Emergency department visits for asthma
                                   0.001900
                                   0.000190
                                   0.007800
                                   0.001300
                                   0.000290
                                   0.000031
                                   0.001200
                                   0.000470
                                   0.005300
                                   0.001400
                                   0.022000
                                   0.004200
 Acute bronchitis
                                   0.005400
                                   0.000510
                                   0.023000
                                   0.003700
                                   0.001300
                                   0.000200
                                   0.005200
                                   0.002100
                                   0.019000
                                   0.005000
                                   0.077000
                                   0.015000
 Lower respiratory symptoms
                                   0.069000
                                   0.006500
                                   0.300000
                                   0.047000
                                   0.016000
                                   0.002500
                                   0.067000
                                   0.026000
                                   0.240000
                                   0.064000
                                   0.970000
                                   0.190000
 Upper respiratory symptoms
                                   0.098000
                                   0.009300
                                   0.420000
                                   0.068000
                                   0.023000
                                   0.003600
                                   0.095000
                                   0.038000
                                   0.340000
                                   0.092000
                                   1.400000
                                   0.270000
 Minor restricted-activity days
                                   2.700000
                                   0.250000
                                   11.000000
                                   1.900000
                                   0.580000
                                   0.078000
                                   2.400000
                                   0.920000
                                   9.400000
                                   2.200000
                                   35.000000
                                   6.800000
 Lost work days
                                   0.450000
                                   0.043000
                                   1.900000
                                   0.310000
                                   0.098000
                                   0.013000
                                   0.410000
                                   0.160000
                                   1.600000
                                   0.380000
                                   6.000000
                                   1.100000
 Asthma exacerbation
                                   0.240000
                                   0.023000
                                   1.000000
                                   0.170000
                                   0.056000
                                   0.008800
                                   0.230000
                                   0.091000
                                   0.840000
                                   0.220000
                                   3.400000
                                   0.650000
 Hospital Admissions, Respiratory
                                   0.001100
                                   0.000100
                                   0.004500
                                   0.000720
                                   0.000150
                                   0.000015
                                   0.000640
                                   0.000260
                                   0.002500
                                   0.000580
                                   0.009400
                                   0.001900
 Hospital Admissions, Cardiovascular
                                   0.001300
                                   0.000120
                                   0.005600
                                   0.000910
                                   0.000200
                                   0.000019
                                   0.000820
                                   0.000330
                                   0.003000
                                   0.000680
                                   0.011000
                                   0.002200
 Non-fatal Heart Attacks (Peters)
                                   0.004100
                                   0.000390
                                   0.018000
                                   0.002800
                                   0.000650
                                   0.000064
                                   0.002800
                                   0.001200
                                   0.011000
                                   0.002400
                                   0.041000
                                   0.007900
 Non-fatal Heart Attacks (All others)
                                   0.000450
                                   0.000042
                                   0.001900
                                   0.000310
                                   0.000070
                                   0.000007
                                   0.000300
                                   0.000130
                                   0.001100
                                   0.000260
                                   0.004400
                                   0.000850
* All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion from precursor emissions to ambient fine particles.

Table 4A-7.	Summary of Regional PM2.5 Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025*
                                Health Endpoint
                                     East
                                     West
                                  California

                                      SO2
                                      NOx
                                     EC+OC
                                    Crustal
                                      SO2
                                      NOx
                                     EC+OC
                                    Crustal
                                      SO2
                                      NOx
                                     EC+OC
                                    Crustal
Premature Mortality

 Krewski et al. (2009)  -  adult
                                   0.003900
                                   0.000350
                                   0.017000
                                   0.002700
                                   0.000750
                                   0.000079
                                   0.003200
                                   0.001300
                                   0.011000
                                   0.002600
                                   0.044000
                                   0.008700
 Lepeule et al. (2012)  -  adult
                                   0.008900
                                   0.000800
                                   0.038000
                                   0.006200
                                   0.001700
                                   0.000180
                                   0.007300
                                   0.003000
                                   0.026000
                                   0.005800
                                   0.099000
                                   0.020000
 Woodruff et al. (1997)  -  infants
                                   0.000008
                                   0.000001
                                   0.000035
                                   0.000006
                                   0.000002
                                   0.000000
                                   0.000007
                                   0.000003
                                   0.000022
                                   0.000007
                                   0.000093
                                   0.000018
Morbidity

 Emergency department visits for asthma
                                   0.002000
                                   0.000200
                                   0.006300
                                   0.001300
                                   0.000320
                                   0.000033
                                   0.001000
                                   0.000510
                                   0.005500
                                   0.001500
                                   0.018000
                                   0.004400
 Acute bronchitis
                                   0.005700
                                   0.000520
                                   0.024000
                                   0.003900
                                   0.001300
                                   0.000210
                                   0.005600
                                   0.002200
                                   0.020000
                                   0.005300
                                   0.080000
                                   0.015000
 Lower respiratory symptoms
                                   0.072000
                                   0.006700
                                   0.310000
                                   0.050000
                                   0.017000
                                   0.002700
                                   0.071000
                                   0.028000
                                   0.250000
                                   0.067000
                                   1.000000
                                   0.200000
 Upper respiratory symptoms
                                   0.100000
                                   0.009600
                                   0.440000
                                   0.071000
                                   0.024000
                                   0.003800
                                   0.100000
                                   0.040000
                                   0.360000
                                   0.096000
                                   1.500000
                                   0.280000
 Minor restricted-activity days
                                   2.800000
                                   0.250000
                                   12.000000
                                   1.900000
                                   0.610000
                                   0.083000
                                   2.500000
                                   0.970000
                                   9.600000
                                   2.300000
                                   36.000000
                                   6.900000
 Lost work days
                                   0.470000
                                   0.043000
                                   2.000000
                                   0.320000
                                   0.100000
                                   0.014000
                                   0.430000
                                   0.160000
                                   1.600000
                                   0.390000
                                   6.100000
                                   1.200000
 Asthma exacerbation
                                   0.250000
                                   0.023000
                                   1.100000
                                   0.170000
                                   0.059000
                                   0.009300
                                   0.250000
                                   0.097000
                                   0.880000
                                   0.230000
                                   3.500000
                                   0.680000
 Hospital Admissions, Respiratory
                                   0.001200
                                   0.000110
                                   0.005100
                                   0.000810
                                   0.000180
                                   0.000017
                                   0.000740
                                   0.000300
                                   0.002800
                                   0.000650
                                   0.011000
                                   0.002100
 Hospital Admissions, Cardiovascular
                                   0.001400
                                   0.000130
                                   0.006200
                                   0.001000
                                   0.000220
                                   0.000022
                                   0.000930
                                   0.000380
                                   0.003300
                                   0.000750
                                   0.012000
                                   0.002400
 Non-fatal Heart Attacks (Peters)
                                   0.004600
                                   0.000430
                                   0.020000
                                   0.003100
                                   0.000740
                                   0.000071
                                   0.003200
                                   0.001300
                                   0.012000
                                   0.002700
                                   0.046000
                                   0.008900
 Non-fatal Heart Attacks (All others)
                                   0.000490
                                   0.000046
                                   0.002100
                                   0.000340
                                   0.000080
                                   0.000008
                                   0.000340
                                   0.000140
                                   0.001300
                                   0.000290
                                   0.004900
                                   0.000950
* All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion from precursor emissions to ambient fine particles.

Table 4A-8.	Summary of Regional PM2.5 Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2030*
                                Health Endpoint
                                     East
                                     West
                                  California

                                      SO2
                                      NOx
                                     EC+OC
                                    Crustal
                                      SO2
                                      NOx
                                     EC+OC
                                    Crustal
                                      SO2
                                      NOx
                                     EC+OC
                                    Crustal
Premature Mortality

 Krewski et al. (2009)  -  adult
                                   0.004200
                                   0.000380
                                   0.018000
                                   0.002900
                                   0.000840
                                   0.000087
                                   0.003600
                                   0.001500
                                   0.013000
                                   0.002800
                                   0.048000
                                   0.009600
 Lepeule et al. (2012)  -  adult
                                   0.009600
                                   0.000850
                                   0.041000
                                   0.006700
                                   0.001900
                                   0.000200
                                   0.008100
                                   0.003400
                                   0.029000
                                   0.006400
                                   0.110000
                                   0.022000
 Woodruff et al. (1997)  -  infants
                                   0.000008
                                   0.000001
                                   0.000033
                                   0.000005
                                   0.000002
                                   0.000000
                                   0.000007
                                   0.000003
                                   0.000021
                                   0.000006
                                   0.000088
                                   0.000017
Morbidity

