Krewski D, Jerrett M, Burnett RT, Ma R, Hughes E, Shi Y, Turner C, Pope CA, Thurston G, Calle EE, Thunt MJ. Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality.  HEI Research Report, 140, 2009; Health Effects Institute, Boston, MA.

Krewski D, Burnett RT, Goldberg MS, et al. Reanalysis of the Harvard Six Cities Study and the American Cancer Society Study of Particulate Air Pollution and Mortality: Special Report. Cambridge, Mass: Health Effects Institute; 2000.

Pope CA, Burnett RT, Thun MJ, Calle EE, Krewski D, Ito K, Thurston GD. Lung Cancer, Cardiopulmonary Mortality, and Long-term Exposure to Fine Particulate Air Pollution. Journal of the American Medical Association, 2002; 287: 1132 -- 1141.

    Appendix B
OAQPS Multimarket Model to Assess the Economic
Impacts of Environmental Regulation
B.1	Introduction
An economic impact analysis (EIA) provides information about a policy's effects (i.e., social costs); emphasis is also placed on how the costs are distributed among stakeholders (EPA, 2010). In addition, large-scale policies that may affect a large number of industries or a substantial part of the whole economy require additional analysis to better understand how costs are passed across the economy. Although several tools are available to estimate social costs, current EPA guidelines suggest that multimarket models "...are best used when potential economic impacts and equity effects on related markets might be considerable" and modeling using a computable general equilibrium model is not available or practical (EPA, 2010, p. 9-21). Other guides for environmental economists offer similar advice (Berck and Hoffmann, 2002; Just, Hueth, and Schmitz, 2004).
Multimarket models focus on "short-run" time horizons and measure a policy's near term or transition costs (EPA, 1999). Recent studies suggest short-run analyses can complement full dynamic general equilibrium analysis.
The multimarket model described in this appendix is a relatively new addition to the Office of Air Quality Planning and Standards' (OAQPS's) economic model tool kit; it is designed to be used as a transparent tool that can respond quickly to requests about how stakeholders in 100 U.S. industries might respond to new environmental policy. It was used to analyze the economic impacts of the industrial boiler NESHAP and CISWI final rules recently signed by EPA. Next, we provide an overview of the model, data, and parameters.
B.2	Multimarket Model
The multimarket model contains the following features:
         Industry sectors and benchmark data set
            100 industry sectors
            a single benchmark year (2015)
         Economic behavior
            industries respond to regulatory costs by changing production rates
            market prices rise to reflect higher energy and other nonenergy material costs
            customers respond to these price increases and consumption falls
         Model scope
            100 sectors are linked with each other based on their use of energy and other nonenergy materials. For example, the construction industry is linked with the petroleum, cement, and steel industries and is influenced by price changes that occur in each sector. The links allow EPA to account for indirect effects the regulation has on related markets.
            Links come from input-output information from IMPLAN as used in OAQPS's computable general equilibrium (CGE) model, the Economic Model for Policy Analysis (EMPAX)
            production adjustments influence employment levels
            international trade (imports/exports) behavior considered
         Model time horizon ("short-run")
            fixed production resources (e.g., capital) leads to an upward-sloping industry supply function
            firms cannot alter input mixes; there is no substitution among intermediate production inputs
            investment and government expenditures are fixed.
         Labor  and Capital Markets and Pre-existing Distortions in Other Markets
            Unlike CGE models, our multimarket model does not include a national labor or capital market.  As a result, we do not estimate real wage changes, changes in labor /leisure choices, or savings and investment decisions within the model.  Therefore we do not consider whether policies interact with existing distortions, particularly tax distortions in a ways that increase or decrease estimates of the social cost.  Since savings and investment decisions are not modeled, social costs associated with capital stock changes are also not considered.
 B.2.1	Industry Sectors and Benchmark Data Set
The multimarket model includes 100 industries. For the benchmark year, the model uses information from OAQPS's computable general equilibrium model's balanced social accounting matrix (SAM) and the following accounting identity holds:
	Output + Imports = Consumption + Investment + Government + Exports	(E.1)
If we abstract and treat each industry as a national market, the identity represents the prepolicy (baseline) market-clearing condition, or benchmark "equilibrium"; supply equals demand in each market. In Table B-1, we identify the 100 industries for the multimarket model; Table B-2 provides the 2015 benchmark data set. Since the benchmark data are reported in value terms, we also use the common "Harberger convention" and choose units where are all prices are one in the benchmark equilibrium (Shoven and Whalley, 1995).
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)
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<PHONE_NUMBER>24
OPM
Other Primary Metals
3314<PHONE_NUMBER>25 331528
Fabricated Metals

FRG
Forging and Stamping
3321
CUT
Cutlery
3322
FMP
Fabricated Metals
3323
BOI
Boilers and Tanks
3324
HRD
Hardware
3325
WIR
Springs and Wires
3326
MSP
Machine Shops
3327
EGV
Engraving
3328
OFM
Other Fabricated Metals
3329
Machinery and Equipment

CEQ
Construction and Agricultural Equipment
3331
IEQ
Industrial Equipment
3332
SEQ
Service Industry Equipment
3333
HVC
HVAC Equipment
3334
MEQ
Metalworking Equipment
3335
EEQ
Engines
3336
GEQ
General Equipment
3339
Electronic Equipment

CPU
Computers
3341
CMQ
Communication Equipment
3342
TVQ
TV Equipment
3343
SMI
Semiconductor Equipment
3344
INS
Instruments
3345
MGT
Magnetic Recording Equipment
3346
LGT
Lighting
3351
APP
Appliances
3352
(continued)
Table 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.
Table B-2.	2015 Benchmark Data Set (billion 2007$)
Industry Label
Industry Description
Output
Imports
Consumption
Investment and Government
Exports
ACM
Accommodations
$940
$7
$919
$20
$8
ADM
Administrative Services
$923
$39
$885
$72
$5
AGR
Agricultural
$349
$71
$390
$6
$25
ALU
Aluminum
$81
$21
$88
$4
$10
ANM
Animal Foods
$50

$41
Less than $1
$10
APP
Appliances
$34
$26
$48
$8
$4
ARC
Aircraft
$257
$57
$68
$116
$129
ART
Arts
$286

$276
$3
$7
ATP
Air Transportation
$174
$34
$98
$30
$80
BAK
Baked Goods
$68
$4
$69
$3
Less than $1
BEV
Beverages and Tobacco
$157
$62
$217
$1
$1
BOI
Boilers and Tanks
$35
$3
$22
$10
$5
CEM
Cement
$74

$68
$4
$3
CEQ
Construction and Agricultural Equipment
$95
$31
$61
$42
$23
CHM
Chemicals and Gases
$355
$124
$409
$12
$58
CLY
Clay
$12
$6
$14
$1
$3
CMQ
Communication Equipment
$96
$43
$60
$57
$23
CNS
Construction
$1,393
$107
$816
$684
Less than $1
COL
Coal
$48
$2
$46

$4
CPP
Converted Paper Products
$60
$2
$48
$7
$7
CPU
Computers
$193
$85
$171
$52
$54
CRU
Crude Oil Extraction
$75
$213
$289

