i Regulatory Impact An a lysis ( 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 2011ii 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 PM 2.5 and Ozone

................................ ................................ ................. 64 4.2.1 Converting CAMx PM 2.5 Outputs to Benefits Inputs

.......................... 64 4.2.2 PM 2.5 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

................................ ................................ ........... 69iii 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 PM 2.5 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 Admiss ions

................ 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 Ecolog ical 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 Vegetationiv and Ecosystems

................................ ................................ ........... 168 5.5.6.1 Ozone Effects on Forests

................................ ................... 170 5.5.6.2 Ozone Effects on Crops a nd Urban Ornamentals............. 176 5.5.7 Unquantified SO 2 and NO 2 - Rel ated 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 De regulation and Restructuring

................................ ................................ ........... 227 6.3 Pollution and EPA Regulation of Emissions

................................ ....................... 228 6.4 Po llution Control Technologies

................................ ................................ ........... 23 0 6.5 Air Re gulation of the Power Sector

................................ ................................ ..... 231 6.6 R evenues, 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 B ackground

................................ ................................ .............................. 243 7.2 Projected SO 2 and NO x Emissions and Reductions

................................ . 250 7.3 Overvie w of Costs and Other Impacts

................................ ..................... 253 7.4 Projected Compliance Costs

................................ ................................ .... 255 7.5 Projected Approa ches 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 f or the Electric Power Sector

........................ 263 7. 10 Project ed Retail Electricity Prices

................................ ........................... 266 7.1 1 Projected Fuel Price Impacts

................................ ................................ ... 267 7.1 2 Key Differences in EPA Model Runs for Transport Rule Modeling

...... 269 7.1 3 Projected Primary PM and Carbon Dioxide Emissions from

Power Plants

................................ ................................ ............................. 270 7.1 4 Limitations of Analysis

................................ ................................ ............ 270 7.1 5 Significant Energy Impact

................................ ................................ ...... 274 7.1 6 References

................................ ................................ ................................ 277

8. MACROECON OMIC 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

................................ ....................... 286v

8.3 Employment Impacts primarily on the regulated industry: Morgenstern, Pizer, and Shih (2002)

................................ ................................ .......................... 287 8.3.1 Lim itations

................................ ................................ ............................... 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 Impa cts of the Transport Rule

................................ .. 295 8.5 Summary

................................ ................................ ................................ .............. 296 8.6 References

................................ ................................ ................................ ............ 298

9. STATUTORY AND EXEC UTIVE ORDER IMPACT ANALYSES

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

................................ ................................ ........................... 299 9.1.1 Identific ation of Small Entities

................................ ................................ 301 9.1.2 Over view of Analysis and Results

................................ ........................... 302 9.1.2.1 Methodology for Estimating Impacts of the Tran sport Rule on Small Entities

................................ ................................ ......... 302 9.1.2.2 Results

................................ ................................ .................... 305 9.1.3 Sum mary of Small Entity Impacts

................................ ........................... 310 9.2 Unfunded Mandates Reform Act (UM RA) Analysis

................................ ......... 310 9.2.1 Identification of Government

- Owned Entities

................................ ......... 311 9.2.2 Over view 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 E ntity Impacts

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

................................ ................................ .................... 319 9.4 Protection of Children from Environmental Health and Safety Risks

........ 320 332 9.5 Executive Order 13132, Federalism

................................ .............................. 321 9.6 Executive Order 13175, Consultation and Coordination with Indian Tribal Governments

................................ ................................ ............................. 321 9.7 Environmental Justic e

................................ ................................ .................. 322 9.7.1 Consideration of Environmental Justice Issues in the Rule Development Process

................................ ................................ ....... 323 9.7.2 Mea ningful Public Participation

................................ ...................... 324

10. COMPA RISON OF BENEFITS AND COSTS

................................ ............................. 325

10 .1 Comp arison of Benefits and Costs

................................ ........................... 325 10.2 References

................................ ................................ ................................ 330

Appendix A: Distribution of the PM 2.5 - Related Benefits Among Vulnerable and Suscep tible

Populations

................................ ................................ ................................ .......... 331vi

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

................................ ................................ ................................ ..................... 3991 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 (SO 2 ) and nitrogen oxide (NO x )
emissions f

rom the electric power industry in 28 eastern states starting in 2012 1

. I n 2014

, this final rule will have annual benefits (in 2007 $) between $1 2 0 to $2 8 0 billion using a 3%
discount rate and $ 11 0 and $2 5 0 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 $ 2 80 billion or $ 11 0 to $ 2 70 billion. The benefits outweigh social costs from
1 50 up to 350 to 1, or from 110 up to 335 to 1. The benefits result primarily from 1 3 ,000 to
3 4 ,000 fewer PM 2.5 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 cle ar 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. T he 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 trad ing ) in
the final rule are in Table 1

- 1. This select ed remedy covers the electric power industry and
allows int er state emissions trading of sulfur dioxide (SO 2 ) and nitrogen oxides (NO x ) in the
covered states as listed in section 2.2 of this RIA

.

1 As finalized, the rule requires emission reductions in 27 states. EPA issued a supplemental proposal to request comment on requiring ozone

- season NOx reductions in additional states; including the states addressed in the supplemental proposal, the total number of states covered by the Transport Rule would be 28.2

Table 1

- 1. Summary of EPA’s Estimates of Benefits, Costs, and Net Benefits of the Select ed Remedy in the Transport Rule in 2014 a (billions of 2007$)

Description Estimate

(3% Discount Rate) Estimate (7% Discount Rate) Social costs b $ 0.8 1 $ 0.8 1 Social benefits c,d $1 2 0 to $2 8 0 + B $1 10 to $2 5 0 + B Health

- related benefits: $1 1 0 to $2 7 0 + B $1 00 to $2 5 0 + B Visibility benefits e $4.1 $4.1 Net benefits (benefits

- costs) $1 2 0 to $2 8 0 $ 11 0 to $2 5 0

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 Ta ble 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 c overs Class I areas. Valuation assumes discounting over the SAB

recommended 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 analys es (U.S. EPA, 2010; OMB, 2003). The estimate of social benefits also includes CO 2 related benefits calculated using the social cost of carbon, discussed further in C hapter 5.

d Potential benefit categories that have not been quantified and monetized are l isted 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 (PM 2.5 ), and nitrogen oxide contributes
to the formation of both PM 2.5 and ground

- le vel ozone.

Our analyses suggest this would yield benefits in 2014 of $ 1 2 0 to $ 2 8 0 billion (based on a 3 percent discount rate) and $ 1 10 to $ 2 5 0 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 PM 2.5 and ozone air pollution. A recent EPA analysis estimated that 2005 levels of PM 2.5 and ozone were responsible for between 130,0003

and 320,000 PM2.5

- related and 4,700 ozone

- related p remature 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 admissi ons 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 PM 2.5 - related premature deaths in 2014 by between 1 3 ,000 and 3 4 ,000 , 1 5 ,000 non

- fatal heart attacks, 8

, 7 00 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 pu blic health burden of PM 2.5 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

r eductions 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 su mmarize 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 s ignificantly larger than those modeled for 2014 because of the much greater incremental SO 2 reductions in 2012 compared to 2014 from the base case. This occurs because the final rule expedites the adoption of SO 2 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

region C Total Premature Mortality (Pope et al. 2002 PM mortality and Bell et al. 2004 ozone mortality estimates)

3% discount rate PM 2.5 & O 3 $100 ($8.3

— $320) $0.3 ($0.01

— $0.9) $100

($8.3

— $320)

7% discount rate PM 2.5 & O 3 $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 PM 2.5 & O 3 $270 ($23

— $770) $0.7 ($0.05

— $2) $270

($23

— $770)

7% discount rate PM 2.5 & O 3 $240 ($21

— $700) $0.6

($0.05

— $1.8) $240

($21

— $700)

Chronic Bronchitis PM 2.5 $4.2 ($0.2

— $19) $0.01

($

- 0.003

-- $0.06) $4.2 ($0.2

— $19)
Non

- fatal heart attacks

3% discount rate PM 2.5 $1.7 ($0.3

— $4.2) $0.004

($0.003

— $0.01) $1.7

($0.3

— $4.2)

7% discount rate PM 2.5 $1.3 ($0.3

— $3.1) $0.004

($0.002

— $0.001) $1.3 ($0.3

— $3.1) Hospital admissions

— respiratory PM 2.5 & O 3 $0.04 ($0.02

— $0.06) --- $0.04 ($0.02

— $0.06) Hospital admissions

— cardiovascular PM 2.5 $0.09 ($0.01

— $0.2) --- $0.09 ($0.01

— $0.2)
Emergency room visits for asthma PM 2.5 & O 3 $0.003 ($0.002

— $0.006) --- $0.003 ($0.002

— $0.006)

Acute bronchitis PM 2.5 $0.008 (<$

- 0.01

— $0.02) D --- $0.008 (<$

- 0.01

— $0.02) c
Lower respiratory symptoms PM 2.5 $0.004 ($0.002

— $0.009) --- $0.004 ($0.002

— $0.009)
Upper respiratory symptoms PM 2.5 $0.005 (<$0.01

— $0.014) --- $0.005 (<$0.01

— $0.014)

Asthma exacerbation PM 2.5 $0.02 ($0.002

— $0.08) --- $0.02 ($0.002

— $0.08)
Lost work days PM 2.5 $0.2 ($0.17

— $0.24) --- $0.2 ($0.17

— $0.24)

School loss days O 3 $0.01 ($0.004

— $0.013) --- $0.01 ($0.004

— $0.013) Minor restricted

- activity PM 2.5 & O 3 $0.7

--- $0.77

days ($0.3

— $1) ($0.3

— $1) Recreational visibility, Class I areas PM 2.5 $4.1
Social cost of carbon (3% discount rate, 2014 value) CO 2 $0.6

Monetized total Benefits (Pope et al. 2002 PM 2.5 mortality and Bell et al. 2004 ozone mortality estimates)

3% discount rate PM 2.5 , O 3 $1 1 0 ($8.8

— $340) $0. 28 ($0.01

— $0.9) $120

($1 4

— $350)

7% discount rate PM 2.5 , O 3 $1 0 0 ($8

— $310) $0.03

($0.0 1

— $0. 85 ) $110

($13

— $320)

Monetized total Benefits (Laden et al. 2006 PM 2.5 mortality and Levy et al. 2005 ozone mortality estimates)

3% discount rate PM 2.5 , O 3 $2 7 0 ($24

— $800) $0.7

($0.05

— $2. 1 ) $280

($29

— $810)

7% discount rate PM 2.5 , O 3 $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 pollutio n 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 bene fits categories that are accounted for in the monetized benefits for this final
rule.8 Figure 1

- 1 summarizes an array of PM 2.5 - related monetized benefits estimates based on alternative epidemiology and expert

- derived PM

- mortality estimate as well as the s um 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 graph ic includes one estimate of ozone

- related mortality and fourteen different PM 2.5 related mortality and a single 3% or 7% discounted cost estimate. 2 Each of the 14 bars in each graph represents a separate point estimate of net benefits under a certain comb ination 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.

2 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 o zone mortality estimate, for simplicity of presentation we have not included this full suite.9

Figure 1

- 1 Estimated Monetized Value of Estimated PM 2.5 - Related Premature Mortalities Avoided According to Epidemiology or Expert

- derived Derived PM Mortality Risk Estimate A

A Column total equals sum of PM 2.5 - related mortality and morbidity benefits and ozone

- related morbidity and mortality benefits using the Bell et al. (200 4) mortality estimate.

$0 $50 $100 $150 $200 $250 $300 $350 $400

Pope et al. Laden et al. Billions (2007$)

PM 2.5 Benefits estimates derived from 2 epidemiology functions and 12 expert functions 3% DR

7% DR10

Figure 1

- 2: Net Benefits of the Transport Rule A ccording to PM 2.5 Epidemiology or Expert

- derived Mortality Risk Estimate A

A Column total equals sum of PM 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

1.1.3

. 1 Alternatives that Are More or Less Stringent

In accordance w ith Circular A

- 4 and EPA‘s for Guidelines for Preparing Economic Analyses , EPA also analyzed the costs and benefits of two options that differed in their stringency from the selected remedy option

– one less stringent, the other more stringent. Both option s have the same 2012 requirements and varied in the requirements for SO 2 

### $0 $50 $100 $150 $200 $250 $300 $350 $400

Pope et al. Laden et al. Billions (2007$)

PM 2.5 Benefits estimates derived from 2 epidemiology functions and 12 expert functions 3% DR

7% DR11

emissions reductions in 2014. Both options only applied to the Group 1 states; requirements
for SO 2 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 select ed remedy, which requires greater SO 2 reductions , reductions of up to $2,300/ton in marginal cost in 1 6 states (Group 1) beginning in 2014 from 2012 emissions levels, the less stringent option only requires SO 2 reductions in 2014 of up to $1,600/ton in marginal cost in Group 1 states. The more stringent option requires SO 2 reductions in 2014 of up to $10,00 0/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 select ed remedy.

Table 1

- 4 . Summary of Annual Benefit s, Costs, and Net Benefits of Versions of the Select ed Remedy Option in 2014 a (billions of 2007 dollars)

Description Preferr ed Remedy Less Stringent Scenario More Stringent

Scenario

Social costs b
3 % discount rate $ 0.8 1 $ 0. 4 3 $ 3. 6 7 % discount rate $ 0.8 1 $ 0. 4 3 $ 3. 6 Health

- related benefits c,d
3 % discount rate 7 % discount rate $1 1 0 to $2 7 0 + B $1 00 to $2 5 0 + 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 $1 1 0 to $2 7 0 $ 10 0 to $2 5 0 $9 8 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 les s 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 impac ts 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 PM 2.5 – relat ed benefits but omit visibility, ozone, and CO 2 - related benefits. We present the PM 2.5 – related benefits of the select ed remedy, omitting these other important benefits, so that readers may compare directly the benefits of the select ed and alternate remedi es. 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 PM 2.5 – related monetized benefits. Benefits in this table are natio nwide and are associated with NO x and SO 2 reductions. Visibility and ozone

- related benefits not calculated for the more and less stringent scenarios because these impacts were estimated using PM 2.5 - related benefit per ton estimates.
d Not all possible ben efits 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 OM B 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 associat ed 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: health a Premature mortality based on cohort study

estimates b 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 impacts c

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 forest13 aesthetics Ecosystem functions UVb exposure (+/

- ) c

NO 2 : health Respiratory hospital admissions Respiratory emergency department visits Asthma exacerbation Acute respiratory symptoms Premature mortality Pulmonary function

NO 2 : 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

SO 2 : health Respiratory hospital admissions Asthma emergency room visits Asthma exacerbation Acute respiratory symptoms Premature mortality Pulmonary function

SO 2 : 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 healt h effects including morphological changes and altered host defense mechanisms. The public health impact of these biological respo nses 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 sh ort term PM exposure not captured in the cohort est imates 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 select ed remedy op tion ( 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 e nforceable controls that happen between 2012 and 2014 in the baseline. These costs represent the total cost to the electricity

- generating industry of reducing NO x and SO 2 emissions to meet the emissions caps set out in the rule. Estimates are in 2007 dol lars. 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 r eductions that would be required by the supplemental propos al 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 f rom 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 th e 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 delivere d 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 5 4 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 increase15

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 R IA.

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 m aintain 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 rel iability 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 add ition to addressing the costs, benefits , and economic impacts of the Tr ansport 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 em ployment 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

. W e 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 2,2 3 0

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 statistical ly 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 (2 4 out of 108 affected) , around 7 0 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 an d 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, anothe r 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 Me gawatt 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 w ith 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
informati on.

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, 2 6 may experience compliance costs in excess of 1 percent of reve nues 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 m ay 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 af fected. Furthermore, the decision to include only units greater than 25 MW in size exempts 3 54 government entities that would otherwise be potentially affected by the Transport Rule.

1.5 Limitations and Uncertainties

Every an alysis 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 underlyi ng 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.

Fo r 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; va riability 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 SO 2 and NO x 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 f ace. 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 PM 2.5 . Of these estimated PM

- related mortalities avoided, about 69 %

occur among p opulations initially exposed to annual mean PM 2.5 level of 10 μg/m 3 and

about 96 % occur among those initially exposed to annual mean PM 2.5 level of 7.5 μg/m 3 ; 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 PM 2.5 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 concentration

response (C

- R) funct ions 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 variabl es 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 an alysis 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
estimat ing the benefits of reductions in air pollution. Enhanced collaboration between air quality modelers, epidemiologists, toxicologists, ecologists, and economists should result20

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

PM 2.5 mortality benefits represent a substantial proportion of total monetized benefits (over 90%), and these estimates have following key assumptions and uncertainties.

1. The PM 2.5 - related benefits of the alternative scenarios were d erived 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

act ual benefits of controlling SO 2
.

