5-1 

CHAPTER 5: COST, ECONOMIC, ENERGY, AND EMPLOYMENT IMPACTS 

Overview 

This chapter reports the compliance cost, emissions, economic, energy and employment 

impact analyses performed for the proposal to update CSAPR. The EPA used the Integrated 

Planning Model (IPM), developed by ICF International, to conduct most of the analysis 

discussed in this Chapter. The EPA conducted additional analysis using the IPM estimates of 

changes in the power and fuels sector as a basis for examining the potential direct labor impacts 

in those sectors. 

IPM is a dynamic linear programming model of the power sector that can be used to 

examine air pollution control policies affecting NOX, CO2, SO2, Hg, HCl, and other air pollutants 

throughout the contiguous United States for the entire power system. Future-year electricity 

demand levels are based on projections from the Energy Information Administration (EIA). 

This chapter of the RIA presents analyses of the proposed rule that include assumptions 

about the possible actions that EGUs may pursue as they reduce their NOX emissions to comply 

with the proposed EGU NOX ozone-season emissions budgets in the 23-state region. Over the 

past two decades, the EPA has used IPM and other analytical methods described in this chapter 

to conduct extensive analyses of proposed and final federal environmental regulatory actions 

affecting the power sector. These previous analytical efforts support the Agency’s understanding 

of key variables that influence the effects of a policy and provide the framework for how the 

Agency estimates the costs and benefits associated with its actions. 

5.1 Power Sector Modeling Framework 

IPM is a state-of-the-art, peer-reviewed, dynamic linear programming model that can be 

used to project power sector behavior under future business-as-usual conditions and examine 

prospective air pollution control policies throughout the contiguous United States for the entire 

electric power system. EPA used IPM to project likely future electricity market conditions with 

and without the proposed updates to CSAPR. 

IPM is a multi-regional, dynamic, deterministic linear programming model of the 

contiguous U.S. electric power sector. It provides estimates of least cost capacity expansion, 
5-2 

electricity dispatch, and emission control strategies while meeting energy demand and 

environmental, transmission, dispatch, and reliability constraints. The EPA has used IPM for 

over two decades to better understand power sector behavior under future business-as-usual 

conditions and to evaluate the economic and emissions impacts of prospective environmental 

policies. The model is designed to reflect electricity markets as accurately as possible. The EPA 

uses the best available information from utilities, industry experts, gas and coal market experts, 

financial institutions, and government statistics as the basis for the detailed power sector 

modeling in IPM. The model documentation provides additional information on the assumptions 

discussed here as well as all other model assumptions and inputs.1

The model incorporates a detailed representation of the fossil-fuel supply system that is 

used to estimate equilibrium fuel prices. The model includes an endogenous representation of the 

North American natural gas supply system through a natural gas module that reflects a partial 

supply/demand equilibrium of the North American gas market, accounting for varying levels of 

potential power sector and non-power sector gas demand and corresponding gas production and 

price levels.2

 This module consists of 118 supply, demand, and storage nodes and 15 liquefied 

natural gas re-gasification facility locations that are tied together by a series of linkages (i.e., 

pipelines) that represent the North American natural gas transmission and distribution network. 

IPM also endogenously models the partial equilibrium of coal supply and EGU coal 

demand levels throughout the contiguous U.S., taking into account assumed non-power sector 

demand and imports/exports. IPM reflects 36 coal supply regions, 14 coal grades, and the coal 

transport network, which consists of over four thousand linkages representing rail, barge, and 

truck and conveyer linkages. The coal supply curves in IPM were developed during a thorough 

bottom-up, mine-by-mine approach that depicts the coal choices and associated supply costs that 

power plants would face if selecting that coal over the modeling time horizon. The IPM 

 

1

 Detailed information and documentation of EPA’s Base Case using IPM (v5.15), including all the underlying 

assumptions, data sources, and architecture parameters can be found on EPA’s website at: 

http://www.epa.gov/airmarkets/powersectormodeling.html 

2

 See Chapter 10 of EPA’s Base Case using IPM (v5.15) documentation, available at: 

http://www.epa.gov/airmarkets/powersectormodeling.html
5-3 

documentation outlines the methods and data used to quantify the economically recoverable coal 

reserves, characterize their cost, and build the 36 coal regions’ supply curves.3 

To estimate the annualized costs of additional capital investments in the power sector, the 

EPA uses a conventional and widely accepted approach that applies a capital recovery factor 

(CRF) multiplier to capital investments and adds that to the annual incremental operating 

expenses. The CRF is derived from estimates of the power sector’s cost of capital (i.e., private 

discount rate), the amount of insurance coverage required, local property taxes, and the life of 

capital.4

 It is important to note that there is no single CRF factor applied in the model; rather, the 

CRF varies across technologies, book life of the capital investments, and regions in the model in 

order to better simulate power sector decision-making. 

The EPA has used IPM extensively over the past two decades to analyze options for 

reducing power sector emissions. Previously, the model has been used to estimate the costs, 

emission changes, and power sector impacts for the Clean Air Interstate Rule, the 2011 CrossState Air Pollution Rule (CSAPR), the Mercury and Air Toxics Standards (MATS), the Clean 

Power Plan (CPP) for Existing Power Plants, and the Carbon Pollution Standards for New Power 

Plants. EPA has also used IPM to estimate the air pollution reductions and power sector impacts 

of water and waste regulations affecting EGUs, including Cooling Water Intakes (316(b)) Rule, 

Disposal of Coal Combustion Residuals from Electric Utilities (CCR) and Steam Electric 

Effluent Limitation Guidelines (ELG). 

The model and EPA's input assumptions undergo periodic formal peer review. The 

rulemaking process also provides opportunity for expert review and comment by a variety of 

stakeholders, including owners and operators of capacity in the electricity sector that is 

represented by the model, public interest groups, and other developers of U.S. electricity sector 

models. The feedback that the Agency receives provides a highly-detailed review of key input 

assumptions, model representation, and modeling results. IPM has received extensive review by 

energy and environmental modeling experts in a variety of contexts. For example, in the late 

 

3

 See Chapter 9 of the IPM v.5.15 documentation. The documentation for EPA’s Base Case v.5.15 using IPM 

consists of a comprehensive document for IPM v. 5.13, and an incremental update document for both v.5.14 and 

v.5.15. All are available at available at: http://www.epa.gov/airmarkets/powersectormodeling.html 

4

 See Chapter 8 of EPA’s Base Case documentation. 
5-4 

1990s, the Science Advisory Board reviewed IPM as part of the CAA Amendments Section 812 

prospective studies that are periodically conducted. The model has also undergone considerable 

interagency scrutiny when it was used to conduct over a dozen legislative analyses (performed at 

Congressional request) over the past decade. The Agency has also used the model in a number of 

comparative modeling exercises sponsored by Stanford University’s Energy Modeling Forum 

over the past 15 years. IPM has also been employed by states (e.g., for RGGI, the Western 

Regional Air Partnership, Ozone Transport Assessment Group), other Federal and state agencies, 

environmental groups, and industry. 