CUT
Cutlery
$13
$6
$11
$6
$3
EDU
Education
$1,122

$296
$810
$15
EEQ
Engines
$46
$18
$37
$8
$20
EGV
Engraving
$26

$11
$6
$9
ELE
Electric Generation
$339
Less than $1
$304
$35
Less than $1
ELQ
Electric Equipment
$46
$21
$31
$22
$14
FIN
Finance and Insurance
$2,345
$157
$2,308
$51
$144
FMP
Fabricated Metals
$85
$4
$77
$9
$3
FRG
Forging and Stamping
$25
Less than $1
$22
$1
$2
FRT
Fertilizer
$53
$6
$40
$5
$14
FRU
Fruits and Vegetables
$82
$14
$85
$5
$6
FUR
Furniture
$78
$42
$104
$14
$2
                                                                    (continued)
Table B-2.	2015 Benchmark Data Set (billion 2007$) (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
$351
                                                                    (continued)
Table B-2.	2015 Benchmark Data Set (billion 2007$) (continued)
Industry Label
Industry Description
Output
Imports
Consumption
Investment and Government
Exports
OTP
Other Transportation Services
$350

$300
$27
$23
OTQ
Other Transport Equip
$26
$9
$17
$11
$7
PAI
Paints and Adhesives
$44
$2
$36
$3
$7
PAP
Pulp and Paper Mills
$151
$25
$154
$6
$16
PFS
Professional Services
$2,439
$87
$2,002
$490
$34
PIP
Pipeline Transport
$44
$101
$50
Less than $1
$95
PLS
Plastic
$173
$19
$169
$5
$17
PLY
Plywood and Veneer
$26
$11
$35
$1
$2
PRN
Printing
$57
$1
$39
$11
$7
PUB
Public Services
$1,248
$54
$406
$896
Less than $1
R_R
Rail Cars
$13
$2
$7
$3
$5
REL
Real Estate
$3,165
$2
$2,975
$111
$81
RSN
Resins
$133
$29
$117
$7
$38
RTL
Retail Trade
$1,688
$58
$1,652
$82
$12
RTP
Railroad Transportation
$79
Less than $1
$49
$7
$23
RUB
Rubber
$45
$24
$43
$17
$10
SAW
Sawmills
$40
$12
$49
$1
$3
SEA
Seafood
$14
$4
$16
$1
$1
SEQ
Service Industry Equipment
$38
$31
$30
$31
$9
SGR
Sugar
$38
$7
$40
$2
$3
SHP
Ships
$43
$6
$15
$25
$8
SMI
Semiconductor Equipment
$188
$85
$197
$14
$61
SOP
Soap
$100
$6
$89
$4
$14
TEX
Textile Mills
$28
$11
$32
$1
$6
TKB
Truck Bodies
$67
$12
$39
$25
$15
TPM
Textile Product Mills
$26
$18
$37
$3
$4
TTP
Freight Truck Transportation
$337
$49
$295
$37
$54
TVQ
TV Equipment
$24
$46
$63
$4
$3
WAP
Wearing Apparel
$23
$90
$112
$1
Less than $1
WHL
Wholesale Trade
$1,535
$37
$1,219
$174
$178
WIR
Springs and Wires
$7

$2
$1
$3
WTP
Water Transportation
$50

$15
$13
$22

B.2.2	Economic Behavior
B.2.2.1	U.S. Supply
In a postpolicy scenario (e.g. the preferred trading remedy in the Transport Rule), industry responds to changes in the new market-clearing "net" price for the good or service sold:
	%Δ"net" price = %Δ market price − %Δ direct costs − %Δ indirect costs	(E.2)
The %Δ direct costs are approximated using the IPM cost analysis and baseline value of output. For example, with the $0.8 billion increase in compliance costs for the electricity sector in 2014, IPM projects a 0.77 percent increase in the retail price of electricity as mentioned in Chapter 7 of this RIA. For the electric power sector (EPS), percentage change in direct costs would be represented in the model as follows:
	%Δ direct costs = 0.77%	(E.3)
To ensure the market-clearing electricity price matches IPM results, we adjust the supply elasticity to reflect a horizontal supply function (i.e. supply is infinitely elastic near market equilibrium).
The multimarket model simultaneously considers how the policy influences other industry supply functions (via changes in energy and other intermediate material prices). As a result, the multimarket model can provide additional information about how policy costs (higher electricity prices) are transmitted through the economy in the short run. As shown in Figure B-1, the higher electricity prices provide other industries with incentives to alter production rates at current market prices; market prices must rise to maintain the original prepolicy production levels (Q). As shown in Figure B-2, the other indirect cost change provides the industry with additional incentives to alter production rates at current market prices.
The %Δ indirect effects associated with each input are approximated using an input "use" ratio and the price change that occurs in the input market.
	%Δ indirect costs = input use ratio x %Δ input price	(B.4)
The social accounting matrix provides an internally consistent estimate of the use ratio and describes the dollar amount of an input that is required to produce a dollar of output. Higher ratios suggest strong links between industries, while lower ratios suggest weaker links. Given the short time horizon such as that for this analysis with a compliance year of 2014 or earlier, we assume the input use ratio is fixed and cannot adjust their input mix; this is a standard assumption in public and commercial input-output (IO) and SAM multiplier models (Berck and Hoffmann, 2002). Morgenstern and colleagues (2004) and Ho and colleagues (2008) also use this assumption when examining near-term effects of environmental policy.
Figure B-1. Higher Electricity Prices Reduce Other Sector Production Rates at Benchmark Prices