 Emergency department visits for asthma
                                   0.001600
                                   0.000160
                                   0.006600
                                   0.001100
                                   0.000260
                                   0.000027
                                   0.001100
                                   0.000420
                                   0.004500
                                   0.001200
                                   0.019000
                                   0.003500
 Acute bronchitis
                                   0.005900
                                   0.000540
                                   0.025000
                                   0.004100
                                   0.001400
                                   0.000220
                                   0.005900
                                   0.002300
                                   0.021000
                                   0.005400
                                   0.083000
                                   0.016000
 Lower respiratory symptoms
                                   0.075000
                                   0.006800
                                   0.320000
                                   0.052000
                                   0.018000
                                   0.002800
                                   0.075000
                                   0.030000
                                   0.260000
                                   0.069000
                                   1.100000
                                   0.200000
 Upper respiratory symptoms
                                   0.110000
                                   0.009800
                                   0.460000
                                   0.074000
                                   0.026000
                                   0.004000
                                   0.110000
                                   0.042000
                                   0.370000
                                   0.099000
                                   1.500000
                                   0.290000
 Minor restricted-activity days
                                   2.900000
                                   0.260000
                                   12.000000
                                   2.000000
                                   0.650000
                                   0.088000
                                   2.700000
                                   1.000000
                                   9.800000
                                   2.300000
                                   37.000000
                                   7.100000
 Lost work days
                                   0.480000
                                   0.043000
                                   2.000000
                                   0.330000
                                   0.110000
                                   0.015000
                                   0.450000
                                   0.170000
                                   1.700000
                                   0.400000
                                   6.300000
                                   1.200000
 Asthma exacerbation
                                   0.260000
                                   0.024000
                                   1.100000
                                   0.180000
                                   0.063000
                                   0.009800
                                   0.260000
                                   0.100000
                                   0.920000
                                   0.240000
                                   3.700000
                                   0.710000
 Hospital Admissions, Respiratory
                                   0.001300
                                   0.000120
                                   0.005600
                                   0.000900
                                   0.000200
                                   0.000019
                                   0.000830
                                   0.000340
                                   0.003200
                                   0.000740
                                   0.012000
                                   0.002400
 Hospital Admissions, Cardiovascular
                                   0.001600
                                   0.000150
                                   0.006800
                                   0.001100
                                   0.000250
                                   0.000024
                                   0.001000
                                   0.000420
                                   0.003800
                                   0.000850
                                   0.014000
                                   0.002700
 Non-fatal Heart Attacks (Peters)
                                   0.005000
                                   0.000460
                                   0.021000
                                   0.003500
                                   0.000830
                                   0.000079
                                   0.003600
                                   0.001500
                                   0.014000
                                   0.003100
                                   0.052000
                                   0.010000
 Non-fatal Heart Attacks (All others)
                                   0.000540
                                   0.000049
                                   0.002300
                                   0.000370
                                   0.000090
                                   0.000009
                                   0.000380
                                   0.000160
                                   0.001500
                                   0.000330
                                   0.005600
                                   0.001100
* All estimates are rounded to two significant figures. All fine particles are assumed to have equivalent health effects, but the incidence-per-ton estimates vary depending on the location and magnitude of their impact on PM2.5 levels, which drive population exposure. The incidence benefit-per-ton estimates incorporate the conversion from precursor emissions to ambient fine particles.
4A.4	Regional Ozone Benefit-per-Ton Estimates
The process for generating the regional ozone benefit-per-ton estimates is consistent with the process for PM2.5. Ozone is not directly emitted, and is a non-linear function of NOx and VOC emissions. For the purpose of estimating benefit-per-ton for this RIA, we assume that all of the ozone impacts from EGUs are attributable to NOx emissions. VOC emissions, which are also a precursor to ambient ozone formation, are insignificant from the EGU sector relative to both NOx emissions from EGUs and the total VOC emissions inventory. Therefore, we believe that our assumption that EGU-attributable ozone formation at the regional-level is due to NOx alone is reasonable.
Similar to PM2.5, this method provides estimates of the regional average benefit-per-ton. Due to the non-linear chemistry between NOx emissions and ambient ozone, using an average benefit-per-ton estimate for NOx adds uncertainty to the ozone co-benefits estimated for the proposed guidelines. Because most of the estimated co-benefits for the proposed guidelines are attributable to changes in ambient PM2.5, the added uncertainty is likely to be small.
In the ozone co-benefits estimated in this RIA, we apply the benefit-per-ton estimates calculated using NOX emissions derived from modeling the Clean Power Plan proposal during the ozone-season only (May to September). As shown in Table 4A-1, ozone-season NOx emissions from EGUs are slightly less than half of all-year NOX emissions. Because we estimate ozone health impacts from May to September only, this approach underestimates ozone co-benefits in areas with longer ozone seasons such as southern California and Texas. When the underestimated benefit-per-ton estimate is multiplied by ozone-season only NOx emission reductions, this results in an underestimate of the monetized ozone co-benefits. For illustrative purposes, Tables 4A-9 through 4A-11 provide the ozone benefit-per-ton estimates using both all-year NOx emissions and ozone-season only NOx for 2020, 2025, and 2030, respectively. Tables 4A-12 through 4A-14 provide the ozone season incidence-per-ton estimates for 2020, 2025, and 2030, respectively. Similar to PM2.5, the ozone benefit-per-ton values for 2020 and 2030 are based on applying the air quality modeling from 2025 to population and health information from 2020 and 2030.  Estimated benefit-per-ton for these years have additional uncertainty relative to 2025 because of potential differences in atmospheric responses to reductions in ozone precursors in those years.  Uncertainties may be somewhat larger in the case of ozone due to high degree of dependence of ozone responses to baseline meteorology and emissions levels.

Table 4A-9.	Summary of Regional Ozone Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020 (2011$)*
                           Ozone precursor Pollutant
                                   National
                                   Regional

                                     East
                                     West
                                  California
                               Ozone season NOx
                               $6,000 to $26,000
                               $6,500 to $28,000
                               $2,000 to $8,900
                              $14,000 to $59,000
* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for ozone. All estimates are rounded to two significant figures. The monetized benefits incorporate the conversion from NOx precursor emissions to ambient ozone.

Table 4A-10.	Summary of Regional Ozone Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025 (2011$)*
                           Ozone precursor Pollutant
                                   National
                                   Regional

                                     East
                                     West
                                  California
                               Ozone season NOx
                               $6,600 to $27,000
                               $7,100 to $30,000
                               $2,300 to $10,000
                              $15,000 to $66,000
* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for ozone. All estimates are rounded to two significant figures. The monetized benefits incorporate the conversion from NOx precursor emissions to ambient ozone.

Table 4A-11.	Summary of Regional Ozone Benefit-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2030 (2011$)*
                           Ozone precursor Pollutant
                                   National
                                   Regional

                                     East
                                     West
                                  California
                               Ozone season NOx
                               $7,100 to $29,000
                               $7,600 to $33,000
                               $2,600 to $11,000
                              $17,000 to $73,000
* The range of estimates reflects the range of epidemiology studies for avoided premature mortality for ozone. All estimates are rounded to two significant figures. The monetized benefits incorporate the conversion from NOx precursor emissions to ambient ozone.

Table 4A-12.	Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2020*
Health Endpoint
                                     East
                                     West
                                  California
Premature Mortality  -  adult

  Bell et al. (2004)
                                   0.000600
                                   0.000190
                                   0.001300
  Levy et al. (2005)
                                   0.002800
                                   0.000880
                                   0.005800
Morbidity

  Hospital Admissions, Respiratory (ages > 65)
                                   0.003500
                                   0.000900
                                   0.006600
  Hospital Admissions, Respiratory (ages < 2)
                                   0.001800
                                   0.000780
                                   0.003300
  Emergency Room Visits, Respiratory
                                   0.002000
                                   0.000500
                                   0.003900
  Acute Respiratory Symptoms
                                   3.500000
                                   1.300000
                                   8.800000
  School Loss Days
                                   1.200000
                                   0.490000
                                   3.000000
* All estimates are rounded to two significant figures. The incidence benefit-per-ton estimates incorporate the conversion from NOx precursor emissions to ambient ozone. These estimates reflect ozone-season NOx emissions.

Table 4A-13.	Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2025*
Health Endpoint
                                     East
                                     West
                                  California
Premature Mortality  -  adult

  Bell et al. (2004)
                                   0.000640
                                   0.000210
                                   0.001400
  Levy et al. (2005)
                                   0.002900
                                   0.000970
                                   0.006400
Morbidity

  Hospital Admissions, Respiratory (ages > 65)
                                   0.004100
                                   0.001100
                                   0.007800
  Hospital Admissions, Respiratory (ages < 2)
                                   0.001800
                                   0.000820
                                   0.003400
  Emergency Room Visits, Respiratory
                                   0.002000
                                   0.000540
                                   0.004100
  Acute Respiratory Symptoms
                                   3.600000
                                   1.400000
                                   8.900000
  School Loss Days
                                   1.300000
                                   0.520000
                                   3.200000
* All estimates are rounded to two significant figures. The incidence benefit-per-ton estimates incorporate the conversion from NOx precursor emissions to ambient ozone. These estimates reflect ozone-season NOx emissions.

Table 4A-14.	Summary of Regional Ozone Incidence-per-Ton Estimates Based on Air Quality Modeling from Proposed Clean Power Plan in 2030*
Health Endpoint
                                     East
                                     West
                                  California
Premature Mortality  -  adult

  Bell et al. (2004)
                                   0.000640
                                   0.000230
                                   0.001800
  Levy et al. (2005)
                                   0.002900
                                   0.001100
                                   0.008200
Morbidity

  Hospital Admissions, Respiratory (ages > 65)
                                   0.004400
                                   0.001300
                                   0.011000
  Hospital Admissions, Respiratory (ages < 2)
                                   0.001800
                                   0.000860
                                   0.004100
  Emergency Room Visits, Respiratory
                                   0.002000
                                   0.000580
                                   0.005000
  Acute Respiratory Symptoms
                                   3.500000
                                   1.500000
                                   11.000000
  School Loss Days
                                   1.200000
                                   0.550000
                                   3.800000
* All estimates are rounded to two significant figures. The incidence benefit-per-ton estimates incorporate the conversion from NOx precursor emissions to ambient ozone. These estimates reflect ozone-season NOx emissions.

