prepared for

Appalachian Regional Commission

prepared by

Inforum and

Keybridge Research

Economic Impact of Energy and 

Environmental Policy in Appalachia

Final Report

December, 2011

Final Report

Economic Impact of Energy and 

Environmental Policy in Appalachia

You can view and download this report by visiting www.arc.gov. 

For more information about the report, please contact Douglas Meade of Inforum (301-

405-4608, meade@econ.umd.edu). 

The authors wish to thank the Appalachian Regional Commission for providing the 

funding for this report. 

Appalachian Regional Commission

1666 Connecticut Avenue, NW, Suite 700

Washington, D.C. 20009-1068 

The Appalachian Regional Commission’s mission is to be an advocate for and partner 

with the people of Appalachia to create opportunities for self-sustaining economic 

development and improved quality of life.

Inforum

4511 Knox Rd. #301 

College Park, MD 20740 

www.inforum.umd.edu

Keybridge Research

3050 K St. NW, Suite 220 

Washington, DC 20007 

www.keybridgeresearch.com

© 2011 by the Appalachian Regional Commission

Reproduction of this document is permitted and encouraged, with credit given to the 

Appalachian Regional Commission
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Economic Impact of Energy and Environmental Policy in Appalachia

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Acknowledgments

This report was funded and prepared for the Appalachian Regional Commission (ARC). 

It was commissioned by ARC to analyze the potential economic impact for Appalachia 

of federal, state and local energy and environmental policies.

This report was prepared by Douglas S. Meade of Inforum and Brendan Fitzpatrick of 

Keybridge Research. Dr. Jeffrey Werling and Dr. Robert Wescott provided overall 

guidance in the research plan and agenda. Troy Wittek provided assistance in data 

management and ARC GIS mapping at Inforum. Jeff Jensen of Keybridge assisted with 

the development of maps and analysis for shale gas. 

The research, analysis and report development were guided by David Carrier, Senior 

Economist with the ARC. Keith Witt, ARC GIS Advisor, also provided assistance with the 

mapping. We benefitted extensively from the opportunity to give a presentation of this 

project at ARC headquarters and receive comments from many members of the 

management and staff.

The research team would also like to acknowledge and thank the many individuals in 

various government agencies that have helped to make this work possible, especially 

the Energy Information Administration of the Department of Energy, the National 

Renewable Energy Laboratory, and Oakridge Laboratory. We acknowledge the 

assistance of the Bureau of Economic Analysis (BEA) in making available the national 

income and product accounts, the input-output tables, and the county and state level 

data on which our models are based.
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Table of Contents

Executive Summary .....................................................................................................................................ES-1

ES.1 Key Study Findings............................................................................................................................ES-3

ES.2 Scenario Comparisons ....................................................................................................................ES-4

Introduction........................................................................................................................................................ 1

1.1 Context and Objectives........................................................................................................................ 1

1.2 Overview of This Study............................................................................................................................ 3

1.3 Scenario Based Analysis......................................................................................................................... 3

2. Policy Review ................................................................................................................................................. 4

2.1 National Climate & Energy Legislation ............................................................................................... 4

2.1.1 Policy Summary ................................................................................................................................ 4

2.1.2 Policy Impacts on Appalachia...................................................................................................... 4

2.2 National Environmental Regulation..................................................................................................... 5

2.2.1 Policy Summary ................................................................................................................................ 5

2.2.2 Policy Impacts on Appalachia...................................................................................................... 6

2.3 State & Regional Climate & Energy Policies...................................................................................... 8

2.3.1 Policy Summary ................................................................................................................................ 8

2.3.2 Policy Impacts on Appalachia...................................................................................................... 8

2.4 Energy Supply Policy............................................................................................................................... 9

2.4.1 Policy Summary ................................................................................................................................ 9

2.4.2 Policy Impacts on Appalachia.................................................................................................... 10

3. Literature Review ......................................................................................................................................... 11

3.1 Policy Choices and Their Impacts on Economic Outcomes........................................................ 11

3.1.1 Cap & Trade Impacts .................................................................................................................... 11

3.1.2 Cap & Trade Policy Features ....................................................................................................... 12

3.1.3 Important Features of Other Climate Policies .......................................................................... 15

3.1.4 Environmental Regulations........................................................................................................... 18

3.1.5 Regional Policies............................................................................................................................. 19

3.1.6 Policy Scenarios in this Analysis.................................................................................................... 20

3.2 Model Type and Use in Policy Analysis.............................................................................................. 21

3.2.1 Economic Models .......................................................................................................................... 21

3.2.2 Modeling the Energy Sector ........................................................................................................ 23

3.2.3 Simulating Regional Variation...................................................................................................... 26

3.2.4 The Inforum LIFT-CUEPS Model ..................................................................................................... 28

3.3 Other Key Assumptions ........................................................................................................................ 28

3.3.1 Full Employment of Resources..................................................................................................... 28
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3.3.2 Technology Assumptions.............................................................................................................. 30

3.3.3 Energy Price Assumptions............................................................................................................. 30

3.3.4 Sources of Fuels and Energy Technologies ............................................................................... 31

4. Methodology: Models and Data............................................................................................................. 34

4.1 Model Capabilities and Objectives .................................................................................................. 34

4.2 Source Data for CUEPS ........................................................................................................................ 35

4.3 Outline of the Modeling System......................................................................................................... 35

4.4 Scenario Analysis................................................................................................................................... 37

5. Scenario Implementation and Assumptions.......................................................................................... 38

5.1 Summary of Policy Scenarios.............................................................................................................. 38

5.2 Reference Case .................................................................................................................................... 39

5.3 Electricity Efficiency .............................................................................................................................. 42

5.4 Carbon Mitigation................................................................................................................................. 45

5.5 Clean Energy Standard Scenario ...................................................................................................... 47

5.6 Expanded Natural Gas Development.............................................................................................. 52

6. Comparisons of Scenario Results............................................................................................................. 54

6.1 Macroeconomic Results...................................................................................................................... 54

6.1.1 Reference Case ............................................................................................................................. 54

6.1.2 Electricity Efficiency ....................................................................................................................... 56

6.1.3 Carbon Mitigation.......................................................................................................................... 57

6.1.4 Clean Energy Standard ................................................................................................................ 58

6.1.5 Expanded Natural Gas Development....................................................................................... 59

6.1.6 Comparisons of All Scenarios....................................................................................................... 60

6.2 Appalachia and State-Level Summaries.......................................................................................... 62

6.2.1 Reference Case ............................................................................................................................. 62

6.2.2 Electricity Efficiency ....................................................................................................................... 63

6.2.3 Carbon Mitigation.......................................................................................................................... 64

6.2.4 Clean Energy Standard ................................................................................................................ 65

6.2.5 Expanded Natural Gas Development....................................................................................... 66

6.2.6 Comparisons of All Scenarios by State ...................................................................................... 67

6.3 Selected County-Level Results ........................................................................................................... 70

6.4 Sample Individual County Results...................................................................................................... 90

7. Conclusions.................................................................................................................................................. 93

7.1 Main Findings from the Scenarios ...................................................................................................... 93

7.2 Policy Conclusions................................................................................................................................. 96

7.3 Extensions of This Research.................................................................................................................. 97

References........................................................................................................................................................ 98
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Appendix A. Data Documentation........................................................................................................... A-1

A.1 County Economic Data.................................................................................................................... A-1

A.2 Electric Utility Data ............................................................................................................................. A-2

A.3 County Utility Bridge........................................................................................................................... A-7

Appendix B. Model Documentation......................................................................................................... B-1

B.1 The LIFT Model...................................................................................................................................... B-1

B.1.1 An Overview of the LIFT Model.................................................................................................. B-2

B.1.2 The Use of LIFT for Energy Modeling.......................................................................................... B-3

B.2 The CUEPS Model ................................................................................................................................ B-6

B.2.1 Elements of the CUEPS Database............................................................................................. B-6

B.2.2 Demand Indicators...................................................................................................................... B-6

B.2.3 The Cross-Sectional Electricity Demand Equations.............................................................. B-7

B.2.4 The Structure of CUEPS.............................................................................................................. B-10

Appendix C. Wind and Biomass Potential............................................................................................... C-1

C.1 Derivation of Wind Assumptions..................................................................................................... C-1

C.2 Derivation of Biomass Assumptions............................................................................................... C-7

Appendix D. Coal Estimates for Appalachia........................................................................................... D-1
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List of Tables

Table ES.1 Macroeconomic Summary of Key Variables Across Scenarios.....................................ES-5 

Table 2.3.1 Sample of CES and RES Policies................................................................................................. 8

Table 5.1. Residential Electricity Savings (GWh)....................................................................................... 43

Table 5.2 Commercial Electricity Savings (GWh)...................................................................................... 44

Table 5.3. Industrial Electricity Savings (GWh)............................................................................................ 45

Table 5.4 Carbon Price .................................................................................................................................. 45

Table 5.5 Electricity Price in the Carbon Mitigation Case...................................................................... 46

Table 5.6 Electricity Generation Mix in Reference and Carbon Mitigation Cases............................. 47

Table 5.7 Electricity Price in CES Case........................................................................................................ 48

Table 5.8 Electricity Generation Mix in Reference and CES Cases...................................................... 48

Table 6.1.1 Macroeconomic Summary Table for Reference Case....................................................... 55

Table 6.1.2 Macroeconomic Comparison of Electricity Efficiency Case with Reference............... 56

Table 6.1.3 Macroeconomic Comparison of Carbon Mitigation Case with Reference .................. 57

Table 6.1.4 Macroeconomic Comparison of Clean Energy Standard with Reference................... 58

Table 6.1.5 Macroeconomic Comparison of Expanded Natural Gas with Reference.................... 59

Table 6.2.1 Appalachia Summary for the Reference Case................................................................... 62

Table 6.2.2 Appalachia Comparison for the Electricity Efficiency Case ............................................ 63

Table 6.2.3 Appalachia Comparison for the Carbon Mitigation Case............................................... 64

Table 6.2.4 Appalachia Comparison for the Clean Energy Standard Case...................................... 65

Table 6.2.5 Appalachia Comparison for the Expanded Natural Gas Development Case ............ 66

Table 6.2.5 Employment Comparison by State Across Scenarios......................................................... 67

Table 6.2.6 Real Personal Income................................................................................................................ 68 

Table 6.2.7 Electricity Sales............................................................................................................................ 69 

Table 6.4.1 Kanawha, West Virginia ............................................................................................................ 90 

Table 6.4.2 Allegheny, Pennsylvania ........................................................................................................... 91 

Table 6.4.3 Mining Share of Employment in West Virginia, 2009 ............................................................ 92 

Table 7.1.1 Macroeconomic Summary of Key Variables Across Scenarios......................................... 94 

Table 7.1.2 National Electric Power Generation by Type Across Scenarios...................................... 95 

Table A-1 Data available from EIA-861 .................................................................................................... A-2

Table A-2 Characteristics in the Generation and Fuel Consumption Database ............................. A-4

Table A-3 Net Generation by State, 2009 ................................................................................................. A-5

Table A-4 Correspondence Between AER Fuel Type and Production Type...................................... A-5

Table A-5 Electricity Generation by Type................................................................................................. A-6

Table B-1 Industry and Government Sectors in CUEPS ....................................................................... B-14

Table B-2 Correspondence of 5 EIA Market Categories ..................................................................... B-14

Table B-3 ERS Beale Codes and Their Definitions..................................................................................... 15

Table C.1 Wind Potential by County and State..................................................................................... C-5
Economic Impact of Energy and Environmental Policy in Appalachia

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List of Figures

Figure 3.1: Carbon Prices and GDP Impacts in Scenarios with Alternative Cap Stringencies......... 12

Figure 5.1. Generation Mix in Carbon Mitigation and Reference Cases............................................ 46

Figure 5.2 Electricity Generation Mix in Reference and CES Cases..................................................... 48

Figure 5.3 Wind Potential in Appalachia .................................................................................................... 49

Figure 5.4 Biomass Potential in Appalachia.............................................................................................. 51

Figure 6.1.1 Comparison of Real GDP........................................................................................................ 60

Figure 6.1.2 Comparison of Employment .................................................................................................. 60

Figure 6.1.3 Comparison of Energy Intensity ............................................................................................. 61

Figure 6.1.4 Comparison of CO2 Emissions ................................................................................................ 61

Figure 6.3.1 Electricity Sales per Customer, Residential, 2020, Reference Case................................. 70

Figure 6.3.3 Electricity Sales per Customer, Residential, 2010-2020 Growth Rate, Electricity 

Efficiency Case ......................................................................................................................................... 72

Figure 6.3.4 Electricity Sales per Customer, Residential, 2010-2020 Growth Rate, Carbon Mitigation 

Case............................................................................................................................................................ 73

Figure 6.3.5 Revenues per Customer, Residential, 2020, Reference Case .......................................... 74

Figure 6.3.6 Revenues per Customer, Residential, 2010-2020 Growth Rate, Reference Case ........ 75

Figure 6.3.7 Revenues per Customer, Residential, 2010-2020 Growth Rate, Efficiency Case .......... 76

Figure 6.3.8 Revenues per Customer, Residential, 2010-2020 Growth Rate, Carbon Mitigation 

Case............................................................................................................................................................ 77

Figure 6.3.9 Residential Rate, 2020, Reference Case ............................................................................... 78

Figure 6.3.10 Residential Rate, 2020, Carbon Mitigation Case .............................................................. 79

Figure 6.3.11 Commercial Rate, 2020, Reference Case.......................................................................... 80

Figure 6.3.12 Commercial Rate, 2020, Carbon Mitigation Case ........................................................... 81

Figure 6.3.13 Industrial Rate, 2020, Reference Case ................................................................................ 82

Figure 6.3.14 Industrial Rate, 2020, Carbon Mitigation Case .................................................................. 83

Figure 6.3.15 Per Capital Real Gross Regional Product, 2020, Reference Case ................................. 84

Figure 6.3.16 Per Capita Real Gross Regional Product, 2010-2020 Growth Rate, Reference Case 85

Figure 6.3.17 Per Capita Real Personal Income, 2020, Reference Case.............................................. 86

Figure 6.3.18 Per Capita Real Personal Income, 2010-2020 Growth Rate, Reference Case ........... 87

Figure 6.3.19 Employment, 2010-2020 Growth Rate, Reference Case ................................................. 88

Figure 6.3.20 Population, 2010-2020 Growth Rate, Reference Case .................................................... 89

Figure 6.4.1 Comparison of Employment in West Virginia: High Efficiency vs. Reference................ 92 

Figure A.1 County Utility Bridge Relationships in West Virginia ............................................................ A-8 

Figure B.1 Historical Data Flow................................................................................................................. B-11

Figure B.2 Flow of the County Utility Policy Simulator .......................................................................... B-13

Figure C.1 Wind Potential in Appalachia Rated by Capacity Factor.............................................. C-2

Figure C.2 Wind Capacity Potential by County.................................................................................... C-3

Figure C.3 Biomass Potential at $20/ton ................................................................................................. C-9

Figure C.4 Biomass Potential at $60/ton ............................................................................................... C-10

Figure C.5 Biomass Potential at $100/ton ........................................................................................... C-111

Figure C.6 Biomass Potential at $200/ton ............................................................................................. C-12 

Figure D.1 EIA Coal Estimates .................................................................................................................... D-2

Figure D.2 Coal Production by Region..................................................................................................... D-3

Figure D.3 County Level Coal Production............................................................................................... D-4

Economic Impact of Energy and Environmental Policy in Appalachia

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Executive Summary

The Appalachian Regional Commission (ARC) has requested that this study be 

performed to analyze the potential economic impact for Appalachia of federal, state 

and local energy and environmental policies. To this end, a modeling system has been 

constructed that can evaluate the impacts of policies at the national, state and county 

level, particularly for the set of counties in the Appalachian region. 

Although the ultimate policies that may be enacted are uncertain, an understanding of 

the likely impacts of such policies can be gained through the use of scenario analysis, 

using the modeling system mentioned above. Quantitative assessment of economic 

policies plays a useful role in informing dialogue among key stakeholders and policy 

decisions. One important type of such assessment is scenario based economic 

modeling, where scenarios of the future are compared to assess the economic benefits 

and costs of alternative policy choices. 

Inforum brings experience to bear in detailed interindustry, regional and energy 

modeling, as well as experience in the analysis of alternative government and corporate 

policy. Inforum’s special expertise is building models that provide a consistent and 

detailed representation of national and regional economic outcomes. For this study, 

Inforum has collaborated with energy policy experts from Keybridge Research. 

Keybridge Research has extensive experience with economic modeling and expertise in 

providing strategic guidance on the economics of federal climate change policy.

The study focuses particularly on issues germane to the electric power industry, and the 

interrelationships between the electric power industry and the local economies. 

However, the modeling system is comprehensive, in that all industry sectors of the 

economy are included, and energy demand is modeled for the residential, commercial 

and industrial sectors. 

The policy scenarios have been developed partly with the objective of understanding 

what are the least cost and highest impact strategies and policies for the states to 

pursue. Since a framework for this understanding has already been developed in the 

ARC Regional Blueprint 1 , we relate the current set of scenarios to the Strategic 

Objectives for economic and energy development are outlined in that blueprint:

1. Promote energy efficiency in Appalachia to enhance the Region’s economic 

competitiveness.

2. Increase the use of renewable energy resources, especially biomass, in 

Appalachia to produce alternative transportation fuels, electricity, and heat.

3. Support the development of conventional energy resources, especially 

advanced clean coal, in Appalachia to produce alternative transportation fuels, 

electricity, and heat.

This report briefly describes the content and structure of the Energy Policy Impact Model 

(EPIM) developed by Inforum for the ARC. A more detailed description of the model is in 

Appendix B. The Energy Policy Impact Model constructed for this project is a dual-level 

 

1 Energizing Appalachia: A Regional Blueprint for Economic and Energy Development, October 2006, at 

http://www.arc.gov/images/newsroom/publications/energyblueprint/energyblueprint.pdf. 
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ES-2 Inforum / Keybridge Research

system, comprised of the Inforum LIFT (Long-term Interindustry Forecasting Tool) model of 

the U.S. national economy, combined with the CUEPS (County-Utility Energy Policy 

Simulator) model of counties and electric utilities. 

The LIFT model works at a detailed sectoral level (about 90 private industries plus 

government sectors) and shows the interactions between industries, and the impacts on 

industry output of changes in exports and imports, personal consumption, investment, 

and government spending. The LIFT model integrates the National Income and Product 

Accounts (NIPA) with the detailed input-output accounts produced by the U.S. Bureau of 

Economic Analysis (BEA). LIFT forecasts at an annual frequency, and the standard 

version has a forecast interval to 2035. 

The CUEPS model includes a detailed database of 3140 counties and 3356 electric utility 

establishments. A county-utility bridge is used to relate economic activity by county to 

service demands by electric utility, and to relate cost changes by utility to the effects on 

the local economies served by that utility. County level economic data is obtained from 

Woods & Poole, and aggregated to a level of 11 private sector industries and 3 

government sectors. These 14 sectors are linked to the LIFT 90 sector forecasts to 

determine county level output, employment, prices and incomes.

In this study, we examine several policy scenarios that explore implications of pursuing 

the Strategic Objectives. These scenarios are compared to a reference case that was 

developed specifically for this study, but is consistent with the reference case of the 

Department of Energy (DOE) 2011 Annual Energy Outlook. For each scenario, consistent 

simulations are developed of the LIFT national model and the CUEPS model of counties 

and utilities.

The following scenarios are analyzed in this study:

1. Reference Case – The reference case for LIFT and CUEPS was developed to be 

consistent with the Annual Energy Outlook (AEO) 2011 Reference Case.

2. Electricity Efficiency – ARC sponsored an earlier study Energy Efficiency in 

Appalachia 2 that summarizes various energy efficiency improvements in the 

residential, commercial, transportation, and industrial sectors in Appalachia, and 

quantifies the energy savings. A scenario has been implemented incorporating 

these savings in LIFT and CUEPS to obtain national and county wide results. This 

scenario relates to the Blueprint Strategic Objective #1.

3. Carbon Mitigation – This scenario assumes the adoption of a carbon price in the 

near future, and is designed to be consistent with the greenhouse gas (GHG) 

price case in the AEO 2011 side cases. This scenario ties to all three Strategic 

Objectives, as the presence of a carbon pricing scheme will spur increased 

energy efficiency, increased use of renewables such as wind and biomass, and 

stimulate the development of novel uses of conventional fossil fuel resources.

4. Clean Energy Standard – This scenario assumes the adoption of a clean energy 

standard for electric power generation, and assumes aggressive development of 

wind and biomass resources for electric power generation in the region. We 

have estimated the development of biomass potential by county and the 

calculation of local output and employment impacts of wind and biomass 

development. This scenario relates to Blueprint Strategic Objective #2.

 

2 Energy Efficiency in Appalachia: How Much More is Available at What Cost, and by When?, May 2009, 

prepared by the Southeast Energy Efficiency Alliance, for ARC, available at 

http://www.arc.gov/assets/research_reports/EnergyEfficiencyinAppalachia.pdf. 
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research ES-3 

5. Expanded Natural Gas Development – This scenario explores the intensive 

development of natural gas resources in the Appalachian region. We use the 

AEO 2011 side case high shale gas scenario to provide the national context, and 

have developed shale gas potential by county and state. This scenario relates to 

Blueprint Strategic Objective #3.

In the Reference case, the Inforum LIFT model was calibrated to the Annual Energy 

Outlook (AEO) 2011 macroeconomic and energy consumption variables. The CUEPS 

model was run in conjunction with LIFT, using national forecasts as determinants of the 

growth of basic industries, changes in prices and earnings rates, and the national 

changes in the interindustry relationships. The other scenarios were developed by 

changing selected assumptions in the Reference case to determine the national and 

county level impacts of policy changes or technology assumptions. 

ES.1 Key Study Findings

This study has developed alternative scenarios to aid in the analysis of likely policy 

outcomes. The scenario choice, as stated above, was informed by decisions of likely 

outcomes, but also guided by the strategic objectives as outlined in the ARC regional 

energy blueprint. Our goal has been to contribute to the dialogue surrounding these 

objectives by providing quantitative, scenario-based analysis. The modeling system used 

provides comprehensive national level summaries of policy impacts, as well as economic 

and electric power sector impacts at the county level. 

Some main policy conclusions which we can draw from this quantitative analysis are:

1. Energy Efficiency stands out as an extremely effective policy to pursue and may 

in fact pay for itself in terms of higher GDP, employment, and government 

revenue. It benefits the economy in terms of increasing aggregate multifactor 

productivity, reducing prices, and increasing employment and real incomes. 

However, certain localities that are dependent on coal are likely to have lower 

employment and income.

2. Natural gas from shale is a very promising avenue for stimulating employment 

and income growth in the northern Appalachian region, as well as contributing to 

a smaller carbon footprint. 

3. Wind resources in Appalachia, though important, are not huge. Wind should be 

relied upon as one component of strategy for economic development of the 

region. According to our estimates, total potential wind capacity in the entire 

Appalachian region is about 16.6 GW, implying a total investment of about $34 

billion (2009$), spread over a period of years. 

4. Biomass resources are also important. We expect a total contribution of $10 

billion to the Agriculture and forestry sector, as well as a contribution of about $5 

billion to the Transportation sector through 2020, if biomass resources are 

aggressively developed.

5. Carbon mitigation strategies would accomplish important objectives through the 

reduction of a large share of U.S. carbon emissions which are generated from 

coal burned by electric utilities. However, unless accompanied by other 

strategies that stimulate development in the Appalachian region, several 

Appalachian counties would be hurt disproportionately.
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ES.2 Scenario Comparisons

Results for selected key macroeconomic variables are summarized in table ES.1. For 

each variable, the first line shows the value of that variable in the Reference case in 2015 

and 2020, and the other lines show the differences from the Reference for each of the 

policy cases. 

Several main conclusions stand out at the macroeconomic level:

1. The High Efficiency case shows by far the largest positive impact. Gains from 

increases in electricity efficiency are significant, whether we use GDP, real 

income, or employment for our criteria. Policy actions that can be taken to 

accelerate increases in the development of efficient technologies or to provide 

incentives for the adoption of existing technologies have a high payoff, and may 

justify public expenditures either from direct subsidies, public assistance, or tax 

credits. By 2020, the High Efficiency case shows real GDP higher by 75 billion 

(2005$), with real Disposable income up by $61 billion. Total employment is higher 

by 526 thousand jobs. Note that the High Efficiency case also shows the largest 

reduction in aggregate energy intensity of all the scenarios examined, and also 

manages to reduce total CO2 emissions by 580 MMT by 2020. 

2. The Carbon Mitigation case shows the largest negative impact. Although it is the 

most effective scenario at reducing carbon emissions (742 MMT reduction by 

2020), it does so at the cost of lower GDP, employment and real income. 

Aggregate energy intensity is reduced, but not by as much as the High Efficiency 

scenario. 

3. The Clean Energy Standard is a highly effective way of reducing carbon 

emissions, without a large cost to GDP, real income or employment. Although the 

changes in these three variables are all slightly negative with respect to the 

Reference case, the differences are not large in percentage terms.

4. Expanded Natural Gas Development is somewhat positive for aggregate GDP 

and real income, significantly positive for employment, and with measurable 

reductions in carbon emissions (285 MMT reduction by 2020). 
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Table ES.1 Macroeconomic Summary of Key Variables Across Scenarios

These national level results are fully consistent with our findings at the Appalachia state 

and county levels. However, we find greater differences in impact at this level due to 

different distributions of production and employment by industry, as well as differences in 

the distribution of coal and natural gas resources, and in the availability of biomass and 

wind development in the Clean Energy Standard scenario.

As we will see in section 6.4, several West Virginia counties suffer from declines in 

employment and output in the High Efficiency case, even though this scenario shows 

strong positive effects at the national level. This is due to the fact that more than half the 

electric power generation in the West Virginia territory is from coal, and this coal 

consumption supports the local mining industry. As electricity efficiency increases, coal 

consumption decreases, with resulting decreases in coal industry employment. 

In section 6.2.6 we show some comparisons at the aggregate state level across 

scenarios. Some of the main conclusions from these are:

1. Consistent with the U.S. national results, the High Efficiency case shows the most 

positive impacts on employment and real income. The Carbon Mitigation case 

shows the largest negative impacts. Results for the CES are mixed, but small, and 

the Expanded Natural Gas case has slightly positive impacts.

 2015 2020

 Real Gross Domestic Product (bil 2005$) Reference 15,474 17,723

High Efficiency 34 75

Carbon Mitigation -60 -154

Clean Energy Standard -2 -26

Expanded Natural Gas 14 21

 Real Disposable Income (bil 2005$) Reference 11,711 13,309

High Efficiency 31 61

Carbon Mitigation -21 -45

Clean Energy Standard 0 -20

Expanded Natural Gas 10 15

 Employment (thousands) Reference 154,733 161,550

High Efficiency 320 526

Carbon Mitigation -401 -999

Clean Energy Standard 49 -39

Expanded Natural Gas 147 284

 GDP Deflator (2005 = 1.0) Reference 1.19 1.31

High Efficiency 0.00 -0.01

Carbon Mitigation 0.02 0.04

Clean Energy Standard 0.00 0.00

Expanded Natural Gas 0.00 -0.01

 Energy Intensity (btus/GDP) Reference 6.66 5.93

High Efficiency -0.20 -0.45

Carbon Mitigation -0.23 -0.24

Clean Energy Standard -0.18 -0.28

Expanded Natural Gas -0.08 -0.10

 Total CO2 Emissions (million metric tons) Reference 5,762 5,791

High Efficiency -225 -580

Carbon Mitigation -555 -742

Clean Energy Standard -408 -718

Expanded Natural Gas -197 -285

Line 1 is reference case value

Other lines are differences from reference
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ES-6 Inforum / Keybridge Research

2. However, there is a significant degree of variation within the states, which is 

reflective of an even greater variation at the county level. For example, although 

all states benefit in the High Efficiency scenario, the benefit to Kentucky and West 

Virginia is not nearly as large, since these are states more heavily dependent on 

coal mining. The state and county impacts in the CES case are a mix of positive 

impacts from Wind and Biomass, countered by negative impacts from reductions 

in coal. Kentucky and Ohio stand out with negative employment changes 

relative to the Reference case. The Expanded Natural Gas case shows states like 

Ohio, New York and Pennsylvania benefitting more than the average, as these 

states contain some of the richer shale resources.
Economic Impact of Energy and Environmental Policy in Appalachia

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Introduction

This report describes a set of tools and analysis used to approach energy and 

environmental problems relevant to Appalachia. The goal of the project is not only to 

develop these tools, but to apply them to help inform policymakers about the costs and 

benefits of different energy strategies, from alternative and renewable energy policies to 

carbon mitigation schemes and energy efficiency regulation.

Inforum has a long and successful history in the development of detailed industry and 

regional models, as well as the development of models focusing on a particular industry 

or region. Working with government and private sector research sponsors, Inforum 

constructs and applies these economic models to investigate a variety of issues,

including energy and environmental public policies. Inforum is widely recognized as a 

pioneer in the construction and application of dynamic, interindustry macroeconomic 

models which portray the economy in a unique "bottom-up" fashion. 

For this project, Inforum has teamed with policy experts from Keybridge Research, an 

international economics and public policy consulting firm. Keybridge serves as energy 

policy advisors to a leading association of Fortune 500 CEOs and has previously teamed 

with Inforum to perform numerous energy-focused economic modeling studies for 

private and non-profit clients. The project team’s experience with economic modeling 

and expertise in providing strategic guidance on the economics of federal climate 

change policy make it uniquely suited to estimate and evaluate the economic impacts 

of new energy policies in Appalachia.

1.1 Context and Objectives

Legislative and regulatory actions at the state, regional, and federal levels have already 

resulted in significant changes to energy markets. In 2005, Congress passed the Energy 

Policy Act (EPAct) with the goals of increasing energy efficiency and stimulating 

renewable power. In 2007, the Energy Independence and Security Act was passed, 

which contained additional incentives for energy efficiency as well as the stimulation of 

biofuels production. Sweeping regulatory and legislative actions related to reducing 

greenhouse gas emissions are yet to come and will have great impacts on the patterns 

of U.S. energy demand, supply, and prices. In turn, such changes will have important 

impacts on the growth and composition of local economies, especially those of 

Appalachia.

At the state and local level, several governments have initiated strategies to help 

promote energy efficiency investments and renewable energy technologies. ARC has 

developed a regional blueprint that established three goals for energy and economic 

development:

1. Promote energy efficiency in Appalachia to enhance the region’s economic 

competitiveness.

2. Increase the use of renewable energy resources to produce alternative 

transportation fuels, electricity, and heat.

3. Support the development of conventional energy resources to produce 

alternative transportation fuels, electricity, and heat.
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At the federal level, Congress has been considering various pieces of legislation ranging 

from renewable energy standards for the electric power sector to an economy-wide 

cap on carbon emissions. For a variety of reasons, including economic weakness, the 

progression of proposals through Congress has been temporarily suspended. Once 

economic growth is sustained, however, energy and climate legislation will likely 

reemerge as a priority for federal policymakers. In addition, the U.S. EPA is in the process 

of developing its regulations of greenhouse gases emissions that will probably take effect 

in 2012. 

Within this context, the ARC has sought to identify and articulate a comprehensive 

regional response to such legislation and regulation, one that would enhance the 

benefits and mitigate the costs of such policies to the region’s economy. As part of this 

task, ARC has expressed an interest in developing modeling infrastructure designed to 

assess the economic opportunities and challenges for the Appalachian region of energy 

policies and regulations of greenhouse gas emissions. Among several capabilities, this 

infrastructure should enable the evaluation of the employment and economic impacts 

of energy and environmental policies and investments. In this project we: 

1. Develop modeling infrastructure that quantifies and models the Appalachian 

regional economy with particular regard to county-by-county makeup of 

industrial output and employment, energy use and production, and household 

income. Quantification has been realized via the compilation and validation of 

available data from U.S. government and other sources for the 13 states and 420 

counties that make up Appalachia. The modeling of these regions has been

accomplished by tying county-level economic models to Inforum’s LIFT (Longterm Interindustry Forecasting Tool) model.

2. Use the modeling infrastructure to develop a baseline projection for the regional 

economy through 2020. Given this baseline, alternative scenarios have been

developed to estimate the employment and economic impacts of plausible 

alternative energy and environmental policies. Once again, particular emphasis 

is on defining those impacts at the county level, as well as at a regional macrolevel. These scenarios provide tangible examples of how the modeling system 

can be used for further research. 

3. Consider the modeled impacts of various policies, and develop a strategy to 

enhance the benefits and mitigate the costs of policies on the region’s industry 

and households. Such strategies could include how state and local governments 

can leverage federal initiatives for regional economic development, energy 

conservation, or alternative technologies. Modeling results could facilitate the 

development of such initiatives.

For example, the advent of carbon pricing under a cap and trade or direct taxation 

scheme might have a large negative impact on Appalachia’s coal industry. However, 

policies to facilitate carbon sequestration or other emission-reducing technologies may 

mitigate these impacts. Moreover, there are many opportunities to leverage the region’s 

other energy resources such as biomass, natural gas, and wind.

A modeling infrastructure, informed with insights on local economic composition, 

resource endowments, and technological possibilities can be used to illustrate the 

tradeoffs among various policy options and regional development strategies. Given the 

uncertainty with regard to the nature of future climate policies, as well as the market and 

technological responses to those policies, the timing and magnitude of economic 

impacts will also be uncertain. However, while the model proposed cannot provide 
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Inforum / Keybridge Research 3 

certainty, it will provide an accounting framework that will identify areas of both 

vulnerability and opportunity and will greatly illuminate the issues which determine the 

timing and magnitude of climate policy impacts. In turn, this analysis can help 

policymakers craft strategies to better mitigate economic hardships and maximize 

economic opportunities associated with structural and technological change.

1.2 Overview of This Study

This study provides a context to the climate and energy issues facing Appalachia and 

uses scenario based analysis to understand aspects of likely policies’ impacts on the 

Appalachian region. Chapter 2 begins with a review of the taxonomies of policies that 

have been proposed in the last ten years or so, including (1) national climate and energy 

legislation; (2) national environmental legislation; (3) state and regional climate and 

energy policies; and (4) energy supply policy. Chapter 3 reviews some of the wealth of 

literature and models that have been used to analyze policies and predict their impacts. 