Figure B-2. Indirect Costs Further Reduce Production Rates at Benchmark Prices

Following guidance in the OAQPS economic resource manual (EPA, 1999), we use a general form for the U.S. industry supply function:
		(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 input (i) price
      	=	price elasticity of supply for output (g)
The key supply parameter that controls the industry production adjustments is the price elasticity of supply (εg). To our knowledge, there is no existing empirical work that estimates short-run supply elasticities for all industry groups used in the multimarket model. As a result, we assume the U.S. supply elasticities are a function of econometrically estimated rest-of-world (ROW) export supply elasticities (see discussion in the next section). We report the values currently available in the model in Table B-5.
B.2.2.2	International Competition
International competition is captured by a single ROW supply function:
		(B.6)
where
      	=	with-policy supply quantity (g)
      c	=	calibrated scale parameter for the supply and price relationship
      	=	with-policy U.S. price for output (g)
      	=	price elasticity of supply of goods from the ROW to the United States (imports) (g)
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 colleagues (2008b).
B.2.2.3	Price Elasticity of Supply: Rest of World (ROW)
Broda and colleagues (2008a and 2008b) provide an empirical basis for the multimarket model supply elasticities. Broda et al. provide over 1,000 long-run trade elasticities that RTI organized to be comparable with the 100-sector model. The first step was to match the Harmonized Trade System (HS) elasticities estimated in the article to the appropriate NAICS codes. Many of the HS codes correspond with a detailed NAICS codes (5- and 6-digit level), while the multimarket sector industries typically correspond with more aggregated sectors (NAICS 2-, 3-, or 4-digit levels). To adapt these labels to our model, we combined the 5- and 6-digit NAICS under their 3- and 4-digit codes and calculated an average elasticity value for codes that fell within the multimarket model's aggregate industrial sectors. 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 have lower elasticity values that are more likely to be consistent with the multimarket model's modeling horizon (i.e., in the short run, importers are likely to have less flexibility to respond to price changes implying the elasticities are low rather than high).
Our ideal preference was to use an exact 3- or 4-digit match from the medium category if one was available. If the multimarket model had a 4-digit code for which there was no direct match, we aggregated up a level and applied the relevant 3-digit elasticity. If a multimarket code was not covered in the medium set of elasticities, we used the low elasticity category. This method was sufficient for mapping the majority of the sectors in the model. After applying our inverse elasticity values to the multimarket sectors, we calculated the inverse of the value to arrive at the actual supply elasticity. Since Broda et al.'s article focused on industrial production goods, some of the multimarket sectors were not covered in the elasticity data. These sectors included mainly service industries, transportation, and energy sources.
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)
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)
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)
Table B-5.	Supply Elasticities (continued)
Industry Label
Industry Description
Rest of World (ROW)
U.S.
WAP
Wearing Apparel
                                      1.2
                                      0.8
WHL
Wholesale Trade
                                      0.7
                                      0.7
WIR
Springs and Wires
                                      1.9
                                      0.8
WTP
Water Transportation
                                      0.7
                                      0.7
[a]	For this analysis, EPA adjusted the domestic supply elasticity parameter to approximate a horizontal market supply function. This allows the multi-market model to replicate the predicted retail price changes estimated by IPM.
Note:	RTI mapped Broda et al. data for their industry aggregation to the multimarket model's 100 industries. Domestic supply elasticities are typically assumed to be within one standard deviation of the sample of supply elasticities used for the ROW. In selected cases where this information is not available, the U.S. supply elasticity is set equal to the ROW.
Source:	Broda, C., N. Limao, and D. Weinstein. 2008a. "Export Supply Elasticities." http://faculty.chicagobooth.edu/christian.broda/website/research/unrestricted/TradeElasticities/TradeElasticities.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).  Although the elasticities in the GTAP model are a different type of international trade elasticity and cannot be directly applied in the multimarket model (e.g., they are based on the Armington structure), the parameters provide us with some additional information about the relative trade elasticity differences between industry sectors. To use the GTAP information to develop assumptions about the multimarket model sectors with missing elasticities, we chose a base industrial sector (iron and steel) for which we had parameter value from Broda et al.  Next, we developed industry-specific ratios for missing industries using the corresponding GTAP sector trade elasticities and the GTAP iron and steel sector.  We multiplied the resulting ratio by the Broda et al. iron and steel parameter (1.0). For example, the GTAP trade elasticity for coal (6.10) is approximately 2.2 times the trade elasticity for iron and steel (2.95).  As a result, the multimarket import supply elasticity for coal is computed as 2.2 (2.2 x 1.0).
B.2.2.4	Price Elasticity of Supply: United States
We also used Broda et al.'s elasticities to derive a set of domestic supply elasticities for the model. We have assumed that a product's domestic supply would be equal to or less elastic than other countries' supply of imports. When we aggregated and averaged the original elasticities to the 3- and 4-digit NAICS level for our foreign supply elasticities, we also calculated the standard deviation of each 3- and 4-digit NAICS sample. By adding the standard deviation to the corresponding foreign supply and then taking the inverse, we were able to calculate a domestic supply elasticity for each sector that was lower than its foreign counterpart while maintaining the structure of the original elasticities. For sectors in which no standard deviation was available, we used professional judgment to apply the closest available substitute from a similar industry. Without a comparable way of scaling our foreign elasticities for the sectors in which we used the GTAP elasticities, we elected to keep the domestic and foreign supply elasticities the same.
B.2.2.5	Demand
Uses for industry output are divided into three groups: investment/government use, domestic intermediate uses, and other final use (domestic and exports). Given the short time horizon, investment/government does not change. Intermediate use is determined by the input use ratios and the industry output decisions.
		(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 function:
		(B.8)
where
      	=	with-policy demand quantity (g)
      	=	calibrated scale parameter for the demand and price relationship
      	=	with-policy price for output (g)
      	=	price elasticity of demand (g)
The key parameter that controls consumption adjustments is the price elasticity of demand (ηg). To approximate the response, we use demand elasticities reported in Ho, Morgenstern, and Shih (2008). To estimate the demand elasticities, Ho, Morgenstern, and Shih used a CGE model and simulate the effects of placing a small tax on output and recording the quantity change. The general equilibrium quantity change associated with the tax considers all price and income changes that led to the quantity change. Table B-6 reports the values taken from Ho, Morgenstern, and Shih (2008) that are currently used for demand responses of other final uses (domestic and exports).
The current version of the multimarket model does not currently consider the consequences of exogenous demand shocks to the scale parameter (a) that the policy may bring about.  For example, IPM explicitly models changes in fuel use (a switch from coal to natural gas) that utilities may use to meet the Transport Rule.  Increases in natural gas demand subsequently lead to price increases for natural gas.  As a result of higher natural gas prices, industries with more intensive natural gas use may shrink while those with less intensive natural gas use may expand. A similar story with opposite effects occurs in the coal market; reduced demand lowers coal prices and may result in surplus loss for the coal industry. The Transport  Rule may also increase the demand for materials for retrofits and increases in the prices of those goods as well as the demand for retrofit equipment; the demand increase will lead to relative expansion and contraction of industries.  EPA acknowledges that the current multimarket model does not account for these types of changes in the market demand curves.
B.2.2.6	Model Scope and Distribution of Impacts
The multimarket model includes 100 sectors covering energy, manufacturing, and service applications. Each sector's production technology requires the purchase of energy and other intermediate goods made by other sectors included in the model. Linking the sectors in this manner allows the model to trace direct and indirect policy effects across different sectors. Therefore, it is best used when potential economic impacts and equity effects on related markets might be important to stakeholders not directly affected by an environmental policy. However, the model can also be run in single-market partial equilibrium mode to support and provide insights for other types of environmental policies.
With respect to distributional impacts, the relative market price elasticities also influence the distribution of social costs across stakeholders. In markets where the demand side is more responsive to price changes than suppliers, industry would bear more of the surplus losses.  In the context of the Multimarket Model, this is more likely to occur in markets where the household has elastic demand and the household is the primary source of market demand (versus demand from intermediate, investment, and government sectors).  However, in markets where the supply side is more responsive to own price changes, consumers would bear more of the losses. Given the Multimarket Model's demand structure, this is more likely to occur in markets where the primary source of demand is intermediate, investment, and government sectors. With electricity having a low elasticity of demand, electric power producers' responsiveness to own price changes is greater than that for the demand side for electricity. Thus, it is not surprising that consumers incur more of the surplus losses associated with this final rule than producers.
B.2.2.7	Model Time Horizon
The model is designed to address short-run and transitional effects associated with environmental policy. Production technologies are fixed; the model does not assess substitution among production inputs (labor, energy intermediates, and other intermediates) and assumes each investment cannot be changed during the time frame of the analysis. These issues are better addressed using other frameworks such as CGE modeling. Similarly, government purchases from each sector do not adjust in response to changes in goods/service prices. Although, employment levels (number of jobs) adjust as production levels change, wages are assumed to be fixed.
Table B-6.	U.S. Demand Elasticities
Industry Label
Industry Description
Demand Elasticity
ηg
ACM
Accommodations
−0.7
ADM
Administrative Services
−0.7
AGR
Agricultural
−0.8
ALU
Aluminum
−1.0
ANM
Animal Foods
−0.6
APP
Appliances
−2.6
ARC
Aircraft
−2.5
ART
Arts
−0.7
ATP
Air Transportation
−0.8
BAK
Baked Goods
−0.6
BEV
Beverages and Tobacco
−0.6
BOI
Boilers and Tanks
−0.5
CEM
Cement
−0.8
CEQ
Construction and Agricultural Equipment
−1.7
CHM
Chemicals and Gases
−1.0
CLY
Clay
−0.8
CMQ
Communication Equipment
−2.6
CNS
Construction
−0.8
COL
Coal
−0.1
CPP
Converted Paper Products
−0.7
CPU
Computers
−2.6
CRU
Crude Oil Extraction
−0.3
CUT
Cutlery
−0.5
EDU
Education
−0.7
EEQ
Engines
−1.7
EGV
Engraving
−0.5
ELE
Electric Generation
−0.2
ELQ
Electric Equipment
−2.6
FIN
Finance and Insurance
−0.7
FMP
Fabricated Metals
−0.5
FRG
Forging and Stamping
−0.5
FRT
Fertilizer
−1.0
FRU
Fruits and Vegetables
−0.6
FUR
Furniture
−0.7
(continued)
Table B-6.	U.S. Demand Elasticities (continued)
Industry Label
Industry Description
Demand Elasticity
ηg
GAS
Natural Gas
−0.3
GEQ
General Equipment
−1.7
GLS
Glass
−0.8
GRN
Grain Milling
−0.6
HLT
Health Care
−0.7
HRD
Hardware
−0.5
HVC
HVAC Equipment
−1.7
I_S
Iron and Steel
−1.0
IEQ
Industrial Equipment
−1.7
INF
Information
−0.7
INS
Instruments
−2.6
LEA
Leather
−1.1
LGT
Lighting
−2.6
LIM
Lime and Gypsum
−0.8
LUM
Other Lumber
−0.7
M_V
Motor Vehicles
−2.5
MEA
Meat Products
−0.6
MED
Drugs and Medicine
−1.0
MEQ
Metalworking Equipment
−1.7
MGT
Magnetic Recording Equipment
−2.6
MIL
Dairy Products
−0.6
MIN
Mining
−0.6
MNG
Management
−0.7
MSC
Miscellaneous Manufacturing
−1.7
MSP
Machine Shops
−0.5
MVP
Motor Vehicle Parts
−2.5
OCM
Other Chemicals
−1.0
OEQ
Other Electric Equipment
−2.6
OFD
Other Food Products
−0.6
OFM
Other Fabricated Metals
−0.5
OIL
Refined Petroleum
−0.1
ONM
Other Non-metallic Minerals
−0.8
OPM
Other Primary Metals
−1.0
OSV
Other Services
−0.7
OTP
Other Transportation Services
−0.8
(continued)
Table B-6.	U.S. Demand Elasticities (continued)
Industry Label
Industry Description
Demand Elasticity
ηg
OTQ
Other Transport Equip
−2.5
PAI
Paints and Adhesives
−1.0
PAP
Pulp and Paper Mills
−0.7
PFS
Professional Services
−0.7
PIP
Pipeline Transport
−0.8
PLS
Plastic
−1.0
PLY
Plywood and Veneer
−0.7
PRN
Printing
−0.7
PUB
Public Services
−0.7
R_R
Rail Cars
−2.5
REL
Real Estate
−0.7
RSN
Resins
−1.0
RTL
Retail Trade
−0.7
RTP
Railroad Transportation
−0.8
RUB
Rubber
−1.0
SAW
Sawmills
−0.7
SEA
Seafood
−0.6
SEQ
Service Industry Equipment
−1.7
SGR
Sugar
−0.6
SHP
Ships
−2.5
SMI
Semiconductor Equipment
−2.6
SOP
Soap
−1.0
TEX
Textile Mills
−1.1
TKB
Truck Bodies
−2.5
TPM
Textile Product Mills
−1.1
TTP
Freight Truck Transportation
−0.8
TVQ
TV Equipment
−2.6
WAP
Wearing Apparel
−2.4
WHL
Wholesale Trade
−0.7
WIR
Springs and Wires
−0.5
WTP
Water Transportation
−0.8
Note: RTI assigned an elasticity using the most similar industry from Ho and colleagues' industry aggregation.
Source:	Ho, M. S, R. Morgenstern, and J. S. Shih. 2008. "Impact of Carbon Price Policies on US Industry." RFF Discussion Paper 08-37. Http://Www.Rff.Org/Publications/Pages/Publicationdetails.Aspx?. Publicationid=20680.  Accessed August 2009. Table B.6.