4A.6	References
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Bell, M.L., A. McDermott, S.L. Zeger, J.M. Sarnet, and F. Dominici. 2004. "Ozone and Short-Term Mortality in 95 U.S. Urban Communities, 1987-2000." Journal of the American Medical Association. 292(19):2372-8.
Bell, M.L., F. Dominici, and J.M. Samet. 2005. "A Meta-Analysis of Time-Series Studies of Ozone and Mortality with Comparison to the National Morbidity, Mortality, and Air Pollution Study." Epidemiology. 16(4):436-45.
ENVIRON International Corporation. 2014. User's Guide: Comprehensive Air Quality Model with Extensions, Version 6.1, Novato, CA. April. Available at <http://www.camx.com>. Accessed June 6, 2015.
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Fann, N., K.R. Baker, and C.M. Fulcher. 2012. "Characterizing the PM2.5-Related Health Benefits of Emission Reductions for 17 Industrial, Area and Mobile Emission Sectors Across the U.S." Environment International. 49:41 - 151.
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Krewski D., M. Jerrett, R.T. Burnett, R. Ma, E. Hughes, Y. Shi, et al. 2009. Extended Follow-Up and Spatial Analysis of the American Cancer Society Study Linking Particulate Air Pollution and Mortality. HEI Research Report, 140, Health Effects Institute, Boston, MA.
Lepeule, J., F. Laden, D. Dockery, and J. Schwartz. 2012. "Chronic Exposure to Fine Particles and Mortality: An Extended Follow-Up of the Harvard Six Cities Study from 1974 to 2009." Environmental Health Perspectives. 120(7):965-70.
Levy, J.I., S.M. Chemerynski, and J.A. Sarnat. 2005. "Ozone Exposure and Mortality: An Empiric Bayes Metaregression Analysis." Epidemiology. 16(4):458-68.
Roman, H.A., K. D. Walker, T. L. Walsh, L. Conner, H. M. Richmond, B. J. Hubbell, and P. L. Kinney. 2008. "Expert Judgment Assessment of the Mortality Impact of Changes in Ambient Fine Particulate Matter in the U.S." Environmental Scientific Technology. 42(7):2268-2274.
Schwartz, J. 2005. "How Sensitive is the Association between Ozone and Daily Deaths to Control for Temperature?" American Journal of Respiratory and Critical Care Medicine. 171(6): 627-31.
U.S. Environmental Protection Agency (U.S. EPA). 2007. Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at <http://www.epa.gov/ttn/scram/guidance/guide/final-03-pm-rh-guidance.pdf>. Accessed June 4, 2015.
U.S. Environmental Protection Agency (U.S. EPA). 2011b Regulatory Impact Analysis for the Final Mercury and Air Toxics Standards. Research Triangle Park, NC: Office of Air Quality Planning and Standards, Health and Environmental Impacts Division. (EPA document number EPA-452/R-11-011, December). Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/matsriafinal.pdf>. Accessed June 4, 2015.
U.S. Environmental Protection Agency (U.S. EPA). 2012a. Regulatory Impact Analysis for the Final Revisions to the National Ambient Air Quality Standards for Particulate Matter. EPA-452/R-12-003. Office of Air Quality Planning and Standards, Health and Environmental Impacts Division, Research Triangle Park, NC. December. Available at: < http://www.epa.gov/ttnecas1/regdata/RIAs/finalria.pdf>. Accessed June 4, 2015.
U.S. Environmental Protection Agency (U.S. EPA). 2013. Technical Support Document: Estimating the Benefit per Ton of Reducing PM2.5 Precursors from 17 Sectors. Office of Air Quality Planning and Standards, Research Triangle Park, NC. February. Available at: < http://www2.epa.gov/sites/production/files/2014-10/documents/sourceapportionmentbpttsd.pdf >. Accessed June 4, 2015.
U.S. Environmental Protection Agency (U.S. EPA). 2014. Regulatory Impact Analysis for the Proposed Carbon Pollution Guidelines for Existing Power Plants and Emission Standards for Modified and Reconstructed Power Plants. EPA-542/R-14-002. Office of Air Quality Planning and Standards, Research Triangle Park, NC. June. Available at <http://www.epa.gov/ttnecas1/regdata/RIAs/111dproposalRIAfinal0602.pdf>. Accessed June 4, 2015.

Chapter 5: Economic Impacts  -  Markets Outside the Utility Power Sector
5.1 Introduction
The energy sector impacts presented in Chapter 3 of this RIA include potential changes in the prices for electricity, natural gas, and coal potentially resulting from the Clean Power Plan Final Rule. This chapter addresses the impact of these potential changes on other markets and discusses some of the determinants of the magnitude of these impacts. We refer to these changes as secondary market impacts.
Under the final emission guidelines, states are not required to use any of the measures that the EPA determines constitute BSER, or use those measures to the same degree of stringency that the EPA determines is achievable at reasonable cost. Rather, CAA section 111(d) allows each state to determine the appropriate combination of, and the extent of its reliance on, measures for its state plan, by way of meeting its state-specific goal. Given the flexibilities afforded states in complying with the emission guidelines, the benefits, cost and economic impacts reported in this RIA are illustrative of actions that states may take. The implementation approaches adopted by the states, and the strategies adopted by affected EGUs, will ultimately drive the magnitude and timing of secondary impacts from changes in the price of electricity, and the demand for inputs by the electricity sector, on other markets that use and produce these inputs.
The flexibility afforded to states in their state plans allows them to encourage compliance methods by affected EGUs, which include design elements that may mitigate or promote particular impacts based on their priorities. For example, states in the Regional Greenhouse Gas Initiative use the revenues from allowance auctions to support direct bill assistance for retail consumers, fund investments in clean energy and electricity demand reduction for business consumers, and support employment in the development of clean and renewable energy technologies. In its recent regulations to limit greenhouse gas (GHG) emissions, California's Air Resources Board designated a portion of allowances to be allocated to electric distribution companies in order to mitigate potential electricity rate increases and their associated impacts. Other states may encourage compliance methods by affected EGUs with particularly robust deployment of renewables, energy efficiency, or natural gas to promote manufacturing demand or employment in those sectors. For example, energy efficiency investments may be targeted towards reducing both electricity consumption and natural gas or heating oil consumption, such as weatherization projects. The state plan approach and the composition of these programs will influence the effects of compliance with the final rulemaking.
To estimate the costs, benefits, and impacts of implementing the CPP guidelines, the EPA modeled two illustrative plan approaches: a rate-based approach and a mass-based approach. Chapter 3 provides a description of the illustrative plan approaches analyzed. This chapter provides a quantitative assessment of the energy price impacts for these illustrative approaches and a qualitative assessment of the factors that will, in part, determine the timing and magnitude of effects in other markets.
5.2	Methods
One potential quantitative approach to evaluating the secondary market impacts is to use a computable general equilibrium (CGE) model. CGE models are able to provide aggregated representations of the whole economy in equilibrium in the baseline and potentially with regulation in place. As such, CGE model may be able to capture interactions between economic sectors and provide information on changes outside of the directly regulated sector. In support of previous rulemakings, such as the 2008 Final Ozone NAAQS (U.S. EPA 2008) and the 2010 Transport Rule proposal (U.S. EPA 2010), the EPA used the Economic Model for Policy Analysis (EMPAX) CGE model to estimate the secondary market effects based on the cost impacts projected by the Integrated Planning Model (IPM) for the directly regulated sector.
When considering the secondary market impacts of a regulation both the effects of the costs, the benefits of improved air quality, and their interaction may be relevant. Therefore, in the Second Prospective Analysis under Section 812 of the Clean Air Act Amendments the EPA incorporated a set of health benefits arising from air quality improvement into the EMPAX CGE model when studying the economy-wide impacts of the Clean Air Act (U.S. EPA 2011). While the external Council on Clean Air Compliance Analysis (Council) review of this study stated that inclusion of benefits in an economy-wide model "represent[ed] a significant step forward in benefit-cost analysis" (Hammitt 2010), the EPA recognizes that serious technical challenges remain when attempting to evaluate the benefits and costs of potential regulatory actions using economy-wide models.
In light of these challenges, the EPA has established a Science Advisory Board (SAB) panel on economy-wide modeling to consider the technical merits and challenges of using this analytical tool to evaluate costs, benefits, and economic impacts in regulatory development. The panel will also be asked to identify potential paths forward for improvements that could address the challenges posed when economy-wide models are used to evaluate the effects of regulations. The final panel membership was announced in March 2015 and the first of multiple face-to-face meetings of the SAB panel has been scheduled for October 2015. The EPA will use the recommendations and advice of this panel as an input into its process for improving benefit-cost and economic impact analyses used to inform decision-making at the agency.
The advice from the Science Advisory Board (SAB) panel formed specifically to address the subject of economy-wide modeling was not available in time for this final action. Given the ongoing SAB panel on economy-wide modeling, the uncertain nature of the ultimate energy price impacts due to the state flexibility in choosing a plan and the compliance flexibility for affected EGUs, and the ongoing challenges of accurately representing costs, benefits, energy efficiency savings in economy-wide modeling, this chapter considers the energy impacts associated with the illustrative plan approaches analyzed and a qualitative assessment of the factors that will, in part, determine the timing and magnitude of effects in other markets.
5.3	Summary of Secondary Market Impacts of Energy Price Changes
Electricity, natural gas, and coal are important inputs to the production of other goods and services. Therefore, changes in the price of these commodities will shift the production costs for sectors that use electricity, natural gas, and coal in the production of other goods and services. Such changes in production costs may lead to changes in the quantities and/or prices of the goods or services produced and changes in imports and exports.
The EPA used IPM to estimate electricity, natural gas, and coal price changes based on the illustrative plan approaches modeled for this rule. IPM is a multi-regional, dynamic, deterministic linear programming model of the U.S. electric power sector that is described in more detail in Chapter 3. The Retail Price Model (RPM) uses forecast changes in wholesale prices and the cost of demand-side energy efficiency programs to forecast changes in average retail prices. The prices are average prices over consumer classes and regions weighted by the amount used. Table 5-1 shows these estimated price changes. For other results generated by IPM and the RPM, please refer to Chapter 3.
There are many factors influencing the projected natural gas prices. IPM (and its integrated gas resource and supply module) models natural gas reserves appropriate natural gas supplies based on a multitude of factors. Since the model simulates perfect foresight, it anticipates future demand for natural gas and responds accordingly. In addition, IPM (and the natural gas module) are viewing a very long time horizon (through 2050), such that the impacts in certain years may be responsive to other modeling assumptions or drivers. The modeling framework is simultaneously solving for all of these key market and policy parameters (both electric and natural gas), resulting in the impacts shown.
Table 5-1.	Estimated Percentage Changes in Average Energy Prices by Energy Type for the Final Emission Guidelines, Rate-based and Mass-based Illustrative Plan Approaches
Rate-based Approach
                                     2020
                                     2025
                                     2030

Electricity Price Change
                                     [X]%
                                     [X]%
                                     [X]%

Delivered Natural Gas Price Change
                                     5.3%
                                     -7.7%
                                     2.5%

Delivered Coal Price Change
                                     -0.8%
                                     -5.0%
                                     -3.8%

Mass-based Approach
                                     2020
                                     2025
                                     2030

Electricity Price Change
                                     [X]%
                                     [X]%
                                     [X]%

Delivered Natural Gas Price Change
                                     [X]%
                                     [X]%
                                     [X]%

Delivered Coal Price Change
                                     [X]%
                                     [X]%
                                     [X]%

