Appendix 1
SO
2Cost Methodology
There are a number of key assumptions on which the analysis supporting Figure III-1 relies. These assumptions fall into the following categories.
• Cost and performance of flue gas desulfurization equipment (FGD).
• Current operations and emissions of the power plants addressed in the study.
• Financial parameters of the plant owners. These are important for converting the capital cost of the emission control equipment into annual costs.
Flue Gas Desulfurization
Two sources were used for cost and performance parameters for FGD equipment. These were the EPA Office of Research and Development report, “Controlling SO2 Emissions: A Review of Technologies,” and the Energy Information Administrations report, "Strategies for Reducing Multiple Emissions from Power Plants." The EIA report offers only a single capital cost for FGD of $195 per kW in 1997 dollars. In 2001 dollars this becomes approximately $215 per kW1. The EPA-ORD offers a more complete set of costs and operational parameters for FGD. In the EPA report, costs are given as a function of plant size and FGD type2. This report uses the costs for the lime spray drying approach. Costs are as follows:
MW Capital Fixed O&M Variable O&M
$/kW $/kW-Year $/MWH < 200 $350 $12.00 $2.00 200-300 $225 $ 8.50 $2.00 300-500 $150 $ 7.00 $2.00 500-800 $150 $ 5.50 $2.00 800-1000 $140 $ 4.50 $2.00 > 1000 $140 $ 4.00 $2.00
These costs were given in 1998 dollars, and were escalated to 2001 dollars using a factor of 1.0763.
The analysis was done using the EIA costs because, in general, the EIA values for capital costs yielded higher overall costs.
The EPA-ORD report suggests that removal efficiencies of 90-95% or higher for spray dryer scrubbers can be expected. This analysis uses 95%.
Operations and emissions from 1999 were used as the base for this analysis. Emissions data come from the EPA Acid Rain Scorecard (CEMS Data). Generation data (MWH produced by each unit) come from FERC Form 767 and 759.
Financial Parameters
Financial parameters such as the cost of debt and equity and the capital structure are used to calculate the annual amortization of the capital costs. This study does not calculate separate amortization factors for each plant owner, but rather calculates a factor based on the general values for investor-owned utilities. The cost of debt, preferred stock, and equity and the capital structure are calculated from the Composite Statement of Income and the Composite Balance Sheet as reported in the 1999 Electric Power Annual issued by the Energy Information Administration. The values for investor-owned utilities in 1999 were as follows: Debt Fraction – 48.7% Cost of Debt – 8.13% Preferred Fraction – 3.5% Cost of Preferred – 5.70% Equity Fraction – 47.8% Cost of Equity – 9.92% Income Tax rate – 35%
Gross Receipts Tax Rate – 2.5%4
A ten year equipment life with a five year tax life was assumed. Sum-of-the-years-digits depreciation was used for the tax depreciation.
These values were used in a capital recovery model to determine the annualized recovery factor. A modest inflation rate of 2.5% per year was included. The capital recovery model returned a annualized factor of 13.99% using the constant dollar approach to capital recovery.
Calculation of Cost per Ton
The cost per ton removed is a function of the emissions per MWH and the capacity factor of the plant. The higher the initial emission rate and the higher the capacity factor, the lower the cost per ton of SO2 removed.
Prepared by David Schoengold MSB Energy Associates, Inc. For the Clean Air Task Force
1 Using an escalation factor of 2.5% per year for four years.
2 The costs are provided in the report in the form of graphs. The values in the table were taken from the
graphs.
3 Using an escalation factor of 2.5% per year for three years.
4 Gross receipts taxes vary greatly from state to state. A value of 2.5% was used as being typical of
Appendix 2 TABLE IX-3. PROJECTED PM2.5
DESIGN VALUES FOR THE 2010
BASE CASE AND REGIONAL STRATEGY SCENARIOS
2010
regional base control IAQR
State County case strategy Benefit
Alabama DeKalb 15.22 13.92 1.3
Alabama Jefferson 20.03 18.85 1.18
Alabama Montgomery 15.69 14.6 1.09
Alabama Russell 17.07 15.77 1.3
Alabama Talladega 16.44 15.26 1.18
Connecticut New Haven 15.43 14.5 0.93
Delaware New Castle 15.43 14.12 1.31
District of Columbia District of Columbia 15.48 13.7 1.78
Georgia Clarke 17.04 15.56 1.48 Georgia Clayton 17.73 16.43 1.3 Georgia Cobb 16.8 15.56 1.24 Georgia DeKalb 18.26 16.92 1.34 Georgia Floyd 16.99 15.65 1.34 Georgia Fulton 19.79 18.37 1.42 Georgia Hall 15.62 14.24 1.38 Georgia Muscogee 16.68 15.41 1.27 Georgia Paulding 15.4 14.17 1.23 Georgia Richmond 15.99 14.65 1.34 Georgia Wilkinson 16.68 15.51 1.17 Illinois Cook 17.9 16.9 1 Illinois Madison 16.41 15.33 1.08 Illinois St. Clair 16.31 15.11 1.2 Illinois Will 15.21 14.25 0.96 Indiana Clark 15.86 14.34 1.52 Indiana Marion 15.89 14.39 1.5 Kentucky Fayette 15.21 13.55 1.66 Kentucky Jefferson 15.79 14.23 1.56
Maryland Baltimore City 16.58 14.82 1.76
Michigan Wayne 18.78 17.65 1.13
Missouri St. Louis City 15.25 14.14 1.11
New York New York 16.3 15.25 1.05
North Carolina Catawba 15.26 13.87 1.39
North Carolina Davidson 15.52 14.22 1.3 North Carolina Mecklenburg 15.18 13.92 1.26
Ohio Butler 16.01 14.53 1.48 Ohio Cuyahoga 19.13 17.68 1.45 Ohio Franklin 16.69 15.04 1.65 Ohio Hamilton 17.75 15.96 1.79 Ohio Jefferson 18.04 16.06 1.98 Ohio Lawrence 15.48 13.67 1.81 Ohio Mahoning 15.39 13.76 1.63
Ohio Scioto 18.4 16.33 2.07 Ohio Stark 17.09 15.19 1.9 Ohio Summit 16.35 14.71 1.64 Ohio Trumbull 15.13 13.56 1.57 Pennsylvania Allegheny 19.52 16.92 2.6 Pennsylvania Berks 15.39 13.84 1.55 Pennsylvania Lancaster 15.46 13.71 1.75 Pennsylvania York 15.68 13.93 1.75
South Carolina Greenville 15.06 13.75 1.31
Tennessee Davidson 15.36 13.92 1.44
Tennessee Hamilton 16.14 14.74 1.4
Tennessee Knox 18.36 16.6 1.76
Tennessee Roane 15.18 13.69 1.49
Tennessee Sullivan 15.24 13.77 1.47
West Virginia Brooke 16.6 14.77 1.83
West Virginia Cabell 16.39 14.41 1.98
West Virginia Hancock 16.69 14.85 1.84
West Virginia Kanawha 17.11 14.81 2.3
West Virginia Marshall 15.53 13.25 2.28
TABLE IX-4. PROJECTED PM2.5 DESIGN VALUES FOR 2015
BASE CASE AND REGIONAL STRATEGY SCENARIOS
2015
regional base control IAQR State County case strategy Benefit Alabama Jefferson 19.57 18.11 1.46 Alabama Montgomery 15.35 14.05 1.3 Alabama Russell 16.68 15.05 1.63 Alabama Talladega 15.97 14.57 1.4 ConnecticuNew Haven 15.13 14.13 1 Georgia Clarke 16.46 14.58 1.88 Georgia Clayton 17.26 15.49 1.77 Georgia Cobb 16.28 14.37 1.91 Georgia DeKalb 17.93 16.22 1.71 Georgia Floyd 16.51 14.71 1.8 Georgia Fulton 19.44 17.62 1.82 Georgia Hall 15.05 13.16 1.89 Georgia Muscogee 16.31 14.71 1.6 Georgia Richmond 15.51 13.82 1.69 Georgia Wilkinson 16.4 14.88 1.52 Illinois Cook 17.52 16.4 1.12 Illinois Madison 16.03 14.88 1.15 Illinois St. Clair 15.91 14.67 1.24 Indiana Clark 15.4 13.69 1.71 Indiana Marion 15.31 13.79 1.52 Kentucky Jefferson 15.32 13.57 1.75 Maryland Baltimore City 16.11 14.2 1.91
