We estimate that by 2014 the rule will have reduced the number of PM2.5 and ozonerelated premature mortalities by between 13,000 and 34,000, produce substantial nonmortality benefits and significantly improve visibility in Class 1 areas. This rule promises to 

yield significant welfare impacts as well, though the quantification of those endpoints in this 

RIA is incomplete. These significant health and welfare benefits suggest the important role 

that pollution from the EGU sector plays in the public health impacts of air pollution. 

Inherent in any complex RIA such as this one are multiple sources of uncertainty. 

Some of these we characterized through our quantification of statistical error in the 
197

concentration response relationships and our use of the expert elicitation-derived PM 

mortality functions. Others, including the projection of atmospheric conditions and sourcelevel emissions, the projection of baseline morbidity rates, incomes and technological 

development are unquantified. When evaluated within the context of these uncertainties, the 

health impact and monetized benefits estimates in this RIA can provide useful information 

regarding the public health impacts attributable to EGUs.

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CHAPTER 6

ELECTRIC POWER SECTOR PROFILE

This chapter discusses important aspects of the power sector that relate to the 

Transport Rule, including the types of power-sector sources affected by the Transport Rule, 

and provides background on the power sector and electric generating units (EGUs). In 

addition, this chapter provides some historical background on EPA regulation of and future 

projections for the power sector.

6.1 Power Sector Overview

The production and delivery of electricity to customers consists of three distinct 

segments: generation, transmission, and distribution.

6.1.1 Generation

Electricity generation is the first process in the delivery of electricity to consumers. 

Most of the existing capacity for generating electricity involves creating heat to rotate 

turbines which, in turn, create electricity. The power sector consists of over 17,000 

generating units, comprising fossil-fuel-fired units, nuclear units, and hydroelectric and other 

renewable sources dispersed throughout the country (see Table 6-1).
223

Table 6-1. Existing Electricity Generating Capacity by Energy Source, 2009

Energy Source

Number of 

Generators

Generator 

Nameplate 

Capacity 

(MW)

Generator 

Net Summer 

Capacity 

(MW)

Coal 1,436 338,723 314,294

Petroleum 3,757 63,254 56,781

Natural Gas 5,470 459,803 401,272

Other Gases 98 2,218 1,932

Nuclear 104 106,618 101,004

Hydroelectric Conventional 4,005 77,910 78,518

Wind 620 34,683 34,296

Solar Thermal and Photovoltaic 110 640 619

Wood and Wood Derived Fuels 353 7,829 6,939

Geothermal 222 3,421 2,382

Other Biomass 1,502 5,007 4,317

Pumped Storage 151 20,538 22,160

Other 48 1,042 888

Total 17,876 1,121,686 1,025,400

Source: EIA Electric Power Annual 2009, Table 1.2

These electric generating sources provide electricity for commercial, industrial, and 

residential uses, each of which consumes roughly a quarter to a third of the total electricity 

produced (see Table 6-2). Some of these uses are highly variable, such as heating and air 

conditioning in residential and commercial buildings, while others are relatively constant, 

such as industrial processes that operate 24 hours a day.

Table 6-2. Total U.S. Electric Power Industry Retail Sales in 2009 (Billion kWh)

Sales/Direct Use 

(Billion kWh)

Share of Total 

End Use

Retail 

Sales

Residential 1,364 37%

Commercial 1,307 35%

Industrial 917 25%

Transportation 8 0.2%

Direct Use 127 3%

Total End Use 3,724 100%

Source: EIA Electric Power Annual 2009, Table 7.2
224

In 2009, electric generating sources produced 3,950 billion kWh to meet electricity

demand. Roughly 70 percent of this electricity was produced through the combustion of 

fossil fuels, primarily coal and natural gas, with coal accounting for almost half of the total 

(see Table 6-3).

Table 6-3. Electricity Net Generation in 2009 (Billion kWh)

Net Generation 

(Billion kWh)

Fuel Source 

Share

Coal 1,756 44.5%

Petroleum 39 1.0%

Natural Gas 921 23.3%

Other Gases 11 0.3%

Nuclear 799 20.2%

Hydroelectric 273 6.9%

Other 151 3.8%

Total 3,950 100%

Source: EIA Electric Power Annual 2009, Table 1.1

Note: Retail sales and net generation are not equal because net generation includes net exported electricity and 

loss of electricity that occurs through transmission and distribution.

Coal-fired generating units typically supply “base-load” electricity, the portion of 

electricity loads which are continually present, and typically operate throughout the day. 

Along with nuclear generation, these coal units meet the part of demand that is relatively

constant. Although much of the coal fleet operates as base load, there can be notable 

differences across various facilities (see Table 6-4). For example, coal-fired units less than 

100 MW in size comprise 37 percent of the total number of coal-fired units, but only 6 

percent of total coal-fired capacity. Gas-fired generation is better able to vary output and is 

the primary option used to meet the variable portion of the electricity load and typically

supplies “peak” power, when there is increased demand for electricity (for example, when 

businesses operate throughout the day or when people return home from work and run 

appliances and heating/air-conditioning), versus late at night or very early in the morning, 

when demand for electricity is reduced.
Table 6-4. Coal Steam Electricity Generating Units, by Size, Age, Capacity, and 

Efficiency (Heat Rate)

Unit Size 

Grouping (MW) No. Units

0 to 25 193

>25 to 49 108

50 to 99 162

100 to 149 269

150 to 249 81

250 and up 453

Totals 1,266

Source: National Electric Energy Data System (NEEDS) v.4.10

Notes: A lower heat rate indicates a higher level of efficiency. Table is 

2010 or earlier, and excludes those units with planned retirements.

Figure 6-1. Fossil Fuel-Fired Electricity Generating Facilities, by Size

Notes/Source: National Electric Energy Data System (NEEDS 4.10) (EPA, Dec

displays facilities in the NEEDS 4.10 IPM frame. NEEDS reflects available capacity on

2011; this includes planned new builds and 

facilities, some facilities may be obscured.

225

4. Coal Steam Electricity Generating Units, by Size, Age, Capacity, and 

% of All 

Units Avg. Age

Avg. Net 

Summer 

Capacity 

(MW)

Total Net 

Summer 

Capacity 

(MW)

% Total 

Capacity

15% 45 15 2,849 1%

9% 42 38 4,081 1%

13% 47 75 12,132 4%

21% 49 141 38,051 12%

6% 43 224 18,184 6%

36% 34 532 241,184 76%

316,480

Source: National Electric Energy Data System (NEEDS) v.4.10

Notes: A lower heat rate indicates a higher level of efficiency. Table is limited to coal-steam units online in 

2010 or earlier, and excludes those units with planned retirements.

Fired Electricity Generating Facilities, by Size

Notes/Source: National Electric Energy Data System (NEEDS 4.10) (EPA, December 2010). This map 

displays facilities in the NEEDS 4.10 IPM frame. NEEDS reflects available capacity on-line by the end of 

new builds and planned retirements. In areas with a dense concentration of 

es may be obscured.

4. Coal Steam Electricity Generating Units, by Size, Age, Capacity, and 

% Total 

Capacity

Avg. Heat 

Rate 

(Btu/kWh)

1% 11,154

1% 11,722

4% 11,328

12% 10,641

6% 10,303

76% 10,193

steam units online in 

ember 2010). This map 

line by the end of 

retirements. In areas with a dense concentration of 
226

6.1.2 Transmission

Transmission is the term used to describe the movement of electricity over a network 

of high voltage lines, from electric generators to substations where power is stepped down 

for local distribution. In the US and Canada, there are three separate interconnected 

networks of high voltage transmission lines50, each operating at a common frequency. 

Within each of these transmission networks, there are multiple areas where the operation of 

power plants is monitored and controlled to ensure that electricity generation and load are 

kept in balance. In some areas, the operation of the transmission system is under the control 

of a single regional operator; in others, individual utilities coordinate the operations of their 

generation, transmission, and distribution systems to balance their common generation and 

load needs.

6.1.3 Distribution

Distribution of electricity involves networks of lower voltage lines and substations 

that take the higher voltage power from the transmission system and step it down to lower 

voltage levels to match the needs of customers. The transmission and distribution system is 

the classic example of a natural monopoly, in part because it is not practical to have more 

than one set of lines running from the electricity generating sources to substations or from 

substations to residences and business.

Transmission has generally been developed by the larger vertically integrated utilities 

that typically operate generation and distribution networks. Distribution is handled by a large 

number of utilities that often purchase and sell electricity, but do not generate it. 

Transmission and distribution have been considered differently from generation in efforts to 

restructure the industry. As discussed below, electricity restructuring has focused primarily 

on efforts to reorganize the industry to encourage competition in the generation segment of 

the industry, including ensuring open access of generation to the transmission and 

distribution services needed to deliver power to consumers. In many state efforts, this has 

 

50 These three network interconnections are the western US and Canada, corresponding approximately to the 

area west of the Rocky Mountains; eastern US and Canada, not including most of Texas; and a third network 

operating in most of Texas. These are commonly referred to as the Western Interconnect Region, Eastern 

Interconnect Region, and ERCOT, respectively.
227

also included separating generation assets from transmission and distribution assets into 

distinct economic entities. Transmission and distribution remain price-regulated throughout 

the country based on the cost of service.

6.2 Deregulation and Restructuring

The process of restructuring and deregulation of wholesale and retail electric markets 

has changed the structure of the electric power industry. In addition to reorganizing asset 

management between companies, restructuring sought a functional unbundling of the 

generation, transmission, distribution, and ancillary services the power sector has historically 

provided, with the aim of enhancing competition in the generation segment of the industry.

Beginning in the 1970s, government policy shifted against traditional regulatory

approaches and in favor of deregulation for many important industries, including 

transportation (notably commercial airlines), communications, and energy, which were all 

thought to be natural monopolies (prior to 1970) that warranted governmental control of 

pricing. However, deregulation efforts in the power sector were most active during the 

1990s. Some of the primary drivers for deregulation of electric power included the desire for 

more efficient investment choices, the possibility of lower electric rates, reduced costs of 

combustion turbine technology that opened the door for more companies to sell power, and 

complexity of monitoring utilities’ cost of service and establishing cost-based rates for 

various customer classes (see Figure 6-1).

The pace of restructuring in the electric power industry slowed significantly in 

response to market volatility and financial turmoil associated with bankruptcy filings of key

energy companies in California. By the end of 2001, restructuring had either been delayed or 

suspended in eight states that previously enacted legislation or issued regulatory orders for its 

implementation (shown as “Suspended” in Figure 6-2 below). Another 18 other states that 

had seriously explored the possibility of deregulation in 2000 reported no legislative or 

regulatory activity in 2001 (DOE, EIA, 2003a) (“Not Active” in Figure 6-2 below). 

Currently, there are 15 states where price deregulation of generation (restructuring) has 

occurred (“Active” in Figure 6-2 below). Eight of these states are in the Transport Rule 

region. The effort is more or less at a standstill; there have been no recent proposals to the 

Federal Energy Regulatory Commission (FERC) for actions aimed at wider restructuring, 
228

and no additional states have begun retail deregulation activity

Figure 6-2. Status of State Electricity Industry Restructuring Activities

Source: EIA http://www.eia.doe.gov/cneaf/electricity/page/restructuring/restructure_elect.html (September, 

2010).

6.3 Pollution and EPA Regulation of Emissions

The burning of fossil fuels, which generates about 70 percent of our electricity

nationwide, results in air emissions of SO2 and NOX, important precursors in the formation of 

fine particles and ozone (NOX only). The power sector is a major contributor of both these 

pollutants, and reductions of SO2 and NOX emissions are critical to EPA’s efforts to bring 

about attainment with the fine particle and ozone NAAQS through programs like the 

Transport Rule. In 2008, the power sector accounted for 66 percent of total nationwide SO2

emissions and 18 percent of total nationwide NOX emissions (see Figure 6-3).
229

Figure 6-3. Emissions of SO2 and NOX from the Power Sector (2008)

Source: EPA http://www.epa.gov/ttn/chief/trends/

Different types of fossil fuel-fired units vary widely in their air emissions levels for 

SO2 and NOX, particularly when uncontrolled. For coal-fired units, NOX emissions rates can 

vary from under 0.05 lbs/mmBtu (for a unit with selective catalytic reduction (SCR) for NOX

removal) to over 1 lb/mmBtu for an uncontrolled cyclone boiler. NOX emissions from 

coal-fired power plants are formed during combustion and are a result of both nitrogen in 

coal and nitrogen in the air. SO2 emissions rates can vary from under 0.1 lbs/mmBtu (for 

some units with flue gas desulfurization (FGD) for SO2 removal) to over 5 lbs/mmBtu for 

units burning higher sulfur coal without any pollution controls. For an uncontrolled coal 

plant, SO2 emissions are directly related to the amount of sulfur in the coal.

Oil- and gas-fired units also have a wide range of NOX emissions depending on both 

the plant type and the controls installed. Gas-fired units with SCR can have emissions rates 

under 0.01 lbs/mmBtu, while completely uncontrolled units can have emissions rates in 

excess of 0.5 lbs/mmBtu. Gas-fired units have very little SO2 emissions. NOX emissions 

rates on oil-fired units can range from under 0.1 lbs/mmBtu (for units with new combustion 

controls) to over 0.6 lbs/mmBtu for units without combustion controls. SO2 emissions for 

oil-fired units can range from under 0.1 lbs/mmBtu for units burning low sulfur distillate oil 

to over 2 lbs/mmBtu for units burning high sulfur residual oil.

Nitrogen Oxides

Power Sector (18%)

Sulfur Dioxide

Power Sector (66%)
230

6.4 Pollution Control Technologies

There are three options for reducing SO2 emissions from coal-burning power plants. 

Units may switch from higher to lower sulfur coal, blend higher sulfur coal with lower sulfur 

coal, or use post-combustion controls, such as FGD (commonly referred to as scrubbers) or 

Dry Sorbent Injection (DSI). According to data submitted to EPA for compliance with the 

Title IV Acid Rain Program, the SO2 emissions rates for coal-fired units without controls 

varied from under 0.4 lbs/mmBtu to over 5 lbs/mmBtu depending on the type of coal 

combusted. With controls, rates range from as low as 0.03 lbs/mmBtu to close to 1 

lb/mmBtu.

It is generally easier to switch to a coal within the same rank (e.g., bituminous or 

sub-bituminous) because these coals will have similar heat contents and other characteristics. 

Switching completely to sub-bituminous coal (which typically has lower sulfur content) from 

bituminous coal is likely to require some modifications to the unit. Limited blending of 

sub-bituminous coal with bituminous coal can often be done with fewer modifications. 

The two most commonly used scrubber types include wet scrubbers and spray dryers, 

also known as dry scrubbers. Wet scrubbers can use a variety of sorbents to capture SO2, 

including limestone and magnesium-enhanced lime. The choice of sorbent can affect the 

performance, size, and capital and operating costs of the scrubber. New wet scrubbers 

typically achieve at least 96 percent SO2 removal. Spray dryers use lime-based slurry and 

can achieve 92 percent removal.

An alternative to wet and dry scrubber technology is dry sorbent injection (DSI), 

which injects an alkaline powdered material (post combustion) to react with SO2 and other 

acid gasses. The reacted product is removed by particulate matter (PM) control device. DSI 

technology is most efficient with a baghouse present downstream but can function with an 

electrostatic precipitator (ESP) downstream as well. Under these circumstances, the ESP 

requires more reagent per molecule of acid gas removed as compared to a similar operation 

with a baghouse. Finally, DSI may employ a multitude of sorbents (Trona,51 sodium 

carbonate, calcium carbonate – and their bicarbonate counterparts) for a more tailored 

approach to reduce emissions based on the source, cost, and unit and fuel characteristics.

 

51 Trona refers to the chemical compound sodium sesquicarbonate.
231

One method of reducing NOX emissions is through the use of combustion controls 

(such as low NOX burners and over-fire air). Combustion controls adjust the coal 

combustion conditions to those where less formation of NOX occurs. Post-combustion 

controls remove the NOX after it has been formed. The most common post-combustion 

control is SCR. In SCR systems ammonia (NH3) is injected, which combines with the NOX

in the flue gas, to form nitrogen and water and uses a catalyst to enhance the reaction. These 

systems can reduce NOX by 90 percent and achieve emissions rates of around 0.06 

lbs/mmBtu. Another post-combustion control is Selective Non-catalytic Reduction (SNCR). 

In this technology NOX also is removed by injecting ammonia, but no catalyst is used. 

SNCR systems can reduce NOX by up to 40 percent.

Some of the same control options available to coal-fired units are also applicable to 

units fueled by oil or gas. Combustion controls, SCR, and SNCR can also be applied to oiland gas-fired boilers for NOX control. Combustion controls and SCR are also routinely used 

for NOX control on gas turbines.

For more detail on the cost and performance assumptions of pollution controls, see 

the documentation for the Integrated Planning Model (IPM), a dynamic linear programming 

model that EPA uses to examine air pollution control policies for SO2 and NOX throughout 

the contiguous United States for the entire power system. Documentation for IPM can be 

found at www.epa.gov/airmarkets/epa-ipm.

6.5 Air Regulation of the Power Sector

At the federal level, efforts to reduce emissions of SO2 have been occurring since 

1970. Policy makers have recognized the need to address these harmful emissions, and 

incremental steps have been taken to ensure that the country meets air quality standards. The 

Transport Rule is the next step towards realizing attainment of the national standards for 

PM2.5 and ozone.

Even before widespread regulation of SO2 and NOX for the power sector, total 

suspended particulate matter (TSP) was a related target of state and federal action. Because 

larger particulates are visible as dark smoke from smokestacks, most states had regulations 

by 1970 limiting the opacity of emissions. Requirements for taller smokestacks also 
232

mitigated local impacts of TSP. Notably, such regulations effectively addressed largediameter, filterable particulate matter rather than condensable particulate matter (such as 

PM2.5) associated with SO2 and NOX emissions, which are not visible at the smokestack and 

have impacts far from their sources.

Federal regulation of SO2 and NOX emissions at power plants began with the 1970 

Clean Air Act. The Act required the Agency to develop New Source Performance Standards 

(NSPS) for a number of source categories including coal-fired power plants. The first NSPS 

for power plants (subpart D) required new units to limit SO2 emissions either by using 

scrubbers or by using low sulfur coal. NOX was required to be limited through the use of low 

NOX burners. A new NSPS (subpart Da), promulgated in 1978, tightened the standards for 

SO2, requiring scrubbers on all new units.

The 1990 Clean Air Act Amendments (CAAA) placed a number of new requirements 

on power plants. The Acid Rain Program, established under Title IV of the 1990 CAAA, 

requires major reductions of SO2 and NOX emissions. The SO2 program sets a permanent 

cap on the total amount of SO2 that can be emitted by electric power plants in the contiguous 

United States at about one-half of the amount of SO2 these sources emitted in 1980. Using a 

market-based cap and trade mechanism allows flexibility for individual combustion units to 

select their own methods of compliance with the SO2 reduction requirements. The program 

uses a more traditional approach to NOX emissions limitations for certain coal-fired electric 

utility boilers, with the objective of achieving a 2 million ton reduction from projected NOX

emission levels that would have been emitted in 2000 without implementation of Title IV.

The Acid Rain Program comprises two phases for SO2 and NOX. Phase I applied 

primarily to the largest coal-fired electric generating sources from 1995 through 1999 for 

SO2 and from 1996 through 1999 for NOX. Phase II for both pollutants began in 2000. For 

SO2, it applies to thousands of combustion units generating electricity nationwide; for NOX it 

generally applies to affected units that burned coal during 1990 through 1995. The Acid 

Rain Program has led to the installation of a number of scrubbers on existing coal-fired units 

as well as significant fuel switching to lower sulfur coals. Under the NOX provisions of Title 

IV, most existing coal-fired units installed low NOX burners.

The CAAA also placed much greater emphasis on control of NOX to reduce ozone 

nonattainment. This led to the formation of several regional NOX trading programs as well 
233

as intrastate NOX trading programs in states such as Texas. The northeastern states of the 

Ozone Transport Commission (OTC) required existing sources to meet Reasonably Available 

Control Technology (RACT) limits on NOX in 1995 and in 1999 began an ozone-season cap 

and trade program to achieve deeper reductions. In 1998, EPA promulgated regulations (the 

NOX SIP Call) that required 21 states in the eastern United States and the District of 

Columbia to reduce NOX emissions that contributed to nonattainment in downwind states 

using the cap and trade approach. This program began in May of 2003 and has resulted in 

the installation of significant amounts of selective catalytic reduction.

The Clean Air Interstate Rule (CAIR) built on EPA’s efforts in the NOX SIP call to 

address specifically interstate pollution transport for ozone, and was EPA’s first attempt to 

address interstate pollution transport for PM2.5. It required significant reductions in 

emissions of SO2 and NOX in 28 states and the District of Columbia (see Figure 6-4 below). 

EGUs were found to be a major source of the SO2 and NOX emissions which contributed to 

fine particle concentrations and ozone problems downwind. Although the D.C. Circuit 

remanded the rule to EPA in 2008, it did so without vacatur, allowing the rule to remain in 

effect while EPA addresses the remand. Thus, CAIR is continuing to help states address 

ozone and PM2.5 nonattainment and improve visibility by reducing transported precursors of 

SO2 and NOX through the implementation of three separate cap and trade compliance 

programs for annual NOX, ozone season NOX, and annual SO2 emissions from power plants.
234

Figure 6-4. States Covered under the Clean Air Interstate Rule

Perhaps in anticipation of complying with CAIR, especially the more stringent second 

phase that was set to begin in 2015, several sources have recently been installing or planning 

to install advanced controls for SO2 and NOX to begin operating in the 2010 to 2015 

timeframe. Many EPA New Source Review (NSR) settlements also require controls in those 

years, as do state rules in Georgia, Illinois, and Maryland. States like North Carolina, New 

York, Connecticut, Massachusetts, and Delaware have also moved to control these emissions 

to address nonattainment. Thus both federal and state efforts are continuing to bring about 

sizeable reductions in SO2 and NOX from the power sector. Section 6-1 below discusses how 

these recent activities are reflected in the Transport Rule base case. Details of the NSR 

settlements and state controls can be found in the IPM documentation referenced earlier.

6.6 Revenues, Expenses, and Prices

Due to lower retail electricity sales, total utility operating revenues declined in 2009 to $276 

billion from a peak of almost $300 billion in 2008. However, operating expenses were 
235

appreciably lower and as a result, net income actually rose modestly compared to 2008 (see 

Table 6-5). Recent economic events have put downward pressure on electricity demand, thus 

dampening electricity prices (utility revenues), but have also reduced the price and cost of 

fossil fuels and other expenses. Electricity sales and revenues associated with the generation, 

transmission, and distribution of electricity are expected to rebound and increase modestly by

2015, where they are projected to be roughly $360 billion (see Table 6-6).

Table 6-5. Revenue and Expense Statistics for Major U.S. Investor-Owned Electric 

Utilities for 2009 ($millions)

 

2008 2009

Utility Operating Revenues 298,962 276,124

Electric Utility 266,124 249,303

Other Utility 32,838 26,822

Utility Operating Expenses 267,263 244,243

Electric Utility 236,572 219,544

Operation 175,887 154,925

Production 140,974 118,816

Cost of Fuel 47,337 40,242

Purchased Power 84,724 67,630

Other 8,937 10,970

Transmission 6,950 6,742

Distribution 3,997 3,947

Customer Accounts 5,286 5,203

Customer Service 3,567 3,857

Sales 225 178

Administrative and General 14,718 15,991

Maintenance 14,192 14,092

Depreciation 19,049 20,095

Taxes and Other 26,202 29,081

Other Utility 30,692 24,698

Net Utility Operating Income 31,699 31,881

 

 Source: EIA Electric Power Annual 2009, Table 8.1

 Note: This data does not include information for public utilities.
236

Table 6-6. Projected Revenues by Service Category in 2015 for Public Power and

Investor-Owned Utilities (billions)

 Generation $195

 Transmission $36

 Distribution $129

$360

Notes: Data is from EIA’s AEO 2011, and is derived by taking either total electricity use (for generation) or 

sales (transmission and distribution) and multiplying by forecasted prices by service category from Table 8 

(Electricity Supply, Disposition, Prices, and Emissions).

Based on EIA's Annual Energy Outlook 2011, Table 6-6 shows that in the base case, 

the power sector is expected to derive revenues of $360 billion in 2015. Table 6-5 shows that 

investor-owned utilities (IOUs) earned income of about 11.5% compared to total revenues in 

2009. Assuming the same income ratio from IOUs (with no income kept by public power), 

and using the same proportion of power sales from public power as observed in 2009, EPA 

projects that the power sector will expend over $320 billion in 2015 alone to generate, 

transmit, and distribute electricity to end-use consumers.

Over the past 50 years, real retail electricity prices have ranged from around 7 cents 

per kWh in the early 1970s, to around 11 cents, reached in the early 1980s. Generally, retail 

electricity prices do not change rapidly and do not display the variability of other energy or 

commodity prices. Retail rate regulation has largely insulated consumers from the rising and 

falling wholesale electricity price signals whose variation on an hourly, daily, and seasonal 

basis is critical for driving lowest-cost matching of supply and demand. In fact, the real price 

of electricity today is lower than it was in the early 1960s and 1980s (see Figure 6-5).
237

Figure 6-5. National Average Retail Electricity Price (1960 – 2009)

 Source: EIA’s Annual Energy Review 2009

On a state-by-state basis, retail electricity prices vary considerably. The Northeast 

and California have average retail prices that can be as much as double those of other states. 

(see Figure 6-6) 

0

2

4

6

8

10

12

1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

Cents / kWh ($2006)
238

Figure 6-6 Average Retail Electricity Price by State (cents/kWh), 2009

6.6.1 Natural Gas Prices

The natural gas market in the United States has historically experienced significant 

price volatility from year to year, between seasons within a year, and can undergo major 

price swings during short-lived weather events (such as cold snaps leading to short-run 

spikes in heating demand). Over the last decade, gas prices (both Henry Hub prices and 

delivered prices to the power sector) have ranged from $3 per mmBtu to as high as $9 on an 

annual average basis (see Figure 6-7). During that time, the daily price of natural gas 

reached as high as $15/mmBtu. Recent forecasts of natural gas have also experienced 

considerable revision as new sources of gas have been discovered and come to market.
239

Figure 6-7. Natural Gas Spot Price, Annual Average (Henry Hub)

Source: EIA

6.7 Electricity Demand and Demand Response

Electricity performs a vital and high-value function in the economy. Historically, 

growth in electricity consumption has been closely aligned with economic growth. Overall, 

the U.S. economy has become more efficient over time, producing more output (GDP) per 

unit of energy input, with per capita energy use fairly constant over the past 30 years. The 

growth rate of electricity demand has also been in overall decline for the past sixty years (see 

Figure 6-8), with several key drivers that are worth noting. First, there has been a significant 

structural shift in the U.S. economy towards less energy-intensive sectors, like services. 

Second, companies have strong financial incentives to reduce energy expenditures. Third, 

companies are responding to the marketplace and continually develop and bring to market 

new technologies that reduce energy consumption. Fourth, complementary policies and 
240

energy efficiency standards at the state and Federal level have helped address market 

failures. These broader changes have altered the outlook for future electricity growth (see 

Figure 6-8). 
241

Figure 6-8. Electricity Growth Rate (3 Year Rolling Average) and Projections from 

the Annual Energy Outlook 2011

 Source: EIA Annual Energy Review 2009 and Annual Energy Outlook 2011

Energy efficiency initiatives have become more common, and investments in energy

efficiency are projected to continue to increase for the next 5 to 10 years, driven in part by

the growing number of states that have adopted energy efficiency resource standards. These 

investments, and other energy efficiency policies at both the state and federal level, create 

incentives to reduce energy consumption and peak load. According to EIA, demand-side 

management provided actual peak load reductions of 31.7 GW in 2009. For context, the 

current coal fleet is roughly 320 GW of capacity.

Demand for electricity, especially in the short run, is not very sensitive to changes in 

prices and is considered relatively price inelastic, although some demand reduction does 

occur in response to price. With that in mind, EPA modeling does not typically incorporate a 

“demand response” in its electric generation modeling (Chapter 8) to the increases in 

electricity prices typically projected for EPA rulemakings. Electricity demand is considered 

to be constant in EPA modeling applications and the reduction in production costs that would 

result from lower demand is not considered in the primary analytical scenario that is 

modeled. This leads to some overstatement in the private compliance costs that EPA 

-2%

0%

2%

4%

6%

8%

10%

12%
242

estimates. Notably, the “compliance costs” are the changes in the electric power generation 

costs in the base case and pollution control options that are evaluated in Chapter 7. In simple 

terms, it is the resource costs of what the power industry will directly expend to comply with 

EPA’s requirements.

6.8 Reference

EIA Electric Power Annual 2009. DOE/EIA-0348 (2009). Available at: 

http://www.eia.doe.gov/cneaf/electricity/epa/epa_sum.htm
243

CHAPTER 7

COST, ECONOMIC, AND ENERGY IMPACTS

This chapter reports the cost, economic, and energy impact analysis performed for the 

Transport Rule. EPA used the Integrated Planning Model (IPM), developed by ICF 

Consulting, to conduct its analysis. IPM is a dynamic linear programming model that can be 

used to examine air pollution control policies for SO2 and NOx (as well as other air 

pollutants) throughout the contiguous United States for the entire power system. 

Documentation for IPM can be found at http://www.epa.gov/airmarkets/progsregs/epa-ipm, 

and updates specific to Transport Rule modeling are available in the document titled: 

“Documentation Supplement for EPA Base Case v.4.10_FTransport – Updates for Final 

Transport Rule,” available on the website and in the docket.

7.1 Background

Over the last decade, EPA has on several occasions used IPM to consider control 

options for reducing power-sector SO2 and NOx for regional transport. The best known 

example of one of these occasions is the Clean Air Interstate Rule (CAIR), the regulation that 

the current rule will replace. , The example of CAIR along with the analysis of the final 

Transport Rule below provide context and suggest alternative approaches to the Transport 

Rule (see Keohane 2009, 34–35 and Wagner 2009, 59).

Many EPA analyses with IPM have focused on legislative changes with national 

programs, such as EPA’s IPM analyses of the Clean Air Planning Act (S.843 in 108th 

Congress), the Clean Power Act (S.150 in 109th Congress), the Clear Skies Act of 2005 

(S.131 in 109th Congress), the Clear Skies Act of 2003 (S.485 in 108th Congress), and the 

Clear Skies Manager's Mark (of S.131). These analyses are available at EPA’s website: 

(www.epa.gov/airmarkets/progsregs/cair/multi.html). EPA’s IPM analysis for CAIR is 
244

another example of how the model has been used in regulatory analysis of SO2 and NOX

control strategies, in this case dealing with a regulatory approach focusing on the eastern US: 

(www.epa.gov/airmarkets/progsregs/epa-ipm/cair/index.html). EPA also analyzed several 

multi-pollutant reduction scenarios in July 2009 at the request of Senator Tom Carper to 

illustrate the costs and benefits of multiple levels of SO2 and NOX control in the power 

sector.

In addition, EPA conducted extensive state-by-state analysis of control levels and the 

associated emissions projections related to upwind pollution that crosses state borders and 

impacts a downwind state’s air quality monitors for the Transport Rule. More details on this 

analysis can be found in the Significant Contribution and State Emissions Budgets TSD.

As discussed in section 6.5, this rule comes during a period when many new SO2 and 

NOx controls are being installed. Many are needed for compliance with NSR settlements and 

state rules, while others may have been planned as a result of CAIR. Because CAIR remains 

in effect until it is replaced, emission reductions continue in the eastern US.

As discussed in section 2.4, the base case in this RIA assumes that CAIR is not in 

effect, but does take into account emissions reductions associated with the implementation of 

all federal rules, state rules and statutes, and other binding, enforceable commitments 

finalized by December 1, 2010, that are applicable the power industry and which govern the 

installation and operation of SO2 and NOx emissions controls in the timeframe covered in the 

analysis.

To address air quality problems, improve public health and the environment, and to 

respond to the Court decision in North Carolina v. EPA, the Agency is finalizing the 

Transport Rule. The Transport Rule requires annual SO2 and NOx reductions in 23 states, 

and also requires ozone season NOx reductions in 20 States.52 Many of the Transport Rule 

states are affected by both the annual SO2 and NOx reduction requirements and the ozone 

season NOx requirements.

 

52 EPA is issuing a supplemental proposal to request comment on requiring ozone-season NOX reductions in six 

additional states under the Transport Rule (Iowa, Kansas, Michigan, Missouri, Oklahoma, and Wisconsin).

Reductions that would be required by the supplemental proposal are included in the cost and impacts estimates 

described in this chapter, and therefore are accounted for in the benefits estimate.
245

The rule affects roughly 3,700 fossil-fuel-fired units with a nameplate capacity

greater than 25 MW located at nearly 1,100 facilities. These sources accounted for roughly 

82 percent of nationwide SO2 emissions and 67 percent of nationwide NOx emissions in 2010 

(see Table 7-1).