B.3	References
Adkins, Liwayway G. 2006. Coordinating Global Trade and Environmental Policy: The Role of Pre-Existing Distortions. Ph.D. dissertation. University of Virginia.
Berck, P., and S. Hoffmann. 2002. "Assessing the Employment Impacts of Environmental and Natural Resource Policy." Environmental and Resource Economics 22(1):133-156.
Broda, C., N. Limao, and D.E. Weinstein. 2008a. "Optimal Tariffs and Market Power: The Evidence." American Economic Review 98(5):2032-2065.
Broda, C., N. Limao, and D. Weinstein. 2008b. "Export Supply Elasticities." <http://faculty.
chicagobooth.edu/christian.broda/website/research/unrestricted/TradeElasticities/
TradeElasticities.html.>
Ho, M.S., R. Morgenstern, and J.S. Shih. 2008. "Impact of Carbon Price Policies on US Industry" (RFF Discussion Paper 08-37). <http://www.Rff.Org/Publications/Pages/Publicationdetails.Aspx?. Publicationid=20680>.
Just, R.E., D.L. Hueth, and A. Schmitz. 2004. The Welfare Economics of Public Policy. Northampton, MA: Edward Elgar.
Morgenstern, R.D., M. Ho, J.S. Shih, and X. Zhang. 2004. "The Near-Term Impacts of Carbon Mitigation Policies on Manufacturing Industries." Energy Policy 32(16): 1825-1841.
Shoven, J.B., and J. Whalley. 1995. Applying General Equilibrium. New York: Cambridge University Press.
U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards. 1999. OAQPS Economic Analysis Resource Document. http://www.epa.gov/ttn/ecas/analguid.html.
U.S. Environmental Protection Agency. 2010. "Guidelines for Preparing Economic Analyses" (EPA 240-R-10-001). <http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/Guidelines.html/$file/cover.pdf

--------------------------------------------------------------------------------
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)
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)
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)
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)
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)

--------------------------------------------------------------------------------
APPENDIX D
--------------------------------------------------------------------------------
EMPLOYMENT ESTIMATES OF DIRECT LABOR IN RESPONSE TO THE FINAL TRANSPORT RULE IN 2014

This appendix presents the short-term employment estimates for the Transport Rule.  The focus of the employment analysis in this study is only on the first order employment impacts related to the compliance actions of the affected entities within the power sector.  It does not include the ripple effects of these impacts on the broader economy (i.e., the "multiplier" effect), nor does it include the wider economy-wide effects of the changes to the energy markets (such as higher electricity prices).  Moreover, this study provides only a static snapshot of the impacts for 2014 and does not account for the dynamic adjustments of the affected entities as they adapt to the Transport Rule such as those arising from technological innovation and learning-by-doing.

The estimates of the employment impacts are divided into several categories: job gains due to the increased demand for pollution control equipment; job losses due to retirements of coal capacity; and job shifts due to changes in demand for fuels.  Results indicate that the Transport Rule has the potential to provide some short-term employment opportunities, primarily driven by the demand for new pollution control equipment.  The employment gains related to the new pollution controls are likely to be tempered by losses due to certain coal retirements, although, as discussed below, some of these workers who lose their jobs due to plant retirements could find replacement employment operating the new pollution controls at nearby units.  Finally, job losses due to reduced coal demand may be partially offset by job gains due to increased natural gas demand, resulting in small negative (about a thousand) net change in employment due to fuel demand changes. Overall, the preliminary results indicate that the Transport Rule could support a net of about 3,500 job-years in 2014.  These results are summarized in Table 1 below.