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 PM 2.5 produced via transported precursors emitted fro m EGUs may differ significantly from direct PM 2.5 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
PM 2.5, 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 PM 2.5 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
loc ations. As a result, the reported confidence intervals and range of estimates give an incomplete picture about the overall uncertainty in the PM 2.5 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 small er 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. Jo urnal 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. Ep idemiology. 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 PM 2.5 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 varia bility 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

- 1 141.
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 An alysis.
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 Perspect ives 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 (NO x ) and sulfur dioxide (SO 2 ) that contribute significantly to nonattainment and maintenance problems with respect to the national ambient air quality standards (NAAQS) for fine particulate matter (PM 2.5 ) that EPA promulgated in 1997 and 2006 and for 8

- hour

ozo ne 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 PM 2.5 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

st ate 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 (SO 2 ), nitrogen oxides (NO x ), an d 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 (PM 2.5 ) NAAQS promulgated in
1997 and 2006. This final rule includes action s to partially or fully address the interstate24

transport of NO x 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 PM 2.5 and 8

- hour ozone NAAQS promulgated in July 1997. In July 2008, the D.C. Circuit Court found CAIR a nd 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 rem ain 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 tha t 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 bud gets. 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 emiss ions 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 SO 2 and NO x budgets for each state covered fo r the 24

- hour and/or annual PM 2.5 NAAQS. EPA25

also establishes an ozone season 3 NO x 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 pl ans (FIPs) to immediately implement the emissions reduction requirements. The FIPs regulate electric generating units (EGUs) in the
2 7 covered states in the final rule 4 . EPA will regulate these sources through a program that

uses state

- specific budgets a nd allows interstate trading.

2.2.3 States Covered by the Final Rule

In th e final rule , EPA requires SO 2 and NO x emissions controls in the following 2 1 jurisdictions that contribute significantly to nonattainment in, or interfere with maintenance by, a downwind area with respect to the 24

- hour PM 2.5 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

, Tennessee, Virginia, West Virginia, and Wisconsin.

EPA requires SO 2 and NO x emissions controls in the following 18 jurisdictions that contribute significantly to nonattainment in, or interfere with maintenance by, a downwind area with respect to the ann ual PM 2.5 NAAQS promulgated in July 1997: Alabama, Georgia, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Missouri, New York, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, Texas, West Virginia, and Wisconsin.

The final rule requi res ozone season NO x emissions controls in 20 states. As discussed in the preamble, EPA issue d a supplemental proposal addressing ozone

- season NO X controls in 6 additional states. In total, EPA identified 26 states that contribute significantly to nonatta inment in, or interfere with maintenance by, a downwind area with

3 Consistent with the approach taken by the Ozone Transport Assessment Group (OTAG), the NO X 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

- spe cific monitoring season.

4 As finalized, the rule requires emission reductions in 27 states. EPA issued a supplemental proposal to request comment on requiring ozone

- season NOx reductions in additional states; including the states addressed in the supplem ental proposal, the total number of states covered by the Transport Rule would be 28.26

respect to the 8

- hour ozone NAAQS promulgated in July 1997: Alabama, Arkansas, Florida, Georgia, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Michigan, Mississippi, Misso uri, New Jersey, New York, North Carolina, Ohio, Oklahoma, Pennsylvania, South Carolina, Tennessee, Texas, Virginia, West Virginia, and Wisconsin. This group of jurisdictions includes 2 0 states covered by the final rule and 6 proposed states (Iowa, Kansas, Michigan, Missouri, Oklahoma, and Wisconsin) in the supplemental proposed rule. EPA is reporting on all 2 6 states in this RIA.

As discussed, EPA is finalizing FIPs to directly regulate EGU SO 2 and/or NO x emissions in the covered states. The FIPs require the states covered for purposes of the 24

- hour and/or annual PM 2.5 NAAQS to reduce SO 2 and NO x emissions by specified amounts. The FIPs require the states covered for purposes of the 8

- hour ozone NAAQS to reduce
ozone season NO x emissions by specified am ounts. For the PM 2.5 NAAQS, EPA would require two phases with an initial phase in 2012 and subsequent phase in 201 4 . 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 sig nificant contribution and interference with maintenance group s the 23 states covered for 24

- hour and/or annual PM 2.5 NAAQS in two tiers reflecting the stringency of SO 2 reductions required to eliminate that state‘s significant contribution and interference with maintenance. There is a more stringent
SO 2 tier comprising 16 states (―group 1‖) and a moderately stringent SO 2 tier comprising 7 states (―group 2‖) with unifo rm stringency within each tier. 5 For these same 23 states, there
is one annual NO x tier with uniform stringency of NO x reductions across all states. Similarly, for the states covered for the 8

- hour ozone NAAQS there is one ozone season NO x tier with unif

orm stringency across all of these states.

The more stringent SO 2 tier (―group 1‖) include s Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Missouri, New Jersey, New York, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia, and W isconsin. The moderately stringent SO 2 tier (―group 2‖) include s Alabama, Georgia, Kansas, Minnesota, Nebraska, South Carolina, and
Texas.

For the 16 states in the more stringent SO 2 tier (―group 1‖), the 2014 phase increase s

5 With regard to interstate trading, the two SO 2 stringency tiers lead to two exclusive SO 2 trading groups.27

the SO 2 reduction requirements (i.e., these states would have smaller SO 2 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 2 4

- hour
PM 2.5 NAAQS. The 2014 annual NO x emissions budgets for all 23 states covered for the 24

- hour and/or annual PM 2.5 NAAQS remain the same as the 2012 annual NO x .
For the 7 states in the moderately stringent SO 2 tier (―group 2‖), the 2014 SO 2 emission reduction requirements remain the same as the 2012 SO 2 emission reduction requirements for these states. See Table 2

- 1 for lists of covered states , which includes the 6 states addressed in the supplemental proposal for ozone

- season NO X .

Table 2

- 1

-- List s of Covered States for PM 2.5 and 8

- Hour Ozone NAAQS

State Covered for 24

- hour

and/or annual PM 2.5 Required to reduce SO 2 and NO x Covered for 8

- hour

ozone Required to reduce

ozone season NO x 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 X28

Missouri X X

Nebraska 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 2 6

The relevant regions for PM 2.5 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 Rule30

2.3 Regulated Entities

This action will directly regulate emissions of NO x and SO 2 from electric generating units (EGUs) with capacity greater than 25 MW in the covered states.

2.4 Baseline and Years of An alysis

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 PM 2.5 NAAQS and interference with mainten ance. To address this significant contribution and interference with maintenance, EPA requires that certain states reduce their emissions of SO 2 and NO x . 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) rul e 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 l evels 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 w ould 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 NO x program will lead to the status quo of the NO x Budget Trading Program (NBP) , which substantially limits ozone

- season NO x emissions from electric power generation in a major part of the Eastern US and ensures th e operation of NO x controls in 20 covered states and the District of Columbia. The base case contains the NBP. Also, without the CAIR SO 2 program, there would remain the broad federal SO 2 emissions requirements for electric generation from fossil fuels i n the lower 48 states for the comparatively less stringent CAA Title IV Acid Rain Program (ARP). As a result, SO 2 emissions in many states would increase markedly in the 2012 base case relative to the present. Efforts to comply with ARP rules at the leas t

- 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 f or both SO 2 and NO x 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 SO 2 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 (E IA). In addition, the model includes policies affecting the power sector: the Title IV of the Clean Air Act (the Acid Rain Program); the NO x SIP Call; various New Source Review (NSR) settlements 6 ; and several

6 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 Co op, 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 NO x , SO 2 , 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 201 4

.32

state rules 7 affecting emissions of SO 2 and NO x , 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 enfo rceable commitments for installation and operation of
advanced NO X and SO 2 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 20 17) 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 Transp ort 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 impleme nting 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 cov ered 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

.

7 These include c urrent and future state programs in Alabama, Connecticut, Delaware, Georgia, Illinois, Kansas, Louisiana, Maine, Maryland, Mass achusetts, 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 $1 2 0 to 2 8 0 billion (3 percent discount rate) or $ 110 to $2 5 0 billion (7 percent discount rate) are expected for the final rule in 2014.

2.7 Cost s 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 r ule 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 i n 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 emiss ion 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 aff ected 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

ana lysis 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 PM 2.5 - Related Benefits Among Vulnerable and Susceptible Populations

Appendix B, OAQPS Multimarket Model to Assess the Economic Impact of
Environmenta l Regulation

Appendix C , State Benefits Results35 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 provi des 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 simu lates 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 (PM 2.5 ). 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) th e 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 base

year emissions and 2005 meteorology for modeling ozone and PM 2.5 with CAMx (see
ht tp://www.camx.com/ ). Version 4.2 of the 2005

- based platform (2005 v4.2 platform) was36

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 CA Mx. 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 future

year scenarios. All inventories include emissions from EGUs, non

- EGU p oint 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 mo deling 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 co vering the lower 48 states using a grid
resolution of 36 x 36 km 8 , 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 co ntrol 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 emi ssions 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

8 The air quality predictions from the 36 km Continental US (CONUS) domain were used to
pro vide 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 platfo rm 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/20 05inventory.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 w ithin 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 receiv ed 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, re flect 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 applie d. Annual resolution. Average

- fire sector: avefire Average

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

Agricultural sector: ag (nonpoint) NH 3 emissions from 2002 NEI nonpoint livestock and fertilizer application, county and annual resolution. Area fugitive dust sector: afdust (nonpoint) PM 10 and PM 2.5 from fugitive dust sources (e.g., building construction, road construction, paved roads, un paved roads, agricultural dust) from the NEI nonpoint inventory (which used 2002 emissions for this sector) after application of county

specific 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 non

C3 commercial marine vessel (CMV): alm_no_c3 (nonroad) 2002 NEI non

- rail maintenance locomotives, and category 1 a nd 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 Emi ssions 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. NH 3 (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 (NO X ), sulfur dioxide (SO 2 ), VOC, ammoni a (NH 3 ), 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 i ncluded (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 t emperature 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. Cali fornia 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 g enerated 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 California 9 for carbon monoxide (CO), nitrogen oxides (NO X ), VOC, PM 2.5 , particulate matter less than ten microns (PM 10 ), sulfur dioxide (SO 2 ), ammonia (NH 3 ), naphthalene, 10 and some VOC
HAPs. 11 To account for the temperature dependence of PM 2.5 , 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 appr oach is available in the Emissions Inventory TSD.

9 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 h azardous air pollutants.

10 Naphthalene emissions were not used in the modeling.

11 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 e missions 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 ―pti pm‖) 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 20 02 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 NO X and SO 2 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 annua l heat input. For EGUs without CEMs, emissions were obtained
from the state

- submitted data in the NEI. Additional ORIS 12 plant and unit code matches were implemented in version 4.2 of the platform, and for a subset of these units, annual
emissions were r ecomputed 13 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
NO X CEM data were used to create NO X - speci fic profiles, the SO 2 data were used to create
SO 2 - 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

12 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.

13 Net change was a decrease in NO X by 1700 tons and a de crease in SO 2 by 600 tons.41

allocate to hourly values. This approach to temporal allocation was used for all base and

c ontrol 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 an d Oklahoma (CO, NO X , PM, SO 2 , 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 Mexi co, 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 Emiss ions 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 c ontain 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 avera ge

- 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 bec ause 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 SO 2 , and NO X . In these tables, ―Nonpoint‖ represents the nonpt sector; ―Area Fugitive Dust‖ (w hich contains only PM 10 and PM 2.5 ) 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 su mmed 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 Ag riculture 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 N onroad 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 area 1 734,587 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 2,010 744,11943

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 14,354,37044 Table 3

- 4. 2005 Base Year NO X 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 13,40045

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

pr eviously 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, benzen e, formaldehyde, acetaldehyde and methanol from the inventory is used in VOC speciation.

The 2012 and 2014 base case EGU emissions projections of SO 2 , and NO X 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 s tate 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 als o made based on the ICR and on comments from the IPM NODA. Units with SO 2 or NO X 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

m odeled 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 PM 2.5 emissions of 85 thousand tons from the EGU sec tor. Other46

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 o f 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 ye ar 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 Transpo rt 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 performe d.

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 PM 2.5 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 a djustment 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 Heavy

Duty Rule, and the Mobile Source Air Toxics (MSAT) final rule. Emission re ductions 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 N MIM 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) comme rcial 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 standard s, 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. 14 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 projectio n 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 a dditional 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
NO X 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 SO 2 and PM 2.5
.
Since aircraft at airports were treated as poi nt 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 nonpo int 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 project ed 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.

14 Controls from the NO X 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 provid ed 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
Trans port 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, SO 2 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, SO 2 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 ye ar projections were not included. Hg, NO X , SO2, PM, HCL

NESHAP: Industrial, Commercial, Institutional (ICI) Boilers, aka ―Boiler MACT‖ (signed 02/21/2011) Hg, SO 2 , HCL, PM New York ozone SIP standards VOC, HAP VOC, NO X 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. NO X , SO 2 , HCL

NESHAP: Reciprocating Internal Combustion Engines (RICE). SO2 controls for RICE were not effective until after 2012, but are applied in 2014. NO X , CO, PM, SO2 Use Phase II WRAP 2018 Oil and Gas, and apply RICE controls to these emissions VOC, SO 2 , NO X , CO Use 2008 Oklahoma and Texas Oil and Gas, and apply RICE controls to these emissions VOC, SO 2 , NO X , CO, PM State fuel sulfur content rules for fuel oil

– effective in 2014 only in Maine and New York SO251 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 incr eases 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 SO 2 and
NO X 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. Sum mary of Modeled Base Case Annual Emissions (tons/year) for 48 States

by Sector: SO 2 and NO X Source Sector SO 2 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 SO 2 , All Sources 14,354,370 10,078,786

Source Sector NO X 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 NO X , All Sources 21,152,309 13,924,51052

Table 3

- 8. 2014 Base Case SO 2 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 14,294 169 159 215 546,70253 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 NO X 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 18,80854 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 Rul e) 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 SO 2 , 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 SO 2 and NO X 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 NO X 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 SO 2 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 NO X for the Lower 48 States

State 2014 Base Case NO X (tons) 2014 Policy Case

NO X (tons) EGU NO X

reduction (tons) EGU NO X

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 NO X (tons) 2014 Policy Case

NO X (tons) EGU NO X

reduction (tons) EGU NO X

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%5960 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 fin e particulate matter (PM 2.5 ) and ground

- level ozone (O 3 )

– as estimated using a national

- scale applications of the C omprehensive A ir Quality M odel with E x tensions (CAMx; Environ, 20 10 );

2. Visibility degradation (i.e., regional haze), as developed using emp irical 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. T hese 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 PM 2.5 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 PM 2.5 and ozone air quality in the contiguous U.S. CAMx is a three

- dimensional grid

- based Eulerian photochemical model61

designed to estimate PM 2.5 and ozone concentrations over annual time periods. Consideration of the different processes that affect pri mary (directly emitted) and secondary (formed by atmospheric processes) PM 2.5 in different locations is fundamental to understanding and assessing the effects of pollution control measures that affect PM 2.5 and
ozone concentrations at the surface. 15 Becaus e 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 PM 2.5 and ozone concentrations. Version 5.3 of CAMx was employed for this Transport Rule model ing, 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 20 05, 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 PM 2.5 component species concentrations. As discussed in the Air Qualit y 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 com pleting this process, we then calculated annual
mean PM 2.5 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

15 Given 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 Euleria n 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 bloc ks 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. Modelin g 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 domain64

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, hourl y 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 applicatio n and remained unchanged for each future

year 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 inpu ts 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 d irection), 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 l ateral boundary and initial species concentrations are provided by a three

dimensional global atmospheric chemistry and transport model (GEOS

- CHEM). The lateral boundary species concentrations varied with height and time (every 3 hours). Terrain
elevatio ns 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 PM 2.5 and Ozone

4.2.1 Converting CAMx PM 2.5 Outputs to Benefits Inputs

CAMx generates predictions of hourly PM 2.5 species concentrations for every grid. The species include a primary fraction and several secondary particles (e.g., sulfates, nitrates, and organics). PM 2.5 is calculated as the sum of the primary and the secondary formed particles. Future

- year estima tes of PM 2.5 were calculated using relative reduction factors (RRFs) applied to 2005 ambient PM 2.5 species concentrations. Gridded fields of species65

concentrations were created by interpolating ambient data from the PM 2.5 speciation network
and IMPROVE da ta. 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 PM 2.5 standard (EPA, 2007). This guidance recommends that model predictions be used in a relative sense to estimate changes expected to occur in each PM 2.5 species. The procedure for calculating future year PM 2.5 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
PM 2.5 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 201 4 base case and 2014 control scenario. In our application of MATS for PM 2.5 we used temporally scaled speciated PM 2.5 monitoring data from 2005 as the set of base

- year measured concentrations. Temporal scaling is based on the ratios of model

- predicted f uture case PM 2.5 species concentrations to the corresponding model

- predicted 2005 concentrations. Output files from this process include both quarterly and annual mean PM 2.5 mass concentrations which are then manipulated within SAS to produce a BenMAP inpu t 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 PM 2.5 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 PM 2.5 data (only the species data are interpolated).