Table 7-1. Annual Emissions of SO2 and NOx in 2010 and Percentage of Emissions in 

the Transport Rule Affected Region (tons)

SO2 NOX

Transport Rule

Annual NOX and SO2 States 4,251,996 1,424,228

Nationwide

(Contiguous 48 States) 5,165,954 2,136,612

Emissions of Transport Rule 

States as Percentage of 

Nationwide Emissions

82% 67%

Source: EPA emissions data from all reporting units.

Note: Transport Rule annual NOX and SO2 states include Alabama, Georgia, Illinois, Indiana, Iowa, Kansas, 

Kentucky, Maryland, Michigan, Minnesota, Missouri, Nebraska, New Jersey, New York, North Carolina, Ohio, 

Pennsylvania, South Carolina, Tennessee, Texas, Virginia, West Virginia, and Wisconsin.

For SO2 and annual NOx, EPA modeled control requirements beginning in 2012 for 

the 23 eastern states shown in blue and green in Figure 7-1 below. In 16 of those states 

(shown in green), more stringent SO2 requirements begin in 2014.53 For ozone-season NOx, 

separate ozone-season requirements were applied to the 26 states shown in green and yellow 

in Figure 7-1. Many of the Transport Rule states are affected by both the annual SO2 and 

NOx reduction requirements and the ozone-season (May–September) NOx requirements. 

Table 7-2 show the emission budgets allotted to each state. For further discussion about the 

scope and requirements of the Transport Rule, see the Transport Rule preamble or Chapter 2 

of this RIA.

 

53 These 16 states include: Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Missouri, New Jersey, New 

York, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia, and Wisconsin
246

As previously stated, the emissions, cost, air quality, and benefits analyses done for 

the Transport Rule are from a modeling scenario that requires annual SO2 and NOx

reductions in 23 states and ozone season NOx requirements in 26 states (See Figure 7-1). 

This modeling differs from the final Transport Rule because it includes ozone season NOX

requirements for six states (Iowa, Kansas, Michigan, Missouri, Oklahoma, and Wisconsin) 

that the final Transport Rule does not cover. (As discussed in the preamble to the final rule, 

EPA is issuing a supplemental proposal to request comment on the inclusion of these six 

states). Results presented in this chapter reflect inclusion of these six states in the Transport 

Rule.

Figure 7-1. Transport Rule Covered States
247

Table 7-2. Transport Rule Annual NOx and SO2 and NOx Ozone Season State 

Emission Budgets (tons)

SO2

Annual

NOX

Annual

NOX

Ozone Season

2012 and 

2013

2014 and 

Later

2012 

and 

2013

2014 

and 

Later

2012 and 

2013

2014 and 

Later

Alabama

 

216,033 

 

213,258 

 

72,691 

 

71,962 

 

31,746 

 

31,499 

Arkansas --- --- --- ---

 

15,037 

 

15,037 

Florida --- --- --- ---

 

27,825 

 

27,825 

Georgia

 

158,527 

 

95,231 

 

62,010 

 

40,540 

 

27,944 

 

18,279 

Illinois

 

234,889 

 

124,123 

 

47,872 

 

47,872 

 

21,208 

 

21,208 

Indiana

 

285,424 

 

161,111 

 

109,726

 

108,424

 

46,876 

 

46,175 

Iowa

 

107,085 

 

75,184 

 

38,335 

 

37,498 

 

16,532* 

 

16,207*

Kansas

 

41,528 

 

41,528 

 

30,714 

 

25,560 

 

13,536* 

 

10,998* 

Kentucky

 

232,662

 

106,284 

 

85,086 

 

77,238 

 

36,167 

 

32,674 

Louisiana --- --- --- ---

 

13,432 

 

13,432 

Maryland

 

30,120 

 

28,203 

 

16,633 

 

16,574 

 

7,179 

 

7,179 

Michigan

 

229,303

 

143,995 

 

60,193 

 

57,812 

 

25,752* 

 

24,727* 

Minnesota

 

41,981 

 

41,981 

 

29,572 

 

29,572 --- ---

Mississippi --- --- --- ---

 

10,160 

 

10,160 

Missouri

 

207,466 

 

165,941 

 

52,374 

 

48,717 

 

22,762* 

 

21,073* 

Nebraska

 

65,052 

 

65,052 

 

26,440 

 

26,440 --- ---

New Jersey

 

5,574 

 

5,574 

 

7,266 

 

7,266 

 

3,382 

 

3,382 
248

New York

 

27,325

 

18,585

 

17,543 

 

17,543 

 

8,331 

 

8,331 

North Carolina

 

136,881 

 

57,620 

 

50,587 

 

41,553 

 

22,168 

 

18,455 

Ohio

 

310,230 

 

137,077 

 

92,703 

 

87,493 

 

40,063 

 

37,792 

Oklahoma --- --- --- ---

 

21,835* 

 

21,835* 

Pennsylvania

 

278,651 

 

112,021 

 

119,986

 

119,194

 

52,201 

 

51,912 

South Carolina

 

88,620 

 

88,620 

 

32,498 

 

32,498 

 

13,909 

 

13,909 

Tennessee

 

148,150 

 

58,833 

 

35,703 

 

19,337 

 

14,908 

 

8,016 

Texas

 

243,954 

 

243,954 

 

133,595

 

133,595

 

63,043 

 

63,043 

Virginia

 

70,820 

 

35,057 

 

33,242 

 

33,242 

 

14,452 

 

14,452 

West Virginia

 

146,174 

 

75,668 

 

59,472 

 

54,582 

 

25,283 

 

23,291 

Wisconsin*

 

79,480 

 

40,126 

 

31,628 

 

30,398 

 

13,704 

 

13,216 

Total

 

3,301,008

 

2,128,264

 

609,435

 

574,107

 

1,245,869

 

1,164,910

*Note: as discussed in the preamble to the final rule, EPA is issuing a supplemental proposal 

to request comment on inclusion of these six states in the Ozone Season program.

Electricity demand is anticipated to grow by roughly 1 percent a year, and total 

electricity demand is projected to be 4,106 billion kWh by 2014. Table 7-3 shows current 

electricity generation alongside EPA’s base case projections using EPA’s IPM modeling. 

This rule relies on EIA’s Annual Energy Outlook for 2010’s electric demand forecast for the 

US and employs a set of EPA assumptions regarding fuel supplies and the performance and 

cost of electric generation technologies as well as pollution controls. The base case 

assumption that CAIR is not in effect does have some modest influence on the fossil 

generation mix in this forecast, because CAIR had increased the costs of coal-fired 

generation relatively more than it had increased the costs to generation units that burned oil 

or natural gas.
249

Table 7-3. 2009 Electricity Net Generation and EPA Base Case Projections for 2012, 

2014, 2020 and 2030 for the Contiguous 48 States (Billion kWh)

Historical Base Case

2009 2012 2014 2020 2030

Coal 1,754 1,958 2,017 2,037 2,071

Oil 29 0.09 0.10 0.14 0.19

Natural Gas 917 721 686 823 1,155

Nuclear 799 812 822 832 811

Hydroelectric 272 281 287 287 286

Non-hydro Renewables 143 233 250 288 328

Other 0 35 45 44 53

Total 3,914 4,041 4,107 4,311 4,704

Source: 2009 data from EIA Electric Power Annual 2009, Table 1.1 (adjusted to represent the 

Contiguous 48 States for consistency with projections, which are from the Integrated Planning Model 

run by EPA, 2011).

As noted above, IPM has been used for evaluating the economic and emission 

impacts of environmental policies for over a decade. The economic modeling presented in 

this chapter has been developed for specific analyses of the power sector. Thus, the model 

has been designed to reflect the industry as accurately as possible. As a result, EPA has used 

discount rates in IPM that are appropriate for the various types of investments and other costs 

that the power sector incurs (for example, the primary real discount rate is 5.5 % for pollution 

control retrofits). The discount rates used in IPM differ from discount rates used in other 

RIA analyses done for the Transport Rule, particularly the discount rates used in the benefits 

and macroeconomic analyses that are assumed to be social discount rates. (See Chapters 5 

and 8 where social discount rates of 3% and 7% are used.) EPA uses the best available 

information from utilities, financial institutions, debt rating agencies, and government 

statistics as the basis for the discount rates used for power sector modeling in IPM. These 

discount rates have undergone review by the power sector and the Energy Information 

Administration.

More detail on IPM can be found in the model documentation, which provides 

additional information on the assumptions discussed here as well as all other assumptions 

and inputs to the model (http://www.epa.gov/airmarkets/progsregs/epa-ipm). Updates 

specific to Transport Rule modeling are also in the document titled: “Documentation 
250

Supplement for EPA Base Case v.4.10_FTransport – Updates for Final Transport Rule.”

7.2 Projected SO2 and NOx Emissions and Reductions

The Transport Rule achieves substantial emission reductions. EPA projects annual 

SO2 emission reductions of 62 percent and annual NOx emission reductions of 11 percent in 

the covered region by 2014 relative to the base case. Additionally, EPA projects ozoneseason NOx reductions of 11 percent in the Transport Rule region (see Table 7-4). There is 

also a small decrease in CO2 emissions as a result of the Transport Rule. 

In Figure 7-2 below, the results of EPA modeling of the Transport Rule show that 

substantial SO2 emission reductions occur in the Midwest and Mid-Atlantic regions of the 

country. Because banking of allowances is allowed to encourage early reductions, 2012 SO2

reductions are greater overall than state budgets alone would require in that year. For many 

coal-fired electric generation units throughout the region it is economically advantageous to 

make extra emissions reductions in 2012 through fuel switching to have allowances to later 

use or sell in 2014 and beyond, when the Transport Rule becomes more stringent and when 

electric generators will also need to meet higher electric demand. Because of the banking 

provisions, the relative economics of making pollution reductions below the state assurance

levels in 2012 versus making emissions reductions later favor doing more in 2012. Annual 

NOx emissions reductions occur across the Transport Rule region (see Figure 7-3), and with 

the Transport Rule, ozone-season NOx emission reductions are lower than they would have 

been with the NOx SIP Call (base case) (see Figure 7-4).
251

Table 7-4. Projected Emissions of SO2, NOX, and CO2 with the Base Case (No Further 

Controls) and with Transport Rule (Million Tons)

2012 2014

Base TR

Chan

ge

% 

Change Base TR

Chan

ge

% 

Change

SO2

(Annual)

Contiguous 

48 States 7.9 3.9 -3.9 -50% 7.2 3.4 -3.8 -53%

TR States 7.0 3.0 -4.0 -57% 6.2 2.4 -3.9 -62%

NOX

(Annual)

Contiguous 

48 States 2.1 2.0 -0.2 -7% 2.1 1.9 -0.2 -9%

TR States 1.4 1.3 -0.1 -9% 1.4 1.2 -0.2 -11%

NOX

(Summer)

Contiguous 

48 States 0.9 0.9 -0.1 -7% 0.9 0.8 -0.1 -9%

TR States 0.7 0.6 -0.1 -9% 0.7 0.6 -0.1 -11%

CO2

(Annual)

Contiguous 

48 States 2,444 2,431 -12 -1% 2,482 2,454 -28 -1%

TR States 1,769 1,749 -19 -1% 1,799 1,765 -24 -2%

Note: Numbers may not add due to rounding. The emissions data presented here are EPA modeling results 

and the Transport Rule region includes states modeled for the annual SO2 and NOx requirements. “Summer” is 

from May 1–September 30, which is the ozone season. Base case includes Title IV Acid Rain Program, NOx

SIP Call, and state rules through December 1, 2010.

Source: Integrated Planning Model run by EPA, 2011.
252

Figure 7-2. SO2 Emissions from the Power Sector in 2012 and 2014 with and without 

the Transport Rule

Source: EPA, IPM, 2011.

Figure 7-3. Annual NOx Emissions from the Power Sector in 2012 and 2014 with 

and without the Transport Rule

Source: EPA, IPM, 2011.
253

Figure 7-4. Ozone-season NOx Emissions from the Power Sector in 2012 and 2014 with 

and without Transport Rule

Source: EPA, IPM, 2011.

7.3 Overview of Costs and Other Impacts

As shown above in Figure 7-1, the Transport Rule directly affects 23 states in 

controlling pollution related to fine particles. For ozone, it also affects a distinct but 

overlapping group of 20

54 states. The states in one or both of these regions constitute most of 

the fossil-fuel-fired generation and capacity in the contiguous US, especially coal-fired (see 

Tables 7-5 and 7-6 below).

 

54 EPA is issuing a supplemental proposal to request comment on requiring ozone-season NOX reductions in 

six additional states under the Transport Rule (Iowa, Kansas, Michigan, Missouri, Oklahoma, and Wisconsin).
254

Table 7-5. 2010 Fossil-fuel Generation Nationwide and in the Transport Region 

(Thousand Megawatthours)

Contiguous 

US

Transport 

Rule Fine 

Particle 

Area

Transport 

Rule Ozone 

Area

Coal-fired 1,829,176 1,405,021 1,510,557

Oil-fired 13,680 5,955 12,080

Natural Gas-fired 894,598 409,534 633,636

Total 3,954,846 2,598,207 2,989,910

Source: EIA Electric Power Monthly with data for December 2010, Tables 1.6B, 1.7.B, 

1.8.B, 1.10.B.

Table 7-6. Total Fossil-fuel Capacity Nationwide and in the Transport Region

Contiguous 

US

Transport Rule 

Fine Particle Area

Transport Rule 

Ozone Area

Pulverized Coal 316 247 266

Combined Cycle 199 97 142

Other Oil/Gas 247 155 196

Total 998 630 743

Source: EPA's NEEDS v4.10.

While most impacts of the Transport Rule affect the covered states themselves, 

national impacts are important. Because the electric grid is connected irrespective of state 

boundaries, effects on electrical generation in one state have spillover effects in other states. 

Likewise, because the Transport Rule states have the vast majority of coal-fired generation, 

changes in their coal consumption and demand affect coal prices nationwide. In some cases, 

such as retail electricity prices and the operation of pollution controls, nationwide 

information would not be as relevant as regional totals. But for most of the following 

sections, nationwide projections provide a more complete picture of the Transport Rule’s 

impacts.
255

7.4 Projected Compliance Costs

The power industry’s “compliance costs” are the changes in electric power generation 

costs in the base case and alternative pollution control approaches that are examined in this 

chapter. In simple terms, these costs are the resource costs of what the power industry will 

directly expend to comply with EPA’s requirements. This is not the “social cost” of the 

control approaches, which is separately explained and estimated in Chapter 8.

EPA projects that the annual incremental compliance costs of the Transport Rule are 

$1.4 billion in 2012 and $0.8 billion in 2014 (see Table 7-7 below). Another measure of this 

impact is the change in electricity prices (discussed in section 7.10). Costs generally are 

higher in 2012 than in 2014 because of reduced compliance flexibility in 2012, which is too 

soon for sources to retrofit new FGD and SCR that were not already planned.

Table 7-7. Annualized Compliance Cost of the Transport Rule

2012 2014 2020

Annualized Compliance Cost

(billions of 2007$)

$1.4 $0.8 $0.6

Note: Numbers rounded to the nearest hundred million for annualized cost.

Source: Integrated Planning Model run by EPA, 2011.

For context, the projected annualized compliance costs are a small fraction of the 

$320 billion in expenditures EPA projects that the power sector will make in 2015 alone to 

generate, transmit, and distribute electricity to end-use consumers. For more information, see 

Chapter 6.

7.5 Projected Approaches to Emissions Reductions 

Fossil-fuel-fired electric generating units in the Transport Region are projected to 

achieve NOx and SO2 emissions reductions through a combination of compliance options. 

These actions include sustained operation of existing controls originally built for CAIR, 

additional pollution control installations at coal-fired generators, coal switching (including 

blending of coals), and increased dispatch of more efficient units and lower-emitting 
256

generation technologies (e.g., some reduction of coal-fired generation with an increase of 

generation from natural gas). In addition, there will be some affected sources that find it 

more economic to retire rather than invest in new pollution control equipment. These 

facilities are generally amongst the oldest, least efficient power plants and typically run 

infrequently. 

In 2012, a small shift from coal- and oil-fired generation to greater use of natural gas 

lowers emissions a small amount (see Table 7-11). NOx emissions reductions largely stem 

from year-round operation of selective catalytic reduction (SCR) controls that would 

otherwise operate only during the ozone season (see Table 7-10). Additionally, a modest 

amount of NOx reduction stems from new low- NOx burners installations on older coal-fired 

units in states that were not in the original CAIR program but are covered in the ozone 

program of this rule. The Transport Rule’s 2012 SO2 emissions reductions largely result 

from sustained operation of all existing FGDs (scrubbers) in states controlled for PM, 

including those scrubbers built originally for CAIR and which may not have reason to 

operatein the absence of the Transport Rule. Other 2012 SO2 reductions are achieved by 

units choosing to burn lower-sulfur coals as the least-cost compliance approach. In 2014, the 

Transport Rule drives the sector to install 5.9 GW of new FGD and 3 GW of new dry sorbent 

injection (DSI), which allow for significant additional SO2 reductions in the Group 1 states.

Table 7-8 below provides a profile of sulfur contents in alternative coal supplies that 

offer substantial SO2 reduction opportunities by increased blending or fuel-switching to 

cleaner grades. The table shows the degree to which uncontrolled coal-fired generation 

switches from relatively dirtier coals to relatively cleaner coals in response to imposition of 

the Transport Rule. 
257

Table 7-8. Percent of Generation without SO2 Controls by Coal Sulfur Content, 2014

Very Low

(<0.8 

lb/mmBtu)

Low

(0.81-1.2 

lbs/mmBtu)

LowMedium

(1.21-1.66 

lbs/mmBtu)

HighMedium

(1.67-3.34 

lb/mmBtu)

High

(>3.35 

lb/mmBtu)

Base Case 27% 37% 8% 15% 13%

Transport Rule 59% 28% 7% 3% 2%

Source: Integrated Planning Model run by EPA, 2011.

Table 7-9 shows total coal use among both controlled (i.e., operating either FGD or 

DSI) and uncontrolled EGUs in the states subject to the SO2 program. The Transport Rule is 

associated with only a slight reduction (3% in 2014) in total coal use compared to the base 

case. More importantly, the table reinforces that the rule drives increased overall use of 

cleaner bituminous and subbituminous coals, especially very low sulfur subbituminous. This 

trend appears even when, as in this table, coal use at controlled units and very small units are 

included.

Table 7-9. Coal Use by Sulfur Category in the PM2.5 Transport Region for the Base 

Case and Transport Rule* (million short tons)

Lignite Subbituminous Bituminous Total

High 

sulfur

Low 

sulfur

Very

low 

sulfur

High 

sulfur

Highmedium 

sulfur

Lowmedium 

sulfur

Low 

sulfur

2012 Base 30 13 145 156 91 211 83 3 732

TR 25 2 77 255 83 195 74 12 723

2014

Base 37 13 145 154 102 216 71 9 747

TR 32 2 108 202 88 202 73 18 724

*These coal usage results are for the 23 states covered by the rule in the trading program to reduce SO2 emissions. 

Source: Integrated Planning Model run by EPA, 2011.

EPA does not project additional SO2 control retrofits in 2012. For SO2, EPA believes 

that to meet the 2012/2013 state assurance levels, compliance strategies would at most 
258

involve: operation of existing controls year-round, operation of controls that are scheduled 

to become operational by 2012, switching to a lower sulfur coal, and/or a general 

optimization of dispatch. EPA projects incremental FGD installations of about 5.9 GW by

2014 (see Table 7-10). These FGD retrofits are primarily wet scrubbers. Additionally, 

approximately 3 GW of DSI is projected to be installed by 2014. EPA believes that the 

January 1, 2014 starting date is as expeditious as practicable for sources to install such 

controls, and that retrofits of this limited extent can be realized in the 30 month interim 

between signature and the start of 2014. For further discussion, see discussion in section 

VII.C.2 of the preamble.

Table 7-10. Newly-Constructed Advanced SO2 Control Retrofits on Coal-fired 

Generation by Technology Built with the Base Case and with the Transport Rule (GW) 

in 2014

Base 

Case

Transport 

Rule

Incremental 

Retrofits

Wet FGD 4.6 10.3 5.7

Dry FGD 3.6 3.8 0.2

DSI 6.8 9.8 3.0

Note: FGD (Flue Gas Desulphurization) and DSI (Dry Sorbent Injection) are advanced SO2 controls. Source: 

Integrated Planning Model run by EPA, 2011.

In 2014, the Transport Rule is also projected to result in the operation of an additional 

25 GW of flue gas desulfurization (scrubbers) for SO2 control and the year-round operation 

of an additional 5 GW of selective catalytic reduction technology (SCR) for NOx control on 

existing coal-fired generation capacity (see “Existing Controls Induced to Operate” in Table 

7-11). These controls were largely built for compliance with CAIR and are thus assumed 

not to operate in the base case. 
259

Table 7-11. Coal-fired Capacity Operating Advanced Pollution Controls with the 

Transport Rule, 2014 (GW)

Existing 

Controls 

Induced to 

Operate

NewlyBuilt 

Controls

Total 

Operational 

Controls

FGD 25 6 209

DSI 0 3 10

SCR 5 0 146

Note: FGD (Flue Gas Desulphurization) and DSI (Dry Sorbent Injection) are advanced SO2 controls. SCR 

(Selective Catalytic Reduction) is an advanced NOX control. Source: Integrated Planning Model run by EPA, 2011.

Projected operation of advanced pollution controls accounts for the vast majority of 

the NOx reductions, while for SO2 the reductions from the FGD and DSI controls are also 

supplemented by the continued and expanded use of relatively cleaner coals at uncontrolled 

units.

7.6 Projected Allowance Prices

Table 7-12 shows the projected allowance prices for the four trading programs under 

the Transport Rule in 2012 and 2014. These are the marginal emission reduction costs (i.e., 

the cost of reducing the last ton of a pollutant subject to the Transport Rule budgets) that 

EPA projected using the Integrated Planning Model (IPM) to analyze the final rule’s remedy, 

in those states where the variability limits do not bind (in states where the variability limits 

bind, the marginal cost of emission reductions is higher than the allowance price). Under the 

trading programs, the marginal emission reduction costs inform the market-clearing price to 

emit that balances the supply of and demand for allowances under the Transport Rule 

programs. 

The projected allowance prices differ from the cost-effective thresholds that were the 

original basis of the state budgets because they reflect the ability of the electricity sector to 

use the trading program flexibilities to achieve the aggregate required cost-effective emission 
260

reductions at minimized marginal costs. Most notably, banking allows entities to make 

emission reductions beyond what is needed in early years (accelerating environmental 

improvements) and smooth the trajectory of emission reductions in later years to minimize 

cost.

EPA believes that these projected marginal costs under the Transport Rule programs 

can inform allowance price discovery as program participants seek cost-effective reductions 

at individual units and in the compliance marketplace.

Table 7-12. Projected allowance prices for the four Transport Rule programs in 2012 

and 2014 (2007$)

Emission Allowance

Prices ($/Ton)

2012 2014

Annual SO2 Group 1 Trading Program 1,000 1,100

Annual SO2 Group 2 Trading Program 600 700

Annual NOx Trading Program 500 600

Ozone Season NOx Trading Program 1,300 1,500

Note: EPA has provided projections rounded to the nearest hundred dollars because the 

model’s point estimates do not reflect the price-discovery process of real-world markets that 

do not feature the same perfect information as in IPM.

7.7 Projected Generation Mix

Table 7-13 and Figure 7-5 show the generation mix with the Transport Rule. Coalfired generation and natural-gas-fired generation are projected to remain relatively

unchanged because of the phased-in nature of the Transport Rule, which allows industry the 
261

appropriate amount of time to install the necessary pollution controls. Additionally, the 

operating costs of complying coal-fired units are not so affected as to result in major changes 

in the electricity generation mix. Both the base case and the Transport Rule case show shifts 

away from oil and natural gas generation and toward increased coal generation between 2012 

and 2014.

Table 7-13. Generation Mix with the Base Case (No Further Controls) and with 

Transport Rule, Contiguous US (Thousand GWh)

2009 2012 2014

Historical Base TR

Change 

from 

Base

Percent 

Change Base TR

Change 

from 

Base

Percent 

Change

Coal 1,754 1,958 1,935 -23 -1.2% 2,017 1,978 -39 -1.9%

Oil 29 0.09 0.09 0.00 -2.2% 0.10 0.11 0.01 5.8%

Natural Gas 917 721 739 18 2.5% 686 714 28 4.1%

Nuclear 799 812 812 0 0.0% 822 829 7 0.8%

Hydroelectric 272 281 282 1 0.2% 287 286 -1 -0.4%

Non-hydro 

Renewables 143 233 239 62.7% 250 250 10.2%

Other 0 35 35 0.0 0.0% 45 45 0.6 1.3%

Total 3,914 4,041 4,043 2 0.0% 4,107 4,102 -4 -0.1%

Note: Numbers may not add due to rounding.

Source: 2009 data from EIA Electric Power Annual 2009, Table 2.1 and Net Generation by State by Type of Producer by Energy 

Source (EIA-906); 2012 and 2014 projections are from the Integrated Planning Model run by EPA, 2011.
262

Figure 7-5. Generation Mix with the Base Case (No Further Controls) and with 

Transport Rule

Source: Integrated Planning Model run by EPA, 2011.

Relative to the base case, about 4.8 GW of coal-fired capacity is projected to be 

uneconomic to maintain (less than 2 percent of all coal-fired capacity in the Transport Rule 

states, or about 1% of all capacity nationally) by 2014. Notably, about 3 GW of capacity 

would have closed by 2014 in the base case without the Transport Rule (for a total of 7.8 GW 

of retirement in 2014 due to economic conditions and the Transport Rule) . Uneconomic 

units, for the most part, are small and infrequently used generating units that are dispersed 

throughout the states covered in the Transport Rule. In practice, units projected to be 

uneconomic to maintain may be “mothballed,” retired, or kept in service to ensure 

transmission reliability in certain parts of the grid. EPA modeling is unable to distinguish 

between these potential outcomes. IPM can only predict that specific generating units are 

uneconomic to maintain, based on their fuel, operating and fixed costs, and whether they are 

needed to meet both demand and reliability reserve requirements.

1.96 1.93 2.02 1.98 2.04 2.00

0.81 0.81 0.82 0.83 0.83 0.84

0.72 0.74 0.69 0.71 0.82 0.85

0.28 0.28 0.29 0.29

0.29 0.29 0.23 0.24 0.25 0.25

0.29 0.29

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

5.0

Base TR Base TR Base TR

Million GWh

Generation Mix

Other

Oil

Non-Hydro 

Renewables

Hydro

Natural Gas

Nuclear

Coal

2012 2014 2020
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7.8 Projected Capacity Additions

In addition, EPA projects that most future growth in electric demand will be met with 

new natural gas-fired capacity (see Table 7-14) in both the base and Transport Rule 

scenarios. 

Table 7-14. Total Generation Capacity by 2014 (GW)

2010 Base TR Change

Pulverized Coal 317 312 307 -5

Natural Gas Combined Cycle 201 205 205 0

Other Oil/Gas 253 232 233 0

Non-Hydro Renewables 31 70 70 0

Hydro 99 99 99 0

Nuclear 102 103 104 1

Other 5 81 85 4

Total 1,009 1,103 1,103 0

Source: 2010 data from EPA's NEEDS v4.10. Projections from Integrated Planning Model run by EPA.

Note: “Renewables” include biomass, geothermal, solar, and wind electric generation capacity.

7.9 Projected Coal Production for the Electric Power Sector

Coal production for electricity generation is expected to increase relative to current 

levels, with or without the Transport Rule (see Table 7-15). Under the Transport Rule, EPA 

projects coal production for use by the power sector will increase above 2009 levels by 21 

million tons in 2012 and by a further 14 million tons in 2014, as opposed to 30 million tons 

in 2012 and a further 26 million tons in 2014 without the Transport Rule in place.

The Transport Rule will encourage the installation of new controls and operation of 

existing pollution controls for SO2 and NOx removal. Many of these pollution controls can 

achieve SO2 removal rates of 95 percent or greater, which allows industry to rely more 

heavily on local bituminous coal in the eastern and central parts of the country that has a 

higher sulfur content and is less expensive to transport than western subbituminous coal. 

Note, for example, the projected increase in Appalachian coal production under the Transport 
264

Rule in 2014, which contrasts with the projected 11 million ton decrease over that time in 

the absence of the rule. Production of low-sulfur western coal also increases as a result of the 

Transport Rule in both 2012 and 2014, as some units pursue fuel-switching to lower-sulfur 

coals to achieve emission reductions. Notwithstanding the Transport Rule, coal production 

in the interior region is still projected to increase more than 60% above 2009 levels by 2014. 
265

Table 7-15. Coal Production for the Electric Power Sector with the Base Case (No 

Further Controls) and with the Transport Rule (Million Tons)

2009 2012 2014

Supply

Area Historical Base TR

Change 

from 

Base Base TR

Change 

from 

Base

Appalachia 246 193 184 -9 182 186 4

Interior 129 209 189 -20 238 210 -27

West 553 544 565 21 554 556 3

Waste Coal 14 14 14 0 14 14 0

Imports 19 31 31 0 30 30 0

Total 961 991 983 -8 1,017 996 -21

Source: Production: U.S. Energy Information Administration (EIA data), Coal Distribution -- Annual (Final), 

web site http://www.eia.doe.gov/cneaf/coal/page/coaldistrib/a_distributions.html (posted February 18, 2011); 

Waste Coal: U.S. EIA, Monthly Energy Review, January 2011 Edition, Table 6.1 Coal Overview, web site 

http://www.eia.doe.gov/emeu/mer/coal.html (posted January 31, 2011). Imports: U.S. Energy Information 

Administration (EIA) and U.S. Census. All projections from Integrated Planning Model run by EPA, 2011. 

Figure 7-6. Total Coal Production by Coal-Producing Region, 2007 (Million Short 

Tons) 

Note: Regional totals do not include refuse recovery

Source: EIA Annual Coal Report, 2007
266

7.10 Projected Retail Electricity Prices

Retail electricity prices for the Transport Rule region are projected to increase a small 

amount with the Transport Rule (see Table 7-16). Regional retail electricity prices in the 

eastern half of the country are projected to range from 0.1 to 3 percent higher with the 

Transport Rule in 2014. By 2014, retail electricity prices in the regions directly affected by

the Transport Rule, on average, are projected to be roughly 0.8 percent higher with the 

Transport Rule.

Table 7-16. Projected Regional Retail Electricity Prices with the Base Case (No Further 

Controls) and with Transport Rule (2007 cents/kWh)

Base Transport Rule Percent Change

2012 2014 2020 2012 2014 2020 2012 2014 2020

ECAR 7.4 7.9 8.1 7.7 8.2 8.3 3.0% 3.1% 2.9%

ERCOT 7.8 8.8 8.7 8.1 8.9 8.8 3.4% 0.3% 1.9%

MAAC 8.4 9.4 10.3 8.7 9.5 10.4 4.0% 1.4% 1.2%

MAIN 7.3 7.9 8.4 7.5 8.1 8.4 2.0% 1.7% 0.9%

MAPP 7.8 8.0 7.9 7.8 8.1 7.9 0.4% 0.8% 0.3%

NY 12.5 13.7 13.3 12.8 13.7 13.4 2.5% 0.4% 0.4%

NE 11.4 12.3 11.8 11.7 12.3 11.9 1.8% -0.1% 0.6%

FRCC 9.5 10.2 9.7 9.6 10.2 9.8 1.1% -0.2% 0.4%

STV 7.7 7.9 7.8 7.8 7.9 7.8 0.9% 0.5% 0.4%

SPP 7.4 7.6 7.4 7.4 7.6 7.4 0.8% 0.1% -0.1%

PNW 6.9 7.1 6.9 6.9 7.1 6.9 0.5% 0.0% 0.0%

RM 8.6 9.1 9.4 8.7 9.2 9.4 0.7% 0.2% 0.3%

CALI 12.7 13.0 12.5 12.9 13.0 12.5 0.8% 0.0% 0.2%

TR Region 

Average 8.5 9.2 9.2 8.7 9.3 9.3 2.2% 0.8% 1.0%

Contiguous 

U.S. Average 8.5 9.0 8.9 8.6 9.0 9.0 1.7% 0.8% 0.9%

Source: EPA’s Retail Electricity Price Model.
267

Figure 7-7. Retail Price Model Regions

7.11 Projected Fuel Price Impacts

The impacts of the Transport Rule on coal prices and natural gas prices are shown 

below in Tables 7-17 and 7-18. Overall, the projected average coal price decrease is related 

to the small decrease in projected coal demand. Projected gas price changes are directly 

related to the projected increase in natural gas consumption under the Transport Rule. 

IPM modeling of natural gas prices uses both short- and long-term price signals to 

balance supply of and demand in competitive markets for the fuel across the modeled time 

horizon. As such, it should be understood that the pattern of IPM natural gas price 

projections over time is not a forecast of natural gas prices incurred by end-use consumers at 

any particular point in time. The natural gas market in the United States has historically

experienced significant price volatility from year to year, between seasons within a year, and 

even sees major price swings during short-lived weather events (such as cold snaps leading to 

short-run spikes in heating demand). These short-term price signals are fundamental for 

allowing the market to successfully align immediate supply and demand needs; however, 

end-use consumers are typically shielded from experiencing these rapid fluctuations in 

natural gas prices by retail rate regulation and by hedging through longer-term fuel supply 

contracts. IPM assumes these longer-term price arrangements take place “outside of the 

model” and on top of the “real-time” shorter-term price variation necessary to align supply
268

and demand. Therefore, the model’s natural gas price projections should not be mistaken 

for traditionally experienced consumer price impacts related to natural gas, but a reflection of 

expected average price changes over the time period 2012 to 2030. 