Michigan Wayne 18.28 17.06 1.22
New York New York (Manhattan) 15.82 14.69 1.13
Ohio Butler 15.39 13.77 1.62 Ohio Cuyahoga 18.58 17.05 1.53 Ohio Franklin 16.18 14.46 1.72 Ohio Hamilton 17.07 15.15 1.92 Ohio Jefferson 17.49 15.51 1.98 Ohio Scioto 17.62 15.49 2.13 Ohio Stark 16.42 14.52 1.9 Ohio Summit 15.78 14.14 1.64 PennsylvanAllegheny 18.64 16.09 2.55 PennsylvanYork 15.13 13.26 1.87 Tennessee Hamilton 15.63 13.91 1.72 Tennessee Knox 17.73 15.59 2.14 West VirginBrooke 16.1 14.26 1.84 West VirginCabell 15.7 13.71 1.99 West VirginHancock 16.18 14.33 1.85 West VirginKanawha 16.45 14.1 2.35 West VirginWood 15.58 13.49 2.09
Red indicates nonattainment in 2010 with IAQR Brown indicates nonattainment in 2015 with IAQR January 2004 per cent of transport remedied in 2015 2015 IAQR benefit per cent of 2010 transport remedied 2010 IAQR Benefit total interstate
impact State Name County Name
2010 Base Case PM2.5 (ug/m3) AL AR CO CT DE FL GA IA IL IN KS KY LA MA MD/DC MI MN MO MS MT NC ND & VT (Combined) NE & ME (Combined) NJ NM NY OH OK PA RI SC SD & NH (Combined) TN TX VA WI WV WY
22.89% 1.3 5.68 Alabama DeKalb County 15.24 1.51 0.11 0.03 0.00 0.01 0.22 1.32 0.09 0.34 0.29 0.04 0.27 0.18 0.00 0.06 0.12 0.05 0.18 0.19 0.01 0.20 0.04 0.03 0.02 0.02 0.04 0.30 0.06 0.15 0.00 0.16 0.02 0.55 0.21 0.10 0.11 0.13 0.03 28.68% 1.46 23.18% 1.18 5.09 Alabama Jefferson County 20.12 2.81 0.12 0.03 0.00 0.01 0.26 0.82 0.10 0.33 0.25 0.05 0.25 0.25 0.00 0.05 0.10 0.06 0.19 0.30 0.01 0.15 0.04 0.03 0.02 0.02 0.04 0.24 0.07 0.14 0.00 0.13 0.02 0.45 0.22 0.09 0.11 0.11 0.03 28.08% 1.3 23.54% 1.09 4.63 Alabama Montgomery County 15.72 2.38 0.10 0.03 0.00 0.01 0.44 0.74 0.08 0.25 0.20 0.05 0.17 0.25 0.00 0.06 0.08 0.05 0.15 0.26 0.01 0.15 0.04 0.03 0.02 0.02 0.05 0.21 0.06 0.15 0.00 0.15 0.01 0.30 0.20 0.09 0.09 0.10 0.03 27.49% 1.63 21.92% 1.3 5.93 Alabama Russell County 17.31 1.25 0.10 0.03 0.00 0.01 0.52 1.52 0.09 0.28 0.23 0.05 0.19 0.22 0.00 0.07 0.10 0.05 0.15 0.22 0.01 0.21 0.04 0.03 0.03 0.02 0.06 0.27 0.06 0.17 0.00 0.26 0.02 0.36 0.21 0.11 0.10 0.11 0.03 28.69% 1.4 24.18% 1.18 4.88 Alabama Talladega County 16.46 2.24 0.10 0.03 0.00 0.01 0.33 0.88 0.09 0.30 0.24 0.05 0.21 0.22 0.00 0.05 0.10 0.06 0.17 0.25 0.01 0.15 0.04 0.03 0.02 0.02 0.04 0.23 0.06 0.14 0.00 0.14 0.01 0.38 0.20 0.09 0.10 0.10 0.03 23.20% 1 21.58% 0.93 4.31 Connecticut New Haven County 15.45 0.05 0.01 0.01 0.23 0.06 0.03 0.08 0.04 0.15 0.13 0.01 0.08 0.03 0.21 0.15 0.20 0.05 0.04 0.02 0.01 0.12 0.06 0.03 0.32 0.00 0.85 0.36 0.01 0.57 0.01 0.04 0.06 0.07 0.05 0.16 0.09 0.14 0.01 24.86% 1.31 5.27 Delaware New Castle County 15.49 0.08 0.02 0.01 0.02 0.24 0.03 0.11 0.05 0.19 0.18 0.02 0.11 0.04 0.04 0.57 0.24 0.05 0.06 0.02 0.01 0.15 0.04 0.02 0.21 0.01 0.33 0.52 0.02 1.17 0.00 0.05 0.02 0.10 0.06 0.35 0.10 0.26 0.01 32.60% 1.78 5.46 District of Colum District of Columbia 15.35 0.12 0.03 0.01 0.01 0.10 0.04 0.15 0.06 0.24 0.23 0.02 0.16 0.05 0.02 0.89 0.24 0.06 0.08 0.04 0.01 0.26 0.04 0.02 0.14 0.01 0.24 0.67 0.02 0.86 0.00 0.09 0.02 0.15 0.08 0.67 0.13 0.37 0.02 34.12% 1.88 26.86% 1.48 5.51 Georgia Clarke County 17.05 0.75 0.10 0.03 0.00 0.02 0.27 2.44 0.07 0.27 0.26 0.04 0.23 0.15 0.00 0.09 0.14 0.04 0.14 0.15 0.01 0.34 0.04 0.03 0.03 0.02 0.06 0.39 0.05 0.22 0.00 0.47 0.01 0.46 0.20 0.15 0.09 0.16 0.03 35.40% 1.77 26.00% 1.3 5.00 Georgia Clayton County 17.82 0.90 0.10 0.03 0.00 0.01 0.30 2.67 0.07 0.26 0.23 0.04 0.20 0.16 0.00 0.07 0.11 0.04 0.14 0.17 0.01 0.23 0.04 0.03 0.02 0.02 0.05 0.30 0.05 0.17 0.00 0.28 0.01 0.40 0.19 0.12 0.09 0.13 0.03 36.73% 1.91 23.85% 1.24 5.20 Georgia Cobb County 17.24 0.97 0.10 0.03 0.00 0.01 0.23 2.53 0.08 0.28 0.26 0.04 0.24 0.16 0.00 0.06 0.12 0.05 0.15 0.16 0.01 0.24 0.04 0.03 0.01 0.02 0.04 0.31 0.06 0.15 0.00 0.24 0.01 0.52 0.21 0.11 0.10 0.13 0.03 32.08% 1.71 25.14% 1.34 5.33 Georgia DeKalb County 18.26 0.93 0.11 0.03 0.00 0.01 0.27 2.91 0.08 0.27 0.25 0.04 0.22 0.17 0.00 0.08 0.12 0.04 0.15 0.17 0.01 0.27 0.04 0.03 0.02 0.02 0.05 0.33 0.06 0.18 0.00 0.30 0.01 0.46 0.21 0.13 0.10 0.14 0.03 31.30% 1.8 23.30% 1.34 5.75 Georgia Floyd County 17.14 1.17 0.11 0.03 0.00 0.01 0.24 2.27 0.09 0.33 0.30 0.05 0.25 0.18 0.00 0.07 0.13 0.05 0.17 0.19 0.01 0.23 0.05 0.03 0.02 0.02 0.05 0.33 0.06 0.16 0.00 0.21 0.02 0.57 0.22 0.11 0.12 0.14 0.03 32.04% 1.82 25.00% 1.42 5.68 Georgia Fulton County 19.79 0.99 0.11 0.03 0.00 0.01 0.28 3.12 0.08 0.29 0.27 0.05 0.23 0.18 0.00 0.08 0.13 0.05 0.16 0.18 0.01 0.29 0.04 0.03 0.02 0.02 0.05 0.36 0.06 0.19 0.00 0.32 0.02 0.49 0.23 0.14 0.11 0.15 0.03 36.28% 1.89 26.49% 1.38 5.21 Georgia Hall County 15.61 0.76 0.10 0.03 0.00 0.01 0.22 2.30 0.07 0.26 0.25 0.04 0.23 0.15 0.00 0.08 0.13 0.04 0.14 0.14 0.01 0.33 0.04 0.03 0.02 0.02 0.05 0.36 0.06 0.18 0.00 0.37 0.01 0.47 0.20 0.14 0.09 0.15 0.03 28.83% 1.6 22.88% 1.27 5.55 Georgia Muscogee County 16.92 1.22 0.10 0.03 0.00 0.01 0.51 1.50 0.08 0.27 0.22 0.05 0.19 0.21 0.00 0.07 0.10 0.05 0.15 0.22 0.01 0.21 0.04 0.03 0.03 0.02 0.06 0.26 0.06 0.17 0.00 0.26 0.02 0.35 0.21 0.11 0.09 0.11 0.03 23.16% 1.23 5.31 Georgia Paulding County 15.52 1.14 0.10 0.03 0.00 0.01 0.26 1.77 0.08 0.29 0.26 0.04 0.22 0.17 0.00 0.06 0.12 0.05 0.16 0.18 0.01 0.21 0.04 0.03 0.02 0.02 0.05 0.29 0.06 0.16 0.00 0.19 0.01 0.47 0.20 0.11 0.11 0.13 0.03 33.40% 1.69 26.48% 1.34 5.06 Georgia Richmond County 16.03 0.55 0.06 0.02 0.00 0.02 0.28 2.03 0.06 0.22 0.21 0.03 0.18 0.12 0.00 0.09 0.12 0.03 0.10 0.12 0.01 0.38 0.04 0.02 0.03 0.02 0.07 0.35 0.04 0.22 0.00 0.72 0.01 0.35 0.17 0.16 0.08 0.16 0.02 32.14% 1.52 24.74% 1.17 4.73 Georgia Wilkinson County 16.89 0.65 0.07 0.02 0.00 0.02 0.37 2.48 0.07 0.22 0.20 0.03 0.18 0.15 0.00 0.07 0.11 0.04 0.11 0.15 0.01 0.26 0.04 0.02 0.03 0.02 0.07 0.30 0.05 0.19 0.00 0.39 0.01 0.35 0.17 0.12 0.08 0.13 0.03 19.96% 1.12 17.83% 1 5.61 Illinois Cook County 18.07 0.08 0.11 0.03 0.00 0.00 0.01 0.04 0.33 2.45 0.79 0.11 0.22 0.08 0.00 0.00 0.73 0.39 0.30 0.05 0.03 0.01 0.12 0.06 0.00 0.02 0.05 0.39 0.07 0.11 0.00 0.00 0.04 0.14 0.21 0.01 1.00 0.04 0.04 21.10% 1.15 19.82% 1.08 5.45 Illinois Madison County 16.48 0.11 0.27 0.04 0.00 0.00 0.02 0.07 0.43 2.38 0.45 0.15 0.20 0.21 0.00 0.01 0.24 0.27 0.89 0.10 0.03 0.02 0.10 0.08 0.00 0.02 0.03 0.33 0.14 0.11 0.00 0.01 0.04 0.22 0.37 0.02 0.35 0.07 0.05 20.98% 1.24 20.30% 1.2 5.91 Illinois St. Clair County 16.32 0.12 0.29 0.04 0.00 0.00 0.02 0.07 0.40 1.61 0.50 0.15 0.22 0.22 0.00 0.01 0.24 0.25 1.28 0.11 0.03 0.02 0.10 0.08 0.00 0.02 0.03 0.34 0.14 0.10 0.00 0.01 0.04 0.24 0.37 0.02 0.32 0.08 0.05 18.82% 0.96 5.10 Illinois Will County 15.54 0.08 0.11 0.03 0.00 0.00 0.01 0.04 0.35 2.19 0.76 0.09 0.18 0.09 0.00 0.00 0.58 0.33 0.30 0.05 0.03 0.01 0.11 0.05 0.00 0.02 0.05 0.36 0.06 0.10 0.00 0.00 0.04 0.12 0.21 0.01 0.84 0.05 0.04 25.30% 1.71 22.49% 1.52 6.76 Indiana Clark County 15.79 0.43 0.12 0.03 0.00 0.00 0.06 0.34 0.19 0.84 0.88 0.06 1.10 0.16 0.00 0.04 0.29 0.13 0.27 0.13 0.02 0.06 0.07 0.04 0.01 0.02 0.05 0.73 0.06 0.16 0.00 0.04 0.02 0.53 0.20 0.08 0.26 0.19 0.03 26.62% 1.52 