5.2 EPA’s Power Sector Modeling Base Case for the Proposal to Update CSAPR 

The IPM “Base Case” for any regulatory impact analysis is a business-as-usual scenario 

that would be expected under market and regulatory conditions in the absence of the rule. As 

such, an IPM base case represents the baseline for this RIA.5

 The EPA frequently updates the 

IPM base case to reflect the latest available electricity demand forecasts from the U.S. Energy 

Information Agency (EIA) as well as expected costs and availability of new and existing 

generating resources, fuels, emission control technologies, and regulatory requirements. 

Our analysis of the proposal to update CSAPR involved two different IPM base cases. The 

EPA used IPM version 5.14 (IPM v.5.14) to provide power sector emissions data for air quality 

modeling. Specifically, IPM v.5.14 was used for the air quality modeling of the 2017 baseline 

and 2017 illustrative control case. This air quality modeling and IPM v.5.14 are detailed in 

Chapter 3. IPM v.5.14 was also used for the air quality modeling to quantify upwind state 

contributions, described in Section V of the preamble for this proposal. 

After these air quality modeling scenarios were underway, the EPA released an updated 

IPM base case, version 5.15 (IPM v.5.15), and the final Clean Power Plan (CPP). The EPA used 

IPM v.5.15 for developing the proposed state NOX emissions budgets discussed in Chapter 4 of 

this RIA, and for analyzing the proposed rule’s cost, benefits and impacts. The EPA relied on 

 

5

 As described in Chapter 5 of EPA’s Guidelines for Preparing Economic Analyses, the baseline “should 

incorporate assumptions about exogenous changes in the economy that may affect relevant benefits and costs (e.g., 

changes in demographics, economic activity, consumer preferences, and technology), industry compliance rates, 

other regulations promulgated by EPA or other government entities, and behavioral responses to the proposed rule 

by firms and the public.“ (USEPA, 2010). 
5-5 

IPM v.5.15 for these analyses so that the base case for this proposal would reflect all on-thebooks policies, including the Clean Power Plan, as well as the most current power sector 

modeling data. Using IPM v.5.15 for these analyses provides EPA with the best information 

available to develop the proposed rule and to provide the public with the most current 

information possible. 

5.2.1 EPA’s IPM Base Case v.5.15 

When the EPA began using the current version of IPM (version 5), the EPA developed a 

comprehensive updated base case (v.5.13), as well as a companion updated database of EGU 

units (the National Electricity Energy Data System, or NEEDS v.5.13) that is used in EPA’s 

modeling applications of IPM. The EPA base case is updated periodically to reflect the annual 

electricity demand forecasts from the EIA, among other data updates. EPA’s IPM modeling 

platform used to analyze this proposed rule (v.5.15) incorporates the version of the model used to 

analyze the potential impacts of the CPP, which was finalized in August, 2015. As discussed 

below in Section 5.2.2, the base case for the proposal to update CSAPR also includes certain 

revisions to the v.5.15 base case used in the CPP analysis. 

The updates to the base case between v.5.14 and v.5.15 include an update to the natural 

gas supply as well as routine calibrations with the EIA’s Annual Energy Outlook (AEO), such as 

updating the electric demand forecast consistent with the AEO 2015.6

 Additional updates, based 

on the most up-to-date information and/or public comments received by the EPA, include unitlevel specifications (e.g., pollution control configurations), planned power plant construction and 

closures, and updated cost and performance for onshore wind and utility-scale solar technologies. 

The IPM v.5.15 modeling platform incorporates federal and most state laws and regulations 

whose provisions were either in effect or enacted and clearly delineated by March 2015. This 

update also includes two non-air federal rules affecting EGUs: Cooling Water Intakes (316(b)) 

Rule and Combustion Residuals from Electric Utilities (CCR). Additionally, all new capacity 

projected by the model is compliant with Clean Air Act 111(b) standards, including the final 

 

6

 The naming conventions used for EPA’s IPM modeling (such as v.5.15) reflect both the major overall version of 

IPM (v.5) and which iteration of the AEO (currently AEO 2015) that supplies the electricity demand forecast used in 

IPM. 
5-6 

standards of performance for GHG emissions from new sources. For a detailed account of all 

updates made to the v.5.15 modeling platform, see the Incremental Documentation for EPA Base 

Case v.5.15 Using IPM.7

 

EPA also updated the National Electric Energy Data System (NEEDS). This database 

contains the unit-level data that is used to construct the "model" plants that represent existing and 

committed units in EPA modeling applications of IPM. NEEDS includes detailed information on 

each individual EGU, including geographic, operating, air emissions, and other data on every 

generating unit in the contiguous U.S. 

5.2.2 The IPM Base Case Used to Analyze the Proposal to Update CSAPR 

As discussed above, IPM Base Case v.5.15 was used as the base case in EPA’s analysis 

of the final Clean Power Plan, which was finalized in August, 2015. We are using a different 

IPM base case for this analysis of the proposal to update CSAPR to reflect the fact that the Clean 

Power Plan is now a finalized and promulgated federal regulation. The Clean Power Plan is now 

a part of the business-as-usual scenario that would be expected under market and existing 

regulatory conditions in the absence of this proposal. 

 The base case used to analyze the proposal to update CSAPR is the IPM configuration 

EPA used to analyze one of the final CPP illustrative policy options. Specifically, the base case 

used in the current analysis is the IPM configuration for the CPP’s “rate-based” illustrative plan 

approach. Using the CPP rate-based IPM configuration for the proposed CSAPR analysis assures 

that the IPM results are the incremental impacts on the power sector of the proposal to update 

CSAPR alone, and avoids “double counting” the impacts of the CPP as well as all other federal 

and state regulations included the v.5.15 base case. Extensive information on the IPM 

configuration and detailed results of the CPP rate-based analysis is available in the Clean Power 

Plan Final Rule – Regulatory Impact Analysis8

 for the CPP as well as at EPA’s Power Sector 

Modeling website. 9

 

7

 Available at: http://www.epa.gov/airmarkets/powersectormodeling.html/ 

8

 Available at: http://www2.epa.gov/cleanpowerplan/clean-power-plan-final-rule-regulatory-impact-analysis

9

 Available at: http://www.epa.gov/airmarkets/powersectormodeling.html/
5-7 

The 2017 baseline EGU emissions include impacts from the Final Mercury and Air Toxics 

Standards announced on December 21, 201110 and CSAPR issued on July 6, 2011. The EPA 

notes that because the modeling for the proposal was performed prior to the D.C. Circuit’s 

issuance of EME Homer City II,11 that modeling assumed in its baseline for all states the 

emission reductions associated with the CSAPR NOX ozone-season phase 2 emissions budgets. 

5.3 Evaluating the Regulatory Control Alternatives 

To estimate the costs, benefits, and economic and energy market impacts of the proposal to 

update CSAPR, the EPA conducted quantitative analysis of three regulatory control alternatives: 

the proposed EGU NOX ozone-season emissions budgets that reflect EGU NOX control strategies 

represented by a uniform NOX cost of $1,300 per ton; and more and less stringent alternative 

EGU NOX ozone-season emissions budgets that reflect EGU NOX control strategies represented 

by uniform NOX costs of $3,400 per ton and $500 per ton, respectively. Details about these 

regulatory control alternatives, including state-specific EGU NOX ozone-season emissions 

budgets for each alternative, are provided in Chapter 4 of this RIA. 

