ENERGY EFFICIENCY IN APPALACHIA 

HOW MUCH MORE IS AVAILABLE, AT WHAT COST, AND BY WHEN? 

Prepared by: 

in partnership with the 

Georgia Institute of Technology 

American Council for an Energy-Efficient Economy and 

Alliance to Save Energy

ENERGY EFFICIENCY IN APPALACHIA 

HOW MUCH MORE IS AVAILABLE, AT WHAT COST, 

AND BY WHEN? 

Marilyn A. Brown,1 John A. “Skip” Laitner,2 Sharon “Jess” Chandler,1 

Elizabeth D. Kelly,1 Shruti Vaidyanathan,2 Vanessa McKinney,2 

Cecelia “Elise” Logan,1 and Therese Langer2 

March 2009

Revised May 2009 

1Georgia Institute of Technology (GA Tech) 

2American Council for an Energy Efficient Economy (ACEEE) 

Table of Contents 

iii

TABLE OF CONTENTS 

EXECUTIVE SUMMARY............................................................................................................. xi 

1. INTRODUCTION ................................................................................................................... 1 

 1.1 RATIONALE AND GOALS OF THE STUDY ............................................................. 1 

 1.2 BACKGROUND ............................................................................................................ 4 

 1.2.1 Overview of the Appalachian Region ............................................................... 4 

 1.2.2 Energy Use in Appalachia .................................................................................. 5 

 1.2.2.1 Energy Consumption by Source ......................................................... 6 

 1.2.2.2 Energy Consumption by Sector .......................................................... 7 

 1.2.2.3 Energy Prices ..................................................................................... 9 

 1.2.2.4 Carbon Footprint ............................................................................... 9 

 1.3 STRUCTURE OF REPORT ......................................................................................... 11 

2. METHODOLOGY ................................................................................................................ 13 

 2.1 THE BASELINE FORECAST ...................................................................................... 13 

 2.2 DEFINITION OF ECONOMIC ENERGY-EFFICIENCY POTENTIAL ................... 15 

 2.2.1 Cost-Effectiveness Tests .................................................................................. 16 

 2.2.2 Life-Cycle of Energy Savings .......................................................................... 17 

 2.3 ENERGY-EFFICIENCY POLICY BUNDLES ............................................................ 18 

 2.4 ALTERNATIVE FUTURES......................................................................................... 20 

 2.4.1 Region-at-Risk Scenario ................................................................................... 21 

 2.4.2 High-Tech Investment Boost Scenario ............................................................. 22 

 2.5 DEEPER MODELING .................................................................................................. 22 

3. ENERGY EFFICIENCY IN RESIDENTIAL BUILDINGS ............................................ 23 

 3.1 INTRODUCTION TO RESIDENTIAL BUILDINGS IN APPALACHIA .................. 23 

 3.2 POLICY OPTIONS FOR RESIDENTIAL ENERGY EFFICIENCY .......................... 26 

 3.2.1 Research, Development, and Demonstration ................................................... 26 

 3.2.2 Financing .......................................................................................................... 26 

 3.2.3 Financial Incentives .......................................................................................... 27 

 3.2.4 Voluntary Agreements ...................................................................................... 27 

 3.2.5 Regulations ....................................................................................................... 27 

 3.2.6 Information Dissemination, Training and Capacity Building .......................... 28 

 3.3 MODELED SAVINGS IN APPALACHIAN RESIDENTIAL BUILDINGS ............. 28 

 3.3.1 Residential Building Codes with Third Party Verification .............................. 28 

 3.3.2 Expanded Low-Income Weatherization Assistance ......................................... 32 

 3.3.3 Residential Retrofit Incentive with Reseal Energy 

 Labeling and On-Bill Financing ....................................................................... 35 

 3.3.4 Super-Efficient Appliance Deployment ........................................................... 38 

 3.4 SUMMARY OF RESULTS .......................................................................................... 43 

4. ENERGY EFFICIENCY IN COMMERCIAL BUILDINGS ........................................... 47 

 4.1 INTRODUCTION TO COMMERCIAL BUILDINGS IN APPALACHIA ................ 47 

 4.2 POLICY OPTIONS FOR COMMERCIAL ENERGY EFFICIENCY ......................... 48 

 4.2.1 Research, Development, and Demonstration ................................................... 50 

 4.2.2 Financing .......................................................................................................... 50 
Energy Efficiency in Appalachia, SEEA 

iv

 4.2.3 Financial Incentives .......................................................................................... 51 

 4.2.4 Voluntary Agreements ...................................................................................... 51 

 4.2.5 Regulations ....................................................................................................... 51 

 4.2.6 Information Dissemination, Training and Capacity Building .......................... 52 

 4.3 MODELED SAVINGS IN APPALACHIAN COMMERCIAL BUILDINGS ............ 52 

 4.3.1 Commercial Model Building Energy Codes with Third Party 

 Verification ....................................................................................................... 52 

 4.3.2 Support for Commissioning of Existing Commercial Buildings ...................... 55 

 4.3.3 Efficient Commercial HVAC and Lighting Retrofit Incentive ........................ 57 

 4.3.4 Tightened Office Equipment Standards with Efficient Use Incentive ............. 63 

 4.4 SUMMARY OF EFFICIENCY POTENTIAL FOR 

 COMMERCIAL BUILDINGS ..................................................................................... 64 

5. ENEGY EFFICIENCY IN INDUSTY ................................................................................ 69 

 5.1 INDUSTRIAL ENERGY USE IN APPALACHIA ...................................................... 69 

 5.2 POLICY OPTIONS FOR INDUSTRIAL ENERGY EFFICIENCY ............................ 70 

 5.2.1 Research, Development, and Demonstration ................................................... 72 

 5.2.2 Financing .......................................................................................................... 72 

 5.2.3 Financial Incentives .......................................................................................... 72 

 5.2.4 Regulations ....................................................................................................... 73 

 5.2.5 Information Dissemination and Training ......................................................... 74 

 5.3 MODELED SAVINGS IN APPALACHIAN INDUSTRY .......................................... 75 

 5.3.1 Expanded Industrial Assessment Center Initiative (IACs) ............................... 75 

 5.3.2 Increasing Energy Savings Assessments .......................................................... 78 

 5.3.3 Supporting Combined Heat and Power with Incentives ................................... 82 

 5.4 SUMMARY OF RESULTS FOR INDUSTRIAL ENERGY EFFICIENCY ............... 86 

6. ENERGY EFFICIENCY IN TRANSPORTATION .......................................................... 89 

 6.1 TRANSPORTATION ENERGY USE IN APPALACHIA .......................................... 89 

 6.2 POLICY OPTIONS FOR TRANSPORTATION ENERGY EFFICIENCY ................ 90 

 6.3 MODELED SAVINGS IN THE TRANSPORTATION SECTOR .............................. 92 

 6.3.1 Enabling Pay-As-You-Drive Insurance ............................................................ 92 

 6.3.2 Clean Car Standards ......................................................................................... 95 

 6.3.3 SmartWay Heavy Truck Efficiency Loan Program ......................................... 98 

 6.3.4 Speed Limit Enforcement ............................................................................... 100 

 6.4 SUMMARY OF RESULTS FOR TRANSPORTATION POLICY BUNDLES ........ 103 

7. MACROECONOMIC RESULTS: EMPLOYMENT, INCOME, AND 

 ECONOMIC ACTIVITY ................................................................................................... 107 

 7.1 METHODOLOGY ...................................................................................................... 107 

 7.2 ILLUSTRATING THE METHODOLOGY: APPALACHIAN JOBS 

 FROM EFFICIENCY GAINS .................................................................................... 108 

 7.3 IMPACTS OF RECOMMENDED ENERGY-EFFICIENCY POLICIES ................. 111 

8. SUMMARY AND CONCLUSIONS.................................................................................. 115 

 8.1 ECONOMY-WIDE RESULTS ................................................................................... 115 

 8.2 RESULTS BY SECTOR ............................................................................................. 122 

 8.2.1 Residential Buildings ...................................................................................... 122 

 8.2.2 Commercial Buildings .................................................................................... 123 
Table of Contents 

v

 8.2.3 Industry ........................................................................................................... 124 

 8.2.4 Transportation ................................................................................................. 124 

 8.3 CONCLUSIONS ......................................................................................................... 125 

9. REFERENCES .................................................................................................................... 127 

ACKNOWLEDGEMENTS ......................................................................................................... 135 

APPENDIX A ARC ENERGY-EFFICIENCY POLICY INVENTORY 

APPENDIX B RESIDENTIAL POLICY AND METHODOLOGY DETAIL 

APPENDIX C COMMERCIAL POLICY AND METHODOLOGY DETAIL 

APPENDIX D INDUSTRIAL SITES

APPENDIX E TRANSPORTATION

APPENDIX F DEEPER MODEL

APPENDIX G SENSITIVITIES AT SECTOR LEVEL
Energy Efficiency in Appalachia, SEEA 

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LIST OF FIGURES

ES.1 Potential Displacement of Appalachian Energy Consumption by Cost-Effective 

 Efficiency Resources ..............................................................................................................xiv 

ES.2 Share of Cost-Effective Efficiency Resources by Sector ........................................................ xv 

ES.3 Annual Investment and Energy Savings: 2010-2030 ............................................................xvi 

ES.4 Sensitivity Analysis of Benefit/Cost Ratios for the Total Resource Cost Test 

With Carbon ―Adders‖ ......................................................................................................... xvii 

1.1 The Economics of Compact Fluorescent Light Bulbs .............................................................. 3 

1.2 Sub-regions in Appalachia ........................................................................................................ 4 

1.3 Energy Consumption Projections of the Appalachian Region by Source, 2006-2030 ............. 6 

1.4 Energy Consumption Shares in the U.S. and Appalachia by End-Use Sectors, 2006 .............. 7 

1.5 Energy Consumption Projections by Sector of the Appalachian Region, 2006-2030 .............. 7 

1.6 Energy Consumption for Electric Power Generation, 2006 ...................................................... 8 

1.7 Energy Consumption for Electric Power Generation in the Appalachian 

 Region, 2006-2030 .................................................................................................................... 8 

1.8 Carbon Footprints of 17 Metropolitan Areas in (or Surrounding) 

 the Appalachian Region, 2005 ................................................................................................ 10 

2.1 ARC Energy Consumption Forecast ....................................................................................... 15 

2.2 ARC 2006 Energy Consumption of Fuels Modeled in this Study .......................................... 18 

3.1 Residential Sector Energy Sources by Fuel, 2006 .................................................................. 23 

3.2 Residential Energy Consumption Forecast for the Appalachian Region ................................ 23 

3.3 Map of Residential Footprints ................................................................................................. 24 

3.4 Annual Investment and Energy Savings from Residential Building Codes, 2010-2030 ........ 31 

3.5 Annual Investment and Energy Savings from Expanded Weatherization, 2010-2030 ........... 35 

3.6 Annual Investment and Energy Savings from Existing Home Retrofits, 2010-2030 ............. 38 

3.7 Percent ENERGY STAR Sales by Appliance by State, 2006 ................................................. 39 

3.8 Annual Investment and Energy Savings from Super-Efficient Appliance 

 Deployment, 2010-2030 .......................................................................................................... 41 

3.9 Schematic of an Air-Source Integrated Heat Pump ................................................................ 42 

3.10 Cumulative Energy Savings from Integrated Heat Pump in Appalachian Region ................. 43 

3.11 Residential Energy Savings by Policy Bundle ........................................................................ 44 

3.12 Residential Primary Energy Consumption With and 

 Without Policy Packages (Quads), 2006-2030 ....................................................................... 44 

3.13 Comparison of Appalachian Residential Delivered Energy Consumption 

 Forecast Under Four Cases (Quads), 2006-2030 .................................................................... 45 

3.14 Annual Investment and Energy Savings from Combined Residential 

 Policy Packages, 2010-2030 ................................................................................................... 45 

4.1 Commercial Sector Energy Sources by Fuel, 2006 ................................................................. 47 

4.2 Commercial Energy Consumption Forecast for the Appalachian Region .............................. 47 

4.3 Annual Investment and Energy Savings from Commercial Building Codes, 2010-2030 ....... 55 

4.4 Annual Investment and Energy Savings for Commissioning, 2010 -2030 ............................. 57 

4.5 Percent of ARC Commercial Floorspace Lit When Buildings are 

 Open and Closed, 2003 ........................................................................................................... 58 

4.6 Percent of ARC Commercial Floorspace Heated and Cooled, 2003 ...................................... 59 

4.7 Annual Investments and Energy Savings from HVAC and Lighting Retrofits, 2010-2030 ... 60 

4.8 Diagram of an LED ................................................................................................................. 61 
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vii

4.9 U.S. Energy Consumption for Lighting Through 2007 .......................................................... 62 

4.10 Annual Investment and Energy Savings from Office Equipment Standards, 2010-2030 ....... 64 

4.11 Commercial Primary Energy Savings by Policy Package (trillion Btu), 2030 ....................... 65 

4.12 Cumulative Savings by Policy Package with Commissioning Included in Retrofits 

 (trillion Btu), 2030 .................................................................................................................. 65 

4.13 Commercial Consumption With and Without Policy Packages 

 (trillion Btu, Primary), 2006-2030 .......................................................................................... 66 

4.14 Annual Investments and Energy Savings for the Commercial Policy 

 Package, 2010-2030 ................................................................................................................ 66 

5.1 Energy Consumption Forecast for Industry ............................................................................ 69 

5.2 Industrial Energy Sources by Fuel, 2006 ................................................................................ 69 

5.3 Annual Investment and Energy Savings from IACs, 2010-2030 ............................................ 78 

5.4 Annual Investment and Savings from Increased Assessments, 2010-2030 ............................ 80 

5.5 Laboratory Prototype Boiler .................................................................................................... 81 

5.6 Comparison of Estimated Energy Savings from Super Boiler to Industrial Baseline 

Consumption (trillion Btu), 2010-2030 ................................................................................... 82 

5.7 Annual Investment and Energy Savings from Supported CHP, 2010-2030 ........................... 85 

5.8 Industrial Energy Savings by Policy Package, (trillion Btu), 2030 ......................................... 86 

5.9 Industrial Energy Consumption With and Without Energy Efficiency................................... 86 

5.10 Annual Investment and Energy Savings from Industrial Policy Package, 2010-2030 ............ 87 

6.1 Appalachian Region Transportation Consumption Forecast (quads), 2006-2030 .................. 89 

6.2 Transportation Sector Energy Sources by Fuel, 2006 ............................................................. 90 

6.3 Pay-As-You-Drive Public Investment and Returns ................................................................ 94 

6.4 Annual Investment and Energy Savings from a New Clean Car Standard, 2010-2030 .......... 98 

6.5 Annual Investments and Energy Savings from SmartWay Loans ........................................ 100 

6.6 Speed Limit Enforcement ...................................................................................................... 102 

6.7 Primary Energy Savings by Policy Package (trillion Btu), 2030 .......................................... 103 

6.8 Transportation Primary Energy Consumption With and Without 

 Policy Packages (quads), 2006-2030 ..................................................................................... 103 

6.9 Annual Investments and Energy Savings from Transportation 

 Policy Packages, 2010-2030.................................................................................................. 104 

7.1 Net Job Impacts for Appalachia (2008-2030) ....................................................................... 113 

7.2 Wages and Gross Regional Product Impacts for Appalachia 

 (million 2006$), 2008-2030 .................................................................................................. 114 

8.1 Potential Displacement of Appalachian Energy Consumption by 

 Cost-Effective Efficiency Resources ..................................................................................... 115 

8.2 Share of Cost-Effective Efficiency Resources by Sector 

 (Primary Energy in trillion Btu, 2030) .................................................................................. 115 

8.3 Primary Energy Savings (excluding Commercial Commissioning), by Sector 

 (2030 million Btu) ................................................................................................................. 115 

8.4 Share of Cost-Effective Efficiency Resources by Fuel Type and Sector 

 (Primary Energy in trillion Btu, 2030) .................................................................................. 119 

8.5 Annual Investment and Energy Savings: 2010-2030 ........................................................... 122 

8.6 Sensitivity Analysis of Benefit/Cost Ratios Based on Alternative Discount Rates .............. 123 

8.7 Sensitivity Analysis of Benefit/Cost Ratios for the Total Resource Cost Test 

 With Carbon ―Adders‖ .......................................................................................................... 124 
Energy Efficiency in Appalachia, SEEA 

viii

LIST OF TABLES

ES.1 Energy-Efficiency Policy Portfolio ........................................................................................ xii 

ES.2 Cost-Effective Efficiency Resources as a Percent of Projected Primary Energy 

 Consumption in the Appalachian Region in 2020 and 2030 ................................................... xv 