             Table 1: Net Employment Changes (Job-years in 2014)
New Pollution Control Equipment
                                     7,200
Retirements of Generating Units
                                    (2,710)
Changes in Fuel Use
                                     (990)
Net Effect
                                     3,500

The job-years estimated here is a snapshot of the first order employment effect of the Transport Rule in 2014.  While there is no temporal dimension to this study, most of these jobs are likely to last over an extended time period, some more than the others.  Most of the construction-related labor demand, for example, is expected to provide a short-term, temporary boost to employment that could last a few years, along with any multiplier effects that are not included in these job-year estimates.  Most of the operational labor n0eeds are likely to be longer term.  Thus, in terms of the impacts of the Transport Rule on economy-wide employment over time, this study shows there is likely to be a temporary boost to employment levels starting around 2014, which would likely recede thereafter as the construction phases for the needed pollution controls wind down.  Over time, the operational jobs, which constitute a large portion of the pollution control related job impacts, will continue to provide a boost to employment over "business as usual" baseline employment levels.  Note that this synopsis does not account for other employment impacts of the Transport Rule, such as those resulting from higher energy prices.

Overall Approach

The estimates for the near-term employment effects of the proposed rule utilize basic methodologies to estimate these job impacts relying on approaches used in prior EPA studies on similar issues.  The basic approach involved using power sector projections and various energy market implications under the Transport Rule from modeling conducted with the EPA Base Case version 4.10, using the Integrated Planning Model (IPM(R)), along with data from secondary sources, to estimate the first order employment impacts for 2014. Throughout this analysis, incremental employment is measured in job-years, since there is no temporal dimension to this analysis.  Also, this TSD does not include estimates of total employment impacts over time, though there is a distinction between short-term construction-related labor needs and more long-term operational labor needs for new pollution controls (though these operational labor requirements are also measured in 2014 job-years only).

Employment Changes due to New Pollution Control Equipment

EPA's Base Case v.4.10 and the Transport Rule policy case IPM projections were used to estimate the incremental pollution control demand.  These are shown in Table 2 below:

Table 2:  Increased Pollution Control Demand due to the Transport Rule, 2014 (GW)
                            Pollution Control Type
                     Existing Controls Induced to Operate
                             Newly-Built Controls
                                Scrubbers (FGD)
                                      25
                                       6
                      Selective Catalytic Reduction (SCR)
                                       5
                                       0
                          Dry Sorbent Injection (DSI)
                                       0
                                       3

The employment impacts due to increased pollution control demand are divided into four categories, two of which are associated with the construction and installation related labor requirements, while the remaining two are associated with the resources required for the ongoing operation of these equipment. The two categories of labor needed for constructing and installing these controls are for construction-related sectors, such as boilermakers, engineers, and other installation labor; and for the labor needed to manufacture the steel used in the control equipment.  The two categories of labor needs for resource requirements include employment in sectors that supply resources needed to run these pollution controls (such as reagents and catalysts); and utility sector jobs to operate these control equipment.  The following sections discuss the approach for each:

   * For the installation related construction labor, per-unit labor needs were taken from an extensive 2002 EPA resource analysis for multi-pollutant strategies that analyzed the resource requirements for installing various pollution control equipment (including FGD and SCR).  For controls not included in the 2002 EPA study, such as DSI, additional information was supplied by Andover Technology Partners (ATP), which was also one of the contributors of the 2002 EPA study.  Using the same resource requirement approach as done in the 2002 EPA study, ATP provided estimates of unit labor needs based on scaling the labor requirements for ACIs (to derive estimates for DSI)   Thus, all of the unit labor needed for different pollution control installations can be traced back to the 2002 EPA study, with appropriate engineering adjustments.  The installation labor was sub-divided into different labor categories, such as boilermakers, engineers and a catch-all "other installation labor", using information from the 2002 EPA study, adjusted for the new pollution controls, such as DSIs.
   * In addition to construction labor, it was assumed that installing the pollution controls requires labor for steel as the primary raw material, leading to employment in the steel manufacturing industry.  Again this approach is consistent with the 2002 EPA study.  The increased steel demand is estimated using the per-MW steel demand from the EPA study along with the estimated increases in pollution controls.  For DSI, the same proportionality assumption is taken from ATP for installation labor to estimate the steel needed for installation.
   * For the longer term, and more significant labor associated with operating the pollution controls, per-unit estimates of the main resources needed for these equipment (see Table 3 below for a list of the resources) are taken from the 2002 EPA study, augmented by DSI resource use estimates from ATP.  These were then multiplied by the incremental GW for each pollution control to obtain the total (physical) quantity of resources needed.  Total tonnage (or volume for SCR catalyst) for each resource was then converted to dollars of increased economic output for these resources using price estimates obtained from publicly available sources (see Table 3).  Finally, the labor productivity for each particular sector was used to estimate the number of job-years these would likely create in 2014.  Labor productivities for each sector were adjusted to account for increased worker productivity in 2014.  Data for baseline worker productivity and corresponding growth rates to account for future productivities came from the Economic Census and the Bureau of Labor Statistics (BLS) estimates.
   * The final employment vector estimated was for the utility sector labor needed to operate these pollution control equipment.  This estimate was based on the incremental Fixed Operation and Maintenance (FOM) costs from the EPA runs using IPM, excluding costs due to retirements. The assumption in this TSD was that the FOM costs -- defined as the maintenance costs incurred by the utility irrespective of whether the equipment is operated (such as those for payroll)  -  are a good proxy for estimating the incremental labor demand to operate these pollution controls.  The FOM costs were then translated into employment based on estimates of payroll per worker for the power sector taken from the 2007 Economic Census and BLS estimates.

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)
                                                                     8,112,715
                      $                                75
Ammonia, SCR (tons)
                                                                        77,387
                      $                               150
Catalyst, SCR (m3)
                                                                            723
                      $                            8,475
Sodium Bicarbonate, DSI (tons)
                                                                      675,725
                      $                               120
      Price Sources: 	Steel: Platts, Steel Markets Daily, Vol 3 Issue 209, October 29, 2009.
            Limestone: FGD Tech Evaluation, March 2007.
                  Ammonia: Development of Supply Curves for Abatement of GHG from Coal-fired Utility Boilers, Air Pollution Prevention and Control Division, US-EPA, RTP and NC State University, 2009.
                  Catalyst: EPA Air Pollution Control Cost Manual, Sixth Edition, January 2002. ;
                  Sodium Bicarbonate:  Communication with Andover Technology Partners, Feb 7, 2011.

Results

Table 4 presents the estimated employment impacts in 2014 resulting from the additional pollution controls needed to meet the Transport Rule requirements.  The Transport Rule is estimated to lead to a total capital investment of about $4 to $5 billion in order for the regulated entities to meet the limits under the proposed rule (this capital cost will be amortized over many years).   According to this analysis, these investments could provide the opportunity to support over 7,000 job-years to design, construct, and operate the needed pollution control equipment in 2014.  Note, some of these jobs are expected to start before, and continue beyond 2014 (such as the resource related job-years), but this analysis only provides a snapshot for 2014.

Table 4:  Jobs Due to Pollution Control Equipment under the Transport Rule
(Job-years in 2014)
Jobs for Construction
Incremental Employment
      Boilermakers
                                                                            890
      Engineers
                                                                            440
      General Construction
                                                                            880
      Steel Manufacturing
                                                                             20
                                                                     Sub-Total:
                                                                          2,230
Jobs for Operation

      Jobs from Increased Operating Resource Use

               Limestone (FGD)
                                                                          2,440
               Ammonia (SCR)
                                                                             30
               Catalyst (SCR)
                                                                             20
               Sodium Bicarbonate (DSI)
                                                                            160
                                                                     Sub-Total:
                                                                          2,650
                        Jobs for Pollution Control Operation
                                                                          2,320
Total Labor:
                                                                          7,200
      Note: Totals may not add due to rounding

The number of job-years estimated for pollution control installation is driven primarily by incremental FGD capacity.  As shown in Table 2, up to 31 GW of additional FGD capacity is projected to be used in 2014 to comply with the Transport Rule.  Out of this, only about 6 GW is estimated to be new scrubber capacity requiring construction and installation of the pollution control equipment.  While the remaining 25 GW is projected to be existing scrubbers that were not operated under the EPA base case assumptions, and hence are not likely to lead to jobs due to construction, these scrubbers do provide significant operational employment opportunities.  The results above show a higher proportion of operational jobs than, for example, was estimated for the utility Toxics Rule.  This implies while the jobs due to the Transport Rule are only a fraction of the total estimated for the Toxics Rule, these jobs are more likely to last longer over time since there is less need for installing new pollution control equipment but a higher demand for operating equipment that may not be operated in absence of the Transport Rule (i.e. under the "business-as-usual" base case).