2) The benefits analysis is anchored by the interpolated PM 2.5 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 PM

2.5 Air Quality Results66 Table 4

- 1 summaries the project ed ambient PM 2.5 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 contr ol 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 PM 2.5 across populated areas of the East declines by roughly 9 percent (or 0.6 μg/m 3 ) in 2014. The population

- weighted average mean

concentr ation declined by 9 percent (or 0.87 μg/m 3 ) in 2014. This indicates the rule generates greater absolute air quality improvements in more populated, urban areas.

Table 4

- 1. Summary of Base Case PM 2.5 Air Quality and Changes Due to the Transport Rule.

2014

Statistic Base Case Control Case Percent

Change a

PM 2.5 (μg/m 3 )

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 PM 2.5 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 PM 2.5 concentrations resulting from the rule. As shown, in 2014,67

about 12 percent of the U.S. population located in the modeling domain are predi cted to experience reductions of greater than 1.75 μg/m 3 . Furthermore, nearly 30 percent of this population will benefit from reductions in annual mean PM 2.5 concentrations of greater than
1.25 μg/m 3 and 68 percent will live in areas with reductions of gr eater than 0.5 μg/m 3

.

Table 4

- 2. Distribution of PM 2.5 Air Quality Improvements Over Population in 2014 Due to the Transport Rule for the Eastern U.S.

Change in Annual Mean PM 2.5 Concentrations (μg/m 3 ) 2014 Population b

Number (millions) Percent (%)

- 0.25 > Δ PM 2.5 Conc < 0 6
, 594
, 134 2% 0 = Δ PM 2.5 Conc 34,315
, 378 11% 0 > Δ PM 2.5 Conc 0.25 36
, 670
, 522 11% 0.25 > Δ PM 2.5 Conc 0.5 27
, 463
, 668 9% 0.5 > Δ PM 2.5 Conc 0.75 43
, 099
, 722 13% 0.75 > Δ PM 2.5 Conc 1.0 53
, 403
, 287 17% 1.0 > Δ PM 2.5 Conc 1.25 28
, 161
, 111 9% 1.25 > Δ PM 2.5 Conc 1.5 30
, 602
, 368 10% 1.5 > Δ PM 2.5 Conc 1.75 21,163,369 7% Δ PM 2.5 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 sea son. 16

, 17 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 we lfare effects, full

- season ozone data are required for every grid cell. Given available ozone monitoring data, we generated full

season ozone profiles for each location in the contiguous 48 States in two steps: (1) we
combine monitored observations and m odeled 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. 18

, 19

16 The 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.

17 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.

18 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.

19 This 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. Ta ble 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 End points Due to the Transport Rule for the Eastern U.S.

2014

Statistic a 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 ligh t

- extinction coefficient, based on the work of Sisler (1996), which shows the total fraction of light that is70

decreased per unit distance. This coefficient accounts for the scattering and absorption of light by both particles and gases, and accounts for t he 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).

Bas ed 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 conditi ons, 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
Shenan doah, 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 <171

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, PM 2.5 , and Regional Haze. Office of Air Quality Planning and Standards, Research Triangle Park, NC.

U.S. Environmental Protection Agency (EPA). 201 1 . 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 a nd 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 SO 2 and NO x from EGUs, which in turn would lower overall ambient levels of PM 2.5 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 o f the selected remedy to be $1 2 0 billion to $2 8 0 billion at a 3% discount rate and $1 10 billion to $2 5 0 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. Al l 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 ben efits 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 expo sure to NO 2 and SO 2 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 PM 2.5 and ozone air pollution. A recent EPA analysis estimated that 2005 lev els of PM 2.5 and ozone were responsible for between 130,000 and 320,000 PM 2.5 - related and 4,700 ozone

- related premature deaths, or about 6.1% of total73

deaths from all causes in the continental U.S. (Fann et al. 2011). This same analysis
attributed almost 2 00,000 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 PM 2.5 - relate d premature deaths in 2014 by between 1 3 ,000 and 3 4 ,000, 1 5 ,000 non

- fatal heart attacks, 8

, 7 00 fewer hospital admissions and 40 0,000 fewer cases of aggravated asthma. By 2014, in combination with other federal and state air quality actions, the Transport R ule will address a substantial fraction of the total public health burden of PM 2.5 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 basel ine (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 PM 2.5 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 PM 2.5 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 PM 2.5 mortality risk, also received a significant reduction in
risk.

Finally, Appendix E provides an alternate presen tation 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 o f the selected74

and alternate rule remedies for the Transport Rule in 2014. The Transport Rule is expected to yield significant aggregate reductions in SO 2 and NO x from EGUs, which in turn would
lower overall ambient levels of PM 2.5 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 impac t calculation for PM 2.5 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 estima te
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 (PM 2.5 ) and ozone air quality resulting from reductions in precursors including NO x and SO 2 ? 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 PM 2.5 and ozone air quality. The 2009 PM 2.5 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. NO x welfare effects include aquat ic and terrestrial acidification and nutrient enrichment (U.S. EPA, 2008f). SO 2 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 P M 2.5 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 contained75

within the primary benefits estimate, those categories that were unquantified due to l imited
data or time.

Table 5

- 1: Estimated monetized benefits of the final Transport Rule remedy (billions of 2007$) A

Benefits Estimate Within Transport Region B Outside

Transport Region Total Pope et al. (2002) PM 2.5 mortality and Bell et al. (2004) ozone mortality estimates Using a 3% discount
rate $1 1 0 +B ($8 .8

— $3 4 0) $ 0.28 +B

($0.0 1

— $0. 9 ) $1 2 0 +B

($ 1 4

— $3 5 0) Using a 7% discount
rate $1 00 +B ($ 8

— $ 31 0) $ 0. 25 +B

($0.0 1

— $0. 85 ) $1 10 +B ($ 13

— $ 32 0)

Laden et al. (2006) PM 2.5 mortality and Levy et al. (2005) ozone mortality estimates
Using a 3% discount
rate $2 7 0 +B ($2 4

— $ 80 0) $0. 7 +B

($0. 05

— $0. 2 1 ) $2 8 0 +B ($2 9

— $ 81 0) Using a 7% discount
rate $2 5 0 +B ($2 2

— $ 72 0) $0. 6 +B

($0.0 4

— $ 1

. 9 ) $2 5 0 +B

($2 6

— $ 73 0)

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 th ose 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 C O 2 - 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 func tions 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 us e 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 statistical76

error may omit im portant sources of uncertainty we also employ the results of an expert elicitation on the relationship between premature mortality and ambient PM 2.5 concentration (Roman et al., 2008); this provides additional insight into the likelihood of different

outco mes 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 domina te 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. EP A, 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 PM 2.5 - 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 PM 2.5 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

- 2 0 ) in which we bin the
estimated number of avoided PM 2.5 - related premature mortalities resulting from the implementation of the Transport Rule according to the projected 2014 baseline PM 2.5 air quality levels. This presentation is consistent with our approach to applying PM 2.5 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 occ ur at or above an annual77

mean PM 2.5 level of 10 μg/m 3 (the LML of the Laden et al. 2006 study); about 9 6 % occur at
or above an annual mean PM 2.5 level of 7.5 μg/m 3 (the LML of the Pope et al. 2002 study). As we model mortality impacts among populations exp osed to levels of PM 2.5 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: health a Premature mortality based on cohort study

estimates b 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 impacts c

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

NO 2 : health Respiratory hospital admissions Respiratory emergency department visits Asthma exacerbation Acute respiratory symptoms Premature mortality Pulmonary function

NO X : 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 for79 currently healthy ecosystems Coastal eutrophication from nitrogen deposition effects

SO 2 : health Respiratory hospital admissions Asthma emergency room visits Asthma exacerbation Acute respiratory symptoms Premature mortality Pulmonary function

SO X : 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 respo nses 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 t his 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
welf are 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 h ealth 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 e t 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 v alued, as is the case for changes in80

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 as sociated 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 th e 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 ter restrial 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 eithe r 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 econ omic 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 PM 2.5 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 expos ure to pollutants (Levy et al. 2009). PC

- based tools such as the environmental B enefits M apping and A nalysis P rogram (BenMAP) can systematize health impact analyses by applying a database of key input parameters, including health impact functions and popul ation 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 others81

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 CAMx

generated projections of P M 2.5 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:

where y 0 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 ch ange 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 d ata 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 affected83

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, fo r 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 impac ts and monetized health benefits for the pr eferred 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

u nable 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 NO 2 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 SO 2 caps and so we
estimate a subset of these health benefits us ing PM 2.5 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
PM 2.5 reductions (see table 5

- 17 below), due in part to the fairly modest summer time NO x

Census Population
Data

Modeled Baseline and Post

- Control 2014 Ambient PM 2.5 and O 3 Concentrations

PM 2.5 & O 3 Health Functions

Economic Valuation Functions 20 14 Population Projections

PM 2.5 & O 3 Incremental Air Quality Change

PM 2.5 & O 3 - Related Health mpacts Woods & Poole Population Projections

Background Incidence and Prevalence Rates

Monetized PM 2.5 and O 3 - related Benefits

Blue identifies a user

- selected input within the BenMAP model Green identifies a data input generated outside of the BenMAP model85

emission reductions under this scenario. The level of summertime NO x emission reductions of the alternate scenarios are very similar to the modeled scenario, suggesting tha t 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 to gether, these factors argued against developing an ozone benefit per ton estimate for this RIA.

For this analysis, EPA applies PM 2.5 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 PM 2.5 - related human and monetized health benefits of the SO 2 emission reducti ons of the proposed remedy . We first quantified the health impacts and monetized benefits of total PM 2.5 mass formed from the SO 2 reductions of the proposed remedy, allowing us to isolate the PM air quality impacts from SO 2 reductions alone. 20 This procedure allowed us to develop PM 2.5 BPT estimates that quantified the PM 2.5 - related benefits of incremental changes in SO 2 emissions. Because reductions in NO x emissions are relatively small in each scenario, and previous EPA modeling indicates that PM 2. 5 formation is less sensitive to NO x emission reductions on a per

- μg/m 3 basis (Fann et al, 2009), we did not quantify the NO x - related PM 2.5 changes.

2. Divided the health impacts and monetized benefits by the emission reduction. This calculation yields BPT e stimates for PM

- related SO 2
.

The resulting BPT estimates were then multiplied by the projected SO 2 emission reductions for the more and less stringent scenarios to produce an estimate of the PM

- and ozone

- related health impacts and monetized benefits. The re is no analogous approach for estimating a BPT for

20 The Tra nsport Rule includes both SO 2 and NOx emissions reductions. In general SO 2 is a precursor to particulate sulfate and NOx is a precursor to particulate nitrate. However, there are also several interactions between the PM 2.5 precursors which cannot be easi ly quantified. For example, under conditions in which SO 2 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. T he 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 p rocess 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 performe d a sensitivity modeling analysis in which only SO 2 emissions were reduced at levels that approximated those of the selected remedy . We calculated benefits from this air quality modeling run to generate an SO 2 - only benefit per ton estimate. The results o f the SO 2 - 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 in ventories, 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 worl d (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 impact s 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 a ppropriate 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 publicat ion 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
C arlo 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 quantit atively.

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 metho ds to generate confidence intervals around the estimated health impact and dollar benefits. The reported standard errors in the87

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 a mbient PM 2.5 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 eff ect 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 esti mates, 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, basel ine 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 re asonable 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 understa nding of ozone

- related mortality. The NAS found88

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 ozon e mortality estimates, including those based on multi

city 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 recommend ations 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 mis specification of model structures, including the use of surrogate variables, such as using PM 10 when PM 2.5 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 imp act 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 m atching 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 PM 2.5 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 liter ature 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 oc cur 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 PM 2.5 monitoring data in reflecting actual PM 2.5 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 no t 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) databas e 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 constr aining 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 a re 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, suc h as outputs from mining, agriculture, and manufacturing with the national economy. The export

based approach requires estimation of demand equations or calculation of historical growth rates for output and employment by sector.

3. Third, population is pr ojected 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 count ies, using the economic region totals as bounds. The age, sex, and race distributions for each region or91

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 a dvice 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 (h ttp://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 resul ts 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 c an 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 epidemiolog y 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

popu lation

- 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 cri teria 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 relation ship 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 d ifferent age groups. For this reason, we consider very carefully the set of studies available examining each92

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 whe n 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 ecologic al 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 o n 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, i t 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 bec ause 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 se veral 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 PM 2.5 are preferred to PM 10 because of the focus on reducing emissions of PM 2.5 precursors, and because air quality modeling was conducted for this size fraction of PM. Where PM 2.5 functions are not availab le, PM 10 functions are used as surrogates, recognizing that there will be potential downward (upward) biases if the fine fraction of PM 10 is more (less) toxic than the coarse fraction. Economically Valuable Health Effects Some health effects, such as fo rced 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 Ass ociates, 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 the n 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 us ed 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.95

Table 5

- 5. Health Endpoints and Epidemiological Studies Used to Quantify Health Impacts

Endpoint Pollutant St udy 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 PM 2.5 (annual avg) Pope et al. (2002)

Laden et al. (2006) >29 years >25 years

Premature mortality, total
exposures PM 2.5 (annual avg) Expert Elicitation (Roman et al., 2008) >24 years

Premature mortality

— all

cause PM 2.5 (annual avg) Woodruff et al. (2006) Infant (<1 year)

Chronic Illness

Chronic bronchitis PM 2.5 (annual avg) Abbey et al. (1995) >26 years

Nonfatal heart attacks PM 2.5 (24

- hour avg) Peters et al. (2001) Adults (>18 years) Hospital Admissions Respiratory O 3 (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 PM 2.5 (24

- hour avg) Pooled estimate:

Moolgavkar (2003)

— ICD 490

– 496 (COPD) Ito (2003)

— ICD 490

– 496 (COPD) >64 years

PM 2.5 (24

- hour avg) Moolgavkar (2000)

— ICD 490

– 496 (COPD) 20

– 64 years

PM 2.5 (24

- hour avg) Ito (2003)

— ICD 480

– 486 (pneumonia) >64 years

PM 2.5 (24

- hour avg) Sheppard (2003)

— ICD 493 (asthma) <65 years96 Endpoint Pollutant St udy Study Population

Cardiovascular PM 2.5 (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

PM 2.5 (24

- hour avg) Moolgavkar (2000)

— ICD 390

– 429 (all

cardiovascular) 20

– 64 years

Asthma

- related ER visits O 3 (8

- hour max) Pooled estimate:

Peel et al. (2005) Wilson et al.(2005) All ages All ages PM 2.5 (24

- hour avg) Norris et al. (1999) 0

– 18 years

Other Health Endpoints

Acute bronchitis PM 2.5 (annual avg) Dockery et al. (1996) 8

– 12 years

Upper respiratory symptoms PM 10 (24

- hour avg) Pope et al. (1991) Asthmatics, 9

– 11 years

Lower respiratory symptoms PM 2.5 (24

- hour avg) Schwartz and Neas (2000) 7

– 14 years

Asthma exacerbations PM 2.5 (24

- hour avg) Pooled estimate:

Ostro et al. (2001) (cough, wheeze and shortness of breath) Vedal et al. (1998) (cough) 6

– 18 years a

Work loss days PM 2.5 (24

- hour avg) Ostro (1987) 18

– 65 years

School absence days O 3 (8

- hour max) Pooled estimate:

Gilliland et al. (2001) Chen et al. (2000) 5

– 17 years b

Minor Restricted Activity Days (MRADs) O3 (8

- hour max) Ostro and Rothschild (1989) 18

– 65 years

PM 2.5 (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 Co sts of the Clean Air Act, 1990

— 2020. EPA

- SAB

COUNCIL

- 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 childr en based on the biological similarity between children aged 5 to 17.97

5.4.2.1 PM

2.5 Premature Mortality Effect Coefficients

Both long

- and short

- term exposures to ambient levels of PM 2.5 air pollution have been associated with increased risk of premature m ortality. 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 PM 2.5 concentrations and increased mortality rat es (U.S. EPA, 2009d). Time

- series
methods have been used to relate short

- term (often day

- to

- day) changes in PM 2.5 concentrations and changes in daily mortality rates up to several days after a period of elevated PM 2.5 concentrations. Cohort methods have be en used to examine the potential relationship between community

- level PM exposures over multiple years (i.e., long

- term exposures) and community

level annual mortality rates. Researchers have found statistically significant associations between
PM 2.5 and p remature 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 ai r 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 PM 2.5 - related p remature mortality. To demonstrate the sensitivity of the benefits estimates to the specific sources of information regarding the impact of PM 2.5 exposures on the risk of premature death, we are providing estimates in our results tables based
on studies de rived 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 ex pert
elicitation results are described in the 2006 PM 2.5 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 PM 2.5 - 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 questions98

about ca usal 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 in dividual 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 earlie r 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 a l. 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 t he 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
characteri stics 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 th eir 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 reex amination lends credibility to the original studies, it99

also highlights sensitivities concerning the relative impact of various pollutants, such as SO 2 , the potential role of education in mediating the association between pollution and mortality, and the
i nfluence 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 publis hed 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 P M 2.5 are likely to result in reductions in the risk of premature death. This additional evidence stems from the observed reductions in PM 2.5 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 PM 2.5
.
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 PM 2.5 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 PM 2.5 , and a less significant relationship between re spiratory

- related mortality and PM 2.5 . 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 follow

up 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

- sui ted 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 e stimate for adults and suggests that alternate estimates of100

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 SO 2 NAAQS, PM NAAQS, CAIR and Nonroad Diesel RIAs), we have included th is 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

- to

date science and was cited by many of the exper ts 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
Po pe 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 newe r 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 cardiopul monary

- 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 o zone 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. 21 This conclusion is also based on recent meta

- analys es 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 th e 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
d etect 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 tha t 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 t hese 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 stu dy 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?