Before undertaking power sector analysis to evaluate compliance with the regulatory 

control alternatives, the EPA first considered available EGU NOX mitigation strategies that could 

be implemented for the first compliance period (i.e., the 2017 ozone season) assuming that this 

rule is finalized in the summer of 2016. The EPA considered all widely used EGU NOX control 

strategies: fully operating existing Selective Catalytic Reduction (SCR) and Selective NonCatalytic Reduction (SNCR) – including optimizing NOX removal by existing, operational SCRs 

and SNCRs as well as turning on and optimizing existing idled SCRs and SNCRs; installation of 

(or upgrading to) state-of-the-art NOX combustion controls; shifting generation to units with 

lower NOX emission rates within the same state; and installing new SCRs and SNCRs. EPA 

determined that the power sector could implement all of these NOX mitigation strategies, except 

installation of new SCRs or SNCRs, for the 2017 ozone-season, assuming this proposed rule is 

finalized in the summer of 2016. As to the installation of new SCRs or SNCRs, the amount of 

 

10 In Michigan v. EPA, the Supreme Court reversed on narrow grounds a portion of the D.C. Circuit decision 

upholding MATS, finding that EPA erred by not considering cost when determining that regulation of EGUs was 

"appropriate" pursuant to CAA section 112(n)(1). 135 S.Ct. 192 (2015). The case was remanded to the D.C. Circuit 

for further proceedings, and MATS currently remain in place. 

11 EME Homer City Generation, L.P., v. EPA, No. 11-1302, slip op. at 19, 36 (July 28, 2015) 
5-8 

time from contract award through commissioning for retrofit with new SCR or SNCR exceeds 18 

and 12 months, respectively. It would therefore not be feasible to retrofit new SCR or SNCR to 

achieve EGU NOX reductions in the 2017 ozone season. For more details on these assessments, 

including the assessment of EGU NOX mitigation costs and feasibility, please refer to the EGU 

NOX Mitigation Strategies TSD, in the docket for this rule. 

The EPA notes that, due to limitations on model size, IPM v.5.15 does not have the 

capacity to determine, within the model, whether or not to operate existing EGU postcombustion NOX controls (i.e., SCR or SNCR) that are idle in the base case.12 In order to 

evaluate compliance with the regulatory control alternatives (including the proposal and more 

and less stringent alternatives), the EPA determined, outside the model, whether or not it would 

be reasonably expected for these controls to operate in order to comply with each of the 

evaluated regulatory control alternatives. After imposing the requirement to operate these control 

systems, IPM then estimated the associated NOX reductions and costs. 

The EGU NOX mitigation strategies that are assumed to operate or be available to reduce 

NOX in order to comply with each of the regulatory control alternatives are shown in Table 5-1; 

more information about the estimated costs of these controls can be found in the EGU NOX

Mitigation Strategies TSD. 

Table 5-1 NOX Mitigation Strategies Implemented for Compliance with the Regulatory 

Control Alternatives 

Regulatory Control 

Alternative NOX Controls Implemented 

Less Stringent Alternative 

(1) Fully operating existing SCRs to achieve 0.075 lb/MMBtu NOX emission 

rate (outside IPM) 

(2) Shift generation to minimize costs (within IPM) 

Proposal 

(All controls above) 

(3) Turn on idled SCRs (within IPM) 

(4) Install or upgrade combustion controls (outside IPM) 

More Stringent Alternative (All controls above) 

(5) Turn on idled SNCRs (outside IPM) 

 

12 The EPA notes that EGUs with idled SCR or SNCR in the base represent a small percentage (less than 10 percent) 

of the EGU fleet that is equipped with NOX post-combustion controls. 
5-9 

5.3.1 Emission Reduction Assessment 

As discussed in Chapter 3 the EPA determined that NOX emissions in 23 eastern states 

affect the ability of downwind states to attain and maintain the 2008 ozone NAAQS. For these 

23 eastern states, the EPA is proposing to issue Federal Implementation Plans (FIPs) that 

generally update the existing CSAPR NOX ozone-season emission budgets for EGUs and 

implement these budgets via the CSAPR NOX ozone-season allowance trading program. 

The EPA analyzed ozone-season NOX emission reductions from implementing each of the 

three regulatory control alternatives. Specifically, the EPA conducted IPM modeling of each of 

the regulatory control alternatives to evaluate the corresponding power sector emissions 

reductions from complying with the EGU NOX ozone-season emissions budgets that are 

provided in Chapter 4 of this RIA. 

The NOX emissions reductions are presented for two time periods – 2017 (the principal 

year of interest for the proposal to update CSAPR) and 2020. However, this version of IPM was 

not structured to simulate 2017 directly. To evaluate the 2017 ozone season EGU NOX

reductions from compliance with the regulatory control alternatives, EPA developed estimates of 

emissions for 2017 by adjusting IPM’s direct estimates for 2018 to account for a number of 

known differences. For example, these adjustments account for emissions from EGUs that are 

expected to operate in 2017, but that have announced plans to retire by 2018 (and whose futureyear emissions are thus not otherwise represented in IPM’s direct 2018 estimates). The EPA has 

only made such adjustments for ozone season NOX emissions given the air quality objective of 

the proposal to address transported ozone pollution by the 2017 ozone season.13 For co-pollutant 

EGU emissions (i.e., annual NOX, SO2, and CO2), the EPA assumes that the 2018 IPM results 

are representative of 2017 with no further adjustment. The EPA believes that this is reasonable 

given that the overall magnitude of the adjustments to ozone season NOX emissions for 2017 is 

relatively small and that the adjustments generally affect both the base case and the regulatory 

control scenarios and therefore have an even smaller influence on the emissions difference 

between the base case and the regulatory control scenarios. 

 

13 The 2018 emissions output from IPM were adjusted to reflect 2017 emissions levels as described in 

http://www.epa.gov/airmarkets/documents/ipm/Adjusted2017.pdf 
5-10 

Table 5-2 presents the reduction in EGU NOX emissions resulting from compliance with 

the regulatory control alternatives (i.e., emissions budgets) in the 23-state region, as well as the 

impact on states not in the region. The emission reductions follow an expected pattern: the less 

stringent alternative produces substantially smaller emission reductions than EPA’s proposed 

emissions budgets, and the more stringent alternative results in modestly more NOX reductions. 

Table 5-2 EGU Ozone Season NOX Emission Reductions (tons) for the Proposal and More 

and Less Stringent Alternatives 

Ozone Season NOX Proposal 

More Stringent 

Alternative 

Less Stringent 

Alternative 

 2017 

23-State Region 85,000 88,000 24,000 

Non-Region -400 -500 -300 

Total 85,000 87,000 24,000 

 2020 

23-State Region 83,000 84,000 24,000 

Non-Region -300 -460 -260 

Total 83,000 84,000 24,000 

In addition to the ozone season NOX reductions, there will also be reductions of other air 

emissions emitted by EGUs burning fossil fuels (i.e., co-pollutants). These other emissions 

include the annual total changes in emissions of NOx, SO2 and CO2. The co-pollutant emission 

reductions are presented in Table 5-3. 