26.27% 1.5 5.71 Indiana Marion County 15.76 0.19 0.10 0.03 0.00 0.00 0.03 0.12 0.25 1.11 1.89 0.07 0.43 0.13 0.00 0.02 0.51 0.19 0.25 0.08 0.02 0.02 0.08 0.04 0.00 0.02 0.05 0.72 0.06 0.18 0.00 0.01 0.03 0.24 0.18 0.03 0.37 0.12 0.03 25.58% 1.66 6.49 Kentucky Fayette County 15.05 0.42 0.10 0.03 0.00 0.00 0.07 0.38 0.17 0.71 0.80 0.05 1.31 0.15 0.00 0.04 0.30 0.12 0.24 0.12 0.02 0.08 0.06 0.04 0.01 0.02 0.06 0.87 0.06 0.17 0.00 0.05 0.02 0.53 0.20 0.09 0.24 0.24 0.03 26.24% 1.75 23.39% 1.56 6.67 Kentucky Jefferson County 15.71 0.42 0.12 0.03 0.00 0.00 0.06 0.35 0.19 0.85 0.90 0.06 1.12 0.16 0.00 0.04 0.30 0.13 0.28 0.13 0.02 0.07 0.07 0.04 0.01 0.02 0.06 0.76 0.07 0.18 0.00 0.04 0.02 0.52 0.20 0.08 0.26 0.20 0.03 34.85% 1.91 32.12% 1.76 5.48 Maryland Baltimore city 16.53 0.10 0.03 0.02 0.01 0.10 0.04 0.14 0.06 0.24 0.23 0.02 0.16 0.05 0.02 1.37 0.25 0.06 0.07 0.03 0.01 0.23 0.04 0.02 0.16 0.01 0.25 0.66 0.02 1.01 0.00 0.08 0.02 0.15 0.08 0.58 0.13 0.38 0.02 23.83% 1.22 22.07% 1.13 5.12 Michigan Wayne County 18.76 0.10 0.06 0.02 0.00 0.00 0.03 0.08 0.16 0.70 0.57 0.05 0.24 0.06 0.00 0.01 2.63 0.19 0.14 0.04 0.02 0.03 0.07 0.04 0.00 0.01 0.15 1.21 0.03 0.20 0.00 0.01 0.02 0.14 0.12 0.03 0.41 0.15 0.03 19.07% 1.11 5.82 Missouri St. Louis city 15.26 0.12 0.27 0.04 0.00 0.00 0.02 0.07 0.38 1.50 0.45 0.14 0.21 0.21 0.00 0.01 0.22 0.23 1.19 0.11 0.02 0.02 0.09 0.08 0.00 0.02 0.03 0.31 0.13 0.09 0.00 0.01 0.04 0.22 0.35 0.02 0.30 0.07 0.04 26.71% 1.13 24.82% 1.05 4.23 New York New York County 16.29 0.05 0.02 0.01 0.07 0.09 0.02 0.08 0.04 0.16 0.15 0.01 0.09 0.03 0.12 0.22 0.21 0.05 0.05 0.02 0.01 0.13 0.05 0.02 0.45 0.00 2.00 0.41 0.01 0.95 0.00 0.05 0.04 0.08 0.05 0.21 0.10 0.17 0.01 25.19% 1.3 5.16 North Carolina Davidson County 15.32 0.27 0.06 0.02 0.00 0.02 0.11 0.54 0.06 0.28 0.29 0.02 0.28 0.08 0.00 0.13 0.16 0.04 0.11 0.06 0.01 1.63 0.03 0.02 0.05 0.01 0.08 0.51 0.04 0.29 0.00 0.38 0.01 0.38 0.13 0.32 0.10 0.25 0.02 23.55% 1.26 5.35 North Carolina Mecklenburg County 15.07 0.33 0.06 0.02 0.00 0.02 0.14 0.74 0.06 0.25 0.26 0.02 0.24 0.09 0.00 0.12 0.14 0.03 0.11 0.08 0.01 1.26 0.03 0.02 0.04 0.01 0.07 0.42 0.04 0.26 0.00 0.66 0.01 0.38 0.14 0.24 0.08 0.21 0.02 28.37% 1.62 25.92% 1.48 5.71 Ohio Butler County 15.87 0.24 0.08 0.02 0.00 0.00 0.04 0.19 0.16 0.75 0.91 0.05 0.60 0.11 0.00 0.03 0.52 0.14 0.20 0.08 0.02 0.05 0.07 0.04 0.00 0.01 0.07 1.86 0.05 0.20 0.00 0.02 0.02 0.30 0.16 0.05 0.28 0.22 0.03 31.55% 1.53 29.90% 1.45 4.85 Ohio Cuyahoga County 18.99 0.14 0.05 0.02 0.00 0.00 0.04 0.11 0.13 0.50 0.47 0.04 0.28 0.07 0.00 0.03 0.88 0.13 0.12 0.05 0.02 0.04 0.06 0.03 0.01 0.01 0.19 2.96 0.03 0.47 0.00 0.02 0.02 0.17 0.11 0.06 0.25 0.27 0.03 32.09% 1.72 30.78% 1.65 5.36 Ohio Franklin County 16.45 0.20 0.06 0.02 0.00 0.00 0.04 0.17 0.14 0.59 0.67 0.04 0.50 0.09 0.00 0.04 0.61 0.13 0.16 0.06 0.02 0.06 0.06 0.03 0.01 0.01 0.08 2.18 0.04 0.30 0.00 0.03 0.02 0.27 0.14 0.08 0.27 0.39 0.03 28.40% 1.92 26.48% 1.79 6.76 Ohio Hamilton County 17.57 0.32 0.09 0.03 0.00 0.00 0.05 0.26 0.18 0.83 1.06 0.06 0.77 0.13 0.00 0.04 0.53 0.15 0.24 0.10 0.02 0.06 0.08 0.04 0.01 0.02 0.07 1.87 0.06 0.22 0.00 0.03 0.03 0.39 0.19 0.07 0.31 0.29 0.03 33.79% 1.98 33.79% 1.98 5.86 Ohio Jefferson County 17.69 0.15 0.04 0.01 0.00 0.01 0.04 0.15 0.10 0.39 0.39 0.03 0.29 0.07 0.00 0.07 0.48 0.09 0.11 0.04 0.01 0.09 0.05 0.02 0.01 0.01 0.11 2.03 0.03 0.73 0.00 0.04 0.02 0.19 0.10 0.12 0.21 1.64 0.02 30.22% 1.81 5.99 Ohio Lawrence County 15.19 0.26 0.06 0.02 0.00 0.00 0.05 0.28 0.12 0.49 0.54 0.03 1.29 0.10 0.00 0.05 0.33 0.08 0.16 0.07 0.01 0.13 0.05 0.03 0.01 0.01 0.06 1.27 0.04 0.21 0.00 0.07 0.02 0.35 0.14 0.13 0.18 0.60 0.02 35.90% 1.63 4.54 Ohio Mahoning County 15.13 0.13 0.04 0.02 0.00 0.00 0.03 0.11 0.10 0.38 0.36 0.03 0.24 0.06 0.00 0.05 0.55 0.09 0.10 0.04 0.01 0.06 0.05 0.03 0.01 0.01 0.16 1.89 0.03 0.70 0.00 0.03 0.02 0.16 0.10 0.09 0.20 0.53 0.02 32.52% 2.13 31.60% 2.07 6.55 Ohio Scioto County 18.02 0.30 0.08 0.02 0.00 0.01 0.06 0.31 0.14 0.59 0.63 0.04 1.05 0.12 0.00 0.06 0.43 0.11 0.19 0.09 0.02 0.13 0.07 0.03 0.01 0.02 0.08 1.62 0.05 0.23 0.00 0.07 0.02 0.42 0.18 0.13 0.22 0.61 0.03 35.65% 1.9 35.65% 1.9 5.33 Ohio Stark County 16.80 0.17 0.05 0.02 0.00 0.00 0.04 0.15 0.13 0.49 0.49 0.04 0.33 0.08 0.00 0.05 0.70 0.11 0.14 0.05 0.02 0.07 0.06 0.03 0.01 0.01 0.15 2.30 0.03 0.71 0.00 0.03 0.02 0.21 0.13 0.09 0.27 0.42 0.03 34.53% 1.64 34.53% 1.64 4.75 Ohio Summit County 16.17 0.14 0.04 0.02 0.00 0.00 0.04 0.12 0.12 0.46 0.44 0.04 0.28 0.07 0.00 0.04 0.71 0.11 0.12 0.05 0.01 0.06 0.06 0.03 0.01 0.01 0.16 2.02 0.03 0.59 0.00 0.03 0.02 0.17 0.11 0.07 0.24 0.32 0.03 35.97% 2.55 36.67% 2.6 7.09 Pennsylvania Allegheny County 18.86 0.17 0.05 0.02 0.00 0.02 0.04 0.19 0.11 0.43 0.43 0.03 0.35 0.08 0.00 0.20 0.50 0.09 0.13 0.06 0.02 0.15 0.06 0.03 0.04 0.02 0.14 1.82 0.04 1.95 0.00 0.05 0.02 0.25 0.14 0.26 0.23 0.89 0.03 34.91% 1.55 4.44 Pennsylvania Berks County 15.28 0.08 0.02 0.01 0.02 0.17 0.03 0.11 0.05 0.20 0.19 0.02 0.13 0.04 0.04 0.54 0.25 0.06 0.06 0.02 0.01 0.14 0.04 0.02 0.21 0.01 0.38 0.60 0.02 1.78 0.00 0.05 0.02 0.11 0.07 0.32 0.11 0.28 0.01 34.45% 1.75 5.08 Pennsylvania Lancaster County 15.27 0.09 0.03 0.02 0.02 0.09 0.03 0.13 0.06 0.23 0.22 0.02 0.15 0.05 0.04 0.68 0.28 0.06 0.07 0.03 0.01 0.16 0.04 0.02 0.23 0.01 0.39 0.72 0.02 2.01 0.00 0.06 0.02 0.13 0.08 0.40 0.12 0.35 0.02 36.74% 1.87 34.38% 1.75 5.09 Pennsylvania York County 15.50 0.09 0.03 0.01 0.01 0.11 0.03 0.13 0.06 0.23 0.22 0.02 0.15 0.04 0.02 0.85 0.26 0.06 0.07 0.03 0.01 0.17 0.04 0.02 0.17 0.01 0.30 0.67 0.02 1.66 0.00 0.06 0.02 0.13 0.08 0.44 0.12 0.39 0.02 21.95% 1.44 6.56 Tennessee Davidson County 15.31 0.85 0.16 0.03 0.00 0.00 0.11 0.49 0.17 0.68 0.54 0.06 0.59 0.24 0.00 0.03 0.19 0.10 0.32 0.22 0.02 0.12 0.06 0.05 0.01 0.02 0.04 0.41 0.08 0.15 0.00 0.08 0.02 1.36 0.27 0.07 0.19 0.16 0.03 27.26% 1.72 22.19% 1.4 6.31 Tennessee Hamilton County 16.11 0.94 0.12 0.03 0.00 0.01 0.17 1.08 0.11 0.41 0.39 0.04 0.37 0.18 0.00 0.05 0.16 0.06 0.20 0.17 0.01 0.21 0.05 0.03 0.01 0.02 0.04 0.40 0.07 0.16 0.00 0.15 0.02 1.56 0.23 0.09 0.14 0.16 0.03 29.08% 2.14 23.91% 1.76 7.36 Tennessee Knox County 18.16 0.77 0.13 0.03 0.00 0.01 0.18 0.98 0.13 0.51 0.51 0.05 0.54 0.18 0.00 0.07 0.22 0.08 0.23 0.16 0.02 0.35 0.06 0.04 0.02 0.03 0.05 0.59 0.08 0.21 0.00 0.23 0.02 1.67 0.28 0.15 0.17 0.24 0.04 23.73% 1.49 6.28 Tennessee Roane County 15.13 0.80 0.13 0.03 0.00 0.01 0.15 0.77 0.12 0.48 0.46 0.05 0.47 0.17 0.00 0.05 0.18 0.07 0.23 0.16 0.01 0.19 0.05 0.04 0.01 0.02 0.04 0.47 0.07 0.18 0.00 0.14 0.02 1.36 0.24 0.10 0.15 0.19 0.03 25.65% 1.47 5.73 Tennessee Sullivan County 15.06 0.43 0.09 0.02 0.00 0.01 0.11 0.57 0.10 0.41 0.43 0.04 0.48 0.11 0.00 0.06 0.20 0.06 0.17 0.10 0.01 0.41 0.04 0.03 0.02 0.02 0.04 0.56 0.05 0.19 0.00 0.18 0.01 1.45 0.20 0.16 0.13 0.26 0.03 32.00% 1.84 31.83% 1.83 5.75 West Virginia Brooke County 16.28 0.13 0.04 0.01 0.00 0.01 0.03 0.14 0.09 0.36 0.36 0.03 0.27 0.06 0.00 0.07 0.44 0.08 0.10 0.04 0.01 0.08 0.04 0.02 0.01 0.01 0.10 1.88 0.03 0.67 0.00 0.03 0.01 0.17 0.10 0.11 0.20 1.51 0.02 31.19% 1.99 31.03% 1.98 6.38 West Virginia Cabell County 15.98 0.28 0.07 0.02 0.00 0.01 0.06 0.31 0.13 0.51 0.54 0.03 0.67 0.10 0.00 0.07 0.35 0.09 0.16 0.08 0.01 0.16 0.06 0.03 0.01 0.02 0.07 1.26 0.04 0.25 0.00 0.09 0.02 0.36 0.16 0.16 0.18 0.80 0.02 32.06% 1.85 31.89% 1.84 5.77 West Virginia Hancock County 16.37 0.13 0.04 0.01 0.00 0.01 0.03 0.13 0.09 0.36 0.36 0.03 0.27 0.06 0.00 0.07 0.45 0.08 0.10 0.04 0.01 0.08 0.04 0.02 0.01 0.01 0.10 1.90 0.03 0.68 0.00 0.03 0.01 0.17 0.09 0.11 0.20 1.50 0.02 38.09% 2.35 37.28% 2.3 6.17 West Virginia Kanawha County 16.67 0.27 0.06 0.02 0.00 0.01 0.06 0.31 0.12 0.47 0.49 0.03 0.60 0.09 0.00 0.08 0.33 0.09 0.15 0.07 0.01 0.19 0.05 0.03 0.01 0.02 0.07 1.20 0.04 0.28 0.00 0.10 0.02 0.36 0.16 0.19 0.17 1.59 0.02 32.35% 2.09 33.28% 2.15 6.46 West Virginia Wood County 15.85 0.23 0.06 0.02 0.00 0.01 0.05 0.25 0.13 0.49 0.53 0.03 0.52 0.09 0.00 0.08 0.41 0.09 0.15 0.07 0.01 0.14 0.06 0.03 0.01 0.01 0.08 1.66 0.04 0.31 0.00 0.07 0.02 0.30 0.15 0.14 0.20 0.93 0.02