For years when the price of electricity, natural gas, or coal increased, one would expect decreases in production and increases in market prices in sectors for which these commodities are inputs, ceteris paribus. Conversely, for years when prices of these inputs decreased, one would expect increases in production and decreases in market prices within these sectors. Smaller changes in input price changes are assumed to lead to smaller impacts within secondary markets. However, a number of factors in addition to the magnitude and sign of the energy price changes, influence the magnitude of the impact on production and market prices for sectors using electricity, natural gas, or coal as inputs to production. These factors are discussed below.
5.3.1	Share of Total Production Costs
The impact of energy price changes in a particular sector depends, in part, on the share of total production costs attributable to those commodities. For sectors in which the directly affected inputs are only a small portion of production costs, the impact will be smaller than for sectors in which these inputs make up a greater portion of total production costs. Therefore, more energy-intensive sectors would potentially experience greater cost increases when electricity, natural gas, or coal prices increase, but would also experience greater reduced costs when these input prices decrease.
5.3.2	Ability to Substitute between Inputs to the Production Process
The ease with which producers are able to substitute other inputs for electricity, natural gas, or coal, or even amongst those commodities, influences the impact of price changes for these inputs. Those sectors with a greater ability to substitute across energy inputs or to other inputs will be able to, at least partially, offset the increased cost of these inputs resulting in smaller market impacts. Similarly, when prices for electricity, natural gas, or coal decrease, some sectors may choose to use more of these inputs in place of other more costly substitutes.
5.3.3	Availability of Substitute Goods and Services
The ability of producers in sectors experiencing changes in their input prices to pass along the increased costs to their customers in the form of higher prices for their products depends, in part, on the availability of substitutes for the sectors' products. Substitutes may be either other domestic products or foreign imports. If close substitutes exist, the demand for the product will in general be more elastic and the producers will be less able to pass on the added cost through a price increase.
Such substitution can also take place between foreign and domestic goods within the same sector. Changes in the price of electricity, natural gas, and coal can influence the quantities of goods imported or exported from sectors using these inputs. When the cost of domestic production increases due to more expensive inputs, imports may increase as consumers substitute towards relatively less costly foreign-produced goods. If imports increase because of a regulation and those imports come from countries with higher emissions per unit of production, this can result in foreign emission increases that offset some portion of domestic decreases, an effect commonly referred to as "leakage." Alternatively, if those imports are less emissions-intensive than the sectors that have contracted, emissions may fall even further. The potential for changes in global pollutants such as carbon dioxide (CO2) and other GHG emissions is noteworthy. Unlike most criteria pollutants and hazardous air pollutants, the impacts of CO2 emissions are not affected by the location from which those emissions originate.
5.4 	Effect of Changes in Input Demand from Electricity Sector
	Section 5.2 focuses on the effects of changes in energy prices, and possible responses to those price changes, on sectors outside of the electricity sector. A change in demand for inputs in the electricity sector, as well as changes in demand for energy efficiency services and products, will also influence economic activity in other sectors of the economy. For example, there will be changes in the demand for new generation sources such as natural gas combined cycle units and renewables, and therefore sectors producing these technologies may expand. Therefore, while a sector that produces say, wind turbine blades, may face higher natural gas and electricity prices, production in that sector may ultimately increase due to higher demand from the electricity sector for wind turbines.
5.5	Conclusions
Changes in the price of electricity, natural gas, and coal can affect markets for goods and services produced by sectors that use these energy inputs in the production process. The direction and magnitude of these impacts are influenced by a number of factors. Changes in cost of production may lead to changes in price, quantity produced, and profitability of firms within secondary markets. Furthermore, the demand for certain inputs in the electricity sector, as well as changes in the demand for energy efficiency services and products, will also affect secondary markets. If regulation results in changes in domestic markets that lead to an increase in imports, increases in production in countries with more energy-intensive production may lead to changes in CO2 emissions elsewhere.
5.6	References
Hammitt, J.K. 2010. Review of the Final Integrated Report for the Second Section 812 Prospective Study of the Benefits and Costs of the Clean Air Act. Available at <http://yosemite.epa.gov/sab/sabproduct.nsf/9288428b8eeea4c885257242006935a3/1E6218DE3BFF682E852577FB005D46F1/$File/EPA-COUNCIL-11-001-unsigned.pdf.> Accessed June 4, 2015.
U.S. Environmental Protection Agency (U.S. EPA). 2008. Final Ozone NAAQS Regulatory Impact Analysis. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at <http://www.epa.gov/ttn/ecas/regdata/RIAs/452_R_08_003.pdf>. Accessed June 4, 2015.
U.S. Environmental Protection Agency (U.S. EPA). 2010. Regulatory Impact Analysis for the Proposed Federal Transport Rule. Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available at <http://www.epa.gov/ttnecas1/regdata/RIAs/proposaltrria_final.pdf>. Accessed June 4, 2015.
U.S. Environmental Protection Agency (U.S. EPA). 2011. The Benefits and Costs of the Clean Air Act from 1990 to 2020, Final Report  -  Rev A. Office of Air and Radiation, Washington, DC. Available at <http://www.epa.gov/cleanairactbenefits/feb11/fullreport_rev_a.pdf>. Accessed June 4, 2015.