ES.3 Financial and Economic Impact of Energy-Efficiency Investment in Appalachia .............. xvii 

1.1 Appalachian Region Energy Consumption Forecast from Annual Energy Outlook, 

 2006 to 2030 (quads) ................................................................................................................. 5 

1.2 Energy Consumption by Source, 2006 ...................................................................................... 6 

1.3 Average Energy Prices to All Users in Appalachia and the United States ............................... 9 

2.1 Population Weighted Census Division Portions ..................................................................... 14 

2.2 Appalachian State Population Portion Estimates and Projections, 2006-2030 ....................... 14 

2.3 Benefits and Costs Included in the Economic Test ................................................................. 17 

2.4 Energy-Efficiency Policy Portfolio ......................................................................................... 19 

2.5 Range of Impacts of a Carbon Cap-and-Trade System on Fossil Energy Prices .................... 21 

3.1 Policy Actions that Support Residential Energy Efficiency ................................................... 25 

3.2 Status of Appalachian State Residential Building Energy Codes and Percent 

 of ENERGY STAR Qualified New Homes ............................................................................ 29 

3.3 Energy Savings from Residential Building Codes .................................................................. 30 

3.4 Costs and Savings from Residential Building Codes .............................................................. 31 

3.5 Insulation Requirements for Manufactured Homes ................................................................ 32 

3.6 Poverty Rates for Appalachian States, 2005 ........................................................................... 33 

3.7 Energy Savings from Expanded Weatherization .................................................................... 34 

3.8 Costs and Savings from Expanded Weatherization ................................................................ 34 

3.9 Energy Savings from Existing Home Retrofits ....................................................................... 37 

3.10 Costs and Savings from Existing Home Retrofits ................................................................... 37 

3.11 Energy Savings from Super-Efficient Appliance Deployment ............................................... 40 

3.12 Costs and Savings from Super-Efficient Appliance Deployment ........................................... 40 

3.13 Results of Economic Tests for Residential Policies ................................................................ 46 

4.1 Median Lifetimes for Selected Building Types ...................................................................... 48 

4.2 Policy Actions that Support Commercial Energy Efficiency .................................................. 49 

4.3 State Commercial Building Energy Codes .............................................................................. 53 

4.4 Energy Savings from Commercial Building Codes ................................................................ 54 

4.5 Costs and Savings from Commercial Building Codes ............................................................ 54 

4.6 Energy Savings from Commissioning of Existing Commercial Buildings ............................. 56 

4.7 Costs and Savings from Commissioning of Existing Commercial Buildings ......................... 57 

4.8 Energy Savings from HVAC and Lighting Retrofits .............................................................. 59 

4.9 Costs and Savings from HVAC and Lighting Retrofits .......................................................... 60 

4.10 Projected Lumens per Watt ..................................................................................................... 61 

4.11 Electricity and Lighting Demand in the Appalachian Region ................................................ 62 

4.12 Energy Savings from Office Equipment Standards ................................................................ 63 

4.13 Costs and Savings from Office Equipment Standards ............................................................ 64 

4.14 Results of Economic Tests for Commercial Policies .............................................................. 67 

5.1 Top Industrial Employers in the Appalachian Region ............................................................ 70 

5.2 Policy Actions that Support Industrial Energy Efficiency ...................................................... 71 

5.3 Current Industrial Net Metering Programs in Appalachian Region ........................................ 74 
Table of Contents 

ix

5.4 IAC Assessments to Date ........................................................................................................ 76 

5.5 Energy Savings from IACs ..................................................................................................... 77 

5.6 Costs and Savings IACs .......................................................................................................... 77 

5.7 Example of Save Energy Now Energy Savings Assessments ................................................. 79 

5.8 Energy Savings from Increasing Assessments and Training .................................................. 80 

5.9 Costs and Savings from Increasing Assessments and Training .............................................. 80 

5.10 Summary of CHP-Supportive Policies in Connecticut ........................................................... 84 

5.11 Energy Savings from Supported CHP ..................................................................................... 85 

5.12 Costs and Savings from Supported CHP ................................................................................. 85 

5.13 Summary of Economic Tests for Industrial Policy Bundles ................................................... 88 

6.1 CAFE Fuel Savings ................................................................................................................. 90 

6.2 Policies That Support Transportation Energy Efficiency ....................................................... 91 

6.3 Estimated Impacts of a Mileage Pay-As-You-Drive Insurance Program ............................... 93 

6.4 Energy Savings and Costs for a Pay-As-You-Drive Insurance Program ................................ 94 

6.5 Proposed Clean Car Standards for Greenhouse Gases, 2009-2020......................................... 96 

6.6 Proposed Fuel Economy Standards 2020-2030 ...................................................................... 97 

6.7 Light-Duty Vehicle Fuel Savings in Appalachia from Adoption of Clean Car Standards ..... 97 

6.8 Energy Savings and Costs from Adoption of Clean Car Standards ........................................ 98 

6.9 Savings from Low-Interest Loans for Heavy Truck Efficiency Improvements ...................... 99 

6.10 Energy Savings and Costs from Heavy Truck Efficiency Improvements............................. 100 

6.11 Estimated Benefits of Improved Speed Limit Enforcement – 

 Light-Duty and Heavy-Duty Vehicles .................................................................................. 101 

6.12 Energy Savings and Costs from Speed Limit Enforcement .................................................. 102 

6.13 Results of Economic Tests for Transportation Policy Bundles ............................................. 105 

7.1 Job Impacts from Commercial Building Efficiency Improvements ..................................... 109 

7.2 Financial Impacts from Energy-Efficiency Policy Scenario ................................................. 112 

7.3 Economic Impact of Energy-Efficiency Investment in Appalachia ...................................... 113 

8.1 Energy Savings from Individual Policies .............................................................................. 117 

8.2 Cost-Effective Efficiency Resources as a Percent of Projected Primary Energy 

 Consumption in the Appalachian Region in 2020 and 2030 ................................................. 120 

8.3 Cost-Effective Efficiency Resources as a Percent of Projected Primary Energy 

 Consumption in the Appalachian Region in 2020 and 2030 (Excluding Commercial 

 Building Commissioning) ..................................................................................................... 121 

8.4 Results of Economic Tests for Four Sectors and All Sectors ............................................... 123 
Energy Efficiency in Appalachia, SEEA 

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Executive Summary 

xi

EXECUTIVE SUMMARY 

In 2006, the Appalachian Regional Commission (ARC) prepared a report – Energizing Appalachia: 

A Regional Blueprint for Economic and Energy Development – that articulates the ARC energy goal: 

―Develop the Appalachian Region‘s energy potential to increase the supply of locally produced, 

clean, affordable energy, and to create and regain jobs‖ (ARC, 2006). The report identified three 

strategic objectives that support this goal, one of which involved developing energy efficiency within 

the Region. 

To more fully articulate this strategic objective, ARC commissioned an assessment of ―the potential 

long-term energy efficiency gains for the Appalachian Region over current baseline projections from 

introducing a range of advanced efficiency standards for each energy end-use sector, and to detail the 

economic and environmental impacts from the technologies and investment required to attain these 

objectives.‖ Energy Efficiency in Appalachia presents the results of this assessment. It addresses 

several essential questions: 

 How big are the energy-efficiency resources in Appalachia? 

 How quickly can these energy-efficiency resources be realized? 

 What policies and programs can most effectively translate these resources into energy 

savings? 

 What impact will such policies and programs have on jobs and wages in Appalachia? 

The Energy Challenges Facing Appalachia 

The Appalachian Region faces daunting energy challenges and opportunities. As an historic center of 

coal production in the United States, Appalachia and energy have long been intertwined. With 7.95 

percent of the U.S. population, Appalachia produces 35 percent of the nation‘s coal output, employs 

two-thirds of the nation‘s coal miners, and generates approximately 15 percent of the total U.S. 

electrical output. The Region‘s annual consumption of 7.98 quadrillion Btu (quads) in 2006 produces 

a per capita energy intensity that slightly surpasses the national average, reflecting the historically 

cheap price of energy in the Region. The Energy Information Administration (EIA) forecasts that this 

comparative energy price advantage will continue through 2030. 

Compared with the rest of the nation, Appalachia spends slightly more of its energy on residential 

and commercial uses, reflecting both its high reliance on electricity for heating and cooling, and its 

relatively inefficient building stock. The Region‘s energy consumption is expected to grow to 10.1 

quads by 2030, a growth of 28 percent over 2006 levels, which is considerably higher than the 19 

percent growth forecast for the United States. As is the case nationwide, the EIA projects that coal 

will increase its share of energy use in the Region as part of the major expansion of coal use that is 

anticipated in the 2015-2030 timeframe, if restrictions on CO2 emissions are not legislated. 

Prior research suggests that residential and commercial consumers in this Region are fairly 

insensitive to short-term to increases in the price of electricity. One study concluded that residential 

and commercial users in Appalachia would need to experience a doubling of electricity prices in 

order to produce a 15 to 17 percent reduction in electricity consumption. This lack of responsiveness 

to electricity price changes, which is similar to behavior in other regions of the country and for other 

fuels, suggests that strong policy interventions will be needed to promote energy-efficient purchases 
Energy Efficiency in Appalachia, SEEA 

xii

and practices. Fortunately, smart policies can transform markets for energy products and services, 

and it is this perspective that is explored in this report. 

Policy Portfolio 

To assess the magnitude of cost-effective and achievable energy-efficiency improvements in 

Appalachia, we assume that a set of transformative energy policies are adopted in the Region 

beginning in the year 2010. These policies build upon the progressive steps Appalachia has already 

taken in the area of clean energy through an array of state and local programs, regional planning 

activities, and utility initiatives. 

The policy portfolio modeled in this report includes a combination of vigorous deployment initiatives 

that increase the achievable potential for energy efficiency, and expanded research, development, and 

demonstration (RD&D) funding that accelerates the advancement of energy-efficient technologies. 

These policy bundles were defined and then modified iteratively as the result of discussions with the 

project‘s Advisory Committee and Stakeholder Group (Table ES.1). 

Table ES.1 Energy-Efficiency Policy Portfolio

Residential

Buildings

Commercial

Buildings Industry Transportation

Improved Building Energy 

Code with Third Party 

Verification and 

Compliance Incentive

Commercial Building 

Energy Codes with Third 

Party Verification and

Compliance Incentives

Expanded Industrial 

Assessment Centers 

Pay-as-You-Drive 

Insurance

Expanded Weatherization 

Assistance Programs

Support for 

Commissioning of Existing 

Commercial Buildings

Increasing Energy Savings 

Assessments Clean Car Standards 

Residential Retrofit 

Incentive with Resale 

Energy Labeling and 

Incremental Cost 

Incentives

Efficient Commercial 

HVAC and Lighting 

Retrofit Incentive

Supporting Combined 

Heat and Power (CHP) 

with Incentive 

SmartWay Heavy Truck 

Efficiency Loan Program

Super-Efficient Appliance 

Deployment

Tightened Office 

Equipment Standards with 

Efficient Use Incentives

Speed Limit Enforcement

Illustrative RD&D Initiative

Air-Source Integrated Heat 

Pump Solid State Lighting Industrial Super Boiler 

In addition to overlaying this energy-efficiency policy portfolio onto an otherwise ―business-asusual‖ forecast, the project‘s analytic team undertook a systematic assessment of alternative possible 

futures for the Appalachian Region. Because it seems likely that some form of national climate or 

carbon policy will be announced early during the study‘s 25-year time horizon, we conduct a 

sensitivity analysis of a carbon constrained scenario where there is a ―price adder‖ of $25 to $100 

per metric ton of carbon dioxide beginning in 2011. In the region-at-risk scenario, a national 

climate policy is assumed to be promulgated early in the time frame (perhaps in 2011), initiating a 
Executive Summary 

xiii

shift in the way energy is produced and used. However, in this scenario the shift takes place without 

the aid of fundamentally different technologies. With a premium on the price of fossil fuels, energyefficient technologies are highly cost-effective; however, the difficult economic conditions dampen 

investments. In the high-tech investment boost scenario, the country produces significant material, 

technology, and process advances in the performance and cost competitiveness of clean-energy 

supply technologies, most notably clean coal. As a result of the successful investment climate that 

results, energy efficiency is also able to play an enhanced role in the Region. These last two scenarios 

are not modeled. 

Methodology 

For the purposes of this study, energy efficiency refers to the long-term reduction in energy 

consumption resulting from the increased deployment and improved performance of energy-efficient 

equipment and practices. Program potential is the cost-effective energy-efficiency improvements 

that would occur in response to specific policies such as subsidies and information dissemination. We 

do not examine the impact of energy-efficiency investments on demand reductions, which is critical 

to electric power planners. Nor do we examine the role of demand-response or load-management 

programs aimed strictly at shifting on-peak consumption to off-peak hours. 

This project uses a variety of sources of data, models, and energy-engineering analyses to estimate 

Appalachia‘s energy-efficiency program potential. Results of past energy-efficiency program 

evaluations is the basis of estimating the administrative and implementation costs of each energyefficiency policy bundle. Our analysis of potential in each sector uses a common baseline forecast, 

common energy price projections, identical discount rates for calculating cost-effectiveness, and the 

same economic tests of cost-effectiveness (the participants cost test and the total resource cost test). 

The specific data sources and methodologies are summarized in each of the sector chapters and are 

described in greater detail in Appendices B through G. The results of these policy analyses are then 

input into a dynamic input-output model to evaluate the macro-economic impacts of proposed 

policies. In addition, the project team created an Advisory Committee and Stakeholder group to 

review and guide the research. 

The following two examples illustrate how the policies are modeled and preview some of the results: 

 Residential Building Codes: All Appalachian states are assumed to adopt the 2006 

International Energy Conservation Code (IECC) by 2009 and more efficient codes every 

three years thereafter. Codes are assumed to become effective the year following adoption. 

Third-party verification of measures occurs, and an incentive to builders is provided for the 

period 2010-2020. This results in an 80 percent compliance rate. To illustrate, the 419,000 

single and multi-family homes projected to be built from 2013 to 2015 in Appalachia are 

assumed to conform to the 2009 IECC code and therefore use 18 percent less energy for 

space heating, space cooling, and water heating than they would have if built to 2005 current 

practice. Homes built from 2016 to 2019 are assumed to use 30 percent less energy. With 

$281.5 million in program spending and an additional $2.1 billion in customer investments 

over the 2010-2030 period, the Appalachian Region could see net cumulative savings of 1.0 

quads of energy and $16.3 billion in energy bills by 2030. 

 Increased Energy Savings Assessments and Training: Large industrial facilities receive 

expanded training and assistance on how to pinpoint energy-efficiency improvements in 

systems throughout their complex. Each assessment takes three days and involves plant 
Energy Efficiency in Appalachia, SEEA 

xiv

7.0

7.5

8.0

8.5

9.0

9.5

10.0

10.5

11.0

11.5

2006 2009 2012 2015 2018 2021 2024 2027 2030 Quadrillion Btu

AEO 2007 AEO 2008 With Policy Packages

personnel to achieve acceptance and training to enable future in-house assessments. It is 

assumed that over 60 percent of available assessments are completed by 2030, and all 

recommendations are executed. On average each assessment results in reducing overall site 

consumption by roughly nine percent. With $23 million in program spending and an 

additional $8 billion in customer investments over the 2010-2030 period, the Appalachian 

Region could see net cumulative savings of 18.7 quads of energy and $43.3 billion in energy 

bills by 2030. 

Magnitude of the Energy-Efficiency Resource in Appalachia 

The engineering-economic modeling 

conducted in this study indicates that an 

ambitious package of energy-efficiency 

policies implemented throughout 

Appalachia in 2010 could result in 

significant energy savings. According 

to the latest EIA ―business-as-usual‖

forecast, Appalachia will require 9.2 

quads of energy in 2020 and 10.1 

quads in 2030. In contrast, a bold 

energy-efficiency initiative could cut 

that consumption by between 9 and 12 

percent to approximately 8 quads in 2020 

and by between 23 and 28 percent to less 

than 8 quads in 2030. (The upper bound 

includes savings from commercial building 

commissioning, while the lower bound 

does not.) Such a bold and aggressive 

initiative could shrink the energy budget 

required by the Region in 2030 to less 

than the Region consumed in 2006 – more than offsetting the forecast growth in energy use (Figure 

ES.1).

Table ES.2 shows that the most significant savings are in electricity overall, representing a reduction 

of between 11 and 15 percent in 2020 rising to between 27 and 33 percent in 2030. Motor gasoline 

consumption is reduced by almost as much: 11 percent in 2020 and 33 percent in 2030. Natural gas 

savings are next in order of magnitude, saving between 5 and 7 percent of the forecast consumption 

in 2020, and between 14 and 20 percent of the forecast consumption in 2030. 