This helps to put the present study in context, and to compare our approach with other 

modelers. Chapter 4 summarizes the EPIM modeling system, which consists of the 

Inforum LIFT model of the U.S. and the CUEPS model of counties and utilities. Chapter 5 

summarizes the set of scenarios we have chosen to examine in this study, and provides 

rationale for those choices. It also describes how the scenarios were devised, including 

information about assumptions used or calibration to other modeling systems such as the 

National Energy Modeling System (NEMS). Chapter 6 presents selected model results for 

the scenarios, at the macro, state and county level. Chapter 7 presents conclusions and 

suggestions as to the usefulness of this study, and suggests directions for further research. 

Several appendices are included that include detailed information not appropriate for 

the main text. Appendix A documents the derivation of county economic data, electric 

utility data, and the county-utility bridge used to link counties and utilities. Appendix B 

describes the operation of the LIFT and CUEPS models in more detail than chapter 4. 

Appendix C describes the development of wind and biomass assumptions that are used 

in the Clean Energy Standard scenario. Finally, Appendix D describes how the estimates 

of coal production by county were developed.

1.3 Scenario Based Analysis

This study is based on the method of scenario based analysis. First, a Reference case is 

developed which embodies assumptions as to likely trajectories of macroeconomic and 

energy variables. In this study, the Reference case is calibrated to the Annual Energy 

Outlook 2011, which is run using the NEMS model. The policy scenarios can be seen as 

alternative states of the world to the Reference case, and they embody specific 

changes in certain assumptions. By comparing results from the policy cases with the 

Reference case, one can determine the “delta” or change of selected variables in 

response to the changed assumptions in the policy scenarios. Section 4.4 describes this 

process in more detail, and concrete examples are discussed in Chapter 5.
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2. Policy Review

2.1 National Climate & Energy Legislation

2.1.1 Policy Summary

Although several significant energy bills have been passed by Congress during the last six 

years, it is widely anticipated that more legislation that directly addresses climate 

change may be passed in the future. Thus far, the most widely discussed approach to 

climate policy has been a cap and trade system. Such a system would require GHG 

emitters to obtain permits for every ton of GHGs emitted, with the economy-wide 

number of permits available capped at a level that would be ratcheted down over time. 

The 111th Congress came the closest to successfully passing a cap and trade bill, with 

the American Clean Energy and Security Act of 2009 (“Waxman Markey”) passing the 

House. However, a similar bill, the American Power Act (“Kerry Lieberman”) stalled in the 

Senate, prompting many legislators to pronounce the cap and trade approach to 

climate policy dead. Had they succeeded, these legislative efforts would have had a 

major impact on the nation’s energy industry, increasing the costs associated with using 

traditional, more GHG-intensive energy sources and making less GHG-intensive sources 

of energy more competitive. 

More recently, policymakers have discussed legislation that focuses on the electric 

power sector. Several legislators and the President have proposed the adoption of a 

national renewable energy standard (RES) or a clean energy standard (CES). Such 

legislation would require that an increasing proportion of electric power be produced 

from renewable energy, in the case of an RES, or with renewable, nuclear, and other 

low- or no-carbon energy sources, in the case of a CES. In his 2011 State of the Union 

address, President Obama suggested that 80% of the nation’s electricity should come 

from “clean” sources by 2040. Similar policies have been proposed by Senator 

Bingaman, Senators Tom Udall and Mark Udall, Senator Lugar, and others. To date, no 

bills that include a national renewable energy standard have been passed.

2.1.2 Policy Impacts on Appalachia

Any of the policies discussed above would likely have a significant impact on 

Appalachia, although the net effects on different sectors within the regional economy 

are likely to vary significantly. For instance, one of the region’s major industries, the coal 

industry, will almost certainly be negatively impacted by an RES or CES. Any climate 

policy coming out of Congress is likely to give electric utilities and/or industrial users of 

electricity an incentive or a mandate to switch from conventional coal-burning 

technologies to less GHG-intensive fuels or technologies. Some experts, including several 

leading utilities, claim that the still largely untested carbon capture and sequestration 

(CCS) technologies can lower the GHG footprint of coal combustion, making the fuel’s 

use more compatible with the achievement of climate goals. Furthermore, because 

capturing, transporting, and pumping GHGs into underground storage require significant 

energy inputs, the adoption of CCS technologies also lowers the efficiency of power 

plants – often referred to as a parasitic loss – increasing the amount of coal required to 

produce a given amount of electricity. Nevertheless, many experts believe that this 
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Inforum / Keybridge Research 5 

technology will be too costly to be widely adopted and that as a result, climate policies 

will further reduce already declining domestic coal demand. 

The same incentives that are expected to hurt conventional coal technologies, however, 

would also increase investment in and demand for renewable fuels. Given that 

Appalachia holds the richest biomass and onshore wind resources in the eastern U.S., 

there is the potential for significant positive economic impacts from national climate or 

energy legislation. 

The impact of climate policy on other key Appalachian industries, however, is far less 

certain. Natural gas production, an industry that is growing rapidly in Appalachia, could 

benefit from a shift away from coal toward less carbon-intensive fuels. However, many 

climate proposals include long-term emissions reduction goals so stringent that even 

electricity produced by natural gas would be considered too carbon-intensive, if not 

accompanied by CCS technology. Additionally, current proposals to establish a CES or 

RES differ significantly in whether they consider natural gas to be a “clean” generation 

fuel, adding more uncertainty to how legislation might affect the industry. 

Finally, the impact of RES and CES policies on manufacturing is also uncertain. 

Manufacturing in Appalachia could be negatively impacted as a legislated shift away 

from cheap fossil fuels drive up electricity prices, although some proposals include 

provisions to protect manufacturing industries from rising costs. Some policy proposals 

even provide incentives for investments in energy efficiency that could enhance the 

competitiveness of domestic manufacturers.

2.2 National Environmental Regulation 

2.2.1 Policy Summary

While climate change and energy legislation have stalled in Congress, the EPA is moving 

closer to regulating GHG emissions under its existing regulatory authority. The 2007 

Supreme Court case Massachusetts v. EPA, mandated both that the EPA assess whether 

or not GHG emissions constitute a danger to society and that the EPA regulate those 

emissions under its Clean Air Act authority if they are found to be a danger. In 

December 2009, the EPA announced that “the current and projected concentrations of 

[greenhouse gases] threaten the public health and welfare of current and future 

generations.” This “endangerment” finding is the basis for national EPA regulation of GHG

emissions. 

EPA has already used its authority under the Clean Air Act to raise CAFE standards for 

light duty cars and trucks, and is currently working to design GHG emissions regulations 

for stationary emitters, including power plants and other industrial facilities. Regulations 

under consideration are likely to require any newly constructed or significantly modified 

emissions source to apply best available control technology (BACT), as determined on a 

case by case basis, and to comply with minimum emissions performance standards. As 

currently envisioned, such regulation would only apply to large stationary emitters, 

responsible for 70% of national stationary emissions, until 2016. Nevertheless, it is currently 

unclear what the final regulation regarding stationary source emissions will look like. 

The EPA is also in the process of revising and updating its regulations regarding criteria 

pollutants – sulfur dioxide (“SO2“), nitrogen oxides (“NOX“), mercury, particulate matter 
Economic Impact of Energy and Environmental Policy in Appalachia

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(“PM”), carbon monoxide (CO), dioxins, and volatile organic compounds (“VOCs”) – 

under the Clean Air Act. Regulations subject to revision include:

1. The Clean Air Transport Rule (Transport Rule): Sets power plant SO2 and NOX 

emissions limits for 31 states (including the 13 Appalachian states) and the District of 

Columbia. 

2. The National Ambient Air Quality Standard (NAAQS): Regulates the emissions of 

ozone forming compounds, including NOX, CO, methane (CH4), and VOCs.

3. National Emissions Standards for Hazardous Air Pollutants (NESHAPs) for Utility 

Boilers: Also known as the Utility Boiler MACT (Maximum Achievable Control 

Technology) Rule, it regulates emissions of hazardous air pollutants (“HAPs”) 

including mercury, hydrochloric acid (“HCl”), VOCs, and others from utility boilers.

4. NESHAPs for Industrial, Commercial, and Institutional Boilers: Also known as the 

Industrial Boiler MACT Rule, it regulates emissions of HAPs from industrial boilers.

EPA is scheduled to issue final rules for each of these regulations in early 2012. Assuming 

that the revision process proceeds in a timely manner, utilities and industrial facilities will 

have until 2014 or 2015 to make the required modifications or be forced to shut down.

In addition to implementing new or revised regulations under the Clean Air Act, the EPA 

is also seeking to regulate power plant and industrial facility energy use under the Clean 

Water Act. One recently proposed regulation is meant to reduce the negative impacts 

that power plants’ and industrial facilities’ water usage has on aquatic life. The new rule 

will require many facilities to make significant investment to their cooling water intake 

structures (“CWIS”) which may be relatively minor investments for some but would 

represent a major expense for facilities that may be force to replace open-loop or oncethrough CWIS with closed-loop CWIS. Although the specific timeline remains uncertain, 

implementation will take place in three phases: (1) new facilities, (2) existing power plants 

that use over 50 million gallons of cooling water per day, and (3) remaining power plants 

and manufacturing facilities. The EPA’s final rule is expected by July 27, 2012.

Finally, the EPA is in the process of adopting a new regulation to govern the disposal of 

coal ash under the Resource Conservation and Recovery Act (“RCRA”). This rule would 

mandate certain safety controls at coal ash impoundments and landfills, and regulate 

the recycling of coal ash. The EPA is still in the process of weighing two alternative 

regulatory paths: one in which it would directly regulate coal ash management and 

disposal, the second in which it would set performance standards and rely on citizen 

lawsuits to enforce them. However, even as the rulemaking process is still underway, 

Republican legislators have introduced legislation that would prevent the EPA from 

enforcing coal ash regulations.

2.2.2 Policy Impacts on Appalachia

These newly proposed and revised regulations are likely to significantly impact the 

Appalachian regional economy. As with proposed GHG legislation, the industry most 

likely to sustain net negative effects is the coal industry. Emissions regulation would make 

coal less attractive relative to other fuels, as coal-burning facilities usually emit more 

greenhouse gases and criteria pollutants than gas-burning facilities in particular. 

Additionally, the CWIS rule is likely to require the most costly changes to water cooling 

systems at older power production and manufacturing plants, which primarily burn coal. 
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Inforum / Keybridge Research 7 

Finally, coal ash regulations will raise utilities’ cost of coal ash removal and storage. The 

resulting retrofit costs and additional operating costs are likely to be substantial, and may 

force a number of coal-fired facilities to close sooner than they otherwise would.

The impacts of these regulations on other energy sources in Appalachia are more 

ambiguous, and could be positive in the case of several fuels. Natural gas combustion is 

a source of far fewer criteria pollutant emissions than coal, does not produce waste 

byproducts that require special disposal, and is about half as GHG intensive as coal 

combustion. Nuclear power has no direct emissions associated with its use and its waste 

management processes will remain unaffected by the EPA’s regulatory changes. Some 

older facilities may, however, face additional costs as a result of the CWIS regulation 

requirements. Renewable power generation facilities will for the most part not be directly 

affected by proposed EPA regulations, with the exception of rules that may regulate 

criteria pollutant emissions from biomass facilities. Hence, to the extent that coal is 

harmed by regulations, these competing energy sources will likely benefit. 

Another major beneficiary of regulatory policies will be the manufacturers of pollution 

control equipment, but other manufacturers could be hurt by these regulations, 

especially the most energy-intensive manufacturers. Many of these manufactures, 

especially those in Appalachia, use coal in their boilers and kilns and will have to invest in 

expensive equipment upgrades, or switch to more expensive, lower emitting fuels. 

Additionally, manufacturers that use a lot of electricity could be hurt by higher electricity 

rates if utilities pass the costs of pollution control equipment through to them. These 

added costs could reduce manufacturers’ competitiveness and, in some cases, prompt 

the closure of Appalachian manufacturing facilities. Meanwhile, if the regulations lead 

to an economy-wide flight from coal to gas or biomass, facilities that are already using 

those fuels may face higher fuel prices as a result of that increased demand. 

Nevertheless, these regulations have significant benefits too. While some pollution 

control equipment require additional energy inputs, some investments, particularly those 

aimed at complying with GHG regulations, may increase plant efficiency and improve 

the long-term competitiveness of those plants. An earlier ARC study, Energizing 

Appalachia3 , identified manufacturing in the region that would benefit from additional 

demand for solar, wind and biomass related equipment. In addition, the region has rich 

resources of biomass and wind that could be developed. Other benefits, however, like 

reductions in pollution, are difficult to put dollar values on. For example, reducing air 

pollution and controlling waste are expected to result in significant health benefits for 

communities near to and downwind from the regulated facilities. These benefits include 

the reduction of pollution related illnesses and mortalities, which would improve 

productivity through reductions in work and school absences and reductions in 

premature deaths of people who contribute to the economy, benefits which are difficult 

to quantify. Another example is the impact of the CWIS regulation on aquatic life and 

the benefits that the rule could have to commercial and recreational fishermen.

 

3 Energizing Appalachia: Global Challenges and the Prospect of a Renewable Future, 

Pennsylvania State University Department of Geography, September, 2007, at 

http://www.arc.gov/assets/research_reports/EnergizingAppalachiaGlobalChallenges.pdf. 
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2.3 State & Regional Climate & Energy Policies

2.3.1 Policy Summary

In addition to various proposed and already approved federal policies, utilities and 

industrial facilities face a patchwork of state-specific energy and climate laws. West 

Virginia, Pennsylvania, New York, Ohio, Maryland, and North Carolina all have 

mandatory renewable or clean energy standards (“RES” and “CES”). The stringency of 

these standards and how they define “renewable” and “clean” vary by state, in addition 

to a range of other policy specifications that aim to create more flexibility.

Table 2.3.1 Sample of CES and RES Policies

Other states use incentives and mandates outside of RES systems to promote the use of 

renewable fuels or energy efficiency. Pennsylvania, for example, requires electric utilities 

to develop and implement plans to reduce consumer demand. A number of states, 

including West Virginia and Kentucky, offer tax incentives and adjustments to encourage 

renewable energy investment. 

Enactment of energy and climate policy varies widely between Appalachian states. 

While Alabama and Tennessee have yet to enact any significant climate or energyrelated legislation, New York and Maryland participate in the Regional Greenhouse Gas 

Initiative (RGGI), which imposes a cap and trade system on power producers in ten 

Northeast and Mid-Atlantic states. Meanwhile, Ohio is participating as an observer to the 

Midwest Greenhouse Gas Reduction Accord (MGGRA), which has proposed 

implementing a similar multi-state cap and trade system.

2.3.2 Policy Impacts on Appalachia

As with the national policies discussed in the previous sections, state and regional climate 

and energy policies are likely to negatively impact the coal industry. States with RESs or 

participating in RGGI will direct power producers away from coal-fired generation, 

reducing the demand for Appalachian coal. Nevertheless, some coal-fired power 

utilities in Appalachia could benefit from regional and state policies through leakage. 

For example, a poorly designed RPS or cap and trade system may result in opportunities 

to sell coal-fired electricity produced in Appalachia to states and regions with more 

stringent regulations.

Appalachia’s fast-growing natural gas industry is likely to feel more ambiguous impacts 

from regional climate and energy policies. Cap and trade systems, including RGGI, are 

Renew. Hydro* Nuclear CCS New Coal

Maryland 20% by 2022 X X

New York 29% by 2015 X X

North Carolina 12.5% by 2021 (investor-owned)

10% by 2018 (cooperative/municipal) X X (up to 

10 MW)

Ohio 25% by 2025 X X X X

Pennsylvania 18% by 2020 X X X

West Virginia 25% by 2025 X X X X

*Most of these RES exclude existing hydro and some large scale or non-run-of-river hydro.

Includes:

State Target
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Inforum / Keybridge Research 9 

likely to favor natural gas in the short and medium terms as a relatively affordable and 

clean alternative to coal. However, while some state regulations explicitly favor natural 

gas, others, particularly those with established RES, treat gas no differently than coal. 

The Appalachian renewable energy industry will unambiguously benefit from all of these 

policies, as they incentivize or mandate the increased use of renewable power. 

Additionally, because Appalachia is home to the most abundant renewable resources in 

the eastern half of the country, Appalachian counties stand to disproportionately benefit 

from investments in renewable energy. Meanwhile, the costs of most regional and state 

policies will be more evenly distributed among ratepayers throughout those jurisdictions. 

Finally, the majority of regional and state regulations focus relatively narrowly on the 

power sector and include no provisions that would directly affect manufacturing. 

However, many heavy manufacturers (e.g., aluminum producers) are highly sensitive to 

changes in electricity prices. These manufacturers could face a significant cost increase 

as a result of policies that make electricity in Appalachia more expensive. Such an 

outcome would negatively affect the competiveness of those heavy manufacturers.

2.4 Energy Supply Policy

2.4.1 Policy Summary

The policies discussed in the previous sections focus on the use of energy. As a major 

energy producer, however, Appalachia could be significantly impacted by new and 

revised regulations concerning natural gas and coal production, particularly those that 

restrict mountaintop removal mining and hydraulic fracturing. Mountaintop removal 

(“MTR”) mining has been widely criticized as environmentally harmful, but continues to 

be utilized by several Appalachian coal mines. One specific criticism is that the debris 

removed from mountaintops has been moved to valley fills that pollute local streams and 

rivers, violating the Clean Water Act. 

The Bush Administration eased regulations on MTR mining by removing a specific 

restriction on depositing mining waste in U.S. waterways in 2002 and relaxing restrictions 

governing when a mining company could alter the flow of a stream in 2008. The Obama 

Administration has been less permissive of the practice, promising to revise the rule 

change made by the Bush Administration in 2008, refusing to grant new permits for new 

MTR mines, and even revoking some permits that had already been granted. 

Additionally, in April 2010, the EPA issued new guidelines aimed at protecting rivers and 

streams from pollution resulting from MTR mines. These guidelines, various legal rulings, 

and prospective new legislation, including the Clean Water Protection Act of 2009 which 

was proposed but not passed, could result in the tighter restrictions on MTR mining. 

The rapidly growing Appalachian natural gas industry could also be significantly 

impacted by the forthcoming regulations and legislation. Most natural gas resources in 

the region are unconventional and can only be accessed through hydraulic fracturing – 

a process in which drillers use high pressure mixtures of water, sand, and chemicals to 

release pockets of natural gas from shale rock. Many opponents of hydraulic fracturing 

worry that the process contaminates groundwater and creates health hazards, while 

studies of the method lack consensus on whether the process is truly hazardous and 

requiring of regulation. 
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Past legislation is also undecided on supposed health risks related to hydraulic fracturing. 

In the Energy Policy Act of 2005, it was deemed a non-hazardous practice and 

exempted from federal regulation under the Safe Drinking Water Act. In 2009, however, 

the Fracturing Responsibility and Awareness of Chemicals Act was introduced in 

Congress. Although it was never passed, it would have removed the prior exemption on 

regulating hydraulic fracturing and both allowed the EPA to regulate the process and 

required companies to disclose the chemical makeup of fracturing fluid. Even if the 

method remains exempt from federal regulation, it could still be subject to state 

regulations, given the increased public scrutiny surrounding the practice.

2.4.2 Policy Impacts on Appalachia

Tighter regulation of both MTR mining and hydraulic fracturing would significantly raise 

the costs of mining Appalachia’s fossil fuel resources and could force energy companies 

to shut down portions of their Appalachian operations if less environmentally damaging 

methods prove to be uneconomical. These shutdowns would, in turn, negatively affect 

communities that are dependent on the mining industry for employment and revenue, in 

the form of royalties and taxes. Although the net effect on the mining industry is likely to 

be negative, opportunities may also exist to offset those effects by ramping up 

production at Appalachian mines that already use or are able to adopt less 

controversial methods. Furthermore, such policies could also help to strengthen 

competing industries including renewable energy. Additional avenues for positive 

impact include positive impacts on the tourism industry in the absence of such highly 

visible mining operations as MTR, and positive health benefits to the general population.
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3. Literature Review

Over the past several years, a host of governmental, academic, private, and non-profit 

institutions have produced scores of analyses that have estimated the impacts of state, 

regional, and national climate and energy policies. The most prominent and cited 

studies have used modeling tools to project the impacts of these policies on national or 

state accounts, incomes, employment, utilities, or industries in affected jurisdictions. 

However, these studies vary greatly with regard to model inputs, outputs, and 

assumptions, as well as the type of modeling tools employed. 

More than 40 studies were reviewed for this analysis. The following section reviews and 

summarizes some of their key insights and methods. Specifically, this section reviews:

• The methodology for translating specific policy features into model inputs and 

economic and energy sector outcomes; 

• The models used for different impact analyses, the strengths and weaknesses of those

models, and their impact on a study’s methodology, results, and conclusions; and

• Key assumptions and their relationship to model outputs and policy conclusions.

3.1 Policy Choices and Their Impacts on Economic Outcomes 

3.1.1 Cap & Trade Impacts

As discussed in the previous section, the U.S. Congress and many state and local 

governments have considered several major climate and energy bills over the past 

several years, including cap and trade systems, renewable fuels standards, renewable 

portfolio standards, energy subsidies, and other regulatory mechanisms. Of these various 

proposals, cap and trade schemes have received the most sustained attention and 

debate. As a result, many of the energy and climate policy analyses that have been 

performed in recent years have focused on cap and trade GHG mitigation systems. 

At the federal level, McCain-Lieberman, then Bingaman-Specter, Lieberman-Warner, 

Waxman-Markey, and finally Kerry-Graham-Lieberman have been analyzed by a host of 

government agencies, think tanks and public policy groups, including EPA, EIA, MIT, the 

Nicholas Institute, the Heritage Foundation, etc. i Meanwhile, other groups have 

evaluated the regional cap and trade policies associated with New England’s Regional 

Greenhouse Gas Initiative (RGGI), California’s AB 32 Implementation Plan, and a 

Proposed Western Climate Initiative (WCI), as well as some cap & trade schemes that are 

not associated with a specific law or policy proposal.ii

The majority of these studies conclude that implementing a cap and trade system will 

have negative impacts on the growth of GDP, employment, income, and other 

economic indicators. Nevertheless, a handful of studies estimate positive impacts, 

highlighting how differences in the geographical scope, model, underlying assumptions, 

and the policy design features can lead to a broad range of impact estimates. iii 

Importantly, none of these analyses estimate the noneconomic costs and benefits 

associated with climate change mitigation and other changes to environmental and 

health outcomes.
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3.1.2 Cap & Trade Policy Features

Cap Stringency

One of the key policy choices driving model results is the stringency of the cap. MIT’s 

Joint Program on the Science and Technology of Climate Change released a series of 

studies that look specifically at this link between policy design features and economic 

impact. Using their Emissions Prediction and Policy Analysis (EPPA) model, the MIT team 

simulates the impacts of scores of different cap and trade policies, with the defining 

difference between many of the policies being the stringency of the cap. As would be 

expected, simulations show that, as the stringency of the cap increases, making 

emissions permits scarcer, the price of emissions permits also increases. Higher permit 

prices, in turn, worsen the policy’s impact on broader economic indicators.iv

Nevertheless, while policymakers and the public often consider the emissions cap 

stringency to be the most important design feature of any cap and trade policy, 

modeling results from a number of studies show that other features can be just as 

important, potentially even more so, in determining a policy’s economic impact.

Figure 3.1: Carbon Prices and GDP Impacts in Scenarios with Alternative Cap Stringencies 

Scope of the Cap

Cap and trade policies vary significantly in the scope of industries that they include 

under the cap. New England’s RGGI, for example, only covers electric utilities, whereas 

AB 32, WCI, and most national climate proposals also include other sectors (i.e., industrial, 

residential, commercial, and transportation). The Beacon Hill Institute’s cost-benefit 

analysis of WCI simulated both a “narrow” scope case, in which the cap and trade 

system covers only large stationary emitters, and a “broad” scope case, which caps 

emissions from smaller transportation, residential, and commercial sources in addition to 

large stationary sources. The Institute’s report shows that the economic costs 

experienced in the narrow case were amplified in the broad case as more sectors were 

forced to reduce emissions in order to achieve a greater amount of economy-wide 

emissions reductions.v However, had the overall level of emissions reductions required 

under both the broad and narrow scope scenarios been the same, then the broader 

scope scenario would have likely lead to the adoption of more cost-effective emissions 

reductions activities than those in the narrow scope scenario as broadening the cap 

would have expanded the pool of emissions reductions sources. 

$0

$20

$40

$60

$80

$100

$120

7.13 GT

Emission Cap

5.19 GT

Emission Cap

4.24 GT

Emission Cap

Carbon Price in 2030

-2.5%

-2.0%

-1.5%

-1.0%

-0.5%

0.0%

7.13 GT

Emission Cap

5.19 GT

Emission Cap

4.24 GT

Emission Cap

GDP reduction in 2030
Economic Impact of Energy and Environmental Policy in Appalachia

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Compliance Flexibility 

The EPA’s analyses of various cap and trade proposals have shown that assumptions 

about the availability of international and domestic offsets can have a very significant 

impact on the estimated effects of a given policy.4 In fact, the EPA estimated that some 

of the costs associated with implementing the Lieberman-Warner Bill would be five times 

higher if no offsets were allowed, compared to allowing the unlimited use of offsets.vi The 

Pew Center’s meta-analysis, which compares analyses conducted by seven different 

groups, also found that the greater availability of offsets is associated with reduced 

economic costs effects on the economy. Across studies, greater use of offsets also led to 

lower allowance prices and overall economic costs. 

Additionally, Terry Barker and the Tyndall Center for Climate Change Research’s metaanalysis of studies estimating the economic impacts of cap and trade systems 

concluded that international carbon permit trading (including offsets) increased

projected 2030 GDP estimates by 0.7 percentage points relative to cap and trade 

scenarios that did not include international carbon permit trading. vii This conclusion 

stems from the fact that the offsets increase the pool of emissions reduction options 

available to regulated entities, and the assumption that many of those options are more 

cost-effective than the options that would be employed if offsets were not available. 

This assumption is based on the experience of existing cap and trade schemes that have 

benefited from the availability of low-cost emissions reduction options through the use of 

offsets. Nevertheless, estimates of the cost and availability of offsets vary significantly 

and can be a major determinant of conclusions regarding the economic cost of cap 

and trade policies.

In addition to offsets, other mechanisms exist that would increase the flexibility of 

compliance obligations under a cap and trade policy. For example, the EIA’s analyses 

of multiple federal cap and trade bills demonstrate that provisions allowing for

intertemporal flexibility in complying with the emissions cap (i.e., the ability to borrow and 

bank emissions permits) can shift costs of the policy from earlier years to later years, but 

that they don’t necessarily lower the cumulative costs of the cap and trade policy.viii 

However, a Stanford University study of federal cap and trade policies similar to those in 

the Waxman-Markey Bill arrives at a different conclusion. According to this study,

allowing for the banking of emissions permits would reduce industry losses and improve 

national economic indicators such as GDP and employment in all years as firms would 

be able to take advantage of less expensive mitigation options in the earlier years of the 

policy, and avoid making more costly emissions reductions in the latter periods of the 

policy.ix

 

4 Offsets are emissions reductions that are made by unregulated entities in order to generate 

emissions allowances that can be sold to entities that are regulated under a cap and trade 

system. Permitting their use, therefore, increases the total number of emissions allowances 

available to regulated entities but offsets that increase with an emission reduction by an 

unregulated entity or in an unregulated jurisdiction that would not have otherwise occurred. 
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Permit Allocation

Perhaps more than any other policy feature, the allocation of permits has been the 

primary focus of many cap and trade policy analyses, and has been shown to be a 

major driver of economic impact estimates. Resources for the Future (RFF) measured 

how five different methods of allocating permits to the electric power sector – 100% 

auction, grandfathering, or load-based allocation based on population, emissions, or 

consumption – would have significantly different impacts on electricity consumers, and 

how those impacts also vary depending on whether consumers live in regulated or 

competitive markets.x The authors show that auctioning of emissions permits would result 

in higher electricity price impacts and lower electricity consumption than under other 

allowance distribution methods. The study also finds that, while alternative allocation 

methods diminish a cap and trade policy’s average price impact on electricity, they also 

distort prices in favor of consumers in regulated markets and against those in competitive 

markets. The reason for this distortion is because customers in regulated markets would 

benefit from the full pass through of the benefits of free allocation while customers in 

competitive markets would not, as those prices would still equal the marginal cost of 

power production, including carbon costs. Finally, RFF’s report also demonstrates that 

free allocation would lower electricity sector emissions reductions, forcing other sectors 

of the economy to increase their own emissions reductions in order to comply with the 

cap, likely resulting in higher overall compliance costs. 

The decision of whether or not to freely allocate permits is most critical to energyintensive industries that are exposed to trade with unregulated jurisdictions. When 

considering subnational policies, this group is more inclusive and includes the electric 

power industry, but for national policies, this group is more limited to industries that 

compete internationally, such as steel, cement, and paper. Most of the federal cap and 

trade bills have included a special mechanism for freely allocating permits in order to 

prevent these industries from being disadvantaged relative to foreign competitors. The 

Stanford study discussed above assesses the impacts of different allocation methods on 

eight emissions-intensive sectors (most of which are also highly exposed to international 

competition). The study finds that the full auction of emissions permits would reduce 

profits and that free allocation would increase profits in those emissions-intensive 

industries relative to what profits would be if no climate policy were adopted.xi The study 

finds that only 14% of all emissions permits would have to be freely allocated to these 

eight industries in order to return them to the same level of profitability that they would 

have achieved had no climate policies been adopted. The remaining 86% could then 

be auctioned without harming those eight vulnerable industries. 

Use of Auction Revenues 

The auctioning of emissions permits can generate substantial government revenues and 

it should be noted that the simulation of any revenue generating policy needs to be 

complemented by revenue use policies. Revenue use assumptions can be categorized 

as falling into one of four revenue use categories:

1. Government spending – Some studies add the revenues collected from a permit 

auction or carbon tax to a government’s general fund and assume that it is spent on 

government programs in the same proportions as other government funds. In 

general, such an assumption would mean that the policy would have a negative 
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economic impact as most models estimate that governments would not spend 

money as efficiently as the private sector would had they not been subject to the 

tax. Other studies assume that all or some of the funds are earmarked for energy 

projects, particularly those that invest in efficiency and renewables. Like regular 

government spending, earmarking funds for a particular purpose is usually modeled 

as being less efficient than leaving revenues in the hands of the private sector. 

However, the case is often made that directing money to certain investments can be 

more efficient than leaving the money in the private sector if the investments help to 

correct existing inefficiencies of private spending caused by market externalities. 

None of the studies reviewed, however, attempt to gauge the accuracy of this 

claim. 

2. Debt reduction – Simulations that assume revenues would be used for debt reduction 

tend to show major negative impacts on measures of economic wellbeing such as 

GDP or income. In contrast, the effect on other economic indicators such as the 

savings and government debt levels are generally ignored. 

3. Rebating – The rebating of carbon revenue to people and businesses that face 

higher costs as a result of the carbon price has essentially the same impact as freely 

allocating emissions permits to GHG emitters. Both strategies offset some or all of the 

carbon costs paid, reducing the negative impacts on targeted industries or 

populations, but in some cases also reducing the effectiveness of the policy by 

reducing the incentive to change behavior or invest in new technologies. 

4. Reduction of other taxes – Several studies show that an efficient use of revenues 

would be to reduce other taxes. If the taxes being reduced are estimated to be less 

efficient (or more distortionary) than collecting carbon revenues, then the net

economy-wide impact is likely to be positive. Like the other choices of how to use 

carbon revenues however, the economy-wide impact estimates could mask

negative impacts on certain industries and population groups.

In the Stanford study discussed above, the question of which allocation method was best 

for the national economy depended largely on how auction revenues were distributed. 

The study found that if all auction revenues were used to reduce income taxes, the full 

auctioning of allowances would not only be better than the other permit allocation 

methods, but it would lead to better national economic outcomes than those that would 

be achieved if no climate policy were adopted. Similarly, several other analyses, 

including EPI’sxii, the University of New Hampshire’s, and EPA’s analysis of the American 

Power Act, have also shown the efficient use of auction revenues, particularly the 

reduction of other distortionary taxes, can significantly improve projected economy-wide 

outcomes. In addition, Terry Barker’s meta-analysis of climate studies showed that cap 

and trade policy simulations that assume the efficient use of auction revenues improved 

2030 GDP estimates by 1.9 percentage points over what they would be with less efficient 

revenue usage assumptions.xiii

3.1.3 Important Features of Other Climate Policies

Fewer sophisticated analyses have been done of carbon taxes, renewable portfolio 

standards, and other non-cap and trade climate policies. Fortunately, however, many 

of the simulations of cap and trade policies either include features that are similar to 

these other policies or simulated those policies in conjunction with cap and trade. 
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Carbon Taxes

Carbon taxes are modeled in essentially the same manner as cap and trade policies. 

Both policies put a price on carbon, encouraging all sectors covered by the cap or tax 

to change behavior or make investments that can reduce emissions for a cost that is less 

than the carbon price. The only difference between modeling carbon taxes and 

modeling cap and trade is the variable that is held constant. Carbon tax simulations fix 

the carbon price and allow the emissions level to vary, whereas cap and trade 

simulations fix the emissions level and vary the carbon price. The latter are generally 

achieved by running iterative simulations of carbon tax scenarios until the tax is 

sufficiently high to induce enough GHG emissions reductions to achieve the cap. The 

Business Roundtable’s modeling analysis of its own policy recommendations 

acknowledges the practical equivalence of simulating either carbon taxes or cap and 

trade, acknowledging that “the study remained agnostic as to the type of instrument 

that is used to establish the carbon price.” That study simulated two carbon price 

trajectories that were based on the carbon prices estimated in various analyses of the 

Lieberman-Warner cap and trade bill. Unlike those studies, however, the Roundtable’s 

simulations fixed the carbon price and allowed the GHG emissions levels to vary.xiv

Renewable Energy Standards (RES) or Clean Energy Standards (CES)

In general, economists consider technology mandates such as these to be less efficient 

mechanisms for reducing GHGs than market mechanisms such as cap and trade and 

carbon taxes. The reason is that market mechanisms provide incentives to make the 

least expensive emissions reductions first and they allow they allow the market to find the 

most cost-effective reductions. In contrast, RES and CES mandate the adoption of 

certain emissions reducing technologies in the electric power sector, which may not be 

the most cost-effective emissions reduction strategies. 

This does not necessarily mean that these standards are more costly policy options than 

carbon taxes or cap and trade. The reason is that RES and CES do not generally require 

the collection of revenues, which as discussed above, can create other economic 

inefficiencies. Because most RES and CES do not generate government revenues, they 

lack this form of inefficiency. An RFF study demonstrates how regulatory approaches 

that do not collect carbon revenues could result in better economic outcomes even if 

they result in less cost-effective emissions reductions than a market based revenue 

collecting scheme.xv

Nevertheless, none of the studies reviewed compare market mechanisms to RES or CES. 