Chapter 6: Employment Impact Analysis
6.1	Introduction
Executive Order 13563 directs federal agencies to consider regulatory impacts on job creation and employment. According to the Executive Order, "our regulatory system must protect public health, welfare, safety, and our environment while promoting economic growth, innovation, competitiveness, and job creation. It must be based on the best available science" (Executive Order 13563, 2011). Although standard benefit-cost analyses have not typically included a separate analysis of regulation-induced employment impacts, we typically conduct employment analyses for economically significant rules. During the current economic recovery, employment impacts are of particular concern and questions may arise about their existence and magnitude. This chapter discusses and projects potential employment impacts for the utility power, coal and natural gas production, and demand-side energy efficiency sectors which may result from the Final Carbon Pollution Emission Guidelines for Existing Stationary Sources: Electric Utility Generating Units (herein referred to as "final emission guidelines" or the "Clean Power Plan Final Rule").
Section 6.2 describes the theoretical framework used to analyze regulation-induced employment impacts, discussing how economic theory alone cannot predict whether such impacts are positive or negative. Section 6.3 presents an overview of the peer-reviewed literature relevant to evaluating the effect of environmental regulation on employment. Section 6.4 provides background regarding recent employment trends in the electricity generation, coal and natural gas extraction, renewable energy, and demand-side energy efficiency-related sectors. Section 6.5 presents the EPA's quantitative projections of potential employment impacts in these sectors. These projections are based in part on a detailed model of the electricity production sector used for this regulatory analysis. Additionally, this section discusses projected employment impacts due to demand-side energy efficiency activities. Section 6.6 offers several conclusions.
6.2	Economic Theory and Employment
Regulatory employment impacts are difficult to disentangle from other economic changes affecting employment decisions over time and across regions and industries. Labor market responses to regulation are complex. They depend on labor demand and supply elasticities and possible labor market imperfections (e.g., wage stickiness, long-term unemployment, etc.). The unit of measurement (e.g., number of jobs, types of job hours worked, and earnings) may affect observability of that response. Net employment impacts are composed of a mix of potential declines and gains in different areas of the economy (e.g., the directly regulated sector, upstream and downstream sectors, etc.) over time. In light of these difficulties, economic theory provides a constructive framework for analysis.
Microeconomic theory describes how firms adjust their use of inputs in response to changes in economic conditions. Labor is one of many inputs to production, along with capital, energy, and materials. In competitive markets, firms choose inputs and outputs to maximize profit as a function of market prices and technological constraints.[,] Berman and Bui (2001) adapt this model to analyze how environmental regulations affect labor demand. They model environmental regulation as effectively requiring certain factors of production, such as pollution abatement capital, at levels that firms would not otherwise choose. Berman and Bui (2001) model two components that drive changes in firm-level labor demand: output effects and substitution effects. Regulation affects the profit-maximizing quantity of output by changing the marginal cost of production. If a regulation causes marginal cost to increase, it will place upward pressure on output prices, leading to a decrease in demand, and resulting in a decrease in production. The output effect describes how, holding labor intensity constant, a decrease in production causes a decrease in labor demand. As noted by Berman and Bui, although many assume that regulations must increases marginal cost, it need not be the case. A regulation could induce a firm to upgrade to less polluting and more efficient equipment that lowers the marginal cost of production. In such a case, output could increase after firms comply with the regulation. For example, in the context of the current rule, improving the heat rate of utility boiler increases fuel efficiency, lowering marginal production costs, and thereby potentially increasing the utility boiler's generation. An unregulated profit-maximizing firm may not have chosen to install such an efficiency-improving technology if the return on investment were too low, but once the investment is required it lowers marginal production costs.
The substitution effect describes how, holding output constant, regulation affects the labor-intensity of production. Although increased environmental regulation may increase use of pollution control equipment and energy to operate that equipment, the impact on labor demand is ambiguous. For example, equipment inspection requirements, specialized waste handling, completing required paperwork, or pollution technologies that alter the production process may affect the number of workers necessary to produce a unit of output. Berman and Bui (2001) model the substitution effect as the effect of regulation on pollution control equipment and expenditures required by the regulation and the corresponding change in the labor-intensity of production.
In summary, as output and substitution effects may be positive or negative, economic theory alone cannot predict the direction of the net effect of regulation on labor demand at the level of the regulated firm. Operating within the bounds of standard economic theory, however, empirical estimation of net employment effects on regulated firms is possible when methods and data of sufficient detail and quality are available. The extant literature, however, illustrates difficulties with empirical estimation. For example, there is a paucity of publicly-available data on plant-level employment, thus most studies must rely on confidential plant-level employment data from the U.S. Census Bureau, typically combined with pollution abatement expenditure data, that are too dated to be reliably informative, or other measures of the stringency of regulation. In addition, the most commonly used empirical methods can only estimate the gross effects of regulation on employment (i.e. the impact of that more stringent regulation has on employment relative to less stringent or no regulation), not the net effects.
The conceptual framework described thus far focused on regulatory effects on plant-level decisions within a regulated industry. Employment impacts at an individual plant do not necessarily represent impacts for the sector as a whole. The theoretical approach must be modified when applied at the industry level.
At the industry-level, labor demand is more responsive if: (1) the price elasticity of demand for the product is high, (2) other factors of production can be easily substituted for labor, (3) the supply of other factors is highly elastic, or (4) labor costs are a large share of total production costs. For example, if all firms in an industry are faced with the same regulatory compliance costs and product demand is inelastic, then industry output may not change much, and output of individual firms may change slightly. In this case, the output effect may be small, while the substitution effect depends on input substitutability. Suppose, for example, that new equipment for heat rate improvements requires labor to install and operate. In this case, the substitution effect may be positive, and with a small output effect, the total effect may be positive. As with potential effects for an individual firm, theory cannot determine the sign or magnitude of industry-level regulatory effects on labor demand. Determining these signs and magnitudes requires additional sector-specific empirical study. For environmental rules, much of the data needed for these empirical studies is not publicly available, would require significant time and resources in order to access confidential U.S. Census data for research, and also would not be necessary for other components of a typical Regulatory Impact Analysis (RIA).
In addition to changes to labor demand in the regulated industry, net employment impacts encompass changes in other related sectors. For example, the final guidelines may increase demand for heat rate improving equipment and services. This increased demand may increase revenue and employment in the firms supporting this technology. At the same time, the regulated industry is purchasing the equipment, and these costs may impact labor demand at regulated firms. Therefore, it is important to consider the net effect of compliance actions on employment across multiple sectors or industries.
If the U.S. economy is at full employment, even a large-scale environmental regulation is unlikely to have a noticeable impact on aggregate net employment. Instead, labor in affected sectors would primarily be reallocated from one productive use to another (e.g., from producing electricity or steel to producing high efficiency equipment), and net national employment effects from environmental regulation would be small and transitory (e.g., as workers move from one job to another). Some workers may retrain or relocate in anticipation of new requirements or require time to search for new jobs, while shortages in some sectors or regions could bid up wages to attract workers. These adjustment costs can lead to local labor disruptions. Although the net change in the national workforce is expected to be small, localized reductions in employment may adversely impact individuals and communities just as localized increases may have positive impacts.
If, on the other hand, the economy is operating at less than full employment, economic theory does not clearly indicate the direction or magnitude of the net impact of environmental regulation on employment; it could cause either a short-run net increase or short-run net decrease (Schmalansee and Stavins, 2011). An important research question is how to accommodate unemployment as a structural feature in economic models. This feature may be important in assessing large-scale regulatory impacts on employment (Smith, 2012).
Environmental regulation may also affect labor supply and productivity. In particular, pollution and other environmental risks may impact labor productivity or employees' ability to work. While the theoretical framework for analyzing labor supply effects is analogous to that for labor demand, it is more difficult to study empirically. There is a small emerging literature described in the next section that uses detailed labor and environmental data to assess these impacts.
To summarize, economic theory provides a framework for analyzing the impacts of environmental regulation on employment. The net employment effect incorporates expected employment changes (both positive and negative) in the regulated sector and other related sectors. Labor demand impacts for regulated firms, and also for the regulated industry, can be decomposed into output and substitution effects which may be either negative or positive. Estimation of net employment effects for regulated sectors is possible when data of sufficient detail and quality are available. Finally, economic theory suggests that labor supply effects are also possible. In the next section, we discuss the empirical literature.
6.3	Current State of Knowledge Based on the Peer-Reviewed Literature
The labor economics literature contains an extensive body of peer-reviewed empirical work analyzing various aspects of labor demand, relying on the theoretical framework discussed in the preceding section. This work focuses primarily on effects of employment policies such as labor taxes and minimum wages. In contrast, the peer-reviewed empirical literature specifically estimating employment effects of environmental regulations is growing, but is more limited. In this section, we present an overview of the latter. As discussed in the preceding section on theory, determining the direction of employment effects in regulated industries is challenging because of the complexity of the output and substitution effects. Complying with a new or more stringent regulation may require additional inputs, including labor, and may alter the relative proportions of labor and capital used by regulated firms (and firms in other relevant industries) in their production processes.
Empirical studies, such as Berman and Bui (2001), suggest that net employment impacts due to regulation were not statistically different from zero in the regulated sector. Other research suggests that more highly regulated counties may generate fewer jobs than less regulated ones, but since this study compares more regulated to less regulated counties it cannot address net employment effects at a national level (Greenstone, 2002). Moreover, environmental regulations may affect sectors that support pollution reduction earlier than the regulated industry. Rules are usually announced well in advance of their effective dates and then typically provide a period of time for firms to invest in technologies and process changes to meet the new requirements. When a regulation is promulgated, the initial response of firms is often to order pollution control equipment and services to enable compliance when the regulation becomes effective. Estimates of short-term increases in demand for specialized labor within the environmental protection sector have been prepared for several EPA regulations in the past, including the Mercury and Air Toxics Standards (MATS). Overall, the peer-reviewed literature does not contain evidence that environmental regulation has a large impact on net employment (either negative or positive) in the long run across the whole economy.
6.3.1	Regulated Sector
Several empirical studies, including Berman and Bui (2001) and Ferris, Shadbegian, and Wolverton (2014), suggest that regulation-induced net employment impacts may be zero or slightly positive, but small in the regulated sector. Gray et al (2014) find that pulp mills that had to comply with both the air and water regulations in EPA's 1998 "Cluster Rule" experienced relatively small, and not always statistically significant, decreases in employment. Other research suggests that more highly regulated counties may generate fewer jobs than less regulated ones (Greenstone 2002, Walker 2011, 2013). However since these latter studies compare more regulated to less regulated counties they overstate the net national impact of regulation to the extent that regulation causes plants to locate in one area of the country rather than another. List et al. (2003) find some evidence that this type of geographic relocation may be occurring. Overall, the peer-reviewed literature does not contain evidence that environmental regulation has a large impact on net employment (either negative or positive) in the long run across the whole economy.
A small literature examines impacts of environmental regulations on manufacturing employment. Greenstone (2002) and Walker (2011, 2013) study the impact of air quality regulations on manufacturing employment, estimating the effects in non-attainment areas relative to attainment areas. Kahn and Mansur (2013) study environmental regulatory impacts on geographic distribution of manufacturing employment, controlling for electricity prices and labor regulation (right to work laws). Their methodology identifies employment impacts by focusing on neighboring counties with different air quality regulations. They find limited evidence that environmental regulations may cause employment to be lower within "county-border-pairs." This result suggests that regulation may cause an effective relocation of labor across a county border, but since one county's loss is another's gain, such shifts cannot be transformed into an estimate of a national net effect on employment. Moreover this result is sensitive to model specification choices.
The few studies in peer-reviewed journals evaluating employment impacts of policies that reduce CO2 emissions in the electric power generation sector are in the European context. In a sample of 419 German firms, 13 percent of which were in the electricity sector, Anger and Oberndorfer (2008) find that the initial allocation of emission permits did not significantly affect employment growth in the first year of the European Union (EU) Emissions Trading Scheme (ETS). Examining European firms from 1996-2007, Commins et al. (2011) find that a 1 percent increase in energy taxes is associated with a 0.01 percent decrease in employees in the electricity and gas sector. Chan et al. (2013) estimate the impact of the EU ETS on a panel of almost 6,000 firms in 10 European countries from 2005-2009. They find that firms in the power sector that participated in the ETS had 2-3 percent fewer employees relative to those that did not participate, but this effect is not statistically significant.
This literature suggests that the employment impacts of controlling CO2 emissions in the European power sector were small. The degree to which these studies' results apply to the U.S. context is unclear. European policies analyzed in these studies effectively put a price on emissions either through taxes or tradable permits. A performance standard may not generate similar employment effects. Moreover, European firms face relative fuel prices and market regulatory structures different from their U.S. counterparts, further complicating attempts to transfer quantitative results from the EU experience to evaluate this rule.
6.3.2 Economy-Wide
As noted above it is very difficult to estimate the net national employment impacts of environmental regulation. Given the difficulty with estimating national impacts of regulations, EPA has not generally estimated economy-wide employment impacts of its regulations in its benefit-cost analyses. However, in its continuing effort to advance the evaluation of costs, benefits, and economic impacts associated with environmental regulation, EPA has formed a panel of experts as part of EPA's Science Advisory Board (SAB) to advise EPA on the technical merits and challenges of using economy-wide economic models to evaluate the impacts of its regulations, including the impact on net national employment. Once EPA receives guidance from this panel it will carefully consider this input and then decide if and how to proceed on economy-wide modeling of employment impacts of its regulations.
EPA received several comments regarding the potential net national employment impact of the proposed emission guidelines. Many of these comments referred to analyses developed using non-transparent, non-peer reviewed models. Additionally, some of the analysis pre-dated the Clean Power Plan proposal. However, one comment was based on an "economy-wide assessment of the employment impacts associated with the U.S. Environmental Protection Agency's (EPA's) proposed Clean Power Plan" using the Long-term Inter-industry Forecasting Tool (LIFT) model. The LIFT model, which is from the Interindustry Forecasting Project (Inforum) at the University of Maryland, has been used in the peer-reviewed academic literature and has also been used to examine the economic impacts of other national policies [Meade (2009); Werling (2011)]. The commenter noted that "While EPA's analysis provides a reasonable first approximation of the proposed rule's employment effects, its focus on direct employment impacts does not capture various indirect employment impacts that may be of interest to policymakers and the public." [...] "These include the employment impact associated with changes in electricity and other energy prices (both positive and negative, depending on the year), the productivity impacts associated with heat rate improvements at power plants, households and businesses re-directing expenditures to other uses because of increased demand-side energy efficiency, expenditures crowded out by energy efficiency expenditures, and changes in investments for air pollution control devices."
As mentioned previously, EPA is currently engaged in an SAB process on economy-wide modeling.  EPA will not make any determinations on whether modeling the economy-wide impacts of its regulations  -  including employment impacts - is feasible and, if so, how and when to do this until it receives guidance from the SAB panel. While the purpose of the SAB process is not to peer review any particular economy-wide model, it is worth noting that the class of models to which LIFT belongs are encompassed in one of the charge questions to the SAB: "Are there other economy-wide modeling approaches that EPA could consider in conjunction with CGE models to evaluate the short run implications of an air regulation (e.g., macro-economic, disequilibrium, input/output models)? What are the advantages or disadvantages of these approaches?"
6.3.3	Labor Supply Impacts
The empirical literature on environmental regulatory employment impacts focuses primarily on labor demand. However, there is a nascent literature focusing on regulation-induced effects on labor supply. Although this literature is limited by empirical challenges, researchers have found that air quality improvements lead to reductions in lost work days (e.g., Ostro, 1987). Limited evidence suggests worker productivity may also improve when pollution is reduced. Graff Zivin and Neidell (2012) used detailed worker-level productivity data from 2009 and 2010, paired with local ozone air quality monitoring data for one large California farm growing multiple crops, with a piece-rate payment structure. Their quasi-experimental structure identifies an effect of daily variation in monitored ozone levels on productivity. They find "ozone levels well below federal air quality standards have a significant impact on productivity: a 10 parts per billion (ppb) decreases in ozone concentrations increases worker productivity by 5.5 percent." (Graff Zivin and Neidell, 2012, p. 3654).
This section has outlined the challenges associated with estimating regulatory effects on both labor demand and supply for specific sectors. These challenges make it difficult to estimate net national employment estimates that would appropriately capture the way in which costs, compliance spending, and environmental benefits propagate through the macro-economy. Quantitative estimates are further complicated by the fact that macroeconomic models often have little sectoral detail and usually assume that the economy is at full employment.
6.4	Recent Employment Trends
The U.S. electricity system includes employees that support electric power generation, transmission and distribution; the extraction of fossil fuels; renewable energy generation; and supply-side and demand-side energy efficiency. This section describes recent employment trends in the electricity system.
6.4.1	Electric Power Generation
In 2014, the electric power generation, transmission and distribution sector (NAICS 2211) employed about 390,000 workers in the U.S. Installation, maintenance, and repair occupations accounted for the largest share of workers (25 percent). These categories include inspection, testing, repairing and maintaining of electrical equipment and/or installation and repair of cables used in electrical power and distribution systems. Other major occupation categories include office and administrative support (18 percent), production occupations (16 percent), architecture and engineering (10 percent), business and financial operations (7 percent) and management (7 percent). As shown in Figure 6.1, employment in the Electric Power Industry averaged about 420,000 workers 2000 to 2005, declining to an average of about 400,000 workers for the rest of the decade, and then declining to about 390,000 workers in 2014.