Figure ES.1 Potential Displacement of Appalachian Energy 

Consumption by Cost-Effective Efficiency Resources
Executive Summary 

xv

Table ES.2 Cost-Effective Efficiency Resources as a Percent

of Projected Primary Energy Consumption in the

Appalachian Region in 2020 and 2030

a

2020 2030

Electricity 10.5 – 15.4 27.2 – 33.1

Natural Gas 4.7 – 6.8 14.2 – 19.5

Gasoline 10.7 33.1

All Fuels 8.8 – 11.9 23.4 – 27.8

a

 The upper bound includes savings from commercial building commissioning, while the lower bound does not. 

Dividing the cost-effective energy-efficiency resources 

by sector helps explain the prominent potential for 

reduced electricity consumption, since the vast majority 

of electricity produced in the U.S. is consumed in 

residential and commercial buildings, which are 

prominently featured in Fig. ES.2. Taking into account 

the energy lost in the generation and transmission of 

electricity as well as losses from ―end-use‖ equipment 

such as motors, lighting, and air conditioning, 68 percent 

of the energy-efficiency potential in Appalachia resides 

in the electricity system. The next largest wedge of 

energy savings potential comes from motor gasoline 

consumption by vehicles (17 percent), followed 

by natural gas savings potential in the 

commercial, residential, and industrial sectors 

(12 percent). 

As Figure ES.3 illustrates, energy savings expand at a slightly increasing pace over the 20-year 

period. In contrast, public investments (including incentives plus program administrative costs) drop 

from approximately $700 million per year during the first decade to slightly less than $500 million in 

the second decade, reflecting the sun-setting of several program subsidies and incentives in the year 

2020. 

Figure ES.2 Share of Cost-Effective Efficiency 

Resources by Sector

(Primary Energy in trillion Btu, 2030)

Primary Energy Savings, by Sector (2030, Trillion Btu)

Residential

374

15%

Industrial

621

25%

Transportation

457

18%

Commercial

1030

42%
Energy Efficiency in Appalachia, SEEA 

xvi

0

1

1

2

2

3

3

2010 2015 2020 2025 2030 Quadrillion BTU

0

1

1

2

2

3

3

4

4

5

5 Billion 2006$

Primary Energy Savings Public Investment Private Investment

Economic and Job Impacts 

In Appalachia, the electric utility and the natural gas sectors directly and indirectly employ about 5.3 

and 3.7 jobs, respectively, for every $1 million of spending. But, sectors vital to energy-efficiency 

improvements, like construction and manufacturing, utilize 13.3 and 8.3 jobs per $1 million of 

spending. Once job gains and losses are netted out in each year, the analysis suggests that, by 

diverting expenditures away from non-labor intensive energy sectors, the cost-effective energy 

policies can positively impact the larger Appalachia economy – even in the early years, but 

especially in the later years of the analysis as the energy savings continue to mount. An early 

program stimulus that drives a higher level of efficiency investments can create more than 15,000 net 

new jobs each year in the first five years of the study, rising to an estimated average of 60,000 net 

new jobs over the last decade of the analysis. 

The annual energy bill savings begins with a modest first year benefit of almost $800 million. As the 

policy portfolio spurs further investment in energy efficiency, the annual consumer energy bill 

savings rise to more than $27 billion by 2030. These savings directly benefit the consumers who 

make these investments, but they also help to moderate energy prices for all consumers because they 

reduce overall demand growth. These investments also increase both wages and Gross Regional 

Product (GRP) throughout Appalachia. 

Figure ES.3 Annual Investment and Energy Savings: 2010-2030
Executive Summary 

xvii

0

2

4

6

8

10 Benefit-to-Cost Ratio 50% Lower than Energy Prices 0.97 3.40 1.04 1.66 1.65

25% Lower than Energy Prices 1.46 5.10 1.56 2.49 2.48

Retail Energy Prices 1.94 6.80 2.08 3.33 3.31

Energy Prices with $25/MtC Tax 2.15 6.59 2.67 3.41 3.45

Energy Prices with $50/MtC Tax 2.35 7.42 3.26 3.49 3.58

Energy Prices with $100/MtC Tax 2.75 9.09 4.45 3.65 3.84

Residential Commercial Industrial Transportation Total

Table ES.3 Financial and Economic Impact of Energy-Efficiency

Investment in Appalachia

Macroeconomic Impacts 2010 2013 2020 2030

Annual Consumer Outlays

(millions $2006)

1,083 2,734 4,564 6,165

Annual Energy Savings

(millions $2006)

788 2,577 9,944 27,567

Annual Net Consumer 

Savings (millions $2006)

(295) (157) 5,380 21,402

Jobs (Actual) 16,231 15,466 37,268 77,378

Wages (million $2006) 517 450 1,169 3,018

GRP (million $2006) 763 444 1,197 3,056

The principal estimate used 

in this project to monetize 

the avoided costs of 

potential energy savings is 

the retail price of energy. 

Figure ES.4 shows what 

would happen to the costeffectiveness tests of the 

energy-efficiency policy 

portfolio if the value of the 

avoided costs were inflated 

as the result of a national 

climate policy that imposed 

a cost of $25 to $100 per 

metric ton of carbon dioxide 

emissions. Such a policy 

would significantly raise the 

benefit/cost ratios of the policy packages in three sectors: commercial, residential, and industrial. 

Because of the lower carbon content of gasoline and diesel, there is a much smaller impact on the 

cost-effectiveness of the transportation sector‘s energy savings potential. Across all of the sectors, the 

carbon-inflated avoided costs make investments in energy efficiency more cost-effective compared 

with the business-as-usual scenario. Figure ES.4 also shows that even if retail energy prices are 50 

percent higher than the cost to the wholesaler or distributor, the combined sector policy packages 

remain cost-effective. 

Figure ES.4 Sensitivity Analysis of Benefit/Cost Ratios for the Total 

Resource Cost Test With Carbon “Adders”
Energy Efficiency in Appalachia, SEEA 

xviii

The Value of Exploiting Appalachia’s Energy-Efficiency Potential 

Policy action aimed at exploiting the energy-efficiency potential described in this report would set 

Appalachia on a course toward a sustainable and prosperous energy future. The Region‘s energyefficiency resources could go a long way toward meeting its future energy needs while ensuring its 

continued economic and environmental health. 

The problem is that energy-efficiency upgrades require consumer and business investment and they 

take time away from other priorities. With so many demands on financial and human capital, energyefficiency improvements tend to be given a low priority. Through a combination of information 

dissemination and education, financial assistance, regulations, and capacity building, consumers, 

businesses, and industry can be encouraged to take advantage of energy-efficiency opportunities. In 

addition, expanded RD&D is needed to innovate and deploy transformational technologies that 

expand the efficiency potential. 

By exploiting the Region‘s substantial energy-efficiency resources, Appalachia can cut the energy 

bills of its households, businesses and industries, create ―green‖ jobs, and grow its economy. The 

ability to convert this vision into a reality will depend on the willingness of business and government 

leaders to implement and champion the kinds of policies modeled here. 

.
Chapter 1: Introduction 

1

1 INTRODUCTION 

Perhaps at no time since the mid-1970s has the Appalachian Region faced so many energy 

challenges. Fuel prices for oil, gasoline, natural gas, and coal have risen dramatically and now match 

or exceed previous all-time highs. These spiraling costs are compounded by double-digit economic 

growth rates in China and India, which are driving up the cost of steel, aluminum, and other materials 

necessary to expand the Region‘s energy infrastructure. The growing reliance on oil imports raises 

questions about the long-term energy security of the Region‘s petroleum-dominant transportation 

system. Concerns about greenhouse gas emissions and mountaintop removal are placing new 

constraints on coal mining and the construction of coal plants, resulting in numerous cancelled 

projects and the loss of billions of dollars.1 ―Not in my backyard‖ (NIMBY) attitudes have allowed

local opposition to routinely trump regional needs for new energy resources and infrastructure. 

After decades of steadily expanding energy consumption, it is hard to imagine how another 25 years 

(or century) of similar growth in energy demand can be accommodated. As a result, policymakers at 

all levels of government want to know how much of the forecasted growth in energy consumption 

can be met by improved energy efficiency. They also want to know what types of policies, programs, 

and technologies hold the greatest potential to curb the growth of energy consumption – at the least 

cost. Energy Efficiency in Appalachia focuses on these issues. 

1.1 RATIONALE AND GOALS OF THE STUDY 

In 2006, the Appalachian Regional Commission (ARC) prepared a report – Energizing Appalachia: 

A Regional Blueprint for Economic and Energy Development – that documented the energy situation 

within the Appalachian Region. It also articulated the ARC energy goal: ―Develop the Appalachian 

Region‘s energy potential to increase the supply of locally produced, clean, affordable energy, and to 

create and regain jobs‖ (ARC, 2006). Three strategic objectives support this goal, focusing on the 

development of energy efficiency, renewable energy, and conventional energy resources within the 

Region. 

In conjunction with the development of Energizing Appalachia, ARC commissioned two studies, one 

on the Economic Development Potential of Conventional and Potential Alternative Sources in 

Appalachian Counties (Glasmeier and Bell, 2006) and another on Energy Efficiency and Renewable 

Energy in Appalachia: Policy and Potential (Center for Business and Economic Research (CBER), 

2006). Together these reports provide a detailed assessment of conventional and unconventional 

fossil energy sources as well as the full range of renewable energy resources. The potential for 

energy-efficiency improvements in the Region, however, is treated more anecdotally by highlighting 

some of the Region‘s innovative energy-saving system designs, reviewing energy-efficiency policies, 

and comparing energy intensity levels in the Region to those of the nation. Although there have been 

a large number of studies highlighting positive opportunities for energy-efficiency investments in 

individual states, across the nation, and even within various states within Appalachia (Laitner and 

 

1

 There are many examples of recently cancelled coal plant projects in the Appalachian region. For example, several years 

ago American Electric Power (AEP) proposed to build the Mountaineer integrated gasification combined cycle (IGCC) 

coal plant next to the existing Mountaineer generating station along the Ohio River in Mason County, West Virginia. In 

April 2008, the West Virginia State Corporation Commission (SCC) rejected the plant after judging that its cost estimates 

were not credible (http://www.sourcewatch.org/index.php?title=Mountaineer).
Energy Efficiency in Appalachia, SEEA 

2

McKinney, 2008a), there is no current quantitative estimate of the economic potential for energyefficiency improvements for the Region as a whole. 

To fill this gap, Energy Efficiency in Appalachia assesses the potential for cost-effective energyefficiency gains across the Region‘s residential, commercial, industrial, and transportation sectors. 

With 2006 as a baseline, it focuses on projections for the years 2013, 2020, and 2030 under the 

assumption that transformative energy policies are adopted within the Region in the year 2010.

Evidence is mounting that energy efficiency is a large, affordable, and feasible energy resource. It 

can be as reliable as the construction of new power plants and the purchase of power via long-term 

contracts or spot markets. It has been shown to be a valuable, ―front-line‖ strategy against global 

climate change because it offers a ―no regrets‖ approach: investments in energy efficiency can save 

consumers and businesses money while reducing pollution and greenhouse gas (GHG) emissions.2

A large potential for improved efficiency exists in numerous appliances and energy-consuming 

equipment and practices. For instance, high-quality adjustable-speed electronic motor drives, once 

exotic and costly, are now mass-produced in Asia and are widely used because of their protective and 

soft-start circuits. High-efficiency compact fluorescent lamps sell for a fifth of their 1983 price, now 

that a billion are made yearly. Real prices have fallen several fold in 15 years for electronic lighting 

ballasts and heat-reflecting window coatings. Hybrid electric cars offer fuel economy performance in 

a standard range vehicle that was unachievable ten years ago. The economic potential for energy 

efficiency continues to grow (Lovins, 2007). 

States across the nation are meeting one to two percent of their electricity consumption each year 

with energy efficiency at a cost of approximately $0.03 per kilowatt-hour (kWh) compared with 

projected costs of $0.05 to $0.07 per kWh of electricity from coal, gas combined cycle, wind or 

nuclear plants (Brown and Chandler, 2008; Kushler, York and Witte, 2004). Results from California, 

New York, Vermont, and other states show that energy efficiency represents a low-cost, low-risk 

energy strategy. 

California, in part due to aggressive and sustained energy-efficiency measures, has kept per capita 

electricity use flat over recent decades (National Academy of Sciences, 2008). This is in direct 

contrast to national trends over the last 25 years, where U.S. per capita electricity use as a whole has 

risen about 50 percent. Rufo and Coito (2002) have shown that the potential for further energyefficiency improvements in California remains strong. A similar potential for aggressive and 

sustained energy-efficiency programs has been demonstrated in Vermont and other states, where 

electricity consumption per capita has remained fairly flat while the state‘s economy has grown 

significantly. Thus, these states have shown that energy demand growth can be significantly reduced 

without compromising economic growth. The challenge is to move these energy-efficiency ―best 

practices‖ to the rest of the country. 

 

2

Indeed as the meta-review provided by Laitner and McKinney (2008a) suggests, the evidence points to a potential 20 to 

30 percent efficiency gain compared to normal business-as-usual projections. Perhaps more critically, the benefits of this 

level of potential efficiency improvement appear to outweigh the costs by roughly two-to-one.
Chapter 1: Introduction 

3

Together, energy efficiency and demand response can delay or completely avoid the need for 

expensive new generation and transmission investments, thus keeping the future cost of electricity 

affordable and freeing up energy dollars to be spent on other resources to expand the Region‘s 

economy. A greater share of the dollars invested in energy efficiency goes to local companies that 

create new jobs compared with conventional electricity resources where much of the money flows 

out of the Region to equipment manufacturers and fuel suppliers. 

Layers of energy inefficiency exist throughout the 

economy. For example, converting coal at the power 

plant into useable light given off by incandescent 

lamps is only three percent efficient (National 

Academy of Sciences, 2008). By simply replacing 

incandescent bulbs with compact fluorescents, a 

four-fold improvement in efficiency can be 

achieved. Consider the economics shown in Figure 

1.1. The payback period can be quite short – in this 

case for compact fluorescent light (CFL) bulbs, less 

than a year or as little as a month, depending on how 

may hours each day the CFL is used. However, as 

with many (but not all) energy-efficiency 

improvements, consumers need to purchase a more 

expensive device in order to generate the energy 

savings. How can reluctant consumers be persuaded 

to pay more up front to save money in the future 

when they often do not understand the sometimes 

complex economic analysis that goes into such a 

purchasing decision? 

Energy-efficiency policy mechanisms are numerous and are implemented at all levels of government 

from the local jurisdiction and state to the regional and national scale. To make matters more 

complicated, energy-efficiency measures and incentives can be delivered by a multiplicity of actors 

and agents, including independent organizations, non-government statewide organizations, fully 

integrated independently owned utilities, unaffiliated distribution companies, as well as government 

agencies (Harrington and Murray, 2003). In this report, we use the typology developed by Geller 

(2003) to inventory existing policies and to consider alternatives (see Appendix A). Geller‘s typology 

includes regulatory policies (regulations, market obligations, and market reforms); fiscal measures 

(financial incentives, financing, and pricing); enabling policies (capacity building, dissemination and 

training, and research, development, and demonstration); and voluntary approaches (planning 

techniques, procurement policies, and voluntary agreements). 

By expanding existing energy-efficiency policies and by implementing new policy approaches that 

tackle key barriers, create new incentives, set minimum standards, and enable change, how much 

energy efficiency can be stimulated? Which technologies hold the greatest potential and what 

policies and programs can most effectively translate that potential into reality? These are the essential 

questions addressed by this study. 

Figure 1.1 The Economics of Compact 

Fluorescent Light Bulbs

(Brown, 2008)
Energy Efficiency in Appalachia, SEEA 

4

1.2 BACKGROUND

1.2.1 Overview of the Appalachian Region

The Appalachian Region tracks the spine of the Appalachian Mountains, starting in northern 

Mississippi and sweeping northeast through southern New York. It includes all of West Virginia and 

parts of twelve additional states: Alabama, Georgia, Kentucky, Maryland, Mississippi, New York, 

North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, and Virginia. With a population of 

23.9 million in 2006, the Region is home to 7.95 percent of the U.S. population. 

In 1965 the Federal government established the 

Appalachian Regional Commission (ARC), an 

economic development agency composed of the 

governors of the 13 states and a co-chair appointed by 

the president. Local participation is provided by local 

development districts. 

In the early years of the ARC, the Region was divided 

into three contiguous and relatively homogeneous subregions based on topography, demographics and 

economics (Figure 1.2). The South sub-region has the 

highest population growth rate (estimated at 1.13 

percent annually), while the North has the slowest 

growth rate (estimated at 0.28 percent). These three 

sub-regions include 410 counties and contain parts of 

four Census Divisions. The cross-walk between these 

three sub-regions, four Census Divisions, and 410 

counties is critical to apportioning numerous data 

elements that are key to the analysis of efficiency 

resources. 