Three government studies, however, do show that RES and CES would have relatively low 

costs. A 2005 EPA study reviewed 22 state level analyses and found that renewable 

energy standards in those states would increase electricity rates by less than 5%.

xviii. None of these studies takes the extra step to show how the 

standards would affect broader economic indicators, but considering the

xvi In 

2009, the EIA performed a modeling analysis of the 25% RES that was a component of the 

American Clean Energy and Security (ACES) Act and found that the standard would 

increase average national electricity rates by less than 3%.xvii Furthermore, a 2007 EIA

analysis showed that a national CES requiring 20% clean electricity production 

(renewable, CCS, plus new nuclear and hydro) by 2025 would cause electricity rates to 

increase by just 1%

small changes 
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in the electricity rates – the prices that would be most directly impacted – it is likely that 

the macroeconomic impacts, if estimated, would have been minimal. 

An MIT study, in contrast, does estimate the full economic impact of a RES, but it does this 

in the context the adoption of an economy-wide cap and trade system, as was 

proposed in the ACES Act. The study concluded that inclusion of a RES requirement in a 

cap and trade bill drove up the overall costs of the policy without reducing GHG 

emissions any further. For example, it found that implementing a 20% RES in conjunction 

with a cap and trade system similar in stringency to that in the ACES ACT would increase 

policy costs from less than 1% to almost 1.7% of the nation’s overall level of economic 

welfare – a composite measure used in the EPPA model to represent economy-wide 

utility.xix This reduction in welfare was caused by forcing the model to adopt emissions 

reduction strategies (i.e., displacing fossil fuel electricity with renewable electricity 

production) that were not most cost-effective strategies available while imposing 

equivalent revenue collection schemes. 

Industry-Specific Climate Policies

While economic modeling and theory suggest that some technology-specific mandates 

like RES and CES result in the adoption of less cost-effective emissions reduction strategies 

than those that would be adopted if market mechanisms were used alone, many 

believe that some technology mandates and standards can result in the adoption of 

more cost-effective strategies than those that would result from market mechanisms 

alone. Fuel economy standards are considered by many to be such a strategy. As part 

of its analysis of the ACES Act, the EIA simulated scenarios that included the adoption of 

the ACES Act, both with and without strengthened fuel economy standards.xx The study 

found that the inclusion of stronger fuel economy standards resulted in slightly more 

positive economic impacts. The implication of this finding is that market mechanisms 

can fail to stimulate some cost-effective emissions reduction strategies because those 

strategies suffer from market failures that are not resolved by the imposition of carbon 

pricing. Many economists would argue that more stringent appliance and building 

efficiency mandates would resolve similar market failures.

Policymakers have a wide variety of other industry-specific policy tools that don’t involve 

mandates that could help advance investments in new technologies and overcome 

market failures. The Business Roundtable’s climate policy analysis, for example, focuses 

on quantifying the benefits of removing barriers to investment in six technology 

categories including energy efficiency, renewable power, nuclear power, CCS, 

renewable fuels, and vehicle efficiency. The study estimates targeted policies and 

investments in these six technology areas can cut the costs that carbon fees would 

impose on the nation’s GDP in half while achieving twice the GHG emissions 

reductions.xxi 

Similarly, a study released by Industrial Energy Consumers of America that measures the 

economic impacts of its own policy proposals indicates that policies aimed at improving 

manufacturing efficiency can have significant positive impacts on the nation’s 

wellbeing. It highlights combined heat and power (CHP) and recycled energy as two 

technologies that currently face non-economic barriers that prevent these efficiency 

improving and money saving investments from taking place. The study estimates that 

implementation of six of the organization’s policy proposals would improve GDP by 0.4% 

by 2020.xxii 
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3.1.4 Environmental Regulations

Policies Affecting Fuel Users

With national climate change policy discussions at an impasse, federal lawmakers, 

industry, think tanks, and academic intuitions have turned their attention to the potential 

impacts of environmental regulations that have been and are being developed by the 

EPA in particular. More than a dozen studies estimating the impact of air, water, and 

waste regulations on power plants and industrial facilities were reviewed. Most of these 

studies only measured the impacts on the electric power sector, but some estimated the 

total impact of regulations on the economy. 

The EPA, for example, accompanies each proposed rule with a Regulatory Impact 

Analysis that includes economic impact estimates of the rule. EPA has used economic 

modeling analyses to show that each of several clean air, water, and waste regulations 

would result in slightly higher electricity prices, and for some regulations, small negative 

impacts on industrial profits and output. Nevertheless, for all but one of the regulations, 

EPA estimates the negative impacts of the regulation on the overall economy to be 

negligible. The exception is the revision of ozone standards, which EPA estimates to cost 

the economy $19-90 billion annually by 2020, depending largely on the level at which the 

standard is set.xxiii

In addition to the costs, the EPA’s analyses of these regulations also include estimates of 

benefits. These estimates are done by quantifying the economic benefits of better 

health outcomes resulting from lower pollution levels. The estimates, however, are done 

independently of the cost estimates and do not utilize economic modeling, although 

EPA is currently working to integrate its health benefit estimates with its economic 

modeling tools for future analyses. 

Studies by other organizations of EPA regulations other than those discussed above 

estimate the impacts that those regulations would have on the electric power sector but 

do not take the extra step of measuring the broader impact on the economy. For 

example, the Edison Energy Institute’s (EEI) and several other industry and non-profit 

groups estimate that multiple air, water, and waste regulations will increase electricity 

prices and force many plants to retire or retrofit with pollution control equipment. The 

range of compliance costs to meet those regulations range from a few billion to more 

than $250 billion over a decade.xxiv 

Only one study (other than those done by EPA) attempts to quantify how such costs to 

the electric power sector filter through to the rest of the economy. A study released by 

the University of Massachusetts-Amherst’s Political Economy Research Institute (PERI) of 

air regulations that are targeted at electric utilities estimates that even without 

accounting for the health benefits of the regulations, they would have positive and 

significant impacts on jobs and investment in the 2010-2015 period. It does not, however, 

discuss the longer term impacts when one might expect electricity prices to be higher as 

utilities pay for their investments. xxv

Policies Affecting Fuel Suppliers

None of the studies reviewed estimated the economic impacts of environmental policies 

that restrict domestic energy production. Among the most actively debated energy 
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supply restrictions that are especially relevant to the Appalachian region are restrictions 

on shale gas drilling and fracking and on mountaintop removal (MTR) coal mining. While 

no studies have been done that directly estimate the impacts of restrictions on shale gas 

drilling and fracking, the EIA’s 2011 Annual Energy Outlook estimates the impact that 

increasing the number of shale gas wells and shale gas production by 50% may have on 

the U.S. economy. xxvi Without factoring in potential environmental costs, the EIA 

estimates that increased shale gas drilling would have a slightly positive impact on 

national GDP, increasing it by an average of 0.07% over the 2011-2035 period, although 

those economic impacts would tend to be more positive in Appalachia because of the 

larger concentration of shale gas resources there. 

No analysis of the impacts of restrictions on MTR were found or reviewed, although 

considering that the tactic is used at just a handful of mines, the economic impact of its 

restriction on the national and even the Appalachian regional economy is likely to be 

negligible. 

3.1.5 Regional Policies 

While many national regulations are pending and several national climate policy bills 

have been dismissed, scores of state and regional policies have been proposed and 

enacted. Many of these were the foci of studies reviewed for this analysis, in particular 

California’s AB 32 climate law, the Western Climate Initiative (WCI), and the Regional 

Greenhouse Gas Initiative (RGGI). These studies yielded additional findings that highlight 

how differences in policies between neighboring jurisdictions can affect the costs of 

policies. 

For example, a report by University of New Hampshire shows that RGGI will have a 

significant impact on the New Hampshire economy, whether or not the state

participates in RGGI’s electricity sector cap and trade program. In its baseline scenario, 

the analysis assumed that the other nine RGGI member states, including all three states 

with which New Hampshire shares a border, would participate. The study found that, 

regardless of whether New Hampshire participates in RGGI, the customers of three of 

New Hampshire’s four utilities would face higher electricity rates because those utilities 

purchase electricity from generation facilities in other RGGI states. The study concludes 

that not participating in RGGI would not only disadvantage those utilities versus the one 

that produces all of its electricity in-state, but also deprive the state of sharing the 

benefits of auction revenue collections, despite the fact that many of its residents would 

be paying higher electricity rates.xxvii

A report by the University of California Energy Institute looks at how differences in 

participation in a WCI cap and trade program would affect the policy’s impact. It 

simulates three policy scenarios in addition to a business-as-usual baseline: one in which 

only California participates, one in which all WCI states participate, and one in which all 

western states participate. The authors find that in the lower participation scenarios, the 

problem of emissions leakage would undermine the effectiveness of the policy in 

lowering GHG emissions because participating states would start importing more 

electricity from GHG emitting sources in neighboring states, offsetting the emissions 

reductions achieved in the participating states. However, the study estimated that once 

all of the western states were participating, the opportunities to import more emissions 

intensive electricity disappeared, incentivizing more energy efficiency and local 
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production of cleaner electricity.xxviii This finding highlights the importance of adopting 

emissions leakage protection measures to prevent the outsourcing of economic activities 

that generate GHG emissions. 

One such measure for reducing emissions leakage in the electric power sector is to treat 

imported electricity the same as domestically produced electricity. Several analyses, 

however, show that utilities can get around such a restriction by using “contract 

shuffling.” This occurs when utilities trade contracts in such a way that the utilities in 

jurisdictions with carbon restrictions buy electricity from low carbon sources while the 

contracts for more carbon intensive electricity are traded to utilities in jurisdictions with 

lower or no carbon restrictions. Navigant Consulting’s analysis of emissions leakage in the 

Eastern Canadian WCI provinces of Quebec, Ontario, and Manitoba demonstrated the 

impact of contract shuffling, concluding that allowing for contract shuffling would 

increase electricity imports and reduce the impact of the GHG policy on emissions.xxix 

Together, these analyses demonstrate the problem of emissions leakage and the 

importance of having well designed policies to prevent it. They also show that emissions 

leakage becomes less of a problem when neighboring jurisdictions adopt similarly 

stringent climate policies. Nevertheless, studies at the national level show that even 

climate policies that cover the entire U.S. can leave the economy vulnerable to 

regulation-related outsourcing and emissions leakage if thoughtful policies to prevent 

leakage are not included.

3.1.6 Policy Scenarios in this Analysis

This study focuses on four policy simulations that estimate how different national climate 

and energy policies may impact Appalachia. While most sectors of the economy are 

likely to be at least mildly effected by major climate or energy policies, the discussion 

above highlights the fact that the most significant impacts are likely to occur in the 

electric power sector. This sector is the largest contributor to greenhouse gas emissions, 

emitting more than a third of all the nation’s GHG emissions, and the industry most

capable of significantly reducing the nation’s GHG emissions profile.xxx 

The scenarios analyzed in this study simulate the adoption of three different electric 

power sector policies or strategies that could profoundly impact the sector, including:

1. End-Use Energy Efficiency

2. Clean Energy Standard 

3. Carbon Mitigation

These three scenarios were chosen because they represent three of the most often 

discussed federal policy proposals and because they are likely to significantly affect 

demand for Appalachia’s coal, natural gas, and renewable energy resources. These 

scenarios also relate directly to the three Strategic Objectives in ARC’s Regional 

Blueprint. They analyze the implications of extending existing legislation, including Epact 

2005, EISA 2007, and the new EPA ruling on GHG emissions. More detailed descriptions of 

the policy scenarios and results are discussed in sections 5 and 6.
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3.2 Model Type and Use in Policy Analysis 

3.2.1 Economic Models

All of the studies reviewed for this project that included economic impact estimates 

made use of economic models. These macroeconomic models ranged from very simple 

spreadsheet models that included few variables to very sophisticated models with 

thousands of variables.

Simple Static Input-Output Models

Simple static input-output models were used in a few of the studies reviewed. These 

models, which include the IMPLAN and RIMS 2 models, use economic multipliers to 

estimate the first order impacts of how a change in costs or prices in one or more sectors 

would filter through the economy to affect other sectors in the economy. For example, 

T2 and Associates performed an analysis of California’s AB 32 cap and trade policy 

assuming a full auction of allowances would result in carbon fees equal to $20, $60, and 

$200 per ton of CO2 by 2020. The authors estimated that, in the scenario where the fee 

was assumed to be $60 per ton, California’s employment would be reduced by nearly 

500,000 jobs in 2020.xxxi 

This loss of jobs would be considered very significant even at a national level, but such a 

loss would be considered catastrophic for just one state. This result, however, is an outlier 

and is a product of using this type of model to simulate a long term, multifaceted policy. 

The T2 simulation shows how the energy sector may react to a tax on fossil fuels, but 

because it does not calculate any secondary effects it assumes that:

1. All people who lose their jobs are unable to find new ones; 

2. Industries affected by the tax shrink and no other industries are able to benefit 

from the resulting glut of labor and capital or to fill the energy supply void that 

those industries left; and 

3. The revenue collected from the permit auctions is not used for a productive 

purpose.

Such assumptions do not reflect the tendencies of the real economy where second and 

third order effects tend to mitigate costs.

In summary, simple static input output models do not do a good job of simulating any 

long term policy effect or even short-term secondary effects. They generally do not 

accommodate structural or industrial change in the economy, efficiency and 

productivity gains, or benefits from a shift in resources toward more competitive or hightech industries. The lack of a macroeconomic component in such models results in a 

calculation of multipliers that are generally too large, as the models ignore the 

constraints of overall GDP potential in the economy.

Computable General Equilibrium Models

In healthy market economies, surpluses of capital and labor resulting from production 

losses in one industry tend to get employed in other industries. The theory is that prices for 

factors of production, goods, and services adjust in such a way that their supply and

demand equalize. When that happens for a particular good or service, that particular
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market is said to be in equilibrium. General equilibrium is said to occur when the prices 

for all goods and services equal values such that the supply and demand for all goods 

and services equalize. Computable general equilibrium (CGE) models assume that 

economies will tend back toward a general equilibrium state. They are run iteratively 

with prices throughout the economy, and therefore supply and demand adjusting until 

the model reaches general equilibrium. Through this process, the model captures the 

second, third, and nth order effects of policy changes that are often ignored by simple 

static input-output models. 

Basic CGE models only simulate the economy for a single time period assuming that the 

process of adjustment to a new equilibrium state is instantaneous. The vast majority of 

the studies reviewed, however, used dynamic CGE models to estimate the economic 

effects of climate and energy policies over time. These models do simulate a time path 

for adjustment to a new equilibrium state, but most require that the modelers project 

values for all exogenous variables for all years. CGE models also must assume that 

households, firms, and governments (the main agents in the model) act with perfect 

knowledge of future prices and that they maximize utility and profitability over time given 

that knowledge. Critics say that this characteristic causes CGE models to overestimate 

the ability of model agents to react intelligently to policy changes. Terry Barker’s metaanalysis of studies estimating the economic impacts of cap and trade systems 

concluded that simulations performed using CGE models tended to improve projected 

2030 GDP estimates by 1.5 percentage points as compared to those that used other 

models.xxxii

Econometric Models

Econometric models are another major model type used for economic analyses of 

climate and energy policies. The major differences between econometric and CGE 

models are:

1. The parameters that define the interrelationships between variables of 

econometric models are determined by econometric analysis of historical data, 

whereas CGE models use input-output matrices. 

2. Unlike CGE models, econometric models do not generally assume that the 

agents of the model have perfect foresight, which allows them to optimize their 

behavior over time. 

The IHS Global Insight U.S. Macroeconomic Model is an econometric model that has 

been used to simulate the macroeconomic impacts in all of the energy policy modeling 

analyses performed by the Energy Information Administration (EIA). This model represents 

the macroeconomic module of a larger system of interlinked models that form EIA’s 

National Energy Modeling System (NEMS). The movement of variables in this model is 

driven largely by econometric equations based on historical relationships, but, like other 

econometric models, they are also constrained by an underlying economic theory, such 

as the general equilibrium theory that underlies CGE models. Nevertheless econometric 

models tend to be more loosely constrained by those theories than CGE models are by 

general equilibrium theory. 

Hybrid Models

Two of the models used in the studies reviewed – the University of Maryland, Inforum’s LIFT 

(Long-term Interindustry Forecasting Tool) and Regional Economic Models, Inc.’s (REMI’s) 

Policy Insight Plus (PI+) – would more accurately be described as hybrid models that 
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include characteristics of both CGE and Econometric models. Like CGE models, they 

both have input-output matrices at their core which guide many of the interrelationships 

among industries, and both include CGE tendencies to return to a general equilibrium 

state. However, like econometric models, much of the movement of model variables 

are driven by econometric equations and activities in those models are not based on the 

assumption of perfect foresight.

3.2.2 Modeling the Energy Sector

While many of the analyses reviewed use modeling tools to simulate the impact of 

energy and environmental policies on the macroeconomy, the studies also employ a 

wide variety of strategies for modeling the energy sector. They include both:

1. Exogenous strategies – estimating energy sector policy responses outside of the 

main economic model used and then inputting those results into the model; and 

2. Endogenous strategies – estimating energy sector policy responses within the 

same modeling system used to estimate economic impacts.

Exogenous Modeling of the Energy Sector

Many of the economic models used for these policy analyses do not contain sufficient 

energy sector detail to suitably simulate the adoption of carbon prices or other policies 

within the model. In lieu of this ability, modelers use the output of other models, side 

calculations, or other tools to approximate policy impacts on the sector. Those estimates 

are then used as inputs for economic model in order to simulate those policies. The 

University of New Hampshire’s analysis of that state’s participation in RGGI does this. The 

authors use a simple “spreadsheet model” that they developed to estimate delivered 

electricity prices in different scenarios by adding carbon costs – the product of carbon 

price data from other climate policy studies and emissions data from EPA’s Clean Air 

Markets database – to business-as-usual electricity rates. These price data were then 

input into a REMI model of the New Hampshire economy. In this model, the utilities were 

not capable of adopting new technologies or switching fuels, limiting its usefulness to 

short run analyses in a region where the existing ability to switch to natural gas is 

limited.xxxiii 

Other studies, including most of the analyses of EPA environmental regulations, use more 

sophisticated electricity market models, often called dispatch models. The models 

include fuel switching as an abatement option in areas where excess gas, nuclear, or 

renewable capacity already exists. Analyses of longer term energy and environmental 

policies, however, require that longer term policy-adaptation strategies, including the 

development of new infrastructure and technologies, be considered. Because many of 

the models used are incapable of automatically estimating levels of new plant 

construction and technological changes, many modelers have to find other ways to 

estimate longer term impacts. 

One strategy is to base technological development and deployment assumptions on 

expert opinions. The Electrification Coalition’s analysis of its own policies to promote the 

electrification of the light-duty vehicle fleet used this strategy. The coalition asked 

subject matter experts from PRTM Consulting to develop a deployment schedule and 

technological cost and efficiency assumptions that corresponded to the policies that the 

coalition was advocating. Those assumptions were then input into Inforum’s LIFT model in 

order to estimate the macroeconomic effects of the policies.xxxiv 
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Similarly, the Business Roundtable’s Balancing Act study of its own energy policy 

recommendations relied on expert opinions. For that analysis, the Roundtable formed six 

technology working groups – building efficiency, biofuels, transportation, and renewable, 

nuclear, and CCS electricity – to provide inputs for the model. The groups included 

technology, policy, and economic experts from the Roundtable companies as well as 

the economic modeling team. Each group was tasked with estimating trajectories for 

the cost, deployment, and technical specs for the most important technologies within its 

scope and for four policy scenarios. Industry modules for each of the technologies were 

then constructed within the LIFT model in order to integrate the inputs from the groups 

and to project the economic impacts of the technologies’ collective deployment.xxxv 

Finally, one other way that modelers estimate energy sector policy impacts without the 

use of a model that has sufficient energy sector detail is to use information from a related 

study that did use such a model. For example, a study done by University of 

Massachusetts-Amherst Political Economy Research Institute (PERI) estimates the 

economic impacts of criteria pollutant regulations using an IMPLAN model that is not 

capable of estimating the direct cost of the regulations to the electric power sector. 

Nevertheless, they were able to borrow results from a Charles River Associates (CRA) 

analysis that used CRA’s National Energy and Environment Model (NEEM) to measure the 

impacts of the same regulations on the electric power sector. PERI then adapted those 

sector level results and pushed them through the IMPLAN model in order to estimate total 

economic effects.xxxvi 

Similarly, the Inforum and Keybridge modeling team have used a similar approach for 

various analyses. In a report for the Clean Air Task Force on the economic effects of 

provisions in the Waxman-Markey Bill that support carbon capture and sequestration, the 

team draws on EIA’s electric power sector results from various scenarios that it does in its 

analysis of the same bill. The team uses electric power sector outputs from two of EIA’s 

Waxman-Markey policy scenarios in order to estimate changes in deployment levels for 

different types of power production including CCS and competing technologies. Those 

deployment levels were then input into the LIFT model in order to capture economic 

effects.xxxvii 

These techniques for exogenously estimating energy sector responses to policies are 

widely-used and well-accepted techniques in energy and environmental policy analysis. 

They save the need to operate a model with greater energy sector detail, which can 

add significantly to the cost and complexity of an analysis without necessarily improving 

the estimates. Nevertheless, one drawback of estimating energy sector impacts 

exogenously is that those estimates – including deployment levels, prices, and 

technological changes – are then set and unlike other model variables, they will not 

change as the model iterates. Some of the modeling analyses, however, include 

endogenous modeling of the energy sector, which allows the modelers to vary more of 

the key energy sector variables and can also allow for a more objective estimation of 

those impacts. 

 
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Endogenous Modeling of the Energy Sector

Endogenously modeling energy sector policy impacts with a macroeconomic model 

was generally employed in one of two ways in the analyses reviewed:

1. Linking an energy sector model(s) to an economic model so that the two would 

feed results to one another with each iteration; and 

2. Using an economic model that includes sufficient energy sector detail to do the 

analysis within that model. 

Two studies that linked energy and economic models together are CRA’s analyses of the 

Lieberman-Warner national cap and trade bill and the California Air Resources Board’s 

analysis of it’s the AB 32 scoping plan for regulating GHG emissions. CRA combined its 

partial equilibrium model of the electricity sector (NEEM) with its computable general 

equilibrium model of the US economy (MRN). NEEM, which is a model of the U.S. electric 

power system and coal industry, was used to forecast electricity price effects, emission 

allowance prices, and EGU retirements and additions among other key energy sector 

variables. Those outputs are then incorporated into the MRN model, which estimated 

impacts on the overall economy. The MRN-NEEM combined model then works iteratively 

with key outputs from each model being sequentially input into one another.xxxviii 

The California Air Resources Board (CARB) used a similar strategy linking the E2020 energy 

sector model with the E-DRAM model of the California economy. CARB faced the 

additional challenge getting two models that were not developed and are not usually 

operated by the same organization to communicate with one another.xxxix 

Other studies have made use of models that include both sufficient energy detail and 

the capability of estimating macroeconomic effects. MIT’s Emissions Prediction and 

Policy Analysis (EPPA), which was used for several of the MIT analyses reviewed for this 

project, is a macroeconomic model that aggregates non-energy activities into just seven 

sectors but includes significant detail in the energy sector, which is divided into 15 

different sectors. In this model, the deployment of various technologies endogenously 

responds to macroeconomic conditions, and technical specifications for those 

technologies – such as their costs and efficiencies – endogenously respond to the 

deployment levels. As the model iterates, both the energy sector and the 

macroeconomic variables adjust until they are in equilibrium with one another.xl 

EIA’s National Energy Modeling System (NEMS) is a modular model that combines energy 

demand, conversion, and supply modules with a macroeconomic module to determine 

the effect that policy changes will have on energy markets and the economy. By far the 

most sophisticated of the models used in the studies reviewed, each of NEMS’s energy 

modules (e.g., electricity, coal, oil & gas, petroleum refining) include a great amount of 

regional and product-specific detail. Additionally, unlike the EPPA model, which includes 

only seven non-energy sectors, NEMS’s macroeconomic module includes a significant 

amount of non-energy sector detail, with 56 non-energy sectors being modeled. 

However, like the EPPA model, most of the energy sector and macroeconomic variables 

adjust as the model iterates until an overall equilibrium is reached.xli
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3.2.3 Simulating Regional Variation

Another major difference between modeling studies is the degree of regional variation 

that the studies include. Most of the analyses reviewed simulate the effects of energy 

policies on a single jurisdiction, estimating only the cumulative national effects on 

industries and households and providing no regional differentiation. The studies that do 

provide regional detail, however, do so for different purposes and often only provide 

regional details for certain variables. 

Regional Variation Due to Policy Differences 

One of the key factors that drive economic impact estimates is the degree to which 

climate policies are symmetric across regions. Some of the studies reviewed simplify their 

analyses by ignoring the impacts on trade with nations, states, and regions that are not 

subject to the policies being considered. The more sophisticated studies, however, 

consider these trade relationships between the region of interest and the rest of the 

world as key determinants of how certain policies may impact economic outcomes. The 

key parameters determining how these relationships are modeled are the assumed trade 

elasticities and changes in relative production costs. 

Several of the studies that consider these relationships are one region studies in which a 

general assumption is made about how economies in the rest of the world may respond 

to the adoption of policies in the region of interest. The Business Roundtable’s Balancing 

Act study, for example, assumes that reciprocal action on climate change by the U.S.’s 

trading partners would result in policy induced price changes that would be 80% the 

magnitude of the equivalent price changes in the U.S. In contrast, many other studies 

assume no price increase for foreign goods, an assumption that would likely increase the 

estimated cost of new regulations or carbon prices to the U.S. economy. 

In several other studies, multiregional models are used to simulate the full economies or 

energy sectors of multiple regions, not just the primary region of interest. The University of 

California Energy Institute’s study of the Western Climate Initiative cap and trade 

program uses such a model. This model splits the Western Electricity Coordinating 

Council into state electricity markets, allowing the modelers to capture the changes in 

the relative costs of power production between states that do and do not participate in 

a cap and trade program. 

Similarly, Navigant consulting uses a regional power production model to analyze the 

Western Climate Initiative’s impacts on the three participating Eastern Canadian 

provinces. The PROMOD model of the power sector includes electricity generating unit 

(EGU)-specific data. The EGUs are then grouped geographically in order to differentiate 

the carbon costs that corresponded with each group’s adopted regulatory scheme – 

WCI, RGGI, or MGGRA. Each EGU’s carbon costs are then added to its production costs, 

which are used in the electricity dispatch model. This regional breakdown also allows the 

modelers to simulate the imposition of import tariffs on electricity transmitted from 

unregulated to regulated regions. As discussed above, both of these studies, like several 

of the single region analyses, demonstrate that asymmetric climate policies can render a 

policy largely ineffective in lowering total GHG emissions while also reducing local 

production of tradable goods like electricity. The unique contribution of these regional 

models, however, is that they allow the modelers to show which regions would be the 

likely winners and which would be the losers under a host of scenarios.
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Regional Variation Due to Differences in Existing Energy Markets 

Other studies used models with regional details to show how a uniform policy across 

multiple regions would have differential impacts due to differences in existing energy 

market policies, energy infrastructure, or resource endowments. A Resources for the 

Future study, for example, uses an electricity market model with regional specifications to 

estimate how different emissions allowance allocation methods would affect consumers 

in different electricity markets. While a uniform national climate policy is applied across 

the continental U.S., 21 regional electricity markets are distinguished not only by their 

mixes of electricity generation sources (e.g., nuclear, coal, wind), but also by whether or 

not they use regulated or market-based pricing. The model then estimates interregional 

electricity trade in the context of these different mixes of energy generation infrastructure 

and regulatory systems, determining price and sales impacts at the regional level.xlii

Many other analyses also focus on how differences in regional electricity markets will 

lead to differential regional impacts of uniform policies. Most of these, however, focus 

solely on the different power generation infrastructure that exists in those regions. Several 

such studies focus on how criteria pollutant regulations will impact electricity prices and 

reliability at the regional level. The analyses estimate that regions that rely more heavily 

on coal-fired power generation, the source of most of the regulated pollutants in the 

electric power sector, will experience more plant closures and require more investment in 

new plants and pollution control retrofits. Meanwhile, regions that are more reliant on 

nuclear, renewable, and gas-fired power are estimated to require fewer investments as 

a result of the policies.xliii

The EIA’s National Energy Modeling System further distinguishes regions from one another 

based on their production of primary energy sources, showing, for example, how policies 

may affect coal production in one region more than another. For example, in its analysis 

of the American Power Act (APA) as well as other climate policy analyses, the EIA 

concludes that putting a price on carbon would have a much larger impact on Western 

coal production than it would on Appalachian production, in large part because global 

metallurgical coal demand, a key export market for Appalachian coal, would be largely 

unaffected by the policy. NEMS includes similar regional detail for petroleum and gas 

markets. xliv 

Regional Variation Due to Firm-level Differences

Some of the models used in the studies reviewed include regional variations with firm and 

geographic specific data in the electric utility sector. One example is the University of 

New Hampshire’s analysis of that state’s participation in RGGI. This study used a simple 

model that simulated the behavior of the four utilities that provide power to New 

Hampshire. The main distinguishing factors between those utilities are their electricity 

generation mix and the fact that only one of them produces in-state power while the 

others import electricity from neighboring states. The study’s simulations show the 

differential effects on consumers living in the areas covered by the different utilities and 

estimates that customers living in areas that are served by the one utility that produces 

power in state would be better off if New Hampshire did not participate in RGGI while 

those living in other areas would be worse off.
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3.2.4 The Inforum LIFT-CUEPS Model

The modeling system used for this analysis is a linkage of Inforum’s National LIFT model 

with its County-Utility Energy Policy Simulator (CUEPS). The combined models constitute a 

tool that can measure policy impacts at the national level with significant industry detail 

while also facilitating the measurement of local impacts at the regional, state, and 

county levels. 

As described earlier, the LIFT model is a hybrid, including characteristics of both CGE and 

econometric models. The model contains full demand and supply accounting for 97 

productive sectors, including variables for investment, prices, imports and exports, 

employment, wages, and other key variables. The LIFT modeling framework is ideal for 

addressing questions where the interactions between industries is crucial. Several 

modules incorporating many of the variables in the Department of Energy National 

Energy Modeling System (NEMS) have been added to LIFT, which makes it possible to 

calibrate the LIFT model to the Annual Energy Outlook (AEO) and to derive alternative 

scenarios. Eight detailed electric power subsectors for different energy generation 

technologies (e.g., wind, nuclear, etc.) allow for the simulation of dynamic changes to 

plant efficiency and fuel use and for greenhouse gas emissions accounting. 

For this project, LIFT is linked to the detailed County-Utility Energy Policy Simulator (CUEPS), 

which is a detailed model that contains economic data at the county level and electric 

power data by utility. CUEPS includes a county-utility bridge that facilitates the 

estimation of power sector efficiency and the generation mix at the county level. This 

allows the modeling team to estimate electricity price impacts and income effects of 

national policies that are specific to each county. Additional assumptions, such as the 

deployment of wind power infrastructure at the county or utility level, can also be 

adopted within CUEPS.

3.3 Other Key Assumptions 

In addition to the policy design and the selection of models and modeling strategies, 

estimated impacts of energy and environmental climate policy are largely dependent 

on several key assumptions, including:

• The degree to which the economy operates at full employment,

• The cost and efficiency of different technologies,

• Energy price assumptions, and

• Whether alternative energy resources and technologies are domestic or 

imported

3.3.1 Full Employment of Resources

A key assumption in any economic modeling analysis is the degree to which resources 

are employed. This is important because, in an economy where resources are being 

employed (as would be expected in normal economic times), the devotion of more 

resources to one purpose will often require reducing the amount of resources dedicated 

to other purposes, often reducing the impact of policies that encourage particular 

investments. However, the assumption that resources are being underemployed (as is 

usually the case during a recession) means that a policy encouraging the employment 

of resources toward a given purpose would not necessarily require that fewer resources 

be available to other industries. Both of these assumptions – the economy is at full 
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employment and the economy is not at full employment – are used in different analyses 

with significant consequences. 

The University of Massachusetts Political Economy Research Institute (PERI) study of the 

criteria pollutant regulations that was discussed above implicitly makes the assumption 

that the economy is not at full employment through its use of the static input-output 

model from IMPLAN. This study assumes that the investments that the electric power 

sector would need to make in order to comply with the regulations would utilize capital 

and labor that would otherwise not have been employed. As a result, there is no 

offsetting reduction in the amount of capital and labor devoted to other purposes as a 

result of the new investments that would be required to reduce criteria pollutant 

emissions. Given this assumption, the study simply applies economic multipliers to the 

cost of the required pollution abatement investments in order to estimate economy-wide 

impacts. As a result, PERI estimates that the regulations would significantly increase 

employment levels in the economy in the 2013-2015 period, as otherwise unemployed 

resources are put to work.xlv 

Terry Barker’s analysis of global climate policy studies, however, points out that while the 

assumption that an economy has excess capital and labor in particular may be 

appropriate for simulations of many developing economies, most analyses of developed 

economies assume that the economy is in full employment in the base year (first year) of 

the simulation. xlvi Nevertheless, given the current post-recession U.S. economy, the 

assumption that there will be an excess of labor and capital in the U.S. over next few 

years is more defensible than it would be in better economic times. 

In longer run analyses, most of the studies reviewed implicitly assume – through their use 

of CGE models – that the U.S. economy tends to operate at full employment and that 

capital and labor are scarce. This means that, while the economy is unlikely to benefit 

from an excess of labor or capital that can be employed without some offsetting 

reductions in their use in other sectors, it is also unlikely to undergo persistent bouts in 

which resources are unemployed. CGE models assume that, after the economy is 

shocked by a policy or price change, it will undergo adjustment after adjustment in order 

to get back towards its equilibrium where resources are employed at their long-run 

equilibrium level. As a result, the job losses and gains attributed to energy policies in the 

studies using such models are mostly short-run job losses due to the shock of the policy 

change. Job losses that are estimated to be persistent and/or increasing over time are 

usually attributable to the continual shock of an increasing carbon price or ever more 

stringent regulation. Meanwhile, the changes in long run equilibrium employment levels 

attributed to energy or environmental policies are often much smaller than the 

temporary initial changes. 