Figure 6.1.	Electric Power Industry Employment
6.4.2	Fossil Fuel Extraction
<IP_ADDRESS>	Coal Mining
The coal mining sector (NAICS 2121) is primarily engaged in coal mining and coal mine site development, excluding metal ore mining and nonmetallic mineral mining and quarrying. In 2014, BLS reported about 74,000 coal mining employees (Figure 6.2). During the 2000 to 2015, period, coal mining employment peaked in 2011 at about 87,000 employees.

Figure 6.2.	Coal Production Employment
<IP_ADDRESS>	Oil and Gas Extraction
In 2014, there were close to 200,000 employees in the oil and gas extraction sector (NAICS 211). This sector includes production of crude petroleum, oil from oil shale and oil sands, production of natural gas, sulfur recovery from natural gas, and recovery of hydrocarbon liquids. Activities include the development of gas and oil fields, exploration activities for crude petroleum and natural gas, drilling, completing, and equipping wells, and other production activities. In contrast with coal, and looking at Figure 6.3, there has been a sharp increase in employment in this sector over the past decade.

Figure 6.3.	Oil and Gas Production Employment
6.4.3	Clean Energy Employment Trends
Clean energy resources, such as energy efficiency and renewable energy, are used to meet energy demand, reduce peak electricity system loads, and reduce reliance on the most carbon-intensive sources of electricity. However, there is not a single clean energy sector in standard national accounts classifications. Renewable generation is not reported to the BLS separately from other electric power generation. Similarly, manufacturers of energy efficient appliances are not reported separately from conventional appliance manufacturers and green building design is not separate from the construction sector. Instead, clean energy technology and services are supported by industries throughout the economy.
Without a specific industrial classification, it is difficult to quantify the exact number of clean energy-related jobs or document the trends. Employees engaged in clean energy can span many job classifications, such as experts required to design and produce a renewable or energy-efficient technology, workers that supply inputs and technicians who install service or operate equipment. As such, there are a variety of definitions of clean or green jobs used, some more expansive than others.
<IP_ADDRESS>	Defining Clean Energy Jobs
Two U.S. Government sources, the 2010 Department of Commerce (DOC) report, Measuring the Green Economy and the 2010 and 2011 BLS Green Goods and Services surveys have subdivided industrial classifications into "green" categories. In both cases the approach was to determine which product classifications, rather than industries, were green. They multiplied green production by product revenue and defined an industrial sector as green if it met a threshold of green revenue as a proportion of total revenue.
DOC broadly defined green jobs in 2010 as those "created and supported in businesses that produce green products and services." They further classified green jobs into a broad and a narrow category. The narrow category includes only products deemed to be green without disagreement, while the broad category is more inclusive definition of green goods and services to over 22,000 product codes in the 2007 Economic Census to estimate their contribution to the U.S. economy. The report found that the number of green jobs in 2007 ranged from 1.8 million to 2.4 million jobs, accounting for between 1.5 and 2 percent of total private sector employment.
BLS used an expansive definition of clean or green jobs in 2010 and 2011. It goes beyond direct clean energy-related investments and includes "those in businesses that produce goods and provide services that benefit the environment or conserve natural resources. These goods and services, which are sold to customers, include research and development, installation, and maintenance services for renewable energy and energy efficiency and education and training related to green technologies and practices" but also include recycling and natural resource conservation, such as forestry management. Based on surveys across the 325 industries it identified as potential producers of green goods and services, BLS counts approximately 2.3 million jobs in the green economy in 2010, rising 7.4 percent to 2.5 million in 2011, compared to increases of about one percent across all occupations in the entire economy over the same period. The table below, Table 6-1, presents BLS green job estimates nationally and for the utility sector.
Table 6-1.	U. S. Green Goods and Services (GGS) Employment (annual average)

                             Total GGS Employment
                            Utility GGS Employment
                           Total GGS Growth 2010-11
                          Utility GGS Growth 2010-11
                                     2010
                                   2,342,562
                                    69,031
                                      NA
                                      NA
                                     2011
                                   2,515,200
                                    71,129
                                     7.4%
                                     3.0%
Source: Bureau of Labor Statistics
<IP_ADDRESS>	Renewable Electricity Generation Employment Trends
The DOC report does not separate renewable energy data and the BLS data include only privately owned electricity generating facilities. As such, neither source isolates renewable electricity generation employment. For historical trends in this sector, we therefore, rely on a Brookings Institution study, Muro et al. (2011). This study built a national database of "clean economy" jobs from the bottom up, verifying each company individually. They include a list of categories similar but not identical to that of BLS, including agricultural and natural resources conservation, education and compliance, energy and resource efficiency, greenhouse gas reduction, environmental management and recycling, and renewable energy. This study found about 138,000 jobs in the renewable energy sector in 2010, with an overall average annual growth rate of 3.1 percent from 2003-2010. Table 6-2 details the national results by energy source.

Table 6-2.	Renewable Electricity Generation-Related Employment
Sector
                                  Jobs, 2010
                   2003-2010 Average Annual Growth Rate (%)
Biofuels/Biomass
                                    20,680
                                      8.9
Geothermal
                                     2,720
                                      6.7
Hydropower
                                    55,467
                                     -3.6
Renewable Energy Services
                                     1,981
                                      6.3
Solar Photovoltaic
                                    24,152
                                     10.7
Solar Thermal
                                     5,379
                                     18.4
Waste-to-Energy
                                     3,320
                                      3.7
Wave/Ocean Power
                                      371
                                     20.9
Wind
                                    24,294
                                     14.9
Total
                                    138,364
                                      3.1
Source: http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf, Appendix A.
More recent industry data, from 2014, indicate higher employment numbers and growth in the solar sector.
<IP_ADDRESS>	Employment Trends in Demand-Side Energy Efficiency Activities
U.S. government data used for calculating the historical trends in the demand-side energy efficiency sector come from the BLS green goods and services surveys. BLS reports an energy efficiency category, finding 1.49 million private sector energy efficiency jobs in 2010 and 1.64 million in 2011.
For a longer term trend the Brookings Institution study (Muro et al., 2011) built a national database of "clean economy" jobs from the bottom up, verifying each company individually. This study found about 428,000 jobs in the Energy and Resource Efficiency sector in 2010, with an overall average annual growth rate of 2.6 percent from 2003-2010. Table 6-3 details the results by energy sector.