EPA performed zero-out modeling for several pairs of geographically distant States in the same model run (i.e., combined State zero-out model runs). Three combined State runs were performed which combined North Dakota and Vermont; Nebraska and Maine; and South Dakota and New Hampshire. The maximum downwind contribution from each of the three Plains States (i.e., Nebraska, North Dakota, and South Dakota) was determined by identifying the highest contribution to nonattainment counties in the Midwest. The maximum downwind contribution from each of the three New England States (i.e., Maine, New Hampshire, and Vermont) was determined by identifying the highest contribution to nonattainment counties in the Northeast.
PM2.5 Contributions (ug/m3) from Source States based on Zero-Out Modeling of 2010 SO2+NOx Emissions.
Values above in bold and left justified represent contributions from a source State to PM2.5 at a receptor within that State (i.e., in-State contribution) or to a receptor in an adjacent State in which the receptor is located in a model grid cell assigned to the source State. Downwind 2010
Nonattainment Counties
Clean Air Task Force 1
Appendix 3
CATF’s Use of EPA’s Benefits Transfer Method in Evaluating the Benefits of a
Proposed IAQR Alternative Control Level
Introduction
EPA has recently proposed its IAQR establishing caps for power plant emissions of NOx and SO2 in a 28 states plus DC region to reduce transported pollution that significantly contributes to downwind PM2.5 nonattainment problems. EPA did not evaluate any alternatives to its proposal, however. Therefore fully modeled benefits analyses are available in the IAQR only for EPA’s proposed caps. However, Clean Air Task Force (CATF) independently contracted ICF Consulting to estimate 2005, 2010, 2015 and 2020 power plant emissions using the Integrated Planning Model (IPM) for an alternate control scenario (CATF 16) with tighter IAQR region NOx and SO2 EGU emission caps than proposed in the IAQR. For this alternative, CATF derived SO2 emissions from the IPM run and estimated health benefits using a simple transfer factor method based on an approach recently used by EPA in several recent rulemakings and other benefits analyses. EPA has developed the simple technique to provide an estimate of the health damages of
emissions reductions from regulatory or legislative alternatives. 1 The method is not ideal for full regulatory impact analyses, but provides useful health benefits estimates in the absence of time-consuming and prohibitively costly modeling. In EPA’s words, “[t]he transfer technique used here provides reasonable approximations. Nevertheless, the method also adds uncertainty to the
analysis and the results may under or overstate actual benefits of the control program.”2 The EPA approach determines health damages (in this case premature deaths) transfer factors expressed in population-adjusted damages/ton/person based on existing air quality modeling and attendant benefits analyses. The method also allows the approximation of monetized benefits based on health benefits for proposed initiatives.
Methodology
The transfer method as used by CATF fundamentally assumes that all PM2.5 comes from the formation of sulfate aerosol from sulfur dioxide emissions. The estimated avoided deaths should not be viewed as benefits strictly from SO2 reductions but a combination of both SO2 and NOx
1 See Sections 10.2 and 10.3 of EPA’s “Final Regulatory Support Document: Control of Emissions from
Unregulated Nonroad Engines,” EPA420-R-02-022, in support of its rule entitled “Control of Emissions From Nonroad Large Spark-Ignition Engines and Recreational Engines (Marine and Land-Based),” 67 Fed. Reg. 68241 (November 8, 2002), available online at http://www.epa.gov/otaq/regs/nonroad/2002/r02022k.pdf. (hereafter “Recreational Vehicle RIA”).
Also see: EPA Memorandum, Bryan Hubbell to Sam Napolitano (July 2, 2001), “Estimated NOx, SO2 and PM Emissions Health Damages for Heavy Duty Vehicle Emissions.
Clean Air Task Force 2
reductions. This being noted, it is reasonable to assume the majority of benefits will come from SO2 reductions (perhaps 90% or more nationally) relative to NOx.
The following briefly describes steps in the transfer factor analysis. In short, we calculate a simple transfer factor derived from EPA IAQR modeling (avoided PM-related death benefits-per-ton of SO2 reduced) and then apply that factor to SO2 emissions generated by the CATF IPM run as follows:
1) Modeled SO2 for the IAQR in 2010 and 2015 (adjusted for banking and trading) were taken from EPA IAQR IPM runs.3 Net millions of tons reduced for 2010 and 2015 were calculated by subtracting the predicted SO2 levels for a given year from the modeled base case.4
2) Avoided deaths used to calculate the transfer factors were taken from the IAQR proposal technical support documents (for 2010, 9,600; for 2015, 13,000).5
3) To calculate the transfer factors for 2010 and 2015, avoided deaths were divided by the net SO2 emissions reductions. Result: 2,560, and 3,403 avoided deaths per ton removed in 2010 and 2015, respectively.
4) Independent IPM SO2 emissions in 2010 and 2015 were generated by CATF for the alternative scenario (CATF 16).
5) For the CATF alternative scenario 2010 and 2015 net emissions were then multiplied times the transfer factor as follows:
IAQR Transfer factor X
Millions of tons SO2 reduced X
Population Factor
6) A population factor is multiplied times the result to account for population growth. In this analysis population adjustment is unnecessary since the modeling is already based on the projected populations for 2010 and 2015.
3The SO2 annual emissions inventories used in the calculations came from IPM modeling runs of the different
scenarios as follows: IPM runs for the IAQR came from the U.S. EPA web site at
http://www.epa.gov/airmarkets/epa-ipm/iaqr.html. The IPM runs for the Alternate Control Scenario were run for the Clean Air Task Force by ICF.