Dynamic non-CGE (or econometric) models were also used in several of the reviewed 

analyses. These models are often not bound by the assumption that an economy will 

eventually return to an equilibrium state, and in some of the analyses the models 

estimate persistent bouts of underemployment of productive resources as a result of 

energy policy. For example, the National Association of Manufacturers (NAM) 

concluded that the Lieberman-Warner Bill would have caused the loss of 1-2 million U.S. 

jobs in 2020 and 3-4 million U.S. jobs in 2030, multiple times larger than the job loss 

estimates in any of the other studies of that particular bill.xlvii 

In contrast, the EIA’s analysis of the same bill uses the same model and estimates that 

economy-wide job losses would be 200,000 in 2020 and 62,000 in 2030. xlviii The NAM 
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analysis includes a number of assumptions that are much more conservative than those 

used by the EIA, such as limited technological progress and limited availability of offsets. 

The EIA presents a number of scenarios with similarly conservative assumptions, but their 

GDP and employment impact estimates in those alternative scenarios still do not 

approach the large impacts projected by NAM. Instead, the difference between the 

studies’ results is likely due to differences in their assumptions governing whether or not 

and with what speed the economy will tend towards full employment of productive 

resources, with the EIA assuming that the economy will tend to return to full employment 

while NAM assumes that the economy will be much less capable of adjusting.

3.3.2 Technology Assumptions

Another key reason why the NAM simulations showed such negative economic 

consequences of climate policy was that they concluded that carbon prices would 

have to be very high in order to meet the emissions reduction targets being proposed, 

making adjustments to those price changes more difficult. Conclusions such as this are 

driven by pessimistic assumptions about the cost, efficiency, and availability of the 

technologies needed to achieve given environmental goals. 

The EIA, in several of its analyses, tests the sensitivity of its technology assumptions by 

simulating alternative scenarios that included higher technology cost assumptions or 

limited technology availability assumptions. Those simulations show higher 

unemployment and reductions in GDP as compared to scenarios with more optimistic 

technological assumptions. The changes in employment and production between 

scenarios, however, never reach the same scale as those estimated in the NAM study. 

Studies of EPA regulation by different organizations show how a major difference in 

technology assumptions can lead to vastly different economic impact estimates. In its 

analysis of potential new ozone standards, the EPA estimates that the new standards will 

require some regions of the country to use technologies that do not yet exist. They 

estimate that the costs of those technologies will be higher but not dramatically higher 

than technologies that are currently available and in use. In contrast, studies by NERA 

Economic Consulting (NERA) and the Manufacturers Alliance (MAPI) estimate 

technology costs that are about ten times higher. While the EPA estimates a significant 

annual compliance cost of $19-90 billion, the MAPI study, which is based on state by 

state cost estimates from NERA, estimates the catastrophic (and largely unheard-of in 

any other policy analysis) annual compliance cost of about $1 trillion. xlix While this 

incredible finding raises a lot of questions about the methodology used, it serves as an 

extreme example of how different technological assumptions can impact the policy cost 

estimates.

3.3.3 Energy Price Assumptions

Another key set of assumptions that could have a major impact on economic outcome 

are the energy price assumptions. Assumptions for baseline scenarios are usually just 

borrowed from existing business-as-usual forecasts, and in most of the analyses reviewed, 

energy price forecasts from the EIA’s Annual Energy Outlook series are used. This is not to 

say that most of these modeling teams believe that the EIA’s forecasts are the most 

accurate, but rather it is simply because this is conventional practice and helps make 

independent studies somewhat more comparable then they would be if they were 

starting with entirely different baseline assumptions. Nevertheless, some studies have 

veered from this convention, which in some instances has significantly influenced results.
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The Business Roundtable’s Balancing Act study was conducted at a time when the AEO’s 

energy price forecasts for crude oil and natural gas differed substantially with existing 

spot and future prices. As a result, the Roundtable chose to assume a flat real crude oil 

price forecast of $100 per barrel. The analysis also assumed that natural gas prices would 

gradually rise to reach parity on a $/Btu basis with oil. The oil price assumptions, which 

now appear to have been more in line with the reality to date than the AEO forecasts 

available in 2008, would have influenced the results by making alternative technologies 

that reduce the use of petroleum seem more economical than they would have been 

had lower oil price forecasts been used. The natural gas price assumptions, however, 

were much higher than both current natural gas price forecasts and the AEO forecasted 

prices that were available in 2008. This higher price forecast would have influenced the 

overall economic cost estimates in two offsetting ways:

1. Making gas, as a lower emissions alternative to coal and petroleum, more 

expensive than it would have otherwise been, increasing the estimated cost of 

the climate policy; and 

2. Making substitution of other technologies for gas seem less costly than they would 

have been had lower gas prices been assumed. 

Either way the study’s use of this higher price forecast would have diminished natural 

gas’s role in the simulated transition to a lower carbon economy.l

 

The studies reviewed also differed in how they estimated changes in energy prices from 

the baseline scenarios to their policy scenarios. Several studies assume that prices would 

not differ between scenarios; some allow the model to determine the change in prices; 

but most keep the prices of some energy sources the same while changing the prices of 

other energy sources. For example, most of the studies reviewed include electricity 

prices that change as a result of policy changes. This is because electricity markets are 

more local than primary energy source markets and because electricity markets are 

expected to be significantly affected by both local and national policies. 

The prices of primary energy sources, however, either changed or did not change 

depending on the scope of the analysis and the type of fuel. For example, some of the 

state and provincial analyses assume that local policy changes would not be enough to 

influence the price of fossil fuels, which are typically determined by national (e.g., natural 

gas) and global (e.g., crude oil) markets. In contrast, many national policies were 

assumed to be significant enough to influence fossil fuel prices, especially prices for 

natural gas, and to a lesser degree, coal. Some studies, such as the Business 

Roundtable’s, estimated these changes in price effect exogenously, although many 

simply allowed the models to determine the changes in prices endogenously.

3.3.4 Sources of Fuels and Energy Technologies

One of the model variables that drives energy prices is energy supply and a key 

characteristic of supply is whether or not it is domestic or international. More 

sophisticated models include industry-specific parameters which estimate the 

proportions of that industry’s output that are domestically produced versus imported, 

assumptions that can largely influence the estimated economic impact of a policy. For 

example, in 2010 the net imports of natural gas represented about 10% of the domestic 

supply and are forecast in the 2011 AEO to represent an even smaller percentage by 
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2030. However, many of the policy scenarios that have been modeled in the different 

studies are estimated to result in significant increases in the natural gas consumption over

baseline scenarios that do not include these policies. In order to meet this increased 

demand for natural gas, the models used and/or the modelers must estimate whether 

that incremental demand will be met with an increase in domestic supply, an increase in 

imports, or an increase in both. 

The EIA, for example, simulates a scenario in its Kerry-Lieberman study in which clean 

coal and nuclear technologies will be more costly than expected and that gas will be 

used as a substitute for them in the production of electricity. In this scenario they 

forecast that natural gas demand would be 2.8 trillion cubic feet (or 12%) higher in 2030 

than it would be in the baseline scenario. They meanwhile estimated that although 

barely 8% of demand would be met by imports in the baseline scenario, over 53% of the 

incremental demand would be. Had the EIA assumed that the incremental demand 

would be met primarily with increased domestic supply, it is likely that estimates of several 

key economic measures, such as GDP, would have been more positive. 

In contrast, the Electrification Coalition study simulates a policy that would encourage 

electric vehicle deployment and use, reducing domestic oil demand. The authors 

estimate that this reduction in demand, although significant, would be unlikely to 

dramatically lower the globally set oil price and would therefore be unlikely to affect the 

profitability of domestic oil production. Given this conclusion, the modeling team 

assumes that any reduction in oil demand would result in an equal reduction of oil 

imports, an assumption that would tend to have a positive impact on estimated 

economic outcomes. Results, however, would likely be less positive if it were assumed 

that domestic oil production would fall as a result of falling domestic demand. 

In many models, similar assumptions about the proportion of a good or service that are 

domestically supplied can be made for other key industries, just as is often done for the 

oil and natural gas industries. For the most part, however, the parameters controlling the 

proportions of an individual industry’s output that are domestic versus imported are left 

unchanged between baseline and policy scenarios, only to be changed when there is a 

compelling reason to believe that the policy may induce such a change. li

Endnotes for Section 3

 

i

Beach, 2007; EIA, 2008a, 2008b, 2010; EPA, 2008; Paltsev, 2008; Murray, 2007; NAM, 2008. ii CARB, 2010; Gittell, 2008; Navigant 2010;Bushnell, 2009; Tanton 2010. iii CARB, 2010; Houser, 2010; Peterson, 2010.

iv Paltsev, 2008. v Tuerck, 2009.

vi EPA, 2008.

vii Barker, 2006. viii EIA, 2008a, 2008b, 2010. ix Goulder, 2009.

x Paul, 2008.

xi Goulder, 2009.
Economic Impact of Energy and Environmental Policy in Appalachia

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 xii Barrett, 2002. xiii Barker, 2006. xiv Business Roundtable, 2009. xv Parry, 2011. xvi Wiser, 2005. xvii EIA 2009b. xviii EIA, 2007. xix Morris, 2010. xx EIA, 2009a. xxi Business Roundtable, 2009. xxii Keybridge Research, 2010a. xxiii EPA, 2010. xxiv ICF 2010; ICF, 2011; .EPRI, 2011; Bernstein, 2010; Brattle, 2010; EPA, 2010b; EPA, 2011. xxv Heintz, 2011.

xxvi EIA, 2011 xxvii Gittell, 2007. xxviii Bushnell, 2009. xxix Navigant, 2010. xxx EPA, 2011. xxxi Tanton, 2010. xxxii Barker, 2006. xxxiii Ross, 2007. xxxiv Electrification Coalition, 2009. xxxv Business Roundtable, 2009. xxxvi Heintz, 2011. xxxvii Keybridge Research, 2010b. xxxviii Smith, 2010. xxxix CARB, 2010 xl Morris, 2010; Paltsev, 2008.

xli EIA 2009c. xlii Paul, 2009. xliii North American Electric Reliability Corporation, 2011; ICF, 2011. xliv EIA, 2009c. xlv Heintz, 2011. xlvi Barker, 2006. xlvii NAM, 2008. xlviii EIA, 2008. xlix MAPI, 2010. l

Business Roundtable, 2009.
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4. Methodology: Models and Data

4.1 Model Capabilities and Objectives

The Energy Policy Impact Model constructed for this project is a dual-level system, 

comprised of the LIFT (Long-term Interindustry Forecasting Tool) model of the U.S. national 

economy, combined with the CUEPS (County-Utility Policy Simulator) model of counties 

and electric utilities. 

The LIFT model works at a detailed sectoral level (about 90 private industries plus 

government sectors) and shows the interactions between industries, and the impacts on 

industry output of changes in exports and imports, personal consumption, investment 

and government spending. The LIFT model integrates the national income accounts 

(NIPA) with the detailed input-output accounts produced by the U.S. Bureau of 

Economic Analysis (BEA). LIFT forecasts at an annual frequency, and the standard 

version has a forecast interval to 2035. 

LIFT also incorporates additional modules that focus on variables of interest for energy 

and environmental modeling, including:

• a biofuels module

• electricity generation module, showing generation by type

• transportation module, with 5 vehicle types

• buildings module

• renewable energy (solar and wind) module

• nuclear module

Other modules can be added flexibly to LIFT to offer additional analytical capacity that 

works with the main LIFT model either in top-down fashion, or simultaneously.

LIFT is used to drive two other Inforum models in a top down manner. The Inforum Iliad

model has a more detailed industry database, forecasting final demands, output, and 

employment for 360 industrial sectors. The Inforum STEMS model forecasts output, 

employment, earnings, personal consumption and other variables by 50 states and the 

District of Columbia, at the 65 industry level.

The CUEPS model includes a detailed database of 3140 counties and 3356 electric utility 

establishments. A county-utility bridge is used to relate economic activity by county to 

service demands by electric utility, and to relate cost changes by utility to the effects on 

the local economies served by that utility. Although not as detailed as the national 

model, the county level data does include data for 11 private sector industries and 3 

government sectors. 
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4.2 Source Data for CUEPS

County level data are derived primarily from data published by the Bureau of Economic 

Analysis (BEA), and include data or estimates for employment, earnings, output, value 

added, costs and capital income by industry. In addition, aggregate data on personal 

income and its components, population, number of households are compiled from BEA 

sources. A small (14x14) IO table consistent with the national level detailed table is used 

to calculate electricity demands for the industrial and commercial sectors.

Utility data is obtained from the detailed Energy Information Administration (EIA) 860 and 

861 datasets, and includes data on revenues, sales, costs, rates, fuel type, and number of 

customers by customer class. The CUEPS model traces the effect of changes in electrical 

generation costs on Kwh electricity rates for residential, commercial, industrial, and other 

customers. 

The model has been tested in a 10 year forecast (to 2020) but should be able to forecast 

as far as 2035, if suitable assumptions can be specified for exogenous variables. 

4.3 Outline of the Modeling System

The Energy Policy Impact Model (EPIM) developed for ARC is comprised of two main 

components:

• The Inforum LIFT model, forecasting macroeconomic and industry variables at the 

national level.

• The CUEPS model, forecasting economic activity by county, electric utility 

generation and revenues by utility, and a county-utility bridge linking counties 

and utilities. 

The LIFT model determines the sources of demand and output growth for each of about 

90 industries. These sources of demand growth may be combinations of the following:

• Personal consumption expenditures

• Equipment investment and software

• Residential and non-residential construction

• Inventory change

• Federal and state and local government spending

• Exports

• Intermediate demand, which consists of sales to other industries

The LIFT model projects each of these components by industry sector. In the case of 

intermediate demand, sales to each of the other industry sectors are identified. Total 

demand may be satisfied by domestic production, or by imports. The import equations 

in LIFT determine what share of demand will be satisfied by imports, based on relative 

foreign and domestic prices, and historical trends of import shares. 

LIFT also forecasts labor productivity, hours worked, and employment by sector, to arrive 

at total economy wide employment. Incomes by industry are forecast for 13 

subcomponents, which can be categorized as either labor compensation, profit-type 

income, or indirect tax. Prices by industry are also forecast, or may be specified 

exogenously.
Economic Impact of Energy and Environmental Policy in Appalachia

36 Inforum / Keybridge Research

The aggregate macroeconomic variables, such as total consumption, investment, 

exports, imports, government spending, employment, personal income, and disposable 

income, are calculated wherever possible as aggregates of detailed industry variables. 

The LIFT model is a fully bottom-up and internally consistent model of the national U.S. 

economy. 

In developing a simulation with LIFT, there are thousands of variables that can be 

specified exogenously, or modified by the user. Indeed, it is a given set of assumptions 

that determine a particular outcome or scenario with the model. The descriptions of 

simulations below provide some examples of how scenarios are developed with LIFT. 

The CUEPS database consists of national level data from the Inforum LIFT model, 

aggregated to the 14 sector level, county level data, based on the Regional Economic 

Information System (REIS) from the U.S. Bureau of Economic Analysis, and electric utility 

data from the Energy Information Administration (EIA). 

The 14 industry sectors used in CUEPS are listed in Appendix B in table B-1. The data from 

LIFT include output in current and constant dollars, prices, employment, productivity, 

labor compensation, proprietors' income, other return to capital, indirect business taxes, 

total value added, personal consumption expenditures, federal defense expenditures, 

federal nondefense expenditures, state & local government expenditures, and total final 

demand.

Data for the 3140 counties comprising the U.S. include population, employment, total 

earnings, number of households, total wages and salaries, other labor income, 

proprietors' income, dividends, interest and rental income, transfer income, social 

insurance contributions, and a residence adjustment. Data for earnings and 

employment are available from each county for the 14 sectors listed in table B-1. In 

addition to these data, estimates were made for current and constant dollar output, 

return to capital, and indirect business taxes. 

The electric utility data is available from EIA for a set of 3356 utilities. These data consist of 

sales in megawatt hours (Mwh), revenue, and number of customers, for five markets: 

Residential, Commercial, Industrial, Public, and Other.

Residential customers are defined as household establishments that consume energy 

primarily for space heating, water heating, air conditioning, lighting, refrigeration, 

cooking, and clothes drying. Commercial actually includes both commercial and 

industrial establishments which have demands generally less than 1000 kilowatt hours 

(kW). Industrial includes commercial and industrial establishments which have demands 

generally greater than 1000 kW per year. Public is energy supplied to ultimate consumers 

for public street and highway lighting. Other includes any customers not included in the 

other four categories, and is primarily for agricultural use. Average price data were also 

calculated by dividing revenue by megawatt hour sales in each market, by utility.

The solution process of CUEPS is described in more detail in Appendix B.
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Inforum / Keybridge Research 37

4.4 Scenario Analysis

The next chapter describes the development of the Reference case scenario and 4 

policy scenarios that incorporate aspects of likely policies or technologies. The minimum 

requirement for the development of a scenario using the LIFT model is to make 

assumptions for the exogenous variables in the model. The word ‘exogenous’ derives 

from Greek, and means “born outside”. Endogenous variables (“born inside”) of the 

model are calculated within the model. Exogenous variables must have assumptions 

supplied for their projected values. Variables that are commonly considered to be 

exogenous include population, labor force, real government consumption and 

investment, tax rates, and social insurance contribution rates. The LIFT model forecasts 

final demand, value added, employment, and prices by industry endogenously. 

However, certain prices, such as energy and commodity prices are often treated as

exogenous, since they are determined on the world market.

The process of calibrating the LIFT model to projections of another model involves making 

assumptions about exogenous variables that are consistent with the other model, and 

making modifications of the projections of endogenous variables to bring them into 

consistency with the projections of the other models. For example, the LIFT model 

forecasts personal income and its components based on income by industry. It derives 

disposable personal income based on personal income and exogenous assumptions 

about tax and contribution rates. The measure of real disposable personal income used 

in the model is nominal disposable income divided by the personal consumption 

deflator. In matching the LIFT real income growth to that of another model such as 

NEMS, many components of the model may need to be adjusted, such as wages, 

proprietors’ income and corporate profits, and taxes and contribution rates. Some of 

these adjustments are on endogenous variables, and some are on exogenous variables. 

In the Annual Energy Outlook 2011, many side cases were developed, and some of these 

side cases were used to calibrate the LIFT model for policy scenarios considered in this 

study. For these side cases, energy prices, energy demands, electricity generation by 

type, energy import shares, and input-output coefficients are some of the variables that 

are adjusted to calibrate to the AEO side case. 

The CUEPS model runs in conjunction with LIFT, and makes a county level projection that 

is consistent with the national model projection. However, additional assumptions can 

be layered on at the county level, that incorporate additional information. For example, 

this is done in the development of wind, biomass, and shale gas assumptions, which are 

further described in Appendices C and D.
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38 Inforum / Keybridge Research

5. Scenario Implementation and Assumptions

5.1 Summary of Policy Scenarios

The Appalachian region is rich in energy resources and will no doubt continue to play an 

important role in the national energy landscape. Which energy policies are chosen in 

the next decade and beyond will have significant implications on the region’s 

contribution to national energy supply, and will have important impacts on economic 

growth and employment in the region. 

In the ARC Regional Blueprint5, several Strategic Objectives for economic and energy 

development are outlined:

1. Promote energy efficiency in Appalachia to enhance the Region’s economic 

competitiveness. 

2. Increase the use of renewable energy resources, especially biomass, in 

Appalachia to produce alternative transportation fuels, electricity, and heat.

3. Support the development of conventional energy resources, especially 

advanced clean coal, in Appalachia to produce alternative transportation fuels, 

electricity, and heat.

In this study, we examine several policy scenarios that explore implications of pursuing 

these objectives. These scenarios are compared to a Reference case that was 

developed specifically for this study, but is consistent with the reference case of the 

Department of Energy (DOE) 2011 Annual Energy Outlook. For each scenario, consistent 

simulations are developed of the LIFT national model and the CUEPS model of counties 

and utilities.

In addition to the Reference case, the following scenarios are analyzed in this study:

1. Energy Efficiency – This scenario is related to Strategic Objective #1, and uses 

assumptions about changes in end use electricity efficiency derived from ARC's 

Energy Efficiency in Appalachia. 6 This study finds that if the set of policy 

recommendations in the energy-efficiency policy portfolio were followed, by 

2020 there would be a 5.4 percent increase in electricity efficiency in the 

residential sector, a 22.3 percent increase in the commercial sector, and an 18.0 

percent increase in efficiency in the industrial sector. These efficiency increases 

are assumed for both the LIFT and the CUEPS models, to determine the economic 

and energy consumption impacts at the regional level and across counties and 

states in the Appalachian region.

2. Carbon Mitigation – While carbon tax or cap and trade policies seem less likely 

now than they did 2 years ago, there is still interest in analyzing the economic 

impacts of mitigating carbon emissions in the U.S., especially from the electric 

power sector. There are major differences between outcomes of these policies, 

 

5 Energizing Appalachia: A Regional Blueprint for Economic and Energy Development, October 

2006, at 

http://www.arc.gov/images/newsroom/publications/energyblueprint/energyblueprint.pdf. 6 Energy Efficiency in Appalachia: How Much More is Available, At What Cost, and By When?, May 

2009, at http://www.arc.gov/assets/research_reports/EnergyEfficiencyinAppalachia.pdf. 
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 39

resulting from the different assumptions about scope, stringency, offsets, 

allowance allocation, and revenue usage. We define the national level LIFT 

scenario to encompass many of the assumptions in the AEO 2011 greenhouse 

gas scenario. Our scenario does not include any offsets or international permit 

trading. We endeavor to estimate the increase in electricity prices in Appalachia 

resulting from the policy, and the resulting impacts on output and employment. 

This scenario ties to all three Strategic Objectives, as the presence of a carbon 

pricing scheme will spur increased energy efficiency, increased use of 

renewables such as wind and biomass, and stimulate the development of novel 

uses of conventional fossil fuel resources, such as advanced clean coal.

3. Clean Energy Standard – This scenario ties into Strategic Objective #2, and 

assumes aggressive development of wind and biomass resources for electric 

power generation in the region. The national scenario in the LIFT model draws on 

a study of the Clean Energy Standard recently published by the Energy 

Information Administration.7 The CUEPS level scenario draws on findings of the 

ARC study Energy Efficiency and Renewable Energy in Appalachia.8 For wind 

power, we make use of information and assumptions from the NREL Eastern Wind 

Integration and Transmission Study (EWITS)9 and for biomass we use information 

from DOE’s 2011 U.S. Billion Ton Update and the EIA’s 2011 Annual Energy Outlook. 

This policy scenario includes the modeling of a national-level context in LIFT and 

county specific investments for wind and biomass. We propose to use multipliers 

available from the NREL Jobs and Economic Development Impact (JEDI) tool10 to 

ascertain the impacts on output and jobs by county in the CUEPS model. 

4. Expanded Natural Gas Development – This scenario ties into Strategic Objective 

#3, exploring the intensive development of natural gas resources in the 

region. While natural gas is a conventional energy resource, the extraction of 

natural gas from the abundant Marcellus Shale deposits represents a very 

promising development for the Appalachian region. We use the AEO 2011 high 

shale gas scenarios to provide the national context, and use GIS software to 

analyze a series of digital datasets published by the USGS to estimate the 

Appalachian specific impact of increased natural gas demand and 

development. 

5.2 Reference Case

The Reference case for the Inforum LIFT model was calibrated to the Annual Energy 

Outlook (AEO) 2011 Reference Case, which was released in March 2011 11 . This 

calibration was done in two stages. In the first stage, industry variables, macroeconomic 

variables, and IO coefficients were modified to produce a macroeconomic forecast 

consistent with the AEO. In the second stage, imports, exports, personal consumption 

expenditures, and IO coefficients were modified to calibrate energy and carbon 

 

7 Analysis of Impacts of a Clean Energy Standard, as requested by Chairman Hall, U.S. Department 

of Energy, Energy Information Administration, October 2011, at 

http://www.eia.gov/analysis/requests/ces_hall/. 8 Energy Efficiency and Renewable Energy in Appalachia: Policy and Potential, July 2006, at 

http://www.arc.gov/assets/research_reports/arc_renewable_energy_full.pdf. 9 See http://www.nrel.gov/wind/systemsintegration/ewits.html. 10 See http://www.nrel.gov/analysis/jedi/about_jedi.html. 

11 The AEO 2011 is produced by the Department of Energy (DOE) Energy Information Administration 

(EIA) The AEO Reference Case and Side Cases are described and documented at 

http://www.eia.gov/forecasts/aeo/. For the reference case, and many of the side cases, detailed 

tables of results are available in Excel format, as well as in PDF.
Economic Impact of Energy and Environmental Policy in Appalachia

40 Inforum / Keybridge Research

projections from the AEO. The current forecasting horizon of both AEO 2011 and LIFT is 

2035. 

The AEO is produced using the National Energy Modeling System (NEMS)12. NEMS is an 

energy-economy modeling system of the U.S. through 2035. NEMS projects the 

production, imports, conversion, consumption, and prices of energy, subject to 

assumptions on macroeconomic and financial factors, world energy markets, resource 

availability and costs, behavioral and technological choice criteria, cost and 

performance characteristics of energy technologies, and demographics. NEMS is 

comprised of several modules which can work separately or be run together using an 

integrating module. NEMS includes demand modules for each of the major consuming 

sectors in the economy, which are residential, commercial, industrial and transportation. 

NEMS also includes extensive detail on the supply sectors, such as electric power, natural 

gas, petroleum-based and alternative liquid fuels, and coal. Within the electric power 

sector, NEMS models several alternative types of power generation. NEMS also projects 

energy prices. The macroeconomic and industry components of NEMS consist of the IHS 

Global Insight macroeconomic and industry models.

The Inforum LIFT model is not as specialized as NEMS with regard to energy production 

and consumption, but includes a greater level of industry detail, and the 

macroeconomic and industry calculations are integrated and internally consistent. LIFT 

forecasts each final demand component at the industry level, including personal 

consumption, equipment investment, construction, government spending, exports, 

imports, and inventory change. Total demand by commodity is calculated using the 

input-output identity, and is ensured to equal total supply, defined as output plus imports 

less inventory change. LIFT also forecasts the major categories of income by industry, 

including labor compensation, corporate profits, proprietors’ income, net interest, rental 

income, and consumption of fixed capital (depreciation). Prices by industry are forecast 

in a way that maintains consistency between input prices, value added, and output 

prices. 

Submodels have been added to LIFT which link to the industry model in both directions. 

These submodels include:

1. An electric power sector disaggregated into 8 generation types: coal, natural 

gas, petroleum, nuclear, hydroelectric, solar, wind, and geothermal and other 

(which includes biomass).

2. A transportation module, which tracks vehicle miles traveled, fuel (including 

electric) efficiency, and sales and stocks of vehicles by 6 major types. The 

transportation module also forecasts fuel use by air, rail, water, and bus and truck 

transportation.

3. Biofuels module, which can be used to analyze corn and cellulosic ethanol and 

biodiesel.

4. Buildings module, which is used to track energy use of residential and commercial 

buildings.

5. Renewable power module, which models costs, efficiency, and penetration of 

solar and wind power.

6. Carbon emissions and carbon tax module, which relates carbon emissions to 

energy consumption and process emissions at the industry level, and traces the 

 

12 NEMS is described in detail at http://www.eia.gov/oiaf/aeo/overview/index.html. 
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 41

industry price impacts of a carbon tax. This module is also used to track the 

“recycling” of carbon tax revenue.

Calculations native to the LIFT model are in either constant 2005 dollars or in current 

dollars. Prices in LIFT are price indices, equal to 1.0 in 2005. Many of the modules link 

energy quantities (barrels, gallons, Kwh, btus) to constant price measures in LIFT. 

The general strategy of deriving a Reference case for LIFT consistent with the NEMS AEO 

forecast consists of a number of steps:

1. Calibrate exogenous variables, such as population and labor force, government 

spending, exports, and oil, natural gas, and coal prices.

2. Calibrate final demand categories, such as personal consumption, equipment 

investment, and construction to AEO. Demand for imports is derived from import 

requirements for other final demands and for intermediate consumption. Adjust 

imports demand to be consistent with AEO. 

3. Once all final demands have been calibrated, derive the components of 

personal income. Change the federal and state and local tax rates to calibrate 

disposable income.

4. Change labor productivity by industry to calibrate to aggregate labor 

productivity in the AEO. Adjust employment to get close to the AEO 

unemployment rate forecast.

5. Calibrate energy consumption by sector by type. Energy consumption can be 

traced in the LIFT model at several different levels. Energy consumed in final 

demand includes personal consumption of gasoline, heating oil, natural gas, and 

electricity; government purchases of fuels and electricity, and energy consumed 

in building residential and nonresidential structures. Energy flows in the 

intermediate demand part of the model include industrial consumption of energy 

for space heat and light, stationary power sources, transportation fuels, and 

electricity for many uses. These flows also include the conversion of energy from 

one type to another, such as the refining of crude oil into petroleum products, 

and the generation of electricity from coal and other fuel sources

6. Calibrate carbon emissions at the level of major demand sector by major energy 

source.

LIFT was calibrated using the above procedures to the AEO 2011 reference case. 

Selected tables of results of the LIFT reference case are presented and discussed in 

section 6.

The CUEPS model forecasts employment, output, income, and other economic variables 

by county. It also presents a detailed projection of electric utilities, and relates utility 

activity to the county markets each utility serves. The CUEPS database of results includes 

county data for 14 industries for 3140 counties, and utility data for 3356 utilities13. 

The Reference case for CUEPS is created using the LIFT AEO2011 reference case to 

obtain some variables exogenous to CUEPS, but forecasts county and utility level data 

endogenously. The CUEPS projection thus is consistent with the national level economic 

environment projected by LIFT, but relates local employment, output, and income to 

both local and national economic activity. CUEPS includes forecasts for about 20 

aggregate county-level variables, and for about 10 economic variables at the 14-

 

13 See Appendix B for a detailed description of the operation of LIFT and CUEPS.
Economic Impact of Energy and Environmental Policy in Appalachia

42 Inforum / Keybridge Research

industry level. It also includes sales, revenues, customers, and electricity rate projections 

for each utility.

5.3 Electricity Efficiency 

The Energy Efficiency Case starts with the assumptions of the Reference case, but energy 

efficiency in the residential, commercial, and industrial sectors are modified to be 

consistent with the recommendations and findings of the ARC report Energy Efficiency in 

Appalachia.14 While this study also assessed the effects of the policies on natural gas and 

petroleum based fuels, our study focuses on the electricity savings possible from this 

policy package.

Electricity efficiency changes were input to the LIFT model through the modification of 

input-output electricity coefficients in the industrial and commercial sector, and through 

the modification of the personal consumption equation for electricity for the residential 

sector. We assume that the efficiency increases occur at the national level, not just in 

the Appalachian region. Other assumptions that are considered in this scenario include 

government expenditures, subsidies, and tax breaks used to implement these policies. 

Other assumptions used in the LIFT model were the same as in the Reference case.

Residential Sector

The Appalachian energy efficiency study models four policy packages to encourage 

energy efficiency in residential buildings15: 

1. Model building energy codes: Residential building energy codes define 

engineering and construction requirements to meet particular efficiency targets 

for new residential buildings. The study assumes that all Appalachian counties 

adopt the International Energy Conservation Codes (IECCs) with third-party 

verification.

2. Expansion of the weatherization assistance program: Weatherization programs 

improve the efficiency of homes for low-income persons. These programs reduce 

energy consumption and therefore lower energy costs while improving comfort, 

health, and safety. Nationally, 25 percent of households are considered to be 

eligible for weatherization assistance under the Weatherization Assistance 

Program (WAP).

3. Existing home retrofits: This policy element encourages homeowners to pursue 

existing home retrofits by reducing financial barriers. The analysis assumes the 

retrofit program runs as an incentive measure for 20 percent of investment cost, 

to accompany two other policies – home energy disclosure and on-bill financing.

4. Super-efficient appliance deployment: This policy element includes policies to 

encourage greater adoption of energy-efficient appliances and electronics. An 

incentive of 40 percent of the incremental cost is offered for adoption of these 

appliances from 2010 to 2015; from 2015 to 2020, the incentive is 20 percent of 

the incremental cost.

 

14 This study can be found on the ARC web site at 

http://www.arc.gov/assets/research_reports/EnergyEfficiencyinAppalachia.pdf. 15 See section 3.3 of Energy Efficiency in Appalachia for a more detailed description of the policies 

targeting residential consumption.
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 43

Table 5.1 summarizes the electricity savings projected to result from these policies for 

selected years through 2030. Total electricity savings are estimated to be 5.4 percent by 

2020, and 11.1 percent by 2030. In the current study, we model the impacts of these 

savings through the year 2020. 

Table 5.1. Residential Electricity Savings (GWh)

Commercial Sector

The study includes four policy packages, coupled with incentives to encourage 

efficiency in commercial buildings16: 

1. Commercial building energy codes with third party verification: As with residential 

buildings, the study assumes that all Appalachian counties adopt the 

International Energy Conservation Codes (IECCs). The savings are assumed to be 

made through 80 percent compliance with model building code legislation 

enabled by third-party verification of code compliance.

2. Support for commissioning of existing commercial buildings: Building 

commissioning is a multi-phase process to ensure building performance is as 

designed and that the building‘s operation meets the needs of its occupants.

Commissioning existing buildings in the Appalachian Region could lead to 

immediate energy savings.

3. Efficient commercial HVAC and lighting retrofit incentive: A commercial retrofit 

program would include incentives and information to accelerate adoption of 

more efficient products. This type of program helps to induce stock turnover – 

removing the least efficient equipment, while also fostering investment in newer 

technology. 

4. Tightened office equipment standards with efficient use incentive: This policy 

package focuses on office equipment standards for computers, copiers, printers, 

monitors, multi-function devices, fax machines, and scanners.

Table 5.2 summarizes the electricity savings resulting from the adoption of the above 

policies. The savings are substantial, especially in policy packages 2 and 3. Total energy 

savings are projected to be 22.3 percent by 2020, and 46.2 percent by 2030. 

 

16 See section 3.4 of Energy Efficiency in Appalachia.

Residential 

Building 

Codes

Expanded 

Weatherization

Existing 

Home 

Retrofits

SuperEfficient 

Appliances

Total 

Residential 

Savings

Total 

Electricity 

Savings 

(Percent)

2010 25.2 114.0 382.0 51.0 572.2

2013 201.2 461.5 1535.0 207.0 2404.7

2020 1587.0 1299.6 4222.0 957.0 8065.6 5.4

2030 4888.7 2612.2 8241.0 2736.0 18477.9 11.1
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44 Inforum / Keybridge Research

Table 5.2 Commercial Electricity Savings (GWh)

Industrial Sector

The study includes 3 policy packages for the industrial sector:

1. Expansion of industrial assessment centers (IACs): Industrial Assessment Centers 

(IACs) are university-based, and teams comprised of both faculty and students 

perform thorough analyses at small to medium-sized industrial facilities within their 

local region. These assessments suggest savings improvements in energy 

efficiency, waste minimization, pollution prevention, and productivity. Expanding

the capacity of Industrial Assessment Centers in Appalachia could greatly 

improve the energy efficiency of industry in the Region.