Table 6-3.	Energy and Resources Efficiency-Related Employment
Sector
                                  Jobs, 2010
                   2003-2010 Average Annual Growth Rate (%)
Appliances
                                    36,608
                                     -3.1
Energy-saving Building Materials
                                    161,896
                                      2.5
Energy-saving Consumer Products
                                    19,210
                                     -2.9
Green Architecture and Construction Services
                                    56,190
                                      6.4
HVAC and Building Control Systems
                                    73,600
                                      3.3
Lighting
                                    14,298
                                     -1.8
Professional Energy Services
                                    49,863
                                      6.9
Smart Grid
                                    15,987
                                      8.6
Total
                                    427,652
                                      2.6
 Source: http://www.brookings.edu/~/media/Series/resources/0713_clean_economy.pdf, Appendix A
In addition, other research institutes and industry groups have clean economy or clean energy employment databases. While definitions and timeframes vary, all show positive employment trends of 1.9 percent or more growth in clean energy-related jobs annually.
6.5	Projected Sectoral Employment Changes due to the Final Emission Guidelines
Affected EGUs may respond to these final CO2 emission performance rates by placing new orders for efficiency-related or renewable energy equipment and services to reduce GHG emissions. Installing and operating new equipment or improving heat rate efficiency could increase labor demand in the electricity generating sector itself, as well as associated equipment and services sectors. Specifically, the direct employment effects of supply-side initiatives include changes in labor demand for manufacturing, installing, and operating higher efficiency or renewable energy electricity generating assets supported by the initiative while reducing the demand for labor that would have been used by less efficient or higher emitting generating assets. Once implemented, increases in operating efficiency would impact the utility power sector's demand for fuel and plans for EGU retirement and new construction.
In addition, EPA expects state compliance plans to also include demand-side energy efficiency policies and programs that typically change energy consumption patterns of business and residential consumers by reducing the quantity of energy required for a given level of production or service. Demand-side initiatives generally aim to increase the use of cost-effective energy efficiency technologies (e.g., including more efficient appliances and air conditioning systems, more efficient lighting devices, more efficient design and construction of new homes and businesses), and advance efficiency improvements in motor systems and other industrial processes. Demand-side initiatives can also directly reduce energy consumption, such as through programs encouraging changing the thermostat during the hours a building is unoccupied or motion-detecting room light switches. Such demand-side energy efficiency initiatives directly affect employment by encouraging firms and consumers to shift to more efficient products and processes than would otherwise be the case. Employment in the sectors that provide these more efficient devices and services would be expected to increase, while employment in the sectors that produce less efficient devices would be expected to contract.
This analysis uses the cost projections from the engineering-based Integrated Planning Model (IPM) to project labor demand impacts of the final emission guidelines on affected electric utility steam generating units (steam generating units) in the electricity power sector and the fuel production sector (coal and natural gas). These projections include effects attributable to heat rate improvements, construction of new EGUs, generation shifts, changes in fuel use, and reductions in electricity generation due to demand-side energy efficiency activities. To project labor requirements for demand-side energy efficiency activities, the analysis uses a different approach that combines data on historic changes in employment and expenditures in the energy efficiency sector with projected changes in expenditures in the sector arising from state implementation of the emission guidelines. We project labor impacts for the rate-based and mass-based illustrative plan approach. Each scenario draws upon the building blocks described above, as well as assumes that demand-side energy efficiency plays an important role in compliance strategies
6.5.1	Projected Changes in Employment in Electricity Generation and Fossil Fuel Extraction
The analytical approach used in this analysis is a bottom-up engineering method combining EPA's cost analysis of the emission guidelines with data on labor productivity, engineering estimates of the amount and types of labor needed to manufacture, construct, and operate different types of generating units, and prevailing wage rates for skilled and general labor categories. This approach is different from the types of economic analyses discussed in section 6.2. Lacking robust peer-reviewed methods to estimate economy-wide impacts, the engineering-based analysis focuses on the direct impact on labor demand in industries closely involved with electricity generation. The engineering approach projects labor changes measured as the change in each analysis year in job-years employed in the utility power sector and directly related sectors (e.g., equipment manufacturing, fuel supply, EGU construction and generating efficiency services). For example, this approach projects the amounts and types of labor required to implement improvements in generating efficiency. It then uses the EPA's estimated effect of efficiency improvements on fuel demand to project reductions in the amount of labor required to produce coal and gas.
This analysis relies on projections and the cost analysis from IPM, which uses industry-specific data and assumptions to estimate costs and energy impacts of the final guidelines (see Chapter 3). The EPA uses IPM to predict coal generating capacity that is likely to undertake improvements in heat rate efficiency (HRI). IPM also predicts the guidelines' impacts on fuel use, retirement of existing units, and construction of new ones.
The methods EPA uses to estimate the labor impacts are based on the analytical methods used in many previous EPA regulatory analyses. The most relevant prior analysis was the Regulatory Impact Analysis for the Mercury and Air Toxics Standards (MATS). While the methods used in this analysis to estimate the recurring labor impacts (e.g., labor associated with operating and maintaining generating units, as well as labor needed to mine coal and natural gas) are the same as we used in MATS (with updated data where available), the methods used to estimate the labor associated with installing new capacity and implementing heat rate improvements were developed for the purpose of the Clean Power Plan RIA.
The bottom-up engineering-based labor analysis in the MATS RIA primarily was concerned with the labor needs of retrofitting pollution control equipment. A central feature of the labor analysis for this RIA, however, involves the quantity and timing of the labor needs of building new renewable and NGCC units and retiring coal units. The estimated response of the utility power sector involves changes in the amount and timing of retirements of existing coal and oil/gas units, as well as changes in the amount and timing of building new NGCC units and renewable generating capacity. In addition to the changes in retirements and building new units, there are also estimated changes in the utilization of existing generating units.
For example, as presented in Chapter 3, the IPM analysis of the rate-based control scenario finds that in 2025 (part way through the 2022-2029 interim plan performance period) less total generating capacity is needed than in the base case. The estimated reduction in capacity by 2025 with the rate-based scenario is 49.4 GW less than the estimated base case capacity (a 4.8 percent net capacity reduction). This 49.4 GW net reduction includes more retirements of coal units (an additional 22.9 GW of coal-fired capacity retired) and oil/gas steam units (an additional 9.3 GW of oil/gas retirement) compared to the base case, as well as a reduction in the amount of new natural gas units needed to be built by 2025 (a decrease of 10.9 GW in new capacity from the amount forecast in the base case) and non-hydro renewables (1.7 GW less renewable capacity built).
An important aspect of the labor analysis is that building new units, and all the associated construction-related labor, occurs before the new units become operational. While the financial costs of building the new units are amortized and recouped over the book life of the new equipment, the labor involved with manufacturing equipment and constructing the new units occurs, and is actually paid for, in a concentrated amount of time before the new capacity begins to generate electricity. IPM assumes that new NGCC units take 3 years to build, and both natural gas combustion turbines and wind-powered renewables take 2 years.
Avoiding some of the need for new capacity due to both demand and supply efficiency improvements results in both a significant net cost savings to consumers and the power sector, as well as reduced emissions of both CO2 and precursor pollutants from fossil fuel-fired generation. The avoided new capacity, however, also has significant labor impacts. A portion of the labor that would have been used to build the new capacity in the base case will not be employed in the power generation sector with the implementation of the GHG guidelines, though it likely will be employed in construction elsewhere. Similarly, less labor involved with operating and providing fuel for new units will be needed with the GHG guidelines than in the base case.
A critical component of the overall labor impacts of implementing the GHG guidelines is the impact of the labor associated with the demand-side energy efficiency activities. The demand-side labor impacts are presented in section 6.5.2. All of the labor impacts of the demand-side energy efficiency activities are increases in labor needs, which more than offset the loss of supply-side jobs associated with the decreasing demand for electricity arising from the demand-side programs. The IPM labor expenditure projections are distributed across different labor categories (e.g., general construction labor, boilermakers and engineering) using data from engineering analyses of labor's overall share of total expenditures, and apportionment of total labor cost to various labor categories. Hourly labor expenditures (including wages, fringe benefits, and employer-paid costs including taxes, insurance and administrative costs) for each category are used to estimate the labor quantity (measured in full-time job-years) consistent with the compliance scenario projections. Projected labor impacts arising from changes in fuel demand are primarily derived from labor productivity data for coal mining (tons mined per employee hour) and natural gas extraction (MMBtu produced/job-year). Tables 6.4 and 6.5 present projected changes relative to the baseline of four labor categories:
   1. manufacturing, engineering and construction for building, designing and implementing heat rate improvements;
   2. manufacturing and construction for new generating capacity;
   3. operating and maintenance for existing generating capacity; and
   4. extraction of coal and natural gas fuel.
All of the employment estimates presented in Tables 6-4 and 6-5 are estimates occurring in a single year. For the construction-related (one-time) labor impacts, including the installation of HRI, Tables 6-4 and 6-5 present the average annual impact occurring in each year of three different intervals. The three intervals are from 2018 through 2020 (a three year interval), during which there are modest labor impacts from the early changes in the power utility sectors operations, from 2021 through 2025 (five years), and 2026 through 2029 (5 years). The construction-related labor analysis are based on the IPM estimates of the net change in capital investment that occurs during each multi-year interval to fund building new units completed during that interval. The new build labor analysis uses the net change in capital investment to estimate the amount and type of labor needed during the interval to build the new capacity. The analysis assumes that the new built labor within each interval is evenly distributed throughout the interval. Tables 6-4 and 6-5 reflect this assumption by presenting the average labor utilization per year during each of the three intervals.
The HRI-related labor impacts are estimated based on the assumed capital cost of $100/kw (see section 3.7.3). The labor estimates for operating and maintaining generating units annually are based on IPMs estimates of Fixed Operating and Maintenance (FOM) Costs. IPM estimates FOM for each year individually, so the net changes in O&M-related labor estimates in Tables 6-4 and 6-5 are single year estimates for 2020, 2025 and 2030. These single year O&M labor estimates are not merely the average annual averages labor needs throughout each multi-year interval. There are O&M labor changes occurring in the all years throughout the entire period 2017-2030, but the labor impacts in each labor category change each year. The fuel-related labor estimates are also single-year estimates, and not multi-year averages. The labor analysis of the impacts on the fuel extraction industries uses IPM's estimates of the net changes in the amount of coal and natural gas in 2020, 2025 and 2030, which are inherently estimates of the fuel usage in a single year. As with the O&M labor impacts, the fuels-related labor impacts occur in every year throughout 2017-2030, and the labor impact changes every year.
It should be noted that the supply-side labor impact estimates in Tables 6-4 and 6-5 reflect the supply-side changes that will potentially occur with each illustrative plan scenario. These labor impacts include not only the impacts of Building Blocks 1 through 3, but also the changes in total generation needed that result from the demand-side energy efficiency activities expected to be an important component of state compliance strategies. The additional labor impact estimates from demand-side energy efficiency activities are presented below in section 6.5.2.
More details on methodology, assumptions, and data sources used to estimate the supply-side labor impacts discussed in this section can be found in Appendix 6A.
Table 6-4.	Engineering-Based[a] Changes in Labor Utilization, Rate-based Scenario (Number of Job-Years[b] of Employment in a Single Year)

                   Construction-related (One-time) Changes*

                                   2018-2020
                                   2021-2025
                                   2026-2030
Heat Rate Improvement: Total
                                       0
                                    15,400
                                     2,200

Boilermakers and General Construction
                                       0
                                    11,000
                                     1,600

Engineering and Management
                                       0
                                     2,800
                                      400

Equipment-related
                                       0
                                     1,200
                                      200

Material-related
                                       0
                                      400
                                       0
New Capacity Construction: Total
                                      500
                                    -15,600
                                      400

Renewables
                                      700
                                    -5,000
                                    23,300

Natural Gas
                                     -200
                                    -10,600
                                    -22,900

                              Recurring Changes**

                                     2020
                                     2025
                                     2030
Operation and Maintenance: Total
                                    -9,100
                                    -17,000
                                    -19,600

Changes in Renewables
                                      600
                                     -100
                                     1,100

Changes in Gas
                                      300
                                    -1,100
                                    -3,700

Retired Coal
                                    -8,000
                                    -13,300
                                    -14,700

Retired Oil and Gas
                                    -2,000
                                    -2,500
                                    -2,300
Fuel Extraction: Total
                                      100
                                    -7,800
                                    -13,900