1.2.2 Energy Use in Appalachia 

As an historic center of coal production in the United States, Appalachia and energy have been 

intimately intertwined. Appalachian mines produce 35 percent of the nation‘s coal output, and the 

Region employs two-thirds of the nation‘s coal miners (ARC, 2006). Much of this coal is burned in 

Appalachian power plants to produce electricity for the Region‘s consumers and for export to 

surrounding markets, especially those in the large metropolitan areas that circle the Region. 

Appalachian coal generated approximately 15 percent of the total U.S. electrical output, worth $16 

billion in 2005 (ARC, 2006). Almost 150,000 jobs are generated by the Appalachian energy industry, 

with hundreds of thousands more producing and distributing energy products and services (ARC, 

2006). 

The intensity of energy use in the Appalachian Region is slightly higher than that of the nation as a 

whole. In 2006, the Region consumed 7.94 quadrillion Btu (quads) of energy, or 332.7 MMBtu per 

capita, slightly more than the U.S. average of 331.6 MMBtu per capita. When indexed to personal 

income, the Region is considerably more energy intensive than the national average (CBER, 2006, p. 

41). According to the CBER (2006), the above-average consumption rates are ―likely due to high 

rates of electrification in some states, which may increase overall energy use, and a somewhat 

Figure 1.2 Sub-regions in Appalachia

(www.arc.gov/index.do?nodeId=938)
Chapter 1: Introduction 

5

elevated share of manufacturing; the ARC counties account for about 26 percent of manufacturing 

income in the ARC states, but only 24.5 percent of the population (p. 42).‖

Based on population weighed extrapolations from the Census Division forecasts of energy 

consumption from EIA‘s Annual Energy Outlook 2008, the Region‘s energy consumption is expected 

to grow by 28 percent to 10.14 quads in 2030. This is considerably higher than the 19 percent growth 

forecast for the U.S. (EIA, 2008a, Table A2). 

Table 1.1 Appalachian Region Energy Consumption Forecast from

Annual Energy Outlook, 2006 to 2030 (quads)

(EIA, 2008a)

Year Total

Residential

Buildings

Commercial

Buildings Industry Transportation

2006 7.94 1.80 1.50 2.42 2.22

2013 8.45 1.96 1.64 2.51 2.34

2020 9.12 2.16 1.89 2.65 2.42

2030 10.14 2.47 2.26 2.87 2.54

1.2.2.1 Energy Consumption by Source3

The Appalachian Region‘s energy consumption of 7.94 quadrillion Btu in 2006 represents 7.98 

percent of the total energy use of the United States. Compared to the share of the energy 

consumption of each fuel in the United States on average, Appalachia consumed six percent more 

energy from coal, and three percent more nuclear energy. On the other hand, the Region consumed 

less energy from oil and natural gas, compared to the national average. 

 

3

 The energy consumption data of the Appalachian region were driven with the projections of business-as-usual scenario 

from the Annual Energy Outlook 2007 (EIA, 2007a). All of the 410 counties included in the region were located over the 

four census divisions such as the East North Central, East South Central, Middle Atlantic, and South Atlantic regions. 

Based on the population proportion of Appalachia in each census division, the energy use of the entire Appalachian 

Region was aggregated. The energy price data of the region was driven from the Annual Energy Outlook 2008 (EIA, 

2008a). Based on the proportional approach that used in the consumption data, the weighted average of the prices of the 

four census regions was calculated for this analysis. 
Energy Efficiency in Appalachia, SEEA 

6

0

1

2

3

4

5

6

7

8

9

10

11

2006 2009 2012 2015 2018 2021 2024 2027 2030 Quadrillion Btu

Other Liquid Fuels Motor Gasoline Natural Gas Coal Biofuels and Renewables Nuclear Power

Table 1.2 Energy Consumption by Source, 2006

(EIA, 2008a)

Source

United States Appalachia

Quadrillion 

Btu Share (%) Quadrillion Btu Share (%)

Liquefied Petroleum Gases 2.65 2.7 0.07 0.8

Motor Gasoline 17.62 17.7 1.43 18.0

Distillate Fuel Oil 8.77 8.8 0.74 9.4

Residual Fuel Oil 1.69 1.7 0.15 1.8

Other Liquid Fuels 9.33 9.4 0.58 7.3

Natural Gas 22.30 22.4 1.34 16.8

Coal 22.50 22.6 2.25 28.4

Biofuels and Renewables 6.27 6.3 0.47 5.9

Nuclear Power 8.21 8.2 0.90 11.3

Total 99.52 7.94 

Fuels may not sum to total due to rounding

As is the case nationwide, coal is forecast to increase its share of energy use in the Region between 

2015 and 2030, in the absence of restrictions on CO2 emissions (Table 1.2 and Figure 1.3). However, 

the market share of western coal is expected to increase, while Appalachian coal production is 

forecast by EIA to decline slightly. 

―Although producers in Central Appalachia 

are well situated to supply coal to new 

generating capacity in the Southeast, that 

portion of the Appalachian basin has been 

mined extensively, and production costs 

have been increasing more rapidly than in 

other Regions.‖ (EIA, 2008a, p. 84) With 

67 percent of the nation‘s jobs in the U.S. 

coal industry supporting only 35 percent of 

U.S. coal production, Appalachia has 

significantly lower levels of labor productivity 

and therefore higher costs. In contrast, the 

Powder River Basin has vast remaining surfaceminable reserves that can be reached by large 

earth-moving equipment with significant 

benefits from economies of scale. 

Figure 1.3 Energy Consumption Projections of the 

Appalachian Region by Source, 2006-2030

(EIA, 2008a)
Chapter 1: Introduction 

7

18.0%

28.4%

18.9%

30.5% 32.7%

20.9%

27.9%

22.7%

0%

5%

10%

15%

20%

25%

30%

35%

Residential Commercial Industrial Transportation

U.S. Appalachia

1.2.2.2 Energy Consumption by Sector 

In 2006, the Appalachian Region spent 

1.85 quadrillion Btu (quads) in the 

residential sector; 1.47 quadrillion Btu 

in the commercial sector; 2.62 

quadrillion Btu in the industrial sector; 

and 2.16 quadrillion Btu in the 

transportation sector (Figure 1.4). 

Compared with the nation as a whole, 

Appalachia consumes slightly more of 

its energy on residential and 

commercial uses and less in the 

industrial and transportation sectors. 

Energizing Appalachia (ARC, 2006, p. 

8) suggests that the significant difference in 

the residential sector ―probably reflects the lower 

efficiency of the Region‘s housing stock.‖ It may 

also be a function of the Region‘s dual heating and 

cooling seasons, which requires either space 

heating or air conditioning most months of the 

year to maintain indoor comfort. 

The energy consumption of each sector is forecast to increase over the next 25 years, expanding 

consumption in 2030 to 2.47 quadrillion Btu (24 percent) in the residential sector, 2.26 quadrillion 

Btu (22 percent) in the commercial sector, 2.87 quadrillion Btu (28 percent) in the industrial sector, 

and 2.54 quadrillion Btu (25 percent) in the transportation sector (Table 1.1). 

0

1

2

3

4

5

6

7

8

9

10

11

2006 2009 2012 2015 2018 2021 2024 2027 2030 Quadrillion Btu

Residential Commercial Industrial Transportation

Figure 1.4 Energy Consumption Shares in the 

U.S. and Appalachia by End-Use Sectors, 2006

(EIA, 2008a)

Figure 1.5 Energy Consumption Projections by Sector of 

the Appalachian Region, 2006-2030

(EIA, 2008a)
Energy Efficiency in Appalachia, SEEA 

8

Appalachia largely depends on coal to generate electric power, as does the United States. Because 

coal mining is a major industry in the Region and Appalachia is an exporter of electric power, coal 

contributes 57 percent of the energy consumption for electric power generation. Compared to the 

nation as a whole, Appalachia depends more on coal and nuclear and less on natural gas and 

renewable sources (Figure 1.6).

United States

Fuel Oil

1.6%

Natural Gas

16.2%

Coal

51.8%

Nuclear

20.8%

Renewable

9.5%

Electricity Imports

0.2%

Appalachia

Fuel Oil

2%

Natural Gas

11%

Coal

57%

Nuclear

25%

Renewable

5%

Electricity Imports

0%

Corresponding to the total energy consumption projections, EIA projects that Appalachia will 

increase its share of coal consumption for electricity generation between 2020 and 2030 (Figure 1.7).

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

5.5

2006 2009 2012 2015 2018 2021 2024 2027 2030 Quadrillion Btu

Fuel Oil Natural Gas Coal

Nuclear Power Renewable Electricity Imports

Figure 1.6 Energy Consumption for Electric Power Generation, 2006

(EIA, 2008a)

Figure 1.7 Energy Consumption for Electric Power Generation 

in the Appalachian Region, 2006-2030

(EIA, 2008a)
Chapter 1: Introduction 

9

1.2.2.3 Energy Prices

Energy in Appalachia is relatively cheap, and EIA forecasts that this comparative advantage will 

continue through 2030 (Table 1.3). Appalachia‘s prices for motor gasoline, distillate fuel oil, natural 

gas, coal, and electricity are all lower than U.S. averages. The only exception is liquefied petroleum 

gases (LPG), which cost more in Appalachia than on average in the United States. This high price 

may explain why LPG usage in Appalachia constitutes such a small fraction of the Region‘s energy 

budget (0.8 percent vs. 2.7 percent for the nation). 

An analysis by the Center for Business and Economic Research (2006) suggests that residential and 

commercial consumers in this Region are fairly insensitive in the short-run to increases in the price 

of electricity. This lack of responsiveness to electricity price changes, which is similar to behavior in 

other Regions of the country, suggests the magnitude of policy change needed to alter the 

consumption of energy. With price elasticities of -0.15 and -0.17, the CBER results indicated that 

residential and commercial users in Appalachia would need to experience a doubling of electricity 

prices in order to produce a 15 to 17 percent reduction in electricity consumption. If this price 

insensitivity applies across all energy sources, which is likely, strong policy interventions will be 

needed to promote energy-efficient purchases and practices. The good news is that smart policies 

can, indeed, get the job done (Brown, et al, 2001; Geller et al., 2006). And it is this perspective that 

we actively explore in the analysis that follows. 

Table 1.3 Average Energy Prices to All Users in Appalachia and the United States

(in 2006 dollars per million Btu)

(EIA, 2008a)

Source

The United States Appalachia 

2006 2013 2020 2030 2006 2013 2020 2030

Liquefied 

Petroleum Gases 20.35 18.61 18.59 19.82 17.39 18.44 18.61 20.10

Motor Gasoline 21.06 19.51 19.64 20.37 15.70 14.73 15.11 16.37

Distillate Fuel Oil 18.56 17.07 17.20 18.74 13.74 13.10 13.33 14.92

Natural Gas 9.22 8.06 7.98 9.36 7.75 6.65 6.78 8.05

Metallurgical Coal 3.54 3.75 3.42 3.60 2.49 2.72 2.53 2.77

Electricity 26.10 25.40 25.23 25.93 18.39 18.94 19.13 20.16

1.2.2.4 Carbon Footprint 

When the slightly greater intensity of energy consumption in Appalachia is compounded by the coalintensity of the Region‘s electricity production and its lower-than-average use of natural gas, the 

Region‘s carbon footprint expands well beyond the national average. Energy use in Appalachia is 
Energy Efficiency in Appalachia, SEEA 

10

estimated to have contributed about 480 million metric tons of carbon dioxide emissions in 2006, 

based on all energy consumption across all sectors: residential, commercial, industrial, and 

transportation. These emissions are expected to grow to about 600 million metric tons by 2030.4 

This translates to about 20.2 metric tons of carbon dioxide per person in 2006 (or 5.5 metric tons of 

carbon), which is forecast to increase to 21 metric tons per person in 2030. In comparison, the U.S. 

carbon footprint was 19.6 metric tons of carbon dioxide in 2006, declining to an estimated 18.7 

metric tons in 2030. 

A recent study by Brown, Southworth and Sarzynski (2008) estimated the per capita carbon footprint 

of the nation‘s largest 100 metropolitan areas. Seventeen of these metro areas lie either entirely 

within the Appalachian Region or span the metro area‘s boundary. (Figure 1.8) The average carbon 

footprint of these seventeen metropolitan areas exceeds the national average by approximately 25 

percent. Thus, from a climate policy perspective, the Appalachian Region is more vulnerable to the 

costs associated with any national climate policy, compared with most areas of the country. 

 

4

 These numbers are from this study‘s population weighted aggregate Appalachian forecast based on the AEO 2008 (EIA, 

2008a).

Figure 1.8 Carbon Footprints of 17 Metropolitan Areas in (or Surrounding) 

the Appalachian Region, 2005*

(Brown, Southworth, and Sarzynski, 2008)

*Carbon footprint refers to the metric tons of carbon emissions per capita from the consumption of 

residential electricity, residential fuels, the energy consumed by light duty vehicles, and the fuels 

used by freight trucks.
Chapter 1: Introduction 

11

1.3 STRUCTURE OF REPORT 

This report is organized into eight chapters followed by references and numerous appendices. 

The chapters can be grouped into four sections: 

Methodology and Policy Analysis (Chapter 2): Provides a broad overview of the 

methodology used in the policy analysis and energy-efficiency resource assessments. This 

chapter also outlines the policy bundles modeled in the analysis and describes the alternative 

future scenarios that could shape their influence. In addition to the ―business-as-usual‖

forecast, these scenarios include the ―region-at-risk‖ and ―high-tech-investment-boost‖

scenarios. 

Energy-Efficiency Resource Assessments (Chapters 3-6): Estimates the total potential for 

cost-effective efficiency in each of the Region‘s major sectors: residential, commercial, 

industrial, and transportation sectors. These assessments begin with a description of energy 

consumption in the Region and the energy-efficiency levels assumed in the ―business-asusual‖ forecast. It then describes each of the policy bundles, the methodology used to analyze 

them, and the estimates of energy savings and costs. The chapters end by describing the 

estimated cost-effectiveness of each policy bundle, both individually and for the sector as a 

whole. 

Economy Wide Results (Chapter 7): Estimates the economy-wide engineering and economic 

results. In addition to presenting the economy-wide cost-effectiveness tests, this chapter 

characterizes the employment impacts and workforce requirements of each scenario. 

Summary and Conclusions (Chapter 8): The report ends with a discussion of its findings. 

This includes a comparison of the results with other assessments of cost-effective energy 

efficiency. It also discusses the package of policy bundles in terms of its political feasibility. 

These chapters are supplemented by detailed appendices that provide additional explanation, 

assumptions and analysis details. Appendix A summarizes the Region-wide inventory of energyefficiency policies. Appendices B through E provide additional information about the methodology 

used to analyze each sector. Appendix F provides further information on the baseline analysis and the 

use of the Dynamic Energy Efficiency Policy Evaluation Routine (DEEPER) model to integrate the 

sector-specific results into a macroeconomic evaluation of the policies as they might impact the 

Region. Finally Appendix G presents a sensitivity analysis of the cost-effectiveness estimates, 

including an assessment of higher fossil fuel prices that could arise in a carbon-constrained future. 
Energy Efficiency in Appalachia, SEEA 

12
Chapter 2: Methodology 

13

2 METHODOLOGY 

A variety of sources of data and models are used in energy-engineering analyses to forecast the 

energy savings potential, administrative, and implementation costs of each energy-efficiency policy 

bundle. These methodologies are summarized in each of the sector chapters and are described in 

greater detail in Appendices B through E. The results of these policy analyses are then input into the 

DEEPER model created by ACEEE to evaluate the macro-economic impacts of proposed policies. 

In addition, the project team created an Advisory Group and Stakeholder group to review and guide 

the research. 

For the purposes of this study, energy efficiency refers to the long-term reduction in energy 

consumption as a result of the increased deployment and improved performance of energy-saving 

equipment and practices. In the electricity sector, energy efficiency is also a low-cost contributor to 

system adequacy – the ability of the electric system to supply the aggregate energy demand at all 

times. When applied to transportation systems, energy efficiency is a major contributor to energy 

security. In addition, environmental benefits often come hand-in-hand with energy efficiency, along 

with productivity gains and job growth. At the same time, energy efficiency typically requires 

increased utility and government costs to transform markets. This chapter describes the methodology 

used to estimate how much energy-efficiency improvement could occur in Appalachia that would be 

cost-effective and feasible given the wide array of associated costs and benefits. 