21 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_pape r.pdf. pp. 5

- 36.102

In 2008 the NAS (NRC, 2008) issued a series of re commendations 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 ―oz one

- related mortality be included in future estimates of the health benefits of reducing ozone exposures...‖ The NAS also recommended that ―...the greatest emphasis be placed on the multicity and NMMAPS studies without exclusion of the meta

analyses‖ (NRC, 200 8).

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 premat ure mortality in a two

- pollutant
model that controls for PM 2.5 . 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) ana lysis of the ACS cohort data. In its advisory, the panel recommended quantifying ozone

- related mortality using risk coefficients drawn from the short

term time

- series studies.

In view of the findings of the Criteria document and the NAS panel, we include u sed
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 B ronchitis

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 As sociation, 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 PM 2.5 , this analysis uses only the Abbey et al. (1995) study, because it is the only study focusing on the relationship between
PM 2.5 and new incidences of CB.

5.4.2.4 Nonfatal M

yocardial Inf

arctions (Heart Attacks)103

Nonfatal heart attacks have been linked with short

- term exposures to PM 2.5 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
PM 2.5 and nonfatal heart attacks. Peters et al. is the only avai lable 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 (Li ao 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 variabil ity 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 ri sk of heart
attacks.

5.4.2.5 Hospital and E

mergency 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

po llution. 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 m ost 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 e mergency 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 hosp ital 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
PM 2.5 , we use d studies by Moolgavkar (2003) and Ito (2003). Additional published studies show a statistically significant relationship between PM 10 and cardiovascular hospital admissions. However, given that the control options we are analyzing are expected to reduce primarily PM 2.5 , we focus on the two studies that examine PM 2.5 . 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 est imate for populations 20 to 64. 22 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 my ocardial 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
admissi ons 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 card iovascular admissions, additional published studies show a statistically significant relationship between PM 10 and respiratory hospital admissions. We used only those focusing on PM 2.5 . Both Moolgavkar (2000) and Ito (2003) provide effect estimates for C OPD 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 populatio ns 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 f

rom 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 PM 10 rather than PM 2.5 . We selected the Norris et al. (1999 ) effect

22 Note 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 PM 2.5 on asthma emergency roo m 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 ad missions 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 hos pital 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 pneumon ia 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), condu cted 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 respir atory 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 oz one 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 maximu m 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 po llution 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 me asured, the spring

- summer months (April through September). Using the106

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

s ignificant 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 p ower 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 d o not result in a hospital admission.

5.4.2.6 Acute H

ealth Events and S chool/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 a mbient 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 Associa tion, 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, 23 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).

Inci dences 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

w ith 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

23 See http://www.nlm.nih.gov/medline plus/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

person al symptoms or from caring for a sick family member). Days of work lost due to PM 2.5 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 PM 2.5 , 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 da y. 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 individu als 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
PM 2.5 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 re commendations 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. 24 Asthma exacerbations occurring in adults are assumed to be captured in the

24 Estimating 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 o f 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 inclu des asthmatics. Therefore, to simply add predictions of asthma

- related symptoms108

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 ch aracterize 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, re cording 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
PM 2.5 , measured as a 12

- hour average, and the daily pre valence 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 o f 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 pe ak expiratory flow (PEF) and the tracking of respiratory symptoms (e.g., cough, phlegm, wheeze, chest tightness) through the use of daily diaries. Association between PM 10 and respiratory symptoms for the asthmatic population was only reported for two end points: 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 cou gh 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

- H ES, 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 ad dition 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 inc lusion 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 da ily 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 R esearch 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 Worke r 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 productiv ity 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 outdoo r 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

rep orted 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 in110

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 hou sehold 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 th e 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 examp le, 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 baselin e 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 b aseline 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 differe nt 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 eme rgency department visits. Consistent with the proposal RIA, we continue to use county

- level mortality rates. We111

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 files a

Asthma ER Visits Daily asthma ER visit rate Age

- , region

- , state

- , county

- and

cause

- specific
rate 2007 HCUP data files a

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 county

specific rate 2007 HCUP data files a ; 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 children b 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 Restricted

Activity Days Daily 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 Association b

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 tha n 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 t o 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 ef

fect, 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 o f the unit114

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 th e 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 r ates. 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 PM 2.5 - 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 t rade 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 ti me (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 t he 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. Howe ver, 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 Agen cy 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 the115

meta

- analytic experts, the SAB

- EEAC advised the Agency to update its guidance using sp ecific, 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 availa ble, 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 Prepa ring Economic Analyses (U.S. EPA, 2000) 25 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 betwee n 1974 and 1991. The mean VSL across these studies is $6.3 million (2000$). 26 The Agency is committed to using scientifically sound, appropriately reviewed evidence in valuing mortality risk reductions and has made significant progress in responding to th e 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 E nvironmental 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. 27

25 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 gui dance would be forthcoming in the near future. Therefore, this report does not represent final agency policy. 26 In this analysis, we adjust the VSL to account for a different currency year (2006$) and to account for income growth to 2014. After applyi ng these adjustments to the $6.3 million value, the VSL is $7.8M. 27 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 w ithin 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 us e 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 an d 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 adjustm ent 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 Morta lity 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 discou nted 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 ). 28 Because of the uncert ainty 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 a n 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 th e 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 whic h 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 th e 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. T his 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

28 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 exp ert 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 exis ting 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 populat ion 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 ris k 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 tim es 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 ris k 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. Som e 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 ri sks 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.

Baseli ne 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

c onsumption 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 pollution

related 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

- re lated 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 Bene fits 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 des cribed 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 Myocardi al 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 even t. 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 age

specific 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 myo cardial 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 incorporated121

therapeuti c 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 year

2000 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 hospit al 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 $3 0,058 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 ) $147,359 a 5 Russell et al. (1998 ) $30,058 b 5 Eisenstein et al. (2001 ) $66,833 b 10 Russell et al. (1998 ) $39,256 b 10

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 wage

related 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 Wittels122

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: Estima ted Costs Over a 5

- Year Period (in 2007$) of a Nonfatal Myocardial Infarction

Age Group Opportunity Cost Medical Cost a Total Cost

0

– 24 $0 $88,709 $88,709

25

– 44 $ 11,080 b $88,709 $99,789

45

– 54 $ 16,331 b $88,709 $105,040

55

– 65 $94,396 b $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) typical ly 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 leng th 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 inform ation 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, WTP 13 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 genera ted 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 pollution

related 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 a l. (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 fo r 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 hospi tal 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 availab le. 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 Hea lthcare 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 mid

range estimates of WTP (IEc, 1994) to avoid each symptom in the cluster and assuming additivity of WTPs. In the absence of information surrounding the fr equency 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 mid

range 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 LR S 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 ast hma 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 sympt oms 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 assumpt ion 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 th e 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 roo m 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 $186.5 mill ion (1987$). 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

eme rgency 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 willingness127

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 lik ely that most MRADs associated with ozone exposure are, in fact, minor respiratory restricted activity days. However, f or 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 th e 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
sympto ms/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 p articipation128

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 multipl ied 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 NO 2 and SO 2 emissions along with the secondary formation of PM 2.5 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
acti vities, 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 th e 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, 199 4 ), 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, elemen tal carbon, and soil (Sisler, 1996). The extent to which any amount of129

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 genera lly 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 15 6 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: Mandator y 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 reg ional 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, 200 9d ). Higher visibility impairment levels in the East are due to gen erally 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 wa ter 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 suffe r from visibility impairment. In eastern parks, average visual131

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, impro ved 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., 199 3; 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 visibi lity; 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 a re 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 recre ational 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 of132

visibility changes in recreational areas. 29 This study serves as an essential input to our
estimates of the benefits of recreat ional 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. 30 A key distinction between recreational
and r esidential 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. 31 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 NPS

managed 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 vari able, the estimating equation also included household income as an explanatory variable.

The Chestnut and Rowe s urve y measure d 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 C lass 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 de scription of the benefits transfer

29 An SAB advisory letter indicates that ―many members of the Council believe that the Chestnut and Rowe study is the best available‖ (EPA

- SAB

- COU NCIL

- 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 ).

30 EPA has designate d 156 areas as mandatory Class I federal areas for visibility protection , including national parks th at exceed 6,000 acres and wilderness areas that exceed 5,000 acres (40 CFR § 81.400 )

. 31 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; 1990 b ), although representing the best available estimates, has a number of limitations. These include the following:

The vintage of the su rve y (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 popul ations 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 su rve y attempted
to control for t his 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 seg regate 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 hav e 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

mos t 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 sub stitutes (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 t o 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

met hodology 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 t o 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
vi sibility (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 Ro we 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 ev idence 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
associ ated 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 benefi ts 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 abo ut 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
anti cipate 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 argu es that WTP for most goods (such as environmental protection) will increase if real incomes increase. There is substantial empirical evidence that the income elasticity 32 of WTP for health risk reductions is positive, although there is uncertainty about it s 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 WT P 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 af fected 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

32 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 E PA 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

- SA B, 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 unc ertainties 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 reconc iled, 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

- serie s 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 fo r 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 t he 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 an d mortality risk137

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 i n 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 Growth a

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 estim ate of national population in 2000. We used projections of real GDP provided in Kleckner and Neumann (1999 ) for the years138

1990 to 2010. 33 We used projections of real GDP (in chained 1996 dollars) provided by
Standard and P oor‘s (2000 ) for the years 2010 to 2014. 34

Using the method outlined in Kleckner and Neumann (1999 ) and the population and income data described above, we calculated WTP adjustment factors f or 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 appropr iate 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 depe ndence 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 year s 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
inco mes 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 Growth a

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.

33 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 growt h rates between 1999 and 2007.

34 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 grow th 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 catego ries associated with reducing NO 2 and SO 2 emissions: health and ecosystem benefits of reducing nitrogen and sulfur emissions and deposition and vegetation benefits from reducing ozone. 35 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 achieve d 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 s mall 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 o r 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 w ell

- 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:

35 Some quantitative estimates of the total value of certain recreational and environmental goods given curren t and historic emission levels are provided below. They do not reflect benefits that would accrue as a result of this result. However, these values wo uld 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 spirit ual 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 pollination141

Figure 5

- 5: Linkages between catego ries 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 willi ng 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

- base d 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 a ssociated 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 poll utants 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 SO 2 , and nitrogen can be emitted as NO, NO 2, or NH 3 . Secondary particles are formed from NO x and SO X 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 terre strial and aquatic ecosystems across the U.S., contributing to the problems of acidification, nutrient enrichment, and methylmercury production as represented in Figure s 5

- 7 and 5

- 8 . Although there is some evidence that nitrogen deposition143

may have positi ve 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, SO 2 can affect vegetation at ambient levels near pollution

sources.144

Figure 5

- 7: Schematics of Ecological Effects of Nitrogen and Sulfur Deposition

NO X Atmospheric Fate and Transport

Deposition Process

Acidification

Aquatic Ecosystems Terrestrial

Ecosystems Nutrient Enrichment

Aquatic Ecosystems Terrestrial

Ecosystems

SO 2 Atmospheric Fate and Transport

Deposition Process

Acidification

Aquatic Ecosystems Terrestrial Ecosystems Methylmercur y Production145

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 gas

phase species th at lead to the dry deposition of acidity are also precursors of particles. Therefore, reductions in NO 2 and SO 2 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 across146

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, 200 8f). 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 inorg anic 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 terre strial 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 t hat 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 flo ws of water and less commonly during low

- flow conditions, except where chronic acidity conditions are severe. Biological effects of episodes include reduced fish
condition factor 36 , changes in species composition and declines in aquatic species richness

36 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

ac ross 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 z ooplankton 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 some149

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. Recrea tional 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)
esti mated 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. 37 For recreation days, consumer sur plus 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 pr ogram, 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 ra tes 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 NO x and SO x emissions were estimated to range from $291 million to $829 million (U.S. EPA, 2009c). 38

37 These estimates reflect the total v alue of the service, not the marginal change in the value of the service as a result of the emission reductions achieved by this rule.

38 These estimates reflect the total value of the service, not the marginal change in the value of the service as a

resu lt 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 f rom 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 suc h 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
acidifi cation 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

- 1 1 shows the distribution of red spruce (brown) and sugar maple (green) in the eastern U.S.152

Figure 5

- 1 1 : 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), dec lines 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 cultural153

ecosystem services

— nonuse (i.e., existence value for threatened and endangered species), recreational, and aesthetic services. Red spruce forests are home to two f ederally 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, m ore 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 wildl ife 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 of154

residents in the Great Lakes area found that roughly 30% of residents reported at least one trip in the previo us 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 p rimarily 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 esti mated 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 p rogram 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)

. 39

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.

39 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 reducti ons achieved by this rule.155

5.5.3 Ecological Effects Associated with Gaseous Sulfur Dioxide

Uptake of gaseous sulfur dio xide in a plant canopy is a complex process involving adsorption to surfaces (leaves, stems, and soil) and absorption into leaves. SO 2 penetrates into leaves through to the stomata, although there is evidence for limited pathways via the
cuticle. Polluta nts 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 susc eptibility 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 u sually 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 SO 2 exposures over the gr owing season. Besides foliar injury, chronic exposure to
low SO 2 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 fo liar injury. As with foliar injury, these effects vary among species and growing environment. SO 2 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 (Hg 0 ), oxidized Hg compounds (Hg +2 ), and particle

bound Hg (Hg P ). 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 atmosp here 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 the156

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 wetland s and aquatic environments (U.S. EPA, 2008f). Specifically, there appears to be a relationship
between SO 4 2

- deposition and mercury methylation; however, the rate of mercury methylation varies according to several spatial and biogeochemical factors whose i nfluence has not been fully quantified (see Figure 5

- 11). Therefore, the correlation between SO 4 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 associa tion 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 forward

looking 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 SO 4 2

- concentrations, acid neutral izing 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 5.1 2 : Preliminary USGS M ap of M ercury M ethylation

– S ensitive W atersheds D erived from More than 55,000 Water Quality Sites and 2,500 Watersheds (Myers et al., 2007)

Interdependent biogeochemical factors preclude the existence of simple sulfate

related 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 SO 4 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 SO 4 2

- and mercury deposition in Little Ro ck Lake, WI (Hrabik and Watras, 2002 ) ; and to decreased SO 4 2

- deposition in Isle Royale in Lake Superior, MI ( Drevnick et al., 2007). Although the possibility exists that reductions in SO 4 2

- emissions could generate a pulse in MeHg production because of de creased sulfide inhibition in sulfate

saturated 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 SO 4 2

- , increased mercury me thylation downstream may still occur in sulfate

enriched 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 s ection. 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 SO X 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 NO x and SO 2 associated with this final rule would also reduce mercury emissions. Thus, the total mercury benefits would reflect b oth 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 fi sh 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 ecosy stem 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 giv en 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 ar e 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 mercury

related 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 methylmer cury 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 affec ted 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 advi sories 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 is sued 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 top

predator 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 concen tration 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 ar e 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 appropria te 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 developm ent 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: ―T he 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 .‖ 40 The NRC also
stated that ―additional studies a re 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

- r esponse 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
bet ween 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

- establishe d neurotoxicity data base.

40 National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology. National Academies Press. Washington, DC. p. 229.162 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 gene tic 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 aut oimmune 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) a nd 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 a nd 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 t han 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 s ystems 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 and163

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 lit terfall (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 repr oductive 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 estab lish 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 qu antify 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, particularly164

in the Mid

- Atlantic region of the United States, is the effect associated with nutrient
enrichment in estuarine waters. A r ecent 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, an d 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 ran ge 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 effe cts

— 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 kill s 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 impairmen ts 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 du e 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 t he 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 commerci al 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

eutrophicat ion in estuaries may also affect the demand for seafood. For example, a well

publicized 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

- b ased 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). 41 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 dai ly 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. 42 Almost 7 million participated in birdwatching fo r 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 risk s 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

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.

42 These estimates reflect the total value of the service, not the marginal c hange 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 I SA 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, 200 8f).

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. 43 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
Mix ed 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 M CF are an integral part of the California landscape, and together the ranges of these habitats include the167

densely populated and valuable coastline and the mountain areas. Numerous threatened and endangered species at both the state and federal levels res ide 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, includ ing 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 i ndividuals 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 an imals 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). 44
In addition, data from California State Parks (2003) indicate that in 2002, 69% of adult residents participated in trail hiking for an average of 2 4 days per year. The aggregate annual benefit for California residents from trail hiking in 2007 was $11 billion (U.S.EPA, 2009c). 45 It is not currently possible to quantify the loss in value of services due to nitrogen deposition
as those losses are alread y 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 im portant 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 C alifornia 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.

44 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.