Table 5-3 EGU Annual Emission Reductions (tons) for SO2 and CO2 for the Proposal and 

More and Less Stringent Alternatives 

Annual NOx Proposal 

More Stringent 

Alternative 

Less Stringent 

Alternative 

 2017 

23-State Region 91,000 93,000 24,000 

Non-Region -560 -700 -500 

Total 90,000 93,000 24,000 

 2020 

23-State Region 92,000 94,000 24,000 

Non-Region -1,000 -1,700 -700 

Total 91,000 92,000 24,000 

Annual SO2 

 2017 

23-State Region 2,000 2,160 1,800 

Non-Region -820 -920 -710 

Total 1,200 1,200 1,100 

 2020 

23-State Region 1,700 1,600 1,300 

Non-Region -1,100 -1,500 -910 

Total 610 100 360 

Annual CO2 

 2017 23-State Region 1,300,000 1,600,000 1,300,000 

Non-Region -670,000 -920,000 -520,000 
5-11 

Annual NOx Proposal 

More Stringent 

Alternative 

Less Stringent 

Alternative 

Total 660,000 710,000 770,000 

 2020 

23-State Region 1,700,000 2,200,000 1,400,000 

Non-Region -1,400,000 -2,400,000 -1,100,000 

Total 270,000 -250,000 360,000,000 

5.3.2 Compliance Cost Assessment 

This section describes EPA’s approach to quantify the costs for compliance with the 

regulatory control alternatives. IPM directly estimated the costs for three of the NOX mitigation 

strategies: turning on idled SCRs, turning on idled SNCRs, and shifting generation to lower-NOX

emitting EGUs. However, the costs of increasing the use and optimizing the performance of 

existing and operating SCRs and SNCRs, and for installing or upgrading NOX combustion 

controls, were estimated using an incremental analysis step that EPA developed to quantify the 

additional costs of these controls. These methods and analysis relies on data and methods used 

within IPM, including NOX control cost equations used in IPM. Therefore, this analysis is 

consistent with IPM and provides the best available quantification of the costs of these NOX

mitigation strategies, consistent with IPM. 

The following steps illustrate this analytical method and demonstrate the application of this 

method to the EPA’s cost estimate of compliance with the proposal: 

Analytical Method

Application to Estimate the Costs of Compliance with 

the Proposal

• Identify the IPM modeled EGU 

NOX reductions that are 

attributable to NOX mitigation 

strategies with costs that are not 

included in the model. 

• Maximizing the use of existing SCRs accounts 

for approximately 33,000 tons of ozone season 

EGU NOX reductions. 

• Installing or upgrading NOX combustion 

controls accounts for approximately 7,800 tons 

of ozone season EGU NOX reductions. 

• Estimate the costs associated with 

these EGU NOX mitigation 

strategies. 

• Maximizing the use of existing SCRs costs 

approximately $500 per ton. 

• Installing or upgrading NOX combustion 

controls costs approximately $1,200 per ton. 

• Calculate the cost of each EGU 

NOX mitigation strategies as 

applied to reduce emissions within 

the model. 

• The cost of reductions from maximizing the use 

of existing SCRs is approximately $17,000,000 

annually for 2017. 
5-12 

Analytical Method

Application to Estimate the Costs of Compliance with 

the Proposal

• The cost of reduction from installing or 

upgrading NOX combustion controls is 

approximately $9,400,000 annually for 2017. 

The total costs of compliance with the regulatory control alternatives are estimated as the 

sum of the costs that are modeled within IPM and the costs that are calculated outside the model. 

The results of EPA’s IPM analysis show that, with respect to compliance with the 

proposed EGU NOX emissions budgets, turning on idle existing SCRs provides the largest 

amount of ozone season NOX emission reductions (51 percent), and maximizing the use of 

existing SCRs produces an additional 39 percent of the total ozone season NOX reductions. 

Combustion controls (9 percent) and generation shifting (1 percent) make up the remainder of 

the ozone season NOX reductions. In the more stringent alternative, compliance by turning on 

idle existing SNCRs makes up 3 percent of the total reductions, while the shares attributed to the 

other four mitigation measures are similar to the shares for compliance with the proposed EGU 

NOX emissions budgets. 

The estimates of the changes in the cost of supplying electricity for the regulatory control 

alternatives are presented in Table 5-4. The costs associated with compliance with monitoring, 

recordkeeping, and reports requirements are not included within the estimates in this table and 

can be found in Chapter 7. 

Table 5-4 Cost Estimates (millions of 2011$) for Proposal and More and Less Stringent 

Alternatives 

 Proposal More Stringent Alternative Less Stringent Alternative 

Annualized* $92.9 $95.7 $4.7 

Annual 2017 $71.6 $63.4 -$47.6

Annual 2020 $48.6 $20.3 -$27.6 

*Levelized annualized costs over the period 2016 through 2040, discounted using the 4.77 discount rate used in 

IPM’s objective function of minimizing the NPV of the stream of total costs of electricity generation. 

There are several notable aspects of the results presented on Table 5-4. The most notable 

result in Table 5-4 is that the estimated annual compliance costs for the less stringent alternative 
5-13 

are negative (i.e., a cost reduction) in 2017 and 2020, although this regulatory control alternative 

reduces NOX emissions by over 25,000 tons as shown in Table 5-3. The estimate of the cost of 

controls determined outside the model are positive. Therefore, the finding of net negative annual 

compliance costs derives from the portion of the annual compliance cost estimate that comes 

from IPM. While seemingly counter-intuitive, such an estimated cost reduction is quite possible 

given the dynamic, perfect foresight linear programing structure of IPM. IPM’s objective 

function is to minimize the discounted NPV of a stream of annual total cost of generation over a 

multi-decadal time period. For example, IPM’s perfect foresight structure makes it possible that 

the least cost way of complying may be to delay a new investment otherwise expected to occur 

in the base case. Such a delay could result in a lowering of annual cost in an early time period 

and increases in later time periods. 

A better understanding of the cause of the negative costs for the less stringent alternative 

can come from considering the entire time path of costs, rather than focusing on the 2017 and 

2020 estimates in isolation. Because IPM only reports estimates for certain years, it is necessary 

to estimate the cost values for the years between the years directly simulated. A simple but 

straightforward method is to use a linear interpolation between the annual cost estimates for 

directly simulated years to estimate a potential annual compliance cost time series across all 

years in a given period. Figure 5-1 shows the annual cost estimates, which are the sum of the 

annual compliance cost estimated by IPM and the annual compliance costs estimated outside the 

model, for each directly simulated year from 2016 through 2040. It also shows the linearly 

interpolated annual cost between those years for all three of the regulatory control alternatives.14

 

14 EPA estimated year-specific outside-the-model cost adjustments only for 2017 and 2020. For this analysis of the 

time series of costs, the outside-the-model compliance cost adjustments estimated for 2020 are added to all years 

between 2021 and 2040. While this approach is a relatively rougher estimate of the likely cost of the exogenouslyimposed NOx controls for those years, it is consistent with an assumption that once the CSAPR update is 

implemented it will continue to have similar costs and emission reductions throughout the rest of the model’s 

forecast horizon. 
5-14 

Figure 5-1 Time series of annual costs for the Proposal and More and Less Stringent 

Alternatives 

Figure 5-1 shows that while two of the IPM year cost estimates (2017 and 2020) in the less 

stringent alternative are negative, the rest of the annual costs are positive, resulting in the 

majority of the years between 2016 and 2040 having positive costs. All years have positive costs 

under compliance with the proposed EGU NOX ozone-season emissions budgets and the more 

stringent alternative. 