For this analysis, in order to represent a natural gas price evolution that end-use 

consumers can anticipate under retail rate regulation and/or typical hedging behavior, EPA is 

displaying the weighted average of IPM’s natural gas price projections for the 2012-2030 

time horizon (see Table 7-18). In that framework, consumer natural gas price impacts are 

anticipated to range from 0.2% to 0.3% based on consumer class in response to the Transport 

Rule. 
269

Table 7-17. Average Minemouth and Delivered Coal Prices with the Base Case and 

with the Transport Rule (2007$/MMBtu)

2012 2014

2007 Base TR

Percent 

Change 

from 

Base Base TR

Percent 

Change 

from 

Base

Minemouth 1.27 1.35 1.29 -4.3% 1.37 1.35 -1.7%

Delivered 1.76 2.08 2.05 -1.4% 2.12 2.10 -0.9%

Source: Historical data from EIA AEO 2010 Reference Case Table 15 (Coal Supply, Distribution, and Prices); 

projections from the Integrated Planning Model run by EPA, 2011.

Table 7-18. 2012-2030 Weighted Average Henry Hub and Delivered Natural Gas Prices 

with the Base Case and with the Transport Rule (2007$/MMBtu)

Base TR

Percent 

Change 

from 

Base

Henry Hub 5.32 5.34 0.4%

Delivered - Electric Power 5.60 5.62 0.3%

Delivered - Residential 10.97 10.99 0.2%

Source: Projections from the Integrated Planning Model run by EPA (2011) adjusted to Henry Hub prices using 

historical data from EIA AEO 2011 reference case to derive residential prices.

7.12 Key Differences in EPA Model Runs for Transport Rule Modeling Geography

As previously stated, the emissions, cost, air quality, and benefits analyses done for 

the Transport Rule are from a modeling scenario that requires annual SO2 and NOx

reductions in 23 states and ozone season NOx requirements in 26 states (See Figure 7-1). 

This modeling differs from the final Transport Rule because it includes ozone season NOX

requirements for six states (Iowa, Kansas, Michigan, Missouri, Oklahoma, and Wisconsin) 

that the final Transport Rule does not cover. (As discussed in the preamble to the final rule, 

EPA is issuing a supplemental proposal to request comment on inclusion of these six states). 
270

7.13 Projected Primary PM Emissions from Power Plants

IPM does not endogenously model primary PM emissions from power plants. These 

emissions are calculated as a function of IPM outputs, emission factors and control 

configuration. IPM-projected fuel use (heat input) is multiplied by PM emission factors 

(based in part on the presence of PM-relevant pollution control devices) to determine PM 

emissions. Primary PM emissions are calculated by adding the filterable PM and 

condensable PM emissions. 

Filterable PM emissions for each unit are based on historical information regarding 

existing emissions controls and types of fuel burned and ash content of the fuel burned, as 

well as the projected emission controls (e.g., scrubbers and fabric filters).

Condensable PM emissions are based on plant type, sulfur content of the fuel, and 

SO2 and PM control configurations. Although EPA’s analysis is based on the best available 

emission factors, these emission factors do not account for the potential changes in 

condensable PM emissions due to the installation and operation of SCRs. The formation of 

additional condensable PM (in the form of SO3 and H2SO4) in units with SCRs depends on a 

number of factors, including coal sulfur content, combustion conditions and characteristics of 

the catalyst used in the SCR, and is likely to vary widely from unit to unit. SCRs are 

generally designed and operated to minimize increases in condensable PM. This limitation 

means that IPM post-processing is potentially underestimating condensable PM emissions for 

units with SCRs. In contrast, it is possible that IPM post-processing overestimates 

condensable PM emissions in a case where the unit is combusting a low-sulfur coal in the 

presence of a scrubber.

EPA plans to continue improving and updating the PM emission factors and 

calculation methodologies. For a more complete description of the methodologies used to 

post-process PM emissions from IPM, see “IPM ORL File Generation Methodology” 

(March, 2011).

7.14 Analysis Approach and Limitations

EPA’s modeling is based on its best judgment for various input assumptions that are 

uncertain. Assumptions for future fuel prices and electricity demand growth deserve 

particular attention because of the importance of these two key model inputs to the power 
271

sector. As a general matter, the Agency selects the best available information from 

available engineering studies of air pollution controls and has set up what it believes is the 

most reasonable modeling framework for analyzing the cost, emission changes, and other 

impacts of regulatory controls.

The annualized cost estimates of the private compliance costs that are provided in this 

analysis are meant to show the increase in production (engineering) costs to the power sector 

of the Transport Rule selected remedy and any alternatives. In simple terms, the private 

compliance costs that are presented are the annual increase in revenues required for the 

industry to be as well off after the Transport Rule is implemented as before. To estimate 

these annualized costs, EPA uses a conventional and widely-accepted approach that is 

commonplace in economic analysis of power sector costs for estimating engineering costs in 

annual terms. For estimating annualized costs, EPA has applied a capital recovery factor 

(CRF) multiplier to capital investments and added that to the annual incremental operating 

expenses. The CRF is derived from estimates of the cost of capital (private discount rate), 

the amount of insurance coverage required, local property taxes, and the life of capital. The 

private compliance costs presented earlier are EPA’s best estimate of the direct private 

compliance costs of the Transport Rule.

The annualized cost of the Transport Rule, as quantified here, is EPA’s best 

assessment of the cost of implementing the Transport Rule. These costs are generated from 

rigorous economic modeling of changes in the power sector due to the Transport Rule. This 

type of analysis using IPM has undergone peer review and federal courts have upheld 

regulations covering the power sector that have relied on IPM’s cost analysis.

The direct private compliance cost includes, but is not limited to, capital investments 

in pollution controls, operating expenses of the pollution controls, investments in new 

generating sources, and additional fuel expenditures. EPA believes that the cost assumptions 

used for the Transport Rule reflect, as closely as possible, the best information available to 

the Agency today. The relatively small cost associated with monitoring emissions, reporting, 

and record keeping for affected sources is not included in these annualized cost estimates, but 

EPA has done a separate analysis and estimated the cost to be approximately $26.2 million 

(see Section 9.3., Paperwork Reduction Act, of this RIA for more information).

Cost estimates for the Transport Rule are based on results from ICF’s Integrated 
272

Planning Model. The model minimizes the costs of producing electricity (including 

abatement costs) while meeting load demand and other constraints (full documentation for 

IPM can be found at http://www.epa.gov/airmarkets/progsregs/epa-ipm and in the document 

titled: “Documentation Supplement for EPA Base Case v.4.10_FTransport – Updates for 

Final Transport Rule”). The structure of the model assumes that the electric utility industry

will be able to meet environmental emission caps at least cost. Montgomery (1972) has 

shown that this least cost solution corresponds to the equilibrium of an emission permit 

system. See also Atkinson and Tietenburg (1982), Krupnick et al. (1980), and McGartland 

and Oates (1985). However, to the extent that transaction and/or search costs, combined with 

institutional barriers, restrict the ability of utilities to exhaust all the gains from emission 

trading, costs are underestimated by the model. Utilities in the IPM model also have “perfect 

foresight.” To the extent that utilities misjudge future conditions affecting the economics of 

pollution control, costs may be understated as well.

The “perfect foresight” of the model is also relevant in the context of the assurance 

provisions required in the Transport Rule. Because of the sizeable penalties associated with 

violating assurance provisions, EPA believes it will be economical for units to comply with 

the provisions. EPA modeled these provisions, which restrict emissions from a state to the 

budget plus variability limits on a 1-year basis, as state-specific emissions caps set at the 

budget plus average variability, or state assurance level. The Power Sector Variability

Technical Support Document contains further details on these assurance provisions.

Modeling the assurance provisions as caps means that the model must meet the same 

limit each year, but it also allows the model to optimize with perfect foresight the present and 

future limits. The model minimizes production costs while meeting required generation and 

reserve margin, but sources in reality may choose to make greater emission reductions than 

required in exchange for more certainty about emissions variability. IPM captures the cost 

associated with making required reductions in each state, but because of its “perfect 

foresight,” the model likely cannot capture the true benefit to sources of having a range of 

allowed variability.

From another vantage point, this modeling analysis does not take into account the 

potential for advancements in the capabilities of pollution control technologies for SO2 and 

NOx removal as well as reductions in their costs over time after 2014. Market-based cap and 
273

trade regulation serves to promote innovation and the development of new and cheaper 

technologies. As an example, cost estimates of the Acid Rain SO2 trading program by 

Resources for the Future (RFF) and MIT’s Center for Energy and Environmental Policy

Research (CEEPR) have been as much as 83 percent lower than originally projected by the 

EPA (see Carlson et al., 2000; Ellerman, 2003). It is important to note that the original 

analysis for the Acid Rain Program done by EPA also relied on an optimization model like 

IPM. Ex ante, EPA cost estimates of roughly $2.7 to $6.2 billion55 in 1989 were an 

overestimate of the costs of the program in part because of the limitation of economic 

modeling to predict technological improvement of pollution controls and other compliance 

options such as fuel switching. Ex post estimates of the annual cost of the Acid Rain SO2

trading program range from $1.0 to $1.4 billion. Harrington et al. have examined cost 

analyses of EPA programs and found a tendency for predicted costs to overstate actual 

implementation costs in market-based programs (Harrington, Morgenstern, and Nelson, 

2000). In recognition of this, EPA’s mobile source program uses adjusted engineering cost 

estimates of pollution control equipment and installation costs to account for this fact, which 

EPA has not done in this case.56 The Agency is considering approaches to make this 

adjustment in the future, or at least to be able to provide a sense of the rough amount by

which costs could be overstated in the analysis that has occurred.

EPA’s latest update of IPM incorporates state rules or regulations and various NSR 

settlements adopted through December, 2010. Documentation for IPM can be found at 

http://www.epa.gov/airmarkets/progsregs/epa-ipm and in the document titled: “ 

Documentation Supplement for EPA Base Case v.4.10_FTransport – Updates for Final 

Transport Rule.” Any state or settlement action since that time has not been accounted for in 

our analysis in this chapter.

As configured in this application, IPM does not take into account demand response 

(i.e., consumer reaction to electricity prices). The increased retail electricity prices shown in 

Table 7-16 would prompt end users to curtail (to some extent) their use of electricity and 

encourage them to use substitutes.57 The response would lessen the demand for electricity, 

 

55

 2010 Phase II cost estimate in $1995.

56

 See regulatory impact analysis for the Tier 2 Regulations for passenger vehicles (1999) and Heavy-Duty 

Diesel Vehicle Rules (2000).

57

 The degree of substitution/curtailment depends on the costs and performance of the goods that substitute for 
274

resulting in electricity price increases slightly lower than IPM predicts, which would also 

reduce generation and emissions. Because of demand response, certain unquantified 

negative costs (i.e., savings) result from the reduced resource costs of producing less 

electricity because of the lower quantity demanded. To some degree, these saved resource 

costs will offset the additional costs of pollution controls and fuel switching that we would 

anticipate with the Transport Rule. Although the reduction in electricity use is likely to be 

small, the cost savings from such a large industry58 is not insignificant. EIA analysis 

examining multi-pollutant legislation under consideration in 2003 indicates that the 

annualized costs of the Transport Rule may be overstated substantially by not considering 

demand response, depending on the magnitude and coverage of the price increases.59

On balance, after consideration of various unquantified costs (and savings that are 

possible), EPA believes that the annual private compliance costs that we have estimated are 

more likely to overstate the future annual compliance costs that industry will incur, rather 

than understate those costs.

Finally, EPA’s projected impacts of this final rule do not reflect minor technical 

corrections to SO2 budgets in three states (KY, MI, and NY). These projections also 

assumed preliminary variability limits that were smaller than the variability limits finalized in 

this rule. EPA conducted sensitivity analysis confirming that these differences do not 

meaningfully alter any of the Agency's findings or conclusions based on the projected cost, 

benefit, and air quality impacts presented for the final Transport Rule. The results of this 

sensitivity analysis are presented in Appendix F in the final Transport Rule RIA.

7.15 Significant Energy Impact

The Transport Rule has a significant impact according to E.O. 13211: Actions that 

 

more energy consuming goods, which is reflected in the demand elasticity.

58 Investor-owned utilities alone accounted for nearly $300 billion in revenue in 2008 (EIA).

59 See “Analysis of S. 485, the Clear Skies Act of 2003, and S. 843, the Clean Air Planning Act of 2003.” 

Energy Information Administration. September, 2003. EIA modeling indicated that the Clear Skies Act of 

2003 (a nationwide cap and trade program for SO2, NOX, and mercury), demand response could lower present 

value costs by as much as 47% below what it would have been without an emission constraint similar to the 

Transport Rule.
275

Significantly Affect Energy Supply, Distribution, or Use. Under the provisions of this final 

rule, EPA projects that approximately 4.8 GW of additional coal-fired generation may be 

removed from operation by 2014. In practice, however, the units projected to be uneconomic 

to maintain may be “mothballed,” retired, or kept in service to ensure transmission reliability

in certain parts of the grid. These units are predominantly small and infrequently-used 

generating units dispersed throughout the area affected by the rule. If current forecasts of 

either natural gas prices or electricity demand were revised in the future to be higher, that 

would create a greater incentive to keep these units operational.

EPA estimates that average retail electricity price could increase in the contiguous 

U.S. by about 1.7 percent in 2012 and 0.8 percent in 2014. This is generally less of an 

increase than often occurs with fluctuating fuel prices and other market factors. Related to 

this, EPA projects limited impacts on coal and gas prices. The average delivered coal price 

decreases by about 1.4 percent in 2012 and 0.9 percent in 2014 relative to the base case as a 

result of decreased coal demand and shifts in the type of coal demanded. As discussed above 

in section 7.11, EPA also projects that electric power sector-delivered natural gas price will 

increase by about 0.3% over the 2012-2030 timeframe and that natural gas use for electricity 

generation will increase by approximately 200 billion cubic feet (BCF) by 2014. These 

impacts are well within the range of price variability that is regularly experienced in natural 

gas markets. Finally, under the Transport Rule, EPA projects that coal production for use by 

the power sector will increase above 2009 levels by 21 million tons in 2012 and a further 14 

million tons in 2014, as opposed to 30 million tons in 2012 and a further 26 million tons in 

2014 without the Transport Rule in place. The Transport Rule is not projected to impact 

production of coal for uses outside the power sector (e.g., export, industrial sources), which 

represent approximately 6% of total coal production in 2009. EPA does not believe that this 

rule will have any other impacts (e.g., on oil markets) that exceed the significance criteria.

EPA believes that a number of features of the rulemaking serve to reduce its impact 

on energy supply. First, the trading component of the Transport Rule provides considerable 

flexibility to the power sector and enables industry to comply with the emission reduction 

requirements in the most cost-effective manner, thus minimizing overall costs and the 

ultimate impact on energy supply. Second, the emission budgets for SO2 are set in two 

phases, providing adequate time for EGUs to install pollution controls ahead of the second 

phase. In addition, both the operational flexibility of trading and the ability to bank 
276

allowances for future years help industry plan for and ensure reliability in the electrical 

system.
277

7.16 References

Atkinson, S., and T. Tietenberg. 1982. “The Empirical Properties of Two Classes of Design 

for Transferable Discharge Permit Markets.” Journal of Environmental Economics 

and Management 9:101-121 

Carlson, Curtis, Dallas R. Burtraw, Maureen, Cropper, and Karen L. Palmer. 2000. “Sulfur 

Dioxide Control by Electric Utilities: What Are the Gains from Trade?” Journal of 

Political Economy 108(#6):1292-1326.

Ellerman, Denny. January 2003. Ex Post Evaluation of Tradable Permits: The U.S. SO2

Cap-and-Trade Program. Massachusetts Institute of Technology Center for Energy

and Environmental Policy Research.

EIA Annual Coal Report 2008. DOE/EIA-0584 (2008). Available at: 

http://www.eia.doe.gov/cneaf/coal/page/acr/acr_sum.html

EIA Annual Energy Outlook 2003. DOE/EIA-0383 (2003). Available at: 

http://www.eia.doe.gov/oiaf/archive/aeo03/index.html

EIA Electric Power Annual 2008. DOE/EIA-0348 (2008). Available at: 

http://www.eia.doe.gov/cneaf/electricity/epa/epa_sum.htm

EIA Electric Power Monthly March 2010 with Data for December 2009. DOE/EIA-0226 

(2010/03). Available at: http://www.eia.doe.gov/cneaf/electricity/epm/epm_sum.html

Freme, Fred. 2009. U.S. Coal Supply and Demand: 2008 Review. EIA. Available at: 

http://www.eia.doe.gov/cneaf/coal/page/special/tbl1.html

Harrington, W., R.D. Morgenstern, and P. Nelson. 2000. “On the Accuracy of Regulatory

Cost Estimates.” Journal of Policy Analysis and Management 19(2):297-322.

Keohane, Nathaniel O. 2009. “The Technocratic and Democratic Functions of the CAIR 

Regulatory Analysis.” In Reforming Regulatory Impact Analysis, eds. Winston 

Harrington, Lisa Heinzerling, and Richard D. Morgenstern, 33–55. Washington, DC: 

Resources for the Future.

Krupnick, A., W. Oates, and E. Van De Verg. 1980. “On Marketable Air Pollution Permits: 

The Case for a System of Pollution Offsets.” Journal of Environmental Economics 

and Management 10:233-47.

Manson, Nelson, and Neumann. 2002. “Assessing the Impact of Progress and Learning 

Curves on Clean Air Act Compliance Costs.” Industrial Economics Incorporated.
278

McGartland, A., and W. Oates. 1985. “Marketable Permits for the Prevention of 

Environmental Deterioration.” Journal of Environmental Economics and 

Management 12:207-228.

Montgomery, W. David. 1972. “Markets in Licenses and Efficient Pollution Control 

Programs.” Journal of Economic Theory 5(3):395-418.

Wagner, Wendy E. 2009. “The CAIR RIA: Advocacy Dressed Up as Policy Analysis.” In 

Reforming Regulatory Impact Analysis, eds. Winston Harrington, Lisa Heinzerling, and 

Richard D. Morgenstern, 56–81. Washington, DC: Resources for the Future.
279

CHAPTER 8

 MACROECONOMIC AND EMPLOYMENT IMPACTS

8.1 Partial Equilibrium Analysis (Multiple Markets)

In this chapter, we provide estimates of the economic impacts to industry sectors 

outside the electric power industry and social costs associated with the selected remedy. 

Economic impacts presented in this chapter are changes in price and outputs for affected 

products from a large number of industry sectors (100). Social costs are estimates of the 

costs to society associated with the control and administrative costs presented in Chapter 7. 

Such costs are estimated from changes in consumer and producer surplus, as discussed in 

more detail below. 

Our partial equilibrium analysis uses 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 and the results for a short-run 

economic impact analysis (in this case, for 2014). While the Multimarket Model has not 

been peer-reviewed, EPA plans to peer-review the model in the near future. More details on 

the economic model, the results, and data used by the model can be found in Appendix B.

8.1.1 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, p. 9-21). Other guides for environmental economists offer 

similar advice (Berck and Hoffmann, 2002; Just, Hueth, and Schmitz, 2004). Multimarket 

models focus on “short-run” time horizons and measure a policy’s near-term or transition 

costs as stated in the EPA Economic Resource Document (EPA, 1999). These models have 

greater levels of aggregation than detailed partial equilibrium models of compliance for 

individual sectors, like IPM, but represent more sectors of the economy. 

A linked partial equilibrium model is useful for evaluating relative economic impacts 
280

across related markets and providing a measure of consumer and producer surplus losses. 

It may not be sufficient for fully estimating social costs, just as a single market model may

not be appropriate for fully estimating social costs. Generally speaking, the smaller the effect 

of a regulation on partial-equilibrium measures of social welfare such as consumer and 

producer surplus, the more accurately those estimates reflect the true social cost.60 However, 

a transition or short-term evaluation using a partial equilibrium model does not explicitly

account for resources to be reallocated across sectors according to relative price changes 

(although the multi-market model does account for certain resource constraints and the 

employment of intermediate inputs). Furthermore, these models (if static) may not capture 

how resource use and consumption shifts over time in response to the regulation, which may

be accounted for with a dynamic computable general equilibrium model. Therefore, we use 

care in referring to the estimate of total change in surplus coming out of the Multimarket 

model as a social cost. For regulations with a relatively modest impact on the economy, 

however, such as this final rule, the change in surplus estimated by this model may provide a 

reasonable approximation of the social cost. 

Our multimarket model contains the following features:

Industry sectors and benchmark data set61

-100 industry sectors

-multiple benchmark years

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 price increases and consumption falls

 

60 A general equilibrium framework (e.g., such as that employed in the EMPAX model used by US EPA) is 

necessary for determining social costs in a theoretically complete way, but general equilibrium models have 

their own drawbacks and their application may not be practical or sufficiently informative to warrant their use.

61 A benchmark data set is included in the model to define the prepolicy equilibrium in the year of analysis (i.e., 

2015 in this rule) for the 100 sectors included. Thus, the dataset provides a benchmark for estimating 

economic impacts and social costs in the year of analysis.
281

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 (2015)62

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

supply function

-firms cannot alter certain input mixes; there is no substitution among 

intermediate production inputs

-there is no explicit labor market (a real wage and labor supply is not determined 

within the model)

-investment and government expenditures are fixed.

Although the model is intended to examine transition or short-term effects of 

this rulemaking, the results may be muted due to the use of annualized capital 

cost as an input to the model rather than the total capital cost.

Labor and Capital Markets and Pre-existing Distortions in Other Markets

Unlike CGE models, our multimarket model does not include a national labor or 

capital market. As a result, we do not estimate real wage changes, changes in 

labor /leisure choices, or savings and investment decisions within the model. 

 

62 For this analysis, we use 2015 as our analysis year and as a proxy for 2014. This allows us to maintain 

consistency with the results of the analysis using IPM (found in Chapter 7) that serve as inputs to this economic 

impact analysis.
282

Therefore we do not consider whether policies interact with existing 

distortions, particularly tax distortions in a ways that increase or decrease 

estimates of the social cost. Since savings and investment decisions are not 

modeled, social costs associated with capital stock changes are also not 

considered.

Although the model is intended to examine transition or short-term effects 

of this rulemaking, the estimate of the short-term effects may be understated due 

to the way the model accounts for changes in capital demand. The cost of new 

capital associated with the rule is accounted for by imposing an increase in the 

annualized capital cost rather than the total capital cost in the year of analysis.

Additionally, using an economic impact model that constrains the physical capital 

stock to be fixed will not fully depict the social costs of an adjustment to a postpolicy equilibrium as discussed in the EPA Economics Guidelines. 

The own-price elasticities used by the Multimarket Model are drawn from 

secondary sources, as fully described in Appendix B. While the Multimarket 

Model is a short-run model that examines transitional costs that arise as a result of 

a new regulation, the estimation methods used by the secondary sources generally 

assume that people and firms have more flexibility to adjust consumption and 

production decisions than they would have in a short-run model. Consequently, 

use of these elasticities within the Multimarket Model may in some cases lead to 

estimates of larger market output changes than would actually occur in the shortrun, all other things being equal. 

On the other hand, while the scale of environmental regulation often is not 

sufficient to influence government spending and investment demand, the Leontief 

structure for intermediate goods and its assumption of fixed investment and 

government spending may make the Multimarket Model less responsive to 

regulatory shocks. Given the competing sources of bias, the ultimate direction of 

the bias with regards to economic impacts and social costs is difficult to ascertain 

a priori. 
283

8.1.2 Economic Impact Analysis Results

Market-Level Results

Market-level impacts include price and quantity adjustments including the changes in 

international trade (Table 8-1).For the final Transport Rule, , the Agency’s economic model 

suggests the average national price increase for energy is 0.16%. Higher energy costs result 

in subsequent manufacturing sector price increases nationwide of 0.01% or less. Imports 

also slightly rise because of higher U.S. prices. The one exception is transportation services; 

since sectors using transportation services are producing less, the demand for transportation 

services declines. The size of the transportation services demand shift outweighs any supply 

side cost increases that place upward pressure on service prices (e.g. higher electricity and 

refined petroleum prices). As a result, the average transportation services price falls.

Social Cost Estimates of Transport Rule

In the short run, the Agency’s partial equilibrium multi-market model suggests that 

industries are able to pass on $0.7 billion (2007$) of the Transport Rule’s costs to U.S. 

households in the form of higher prices (Table 8-2). Existing U.S. industries’ surplus falls by

$0.2 billion and the net U.S. loss in aggregate, is $0.9 billion (2007$). This is slightly higher 

than the annualized nationwide compliance cost estimate of the final rule as shown in 

Chapter 7 of the RIA because it excludes gains to other countries discussed below.

Table 8-1. Short-Term Market-Level Changes (in percent) within the U.S. Economy 

in 2014

Industry Sector

U.S. 

Prices

U.S. 

Production Imports

U.S. 

Consumption Exports

Energy -0.163% -0.031%

0.009%

-0.016% -0.031%

Coal -0.017 -0.047 -0.048 -0.052 0.003

Crude Oil Extraction 0.004 -0.053 0.027 −0.003 0.000

Electric generation 0.770 -0.059 0.000 -0.059 -0.137

Natural Gas 0.004 -0.033 0.057 -0.018 -0.002

Refined Petroleum 0.002 -0.002 0.003 -0.001 -0.0002

Nonmanufacturing 0.0004 -0.002 0.001 -0.0016 -0.001

Manufacturing

Food, beverages, and textiles 0.004 -0.005 0.006 -0.003 -0.003
284

Lumber, paper, and printing 0.008 -0.005 0.009 -0.003 -0.006

Chemicals 0.002 -0.006 0.003 -0.003 -0.002

Plastics and Rubber 0.005 -0.006 0.008 −0.003 -0.005

Nonmetallic Minerals 0.009 −0.006 0.011 -0.003 -0.009

Primary Metals 0.007 −0.007 0.007 -0.004 −0.007

Fabricated Metals 0.006 -0.002 0.007 −0.002 -0.003

Machinery and Equipment 0.0007 -0.002 0.0005 -0.002 -0.0008

Electronic Equipment 0.0007 -0.002 0.0007 -0.002 -0.001

Transportation Equipment 0.0008 -0.003 0.001 -0.002 -0.001

Other 0.002 -0.007 0.004 -0.003 -0.003

Wholesale and Retail Trade 0.001 −0.002 0.001 -0.002 -0.002

Transportation Services -0.003 −0.003 −0.003 -0.003 0.003

Other Services 0.001 −0.002 0.0007 -0.002 -0.001

Note: Approximated using the IPM cost analysis. For example, with the $0.8 billion increase in costs for the 

electric power sector, IPM projects a 0.77 percent increase in the retail price of electricity in 2015. All other 

energy market-level changes are determined within the multimarket model. Appendix B provides additional 

details.

As U.S. prices rise, other countries are affected through international trade 

relationships. The price of goods produced in the United States increase, domestic exports 

decline, and domestic production is replaced to a certain degree by imports; the model 

estimates a net gain of about $0.04 billion for other countries. The net change in total surplus 

is lower than the annualized nationwide compliance cost estimate of the final rule as shown 

in Chapter 7 of the RIA. Our estimate of social costs for the rule incorporates the net change 

in total surplus, and this estimate is $0.81 billion (2007 dollars) as shown in Table 8-2, or 

nearly identical to the compliance costs.63 Compliance costs based on the pre-policy output 

levels would be overstated if we do not consider the new lower levels of consumption as a 

result of higher market prices.64

 

63 The same is true for many recent rulemakings, including the Boiler MACT.

64 There are small additional losses associated with the foregone benefits associated with reduced consumption 

(e.g. deadweight loss). However, in a perfectly competitive market without pre-existing distortions, the costs 

represent only a small fraction of total social costs. A more detailed discussion of the economic costs of 

regulation is found in Chapter 8 of EPA (2010).
285

Table 8-2. Distribution of Social Costs (billions, 2007$): 2015

Change in U.S. consumer surplus −$0.68

Change in U.S. producer surplus −$0.17

Net Change in U.S. Surplus −$0.85

Net change in rest of world surplus $0.04

Net change in Total Surplus −0.81$

The surplus losses are concentrated in the electric generation sector (percent) and 

other services (percent). Other services include information, finance and insurance, real 

estate, professional services, management, administrative services, education, health care, 

arts, accommodations, and public services. Although electricity costs represent a small share 

of total service industry production costs, the service sectors represent a significant economic 

sector within the U.S. economy and use a large amount of electricity. The transition or shortterm evaluation using a partial equilibrium model does not allow for resources to be allocated 

according to price changes. So the results of the model do not capture any distortions in the 

economy that may results as the price of electricity changes. If the distortions are significant, 

the “true” social cost would be higher than the compliance cost and the results of this partial 

equilibrium model.

8.1.3 Alternative Approach to Estimating Social Cost

In the Transport Rule proposed last summer, EPA used a different model to estimate 

the social cost of the regulatory approach than applied in this RIA. That model, EPA’s 

Economic Model for Policy Analysis (EMPAX), is a computable general equilibrium model 

(CGE) which dynamically cascades the cost of a regulation through the entire economy. 

However, since that rule was proposed, an updated version of EMPAX was used to estimate 

the social cost of the Clean Air Act in a new EPA report entitled “The Benefits and Costs of 

the Clean Air Act from 1990 to 2020. This report is available at 

http://www.epa.gov/air/sect812/feb11/fullreport.pdf.

This updated version of EMPAX added in the benefit-side effects (incorporating 

labor-force and health care expenditures) which significantly changed the social cost estimate 

from the previous edition. In December 2010, EPA’s Science Advisory Board (SAB) found 

that “The inclusion of benefit-side effects (reductions in mortality, morbidity, and health-
286

care expenditures) in a computable general equilibrium (CGE) model represents a 

significant step forward in benefit-cost analysis.” 

http://yosemite.epa.gov/sab/sabproduct.nsf/1E6218DE3BFF682E852577FB005D46F1/$File/

EPA-COUNCIL-11-001-unsigned.pdf. A description of the changes to the model and 

implications are covered in detail in chapter 8 of the section 812 report. EPA has determined

that it needs to update the EMPAX model version used for RIAs to add this benefit-side 

effect prior to use in any additional regulatory analysis. In addition, the total nationwide 

annual compliance costs of the Transport Rule are now much lower than estimated for the 

proposal, and now are low enough so that EMPAX may not be as appropriate a model to use 

to for social costs and other economic impact estimation for this final rule. With the benefitside effects updates now in the process of being incorporated into the model in response to 

SAB’s guidance, and the relatively low nationwide annual compliance cost incurred by the 

electric power sector for this final rule as mentioned earlier in this RIA, EPA will not use 

EMPAX for this final RIA.

8.2 Employment Impacts for the Transport Rule

In addition to addressing the costs and benefits of the Transport Rule, EPA has 

estimated the impacts of this rulemaking on labor demand, and the results are presented in 

this section.65 While a standalone analysis of employment impacts is not included in a 

standard cost-benefit analysis, such an analysis is of particular concern in the current 

economic climate. Executive Order 13563, 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

partial employment analysis to inform the discussion of labor demand and job impacts. We 

provide an estimate of the employment impacts on the regulated industry over time. We also 

provide the short-term employment impacts (increase in labor demand) associated with the 

construction of needed pollution control equipment.

We have not quantified the rule’s effects on all labor in other sectors not regulated by

this rule, or the effects induced by changes in workers’ incomes. What follows is an 

 

65 See TSD as part of the Transport Rule Docket: “Employment Estimates of Direct Labor in Response to the 

Transport Rule in 2014.”
287

overview of the various ways that environmental regulation can affect employment,

followed by a discussion of the estimated impacts of this rule. EPA continues to explore the 

relevant theoretical and empirical literature and seeks public comment in order to ensure that 

such estimates are as accurate, useful and informative 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 at full employment, we would not expect an environmental regulation to have an 

impact on overall employment since labor is being shifted from one sector to another. On the 

other hand, in periods of high unemployment, an increase in labor demand due to regulation 

may have the potential to result 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 smaller.

To provide a partial picture of the employment consequences of this rule, EPA takes 

two approaches. First, the analysis uses the results of Morgenstern, Pizer, and Shih (2002) to 

estimate the effects of the regulation on the regulated industry, the electric power industry in 

this case. This approach has been used by EPA in recent Regulatory Impact Analyses. (See, 

for example, the Regulatory Impact Analysis for the recently finalized Industrial Boilers and 

Commercial and Institutional Solid Waste (CISWI) rulemakings, promulgated on February

21, 2011). Second, EPA uses projections from IPM to estimate the short-term employment 

effects resulting from construction and resource requirements as a result of the additional 

demand for pollution control equipment. Historically, EPA has only reported employment 

impacts on a few regulations. Section 8.3 discusses the estimates of the employment 

consequences in the electricity sector, using the Morgenstern, et al. approach. Section 8.4 

estimates the employment effects in the environmental protection sector, using the second 

approach.