45 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.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 va riable 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 pote ntial 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 (CO 2 ) and other gaseous substances, ozone enters plant tissues primarily through the stomata in leaves in a process called ―uptak e‖ (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 grow th 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 c an 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 o ther 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 symbi otic 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
likew ise, 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 from169

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 aes thetic 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 durat ion 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, howe ver, 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 va lues 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 sup port 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 acq uired through long

term observations in highly damaged forests in the United States (U.S EPA, 2006a).170

5.5.6.1 Ozone Effects on Forests

Air pollution can affect the environment and affect eco logical systems, leading to changes in the ecological com munity 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 impor tant 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 H ealth Monitoring, FIA examines ozone injury to ozone

- sensitive plant species at ground moni toring 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 US DA Forest Service has expanded over
the last 1 5 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. 46 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

- rel ated 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 t o be equivalent from site to site. Ranges of biosite values translate to no injury, low or moderate foliar injury (visible foliar injury to highly sensi tive or moderately sensitive plants, respectively), and high or severe foliar injury, which would be e xpected 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

46 The 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 se nsitivity, 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. E PA, 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 c oncentrations 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 ozone

exposure/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 leave s 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 sig nificant. 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 seedlings174

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 ), t ulip

- 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 poss ible to estimate a ―biomass loss‖ for each species across their range. As shown in Figure 5

- 1 5

, current ambient levels of ozone are associated with significant biomass loss across large geographic areas (U.S. EPA, 2009b). However, this information is una vailable for this final
rule.

To estimate the biomass loss for forest ecosystems across the eastern Unit ed States, the biomass loss for each of the seven tree spe cies 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 con ditions. 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 ( Chappel ka, 1998 ).175

Figure 5

- 1 5 : 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 t he 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 affec ted 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 ab ility to sequester carbon (Felzer et al., 2005).176

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, cond ucted 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 the se crops, associated with observe d 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 e vidence 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 standa rd
of 13 ppm

- hr would produce monetized benefits of approximately $ 40 0 million to $ 62 0
million in 2006 (inflated to 2006 dollars) (U.S. EPA, 2007b). 47

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 vis ible 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 (f

ertilizer 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, Chr istmas

47 These estimates illustrate the value of vegetation effects from a substantial reduction of o zone 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 enjoym ent 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 SO 2 and NO 2 - Re lated 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 effect s and short

- term exposure to SO 2 (U.S. EPA, 2008). The immediate effect of SO 2 on the respiratory system in humans is
bronchoconstriction. Asthmatics are more sensitive to the effects of SO 2 likely resulting from preexisting inflammation associated with this disease. A clear concentration

- response relationship has been demonstrated in laboratory studies following exposures to SO 2 at concentrations between 20 and 100 ppb, both in terms of increasing severity of effect and percentage of asthmatics adversel y affected. Based on our review of this information, we
identified four short

- term morbidity endpoints that the SO 2 ISA identified as a ―causal relationship‖: asthma exacerbation, respiratory

- related emergency department visits, and respiratory

- related hos pitalizations. The differing evidence and associated strength of the
evidence for these different effects is described in detail in the SO 2 ISA. The SO 2 ISA also concluded that the relationship between short

- term SO 2 exposure and premature mortality

was ―suggestive of a causal relationship‖ because it is difficult to attribute the mortality risk
effects to SO 2 alone. Although the SO 2 ISA stated that studies are generally consistent in reporting a relationship between SO 2 exposure and mortality, there was a lack of robustness178

of the observed associations to adjustment for pollutants. We did not quantify these benefits
due to time constraints.

Epidemiological researchers have associated NO 2 exposure with adverse health effects in numerous toxicological, cl inical and epidemiological studies, as described in the Integrated Science Assessment for Oxides of Nitrogen

- Health Criteria (Final Report) (U.S. EPA, 2008c). The NO 2 ISA provides a comprehensive review of the current evidence of
health and environmenta l effects of NO 2 . The NO 2 ISA concluded that the evidence ―is sufficient to infer a likely causal relationship between short

- term NO 2 exposure and adverse effects on the respiratory system‖ (ISA, section 5.3.2.1). These epidemiologic and experimental stu dies 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 NO 2 ISA concluded that the relationship between short

- term NO 2 exposure and prema ture mortality was ―suggestive but not sufficient to infer a causal relationship‖ because it is difficult to attribute the mortality risk effects to NO 2 alone. Although the NO 2 ISA stated that studies consistently reported a relationship between NO 2 expos ure 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 reductio ns in carbon dioxide (CO 2 ) 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) change s 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 EP A 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 Gas179

Emission Standards and Corporate Avera ge 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. 48

The intera gency 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 CO 2 emissions 49 in 2010, in 2007 dollars. The first three values are based on the average SCC from three
in tegrated 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 functi on 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 pro duce 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

48 Docket ID EPA

- HQ

- OAR

- 2009

- 0472

- 114577, Technical Support Document: Social Cost of Carbon for Regulatory Impact A nalysis 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, Departme nt 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

49 The interagency group decided that these estimates apply only to CO 2 emissions. Given that warming profiles and impacts other than temperature change (e.g. ocean acidification) vary across GHGs, the group concluded ―transforming gases into CO 2 - equivalents using GWP, and then multiplying the carbon

- equivalents by the SCC, would not result in accurate estimates of the social costs of non

- CO 2 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, specu lation, 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 interage ncy group noted a number of limitations to the SCC analysis, including the incomplete way in which the integrated assessment models capture catastrophic and non

catastrophic impacts, their incomplete treatment of adaptation and technological change,

uncert ainty 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 g roup 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, th e 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 estim ated reductions in CO 2 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%. 50 These estimates are provided in Table 5

- 16.

Table 5

- 15. Social Cost of Carbon (SCC) Estimates (per tonne of CO 2 ) for 2014 (in 2007$) a Discount Rate and Stat istic 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 CO 2 Emissions Reductions in 2014 (in millions of 2007$) a Discount Rate and Statistic SCC

- derived benefits Tons of CO 2 reduced (millions) 2 5

5% Average $140 3% Average $ 59 0 2.5% Average $9 5 0 3% 95%ile $1, 8 00

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 PM 2.5 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

- 1 8 , along with total aggregate monetized benefits. All of the monetary benefits are in constant

- year 2007 dollars. The PM 2.5 - related benefits of the more and less stringent scenarios were within about 15 % of the selected remedy. The results

50 It is possible that other benefits or costs of proposed regulations unrelated to CO 2 emissions will be discounted at rates that differ from those used to dev elop the SCC estimates.182

of th at 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 Bey ond 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 M u l t i - c i t y a n d N M M A P S

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

region B Total Premature Mortality (Pope et al. 2002 PM mortality and Bell et al. 2004 ozone mortality estimates)

3% discount rate PM 2.5 & O 3 $100 ($8.3

— $320) $0.3 ($0.01

— $0.9) $100

($8.3

— $320)

7% discount rate PM 2.5 & O 3 $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 PM 2.5 & O 3 $270 ($23

— $770) $0.7 ($0.05

— $2) $270

($23

— $770)

7% discount rate PM 2.5 & O 3 $240 ($21

— $700) $0.6

($0.05

— $1.8) $240

($21

— $700)

Chronic Bronchitis PM 2.5 $4.2 ($0.2

— $19) $0.01

($

- 0.003

-- $0.06) $4.2 ($0.2

— $19)
Non

- fatal heart attacks

3% discount rate PM 2.5 $1.7 ($0.3

— $4.2) $0.004

($0.003

— $0.01) $1.7

($0.3

— $4.2)

7% discount rate PM 2.5 $1.3 ($0.3

— $3.1) $0.004

($0.002

— $0.001) $1.3 ($0.3

— $3.1) Hospital admissions

— respiratory PM 2.5 & O 3 $0.04 ($0.02

— $0.06) --- $0.04 ($0.02

— $0.06) Hospital admissions

— cardiovascular PM 2.5 $0.09 ($0.01

— $0.2) --- $0.09 ($0.01

— $0.2)
Emergency room visits for asthma PM 2.5 & O 3 $0.003 ($0.002

— $0.006) --- $0.003 ($0.002

— $0.006)

Acute bronchitis PM 2.5 $0.008 (< $

- 0.01

— $0.02) c --- $0.008 (<$

- 0.01

— $0.02) c
Lower respiratory symptoms PM 2.5 $0.004 ($0.002

— $0.009) --- $0.004 ($0.002

— $0.009)
Upper respiratory symptoms PM 2.5 $0.005 (<$0.01

— $0.014) --- $0.005 (<$0.01

— $0.014)

Asthma exacerbation PM 2.5 $0.02 ($0.002

— $0.08) --- $0.02 ($0.002

— $0.08)
Lost work days PM 2.5 $0.2 ($0.17

— $0.24) --- $0.2 ($0.17

— $0.24)

School loss days O 3 $0.01 ($0.004

— $0.013) --- $0.01 ($0.004

— $0.013) Minor restricted

- activity days PM 2.5 & O 3 $0.7 ($0.3

— $1) --- $0.7 ($0.3

— $1)186

Recreational visibility, Class I areas PM 2.5 $4.1
Social cost of carbon (3% discount rate, 2014 value) CO 2 $0.6

Monetized total Benefits (Pope et al. 2002 PM 2.5 mortality and Bell et al. 2004 ozone mortality estimates)

3% discount rate PM 2.5 , O 3 $110 ($8.8

— $340) $0. 28 ($0.01

— $0.9) $120

($1 4

— $350)

7% discount rate PM 2.5 , O 3 $100 ($8

— $310) $0. 25

($0.0 1

— $0. 85 ) $110

($13

— $320)

Monetized total Benefits (Laden et al. 2006 PM 2.5 mortality and Levy et al. 2005 ozone mortality estimates)

3% discount rate PM 2.5 , O 3 $270 ($24

— $800) $0.7

($0.05

— $2. 1 ) $280

($29

— $810)

7% discount rate PM 2.5 , O 3 $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 bene fits 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 c ategories 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 pr eferr ed remedy will result in between 1 3 ,000 and 3 4 ,000
PM 2.5 and ozone

- rel ated avoided premature deaths annually in 2014. Our estimate of total
monetized benefits in 2014 for the select ed remedy is between $1 2 0 billion and $2 8 0 billion using a 3 percent discount rate and between $1 10 billion and $2 5 0 using a 7 percent discount rate. Health benefits account for between 97 and 99 percent of total benefits depending on
the PM 2.5 and ozone mortality estimates used, in part because we are unable to quantify most
of the non

- health benefits. The monetized benefit associated with redu ctions in the risk of187

premature mortality, which accounts for between $ 100 and $2 7 0 billion in 2014, depending again on the PM and ozone mortality risk estimates used, is between 9 3 and 9 7 percent of total monetized health benefits. The next largest benef it 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 m any 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 PM 2.5 and

ozone

- related mortalities estimated to result from the select ed remedy. Figure 5

- 17 plots the
cumulative distribution of the percentage of deaths due to PM2.5 and ozone in 2 014, 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 PM 2.5 and ozone. While not q uantified 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 qualit y 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 t he 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 Environmental188

Management

.
These figures show that while there are very large health benefits throughout most of
th e 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 throu ghout 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, su ch as the recent Colorado Clean Air

– Clean Jobs Act of April 2010 that is likely to convert much of the F ront R ange coal

- fired generation in Colorado to natural gas in the near future.189

Figure 5

- 15: Estimated reduction in excess PM 2.5 - related premature mortalities estimated to occur in each county in 2014 as a result of the select ed 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 select ed remedy191

Figure 5

- 17: Cumulative percentage of the reduction in all

- cause mortality attributable to reductions in PM 2.5 and Ozone resulting from the selected remedy by county in 2014 A

A Bell et al. 2005 ozone mortality estimate and Pope et al. 2002 PM 2.5 mortality estimates.192 Figure 5

- 18 summarizes an array of PM 2.5 - 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 scien tific literature, EPA estimated PM

related 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 Commi ttee (U.S. EPA

- SAB, 2009a; U.S. EPA

- SAB, 2009b), concluded that the scientific literature consistently finds that a no

- threshold log

linear model most adequately portrays the PM

- mortality concentration

- response relationship while recognizing potential unce rtainty 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) PM 2.5 levels experienced by the population receiving the PM 2.5 mortality benefit (Figure 5

- 19). We identify on this figure the lowest air quality levels measured in each of the two primary epidemiological studies E PA 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 con sider 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 (Figu res 5

- 19 ) increasing our confidence in the PM mortality analysis. Approximately 69 % of the avoided impacts occur at or above an annual mean PM 2.5 level of 10 μg/m 3 (the LML of the Laden et al. 2006 study); about 9 6 % occur at or above an annual mean PM 2.5 l evel of 7.5 μg/m 3 (the193

LML of the Pope et al. 2002 study). As we model mortality impacts among populations exposed to levels of PM 2.5 that are successively lower than the LML of each study our confidence in the results diminishes. However, the analysis abo ve 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 μ g/m3, we would place greater confidence in
estimat es 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 abov e 10 μ g/m3 because this value is at or above the LML of each study.

Finally, Figure 5

- 2 0 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
PM 2.5 concentrations are below 7.5 μ g /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 P M 2.5 . 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 R ule, 85% of the population live in areas where PM 2.5 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 pollu tion 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 the194

American Cancer Society cohorts there are innumerable other attribu tes 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 representati on of one key difference between
the two studies.

Figure 5

- 18: Estimated PM 2.5 - related premature mortalities avoided according to epidemiology or expert

- derived PM mortality risk estimate A

A Column total equals sum of PM 2.5 - related mortality and morbidity benefits and ozone

- related morbidity and mortality benefits using the Bell et al. (2004) mortality estimate. $0 $50 $100 $150 $200 $250 $300 $350 $400

Pope et al. Laden et al. Billions (2006$)

PM 2.5 Benefits estimates derived from 2 epidemiology functions and 12 expert functions 3% DR 7% DR195

Figure 5

- 19: Distribution of PM 2.5 - related mortality impacts by baseline PM 2.5 levels, PM 2.5 epidemiology study and lowest measured l evel (LML) of each study196

Figure 5

- 20: Cumulative percentage of adult population at annual mean PM 2.5 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 PM 2.5 and ozone

related premature mortalities by between 1 3 ,000 and 3 4 ,000, produce substantial non

mortality 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 t he 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 the197

concentration response relationships and our use of the expert elicitation

- derived PM mortality functions. Others, including the projection of atmospheric conditions and source

level emissions, the projection of baseline morbidity rates, incomes and technological development are unquantified. When e valuated 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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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 c ustomers 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). Som e 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 Industr y 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 7.2224 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 ga s, 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 nucl ear 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 th an 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 electrici ty 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.225

Table 6

- 4. Coal Steam Electricity Generating Units, by Size, Age, Capacity, and Efficiency (Heat Rate)

Unit Size Grouping (MW) No. Units % of All Units Avg. Age Avg. Net

Summer Capacity (MW) Total Net

Summer Capacity (MW) % Total

Capacity Avg. Heat

Rate (Btu/kWh) 0 to 25 193 15% 45 15 2,849 1% 11,154 >25 to 49 108 9% 42 38 4,081 1% 11,722 50 to 99 162 13% 47 75 12,132 4% 11,328 100 to 149 269 21% 49 141 38,051 12% 10,641 150 to 249 81 6% 43 224 18,184 6% 10,303 250 and up 453 36% 34 532 241,184 76% 10,193 Totals 1,266 316,480

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.

Figure 6

- 1. Fossil Fuel

- Fired Electricity Generating Facilities, by Size

Notes/Source: National Electric Energy Data System (NEEDS 4.10) (EPA, Dec ember 2010). This map displays facilities in the NEEDS 4.10 IPM frame. NEEDS reflects available capacity on

- line by the end of 2011; this includes planned new builds and planned retirements. In areas with a dense concentration of facilities, some faciliti es may be obscured.22 6

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 lines 51 , 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 c ontrolled 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

ge neration, 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 transmis sion 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 fro m 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. Distri bution 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 d eliver power to consumers. In many state efforts, this has

51 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 b ased 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 indust ry.

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

t hought 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 t he 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 esta blishing 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 c ompanies 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 o ther 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 generat ion (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) fo r 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 ai r emissions of SO 2 and NO X , important precursors in the formation of fine particles and ozone (NO X only). The power sector is a major contributor of both these pollutants, and reductions of SO 2 and NO X emissions are critical to EPA‘s efforts to bring
abou t 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 SO 2 emissions and 18 percent of total nationwide NO X emissions (see Figure 6

- 3).229

Figure 6

- 3. Emissions of SO 2 and NO X 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
SO 2 and NO X , particularly when uncontrolled. For coal

- fired units, NO X emissions rates can vary from under 0.05 lbs/mmBtu (for a unit with selective catalytic reduction (SCR) for NO X removal) to over 1 lb/mmBtu for an uncontrolle d cyclone boiler. NO X emissions from
coal

- fired power plants are formed during combustion and are a result of both nitrogen in coal and nitrogen in the air. SO 2 emissions rates can vary from under 0.1 lbs/mmBtu (for some units with flue gas desulfurizati on (FGD) for SO 2 removal) to over 5 lbs/mmBtu for units burning higher sulfur coal without any pollution controls. For an uncontrolled coal plant, SO 2 emissions are directly related to the amount of sulfur in the coal.