Using the estimated annual cost time series, it is possible to use a standard NPV 

calculation to estimate an annualized cost (i.e., levelized annual cost) of the annual cost stream 

associated with compliance with each regulatory control alternative.15 For this analysis we first 

calculated the NPV of the stream of costs from 2016 through 2040. We calculate the NPV in 

2016 of each cost stream using a 4.77 percent discount rate.16 While EPA typically uses a 3 and a 

7 percent discount rate to discount future year benefits and costs in regulatory impact analyses 

(USEPA 2010), in this cost annualization approach we use a 4.77 percent rate to be consistent 

with the optimization approach used within IPM. IPM’s cost minimization objective function 

 

15 The XNPV() function in Microsoft Excel 2013 was used to calculate the NPV of the variable stream of costs. 

16 While IPM also reports the costs for 2050, we do not include the 2050 estimates in this analysis. In this situation, 

extending the present value calculations to include 2050 does not materially change the results. 

-$50

$0

$50

$100

$150

$200

2016 2020 2024 2028 2032 2036 2040

Annual Costs (Millions of 2011$)

Less Stringent Proposal More Stringent
5-15 

estimates the lowest possible NPV, and uses a 4.77 percent discount rate to calculate the NPV. 

IPM v.5.15 uses a 4.77 percent discount rate as an estimate of the real opportunity cost of capital 

in the power sector.17

After calculating the NPV of the cost streams, the same 4.77 percent discount rate and 

2016-2040 time period is used to calculate the levelized annualized cost estimates shown in 

Table 5-3.18 

Figure 5-2 depicts the annualized cost estimates on top of the time stream of annual costs 

previously presented on Figure 5-1. 

Figure 5-2 Time series of annual costs and annualized costs for the Proposal and More and 

Less Stringent Alternatives 

As can be seen in Table 5-3 and Figure 5-2, the annualized costs are positive for 

compliance with each of the three regulatory control alternatives. Furthermore, the annualized 

 

17 The IPM Base Case documentation (Section 8.2.1 Introduction to Discount Rate Calculations) states “The real 

discount rate for expenditures (e.g., capital, fuel, variable operations and maintenance, and fixed operations and 

maintenance costs) in the EPA Base Case v.5.13 is 4.77%. This serves as the default discount rate for all 

expenditures.” Section 8.2 in that documentation describes the rationale for using a real opportunity cost of capital 

specific to the power sector, and describes the data used to derive the 4.77 percent rate. 

18 The PMT() function in Microsoft Excel 2013 is used to calculate the level annualized cost from the estimated 

NPV. 

-$50

$0

$50

$100

$150

$200

2016 2020 2024 2028 2032 2036 2040

Annual Costs (Millions of 2011$)

Less Stringent Proposal More Stringent

Annualized Annualized Annualized
5-16 

costs have the expected relationship; the annualized costs are lowest for the less stringent 

alternatives, and highest for the more stringent alternative. 

5.3.3 Impacts on Fuel Use, Prices and Generation Mix 

The proposal to update CSAPR is estimated to have a variety of different impacts to the 

power sector. While all the impacts are relatively small in percentage terms, considering the 

potential impacts in addition to the cost and emissions estimates presented previously is an 

important component of assessing the overall impact of the proposal. In this section of the RIA 

we discuss the estimated changes in fuel use, prices of fuel and retail electricity, generation by 

fuel type, and capacity by fuel type. 

Table 5-5 presents the percentage changes in national coal and natural gas usage by EGUs 

in 2017. Percent changes in fuel prices are also shown. As will be seen for the other measures of 

impacts on the power sector, the fuel use estimates in Table 5-5 estimates reflect a modest shift 

to natural gas from coal. 

Table 5-5 Percent Changes* in Coal and Natural Gas Usage by EGUs for the Proposal and 

More and Less Stringent Alternatives for 2017 

 Proposal More Stringent Alternative Less Stringent Alternative 

Change in Fuel Quantity (TBtu) 

 Coal -0.077% -0.072% -0.091% 

 Natural Gas 0.105% 0.090% 0.134% 

Change in Fuel Prices 

 Coal** -0.211% -0.279% -0.321% 

 Natural Gas*** 0.002% -0.004% 0.004% 

*Fuel use changes measured in TBtu 

**Coal price changes are minemouth prices per MMBtu 

***Natural gas price changes are delivered gas prices per MMBtu 

Table 5-6 presents the projected percentage changes in the amount of electricity generation 

nationally in 2017 by 3 major types of electricity generation: coal, gas, and conventional hydro. 

The amount generated by nuclear energy is unchanged in all three alternatives. 

Table 5-6 will be expanded to include details by prime mover type (in addition to by fuel 

type already shown). 
5-17 

Table 5-6 Percent Changes* in Generation by Major Type for the Proposal and More and 

Less Stringent Alternatives for 2017 

 Proposal More Stringent Alternative Less Stringent Alternative 

Coal -0.084% -0.079% -0.103% 

Natural Gas 0.108% 0.101% 0.127% 

Hydro 0.062% 0.064% 0.068% 

Total Generation** 0.009% 0.008% 0.009% 

*Changes in GWh generated in 2017. 

**Changes in total generation is from all types of generation nationally, including fossil, non-fossil and renewable 

EGUs dispatching electricity to the grid. 

Table 5-7 presents the projected percentage changes in the amount of generating capacity 

in 2017 for coal, natural gas and total capacity. 

Table 5-7 will be expanded to include all coal production regions 

Table 5-7 Changes* in Generating Capacity by Major Type for the Proposal and More 

and Less Stringent Alternatives in 2017 

 Proposal More Stringent Alternative Less Stringent Alternative 

Percent Change 

Coal -0.144% -0.136% -0.162% 

Natural Gas -0.006% -0.006% -0.006% 

Total Capacity -0.006% -0.004% -0.015% 

Capacity Change (MW) 

Coal -282 -318 -266 

Natural Gas -24 -95 -22 

Total Capacity -60 -146 -36 

*Changes in generating capacity available in 2017.