8.3 Employment Impacts primarily on the regulated industry: Morgenstern, Pizer, 

and Shih (2002)

EPA examined possible employment effects within the electric utility sector using a 

peer-reviewed, published study that explores historical relationships between industrial 
288

employment and environmental regulations (Morgenstern, Pizer, and Shih, 2002). The 

fundamental insight of Morgenstern, et al. 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 66;

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.

Using plant-level Census information between the years 1979 and 1991, Morgenstern 

et al. estimate the size of each effect for four polluting and regulated industries (petroleum, 

 

66 The Morgenstern et al. results rely on industry demand and supply elasticities to determine cost pass-through 

and reductions in output.
289

plastic material, pulp and paper, and steel). On average across the four industries, each 

additional $1 million ($1987) spending on pollution abatement results in a (statistically

insignificant) net increase of 1.55 (+/- 2.24) jobs. As a result, the authors conclude that 

increases in pollution abatement expenditures do not necessarily cause economically

significant employment changes. The conclusion is similar to Berman and Bui (2001) who 

found that increased air quality regulation in Los Angeles did not cause in large employment 

changes67.

Since the Morgenstern, et al. parameter estimates are expressed in jobs per million 

($1987)68 of environmental compliance expenditures, their study offers a transparent and 

simple way to transfer estimates for other employment analysis. For each of the three job 

effects outlined above, EPA used the Morgenstern et al. four industry average parameters and 

standard errors along with the estimated private compliance costs to provide a range of 

electricity sector employment effects associated with the Transport Rule.

By applying these estimates to pollution abatement costs for the final rule for the 

electric power sector, we estimated each effect. The results are

Demand effect: −to + jobs in the directly affected sector with a central estimate of −;

Cost effect: + to + jobs in the directly affected sector with a central estimate of +; 

and

Factor-shift effect: + to + jobs in the directly affected sector with a central estimate 

of +.

EPA estimates the net employment effect to range from - to + jobs in the directly

affected sector with a central estimate of +. 69, 70

 

67 For alternative views, see Henderson (1996) and Greenstone (2002). 

68 The Morgenstern et al. analysis uses “production worker” as defined in the US Census Bureau’s Annual 

Survey of Manufactures (ASM) in order to define a job. This definition can be found on the Internet at 

http://www.census.gov/manufacturing/asm/definitions/index.html. 

69 Since Morgenstern’s analysis reports environmental expenditures in $1987, we make an inflation adjustment 

the IPM costs using the ratio of the consumer price index, U.S. city, all items reported by the U.S. Bureau of 

Labor Statistics: CPI1987/ CPI2007 = (113.6/207.3) = 0.55
290

These estimates are shown in Table 8-3.

Table 8-3. Employment Impacts Using Peer-Reviewed Study

Estimates using Morgenstern et al. (2002)

Demand Effect Cost Effect

Factor Shift 

Effect Net Effect

Change in 

Full-Time Jobs 

per Million 

Dollars of 

Environmental 

Expenditurea

−3.56 2.42 2.68 1.55

Standard Error 2.03 0.83 1.35 2.24

EPA estimate 

for Transport 

Ruleb

+200 to -3,000 +400 to 2,000 0 to +2,000 -1,000 to 3,000

a Expressed in 1987 dollars. See footnote 8 for inflation adjustment factor used in the analysis.

b According to the 2007 Economic Census, the electric power generation, transmission and distribution sector 

(NAICS 2211) had approximately 510,000 paid employees. Both the midpoint and range for each effect are 

reported in the last row of the table.

All ranges for these job changes are based on the 95th percentile of results. EPA 

recognizes there will be other employment effects which are not considered in the 

Morgenstern et al. study. For example, employment in environmental protection industries 

may increase as firms purchase more pollution control equipment and services to meet the 

rule’s requirements. EPA does provide such an estimate of employment change later in this 

section in a separate analysis. On the other hand, industries that use electricity will face 

higher electricity prices as the result of the transport rule, reduce output, and demand less 

labor. In the earlier part of Chapter 8 we use a Multimarket Model to estimate that there will 

be a .002% reduction in output in nonmanufacturing industries and .002 to .007 percent 

reductions in output in various manufacturing industries. It is difficult, however, to extend 

these results to an estimate of employment effects across these many different industries; 

 

70 Net employment effect = 1.55× $832 million × 0.55. This estimated net result is not statistically different 

from zero.
291

therefore, we feel we do not currently have sufficient information to quantify these impacts 

as potential employment losses.

8.3.1 Limitations

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 8-3 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). 

Morgenstern, et al. present those industries’ estimates separately, and they range from -1.13 

jobs per $1 million (in 1987 dollars) of environmental expenditures for pulp and paper, to 

+6.90 jobs for plastics. Only two of the total jobs estimates are statistically significantly 

different from zero, and the overall weighted average used here, 1.55 jobs per $1 million, is 

not statistically significant. Moreover, here we are applying the estimate to the electricity 

generating industry.

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.

8.4 Employment Impacts of the Transport Rule - Environmental Protection Sector 

Approach, by 2014 71

The Transport Rule will require additional pollution control equipment and services 

in order to reduce emissions from the power sector and address nonattainment and the 

transport of air emissions cross state borders. Historically, new categories of employment 

have been created in the process of implementing regulations that reduce air emissions and 

other forms of pollution.. When a regulation is promulgated, the first response of industry is 

to order pollution control equipment and services in order to comply with the regulation 

 

71 EPA expects that the installation of retrofit control equipment in response to the requirements of this rule will 

primarily take place within 2014. 
292

when it becomes effective. Revenue and employment in the environmental technology

industry have grown steadily between 2000 and 2008, reaching an industry total of 

approximately $300 billion in revenues and 1.7 million employees in 2008.72 While these 

revenues and employment figures represent gains for the environmental technologies 

industry, they are costs to the regulated industries required to install the equipment. 

Moreover, it is not clear the 1.7 million employees in 2008 represent anything other than 

workers diverted from other productive employment as opposed to new additional 

employment.

Regulated firms hire workers to design and construct pollution control equipment. 

Once the equipment is installed, regulated firms hire workers to operate and maintain the 

pollution control equipment – much like they hire workers to produce more output. 

Morgenstern, Pizer, and Shih (2002) examined how regulated industries respond to 

regulation. They found that on average, employment goes up in regulated firms. Of course, 

these firms may also reassign existing employees to do these activities.

Environmental regulations support 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 support employment in 

industries that provide intermediate goods to the environmental protection industry. For 

example, $1 billion in capital expenditures to reduce air pollution involves the purchase of 

abatement equipment. The equipment manufacturers, in turn, order steel, tanks, vessels, 

blowers, pumps, and chemicals to manufacture and install the equipment.

A study (2008) by Bezdek, Wendling, and DiPernab found that “investments in 

environmental protection industries create jobs and displace jobs, but the net effect on 

employment is positive.”

 

72 In 2008, the industry totaled approximately $315 billion in revenues and 1.9 million employees including 

indirect employment effects, pollution abatement equipment production employed approximately 4.2 million 

workers in 2008. These indirect employment effects are based on a multiplier for indirect employment = 2.24 

(1982 value from Nestor and Pasurka - approximate middle of range of multipliers 1977-1991). Environmental 

Business International (EBI), Inc., San Diego, CA. Environmental Business Journal, monthly (copyright). 

http://www.ebiusa.com/ EBI data taken from the Department of Commerce International Trade Administration 

Environmental Industries Fact Sheet from April 2010: 

http://web.ita.doc.gov/ete/eteinfo.nsf/068f3801d047f26e85256883006ffa54/4878b7e2fc08ac6d85256883006c4

52c?OpenDocument
293

The focus of this part of the employment analysis is on short-term jobs related to the 

compliance actions of the affected entities and includes estimates of the employment impacts 

due to the increased demand for pollution control equipment.73 Results indicate that the 

Transport Rule has the potential to result in a net increase of labor in these industries, driven 

by the increased demand for new pollution controls. Overall, the preliminary results of the 

environmental protection sector approach indicate that the Transport Rule could support an 

increase of about 2,230 job-years74 by 2014.

8.4.1 Overall Approach and Methodology for Environmental Protection Sector 

Approach

EPA commissioned ICF International to provide estimates for the Environmental 

Protection Sector, and the analysis utilizes a bottom-up engineering based methodology

combined with macroeconomic data on industrial output and productivity, to estimate 

employment impacts in the environmental control sector of the economy. It relies on 

projections from the IPM model and projections for new pollution control equipment and 

related costs, along with data from the Bureau of Labor Statistics and other sources. The 

approach also relies upon prior EPA studies on similar issues, and in particular uses data and 

information from an extensive resource study conducted in 2002, which was updated in the 

Spring of 2011 and reflects more recent information.75 The approach involves using IPM 

projected results from the Transport Rule analysis for the set of pollution control 

technologies expected to be installed to comply with the rule, along with data from secondary

sources, to estimate the job impacts using this approach.76 This includes the labor needed to 

design, manufacture and install the needed pollution control equipment over the years 

leading up to compliance in 2014.

For construction labor, the labor needs are derived from the 2002 EPA resource 

analysis for installing various retrofits (FGD – Flue Gas Desulfurization scrubbers, SCR-

 

73 For more detail on methodology, approach, and assumptions, see Appendix D in this RIA: “Employment 

Estimates of Direct Labor in Response to the Transport Rule in 2014.”

74 Numbers of job years are not the same as numbers of individual jobs, but represents the amount of work that 

can be performed by the equivalent of one full-time individual for a year (or FTE). For example, 25 job years 

may be equivalent to five full-time workers for five years, twenty-five full-time workers for one year, or one 

full-time worker for twenty-five years.

75 Engineering and Economic Factors Affecting the Installation of Control Technologies for Multipollutant 

Strategies EPA-600/R-02/073 (2002).

76 Detailed results from IPM for the Transport Rule can be found in Chapter 7 of the RIA.
294

selective catalytic reduction, and DSI - dry sorbent injection) and are further classified into 

different labor categories, such as boilermakers, engineers and a catch-all “other installation 

labor.” For the inputs needed (e.g., steel), the 2002 resource study was used to determine the 

steel demand for each MW of additional pollution control and combined with labor 

productivity data from the Economic Census and BLS for relevant industries.

More detail on methodology, assumptions, and data sources can be found in 

Appendix D of this RIA - “Employment Estimates of Direct Labor in Response to the 

Transport Rule in 2014.”

Projections from IPM were used to estimate the incremental retrofit capacities 

projected in response to the final rule. These additional pollution controls are shown in 

Table 8-4 below, and reflect the added pollution controls needed to meet the requirements of 

the rule. Additional information on the power sector impacts can be found in Chapter 7 of 

the RIA.

Table 8-4. Increased Pollution Control Demand due to the Transport Rule, by 2014 

(GW)

Retrofit Type

Existing Controls 

Induced to Operate Newly Built Controls

FGD 25 6

SCR 5 0

DSI 0 3

8.4.2 Summary of Employment Estimates from Environmental Protection Sector 

Approach

Table 8-5 presents additional detail on the estimated employment impacts using the 

environmental protection sector approach resulting from the Transport Rule. Results for the 

Environmental Protection Sector Approach indicate the Transport Rule could support or 

create roughly 2,230 one-time job-years of increased demand for direct labor, driven by the 

need to design and build the pollution control equipment.
295

Table 8-5. Employment Effects Using the Environmental Protection Sector 

Approach for the Transport Rule by 2014 (in Job-Years) 

Employment Incremental Employment

One-Time Employment Changes for Construction

1. Boilermakers 890

2. Engineers 440

3. General Construction 880

4. Steel Manufacturing 20

8.4.3 Other Employment Impacts of the Transport Rule

We expect ongoing employment impacts on regulated and non-regulated entities for a 

variety of reasons. These include labor changes in the regulated entities resulting from shifts 

in demand for fuels, increased demand for materials and the labor required to provide them to 

operate pollution control equipment, reductions in employment resulting from coal 

retirements, and reductions in other industries due to slight projected increases in the price of 

electricity and natural gas. The most notable of the ones we are unable to estimate are the 

impacts on employment as a result of the increase in electricity and other energy prices in the 

economy. Because of this inability to estimate all the important employment impacts, EPA 

neither sums the impacts that the Agency is able to estimate for the ongoing non-regulated 

group or make any inferences of whether there is a net gain or loss of employment for the 

non-regulated group. These other ongoing employment impacts are found in Table 8-6, and 

these employment impacts are not included in the Agency estimate of employment impacts 

for this Transport Rule for the reasons mentioned in this paragraph. The data in Table 8-6 

come from an analysis of short-term employment impacts presented in Appendix D. That 

analysis focuses on the first-order employment impacts related to the compliance actions of 

the affected entities within the power sector, but not the ripple effects of these impacts on the 

broader economy (i.e., the “multiplier” effect) or economy-wide effects of changes to the 

energy markets.

Table 8-6. Employment Impacts for Entities Not Regulated by the Transport Rule

Employment Changes for Ongoing Annual Operation
296

Employment Changes from Changes to Demand in 

Materials77

1. Limestone (FGD) 2,440

2. Ammonia (SCR) 30

3. Catalyst (SCR) 20

4. Sodium Bicarbonate (DSI) 160

Sub-Total: 2,650

Employment Changes for Ongoing Annual Retrofit 

Operation

2,320

Employment Annual Changes due to Coal Capacity 

Retirements

(2,710)

Annual Employment Changes due to Changes in Fuel Use

1. Coal (2,030)

2. Natural Gas 810

3. New Natural Gas Pipeline 230

Sub-Total: (990)

8.5 Summary

The two approaches use different analytical techniques and are applied to different 

industries during different time periods, and they use different units of analysis. These 

estimates should not be summed because of the different metrics, length and methods of 

analysis as mentioned in Section 8.4. The Morgenstern estimates are used for the ongoing 

employment impacts for the regulated entities (the electric power sector). The short term 

estimates for employment needed to design, construct, and install the pollution control 

equipment in the period leading up to the compliance date are also provided. Finally some of 

the other types of employment impacts that will be ongoing are not included in the 

employment impacts estimated for the final Transport Rule.

Table 8-7 shows the employment impacts of the final Transport Rule as estimated by

the environmental protection sector approach and by the Morgenstern approach.

 
297

Table 8-7. Estimated Employment Impact Table 

Annual (reoccurring) One time (construction 

during compliance period)

Environmental Protection 

Sector approach*

Not Applicable 2,230

Net Effect on Electric Utility

Sector Employment from 

Morgenstern et al. 

approach***

**+700

- 1,000 to +3,000****

Not Applicable

*These one-time impacts on employment are estimated in terms of job-years.

**This estimate is not statistically different from zero.

***These annual or recurring employment impacts are estimated in terms of production workers as defined by 

the US Census Bureau’s Annual Survey of Manufacturers (ASM).

**** Based on the 95% confidence interval of the Morgenstern study.
298

8.6 References

Berman, E., and L. T. M. Bui. 2001. “Environmental Regulation and Labor Demand: 

Evidence from the South Coast Air Basin.” Journal of Public Economics 79(2):265-

295.

Bezdek, R. H., Wendling, R. M., and DiPerna, P. 2008. “Environmental Protection, the 

Economy, and Jobs: National and Regional Analyses.” Journal of Environmental 

Management 86(1):63-79.

Greenstone, M. 2002. “The Impacts of Environmental Regulations on Industrial Activity: 

Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of 

Manufactures. Journal of Political Economy 110(6):1175-1219.

Henderson, J. V. 1996. “Effects of Air Quality Regulation.” American Economic Review

86(4):789-813.

Morgenstern, R. D., W. A. Pizer, and J. S. Shih. 2002. “Jobs versus the Environment: An 

Industry-Level Perspective.” Journal of Environmental Economics and Management

43(3):412-436.

US Environmental Protection Agency (EPA). “Impacts of the Acid Rain Program on Coal 

Industry Employment.” EPA 430-R-01-002, (2001).

US Environmental Protection Agency. Engineering and Economic Factors Affecting the 

Installation of Control Technologies for Multipollutant Strategies, EPA-600/R-02/073 

(2002).

US Energy Information Administration (EIA), Annual Energy Review 2009. “Coal Mining 

Productivity By State and Mine Type.”

US Census Bureau, 2007 and 2002 Economic Census. “Manufacturing and Mining: Detailed 

Statistics by Ind for the US, Utiltiities: Summary Statistics for the US.”

US Bureau of Labor Statistics, United States Department of Labor. “Industry Labor 

Productivity and Cost Data Tables, Annual Percent Changes.” (2010).
299

CHAPTER 9

 STATUTORY AND EXECUTIVE ORDER ANALYSES 

This chapter presents discussion and analyses relating to relevant Executive Orders 

and statutory requirements relevant for the Transport Rule. We discuss potential impacts to 

affected small entities as required by the Regulatory Flexibility Act (RFA), as amended by 

the Small Business Regulatory Enforcement Fairness Act (SBREFA). We also describe the 

analysis conducted to meet the requirements of the Unfunded Mandates Reform Act of 1995 

(UMRA) that assess the impact of the Transport Rule for state, local and Tribal governments 

and the private sector. Analyses conducted to comply with the Paperwork Reduction Act 

(PRA) are also discussed. In addition, we address the requirements of Executive Order (EO) 

13045: Protection of Children from Environmental Health and Safety Risks; EO 13175: 

Consultation and Coordination with Indian Tribal Governments; and EO 12898: Federal 

Actions to Address Environmental Justice in Minority Populations and Low-Income 

Populations. The Discussion of Executive Order 13211: Actions that Significantly Affect 

Energy Supply, Distribution or Use is provided in Chapter 7 of this RIA. 

9.1 Small Entity Impacts

The Regulatory Flexibility Act (5 U.S.C. 601 et seq.), as amended by the Small 

Business Regulatory Enforcement Fairness Act (Public Law No. 104-121), provides that 

whenever an agency is required to publish a general notice of proposed rulemaking, it must 

prepare and make available an initial regulatory flexibility analysis, unless it certifies that the 

proposed rule, if promulgated, will not have a significant economic impact on a substantial 

number of small entities (5 U.S.C. 605[b]). Small entities include small businesses, small 
300

organizations, and small governmental jurisdictions. 

For the purposes of assessing the impacts of the Transport Rule on small entities, a small 

entity is defined as:

(1) A small business according to the Small Business Administration size 

standards by the North American Industry Classification System (NAICS) category of 

the owning entity. The range of small business size standards for electric utilities is 4 

billion kilowatt-hours of production or less;

(2) a small government jurisdiction that is a government of a city, county, town, 

district, or special district with a population of less than 50,000; and

(3) a small organization that is any not-for-profit enterprise that is independently

owned and operated and is not dominant in its field. 

Table 9-1 lists entities potentially affected by this final rule with the applicable 

NAICS code. 

Table 9-1. Potentially Regulated Categories and Entitiesa

Category NAICS 

Codeb Examples of Potentially Regulated Entities

Industry 221112 Fossil fuel-fired electric utility steam generating units.

Federal 

Government

221112cFossil fuel-fired electric utility steam generating units owned by 

the federal government.

State/Local/

Tribal 

Government

221112cFossil fuel-fired electric utility steam generating units owned by 

municipalities.

921150 Fossil fuel-fired electric utility steam generating units in Indian 

Country.

a

Include NAICS categories for source categories that own and operate electric generating units only.

bNorth American Industry Classification System.

c

Federal, state, or local government-owned and operated establishments are classified according to the activity 
301

in which they are engaged.

EPA examined the potential economic impacts to small entities associated with this 

rulemaking based on assumptions of how the affected entities will implement control 

measures to meet their NOx and SO2 budgets. This analysis does not examine potential 

indirect economic impacts associated with the Transport Rule, such as employment effects in 

industries providing fuel and pollution control equipment, or the potential effects of

electricity price increases on industries and households. 

9.1.1 Identification of Small Entities

EPA used Velocity Suite’s Ventyx data as a basis for identifying plant ownership and 

compiling the list of potentially affected small entities, in conjunction with IPM outputs.78 

The Ventyx data set contains detailed ownership and corporate affiliation information. For 

plants burning fossil fuel as the primary fuel, plant-level boiler and generator capacity, heat 

input, generation, and emissions data were aggregated by owner and then parent company. 

Entities with more than 4 billion kWh of annual electricity generation were removed from the 

list, as were municipal-owned entities serving a population greater than 50,000. For 

cooperatives, investor-owned utilities, and subdivisions that generate less than 4 billion kWh 

of electricity annually but may be part of a large entity, additional research on power sales, 

operating revenues, and other business activities was performed to make a final 

determination regarding size. Because the rule does not affect units with a generating 

capacity of less than 25 MW, small entities that do not own at least one generating unit with 

a capacity greater than or equal to 25 MW were dropped from the data set. According to 

EPA’s analysis, about 390 small entities were exempted by this provision. Finally, small 

entities for which IPM does not project generation in 2014 in the base case were omitted 

from the analysis because they are not projected to be operating and thus will not face the 

costs of compliance with the Transport Rule. After omitting entities for the reasons above, 

 

78 For details, see http://www.ventyx.com/
302

EPA identified a total of 108 potentially affected small entities, out of a possible 610.79 The 

number of potentially affected small entities by ownership type is listed in Table 9-2. 

9.1.2 Overview of Analysis and Results

This section presents the methodology and results for estimating the impact on the 

Transport Rule to small entities in 2014 based on the following endpoints:

 annual economic impacts of the Transport Rule on small entities and 

 ratio of small entity impacts to revenues from electricity generation.

9.1.2.1 Methodology for Estimating Impacts of the Transport Rule on Small Entities

An entity can comply with the Transport Rule through some combination of the 

following: installing retrofit technologies, purchasing allowances, switching to a cleaner fuel, 

or reducing emissions through a reduction in generation or improved efficiency. 

Additionally, units with more allowances than needed can sell these allowances in the 

market. The chosen compliance strategy will be primarily a function of the unit’s marginal 

control costs and its position relative to the marginal control costs of other units.

To attempt to account for each potential control strategy, EPA estimates compliance 

costs as follows:

CCompliance = Δ COperating+Retrofit + Δ CFuel + Δ CAllowances + Δ CTransaction + Δ R

where C represents a component of cost as labeled, and Δ R represents the value of foregone 

electricity generation, calculated as the difference in revenues between the base case and the 

Transport Rule. 

In reality, compliance choices and market conditions can combine such that an entity

may actually experience a savings in any of the individual components of cost. Under the 

Transport Rule, some units will forgo some level of electricity generation (and thus revenues) 

to comply and this impact will be lessened on these entities by the projected increase in 

electricity prices under the Transport Rule. On the other hand, those maintaining or 

 

79 There are 95 entities that are not technically electricity generating utilities so we applied other criteria that 

would apply to financial or industrial companies and found that they did not meet the definitions of small 

entities in this context.
303

increasing generation levels will see an increase in electricity revenues and as a result, lower 

net compliance costs. If entities are able to increase revenue more than an increase in fuel 

cost and other operating costs, ultimately they will have negative net compliance costs (or 

savings). Elsewhere, units burning high or medium sulfur coal might decide to pay relatively 

more for low-sulfur coal under the Transport Rule and sell allowances on the market, in the 

hopes of negating some or all of their compliance costs. Overall, small entities are not 

projected to install relatively costly emissions control retrofits, but may choose to do so in 

some instances. Because this analysis evaluates the total costs along each of the compliance 

strategies laid out above for each entity, it inevitably captures savings or gains such as those 

described. As a result, what we describe as cost is really more of a measure of the net 

economic impact of the rule on small entities.

For this analysis, EPA used IPM-parsed outputs to estimate costs based on the 

parameters above, at the unit level. These impacts were then summed for each small entity, 

adjusting for ownership share. Net impact estimates were based on the following: operating 

and retrofit costs, sale or purchase of allowances, and the change in fuel costs or electricity 

generation revenues under the Transport Rule relative to the base case. These individual 

components of compliance cost were estimated as follows:

(1) Operating and retrofit costs: Using the IPM-parsed output for the base case and 

the Transport Rule, EPA identified units that install control technology under the 

Transport Rule and the technology installed. The equations for calculating retrofit 

costs were adopted from EPA’s version of IPM. The model calculates the capital cost 

(in $/MW); the fixed operation and maintenance (O&M) cost (in $/MW-year); the

variable O&M cost (in $/MWh); and the total annualized retrofit cost for units 

projected to install FGD, DSI, SCR, or SNCR. 

(2) Sale or purchase of allowances: EPA estimated the value of initial SO2 and 

NOx annual and NOx ozone season allowance holdings. For both SO2 and NOx, the 

state emission budgets were assumed to be apportioned to units based on their share 

of the state's historic heat input (but not exceeding the unit's maximum historic 

emissions). Using 2006-2010 data, the three highest, non- zero annual heat input 

values within the 5 year period are selected and averaged for each unit. EPA

calculated each unit's SO2 and NOx annual and NOx ozone season allowance 
304

allocations as the ratio of that unit's historic heat input to the sum of all units' historic 

heat input in the applicable state, times the final state budget for existing units. Thus 

each unit's allocation is the unit's proportional share of the state budget, based on 

historic heat input. See Allowance Allocations in the Existing and New Units under 

the Transport Rule Federal Implementation Plans TSD.

To estimate the value of allowances holdings, allocated allowances were subtracted 

from projected emissions, and the difference was then multiplied by the allowance 

prices projected by IPM for 2014. Units were assumed to purchase or sell allowances 

to exactly cover their projected emissions under the Transport Rule. 

(3) Fuel costs: Fuel costs were estimated by multiplying fuel input (MMBtu) by

region and fuel-type-adjusted fuel prices ($/MMBtu) from IPM. The change in fuel 

expenditures under the Transport Rule was then estimated by taking the difference in 

fuel costs between the Transport Rule and the base case.

(4) Value of electricity generated: EPA estimated electricity generation by first 

estimating unit capacity factor and maximum fuel capacity. Unit capacity factor is 

estimated by dividing fuel input (MMBtu) by maximum fuel capacity (MMBtu). The 

maximum fuel capacity was estimated by multiplying capacity (MW) * 8,760 

operating hours * heat rate (MMBtu/MWh). The value of electricity generated is then 

estimated by multiplying capacity (MW) * capacity factor * 8,760 * regional-adjusted 

retail electricity price ($/MWh), for all entities except those categorized as “Private” 

in Ventyx. For private entities, EPA used wholesale electricity price instead retail 

electricity price because most of the private entities are independent power producers 

(IPP). IPPs sell their electricity to wholesale purchasers and do not own transmission 

facilities and thus their revenue was estimated with wholesale electricity prices. 

As discussed later in this analysis, 70 percent of small entities projected to incur 

positive costs under the Transport Rule do not have to operate in a competitive 

market environment and thus should be able to pass compliance costs on to 

consumers. We defined cost of service regions as regions with a deregulation 

percentage of less than 20 percent. The deregulation percentage is defined for this 

analysis as a percentage estimating the degree of competition in electricity market, as 

provided by EIA. The lower this percentage means that there are more areas with 
305

cost of service market characteristics. We have used the estimates published in AEO 

2010.

To somewhat account for this cost pass-through, we incorporated the projected 

regional-adjusted retail electricity price calculated under the Transport Rule in our 

estimation of generation revenue under the Transport Rule. 

(5) Administrative costs: Because most affected units are already monitored as a 

result of other regulatory requirements, EPA considered the primary administrative 

cost to be transaction costs related to purchasing or selling allowances. EPA assumed 

that transaction costs were equal to 1.5 percent of the total absolute value of a unit’s 

allowances. This assumption is based on market research by ICF International. 

9.1.2.2 Results

The potential impacts of the Transport Rule on small entities are summarized in Table 

9-2. All costs are presented in $2007. EPA estimated the annualized net compliance cost to 

small entities to be approximately $38 million in 2014. As the table below indicates, net 

compliance costs for some ownership groups are actually negative, implying the group as a 

whole is estimated to have cost savings under the Transport Rule. The fact that these groups 

have net savings does not mean that each small entity in that group would benefit from the 

Transport Rule. In most cases, the net savings are driven by a few entities that are able to 

increase their electricity revenues by increasing generation and taking advantage of higher 

electricity prices.
306

Table 9-2. Projected Impact of the Transport Rule on Small Entities in 2014

EGU 

Ownership 

Type

Number of 

Potentially 

Affected 

Entities

Total Net 

Compliance 

Cost ($2007 

millions)

Number of Small 

Entities with 

Compliance Costs 

>1% of Generation 

Revenues

Number of Small 

Entities with 

Compliance Costs 

>3% of Generation 

Revenues

Cooperative 25 $52.9 7 5

Investor-Owned 

Utility 9 -$9.9 0 0

Municipal 51 $3.0 15 5

Subdivision 5 -$2.1 0 0

Private 18 -$5.7 2 1

Total 108 $38.1 24 11

Note: The total number of potentially affected entities in this table excludes around 390 entities that have 

been dropped because they will not be affected by the Transport Rule, because they only own units less 

than 25 MW.. Also, the total number of entities with costs greater than 1 percent or 3 percent of 

revenues includes only entities experiencing positive costs. A negative cost value implies that the 

group of entities experiences a net savings under the Transport Rule. Definitions of types of entities 

are the following: Cooperative = User-owned utility company; Investor-Owned Utility = Utility owned 

by stockholders; Municipal = Publicly owned utility; Subdivision = County, municipality, hospital 

district or school district that receives electric service from an entity; Private = Privately owned electric 

companies not openly traded on stock markets

Source: IPM analysis

EPA assessed the economic and financial impacts of the rule using the ratio of 

compliance costs to the value of revenues from electricity generation, focusing in particular 

on entities for which this measure is greater than 1 percent. Although this metric is 

commonly used in EPA impact analyses, it makes the most sense when as a general matter an 

analysis is looking at small businesses that operate in competitive environments. However, 

small businesses in the electric power industry often operate in a price-regulated environment 

where they are able to recover expenses through rate increases. Given this, EPA considers 

the 1 percent measure in this case a crude measure of the price increases these small entities 

will be asking of rate commissions or making at publicly owned companies. 
307

EPA assessed the potential impact of this action on small entities and found that 

there are about 660 potentially affected small units (i.e., greater than 25 MW and generating 

less than 4MM MWh) out of 3,625 existing units greater than 25 MW in the TR region. The 

majority of these EGUs are owned by entities that do not meet the small entity definition. 

The remaining 271 of 660 EGUs are owned by 108 potentially affected small entities and are 

likely to be affected by today’s rule. Of the 108 small entities considered in this analysis, 24

entities may experience compliance costs greater than 1 percent of generation revenues in 

2014. Entities that experience negative net costs under the Transport Rule are excluded from 

these totals. These results do not fully account for the reality that about 70 percent of these 

entities operate in cost of service markets and thus should be able to recover all of their costs 

of complying with the Transport Rule. In EPA’s modeling, most of the cost impacts for 

these small entities and their associated units are driven by lower electricity generation 

relative to the base case. Specifically, two units reduce their generation by significant 

amounts, driving the bulk of the costs for all small entities. Excluding these two units, 

another driver of small entity impacts for sub-divisions and private small entities is higher 

fuel costs, which the affected units would be expected to use irrespective of whether they had 

to comply with this rule. Increased fuel costs are often passed through to rate-payers as 

common practice in many areas of the U.S. due to fuel adder arrangements instituted by state 

public utility commissions. Finally, EPA’s decision to exclude units smaller than 25 MW has 

already significantly reduced the burden on potentially small entities by nearly 390. Hence, 

EPA has concluded that there is no significant economic impact on a substantial number of 

small entities (No SISNOSE) for this rule. The number of entities with compliance costs 

exceeding 3 percent of generation revenues is also included in Table 9-2. 

The distribution across entities of economic impacts as a share of base case revenue is 

summarized in Table 9-3. Although the distributions of economic impacts on each 

ownership type are in general fairly tight, there are a few outliers for which the percentage of 

economic impacts as a share of revenue is either very low or very high relative to the 

capacity-weighted average. In the cases where entities are projected to experience negative 

net impacts that are a high percentage of revenues, these entities have units that are able to 

increase generation with the Transport Rule or sell excess allowances, thus increasing 

revenues. In the cases where entities are projected to experience positive net impacts that are 

a high percentage of revenues, these entities do not find it economic to retrofit and are unable 

to switch to a lower sulfur coal. Thus, another reason for entities incurring impacts is that 
308

they are expected to reduce their generation under the Transport Rule which reduces 

revenues collected from electricity sales and inflates net costs. 

Table 9-3. Summary of Distribution of Economic Impacts of the Transport Rule on 

Small Entities in 2014

EGU Ownership 

Type

Capacity-Weighted 

Average Economic 

Impacts as a % of 

Generation Revenues

Min Max

Cooperative -0.1% -13.1% 47.8%

Investor-owned utility -2% -13.6% -0.4%

Municipal -0.7% -78.1% 26.0%

Subdivision -0.4% -13.2% 6.7%

Private -3% -2.0% 0.2%

All -1.0% -78.1% 47.8%

Source: IPM analysis
309

The separate components of annualized costs to small entities under the Transport 

Rule are summarized in Table 9-4. The most significant components of incremental cost to 

these entities under the Transport Rule are due to lower electricity revenues as some EGUs 

reduce their generation under the policy case in IPM. This effect is most prominent for 

Cooperatives in particular, as two coal units reduce a significant portion of their generation. 