2. Energy savings assessment (ESA) training: There are several programs identified in 

the EEA report that support energy savings assessment and training. Details of the 

estimates of energy savings gained from these programs are discussed in 

Appendix D.2 of that study.

3. Combined heat and power (CHP) incentives: Combined heat and power (CHP) 

can offer significant energy use reductions by avoiding energy waste through 

heat loss. The suite of policies suggested in EEA includes grants, loans, special 

rates, and ease of interconnection with the electrical grid. Energy savings from 

these policies are discussed in Appendix D.3 of that study.

Table 5.3 summarizes the electricity savings estimated to be achieved through the above 

policies. Total energy savings are estimated to be 18 percent by 2020, and 42.4 percent 

by 2030. 

Commercial 

Building 

Codes

Commissioning of 

Existing Buildings

Efficient 

HVAC and 

Lighting 

Retrofits

Office 

Equipment 

Standards

Total 

Commercial 

Savings

Percent 

Savings

2010 84 213 712 387 1396

2013 391 1868 3489 1060 6808

2020 1551 9886 14129 4673 30239 22.3

2030 3993 26611 31661 12017 74282 46.2
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Table 5.3. Industrial Electricity Savings (GWh)

5.4 Carbon Mitigation

The Carbon Mitigation case examines the implications of a national greenhouse gas 

(GHG) price policy on electricity prices, output, and employment in Appalachia. The 

objective of this scenario is not to model any particular legislation, but to examine a 

generic GHG price scenario. Since the Reference case for this exercise is calibrated to 

the AEO2011 Reference case, it was convenient to calibrate the LIFT model to the 

AEO2011 side case “GHG Price Economywide”.17 The carbon price for this scenario is 

specified to begin in 2013 at $25 per metric ton CO2, and rise to $75 per metric ton by 

2035. Table 5.4 shows the carbon price assumption in both 2009$ and in nominal dollars. 

The average growth rate of the nominal price over this period is about 9 percent.

Table 5.4 Carbon Price

The average electricity price is higher relative to the base case, due to the carbon costs 

imposed upon fossil fuels such as coal, natural gas, and petroleum. However, the AEO 

GHG case also includes a switch from coal to other fuel sources, which reduces the 

impact of the carbon tax somewhat. This switch in generation types was also adopted in 

the LIFT model. Table 5.5 below shows the impact on the average delivered electricity 

price in the Reference case and in LIFT Carbon Mitigation case.

 

17 The AEO 2011 was distinguished by the publication of a Reference Case and 58 side cases, 

described in Appendix E of the AEO document. The Annual Energy Outlook 2011 can be 

accessed at http://www.eia.gov/forecasts/aeo/pdf/0383%282011%29.pdf. The full set of side 

cases is described in table E1 beginning on p. 214. The cases are summarized starting on p. 218. 

The GHG cases are described on p. 223.

Expanded 

Industrial 

Assessment 

Center 

Initiative

Increasing 

Assessments and 

Training

Supporting 

Combined 

Heat and 

Power (CHP) 

with 

Incentives

Total 

Industrial 

Savings

Percent 

Savings

2010 10 106 0 116

2013 631 914 2793 4338

2020 3243 6422 9655 19320 18.0

2030 7261 21344 21081 49686 42.4

2009$ Nominal

2012 0.00 0.00

2013 25.00 26.35

2015 29.73 32.22

2016 32.09 35.31

2020 41.55 49.58

Avg. Growth 7.26 9.03
Economic Impact of Energy and Environmental Policy in Appalachia

46 Inforum / Keybridge Research

Table 5.5 Electricity Price in the Carbon Mitigation Case

Figure 5.1 shows the changes in nationwide electricity generation by type, and table 5.6 

provides the data underlying this figure. Note that separate generation for wind, solar 

and geothermal and other is accounted for in LIFT, but that these have been combined 

into “Renewable” for this figure and table.

Figure 5.1. Generation Mix in Carbon Mitigation and Reference Cases

By 2020, generation from coal in the Carbon Mitigation case is significantly reduced (by 

40.2 percent) relative to the Reference. Natural gas and Renewables are significantly 

higher (19 percent and 67 percent, respectively). Total electricity generation is about 

10.2 percent lower in the Carbon Mitigation case, due to demand response to higher 

electricity prices in the residential, commercial, and industrial sectors.

Reference GHG Price

Percent 

Difference

2012 9.31 9.31

2013 9.40 10.93 16.2

2015 9.70 11.72 20.8

2020 10.68 13.46 26.0

0

500

1000

1500

2000

2500

3000

3500

4000

4500

 2010 2015 2020 2010 2015 2020

Reference Case Carbon Mitigation

Bil Kwh

Electricity Generation Mix

Renewable

Hydro

Nuclear

Oil/Gas

Coal
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 47

Table 5.6 Electricity Generation Mix in Reference and Carbon Mitigation Cases

(billions of KWh)

5.5 Clean Energy Standard Scenario

This scenario examines possible responses to a clean energy standard, specifically the 

aggressive development of wind and biomass resources for electric power in the 

Appalachian region. The national-level scenario developed for the LIFT model draws on 

the recently published EIA study Analysis of Impacts of a Clean Energy Standard18. 

In general, a Clean Energy Standard (CES) is a policy that requires covered electricity 

sources to supply a specified share of their electricity sales from qualifying clean energy 

sources. The EIA report analyzes a version of the CES that was specified by 

Representative Ralph M. Hall, Chairman of the House Committee on Science, Space, 

and Technology. In this version, electric generators are granted clean energy credits for 

every megawatt-hour (MWh) of electricity produced using qualifying clean energy 

sources. Generators can use some combination of credits granted to their own 

generation of electricity, or acquire credits from other generators, to meet their CES 

obligations. Generators without retail customers that generate more clean energy 

credits than needed to meet their own obligation can sell CES credits to other 

companies.

Eligible generation types include hydroelectric, wind, solar, geothermal, biomass power, 

municipal solid waste, landfill gas, nuclear, coal-fired plants with carbon capture and 

sequestration (CCS), and natural gas plants with either CCS or utilizing combined cycle 

technology.19 The target starts from an initial share of 44.8 percent in 2013, and rises 

linearly to 80 percent in 2035. Beyond 2035, the target remains at 80 percent. There is no 

option to purchase or sell credits to the government, and there is no banking of credits 

for use in future years.

 

18 Analysis of Impacts of a Clean Energy Standard, as requested by Chairman Hall, U.S. 

Department of Energy, Energy Information Administration, October 2011, at 

http://www.eia.gov/analysis/requests/ces_hall/. 19 Generators earn 0.5 Mwh credit for 1 Mwh generation from natural gas combined cycle 

(NGCC), and 0.9 Mwh credit for 1 Mwh generated by coal or gas CCS.

Reference Case

2010 2015 2020 2015 2020 2015 2020

Coal 1818 1787 1850 1266 1105 -29.2 -40.3

Oil/Gas 934 905 868 1008 1036 11.3 19.4

Nuclear 803 839 877 839 877 0.0 0.0

Hydro 240 293 301 299 306 1.8 1.6

Renewable 133 201 224 330 375 64.1 67.2

 Total Generation 3929 4026 4121 3742 3699 -7.1 -10.2

Carbon Mitigation

Levels

Percent 

Difference from 

Reference
Economic Impact of Energy and Environmental Policy in Appalachia

48 Inforum / Keybridge Research

The policy goal of the CES is to shift generation to a mix which generates less CO2 and 

other emissions. In the scenario, there is indeed a significant shift in the generation mix. 

The electricity price is also significantly higher than in the Reference case.

Table 5.7 shows the impact of the CES on the delivered electricity price, in selected 

years. 

Table 5.7 Electricity Price in CES Case

Figure 5.2 Electricity Generation Mix in Reference and CES Cases

Table 5.8 Electricity Generation Mix in Reference and CES Cases

Reference GHG Price

Percent 

Difference

2012 9.31

2015 9.70 9.82 1.2

2020 10.68 11.58 8.4

 2010 2015 2020 2015 2020

Coal 1818 1787 1850 1414 1135 -20.9 -38.7

Oil/Gas 934 905 868 893 903 -1.3 4.1

Nuclear 803 839 877 839 884 0.0 0.7

Hydro 240 293 301 299 306 1.8 1.6

Renewable 133 201 224 325 359 61.2 60.0

 Total Generation 3929 4026 4121 3769 3587 -6.4 -13.0

Reference Case Clean Energy Standard

Levels

Percent Difference 

from Reference

0

500

1000

1500

2000

2500

3000

3500

4000

4500

 2010 2015 2020 2010 2015 2020

Reference Case Clean Energy Standard

Bil Kwh

Electricity Generation Mix

Renewable

Hydro

Nuclear

Oil/Gas

Coal
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Generation by coal is significantly lower in the CES Case by 2020, at a level of 1,135 billion 

Kwh, compared with 1,850 billion Kwh in the Reference case. Renewables are 

significantly higher, with generation of 359 billion Kwh compared with 224 billion Kwh in 

the Reference. Among the renewable sources, wind and biomass have the largest 

generation increases under the CES.

Wind

Since wind and biomass are two technologies that are expected to benefit from the 

CES, our question is: How much will these technologies expand in the Appalachian 

region? A study commissioned by ARC and completed in 2006 suggested that the 

Appalachian region had the potential of about 15,000 MW of wind capacity, with the 

three largest states being New York, Pennsylvania, and West Virginia.20 The map below in 

Figure 5.3 shows bands of wind potential, categorized by wind speed21. 

Figure 5.3 Wind Potential in Appalachia

 

20 Energy in Appalachia: Policy and Potential Energy Efficiency and Renewable, July 2006, at 

http://www.arc.gov/assets/research_reports/arc_renewable_energy_full.pdf, pp. 3-4. 21 This map was taken from the ARC study, and they obtained it from TrueWind Solutions, LLC.
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Since the earlier ARC study was published, the Eastern Wind Intregation and Transmission 

Study (EWITS) has published an executive summary and project overview as well as a full 

report22. This study, which is done under contract with the DOE National Renewable 

Energy Laboratory (NREL) is being led by Enernex, with support from Ventyx and the 

Midwest Independent System Operator (MISO). This study is one of the largest regional 

wind integration studies to date. It was initiated in 2008 to examine the operational 

impact of up to 20-30% wind on the power system in the Eastern Interconnect of the 

United States, of which the Appalachian Region is a part. An output of the study has 

been the valuable eastern wind dataset, which is based on three years (2004-2006) of 

data of 10-minute wind speed and plant output values for simulated wind plants at 

selected locations. This data was created by AWS-Truewind, and includes a database of 

selected sites for potential wind farms, with information including longitude and latitude, 

elevation, cost of energy, average wind speed, estimated capacity factor, size and 

density of the area, and total installed power potential. The derivation of the 

assumptions for wind capacity installation by Appalachian county are described in 

Appendix C. 

Biomass

The other major component of the Clean Energy Standard Scenario for this project is 

biomass development in Appalachia. Figure 8.6 shows the map of biomass potential by 

county from the ARC study Energy Efficiency and Renewable Energy in Appalachia.

 

22 These two documents, related information, maps, data and FAQs can be found at 

http://www.nrel.gov/wind/systemsintegration/ewits.html.
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Figure 5.4 Biomass Potential in Appalachia

In this scenario, it is assumed that all coal-fired power plants retrofit in order to replace 

15% of the coal that they burn with biomass by 2025. To be consistent with the EIA it was 

assumed upfront investments of $123 to $282 per kilowatt were needed in order to 

facilitate this ability. Increases in the percent of cofiring was assumed to increase linearly 

from the levels projected for 2012 until reaching 15% in 2025. 

 
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5.6 Expanded Natural Gas Development 

This scenario explores the intensive development of natural gas resources in the region. 

As with the other scenarios, we first developed a national-level macroeconomic and 

industry context using the Inforum LIFT model, and then used geographic information to 

determine the impacts by county using the CUEPS model.

One of the side cases developed for the Annual Energy Outlook 2011 is called ‘High 

Shale EUR Case’. In this scenario, the estimated ultimate recovery (EUR) per shale gas 

well is assumed to be 50 percent higher than in the Reference case, decreasing the perunit cost of developing this resource. The total unproved technically available shale gas 

resource is currently estimated by USGS to be about 84 trillion cubic feet23. 

A LIFT model scenario was developed by calibrating LIFT to the AEO High Shale EUR case. 

The process was very similar to that described in section 8.1 on the Reference case. 

Increased supply of natural gas in the model is incorporated as higher production levels 

from the base case. As was shown in the AEO High Shale EUR case, there is increased 

supply of natural gas compared to the Reference case, which in turn leads to increased 

use of natural gas in electric power generation, higher gas exports and lower imports, 

and increased use in the residential, commercial, and industrial sectors. The gas price is 

also significantly reduced from the Reference case.

In developing the county level (CUEPS) assumptions for this scenario, regional changes in 

natural gas supply were differentiated based on regional differences in NEMS’s 6 onshore 

and 3 offshore natural gas production regions. In all but the Northeast region, which 

includes all of the Appalachia counties, expanded natural gas production was shared 

down to the county level based on the overall size of the mining sector in each county 

relative to that of the region. 

However, in the sixth region, the northeast, which includes all of the gas-producing 

Appalachian counties, the degree to which different Appalachian counties would 

benefit from increased production of shale gas, the modeling team used digital data 

published by the U.S. Geological Survey (USGS) on undeveloped shale gas resources in 

the Appalachian Basin. The USGS data focus on the Devonian Marcellus Shale, which 

stretches across parts of New York, Pennsylvania, Maryland, Virginia, West Virginia, Ohio, 

Kentucky, and Tennessee. The data are comprised of 21 separate shale formations 

within the Devonian Marcellus Shale and, among other information, include USGS 

estimates for the undiscovered gas resources that are likely to reside within each 

formation. Using GIS software, Keybridge analyzed the geographic extent of each 

formation relative to county boundaries in the region and generated estimates for the 

total amount of undiscovered shale gas in each county. 

To facilitate analysis, certain key assumptions were necessary, and some factors that are 

likely to affect a county’s suitability for shale gas exploration were not considered. For 

example, due to limitations with the USGS data, each formation’s estimated gas 

resources were assumed to be distributed evenly throughout the formation. As such, 

counties were allocated gas resources based on their size and the proportion of the 

 

23 This estimated is significantly lower than the estimate of 410 trillion cubic feet published earlier this 

year by the federal Energy Information Administration. The high shale case calls for an addition 3 

trillion cubic feet of production by 2020. While the revised estimates are significant, drilling costs 

and well performance will have a larger impact on natural gas production than the size of the 

reserves.
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county located within each formation. In addition, factors that could affect a county’s 

capacity to extract its gas resources—for example, proximity to major urban areas, the 

presence of environmentally sensitive areas (e.g., national forests and parks), and the 

existence of ongoing drilling activities within its borders—were not included in the 

analysis. Moreover, the analysis is limited to the Devonian Marcellus Shale and does not 

include any gas resources that may be present in the Utica Shale. Although most of the 

attention surrounding undiscovered natural gas resources in the Appalachian Basin has 

centered on the Devonian Marcellus Shale, the Utica Shale is an even larger formation 

which, based on early testing, may also contain vast natural gas deposits.
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6. Comparisons of Scenario Results

This chapter presents a comparison of the results of the scenarios implemented using the 

LIFT and CUEPS models. Section 6.1 compares the macroeconomic results produced 

using LIFT. Section 6.2 shows state summaries, which are of state level aggregates of 

counties within the Appalachian region. Section 6.4 shows some selected county-level 

results, in map form. Finally, section 6.5 shows results for certain individual counties. 

Tables such as those in section 6.5 could be generated for any of the 420 counties 

comprising Appalachia.

6.1 Macroeconomic Results

The LIFT model makes projections of final demand, value added, employment, and 

prices for each industry. These are summed to form the macroeconomic, energy, and 

emission aggregates. In this section, we first present a macroeconomic summary for 

each scenario. The Reference case is presented in the most detail and in a standalone 

table. Each other scenario is compared with respect to the Reference case. Finally, in 

section 6.1.5, we show global results, comparing all of the scenarios.

6.1.1 Reference Case

Table 6.1.1 shows a summary of important macroeconomic and energy-related variables 

for the Reference case, forecast to 2020. The table shows values for 3 selected years in 

the first three columns, and shows the average annual exponential growth rate of the 

variable in the column on the far right. 

Average real GDP growth in the Reference case from 2010 to 2020 is projected to be 

about 3%. Personal consumption growth is expected to be somewhat slower, growing at 

an average rate of 2.6%. The nominal oil price is expected to reach $130.6 per barrel by 

2020, an average growth of 5.1% per year. The average national electricity price is 

projected to grow from a value of 9.7 cents/Kwh in 2010 to 10.7 cents/Kwh in 2020, an 

average annual growth rate of 1.0%. Total energy consumption in Btus is expected to 

grow from a value of 97.8 quadrillion Btus (quads) in 2010 to 105.1 quads in 2020, an 

average annual growth of 0.7%. Total carbon dioxide emissions are projected to grow 

from 5,651 million metric tons (Mmt) in 2010 to 5791 Mmt in 2020, an average growth of 

0.2%.

The policy scenarios in the next few sections are compared vis-à-vis the Reference case.
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Table 6.1.1 Macroeconomic Summary Table for Reference Case

Growth Rate

 2010 2015 2020 2010-2020

 GDP AND MACROECONOMIC SUMMARY

 (Billions of chained 2005 dollars)

 Gross Domestic Product 13,100 15,474 17,723 3.0

 Personal Consumption Expenditures 9,346 10,673 12,147 2.6

 Gross Private Fixed Investment 1,607 2,641 3,007 6.3

 Exports 1,521 2,275 3,248 7.6

 Imports 1,955 2,586 3,182 4.9

 Government 2,569 2,547 2,694 0.5

 Gross Domestic Product, bil cu$ 14,578 18,486 23,233 4.7

 Crude Oil Imports (mbd) 8.7 8.7 8.3 -0.5

 GDP Deflator 111.3 119.5 131.1 1.6

 Real Disp Income, bil 05$ 9,935 11,711 13,309 2.9

 ENERGY PRICES

 Crude low sulphur ($/bbl) 78.7 103.2 130.6 5.1

 Average electricity (c/Kwh) 9.7 9.7 10.7 1.0

 ENERGY CONSUMPTION BY SECTOR

 (quadrillion Btus)

 Residential 22.1 20.6 20.9 -0.5

 Commercial 18.3 19.2 20.3 1.0

 Industrial 30.0 34.5 34.8 1.5

 Transportation 27.5 28.7 29.1 0.6

 Total 97.8 103.1 105.1 0.7

 Total electricity generation 3,929.0 4,025.6 4,120.7 0.5

 CARBON DIOXIDE EMISSIONS BY SECTOR

 (million metric tons)

 Residential 339 337 333 -0.2

 Commercial 214 231 235 0.9

 Industrial 931 1,078 1,072 1.4

 Transportation 1,859 1,925 1,922 0.3

 Electric power 2,308 2,191 2,229 -0.3

 Total 5,651 5,762 5,791 0.2

 Carbon Dioxide Emissions

 (tons per person) 18.1 17.6 16.9 -0.7
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6.1.2 Electricity Efficiency

Table 6.1.2 shows a summary of the national level macroeconomic results in LIFT for the 

Energy Efficiency Case. The most notable differences from the Reference case are the 

differences in energy consumption by sector, total electricity generation, and carbon 

dioxide emissions in the electric power sector. Total energy consumption rises to 100.2 

quad btus in 2015, but then falls back to 97.6, slightly below the starting point of 97.8 in 

2010. The biggest difference is in the commercial sector, which reached 20.3 quads by 

2020 in the Reference case, but only reaches 15.77 quads in the Energy Efficiency Case. 

Total electricity generation actually declines to 3,426 bil Kwh by 2020 in the Energy 

Efficiency case, compared to a rise to 4,121 billion Kwh in the Reference case. Total 

carbon emissions reach 5,211 by 2020, compared to 5,791 in the Reference case.

Table 6.1.2 Macroeconomic Comparison of Electricity Efficiency Case with Reference

2015 2020 10-20 2015 2020 2015 2020 10-20

 GDP AND MACROECONOMIC SUMMARY

 (Billions of chained 2005 dollars)

 Gross Domestic Product 15,474 17,723 3.0 0.2 0.4 15,508 17,798 3.1

 Personal Consumption Expenditures 10,673 12,147 2.6 0.3 0.4 10,700 12,201 2.8

 Gross Private Fixed Investment 2,641 3,007 6.3 0.5 0.6 2,653 3,024 6.3

 Exports 2,275 3,248 7.6 0.1 0.4 2,276 3,259 7.5

 Imports 2,586 3,182 4.9 0.3 0.4 2,593 3,193 4.9

 Gross Domestic Product, bil cu$ 18,486 23,233 4.7 0.1 -0.2 18,501 23,198 4.9

 GDP Deflator 119.5 131.1 1.6 -0.1 -0.6 119.3 130.3 1.8

 Real Disp Income, bil 05$ 11,711 13,309 2.9 0.3 0.5 11,742 13,371 3.1

 Average electricity price (c/Kwh) 9.7 10.7 1.0 -1.3 -3.5 9.6 10.3 0.7

 ENERGY CONSUMPTION BY SECTOR

 (quadrillion Btus)

 Residential 20.65 20.94 -0.5 -2.5 -4.6 20.14 19.97 -1.0

 Commercial 19.24 20.29 1.0 -8.5 -22.3 17.60 15.77 -1.5

 Industrial 34.48 34.82 1.5 -2.3 -6.0 33.69 32.72 0.9

 Transportation 28.73 29.08 0.6 0.0 0.0 28.74 29.09 0.6

 Total Energy Consumption 103.10 105.13 0.7 -2.8 -7.2 100.18 97.55 0.0

 Total electricity generation (bil Kwh) 4,026 4,121 0.5 -6.7 -16.9 3,755 3,426 -1.3

 Total carbon dioxide emissions (MMT) 5,762 5,791 0.2 -3.9 -10.0 5,537 5,211 -0.8

 (tons per person) 17.6 16.9 -0.7 -3.9 -10.0 16.9 15.2 -1.7

Reference Case

Percent 

Differences 

from 

Reference Levels

Electricity Efficiency Case
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6.1.3 Carbon Mitigation

Table 6.1.3 is the macroeconomic summary for the Carbon Mitigation case. By 2020 GDP 

is slightly lower than in the Reference case, a difference of about 0.9 percent. Exports 

decline by about 3.4 percent relative to the base by 2020 due to an increase in the 

relative domestic to foreign price. Total carbon tax revenue collected in 2020 is $248 

billion. In the LIFT scenario, we have rebated two-thirds of this tax in the form of a lump 

sum personal tax rebate. One-third has been rebated to the corporate sector through a 

reduction in corporate income taxes. Total energy consumption in Btus reaches 99.95 

quads by 2020, compared with 105.1 in the Reference case, a decline of 4.9 percent. 

Total carbon dioxide emissions reach a total of 5,048 by 2020, compared with 5,791 in the 

Reference case, a decline of about 13 percent. This decline is due partly to reduced 

energy consumption, but more importantly from a switch away from coal in electricity 

generation.

Table 6.1.3 Macroeconomic Comparison of Carbon Mitigation Case with Reference

 

2015 2020 10-20 2015 2020 2015 2020 10-20

 GDP AND MACROECONOMIC SUMMARY

 (Billions of chained 2005 dollars)

 Gross Domestic Product 15,474 17,723 3.0 -0.4 -0.9 15,414 17,569 3.1

 Personal Consumption Expenditures 10,673 12,147 2.6 -0.2 -0.2 10,656 12,119 2.8

 Gross Private Fixed Investment 2,641 3,007 6.3 -0.7 -2.0 2,622 2,947 6.3

 Exports 2,275 3,248 7.6 -1.9 -3.4 2,232 3,137 7.5

 Imports 2,586 3,182 4.9 -0.8 -1.2 2,565 3,145 4.9

 Gross Domestic Product, bil cu$ 18,486 23,233 4.7 1.6 2.5 18,775 23,816 4.9

 GDP Deflator 119.5 131.1 1.6 2.0 3.4 121.8 135.6 1.8

 Real Disp Income, bil 05$ 11,711 13,309 2.9 -0.2 -0.3 11,690 13,265 3.1

 Average electricity price (c/Kwh) 9.7 10.7 1.0 20.8 26.0 11.7 13.5 0.7

 ENERGY CONSUMPTION BY SECTOR

 (quadrillion Btus)

 Residential 20.65 20.94 -0.5 -6.8 -8.3 19.24 19.20 -1.0

 Commercial 19.24 20.29 1.0 -3.2 -5.3 18.62 19.22 -1.5

 Industrial 34.48 34.82 1.5 -4.9 -5.7 32.78 32.84 0.9

 Transportation 28.73 29.08 0.6 -0.9 -1.4 28.47 28.68 0.6

 Total Energy Consumption 103.10 105.13 0.7 -3.9 -4.9 99.10 99.95 0.0

 Total electricity generation (bil Kwh) 4,026 4,121 0.5 -7.1 -10.2 3,742 3,699 -1.3

 Total carbon dioxide emissions (MMT) 5,762 5,791 0.2 -9.6 -12.8 5,207 5,048 -0.8

 (tons per person) 17.6 16.9 -0.7 -9.6 -12.8 15.9 14.7 -1.7

Reference Case Carbon Mitigation Case

Percent 

Differences 

from 

Reference Levels
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6.1.4 Clean Energy Standard

The main impacts of the Clean Energy Standard (CES) are on the choice of electric 

power generation by type, which result in only a slight increase (1.3%) in the electricity 

price by 2015, and a larger increase (8.5%) by 2020. GDP and its components are hardly 

affected, with only a 0.1% decline in GDP relative to the Reference case by 2020. Energy 

consumption is down more than would be warranted by the electricity price alone. This 

case was calibrated to the CES case published by DOE, which also showed an increase 

in natural gas prices due to demand pressure from electric power producers switching 

out of coal into natural gas. Total energy consumption reaches 100 quads by 2020, 

compared with 105.1 in the Reference case, a difference of -4.9%. Total carbon 

emissions are 12.4% lower than the Reference by 2020, due both to reduction in 

consumption as well as the greener mix of electric power production.

Table 6.1.4 Macroeconomic Comparison of Clean Energy Standard with Reference

 

2015 2020 10-20 2015 2020 2015 2020 10-20

 GDP AND MACROECONOMIC SUMMARY

 (Billions of chained 2005 dollars)

 Gross Domestic Product 15,474 17,723 3.0 0.0 -0.1 15,472 17,697 3.0

 Personal Consumption Expenditures 10,673 12,147 2.6 0.0 -0.1 10,672 12,130 2.6

 Gross Private Fixed Investment 2,641 3,007 6.3 0.0 -0.2 2,640 3,001 6.2

 Exports 2,275 3,248 7.6 0.0 -0.1 2,275 3,243 7.6

 Imports 2,586 3,182 4.9 0.0 -0.1 2,585 3,177 4.9

 Gross Domestic Product, bil cu$ 18,486 23,233 4.7 0.0 0.1 18,481 23,253 4.7

 GDP Deflator 119.5 131.1 1.6 0.0 0.2 119.4 131.4 1.7

 Real Disp Income, bil 05$ 11,711 13,309 2.9 0.0 -0.2 11,710 13,289 2.9

 Average electricity price (c/Kwh) 9.7 10.7 1.0 1.3 8.5 9.8 11.6 1.8

 ENERGY CONSUMPTION BY SECTOR

 (quadrillion Btus)

 Residential 20.65 20.94 -0.5 -8.9 -17.3 18.81 17.32 -3.2

 Commercial 19.24 20.29 1.0 -1.2 -1.9 19.01 19.90 0.8

 Industrial 34.48 34.82 1.5 -2.1 -3.1 33.76 33.73 1.2

 Transportation 28.73 29.08 0.6 0.0 -0.1 28.72 29.05 0.6

 Total Energy Consumption 103.10 105.13 0.7 -2.7 -4.9 100.30 100.00 0.0

 Total electricity generation (bil Kwh) 4,026 4,121 0.5 -6.4 -13.0 3,769 3,587 -1.4

 Total carbon dioxide emissions (MMT) 5,762 5,791 0.2 -7.1 -12.4 5,354 5,072 -1.4

 (tons per person) 17.6 16.9 -0.7 -7.1 -12.4 16.4 14.8 -2.4

Reference Case Clean Energy Standard

Percent 

Differences 

from 

Reference Levels
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6.1.5 Expanded Natural Gas Development

This scenario is characterized by an aggressive development of natural gas from shale. 

As a result, gas prices are 15.5% lower than the Reference in 2015, and 16.1% lower in 

2020, reaching a level of 5.2 $/tcf instead of 4.2 $/tcf in the Reference case. The 

electricity generation mix shifts in favor of the cheaper natural gas and the overall 

electric power price declines 3.4% by 2020. Residential consumption actually decreases 

relative to the base in 2020, but all other sectors expand slightly, due to the lower prices. 

The net effect on carbon emissions is a difference of -4.9% relative to the Reference case 

by 2020. 

Table 6.1.5 Macroeconomic Comparison of Expanded Natural Gas with Reference

2015 2020 10-20 2015 2020 2015 2020 10-20

 GDP AND MACROECONOMIC SUMMARY

 (Billions of chained 2005 dollars)

 Gross Domestic Product 15,474 17,723 3.0 0.1 0.1 15,488 17,744 3.0

 Personal Consumption Expenditures 10,673 12,147 2.6 0.1 0.0 10,679 12,153 2.6

 Gross Private Fixed Investment 2,641 3,007 6.3 0.0 0.2 2,641 3,014 6.3

 Exports 2,275 3,248 7.6 0.3 0.5 2,283 3,263 7.6

 Imports 2,586 3,182 4.9 0.1 0.2 2,588 3,187 4.9

 Gross Domestic Product, bil cu$ 18,486 23,233 4.7 -0.3 -0.3 18,427 23,160 4.6

 GDP Deflator 119.5 131.1 1.6 -0.4 -0.4 119.0 130.5 1.6

 Real Disp Income, bil 05$ 11,711 13,309 2.9 0.1 0.1 11,721 13,325 2.9

 Natural gas ($/tcf) 4.9 6.2 4.5 -15.5 -16.1 4.2 5.2 3.9

 Average electricity price (c/Kwh) 9.7 10.7 1.0 -3.9 -3.4 9.3 10.3 0.7

 ENERGY CONSUMPTION BY SECTOR

 (quadrillion Btus)

 Residential 20.65 20.94 -0.5 -5.1 -10.9 19.60 18.67 -2.4

 Commercial 19.24 20.29 1.0 -0.1 1.0 19.23 20.49 1.1

 Industrial 34.48 34.82 1.5 -0.3 1.3 34.39 35.27 1.6

 Transportation 28.73 29.08 0.6 0.0 0.1 28.74 29.09 0.6

 Total Energy Consumption 103.10 105.13 0.7 -1.1 -1.5 101.96 103.52 0.4

 Total electricity generation (bil Kwh) 4,026 4,121 0.5 -5.0 -8.2 3,825 3,785 -0.8

 Total carbon dioxide emissions (MMT) 5,762 5,791 0.2 -3.4 -4.9 5,565 5,506 -0.5

 (tons per person) 17.6 16.9 -0.7 -3.4 -4.9 17.0 16.1 -1.5

Reference Case Expanded Natural Gas Development

Percent 

Differences 

from 

Reference Levels
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6.1.6 Comparisons of All Scenarios

Figures 6.1.1 through 6.1.4 compare 4 selected macroeconomic variables across the 5 

cases: Real GDP, total employment, energy intensity (btus/GDP), and CO2 emissions. The 

biggest negative impact to real GDP and employment is in the Carbon Mitigation case. 

The efficiency and gas development cases have higher GDP than the Reference and 

the CES is very close to the Reference.

Figure 6.1.1 Comparison of Real GDP

Figure 6.1.2 Comparison of Employment
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In terms of total economy-wide energy intensity, all scenarios are somewhat more 

efficient than the Reference case. The biggest increase in efficiency is in the electricity 

efficiency case. Carbon emissions are reduced the most in the Carbon Mitigation case, 

followed by the Clean Energy Standard. However, by 2020, the efficiency case also has 

significant carbon reductions.

Figure 6.1.3 Comparison of Energy Intensity

Figure 6.1.4 Comparison of CO2 Emissions
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6.2 Appalachia and State-Level Summaries

The CUEPS model makes projections for 3140 counties and 3356 utilities. To make sense 

of this mass of detail it is necessary to aggregate. In the following sections, we present 

results at the Appalachia 420 county level, and for state-level aggregates of 

Appalachian counties. In the following section, results are presented in maps of all 

Appalachian counties for selected variables in the model. 

6.2.1 Reference Case

The projections in table 6.2.1 are aggregated from the county level. There are 420 

counties comprising the Appalachian region, scattered across 13 states. Real personal 

income in the aggregate Appalachian region is about $723.8 billion (2005$) in 2010, 

growing to $1,031.7 billion by 2020. Per capita real income is about $28.8 thousand in 

2010, growing to about $38.1 thousand by 2020. Employment in the region is 12.9 million 

jobs in 2010, growing to about 16 million by 2020. 

Table 6.2.1 Appalachia Summary for the Reference Case

The data for electricity sales, revenues, customers, and rates is originally projected by 

utility, and then converted to the county level using the county-utility bridge which 

relates county economic activity to each utility’s market. Total electricity sales in 

Appalachia is 389.5 million Mwh in 2010, and grows to 508.4 million Mwh by 2020, an 

average annual growth of about 2.7 percent. Sales per customer is obtained by dividing 

total sales by market (residential, commercial, and industrial) by the number of 

Growth 

rate

Units 2010 2015 2020 10-20

County Economic Variables

 Real personal income Millions of 2005$ 723,817 858,671 1,031,721 3.5

 Per capita Thousands of 2005$ 28.76 32.89 38.09 2.8

 Employment Thousand jobs 12,885 14,530 16,044 2.2

Electric Utility Variables

 Electricity sales Thousand Mwh 389,502 453,021 508,445 2.7

 Sales per customer Mwh

 Residential 12.64 13.02 12.92 0.2

 Commercial 63.36 62.86 60.93 -0.4

 Industrial 2,180.52 1,945.93 1,752.04 -2.2

 Electricity revenues Millions of dollars 30,411 35,611 44,125 3.7

 Revenues per customer Thousands of dollars

 Residential 1.21 1.25 1.37 1.2

 Commercial 5.14 5.15 5.53 0.7

 Industrial 119.41 107.21 106.38 -1.2

 Electricity rate Dollars/Kwh

 Residential 0.096 0.096 0.106 1.0

 Commercial 0.081 0.082 0.091 1.1

 Industrial 0.055 0.055 0.061 1.0

Levels
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 63

customers projected in each market. The Reference case projection includes a slight 

increase in residential sales per customer (0.22 percent growth), but for slightly declining 

commercial sales per customer (-0.39 percent), and fairly strong declines in industrial 

sales per customer (-2.2 percent). The different absolute sizes of the sales per customer 

number are typical of the database, although a few large industrial customers bring up 

the industrial average considerably.