The EPA estimated the change in the retail price of electricity (2011$) using the Retail 

Price Model (RPM).19 The RPM was developed by ICF International for the EPA, and uses the 

IPM estimates of changes in the cost of generating electricity to estimate the changes in average 

retail prices. The prices are average prices over consumer classes (i.e.., consumer, commercial 

and industrial) and regions, weighted by the amount of electricity used by each class and in each 

region. The RPM combines the IPM cost estimates in each of the 64 IPM regions with EIA 

electricity market data for each of the 22 electricity supply regions in the electricity market 

module of the National Energy Modeling System (NEMS).20

 

19 See documentation available at: http://www.epa.gov/powersectormodeling/ 

20 See documentation available at: 

http://www.eia.gov/forecasts/aeo/nems/documentation/electricity/pdf/m068(2014).pdf 
5-18 

Table 5-8 presents the projected percentage changes in the retail price of electricity for 

the proposal and more and less stringent alternatives. Estimates are presented for both 2017 and 

2020. While all of the estimated changes in prices are quite small, Table 5-8 includes both 

positive and negative retail electricity price changes. This is consistent with the pattern of 

positive and negative annual cost estimates discussed above in Section 5.3.3, and also reflects 

some influence from aggregating results for the 22 NEMS regions into separate estimates for the 

23 State region affected by the proposal to update CSAPR, and the rest of the country. 

Table 5-8 Percent Changes* in Retail Electricity Price for the Proposal and More and Less 

Stringent Alternatives for 2017 & 2020 

 Base Case Percent Change in Retail Electricity Price 

Price 

(cents/kWh) Proposal 

More Stringent 

Alternative 

Less Stringent 

Alternative 

2017 

 National Price 9.93 -0.069% -0.099% -0.025% 

 23 State Region 9.69 -0.161% -0.371% -0.047% 

 Non-Region 10.75 -0.154% -0.371% -0.027% 

2020 

 National Price 10.33 0.031% 0.034% 0.009% 

 23 State Region 10.26 0.027% 0.028% 0.022% 

 Non-Region 11.11 0.018% 0.022% -0.001% 

*Changes in GW capacity available in 2017.

5.4 Employment Impacts 

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,21 we typically conduct 

employment analyses for economically significant rules. This section discusses and projects 

 

21 Labor expenses do, however, contribute toward total costs in the EPA’s standard benefit-cost analyses. 

Formatted Table
5-19 

potential employment impacts related to today’s proposal, but the employment impacts have not 

been quantified.22 

Section 5.5.1 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 5.5.2 presents an overview of the peer-reviewed 

literature relevant to evaluating the effect of environmental regulation on employment. Section 

5.5.3 provides background regarding recent employment trends in the electricity generation, coal 

and natural gas extraction sectors. Section 5.5.4 discusses the potential direct employment 

impacts in these sectors. The direct employment impacts are not quantified in this RIA. 

5.4.1 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 jobs, 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, the 

environmental protection 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.23 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.24,25 Berman and Bui (2001) adapt this 

model to analyze how environmental regulations affect labor demand.26 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.27 Regulation affects the profit-maximizing quantity of output by changing 

the marginal cost of production. If a regulation causes marginal production cost to increase, it 

 

22 The employment analysis in this RIA is part of EPA’s ongoing effort to “conduct continuing evaluations of 

potential loss or shifts of employment which may result from the administration or enforcement of [the Act]” 

pursuant to CAA section 321(a). 

23 See Layard and Walters (1978), a standard microeconomic theory textbook, for a discussion, in Chapter 9. 

24 See Hamermesh (1993), Ch. 2, for a derivation of the firm’s labor demand function from cost-minimization. 

25 In this framework, labor demand is a function of quantity of output and prices (of both outputs and inputs). 

26 Morgenstern, Pizer, and Shih (2002) develop a similar model. 

27 The authors also discuss a third component, the impact of regulation on factor prices, but conclude that this effect 

is unlikely to be important for large competitive factor markets, such as labor and capital. Morgenstern, Pizer and 

Shih (2002) use a very similar model, but they break the employment effect into three parts: 1) a demand effect; 2) a 

cost effect; and 3) a factor-shift effect. 
5-20 

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 increase marginal cost, in some cases they may 

decrease it. 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. 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 technology is in place it lowers marginal production costs. 

The substitution effect describes how, holding output constant, regulation affects the laborintensity 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. In addition, the 

empirical literature illustrates difficulties with estimation of net employment impacts. The most 

commonly used empirical methods, for example, Greenstone (2002), likely overstate 

employment impacts because they rely on relative comparisons between more regulated and less 

regulated counties, which can lead to “double counting” of impacts when production and 

employment shift from more regulated towards less regulated areas. Thus these empirical 

methods cannot be used to estimate net employment effects.28

The conceptual framework described thus far focused on regulatory effects on plant-level 

decisions within a regulated industry, but employment impacts at an individual plant do not 

necessarily represent impacts for the sector as a whole. 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.29 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.30

 

In addition to changes to labor demand in the regulated industry, net employment impacts 

encompass changes in other related sectors, for example, the environmental protection sector. 

This proposal may increase demand for the nitrogenous reagent (typically ammonia or urea) used 

in SCRs and SNCRs to reduce NOx, which may increase revenue and employment in the firms 

providing these chemicals. 

 

28 See Greenstone (2002) p. 1212. 

29 See Ehrenberg & Smith, p. 108. 

30 This discussion draws from Berman and Bui (2001), pp. 293. 
5-21 

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.31 Instead, labor in affected 

sectors would primarily be reallocated from one productive use to another (e.g., from producing 

electricity to manufacturing, installing, or operating and maintaining pollution-abatement 

equipment), and net national employment effects from environmental regulation would be small 

and transitory (e.g., as workers move from one job to another).32 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. 

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). For example, the Congressional Budget Office considered 

EPA’s Mercury Air Toxics Standards and regulations for industrial boilers and process heaters as 

potentially leading to short-run net increases in economic growth and employment, driven by 

capital investments for compliance with the regulations (Congressional Budget Office, 2011). 

Environmental regulation may also affect labor supply and productivity. In particular, reducing 

pollution and other environmental risks may improve labor productivity or employees’ ability to 

work.33 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 

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 including the environmental protection sector. 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. 

5.4.1.2 Current State of Knowledge Based on the Peer-Reviewed Literature 

 The peer-reviewed empirical literature specifically estimating employment effects of 

environmental regulations is limited but growing. We summarize it briefly in this section. 

 

31 Full employment is a conceptual target for the economy where everyone who wants to work and is available to do 

so at prevailing wages is actively employed. The unemployment rate at full employment is not zero. 

32 Arrow et al. 1996; see discussion on bottom of p. 8. In practice, distributional impacts on individual workers can 

be important, as discussed in later paragraphs of this section.

33 E.g. Graff Zivin and Neidell (2012). 
5-22 

5.4.1.3 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 

on regulated sectors suggests that employment growth may be lower in more regulated areas 

(Greenstone 2002, Walker 2011, 2013). However, since these latter studies compare more 

regulated to less regulated counties, this methodological approach likely overstates employment 

impacts to the extent that regulation causes plants to locate in one area of the country rather than 

another, which would lead to “double counting” of the employment impacts. List et al. (2003) 

find some evidence that this type of geographic relocation may be occurring. 

5.4.1.4 Economy-Wide 

Given the difficulty noted above 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.34 Once EPA receives guidance from this 

panel, it will carefully consider this input and then decide if and how to proceed on economywide modeling of employment impacts of its regulations. 