As discussed above, excluding these two units from the sample, leads to an overall cost 

savings for the rest of the Co-ops. Allowances purchases also account for a portion of the 

total costs to small entities. All ownership types, with the exception of municipals, are net 

purchasers of allowances. Additionally, fuel costs and operating costs appear to decrease for 

cooperatives, but this is mostly due to one large unit significantly cutting its generation, thus 

translating to lower variable costs for the whole group. Across all other ownership types, 

with the exception of municipals, electricity revenues actually increase. This is due largely to 

the projected increase in electricity prices under the Transport Rule. 

Table 9-4. Incremental Annualized Costs under the Transport Rule Summarized by 

Ownership Group and Cost Category in 2014 ($2007 millions)

EGU 

Ownership 

Type

Retrofit + 

Operating 

Cost

Net Purchase 

of Allowances Fuel Cost

Lost 

Electricity 

Revenue

Administrative 

Cost

Cooperative -$11.3 $4.9 -$50.7 -$109.8 $0.2

InvestorOwned Utility $0.2 $1.0 -$0.6 $10.6 $0.0

Municipal $4.1 -$3.4 -$0.8 -$3.0 $0.1

Subdivision $0.2 $0.6 $3.1 $6.0 $0.0

Private $0.3 $3.2 $7.2 $16.5 $0.1

TOTAL -$6.5 $6.3 -$41.8 -$79.6 $0.5

Source: IPM analysis.

Furthermore, 601 MW of total small entity capacity, or about 1 percent of total small 

entity capacity in the Transport Rule region, is projected to be uneconomic to maintain under 

the Transport Rule relative to the base case. To put these numbers in context, of all affected 

capacity under the Transport Rule, about 4.8 GW (1.6 percent) of coal-fired capacity is 
310

projected to be uneconomic to maintain relative to the base case. This comparison suggests 

that small entities should not be disproportionately affected by the Transport Rule. In 

practice, units projected to be uneconomic to maintain may be mothballed, retired, or kept in 

service to ensure transmission reliability in certain parts of the grid. Our IPM modeling is 

unable to distinguish between these potential outcomes. 

9.1.3 Summary of Small Entity Impacts

EPA examined the potential economic impacts to small entities associated with this 

rulemaking based on assumptions of how the affected states will implement control measures 

to meet their emissions. To summarize, of the 108 small entities potentially affected, and the 

610 small entities in the Transport Rule region that are included in EPA’s modeling, 25 may

experience compliance costs in excess of 1 percent of revenues in 2014, based on 

assumptions of how the affected states implement control measures to meet their emissions 

budgets as set forth in this rulemaking. Potentially affected small entities experiencing 

compliance costs in excess of 1 percent of revenues have some potential for significant 

impact resulting from implementation of the Transport Rule. However, as noted above, it is 

EPA’s position that because very few of the affected entities currently operate in a 

competitive market environment, they should generally be able to pass the costs of 

complying with the Transport Rule on to rate-payers. Furthermore, the decision to include 

only units greater than 25 MW in size exempts around 390 potentially small entities that 

would otherwise be affected by the Transport Rule. 

9.2 Unfunded Mandates Reform Act (UMRA) Analysis

Title II of the UMRA of 1995 (Public Law 104-4)(UMRA) establishes requirements 

for federal agencies to assess the effects of their regulatory actions on state, local, and Tribal 

governments and the private sector. Under Section 202 of the UMRA, 2 U.S.C. 1532, EPA 

generally must prepare a written statement, including a cost-benefit analysis, for any

proposed or final rule that includes any Federal mandate that may result in the expenditure by

State, local, and Tribal governments, in the aggregate, or by the private sector, of 

$100,000,000 or more ... in any one year. A Federal mandate is defined under Section 

421(6), 2 U.S.C. 658(6), to include a Federal intergovernmental mandate and a Federal 

private sector mandate. A Federal intergovernmental mandate, in turn, is defined to include a 
311

regulation that would impose an enforceable duty upon State, Local, or Tribal governments, 

Section 421(5)(A)(i), 2 U.S.C. 658(5)(A)(i), except for, among other things, a duty that is a 

condition of Federal assistance, Section 421(5)(A)(i)(I). A Federal private sector mandate 

includes a regulation that would impose an enforceable duty upon the private sector, with 

certain exceptions, Section 421(7)(A), 2 U.S.C. 658(7)(A).

Before promulgating an EPA rule for which a written statement is needed under 

Section 202 of the UMRA, Section 205, 2 U.S.C. 1535, of the UMRA generally requires 

EPA to identify and consider a reasonable number of regulatory alternatives and adopt the 

least costly, most cost-effective, or least burdensome alternative that achieves the objectives 

of the rule. EPA included descriptions of three remedy options that it considered when 

developing its proposed rule: (1) the proposed remedy of State Budgets/Limited Trading, (2) 

State Budgets/Intrastate Trading, and (3) Direct Controls. Moreover, section 205 allows EPA 

to adopt an alternative other than the least costly, most cost-effective or least burdensome 

alternative if the Administrator publishes with the final rule an explanation why that 

alternative was not adopted.

Furthermore, as EPA stated in the proposal and final rule, EPA is not directly

establishing any regulatory requirements that may significantly or uniquely affect small 

governments, including Tribal governments. Thus, under the Transport Rule, EPA is not 

obligated to develop under Section 203 of the UMRA a small government agency plan.

EPA analyzed the economic impacts of the Transport Rule on government entities 

and this section presents the results of that analysis. The analysis does not examine potential 

indirect economic impacts associated with the Transport Rule, such as employment effects in 

industries providing fuel and pollution control equipment, or the potential effects of 

electricity price increases on industries and households. Analyses of employment effects and 

the potential effects of electricity price increases on industries can be found in Chapter 8 of 

this RIA.

9.2.1 Identification of Government-Owned Entities

Using Ventyx data, EPA identified state- and municipality-owned utilities and 

subdivisions in the Transport Rule region. EPA then used IPM-parsed output to associate 
312

these plants with individual generating units. Entities that did not own at least one unit with 

a generating capacity of greater than 25 MW were omitted from the analysis because of their 

exemption from the rule. This exempts 354 entities owned by state or local governments. 

Additionally, government-owned entities for which IPM does not project generation in 2014 

under the base case or the Transport Rule were exempted from this analysis, because they are 

not projected to be operating and thus will not face the costs of compliance with the 

Transport Rule. Eleven additional entities were dropped from the analysis for this reason. 

EPA identified 98 state or municipality-owned utilities that are potentially affected by the 

Transport Rule, out of a possible 463. These results are summarized in Table 9-5. 

9.2.2 Overview of Analysis and Results

After identifying potentially affected government entities, EPA estimated the impact 

of the Transport Rule in 2014 based on the following:

 total impacts of compliance on government entities and

 ratio of government entity impacts to revenues from electricity generation.

The financial burden to owners of EGUs under the Transport Rule is composed of 

compliance and administrative costs. This section outlines the compliance and 

administrative costs for the 98 potentially affected government-owned units in the Transport 

Rule region. 

9.2.2.1 Methodology for Estimating Impacts of the Transport Rule on Government Entities

The primary burden on state and municipal governments that operate utilities under 

the Transport Rule is the cost of installing control technology on units to meet SO2 and NOx

emission limits or the cost of purchasing allowances. However, an entity can comply with 

the Transport Rule through any combination of the following: installing retrofit 

technologies, purchasing allowances, switching to a cleaner fuel, or reducing emissions 

through a reduction in generation. Additionally, units with more allowances than needed can 

sell these allowances on the market. The chosen compliance strategy will be primarily a 

function of the unit’s marginal control costs and its position relative to the marginal control 

costs of other units. 

To attempt to account for each potential control strategy, EPA estimates compliance 
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costs as follows:

CCompliance = Δ COperating+Retrofit + Δ CFuel + Δ CAllowances + Δ CTransaction + Δ R

where C represents a component of cost as labeled, and Δ R represents the retail value of 

foregone electricity generation. 

In reality, compliance choices and market conditions can combine such that an entity 

may actually experience a savings in any of the individual components of cost. Under the 

Transport Rule, for example, some units will forgo some level of electricity generation (and 

thus revenues) to comply. The impact of forgone generation revenues will be lessened by 

the projected increase in electricity prices under the Transport Rule; while those not reducing 

generation levels will see an increase in electricity revenues. Because this analysis evaluates 

the total costs along each of the four compliance strategies laid out above for each entity, it 

inevitably captures savings or gains such as those described. As a result, what we describe as 

cost is really more of a measure of the net economic impact of the rule on small entities.

In this analysis, EPA used unit-level IPM-parsed outputs for the base case and the 

Transport Rule to estimate compliance costs based on the parameters above. These costs 

were then aggregated for each small government entity, adjusting for ownership share. 

Compliance cost estimates were based on the following: operating and retrofit costs, sale or 

purchase of allowances, and the change in fuel costs or electricity generation revenues under 

the Transport Rule relative to the base case. These components of compliance cost were 

estimated as follows:

(1) Retrofit and operating costs: Using the IPM-parsed output for the base case and 

the Transport Rule, EPA identified units that install control technology under the 

Transport Rule and the technology installed. The equations for calculating retrofit 

costs for SCR, SNCR, and FGD were adopted from EPA’s version of IPM (version 

4.10). The model calculates the capital cost (in $/MW), the fixed O&M cost (in 

$/MW-year), the variable O&M cost (in $/MWh). 

(2) Sale or purchase of allowances: EPA estimated the value of initial SO2 and 

NOx annual and NOx ozone season allowance holdings. For both SO2 and NOx, the 

state emission budgets were assumed to be apportioned to units based on their share 

of the state's historic heat input (but not exceeding the unit's maximum historic 
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emissions). Using 2006-2010 data, the three highest, non- zero annual heat input 

values within the 5 year period are selected and averaged for each unit. EPA 

calculated each unit's SO2 and NOx annual and NOx ozone season allowance 

allocations as the ratio of that unit's historic heat input to the sum of all units' historic 

heat input in the applicable state, times the final state budget for existing units. Thus 

each unit's allocation is the unit's proportional share of the state budget, based on 

historic heat input. See Allowance Allocations in the Transport Rule Federal 

Implementation Plans TSD.

To estimate the value of allowances holdings, allocated allowances were subtracted 

from projected emissions, and the difference was then multiplied by the allowance 

price projected by IPM. Units were assumed to purchase or sell allowances to exactly

cover their projected emissions under the Transport Rule. 

(3) Fuel costs: Fuel costs were estimated by multiplying fuel input (MMBtu) by

region and fuel type-adjusted fuel prices ($/MMBtu) from IPM. The change in fuel 

expenditures under the Transport Rule was then estimated by taking the difference in 

fuel costs between the Transport Rule and the base case. 

(4) Value of electricity generated: EPA estimated electricity generation by first 

estimating the unit capacity factor and maximum fuel capacity. The unit capacity

factor is estimated by dividing fuel input (MMBtu) by maximum fuel capacity

(MMBtu). The maximum fuel capacity was estimated by multiplying capacity (MW) 

* 8,760 operating hours * heat rate (MMBtu/MWh). The value of electricity

generated was then estimated by multiplying capacity (MW) * capacity factor * 8,760 

* regional-adjusted retail electricity price ($/MWh). 

(5) Administrative costs: Because most affected units are already monitored as a 

result of other regulatory requirements, EPA considered the primary administrative 

cost to be transaction costs related to purchasing or selling allowances. EPA assumed 

that transaction costs were equal to 1.5 percent of the total absolute value of a unit’s 

allowances. This assumption is based on market research by ICF International. 
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9.2.2.2 Results

A summary of economic impacts on government-owned entities is presented in 

Table 9-5. According to EPA’s analysis, the total net economic impact on each category of 

government-owned entity (state- and municipality-owned utilities and subdivisions) is 

expected to be about $26 million in 2014.80 

 

80All costs are reported in 2007 dollars. 
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Table 9-5. Summary of Potential Impacts on Government Entities under the 

Transport Rule in 2014

EGU Ownership 

Type

Potentially 

Affected Entities

Projected 

Annualized 

Costs ($2007

millions)

Number of 

Government 

Entities with 

Compliance 

Costs >1% of 

Generation 

Revenues

Number of 

Government 

Entities with 

Compliance 

Costs >3% of 

Generation 

Revenues

Municipal 86 $15.5 25 12

State 6 $0.9 0 0

Subdivision 6 $9.5 1 0

Total 98 $25.9 26 12

Note: The total number of potentially affected entities in this table excludes the 365 entities that have been 

dropped because they will not be affected by the Transport Rule, either because they only own units less than 25 

MW (354 entities) or because they would not operate in either the base or the policy cases (the remaining 11 

entities). Also, the total number of entities with costs greater than 1 percent or 3 percent of revenues includes 

only entities experiencing positive costs. 

Source: IPM analysis

As was done for the small entities analysis, EPA further assessed the economic and 

financial impacts of the rule using the ratio of compliance costs to the value of revenues from 

electricity generation in the base case, also focusing specifically on entities for which this 

measure is greater than 1 percent. EPA projects that 26 government entities will have 

compliance costs greater than 1 percent of revenues from electricity generation in 2014. As 

was true with the small entity analysis, one main driver of cost increases for these 

government entities appear to be revenue losses due to lower electricity generation relative to 

the base case. As will be discussed below in more detail, while government entities do face 

higher costs under the Transport Rule than under the base case, these cost increases are 

actually lower than what EPA estimated under the proposed rule. Revenues from electricity

generation for these entities, however, go down more than what EPA estimated under the 

proposed Transport Rule, leading to a net cost increase for these entities. Entities that are 

projected to experience negative compliance costs under the Transport Rule are not included 

in those totals. This approach is more indicative of a significant impact when an analysis is 
317

looking at entities operating in a competitive market environment. Government-owned 

entities do not operate in a competitive market environment and therefore will be able to 

recover expenses under the Transport Rule through rate increases. Given this, EPA considers 

the 1 percent measure in this case a crude measure of the extent to which rate increases will 

be made at publicly owned companies. 

The distribution across entities of economic impacts as a share of base case revenue is 

summarized in Table 9-6. Although the distributions of economic impacts on each 

ownership type are in general fairly tight, there are a few outliers for which the percentage of 

economic impacts as a share of revenue is either very low or very high relative to the 

capacity-weighted average. This is especially true for municipality-owned entities where 

the maximum economic impact as a share of base case revenues is approximately 35 percent. 

Thus, a few municipality-owned entities experience economic impacts that are significantly 

higher than the capacity-weighted average for this group. Another reason for entities 

incurring impacts is that they are expected to reduce their generation under the Transport 

Rule which reduces revenues collected from electricity sales and inflates net costs. In the 

cases where entities are projected to experience negative net impacts that are a high 

percentage of revenues, these entities have units that are able to increase generation with the 

Transport Rule or sell excess allowances, thus increasing revenues.

Table 9-6. Distribution of Economic Impacts on Government Entities under the 

Transport Rule in 2014

EGU Ownership 

Type

Capacity-Weighted 

Average Economic 

Impacts as a % of 

Generation Revenues

Min Max

Municipal -0.00% -78.1% 34.5%

State 0.02% -2.6% 0.6%

Sub-division 0.65% -2.0% 1.3%

All -0.04% -78.1% 34.5%

Source: IPM analysis
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The separate components of annualized incremental cost under the Transport Rule to 

each group of government entities are summarized in Table 9-7. While all groups experience 

higher costs in general, there is a wide variation in these increases across groups. Municipal 

entities have the highest retrofit and operating costs as they dominate the universe of affected 

government entities. Municipal and state entities are net sellers of allowances, which lessens 

the net impact of the rule for these entities. Sub-divisions are net purchasers of allowances, 

though the higher electricity revenues for them negate those cost increases as a group. Both 

sub-divisions and state-owned entities benefit from higher electricity revenues, but they also 

incur higher fuel costs as they increase their electricity generation. In aggregate, cost 

increases across the three groups are about $29 million relative to the base case (compared to 

about $135 million projected in the proposal). As a group, these entities also gain about $3 

million in higher electricity revenues (compared to $150 million increased revenues 

projected in the proposal), resulting in a net increase of about $26 million in impacts under 

the final rule. 

Table 9-7. Incremental Annualized Costs under the Transport Rule 

Summarized by Ownership Group and Cost Category ($2007 millions) in 2014

EGU 

Ownership 

Type

Retrofit + 

Operating 

Cost

Net Purchase 

of Allowances Fuel Cost

Increased 

Electricity 

Revenue

Administrative 

Cost

Municipal $28. 8 -$6.9 -$21.6 -$14.4 $0.8

State $3.6 -$2.8 $7.6 $7.7 $0.1

Subdivision $0.2 $4.6 $14.5 $9.9 $0.1

Source: ICF International analysis based on IPM analysis

IPM modeling of the Transport Rule projects that approximately 76 MW (4 units) of 

municipality-owned capacity would be uneconomic to maintain under the Transport Rule, 

beyond what is projected in the base case. This represents about 0.4 percent of all 

subdivision, state, and municipality capacity in the Transport Rule region. For comparison, 

overall affected capacity under the Transport Rule, about 4.2 GW, or 1.6 percent of all 
319

coal-fired capacity is projected to be uneconomic to maintain relative to the base case. This 

comparison suggests that government entities should not face a disproportionate burden 

under the Transport Rule. In practice, units projected to be uneconomic to maintain may be 

“mothballed,” retired, or kept in service to ensure transmission reliability in certain parts of 

the grid. Our IPM modeling is unable to distinguish between these potential outcomes.

9.2.3 Summary of Government Entity Impacts

EPA examined the potential economic impacts on state and municipality-owned 

entities associated with this rulemaking based on assumptions of how the affected states will 

implement control measures to meet their emissions. According to EPA’s analysis, the total 

net economic impact on government-owned entities is expected to be about $26 million in 

2014. Of the 98 government entities considered in this analysis and the 463 government 

entities in the Transport Rule region that are included in EPA’s modeling, 26 may experience 

compliance costs in excess of 1 percent of revenues in 2014, based on our assumptions of 

how the affected states implement control measures to meet their emissions budgets as set 

forth in this rulemaking.

Government entities projected to experience compliance costs in excess of 1 percent 

of revenues have some potential for significant impact resulting from implementation of the 

Transport Rule. However, as noted above, it is EPA’s position that because these 

government entities can pass on their costs of compliance to rate-payers, they will not be 

significantly affected. Furthermore, the decision to include only units greater than 25 MW in 

size exempts 354 government entities that would otherwise be potentially affected by the 

Transport Rule. 

9.3 Paperwork Reduction Act

In compliance with the Paperwork Reduction Act (44 U.S.C. 3501 et seq.), EPA has 

submitted a proposed Information Collection Request (ICR) (EPA ICR number 2391.02) to 

the Office of Management and Budget (OMB) for review and approval. The ICR describes 

the nature of the information collection and its estimated burden and cost associated with the 

final rule, including estimates of the anticipated monitoring, reporting, and record-keeping 

burden and associated costs for states, local governments, and sources. In cases where 

information is already collected by a related program, the ICR takes into account only the 
320

additional burden. This situation arises in states that are also subject to requirements of the 

Consolidated Emissions Reporting Rule (EPA ICR number 0916.10; OMB control number 

2060-0088) or for sources that are subject to the Acid Rain Program (EPA ICR number 

1633.13; OMB control number 2060-0258), NOx SIP Call (EPA ICR number 1857.03; OMB 

control number 2060-0445), or CAIR (EPA ICR number 2152.04; OMB control number 

2060-0570) requirements.

The record-keeping and reporting burden to sources resulting from states choosing to 

participate in a regional cap-and-trade program is approximately $26 million annually. The 

total number of burden hours associated with the record-keeping and reporting burden to 

Transport Rule-affected sources, states, and EPA for states choosing to participate in a 

regional cap-and-trade program is approximately 185,000 hours annually. These estimates 

include the annualized cost of installing and operating appropriate SO2 and NOx emissions 

monitoring equipment to measure and report the total emissions of these pollutants from 

affected EGUs (serving generators greater than 25 megawatts capacity). The burden to state 

and local air agencies includes any necessary SIP revisions, performance of monitoring 

certification, and fulfilling of audit responsibilities.

EPA received no adverse comment on the ICR submitted for the proposed Transport 

Rule (EPA ICR number 2391.01). More information on the ICR analysis is included in the 

Transport Rule docket.

9.4 Protection of Children from Environmental Health and Safety Risks

Executive Order 13045 (62 FR 19885, April 23, 1997) applies to any rule that: (1) is 

determined to be “economically significant” as defined under EO 12866, and (2) concerns an 

environmental health or safety risk that EPA has reason to believe may have a 

disproportionate effect on children. If the regulatory action meets both criteria, the Agency

must evaluate the environmental health or safety effects of this planned rule on children, and 

explain why this planned regulation is preferable to other potentially effective and reasonably 

feasible alternatives considered by the Agency.

This rule is not subject to EO 13045 because it does not involve decisions on 

environmental health or safety risks that may disproportionately affect children. The EPA 

believes that the emission reductions from the strategies in this final rule will further improve 
321

air quality and will further improve children’s health. 

9.5 Executive Order 13132, Federalism

Under EO 13132, EPA may not issue an action that has federalism implications, that 

imposes substantial direct compliance costs, and that is not required by statute, unless the 

Federal government provides the funds necessary to pay the direct compliance costs incurred 

by State and local governments, or EPA consults with State and local officials early in the 

process of developing the proposed action.

EPA has concluded that this action does not have federalism implications. It will not

impose substantial direct effects on State or local governments, or on the relationship 

between the Federal government and the States, or on the distribution of power and 

responsibilities among the various levels of government, as specified in EO 13132. Thus, 

Executive Order 13132 does not apply to the final rule. 

In the spirit of EO 13132, and consistent with EPA policy to promote 

communications between EPA and State and local governments, EPA did provide 

information to State and local officials. EPA sent a letter to the ten Representative National 

Organizations to draw their attention to the Transport Rule Notice of Data Availability

(NODA) on allowance allocations and other related matters and to invite their comments. 

Following that letter in early 2011, EPA participated in informational calls with the 

Environmental Councils of the States (ECOS) and the National Governors Association to 

provide information about the NODA directly to State and local officials. There were no 

new concerns raised during these informational calls. 

9.6 Executive Order 13175, Consultation and Coordination with Indian Tribal 

Governments

Subject to EO 13175 (65 FR 67249, November 9, 2000) EPA may not issue a 

regulation that has Tribal implications, that imposes substantial direct compliance costs, and 

that is not required by statute, unless the Federal government provides the funds necessary to 

pay the direct compliance costs incurred by Tribal governments, or EPA consults with Tribal 

officials early in the process of developing the proposed regulation and develops a Tribal 

summary impact statement. EO 13175 requires EPA to develop an accountable process to 

ensure “meaningful and timely input by tribal officials in the development of regulatory
322

policies that have tribal implications.”

EPA has concluded that this action may have Tribal implications. However, it will 

neither impose substantial direct compliance costs on Tribal governments, nor preempt Tribal 

law. EPA consulted with tribal officials during the process of finalizing this regulation to 

permit them to have meaningful and timely input into its development.

EPA received comments on the proposed rule that the Agency did not properly

conduct consultation during the proposal phase of the rulemaking. In response to these 

comments, EPA sent a letter to all federally-recognized tribes in the country offering 

consultation. In addition, several comments also noted that the Agency did not adequately 

consider opportunities for tribes to enter into any of the trading programs and, in particular, 

did not consider sovereignty issues when addressing how to distribute allowances to potential 

new units in Indian country. On January 7, 2011, EPA issued a NODA requesting comment 

on allocations for new units in Indian country, among other topics. The Agency held a 

formal consultation call with three tribes on January 21, 2011. A follow up call was held on 

Feb. 4, 2011 with 11 tribes. EPA considered the additional input from these consultation 

and information calls, in conjunction with the public comments, in the development of the 

final rule. Accordingly, EPA created an Indian country new unit set-aside to specifically 

address tribes’s concerns regarding the protection of tribal sovereignty in the distribution of 

allowances for new units in Indian country. 

As required by section 7(a) of the Executive Order, EPA’s Tribal Consulation official 

has certified that the requirements of the Executive Order have been met in a meaningful and 

timely manner. A copy of the certification is included in the docket for this action.

9.7 Environmental Justice

 Executive Order (EO) 12898 (59 FR 7629 (Feb. 16, 1994)) establishes federal 

executive policy on environmental justice. Its main provision directs federal agencies, to the 

greatest extent practicable and permitted by law, to make environmental justice part of their 

mission by identifying and addressing, as appropriate, disproportionately high and adverse 

human health or environmental effects of their programs, policies, and activities on minority
323

populations and low-income populations in the United States. 

9.7.1. Consideration of Environmental Justice Issues in the Rule Development Process

During development of this final Transport Rule, EPA considered its impacts on lowincome, minority, and tribal communities in several ways. EPA’s assessment included a) the 

structure of the rule and responses to comments received on the proposed rule on issues 

specific to these communities; b) expected SO2 and NOX emission reductions; c) expected 

PM2.5 and ozone air quality improvements; d) expected health benefits, including asthma 

and other health effects of particular concern for environmental justice communities; and e) a 

quantitative assessment of the expected socioeconomic distribution of a key health benefit 

(reduction in premature mortality). This last analysis, the distributional analysis, estimated 

the PM2.5 mortality risks according to race, income, and educational attainment before and 

after implementation of the Transport Rule. A description of these analyses and the 

conclusions reached can be found in section XII. J of the preamble for this final Transport 

Rule.

Briefly, all of these analyses indicate large health and environmental benefits for 

susceptible and vulnerable populations as a result of implementing this Transport Rule. The 

final rule will provide significant health and environmental benefits to numerous lowincome, minority, and tribal individuals in both rural areas and inner cities in the region 

affected by this rule. This includes, among others, people with asthma, people with heart 

disease, and people living in ozone or fine particle (PM2.5) nonattainment areas. 

The distributional analysis indicates that the populations with the largest 

improvement (i.e. largest decline) in PM2.5 mortality risk as a result of the Transport Rule in 

2014 (compared to the base case in 2014) are people living in the highest-risk counties. 

Among these counties, the largest improvements are for people with less than high school or 

high school education. These reductions in risk within the highest-risk counties, as well as 

the reductions in risk within the other 95 percent of counties, are distributed among 

populations of different races fairly evenly. Therefore, there is no indication that people of 

particular race receive a greater benefit (or smaller benefit) than others. None of the analyses, 

including the distributional analysis, showed evidence of adverse effects for any
324

socioeconomic group. 

These analyses are based on the same data and modeling tools used for the other 

analyses of this Transport Rule. A detailed description of the underlying data and analytical 

techniques used can be found in Chapter 3 of this RIA (emissions), Chapter 4 (air quality), 

and Chapter 5 (benefits). The analysis of the human health benefits of the rule, including the 

socioeconomic distribution of mortality benefits among different populations, is described in 

Appendix A of this RIA.

9.7.2. Meaningful Public Participation

During the comment period for the proposed rule, EPA reached out specifically to 

environmental justice communities and organizations to notify them of the opportunity to 

provide comments on this rule and to solicit their comments on both the proposed rule and 

upcoming actions. EPA held public hearings on this rule and received an extensive number 

of comments in writing. EPA has responded to these comments as part of this final 

rulemaking.
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CHAPTER 10

COMPARISON OF BENEFITS AND COSTS

10.1 Comparison of Benefits and Costs

As shown in Chapter 8, the estimated social costs to implement the final Transport 

Rule, as described in this document, are approximately $0.81 billion in 2014 (2007 dollars). 

As shown in Chapter 5, the estimated benefits from implementation of the final Transport 

Rule are approximately $120 to 280 billion + $110 to 250 billion + B in 2014 (2007 dollars, 

based on a discount rate of 3 percent and 7 percent, respectively and rounded to three 

significant figures) EPA can thus calculate the net benefits of the final rule by subtracting the 

estimated social costs from the estimated benefits in 2014. The annual net benefits of the 

final Transport rule are approximately $120 to 280 + B billion using a 3 percent discount rate 

or $110 to 250 + B billion using a 7 percent discount rate. (B represents the sum of all 

unquantified benefits and disbenefits of the regulation.) Therefore, EPA expects that 

implementation of this rule, based solely on economic efficiency criteria, will provide society

with a significant net gain in social welfare, notwithstanding the expansive set of health and 

environmental effects we were unable to quantify. Further quantification of directly emitted 

PM2.5-, mercury-, acidification-, and eutrophication-related impacts would increase the 

estimated net benefits of the rule. Table 10-1 presents a summary of the benefits, costs, and 

net benefits of the final rule. 

As explained in Chapter 5, EPA presents two types of probabilistic approaches to 

characterize uncertainty in the benefit estimates for the Transport Rule. The first approach 

generates a distribution of benefits based on the classical statistical error expressed in the 

underlying health and economic valuation studies used in the benefits modeling framework. 

The second approach uses the results from a pilot expert elicitation project designed to 

characterize key aspects of uncertainty in the ambient PM2.5/mortality relationship, and 

augments the uncertainties in the mortality estimate with the statistical error reported for 

other endpoints in the benefit analysis. 
326

EPA also analyzed the costs and benefits of two alternative scenarios that impose 

relatively more stringent and relatively less stringent SO2 budgets in Group 1 states, 

compared to the final Transport Rule, beginning in 2014, in accordance with Circular A-4 

Guidance (OMB, 2003). These scenarios are illustrative of the cost and benefit impacts of 

varying program stringency. They are designed to show the effects of more stringent and 

less stringent SO2 reduction requirements in a regulatory structure that is otherwise the same 

as the remedy.

Both of the alternative scenarios represent an interstate trading program to achieve 

emission reductions, as in the final Transport rule remedy. Both alternative scenarios 

impose the same NOx and SO2 emission reduction requirements in 2012 as imposed by the 

final Transport Rule. The options vary in stringency for the SO2 budgets in Group 1 states 

that exist in the final rule’s remedy, which are modeled on emission reductions in 2014 

available for those states up to a marginal cost of $2,300 per ton.81 Beginning in 2014, the 

less stringent alternative scenario imposes higher SO2 budgets in Group 1 states based on 

modeled emissions from the $1,600 per ton cost threshold analysis described in preamble 

section VI.B. The more stringent alternative scenario reflects 2014 reduction requirements 

based on modeled emissions at the $10,000 per ton cost threshold. 

Air quality modeling was not conducted for these two alternatives. EPA applied a 

benefit- per-ton approach appropriate for deriving PM-related benefits for these alternative 

scenarios from the full benefit estimation methodology conducted for the final Transport 

Rule’s remedy, as described in Chapter 5. The alternative scenarios’ social costs are 

estimated using the Multimarket Model while using the private compliance costs are 

projected using IPM. Table 10-2 presents the emissions forecast for SO2 and NOx in 2014 

under the Transport Rule and the more or less stringent scenarios. Table 10-3 presents the 

estimated social costs and health benefits, including net social benefit, of the two alternative 

scenarios alongside corresponding estimates for the final Transport Rule remedy.

 

81 The Group 1 states are Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Missouri, New Jersey, New 

York, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia, and Wisconsin.
327

Table 10-1. Summary of Annual Benefits, Costs, and Net Benefits of the Final 

Transport Rule in 2014a(billions of 2007 dollars)

Description Preferred Remedy

Social costsb

3 percent discount rate $0.81

7 percent discount rate $0.81

Social benefitsc,d,e

3 percent discount rate

7 percent discount rate

$120 to $280 + B

$110 to $250 + B

Health-related benefits:

3 percent discount rate

7 percent discount rate

$110 to $270 + B

$100 to $250 + B

Visibility benefits $4.1 

Net benefits (benefits-costs)

e,f

3 percent discount rate

7 percent discount rate

$120 to $280

$110 to $250

a Estimated rounded to two significant figures. 

b Note that costs are the annualized total social costs of reducing pollutants including NOx and SO2 for the EGU 

source category in the final Transport Rule region in 2014. Social costs are estimated using the MultiMarket 

model, the model employed by EPA in this RIA to estimate economic impacts to industries outside the 

electric power sector. This model does not estimate indirect impacts associated with a regulation such as this 

one. More information on the social costs can be found in Chapter 8 and Appendix B of this RIA.

c Total benefits are comprised primarily of monetized PM-related health benefits. The reduction in premature 

fatalities each year accounts for over 90 percent of total monetized benefits. Benefits in this table are 

nationwide (with the exception of ozone and visibility) and are associated with NOx and SO2 reductions. 