Total electricity revenues in the Appalachian region in the Reference case are $30.4 

billion in 2010 and are projected to grow to $44.1 billion by 2020, an average growth rate 

of 3.7 percent. This overall revenue growth is a combination of the growth in electricity 

sales (Mwh), electricity rate, and the growth in the number of customers, which are 

projected for each market. The electricity rate is highest in the residential sector, at 9.6 

cents/Kwh in 2010. The commercial rate is slightly lower, at 8.1 cents/Kwh, and the 

industrial rate is the lowest, at 5.5 cents/Kwh. All of the electricity prices are projected to 

grow at between 1 percent and 1.1 percent over the period.

6.2.2 Electricity Efficiency

Table 6.2.2 presents results at the all Appalachia level for the Electricity Efficiency case. 

The most important driver of this simulation is the reduction in electricity use. The 

reduction in demand pressure also causes the electricity price to grow somewhat more 

slowly. Electricity sales by 2020 are 15.6 percent below the Reference case, as shown in 

the fifth column. Electricity revenues are 17.6 percent below the Reference case. Both 

personal income and employment are somewhat higher than the Reference case, due 

both to improvements in efficiency and the slight reduction in the electricity price. 

Table 6.2.2 Appalachia Comparison for the Electricity Efficiency Case

Growth 

rate

Growth 

rate

2015 2020 10-20 2015 2020 2015 2020 10-20

County Economic Variables

 Real personal income 858,671 1,031,721 3.5 0.1 0.3 859,283 1,035,120 3.6

 Per capita 32.89 38.09 2.8 0.1 0.3 32.92 38.21 2.8

 Employment 14,530 16,044 2.2 0.2 0.4 14,559 16,113 2.2

Electric Utility Variables

 Electricity sales 453,021 508,445 2.7 -5.7 -15.6 427,337 428,961 1.0

 Electricity revenues 35,611 44,125 3.7 -6.5 -17.6 33,312 36,376 1.8

 Electricity rate

 Residential 0.096 0.106 1.0 -1.3 -3.5 0.095 0.102 0.7

 Commercial 0.082 0.091 1.1 -1.3 -3.5 0.081 0.088 0.8

 Industrial 0.055 0.061 1.0 -1.3 -3.5 0.054 0.059 0.7

Reference Case Electricity Efficiency Case

Percent 

Differences 

from 

Levels Reference Levels
Economic Impact of Energy and Environmental Policy in Appalachia

64 Inforum / Keybridge Research

6.2.3 Carbon Mitigation

Table 6.2.3 presents results at the all Appalachia level for the Carbon Mitigation case. 

The economic results for Appalachia are mixed. By 2020, employment is down by 0.3 

percent relative to the Reference case. Revenue from the carbon tax is recycled to 

consumers, so there is some benefit to real personal income, an increase of 1.2 percent 

relative to the Reference case in 2020.

The carbon charge has a large impact on electricity prices, with an increase of 26 

percent over the Reference case by 2020. There is a reduction of electricity sales of 7.9 

percent by 2020, relative to the Reference case. Revenues (which include the tax) 

actually increase, since the decline in sales is not as large as the increase in price.

Table 6.2.3 Appalachia Comparison for the Carbon Mitigation Case

Growth 

rate

Growth 

rate

2015 2020 10-20 2015 2020 2015 2020 10-20

County Economic Variables

 Real personal income 858,671 1,031,721 3.5 0.6 1.2 863,895 1,044,059 3.7

 Per capita 32.89 38.09 2.8 0.6 1.2 33.09 38.54 2.9

 Employment 14,530 16,044 2.2 -0.1 -0.3 14,515 15,991 2.2

Electric Utility Variables

 Electricity sales 453,021 508,445 2.7 -6.3 -7.9 424,525 468,453 1.8

 Electricity revenues 35,611 44,125 3.7 13.3 16.3 40,360 51,308 5.2

 Electricity rate

 Residential 0.096 0.106 1.0 20.8 26.0 0.116 0.134 3.3

 Commercial 0.082 0.091 1.1 20.8 26.0 0.099 0.114 3.4

 Industrial 0.055 0.061 1.0 20.8 26.0 0.067 0.076 3.3

Reference Case Carbon Mitigation Case

Levels

Percent 

Differences 

from 

Reference Levels
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 65

6.2.4 Clean Energy Standard

In the Clean Energy Standard scenario, electricity prices also increase, though not by as 

much as in the Carbon Mitigation case. Electricity prices increase by 8.5 percent relative 

to the Reference by 2020. Sales decline by 4 percent, with the result that revenues 

increase by 4.4 percent. Macroeconomic and income effects are negligible, with an 

increase of 0.1 percent in real personal income by 2020.

Table 6.2.4 Appalachia Comparison for the Clean Energy Standard Case

Growth 

rate

Growth 

rate

2015 2020 10-20 2015 2020 2015 2020 10-20

County Economic Variables

 Real personal income 858,671 1,031,721 3.5 0.0 0.1 858,609 1,032,632 3.6

 Per capita 32.89 38.09 2.8 0.0 0.1 32.89 38.12 2.8

 Employment 14,530 16,044 2.2 0.1 0.0 14,540 16,050 2.2

Electric Utility Variables

 Electricity sales 453,021 508,445 2.7 -1.2 -4.0 447,667 488,051 2.2

 Electricity revenues 35,611 44,125 3.7 0.3 4.4 35,702 46,086 4.1

 Electricity rate

 Residential 0.096 0.106 1.0 1.3 8.5 0.098 0.115 1.8

 Commercial 0.082 0.091 1.1 1.3 8.5 0.083 0.098 1.9

 Industrial 0.055 0.061 1.0 1.3 8.5 0.056 0.066 1.8

Reference Case Clean Energy Standard

Levels

Percent 

Differences 

from 

Reference Levels
Economic Impact of Energy and Environmental Policy in Appalachia

66 Inforum / Keybridge Research

6.2.5 Expanded Natural Gas Development

In this scenario, use of natural gas increases and coal decreases. The net effect on 

Appalachian counties depends partly on the relative size of the impact on the natural 

gas industry versus the coal and electric power sectors. Personal income is 0.3 percent

lower than the Reference case and employment is up slightly, by 0.1 percent. Electricity 

prices are lower than the Reference by 3.4 percent in 2020. Electricity sales are up by 

0.6 percent in 2015, but about the same as the Reference case by 2020. 

Table 6.2.5 Appalachia Comparison for the Expanded Natural Gas Development Case

Growth 

rate

Growth 

rate

2015 2020 10-20 2015 2020 2015 2020 10-20

County Economic Variables

 Real personal income 858,671 1,031,721 3.5 -0.3 -0.3 856,119 1,029,095 3.5

 Per capita 32.89 38.09 2.8 -0.3 -0.3 32.79 37.99 2.8

 Employment 14,530 16,044 2.2 0.1 0.1 14,539 16,067 2.2

Electric Utility Variables

 Electricity sales 453,021 508,445 2.7 0.6 0.0 455,567 508,665 2.6

 Electricity revenues 35,611 44,125 3.7 -3.2 -3.1 34,464 42,749 3.4

 Electricity rate

 Residential 0.096 0.106 1.0 -3.9 -3.4 0.093 0.102 0.7

 Commercial 0.082 0.091 1.1 -3.9 -3.4 0.079 0.088 0.8

 Industrial 0.055 0.061 1.0 -3.9 -3.4 0.053 0.059 0.8

Reference Case Expanded Natural Gas Development

Levels

Percent 

Differences 

from 

Reference Levels
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 67

6.2.6 Comparisons of All Scenarios by State

Table 6.2.5 shows a comparison of total employment in each Appalachian state across 

the Reference case and the four policy scenarios. The units are thousands of jobs. The 

first two columns show the values in the Reference case for 2010 and 2020. The next 4 

groups of 2 columns each show the levels of employment in each policy scenario in 

2020, and the percentage difference from the Reference case in 2020. 

The Efficiency case has the largest positive impacts on employment by 2020, with an 

average increase relative to the Reference case of 0.43 percent within all Appalachia. 

Results by state vary from an increase of 0.30 percent in Kentucky, to 0.47 percent in 

Maryland, North Carolina, and South Carolina. The Expanded Natural Gas case also 

shows positive employment impacts, with an average increase of 0.15 percent for all 

Appalachia, and increases ranging from 0.13 percent in several states, to 0.18 percent in 

Ohio. Results for the Clean Energy Standard are mixed, and with a wide variation across 

states. The average impact across all Appalachia is nearly zero (0.04 percent), with 

results ranging from -0.03 percent in Kentucky to 0.24 percent in New York. Finally, the 

Carbon Mitigation case shows consistently negative employment impacts relative to the 

Reference case by 2020 with an average of -0.33 percent across all Appalachia, and a 

range of -0.31 percent to -0.40 percent across the state aggregates. 

Table 6.2.5 Employment 

Table 6.2.6 has the same format as the employment table, but shows aggregates of real 

personal income. It provides a somewhat different picture of the changes across the 

scenarios by state. For example, from the perspective of real income, the improvement 

in the Efficiency case is larger than employment, with an average improvement in all 

Appalachia of 0.91 percent by 2020. This increase in real income is due to two factors: 

the additional efficiency stimulates additional economic activity; and the overall price 

level is also reduced. Since real income is calculated as nominal income divided by the 

price level, the reduction in the price level implies this makes real income higher than it 

Level % Diff. Level % Diff. Level % Diff. Level % Diff.

2010 2020 2020 2020 2020 2020 2020 2020 2020 2020

 Appalachia 12,885 16,044 16,113 0.43 15,991 -0.33 16,050 0.04 16,067 0.15

 Alabama 1,591 1,970 1,979 0.45 1,963 -0.34 1,970 0.00 1,973 0.13

 Georgia 1,356 2,568 2,579 0.46 2,559 -0.32 2,568 0.02 2,571 0.14

 Kentucky 513 604 606 0.30 601 -0.40 604 -0.03 605 0.16

 Maryland 135 167 168 0.47 166 -0.33 167 0.17 167 0.14

 Mississippi 305 340 342 0.40 339 -0.40 340 0.08 341 0.13

 New York 556 586 589 0.44 585 -0.30 588 0.24 587 0.16

 North Carolina 889 1,105 1,110 0.47 1,102 -0.26 1,106 0.04 1,107 0.13

 Ohio 914 1,069 1,073 0.41 1,065 -0.38 1,069 -0.01 1,071 0.18

 Pennsylvania 3,184 3,513 3,529 0.45 3,502 -0.31 3,515 0.06 3,518 0.15

 South Carolina 648 772 776 0.47 769 -0.34 772 0.01 773 0.14

 Tennessee 1,515 1,912 1,921 0.45 1,906 -0.33 1,913 0.03 1,915 0.14

 Virginia 387 424 426 0.36 422 -0.38 424 0.07 425 0.13

 West Virginia 893 1,014 1,017 0.32 1,010 -0.33 1,014 0.02 1,015 0.16

Units: Thousands of jobs

Reference Efficiency Carbon Mitigation

Clean Energy 

Standard

Expanded Natural 

Gas
Economic Impact of Energy and Environmental Policy in Appalachia

68 Inforum / Keybridge Research

would be otherwise. Similarly, in the Carbon Mitigation case, the negative impacts on 

real income are larger than on employment because of the higher GDP price in the 

Carbon Mitigation case. The average reduction of real income across all Appalachia is -

2.14 percent by 2020, compared to an employment difference of -0.33 percent. 

Whereas the employment effects of the Clean Energy Standard are mixed, the real 

income effects are all slightly negative, since the overall price level is slightly higher than 

the Reference case. Personal income impacts in the Expanded Natural Gas case are 

mixed, due to the countervailing effects of increased natural gas production combined 

with decreases in coal production. The negative impacts on income are largest in West 

Virginia, with a 0.31 percent difference from the Reference case.

Table 6.2.6 Real Personal Income

The final table shown at the aggregate Appalachian state level is 6.2.7, which compares 

electricity sales the Reference case with the four policy scenarios. This table 

demonstrates clearly the differences in electricity demand due to each set of policy 

assumptions. The largest decrease is in the Efficiency case, with an average of 15.63 

percent decline relative to the Reference across all Appalachia by 2020. The demand 

effect in the Carbon Mitigation case is price induced, with an average 7.87 percent 

decline relative to the Reference. The demand effect in the Clean Energy Standard case 

is also price induced, with an average 4 percent decline relative to the Reference. 

There is a slight electricity price reduction in the Expanded Natural Gas case, with a 

resulting slight increase in demand, about 0.04 percent for total Appalachia.

Level Perc. Diff. Level Perc. Diff. Level Perc. Diff. Level Perc. Diff.

2010 2020 2020 2020 2020 2020 2020 2020 2020 2020

 Appalachia 722,664 1,030,727 1,040,110 0.91 1,008,712 -2.14 1,029,259 -0.14 1,032,584 0.18

 Alabama 92,476 133,874 135,168 0.97 131,020 -2.13 133,646 -0.17 134,118 0.18

 Georgia 82,522 173,145 174,761 0.93 169,232 -2.26 172,926 -0.13 173,702 0.32

 Kentucky 27,047 36,077 36,328 0.69 35,298 -2.16 35,972 -0.29 36,035 -0.12

 Maryland 7,271 10,506 10,607 0.96 10,281 -2.14 10,505 -0.01 10,533 0.25

 Mississippi 15,187 19,328 19,492 0.85 18,887 -2.28 19,308 -0.10 19,376 0.25

 New York 30,377 37,171 37,536 0.98 36,373 -2.15 37,171 0.00 37,259 0.24

 North Carolina 47,894 68,057 68,718 0.97 66,613 -2.12 67,980 -0.11 68,257 0.29

 Ohio 52,807 69,714 70,302 0.84 68,096 -2.32 69,585 -0.19 69,862 0.21

 Pennsylvania 187,174 237,444 239,618 0.92 232,591 -2.04 237,170 -0.12 237,804 0.15

 South Carolina 32,928 45,230 45,674 0.98 44,271 -2.12 45,175 -0.12 45,365 0.30

 Tennessee 77,853 111,578 112,612 0.93 109,209 -2.12 111,442 -0.12 111,876 0.27

 Virginia 20,015 25,720 25,928 0.81 25,166 -2.16 25,672 -0.19 25,709 -0.05

 West Virginia 49,114 62,884 63,366 0.77 61,676 -1.92 62,709 -0.28 62,688 -0.31

Units: Millions of 2005$

Reference Efficiency Carbon Mitigation

Clean Energy 

Standard

Expanded Natural 

Gas
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 69

Table 6.2.7 Electricity Sales 

 

Level Perc. Diff. Level Perc. Diff. Level Perc. Diff. Level Perc. Diff.

2010 2020 2020 2020 2020 2020 2020 2020 2020 2020

 Appalachia 389,502 508,445 428,961 -15.63 468,453 -7.87 488,051 -4.01 508,665 0.04

 Alabama 52,803 67,530 57,210 -15.28 62,198 -7.90 64,815 -4.02 67,513 -0.02

 Georgia 41,382 66,252 55,789 -15.79 61,228 -7.58 63,828 -3.66 66,379 0.19

 Kentucky 19,048 22,899 19,607 -14.37 21,036 -8.14 21,926 -4.25 22,917 0.08

 Maryland 3,907 5,594 4,790 -14.37 5,163 -7.70 5,390 -3.64 5,607 0.25

 Mississippi 8,719 10,665 9,113 -14.55 9,844 -7.69 10,265 -3.75 10,679 0.14

 New York 9,431 11,396 9,659 -15.24 10,528 -7.61 10,981 -3.64 11,434 0.33

 North Carolina 23,847 32,945 27,888 -15.35 30,432 -7.63 31,737 -3.67 33,016 0.21

 Ohio 31,547 41,381 34,719 -16.10 38,126 -7.87 39,709 -4.04 41,418 0.09

 Pennsylvania 71,858 88,071 74,089 -15.87 81,213 -7.79 84,634 -3.90 88,238 0.19

 South Carolina 19,474 26,794 22,630 -15.54 24,750 -7.63 25,810 -3.67 26,851 0.21

 Tennessee 63,561 81,384 68,213 -16.18 74,707 -8.20 77,682 -4.55 81,042 -0.42

 Virginia 13,834 16,961 14,320 -15.57 15,602 -8.01 16,247 -4.21 16,957 -0.02

 West Virginia 30,091 36,575 30,933 -15.43 33,624 -8.07 35,028 -4.23 36,613 0.10

Reference Efficiency Carbon Mitigation

Clean Energy 

Standard

Expanded Natural 

Gas
Economic Impact of Energy and Environmental Policy in Appalachia

70 Inforum / Keybridge Research

6.3 Selected County-Level Results

Figure 6.3.1 shows electricity sales per customer, in thousands of Mwh. Regional 

influences appear very strongly here. Higher demand characterizes southern regions, 

which require more air conditioning. Intensity of usage is also related to average price

and average income, but the location effect is most important.

 

Figure 6.3.1 Electricity Sales per Customer, Residential, 2020, Reference Case
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 71

Figure 6.3.2 displays the growth of electricity sales per customer in the Reference case. 

Residential efficiency is projected to improve over time, so these growth rates are fairly 

low. The pattern of growth is related in adjoining regions, showing that price and 

demand indicator patterns in adjoining regions are similar. The area of fastest sales 

growth is concentrated in the south.

Figure 6.3.2 Electricity Sales per Customer, Residential, 2010-2020 Growth Rate, 

Reference Case
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Figure 6.6.3 shows the growth rates by county of residential sales per customer for the 

Efficiency case. The Efficiency case assumes reductions in electricity use of up to 16% by 

2020, relative to the Reference case. This results in absolute declines in per residential 

customer use, of up to -0.7 percent annual growth. The greatest declines are in the 

northern region of Appalachia, in New York, Pennsylvania, and West Virginia. Declines in 

the high consumption regions of the south are not as big.

Figure 6.3.3 Electricity Sales per Customer, Residential, 2010-2020 Growth Rate, Electricity 

Efficiency Case 
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 73

Figure 6.3.4 shows the growth rates by county of residential sales per customer for the 

Carbon Mitigation Case. In this case, residential electricity consumption per customer 

declines in response to the electricity price increase. In some counties is larger than in 

the high efficiency case. Since the proportion of the price increase is very similar across 

counties, and the assumed price response is also the same, the patterns of change are 

similar, but with the largest declines still concentrated in the northern and central areas 

of Appalachia. 

Figure 6.3.4 Electricity Sales per Customer, Residential, 2010-2020 Growth Rate, Carbon 

Mitigation Case 
Economic Impact of Energy and Environmental Policy in Appalachia

74 Inforum / Keybridge Research

Figure 6.3.5 shows revenues per customer, which can be thought of as the product of 

Mwh sales per customer times the electricity price for each region. Higher revenues are 

still concentrated in the southern counties, as with sales. Note that large pockets of West 

Virginia and western Pennsylvania have low revenues per customer, whereas most of 

Appalachian Alabama has higher than average revenues.

Figure 6.3.5 Revenues per Customer, Residential, 2020, Reference Case
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 75

The growth rate in revenues per customer is the growth rate in sales per customer plus the 

growth in prices. Figure 6.3.6 shows the growth in the Reference case. Since the 

percentage growth in prices is assumed to be the same across counties, this map is

essentially reflecting differences in the growth rates of sales. This growth is partly related 

to overall economic growth in each county, but also to changes in the number of 

households.

6.3.6 Revenues per Customer, Residential, 2010-2020 Growth Rate, Reference Case
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Figure 6.3.7 shows the same data for the High Efficiency Case. The growth rates of 

residential revenues per customer are much lower overall than in the Reference case.

6.3.7 Revenues per Customer, Residential, 2010-2020 Growth Rate, Efficiency Case 
Economic Impact of Energy and Environmental Policy in Appalachia

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Figure 6.3.8 shows the increase in revenues per customer for the Carbon Mitigation Case. 

As we saw in the map for Mwh sales, sales growth in this case is lower than the Reference 

case, in response to the higher price due to the carbon cost. However, the growth rates 

of residential revenues per customer are higher overall than in the Reference case in 

response to higher electricity prices.

6.3.8 Revenues per Customer, Residential, 2010-2020 Growth Rate, Carbon Mitigation 

Case 
Economic Impact of Energy and Environmental Policy in Appalachia

78 Inforum / Keybridge Research

Figure 6.3.9 shows the distribution of electricity prices by county. These county prices are 

calculated by starting with the average electricity rates by utility, and distributing them to 

the counties they serve based on the county-utility bridge. The lowest rates are 

concentrated in West Virginia, western Virginia, and eastern Kentucky. The highest price 

regions are New York, Pennsylvania, and areas of Alabama and Georgia. This map is 

identical for the efficiency case, as we assumed no change in rates for that case.

6.3.9 Residential Rate, 2020, Reference Case 
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 79

Figure 6.3.10 shows the residential rate by county for the Carbon Mitigation Case, where 

all electricity prices are about 25% higher than the Reference case by 2020. The higher 

electricity cost does have a significant impact on rates, although counties appear to 

preserve their relative standing.

6.3.10 Residential Rate, 2020, Carbon Mitigation Case
Economic Impact of Energy and Environmental Policy in Appalachia

80 Inforum / Keybridge Research

Figure 6.3.11 shows the distribution of commercial electricity rates by county in 2020 in the 

Reference case. While highly correlated with the distribution of residential rates, the 

distribution is not identical. Although commercial rates are lower than residential rates, 

they are not lower by the same percentage in every utility and county. This map also 

applies to the Efficiency case, as there was no change in rates.

6.3.11 Commercial Rate, 2020, Reference Case 
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 81

Figure 6.3.12 shows the distribution of commercial rates in the Carbon Mitigation Case in 

2020. The areas of highest commercial rates continue to be the southern region of 

Appalachia, with scattered pockets elsewhere. The lowest commercial rates are in 

northern West Virginia.

6.3.12 Commercial Rate, 2020, Carbon Mitigation Case 
Economic Impact of Energy and Environmental Policy in Appalachia

82 Inforum / Keybridge Research

Figure 6.3.13 shows the distribution of industrial rates in the Reference case in 2020. These 

tend to be substantially lower than residential or commercial rates, as industrial users buy 

in bulk and also tend to use more off-peak power. The area centered on West Virginia 

and Virginia still has the lowest rates, with the southern regions generally paying higher 

industrial prices. Note that this map is also valid for the Efficiency Case, where there were 

no changes in rates.

6.3.13 Industrial Rate, 2020, Reference Case 
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 83

This figure shows the distribution of industrial electricity rates by county in 2020, in the 

Carbon Mitigation Case. The relative standing of the counties is similar to that in Figure 

6.3.13. 

6.3.14 Industrial Rate, 2020, Carbon Mitigation Case
Economic Impact of Energy and Environmental Policy in Appalachia

84 Inforum / Keybridge Research

Figure 6.3.15 shows the distribution of per capita real Gross Regional Product (GRP) in 

2020. GRP can be high due either to higher incomes or to higher industrial output per 

person. This map shows quite a variation in distribution of GRP across counties. An area 

with highly valued production will show higher GRP per capita, but not necessarily higher 

incomes. The reverse is also true. 

6.3.15 Per Capita Real Gross Regional Product, 2020, Reference Case
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 85

Reference case GRP growth rates, shown in Figure 6.3.16, can be traced back to the 

growth of industrial output in each county. In general, the Appalachian counties of 

north Georgia grow the fastes. There are other pockets of high growth scattered 

throughout Appalachia. Keep in mind that high growth does not necessarily imply high 

GRP, as the county may be starting from a lower base in 2010.

6.3.16 Per Capita Real Gross Regional Product, 2010-2020 Growth Rate, Reference Case
Economic Impact of Energy and Environmental Policy in Appalachia

86 Inforum / Keybridge Research

Levels of per capita real income in 2020 are projected to follow similar relative patterns 

to those of today. Large pockets of relatively high personal income can be found in 

near metro areas in north Georgia, parts of central and north Alabama, southwestern 

Pennsylvania, West Virginia, North Carolina, and Tennessee. Only two counties are 

projected to have average income below $20 thousand in 2020. 

6.3.17 Per Capita Real Personal Income, 2020, Reference Case 
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 87

Per capita income growth varies widely across the region, with north Georgia being the 

strongest. Pockets of high growth and low growth are fairly well dispersed.

6.3.18 Per Capita Real Personal Income, 2010-2020 Growth Rate, Reference Case
Economic Impact of Energy and Environmental Policy in Appalachia

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The strongest employment growth is projected in north Georgia, but there are also 

pockets of high growth in western North Carolina and east Tennessee. 

6.3.19 Employment, 2010-2020 Growth Rate, Reference Case
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 89

Population growth is projected to be most rapid in certain relatively urban counties in 

Georgia and Alabama. Regions of northern Pennsylvania, southwestern New York, and 

parts of West Virginia and Ohio are projected to experience the slowest growth.

6.3.20 Population, 2010-2020 Growth Rate, Reference Case
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6.4 Sample Individual County Results

This section explores a sampling of simulation results at the county level. Here we give a 

flavor for the capabilities of the model, as well as what is happening in each of the four 

alternate policy simulations.

Table 6.4.1 is a comparison table of Kanawha county West Virginia. The first two columns 

show the values in the Reference case for 2010 and 2020. The next 4 groups of 2 columns 

each show the levels of variables in each policy scenario in 2020 and the percentage 

difference from the Reference case in 2020. The units of each variable are shown in 

parenthesis next to the title.

West Virginia is at the center of our analysis, being a state wholly contained within the 

Appalachian region, and experiencing intimately the interplay of coal, wind, biomass, 

and natural gas issues in the region. Kanawha is the largest county in West Virginia, both 

in population and in income.

In the efficiency case, employment increases relative to the Reference case by 0.35 

percent, which is higher than the West Virginia average of 0.33, but lower than the 

Appalachian average of 0.43 (see table 6.2.5). Real personal income increases by 0.77 

percent. This is the same percentage as the West Virginia average, but lower than the 

Appalachia average of 0.91 percent. Total real output is up by 0.10 percent. The results 

for the electric power sector are in line with the rest of West Virginia.

In the Carbon Mitigation case, employment is down by 0.23 percent, not as much as the 

West Virginia average of 0.33 percent. This is most likely due to the fact that Kanawha is 

a large and diversified county, with a strong service sector and government presence. 

Real personal income is down 1.34 percent, also less than the West Virginia average of 

1.92 percent, and less than the overall Appalachia average of 2.14 percent. 

In the CES case, employment is down relative to the base by 0.14 percent, compared 

with 0.02 percent for all West Virginia, and .04 percent for all Appalachia. Personal 

income is down by 0.55 percent, which is more than the West Virginia average (-0.28 

percent) or the all Appalachia average (0-.14 percent). 

In the Expanded Natural Gas cases employment shows almost no change relative to the 

Reference case, but real personal income is down by 0.93 percent. These results relate 

to the relative strength of coal and natural gas in this county’s economy.

Table 6.4.1 Kanawha, West Virginia 

Level Perc. Diff. Level Perc. Diff. Level Perc. Diff. Level Perc. Diff.

2010 2020 2020 2020 2020 2020 2020 2020 2020 2020

 Total employment (thous) 131,738 142,421 142,920 0.35 142,091 -0.23 142,216 -0.14 142,405 -0.01

 Real personal income (mil 2005$) 6,716 7,788 7,848 0.77 7,683 -1.34 7,745 -0.55 7,715 -0.93

 Real output (mil 2005$) 12,796 16,879 16,895 0.10 16,743 -0.81 16,810 -0.41 16,895 0.10

 Household consumption (mil $) 6,276 7,985 7,997 0.14 8,215 2.88 7,961 -0.31 7,869 -1.46

 Total earnings (mil $) 6,131 9,164 9,185 0.23 9,321 1.72 9,136 -0.30 9,088 -0.82

 Sales (Mwh) 3,057 3,648 3,097 -15.10 3,355 -8.03 3,496 -4.17 3,651 0.08

 Revenue ($) 217 285 236 -17.20 331 16.03 297 4.19 276 -3.15

 Rate ($/Kwh) 0.07 0.08 0.08 -2.48 0.10 26.15 0.09 8.73 0.08 -3.23

Reference Efficiency Carbon Mitigation

Clean Energy 

Standard Expanded Natural Gas
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 91

Table 6.4.2 is a similar table for Allegheny, Pennsylvania. This county is the largest in 

Appalachia, both in terms of personal income and population. The county has both 

coal and gas resources, but they are not huge in proportion to the county economy. 

This county benefits to a greater extent than Kanawha from the Efficiency scenario, with 

a 0.57 percent increase in employment and a 1.12 percent increase in real income. 

Impacts on the electric power sector are similar to those of Kanawha county. With 

regard to the Carbon Mitigation case, the negative impacts are proportionally smaller 

than those on Kanawha, while the effects on real income are larger. The Clean Energy 

Standard scenario has much smaller negative employment and income impacts than 

Kanawha. This is partly due to the fact that Allegheny has both wind and biomass 

development contributing positive impacts. Over the course of the scenario, we assume 

that 161 MW of wind capacity gets built in this county at a cost of $338 million. This 

county also benefits from biomass development. Finally, in the Expanded Natural Gas 

scenario, this county experiences a small improvement relative to the base, both in 

employment and in income. 

Table 6.4.2 Allegheny, Pennsylvania 

Figure 6.4.1 shows a graphical comparison of employment in the High Efficiency case 

with the Reference case in 2020. Consistent with the previous table, Kanawha county 

has an increase of 0.35 percent in employment relative to the Reference case. There 

are four counties which suffer employment declines relative to the base from -0.66 to -0.3 

percent. Those counties are Calhoun, Boone, Mingo, and McDowell. Table 6.4.3 shows 

how dependent these counties are on the mining sector, which includes a large share of 

coal in these counties. In Calhoun county, 23.5 percent of all employment is in this 

sector, making it very susceptible to reductions in coal consumption.

Level Perc. Diff. Level Perc. Diff. Level Perc. Diff. Level Perc. Diff.

2010 2020 2020 2020 2020 2020 2020 2020 2020 2020

 Total employment (thous) 861,355 923,233 928,452 0.57 921,748 -0.16 923,387 0.02 924,445 0.13

 Real personal income (mil 2005$) 52,660 66,734 67,480 1.12 65,611 -1.68 66,643 -0.14 66,828 0.14

 Real output (mil 2005$) 89,866 128,766 129,516 0.58 127,992 -0.60 128,722 -0.03 129,096 0.26

 Household consumption (mil $) 49,211 68,419 68,754 0.49 70,146 2.52 68,489 0.10 68,152 -0.39

 Total earnings (mil $) 48,447 72,048 72,430 0.53 73,164 1.55 72,084 0.05 71,881 -0.23

 Sales (Mwh) 13,341 16,372 13,594 -16.97 15,137 -7.55 15,767 -3.70 16,409 0.23

 Revenue ($) 828 1075 901 -16.20 1253 16.59 1128 4.95 1045 -2.77

 Rate ($/Kwh) 0.06 0.07 0.07 0.93 0.08 26.10 0.07 8.98 0.06 -2.99

Reference Efficiency Carbon Mitigation

Clean Energy 

Standard Expanded Natural Gas
Economic Impact of Energy and Environmental Policy in Appalachia

92 Inforum / Keybridge Research

Figure 6.4.1 Comparison of Employment in West Virginia: High Efficiency vs. Reference

Table 6.4.3 Mining Share of Employment in West Virginia, 2009

 

County Percent

54013 Calhoun, WV 23.2

54005 Boone, WV 22.5

54047 McDowell, WV 20.4

54059 Mingo, WV 20.3

54015 Clay, WV 18.6

 West Virginia Average 3.0
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 93

7. Conclusions 

For this study, we have constructed a new economic modeling system and used that 

system to set up a Reference case and then to explore the impacts of four alternative 

policy scenarios on the U.S. and the Appalachian region. The focus has been on the 

electric power sector, which is particularly important in this region. We have chosen the 

policy scenarios to encompass a range of policy and technology outcomes which we 

anticipate may be likely to occur in the next ten to fifteen year period. These outcomes 

include:

• Increases in end-use electricity efficiency in the residential, commercial, and 

industrial sectors.

• A carbon mitigation policy, which creates a market for carbon emissions or 

through the imposition of a tax.

• A Clean Energy Standard or similar policy which encourages development of 

renewables, nuclear, and to some extent gas combined cycle generation.

• Expanded and accelerated development of shale gas resources.

The actual evolution of policy and the economy may include some mix of these 

outcomes, and there may well be other policy scenarios well worth investigating with this 

modeling system. 

This study also contributes a review of the broader policy environment described in 

Chapter 2 to put the current study in context. Chapter 3 reviews literature on policy 

choices, economic models and modeling exercises, and discusses assumptions that are 

typically used in energy/environmental modeling. 

A brief review of the components of the EPIM (Economic Policy Impact Model) is given in 

Chapter 4 and is supplemented by additional detailed information in Appendices A and 

B. Chapter 5 reviews the assumptions and techniques used to develop the Reference 

case and each policy scenario. Results are summarized and sampled in the various 

sections of Chapter 6 at the national, Appalachian state, and Appalachian county level. 

7.1 Main Findings from the Scenarios

Results for selected key macroeconomic variables are summarized in table 7.1.1. For 

each variable, the first line shows the value of that variable in the Reference case in 2015 

and 2020. The other lines show the differences from the Reference for each of the policy 

cases. 

Several main conclusions stand out at the macroeconomic level:

1. The High Efficiency case shows by far the largest positive impact. Gains from 

increases in electricity efficiency are significant, whether we use GDP, real 

income or employment for our criteria. Policy actions that can be taken to 

accelerate increases in the development of efficient technologies or to provide 

incentives for the adoption of existing technologies have a high payoff and may 

justify public expenditures either from direct subsidies, public assistance, or tax 

credits. By 2020, the High Efficiency case shows real GDP higher by 75 billion 
Economic Impact of Energy and Environmental Policy in Appalachia

94 Inforum / Keybridge Research

(2005$), with real Disposable income up by $61 billion. Total employment is higher 

by 526 thousand jobs. Note that the High Efficiency case also shows the largest 

reduction in aggregate energy intensity of all the scenarios examined and also 

manages to reduce total CO2 emissions by 580 MMT by 2020. 