Moreover, of the roughly 7,200 job-years estimated in Table 4, over 2,300 job-years, or over 30 percent, are estimated to occur within the utility sector for labor needed to operate the pollution controls (referred to as "Jobs for Pollution Control Operation" in Table 4).  The rest of the labor demand will benefit the pollution control industry and other economic sectors.  The increased demand for resources and chemicals needed to operate the pollution controls will provide a boost to employment in sectors such as mining, chemicals, other manufacturing sectors, and to a smaller extent, in construction-related sectors.

Employment Changes due to Coal Retirements

Employment changes due to plant retirements were estimated by first identifying the retiring units from EPA's modeling using IPM,all of which are coal-fired units.  EPA projects that there will be roughly 4.8 GW of additional coal retirements by 2014 with the Transport Rule in place.

In order to convert the retired coal capacity into potential employment losses, it was assumed that changes in the operating costs for the retired coal units can be used as a proxy for the lost economic output due to fossil retirements. Thus, the changes in the O&M costs (both FOM and VOM) for these particular retiring units were derived from the EPA Base Case and Transport Rule policy case runs using IPM, and converted to lost jobs using data from the Economic Census and BLS output/worker estimates for the utility sector.

Note that the lost economic output due to fossil retirements is likely to affect not just the utility sector, but others as well.  Employment losses due to plant retirements will not only affect those that are directly working at the plant (i.e., plant operators), but would also affect administrative and other "back-office" workers for those utilities and their support organizations.  This TSD assumes that the FOM costs related to retiring plants are a good proxy for these types of job losses.  Moreover, because the VOM costs represent the variable costs of operating the plant, including costs of materials needed to run any installed pollution control equipment (such as cost of reagents, etc.), this TSD assumes that the VOM costs can be used as an indicator of the employment effects on the resource suppliers to these plants and units.  Thus, the retirement related employment losses shown below are assumed to include losses directly affecting the utility sector as well as other "upstream" sectors that provide material inputs needed to operate these plants.  Table 5 shows those results.

             Table 5: Job Losses due to Coal Capacity Retirements
O&M Decrease from Retirements ($MM)
                                                                            303
Workers Per Million$ in payroll
                                                                            8.9
Workers lost due to retirements (job-year):
                                                                          2,710

Results indicate there could potentially be about 2,700 job losses (measured in job-years for 2014, but any net job losses under this category are likely to be permanent), due to coal retirements.  However, two mitigating factors could reduce the negative employment impacts due to retirements.  First, many of the retiring units are at plants that are likely to have other units operating under the policy scenario.  In such cases, some of the excess labor pool at the retiring units could well be absorbed at other units within the same firm.

Second, as Table 4 indicates, utilities are expected to have the need to fill over two thousand additional job slots to operate the pollution controls needed to meet the requirements of the Transport Rule.  If workers with experience at existing coal facilities become available through plant retirements, it would seem logical for many of these workers to be absorbed in operating these new pollution controls.  While it is not possible to determine how many of these workers from particular coal retirements could be employed to operate the new pollution controls, it is likely that a significant portion of them could find gainful employment at nearby units.

Employment Changes due to Changes in Fuel Use

Two types of employment impacts due to projected fuel use changes are estimated in this TSD.  First, employment losses due to reductions in coal demand were estimated using an approach similar to EPA's coal employment analyses under Title IV of the Clean Air Act Amendments.  Using this approach, changes in coal demand (in short tons) for various coal supplying regions were taken from EPA's Base and Policy Case runs. These changes were converted to job-years using EIA data on regional coal mining productivity (in short tons per employee hour), using 2008 labor productivity estimates.   Results of the coal employment impacts of the Transport Rule are presented in Table 6 below.

                Table 6: Employment Due To Changes in Coal Use
Coal by Region
Change in Coal Demand (MM Tons)
Labor Productivity
Job-year Change
          Appalachia
                                                                            3.8
                                                                           2.91
                                                                            630
          Interior
                                                                         (27.2)
                                                                           4.81
                                                                        (2,720)
          West
                                                                            2.9
                                                                          19.91
                                                                             70
          Waste Coal
                                                                         (0.03)
                                                                           5.96
                                                                            (0)
Net Total
                                                                         (20.5)
                                                                           --
                                                                        (2,030)
      Notes:	Used US national coal productivity for waste coal
      Totals may not add due to rounding

For natural gas demand, labor productivity per unit of natural gas was unavailable, unlike coal labor productivities used above.  Most secondary data sources (such as Census and EIA) provide estimates for the combined oil and gas extraction sector. This TSD thus uses an adjusted labor productivity estimate for the combined oil and gas sector that accounts for the relative contributions of oil and natural gas in the total sector output (in terms of the value of energy output in MMBtu).  This estimate of labor productivity is then used with the incremental natural gas demand for the Transport Rule to estimate the job-years for 2014. In addition, the pipeline construction costs were estimated using proportionality assumptions from those used for the Toxics Rule TSD.   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 Transport Rule is expected to provide notable small increases in short-term employment resulting from investments in new pollution control equipment.  The Transport Rule is estimated to lead to a total pollution control-related capital investments of about $4 to $5 billion and results for 2014 indicate this could support or create around 3,500 job-years, driven primarily by the need to operate new and existing pollution control equipment.  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 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, though the operational jobs estimated here appear to be the largest driver of positive job impacts and are likely to provide a permanent and small boost to employment over time.

The employment impacts estimated in this study are mostly based on approaches in prior EPA studies on similar topics.  The main source for resource estimates used here is the 2002 EPA multi-pollutant resource analysis, which was also the basis for subsequent EPA labor analyses, such as the 2005 Clean Air Interstate Rule (CAIR) TSD on boilermaker labor, as well as the more recent utility Toxics Rule employment TSD.  Thus, results presented here are consistent with prior EPA studies on similar topics.

                           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 using the percentages, shown in Table 8.  These percentages were estimated from the 2002 study and supplemented by ATP information for DSI.

   * Installation Resource Labor
                     Table 9:  Installation Resource Needs
                            Pollution Control Type
                                Steel (Tons/MW)
                                      FGD
                                     2.25
                                      SCR
                                      2.5
                                      DSI
                                      2.2
      Sources: Steel and Catalyst Usage: EPA Multi-pollutant Strategies 2002 report;
      Steel Price: Platts, Steel Markets Daily, Vol 3 Issue 209, October 29 2009;
      Productivity: 2000 Annual Survey of Manufacturers: Iron and Steel Pipes and Tube Manufacturing, BLS for Productivity Growth Rate.