4 Base case is same as for Clear Skies (2003) and assumes full implementation of Title IV of the CAA but does not
include other additional SO2 reductions requirements, e.g. PM2.5, regional haze, or BART implementation.
5 Health benefits are documented in EPA’s technical support document: “ Benefits of the Proposed Interstate Air
Clean Air Task Force 3
The data used to derive the transfer factors are summarized in the following table:
SUMMARY OF EPA REMSAD DATA UTILIZED FOR MORTALITY TRANSFER FACTORS ESTIMATES
SO2 Nominal Cap (Million TPY) IPM EGU SO2 Emissions (Million TPY) Net SO2 Reduction (Million TPY) Mortality Reduction from Base (from IAQR) *FACTOR* Mortality Reduction Per million TPY SO2 Base 2010 9.9 IAQR 2010 3.9 6.1 3.8 9,600 2,560 Base 2015 9.2 IAQR 2015 2.7 5.4 3.8 13,000 3,403 CATF (16) Alternative, 2010 1.84 2.8 7.1 CATF (16) Alternative, 2015 1.84 2.8 6.4
RESULTS
PM-related Avoided Premature Mortality:
Estimated PM-related avoided deaths for the three scenarios in 2010 and 2015 are listed below. Avoided deaths for the EPA’s IAQR proposal were taken directly from 69 Fed. Reg. 4645, and from EPA’s supporting Benefits Analysis document.6 The table below lists the results of the CATF alternative control scenario (CATF 16) analysis:
2010 Avoided Deaths 2015 Avoided Deaths EPA IAQR
9,600 13,000
CATF Alternate Control Scenario (CATF 16)718,000 22,000
6http://www.epa.gov/air/interstateairquality/tsd0175.pdfClean Air Task Force 4
Monetized Benefits
The monetized benefits of the estimated PM-related mortality associated with the two regulatory options in 2010 and 2015 (above) are summarized below.8,9
8 In calculating the monetized value of the PM-related avoided deaths, CATF utilized the same value used by EPA in
the IAQR benefits analysis for the statistical value of a human life, $5.5 million 1999 dollars in 2010. We note that this value is significantly lower than the value of a human life utilized by EPA in the benefits analysis of the Clear Skies Act of 2003. This value has been scaled proportionately assuming an annual increase in GDP per capita (smoothed) between 2010 and 2020 of 1.38%. Because the value of reduced mortality is related to the GDP per capita, it is appropriate to increase the mortality value used in the IAQR analysis by this rate of 1.38% per year between 2010 and 2015.
9 Monetized benefits were calculated with unrounded avoided deaths. Monetized benefits were subsequently rounded.
Raw results were: 2010: $99.4 billion; 2015 $128.9 billion. 2010 Avoided Deaths Benefit [1999 dollars] 2015 Avoided Death Benefits [1999 dollars]
EPA IAQR
$53 billion
$77 billion
CATF Alternate Control Scenario (CATF 16)
Appendix 4
Estimating PM2.5 Nonattainment for Different Scenarios
Prepared by David Schoengold MSB Energy Associates, Inc. For the Clean Air Task Force March 25, 2004
In order to assist in the preparation of comments on the EPA’s IAQR Proposal, we were asked by CATF to prepare a comparative residual nonattainment analysis looking at a policy alternative to the EPA proposal. Detailed REMSAD calculations of nonattainment were not available for this scenario. As a result, it was necessary to use an estimation procedure which we had developed earlier after consultation with EPA staff.
Methodology
In mid-February 2003, Bruce Hill and I had a phone conversation with John Bachman of the U.S. EPA to discuss ways to estimate the impact of changing emissions on PM2.5 nonattainment without doing a full fledged run of the REMSAD model. Bachmann suggested that we could get a reasonable approximation by assuming that, for any given area, there was a roughly linear relationship between the national total power sector emissions of SO2 and the PM2.5 concentration in ug/m3. Based on this conversation we used the EPA’s base case and IAQR Proposal REMSAD runs to create a model of PM2.5 nonattainment. Since the EPA had REMSAD runs for 2010 and 2015, we created separate nonattainment models for each of those years.1
The REMSAD model runs provided PM2.5 concentrations for 61 separate counties in the eastern US for 2010 and 41 separate counties for 2015. Our models have separate estimation equations for each county for each year. The description below describes the model for one county. The same approach was used for all the other counties. Sample Equation Estimation for One Typical County
The development of an estimation model for Dekalb County, Alabama for 2010 was done as follows.
1 The EPA used a similar estimation method in a July 2, 2001 memo estimating the health damages from
heavy duty vehicle emissions. In a memo from Bryan Hubbell to Sam Napolitano, Hubbell suggests that a reasonable approximation can be developed assuming a linear relationship between tons of emissions and health impacts. Since health impacts are directly related to pollutant concentrations, this suggests a linear relationship between tons of emissions and pollutant concentrations. Bachmann suggested that the appropriate pollutant to focus on relative to PM2.5 concentrations would be SO2 .
1. According to the EPA IPM runs, total US power sector SO2 emissions in 2010 under the base case would be 9.861 million tons, while under the IAQR Proposal the
emissions would be 6.111 million tons.
2. According to the EPA base case REMSAD model, Dekalb County would have a PM2.5 concentration in 2010 of 15.22 ug/m3. Under the IAQR Proposal the PM2.5 concentration would be 13.92 ug/m3.
3. The difference in national level SO2 emissions between the two cases is 9.861 – 6.111, or 3.75 million tons.
4. The difference in PM2.5 concentration for Dekalb County between the two cases is 1.30 ug/m3.
5. The change in PM2.5 concentration in Dekalb County per million ton change in national level SO2 is 1.30 / 3.75, or 0.347 ug/m3 per million tons of SO2.
6. Using this relationship we develop an equation for Dekalb County PM2.5 concentration. This equation is as follows:
Concentration = 15.22 – 0.347 * (9.861 – National SO2 in Millions of Tons). Testing, we substitute the IAQR SO2 total in this equation:
Concentration = 15.22 – 0.347 * (9.861 – 6.111) = 13.92 ug/m3. This is the PM2.5 concentration calculated by REMSAD.
7. If, instead, the national total SO2 for a different case were 4 million tons, we would calculate as follows:
Concentration = 15.22 – 0.347 * (9.861 – 4.00) = 13.19 ug/m3. Other Counties
Each county has its own linear slope of PM2.5 concentration versus national SO2 totals. Counties close to each other will have slopes which are similar. Also, each county has a separate PM2.5 versus SO2 relationship for 2010 and 2015.
Using the model we can estimate the number of counties in nonattainment for PM2.5 in 2010 and 2015 for any level of national SO2 emissions.
Appendix 5
Benefits and Costs for the Alternate Control Scenario
The Clean Air Task Force contracted to have an additional IPM run performed evaluating the costs for a scenario which included much deeper emission reductions than those included in the EPA IAQR Proposal. This Alternate Control Scenario achieves SO2 levels of less than 2.8 million tons in 2010 and 2015 and NOx levels of 3.74 million tons in 2010 and 1.9 million tons in 2015. The incremental cost of this scenario (compared to the base case) is $8.16 billion in 2010 and $8.97 billion in 2015. The benefits of the Alternate Control Scenario have been show in a different part of these comments to be $99 billion in 2010 and $129 billion in 2015 (compared to the base case). The benefits far outweigh the costs – the benefit cost ratios are 12 in 2010 and over 14 in 2015.
Calculating the cost per ton of SO2 and NOx removed is complicated for the Alternate Control Scenario by virtue of the fact that we were not able to do additional IPM runs such as those done by EPA holding one pollutant constant while reducing the other so as to isolate the cost per ton of each pollutant. Therefore we have had to use some approximate methods to try to estimate the cost per ton removed for SO2 and NOx. SO2 and NOx Averaged Together
Looking at the EPA’s cost per ton calculations, we see that the cost per ton for SO2 and for NOx removal are fairly close (the biggest differential is in 2020 when the cost per ton for SO2 removal is $900 while the cost per ton for NOx removal is $700). Let us
assume that the costs are roughly equal for the Alternate Control Scenario. Then we can calculate the cost per ton by dividing the cost differential by the total reduction in SO2 plus NOx. Doing so gives a cost per ton removed (compared to the base case) of $1,125 per ton in 2010 and $1,050 per ton in 2015. This estimated cost of removal is for both SO2 and NOx.1
Worst Case for SO2
We can get the worst case cost estimate for SO2 by assuming that all of the
incremental costs of the Alternate Control Scenario are allocated to SO2 removal. This is clearly incorrect, but sets the upper limit of the cost of SO2 removal. Using this
approach we find that the cost of SO2 removal is $1,150 per ton in 2010 and $1,400 per ton in 2015 (compared to the base case).
Marginal Cost of Removal
1 This approach will not work if there is a very large difference in the cost per ton for SO2 and NOx.
However, all of the analyses done by EPA make it clear that there is not a large difference in cost per ton for these two pollutants in the range of removal under consideration here.
The marginal cost of removal is the cost per ton for the most expensive ton removed. This is always much higher than the average cost per ton removed. The IPM modeling gives the marginal cost of SO2 removal as $2,075 in 2010 and $1,960 in 2015. The marginal cost of NOx removal is $1,860 in 2010 and $1,610 in 2015. Typically, over most of the range of removals, the average cost is about half of the marginal cost. Using this approximation suggests an average cost of removal of about $1,000 per ton for SO2 and $800-$900 per ton for NOx.
Conclusions
We cannot say for sure what the precise cost per ton for SO2 removal and NOx removal are for the Alternate Control Scenario. But looking at the various approaches suggests strongly that the cost per ton (compared to the base case) would be close to $1,000-$1,100 per ton for SO2 in both 2010 and 2015, and $800-$1,000 per ton for NOx removal in both 2010 and 2015.