2. The Carbon Mitigation case shows the largest negative impact. Although it is the 

most effective scenario at reducing carbon emissions (742 MMT reduction by 

2020), it does so at the cost of lower GDP, employment, and real income. 

Aggregate energy intensity is reduced, but not by as much as the High Efficiency 

scenario. 

3. The Clean Energy Standard is a highly effective way of reducing carbon 

emissions, without a large cost to GDP, real income or employment. Although the 

changes in these three variables are all slightly negative with respect to the 

Reference case, the differences are not large in percentage terms.

4. Expanded Natural Gas Development is somewhat positive for aggregate GDP 

and real income, significantly positive for employment, and with measurable 

reductions in carbon emissions (285 MMT reduction by 2020). 

Table 7.1.1 Macroeconomic Summary of Key Variables Across Scenarios

These national level results are fully consistent with our findings at the Appalachia state 

and county levels. However, we find greater differences in impact at this level due to 

different distributions of production and employment by industry, as well as differences in 

 2015 2020

 Real Gross Domestic Product (bil 2005$) Reference 15,474 17,723

High Efficiency 34 75

Carbon Mitigation -60 -154

Clean Energy Standard -2 -26

Expanded Natural Gas 14 21

 Real Disposable Income (bil 2005$) Reference 11,711 13,309

High Efficiency 31 61

Carbon Mitigation -21 -45

Clean Energy Standard 0 -20

Expanded Natural Gas 10 15

 Employment (thousands) Reference 154,733 161,550

High Efficiency 320 526

Carbon Mitigation -401 -999

Clean Energy Standard 49 -39

Expanded Natural Gas 147 284

 GDP Deflator (2005 = 1.0) Reference 1.19 1.31

High Efficiency 0.00 -0.01

Carbon Mitigation 0.02 0.04

Clean Energy Standard 0.00 0.00

Expanded Natural Gas 0.00 -0.01

 Energy Intensity (btus/GDP) Reference 6.66 5.93

High Efficiency -0.20 -0.45

Carbon Mitigation -0.23 -0.24

Clean Energy Standard -0.18 -0.28

Expanded Natural Gas -0.08 -0.10

 Total CO2 Emissions (million metric tons) Reference 5,762 5,791

High Efficiency -225 -580

Carbon Mitigation -555 -742

Clean Energy Standard -408 -718

Expanded Natural Gas -197 -285

Line 1 is reference case value

Other lines are differences from reference
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 95

the distribution of coal and natural gas resources, and in the availability of biomass and 

wind development in the Clean Energy Standard scenario.

As we saw in section 6.4, several West Virginia counties suffer from declines in 

employment and output in the High Efficiency case, even though this scenario shows 

strong positive effects at the national level. This is due to the fact that more than half the 

electric power generation in the West Virginia territory is coal-based and the coal 

consumed by power generation supports the local mining industry. As electricity 

efficiency increases, coal consumption decreases, with resulting decreases in coal 

industry employment. 

In fact, all of the policy scenarios imply a reduction in electric power generation from 

coal. Table 7.1.2 summarizes the production of electricity by eight different generation 

types across the Reference case and the four policy scenarios. In the Reference case, 

total generation rises to 4,121 billion Kwh by 2020, with 1,850, or about 45 percent, from 

coal. In the High Efficiency case, total generation is 16.9 percent lower by 2020, at 3,426 

billion Kwh. Our simulation rule was to take the reduction in fossil fuels, so coal, gas and 

petroleum based generation were all cut by the same percentage of 25.1. In the 

Carbon Mitigation case, coal takes a much bigger hit, -40.3 percent. Total generation 

declines by 10.2 percent. Gas, wind, and geothermal and other increase in this scenario. 

In the CES scenario, there is a 13 percent reduction in total generation, but nearly 39 

percent reduction in coal generation, with increases in gas, nuclear, hydro, wind, and 

geothermal and other. Finally, the expanded natural gas case has more gas (8.4 

percent), but not quite as much more as the Carbon Mitigation case, which had an 

increase of 20.6 percent.24

Table 7.1.2 National Electric Power Generation by Type Across Scenarios

In section 6.2.6 we presented some comparisons at the aggregate state level across 

scenarios. Some of the main conclusions from these comparisons were:

1. Consistent with the U.S. national results, the High Efficiency case shows the most 

positive impacts on employment and real income. The Carbon Mitigation case 

shows the largest negative impacts. Results for the CES are mixed, but small, and 

the Expanded Natural Gas case has slightly positive impacts.

 

24 Note that these generation shares are a result of our calibrations to AEO 2011 side cases, and to 

a special study on the CES done by EIA.

Level Perc. Diff. Level Perc. Diff. Level Perc. Diff. Level Perc. Diff.

2010 2020 2020 2020 2020 2020 2020 2020 2020 2020

Production by Type

 Coal 1,818 1,850 1,386 -25.1 1,105 -40.3 1,135 -38.7 1,485 -19.7

 Natural gas 895 829 621 -25.1 1,000 20.6 871 5.0 899 8.4

 Petroleum 39 39 29 -25.1 36 -6.5 32 -17.0 35 -9.9

 Nuclear 803 877 877 0.0 877 0.0 884 0.7 859 -2.1

 Hydro 240 301 301 0.0 306 1.6 306 1.6 301 -0.1

 Wind 91 143 143 0.0 186 29.8 186 29.8 120 -15.8

 Solar 1 3 3 0.0 3 0.0 3 0.0 3 0.0

 Geothermal & other 41 78 65 -16.9 186 138.2 170 117.7 82 5.5

 Total 3,929 4,121 3,426 -16.9 3,699 -10.2 3,587 -13.0 3,785 -8.2

Units: Billions of Kwh

Reference Efficiency Carbon Mitigation

Clean Energy 

Standard Expanded Natural Gas
Economic Impact of Energy and Environmental Policy in Appalachia

96 Inforum / Keybridge Research

2. However, there is a significant degree of variation within the states, which is 

reflective of an even greater variation at the county level. For example, although 

all states benefit in the High Efficiency scenario, the benefit to Kentucky and West 

Virginia is not nearly as large, since these are states more heavily dependent on 

coal mining. The state and county impacts in the CES case are a mix of positive 

impacts from Wind and Biomass, countered by negative impacts from reductions 

in coal. Kentucky and Ohio stand out with negative employment changes 

relative to the Reference case. The Expanded Natural Gas case shows states like 

Ohio, New York, and Pennsylvania benefitting more than the average, as these 

states contain some of the richer shale resources.

7.2 Policy Conclusions

This study has developed alternative scenarios to aid in the analysis of likely policy 

outcomes. The scenario choice, as stated above, was informed by decisions of likely 

outcomes, but also guided by the strategic objectives as outlined in the ARC Regional 

Energy Blueprint. Our goal has been to contribute to the dialogue surrounding these 

objectives by providing quantitative, scenario-based analysis. The modeling system used 

provides comprehensive national level summaries of policy impacts, as well as economic 

and electric power sector impacts at the county level. 

Some main policy conclusions which we can draw from this quantitative analysis are:

1. Energy Efficiency stands out as an extremely effective policy to pursue, and may

in fact pay for itself in terms of higher GDP, employment, and government 

revenue. It benefits the economy in terms of increasing aggregate multifactor 

productivity, reducing prices, and increasing employment and real incomes. 

However, certain localities that are dependent on coal are likely to have lower 

employment and income.

2. Natural gas from shale is a very promising avenue for stimulating employment 

and income growth in the northern Appalachian region, as well as contributing to 

a smaller carbon footprint. 

3. Wind resources in Appalachia, though important, are not huge. Wind should be 

relied upon as one component of strategy for economic development of the 

region. According to our estimates, total potential wind capacity in the entire 

Appalachian region is about 16.6 GW, implying a total investment of about $34 

billion (2009$), spread over a period of eight years. Wind capacity will most likely 

be concentrated in Pennsylvania and New York.25

4. Biomass resources are also important. If biomass resources are aggressively 

developed, we expect a total contribution of $10 billion to the Agriculture, and 

forestry sector, as well as a contribution of about $5 billion to the Transportation 

sector over the period until 2020, 

5. Carbon mitigation strategies would accomplish important objectives through the 

reduction of a large share of U.S. carbon emissions which are generated from 

coal burned by electric utilities. However, unless accompanied by other 

strategies that stimulate development in the Appalachian region, several 

Appalachian counties would be hurt disproportionately.

 

25 See Appendix C for detail on the derivation of assumptions for Wind in the CES case.
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 97

7.3 Extensions of This Research

A key contribution of this study, in addition to the development of the EPIM modeling 

system, has been the framing of policy scenarios to explore likely outcomes for 

Appalachia. Another valuable contribution has been the detailed study of the electric 

power sector in Appalachia, including how the power sector relates to the economies of 

the Appalachian counties. Finally, a significant effort was invested in obtaining detailed 

information about coal, natural gas, wind, and biomass resources at the detailed county 

level for Appalachia.

Several extensions of this research could benefit the Appalachian Regional Commission:

1. The exploration of further policy scenarios, particularly along the lines of the CES 

scenario used in this study, where a particular concrete policy proposal was 

evaluated. The availability of analysis from the Department of Energy EIA NEMS 

model is always particularly valuable for this type of exercise. The results from 

EPIM can be considered to be an extension of the NEMS results in the direction of 

industry level detail at the national level, and county level detail for Appalachia.

2. Further research on shale gas potential in the region and the likely course of 

development. Such analysis could look at key uncertainties affecting shale gas 

development, such as environmental constraints imposed by potential water 

supply degradation.

3. Further research on biomass development by county, also examining key 

constraints which may slow the development of the region’s biomass resources.

4. More detailed examination of the simulation results for particular counties and 

regions. Stakeholders in this analysis may be interested to have a more 

comprehensive view of the local impacts.

The Inforum LIFT model is continuously updated, and each year we make a new 

calibration to the latest Annual Energy Outlook.26 We have also started to update the 

county level economic data used in the CUEPS database. 

Energy and environmental issues will become an ever larger issue for the counties in 

Appalachia, no matter what the economic and policy environment may bring. The 

modeling system and strategies presented in this study should continue to contribute to 

the understanding of these issues.

 

26 The AEO for 2012 will be published in March, 2012.
Economic Impact of Energy and Environmental Policy in Appalachia

Inforum / Keybridge Research 98

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U. S. Department of Energy, Energy Information Administration, Annual Energy Outlook, 2011 with 

Projections to 2035, April 2011, at 

http://www.eia.gov/forecasts/aeo/pdf/0383%282011%29.pdf. 

U.S. Department of Energy, Energy Information Administration, Analysis of Impacts of a Clean 

Energy Standard, as requested by Chairman Hall, October 2011, at 

http://www.eia.gov/analysis/requests/ces_hall/. 

U.S. Department of Energy, Energy Information Administration, Energy Market and Economic 

Impacts of H.R. 2454, the American Clean Energy and Security Act of 2009, August 2009, at 

http://www.eia.gov/oiaf/servicerpt/hr2454/pdf/sroiaf%282009%2905.pdf. 

U.S. Department of Energy, Energy Information Administration, Energy Market and Economic 

Impacts of S. 1766, the Low Carbon Economy Act of 2007, (BingamanSpecter) January 2008, at 

http://www.eia.gov/oiaf/servicerpt/lcea/pdf/sroiaf%282007%2906.pdf. 

U.S. Department of Energy, Energy Information Administration, Energy Market and Economic 

Impacts of S.2191, the Lieberman-Warner Climate Security Act of 2007, April 2008, at 

http://www.eia.gov/oiaf/servicerpt/s2191/pdf/sroiaf%282008%2901.pdf. 

U.S. Department of Energy, Energy Information Administration, Energy Market and Economic 

Impacts of the American Power Act of 2010, (McCain-Lieberman) July 2010 at 

http://www.eia.gov/oiaf/servicerpt/kgl/pdf/sroiaf%282010%2901.pdf. 

U.S. Department of Energy, Energy Information Administration, Impacts of a 25-Percent 

Renewable Electricity Standard as Proposed in the American Clean Energy and Security Act 

Discussion Draft, April 2009, at 

http://www.eia.gov/oiaf/servicerpt/acesa/pdf/sroiaf%282009%2904.pdf. 

U.S. Department of Energy, Energy Information Administration, The National Energy Modeling 

System: An Overview 2009, October 2009, at 

http://www.eia.gov/oiaf/aeo/overview/pdf/0581%282009%29.pdf. 

U.S. Department of Energy, U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and 

Bioproducts Industry, R.D. Perlack and B.J. Stokes (Leads), ORNL/TM-2011/224. Oak Ridge 

National Laboratory, Oak Ridge, TN. August 2011, at 

http://www1.eere.energy.gov/biomass/pdfs/billion_ton_update.pdf. 

U.S. Environmental Protection Agency, Inventory of U.S. Greenhouse Gas Emissions and Sinks: 

1990 – 2009, , April 2011, at http://epa.gov/climatechange/emissions/downloads11/US-GHGInventory-2011-Complete_Report.pdf. 

U.S. Environmental Protection Agency, EPA Analysis of the Lieberman-Warner Climate Security 

Act of 2008 S. 2191 in 110th Congress. March 2008, at 

http://www.epa.gov/climatechange/downloads/s2191_EPA_Analysis.pdf. 

U.S. Environmental Protection Agency, Summary of the Updated Regulatory Impact Analysis 

(RIA) for the Reconsideration of the 2008 Ozone National Ambient Air Quality Standard 

(NAAQS), 2010, at http://www.epa.gov/ttnecas1/regdata/RIAs/s1-

supplemental_analysis_summary11-5-09.pdf. 

Wiser, Ryan H., A Review of the Results and Methods of State RPS Cost-Benefit Projections, June 

2005, at 

http://www.epa.gov/statelocalclimate/documents/pdf/wiser_rps_20cost_20studies_2.pdf. 

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Appendix A. Data Documentation

A.1 County Economic Data

County level data are derived primarily from data published by the Bureau of Economic 

Analysis (BEA), and include data or estimates for employment, earnings, output, value 

added, costs and capital income by industry. In addition, aggregate data on personal 

income and its components, population, number of households are compiled from BEA 

sources. A small (14x14) IO table consistent with the national level detailed table has 

been compiled to calculate electricity demands for the industrial and commercial 

sectors.

The CEDDS 201127 data was obtained from Woods & Poole, and this was used to update 

the county level economic data in CUEPS. This data is now available with historical 

economic data through 2009. Projections of population and other variables are 

available as far out as 2040, at five-year intervals. These projections are used to 

compare with the results of CUEPS projections.

Data from CEDDS were incorporated and updated for the following variables by county:

1. Population

2. Total employment

3. Personal income

4. Total earnings

5. Wages and salaries

6. Other labor income

7. Proprietor income

8. Dividends interest and rental income

9. Transfer payments

10. Social insurance contributions

11. Residence adjustment

12. Gross regional product 

13. Number of households

14. Employment for 14 NAICS industries (11 private and 3 government) 

15. Earnings for 14 industries

Historical data by county were then estimated for the following variables by county:

1. Real and nominal output for 14 industries

2. Personal consumption expenditures by 14 industries

3. Capital type income by 14 industries

4. Indirect taxes by 14 industries

5. Electricity use in dollars by 14 industries

6. Electricity use in Mwh by 14 industries

 

27 The Complete Economic and Demographic Data Source, Woods & Poole Economics, 

Washington, D.C.
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Historical data for many economic variables were also allocated to the respective 

utilities serving the county areas by using the county utility bridge. The following are 

some of the economic variables are allocated to electric utility service areas: 

1. Population

2. Personal income

3. Employment

4. Personal consumption

5. Current and constant price output

6. Households

Demand indicators are developed for each major utility market. The residential demand 

indicator is moved by the number of households. The industrial and commercial 

demand indicators are moved by the growth of real output in the industrial or 

commercial sectors.

A.2 Electric Utility Data

EIA-861 Data

The EIA-861 is an electric utility data file that includes such information as peak load, 

generation, electric purchases, sales, revenues, customer counts and demand-side 

management programs, green pricing and net metering programs, and distributed 

generation capacity. The data source is the survey Form EIA-861, Annual Electric Power 

Industry Report. The EIA-861, along with the EIA-923, and several other data files, are the 

main sources of information for EIAs Electric Power Annual and Electric Power Monthly. 

The data are distributed in zipped executable files. For example, the file F861yr09.exe is a 

file of data collected on the Form EIA-861, Annual Electric Power Industry Report, for the 

reporting period, calendar year 2009. The zipped .exe file contains 11 .xls files and one 

Word file describing the tables and file layout, and a .pdf of the Form EIA-861. The 

complete set of data is described in table A-1: 

Table A-1. Data available from EIA-861

File Description of Contents

File1 Aggregate operational data on energy balance, and revenue information from each electric utility, including 

power marketers and Federal power marketing administrators.

File1_cao (2005 forward) Data on control area operators

File1_a (2007 forward) Data on the types of activities each utility engages in, the NERC regions of operation, whether 

the utility generates power, and if it operates alternative-fueled vehicles.

File2 Retail revenue, sales and customer counts, by state and class of services, for each distribution utility or energy 

service provider.

File3 Information on demand-side management programs.

File3a (2007 forward) Cost percentages by state of energy efficiency and load management programs

File4 Names of the counties, by state, in which each respondent has equipment for the distribution of electricity to 

ultimate consumers

File5 Aggregate data of the number of customers by state and customer class for Green Pricing and net Metering 

programs.

File6 Information on utility or customer-owned distributed and dispersed generation such as the number, capacity 

and types of generators.

File7 (2007 forward) Data on mergers and acquisitions

File8 (2007 forward) Data on advanced meters.

The main data file used in the CUEPS model is File 2. These data on revenues, sales, 

customers and electricity rates by utility, for each major market from File 2 have been
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combined across the years 1999 to 2009. There are 3356 utilities in this database. A 

bridge was also constructed that maps the utility service regions to the counties 

comprising each service region. The mapping weights are numbers of households. File 4 

was also used in the construction of the county-utility bridge. This file shows the counties 

in which each utility has equipment for the distribution of electricity to the ultimate 

consumers. 

File 1 of the database includes data on net generation, wholesale purchases, electricity 

exchanged/received, exchanged/delivered, wheeled/received, wheeled/delivered, 

total sources, retail sales, sales for resale, and other information. 

EIA-923 Data

For this study, the 2008 and 2009 files of the EIA-923 data were used28. This dataset 

identifies generation by type, and consumption of fuels, as well as dollar values of various 

costs by electric generating unit (EGU). 

The form EIA-923 Preliminary 2010 is published in spreadsheet format. We have worked 

with the tab entitled Generation and Fuel Consumption. Other data in the file include

Fuel Stocks, Boiler Fuel Data, Generator Data, Fuel Receipts and Cost Data, Combustion 

By-Products Disposition and Costs, and operational data for Cooling Systems, NOX 

control, FGP control, and FGD control.

The generation and fuel consumption table has 9927 rows, each representing a separate 

generating unit. Monthly data and some other fields that are not used were removed 

from this table, leaving the following fields:

 

28 Before 2008, the format of the data files was different. Since we are interested in 

generator characteristics in a forecasting framework, we find that the 2008-9 data is 

sufficient to establish generation characteristics for the near future.
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Table A-2 – Characteristics in the Generation and Fuel Consumption Database

The first task was to check that rollups of the data corresponded to tables published in 

the Electric Power Annual, 2009 (EPA). The data were cross-tabbed by state, and the 

results put into a table showing total generation by state. The results of this crosstab for 

2009 are shown in table A-3. This table corresponds with the map on page 27 of the 

Electric Power Annual 2009. 

Fld# Description

1 Plant ID

2 Combined Heat & Power Plant

3 Nuclear Unit I.D.

4 Plant Name

5 Operator Name

6 Operator ID

7 State

8 Census Region

9 NERC Region

10 NAICS Code

11 EIA Sector Number

12 Sector Name

13 Reported Prime Mover

14 Reported Fuel Type Code

15 AER Fuel Type Code

16 Physical Unit Label

17 TOTAL FUEL CONSUMPTION QUANTITY

18 ELECTRIC FUEL CONSUMPTION QUANTITY

19 TOTAL FUEL CONSUMPTION MMBTUS

20 ELEC FUEL CONSUMPTION MMBTUS

21 NET GENERATION (megawatthours)

22 Year
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Table A-3. Net Generation by State, 2009

The data were also cross-tabbed by generation type, using the AER Fuel Type Code. The 

results were compared with the top part of table 2.1 on page 26, and these also 

checked out.

The table A-4 shows how generation was allocated to each type of production, based 

on the AER code.

Table A-4. – Correspondence Between AER Fuel Type and Production Type

Production by Type AER Fuel Type Code AER Description

Coal COL Coal

WOC Waste Coal

Petroleum DFO Distillate Petroleum

WOO Waste Oil

PC Petroleum Coke

RFO Residual Fuel Oil

Natural Gas NG Natural Gas

Other Gases OOG Other Gases

State Generation (Mwh) State Generation (Mwh)

Alabama 143,255,557 Montana 26,712,736

Alaska 6,702,159 Nebraska 34,001,893

Arizona 111,971,251 Nevada 37,705,133

Arkansas 57,457,739 New Hampshire 20,164,122

California 204,776,132 New Jersey 61,811,239

Colorado 50,565,952 New Mexico 39,674,339

Connecticut 31,206,222 New York 133,150,550

Delaware 4,841,563 North Carolina 118,407,402

District of Columbia 35,499 North Dakota 34,196,467

Florida 217,952,309 Ohio 136,090,225

Georgia 128,698,377 Oklahoma 75,066,810

Hawaii 11,010,533 Oregon 56,690,856

Idaho 13,100,152 Pennsylvania 219,496,144

Illinois 193,864,358 Rhode Island 7,696,824

Indiana 116,670,280 South Carolina 100,125,487

Iowa 51,860,063 South Dakota 8,196,531

Kansas 46,677,308 Tennessee 79,716,889

Kentucky 90,630,427 Texas 397,167,910

Louisiana 90,993,676 Utah 43,542,946

Maine 16,349,849 Vermont 7,282,348

Maryland 43,774,832 Virginia 70,082,066

Massachusetts 38,966,651 Washington 104,470,132

Michigan 101,202,606 West Virginia 70,782,514

Minnesota 52,491,848 Wisconsin 59,959,060

Mississippi 48,701,484 Wyoming 46,029,212

Missouri 88,354,271

Total U.S. 3,950,330,932
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Nuclear NUC Nuclear

Hydroelectric 

Conventional

HYC Hydroelectric Conventional

Other Renewables GEO Geothermal

SUN Solar PV and Thermal

WND Wind

ORW Other Renewables

WWW Wood and Wood Waste

MLG Biogenic Municipal Solid Waste 

and Landfill Gas

Hydroelectric 

Pumped Storage

HPS Hydroelectric Pumped Storage

Other OTH Other (including nonbiogenic 

MSW)

Table A-5 summarizes the production by type in 2009.

Table A-5 Electricity Generation by Type

The EIA-923 data is a database on generating units. The database of electric utilities in 

EIA-861 is by sellers of electricity. Some of these sellers are generators as well. However, 

many of them are transmitting and distributing electricity generated by others. Some are 

power marketers, buying and reselling electricity. There is difficulty in determining from 

these two databases the generating source of the power which is ultimately sold at the 

residential, commercial or industrial market. Even if this is not possible, our goal is to 

determine the average content by production type in any given market. We can make 

some simplifying assumptions to assist in solving this problem.

Type Generation (Mwh)

Coal 1,755,904,256

Petroleum 38,936,515

Natural Gas 920,978,681

Other Gases 10,632,107

Nuclear 798,854,585

Hydroelectric Conventional 273,445,094

Other Renewables 144,278,704

Hydroelectric Pumped Storage (4,627,345)

Other 11,928,335

Total 3,950,331,136
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We first performed a matching exercise on the utility IDs in the 923 and 861 databases. A 

simple tally was made to see how many records, and how much net generation were in 

the utility IDs that match those in the model database. Out of 9927 records for 2009, only 

4429 records are matched in the utility key, and 5498 are not. Of the total net 

generation of 3950 million Mwh, 2247 is accounted for by matched utility IDs, and 1703 is 

not. 

The next step is to find a link from the generators to the sellers. The objective will be to 

maintain one database of generators, and compile the information needed by 

generator. Then, we construct a bridge matrix between the generator list and the seller 

list. Except for the direct matches, this is done on the basis of judgment.

Here are the steps:

1. Build the generator list

2. Build a link from the generators to sellers, where the ID is the same.

3. Use information provided by ARC on the generator characteristics of electricity 

by seller.

4. Use geographic location (state) to match up others not identified above.

We have linked these data with existing data in CUEPS from the EIA-861. We use these 

data in modeling the effects of fuel costs and taxes on electricity prices.

A.3 County Utility Bridge

An immediate problem that arises is that of relating economic activity in a given county 

to demand for a utility, as well as relating utility prices of a given utility to demand for 

electricity in a county. The solution to this problem was the development of a countyutility bridge matrix, which is a crucial component of the model. The bridge is used to 

convert data available by county to data by utility, and vice versa. The rows of this 

bridge matrix correspond to the 3140 counties, and the columns to the 3356 utilities. The 

bridge was initially filled with some data that could be used to relate utility service area 

to counties. For this purpose, we have used the number of households (meters) served. 

Although the matrix is quite large, only the nonzero cells are stored, of which there are at 

present only about 8000 out of a total possible of 11.4 million. This bridge is conceptually 

similar to the product-industry bridge, used in the Inforum LIFT model, which distributes 

value added by industry to value added by product, and vice-versa. 

Figure A.3.1 shows the mapping of utilities to counties in West Virginia by the county utility 

bridge.
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Figure A.1 County Utility Bridge Relationships in West Virginia
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Appendix B. Model Documentation

B.1 The LIFT Model

The Inforum LIFT (Long-term Interindustry Forecasting Tool) model is unique among largescale models of the U.S. economy in that it is based on an input-output (IO) core, and 

builds up macroeconomic forecasts from the bottom up. In fact, this characteristic of 

LIFT is one of the principles that has guided the development of Inforum models from the 

beginning. This is in part because the understanding of industry behavior is important in 

its own right, but also because this parallels how the economy actually works. 

Investments are made in individual firms in response to market conditions in the industries 

in which those firms produce and compete. Aggregate investment is simply the sum of 

these industry investment purchases. Decisions to hire and fire workers are made jointly 

with investment decisions with a view to the outlook for product demand in each 

industry. The net result of these hiring and firing decisions across all industries determines 

total employment, and hence the unemployment rate. In the real world economy 

pricing decisions are made at the detailed product level. Modeling price changes at 

the commodity level certainly captures the price structure of the economy better than 

an aggregate price equation. In LIFT, prices and incomes are forced into consistency 

through the fundamental input-output identity, and the aggregate price level is 

determined as current price GDP divided by constant price GDP. 

Despite its industry basis, LIFT is a full macroeconomic model, with more than 1200 

macroeconomic variables determined either by econometric equation, exogenously or 

by identity. The econometric equations tend to be those where behavior is more 

naturally modeled in the aggregate. Many aggregates are formed the sum of industry 

detail, such as total corporate profits. An equation for the effective corporate tax rate is 

used to determine total profits taxes, which is a source of revenue in the Federal 

government account. Equations for contribution rates for social insurance programs and 

equations for transfer payments out of these programs can be used to study the future 

solvency of the trust funds. Certain macrovariables provide important levers for studying 

effects of government policy. Examples are the monetary base and the personal tax 

rate. Other macrovariables, such as potential GDP and the associated GDP gap 

provide a framework for perceiving tightness or slack in the economy.

Since its inception, LIFT has continued to develop and change. We have learned much 

about the properties of the model through analytical studies and simulation tests. We 

have learned about the behavior of the general Inforum type of model, from work with 

Inforum partners in other countries, including China, Japan, Germany and Italy. 

In the last several years, the LIFT model has been extended through the incorporation of 

several modules that can be used to study energy demand and supply, and the 

implications of energy use on carbon emissions.
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B.1.1 An Overview of the LIFT Model

We first focus on the “real side” of the model, where the expenditure components of 

GDP are calculated in constant prices. First personal savings are determined, which 

affect how much of real disposable income will result in total expenditures on 

consumption. Personal consumption is modeled in the PADS (consumer demand system) 

function to get consumption by category. PADS allows the classification of consumption 

goods into related expenditure groups, for example food, transportation or medical 

care. In PADS, motor vehicles prices affect the demand for public transportation, since 

motor vehicles and public transport are substitutes.

Exports by commodity may be determined outside the model, from the Inforum bilateral 

trade model (BTM) or by equations use information from BTM in the form of weighted 

foreign demands and foreign prices. The equipment investment equations are based on 

a Diewert cost function, that models the substitution (or complementarity) of equipment 

capital with labor and energy. The equations use a cost of capital measure that 

includes real interest rates, present value of depreciation, investment tax credit and 

corporate profits tax. The construction equations are for the roughly 20 categories of 

private construction. Though each has a different form, common variables are interest 

rates, disposable income and sectoral output.

Federal and state and local consumption and investment expenditures are specified 

exogenously in real terms, but LIFT allows for detailed control of these expenditures. For 

example, defense purchases of aircraft can be specified independently of missiles, ships 

or tanks. 

The input-output solution solves jointly for output, imports and inventory change. Note 

that the IO matrix coefficients are specified to change over time, according to trends for 

each row. However, individual coefficients can also be fixed, to model changes in price 

or technology.

Labor productivity equations are used to determine the ratio of output to hours worked 

by industry. Average hours equations determine the average hours per employed

person per year. Together, the productivity, average hours and output forecast 

generate employment by industry in the private sector. Adding in exogenous projections 

of government and domestic employment, total civilian employment is obtained. 

Subtracting total employment from projected labor force yields unemployment, and the 

unemployment rate, which is a pivotal variable in the model. 

Prices in LIFT are determined as a markup over unit intermediate and labor costs. 

However, all components of value added are calculated first. Some are then scaled so 

that value added by commodity and prices are consistent. The largest component of 

value added is labor compensation by industry, which we call simply the “wage rate”, 

although it also includes supplements. The “wage” equations relate the growth of the 

wage rate to growth in the ratio of M2 to GDP, expected inflation, and the growth in 

labor productivity. Multiplying the wage rate by the total hours worked per industry gives 

total labor compensation per industry.

It is also important to determine the components of capital income. Such items as 

Corporate profits, proprietors’ income and capital consumption allowances are 

calculated in LIFT by industry. The value added relationships not only play a role in the 

determination of prices, but are also needed to be able to calculate corporate profits 
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taxes, and retained earnings and capital consumption allowances are the large 

components of business savings, which is an important part of the savings-investment

identity. Furthermore, dividends, proprietors’ income, interest income and rental income 

all contribute to personal income.

Finally, there is a block of the model called “the Accountant”, which is a large set of 

equations and identities that aggregate industry and commodity level variables up to 

the aggregate level, and calculate many of the main variables in the National Income 

and Product Accounts (NIPA). Part of the job of the accountant is to estimate all of the 

components of national income, personal income and disposable income. It also 

calculates federal and state and local government receipts and expenditures, as well as 

transfer payments and social insurance contributions. All of the fundamental national 

accounts identities are also calculated by the Accountant.

The standard solution interval for LIFT has recently been to 2030. For the calibration to the 

2010 Annual Energy Outlook, the solution interval will be extended to 2035. Note that we 

have also developed special versions of LIFT that forecast to 2050 (for carbon emissions 

modeling) or to 2085 (for long-term health care projects).

B.1.2 The Use of LIFT for Energy Modeling

As described above, LIFT is an interindustry macroeconomic (IM) model. Price and 

quantity calculations are grounded in the IO relationships. To a large extent, the 

macroeconomic forecasts are aggregates of detailed industry equations. The LIFT

model embodies industry and interindustry detail for about 90 commodities, as well as a 

full set of NIPA (national accounts) variables. While not an energy model per se, LIFT 

maintains detail for the following energy industries:

LIFT shows constant and current price sales of these industries to all other industries and to 

final demand, as well as showing the purchases of these industries from other industries in 

the economy.

The calculation of prices in LIFT is also based on IO relationships. Prices are based on the 

prices of domestic and imported inputs, and the value added generated in production, 

including labor compensation, gross operating surplus and indirect taxes. Energy taxes, 

such as those analyzed in this study, are implemented as an indirect tax, which affects 

the price of the target industry directly, and the prices of all other industries indirectly.

Residential energy demand, and household transportation are modeled as part of a 

system of personal consumption expenditure equations. These consumption equations 

respond to disposable income, relative prices and other variables. Industrial, commercial 

and non-household transportation energy demand is modeled via IO relationships. The 

IO relationships are not static, but may be modeled to incorporate efficiency 

improvements, price-induced substitution, or changes in structure due to technological 

3. Coal

4. Natural gas extraction

5. Crude petroleum

24. Petroleum refining

25. Fuel oil

66. Electric utilities

67. Natural gas distribution
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change. The structure of the electric power generating industry is represented as a 

disaggregation into the following list of 8 separate components, based on the 

technology or fuel type:

Additional modules have been attached to LIFT, which perform side calculations. These 

modules take output, price and other variables from the model, solve, and then provide 

variables to feed back to the main model. Examples of modules now functioning with 

LIFT include:

 Biofuels 

 Light-duty vehicles 

 Building efficiency 

 CCS 

 Renewable power (wind and solar)

 Nuclear power

 Carbon and carbon tax calculator

 Electricity generation by type

A module such as the building efficiency or light duty vehicles calculates variables such 

as residential and commercial energy demand for which LIFT would normally use the 

personal consumption equations or the IO coefficients. With the addition of the module, 

these default calculations are either replaced or modified. Personal consumption 

expenditures on gasoline may then be calculated as the sum of fuels of vehicles of 

different types, based on MPG and vehicle miles traveled instead of the default 

equations which rely on income and price. Changes in commercial energy demand 

coming through building or vehicle efficiency are implemented as changes in IO 

coefficients.