For installation resource labor, steel is the primary resource input used for new pollution control installations, consistent with the 2002 EPA multi-pollutant study. The first step used the incremental installed pollution control capacity and estimates of steel usage (tons/MW), as shown in Table 9 above, to calculate the total steel needed in tons, by pollution control type.  Steel usage estimates were used from the 2002 EPA study, supplemented by ATP information for DSI.  The total amount of steel was then converted into a dollar value for steel expenditure using an average steel price ($550/ton), using data from Platts Steel Markets Daily (see notes to Table 9 above).  Resulting total steel expenditure was then converted to labor using the productivity estimates calculated from the Economic Census data using workers per $ Million in total output for the iron and steel manufacturing industry (= 2.2 workers/$Million).

   *       Operating Resource Labor
                   Table 10:  Resources Needed for Operation
                            Pollution Control Type
                Incremental Total GW (Includes Existing Units)
                                   Resource
                            (Units in parenthesis)
                                Usage Estimates
                                Price ($/unit)
                Industry Assumed for Productivity Calculations
                                 Productivity*
                                      FGD
                                     30.3
Limestone (Tons/MWh)
                                     0.036
75
Lime Manufacturing
                                      4.0
                                      SCR
                                       5
                       (NOx reduction: 396,870,600 lbs)
Ammonia (lbs/lb NOx Reduced)
                                     0.39
150
All Other Basic Inorganic Chemical Manufacturing
                                      2.1

Initial Catalyst (m3/MW)
                                      1.2
8,475
Combined: Other Nonferrous Metal Primary and Secondary Smelting and Refining
                                      3.2

Operational Catalyst (m3/MWh)
                                    0.00002

                                      DSI
                                       3
Sodium Bicarbonate (Tons/MWh)
                                     0.03
120
Potash Soda and Borate Mineral Mining
                                      2.0
      *Workers/$Million in Output, Forecasted to 2014
Sources: Usage: EPA Multi-pollutant Strategies 2002 report, DSI from Andover Technology Partners;
      Prices: Limestone: FGD Tech Evaluation, March 2007
      Ammonia: Development of Supply Curves for Abatement of GHG from Coal-fired Utility Boilers, Air Pollution Prevention and Control Division, US-EPA, RTP and NC State University, 2009
      Catalyst: EPA Air Pollution Control Cost Manual, Sixth Edition, January 2002.
      Sodium Bicarbonate:  Communication with Andover Technology Partners, Feb 7 2011;
       Productivity: 2000 and 2007 Economic Census, Productivity Growth rates from BLS.

Labor related to resources used in operating pollution control equipment was estimated using the total incremental GW of pollution control capacity which was first converted to total MWh of incremental capacity assuming 85 percent capacity factor.  For each pollution control type, the next step involved choosing the primary operating resource, except for SCRs for which two resources  -  ammonia and catalyst  -  were chosen.  This approach is consistent with the 2002 EPA study.  The next step involved estimating the resource needs by each control type, generally in tons of material (cubic meters (m3) for catalyst), using the resource usage estimates as shown in Table 10.  Using the total usage for each pollution control input (in tons or m3) and associated average prices, total expenditure by each resource type was then calculated.  This total expenditure was then converted to labor using workers per $Million in total output for the industry associated with producing each respective input material.

   *       Operating Labor
                    Table 11:  Operating Labor Assumptions
Incremental FOM ($ Million)
                                      259
Productivity*
                                      8.9
      *Workers per $Million in Payroll for Electricity Generating Sector, Forecast to 2014
      Sources: Productivity from 2007 Economic Census and Growth Rate from BLS.

Labor requirement to operate the controls is estimated for all equipment types combined, using the incremental FOM costs based on EPA modeling assumptions for the Transport Rule. Resulting FOM cost estimate was then converted to labor needs using the workers/$ Million in total payroll for the Electric Generating Sector.

Retirement Labor

                  Table 12:  Inputs to Labor from Retirements

Capacity of Incremental Retirements (MW)
                                     4,772
O&M Decrease scaled to SSR Retirements ($MM) (To account for Partial Retirements)
                                     302.8
Workers Per $Million in payroll, forecast to 2014
                                      8.9
      Sources: Productivity from 2007 Economic Census and Growth Rate from BLS.

Retirement labor was estimated by first identifying the retiring units from the parsed outputs (using incremental retirements in EPA's Transport Rule policy case run).  The next step involved estimating the capacity of incremental retirements as well as the change in the O&M costs due to these retirements.  Because of the discrepancies between partial retirements in parsed outputs and System Summary Reports (SSR), O&M costs were scaled proportionately to reflect the lower SSR-based estimates, as shown in Table 12 above.  O&M cost decreases were then converted to job-years lost due to retirements using workers per $Million in payroll.

Fuel Use Labor

                    Table 13:  Inputs to Labor for Fuel Use
Coal by Region
                                                    Incremental Fuel Use (Tons)
                                                  2008 Short Tons/Employee hour
Appalachia
                                                                      3,780,000
                                                                            2.9
Interior
                                                                   (27,190,000)
                                                                            4.8
West
                                                                      2,870,000
                                                                           19.9
Waste Coal
                                                                       (30,000)
                                                                            6.0

Natural Gas

EIA Total Natural Gas Production 2007 (TCF)
                                                                         24.664
EIA Total Crude Oil Production 2007 (Barrels)
                                                                  1,848,450,000
EIA Natural Gas Heat Content 2007 (Btu/cf)
                                                                          1,027
EIA Petroleum Heat Content (MMBtu/Barrel)
                                                                          6.151
Total Crude Oil and Natural Gas Production (MMBtu)
                                                                 36,699,744,000
Economic Census 2007 Oil and Gas Extraction Employees
                                                                        140,160
MMBtu per Man-year for Oil and Gas Extraction
                                                                        261,842
Incremental Natural Gas from EPA (IPM) Results (TCF)
                                                                          0.207
Incremental Natural Gas from EPA (IPM) Results (Converted to MMBtu)
                                                                    212,279,903

Pipeline
Capital Cost of New Pipeline ($MM)
                                                                  Productivity

                                  44.2
                                                                            5.1
      Note: Heat Contents from EIA are assumed to be for fuels used in Electric Power Sector
      Sources: Short Tons per hour from US EIA, Coal Industry Annual. Total Production from 2009 EIA Annual Energy Review. Heat Contents from EIA, Heat Content of Natural Gas Consumed and 2009 Annual Energy Review. Employment Data from 2007 Economic Census.

Fuel use related employment impacts were estimated using EPA's Base Case and the Transport Rule runs using IPM for incremental changes in coal and natural gas use.  For coal, estimates of coal use in tons by region from the relevant EPA runs were used in conjunction with labor productivity estimates from EIA for each region (in short tons/ employee hour), to calculate the change in job-hours needed.  These were then converted to job-years, assuming 2,080 working hours per year.  As discussed in the main report above, because of the high variability in coal mining labor productivity in recent years, no attempt was made to forecast coal (and natural gas, for consistency) productivities, instead the most recent historical estimates were used in this TSD (which was the 2008 labor productivity for coal).

For natural gas, the first step was estimating labor productivity since such information was not available directly from any reliable source.  EIA production data from the Annual Energy Review for natural gas and crude oil (in TCF and barrels, respectively), along with EIA heat content estimates were used to find total crude oil and natural gas production in MMBtu for 2007.  Labor productivity in MMBtu per job-year for the Oil and Gas Extraction sector was then estimated using data from the Census on oil and gas extraction employment.  Then, incremental natural gas demand (in Tcf) from the relevant EPA runs was converted to MMBtu of natural gas demand using EIA data on natural gas heat content.  This was then used with the labor productivity estimated above to calculate the total job-years needed for increased natural gas demand for the Transport Rule.