Prepared by David Schoengold MSB Energy Associates, Inc. For the Clean Air Task Force
Appendix 6
Cost Summary for CATF16 ( Million $)
2005 2010 2015 2020
Total Cost 77082 93873 104536 117048
Allowance Price for CATF16 ($/Ton)
2005 2010 2015 2020
SO2 Title IV 0 0 0 0
PM SO2 Constraint (28 States Plus DC) 0 2076 1958 2000 NOx SIPCall & PM (28 States Plus DC) 2176 1861 1608 1617
National MER ($/Lb) 0 0 0 0
CO2 0 0 0 0
Air Emissions for CATF16
2005 2010 2015 2020
SO2 [Thousand Tons] 11559 2798 2796 2800
NOX [Thousand Tons] 3797 3744 1903 1924
CO2 [Million Tonnes] 2204 2348 2477 2619
Carbon [Million Tonnes] 601 640 676 714
MER - Coal [Tons] 47 35 25 25
Fuel Consumption for CATF16 (TBtu)
2005 2010 2015 2020
Coal 20562 20818 20972 21185
Oil 0 0 0 0
Gas 5014 7365 9538 11834
Biomass 106 110 113 114
Delivered Fuel Prices for CATF16 ($/MMBtu)
2005 2010 2015 2020
Coal 1.16 1.07 1.02 0.95
Oil 0 0 0.00 0.00
Gas 2.97 3.31 3.13 3.04
Biomass 1.43 1.48 1.48 1.47
Wholesale Electric Prices for CATF16 (mills/kWh)
2005 2010 2015 2020
Total Generation for CATF16 (GWh) UnitType 2005 2010 2015 2020 Scrubbed Coal_NOx 1 199393 202138 202602 206662 Scrubbed Coal 2 432854 428265 287894 280493 Unscrubbed Coal_NOx 3 348154 15622 15738 15258 Unscrubbed Coal 4 815307 215449 197770 202554 Oil/Gas Steam 5 45910 60957 27307 19875 Oil/Gas Steam_Nox 6 1482 3148 3458 2395 Nuclear 7 785779 790910 792019 786984 Hydro 8 269380 269380 269380 269380 Comb.Cycle Gas 9 474619 665628 916968 1214223 IGCC 10 4702 4702 4702 4702 Turbine 11 19538 31108 52827 92054 Biomass 12 8886 9358 9873 10120 Geothermal 13 22429 23518 24571 24571 Landfill Gas 14 3958 5206 6609 7694 Wind 15 14310 16026 17327 18272 Fuel Cell 16 12 42 67 66 Solar 17 886 892 892 892 Non Fossil_Other 18 17936 17936 17936 17936 Fossil_Other 19 985 985 985 985 Pump Storage 20 9201 10056 11345 8989 Int. Imports 21 44616 28078 21538 22657 Cgn_Coal 22 27474 18834 18624 19032 Cgn_Gas 23 88879 125937 153950 156034 Cgn_Oil 24 3162 3175 2894 2894 Cgn_Other 25 7345 7112 7002 6941 NonCG IPP_Coal 26 0 0 0 0 NonCG IPP_Gas 27 0 0 0 0 NonCG IPP_Other 28 0 0 0 0 Blr_Coal 29 0 0 0 0 Blr_Gas 30 0 0 0 0 Blr_Oil 31 0 0 0 0 Blr_Other 32 0 0 0 0 SteamOnly Cogen 33 0 0 0 0 Rep.Coal-CC 34 0 1988 3591 3591 Rep.O/G-CC 35 0 34654 58564 60113 Rep.Coal-IGCC 36 0 0 0 1414 Ret.Scrubber 37 718 563787 119269 111459
Ret.ExistSCR & Scrub 38 0 285539 285644 285644
Ret.ExistSNCR & Scrub 39 0 42758 43641 44282
Ret.SCR 40 171043 801 19684 17715 Ret.ExistScrub & SCR 41 73137 73076 212548 216492 Ret.SNCR 42 0 0 0 1852 Ret.ExistScrub & SNCR 43 0 1664 3410 3505 Ret.SCR+Scrb 44 1298 227186 683543 705801 Ret.SNCR+Scrub 45 0 0 0 0 Ret.Gas Reburn 46 0 0 0 0 Ret.ExistScrub+GasR 47 0 0 0 0 Ret.GasReburn+Scrub 48 0 0 0 0
Ret.ACI 49 0 0 0 0 Ret.ExistScrub&ACI 50 0 0 0 0 Ret.ExistSCR&ACI 51 0 0 0 0 Ret.ExistSNCR&ACI 52 0 0 0 0 Ret.ACI & SCR 53 0 0 0 0 Ret.ExistScrub&ACI&SCR 54 0 0 0 0 Ret.ACI & SNCR 55 0 0 0 0 Ret.ExistScrub&ACI&SNCR 56 0 0 0 0
Ret.ACI & Srub 57 0 0 0 0
Ret.ExistSCR&ACI&Scrub 58 0 0 0 0
Ret.ExistSNCR&ACI&Scrub 59 0 0 0 0
Ret.ExistSNCR&ExistScrub&ACI 60 0 0 0 0
Ret.ExistSCR&ExistScrub&ACI 61 0 0 0 0
Ret.ExistNOx&ExistScrub&ACI 62 0 0 0 0
Ret.SCR & Scrub & ACI 63 0 0 0 0
Ret.SNCR & Scrub & ACI 64 0 0 0 0
Ret.O/G SCR 65 0 0 0 0
Ret.O/G SNCR 66 0 8062 12219 7365
Ret.Nuclear (age 30+10 yrs) 67 0 0 0 0
Ret.Nuclear (age 40+20 yrs) 68 0 0 0 0
Ret. Biomass Cofiring 69 0 0 0 0
CT Early Retirement 70 0 0 0 0
CC Early Retirement 71 0 0 0 0
O/G Early Retirement 72 0 0 0 0
Coal Early Retirement 73 0 0 0 0
Nuke Early Retirement 74 0 0 0 0
Total Capacity for CATF16 (MW) UnitType 2005 2010 2015 2020 Scrubbed Coal_NOx 1 27267 27267 27267 27779 Scrubbed Coal 2 61395 60489 41266 40981 Unscrubbed Coal_NOx 3 49018 2555 2505 2505 Unscrubbed Coal 4 124155 39223 35670 33988 Oil/Gas Steam 5 94043 89991 85411 85325 Oil/Gas Steam_Nox 6 7720 5668 5668 5668 Nuclear 7 99087 99289 99480 99594 Hydro 8 89869 89869 89869 89869 Comb.Cycle Gas 9 133549 137234 160216 198052 IGCC 10 612 612 612 612 Turbine 11 130505 131221 136242 165355 Biomass 12 1347 1404 1472 1510 Geothermal 13 2943 3086 3223 3223 Landfill Gas 14 502 670 858 1003 Wind 15 5078 5702 6159 6488 Fuel Cell 16 17 17 17 17 Solar 17 413 416 416 416 Non Fossil_Other 18 2275 2275 2275 2275 Fossil_Other 19 125 125 125 125 Pump Storage 20 22854 22854 22854 22854 Int. Imports 21 11000 11000 11000 11000 Cgn_Coal 22 3992 2852 2852 2852 Cgn_Gas 23 42570 42570 42570 42570 Cgn_Oil 24 1069 1069 1069 1057 Cgn_Other 25 1075 1075 1075 1075 NonCG IPP_Coal 26 0 0 0 0 NonCG IPP_Gas 27 0 0 0 0 NonCG IPP_Other 28 0 0 0 0 Blr_Coal 29 0 0 0 0 Blr_Gas 30 0 0 0 0 Blr_Oil 31 0 0 0 0 Blr_Other 32 0 0 0 0 SteamOnly Cogen 33 0 0 0 0 Rep.Coal-CC 34 0 251 521 521
Rep.O/G-CC 35 0 4376 7395 7590
Rep.Coal-IGCC 36 0 0 0 184
Ret.Scrubber 37 96 76877 16134 14970
Ret.ExistSCR & Scrub 38 0 38361 38361 38361
Ret.ExistSNCR & Scrub 39 0 5898 5947 5947
Ret.SCR 40 23600 378 3072 2666 Ret.ExistScrub & SCR 41 10082 10082 29043 29328 Ret.SNCR 42 0 0 0 387 Ret.ExistScrub & SNCR 43 0 256 518 518 Ret.SCR+Scrb 44 174 30792 92245 94983 Ret.SNCR+Scrub 45 0 0 0 0 Ret.Gas Reburn 46 0 0 0 0 Ret.ExistScrub+GasR 47 0 0 0 0 Ret.GasReburn+Scrub 48 0 0 0 0 Ret.ACI 49 0 0 0 0 Ret.ExistScrub&ACI 50 0 0 0 0 Ret.ExistSCR&ACI 51 0 0 0 0 Ret.ExistSNCR&ACI 52 0 0 0 0 Ret.ACI & SCR 53 0 0 0 0 Ret.ExistScrub&ACI&SCR 54 0 0 0 0 Ret.ACI & SNCR 55 0 0 0 0 Ret.ExistScrub&ACI&SNCR 56 0 0 0 0
Ret.ACI & Srub 57 0 0 0 0
Ret.ExistSCR&ACI&Scrub 58 0 0 0 0
Ret.ExistSNCR&ACI&Scrub 59 0 0 0 0
Ret.ExistSNCR&ExistScrub&ACI 60 0 0 0 0
Ret.ExistSCR&ExistScrub&ACI 61 0 0 0 0
Ret.ExistNOx&ExistScrub&ACI 62 0 0 0 0
Ret.SCR & Scrub & ACI 63 0 0 0 0
Ret.SNCR & Scrub & ACI 64 0 0 0 0
Ret.O/G SCR 65 0 0 0 0
Ret.O/G SNCR 66 0 3915 6986 6986
Ret.Nuclear (age 30+10 yrs) 67 0 0 0 0
Ret.Nuclear (age 40+20 yrs) 68 0 0 0 0
Ret. Biomass Cofiring 69 0 0 0 0
CT Early Retirement 70 331 331 331 331
O/G Early Retirement 72 28866 28866 28866 28866
Coal Early Retirement 73 6395 7769 7769 7769
Nuke Early Retirement 74 0 0 0 0
Appendix 6A National&RegionalPolicies
Appendix 6A Environmental Policy Specifications - CATF16
SIP Call & PM Rule SIP Call & PM Rule SIP Call & PM Rule PM Rule Title IV
NOx NOx NOx SO2 SO2 SIP Call States PM Region: 28 states + DC - Nox PM Region: 28 states + DC - SO2
Policy Type Cap Cap Cap Cap Cap Alabama Alabama Alabama
Trading Yes Yes Yes Yes Yes Connecticut Arkansas Arkansas
Banking Yes Yes Yes Yes Yes Delaware Delaware Delaware
Start 2005 2005 2005 2008 2005 Average District of Columbia District of Columbia District of Columbia
Season 2005-2009 (Summer), 2010- (Annual) Summer Summer Annual Annual Georgia Florida Florida
Scope SIP Call (starting 2005) and PMRegion (starting 2010) Connecticut Rhode Island PM region National SO2 Illinois Georgia Georgia
Allowance Use to
Emission Ratio Indiana Illinois Illinois
Affected Units All Fossil > 25 MW All Fossil > 25 MW All Fossil > 25 MW All Fossil > 25 MW All Fossil > 25 MW Kentucky Indiana Indiana