2.1 THE BASELINE FORECAST 

The ―business-as-usual‖ baseline forecast of Appalachian energy consumption derived for this study 

is based on supplemental data from the National Energy Modeling System (NEMS) used to support 

the Annual Energy Outlook (EIA, 2007a; 2008a). The Appalachian Region baseline forecast is 

derived from population-weighted portions of the four census divisions comprising the 410-county 

Region. Regional populations within each census division were calculated using the Regional 

Economic Models, Inc. (REMI)5estimates of population by county for 2002, based on ARC subregion growth rates (North 0.28 percent, Central 0.39 percent, and South 1.13 percent). 

While the AEO-based method used here does not result in county-by-county populations that exactly 

match REMI‘s 2020 and 2030 forecast, they are generally within a close margin. For example, the 

total Appalachian Region‘s population is less than 0.2 percent higher in 2020 and less than two 

percent lower in 2030 in this study compared with the REMI forecast. Total census division 

populations are based on the AEO 2007 population forecast (EIA, 2007a). Over the study horizon, 

the Appalachian Region is increasingly weighted in three of the four census divisions. In contrast, the 

proportion of the Region‘s population residing in the South Atlantic census division (from Virginia 

through Georgia) shrinks over time; this is likely due to much higher growth rates in the nonAppalachian portions (especially coastal areas) of the South Atlantic division (Table 2.1). 

 

5

 REMI, 2007
Energy Efficiency in Appalachia, SEEA 

14

Table 2.1 Population Weighted Census Division Portions

(EIA, 2007a, REMI, 2007)

Year Total ARC 

Population

%

Middle 

Atlantic

East 

North 

Central

South 

Atlantic

East 

South 

Central

2006 23,862,608 16.79 3.18 14.29 41.56

2013 25,056,158 17.11 3.18 13.56 43.27

2020 26,331,378 17.26 3.19 13.14 44.80

2030 28,307,471 17.66 3.26 12.61 47.10

Using the same estimates of county population described above, portions of each state population 

were also developed (Table 2.2). These state population portions are used when modeling 

administrative costs for several of the policy packages. 

Table 2.2 Appalachian State Population Portion Estimates and Projections,

2006-2030 (WV = 100%)

YEAR

%

AL GA KY MD MS NY NC OH PA SC TN VA

2006 25.7 29.0 16.4 32.2 50.6 5.6 66.5 12.9 24.1 15.1 41.7 22.7

2013 25.9 28.2 16.7 30.6 50.4 5.7 61.5 13.0 25.7 15.3 42.4 22.5

2020 26.1 27.8 17.2 29.3 50.6 5.8 57.0 13.2 27.5 15.7 43.3 22.5

2030 26.3 27.7 18.1 27.9 51.3 6.0 51.3 13.7 30.8 16.4 44.5 22.8

Using REMI estimates, the Gross Appalachian Regional Product (GRP) would almost double 

between 2006 and 2030, growing to $1,320 billion (in 1996-$). The Region‘s annual growth rate of 

about 2.4 percent is significantly lower than the EIA forecast of a 2.9 percent annual GDP growth 

nationwide. Given the inertia that characterizes most economic systems, we can imagine that the 

distribution of distressed and prosperous counties in the Region would not change much over our 

planning horizon. 

This business-as-usual ―baseline‖ future paints the Appalachian Region over the next 25 years, much 

as it is today. In this scenario, the nation remains uncommitted to climate policy, coal continues to 

be an economically competitive energy resource, and oil persists as the dominant transportation fuel. 

As such, energy efficiency still is expected to carry the external benefits of reduced greenhouse gas 

emissions and improved energy security. Many energy-efficiency investments are more costeffective than many supply-side options, but numerous barriers including the policy environment 

often hinder energy-efficiency investments (Prindle, 2007; Brown and Chandler, 2008). 
Chapter 2: Methodology 

15

7.0

7.5

8.0

8.5

9.0

9.5

10.0

10.5

11.0

11.5

2006 2009 2012 2015 2018 2021 2024 2027 2030 Quadrillion Btu

AEO 2007 AEO 2008

Nevertheless, some amount of ―naturally occurring energy-efficiency improvement‖ is incorporated 

in the baseline forecast. The magnitude of this can be highlighted by comparing the Annual Energy 

Outlooks published in 2007 and 2008 

(EIA, 2007a; 2008a) (Figure 2.1). The 

AEO 2008 includes several strong 

efficiency policies promulgated in the 

Energy Independence and Security Act 

of 2007 (EISA, 2007), which were not 

reflected in the AEO 2007. In addition, 

the AEO 2008 uses higher energy prices 

and a slower GDP growth rate. Based 

on the AEO 2007, energy consumption in 

the Appalachian Region was forecast to 

grow to 11.2 quads by 2030. In contrast, 

the forecast based on the AEO 2008 is 11 

percent lower, projecting 10.1 quads of energy 

consumed in the Appalachian Region in 2030. The 

biggest difference is in the transportation 

sector where 40 percent stricter fuel 

economy standards for vehicles are 

required in 2020. 

2.2 DEFINITION OF ECONOMIC ENERGY-EFFICIENCY POTENTIAL 

When evaluating the potential for any energy alternative to be deployed in future years, four types of 

estimates are generally used (Rufo and Coito, 2002; NYSERDA, 2003; Eldridge, Elliott, Neubauer, 

2008). 

 Technical potential refers to the complete penetration of all energy-efficient applications that 

are technologically feasible, regardless of economic cost-effectiveness. 

 Economic potential is defined as that portion of the technical potential that is judged costeffective. 

 Maximum achievable potential is defined as the amount of cost-effective (economic) 

potential that is achievable over time under the most aggressive program scenario possible. It 

takes into account administrative and program costs as well as market barriers that prevent 

100 percent market penetration. 

 Program potential is the subset of maximum achievable potential that would occur in 

response to specific policies such as subsidies and information dissemination aimed at 

promoting the deployment of cost-effective energy efficiency. 

Energy Efficiency in Appalachia estimates the program potential for energy-efficiency improvements 

in each of the Region‘s four sectors. Our analysis of potential in each sector uses a common baseline 

forecast, identical energy price projections (Table 1.2), the same discount rates for calculating costeffectiveness, and the same economic tests of cost-effectiveness. 

Figure 2.1 ARC Energy Consumption Forecast (Quads)

(EIA, 2007a, 2008a)
Energy Efficiency in Appalachia, SEEA 

16

2.2.1 Cost-Effectiveness Tests 

Many cost-effectiveness tests have been used to estimate the economic payback to investments in 

energy efficiency. Four tests, in particular, are most common: the participants cost test, total resource 

cost test, rate impact measure, and societal test. Each of these tests answers a distinct set of questions 

(NAPEE, 2007a, p. 5-2). 

Participants Cost Test: 

Is it worth it to the customer to install energy efficiency? 

Is a customer likely to want to participate in a program that promotes energy efficiency? 

Total Resource Cost Test: 

What is the Regional benefit of the energy-efficiency project including the net costs and 

benefits to the utility and its customers? 

Is more or less money required by the Region to pay for energy needs? 

Ratepayer Impact Measure: 

What is the impact of the energy-efficiency program on the utility‘s operating margin? 

Would the project require an increase in rates to reach the same operating margin? 

Societal Test: 

What is the overall benefit to the community of the energy-efficiency program, including 

indirect benefits? 

Are all of the benefits (including indirect benefits) greater than all of the costs regardless of 

who pays the costs and who receives the benefits? 

We use the participants test and the total resource cost test to evaluate the cost-effectiveness of each 

of the modeled energy policies and each sector‘s bundle of policies. 

The participants test compares the costs and benefits experienced by participants in the energy policy 

or program. If the net present value of their benefits is greater than the net present value of their 

costs, then the benefit/cost ratio is greater than 1.0 and the policy is cost-effective. Typically these 

net present value calculations use a discount value of 10 percent to reflect the private-sector nature of 

the investments made. 

The total resource cost (TRC) test was developed originally to evaluate demand-side management 

(DSM) programs operated by utilities (OTA, 1993). It is a measure of the total net benefits of a 

program from the point of view of the utility and its ratepayers as a whole. A policy or program is 

cost-effective if it does not increase the total costs of meeting the customers‘ service needs. We use a 

seven percent discount rate to calculate these net present values, which is the rate recommended by 

the Office of Management and Budget‘s Circular No. A-94 (p. 8). 

The benefits and costs included in these two economic tests, along with their discount rates, are 

summarized in Table 2.3. 
Chapter 2: Methodology 

17

Table 2.3 Benefits and Costs Included in the Economic Tests

Energy-Efficiency Benefits Energy-Efficiency Costs

Participants Cost Test

Reduction in energy bill, plus 

incentives from utility and 

government programs (10

percent discount rate)

Participants‘ direct investment, 

plus incentives from utility and 

government programs (10

percent discount rate) 

Total Resource Cost Test

Avoided supply costs (based 

on retail energy prices) (seven 

percent discount rate)

Utility and government 

program costs (including 

administrative costs and 

incentives to participants) plus 

participants‘ direct investment 

(seven percent discount rate)

We are not able to use the ratepayer impact measure (RIM) test because we are unable to estimate the 

utility‘s change in revenues or costs as the result of the policies modeled here. We are also not able to 

use the societal test because of the wide range of co-benefits and costs produced by energy-efficiency 

policies. For example, no consensus exists today to place a value on avoiding the emission of a ton of 

carbon dioxide (Tol, 2005). Similarly, it is difficult to put a value on the time lost and activities 

foregone by drivers and passengers as a result of speed limit enforcement, or the lives saved. 

Typically, the RIM test is the most difficult to pass, while the societal and participants tests are the 

easiest. 

2.2.2 Life-Cycle of Energy Savings

The energy required to produce a unit of fuel or electricity for consumption by an ―end-user‖ can be 

large relative to the energy contained in the ―delivered‖ unit of fuel or electricity. Energy is required 

to mine coal and drill for petroleum; energy is used to create the compressed air that drives natural 

gas pipelines; fuels are used to propel the trains and barges that ship coal; and energy is lost in the 

transmission of electricity from the power plant to the consumer. Energy is also embodied in the 

power plants, trucks, trains, and other equipment that comprises the energy production and delivery 

supply chain. As a result, various ―adders‖ have been created to augment the energy contained in the 

delivered fuel or electricity to account for the full life cycle of energy consumed. As explained 

below, we use the electricity adder in this study, but we do not use adders for other fuels. 

In the case of electricity, 2.2 million Btu are lost in the electric generation, transmission and 

distribution steps that deliver 1 million Btu to the consumer in the form of delivered energy. That is, 

68.5 percent of the energy embodied in the fuel used to generate electricity in the United States in 

2006 is lost principally in the form of waste heat (EIA, 2008a, Table A2). These electricity-related 

losses do not include the energy required to mine the coal or the energy embodied in the various 

supply chain equipment. However, this adder of 2.2 is a typical factor used to more completely 

account for the energy saved when less energy is used by the consumer, and we use it in Energy 
Energy Efficiency in Appalachia, SEEA 

18

Efficiency in Appalachia. This adder is justifiable because most electricity-related losses occur in the 

Appalachian Region. 

The same is not true of the energy-related losses associated with the delivery of other fuels to 

consumers in Appalachia. EPA (2006, Table 14, p. 55) suggests a range of ―adders‖ that convert the 

greenhouse gas content of fuels into life-cycle measures, based on the energy used to refine and 

transport fuels. For passenger cars, the fuel cycle add-on for gasoline ranges from 0.24 to 0.31, which 

means that saving a million Btu of energy by consuming less gasoline in fuel-efficient cars, actually 

saves 1.24 to 1.31 million Btu when the energy lost in refining and transportation is included. The 

adder for truck diesel is slightly lower, ranging from 0.15 to 0.25. However, most of these life cycle 

energy losses occur outside of the Appalachian Region, since little petroleum refining occurs within 

the Region. As a result, we do not plus up the energy content of delivered petroleum fuels. 

Figure 2.2 estimates how much of each modeled fuel was consumed in each of the four sectors in 

2006 in Appalachia. Thus, it ignores the consumption of fuels by sectors that we do not address 

explicitly by our policy bundles, such as natural gas and electricity in transportation and coal and 

petrochemical feedstocks and coal used by industry. Altogether, we model 6.9 quads of the 7.9 

quads that comprised the Appalachian energy budget in 2006. Slightly more than half (3.6 quads) of 

the modeled energy is electricity (delivered + electric-related losses). 

0

500

1000

1500

2000

2500

3000

3500

4000 trillion Btu

Transportation 719.79 1430.04

Industrial 1084.98 378.82 96.48

Commercial 1198.85 220.10 43.31

Residential 1364.22 302.29 60.69

Electricity Natural Gas Fuel Oil Diesel Motor Gasoline

3648.05

901.21

200.47

719.79

1430.04

2.3 ENERGY-EFFICIENCY POLICY BUNDLES 

To assess the magnitude of cost-effective and achievable energy-efficiency improvements in 

Appalachia, we assume that a set of transformative energy policies are adopted in the Region 

beginning in the year 2010. The policy bundles include a combination of vigorous deployment 

policies that increase the achievable potential for energy efficiency, and expanded RD&D funding 

that accelerates the advancement of energy-efficient technologies. These policy bundles were defined 

and then modified iteratively as the result of discussions with the project‘s Advisory Committee. 

Figure 2.2 ARC 2006 Energy Consumption of Fuels Modeled in this Study
Chapter 2: Methodology 

19

To aid in the definition of these bundles of policies, a policy inventory was created for each state, 

detailing active and imminent (promulgated but not in effect) policies. These inventories were 

reviewed by state energy offices in the Region and revised accordingly to reflect the latest policy 

actions. The inventory of state policies is described further in Appendix A. 

Forecasting the economic payback to energy R&D has traditionally been challenging. As a result, we 

chose an illustrative case study approach. The potential impact of three specific R&D projects is 

illustrated in independent assessments to highlight the potential benefits of transformational 

technologies. Specifically, we examine the: 

 Air-source integrated heat pump 

 Solid state lighting 

 Industrial super boiler 

Table 2.4 summarizes the package of fifteen policies that are assumed to be implemented in each 

sector. In many cases, policies could be feasibly adopted and implemented at any level of 

government in this Region (national, regional, state, or local). The Energy-Efficiency Resource 

Assessments developed for each sector (Chapters 3-6) describe these policies in fuller details; 

however, they do not proscribe the governmental agencies that should administer them. 
Energy Efficiency in Appalachia, SEEA 

20

Table 2.4 Energy-Efficiency Policy Portfolio

Residential

Buildings

Commercial

Buildings Industry Transportation

Improved Building Energy 

Code with Third Party 

Verification and 

Compliance Incentive

Commercial Building 

Energy Codes with Third 

Party Verification and

Compliance Incentives

Expanded Industrial 

Assessment Centers 

Pay-as-You-Drive 

Insurance

Expanded Weatherization 

Assistance Programs

Support for 

Commissioning of Existing 

Commercial Buildings

Increasing Energy Savings 

Assessments Clean Car Standards 

Residential Retrofit 

Incentive with Resale 

Energy Labeling and 

Incremental Cost 

Incentives

Efficient Commercial 

HVAC and Lighting 

Retrofit Incentive

Supporting Combined 

Heat and Power (CHP) 

with Incentive 

SmartWay Heavy Truck 

Efficiency Loan Program

Super-Efficient Appliance 

Deployment

Tightened Office 

Equipment Standards with 

Efficient Use Incentives

Speed Limit Enforcement

Illustrative RD&D Initiative

Air-Source Integrated Heat 

Pump (IHP). Accelerated 

RD&D is assumed to result 

in the commercialization 

of a single system based on 

heat pumping technology 

that provides space heating 

and cooling, water heating, 

ventilation and 

dehumidification and 

humidification.

Solid State Lighting.

Accelerated RD&D is 

expected to produce 

technology improvements 

that bring brighter LEDs 

and provide light 

equivalent to existing 

fluorescent fixtures with 

25 to 45 percent less 

electricity usage.

Super Boiler. A 

combination of enhanced 

design features could 

increase industrial package 

boiler efficiency from 75

percent to 95 percent. 

Many boilers used today 

are more than 40 years old, 

suggesting a large energysavings opportunity.

2.4 ALTERNATIVE FUTURES 

In keeping with Peter Schwartz‘s The Art of the Long View (1991) and other advocates of scenario 

analysis, the project‘s analytic team undertook a systematic assessment of alternative possible futures 

for the Appalachian Region. Our goal was to consider the range of drivers and change agents that 

could cause energy efficiency in the Region to play a role quite distinct from simply imposing an 

aggressive energy-efficiency campaign on an otherwise ―business-as-usual‖ trajectory. The process 

involved identifying possible drivers of change, brainstorming a wide range of possible futures they 

could create, and then down-selecting to a small number of scenarios for further consideration. 