Ozone benefits represent benefits nationwide. Visibility benefits represent benefits in Class I areas in the 

southeast, southwest and California. The estimate of social benefits also includes CO2-related benefits 

calculated using the social cost of carbon, discussed further in Chapter 5.

d Not all possible benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all 

unquantified benefits and disbenefits. Potential benefit categories that have not been quantified and 

monetized are listed in Table 1-4.

e Valuation assumes discounting over the SAB-recommended 20-year segmented lag structure. Results reflect 

the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing 

economic analyses (EPA, 2010; OMB, 2003).

f Net benefits are rounded to three significant figures. Columnar totals may not sum due to rounding.
328

Table 10-2. Projected 2014 National Emissions of SO2 and NOX with the Base Case (No 

Further Controls) and with Transport Rule (Million Tons) and for More and Less 

Stringent Scenarios.

Pollutant Base Case Selected Remedy Less Stringent 

Scenario

More Stringent 

Scenario

SO2 (Annual) 7.2 3.4 3.5 2.4

NOX (Annual) 2.1 1.9 1.8 1.8

NOX (Ozone 

Season)

0.9 0.8 0.8 0.8

Table 10-3. Summary of PM Annual Health Benefits, Costs, and Net Benefits of 

Versions of the Selected Remedy Option in 2014a(billions of 2007 dollars)

Description Selected Remedy Less Stringent 

Scenario

More Stringent 

Scenario

Social costsb

3 percent discount rate $0.81 $0.43 $3.6

7 percent discount rate $0.81 $0.43 $3.6

Health PM benefitsc,d

3 percent discount rate

7 percent discount rate

$110 to $270 + B

$100 to $250 + B

$98 to $240 + B

$89 to $220 + B

$130 to $320 + B

$120 to $290 + B

Net benefits (benefits-costs)

e,f

3 percent discount rate

7 percent discount rate

$110 to $270 + B

$100 to $250 + B

$98 to $240 + B

$88 to $220 + B

$130 to $320 + B

$120 to $290 + B

aEstimates rounded to two significant figures.

b Note that costs are the annualized total social costs of reducing pollutants including NOx and SO2 for the EGU 

source category in the final Transport Rule region in 2014. Social costs are estimated using the MultiMarket 

model, the model employed by EPA in this RIA to estimate economic impacts to industries outside the 

electric power sector. This model does not estimate indirect impacts associated with a regulation such as this 

one. More information on the social costs can be found in Chapter 8 and Appendix B of this RIA.

c Due to methodological limitations, the health benefits of these remedy options include PM2.5 –related benefits 

but omit visibility, ozone, and CO2-related benefits. We present the PM2.5 –related benefits of the preferred 
329

remedy, omitting these other important benefits, so that readers may compare directly the benefits of the 

preferred and alternate remedies. Total benefits are comprised primarily of the value of PM-related avoided 

premature mortalities. The reduction in these premature mortalities in each year account for over 90 percent 

of total PM2.5-related monetized benefits. Benefits in this table are nationwide and are associated with NOx

and SO2 reductions. To ensure that the benefits of the selected remedy and the more and less stringent 

scenarios are directly comparable, we exclude the visibility-related benefits of the selected remedy from this 

table; these visibility-related benefits are approximately $ 4.1 billion (2007$). 

d Not all possible benefits or disbenefits are quantified and monetized in this analysis. B is the sum of all 

unquantified benefits and disbenefits. Potential benefit categories that have not been quantified and 

monetized are listed in Table 1-5.

e Valuation assumes discounting over the SAB-recommended 20-year segmented lag structure. Results reflect 

the use of 3 percent and 7 percent discount rates consistent with EPA and OMB guidelines for preparing 

economic analyses (EPA, 2010; OMB, 2003).

f Net benefits are rounded to three significant figures. Columnar totals may not sum due to rounding.

* The 2014 compliance costs (incremental to the base case) for the selected remedy, less stringent scenario, 

and more stringent scenario are approximately $0.8, $0.4 and $3.6 billion in 2007 dollars.
330

10.2 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. 

U.S. Environmental Protection Agency (EPA). December 2010. Guidelines for Preparing 

Economic Analyses. EPA 240-R-10-001. 

U.S. Office of Management and Budget (OMB). 2003. Circular A-4 Guidance to Federal 

Agencies on Preparation of Regulatory Analysis.

Woodruff, T.J., J. Grillo, and K.C. Schoendorf. 1997. “The Relationship Between Selected 

Causes of Postneonatal Infant Mortality and Particulate Infant Mortality and Particulate 

Air Pollution in the United States.” Environmental Health Perspectives 105(6):608-612.
331

Appendix A 

Distribution of the PM2.5-Related Benefits Among Vulnerable and Susceptible Populations

Characterizing the distribution of health impacts across the population

This analysis aims to answer two principal questions regarding the distribution of PM2.5-related 

benefits resulting from the implementation of the final Transport Rule:

1. What is the baseline distribution of PM2.5-related mortality risk according to the race, income and 

education of the population?

2. How does the Transport Rule change the distribution of risk among populations of different 

races—particularly among those populations at greatest risk in the baseline?82

In this analysis we find that the level of PM2.5 mortality risk is not distributed equally throughout 

the U.S., or among populations of different levels of educational attainment—though the level of 

mortality risk appears to be shared fairly equally among populations of different races. We find 

that the Transport Rule provides air quality improvements, and lowers PM2.5-related mortality

risk, fairly equally among minority populations and greatly reduces PM2.5 mortality risk among 

those populations at greatest risk in the baseline.

Methodology

As a first step, we estimate the level of PM2.5-related mortality risk in each county in the 

continental U.S. based on 2005 air quality levels, which provides a baseline against which 

projected changes in PM2.5 risk attributable to the Transport rule may be compared. This portion 

of the analysis follows an approach described elsewhere (Fann et al. 2011a, Fann et al. 2011b), 

wherein modeled 2005 PM2.5 levels are used to calculate the proportion of all-cause mortality

risk attributable to total PM2.5 levels in each county in the Continental U.S. Within each county 

we estimate the level of all-cause PM2.5 mortality risks for adult populations as well as the level 

of PM2.5 mortality risk according to the race, income and educational attainment of the 

population. 

 

82 In this analysis we assess the change in risk among populations of different race, income and 

educational attainment. As we discuss further in the methodology, we consider this last variable 

because of the availability of education-modified PM2.5 mortality risk estimates.
332

Our approach to calculating PM2.5 mortality risk is generally consistent with the primary analysis 

with the two exceptions: the PM mortality risk coefficients used to quantify impacts and the 

baseline mortality rates used to calculate mortality impacts (a detailed discussion of how both the 

mortality risk coefficients and baseline incidence rates are used to estimate the incidence of 

PM2.5-related deaths may be found in the benefits chapter). We substitute risk estimates drawn 

from the Krewski et al. (2009) extended analysis of the ACS cohort. In particular, we applied the 

all-cause mortality risk estimate random effects Cox model that controls for 44 individual and 7 

ecological covariates, using average exposure levels for 1999-2000 over 116 U.S. cities 

(Krewski et al. 2009) (RR=1.06, 95% confidence intervals 1.04—1.08 per 10μg/m3increase in 

PM2.5. This mean relative risk estimate is identical to the Pope et al. (2002) risk estimate applied 

for the primary analysis (thought the standard error around the mean RR estimate is slightly

different).

Within both this and other analyses of the ACS cohort (see: Krewski et al. 2000), 

educational attainment has been found to be inversely related to the risk of all-cause mortality. 

That is, populations with lower levels of education (in particular, < grade 12) are more 

vulnerable to PM2.5-related mortality. Krewski and colleagues note that “...the level of education 

attainment may likely indicate the effects of complex and multifactorial socioeconomic 

processes on mortality...”, factors that we would like to account for in this EJ assessment. When 

estimating PM mortality impacts among populations according to level of education, we applied 

PM2.5 mortality risk coefficients modified by educational attainment: less than grade 12 (RR = 

1.082, 95% confidence intervals 1.024—1.144 per 10 μg/m3change), grade 12 (RR = 1.072, 

95% confidence intervals 1.020—1.127 per 10 μg/m3change), and greater than grade 12 (RR = 

1.055, 95% confidence intervals 1.018—1.094 per 10 μg/m3change). The Pope et al. (2002) 

study does not provide education-stratified RR estimates. The principal reason we applied risk 

estimates from the Krewski study was to ensure that the risk coefficients used to estimate allcause mortality risk and education-modified mortality risk were drawn from a consistent 

modeling framework. 

The other key difference between this distributional analysis and the primary analysis 

relates to the baseline mortality rates. As described in the benefits chapter, we calculate PMrelated mortality risk relative to baseline mortality rates in each county. Traditionally we have 

applied county-level age- and sex-stratified rates when calculating these impacts (Abt, 2008). For 

the calculation of PM impacts by race, we incorporated race-specific (stratified by

White/Black/Asian/Native American) baseline mortality rates. This approach improves our 
333

ability to characterize the relationship between race and susceptibility to PM2.5-related mortality.

The result of this analysis is a distribution of PM2.5 mortality risk estimates by county, 

stratified by each of the three population variables (race, income and educational attainment). 

We next identified the counties at or above the median and upper 95th percentile of the PM2.5

mortality risk distribution. We selected this percentile cut-off to capture the very highest levels 

of PM2.5 mortality risk. The second step of the analysis was to repeat the sequence above by 

estimating PM2.5 mortality risk in 2014 prior to, and after, the implementation of the Transport 

Rule. We also report the per-person PM2.5 exposure for each of the three scenarios (2005, 2014, 

2014 post-Transport Rule) by populations of different race. 

Results

The level of PM2.5 exposure and mortality risk among all populations declines 

significantly between 2005, 2014 prior to, and then after, the implementation of the Transport 

Rule (Table 1, Figures 1—3). In each figure we outline in yellow those counties at or above the 

2005 median risk level. The number of counties at or above this level falls significantly between 

2005 and the implementation of the 2014 Transport Rule, suggesting that the combination of this 

rule as well as others being implemented between 2005 and 2014 greatly reduce the level of PM 

mortality risk among adult populations. 

We next stratify the PM mortality risk according to race, income and educational 

attainment. For these analyses we estimated the change in PM mortality risk between 2005 and 

2014 among populations living in those counties at the upper 95th percentile of the mortality risk 

in the 2005 baseline; we then compared the change in risk among these populations living in 

high-risk counties with populations living in all other counties. Figures 4—6 summarize these 

results. Tables 2-4 enumerate the data used to construct these figures.

In Figure 4, we plot the level of PM mortality risk among populations of different races 

according to whether those populations live in counties identified as “greater risk” counties or 

“all other counties.” As described above, we define “greater risk” counties as those at or above 

the 95th percentile of PM2.5 mortality risk in 2005, and “all other counties” as those with PM2.5

mortality risk below this level. The results of this analysis suggest that the PM2.5 mortality risk 

among these populations at “greater risk” falls significantly between 2005, 2014 and the 

implementation of the 2014 Transport Rule. Upon implementing the 2014 Transport Rule, the 

estimated PM2.5 mortality risk among these populations is roughly equal to the level of PM2.5
334

mortality risk experienced by those populations in “all other counties” in 2005. These results also 

suggest that all populations, irrespective of race, are receiving a reduction in PM2.5 mortality risk; 

limits to data resolution prevent us from delineating the PM mortality risk according to 

population race with confidence. 

In Figure 5, we illustrate the change in the level of PM2.5 mortality risk among 

populations living in those counties that meet two criteria: (1) the county is at the upper 95th

percentile of mortality risk in 2005; (2) the county is at the upper 95th percentile in terms of the 

number of individuals living below the poverty line. We also estimate the change in PM2.5 risk 

among all other counties. The analysis indicates that people living in the highest risk and poorest 

counties will experience a larger improvement in PM2.5 mortality risk than those living in lower 

risk counties containing a smaller number of individuals living below the poverty line..

In Figure 6, we summarize the change in PM2.5 mortality risk among populations who 

have attained three alternate levels of education—less than highschool, highschool and greater 

than highschool. As described above, we apply education-stratified PM2.5 mortality risk 

coefficients for this analysis. These results indicate that populations with less than a highschool 

education are at higher risk of PM2.5 mortality in 2005, irrespective of whether these populations 

live in “greater risk” counties, according to the definition described above. Between 2005 and the 

2014 Transport Rule, all populations see their mortality risk fall, regardless of educational 

attainment. 
335

Figure 1: Distribution of PM2.5 mortality risk in 2005

Percent of total deaths due to PM2.5

2005

0.85% to 2.6%

2.7% to 3.9%

4% to 5.1%

5.2% to 6.1%

6.2% to 9%

Counties at or above the 2005 median risk level
336

Figure 2: Distribution of PM2.5 mortality risk in 2014 (prior to the implementation of the 

Transport Rule)

Percent of total deaths due to PM2.5

2014

0.6% to 2.6%

2.7% to 3.9%

4% to 5.1%

5.2% to 6.1%

6.2% to 9%

Counties at or above the 2005 median risk level
337

Figure 3: Distribution of PM2.5 mortality risk in 2014 (after the implementation of the 

Transport Rule)

Table 1: Per-person level of annual mean PM2.5 exposure (μg/m3/person) between 2005 and 

the 2014 Transport Rule 

Year

Race

Asian Black Native 

American White

2005 11.5 12.1 9 10.7

2014 (pre-Transport Rule) 9.4 9.6 7.7 8.7

2014 (post-Transport Rule 8.9 8.5 7.1 7.8

Percent of total deaths due to PM2.5

2014 Transport Rule

0.6% to 2.6%

2.7% to 3.9%

4% to 5.1%

5.2% to 6.1%

6.2% to 9%

Counties at or above the 2005 median risk level
Figure 4: Change in the percentage of 

the implementation of the 2014 Transport Rule

Table 2: Numerical values used for Figure 4 above

Year

Among populations at greater 

risk

2005

2014 (pre-Transport Rule)

2014 (post-Transport Rule

Among all other populations

2005

2014 (pre-Transport Rule)

2014 (post-Transport Rule

338

Figure 4: Change in the percentage of PM2.5-attributable deaths by race between 2005 and 

the implementation of the 2014 Transport Rule

Table 2: Numerical values used for Figure 4 above

Race

Asian Black Native 

American

7.9% 7.8% 7.9%

6.2% 5.9% 6.1%

5.1% 5% 5.1%

4.9% 5.0% 4.5%

4.2% 4.2% 4.2%

3.5% 3.6% 3.4%

attributable deaths by race between 2005 and 

White

8.0%

6.2%

5.2%

5.2%

4.2%

3.8%
Figure 5: Change in the percentage of PM

poverty level between 2005 and the implementation of the 2014 Transport Rule

Table 3: Numerical values used for Figure 5 above

Year

Among populations living in 

counties with the largest number of 

individuals living below the poverty 

line

2005

2014 (pre-Transport Rule)

2014 (post-Transport Rule

Among all other populations

2005

2014 (pre-Transport Rule)

2014 (post-Transport Rule

339

Figure 5: Change in the percentage of PM2.5-attributable deaths among populations by 

poverty level between 2005 and the implementation of the 2014 Transport Rule

Table 3: Numerical values used for Figure 5 above

Race

Asian Black Native 

American

counties with the largest number of 

individuals living below the poverty 

7.9% 7.8% 7.9%

6.1% 6.1% 6.2%

5.3% 5.3% 5.4%

4.9% 5% 4.6%

4.1% 4.2% 3.9%

3.5% 3.6% 3.4%

attributable deaths among populations by 

poverty level between 2005 and the implementation of the 2014 Transport Rule

White

8%

6.3%

5.4%

5.3%

4.4%

3.8%
Figure 6: Change in the percentage of PM

educational attainment between 2005 and the implementation of the 2014 Transport Rule

Table 4: Numerical values used for Figure 6 above

Year

Among populations at greater risk

2005

2014 (pre-Transport Rule)

2014 (post-Transport Rule

Among all other populations

2005

2014 (pre-Transport Rule)

2014 (post-Transport Rule

340

Figure 6: Change in the percentage of PM2.5-attributable deaths among populations by 

attainment between 2005 and the implementation of the 2014 Transport Rule

Table 4: Numerical values used for Figure 6 above

Race

< Grade 12 = Grade 

12 > Grade 12

Among populations at greater risk

11.0% 9.1%

Rule) 8.4% 7.4%

Transport Rule 7.1% 6.3%

Among all other populations

7.2% 6.4%

Transport Rule) 6% 5.3%

Transport Rule 5.2% 4.6%

attributable deaths among populations by 

attainment between 2005 and the implementation of the 2014 Transport Rule

> Grade 12

7.5%

5.8%

4.9%

4.9%

4.1%

3.6%
341

References

Fann N, Roman HR, Fulcher CF, Gentile M, Wesson K, Hubbell BJ, Levy JI. Maximizing 

Health Benefits and Minimizing Inequality: Incorporating Local Scale Data in the Design and 

Evaluation of Air Quality Policies. Risk Analysis; 2011a. In Press.

Fann N, Lamson A, Wesson K, Risley D, Anenberg SC, Hubbell BJ. Estimating the National 

Public Health Burden Associated with Exposure to Ambient PM2.5 and Ozone. Risk Analysis; 

2011b. In Press. 

Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi Y, Turner C, Pope CA, Thurston G, 

Calle EE, Thunt MJ. Extended follow-up and spatial analysis of the American Cancer Society 

study linking particulate air pollution and mortality. HEI Research Report, 140, 2009; Health 

Effects Institute, Boston, MA.

Krewski D, Burnett RT, Goldberg MS, et al. Reanalysis of the Harvard Six Cities Study and the 

American Cancer Society Study of Particulate Air Pollution and Mortality: Special 

Report. Cambridge, Mass: Health Effects Institute; 2000.

Pope CA, Burnett RT, Thun MJ, Calle EE, Krewski D, Ito K, Thurston GD. Lung Cancer, 

Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution. Journal 

of the American Medical Association, 2002; 287: 1132—1141. 
342

APPENDIX B

OAQPS MULTIMARKET MODEL TO ASSESS THE ECONOMIC 

IMPACTS OF ENVIRONMENTAL REGULATION

B.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, 

2010). In addition, large-scale policies that may affect a large number of industries or a 

substantial part of the whole economy 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, 2010, p. 9-21). 

Other guides for environmental economists offer similar advice (Berck and Hoffmann, 2002; 

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). Recent studies suggest short-run analyses can complement full 

dynamic general equilibrium analysis.

The multimarket model described in this appendix is a relatively 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. It was used to 

analyze the economic impacts of the industrial boiler NESHAP and CISWI final rules recently 

signed by EPA. Next, we provide an overview of the model, data, and parameters.

B.2 Multimarket Model

The multimarket model contains the following features:

Industry sectors and benchmark data set

100 industry sectors

a single benchmark year (2015)83

 

83 As mentioned in Chapter 8, we use 2015 as a proxy for 2014 in order to maintain consistency between the analysis 

from this model and the IPM outputs (generated for 2015, as mentioned in Chapter 7) that serve as inputs to this 

analysis.
343

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 from IMPLAN as used in OAQPS’s 

computable general equilibrium (CGE) model, the Economic Model for Policy

Analysis (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

investment and government expenditures are fixed.

Labor and Capital Markets and Pre-existing Distortions in Other Markets

Unlike CGE models, our multimarket model does not include a national labor or 

capital market. As a result, we do not estimate real wage changes, changes in 

labor /leisure choices, or savings and investment decisions within the model. 

Therefore we do not consider whether policies interact with existing distortions, 

particularly tax distortions in a ways that increase or decrease estimates of the 
344

social cost. Since savings and investment decisions are not modeled, social costs 

associated with capital stock changes are also not considered.

B.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 (E.1)

If we abstract and treat each industry as a national market, the identity represents the 

prepolicy (baseline) market-clearing condition, or benchmark “equilibrium”; supply equals 

demand in each market. In Table B-1, we identify the 100 industries for the multimarket model; 

Table B-2 provides the 2015 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).
345

Table B-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)
346

Table B-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)
347

Table B-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.
348

Table B-2. 2015 Benchmark Data Set (billion 2007$)

Industry 

Label Industry Description Output Imports Consumption

Investment 

and 

Government Exports

ACM Accommodations $940 $7 $919 $20 $8 

ADM Administrative 

Services

$923 $39 $885 $72 $5 

AGR Agricultural $349 $71 $390 $6 $25 

ALU Aluminum $81 $21 $88 $4 $10 

ANM Animal Foods $50 $41 Less than $1 $10 

APP Appliances $34 $26 $48 $8 $4 

ARC Aircraft $257 $57 $68 $116 $129 

ART Arts $286 $276 $3 $7 

ATP Air Transportation $174 $34 $98 $30 $80 

BAK Baked Goods $68 $4 $69 $3 Less than $1

BEV Beverages and 

Tobacco

$157 $62 $217 $1 $1 

BOI Boilers and Tanks $35 $3 $22 $10 $5 

CEM Cement $74 $68 $4 $3 

CEQ Construction and 

Agricultural 

Equipment

$95 $31 $61 $42 $23 

CHM Chemicals and Gases $355 $124 $409 $12 $58 

CLY Clay $12 $6 $14 $1 $3 

CMQ Communication 

Equipment

$96 $43 $60 $57 $23 

CNS Construction $1,393 $107 $816 $684 Less than $1

COL Coal $48 $2 $46 $4 

CPP Converted Paper 

Products

$60 $2 $48 $7 $7 

CPU Computers $193 $85 $171 $52 $54 

CRU Crude Oil Extraction $75 $213 $289 

CUT Cutlery $13 $6 $11 $6 $3 

EDU Education $1,122 $296 $810 $15 

EEQ Engines $46 $18 $37 $8 $20 

EGV Engraving $26 $11 $6 $9 

ELE Electric Generation $339 Less than $1 $304 $35 Less than $1

ELQ Electric Equipment $46 $21 $31 $22 $14 

FIN Finance and 

Insurance

$2,345 $157 $2,308 $51 $144 

FMP Fabricated Metals $85 $4 $77 $9 $3 

FRG Forging and 

Stamping

$25 Less than $1 $22 $1 $2 

FRT Fertilizer $53 $6 $40 $5 $14 

FRU Fruits and Vegetables $82 $14 $85 $5 $6 

FUR Furniture $78 $42 $104 $14 $2 

(continued)
349

Table B-2. 2015 Benchmark Data Set (billion 2007$) (continued)

Industry 

Label Industry Description Output Imports Consumption

Investment 

and 

Government Exports

GAS Natural Gas $150 $34 $170 $7 $7 

GEQ General Equipment $72 $41 $62 $31 $20 

GLS Glass $37 Less than $1 $21 $3 $12 

GRN Grain Milling $86 $10 $83 $2 $11 

HLT Health Care $2,154 $2,108 $22 $24 

HRD Hardware $10 $5 $6 $4 $4 

HVC HVAC Equipment $46 $12 $36 $13 $8 

I_S Iron and Steel $156 $53 $181 $12 $16 

IEQ Industrial Equipment $35 $18 $21 $18 $15 

IFN Information $1,502 $84 $1,409 $162 $13 

INS Instruments $145 $47 $89 $64 $38 

LEA Leather $4 $25 $28 Less than $1 $1 

LGT Lighting $16 $15 $23 $7 $2 

LIM Lime and Gypsum $9 $2 $1 $7 

LUM Other Lumber $57 $3 $45 $12 $3 

M_V Motor Vehicles $304 $180 $346 $86 $52 

MEA Meat Products $193 $11 $189 $5 $10 

MED Drugs and Medicine $318 $131 $379 $22 $49 

MEQ Metalworking 

Equipment

$30 $13 $20 $17 $6 

MGT Magnetic Recording 

Equipment

$19 $2 $15 $3 $4 

MIL Dairy Products $96 $4 $94 $5 $2 

MIN Mining $65 $3 $39 $15 $13 

MNG Management $560 $10 $453 Less than $1 $116 

MSC Miscellaneous 

Manufacturing

$213 $134 $221 $60 $65 

MSP Machine Shops $48 $2 $40 $7 $4 

MVP Motor Vehicle Parts $246 $92 $254 $19 $64 

OCM Other Chemicals $56 $2 $28 $11 $19 

OEQ Other Electric 

Equipment

$43 $22 $38 $10 $16 

OFD Other Food Products $102 $9 $102 $2 $7 

OFM Other Fabricated 

Metals

$71 $35 $64 $27 $15 

OIL Refined Petroleum $650 $171 $728 $19 $74

ONM Other Non-Metallic 

Minerals

$18 $7 $22 $1 $3 

OPM Other Primary Metals $51 $34 $66 $3 $16 

OSV Other Services $2,628 $1,676 $602 $351 

(continued)
350

Table B-2. 2015 Benchmark Data Set (billion 2007$) (continued)

Industry 

Label Industry Description Output Imports Consumption

Investment 

and 

Government Exports

OTP Other Transportation

Services

$350 $300 $27 $23 

OTQ Other Transport 

Equip

$26 $9 $17 $11 $7 

PAI Paints and Adhesives $44 $2 $36 $3 $7 

PAP Pulp and Paper Mills $151 $25 $154 $6 $16 

PFS Professional Services $2,439 $87 $2,002 $490 $34 

PIP Pipeline Transport $44 $101 $50 Less than $1 $95 

PLS Plastic $173 $19 $169 $5 $17 

PLY Plywood and Veneer $26 $11 $35 $1 $2 

PRN Printing $57 $1 $39 $11 $7 

PUB Public Services $1,248 $54 $406 $896 Less than $1

R_R Rail Cars $13 $2 $7 $3 $5 

REL Real Estate $3,165 $2 $2,975 $111 $81 

RSN Resins $133 $29 $117 $7 $38 

RTL Retail Trade $1,688 $58 $1,652 $82 $12 

RTP Railroad 

Transportation

$79 Less than $1 $49 $7 $23 

RUB Rubber $45 $24 $43 $17 $10 

SAW Sawmills $40 $12 $49 $1 $3 

SEA Seafood $14 $4 $16 $1 $1 

SEQ Service Industry 

Equipment

$38 $31 $30 $31 $9 

SGR Sugar $38 $7 $40 $2 $3 

SHP Ships $43 $6 $15 $25 $8 

SMI Semiconductor 

Equipment

$188 $85 $197 $14 $61 

SOP Soap $100 $6 $89 $4 $14 

TEX Textile Mills $28 $11 $32 $1 $6 

TKB Truck Bodies $67 $12 $39 $25 $15 

TPM Textile Product Mills $26 $18 $37 $3 $4 

TTP Freight Truck 

Transportation

$337 $49 $295 $37 $54 

TVQ TV Equipment $24 $46 $63 $4 $3 

WAP Wearing Apparel $23 $90 $112 $1 Less than $1

WHL Wholesale Trade $1,535 $37 $1,219 $174 $178 

WIR Springs and Wires $7 $2 $1 $3 

WTP Water Transportation $50 $15 $13 $22 
351

B.2.2 Economic Behavior

B.2.2.1 U.S. Supply

In a postpolicy scenario (e.g. the preferred trading remedy in the Transport Rule), 

industry responds to changes in the new market-clearing “net” price for the good or service sold:

%Δ”net” price = %Δ market price − %Δ direct costs − %Δ indirect costs (E.2)

The %Δ direct costs are approximated using the IPM cost analysis and baseline value of 

output. For example, with the $0.8 billion increase in compliance costs for the electricity sector 

in 2014, IPM projects a 0.77 percent increase in the retail price of electricity as mentioned in 

Chapter 7 of this RIA. For the electric power sector (EPS), percentage change in direct costs 

would be represented in the model as follows:

%Δ direct costs = 0.77% (E.3)

To ensure the market-clearing electricity price matches IPM results, we adjust the supply

elasticity to reflect a horizontal supply function (i.e. supply is infinitely elastic near market 

equilibrium).

The multimarket model simultaneously considers how the policy influences other 

industry supply functions (via changes in energy and other intermediate material prices). As a 

result, the multimarket model can provide additional information about how policy costs (higher 

electricity prices) are transmitted through the economy in the short run. As shown in Figure B-1, 

the higher electricity prices provide other industries with incentives to alter production rates at 

current market prices; market prices must rise to maintain the original prepolicy production 

levels (Q). As shown in Figure B-2, the other 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 (B.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 such as that for this analysis with a compliance year of 2014 or earlier, we 
352

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). Morgenstern and colleagues (2004) and Ho and colleagues (2008) also use 

this assumption when examining near-term effects of environmental policy.

Figure B-1. Higher Electricity Prices Reduce Other Sector Production Rates at Benchmark 

Prices

Figure B-2. Indirect Costs Further Reduce Production Rates at Benchmark Prices

Following guidance in the OAQPS economic resource manual (EPA, 1999), we use a 

general form for the U.S. industry supply function:
353

g

ε

n

i

g g gi i i Q b P t (P P )











     

1



(E.5)

where

Qg

 = with-policy supply quantity (g)

b = calibrated scale parameter for the supply price relationship

Pg

 = with-policy price for output (g)

t = direct compliance costs per unit of supply

gi = input use ratio (g using input i)

Pi

 = with-policy input (i) price

Pi = benchmark input (i) price

g

 = 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 B-5.

B.2.2.2 International Competition

International competition is captured by a single ROW supply function:

 

ROW

g

ε

g Pg Q  c 

(B.6)

where

Qg

 = with-policy supply quantity (g)

c = calibrated scale parameter for the supply and price relationship

Pg

 = with-policy U.S. price for output (g)

ROW

g

 = price elasticity of supply of goods from the ROW to the United States (imports) 

(g)

The key supply parameter that controls the ROW supply adjustments is the price 

elasticity of supply ( ROW

g

 ). We obtained these estimates for a variety of industry groups from a 

recently published article by Broda and colleagues (2008b).
354

B.2.2.3 Price Elasticity of Supply: Rest of World (ROW)

Broda and colleagues (2008a and 2008b) provide an empirical basis for the multimarket 

model supply elasticities. Broda et al. provide over 1,000 long-run trade 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 elasticity value for codes 

that fell within the multimarket model’s aggregate industrial sectors.84 This gives a crude way to 

account for the variety of products detailed in the original data set. We also restricted the longrun elasticity sample to those that Broda et al. classify as “medium” and “low” long-run 

categories; these categories tend to have lower elasticity values that are more likely to be 

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 implying the elasticities are low rather 

than high).85

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.

 

84 Given Broda et al.’s research design, the parameter estimates reported are inverse export supply elasticities. For 

example, a reported parameter estimate for inverse export supply elasticity of 1.6 would imply a ROW supply 

elasticity of 1/1.6, or 0.6. A one percent increase in the domestic price lead to an 0.6 increase in the volume of goods 

supplied (i.e., exported) to the U.S. by other countries (p. 2043). 

85 Broda et al.’s intent was to use these categories to describe or proxy for domestic market power.
355

Table B-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 a a

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)
356

Table B-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)
357

Table B-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)
358

Table B-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

a

For this analysis, EPA adjusted the domestic supply elasticity parameter to approximate a horizontal market 

supply function. This allows the multi-market model to replicate the predicted retail price changes estimated by 

IPM.

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. “Export Supply Elasticities.” 

http://faculty.chicagobooth.edu/christian.broda/website/research/unrestricted/TradeElasticities/TradeElastic

ities.html. Accessed September 2009.

In order to fill these gaps, we turned to the source substitution elasticities from Purdue 

University’s Global Trade Analysis Project (GTAP).86 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 structure87), 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).

 

86 See Chapter 14 of the GTAP 7 Database Documentation for the full description of the parameters at 

https://www.gtap.agecon.purdue.edu/resources/download/4184.pdf; see Table 14.2 for elasticities. 

87 Detailed documentation of the entire GTAP 7 Database is available at 

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.
359

B.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,88 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.

B.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.

Qi giQg

   

(B.7)

Qi

 = with-policy input demand quantity (i)

gi = input use ratio (g using input i)

Qg

 = with-policy output quantity (g)

Other final use does respond to market price changes. Following guidance in the OAQPS 

economic resource manual (EPA, 1999), we use a general form for the U.S. industry demand 

function:

 

g

g Pg Q a



  

(B.8)

where

Qg

 = with-policy demand quantity (g)

 

88No 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).
360

a = calibrated scale parameter for the demand and price relationship

Pg

 = with-policy price for output (g)

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 reported in Ho, 

Morgenstern, and Shih (2008). To estimate the demand elasticities, Ho, Morgenstern, and Shih 

used a CGE model89 and simulate the effects of placing a small tax on output and recording the 

quantity change. The general equilibrium quantity change associated with the tax considers all 

price and income changes that led to the quantity change. Table B-6 reports the values taken 

from Ho, Morgenstern, and Shih (2008) that are currently used for demand responses of other 

final uses (domestic and exports).

The current version of the multimarket model does not currently consider the 

consequences of exogenous demand shocks to the scale parameter (a) that the policy may bring 

about. For example, IPM explicitly models changes in fuel use (a switch from coal to natural 

gas) that utilities may use to meet the Transport Rule. Increases in natural gas demand 

subsequently lead to price increases for natural gas.90 As a result of higher natural gas prices, 

industries with more intensive natural gas use may shrink while those with less intensive natural 

gas use may expand. A similar story with opposite effects occurs in the coal market; reduced 

demand lowers coal prices and may result in surplus loss for the coal industry. The Transport 

Rule may also increase the demand for materials for retrofits and increases in the prices of those 

goods as well as the demand for retrofit equipment; the demand increase will lead to relative 

expansion and contraction of industries. EPA acknowledges that the current multimarket model 

does not account for these types of changes in the market demand curves.