Units MTons MTons MTons MTons MTons Maryland Iowa Iowa
2005 503.2 15884 11608 Massachusetts Kansas Kansas
2006 503.2 9470 Michigan Kentucky Kentucky
2007 503.2 9470 Run Year 1 Missouri Louisiana Louisiana
2008 503.2 1840 9470 9098 New Jersey Maryland Maryland
2009 503.2 1840 9355 New York Massachusetts Massachusetts
2010 503.2 1840 8888 Run Year 2 North Carolina Michigan Michigan
2011 503.2 1840 8888 Ohio Minnesota Minnesota
2012 503.2 1840 8888 Pennsylvania Mississippi Mississippi
2013 1040.0 3.63 1.00 1840 8813 8813 Rhode Island Missouri Missouri
2014 1040.0 3.63 1.00 1840 8813 South Carolina New Jersey New Jersey
2015 1040.0 3.63 1.00 1840 8813 Run Year 3 Tennessee New York New York
2016 1040.0 3.63 1.00 1840 8813 Virginia North Carolina North Carolina
2017 1040.0 3.63 1.00 1840 8813 West Virginia Ohio Ohio
2018 1040.0 3.63 1.00 1840 8813 8813 Pennsylvania Pennsylvania
2019 1040.0 3.63 1.00 1840 8813 South Carolina South Carolina
2020 1040.0 3.63 1.00 1840 8813 Run Year 4 Tennessee Tennessee
2021 1040.0 3.63 1.00 1840 8813 Texas Texas
2022 1040.0 3.63 1.00 1840 8813 Virginia Virginia
2023 1040.0 3.63 1.00 1840 8813 West Virginia West Virginia
2024 1040.0 3.63 1.00 1840 8813 8813 Wisconsin Wisconsin 2025 1040.0 3.63 1.00 1840 8813 2026 1040.0 3.63 1.00 1840 8813 Run Year 5 2027 1040.0 3.63 1.00 1840 8813 2028 1040.0 3.63 1.00 1840 8813 2029 1040.0 3.63 1.00 1840 8813 2030 1040.0 3.63 1.00 1840 8813
Appendix 7
TABLE 1. PROJECTED PM2.5
DESIGN VALUES FOR 2010 (ug/m3)
Regional Alternate
Base Control Control
State County Case Strategy Scenario
--- --- --- --- --- --- Alabama DeKalb 15.22 13.92 12.77 Alabama Jefferson 20.03 18.85 17.81 Alabama Montgomery 15.69 14.60 13.64 Alabama Russell 17.07 15.77 14.62 Alabama Talladega 16.44 15.26 14.22
Connecticut New Haven 15.43 14.50 13.68
Delaware New Castle 15.43 14.12 12.96
District of Columbia District of Columbia 15.48 13.70 12.13 Georgia Clarke 17.04 15.56 14.25 Georgia Clayton 17.73 16.43 15.28 Georgia Cobb 16.80 15.56 14.46 Georgia DeKalb 18.26 16.92 15.74 Georgia Floyd 16.99 15.65 14.47 Georgia Fulton 19.79 18.37 17.12 Georgia Hall 15.62 14.24 13.02 Georgia Muscogee 16.68 15.41 14.29 Georgia Paulding 15.40 14.17 13.08 Georgia Richmond 15.99 14.65 13.47 Georgia Wilkinson 16.68 15.51 14.48 Illinois Cook 17.90 16.90 16.02 Illinois Madison 16.41 15.33 14.38 Illinois St. Clair 16.31 15.11 14.05 Illinois Will 15.21 14.25 13.40 Indiana Clark 15.86 14.34 13.00 Indiana Marion 15.89 14.39 13.06 Kentucky Fayette 15.21 13.55 12.08 Kentucky Jefferson 15.79 14.23 12.85
Maryland Baltimore City 16.58 14.82 13.27
Michigan Wayne 18.78 17.65 16.65
Missouri St. Louis City 15.25 14.14 13.16
New York New York 16.30 15.25 14.32
North Carolina Catawba 15.26 13.87 12.64
North Carolina Davidson 15.52 14.22 13.07
Ohio Butler 16.01 14.53 13.22 Ohio Cuyahoga 19.13 17.68 16.40 Ohio Franklin 16.69 15.04 13.58 Ohio Hamilton 17.75 15.96 14.38 Ohio Jefferson 18.04 16.06 14.31 Ohio Lawrence 15.48 13.67 12.07 Ohio Mahoning 15.39 13.76 12.32 Ohio Scioto 18.40 16.33 14.50 Ohio Stark 17.09 15.19 13.51 Ohio Summit 16.35 14.71 13.26 Ohio Trumbull 15.13 13.56 12.17 Pennsylvania Allegheny 19.52 16.92 14.62 Pennsylvania Berks 15.39 13.84 12.47 Pennsylvania Lancaster 15.46 13.71 12.16 Pennsylvania York 15.68 13.93 12.38
South Carolina Greenville 15.06 13.75 12.59
Tennessee Davidson 15.36 13.92 12.65
Tennessee Hamilton 16.14 14.74 13.50
Tennessee Knox 18.36 16.60 15.05
Tennessee Roane 15.18 13.69 12.37
Tennessee Sullivan 15.24 13.77 12.47
West Virginia Brooke 16.60 14.77 13.15
West Virginia Cabell 16.39 14.41 12.66
West Virginia Hancock 16.69 14.85 13.22
West Virginia Kanawha 17.11 14.81 12.78
West Virginia Marshall 15.53 13.25 11.24
West Virginia Wood 16.30 14.15 12.25
Total Number of Nonattainment Counties
TABLE 2. PROJECTED PM2.5
DESIGN VALUES FOR 2015 (ug/m3)
Regional Alternate
Base Control Control
State County Case Strategy Scenario
--- --- --- --- --- ---
Alabama Jefferson 19.57 18.11 17.07
Alabama Montgomery 15.35 14.05 13.12
Alabama Russell 16.68 15.05 13.89
Alabama Talladega 15.97 14.57 13.57
Connecticut New Haven 15.13 14.13 13.42
Georgia Clarke 16.46 14.58 13.24 Georgia Clayton 17.26 15.49 14.23 Georgia Cobb 16.28 14.37 13.01 Georgia DeKalb 17.93 16.22 15.00 Georgia Floyd 16.51 14.71 13.42 Georgia Fulton 19.44 17.62 16.32 Georgia Hall 15.05 13.16 11.81 Georgia Muscogee 16.31 14.71 13.57 Georgia Richmond 15.51 13.82 12.61 Georgia Wilkinson 16.40 14.88 13.79 Illinois Cook 17.52 16.40 15.60 Illinois Madison 16.03 14.88 14.06 Illinois St. Clair 15.91 14.67 13.78 Indiana Clark 15.40 13.69 12.47 Indiana Marion 15.31 13.79 12.70 Kentucky Jefferson 15.32 13.57 12.32
Maryland Baltimore City 16.11 14.20 12.84
Michigan Wayne 18.28 17.06 16.19
New York New York (Manhattan) 15.82 14.69 13.88
Ohio Butler 15.39 13.77 12.61 Ohio Cuyahoga 18.58 17.05 15.96 Ohio Franklin 16.18 14.46 13.23 Ohio Hamilton 17.07 15.15 13.78 Ohio Jefferson 17.49 15.51 14.10 Ohio Scioto 17.62 15.49 13.97 Ohio Stark 16.42 14.52 13.16 Ohio Summit 15.78 14.14 12.97 Pennsylvania Allegheny 18.64 16.09 14.27 Pennsylvania York 15.13 13.26 11.92 Tennessee Hamilton 15.63 13.91 12.68 Tennessee Knox 17.73 15.59 14.06
West Virginia Brooke 16.10 14.26 12.95
West Virginia Hancock 16.18 14.33 13.01
West Virginia Kanawha 16.45 14.10 12.42
West Virginia Wood 15.58 13.49 12.00
Total Number of Nonattainment Counties
1
IAQR Projected 2015 Control Technologies
can be Installed by 2010
The time and resources required to install air pollution control equipment has historically been one of the primary subjects of debate for proposed environmental regulations. That debate has again arisen in some of US EPA’s more recent regulatory efforts including the Clear Skies Initiative and Interstate Air Quality Rule (IAQR). More specifically, the resource that EPA has identified as the most limiting in regards to the implementation of regional or national emission reduction programs is boilermaker labor. Predominantly employed in the power industry, boilermakers are skilled laborers that perform welding, rigging and hoisting for the construction and maintenance of boilers and high pressure vessels. They are an integral part of the
construction of certain types of air pollution control equipment. During the late 1990s, their numbers declined significantly raising concerns during the NOx SIP call as to whether there would be enough boilermaker laborers to complete the installation of air pollution control equipment.
The concern that there will be enough boilermaker laborers to implement the proposed EPA rules has again been raised. The following discussion will provide information indicating that there will be enough boilermaker labor available to implement the Interstate Air Quality Rule.
Further, it is projected that there will be enough boilermaker labor to implement the 2015 targets of the IAQR rulemaking in the 2010 timeframe. Below is a short list of topics that will be discussed in further detail supporting these statements.