Because it seems likely that some form of national climate or carbon policy will be announced early 

during the study‘s 25-year time horizon, we assume that in any alternative future a price will be 

placed on greenhouse gas emissions. We also model the impact of this possible future policy, at least 

partially, by conducting a sensitivity analysis of the policy bundle‘s cost-effectiveness. Specifically, 

we consider a range of carbon prices (from $25 to $100 per metric ton of carbon dioxide) beginning 

in 2011. These carbon ―adders‖ result in higher retail prices for fossil-based fuels, as shown in Table 

2.5 with respect to today‘s retail energy prices. Using these higher prices, we calculate alternative net 
Chapter 2: Methodology 

21

present benefits from the energy saved by the policy bundles, resulting in a range of higher 

benefit/cost ratios. The results of this sensitivity analysis are summarized in Chapter 8 and detailed in 

Appendix G for each of the study‘s fifteen policies. 

Table 2.5 Range of Impacts of a Carbon Cap-and-Trade System on Fossil Energy Prices

(Source: Brown and Atamturk, 2008)

Carbon 

Tax/Penalty 

($/MtC)

Natural 

Gas

($/ccf)

Coal

($/short 

ton)

Distillate 

Fuel Oil 

($/gal)

Motor 

Gasoline

($/gal)

Electricity

($/MWh)

Average CCGT Coal

$25 $0.04

(0.49%)

$13.00

(52.20%)

$0.07 

(3.13%)

$0.06 

(2.77%)

$4.43 

(4.17%)

$2.50 

(2.36%)

$6.50 

(6.13%)

$50 $0.07

(0.98%)

$26.00

(104.39%)

$0.14 

(6.26%)

$0.12

(5.55%)

$8.85 

(8.35%)

$5.00 

(4.72%)

$13 

(12.26%)

$100 $0.15

(1.96%)

$53.00

(208.79%)

$0.28 

(12.52%)

$0.24

(11.09%)

$17.70 

(16.70%)

$10.00 

(9.43%)

$26 

(24.53%)

In addition, two scenarios emerged from our brainstorming session that appeared to bracket distinct 

futures for the Region: a ―region-at-risk‖ scenario and a ―high-tech investment boost‖ scenario. As 

with any scenario analysis, we do not expect that either of these alternatives will exactly come to 

pass. Rather, we assume that they characterize a range of plausible possibilities. 

2.4.1 Region-at-Risk Scenario

In the region-at-risk future, a national climate policy is assumed to be promulgated early in the time 

frame (perhaps in 2011), initiating a shift in the way energy is produced and used. However, in this 

scenario the shift takes place without the aid of fundamentally different technologies. For example, 

there is no great leap forward in cellulosic ethanol, clean coal, or hydrogen fuel cell vehicles. 

In this alternative reference scenario, the Region faces economic troubles due to the higher cost of 

operating coal plants and the subsequent reduced demand for coal across the country. The Region‘s 

annual GRP growth is significantly dampened in this scenario, especially in counties where coal 

mining dominates. Counties near metropolitan areas or with a more varied economic base may not be 

impacted as heavily. 

Overlaying on this scenario a set of vigorous deployment policies would result in public-private 

investments that are able to cushion these negative economic impacts and could help the Region 

adapt to a low-carbon future. With a premium on the price of fossil fuels, energy-efficient 

technologies are highly cost-effective; however, the difficult economic conditions dampen 

investments. 
Energy Efficiency in Appalachia, SEEA 

22

2.4.2 High-Tech Investment Boost Scenario 

In the high-tech investment boost scenario, a national climate policy is assumed to be promulgated 

early in the time frame, identical to the policy assumed in the Region-at-risk case. But in this case, by 

2015-2020 the country produces significant material, technology, and process advances in the 

performance and cost competitiveness of clean energy supply technologies, most notably clean coal. 

The ability to cost-effectively capture and sequester carbon allows the Region to maintain its 

economic base in industrial and coal sectors even in the face of public concern over climate. 

Technological breakthroughs also allow coal to be gasified and used to produce hydrogen for the 

growing demand for fuel cell technologies, and cellulosic ethanol becomes cost-competitive in the 

2015-2030 timeframe. This future offers a picture of optimism for the Appalachian Region as coal 

retains its value and receives a new use for producing vehicle fuels. The Region‘s annual GRP 

growth rate is expected to rise as a result. 

Without the advancement of energy-efficient technologies and vigorous deployment policies in 

combination with more cost-competitive low-carbon supply options, energy-efficiency investments 

may have more difficulty gaining market share. In contrast, overlaying on this more prosperous hightech boost, a set of vigorous deployment policies would result in public-private investments that can 

significantly decrease the Region‘s energy intensity. With capital made available from the successful 

launch of clean coal and other low-carbon fuels and motivated by effective energy-efficiency 

policies, consumers are able to trim their energy consumption and cut their energy bills. The 

successful investment climate can thus greatly enhance the role energy efficiency plays in the 

Region. 

2.5 DEEPER MODELING 

The ACEEE model – Dynamic Energy Efficiency Policy Evaluation Routine (DEEPER) – was used 

to assess the macroeconomic impacts of the policy scenarios. This includes estimates of the net 

employment and income effects as well as the impact on GRP. DEEPER is a dynamic input-output 

model that adapts the policy scenario results into a form that enables us to provide a richer 

assessment of economic impacts that would result from the policy suite. See Appendix F for a 

detailed description of the DEEPER model. 
Chapter 3: Residential Buildings 

23

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

2006 2009 2012 2015 2018 2021 2024 2027 2030 Quadrillion Btu

AEO 2007 AEO 2008

3 ENERGY EFFICIENCY IN RESIDENTIAL BUILDINGS 

3.1 INTRODUCTION TO RESIDENTIAL BUILDINGS IN APPALACHIA

The Appalachian residential sector consumed about 1.8 quads of energy in 2006 at a cost of about 

$14 billion (2006 dollars).6 Electricity and natural gas comprised the majority of delivered energy at 

49 percent and 35 percent, respectively, excluding electricity related losses; these drop to 24 percent 

and 17 percent, respectively, when losses are included (Figure 3.1) (EIA, 2008a). The primary end 

use for energy was space heating (36.8 percent), followed by water heating (13.3 percent), and 

miscellaneous electric load (8.2 percent) (EIA, 2008a). 

Liquid Fuels 

Subtotal

6%

Natural Gas

17%

Electricity

24%

Electricity 

Related 

Losses

51%

Renewable 

Energy

2%

From 2008 to 2030, residential energy 

consumption in the Appalachian Region 

is expected to increase between 30 

percent and 32 percent up to between 

2.47 and 2.58 quads, see Figure 3.2 

(EIA, 2007a; 2008a). The lower 

forecasted growth in residential 

energy consumption is the result of 

the AEO 2008 forecast, which 

projects higher energy prices, slower 

economic growth, and stronger lighting 

and appliance standards as a result of 

the Energy Independence and Security 

Act of 2007. Each of these factors subdues 

the growth in energy use, compared with the 

AEO 2007 forecast. 

 

6

 Costs include those for liquid propane gas, distillate fuel oil, natural gas, and electricity based on population-weighted 

average Appalachian prices. Other fuels, such as kerosene, coal, and renewable energy were also used by Appalachian 

households in 2006 but excluded from the cost given. 

Figure 3.2 Residential Energy Consumption Forecast 

for the Appalachian Region (Quads)

(EIA, 2007a; 2008a)

Figure 3.1 Residential Sector Energy Sources by Fuel, 2006

(EIA, 2008a)
Energy Efficiency in Appalachia, SEEA 

24

Figure 3.3 Map of Residential Footprints

(values can be found in Appendix B)

On a per capita basis, Appalachia, as a whole, had residential electric energy intensity of 58 million 

Btu and residential fuel intensity of 20 million Btu in 2005, compared to a national average of 50 

million Btu and 22 million Btu (EIA, 2008a). The higher intensity of residential electricity use in the 

Region is possibly a function of the Region‘s reliance on electricity for home heating and air 

conditioning and often high numbers of Heating Degree Days (HDD) and Cooling Degree Days 

(CDD) in the mixed climate. It may also reflect the Region‘s relatively inefficient building stock. 

The Appalachian Region does have lower residential fuel intensity than the national average, 

reflecting lower use of propane, natural gas, and fuel oil; 

likely due to relative accessibility of electricity to fuels, 

at least outside of the metropolitan areas. 

While the Appalachian Region is largely rural, 

seventeen metropolitan areas are at least partially within 

the Region. Also in 2005, Appalachian metropolitan 

areas averaged per capita residential electric intensity of 

63 million Btu and residential fuel intensity of 28 

million Btu, compared to the top 100 metropolitan 

average of 41 and 21 million Btu, respectively, see 

Figure 3.3 (Brown, Southworth, and Sarzynski, 2008). 

These data, while averages, show that the largest 100 

metropolitan areas are more energy efficient for 

residential fuels and residential electricity, per capita, 

than the nation as a whole, while Appalachian 

metropolitan areas are more energy intense than the 

Appalachian Region as a whole. Comparing Appalachia 

to the rest of the nation offers anecdotal evidence to the 

potential for energy efficiency. 

3.2 POLICY OPTIONS FOR RESIDENTIAL ENERGY EFFICIENCY 

This study models four policy packages to encourage energy efficiency in residential buildings: 

Model Building Energy Codes, Expansion of the Weatherization Assistance Program, Existing Home 

Retrofits, and Super-Efficient Appliance Deployment. There are several other kinds of policies that 

could be used to encourage more efficient use of energy in residential buildings. Table 3.1 lists 

examples of policy actions, including those modeled; these policies could be used as substitutions to 

the modeled packages, or as complementary actions. While this study sought to model policies that 

have been shown to reduce energy consumption in the past by overcoming barriers to efficiency in 

residential buildings, policy makers in Appalachia may seek to target different barriers or segments 

of the residential market. For example, the policy of expanding Weatherization Assistance targets 

the barriers of high first costs and lack of access to capital for the low-income market segment; the 

other policies do not specifically target this segment of the population (Brown et al., 2008). Many 

current polices, see Appendix A, are focused on reaching different segments of the market 

(consumers of all income groups, construction contractors, manufacturers) to overcome the barrier of 

lack of trusted information; policy makers may choose to continue to target this barrier with their 

limited resources rather than attempting to target the barrier of ―high costs‖ (Brown et al., 2008) That 

being said, the actual form of policies adopted within the Appalachian Region will depend on the 

specific goals and capacity of each policy making body. 
Chapter 3: Residential Buildings 

25

Table 3.1 Policy Actions that Support Residential Energy Efficiency

Actions Residential Building 

Codes Weatherization Retrofits

Super-Efficient 

Appliance 

Deployment

Research, 

Development, and 

Demonstration

Support for research 

and development in 

advanced building 

processes and materials

Development of new 

insulation, heating, 

and cooling 

technologies useful 

for the local climate

Development of new 

insulation, heating, 

and cooling 

technologies useful 

for the local climate

Support for research 

and development for 

innovation in 

appliance performance

Financing

Low or no-interest 

loans for incremental 

cost of improvements 

for new construction

Support for EnergyEfficiency Mortgages 

(EEMs)

N/A

Low or No-Interest 

Loans for 

Incremental Cost of 

Improvements for 

Existing Buildings

Low or No-Interest 

Loans for ENERGY 

STAR® Appliances

Financial 

Incentives

Incremental cost 

rebates to builders for 

homes that meet or 

exceed building energy 

code

Permit fee or Property 

tax reductions for 

efficient homes

Grants or publicly 

funded provision of 

retrofits

Incremental cost 

rebates or grants for 

retrofits

Tax credits for 

efficient purchases

Incremental cost 

rebates or grants for 

efficient appliances

Tax credits for 

efficient purchases

Appliance Buyback 

Programs

Pricing N/A N/A N/A N/A

Voluntary 

Agreements

Agreement between 

major builders in the 

area to meet or exceed 

code

N/A N/A N/A

Regulations

Model Building Energy 

Code legislation

Allowing third party 

compliance inspection

Energy-efficiency 

rating and labeling

N/A

Allowing third party 

compliance 

inspection 

Resale energy rating 

and labeling

Broad appliance 

standards with tighter 

requirements

Standby Efficiency 

Standards

Information 

Dissemination & 

Training

Training architects, 

builders, contractors, 

and code enforcement 

officials

Training contractors, 

weatherization 

officials, and 

community providers

Public Awareness

campaigns to inform 

consumers of the 

benefits of 

conservation and 

efficiency measures

Training architects 

and contractors

Public Awareness 

campaigns to inform 

consumers of the 

benefits of 

conservation and 

efficiency measures

Advanced metering 

(interior, real-time, 

with price signal)

Public Awareness 

campaigns to inform 

consumers of the 

benefits of 

conservation and 

efficiency measures

Advanced metering 

(interior, real-time, 

with price signal)

Procurement N/A N/A N/A

Government efficient 

appliance lead by 

example programs
Energy Efficiency in Appalachia, SEEA 

26

Table 3.1 Policy Actions that Support Residential Energy Efficiency

Actions Residential Building 

Codes Weatherization Retrofits

Super-Efficient 

Appliance 

Deployment

Market Reforms N/A N/A

Enable On-bill 

Financing for 

Retrofits

N/A

Planning 

Techniques

Evaluation and 

monitoring for 

feedback

N/A N/A N/A

Capacity Building

Centers for energy 

efficiency to train next 

generation of 

architects, builders, 

retrofitters

Centers for energy 

efficiency to train 

next generation of 

architects, builders, 

retrofitters

Centers for energy 

efficiency to train 

next generation of 

architects, builders, 

retrofitters

N/A

This table describes policy actions available that could further the savings from the policy packages modeled in this study. The policy 

actions shown in italics are modeled in this study, while the others are not.

3.2.1 Research, Development, and Demonstration (RD&D) 

Developing advanced building processes and technologies can help to improve the performance of 

new buildings (and some retrofit buildings). Research in this area drives the capability to meet 

greater efficiency levels over time. In addition, RD&D programs help to offset commercialization 

barriers, especially those of uncertain performance and costs, by pushing innovations out of the 

laboratory (Brown et al., 2008). Having a research program, especially when combined with 

commercialization and deployment efforts, allows a state or locality to keep talented researchers and 

money for new technology from leaving the area. Research and development programs can work 

hand in hand with capacity building and technology pull measures. Both South Carolina‘s ―SC 

Launch!‖ and Kentucky‘s ―Energy Research and Development Grants for Renewable Energy and 

Energy Efficiency‖ are examples of state efforts to encourage innovation. 

3.2.2 Financing 

Financing policies can help to reduce the ―first cost‖ burden, making efficient investments more 

affordable. Loans available for incremental costs to builders and homeowners allow them to invest 

in more efficient equipment and materials; buyers (who are passed the cost through builders) and 

homeowners then benefit from lower energy consumption and greater comfort levels. Many utilities 

offer loans for efficiency improvements; for example, Bristol Tennessee Essential Services (BTES) 

offers loans up to $10,000 for qualified homeowners through their ―Energy Savers Loan Program‖

(BTES, 2008). Because incremental costs for more efficient homes and retrofit materials are not 

very high, and the turnaround is fairly short, revolving loan funds can be utilized. Supporting 

Energy-Efficiency Mortgages, which offer lower rates for qualified efficient homes, by streamlining 

verification of the residence‘s performance and connecting consumers with suitable lenders, can ease 

the first cost to buyers of efficient new homes without significant public cost. 
Chapter 3: Residential Buildings 

27

3.2.3 Financial Incentives 

Financial incentive policies can provide carrots to builders and new home buyers, also addressing the 

barrier of high costs. For example, PG&E (California) operates a residential new construction 

program that provides an incentive of $400 or $500 to builders per ENERGY STAR home; it also 

provides incentives for outfitting compliant homes with energy-efficient appliances (PG&E, 2008). 

Vine (1996) presented compliance levels from California, Oregon, and Washington and found that 

utility residential new construction programs achieved near 100 percent compliance from builders 

while residences built outside of the program were found to fall short of the code-prescribed level of 

efficiency by six percent or more.7

Within the Appalachian Region, the New York State Energy Research and Development Authority 

(NYSERDA) offers incentives for energy-efficient multifamily buildings through their ―EnergySmart 

Multifamily Performance Program‖ and ―Green Affordable Housing Options.‖ Using another 

approach, the city of Asheville, NC, offers rebates on building permit fees for certain sustainable 

building practices. By offering incentives to builders, such policies increase the supply of efficient 

buildings. 

Virginia adopted legislation in 2007 allowing local governments the ability to create a new 

classification and incentivize tax rates for buildings that are 30 percent more efficient than the 

Virginia Uniform Building Code; this type of policy, when implemented by towns and cities that can 

afford such incentives, can increase demand for efficient homes by offering buyers lower taxes. 