B.2.2.6 Model Scope and Distribution of Impacts

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, 

 

89 The authors use the Adkins–Garbaccio CGE Model (Adkins, 2006). 

90 However, IPM treats the natural gas price increase as a cost to the electricity sector, but does not simultaneously 

consider that higher prices may result in a surplus gain to owners of natural gas reserves. 
361

the model can also be run in single-market partial equilibrium mode to support and provide 

insights for other types of environmental policies. 

With respect to distributional impacts, the relative market price elasticities also influence 

the distribution of social costs across stakeholders. In markets where the demand side is more 

responsive to price changes than suppliers, industry would bear more of the surplus losses. In 

the context of the Multimarket Model, this is more likely to occur in markets where the 

household has elastic demand and the household is the primary source of market demand (versus 

demand from intermediate, investment, and government sectors). However, in markets where 

the supply side is more responsive to own price changes, consumers would bear more of the 

losses. Given the Multimarket Model's demand structure, this is more likely to occur in markets 

where the primary source of demand is intermediate, investment, and government sectors. With 

electricity having a low elasticity of demand, electric power producers’ responsiveness to own 

price changes is greater than that for the demand side for electricity. Thus, it is not surprising that 

consumers incur more of the surplus losses associated with this final rule than producers. 

B.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 CGE 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.
362

Table B-6. U.S. Demand Elasticities

Industry Label Industry Description

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)
363

Table B-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)
364

Table B-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. “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.
365

B.3 References

Adkins, Liwayway G. 2006. Coordinating Global Trade and Environmental Policy: The Role of 

Pre-Existing Distortions. Ph.D. dissertation. University of Virginia.

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/TradeElasticities/

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). 

<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. 

http://www.epa.gov/ttn/ecas/analguid.html.

U.S. Environmental Protection Agency. 2010. “Guidelines for Preparing Economic Analyses” 

(EPA 240-R-10-001). 

<http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf
366

APPENDIX C

SUMMARY OF STATE-LEVEL MORTALITY IMPACTS

Table C-1: Avoided PM2.5-related premature deaths in 2014 for the selected remedy by 

state

State Pope et al. (2002) mortality 

estimate

Laden et al. (2006) mortality 

estimate

Alabama

380

(150--610)

980

(530--1400)

Arizona

8.9

(3.5--14)

23

(12--33)

Arkansas

200

(79--320)

510

(280--740)

California

7.9

(3.1--13)

20

(11--30)

Colorado

-4.4

(-8.2---0.52)

-11

(-19---3.8)

Connecticut

130

(50--200)

320

(180--470)

Delaware

55

(22--89)

140

(77--200)

District of Columbia

29

(11--46)

74

(40--110)

Florida

600

(230--960)

1,500

(830--2200)

Georgia

580

(230--930)

1500

(810--2200)

Idaho

0.038

(0.012--0.064)

0.098

(0.048--0.15)

Illinois

590

(230--950)

1,500

(820--2200)

Indiana

530

(210--850)

1,300

(740--2000)

Iowa

93

(36--150)

240

(130--340)

Kansas

82

(32--130)

210

(110--300)

Kentucky

530

(210--850)

1,400

(740--2000)

Louisiana

200

(79--320)

520

(280--750)

Maine

23

(9.2--38)

60

(33--88)
367

Maryland

400

(160--650)

1,000

(560--1500)

Massachusetts

150

(60--250)

390

(210--570)

Michigan

540

(210--870)

1,400

(760--2000)

Minnesota

75

(29--120)

190

(110--280)

Mississippi

220

(87--360)

570

(310--820)

Missouri

330

(130--530)

840

(460--1200)

Montana

-0.12

(-0.23---0.0086)

-0.3

(-0.51---0.093)

Nebraska

31

(12--49)

78

(43--110)

Nevada

-0.59

(-1.1---0.087)

-1.5

(-2.5---0.55)

New Hampshire

31

(12--49)

78

(43--110)

New Jersey

450

(180--730)

1,200

(630--1700)

New Mexico

9

(3.5--15)

23

(13--34)

New York

780

(300--1200)

2,000

(1100--2900)

North Carolina

750

(300--1200)

1,900

(1000--2800)

North Dakota

3.1

(1.2--5)

7.9

(4.3--12)

Ohio

1,300

(500--2000)

3,200

(1800--4600)

Oklahoma

160

(61--250)

400

(220--580)

Oregon

0.06

(0.0055--0.11)

0.15

(0.049--0.26)

Pennsylvania

1,200

(450--1800)

2,900

(1600--4200)

Rhode Island

31

(12--50)

80

(44--120)

South Carolina

380

(150--600)

960

(530--1400)

South Dakota

8.4

(3.3--13)

22

(12--31)

Tennessee

650

(260--1000)

1,700

(910--2400)
368

Texas

670

(260--1100)

1,700

(940--2500)

Utah

0.28

(0.11--0.46)

0.73

(0.4--1.1)

Vermont

17

(6.7--28)

44

(24--64)

Virginia

610

(240--980)

1,600

(850--2300)

Washington

0.07

(0.025--0.11)

0.18

(0.092--0.26)

West Virginia

280

(110--450)

700

(390--1000)

Wisconsin

170

(66--270)

430

(230--630)

Wyoming

-0.14

(-0.23---0.045)

-0.36

(-0.54---0.18)

Table C-2: Avoided ozone-related premature deaths in 2014 for the selected remedy by 

state

State Bell et al. (2004) mortality 

estimate

Levy et al. (2006) mortality 

estimate

Alabama

0.76

(0.33--1.2)

3.5

(2.6--4.4)

Arizona

0.013

(0.0056--0.02)

0.06

(0.044--0.076)

Arkansas

0.31

(0.14--0.48)

1.4

(1.1--1.8)

California

0.015

(0.0067--0.024)

0.071

(0.052--0.089)

Colorado

0.002

(-0.0066--0.011)

0.009

(-0.01--0.028)

Connecticut

0.1

(0.046--0.16)

0.48

(0.35--0.6)

Delaware

0.063

(0.028--0.098)

0.29

(0.21--0.36)

District of Columbia

0.041

(0.018--0.063)

0.19

(0.14--0.24)

Florida

2.1

(0.67--3.5)

9.7

(6.6--13)

Georgia

1

(0.45--1.6)

4.8

(3.5--6.1)

Idaho

0.0005

(0.0002--0.0007)

0.0021

(0.0016--0.0027)

Illinois

1.4

(0.62--2.2)

6.4

(4.7--8.1)
369

Indiana

0.88

(0.39--1.4)

4.1

(3--5.1)

Iowa

0.65

(0.29--1)

3

(2.2--3.7)

Kansas

0.76

(0.33--1.2)

3.5

(2.6--4.4)

Kentucky

0.83

(0.37--1.3)

3.9

(2.8--4.9)

Louisiana

0.3

(0.13--0.46)

1.4

(1--1.8)

Maine

0.023

(0.01--0.036)

0.11

(0.077--0.13)

Maryland

0.62

(0.28--0.97)

2.9

(2.1--3.6)

Massachusetts

0.15

(0.065--0.23)

0.66

(0.49--0.84)

Michigan

1.2

(0.51--1.8)

5.3

(3.9--6.7)

Minnesota

0.58

(0.25--0.91)

2.7

(1.9--3.4)

Mississippi

0.3

(0.13--0.47)

1.4

(1--1.8)

Missouri

1.3(0.5 7--2)6

(4.4--7.5)

Montana

0.0026

(0.0011--0.0041)

0.012

(0.0089--0.015)

Nebraska

0.35

(0.15--0.54)

1.6

(1.2--2)

Nevada

-0.022

(-0.037---0.0065)

-0.1

(-0.14---0.069)

New Hampshire

0.04

(0.018--0.063)

0.18

(0.13--0.23)

New Jersey

0.55

(0.24--0.86)

2.5

(1.8--3.1)

New Mexico

0.024

(0.011--0.038)

0.12

(0.085--0.15)

New York

1

(0.44--1.6)

4.5

(3.3--5.7)

North Carolina

1.1

(0.49--1.8)

5.2

(3.8--6.6)

North Dakota

0.025

(0.011--0.039)

0.12

(0.085--0.15)

Ohio

2

(0.88--3.1)

9.1

(6.7--11)

Oklahoma

1.1

(0.48--1.7)

5.1

(3.8--6.5)
370

Oregon

0.0003

(0.0001--0.0004)

0.0012

(0.0009--0.0015)

Pennsylvania

2.4

(1.1--3.8)

11

(8.2--14)

Rhode Island

0.025

(0.011--0.039)

0.11

(0.084--0.14)

South Carolina

0.72

(0.32--1.1)

3.3

(2.4--4.2)

South Dakota

0.076

(0.034--0.12)

0.35

(0.26--0.45)

Tennessee

1

(0.46--1.6)

4.9

(3.6--6.2)

Texas

0.86

(0.33--1.4)

4

(2.8--5.2)

Utah

0.0019

(0.0008--0.003)

0.009

(0.0067--0.011)

Vermont

0.029

(0.013--0.044)

0.13

(0.097--0.17)

Virginia

0.6

(0.26--0.94)

2.8

(2--3.5)

Washington

0.0011

(0.0005--0.0017)

0.0052

(0.0038--0.0065)

West Virginia

0.5

(0.22--0.78)

2.3

(1.7--2.9)

Wisconsin

0.83

(0.37--1.3)

3.8

(2.8--4.8)

Wyoming

0.001

(-0.0001--0.0022)

0.0048

(0.0022--0.0074)
371

APPENDIX D

EMPLOYMENT ESTIMATES OF DIRECT LABOR IN RESPONSE TO THE FINAL 

TRANSPORT RULE IN 2014

This appendix presents the short-term employment estimates for the Transport Rule. The 

focus of the employment analysis in this study is only on the first order employment impacts 

related to the compliance actions of the affected entities within the power sector. It does not 

include the ripple effects of these impacts on the broader economy (i.e., the “multiplier” effect), 

nor does it include the wider economy-wide effects of the changes to the energy markets (such as 

higher electricity prices).91 Moreover, this study provides only a static snapshot of the impacts 

for 2014 and does not account for the dynamic adjustments of the affected entities as they adapt 

to the Transport Rule such as those arising from technological innovation and learning-by-doing. 

The estimates of the employment impacts are divided into several categories: job gains 

due to the increased demand for pollution control equipment; job losses due to retirements of 

coal capacity; and job shifts due to changes in demand for fuels. Results indicate that the 

Transport Rule has the potential to provide some short-term employment opportunities, primarily

driven by the demand for new pollution control equipment. The employment gains related to the 

new pollution controls are likely to be tempered by losses due to certain coal retirements, 

although, as discussed below, some of these workers who lose their jobs due to plant retirements 

could find replacement employment operating the new pollution controls at nearby units. 

Finally, job losses due to reduced coal demand may be partially offset by job gains due to 

increased natural gas demand, resulting in small negative (about a thousand) net change in 

employment due to fuel demand changes. Overall, the preliminary results indicate that the 

Transport Rule could support a net of about 3,500 job-years in 2014. These results are 

summarized in Table 1 below. 

Table 1: Net Employment Changes (Job-years in 2014) 

New Pollution Control Equipment 7,200

Retirements of Generating Units (2,710)

Changes in Fuel Use (990)

Net Effect 3,500

The job-years estimated here is a snapshot of the first order employment effect of the 

 

91 For more detail on the costs and energy impacts of the proposed rule, see Chapter 7 of the Regulatory Impact 

Analysis accompanying the Transport Rule.
372

Transport Rule in 2014. While there is no temporal dimension to this study, most of these jobs 

are likely to last over an extended time period, some more than the others. Most of the 

construction-related labor demand, for example, is expected to provide a short-term, temporary

boost to employment that could last a few years, along with any multiplier effects that are not 

included in these job-year estimates. Most of the operational labor n0eeds are likely to be longer 

term. Thus, in terms of the impacts of the Transport Rule on economy-wide employment over 

time, this study shows there is likely to be a temporary boost to employment levels starting 

around 2014, which would likely recede thereafter as the construction phases for the needed 

pollution controls wind down. Over time, the operational jobs, which constitute a large portion 

of the pollution control related job impacts, will continue to provide a boost to employment over 

“business as usual” baseline employment levels. Note that this synopsis does not account for 

other employment impacts of the Transport Rule, such as those resulting from higher energy

prices. 

Overall Approach 

The estimates for the near-term employment effects of the proposed rule utilize basic 

methodologies to estimate these job impacts relying on approaches used in prior EPA studies on 

similar issues. The basic approach involved using power sector projections and various energy

market implications under the Transport Rule from modeling conducted with the EPA Base Case 

version 4.10, using the Integrated Planning Model (IPM®), along with data from secondary

sources, to estimate the first order employment impacts for 2014. Throughout this analysis, 

incremental employment is measured in job-years, since there is no temporal dimension to this 

analysis.92 Also, this TSD does not include estimates of total employment impacts over time, 

though there is a distinction between short-term construction-related labor needs and more longterm operational labor needs for new pollution controls (though these operational labor 

requirements are also measured in 2014 job-years only).

 

92 A job-year is defined as the amount of work that can be performed by the equivalent of one full-time individual 

for one year (or FTE). 
373

Employment Changes due to New Pollution Control Equipment 

EPA’s Base Case v.4.10 and the Transport Rule policy case IPM projections were used to 

estimate the incremental pollution control demand.93 These are shown in Table 2 below:94 

Table 2: Increased Pollution Control Demand due to the Transport Rule, 2014 (GW)

Pollution Control 

Type

Existing Controls 

Induced to Operate Newly-Built Controls

Scrubbers (FGD) 25 6

Selective Catalytic 

Reduction (SCR) 5 0

Dry Sorbent 

Injection (DSI) 0 3

The employment impacts due to increased pollution control demand are divided into four 

categories, two of which are associated with the construction and installation related labor 

requirements, while the remaining two are associated with the resources required for the ongoing 

operation of these equipment. The two categories of labor needed for constructing and installing 

these controls are for construction-related sectors, such as boilermakers, engineers, and other 

installation labor; and for the labor needed to manufacture the steel used in the control 

equipment. The two categories of labor needs for resource requirements include employment in 

sectors that supply resources needed to run these pollution controls (such as reagents and 

catalysts); and utility sector jobs to operate these control equipment. The following sections 

discuss the approach for each: 

 For the installation related construction labor, per-unit labor needs were taken from an 

extensive 2002 EPA resource analysis for multi-pollutant strategies that analyzed the 

 

93 Results for this analysis were developed using various outputs from EPA’s Base Case v.4.10 using ICF’s 

Integrated Planning Model (IPM®). This case includes all of the underlying modeling that was developed by EPA 

with technical support from ICF Consulting, Inc. IPM is a multi-regional, dynamic, deterministic linear 

programming model of the U.S. electric power sector. It provides forecasts of least cost capacity expansion, 

electricity dispatch, and emission control strategies while meeting energy demand and environmental, transmission, 

dispatch, and reliability constraints. See http://www.epa.gov/airmarkets/progsregs/epaipm/BaseCasev410.html for more info.

94 According to the EPA Policy Case results, there is some overlap between the different types of pollution control 

equipment demand at individual facilities. To the extent that there could be some efficiency gains at plants 

installing multiple controls due to economies of scale, the job estimates presented here could overstate the impacts. 
374

resource requirements for installing various pollution control equipment (including FGD 

and SCR).95 For controls not included in the 2002 EPA study, such as DSI, additional 

information was supplied by Andover Technology Partners (ATP), which was also one of 

the contributors of the 2002 EPA study. Using the same resource requirement approach as 

done in the 2002 EPA study, ATP provided estimates of unit labor needs based on scaling 

the labor requirements for ACIs (to derive estimates for DSI) 96 Thus, all of the unit labor 

needed for different pollution control installations can be traced back to the 2002 EPA 

study, with appropriate engineering adjustments. The installation labor was sub-divided 

into different labor categories, such as boilermakers, engineers and a catch-all “other 

installation labor”, using information from the 2002 EPA study, adjusted for the new 

pollution controls, such as DSIs. 

 In addition to construction labor, it was assumed that installing the pollution controls requires 

labor for steel as the primary raw material, leading to employment in the steel manufacturing 

industry. Again this approach is consistent with the 2002 EPA study. The increased steel 

demand is estimated using the per-MW steel demand from the EPA study along with the 

estimated increases in pollution controls. For DSI, the same proportionality assumption is 

taken from ATP for installation labor to estimate the steel needed for installation. 

 For the longer term, and more significant labor associated with operating the pollution 

controls, per-unit estimates of the main resources needed for these equipment (see Table 3 

below for a list of the resources) are taken from the 2002 EPA study, augmented by DSI 

resource use estimates from ATP. These were then multiplied by the incremental GW for 

each pollution control to obtain the total (physical) quantity of resources needed.97 Total 

tonnage (or volume for SCR catalyst) for each resource was then converted to dollars of 

increased economic output for these resources using price estimates obtained from publicly 

available sources (see Table 3). Finally, the labor productivity for each particular sector was 

used to estimate the number of job-years these would likely create in 2014. Labor 

productivities for each sector were adjusted to account for increased worker productivity in 

2014. Data for baseline worker productivity and corresponding growth rates to account for 

future productivities came from the Economic Census and the Bureau of Labor Statistics 

 

95 “Engineering and Economic Factors Affecting the Installation of Control Technologies for Multi-pollutant 

Strategies”. EPA/ORD, 2002.

96 DSI and Fabric Filter System Installation Labor Estimate. Memorandum by Andover Technology Partners to 

ICF International. January 26, 2011. 

97 Except for Ammonia, where the usage was calculated based on the total predicted NOx reduced, consistent with 

the 2002 EPA study approach. 
375

(BLS) estimates.98

 The final employment vector estimated was for the utility sector labor needed to operate 

these pollution control equipment. This estimate was based on the incremental Fixed 

Operation and Maintenance (FOM) costs from the EPA runs using IPM, excluding costs due 

to retirements. The assumption in this TSD was that the FOM costs -- defined as the 

maintenance costs incurred by the utility irrespective of whether the equipment is operated 

(such as those for payroll) – are a good proxy for estimating the incremental labor demand to 

operate these pollution controls. The FOM costs were then translated into employment based 

on estimates of payroll per worker for the power sector taken from the 2007 Economic 

Census and BLS estimates.99

 

98 Total value of shipments in 2007 and total employees were taken from 2007 Economic Census, Statistics by 

Industry for Mining and Manufacturing sectors. The average annual growth rate of labor productivity was taken 

from the Bureau of Labor Statistics. Average growth rate calculated for years 1992-2007, applied to 2007 

productivity to determine 2014 estimates of productivity. For steel, limestone, ammonia, and SCR catalyst, the 

value of shipments and employment estimates were taken from the 2000 Survey of Manufacturers. See the 

Appendix for more details about the data used for these calculations. 

99 Same sources as other productivity estimates (2007 Economic Census and BLS), however, uses employees and 

total payroll rather than revenue or value of shipments. 
376

Table 3: Estimated Pollution Control Resource Needs (Quantity and Prices Used)

Amount of Resource 

Used Price Used ($/unit)

Steel (tons)

 

19,906 $ 550

Limestone, FGD (tons) 100

 

8,112,715 $ 75

Ammonia, SCR (tons)

 

77,387 $ 150

Catalyst, SCR (m3)

 

723 $ 8,475

Sodium Bicarbonate, DSI 

(tons)

 

675,725 $ 120

Price Sources: Steel: Platts, Steel Markets Daily, Vol 3 Issue 209, October 29, 2009. 

Limestone: FGD Tech Evaluation, March 2007. 

Ammonia: Development of Supply Curves for Abatement of GHG from 

Coal-fired Utility Boilers, Air Pollution Prevention and Control Division, 

US-EPA, RTP and NC State University, 2009. 

Catalyst: EPA Air Pollution Control Cost Manual, Sixth Edition, January

2002. ; 

Sodium Bicarbonate: Communication with Andover Technology Partners, 

Feb 7, 2011.

Results

Table 4 presents the estimated employment impacts in 2014 resulting from the additional 

pollution controls needed to meet the Transport Rule requirements. The Transport Rule is 

estimated to lead to a total capital investment of about $4 to $5 billion in order for the regulated 

entities to meet the limits under the proposed rule (this capital cost will be amortized over many 

years). According to this analysis, these investments could provide the opportunity to support 

over 7,000 job-years to design, construct, and operate the needed pollution control equipment in 

2014. Note, some of these jobs are expected to start before, and continue beyond 2014 (such as 

 

100 EPA’s modeling for the Transport Rule indicates most of the incremental scrubber capacity is likely to be wet 

scrubbers. EPA assumes a lower price for the limestone used in wet scrubbers relative to the reagent used in dry 

scrubbers. The $75/ton price for limestone was used to be consistent with a similar analysis conducted for the 

proposed Mercury and Air Toxics Standards (March 2011) where EPA projected mostly dry scrubbers that use a 

more expensive lime reagent. This simplification is likely to overestimate the job impacts. However, this study 

does not include the positive employment impacts arising from the waste disposal costs assumed in EPA’s 

modeling, again to be consistent with the Toxics Rule TSD, which is likely to lead to underestimation of the total 

job impacts. 
377

the resource related job-years), but this analysis only provides a snapshot for 2014. 

Table 4: Jobs Due to Pollution Control Equipment under the Transport Rule 

(Job-years in 2014)

Jobs for Construction Incremental Employment

Boilermakers 890

Engineers 440

General Construction 880

Steel Manufacturing 20

Sub-Total: 2,230

Jobs for Operation

Jobs from Increased Operating 

Resource Use

Limestone (FGD) 2,440

Ammonia (SCR) 30

Catalyst (SCR) 20

Sodium Bicarbonate 

(DSI) 160

Sub-Total: 2,650

Jobs for Pollution Control 

Operation 2,320

Total Labor: 7,200

Note: Totals may not add due to rounding

The number of job-years estimated for pollution control installation is driven primarily by 

incremental FGD capacity. As shown in Table 2, up to 31 GW of additional FGD capacity is 

projected to be used in 2014 to comply with the Transport Rule. Out of this, only about 6 GW is 

estimated to be new scrubber capacity requiring construction and installation of the pollution 

control equipment. While the remaining 25 GW is projected to be existing scrubbers that were 

not operated under the EPA base case assumptions, and hence are not likely to lead to jobs due to 

construction, these scrubbers do provide significant operational employment opportunities. The 

results above show a higher proportion of operational jobs than, for example, was estimated for 

the utility Toxics Rule. This implies while the jobs due to the Transport Rule are only a fraction 

of the total estimated for the Toxics Rule, these jobs are more likely to last longer over time 
378

since there is less need for installing new pollution control equipment but a higher demand for 

operating equipment that may not be operated in absence of the Transport Rule (i.e. under the 

“business-as-usual” base case). 

Moreover, of the roughly 7,200 job-years estimated in Table 4, over 2,300 job-years, or 

over 30 percent, are estimated to occur within the utility sector for labor needed to operate the 

pollution controls (referred to as “Jobs for Pollution Control Operation” in Table 4). The rest of 

the labor demand will benefit the pollution control industry and other economic sectors. The 

increased demand for resources and chemicals needed to operate the pollution controls will 

provide a boost to employment in sectors such as mining, chemicals, other manufacturing 

sectors, and to a smaller extent, in construction-related sectors. 

Employment Changes due to Coal Retirements

Employment changes due to plant retirements were estimated by first identifying the 

retiring units from EPA’s modeling using IPM,all of which are coal-fired units. EPA projects 

that there will be roughly 4.8 GW of additional coal retirements by 2014 with the Transport Rule 

in place.101 

In order to convert the retired coal capacity into potential employment losses, it was 

assumed that changes in the operating costs for the retired coal units can be used as a proxy for 

the lost economic output due to fossil retirements. Thus, the changes in the O&M costs (both 

FOM and VOM) for these particular retiring units were derived from the EPA Base Case and 

Transport Rule policy case runs using IPM, and converted to lost jobs using data from the 

Economic Census and BLS output/worker estimates for the utility sector.102 

Note that the lost economic output due to fossil retirements is likely to affect not just the 

utility sector, but others as well. Employment losses due to plant retirements will not only affect 

those that are directly working at the plant (i.e., plant operators), but would also affect 

administrative and other “back-office” workers for those utilities and their support organizations. 

This TSD assumes that the FOM costs related to retiring plants are a good proxy for these types 

of job losses. Moreover, because the VOM costs represent the variable costs of operating the 

plant, including costs of materials needed to run any installed pollution control equipment (such 

as cost of reagents, etc.), this TSD assumes that the VOM costs can be used as an indicator of the 

employment effects on the resource suppliers to these plants and units. Thus, the retirement 

 

101 Retirement estimates are based on system-level results for the EPA runs using IPM. Where applicable, parsed 

data from the IPM for the relevant EPA runs were adjusted to account for partial retirements. 

102 The same specific sources as cited before, however, used workers and total payroll.
379

related employment losses shown below are assumed to include losses directly affecting the 

utility sector as well as other “upstream” sectors that provide material inputs needed to operate 

these plants. Table 5 shows those results. 

Table 5: Job Losses due to Coal Capacity Retirements

O&M Decrease from Retirements ($MM) 303

Workers Per Million$ in payroll 8.9

Workers lost due to retirements (job-year): 2,710

Results indicate there could potentially be about 2,700 job losses (measured in job-years 

for 2014, but any net job losses under this category are likely to be permanent), due to coal 

retirements. However, two mitigating factors could reduce the negative employment impacts 

due to retirements. First, many of the retiring units are at plants that are likely to have other units 

operating under the policy scenario. In such cases, some of the excess labor pool at the retiring 

units could well be absorbed at other units within the same firm.103

Second, as Table 4 indicates, utilities are expected to have the need to fill over two 

thousand additional job slots to operate the pollution controls needed to meet the requirements of 

the Transport Rule. If workers with experience at existing coal facilities become available 

through plant retirements, it would seem logical for many of these workers to be absorbed in 

operating these new pollution controls. While it is not possible to determine how many of these 

workers from particular coal retirements could be employed to operate the new pollution 

controls, it is likely that a significant portion of them could find gainful employment at nearby 

units. 

Employment Changes due to Changes in Fuel Use

Two types of employment impacts due to projected fuel use changes are estimated in this 

TSD. First, employment losses due to reductions in coal demand were estimated using an 

approach similar to EPA’s coal employment analyses under Title IV of the Clean Air Act 

 

103 According to EPA modeling results, approximately 70 percent of the coal retirements (in terms of capacity) 

occur at plants with other operating units under the Transport Rule policy case. 
380

Amendments.104 Using this approach, changes in coal demand (in short tons) for various coal 

supplying regions were taken from EPA’s Base and Policy Case runs. These changes were 

converted to job-years using EIA data on regional coal mining productivity (in short tons per 

employee hour), using 2008 labor productivity estimates.105 106 Results of the coal employment 

impacts of the Transport Rule are presented in Table 6 below. 

 

Table 6: Employment Due To Changes in Coal Use

Coal by Region

Change in Coal 

Demand (MM 

Tons)

Labor 

Productivity 

Job-year 

Change

 Appalachia 3.8 2.91 630

 Interior (27.2) 4.81 (2,720)

 West 2.9 19.91 70

 Waste Coal (0.03) 5.96 (0)

Net Total (20.5) -- (2,030)

Notes: Used US national coal productivity for waste coal

Totals may not add due to rounding

For natural gas demand, labor productivity per unit of natural gas was unavailable, unlike 

coal labor productivities used above. Most secondary data sources (such as Census and EIA) 

provide estimates for the combined oil and gas extraction sector. This TSD thus uses an adjusted 

labor productivity estimate for the combined oil and gas sector that accounts for the relative 

contributions of oil and natural gas in the total sector output (in terms of the value of energy

output in MMBtu). This estimate of labor productivity is then used with the incremental natural 

gas demand for the Transport Rule to estimate the job-years for 2014. In addition, the pipeline 

construction costs were estimated using proportionality assumptions from those used for the 

 

104 Impacts of the Acid Rain Program on Coal Industry Employment. EPA 430-R-01-002

March 2001.

105 From US Energy Information Administration (EIA) Annual Energy Review, Coal Mining Productivity Data. 

Used 2008. 

106 Unlike the labor productivity estimates for various equipment resources which were forecasted to 2014 using 

BLS average growth rates, this study uses the most recent historical productivity estimates for fuel sectors. In 

general, labor productivity for the fuel sectors (both coal and natural gas) showed a significantly higher degree of 

variability in recent years than the manufacturing sectors, which would have introduced a high degree of uncertainty 

in forecasting productivity growth rates for future years. 
381

Toxics Rule TSD.107 These results are summarized in Table 7 below. 

 

Table 7: Total Employment Impact due to Changes in Fuel Use (2014) 

Fuel Type Employment

Coal Job Years Lost (2,030)

Natural Gas 

 Incremental Natural Gas Use (MMBtu) 212,279,900

 Labor Productivity (MMBtu/job-year) 261,840

 Job-years gained

 

810

Gas Pipeline

 Capital Cost for New Pipeline Capacity 

 (US total: MM $/year) 44.2

 Workers/million $ in Pipeline 

Construction 5.1

 Job-years gained 230

Net Employment Effects of Fuel use changes 

 

(990)

Note: Totals may not add due to rounding 

Thus, about 2,000 job losses in the coal mining sector are likely to be partially offset by

about 1,000 job gains in the production and transportation-related jobs in the natural gas related 

sectors, for a net effect of less than a thousand job losses due to changes in fuel use. 

Conclusion 

Overall, the impact of the Transport Rule on short-term employment resulting from 

investments in pollution control equipment is expected to be small. The Transport Rule is 

estimated to lead to a total pollution control-related capital investment of about $4 to $5 billion. 

Of the jobs associated with pollution controls, over two-thirds are likely to be associated with 

operating these controls while the remaining one-third is estimated to be driven by the need to 

install new controls. Employment losses due to coal retirements that will likely have a negative 

effect on some utilities and the coal mining sector could offset some of the positive gains from 

 

107 See “Employment Estimates of Direct Labor in Response to the Proposed Toxics Rule in 2015”. Technical 

Support Document, March 2011.
382

pollution control and in the natural gas sector. As previously discussed, this assessment does not 

account for the long-run economy-wide effects of the Transport Rule. The employment impacts 

estimated in this study are mostly based on approaches in prior EPA studies on similar topics. 

The main source for resource estimates used here is the 2002 EPA multi-pollutant resource 

analysis, which was also the basis for subsequent EPA labor analyses, such as the 2005 Clean 

Air Interstate Rule (CAIR) TSD on boilermaker labor, as well as the more recent utility Toxics 

Rule employment TSD.108 Thus, results presented here are consistent with prior EPA studies on 

similar topics. 

 

108 CAIR Technical Support Document. Boilermaker Labor Analysis and Installation Timing. March 2005.
383

Supplemental Information

This section provides further details on the inputs and methodology used in the labor 

calculations presented above. The Appendix begins with the pollution control labor section, 

presenting the inputs for the four primary sections of equipment labor and describing the process 

by which the labor estimates were obtained from the available data, followed by similar 

discussions for coal retirements and fuel use change employment analysis. 

Pollution Control Equipment Labor

 Installation Labor 

Table 8: Installation Labor Requirement

Pollution 

Control 

Type

Incremental 

GW 

Installed

Manhours/MWBoilermakers 

(%)

Engineers 

(%)

Others 

(%)

FGD 6 760 40 20 40

SCR 0 700 45 7 48

DSI 3 44 50 17 33

Source: EPA Multi-pollutant Strategies 2002 report; DSI and FF adjustments based on 

Andover Technology Partners.

Installation labor is estimated using the incremental GW installed for each pollution 

control type from EPA’s modeling using IPM. This was then converted into total man-hours 

needed for installation using estimates of man-hours/MW from the 2002 EPA study,

supplemented by information from ATP. Total man-hours for each pollution control type were 

then converted into man-years assuming 2,080 working hours per year. 

Total man-years for each pollution control type were then broken down into various 

sectors using the percentages, shown in Table 8. These percentages were estimated from the 

2002 study and supplemented by ATP information for DSI.

 
384

 Installation Resource Labor

Table 9: Installation Resource Needs

Pollution 

Control Type

Steel 

(Tons/MW)

FGD 2.25

SCR 2.5

DSI 2.2

Sources: Steel and Catalyst Usage: EPA Multi-pollutant Strategies 2002 report; 

Steel Price: Platts, Steel Markets Daily, Vol 3 Issue 209, October 29 2009; 

Productivity: 2000 Annual Survey of Manufacturers: Iron and Steel Pipes and Tube 

Manufacturing, BLS for Productivity Growth Rate.

For installation resource labor, steel is the primary resource input used for new pollution 

control installations, consistent with the 2002 EPA multi-pollutant study. The first step used the 

incremental installed pollution control capacity and estimates of steel usage (tons/MW), as 

shown in Table 9 above, to calculate the total steel needed in tons, by pollution control type. 