Oil

- and gas

- fired units also have a wide range of NO X 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 SO 2 emissions. NO X 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. SO 2 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 SO 2 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 SO 2 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 contro ls, 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.
Switc hing 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 modificat ions.

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 SO 2 , 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 SO 2 removal. Spray dryers use lime

- based slurry and can achieve 92 percent removal.

An alternative to wet and dry scrub ber technology is dry sorbent injection (DSI), which injects an alkaline powdered material (post combustion) to react with SO 2 and other acid gasses. The reacted product is removed by particulate matter (PM) control device. DSI technology is most efficie nt 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 baghous e. Finally, DSI may employ a multitude of sorbents (Trona, 52 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.

52 Trona refers to the chemical compound sodium sesquicarbonate.231 One metho d of reducing NO X emissions is through the use of combustion controls (such as low NO X burners and over

- fire air). Combustion controls adjust the coal
combustion conditions to those where less formation of NO X occurs. Post

- combustion
controls remove the NO X after it has been formed. The most common post

- combustion control is SCR. In SCR systems ammonia (NH 3 ) is injected, which combines with the NO X in the flue gas, to form nitrogen and water and uses a catalyst to enhance the reaction. These
systems ca n reduce NO X 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 NO X also is removed by injecting ammonia, but no catalyst is used. SNCR syst ems can reduce NO X 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 oil

and gas

- fired boilers for NO X control. Combustion controls and SCR are also routinely used
for NO X 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 programmin g model that EPA uses to examine air pollution control policies for SO 2 and NO X 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 Sec tor

At the federal level, efforts to reduce emissions of SO 2 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 sta ndards. The Transport Rule is the next step towards realizing attainment of the national standards for
PM 2.5 and ozone.

Even before widespread regulation of SO 2 and NO X for the power sector, total suspended particulate matter (TSP) was a related target o f 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 also232

mitigated local impacts of TSP. Notably, such regulations effectively addressed large

diameter, filterable particulate matter rather than condensable particulate matter (such as
PM 2.5 ) associated with SO 2 and NO X emissions, which are not visible at the smokestack and have impacts far from their s ources.

Federal regulation of SO 2 and NO X 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 SO 2 emissions either by using scrubbers or by using low sulfur coal. NO X was required to be limited through the use of low
NO X burners. A new NSPS (subpart Da), promulgated in 1978, tig htened the standards for
SO 2 , 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 SO 2 and NO X emissions. The SO 2 program sets a permanent cap on the total amount of SO 2 that can be emitted by electric power plants in the contiguous
United States at about one

- half of the amount of SO 2 these sources emitted in 1980. Using a
market

- b ased cap and trade mechanism allows flexibility for individual combustion units to select their own methods of compliance with the SO 2 reduction requirements. The program uses a more traditional approach to NO X emissions limitations for certain coal

- fired electric utility boilers, with the objective of achieving a 2 million ton reduction from projected NO X emission levels that would have been emitted in 2000 without implementation of Title IV.

The Acid Rain Program comprises two phases for SO 2 and NO X . Phase I applied primarily to the largest coal

- fired electric generating sources from 1995 through 1999 for
SO 2 and from 1996 through 1999 for NO X . Phase II for both pollutants began in 2000. For
SO 2 , it applies to thousands of combustion units generating electricity nationwide; for NO X 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 switchin g to lower sulfur coals. Under the NO X provisions of Title IV, most existing coal

- fired units installed low NO X burners.

The CAAA also placed much greater emphasis on control of NO X to reduce ozone nonattainment. This led to the formation of several regi onal NO X trading programs as well233

as intrastate NO X 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 NO X in 1995 and in 1999 began an ozone

- season cap and trade program to achieve deeper reductions. In 1998, EPA promulgated regulations (the
NO X SIP Call) that required 21 states in the eastern United States and the District of
Columbia to reduce NO X emissions t hat 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) buil t on EPA‘s efforts in the NO X SIP call to address specifically interstate pollution transport for ozone, and was EPA‘s first attempt to address interstate pollution transport for PM 2.5 . It required significant reductions in
emissions of SO 2 and NO X in 28 states and the District of Columbia (see Figure 6

- 4 below). EGUs were found to be a major source of the SO 2 and NO X emissions which contributed to fine particle concentrations and ozone problems downwind. Although the D.C. Circuit
remanded the rule to EP A 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 PM 2.5 nonattainment and improve visibility by reducing transported precursors of
SO 2 and NO X through the implementation of three separate cap and trade compliance programs for annual NO X , ozone season NO X , and annual SO 2 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 SO 2 and NO X 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 SO 2 and NO X 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 p eak of almost $300 billion in 200 8 . However, operating expenses were235

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 damp ening 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 inc ome 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 w ill 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 el ectricity 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 t hat 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 natu ral 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 pe rforms 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 e nergy 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 co ntinually develop and bring to market new technologies that reduce energy consumption. Fourth, complementary policies and240

energy efficiency standards at the state and Federal level have helped address market failures. These broader changes have altered t he 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 standa rds. 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 20 09. 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 i s 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 compli ance 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 po wer 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.htm243 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 SO 2 and NO x (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.ep a.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 SO 2 and NO x 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.13 1 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 is244

another example of how the model has been used in regulatory analysis of SO 2 and NO X 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
illus trate the costs and benefits of multiple levels of SO 2 and NO X 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 sta te 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 SO 2 and
NO x 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, t he 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 SO 2 and NO x 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 A gency is finalizing the Transport Rule. The Transport Rule requires annual SO 2 and NO x reductions in 23 states, and also requires ozo ne season NO x reductions in 2 0 States. 53 Many of the Transport Rule

s tates are affected by both the annual SO 2 and NO x reduction requirements and the ozone
season NO x requirements.

53 EPA is issuing a supplemental proposal to request comment on requiring ozone

- season NO X 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 SO 2 emissions and 67 percent of nationwide NO x emissions in 2010 (see Table 7

- 1).

Table 7

- 1. Annual Emissions of SO 2 and NO x in 2010 and Percentage of Emissions in the Transport Rule Affected Region (tons)

SO 2 NO X Transport Rule Annual NO X and SO 2 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 NO X and SO 2 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 Wiscons in.

For SO 2 and annual NO x , 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 SO 2 requirements begin in 2014. 54 For ozone

- seaso n NO x , 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 SO 2 and
NO x reduction requirements and the ozone

- season (May

– September) NO x 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.

54 These 1 6 states include: Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Missouri, New Jersey, New York, North Carolina, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia, and Wisconsin246 As previously stated, the emissions, cost, air quality, and benefits analyses done for the Transport Rule are from a modeling scenario that requires annual SO 2 and NO x reductions in 23 states and ozone season NO x requirements in 26 states (See Figure 7

- 1). This modeling differs from the final Tran sport Rule because it includes ozone season NO X requirements for six states (Iowa, Kansas, Michigan, Missouri, Oklahoma, and Wisconsin) that the final Transport Rule does not cover

. ( A s discussed in the preamble to the final rule,
EPA is issu ing a suppleme ntal 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 States247

Table 7

- 2. Transport Rule Annual NO x and SO 2 and NOx Ozone Season State Emission Budgets (tons)

SO 2 Annual NO X

Annual NO X

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 3,382248

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 issu ing 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. Ta ble 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 in creased 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 C ontiguous 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 fo r 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 t he 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 rate s 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 ar e also in the document titled: ―Documentation250

Supplement for EPA Base Case v.4.10_FTransport

– Updates for Final Transport Rule.‖

7.2 Projected SO 2 and NO x Emissions and Reductions

The Transport Rule achieves substantial emission reductions. EPA projects annual
SO 2 emission reductions of 62 percent and annual NO x emission reductions of 11 percent in the covered region by 2014 relative to the base case. Additionally, EPA projects ozone

season NO x reductions of 11 percent in the Transport Rule region (see Table 7

- 4). There is
also a small decrease in CO 2 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 SO 2 emission reductions occur in the Midwest and Mid

- Atlantic regio ns of the country. Because banking of allowances is allowed to encourage early reductions, 2012 SO 2 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 h igher 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
NO x emissions reductions oc cur across the Transport Rule region (see Figure 7

- 3), and with the Transport Rule, ozone

- season NO x emission reductions are lower than they would have
been with the NO x SIP Call (base case) (see Figure 7

- 4).251

Table 7

- 4. Projected Emissions of SO 2 , NO X , and CO 2 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

SO 2 (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%

NO X (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% NO X (S ummer) 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%
CO 2 ( Annua l ) 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 s tates modeled for the annual SO 2 and NO x requirements. ―Summer‖ is from May 1

– September 30, which is the ozone season. Base case includes Title IV Acid Rain Program, NO x SIP Call, and s tate rules through December 1, 2010. Source: Integrated Planning Model run by EPA, 2011.252

Figure 7

- 2. SO 2 Emissions from the Power Sector in 2012 and 2014 with and without the Transport Rule

S ource: EPA, IPM, 2011. Figure 7

- 3. Annual NO x 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 NO x 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 2 0 55 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).

55 EPA is issuing a supplemental proposal to request comment on re quiring ozone

- season NO X 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 t he 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 c ase 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 th e 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 thi s 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 pl anned.

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 project ed to
achieve NO x and SO 2 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
blen ding of coals), and increased dispatch of more efficient units and lower

- emitting256

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 tha t 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 gene ration to greater use of natural gas lowers emissions a small amount (see Table 7

- 11). NO x emissions reductions largely stem from year

- round operation of selective catalytic reduction (SCR) controls that would otherwise operate only during the ozone seaso n (see Table 7

- 10). Additionally, a modest
amount of NO x reduction stems from new low

- NO x 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 Transpo rt Rule‘s 2012 SO 2 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
operate in the absence of the Transport Rule . Other 2012 SO 2 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 (D SI), which allow for significant additional SO 2 reductions in the Group 1 states.

Table 7

- 8 below provides a profile of sulfur contents in alternative coal supplies that
offer substantial SO 2 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 SO 2 Controls by Coal Sulfur Content, 2014

Very Low (<0.8 lb/mmBtu) Low

(0.81

- 1.2 lbs/mmBtu) Low

Medium (1.21

- 1.66 lbs/mmBtu) High

Medium (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 SO 2 program. The Transport Rule is associated with only a slight reduc tion (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 e ven 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 PM 2.5 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 High

medium sulfur Low

medium 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 r educe SO 2 emissions. Source: Integrated Planning Model run by EPA, 2011.

EPA does not project additional SO 2 control retrofits in 2012. For SO 2 , EPA believes

that to meet the 2012/2013 s tate assurance levels , compliance strategies would at most258

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 source s 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

- Construct ed Advanced SO 2 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 SO 2 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 des ulfurization (scrubbers) for SO 2 control and the year

- round operation of an additional 5 GW of selective catalytic reduction technology (SCR) for NO x 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 Newly

Built 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 SO 2 controls. SCR (Selective Catalytic Reduction) is an advanced NO X control. Source: Integrated Planning Model run by EPA, 2011

.

Projected operation of advanced pollution controls accounts for the vast majority of

the NO x reduction s , while for SO 2 the red uctions 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 analyz e 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

- effec tive 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 emission260

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 th ese 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 th e four Transport Rule programs in 2012 and 2014 (2007$)

Emission Allowance

Prices ($/Ton)

2012 2014

Annual SO2 Group 1 Trading Program 1,000 1,1 00

Annual SO2 Group 2 Trading Program 600 700

Annual NOx Trading Program 500 600

Ozone Season NOx Trading Program 1,3 00 1,5 00

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

- 1 3 and Figure 7

- 5 show the generation mix with the Transport Rule. Coal

fired generation and natural

- gas

- fired generation are projected to remain relatively unchanged because of the phased

- in nature of the Tr ansport Rule, which allows industry the261

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

- 1 3 . 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 6 2.7% 250 250 1 0.2% 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 capacit y 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
transmissio n 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 whethe r they are needed to meet both demand and reliability reserve requirements.263

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

- 1 4 ) in both the base and Transport Rule
scenarios.

Table 7

- 1 4 . Total Generation Capacity by 20 14 (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

- 1 5 ). 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 withou t the Transport Rule in place.

The Transport Rule will encourage the installation of new controls and operation of existing pollution controls for SO 2 and NO x removal. Many of these pollution controls can
achieve SO 2 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 Transport264

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 i ncrease more than 60% above 2009 levels by 2014.265

Table 7

- 1 5 . 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 Ap palachia 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); Wa ste 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, 2007266

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

- 1 6 ). Regional retail electricity prices in the eastern half of the country are projected to ran ge 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

- 1 6 . Pr ojected 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

7.1 1 Projected Fuel Price Impacts

The impacts of the Transport Rule on coal prices and natural gas prices are shown
below in Tables 7

- 1 7 and 7

- 1 8 . 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 increa se 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 sho uld 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 experienc ed 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 fun damental 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 lo nger

- 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 supply268

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

- 1 8 ). In that fram ework, 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

- 1 7 . 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

- 1 8 . 2012

- 2030 Weighted Average Henry Hub and Delivered Natural Gas Prices with the Base Case and with the Transport Rule (2007$/M MBtu)

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.

7.1 2 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 SO 2 and NO x reductions in 23 states and ozone season NO x requirements in 26 states (See Figure 7

- 1). This modeling differs from the final Transport Rule because it includes ozone season NO X requirements for six states (Iowa, Kansas, Michigan, Missouri, Oklahoma, and Wisconsin) that the final Transport Rule does not cover

. ( A s discussed in the preamble to the final rule,
EPA is issuing a supplemental proposal to request comment on inclusion of these six states).270

7.1 3 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 fabr ic filters).

Condensable PM emissions are based on plant type, sulfur content of the fuel, and
SO 2 and PM control configurations. Although EPA‘s analysis is based on the best available emission factors, these emission factors do not account for the potent ial changes in condensable PM emissions due to the installation and operation of SCRs. The formation of additional condensable PM (in the form of SO 3 and H 2 SO 4 ) in units with SCRs depends on a number of factors, including coal sulfur content, combustion c onditions 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).

7.1 4 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 k ey model inputs to the power271

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 analyzin g 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 ca pital 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 p roperty 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

asses sment 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 up held 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 mo nitoring 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 $2 6

. 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 Integrated272

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 docum ent 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. Montgome ry (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/o r 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 ut ilities 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. T he 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 ―per fect 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 pollu tion control technologies for SO 2 and
NO x removal as well as reductions in their costs over time after 2014. Market

- based cap and273

trade regulation serves to promote innovation and the development of new and cheaper technologies. As an example, cost estim ates of the Acid Rain SO 2 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 billion 56 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 SO 2 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 recogniti on 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. 57 The Agency is considering approaches to make this adjust ment 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 t hrough 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 sett lement 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 p rices shown in
Table 7

- 16 would prompt end users to curtail (to some extent) their use of electricity and encourage them to use substitutes. 58 The response would lessen the demand for electricity,

56 2010 Phase II cost estimate in $1995.

57 See regulatory impact analysis for the Tier 2 Regulations for passenger vehicles (1999) and Heavy

- Duty Diesel Vehicle Rules (2000).

58 The degree of substitution/curtailment depends on the costs and performance of the goods that substitute for274

resulting in electricity price increases slightly lower tha n 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 industry 59 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, depen ding on the magnitude and coverage of the price increases. 60

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). Thes e 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 c onclusions 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.

7.1 5 Significant Energy Impact

The Transport Rule has a significant impact according to E.O. 13211: Actions that

more ene rgy consuming goods, which is reflected in the demand elasticity.

59 Investor

- owned utilities alone accounted for nearly $300 billion in revenue in 2008 (EIA). 60 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 SO 2 , NO X , and mercury), demand response could lower present value costs by as much as 47% below wh at 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 kee p 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 deman d and shifts in the type of coal demanded. As discussed above
in section 7.1 1 , 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

gener ation 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 produc tion 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 Transpo rt 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 SO 2 are set in two phases, pr oviding 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 bank276

allowances for future years help industry plan for and ensure reliability in the elect rical
system.277

7.1 6 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. SO 2 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 Dem ocratic 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 Ver g. 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 Harr ington, Lisa Heinzerling, and Richard D. Morgenstern, 56

– 81. Washington, DC: Resources for the Future.279 CHAPTER 8
MACROECONOMIC AND EM PLOYMENT 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 select ed 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) mig ht 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 guide lines 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). Othe r 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, lik e IPM, but represent more sectors of the economy.

A linked partial equilibrium model is useful for evaluating relative economic impacts280

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 produce r surplus, the more accurately those estimates reflect the true social cost. 61 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 re latively 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 set 62

- 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

61 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.

62 A benchmark data set is included in the model to define the prepolicy equilibrium in the year of analysi s (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 petrole um, 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 tra de (imports/exports) responds to domestic price changes

Model time horizon (―short run‖) for a single period (2015) 63

- fixed production resources (e.g., capital) lead to an upward

- sloping industry supply function

- firms cannot alter certain input mixes; the re 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 tra nsition 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

Unlik e 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.