Wisconsin offers ―cash-back‖ to builders or homebuyers for meeting Wisconsin‘s ENERGY STAR 

heating, cooling, and lighting performance objectives (WFOE, 2008). 

3.2.4 Voluntary Agreements 

Voluntary agreements have been used by the U.S. DOE to motivate private builders to construct 

more energy-efficient homes. DOE‘s Building America program is an example of voluntary 

innovation by many leading builders, and it offers a competitive advantage to participating builders.8

 

Programs like these can reduce uncertainties and prevent any one builder from facing all the costs of 

innovating while knowing that imitators will also be able to reap the rewards. 

3.2.5 Regulations 

Regulating building practices, enabling innovative financing and verification mechanisms, and 

requiring the provision of information can lead to more efficient homes. Model energy codes set new 

minimum levels of efficiency; and by periodically reviewing and updating code requirements, current 

building practices can keep up with advances in construction materials and practices. 

Some regulations set the foundation for other policies to work; for example, regulating contractors 

enables third party verification of savings and labeling while regulating lenders or utility actions 

enables on-bill financing or energy-efficient mortgages.9 With a third-party contracting program, 

 

7

 Utility residential new construction programs offer incentives to builders to meet or exceed model energy codes. 

8

http://www.buildingamerica.gov 

9 On-bill financing refers to programs, run through utility or municipal energy retailers, that allow consumers to acquire a 

favorable loan for energy-efficient retrofits or upgrades that are paid back through their energy savings; this is 

conceptually similar to the services offered by energy services companies (ESCOs) through ―performance contracting.‖ 
Energy Efficiency in Appalachia, SEEA 

28

builders or retrofitters would be required to contract with a state or locally certified third party to 

verify compliance; any expenses associated with inspection and verification would be undertaken by 

the builder or contractor rather than the jurisdiction. California, New York, and Washington already 

use this type of program for building energy codes, with high compliance rates (EPA, 2006; Smith 

and McCullough, 2001; Vine, 1996). However, allowing for third party contracting takes time; for 

example, the state of Washington spent three years (with utility funding) setting up training and 

certification programs to move their non-residential code to a system allowing for third party 

inspection (Kunkle, 1997). 

3.2.6 Information Dissemination, Training, and Capacity Building 

Training and information as well as capacity building programs can support the goals of improved 

energy performance by ensuring that a knowledgeable workforce is prepared to produce efficient 

homes. Information dissemination programs include TV and radio outreach, flyers, conferences, 

websites, school visits, and other media. Training programs include certification for particular 

trades, seminars to keep government officials current, and testing for professional licenses. Capacity 

building refers to developing schools, centers, and technology specific parks; in general, they are 

designed specifically to build the capacity of an area or a people to achieve a goal – in this case, 

energy efficiency. There are several examples of training, information, and capacity building 

programs already at work in the Appalachian Region (see Appendix A). To illustrate, Kentucky 

offers financial support to public universities and colleges participating in energy efficiency and 

renewable energy research. Also, West Virginia‘s Building Professional‘s Energy Training Program 

offers seminars on current building and code topics. 

3.3 MODELED SAVINGS IN APPALACHIAN RESIDENTIAL BUILDINGS 

The following sections describe each of the modeled policies in more 

detail and estimate their projected savings. At the end of the chapter, 

aggregated results for the sector are reported along with a discussion of the 

findings. Appendix B provides greater detail on the modeling 

methodology. 

3.3.1 Residential Building Codes with Third Party Verification 

Residential building energy codes define engineering and construction 

requirements to meet particular efficiency targets for new residential 

buildings.10

 Building energy codes impact consumption based on 

structural changes – as such, they primarily impact heating and cooling 

loads. Appendix B.1 presents the methodology for estimating savings for 

residential energy codes. 

Ten of the 13 states in the Appalachian Region require new construction of residential buildings to 

meet recent building codes. Five of these 10 states enforce the most recent 2006 IECC code (Table 

 

10 These codes affect residential structures with fewer than three stories; residential structures with more than three stories 

are considered commercial buildings. Since the number of floors is not included in building data, all residential units in 

Appalachia are assumed to be under three stories. This could be a slight distortion if there are a great number of high rise 

apartment complexes in the region, but the generally rural and suburban region is not expected to have many high rise 

units. 
Chapter 3: Residential Buildings 

29

3.2). However, compliance rates are unknown and efforts to improve new buildings beyond code are 

uneven. Nine Appalachian states have less than three percent penetration rates for ENERGY STAR 

Qualified New Homes, while the top performer, Nevada, had a 71 percent penetration rate 

(ENERGY STAR, 2008).11 The higher penetration rates are found in states where state and local 

governments and homebuilders have publicly committed to ENERGY STAR goals. For example, in 

Nevada, 23 of 54 home builders who are ENERGY STAR partners have committed to build all their 

homes to ENERGY STAR specifications.12

Table 3.2 Status of Appalachian State Residential Building Energy 

Codes and Percent of ENERGY STAR Qualified New Homes

State Residential 

Energy Code Mandatory?

ENERGY STAR 

Qualified

%

Alabama 2000 IECC No <3

Georgia 2006 IECC Yes <3

Kentucky 2006 IRC Yes 3-11

Maryland 2006 IRC Yes 3-11

Mississippi PRIOR 92 MEC No <3

New York 2004 IECC Yes 13

N. Carolina 2003 IECC Yes <3

Ohio 2006 IECC Yes 13

Pennsylvania 2006 IECC Yes <3

S. Carolina 2003 IECC Yes <3

Tennessee 92 MEC No <3

Virginia 2003 IECC Yes <3

W. Virginia 2003 IRC Yes <3

Establishing mandatory residential energy codes in Alabama, Mississippi, and Tennessee, keeping all 

of the Appalachian states up-to-date with codes, and driving greater code compliance could have a 

significant impact on consumption across the Region over time. Building energy codes are most 

successful when suppliers and consumers of new residences are motivated to improve the energy 

performance of the new home, and when their compliance can be verified. Ensuring greater 

compliance will require third-party verification of measures; accordingly, staff engineers or 

inspectors would need to be hired and trained to verify installation of proper measures. Greater 

education of consumers could also encourage market demand for compliance from builders. 

This study assumes that all Appalachian counties adopt, or are otherwise subject to, the 2006 IECC 

by 2009 and subsequently more efficient codes every three years thereafter; codes are assumed to 

become effective the year following adoption. To illustrate, the 419,000 single and multi-family 

homes projected to be built from 2013 to 2015 in Appalachia are assumed to be built to the 2009 

IECC code and therefore use 18 percent less energy for space heating, space cooling, and water 

 

11 ENERGY STAR penetration rates reflect ENERGY STAR‘s calculation of the portion of new site built single family 

homes in a state that meet ENERGY STAR requirements. 

12 Nevada‘s ENERGY STAR factsheet describes how Nevada is achieving high penetration: 

http://www.naseo.org/taskforces/energystar/factsheets/Nevada07.pdf 
Energy Efficiency in Appalachia, SEEA 

30

heating than they would have if built to 2005 current practice. Homes built from 2016 to 2019 are 

assumed to use 30 percent less energy (Appendix B, p. 3). Third-party verification of measures is 

also assumed, and the administrative personnel are assumed to serve in training and liaison roles. 

While the actual verification of compliance is completed by a third-party hired by the builder, the 

codes officials train and approve these verification firms; officials also provide random verification 

spot checks as well as ongoing training and support to verifiers and construction firms. 

These codes lead to substantial energy savings as shown in Table 3.3. By 2030, four percent of the 

forecast residential energy consumption in Appalachia is offset by this one policy. These savings are 

similar to those modeled recently by the Eldridge et al. (2008) assessment of energy-efficiency 

potential in Maryland. Advanced building codes generated electricity savings of two percent of 

Maryland‘s projected consumption in 2025. 

Table 3.3 Energy Savings from Residential Building Codes

Year Electricity 

Savings

Natural Gas 

Savings

Fuel Oil 

Savings

Total 

Primary 

Energy 

Saved

% of 

Sector 

Primary 

Energya

(GWh) (trillion Btu) (trillion Btu) (trillion Btu)

2010 25.23 0.15 0.03 0.54 0.03

2013 201.19 1.14 0.19 4.22 0.22

2020 1,586.97 8.01 1.21 32.42 1.50

2030 4,888.73 22.46 3.15 98.66 4.00

a Based on the EIA, 2008a forecast.

The costs reported in Table 3.4 include an incremental investment cost for more efficient building of 

$1,000 real 2006 dollars per new home constructed. The administrative costs reflect new personnel; 

two training staff per state (costs apportioned) and one verification liaison per 10,000 constructed 

homes per year who works with third-party verification firms and construction firms to ensure that 

compliance is achieved. The annual energy savings increase from $10 million in 2010 to $1.6 billion 

in 2030. 
Chapter 3: Residential Buildings 

31

Table 3.4 Costs and Savings from Residential 

Building Codes

Year

Energy 

Savings

Admin 

Costs

Investment 

Costs

(million 

2006$)

(million 

2006$)

(million 

2006$)

2010 10.30 10.21 102.00

2013 73.70 11.18 113.03

2020 530.71 10.42 102.21

2030 1,607.90 9.83 91.48

Figure 3.4 shows how investment and energy savings vary over the study period. Public investment 

is the administrative costs of the program while private investment is the incremental costs of 

improvement. If there were some form of public incentive for meeting or exceeding the codes, 

public investment would be higher while private investment would be lower. 

0

20

40

60

80

100

120

2010 2015 2020 2025 2030 Trillion BTU

0

20

40

60

80

100

120

140 Million 2006$

Energy Savings Public Investment

Private Investment

The Residential Building Energy Code with Third-Party Verification is cost-effective with a benefitto-cost ratio of about 3.4 for participants and about 3.7 for total resource costs. With $220.5 million 

in program spending and an additional $2.2 billion in customer investments over the 2010-2030 

period, the Appalachian region could see net cumulative savings of 802.5 trillion Btu, saving $13.1 

billion in energy bills by 2030. This is the equivalent of about 4.0 percent of the EIA‘s forecast 

consumption of residential energy in the Appalachian Region in 2030 or 18.9 percent of forecast 

growth (EIA, 2008a). 

Figure 3.4 Annual Investment and Energy Savings 

from Residential Building Codes, 2010-2030
Energy Efficiency in Appalachia, SEEA 

32

Box 3.1. Manufactured Housing Codes

The Appalachian Region has a growing proportion of homes that are manufactured off-site. This 

type of single family home has a higher stock turnover rate than stick built homes, but a lower 

average energy efficiency. Requirements for manufactured housing efficiency have not changed 

since 1994, but EISA 2007 requires DOE to establish new standards based on the 2009 IECC by 

2011; see Table 3.5 for insulation requirements for the current code (EISA, 2007, Sec. 413, 24 CFR 

3280).

Table 3.5 Insulation Requirements for Manufactured Homes (24 CFR 3280)

Zone # Single-Wide Double-Wide

1 2 3 1 2 3

Ceiling R-14 R-19 R-19 R-14 R-22 R-22

Walls R-11 R-13 R-19 R-11 R-13 R-19

Floor R-11 R-19 R-19 R-14 R-19 R-22

ASHRAE (2005) found savings of 24-29 percent in heating and cooling energy needs for 

manufactured homes meeting the ENERGY STAR requirements compared to those meeting current 

codes; further, they found that these ENERGY STAR homes would just barely meet the requirements 

of 2006 IECC.

ENERGY STAR qualified manufactured homes must be designed, produced, and installed in 

accordance with EPA guidelines by an ENERGY STAR certified manufactured housing plant. 

Manufactured housing plants can be certified by a third party Quality Assurance Provider who is 

certified by the EPA to perform plant inspections. After meeting several requirements for design and 

installation, at least three homes of the same design must be proven in the field before the plant can 

apply for ENERGY STAR certification. Many manufactured housing plants in Appalachian states 

are already ENERGY STAR partners; however, only a few have produced a significant number of 

ENERGY STAR labeled manufactured homes (EPA, 2008). Because a large portion of national 

manufactured homes are produced in Appalachian states by a few companies, it may be easier to 

influence building practices in this area than it is with stick-built homes.

If all new mobile homes in the Appalachian region were built to ENERGY STAR requirements, 

saving 25 percent in heating and cooling end-uses, the region could have cumulative annual savings 

of 4 trillion Btu (site) by 2030 or about 6.8 percent of all mobile home consumption (site). A more 

aggressive mobile home standard that increases from 25 percent in 2010 to 50 percent by 2030 could 

save more than six trillion Btu (site) or about 10.3 percent of all mobile home energy consumption 

(site).

3.3.2 Expanded Low-Income Weatherization Assistance 

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); eligibility is determined by income 
Chapter 3: Residential Buildings 

33

at or below 150 percent of the poverty level by DOE, but states can set their own criteria.13 Across 

the Appalachian Region, a greater percentage of persons live in poverty than the national average (in 

2000, the national poverty rate was 12.4 percent). 

Table 3.6 Poverty Rates for Appalachian States, 2005

(Census, 2008)

United States

Individuals Families

13.3 10.2

Alabama 17.0 13.7

Georgia 14.4 11.6

Kentucky 16.8 13.4

Maryland 8.2 6.0

Mississippi 21.3 16.8

New York 13.8 11.1

North Carolina 15.1 11.7

Ohio 13.0 9.9

Pennsylvania 11.9 8.6

South Carolina 15.6 12.5

Tennessee 15.5 12.5

Virginia 10.0 7.4

West Virginia 18.0 14.0

States can provide for additional weatherization above that provided through Department of Energy 

funding; sources for these funds include utilities, community organizations, and public benefits 

charges. 

This study assumes that one percent of single family and manufactured homes are weatherized each 

year through this expanded program, reaching 15 percent of Appalachian homes by 2030 (beyond the 

homes reached by the existing Weatherization Assistance Program). Energy savings estimates for 

this program are shown in Table 3.7. 

 

13 Current year documentation can be found at http://www.waptac.org/sp.asp?id=6878 
Energy Efficiency in Appalachia, SEEA 

34

Table 3.7 Energy Savings from Expanded Weatherization

Year

Electricity 

Savings

Natural Gas 

Savings

Fuel Oil 

Savings

Total 

Primary 

Energy 

Saved

% of 

Sector 

Primary 

Energy (GWh) (trillion Btu) (trillion Btu) (trillion Btu)

2010 113.95 0.54 0.11 2.27 0.12

2013 461.47 2.19 0.43 9.24 0.47

2020 1,299.61 6.19 1.19 26.23 1.22

2030 2,612.21 12.13 2.21 53.00 2.15

Table 3.8 investment costs are based on an investment of $2,300 per home and administrative cost 

are set at a level of 10 percent of the investment (the Federally defined limit for such costs). Energy 

savings reflect the consumer‘s bill savings based on their reduced consumption and forecast energy 

prices (EIA, 2008a). 

Table 3.8 Costs and Savings from Expanded Weatherization

Year

Energy Savings Admin Costs Investment Costs

(million 2006$) (million 2006$) (million 2006$)

2010 20.41 17.18 171.77

2013 78.35 17.80 177.97

2020 217.81 19.22 192.22

2030 455.48 21.01 210.11

Figure 3.5 shows how investments in weatherization and energy savings change over the study 

period. There is no private investment assumed in this model for expanded weatherization. The 

public investment includes the administrative costs and the cost of improvements; if the low-income 

weatherization program were designed as a cost-share program; public investments would be lower 

while private investment would be higher. 
Chapter 3: Residential Buildings 

35

0

10

20

30

40

50

60

2010 2015 2020 2025 2030 Trillion BTU

0

50

100

150

200

250

Million 2006$

Energy Savings Public Investment

Weatherization of low-income homes 

has been touted as an effective 

program for more than 20 years. As 

mentioned above, not only does 

weatherization reduce energy bills for 

low-income consumers, it also 

improves comfort, health, and safety 

for the families served – the existing 

program favors homes with children 

and the elderly. In addition, if energy 

bills are lower, consumers will be 

more likely to be able to pay their 

bills, and less likely to request heating 

bill assistance funds, like LIHEAP, or 

shirk on payment, leading to chargeoffs. These non-energy benefits can be 

considerable; Schweitzer and Tonn (2002) 

determined that the non-energy benefits of 

weatherization were about $3,809 over the lifetime of the retrofits, and most of these benefits accrue 

to society as a whole.14

 More detail on benefits and costs are in the summary of this chapter. 

The Expanded Low-Income Weatherization Assistance Program is cost-effective over the lifetime of 

the measures with a benefit-to-cost ratio of about 2.1 for participants and about 1.3 for total resource 

costs. With $4 billion in program spending over the 2010-2030 period, the Appalachian Region 

could see net cumulative savings of 1.1 quads, saving $11.4 billion in energy bills by 2050. This is 

the equivalent of about 2.1 percent of the EIA‘s forecast consumption in 2030 or 10.2 percent of 

forecast growth (EIA, 2008a). 