5.4.1.5 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.35 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).36

 

34 For further information see: 

http://yosemite.epa.gov/sab/sabproduct.nsf/0/07E67CF77B54734285257BB0004F87ED?OpenDocument 

35 For a recent review see Graff-Zivin and Neidell (2013). 

36 The EPA is not quantifying productivity impacts of reduced pollution in this rulemaking using this study. In light 

of this recent research, however, the EPA is considering how best to incorporate possible productivity effects in the 

future. 
5-23 

5.4.1.6 Conclusion 

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 improvements propagate through the macro-economy. 

5.4.2 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. 

5.4.2.1 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 5-3, 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. 
5-24 

Figure 5-3 Electric Power Industry Employment 

5.4.2.2 Fossil Fuel Extraction 

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 5-4). During the 

2000 to 2014 period, coal mining employment peaked in 2011 at about 87,000 employees. 

0

50

100

150

200

250

300

350

400

450

500

Power Generation and Supply Employment 

(NAICS = 2211, Annual Average, 1000s of Employees)
5-25 

 

Figure 5-4 Coal Production Employment 

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, Figure 5-5 shows there has been a sharp 

increase in employment in this sector over the past decade. 
5-26 

 

Figure 5-5 Oil and Gas Extraction Employment 

5.4.3 Power and Fuels Sector Direct Employment Impacts 

As described above, affected EGUs may respond to the CSAPR update by upgrading or 

improving performance of existing combustion controls, or by upgrading, improving, or utilizing 

post-combustion NOX systems already in place. In addition, some net generation may shift from 

higher NOX-emitting EGUs to units with lower NOX emission rates. All of these changes will 

likely involve some amount of change in various types of amount of labor needed in different 

parts of the fuels and utility power sectors. There also may be other labor impacts in sectors that 

provide products and materials used in reducing NOX emissions at EGUs, such as catalysts used 

in SCR control systems. These direct labor impacts will likely include both increased demand for 

certain types of labor in some portions of the affected sectors, and reduced demand for labor in 

other portions of the affected sectors. 

Installing and operating new equipment could change labor demand in the electricity 

generating sector itself, as well as associated equipment and services sectors. Specifically, the 

direct employment effects in the power sector that could occur because of actions taken by the 

2017 ozone season include: 

0

50

100

150

200

250

Oil and Gas Extraction Employment 

(NAICS = 211, Annual Average, 1000s of Employees)
5-27 

• Optimizing NOX removal from existing and operational SCR and SNCR systems; 

• Turning on and optimizing idled SCR and SNCR systems; 

• Installing or upgrading combustion-side improvements resulting in reduced NOX

emissions; 

• Shifting generation from units with higher NOX emission rates to units with lower 

emission rates. 

In addition, there could be directly induced employment impacts (both positive and 

negative) in the labor demand in the fossil fuels industry supplying fuels to the power sector. 

Once implemented, both the potential increases in operating efficiency and NOX reductions, as 

well as and shifting generation to lower NOX emitting assets, could impact the utility power 

sector’s demand for fossil fuels, and hence the demand for labor needed in the coal mining and 

gas extraction sectors. 

The direct net employment impacts of this proposed rule, however, are anticipated to be 

relatively small. This is consistent with the relatively small estimated changes in the power 

sector’s overall cost of generation, as well as relatively small changes in generation, fuel use, 

capacity, and the percent of total generation produced by each type of fuel. 

For example, for the proposal in 2017, the estimated impacts relevant to changes in labor 

demand include: 

• The overall total national cost of generation in 2017 increases by 0.046 percent (the 

levelized annualized total cost of generation increases by 0.01 percent); 

• Total net generation increases by 0.009 percent (coal generation decreases by .084 percent, 

and natural gas generation increases by 0.11 percent); 

• The power sector’s total tons of coal used for electricity generation decreases by 0.04 

percent (or 0.09 percent increase in BTUs); 

• Total natural gas increases by 0.11 percent. 

While the results of the power sector modeling suggest the direction and magnitude of the 

potential labor impacts, because of the very small changes in the power and fuels sector, the 

direct labor impacts are not quantified in this report. 
5-28 

5.5 Social Costs 

As discussed in the EPA Guidelines for Preparing Economic Analyses, social costs are the 

total economic burden of a regulatory action (USEPA, 2010). This burden is the sum of all 

opportunity costs incurred due to the regulatory action, where an opportunity cost is the value 

lost to society of any goods and services that will not be produced and consumed as a result of 

reallocating some resources towards pollution mitigation. Estimates of social costs may be 

compared to the social benefits expected as a result of a regulation to assess its net impact on 

society. The social costs of a regulatory action will not necessarily be equivalent to the 

expenditures associated with compliance. Nonetheless, here we use compliance costs as a proxy 

for social costs. 

The cost estimates for the three alternative NOX cost threshold scenarios presented in this 

chapter are the change in expenditures required by the electricity sector for compliance under 

each alternative, with a focus on the proposed remedy scenario. The change in the expenditures 

required by the electricity sector to maintain compliance reflect the changes in electricity 

production costs due to changes in electricity demand resulting from application of the control 

strategies described in Chapter 4 and changes in electricity production costs required to achieve 

the additional emission reductions necessary to comply with the emissions budgets that are 

described earlier in this RIA. The changes in electricity production costs and changes in demand 

are estimated by IPM as described earlier in Chapter 5, and the change in expenditures required 

by the electricity sector to maintain compliance are also described earlier in Chapter 5. 

5.6 Secondary Economic Impacts 

The energy sector impacts presented earlier in Chapter 5 of this RIA include potential 

changes in the prices for electricity, natural gas, and coal potentially resulting from the proposal. 

This chapter addresses the impact of these potential changes on other markets outside of the 

power sector and discusses some of the determinants of the magnitude of these impacts. We refer 

to these changes as secondary market impacts. 

The analysis of costs for this proposal includes strategies for affected states to reduce NOX

emissions from EGUs to comply with the proposed EGU NOX ozone-season emissions budgets 

in 2017. Ultimately, given the flexibilities afforded EGUs and states in complying with the 
5-29 

proposed rule, 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. 

To estimate the costs, benefits, and impacts of implementing the proposal, the EPA 

modeled an illustrative compliance approach for the proposal and more and less stringent 

alternatives. Chapter 4 provides a description of the proposal and the more and less stringent 

alternatives considered. 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. 

5.6.1 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, a 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 2010a), 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, 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 
5-30 

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. In 

addition, EPA is asking the panel to identify potential paths forward for improvements that could 

address the challenges posed when using economy-wide models 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 benefitcost 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, and the ongoing challenges of accurately 

representing costs, benefits, energy efficiency improvements in economy-wide modeling, this 

chapter considers the energy impacts associated with the proposed alternatives analyzed and a 

qualitative assessment of the factors that will, in part, determine the timing and magnitude of 

effects in other markets. 

5.6.2 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. 