63 For this analysis, we use 2015 as our analysis year and as a proxy for 2014. Thi s 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 consi der 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 chan ges 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

st ock to be fixed will not fully depict the social costs of an adjustment to a post

policy equilibrium as discussed in the EPA Economics Guidelines.

The own

- price elasticities used by the Multimarket Model are drawn from secondary sources, as fully describ ed 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 flexibili ty 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 short

run, 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 assumptio n 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 a scertain a priori

.2 83

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 0.1 6 %. 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 p ressure 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 s uggests 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 201 4

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

- 0.003284 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 ch anges 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. 8 1 billion (2007 dollars) as shown in Table 8

- 2, or nearly identical to the compliance cost s. 64 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. 65

64 The same is true for many recent rulemakings, including the Boiler MACT.

65 There are small addition al 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 mo re 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 −0.8 1 $

T he 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 electr icity 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 short

term evaluation using a part ial 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 signific ant, 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 econo my. 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 c hanged 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 e quilibrium (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 esti mated 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 benefit

side effects updates now in the process of being incorpora ted 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 Employm ent 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. 66 While a standalone analysis of empl oyment 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 enviro nment 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 e mployment 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

66 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 la bor 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 provi de 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 ) rulemaki ngs, 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. Histor ically, 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 t he 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

- reviewe d, published study that explores historical relationships between industrial288

employment and environmental regulations (Morgenstern, Pizer, and Shih, 2002 ) . The fundamental insight of Morgenstern, et al. is that e nvironmental 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 wi th 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, Morg enstern 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 wit hin the regulated industry 67 ;

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, r egulated 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 o f 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,

67 The Morgenstern et al. resul ts 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 changes 68

.
Since the Morgenstern, et al. parameter estimates are expressed in jobs per million ($1987) 69 of environmental compliance expenditures, their study o ffers 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 result s 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 wit h 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 +. 70

, 71

68 For alternative views, see Henderson (1996 ) and Greenstone (2002 )

.
69 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

.
70 Since Morgenstern‘s analysis reports environmental expenditures in $1987, we make an inflation adjustment the IPM costs using the ratio of the consumer price ind ex, U.S. city, all items reported by the U.S. Bureau of Labor Statistics: CPI 1987 / CPI 2007 = (113.6/207.3) = 0.55290 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 Expenditure a −3.56 2.42 2.68 1.55

Standard Error 2.03 0.83 1.35 2.24

EPA estimate for Transport Rule b +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 95 th 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 t o 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 rul e, 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 manufac turing industries. It is difficult, however, to extend these results to an estimate of employment effects across these many different industries;

71 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 em ployment 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. F irst, 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
diff erent 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 a ssumes 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 burde n 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 dispropor tionately fall on smaller or larger plants.

8.4 Employment Impacts of the Transport Rule

- Environmental Protection Sector Approach, by 2014 72

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 employ ment 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 co mply with the regulation

72 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 i n 2008. 73 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 re present 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

re gulation. 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 inc rease 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 s tudy (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 .‖

73 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

- appr oximate 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 De partment of Commerce International Trade Administration Environmental Industries Fact Sheet from April 2010: http://web.ita. doc.gov/ete/eteinfo.nsf/068f3801d047f26e85256883006ffa54/4878b7e2fc08ac6d85256883006c4 52c?OpenDocument293 The focus of this part of the employment analysis is on short

- term jo bs 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. 74 Results indicate that the Transport Rule has the potential to result in a net inc rease 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 2,2 30 job

- y ears 75 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 meth odology 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 equipm ent 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. 76 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 r ule, along with data from secondary sources, to estimate the job impacts using this approach. 77 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 co nstruction labor, the labor needs are derived from the 2002 EPA resource analysis for installing various retrofits (FGD

– Flue Gas Desulfurization scrubbers, SCR

-

74 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.‖

75 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.

76 Engineering and Economic Factors Affecting the Installation of Control Technologies for Multipollutant Strateg ies EPA

- 600/R

- 02/073 (2002).

77 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 i nto 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 nee ded 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

Induce d 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 2,2 3 0 one

- time job

- years of increased demand for direct labor, driven by the
need to de sign 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 Constructio n 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 t o 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 al l 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 on going 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
broad er 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 Operation296

Employment Changes from Changes to Demand in Materials 78

1. Limestone (FGD) 2,440
2. Ammonia (SCR) 30
3. Catalyst (SCR) 20
4. Sodium Bicarbonate (DSI) 160 Sub

- Total: 2,6 50

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 (2,0 30 )
2. Natural Gas 8 1 0
3. New Natural Gas Pipeline 2 30
Sub

- Total: (990)

8.5 Summary

The t wo 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 Morgenster n 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 th e 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 f inal Transpor t 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 appro ach* Not Applicable 2,2 30

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 Morg enstern 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. ―Env ironmental 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 o f 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: Detai led 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 EXECUT IVE 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 Heal th 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: Act ions 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
Busine ss 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 certi fies 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, small300

organizations, and small governmental jurisdictions.

Fo r 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) c ategory 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 distr ict 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 Entities a

Category NAICS

Code b Examples of Potentially Regulated Entities

Industry 221112 Fossil fuel

- fired electric utility steam generating units.

Federal Government 221112 c Fossil fuel

- fired electric utility steam generating units owned by the federal government.

State/Local/ Tribal Government 221112 c Fossil 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.
b North American Industry Classification System.

c Federal, state, or local government

- owned and operated establishments are classified according to the activity301

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 NO x and SO 2 budgets. This analysis does not examine potential indirect economic impacts associated with the Transport Rule, such as employ ment 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. 79 The Ventyx data set contains detailed ownership and corporate affiliation in formation. 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 electri city 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 pa rt 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, s mall 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 w hich 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 abo ve,

79 For details, see http://www.ventyx.com/302

EPA identified a total of 108 potentially affected small entities, out of a possible 610. 80 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:

annua l 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 reduct ion 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 rela tive to the marginal control costs of other units.

To attempt to account for each potential control strategy, EPA estimates compliance
costs as follows:

C Compliance = Δ C

Operating+Retrofit + Δ C Fuel + Δ C

Allowances + Δ C

Transaction + 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 wi ll be lessened on these entities by the projected increase in electricity prices under the Transport Rule . On the other hand, those maintaining or

80 The re are 95 entities that are not technically electricity generating utilities so we applie d 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 complian ce 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 retrof its, 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 ea ch 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 relat ive 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 u nder 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 SO 2 and
NO x annual and NO x ozone season allowance holding s. For both SO 2 and NO x , 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

- ze ro annual heat input values within the 5 year period are selected and averaged for each unit. EPA
calculated each unit's SO 2 and NO x annual and NO x ozone season allowance304 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 N ew 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 b y 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 h ours * 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 Venty x. 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
faci lities 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 env ironment 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 estim ating the degree of competition in electricity market, as provided by EIA. The lower this percentage means that there are more areas with305 cost of service market characteristics. We have used the estimates published in AEO
2010.

To somewhat account for thi s 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 e ntities 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 wi th 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 poten tially 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 re maining 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, 2 4 entities may experience compliance costs greater than 1 percent of generat ion 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 t hus 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. S pecifically, 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

- division s and private small entities is higher
fuel cost s, 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 institu ted 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 i mpact 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 s witch to a lower sulfur coal. Thus, another reason for entities incurring impacts is that308

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. S ummary 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 analysis309 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 o verall 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 op erating 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 municip als, 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

Cos t

Cooperative

- $11.3 $4.9

- $50.7

- $109.8 $0.2

Investor

Owned 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 is310

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 su mmarize, 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 impac t 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

c omplying 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 R eform 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, a nd 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 intergovernmenta l mandate and a Federal private sector mandate. A Federal intergovernmental mandate, in turn, is defined to include a311

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), e xcept 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, Sectio n 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 regulator y 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 propos ed 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 Admi nistrator 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 s mall 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 p otential 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

- O wned 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 associate312

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 gov ernments. 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 re sults 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 SO 2 and NO x emis sion 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 compliance313

costs as follows:

C Compliance = Δ C

Operating+Retrofit + Δ C Fuel + Δ C

Allowances + Δ C

Transaction + 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 componen ts of cost. Under the Transport Rule, for example, s ome 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 capture s 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 cos ts, 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 f rom 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 historic314 emissions). Using 2006

- 201 0 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 h eat 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) Adminis trative 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 transactio n 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.315

9.2.2.2 Results

A summary of economic impacts on government

- owned entit ies 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. 81

81 All costs are reported in 2007 dollars.316

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 Revenue s Number of

Government Entities with Compliance Costs >3% of Generation Revenue s

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 bec ause 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 2 6 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 is317

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 t o 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 i n 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 ne t 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 Entitie s 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 analysis318 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 f or 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 enti ties 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 C osts 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 all319

coal

- fired capacity is projected to be uneconomic to ma intain 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 ser vice 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 tota l 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 m ay 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 Ru le.

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

- kee ping 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 the320

additional burden. This situation arises in states that are also subjec t 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), NO x SIP Call (EPA ICR num ber 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 $2 6 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 app roximately 185,000 hours annually. These estimates include the annualized cost of installing and operating appropriate SO 2 and NO x emissions monitoring equipment to measure and report the total emissions of these pollutants from affected EGUs (serving gen erators 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
feasib le alternatives considered by the Agency.

This rule is not subject to EO 13045 because it does not involve decisions on
environmental health or safety risk s that may disproportionate ly affect children. The EPA
believes that the emission reductions from the strategies in this final rule will further improve321

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 direc t 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

proc ess of developing the proposed action.

EPA has concluded that this action does not have federalism implications. It will not impose sub stantial 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 polic y 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 D ata 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 Gove rnors 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

Sub ject 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 account able process to ensure ―meaningful and timely input by tribal officials in the development of regulatory322

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 t he 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, sev eral 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 un its 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 he ld 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 ce rtified 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 minority323

populations and low

- income populations in the United States.

9. 7

. 1. Consideration of Enviro nmental Justice Issues in the Rule Development Process

During development of this final Transport Rule, EPA considered its impacts on low

income, 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 PM 2.5 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 envi ronmental benefits to numerous low

income, 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 (PM 2.5 ) 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 ris k 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 any324

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 under lying 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 ben efits among different populations, is described in Appendix A of this RIA.

9. 7

. 2 . Meaningful Public Participation

During the comment period for th e proposed rule, EPA reach ed out specifically to environmental justice communities and organizations to notify them of the opportunity to provide comments on this rule and to sol icit their comments on both the proposed rule and

upcom ing actions. EPA he ld publi c hearings on this rule and received an extensive number of comments in writing. EPA has responded to the se comments as part of this final rulemaking.325 CHAPTER 10

COMPARISON OF BENEFITS AND COSTS

10.1 Comparison of Benefits and Costs

As shown in Chapter 8, t he 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 appr oximately $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 subtract ing the
estimated social costs from the estimated benefits in 2014. T he annual net benefits of the final Transport rule are approximately $ 120 to 2 80 + 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 sig nificant net gain in social welfare, notwithstanding the expansive set of health and
environmental effects we were un able to quantify. Further quantification of directly emitted
PM 2.5 - , mercury

- , acidification

- , and eutrophication

- related impacts would in crease 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 bene fit 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 a pproach uses the results from a pilot expert elicitation project designed to characterize key aspects of uncertainty in the ambient PM 2.5 /mortality relationship, and augments the uncertainties in the mortality estimate with the statistical error reported f or 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 SO 2 budgets in Group 1 states, compared to the final Transport Rule, beg inning 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

. T hey are designed to show the effects of more stringent and less stringent SO 2 reduction requirements in a regulatory structure that is otherwise the same as the remedy.

Both of the alternative scenarios represent a n interstate trading program to achieve
emission reductions , as in the final Transport rule remedy

. Both alternative scena rios impose the same NOx and SO 2 emission reduction requirements in 2012 as imposed by the final Transport R ule. The option s vary in stringency for the SO 2 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. 82 Beginning in 2014, the
less stringen t alternative scenario imposes higher SO 2 budgets in G roup 1 states based on
modeled emissions from the $ 1

, 6 00 per ton cost threshold analysi s described in preamble
section VI.B

. The more stringent alternative scenario reflects 2014 reduction requirements
based on modeled emissions at the $ 10

, 0 00 per ton cost threshold.

Air quality modeling was not conducted for these two alternatives

. EPA appl ied a benefit

- per

- ton approach appropriate for deriving PM

- related benefits for the se alternative
scenarios from the full benefit estimation methodology conducted for the final Tra nsport Rule‘s remedy, as described in Chapter 5

. The alternative scenarios‘ social costs are estimated using the Multimarket Model while using t he private compliance costs are projected using IPM. Table 10

- 2 presents the emissions forecast for SO 2 and NO x 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.

82 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 2014 a (billions of 2007 dollars)

Description Preferred Remedy

Social costs b

3 percent discount rate $0.81

7 percent discount rate $0.81

Social benefits c,d,e

3 percent discount rate
7 percent discount rate $1 2 0 to $2 8 0 + B

$1 10 to $2 5 0 + B

Health

- related benefits:
3 percent discount rate
7 percent discount rate $1 1 0 to $2 7 0 + B

$1 00 to $2 5 0 + B
Visibility benefits $ 4.1

Net benefits (benefits

- costs ) e,f

3 percent discount rate
7 percent discount rate $1 2 0 to $2 8 0

$ 11 0 to $2 5 0

a Estimated rounded to two significant figures.

b Note that costs are the annualized total social costs of reducing pollutants including NO x and SO 2 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 th e 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 NO x and SO 2 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 CO 2 - related benefits calculated using the social cost of carbon, discussed further in C hapter 5.

d Not all possible benefits or disbenefits are quantified and monetized in this analysi s. 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. Res ults 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 round ing.328

Table 10

- 2. Projected 2014 National Emissions of SO 2 and NO X 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

SO 2 (Annual) 7.2 3.4 3.5 2.4

NO X (Annual) 2.1 1.9 1.8 1.8

NO X ( 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 Select ed Remedy Option in 2014 a (billions of 2007 dollars)

Description Select ed Remedy Less Stringent
Scenario More Stringent

Scenario

Social costs b

3 percent discount rate $0.81 $0.43 $3.6

7 percent discount rate $0.81 $0.43 $3.6

Health PM benefits c,d

3 percent discount rate
7 percent discount rate $1 1 0 to $2 7 0 + B $1 00 to $2 5 0 + 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 $1 1 0 to $2 7 0 + B $ 1 0 0 to $2 5 0 + B $9 8 to $240 + B $88 to $220 + B $130 to $320 + B

$120 to $290 + B

a Estimates rounded to two significant figures

. b Note that costs are the annualized total social costs of reducing pollutants including NO x and SO 2 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 in clude PM 2.5 – related benefits but omit visibility, ozone, and CO 2 - related benefits. We present the PM 2.5 – related benefits of the preferred329

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 PM 2.5 - related monetized benefits. Be nefits in this table are nationwide and are associated with NO x and SO 2 reductions. To ensure that the benefits of the select ed remedy and the more and less stringent scenarios are directly comparable, we exclude the visibility

- related benefits of the sele ct ed 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 c onsistent 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 select ed 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. Thurst on. 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 Prepari ng 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 PM 2.5 - 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 PM 2.5 - related benefits resulting fro m the implementation of the final Transport Rule:

1. What is the baseline distribution of PM 2.5 - 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? 83

In this analysis we find that the level of PM 2.5 mortality risk is not distributed equally throughout the U.S., or among populations of different levels of educatio nal 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 PM 2.5 - related mortality risk, fairly equally among min ority populations and greatly reduces PM 2.5 mortality risk among those populations at greatest risk in the baseline.

Methodology

As a first step, we estimate the level of PM 2.5 - 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 PM 2.5 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),
whe rein modeled 2005 PM 2.5 levels are used to calculate the proportion of all

- cause mortality
risk attributable to total PM 2.5 levels in each county in the Continental U.S. Within each county
we estimate the level of all

- cause PM 2.5 mortality risks for adult populations as well as the level
of PM 2.5 mortality risk according to the race, income and educational attainment of the population.

83 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 PM 2.5 mortality risk estimates.332

Our approach to calculating PM 2.5 mortality risk is generally consistent with the primary analysis with the two exception s: 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 inc idence of
PM 2.5 - 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/m 3 increase in
PM 2.5. This mean relative risk es timate 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),

e ducational 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 PM 2.5 - related mortality. Krewski and colleagues note tha t ―...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 populati ons according to level of education, we applied
PM 2.5 mortality risk coefficients modified by educational attainment: less than grade 12 (RR = 1.082, 95% confidence intervals 1.024

— 1.144 per 10 μg/m 3 change), grade 12 (RR = 1.072,
95% confidence intervals 1.020

— 1.127 per 10 μg/m 3 change), and greater than grade 12 (RR = 1.055, 95% confidence intervals 1.018

— 1.094 per 10 μg/m 3 change). The Pope et al. (2002) study does not provide education

- stratified RR estimates. The principal reason we applied risk
estima tes from the Krewski study was to ensure that the risk coefficients used to estimate all

cause mortality risk and education

- modified mortality risk were drawn from a consistent modeling framework.