• Installation Experience • Trade Crossover • Labor Utilization • Installation Timing
Installation Experience
The air pollution control (APC) industry has extensive
experience both in the US and worldwide with the installation of SO2 and NOx control technologies for the electric power sector. This experience has resulted in improved designs and higher equipment reliability.1 Over the last three decades in the US, vendors and power plant operators have gained experience with the control of SO2 from coal-fired power plants through
Table 1. SCR Start-Up Dates on Coal-Fired Units in U.S. 1660 L Street NW
Suite 1100
Washington, DC 20036-5603 Telephone 202.457.0911 Fax 202.331.1388
David C. Foerter, Executive Director
Email: dfoerter@icac.com
Chad S. Whiteman, Deputy Director
2 0 10 20 30 40 50 60 70 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 Capacity (GW)
Figure 3. Natural Gas Capacity Additions from 1960 - 2010a
the installation and operation of over 100 GW of flue gas desulfurization (FGD) systems. For SCR systems in the US, a few installations were in place in the mid-1990s with the majority of the close to 100 GW of SCR installations taking place in the last four years. According to the National Association of Construction Boilermaker Employers (NACBE), the boilermaker labor demands due to the NOx SIP call accounted for about 38 million union boilermaker man-hours between 2000-2004, with an estimated 22 million (close to 60%) being used in the 2001-2002 time period. This is significant, in that the requirements for the IAQR will require a large
number of control installations in a similarly short time period. Table 1 demonstrates that the air pollution control industry has the ability and labor force available as 63 GW of SCR,
approximately 111 units, were started-up from 2001 through 2003. The overall experience gained from SCR installations equates to almost ten years of operational and installation experience with SCR systems on coal-fired power plants in the US alone.2
Outside of the US, there is also extensive experience with the installation of SO2 and NOx controls on coal-fired units. It is estimated that over 125 GW of FGD installations and close to 90 GW of SCR installations have been completed in Japan and Europe.3 Figure 2 exemplifies that Germany has installed a large numbers of SCR systems over a short period of time as 97 of their 137 units were installed during two consecutive years (1989-1990).4 This pattern of installations demonstrates that the air pollution control industry is able to quickly respond to environmental regulations that require a surge of control installations in a short period of time.
In addition to the large number of SCRs installed from 2001 to 2004, boilermakers were also working on an unusually high number of natural gas-fired power plants during the same period. In the three decades prior to 2000, an average of 5 GW of new natural gas capacity was
constructed in the United States per year. However, in the period from 2000 to 2004 there was on average 41 GW of new natural gas-fired power plant builds for a total of 205 GW as shown in Figure 3. Even more impressive is the fact that the peak year for new gas power plant builds, 60 GW in 2003, coincides with the peak year for SCR start-ups for the NOx SIP call. The surge in builds was due to a number of market forces including lower natural gas prices making gas power plants cost effective builds and increased consumer demand for electricity combined with low electricity reserve margins. The reserve margins have increased to levels where a large number of the natural gas plants planned for the next several years have been canceled. The
0 10 20 30 40 50 60 1985 1986 1987 1988 1989 1990 1991 1992
# of Installations Per Year
3
future projections for new gas power plant demand by the Department of Energy, suggest that natural gas power plant builds will return to the approximate levels, 8 GW per year, seen before the building surge that began in 2000.
Trade Crossover
In general, boilermakers in the construction division are trained to assemble, erect and maintain boilers, tanks, pressure vessels, heat exchangers, pollution control systems, furnaces and other pieces of equipment that require hoisting, rigging, and welding. Boilermakers are used in
numerous industries but are predominantly used in the electric power sector which has accounted for between 52 – 73 % of their demand over the last decade. During the last several years, due to the installation of SCRs for the NOx SIP call, electric power sector demand for boilermaker labor has been as high as 73 % of their total demand.
In order to become a union journeyman, the prospective worker will typically enroll in a 3.5 to 4 year training program offered by the International Brotherhood of Boilermakers, Iron
Shipbuilders, Blacksmiths, Forgers and Helpers. The apprenticeship program provides both classroom as well as on-the-job training. Membership in the boilermaker union has varied considerably over the last decade depending on the market demand for their services. During the past several years the boilermaker numbers have quickly expanded due to the demand exerted by the construction of SCRs required for the NOx SIP call and construction of natural gas power plants to meet the increasing demand for electricity. As shown in Figure 4, there was more than a 10,000 member increase in the union boilermaker numbers in a two year period from 1999 -2001. The rapid increase in membership over a short period of time demonstrates that the boilermaker union is able to respond to abrupt changes in market demand. Additionally, the boilermaker’s union in Canada has 4000 members of which some workers would be available to work on these environmental projects. Air pollution control vendors have made arrangements to use Canadian labor and have expedited immigration agreements in place to quickly move
Canadian boilermakers to the US should a labor shortage arise.
The number of boilermakers in the construction division is expected to decrease slightly in 2004 to approximately 26,000 members but is expected to rise to between 28,000 to 30,000 members by 2006. The expected increase in numbers are in large part due to the projected number of jobs
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 1994 1996 1998 2000 2002 2004 2006 2008 2010 Active Members
4
due to air pollution control equipment work from EPA’s proposed rule and less significantly to new power plant construction. This demand will provide long term work opportunities that will attract and maintain workers in this field. When the demand for labor in the construction division grows, members of the other boilermaker divisions such as the iron, steel, and ship builders divisions are able to move to the construction division. The sister divisions have skilled laborers that perform similar work as the construction division which would negate the need for the full four years of apprenticeship training that is typically required in order to become a journeyman. There are around 150,000 members in the iron and steelworkers division and another 30,000 members in the ship builders division providing a large field of potential labor.5 The higher pay found more recently in the boilermaker construction division would also be an incentive for this crossover into the boilermaker division. With the drastic increase of 10,000 new boilermakers in such a short period of time, there were instances where the cost of construction increased due to overtime pay or wage increases to boilermaker laborers. Due to these increases, EPA adjusted their cost estimates for SCR installations in their modeling analysis for the IAQR. Labor cost increases are not expected to occur under the IAQR as EPA projects that only a 2,000 member increase in boilermaker numbers may occur during the first few years of control technology installations.
Labor Utilization
Another part of the labor equation that needs to be accounted for is the type of construction method implemented for air pollution control equipment. In general, performing fabrication work on the ground reduces the amount of field labor hours needed for a project. Large sections of equipment can be assembled into modules in less time on the ground than if each individual piece were hoisted into the air by a crane and welded or bolted into place. If modular
construction is chosen, up to a 30% savings in boilermaker labor may be realized on a particular project. The decision to use modular construction is typically driven by cost so as the labor demand increases, the pressure to perform modular construction will likely increase with it. Modularization will look especially favorable in states that have deregulated electricity markets.a In these States, there will be an added incentive to cut costs as the cost of air pollution controls cannot be passed on to the ratepayer. EPA projects that there will be approximately 26 GW of SCR and 37 GW of FGD installations, displayed in Figure 5 below, required in these states by 2015.b
a The States affected by the IAQR program that have retail electricity markets include: Connecticut, Delaware,
Illinois, Maryland, Massachusetts, Michigan, New Jersey, New York, Ohio, Pennsylvania, Texas, and Virginia. The retail market information was obtained from a February 2003 EIA document at
http://www.eia.doe.gov/cneaf/electricity/chg_str/regmap.html.
b This includes the cumulative total number of control installations due to the current set of regulations modeled in
the IPM model (e.g. NOx SIP call, State specific regulations, Acid Rain Program, etc.) as well as the proposed IAQR and UMRR.
0 2,000 4,000 6,000 8,000 10,000 12,000 CT IL MD MA NJ NY OH PA TX VA Capacity (MW) SCR by 2015 FGD by 2015
Figure 5. Projected SCR and FGD Installations in States with Deregulated Electricity Markets
0 2,000 4,000 6,000 8,000 10,000 12,000 AL AR FL GA IA KY LA NC SC TX Capacity (MW) SCR by 2015 FGD by 2015
Figure 6. Projected FGD Installations in States with Traditional Non-Union Labor Markets
5
Additionally, some states have less union presence than others which means that less union labor is used in certain States than in others. This means that the labor pool for skilled crafts such as boilermakers, electricians, etc., is larger than merely the national number for union members. For instance, it is estimated that as much as 50% of heavy machinery construction labor is performed by non-union members. Non-union members are also employed in merit shops, where craft workers and subcontractors are hired regardless of their labor union affiliation. It is estimated that merit shop workers will help reduce demand by 30-40% in non-union areas. For the IAQR, there are ten States that have traditionally relied on non-union labor.c Figure 6 demonstrates that in these States, there are approximately 42 GW of SCR and 25 GW of FGD installations projected to occur under the IAQR by 2015.
Installation Timing
One of the constraining assumptions that EPA makes concerning the usage of boilermakers is the assumed time that boilermakers will have to construct the equipment so that it will be in place for the compliance deadline. When EPA proposed the IAQR, they expected to release the final rule in June 2005 but EPA has since moved up that timeline by six months to a December 2004 release of the final rule.6 The purpose for doing this was to harmonize the release of the IAQR final rule with the release of the final Utility Mercury Reduction Rule (UMRR). According to the proposal, EPA plans to give States 18 months to have approved SIPs in place, this would mean that affected sources would know their compliance obligations by July 2006 with
compliance starting in January 2010. This has increased the time window for compliance from 36 to 42 months as shown in the Figure 8 scenario titled “EPA Revised.” EPA further assumes that during the first 15 months of the 42 month compliance period there will be no boilermaker construction work taking place. EPA assumes that this 15 month period will be used for designing the control equipment and installing foundations. They assume that boilermaker construction period will consist of 24 months from October 2007 lasting until September 2009. EPA also assumed that only 18 months of the 42 month compliance period would allow for boilermaker construction activities. With the release of the supplemental rule information, EPA moved the release date for the final rule forward six months to December 2004. This increases the time for boilermaker construction activities from 18 to 24 months thus spreading out their demand over a longer period of time. Beginning in October 2009, EPA assumed that all
boilermaker construction will be complete allowing for three months of testing and optimization of the technologies before compliance starts in January 2010.
In order to increase the time available for control equipment construction beyond 24 months, ICAC would recommend that EPA provide a 12 month window as opposed to the proposed 18 month window for States to get their SIPs approved. This would increase the compliance
window from 42 to 48 months and provide 30 months, an additional six months, for boilermaker construction activities. As recently as a few years ago, EPA allowed States 12 months to submit their SIPs under the NOx SIP call requirements. Since all of the affected States are currently participating in the Acid Rain Program, a national SO2 trading program, and all but ten States are participating in the NOx SIP call, a northeastern regional NOx trading program, it will be easier for States to complete their rules. Additionally, the affected sources have been monitoring
c Alabama, Arkansas, Florida, Georgia, Iowa, Kentucky, Louisiana, North Carolina, South Carolina, and Texas are
States that have traditionally been non-union States. Some of these States (e.g. Alabama, Georgia, Iowa and Texas) use a mix of union and non-union labor.