Changes in the types and levels of inputs used by producers in response to electricity and fuel 

price changes may mitigate the production cost changes in these sectors. 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 

compliance with the regulatory control alternatives for this rule. IPM is a multi-regional, 

dynamic, deterministic linear programming model of the U.S. electric power sector described in 
5-31 

more detail earlier in Chapter 5. The Retail Price Model (RPM) uses estimated changes in 

wholesale prices to estimate changes in average retail prices. The prices are average prices over 

consumer classes and regions weighted by the amount used. Table 5-9 shows these estimated 

price changes for the proposal in 2017 and, for illustrative purposes only, 2020. For other results 

generated by IPM and the RPM, please refer to earlier sections in Chapter 5. 

There are many factors influencing the projected natural gas prices. IPM (and its integrated 

gas resource and supply module) models natural gas 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 

discussed in previous sections and shown in Table 5-9. 

Table 5-9. Estimated Percentage Changes in Average Energy Prices by Energy Type for 

the Proposed Alternative* 

Proposed Rule 2017 2020 

Electricity Price Change -0.1% -0.2% 

Delivered Natural Gas Price Change 0.01% -0.1% 

 Delivered Coal Price Change -0.3% -0.4% 

 

*A minus sign in front of the number denotes a negative change, or a price decrease. 

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 would lead to smaller impacts within secondary markets. For compliance 

with the proposal and more and less stringent alternatives these price changes are quite small, or 

even negative, in 2017. Thus, one could readily surmise that the impacts of this proposed rule 

upon secondary markets should be minimal. As seen in Table 5-9, most of the price changes are 

negative, thus showing an estimated decrease in energy prices, albeit very small. The variation 

for these prices at the regional level is not substantially different from the national-level price 

changes, and the variation of energy prices related to the proposal and the regional level can be 

found among the IPM outputs in the public docket for this rulemaking. 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. 
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5.6.3 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. 

5.6.4 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. 

5.6.5 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, and vice versa. 
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5.6.6 Effect of Changes in Input Demand from Electricity Sector 

 Section 5.7.2 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 could 

be changes in the demand for new generation sources such as natural gas combined cycle units, 

and therefore sectors producing technologies such as these may expand. 

5.6.7 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. For example, the more 

producers in these sectors are able to substitute away from the use of these energy inputs, the 

smaller the effect of energy prices changes will be on their production cost. Changes in cost of 

production may lead to changes in price, quantity produced, and profitability of firms within 

secondary markets. Furthermore, the demand inputs in the electricity sector will also affect 

secondary markets. If regulation results in changes in domestic markets that lead to an increase 

in imports, then domestic producers may experience less demand from their consumers, and vice 

versa. 

Modeling choices in IPM influence the estimated changes in electricity, natural gas, and 

coal prices in this RIA. Actual market conditions will ultimately influence the price changes of 

these energy inputs and consequent effects on secondary markets, as will the plan approaches 

that states adopt. 

5.7 References 

Arrow, K. J.; M. L. Cropper; G. C. Eads; R. W. Hahn; L. B. Lave; R. G. Noll; Paul R. Portney; 

M. Russell; R. Schmalensee; V. K. Smith and R. N. Stavins. 1996. “Benefit-Cost Analysis in 

Environmental, Health, and Safety Regulation: A Statement of Principles.” American Enterprise 

Institute, the Annapolis Center, and Resources for the Future; AEI Press. Available at: 

<http://www.hks.harvard.edu/fs/rstavins/Papers/Benefit Cost Analysis in 

Environmental.AEI.1996.pdf>. Accessed June 5, 2015. 
5-34 

Berman, E. and L. T. M. Bui. 2001. “Environmental Regulation and Labor Demand: Evidence 

from the South Coast Air Basin.” Journal of Public Economics. 79(2): 265-295. 

Congressional Budget Office (2011), Statement of Douglas W. Elmendorf, Director, before the 

Senate Budget Committee, “Policies for Increasing Economic Growth and Employment in 2012 

and 2013” (November 15)<http://www.cbo.gov/sites/default/files/11-15-

Outlook_Stimulus_Testimony.pdf> 

Ehrenberg, R. G. and R. S. Smith. 2000. Modern Labor Economics: Theory and Public Policy.

Addison Wesley Longman, Inc., Chapter 4. 

Executive Order 13563 (January 21, 2011). “Improving Regulation and Regulatory Review. 

Section 1. General Principles of Regulation.” Federal Register 76(14): 3821-3823. 

Ferris, A. E., R. J. Shadbegian, A. Wolverton. 2014. “The Effect of Environmental Regulation on 

Power Sector Employment: Phase I of the Title IV SO2 Trading Program.” Journal of the 

Association of Environmental and Resource Economists. 1(4): 521-553. 

Graff Zivin J. and M. Neidell. 2012. “The Impact of Pollution on Worker Productivity.” 

American Economic Review. 102(7):3652-73. 

Gray, W., R. J. Shadbegian, C. Wang and M. Meral. 2014 “Do EPA Regulations Affect Labor 

Demand? Evidence from the Pulp and Paper Industry”, Journal of Environmental Economics 

and Management. 68: 188-202. 

Greenstone, M. 2002. “The Impacts of Environmental Regulations on Industrial Activity: 

Evidence from the 1970 and 1977 Clean Air Act Amendments and the Census of Manufactures.” 

Journal of Political Economy. 110(6): 1175-1219. 

Hamermesh, D. S. 1993. Labor Demand. Princeton, NJ: Princeton University Press. Chapter 2. 

Layard, P.R.G. and A. A. Walters. 1978. Microeconomic Theory. McGraw-Hill, Inc. Chapter 9. 

List, J. A.; D. L. Millimet; P. G. Fredriksson and W. W. McHone. 2003. “Effects of 

Environmental Regulations on Manufacturing Plant Births: Evidence from a Propensity Score 

Matching Estimator.” The Review of Economics and Statistics. 85(4): 944-952. 

Ostro, B.D. 1987. “Air Pollution and Morbidity Revisited: A Specification Test.” Journal of 

Environmental Economics Management. 14:87-98. 

Schmalansee, R. and R. Stavins (2011). “A Guide to Economic and Policy Analysis for the 

Transport Rule.” White Paper. Boston, MA. Exelon Corp. 
5-35 

Walker, W. R. 2011.“Environmental Regulation and Labor Reallocation." American Economic 

Review. 101(2): 442-47. 

Walker, W. R. 2013.“The Transitional Costs of Sectoral Reallocation: Evidence From the Clean 

Air Act and the Workforce." The Quarterly Journal of Economics 128 (4): 1787-1835. 

U.S. Energy Information Agency (EIA). 2014. The Electricity Market Module of the National 

Energy Modeling System: Model Documentation 2014. Available at: 

<http://www.eia.gov/forecasts/aeo/nems/documentation/electricity/pdf/m068(2014).pdf>. 

Accessed 9/17/2015. 

U.S. EPA. 2010. EPA Guidelines for Preparing Economic Analyses. Available at: 

<http://yosemite.epa.gov/ee/epa/eed.nsf/webpages/guidelines.htm>. Accessed 9/21/2015. 

U.S. EPA. 2010a. Regulatory Impact Analysis for the Proposed Federal Transport Rule 

Analyses. Available at: < http://www3.epa.gov/ttnecas1/ria.html>. Accessed 9/21/2015.

 
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