Jobs related to pipeline construction were estimated in proportion to the increased natural gas demand under this rule relative to those estimated for the utility Toxics Rule.  More details on the approach used to convert pipeline information into jobs for the Toxics Rule are provided in the Toxics Rule employment TSD.

References
DSI and Fabric Filter System Installation Labor Estimate.  Memorandum by Andover Technology Partners to ICF International.  January 26, 2011.
"Flue Gas Desulfurization Technology Evaluation (Dry Lime vs. Wet Limestone FGD)."  Prepared for National Lime Association, by Sargent & Lundy LLC. March 2007.

Staudt, Jim. Email Communication with Andover Technology Partners. Feb 7, 2011.

Platts, Steel Markets Daily, Vol 3 Issue 209, October 29, 2009.

United States Department of Labor. Bureau of Labor Statistics. Industry Labor Productivity and Cost Data Tables, Annual Percent Changes. 2010.
US Census Bureau. 2007 and 2002 Economic Census, 2000 Annual Survey of Manufacturers, Manufacturing and Mining: Detailed Statistics by Industry for the US and Utilities: Summary Statistics for the US.
US Department of Energy, National Energy Technology Laboratory. Hoffmann, Jeff and Ratafia-Brown, Jay, Science Applications International Corporation.   "Preliminary Cost Estimate of Activated Carbon Injection for Controlling Mercury Emissions from an Un-Scrubbed 500 MW Coal-Fired Power Plant."  November 2003.

US Energy Information Administration (EIA). Annual Energy Review 2009, Assorted Data for Coal, Natural Gas, and Petroleum. 2009.
US Energy Information Administration (EIA). Natural Gas 2009, Heat Content of Natural Gas Consumed 2009.
US Environmental Protection Agency (EPA), Air Pollution Prevention and Control Division, Office of research and Development and Department of Civil and Environmental Engineering, North Carolina State University. Vijay, Samudra, Yelverton, William and DeCarolis, Joseph. "Development of Supply Curves for Abatement of GHG from Coal-fired Utility Boilers" June, 2009.

US EPA. Clear Skies Act, Technical Package, Section D. "Projected Impacts on Generation and Fuel Use". 2003.
US EPA. Clean Air Interstate Rule, Technical Support Document.  "Boilermaker Labor Analysis and Installation Timing." OAR-2003-0053 (March, 2005).
US EPA. Employment Estimates of Direct Labor in Response to the Proposed Toxics Rule in 2015.  Technical Support Document, (March 2011).
US EPA. "Impacts of the Acid Rain Program on Coal Industry Employment."  EPA 430-R-01-002 (March, 2001).
US EPA, Office of Research and Development.  "Engineering and Economic Factors Affecting 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).

                                  APPENDIX E
  ALTERNATE PRESENTATION OF THE BENEFITS OF THE FINAL FEDERAL TRANSPORT RULE

      In this appendix, we provide an alternate presentation of the benefits of the final Federal Transport Rule.  This alternate presentation is an attempt to incorporate the recommendations from EPA's recently published Guidelines for Preparing Economic Analyses (U.S. EPA, 2010).  The Guidelines provide four basic principles for constructing thorough and transparent results tables:
   * All meaningful benefits and costs are included,
   * The types of benefits and costs are briefly described in plain terms,
   * The benefits are expressed first in natural or physical units, and
   * Explanatory notes accompany each benefit and cost entry.
      By adopting these general principles, the tables provided in this appendix do not exactly match the tables provided in the benefits chapter of this RIA and previous RIAs, but they are consistent with the types of information previously provided.  In Part A, we identify the quantified and unquantified benefits of this rule, indicate whether each specific effect has been quantified and monetized in this assessment, and explain the lack of quantification for certain effects.  In Part B, we provide the benefits in natural units for each specific effect that was quantified in this assessment, including the 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 of unquantified effects is not exhaustive, nor is quantification of each effect complete.  In order to identify the most meaningful human health and environmental benefits, we excluded effects not identified as having at least a causal, likely causal, or suggestive relationship with the affected pollutants in the most recent comprehensive scientific assessment, such as an Integrated Science Assessment.  This does not imply that additional relationships between these and other human health and environmental benefits and the affected pollutants do not exist.  Due to this decision criterion, some effects that were identified in previous lists of unquantified benefits have been dropped (e.g., UVb exposure).  In addition, some quantified effects represent only a partial accounting of likely impacts due to limitations in the currently available data (e.g., climate effects from CO2, etc).
	As the use of these tables evolves, further refinements will be made to improve the information provided and to assure consistency with previous EPA analysis and EPA and OMB guidelines.

References

Intergovernmental Panel on Climate Change (IPCC). 2007. Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (AR4) [Core Writing Team, Pachauri, R.K and Reisinger, A. (eds.)]. IPCC, Geneva, Switzerland, 104 pp. Available on the Internet at <http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_assessment_report_synthesis_report.htm>.
National Research Council (NRC). 2000. Toxicological Effects of Methylmercury. Committee on the Toxicological Effects of Methylmercury, Board on Environmental Studies and Toxicology, Commission on Life Sciences, National Research Council. National Academy Press, Washington, DC.
U.S. Environmental Protection Agency (U.S. EPA). 1997. Mercury Study Report to Congress. EPA-452/R-97-003. Washington, DC: U.S. EPA. December. Available on the Internet at <http://www.epa.gov/hg/report.htm>.
U.S. Environmental Protection Agency (U.S. EPA). 2006. Air Quality Criteria for Ozone and Related Photochemical Oxidants (Final). EPA/600/R-05/004aF-cF. Washington, DC: U.S. EPA. February. Available on the Internet at http://cfpub.epa.gov/ncea/CFM/recordisplay.cfm?deid=149923.
U.S. Environmental Protection Agency (U.S. EPA). 2008. Integrated Science Assessment for Oxides of Nitrogen - Health Criteria (Final Report). National Center for Environmental Assessment, Research Triangle Park, NC. July. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=194645>.
U.S. Environmental Protection Agency (U.S. EPA). 2008. Integrated Science Assessment for Sulfur Oxides - Health Criteria (Final Report). National Center for Environmental Assessment, Research Triangle Park, NC. September. Available on the Internet at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=198843>.
U.S. Environmental Protection Agency (U.S. EPA). 2008. Integrated Science Assessment for Oxides of Nitrogen and Sulfur -- Ecological Criteria National (Final Report). National Center for Environmental Assessment, Research Triangle Park, NC. EPA/600/R-08/139. December. Available on the Internet at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=201485>.
U.S. Environmental Protection Agency (U.S. EPA). 2009. Integrated Science Assessment for Particulate Matter (Final Report). EPA-600-R-08-139F. National Center for Environmental Assessment -- RTP Division. December. Available at <http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=216546>.
U.S. Environmental Protection Agency (U.S. EPA). 2010. Guidelines for Preparing Economic Analyses.  EPA 240-R-10-001. National Center for Environmental Economics, Office of Policy. Washington, DC. December. Available on the Internet at <http://yosemite.epa.gov/EE/epa/eed.nsf/pages/Guidelines.html/$file/Guidelines.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2011. Integrated Science Assessment for Ozone and Related Photochemical Oxidants (First External Review Draft).  U.S. Environmental Protection Agency, Washington, DC, EPA/600/R-10/076A. National Center for Environmental Assessment -- RTP Division. March. Available at <http://www.epa.gov/ncea/isa/pdfs/ozone/Ozone_ISA_ERD1.pdf>.
U.S. Environmental Protection Agency (U.S. EPA). 2011. Methylmercury (MeHg) (CASRN 22967-92-6).  Integrated Risk Information System (IRIS). Available at <http://www.epa.gov/iris/subst/0073.htm>.