Coal
                                    -1,300
                                    -7,300
                                    -13,300

Natural Gas
                                     1,400
                                     -500
                                     -600
Supply-Side Employment Impacts - Quantified
                                    -8,500
                                    -25,000
                                    -30,900
[a] Job-year estimates are derived from IPM investment and O&M cost estimates, as well as IPM fuel use estimates (tons coals or MMBtu gas).
b All job-year estimates on this are full-time equivalent (FTE) jobs. Job estimates in the Demand-Side energy efficiency section (below) include both full-time and part-time jobs.
*Construction-related job-year changes are one-time impacts, occurring during each year of the 2 to 4 year period during which construction and HRI installation activities occur. Construction-related figures in table are average job-years during each of the years in each range. Negative construction job-year estimates occur when additional generating capacity must be built in the base case, but is avoided in the final rule due to HRI or Demand-side energy efficiency programs.
**Recurring Changes are job-years associated with annual recurring jobs including operating and maintenance activities and fuel extraction jobs. Newly built generating capacity creates a recurring stream of positive job-years, while retiring generating capacity, as well as avoided new built capacity, create a stream of negative job-years. In addition, there are recurring jobs prior to 2020 to fuel and operate new generating capacity brought online before 2020; the recurring jobs prior to 2020 are not estimated.
Table 6-5.	Engineering-Based[a] Changes in Labor Utilization, Mass-Based Illustrative Plan Approach (Number of Job-Years of Employment in Year)

                   Construction-related (One-time) Changes*

                                   20XX-20XX
                                   20XX-20XX
                                   20XX-20XX
Heat Rate Improvement: Total
                                      [X]
                                      [X]
                                      [X]

Boilermakers and General Construction
                                      [X]
                                      [X]
                                      [X]

Engineering and Management
                                      [X]
                                      [X]
                                      [X]

Equipment-related
                                      [X]
                                      [X]
                                      [X]

Material-related
                                      [X]
                                      [X]
                                      [X]
New Capacity Construction: Total
                                      [X]
                                      [X]
                                      [X]

Renewables
                                      [X]
                                      [X]
                                      [X]

Natural Gas
                                      [X]
                                      [X]
                                      [X]

                              Recurring Changes**

                                     2020
                                     2025
                                     2030
Operation and Maintenance: Total
                                      [X]
                                      [X]
                                      [X]

Changes in Renewables
                                      [X]
                                      [X]
                                      [X]

Changes in Gas
                                      [X]
                                      [X]
                                      [X]

Retired Coal
                                      [X]
                                      [X]
                                      [X]

Retired Oil and Gas
                                      [X]
                                      [X]
                                      [X]
Fuel Extraction: Total
                                      [X]
                                      [X]
                                      [X]

Coal
                                      [X]
                                      [X]
                                      [X]

Natural Gas
                                      [X]
                                      [X]
                                      [X]
Supply-Side Employment Impacts - Quantified
                                      [X]
                                      [X]
                                      [X]
[a] Job-year estimates are derived from IPM investment and O&M cost estimates, as well as IPM fuel use estimates (tons coals or MMBtu gas).
b All job-year estimates on this are full-time equivalent (FTE) jobs. Job estimates in the Demand-Side energy efficiency section (below) include both full-time and part-time jobs.
*Construction-related job-year changes are one-time impacts, occurring during each year of the 2 to 4 year period during which construction and HRI installation activities occur. Construction-related figures in table are average job-years during each of the years in each range. Negative construction job-year estimates occur when additional generating capacity must be built in the base case, but is avoided in the final rule due to HRI or Demand-side energy efficiency programs.
**Recurring Changes are job-years associated with annual recurring jobs including operating and maintenance activities and fuel extraction jobs. Newly built generating capacity creates a recurring stream of positive job-years, while retiring generating capacity, as well as avoided new built capacity, create a stream of negative job-years. In addition, there are recurring jobs prior to 2020 to fuel and operate new generating capacity brought online before 2020; the recurring jobs prior to 2020 are not estimated.

6.5.2	Projected Changes in Employment in Demand-Side Energy Efficiency Activities
While not a component of the best system of emission reduction (BSER), EPA anticipates that this rule may stimulate investment in clean energy technologies and services, resulting in considerable increases in energy efficiency. We expect these increases in energy efficiency, specifically, to support a significant number of jobs in related industries. For more information on EPA's illustrative investment levels in demand-side energy efficiency activities, assumed to be adopted in response to the CPP, please see Section 3.6 "Demand-Side Energy Efficiency" in Chapter 3 of this RIA.
 In this section, we project employment impacts in demand-side energy efficiency activities arising from these guidelines using illustrative calculations. The approach uses information from power sector modeling and projected impacts on energy efficiency investments analyzed (see Chapter 3), and U.S. government data on employment and expenditures in energy efficiency. This approach is limited by the fact that we do not know which options states will choose for demand-side energy efficiency activities and by uncertainties associated with methods. These illustrative employment projections are gross; thus they do not include impacts of any shift in resources from other sectors. Nor does this analysis attempt to quantify employment impacts arising from changes in consumer expenditures away from energy towards other sectors. In other words, these projections are not attempts at estimating net national job creation. Also, this approach attempts to calculate the number of employees (full-time and part-time) rather than job-years as discussed in section 6.5.1.
Investments in demand-side energy efficiency reduce energy required for a given activity by encouraging more efficient technologies (e.g., ENERGY STAR appliances), implementing energy improvements for existing systems (e.g., weatherization of older homes), or encouraging changes in behavior (e.g., reducing air conditioning during periods of high electricity demand).
Employment impacts of demand-side energy efficiency programs have not been extensively studied in the peer-reviewed, published economics literature. Instead, most research has focused on consumer response to and amount of energy savings achieved by these programs (e.g., Allcott (2011a, 2011b), Arimura et al. (2012)). Results suggest that demand-side energy efficiency programs reduce energy use and generate small increases in consumer welfare. These policy impacts are due to low investment in energy efficiency as described in "energy paradox" literature (Gillingham, Newell, and Palmer (2009), Gillingham and Palmer (2014)).
Two recent articles discuss employment effects of demand-side energy efficiency programs. Aldy (2013) describes clean energy investments funded by the American Recovery and Reinvestment Act of 2009, which "included more than $90 billion for strategic clean energy investments intended to promote job creation and the deployment of low-carbon technologies" (p. 137), with nearly $20 billion for energy efficiency investments. The Council of Economic Advisors (CEA) (2011) estimated higher economic activity and employment than would have otherwise occurred without the American Recovery and Reinvestment Act. Using CEA's methods to quantify job creation for the Recovery Act, Aldy uses the share of stimulus funds for clean energy investments to estimate job-years supported by the Recovery Act. The largest sources of job creation in clean energy are those that received the largest shares of stimulus funds: renewable energy, energy efficiency, and transit. Aldy's estimates, while informative, are not directly applicable for employment analysis in this rulemaking as there are important differences in expected employment impacts from a historically large fiscal stimulus specifically targeting job creation during a period of exceptionally high unemployment versus environmental regulations taking effect several years from now.
Yi (2013) analyzes clean energy policies and employment for U.S. metropolitan areas in 2006, prior to the Recovery Act, to evaluate impacts on clean energy job growth. Implementing an additional state clean energy policy tool (renewable energy policies, GHG emissions policies, and energy efficiency polices such as energy efficiency resource standards, appliance or equipment energy efficiency standards, tax incentives, and public building energy efficiency standards) is associated with 1 percent more clean energy employment within that MSA. These estimates are not transferable to this rulemaking since states are likely to change intensity as well as number of clean energy programs.
Lacking a peer-reviewed methodology, we use the following approach to illustrate possible effects on labor demand in the energy efficiency sector due to demand-side management strategies. We use U.S. government data and divide energy efficiency employment by expenditures on energy efficiency activities to calculate an estimate of jobs per million dollars. We then multiply this fraction by projected expenditure in energy efficiency activities undertaken in response to these final guidelines.
Data used for calculating employment in energy efficiency sectors comes from the "energy efficiency" industry category of the BLS Green Goods and Services survey. Using BLS Green Goods and Services information on 132 energy efficiency industries, as identified by BLS, we adjusted the list to remove ten industries expected to not be directly affected by the rule, e.g. transportation. Next we used this detailed list of 122 industries to extract 2011 BLS data on green employment. Employment data at the most-detailed industry level (6-digit NAICS) is available only for a portion of these 122 industries. Therefore we use both the most-detailed industry level (6-digit NAICS) and also a more aggregate level (4-digit NAICS) to estimate a range of energy efficiency employment with the 2011 BLS Green Goods and Services data.
BLS does not collect data on energy efficiency expenditures directly, however. Instead, BLS collects data on the share of revenues associated with green goods and services, at the establishment level. We multiply data on total revenues by NAICS by the share of green revenues reported by BLS to obtain a measure of green revenues by industry. The only U.S. Government data source containing this revenue information for all NAICS sectors is the U.S. Economic Census. This Census is conducted at 5-year intervals (the latest available year is 2012), however, making it unsuitable for directly pairing with 2011 data from BLS. Instead, we use U.S. Census Bureau data on total value of shipments by industry, for 2011, from the Annual Survey of Manufacturers. The disadvantage of this data source is that the manufacturing sector makes up only 50 percent of the 132 NAICS codes belonging to the energy efficiency sector as defined by the BLS Green Goods and Services surveys, with the remainder in the construction or service sectors. Thus, this analysis implicitly projects that the same number of jobs per dollar are supported in construction and service sectors as in manufacturing. Also, the Annual Survey of Manufacturers contains data for some, but not all, detailed industry codes, e.g. 4-digit and 6-digit NAICS. We pair our BLS GGS data by industry, either by 4-digit or 6-digit NAICS, with data from the Annual Survey of Manufacturers. At the more detailed, 6-digit level, 17 industries have data available for both employment and total value of shipments. At the less detailed, 4-digit level, 15 industries have data available for both employment and total value of shipments. Using this approach we obtain estimates of 2.07 demand-side energy efficiency jobs per million 2011 dollars of expenditure, using the less-detailed industry level (4-digit NAICS), and 3.29 demand-side energy efficiency jobs per million 2011 dollars of expenditure, using the more-detailed industry level (6-digit NAICS).