The other key difference between this distributional analy sis and the primary analysis relates to the baseline mortality rates. As described in the benefits chapter, we calculate PM

related 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 our333

ability to characte rize the relationship between race and susceptibility to PM 2.5 - related mortality.

The result of this analysis is a distribution of PM 2.5 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 95 th percentile of the PM 2.5 mortality risk distribution. We selected this percentile cut

- off to capture the very highest levels
of PM 2.5 mortality risk. The second step of the a nalysis was to repeat the sequence above by estimating PM 2.5 mortality risk in 2014 prior to, and after, the implementation of the Transport Rule. We also report the per

- person PM 2.5 exposure for each of the three scenarios (2005, 2014, 2014 post

- Transport Rule) by populations of different race.

R esults

The level of PM 2.5 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 thi s 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 esti mated the change in PM mortality risk between 2005 and 2014 among populations living in those counties at the upper 95 th 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 t o 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 95 th percentile of PM 2.5 mortality risk in 2005, and ―all other count ies‖ as those with PM 2.5 mortality risk below this level. The results of this analysis suggest that the PM 2.5 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 PM 2.5 mortality risk among these populations is rou ghly equal to the level of PM 2.5334

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 PM 2.5 mortality risk;
limits to data resol ution 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 PM 2.5 mortality risk among populations living in those counties that meet two criteria: (1) the cou nty is at the upper 95 th percentile of mortality risk in 2005; (2) the county is at the upper 95 th percentile in terms of the number of individuals living below the poverty line. We also estimate the change in PM 2.5 risk among all other counties. The anal ysis 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 F igure 6, we summarize the change in PM 2.5 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 PM 2.5 mortality risk coefficients for this analysis. These results indicate that populations with less than a highschool education are at higher risk of PM 2.5 mortality in 2005, irrespective of whether these populations live in ―greater risk‖ counties, according to the d efinition 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 PM 2.5 mortality risk in 2005336

Figure 2: Distribution of PM 2.5 mortal ity risk in 2014 ( prior to the implementation of the Transport Rule)337

Figure 3: Distribution of PM 2.5 mortality risk in 2014 ( after the implementation of the Transport Rule)

Table 1: Per

- person level of annual mean PM 2.5 exposure (μg/m 3 /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 7.8338

Figure 4: Change in the percentage of PM 2.5 - attributable deaths by race between 2005 and the implementation of the 2014 Transport Rule

Table 2: Numerical values used for Figure 4 above

Year Race

Asian Black Native American White Among populations at greater risk 2005 7.9% 7.8% 7.9% 8.0% 2014 (pre

- Transport Rule) 6.2% 5.9% 6.1% 6.2% 2014 (post

- Transport Rule 5.1% 5% 5.1% 5.2% Among all other populations

2005 4.9% 5.0% 4.5% 5.2% 2014 (pre

- Transport Rule) 4.2% 4.2% 4.2% 4.2% 2014 (post

- Transport Rule 3.5% 3.6% 3.4% 3.8%339

Figure 5: Change in the percentage of PM 2.5 - 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

Year Race

Asian Black Native American White Among populations living in counties with the largest number of individuals living below the poverty line 2005 7.9% 7.8% 7.9% 8% 2014 (pre

- Transport Rule) 6.1% 6.1% 6.2% 6.3% 2014 (post

- Transport Rule 5.3% 5.3% 5.4% 5.4% Among all other populations

2005 4.9% 5% 4.6% 5.3% 2014 (pre

- Transport Rule) 4.1% 4.2% 3.9% 4.4% 2014 (post

- Transport Rule 3.5% 3.6% 3.4% 3.8%340

Figure 6: Change in the percentage of PM 2.5 - attributable deaths among populations by educational attainment between 2005 and the implementation of the 2014 Transport Rule

Table 4: Numerical values used for Figure 6 above

Year Race

< Grade 12 = Grade

12 > Grade 12
Among populations at greater risk

2005 11.0% 9.1% 7.5% 2014 (pre

- Transport Rule) 8.4% 7.4% 5.8% 2014 (post

- Transport Rule 7.1% 6.3% 4.9% Among all other populations

2005 7.2% 6.4% 4.9% 2014 (pre

- Transport Rule) 6% 5.3% 4.1% 2014 (post

- Transport Rule 5.2% 4.6% 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, Anen berg SC, Hubbell BJ. Estimating the National Public Health Burden Associated with Exposure to Ambient PM 2.5 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, Thun t 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. Reanalysi s 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 ENVIRONME NTAL 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 a mong 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 e conomy. 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 pro vide 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) 84

84 As mentioned in Chapter 8, we use 2015 as a proxy for 2014 in order to maintain consis tency 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 resp ond 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 le vels

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 in termediate 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 resu lt, 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 tha t increase or decrease estimates of the344 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 i ncludes 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 identif y 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 benchma rk equilibrium (Shoven and Whalley, 1995 ).345 Table B

- 1. Industry Sectors Included in Multimarket Model
Industry Label Description Representative NAICS a 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 NAICS a

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 NAICS a 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 In vestment 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 $22351

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 ener gy 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 el ectricity 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 co sts = 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 l inks 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, we352

assume the input use ratio is fixed and cannot adjust their input mix; this is a sta ndard 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 poli cy.
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 (E.5)

where

= with

- policy supply quantity (g)

= calibrated scale parameter for the supply price relationship

= with

- policy price for output (g)

= direct compliance costs per unit of supply

= input use ratio (g using input i)

= with

- policy input (i) price

= benchmark i nput (i) price

= price elasticity of supply for output (g)

The key supply parameter that controls the industry production adjustments is the price elasticity of supply (ε g ). 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:

(B.6)

where

= wi th

- policy supply quantity (g)

c = calibrated scale parameter for the supply and price relationship

= with

- policy U.S. price for output (g)

= price elasticity of supply of goods from the ROW to the United States (imports) (g)

The key supply parameter that controls the ROW supply adjustments is the price elasticity of supply ( ). We obtained these estimates for a variety of industry groups from a
recently published article by Broda and colleag ues (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

organ ized 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. 85 This gives a crude way to account for the variety of products detailed in the original data set. We also restricted the long

run elasticity sample to those that Broda et al. classify as ―medium‖ and ―low‖ long

- run categories; these categories tend to hav e 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 h igh). 86

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 el asticity. 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 mul timarket 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.

85 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 on e 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).

86 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 deviati on 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/unrestri cted/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). 87 Although the elasticities in the GTAP

mo del are a different type of international trade elasticity and cannot be directly applied in the multimarket model (e.g., they are based on the Armington structure 88 ), the parameters provide us
with some additional information about the relative trade elast icity 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

ex ample, 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).

87 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.

88 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 sy stem 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 aggreg ated 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, 89 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 dom estic 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, investme nt/government does not change. Intermediate use is determined by the input use ratios and the industry output decisions.

( B .7)

= with

- policy input demand quantity (i)

= input use ratio (g using input i)

= with

- policy output quantity (g)

Other final use does respond to market price changes. Following guidance in the OAQPS economic resource manual (EPA, 1999), we use a general form for the U.S. industry demand
functi on:

( B .8)

where

= with

- policy demand quantity (g)

89 No standard deviations were calculated for the 3

- and 4

- digit codes that had only one observation (i.e., Broda
et al.‘s model used the exact 3

- or 4

- digit code).360 = calibrated scale parameter for the demand and price relationship

= with

- policy price for output (g)

= price elasticity of demand (g)

The key parameter that controls consumption adjustments is the price elasticity of demand (η g ). 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 model 90 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 a nd 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 mode l 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. 91 As a result of higher natural gas prices, industries with more intensive natural gas use may shrink while those with less intensive natural

gas us e 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 increase s 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,

90 The a uthors use the Adkins

– Garbaccio CGE Model (Adkins, 2006).

91 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 p olicies.

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 wou ld 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, investme nt, 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 prim ary 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 i s 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. Producti on 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
ad dressed 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 a ssumed to be
fixed.362 Table B

- 6. U.S. Demand Elasticities

Industry Label Industry Description Demand Elasticity
η g 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
η g 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
η g 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/Publicationdetai ls.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.‖ Ameri can 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/Publ icationdetails.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 Mitig ation 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 Prote ction Agency. 2010. ―Guidelines for Preparing Economic Analyses‖ (EPA 240

- R

- 10

- 001).

< http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cov er.pdf366 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 t o the energy markets (such as higher electricity prices). 92 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 t hose 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 c apacity; 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 t he 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

92 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 n 0 eeds 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 ther eafter 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 T ransport 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 em ployment is measured in job

- years, since there is no temporal dimension to this analysis. 93 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 a nd more long

term operational labor needs for new pollution controls (though these operational labor requirements are also measured in 2014 job

- years only).

93 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 Transp ort Rule policy case IPM projections were used to estimate the incremental pollution control demand. 94 These are shown in Table 2 below: 95

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 requ ired 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 need ed 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

94 Results for this analysis were developed using various outputs from EPA‘s Base Case v.4.10 using ICF‘s Integrated Pl anning 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/epa

ipm/BaseCasev410.html for more info.

95 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). 96 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 provi ded estimates of unit labor needs based on scaling the labor requirements for ACIs (to derive estimates for DSI) 97 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 w ith 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 lab or 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 r esources) 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. 98 Total tonnage (or vol ume 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

96 ― Engineering and Economic Factors Affecting the Installation of Control Technologies for Multi

- pollutant Strategies ‖. EPA/ORD, 2002.

97 DSI and Fabric Filter System In stallation Labor Estimate. Memorandum by Andover Technology Partners to ICF International. January 26, 2011.

98 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. 99

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 pay roll per worker for the power sector taken from the 2007 Economic
Census and BLS estimates. 100

99 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. A verage 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 o f Manufacturers. See the Appendix for more details about the data used for these calculations.

100 Same sources as other productivity estimates (2007 Economic Census and BLS), however, uses employees and total payroll rather than revenue or value of shipm ents.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) 101 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 o rder 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, const ruct, 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

101 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/to n 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 underestima tion 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 3 1 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 2 5 GW is projected to be existing scrubbers that were not operated under the EPA base ca se 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 time378

since there is less need for installing new pollution contro l 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 indust ry 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 cons truction

- 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. 102

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 BL S output/worker estimates for the utility sector. 103

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 t hat 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 g ood 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 a ssumes 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

102 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.

103 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 se ctor 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. 104

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 facil ities 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 co uld 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 us e 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

104 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 Transpor t Rule policy case.380

Amendments. 105 Using this approach, changes in coa l 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 p roductivity estimates
. 106 107 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 TS D 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 labo r 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

105 Impacts of the Acid Rain Program on Coal Industry Employment. EPA 430

- R

- 01

- 002 March 2001.

106 From US Energy Information Administration (EIA) Annual Energy Review, Coal Mining Productivity Data. Used 2008.

107 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. 108 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

108 See ―Empl oyment 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 E PA 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 boilerma ker labor, as well as the more recent utility Toxics Rule employment TSD. 109 Thus, results presented here are consistent with prior EPA studies on similar topics.

109 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 Man

hours/MW Boilermakers

(%) 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 us ing 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 e xpenditure 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). 110

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

110 In general, the productivit y 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 Statist ic (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 eac h pollution control type, the next step involved choosing the primary operating resource, except for SCRs for which

two resources

– ammonia and catalyst

– were chosen. 111 This approach is consistent with the 2002 EPA study. The next step involved estimatin g 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

. 112 Using the total usage for each pollution control input (in tons or m3)
and associated aver age 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.

111 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 n umber and the associated labor needs are included to estimate the total volume of catalyst required.

112 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 La bor

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 worke rs/$ 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 th e 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 Ta ble 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 Co ntents 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 c onjunction with3 88

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 rep ort 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 an d 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

e stimated 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 thi s 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

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DSI and Fabric Filter System Installation Labor Estimate. Memorandum by Andover Technology Partners to ICF International. January 26, 2011.
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Staudt, Jim. Email Communication with Andover Technology Partners. Feb 7, 2011.

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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: Su mmary Statistics for the US.

US Department of Energy, National Energy Technology Laboratory. Hoffmann, Jeff and Ratafia

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Scrubbed 500 MW Coal

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US Energy Information Administration (EIA). Annual Energy Review 2009, Assorted Data for Coal, Natural Gas, and Petroleum. 2009.

US Energy Information Administration (E IA). 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 Carol ina State University. Vijay, Samudra, Yelverton, William and DeCarolis, Joseph. ―Development of Supply Curves for Abatement of GHG from Coal

fired Utility Boilers‖ June, 2009.

US EPA. Clear Skies Act, Technical Package, Section D. ―Projected Impacts on G eneration 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 Affectin g the Installation of Control Technologies for Multi

- pollutant Strategies.‖ EPA

- 600/R

02/073 (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 FINA L 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 bene fits 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 assess ment, including the 95 th 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 95 th 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 o f 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 su ggestive 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 b enefits 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 p artial accounting of likely impacts due to limitations in the currently available data (e.g., climate effects from CO 2 , 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 guidelines393394395396397

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). Nation al 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

- H

ealth 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 at398

<http://yosemite.epa.gov/EE/epa/eed.nsf/pages/Guidelines.html/$file/Guidelines.pdf>.

U.S. Environmental Protection Agenc y (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 Divisio n. 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 REME DY SENSITIVTY WITH R EVISED VARIABILITY LIMITS

The projected impacts of the final Transport R ule reported throughout the preamble and
RIA do not reflect minor technical corrections to SO2 budgets in thre e states (KY, MI, and NY). The modeling results reported in the preamble and RIA also assumed preliminary variability
limits that were smaller than the variabil i ty limits finalized in this r ule. 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 result s of this sensitivity analysis are presented in the
tables below

.

Table F

- 1. Annual Emissions of SO 2 and NO x in 2010 and Percentage of Emissions in the Transport Rule Affected Region (tons)

SO 2 NO X Transport Rule Annual NO X and SO 2 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 NO X and SO 2 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 Wiscons in.400

Table F

- 2. Transport Rule Annual NO x and SO 2 and NOx Ozone Season State Emission Budgets (tons)

SO 2 Annual NO X

Annual NO X

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 8,331401

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)

Hi storical 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 SO 2 , NO X , and CO 2 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

SO 2 (Annual) Contiguous

48 States 7.9 3.9

- 3.9

- 50% 7.2 3.4

- 3.8

- 5 2 % TR States 7.0 3.0

- 4.0

- 57% 6.2 2.4

- 3. 8

- 6 1 %

NO X (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% NO X (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%
CO 2 ( Annua l ) Contiguous

48 States 2444 24 30

- 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 SO 2 and NO x requirements. ―Summer‖ is from May 1

– September 30, which is the ozone season. Base case includes Title IV Acid Rain Program, NO x 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 SO 2 Controls by Coal Sulfur Content, 2014

Very Low (<0.8 lb/mmBtu) Low

(0.81

- 1.2 lbs/mmBtu) Low

Medium (1.21

- 1.66 lbs/mmBtu) High

Medium (1.67

- 3.34 lb/mmBtu) High

(>3.35 lb/mmBtu) Base Case 27% 37% 8% 15% 13% Transport Rule 5 8 % 2 9 % 8 % 3% 2% Source: Integrated Planning Model run by EPA, 2011.404

Table F

- 9. Coal Use by Sulfur Category in the PM 2.5 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 High

medium sulfur Low

medium sulfur Low sulfur

2012 Base 30 13 145 156 91 211 83 3 732
TR 25 2 7 9 25 6 77 19 2 7 8 1 3 723

2014 Base 37 13 145 154 102 216 71 9 747

TR 32 2 10 6 20 0 92 202 7 2 1 7 724

*These coal usage results are for the 23 states covered by the rule in the trading program to r educe SO 2 emissions. Source: Integrated Planning Model run by EPA, 2011.

Table F

- 10. Newly

- Constructed Advanced SO 2 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 SO 2 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 Newly

Built Controls Total

Operational Controls FGD 2 4 7 209 DSI 0 2 9 SCR 5 0 146 Note: FGD (Flue Gas Desulphurization) and DSI (Dry Sorbent Injection) are advanced SO 2 controls. SCR (Selective Catalytic Reduction) is an advanced NO X control. Source: Integrated Planning Model run by EPA, 2011.405

Table F

- 12. Projected allowance prices for the four Transpo rt 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 1,93 3

- 2 4

- 1.2% 2,017 1,97 7

- 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 7 41 18 2. 7 % 686 71 5 2 9 4.1% Nuclear 799 812 812 0 0.0% 822 82 8 6 0.8% Hydroelectric 272 281 282 1 0. 3 % 287 286

- 1

- 0. 3 % Non

- hydro Renewables 143 233 239 6 2.7% 250 250 1 0. 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 20 14 (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 Tra nsport Rule (Million Tons)

2009 2012 2014

Supply Area Historical Base TR Change from Base Base TR Change from Base Ap palachia 246 193 18 5

- 9 182 18 3 1
Int erior 129 209 18 4

- 2 6 238 21 6

- 2 2
West 553 544 5 70 2 6 554 55 2

- 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 99 5

- 2 2 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); Wa ste 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.