Steel usage estimates were used from the 2002 EPA study, supplemented by ATP information 

for DSI. The total amount of steel was then converted into a dollar value for steel expenditure 

using an average steel price ($550/ton), using data from Platts Steel Markets Daily (see notes to 

Table 9 above). Resulting total steel expenditure was then converted to labor using the 

productivity estimates calculated from the Economic Census data using workers per $ Million in 

total output for the iron and steel manufacturing industry (= 2.2 workers/$Million).109

 Operating Resource Labor 

Table 10: Resources Needed for Operation

Pollutio

n 

Control 

Type

Incrementa

l Total GW 

(Includes 

Existing 

Units)

Resource

(Units in 

parenthesis)

Usage 

Estimate

s

Price 

($/unit

)

Industry 

Assumed for 

Productivity 

Calculations

Productivity

*

FGD 30.3 Limestone 

(Tons/MWh) 0.036 75 Lime Manufacturing 4.0

 

109 In general, the productivity estimates for various sectors were derived using data from the 2000 Annual Survey 

of Manufacturers or 2007 Economic Census. These were then forecasted to 2014 using corresponding average 

growth rates estimated using data from the Bureau of Labor Statistic (BLS) information on productivity growth 

rates.
385

SCR

5

(NOx 

reduction: 

396,870,600 

lbs) 

Ammonia 

(lbs/lb NOx 

Reduced)

0.39 150

All Other Basic 

Inorganic 

Chemical 

Manufacturing

2.1

Initial 

Catalyst 

(m3/MW)

1.2

8,475

Combined: Other 

Nonferrous 

Metal Primary

and Secondary

Smelting and 

Refining

3.2 Operational 

Catalyst 

(m3/MWh)

0.00002

DSI 3

Sodium 

Bicarbonate 

(Tons/MWh)

0.03 120

Potash Soda and 

Borate Mineral 

Mining

2.0

*Workers/$Million in Output, Forecasted to 2014 

Sources: Usage: EPA Multi-pollutant Strategies 2002 report, DSI from Andover 

Technology Partners; 

Prices: Limestone: FGD Tech Evaluation, March 2007

Ammonia: Development of Supply Curves for Abatement of GHG from Coal-fired Utility 

Boilers, Air Pollution Prevention and Control Division, US-EPA, RTP and NC State 

University, 2009

Catalyst: EPA Air Pollution Control Cost Manual, Sixth Edition, January 2002.

Sodium Bicarbonate: Communication with Andover Technology Partners, Feb 7 2011;

Productivity: 2000 and 2007 Economic Census, Productivity Growth rates from BLS.

Labor related to resources used in operating pollution control equipment was estimated 

using the total incremental GW of pollution control capacity which was first converted to total 

MWh of incremental capacity assuming 85 percent capacity factor. For each pollution control 

type, the next step involved choosing the primary operating resource, except for SCRs for which 

two resources – ammonia and catalyst – were chosen. 110 This approach is consistent with the 

2002 EPA study. The next step involved estimating the resource needs by each control type, 

generally in tons of material (cubic meters (m3) for catalyst), using the resource usage estimates 

as shown in Table 10.

111

 Using the total usage for each pollution control input (in tons or m3) 

and associated average prices, total expenditure by each resource type was then calculated. This 

total expenditure was then converted to labor using workers per $Million in total output for the 

industry associated with producing each respective input material.

 

110 SCRs have an operational input of catalyst as well as an initial input needed when the unit is first installed. 

While the initial value is calculated using only the installed new control capacity, that number and the associated 

labor needs are included to estimate the total volume of catalyst required. 

111 Ammonia has a usage rate based on lbs of NOx reduced, so we estimated our ammonia usage using the total 

NOx reduced between the base and policy cases.
386

 Operating Labor 

Table 11: Operating Labor Assumptions

Incremental FOM ($ Million) 259

Productivity* 8.9

*Workers per $Million in Payroll for Electricity Generating Sector, Forecast to 2014

Sources: Productivity from 2007 Economic Census and Growth Rate from BLS.

Labor requirement to operate the controls is estimated for all equipment types combined, 

using the incremental FOM costs based on EPA modeling assumptions for the Transport Rule. 

Resulting FOM cost estimate was then converted to labor needs using the workers/$ Million in 

total payroll for the Electric Generating Sector. 

Retirement Labor

Table 12: Inputs to Labor from Retirements

Capacity of Incremental Retirements (MW) 4,772

O&M Decrease scaled to SSR Retirements ($MM) 

(To account for Partial Retirements) 302.8

Workers Per $Million in payroll, forecast to 2014 8.9

Sources: Productivity from 2007 Economic Census and Growth Rate from BLS.

Retirement labor was estimated by first identifying the retiring units from the parsed 

outputs (using incremental retirements in EPA’s Transport Rule policy case run). The next step 

involved estimating the capacity of incremental retirements as well as the change in the O&M 

costs due to these retirements. Because of the discrepancies between partial retirements in 

parsed outputs and System Summary Reports (SSR), O&M costs were scaled proportionately to 

reflect the lower SSR-based estimates, as shown in Table 12 above. O&M cost decreases were 

then converted to job-years lost due to retirements using workers per $Million in payroll. 
387

Fuel Use Labor

Table 13: Inputs to Labor for Fuel Use

Coal by 

Region Incremental Fuel Use (Tons) 2008 Short Tons/Employee hour

Appalachi

a 3,780,000 2.9

Interior (27,190,000) 4.8

West 2,870,000 19.9

Waste 

Coal (30,000) 6.0

Natural Gas 

EIA Total Natural Gas Production 2007 (TCF) 24.664

EIA Total Crude Oil Production 2007 (Barrels) 1,848,450,000

EIA Natural Gas Heat Content 2007 (Btu/cf) 1,027

EIA Petroleum Heat Content (MMBtu/Barrel) 6.151

Total Crude Oil and Natural Gas Production (MMBtu) 36,699,744,000

Economic Census 2007 Oil and Gas Extraction 

Employees

140,160

MMBtu per Man-year for Oil and Gas Extraction 261,842

Incremental Natural Gas from EPA (IPM) Results 

(TCF) 0.207

Incremental Natural Gas from EPA (IPM) Results 

(Converted to MMBtu) 212,279,903

Pipeline Capital Cost of New Pipeline ($MM) Productivity

 44.2 5.1

Note: Heat Contents from EIA are assumed to be for fuels used in Electric Power Sector

Sources: Short Tons per hour from US EIA, Coal Industry Annual. Total Production from 

2009 EIA Annual Energy Review. Heat Contents from EIA, Heat Content of Natural Gas 

Consumed and 2009 Annual Energy Review. Employment Data from 2007 Economic 

Census.

Fuel use related employment impacts were estimated using EPA’s Base Case and the 

Transport Rule runs using IPM for incremental changes in coal and natural gas use. For coal, 

estimates of coal use in tons by region from the relevant EPA runs were used in conjunction with 
388

labor productivity estimates from EIA for each region (in short tons/ employee hour), to 

calculate the change in job-hours needed. These were then converted to job-years, assuming 

2,080 working hours per year. As discussed in the main report above, because of the high 

variability in coal mining labor productivity in recent years, no attempt was made to forecast coal 

(and natural gas, for consistency) productivities, instead the most recent historical estimates were 

used in this TSD (which was the 2008 labor productivity for coal). 

For natural gas, the first step was estimating labor productivity since such information 

was not available directly from any reliable source. EIA production data from the Annual 

Energy Review for natural gas and crude oil (in TCF and barrels, respectively), along with EIA 

heat content estimates were used to find total crude oil and natural gas production in MMBtu for 

2007. Labor productivity in MMBtu per job-year for the Oil and Gas Extraction sector was then 

estimated using data from the Census on oil and gas extraction employment. Then, incremental 

natural gas demand (in Tcf) from the relevant EPA runs was converted to MMBtu of natural gas 

demand using EIA data on natural gas heat content. This was then used with the labor 

productivity estimated above to calculate the total job-years needed for increased natural gas 

demand for the Transport Rule. 

Jobs related to pipeline construction were estimated in proportion to the increased natural 

gas demand under this rule relative to those estimated for the utility Toxics Rule. More details 

on the approach used to convert pipeline information into jobs for the Toxics Rule are provided 

in the Toxics Rule employment TSD. 
389

References

DSI and Fabric Filter System Installation Labor Estimate. Memorandum by Andover 

Technology Partners to ICF International. January 26, 2011. 

“Flue Gas Desulfurization Technology Evaluation (Dry Lime vs. Wet Limestone FGD).” 

Prepared for National Lime Association, by Sargent & Lundy LLC. March 2007.

Staudt, Jim. Email Communication with Andover Technology Partners. Feb 7, 2011.

Platts, Steel Markets Daily, Vol 3 Issue 209, October 29, 2009. 

United States Department of Labor. Bureau of Labor Statistics. Industry Labor Productivity and 

Cost Data Tables, Annual Percent Changes. 2010. 

US Census Bureau. 2007 and 2002 Economic Census, 2000 Annual Survey of Manufacturers, 

Manufacturing and Mining: Detailed Statistics by Industry for the US and Utilities: 

Summary Statistics for the US.

US Department of Energy, National Energy Technology Laboratory. Hoffmann, Jeff and 

Ratafia-Brown, Jay, Science Applications International Corporation. “Preliminary Cost 

Estimate of Activated Carbon Injection for Controlling Mercury Emissions from an UnScrubbed 500 MW Coal-Fired Power Plant.” November 2003. 

US Energy Information Administration (EIA). Annual Energy Review 2009, Assorted Data for 

Coal, Natural Gas, and Petroleum. 2009. 

US Energy Information Administration (EIA). Natural Gas 2009, Heat Content of Natural Gas 

Consumed 2009.

US Environmental Protection Agency (EPA), Air Pollution Prevention and Control Division, 

Office of research and Development and Department of Civil and Environmental 

Engineering, North Carolina State University. Vijay, Samudra, Yelverton, William and 

DeCarolis, Joseph. “Development of Supply Curves for Abatement of GHG from Coalfired Utility Boilers” June, 2009. 

US EPA. Clear Skies Act, Technical Package, Section D. “Projected Impacts on Generation and 

Fuel Use”. 2003. 

US EPA. Clean Air Interstate Rule, Technical Support Document. “Boilermaker Labor Analysis 

and Installation Timing.” OAR-2003-0053 (March, 2005).

US EPA. Employment Estimates of Direct Labor in Response to the Proposed Toxics Rule in 

2015. Technical Support Document, (March 2011).

US EPA. “Impacts of the Acid Rain Program on Coal Industry Employment.” EPA 430-R-01-

002 (March, 2001).
390

US EPA, Office of Research and Development. “Engineering and Economic Factors Affecting 

the Installation of Control Technologies for Multi-pollutant Strategies.” EPA-600/R02/073 (October, 2002).

US EPA, Clean Air Technology Center. Air Pollution Control Cost Manual, Sixth Edition, EPA 

452/B-02-001 (January, 2002). 
391

APPENDIX E

ALTERNATE PRESENTATION OF THE BENEFITS OF THE FINAL FEDERAL 

TRANSPORT RULE

In this appendix, we provide an alternate presentation of the benefits of the final Federal 

Transport Rule. This alternate presentation is an attempt to incorporate the recommendations 

from EPA’s recently published Guidelines for Preparing Economic Analyses (U.S. EPA, 2010). 

The Guidelines provide four basic principles for constructing thorough and transparent results 

tables:

 All meaningful benefits and costs are included,

 The types of benefits and costs are briefly described in plain terms,

 The benefits are expressed first in natural or physical units, and

 Explanatory notes accompany each benefit and cost entry. 

By adopting these general principles, the tables provided in this appendix do not exactly

match the tables provided in the benefits chapter of this RIA and previous RIAs, but they are 

consistent with the types of information previously provided. In Part A, we identify the 

quantified and unquantified benefits of this rule, indicate whether each specific effect has been 

quantified and monetized in this assessment, and explain the lack of quantification for certain 

effects. In Part B, we provide the benefits in natural units for each specific effect that was 

quantified in this assessment, including the 95th percentile confidence intervals. In Part C, we 

provide the dollar value of the benefits monetized in this assessment at discount rates of 3% and 

7%, including the 95th percentile confidence intervals. To avoid repetition, we have limited Parts 

B and C to the quantified effects for the selected regulatory option. In each table, we identify the 

section of the RIA where each effect and the underlying assumptions are discussed in detail.

It is important to emphasize that the list of unquantified effects is not exhaustive, nor is 

quantification of each effect complete. In order to identify the most meaningful human health 

and environmental benefits, we excluded effects not identified as having at least a causal, likely

causal, or suggestive relationship with the affected pollutants in the most recent comprehensive 

scientific assessment, such as an Integrated Science Assessment. This does not imply that 

additional relationships between these and other human health and environmental benefits and 

the affected pollutants do not exist. Due to this decision criterion, some effects that were 

identified in previous lists of unquantified benefits have been dropped (e.g., UVb exposure). In 

addition, some quantified effects represent only a partial accounting of likely impacts due to 

limitations in the currently available data (e.g., climate effects from CO2, etc).
392

As the use of these tables evolves, further refinements will be made to improve the 

information provided and to assure consistency with previous EPA analysis and EPA and OMB 

guidelines
393
394
395
396
397

References

Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Change 2007: Synthesis 

Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of 

the Intergovernmental Panel on Climate Change (AR4) [Core Writing Team, Pachauri, 

R.K and Reisinger, A. (eds.)]. IPCC, Geneva, Switzerland, 104 pp. Available on the 

Internet at

<http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report

_synthesis_report.htm>. 

National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on 

the Toxicological Effects of Methylmercury, Board on Environmental Studies and 

Toxicology, Commission on Life Sciences, National Research Council. National 

Academy Press, Washington, DC.

U.S. Environmental Protection Agency (U.S. EPA). 1997. Mercury Study Report to Congress. 

EPA-452/R-97-003. Washington, DC: U.S. EPA. December. Available on the Internet at 

<http://www.epa.gov/hg/report.htm>.

U.S. Environmental Protection Agency (U.S. EPA). 2006. Air Quality Criteria for Ozone and 

Related Photochemical Oxidants (Final). EPA/600/R-05/004aF-cF. Washington, DC: 

U.S. EPA. February. Available on the Internet at 

http://cfpub.epa.gov/ncea/CFM/recordisplay.cfm?deid=149923.

U.S. Environmental Protection Agency (U.S. EPA). 2008. Integrated Science Assessment for 

Oxides of Nitrogen - Health Criteria (Final Report). National Center for Environmental 

Assessment, Research Triangle Park, NC. July. Available at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645>. 

U.S. Environmental Protection Agency (U.S. EPA). 2008. Integrated Science Assessment for 

Sulfur Oxides - Health Criteria (Final Report). National Center for Environmental 

Assessment, Research Triangle Park, NC. September. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843>.

U.S. Environmental Protection Agency (U.S. EPA). 2008. Integrated Science Assessment for 

Oxides of Nitrogen and Sulfur—Ecological Criteria National (Final Report). National 

Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-08/139. 

December. Available on the Internet at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485>.

U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for 

Particulate Matter (Final Report). EPA-600-R-08-139F. National Center for 

Environmental Assessment—RTP Division. December. Available at 

<http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.

U.S. Environmental Protection Agency (U.S. EPA). 2010. Guidelines for Preparing Economic 

Analyses. EPA 240-R-10-001. National Center for Environmental Economics, Office of 

Policy. Washington, DC. December. Available on the Internet at 
398

<http://yosemite.epa.gov/EE/epa/eed.nsf/pages/Guidelines.html/$file/Guidelines.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011. Integrated Science Assessment for 

Ozone and Related Photochemical Oxidants (First External Review Draft). U.S. 

Environmental Protection Agency, Washington, DC, EPA/600/R-10/076A. National 

Center for Environmental Assessment—RTP Division. March. Available at 

<http://www.epa.gov/ncea/isa/pdfs/ozone/Ozone_ISA_ERD1.pdf>.

U.S. Environmental Protection Agency (U.S. EPA). 2011. Methylmercury (MeHg) (CASRN 

22967-92-6). Integrated Risk Information System (IRIS). Available at 

<http://www.epa.gov/iris/subst/0073.htm>.
399

APPENDIX F

ALTERNATE TABLES BASED ON REMEDY SENSITIVTY WITH REVISED 

VARIABILITY LIMITS

The projected impacts of the final Transport Rule reported throughout the preamble and 

RIA do not reflect minor technical corrections to SO2 budgets in three states (KY, MI, and NY). 

The modeling results reported in the preamble and RIA also assumed preliminary variability

limits that were smaller than the variability limits finalized in this rule. EPA conducted 

sensitivity analysis confirming that these differences do not meaningfully alter any of the 

Agency's findings or conclusions based on the projected cost, benefit, and air quality impacts 

presented for the final Transport Rule. The results of this sensitivity analysis are presented in the 

tables below. 

Table F-1. Annual Emissions of SO2 and NOx in 2010 and Percentage of Emissions in the 

Transport Rule Affected Region (tons)

SO2 NOX

Transport Rule

Annual NOX and SO2 States 4,251,996 1,424,228

Nationwide

(Contiguous 48 States) 5,165,954 2,136,612

Emissions of Transport Rule 

States as Percentage of 

Nationwide Emissions

82% 67%

Source: EPA emissions data from all reporting units.

Note: Transport Rule annual NOX and SO2 states include Alabama, Georgia, Illinois, Indiana, Iowa, Kansas, 

Kentucky, Maryland, Michigan, Minnesota, Missouri, Nebraska, New Jersey, New York, North Carolina, Ohio, 

Pennsylvania, South Carolina, Tennessee, Texas, Virginia, West Virginia, and Wisconsin.
400

Table F-2. Transport Rule Annual NOx and SO2 and NOx Ozone Season State Emission 

Budgets (tons)

SO2

Annual

NOX

Annual

NOX

Ozone Season

2012 and 

2013

2014 and 

Later

2012 

and 

2013

2014 

and 

Later

2012 and 

2013

2014 and 

Later

Alabama

 

216,033 

 

213,258 

 

72,691 

 

71,962 

 

31,746 

 

31,499 

Arkansas --- --- --- ---

 

15,037 

 

15,037 

Florida --- --- --- ---

 

27,825 

 

27,825 

Georgia

 

158,527 

 

95,231 

 

62,010 

 

40,540 

 

27,944 

 

18,279 

Illinois

 

234,889 

 

124,123 

 

47,872 

 

47,872 

 

21,208 

 

21,208 

Indiana

 

285,424 

 

161,111 

 

109,726

 

108,424

 

46,876 

 

46,175 

Iowa

 

107,085 

 

75,184 

 

38,335 

 

37,498 

 

16,532* 

 

16,207*

Kansas

 

41,528 

 

41,528 

 

30,714 

 

25,560 

 

13,536* 

 

10,998* 

Kentucky

 

232,662

 

106,284 

 

85,086 

 

77,238 

 

36,167 

 

32,674 

Louisiana --- --- --- ---

 

13,432 

 

13,432 

Maryland

 

30,120 

 

28,203 

 

16,633 

 

16,574 

 

7,179 

 

7,179 

Michigan

 

229,303

 

143,995 

 

60,193 

 

57,812 

 

25,752* 

 

24,727* 

Minnesota

 

41,981 

 

41,981 

 

29,572 

 

29,572 --- ---

Mississippi --- --- --- ---

 

10,160 

 

10,160 

Missouri

 

207,466 

 

165,941 

 

52,374 

 

48,717 

 

22,762* 

 

21,073* 

Nebraska

 

65,052 

 

65,052 

 

26,440 

 

26,440 --- ---

New Jersey

 

5,574 

 

5,574 

 

7,266 

 

7,266 

 

3,382 

 

3,382 

New York

 

27,325

 

18,585

 

17,543 

 

17,543 

 

8,331 

 

8,331 
401

North Carolina

 

136,881 

 

57,620 

 

50,587 

 

41,553 

 

22,168 

 

18,455 

Ohio

 

310,230 

 

137,077 

 

92,703 

 

87,493 

 

40,063 

 

37,792 

Oklahoma --- --- --- ---

 

21,835* 

 

21,835* 

Pennsylvania

 

278,651 

 

112,021 

 

119,986

 

119,194

 

52,201 

 

51,912 

South Carolina

 

88,620 

 

88,620 

 

32,498 

 

32,498 

 

13,909 

 

13,909 

Tennessee

 

148,150 

 

58,833 

 

35,703 

 

19,337 

 

14,908 

 

8,016 

Texas

 

243,954 

 

243,954 

 

133,595

 

133,595

 

63,043 

 

63,043 

Virginia

 

70,820 

 

35,057 

 

33,242 

 

33,242 

 

14,452 

 

14,452 

West Virginia

 

146,174 

 

75,668 

 

59,472 

 

54,582 

 

25,283 

 

23,291 

Wisconsin*

 

79,480 

 

40,126 

 

31,628 

 

30,398 

 

13,704 

 

13,216 

Total

 

3,301,008

 

2,128,264

 

609,435

 

574,107

 

1,245,869

 

1,164,910

*Note: as discussed in the preamble to the final rule, EPA is issuing a supplemental proposal to 

request comment on inclusion of these six states in the Ozone Season program.

Table F-3. 2009 Electricity Net Generation and EPA Base Case Projections for 2012, 2014, 

2020 and 2030 for the Contiguous 48 States (Billion kWh)

Historical Base Case

2009 2012 2014 2020 2030

Coal 1,754 1,958 2,017 2,037 2,071

Oil 29 0.09 0.10 0.14 0.19

Natural Gas 917 721 686 823 1,155

Nuclear 799 812 822 832 811

Hydroelectric 272 281 287 287 286

Non-hydro Renewables 143 233 250 288 328

Other 0 35 45 44 53

Total 3,914 4,041 4,107 4,311 4,704

Source: 2009 data from EIA Electric Power Annual 2009, Table 1.1 (adjusted to represent the Contiguous 

48 States for consistency with projections, which are from the Integrated Planning Model run by EPA, 

2011).
402

Table F-4. Projected Emissions of SO2, NOX, and CO2 with the Base Case (No Further 

Controls) and with Transport Rule (Million Tons)

2012 2014

Base TR

Chan

ge

% 

Change Base TR

Chan

ge

% 

Change

SO2

(Annual)

Contiguous 

48 States 7.9 3.9 -3.9 -50% 7.2 3.4 -3.8 -52%

TR States 7.0 3.0 -4.0 -57% 6.2 2.4 -3.8 -61%

NOX

(Annual)

Contiguous 

48 States 2.1 2.0 -0.2 -7% 2.1 1.9 -0.2 -9%

TR States 1.4 1.3 -0.1 -9% 1.4 1.2 -0.2 -11%

NOX

(Summer)

Contiguous 

48 States 0.9 0.9 -0.1 -8% 0.9 0.8 -0.1 -9%

TR States 0.7 0.6 -0.1 -9% 0.7 0.6 -0.1 -11%

CO2

(Annual)

Contiguous 

48 States 2444 2430 -13 -1% 2482 2454 -28 -1%

TR States 1769 1747 -22 -1% 1799 1765 -44 -2%

Note: Numbers may not add due to rounding. The emissions data presented here are EPA modeling results and 

the Transport Rule region includes states modeled for the annual SO2 and NOx requirements. “Summer” is from 

May 1–September 30, which is the ozone season. Base case includes Title IV Acid Rain Program, NOx SIP Call, 

and state rules through December 1, 2010.

Source: Integrated Planning Model run by EPA, 2011.

Table F-5. 2010 Fossil-fuel Generation Nationwide and in the Transport Region 

(Thousand Megawatthours)

Contiguous 

US

Transport 

Rule Fine 

Particle 

Area

Transport 

Rule Ozone 

Area

Coal-fired 1,829,176 1,405,021 1,510,557

Oil-fired 13,680 5,955 12,080

Natural Gas-fired 894,598 409,534 633,636

Total 3,954,846 2,598,207 2,989,910

Source: EIA Electric Power Monthly with data for December 2010, Tables 1.6B, 1.7.B, 1.8.B, 

1.10.B.
403

Table F-6. Total Fossil-fuel Capacity Nationwide and in the Transport Region

Contiguous 

US

Transport Rule 

Fine Particle Area

Transport Rule 

Ozone Area

Pulverized Coal 316 247 266

Combined Cycle 199 97 142

Other Oil/Gas 247 155 196

Total 998 630 743

Source: EPA's NEEDS v4.10.

Table F-7. Annualized Compliance Cost of the Transport Rule

2012 2014 2020

Annualized Compliance Cost

(billions of 2007$)

$1.7 $0.7 $0.4

Note: Numbers rounded to the nearest hundred million for annualized cost.

Source: Integrated Planning Model run by EPA, 2011.

Table F-8. Percent of Generation without SO2 Controls by Coal Sulfur Content, 2014

Very Low

(<0.8 

lb/mmBtu)

Low

(0.81-1.2 

lbs/mmBtu)

LowMedium

(1.21-1.66 

lbs/mmBtu)

HighMedium

(1.67-3.34 

lb/mmBtu)

High

(>3.35 

lb/mmBtu)

Base Case 27% 37% 8% 15% 13%

Transport Rule 58% 29% 8% 3% 2%

Source: Integrated Planning Model run by EPA, 2011.
404

Table F-9. Coal Use by Sulfur Category in the PM2.5 Transport Region for the Base Case 

and Transport Rule* (million short tons)

Lignite Subbituminous Bituminous Total

High 

sulfur

Low 

sulfur

Very

low 

sulfur

High 

sulfur

Highmedium 

sulfur

Lowmedium 

sulfur

Low 

sulfur

2012 Base 30 13 145 156 91 211 83 3 732

TR 25 2 79 256 77 192 78 13 723

2014

Base 37 13 145 154 102 216 71 9 747

TR 32 2 106 200 92 202 72 17 724

*These coal usage results are for the 23 states covered by the rule in the trading program to reduce SO2 emissions. 

Source: Integrated Planning Model run by EPA, 2011.

Table F-10. Newly-Constructed Advanced SO2 Control Retrofits on Coal-fired Generation 

by Technology Built with the Base Case and with the Transport Rule (GW) in 2014

Base 

Case

Transport 

Rule

Incremental 

Retrofits

Wet FGD 4.6 10.7 6.0

Dry FGD 3.6 4.1 0.5

DSI 6.8 8.9 2.1

Note: FGD (Flue Gas Desulphurization) and DSI (Dry Sorbent Injection) are advanced SO2 controls. Source: Integrated 

Planning Model run by EPA, 2011.

Table F-11. Coal-fired Capacity Operating Advanced Pollution Controls with the Transport 

Rule, 2014 (GW)

Existing 

Controls 

Induced to 

Operate

NewlyBuilt 

Controls

Total 

Operational 

Controls

FGD 24 7 209

DSI 0 2 9

SCR 5 0 146

Note: FGD (Flue Gas Desulphurization) and DSI (Dry Sorbent Injection) are advanced SO2 controls. SCR (Selective 

Catalytic Reduction) is an advanced NOX control. Source: Integrated Planning Model run by EPA, 2011.
405

Table F-12. Projected allowance prices for the four Transport Rule programs in 2012 and 

2014 (2007$)

Emission Allowance

Prices ($/Ton)

2012 2014

Annual SO2 Group 1 Trading Program 1,100 1,300

Annual SO2 Group 2 Trading Program 600 700

Annual NOx Trading Program 500 600

Ozone Season NOx Trading Program 1,600 1,800

Table F-13. Generation Mix with the Base Case (No Further Controls) and with Transport 

Rule, Contiguous US (Thousand GWh)

2009 2012 2014

Historical Base TR

Change 

from 

Base

Percent 

Change Base TR

Change 

from 

Base

Percent 

Change

Coal 1,754 1,958 1,933 -24 -1.2% 2,017 1,977 -40 -1.9%

Oil 29 0.09 0.09 0.00 -0.0% 0.10 0.11 0.01 5.8%

Natural Gas 917 721 741 18 2.7% 686 715 29 4.1%

Nuclear 799 812 812 0 0.0% 822 828 6 0.8%

Hydroelectric 272 281 282 1 0.3% 287 286 -1 -0.3%

Non-hydro 

Renewables 143 233 239 62.7% 250 250 10.3%

Other 0 35 35 0.0 0.0% 45 45 0.6 1.3%

Total 3,914 4,041 4,043 2 0.1% 4,107 4,102 -4 -0.1%

Note: Numbers may not add due to rounding.

Source: 2009 data from EIA Electric Power Annual 2009, Table 2.1 and Net Generation by State by Type of Producer by Energy Source 

(EIA-906); 2012 and 2014 projections are from the Integrated Planning Model run by EPA, 2011.
406

Table F-14. Total Generation Capacity by 2014 (GW)

2010 Base TR Change

Pulverized Coal 317 312 307 -6

Natural Gas Combined Cycle 201 205 205 0

Other Oil/Gas 253 232 233 0

Non-Hydro Renewables 31 70 70 0

Hydro 99 99 99 0

Nuclear 102 103 104 1

Other 5 81 85 4

Total 1,009 1,029 1,025 0

Source: 2010 data from EPA's NEEDS v4.10. Projections from Integrated Planning Model run by EPA.

Note: “Renewables” include biomass, geothermal, solar, and wind electric generation capacity.

Table F-15. Coal Production for the Electric Power Sector with the Base Case (No Further 

Controls) and with the Transport Rule (Million Tons)

2009 2012 2014

Supply

Area Historical Base TR

Change 

from 

Base Base TR

Change 

from 

Base

Appalachia 246 193 185 -9 182 183 1

Interior 129 209 184 -26 238 216 -22

West 553 544 570 26 554 552 -1

Waste Coal 14 14 14 0 14 14 0

Imports 19 31 31 0 30 30 0

Total 961 991 983 -8 1,017 995 -22

Source: Production: U.S. Energy Information Administration (EIA data), Coal Distribution -- Annual (Final), web 

site http://www.eia.doe.gov/cneaf/coal/page/coaldistrib/a_distributions.html (posted February 18, 2011); Waste 

Coal: U.S. EIA, Monthly Energy Review, January 2011 Edition, Table 6.1 Coal Overview, web site 

http://www.eia.doe.gov/emeu/mer/coal.html (posted January 31, 2011). Imports: U.S. Energy Information 

Administration (EIA) and U.S. Census. All projections from Integrated Planning Model run by EPA, 2011. 
407

Table F-16. Projected Regional Retail Electricity Prices with the Base Case (No Further 

Controls) and with Transport Rule (2007 cents/kWh)

Base Transport Rule Percent Change

2012 2014 2020 2012 2014 2020 2012 2014 2020

ECAR 7.4 7.9 8.1 7.7 8.1 8.3 3.5% 2.9% 2.2%

ERCOT 7.8 8.8 8.7 8.1 8.8 8.8 4.0% 0.2% 1.8%

MAAC 8.4 9.4 10.3 8.8 9.5 10.4 4.6% 1.4% 1.1%

MAIN 7.3 7.9 8.4 7.5 8.1 8.4 2.4% 1.7% 0.9%

MAPP 7.8 8.0 7.9 7.8 8.1 7.9 0.4% 0.7% 0.2%

NY 12.5 13.7 13.3 12.9 13.7 13.4 2.9% 0.4% 0.4%

NE 11.4 12.3 11.8 11.7 12.3 11.8 2.1% -0.2% 0.4%

FRCC 9.5 10.2 9.7 9.6 10.2 9.8 1.3% -0.3% 0.3%

STV 7.7 7.9 7.8 7.8 7.9 7.8 1.0% 0.5% 0.4%

SPP 7.4 7.6 7.4 7.4 7.6 7.4 0.9% 0.0% -0.2%

PNW 6.9 7.1 6.9 7.0 7.1 6.9 0.6% 0.0% 0.0%

RM 8.6 9.1 9.4 8.7 9.2 9.4 0.9% 0.2% 0.2%

CALI 12.7 13.0 12.5 12.9 13.0 12.5 1.0% -0.1% 0.2%

TR Region 

Average 8.5 9.2 9.2 8.7 9.3 9.3 2.6% 0.8% 0.8%

Contiguous 

U.S. Average 8.5 9.0 8.9 8.6 9.0 9.0 2.0% 0.7% 0.7%

Source: EPA’s Retail Electricity Price Model.

Table F-17. Average Minemouth and Delivered Coal Prices with the Base Case and with 

the Transport Rule (2007$/MMBtu)

2012 2014

2007 Base TR

Percent 

Change 

from 

Base Base TR

Percent 

Change 

from 

Base

Minemouth 1.27 1.35 1.29 -4.6% 1.37 1.35 -1.7%

Delivered 1.76 2.08 2.05 -1.4% 2.12 2.10 -0.9%

Source: Historical data from EIA AEO 2010 Reference Case Table 15 (Coal Supply, Distribution, and Prices); 

projections from the Integrated Planning Model run by EPA, 2011.
408

Table F-18. 2012-2030 Weighted Average Henry Hub and Delivered Natural Gas Prices 

with the Base Case and with the Transport Rule (2007$/MMBtu)

Base TR

Percent 

Change 

from 

Base

Henry Hub 5.32 5.34 0.5%

Delivered - Electric Power 5.60 5.62 0.4%

Delivered - Residential 10.97 10.99 0.2%

Source: Projections from the Integrated Planning Model run by EPA (2011) adjusted to Henry Hub prices using 

historical data from EIA AEO 2011 reference case to derive residential prices.