Types of Electricity Generation

1. Coal

2. Natural gas

3. Petroleum

4. Nuclear

5. Hydro

6. Wind

7. Solar

8. Geothermal, biomass and other
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Producing Sectors of the LIFT Model of the U.S. Economy

1 Agriculture, forestry, and fisheries Instruments and Miscellaneous Manufacturing

54 Search & navigation equipment

Mining 55 Medical instruments & supplies

2 Metal mining 56 Opthalmic goods

3 Coal mining 57 Other instruments

4 Natural gas extraction 58 Miscellaneous manufacturing

5 Crude petroleum

6 Non-metallic mining Transportation

59 Railroads

Construction 60 Trucking, highway passenger transit

7 New construction 61 Water transport

8 Maintenance & repair construction 62 Air transport

63 Pipeline

Non-Durables 64 Transportation services

9 Meat products

10 Dairy products Utilities

11 Canned & frozen foods 65 Communications services

12 Bakery and grain mill products 66 Electric utilities

13 Alcoholic beverages 67 Gas utilities

14 Other food products 68 Water and sanitary services

15 Tobacco products

16 Textiles and knitting Trade

17 Apparel 69 Wholesale trade

18 Paper 70 Retail trade

19 Printing & publishing 71 Restaurants and bars

20 Agricultural fertilizers and chemicals

21 Plastics & synthetics Finance and Real Estate

22 Drugs 72 Finance & insurance

23 Other chemicals 73 Real estate and royalties

24 Petroleum refining 74 Owner-occupied housing

25 Fuel oil

26 Rubber products Services

27 Plastic products 75 Hotels

28 Shoes & leather 76 Personal and repair services, exc. auto

77 Professional services

Durable Material and Products 78 Computer & data processing

29 Lumber 79 Advertising

30 Furniture 80 Other business services

31 Stone, clay & glass 81 Automobile services

32 Primary ferrous metals 82 Movies and amusements

33 Primary nonferrous metals 83 Private hospitals

34 Metal products 84 Physicians

85 Other medical services & dentists

Non-Electrical Machinery 86 Nursing homes

35 Engines and turbines 87 Education, social services, membership organizations

36 Agriculture, construction, mining & oilfield equipment

37 Metalworking machinery Miscellaneous

38 Special industry machinery 88 Federal & state and local government enterprises

39 General and miscellaneous industrial machinery 89 Non-competitive imports

40 Computers 90 Miscellaneous tiny flows

41 Office equipment 91 Scrap and used goods

42 Service industry machinery 92 Rest of the world industry

93 Government industry

Electrical Machinery 94 Domestic servants

43 Electrical industrial apparatus & distribution equipment 95 INFORUM statistical discrepancy

44 Household appliances 96 NIPA statistical discrepancy

45 Electric lighting and wiring equipment and misc. electrical 

supplies 97 Chain weighting residual

46 TV's, VCR's, radios & phonographs

47 Communication equipment

48 Electronic components

Transportation Equipment

49 Motor vehicles

50 Motor vehicle parts

51 Aerospace

52 Ships & boats

53 Other transportation equipment
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B.2 The CUEPS Model

B.2.1 Elements of the CUEPS Database

The CUEPS database consists of national level data from the Inforum LIFT model, 

aggregated to the 14 sector level, county level data, based on the Regional Economic 

Information System (REIS) from the U.S. Bureau of Economic Analysis, and electric utility 

data from the Energy Information Administration (EIA). 

The 14 sectors used in CUEPS are listed in table B-1. The data from LIFT include output in 

current and constant dollars, prices, employment, productivity, labor compensation, 

proprietors' income, other return to capital, indirect business taxes, total value added, 

personal consumption expenditures, federal defense expenditures, federal nondefense 

expenditures, state & local government expenditures and total final demand.

Data for the 3140 counties comprising the U.S. include population, employment, total 

earnings, number of households, total wages and salaries, other labor income, 

proprietors' income, dividends, interest and rental income, transfer income, social 

insurance contributions, and a residence adjustment. 29 Data for earnings and 

employment are available from each county for the 14 sectors listed in table A-1. In 

addition to these data, estimates were made for current and constant dollar output, 

return to capital, and indirect business taxes. Table X-X shows a sample of the 3140 

counties.

The electric utility data is available from EIA for a set of 3356 utilities, derived from the EIA861 and EIA-860 datasets. These data consist of sales in megawatt hours (Mwh), 

revenue, and number of customers, for five markets: Residential, Commercial, Industrial, 

Public and Other. Table B-2 shows a sample of the list of utilities.

Residential customers are defined as household establishments that consume energy 

primarily for space heating, water heating, air conditioning, lighting, refrigeration, 

cooking and clothes drying. Commercial actually includes both commercial and 

industrial establishments which have demands generally less than 1000 kilowatt hours 

(kW). Industrial includes commercial and industrial establishments which have demands 

generally greater than 1000 kW. Public is energy supplied to ultimate consumers for 

public street and highway lighting. Other includes any customers not included in the 

other four categories, and is primarily for agricultural use. Average price data were also 

calculated by dividing revenue by megawatt hour sales in each market, by utility.

B.2.2 Demand Indicators

The demand indicators are variables constructed for the regression equations of 

electricity demand (in Mwh sales) by major sector. The demand indicators are 

calculated based on sectoral outputs and the national input-output coefficients for 

 

29 The residence adjustment accounts for individuals who live in one county or area, but earn their 

income in another. The earnings variable includes them in their county of employment, but the 

personal income variable includes them in the county of residence.
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electricity consumption. For example, the demand indicator for industrial electricity 

consumption is formed as:

∑∈

= j Industrial

Industrial jj D 6 qA

where:

A is the national input-output direct coefficients matrix, aggregated to 14 sectors

q is real sectoral output

the “industrial” group is mining, construction, manufacturing, and transportation, 

communication and public utilities, except electric utilities

Sector 6 is the electric utilities sector

The interpretation of this demand indicator is the amount of electricity input in constant 

dollars that would be needed in a certain county, given the levels of sectoral outputs of 

each buying sector, and the input-output relationships that we observe at the national 

level. The demand indicator is necessarily imperfect since the national input-output 

matrix is only an approximate measure of electricity use per sector at the county level. 

However, the demand indicator constructed in this way gives a better measure than the 

sum of sectoral outputs for how electricity demand should change with changes in 

outputs, especially if the composition of outputs is changing within the industrial group.

The demand indicator for the commercial group is formed in the same way, but using 

the I-O coefficients and output levels of the agricultural services, wholesale trade, retail 

trade, finance insurance and real estate, and services sectors.

The demand indicator for “other” is simply the output of the agricultural industry. The 

demand indicator for the government sector is total government spending in the county.

Before they can be used in the demand regression equations described below, the 

demand indicators are converted to the utility basis using the county-utility bridge matrix.

B.2.3 The Cross-Sectional Electricity Demand Equations

To relate demand by market to electricity price changes, cross-sectional demand 

regressions were estimated for each of the five markets, using the EIA utility data 

described above. The quantity variable used was Mwh sales, and the price variable was 

the average utility rate per Kwh by market. Demographic data were also used in the 

residential regressions. Finally, the demand indicators described above were bridged to 

the utility level to adjust for demand differences.

The Commercial Sector

Of the total of roughly 3356 utilities in the database, only about 1840 had enough 

variables present to be included in the regressions for commercial and industrial. The 

final estimated equation for the commercial sector was:

LSALECOM = C - 0.87*LRATECOM + -.09*LDEMCOM + 0.92*LCUSTCOM
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where: C = function of state dummies and ERS Beale codes.

LSALECOM = log of utility sales to the commercial sector

LRATECOM = log of utility rate per Kwh for the commercial sector

LDEMCOM = log of demand indicator for the commercial sector

LCUSTCOM = log of number of commercial utility customers

The constant term C is consists of the actual constant term of the equation plus two 

dummy variable effects, and thus varies by utility. The meaning of the state dummies is 

clear. The ERS Beale codes are classifications for the rural/urban classification of the 

county, and are defined in table 3.30 

The omitted state dummy is for Alaska, and the omitted ERS Beale code was 0. The 

coefficient on LRATECOM (0-0.87) can be interpreted as the long run price-elasticity. 

Both the demand indicator and number of customers were included as demand 

variables. Each customer (utility meter) accounts for some fixed amount or minimum of 

electricity consumption, and the demand variable picks up electricity consumption that 

varies with economic activity. In the case of commercial customers, the bulk of the 

demand is estimated to arise from the customers variable. This makes sense, since most 

commercial use is for lighting, air-conditioning, computers and registers.31 The sum of the 

elasticities of sales with respect to the demand indicator and number of customers is 

approximately one, which is in accord with our a priori expectation. The fit of this 

equation was rather good for a cross-sectional regression, with an R-squared of .915. 

The Industrial Sector

The final estimated equation for the industrial sector was:

LSALEIND = C -2.58*LRATEIND + 0.34*LDEMIND + 0.48*LCUSTIND

where the variables are defined just as in the commercial sector. The estimated price 

elasticity for this equation is rather high. Some plotting and scanning of the price and 

sales data verified that the coefficient reflected the patterns observed in the data. 

However, this elasticity may be partly due to location decisions of industrial firms, rather 

than being a true price elasticity. In other words, firms that anticipate that they will have 

a high share of electricity cost will tend to locate in areas where electricity is relatively 

cheap. This phenomena will bias our coefficient upwards for use in time-series 

forecasting. We are considering constraining this elasticity to a smaller number. The 

industrial equation allocates a larger share of demand to the demand indicator variable 

than the commercial equation. Industrial customers' consumption of electricity is driven 

by electric motors, electro-chemical process needs, welding, and other production 

activities, which vary with the level of output. However, there are still space heat, lighting 

and air-conditioning requirements, so the number of customers is still important. The total 

of the demand indicator and customers elasticities was 0.82. This is smaller than our a 

priori elasticity of 1.0, but it could be there are more significant economies of scale in 

electricity use in the industrial sector. The fit of this equation was also quite good, with an 

R-squared of .761.

 

30 The ERS is the U.S. Department of Agriculture, Economic Research Service. 

31 Ideally, one would like to obtain data on number of square feet of space. However, this data 

was not available to us.
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The Residential Sector

For the residential sector, 2240 utilities had sufficient data to be included in the regression. 

The estimated equation is:

LSALERES = C -0.35*LRATERES + 0.71*HIAGESHARE + 0.13*LCUSTMI

where LSALERES = log of sales to the residential sector

LRATERES = log of utility rate per Kwh for the residential sector

HIAGESHR = share of over 65 population

LCUSTMI = log of customers per mile

The price elasticity estimated for the residential sector is -.35, much lower than that of the 

commercial or industrial sector. After performing several exploratory regressions, the 

share of elderly and the population density were chosen as useful non-price explainers of 

the variation of electricity demand by utility. Note that no measure of number of 

customers was used in this regression, nor was the left-hand side variable put in percapita form. The residential demand equations were much harder to fit than the 

commercial or industrial equations. The R-squared of the final equation used was only 

.387. 

The Other Sector

As described above, this sector is comprised mostly of agriculture. In the total sample, 

1408 observations had the necessary data to be included in this regression. The final 

estimated equation for the other sector was:

LSALEOTH = C - 1.35*LRATEOTH + .27*LDEMOTH + .47*LCUSTOTH

where: C = ERS Beale codes

 LSALEOTH = log of utility sales to the other sector

 LRATEOTH = log of utility rate per Kwh for the other sector

 LDEMOTH = log of demand indicator for the other sector

 LCUSTOTH = log of number of utility customers (meters) in the other sector

Note that state dummies were not used in this regression, as they did not add much to 

the explanatory power. The estimated price elasticity in this sector of -1.35 is between 

that of industrial and commercial. As in the commercial sector, the elasticity of demand 

with respect to the demand indicator variable was significantly less than 1.0 (.27). Even 

the sum of the demand indicator elasticity and the elasticity with respect to the number 

of customers (.47) was less than 1.0 (.74). This equation should be revisited, as it implies 

significant economies of scale in electricity use in the agriculture sector, which may not 

be realistic.

The Public Sector

The public sector regression followed the form of the other sector, i.e.:
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LSALEPUB = C - .79*LRATEPUB+ .46*LCUSTPUB

where: C = State dummies and ERS Beale codes

 LSALEPUB = log of utility sales to the public sector

 LRATEPUB = log of utility rate per Kwh for the public sector

 LCUSTPUB = log of number of utility customers (meters) in the public sector

The price elasticity for the public sector is reasonable (-.79). The elasticity with respect to 

number of customers is significantly less than one.

B.2.4 The Structure of CUEPS

The construction of the County Utility Policy Simulator was done in two stages. In the first 

stage, historical data at the local level was estimated based on national relationships. 

This data set was then used as the historical starting point for making model projections.

B.2.4.1 Estimation of County Historical Data 

The step of estimating data not available from the county or utility database is done by a 

program that works like a model, but over the historical interval (1980 to 2009) 

The first step of this program is to reads data from the national model LIFT, aggregate 

that data, and insert it into the model database. These variables include the A-matrix, or 

direct coefficients matrix, output in constant and current prices, domestic prices, 

employment, labor productivity (output/employment), personal consumption by 

commodity in constant and current prices, total final demand, labor compensation, 

proprietors’ income, total earnings (labor compensation plus proprietors’ income), total 

return to capital, indirect business taxes, total value added, total federal government 

final demand, and total state & local government final demand. This data is used to 

form other variables described below. In running the simulator, the forecast data is in 

many cases calculated in the same way as the historical data.

After filling the historical data file with 14-sector national data, the program converts the 

revenue and sales data by utility to county level, using the county-utility bridge. With 

total electric utility revenue by county in hand, the sector Transportation, communication 

and public utilities (TCPU) can be split into two parts. First, total revenue for the group as 

a whole is estimated by multiplying the ratio of current price output to employment at 

the national level by employment at the county level. Then, the share of electric utility 

revenue to this total is used to split employment and constant price output. In a few 

cases, electric utility revenue obtained by passing through the bridge was greater than 

current price output in the original TCPU sector. In these cases, we used the national 

shares of the two industries to do the split.

Constant and current price output for the other 12 sectors is obtained by multiplying the 

national ratio of these variables to employment by the employment by county by 

industry. Indirect business taxes by industry are derived as a share of current price output 

equal to that of the national data, for each industry. Capital type income by industry is 

calculated as a ratio to total earnings (labor compensation plus proprietors’ income) 

equal to that of the national data for each industry. Total value added is then the sum of 

earnings, return to capital and indirect business taxes. In a small number of cases, the 
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value added so estimated is larger than nominal output. In these cases, nominal output 

is set equal to total value added.

Electric utility sales (Mwh) and revenue by the five market areas are then distributed to 

the 14 industry sectors, using shares of intermediate sales of electricity in the national 14 

sector I-O table to do the distribution. The link to the twelve industries and to 

consumption is as described in table 2. Note that Residential revenue is defined to be 

equal to personal consumption of Electric utilities in current prices.

Next, total consumption by county is estimated by applying the ratio of personal 

consumption to personal income at the national level to personal income by county. 

Personal consumption of electricity in current prices is set equal to residential 

consumption of electricity, derived above. Consumption of the other commodities is 

then determined by using the national shares of the non-electric part of current price 

consumption by commodity. Figure B-1 summarizes the historical data flow.

Figure B.1 Historical Data Flow

INFORUM

LIFT

EIA Utility

Data

BEA County 

Data

Aggregated to

14 Sectors

Calculate Employment,

Current Price Output,

Value Added,

Capital Income by 

Industry

County

Utility

Bridge

Convert Industry

Data to 14 

Sectors

Convert Utility

Data to County

Attribute Utility

Sales and Revenue

to Industry

Personal 

Consumption
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B.2.4.2 The County Utility Policy Simulator (CUEPS) Program 

Like the historical data program CUEPS first collects and aggregates various data from 

LIFT which are used both as driver variables and allocator variables. Particularly 

important is sectoral output, since regressions have been developed by 14 sectors that 

relate county output to national output.

The model starts by making a first pass estimate of personal income by county. This 

estimate is made by growing lagged personal income by county by the growth rate of 

personal income at the national level. The model iteration loop begins by figuring how 

much of personal income is personal consumption, using the national share of personal 

consumption in personal income. Then the first pass estimate of the electricity share in 

that total consumption is calculated by moving the lagged share by the growth of the 

national share. That share is then used to derive consumption of electricity, in current 

prices. Personal consumption of the non-electricity commodities is obtained by sharing 

out the non-electricity consumption by county by the corresponding national shares. 

Next, output by industry for each county is estimated using estimated output regressions. 

These are of two types. “National” industries are related to the national output of the 

same industry. “Local” industries, which include Retail trade, Construction, Services and 

State & local government, are related to total personal consumption in each county. 

Federal government output is moved forward by the growth rate of the total national 

federal government spending. Current price output by industry is derived by multiplying 

the national price level for that industry by the constant price output estimate.

Next, demand indicators for each major market are derived by county, and then 

converted to demand indicators by utility using the bridge matrix. Electricity prices by 

market by utility are moved forward by the growth rate of the national electricity price. 

The number of customers by utility is set to grow at the same rate as the demand 

indicator, but this can be changed by the user of the model.

Once the price, demand indicators and customers variables have been calculated, the 

electricity demand equations described above are calculated. The result of these 

equations is Mwh sales of electricity by market, by utility. These sales are then converted 

back to the county level using the bridge. Total utility revenue by market is calculated 

by multiplying the Mwh sales by the price per Mwh. Mwh sales by county by market are 

aggregated to a total for each county, and this figure is used to move the estimate of 

the electric utility industry output.

Employment by industry is formed using national employment/output ratios times sectoral 

constant price output. Total earnings are formed by first growing the 

earnings/employment ratio by county by the national growth rate in employment to 

earnings for each industry, and then multiplying by employment. Current price output by 

industry is formed simply by multiplying constant price output by industry by the national 

price index. Value added by industry is calculated based on the national ratio of value 

added to current price output, and capital income is formed as the difference of total 

value added and total earnings by industry. Population in each county is assumed to 

move in step with employment. This assumes a constant jobs/population ratio in each 

county. 

Finally, personal income by county is formed by moving the lagged value forward by the 

growth in total earnings. The model compares the personal income vector with the last 

guess of personal income, and returns to the top of the model loop if they are not 
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sufficiently close. Several iterations are usually required before the model reaches 

convergence. 

Many of the variables in the model can be fixed exogenously by the user. These fixes 

may be overrides, indexes, growth rates, add-factors or multiplication factors. By 

changing the path of certain variables, we can investigate the effects of these variables 

on other variables, either for an individual county, or a set of counties grouped by some 

criterion.

Figure B.2 Flow of the County Utility Policy Simulator

INFORUM

LIFT Drivers

Aggregate to

14 Sectors

Assume: Personal 

Income by County

Calculate Personal 

Consumption, and 

Residential Electricity

Employment and 

Population

Personal Income

Earnings, Value Added

& Capital Income

Output for Basic and 

Non-Basic Industries

Electricity Sales 

& Revenue by County

County

Utility

Bridge

Convert to 

Utility

Basis
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Table B-1: Industry and Government Sectors in CUEPS

# Industry Title NAICS Definition

1 Farming 111, 112

2 Agricultural services, forestry, fisheries 113, 114, 115

3 Mining 21

4 Construction 23

5 Manufacturing 31, 32, 33

6 Electric Utilities 2211

7 Transportation, communications, and public utilities, except 

electric utilities

48, 49, 513, 2212, 2213

8 Wholesale trade 42

9 Retail trade 44, 45

10 Finance, insurance and real estate 52, 53

11 Services 51, 54, 55, 56, 61, 62, 71, 72, 81

12 Federal civilian N/A

13 Federal military N/A

14 State & local N/A

Table B-2: Correspondence of 5 EIA Market Categories to 14 Sectors and Personal 

Consumption

Market Sectoral Correspondence

Residential Personal consumption of electricity

Industrial Mining, Construction, Manufacturing, Transportation, 

communication and public utilities, except electric

Commercial Wholesale trade, Retail trade, Finance, insurance and real estate, 

Services

Public Federal government, State & local government

Other Agriculture, agricultural services, forestry and fisheries
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Table B-3: ERS Beale Codes and Their Definitions

ERS Beale 

Code Definition

METROPOLITAN COUNTIES (0-3)

0 Central counties of metropolitan areas of 1 million population or more

1 Fringe counties of metropolitan areas of 1 million population or more

2 Counties in metropolitan areas of 250 thousand to 1 million population

3 Counties in metropolitan areas of less than 250 thousand population

NONMETROPOLITAN COUNTIES (4-9)

4 Urban population of 20 thousand or more, adjacent to a metropolitan area

5 Urban population of 20 thousand or more, not adjacent to a metropolitan area

6 Urban population of 2,500 to 19,999, adjacent to a metropolitan area

7 Urban population of 2,500 to 19,999, not adjacent to a metropolitan area

8

Completely rural (no places with a population of 2,5000 or more, adjacent to a 

metropolitan area

9

Completely rural (no places with a population of 2,5000 or more, not adjacent to a 

metropolitan area

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Appendix C. Wind and Biomass Potential

C.1 Derivation of Wind Assumptions

The Eastern Wind Intregation and Transmission Study (EWITS) has published an executive 

summary and project overview as well as a full report32. This study, which is done under 

contract with the DOE National Renewable Energy Laboratory (NREL) is being led by 

Enernex, with support from Ventyx and the Midwest Independent System Operator 

(MISO). This study is one of the largest regional wind integration studies to date. It was 

initiated in 2008 to examine the operational impact of up to 20-30% wind on the power 

system in the Eastern Interconnect of the United States, of which the Appalachian Region 

is a part. An output of the study has been the valuable eastern wind dataset, which is 

based on three years (2004-2006) of data of 10-minute wind speed and plant output 

values for simulated wind plants at selected locations. This data was created by AWSTruewind, and includes a database of selected sites for potential wind farms, with 

information including longitude and latitude, elevation, cost of energy, average wind 

speed, estimated capacity factor, size and density of the area, and total installed power 

potential. 

For the current study, we isolated the locations that fell within the Appalachian area. 

Figure C.1 shows the locations pinpointed as circles, proportional to capacity factor. The 

capacity factor is defined as the ratio of actual energy produced in a given period to 

the maximum attainable energy, running full time at the rated power of the wind turbine. 

Since wind is intermittent, and wind speeds vary greatly over time, the capacity factor for 

wind is generally less than 40 percent. The capacity factor is thus an indicator of how 

much energy potential wind turbines would have in a given location. 

The EWITS database also shows the total power generation potential in each location, 

based on estimates of optimal wind farm size and other factors. Figure C.2 shows a map 

of counties by potential total installed power generation potential. 

 

32 These two documents, related information, maps, data and FAQs can be found at 

http://www.nrel.gov/wind/systemsintegration/ewits.html. 
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Figure C.1 Wind Potential in Appalachia Rated by Capacity Factor

 
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Figure C.2 Wind Capacity Potential by County

Table C.1 summarizes the wind capacity potential, calculated investment cost, and 

generation potential by county and state. The states within the Appalachian region that 

have the largest wind potential are Pennsylvania (6.7 GW), New York (3.9 GW) and West

Virginia (2.3 GW). 
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The estimated cost of construction of new wind capacity used for our calculations was 

1.9 million 2009 dollars per MW of generation capacity, and an additional 0.2 million 2009 

dollars per MW of transmission capacity.33 These data are summarized by county and 

state in column 4 of table 8.15. Total estimated investment requirements in Pennsylvania 

amount to 13.3 billion 2009 dollars.

The last column of table 8.15 shows total power generation potential based on the 

capacity factor and potential installed capacity at each location. The formula used to 

make this conversion was:

G = CF ∗ C ∗ 365.25 ∗ 24/1000

Where G is total potential generation in millions of KWh, CF is the capacity factor, C is the 

capacity in GW, and the constants 365.25 and 24 refer to the average number of days in 

a year, and the number of hours in a day. According to these calculations, total 

potential wind generation attainable in Pennsylvania would be 14.9 billion KWh.

For the CUEPS Clean Energy Standard Scenario, we assume that all of the capacity listed 

in Table C.1 will be built. Two major questions to address at this point are:

What is the time period over which construction can be expected to occur?

How much local impact will the construction have on the local economies?

We have decided to assume a five-year construction period, lasting from 2013 to 2020, 

at which point the capacity listed in table is assumed to be in place. 

A tool for determining the output and job impacts from wind turbine farm construction is 

the Jobs and Economic Development Impact (JEDI) models, supported and maintained 

by the National Renewable Energy Laboratory.34 

At this stage, we are exploring the JEDI module for the analysis of local impacts of wind 

power construction. Our next step will be the integration of the outputs of JEDI as 

assumptions on output and employment changes by county in the CUEPS model.

 

33 These figures are an average of estimates from several sources, including the NEMS 

documentation for the Renewable Fuels Module in the Assumptions to the Annual Energy Outlook 

2011 (at http://38.96.246.204/forecasts/aeo/assumptions/pdf/0554%282011%29.pdf), p. 168, the 

EWITS Executive summary, and Wind Power: Performance and Economics, Fact Sheet #2 at the Renewable

Energy Research Laboratory, at 

http://www.umass.edu/windenergy/publications/published/communityWindFactSheets/index.html

. 

34 The home page for JEDI is http://www.nrel.gov/analysis/jedi
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Table C.1. Wind Potential by County and State

State County

Potential 

Wind 

Capacity 

(MW)

Investment 

Requirements 

(Mil. 2009$)

Potential 

Generation 

(mil KWh)

Kentucky

Bell 100 209 239

Harlan 200 421 511

300 630 750

Maryland

Garrett 634 1332 1577

Washington 155 326 348

789 1658 1925

New York

Allegany 127 266 340

Chautauqua 2056 4317 5384

Delaware 643 1351 1736

Otsego 464 974 1318

Schoharie 222 466 622

Steuben 376 790 994

3888 8164 10393

North Carolina

Alleghany 100 209 247

Hyde 127 267 278

Madison 100 210 276

Stokes 114 240 269

Watauga 100 209 230

541 1136 1300

Pennsylvania

Allegheny 161 338 346

Bedford 154 323 357

Blair 332 697 829

Bradford 325 683 751

Cambria 593 1246 1386

Carbon 100 210 218

Centre 150 316 339

Clearfield 100 210 217

Columbia 100 210 233

Erie 580 1217 1266

Fayette 539 618 622

Fulton 136 286 312

Luzerne 199 419 431

Mifflin 100 420 452

Potter 301 631 660

Schuylkill 355 746 805

Somerset 1220 2562 3238

Susquehanna 145 304 352

Tioga 100 210 220

Wayne 426 895 952

Westmoreland 316 663 780

Wyoming 255 536 644

6687 13285 14937
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Table C.1 Wind Potential by County and State (continued)

State County

Potential 

Wind 

Capacity 

(MW)

Investment 

Requirements 

(Mil. 2009$)

Potential 

Generation 

(mil KWh)

Tennessee

Anderson 114 239 268

Campbell 100 211 223

Cumberland 100 210 232

Franklin 256 537 602

Warren 216 454 493

786 1650 1818

Virginia

Accomack 111 234 291

Floyd 572 1201 1337

Highland 212 444 586

Patrick 100 210 280

Tazewell 201 421 490

1397 2934 3562

West Virginia

Grant 710 1490 1994

Greenbrier 100 211 245

Hampshire 100 211 268

Hardy 300 629 889

Jefferson 102 213 286

Mineral 114 452 598

Morgan 127 267 320

Pendleton 200 420 619

Preston 423 889 1034

Randolph 302 633 763

2275 4778 6152

Appalachia Total 16663 34235 40837
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C.2 Derivation of Biomass Assumptions

In the Clean Energy Standard scenario, the modeling team adopted national biomass 

power generation assumptions from the EIA’s Analysis of Impacts of a Clean Energy 

Standard as requested by Chairman Hall. In this analysis, EIA assumes virtually no 

increase in the construction of or power generation from dedicated biomass power 

plants. They do, however, project a major increase in the cofiring of biomass with coal at 

the nation’s coal-fired power plants. Overall, they project the most co-fired power plants 

will be fueled by a 15% biomass/85% coal fuel mixture, a level that is broadly considered 

the maximum biomass content that could be burned efficiently at those plants without 

dramatically increasing the associated investment costs that would be needed at a 

plant in order to facilitate biomass co-firing. 

Based on these projections, the biomass portion of the fuel mix at existing coal-fired 

power plants is projected to grow to 11% by 2020 and reach 15% in 2025. It was also 

assumed that coal-fired power plants would need to invest $200 per kW of capacity 

between now and 2025 in order to facilitate biomass co-firing, based on the EIA’s cost 

estimates of $123 to $282 per kilowatt. 

County-level biomass supplies were estimated by using the county-level data from the 

Department of Energy’s Billion Ton Study Update35. Non-cellulosic resources such as 

grains, animal fat, and animal wastes were assumed to be used for other purposes and 

cellulosic mill residues were assumed to be used for power production by end users such 

as paper manufacturers. The remaining biomass resources that were assumed to be 

available for either cellulosic biofuels production or electricity production include annual 

energy crops, perennial grasses, other woody agricultural crops, forestry resources, urban 

wood waste, and agricultural residues such as corn stover. County-level supplies were 

then calculated by summing all of the cellulosic biomass resources available in each 

county in for all years from 2012-2030 at $10 per ton increments from $10 to $200, with 

more and more resources becoming accessible at higher biomass price levels. The 

county-level biomass resource estimates were summed for 22 regions that correspond to 

NEMS’s Electricity Market Module (EMM) regions. Because these regions do not align 

with state borders, the modeling team used GIS software in order to approximate which 

counties are within each region.

Demand in each of these 22 regions was then calculated using the regional cellulosic 

ethanol production and biomass electricity projections from the 2011 AEO Reference 

case and a few core assumptions. The assumptions included:

1. Each ton of biomass had energy content of biomass equal to 16 mmBtus, the 

assumption used in the Billion Ton Study Update, and 

2. Coal-fired power plants could convert biomass into electricity with an efficiency 

of nearly 33%, the average efficiency used by the EIA in their analyses. 

After calculating demand for biomass in each of the 22 regions, the modeling team 

determined the price at which each region could supply sufficient biomass resources for 

each year and then reduced the amount of biomass resources estimated to be 

available for cellulosic ethanol production. 

 

35 See https://bioenergykdf.net/content/billiontonupdate
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The remaining amounts of biomass available were then summed for each price level 

and year and compared with the amount of biomass needed to produce cellulosic 

ethanol at the levels projected in the AEO Reference case. The demand from the 

ethanol industry assumed a conversion efficiency from cellulosic biomass feedstock into 

ethanol of 28 percent, the same efficiency assumption that the EIA uses. The modeling 

team then estimated the lowest biomass price at which the remaining biomass resources 

would be sufficient to supply the cellulosic ethanol industry and further reduced 

available biomass supplies by that amount in the EMM regions that were able to provide 

resources at that price. In addition an average biomass transportation cost of $18 per 

ton was added to the biomass price in each region to account for the transportation of 

biomass within a region. 

Some EMM regions were estimated to be unable to produce sufficient biomass resources 

to supply electric power sector demand for biomass in the region at a reasonable price. 

The modeling team assumed that these regions could obtain fill the gap between 

biomass demand and supply by purchasing excess biomass resources from other regions 

but that they would have to pay an additional $30 per ton of biomass for the additional 

cost of transporting the resources to the region. The regions that were able to contribute 

still more biomass resources at the lowest prices were then assumed to export the 

resources to the EMM regions that were unable to supply their own power sectors. 

Revenues from biomass production were then calculated by multiplying the regional 

price, which varied from $30 to $70 per metric ton in the Reference case, and the 

amount supplied by each region. These amounts were summed to reflect the biomass 

revenues collected by the agriculture and forestry industry at the national level. The 

amounts were also shared down to the county level based on the available supplies in 

each county at the projected county-level biomass price. 

These same calculations were repeated for the clean energy standard scenario, which, 

as explained above, includes significantly more biomass co-firing at coal-fired power 

plants than the Reference case does. This drives the demand for biomass resources up 

significantly as well as the price of biomass in many regions. The resulting increases in 

biomass production and prices were modeled as transfers from the utility industry to the 

agriculture and forestry industries. Transportation costs were represented as transfers 

from the utility sector to the trucking and rail industries. 

Figures C.3 to C.6 show the estimated biomass potential by Appalachian county at 

different biomass prices, from $20/ton to $200/ton. 
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Figure C.3 Biomass Potential at $20/ton
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Figure C.4 Biomass Potential at $60/ton
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Figure C.5 Biomass Potential at $100/ton
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Figure C.6 Biomass Potential at $200/ton
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Appendix D. Coal Estimates for Appalachia

Like other variables, coal production in the Reference case was calibrated to the 

levels projected in the EIA’s 2011 Annual Energy Outlook Reference case. The EIA 

divides coal production into three coal producing regions and various subregions. Based on these regions and sub-regions, CUPS assigns coal-producing 

counties to one of 5 regions/sub-regions: Northern, Central, and Southern 

Appalachia, Interior U.S. (includes the Great Plains non-Appalachian parts of the 

Midwest and South), and Western U.S.. Coal production for all counties in each 

of these five regions was calibrated to equal the total in the AEO Reference Case 

and, within those regions, shares of those totals were estimated using 2009 

county-level coal production data from the EIA. 

In the alternative scenarios it was assumed that additional electricity production 

from alternative resources – efficiency, wind, biomass, and gas – would displace 

coal-fired power production. The impact on coal production was estimated by 

using projections from the EIA’s Analysis of Impacts of a Clean Energy Standard as 

requested by Chairman Hall. Elasticities of coal fired power production in 

regional electricity markets to regional coal production were calculated from the 

EIA’s Reference and CES-Hall scenarios and then applied in CUPS. Important 

regional differences in the EIA’s projected impacts on coal production were then 

captured within CUPS. 

In particular, the EIA estimates that reductions in coal demand from the power 

sector would have milder economic impacts on the Appalachian coal industry 

than it would have on the Western coal production. This is driven by the fact that 

much of the coal produced in Appalachia’s is metallurgical coal that is either 

used in the domestic steel industry or exported. The EIA estimates that production 

of this type of coal would be largely unaffected by a domestic policy aimed at 

the power sector. The EIA’s projections are that Southern Appalachian coal 

production would be the least impacted by reductions in demand from the 

power sector, followed by Central Appalachia, the Interior, Northern Appalachia, 

and Western U.S. 
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Figure D.1 EIA Coal Estimates

 

The EIA estimates coal production for 3 

regions and 14 sub-regions.
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EIA’s modeling results provided a basis for differentiating coal production by 

region, with Southern Appalachian production being the least impacted.

Figure D.2 Coal Production by Region

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

South

Appalachia

Central

Appalachia

North

Appalachia

Interior Western
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Regional coal impacts were then further broken down to the county level using 

2009 county-level production data.

Figure D.3 County Level Coal Production