3.3.3 Residential Retrofit Incentive with Resale Energy Labeling and On-Bill Financing

Policies to encourage existing home retrofits can reduce the financial barriers faced by homeowners 

in that they tend to have high discount rates and lack adequate access to capital (Brown and 

Chandler, 2008). In addition, retrofit of existing homes innovates within the current stock of homes 

rather than waiting for new, more efficient homes to be built. This innovation alleviates efficiency 

problems with slow housing turnover (survival rates of 98-99.7 percent). Homeowners are already 

turning to retrofits for comfort and safety; remodeling expenditures doubled for owner-occupied 

homes over the 1995-2005 decade with extra insulation as a popular project (AIA, 2006a, b). 

However, the interval for a major home renovation, including the envelope/shell is 30-50 years. This 

long interval suggests that each renovation not bringing a home to meet current standards is a lost 

opportunity (Jakob, 2006). Banfi et al. (2008) found that consumers (both renters and owners) report 

a willingness to pay for energy-efficient measures, such as windows, insulation, and ventilation 

technologies, that is higher than the cost of the same measure‘s installation; they suggest that this 

may represent either an overstatement of willingness to pay or an indication that the market has not 

fully developed. 

 

14 Schweitzer and Tonn (2002) report savings in 2001 dollars; to remain consistent with the currency of this report, the 

$3,346 in reported 2001 savings was converted to 2006 dollars. 

Figure 3.5 Annual Investment and Energy Savings 

from Expanded Weatherization, 2010-2030
Energy Efficiency in Appalachia, SEEA 

36

The present 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. 

While the incentive only lasts for 10 years – to 2020 – the program continues to provide support for 

the disclosure and financing mechanisms as well as public awareness campaigns until 2030. 

Home energy disclosure would provide information to home buyers (for new and resale residences) 

on the energy efficiency of the home. In Kansas, home energy disclosures have been required on 

new residential construction since 2001, but legislation in 2007 extended the requirement to include 

resale homes (Kansas, 2007a; KEC, 2008). While policy makers might expect opposition from 

industry, realtors, and architects, for such a requirement, recent history shows that this is not a 

concern. The Kansas experience shows strong support from these groups (Aron, 2007; Bell, 2007; 

Neu Smith, 2007). Also, resale energy labeling could encourage home buyers to recognize the energy 

costs of the homes they are considering and may drive greater investment in retrofits before or after 

homes are sold. 

On-bill financing refers to programs, run through utility or municipal energy retailers that allow 

consumers to acquire a favorable loan for energy-efficient retrofits or upgrades that are paid back 

through their energy savings. This is conceptually similar to the services offered by energy services 

companies as ―performance contracting.‖ On-bill financing reduces the first cost to the consumers 

(and allows for pass-through of costs to the next owner). This could make the costs less daunting and 

allow for homeowners to pass the costs on if they have to sell their home before these costs are paid 

for by the energy savings (Brown, Southworth, and Sarzynski, 2008). Kansas has also passed 

legislation to allow utilities to enter into contracts with customers, or landlords of customers, to 

finance energy-efficiency improvements; the amount must be approved by the Kansas Corporation 

Commission and would be repaid through energy savings (Kansas, 2007b). Some utilities in the 

Appalachian Region already offer on-bill payment of financing for energy efficiency. An example is 

Cherokee Electric Cooperative in Alabama which offers the Energy Conservation Home 

Improvement Loan Program with low-interest loans for up to 10 years and on-bill payment 

(Cherokee, 2008). 

Regardless of the form of the program, retrofits of homes would offer greater savings per home than 

weatherization and are not limited to low-income homeowners. Programs of this nature could 

encourage investment in rental properties and larger homes. For this policy, we assume that two 

percent of all existing single family homes are retrofit each year from 2010 to 2030. The costs per 

home are more than under the weatherization program – $3,400 per home. 

Resale energy labeling would require an additional field in the Multiple Listing Service for home 

energy consumption. It is not envisioned to require a Home Energy Rating System or audit; both of 

these have been shown to be costly and therefore engender opposition from real estate professionals. 

Instead, utilities (or other energy providers) would be required to provide the information on average 

consumption. This is information that the utilities (or other energy providers) would already have and 

does not require an audit as utilities generally have incentive to have correct consumption 

information for billing purposes. Administrative costs on the part of the government are expected to 

be minimal. Energy consumption information should be provided on an average annual or average 

monthly basis for the whole residence and per square foot for each fuel used in the home provided by 

an energy company (electricity, natural gas, fuel oil, etc). 

Labeling programs provide potential buyers with information on the energy integrity of homes. They 

do not mandate efficiency improvements, but may encourage improvements when energy costs are 
Chapter 3: Residential Buildings 

37

high. These can easily be compared to the provision of the U.S. Environmental Protection Agency 

(EPA) mileage estimates provided for new vehicle sales; consumers may choose more efficient 

vehicles based on this information. 

The energy savings reported in Table 3.9 amounts to a 7.3 percent reduction of residential energy 

consumption. It translates to a simple payback of about six years, following an incremental 

investment of $3,400 per retrofit home. This payback period is considerably longer than some 

studies claim for retrofits (see Appendix B.3); however, lower energy prices in Appalachia and a 

greater reliance on electricity for heat can explain some of this difference. 

Table 3.9 Energy Savings from Existing Home Retrofits

Year

Electricity 

Savings

Natural Gas 

Savings

Fuel Oil 

Savings

Total 

Primary 

Energy 

Saved

% of 

Sector 

Primary 

Energy (GWh) (trillion Btu) (trillion Btu) (trillion Btu)

2010 382 2.23 0.43 8.11 0.42

2013 1,535 9.04 1.72 32.76 1.67

2020 4,222 25.52 4.68 91.47 4.24

2030 8,241 49.75 8.58 180.20 7.30

The costs reported in Table 3.10 are based on the incremental cost of $3,400 for efficient retrofits, 

and a small administrative staff to support oversight of labeling and incentive distribution. The 

energy bill savings are based on forecast energy prices and modeled energy savings by census 

division (EIA, 2008a). 

 

Table 3.10 Costs and Savings from Existing Home Retrofits

Year

Energy Savings Admin Costs Investment 

Costs

(million 2006$) (million 2006$) (million 2006$)

2010 75 0.99 466

2013 288 0.99 483

2020 790 0.98 523

2030 1,631 0.33 572
Energy Efficiency in Appalachia, SEEA 

38

0

20

40

60

80

100

120

140

160

180

200

2010 2015 2020 2025 2030 Trillion BTU

0

100

200

300

400

500

600

700 Million 2006$

Energy Savings Public Investment

Private Investment

Figure 3.6 shows how investments and 

energy savings change over the study 

horizon. The modeled program assumes 

that private investment continues, and 

picks up the difference when the 

incentive for efficient retrofits is 

removed. As such, this program is 

modeled as a market transformation. 

Over the first 10 years, when there is an 

incentive, consumers become familiar 

with the energy labeling, and on-bill 

financing mechanisms become 

commonplace. By 2020, consumers and 

the contractors performing retrofits are 

interested in ensuring greater energy 

efficiency with their retrofits and are willing 

to cover the entire incremental costs of these 

improvements. 

In total, the Efficient Residential Retrofit Incentive with Enabling Home Labeling and On-Bill 

Financing is cost-effective with a benefit-to-cost ratio of about 1.5 for participants (all retrofit home 

occupants) and about 1.7 for total resource costs; more detail on benefits and costs are in the 

summary of this chapter. With $1.1 billion in program spending and an additional $9.86 billion in 

private investments over the 2010-2030 period, the Appalachian Region could see net cumulative 

savings of 3.8 quads, saving $33.5 billion in energy bills by 2050. This is the equivalent of 7.3 

percent of the EIA‘s forecast consumption in 2030 or 34.6 percent of forecast growth (EIA, 2008a). 

3.3.4 Super-Efficient Appliance Deployment 

About one-quarter of residential energy consumption goes to support lighting and appliances. While 

new incandescent bulb efficiency requirements within EISA 2007 are expected to reduce the lighting 

load, significant reductions in other appliances and electronics are not currently forecast. While 

energy-efficient dishwashers have nearly achieved sales saturation, less than one-third of clothes 

washers and refrigerators sold in 2006 met ENERGY STAR requirements (ENERGY STAR, 2007). 

Figure 3.6 Annual Investment and Energy Savings 

from Existing Home Retrofits, 2010-2030
Chapter 3: Residential Buildings 

39

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

AL GA KY MD MS NC NY OH PA SC TN VA WV U.S.

Average

Air-Conditioners Clothes Washers

Dish Washers Refrigerators

Policies to encourage greater adoption of energy-efficient appliances and electronics come in many 

forms. States offer sales tax holidays, tax credits, and rebates for energy-efficient appliances. 

Georgia started an ENERGY STAR sales tax holiday in 2005, with other states, like North Carolina, 

Virginia, and West Virginia following; in the same spirit, New York is considering eliminating sales 

tax on ENERGY STAR labeled appliances and light bulbs (Hayes, 2008). In Japan, the most efficient 

equipment and appliances set the new target consumption rates (as opposed to a minimum standard 

set before development like our ENERGY STAR program); this ―Top Runner‖ program has 

exceeded the savings expectations of the Energy Conservation Center, Japan (ECCJ, 2008). 

Regional support to remove poor performing appliances from the market could significantly reduce 

energy consumption in newly purchased products while a companion replacement effort could 

accelerate stock turnover for outdated appliances and equipment. For example, New York Power 

Authority has a refrigerator replacement program in place for public housing residents.15

This study assumes that super-efficient appliances are available at technology development rates of 

three percent more efficient than forecast every five years, so the most efficient appliances in 2025 

are nine percent more efficient than stock efficiency in the Annual Energy Outlook 2008 baseline 

forecast (EIA, 2008a). The model considers eleven residential end-uses (see Appendix B.4). 

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. These 

incentives are expected to drive 50 percent of appliance replacements and new purchases to superefficient appliances available for that end-use from 2010 to 2020; after 2020, only 40 percent of new 

purchases are of super-efficient appliances. While this policy represents an aggressive demand pull 

mechanism, higher demand could drive the technology improvements faster and lead to lower 

incremental costs in the long run. It is envisioned that the incentive would target manufacturers, 

 

15

 For information about New York Power Authority‘s refrigerator program see 

http://www.nypa.gov/services/esprograms2.htm 

Figure 3.7 Percent ENERGY STAR Sales by Appliance by State, 2006

(ENERGY STAR, 2007)
Energy Efficiency in Appalachia, SEEA 

40

distributers, and contractors; and that it would be coupled with public information campaigns for 

consumers. By 2020, 0.7 percent of the residential sector‘s energy demand would be saved as a result 

of this policy, increasing to 1.7 percent in 2030. 

The energy savings expected from this program are shown in Table 3.11. 

Table 3.11 Energy Savings from Super-Efficient Appliance Deployment

Year

Electricity 

Savings

Natural Gas 

Savings

Fuel Oil 

Savings

Total 

Primary 

Energy 

Saved

% of 

Sector 

Primary 

Energy (GWh) (trillion Btu) (trillion Btu) (trillion Btu)

2010 51 0.02 0.00 0.75 0.04

2013 207 0.06 0.00 3.03 0.15

2020 957 0.30 0.00 14.25 0.66

2030 2,736 0.82 0.00 41.65 1.69

Table 3.12 shows the costs and energy bill savings from the Super-Efficient Appliance Deployment. 

The energy bill savings are based on modeled energy savings and forecast energy prices by fuel and 

census division (EIA, 2008a). Administrative costs include only the program staff costs while 

investment costs include the whole incremental cost of more energy-efficiency appliances. 

Table 3.12 Costs and Savings from Super-Efficient

Appliance Deployment

Year

Energy Savings Admin Costs Investment 

Costs

(million 2006$) (million 2006$) (million 2006$)

2010 5.68 0.02 5.57

2013 22.12 0.02 5.67

2020 101.62 0.08 17.97

2030 294.31 0.11 26.31
Chapter 3: Residential Buildings 

41

Figure 3.8 shows how investments and energy savings change over time. It is clear that the five year 

cycle represents a significant cost increase as the incremental cost of the super-efficient appliances 

are assumed to rise with greater efficiency required. Public investment remains quite low, although it 

does reflect an incentive of 40 percent of the incremental cost until 2015 and 20 percent until 2020 

on top of the administrative costs. Private investment is lower until 2020 due to the incentive. 

Because the policy is a designed to pull demand towards efficient products, the policy transforms the 

market to a more efficient equilibrium. 

0

10

20

30

40

50

60

70

80

90

100

2010 2015 2020 2025 2030 Trillion BTU

0

5

10

15

20

25

30 Million 2006$

Energy Savings Public Investment

Private Investment

The Super-Efficient Appliance Deployment Program is cost-effective with a benefit-to-cost ratio of 

about 6.7 for participants (everyone who uses appliances replaces something during this period 

because all modeled appliances have lifetimes of less than 20 years) and about 7.0 for total resource 

costs. With $27.8 million in program spending and an additional $271 million in customer 

investments over the 2010-2030 period, the Appalachian region could see net cumulative savings of 

345 trillion Btu, saving $2.4 billion in energy bills by 2030. This is the equivalent of about 1.7 

percent of the EIA‘s forecast consumption in 2030 or 8.0 percent of forecast growth (EIA, 2008a). 

Figure 3.8 Annual Investment and Energy Savings 

from Super-Efficient Appliance Deployment, 2010-2030
Energy Efficiency in Appalachia, SEEA 

42

Box 3.2 Research and Development Example: 

Air-Source Integrated Heat Pumps

A major manufacturer has partnered with Oak Ridge National Laboratory (ORNL) on the 

development of air-source integrated heat pumps (AS-IHP). Integrated heat pumps provide space 

heating and cooling, water heating, ventilation, and dehumidification into a single system. 

Efficiencies are gained over traditional HVAC systems by making use of otherwise wasted energy 

(e.g., heat rejected by the space cooling operation can be used for water heating). Heat pumps are a 

central part of the DOE‘s efforts to develop net-zero energy housing, or homes that produce as much 

energy as they consume. This technology could provide nearly 60 percent savings in both cold and 

mixed humid climates (even greater in hot or temperate climates), relative to a baseline system. 

Incremental capital costs for this advanced HVAC system are not expected to be prohibitive, ranging 

from $2,500 to $3,200 (2006 dollars) greater than a baseline system, with payback times averaging 

around eight years in cold and mixed climates (Baxter, 2006). The incremental costs are kept low 

because of shared components of the HVAC system and the ability to use otherwise wasted energy. 

Figure 3.9 Schematic of an Air-Source Integrated Heat Pump

(Baxter, 2006)

The residential sector is responsible for approximately one third of all energy consumed in the U.S., 

and this holds true for the Appalachian Region as well. A new HVAC technology such as the ASIHP that is 60 percent more efficient than traditional technologies could dramatically reduce 

residential energy demand (Baxter, 2006). Space heating and cooling, and water heating combined 

account for about 70 percent of all residential energy consumption.
Chapter 3: Residential Buildings 

43

0

5

10

15

20

25

30

35

40

2010 2015 2020 2025 2030 Trillion Btu

 Figure 3.10 Cumulative Energy Savings from Integrated Heat

 Pump in Appalachian Region (Trillion Btu)

The AS-IHP is still in the development stages and is not yet commercially available. Within an 

average Appalachian home, installation of the AS-IHP would save approximately 37 million Btu per 

year. This estimate is based on the energy use of the AS-IHP in Appalachian climates as compared to 

the average amount of energy used in an Appalachian home on space heating and cooling, and water 

heating. As with any new technology it is difficult to estimate what the market potential will be, but a 

study from the IEA Heat Pump Programme (2006) suggests that heat pumps could reach 30 percent 

of the market. Figure 3.10 shows the estimated the amount of energy that could be saved from 

installing the AS-IHP in 30 percent of all new homes built between 2010 and 2030, assuming 

household energy consumption remains consistent. Roughly 1.6 trillion Btu would be saved annually 

under this scenario. Because the integrated heat pump is not easily adaptable to retrofits, they were 

not included in these estimates. 

The air source integrated heat pump could provide substantial energy savings to households in the 

Appalachian Region. However, high upfront costs may stall its adoption by the market. Policies that 

support or require installment of energy-efficient technologies in all new housing, as well as 

incentivize retrofitting energy-efficient technologies in existing houses, would help push integrated 

heat pumps into the market more quickly. 

3.4 SUMMARY OF RESULTS 

The residential sector can quickly deliver cost-effective energy savings to consumers (Brown, et al., 

2001; McKinsey, 2007). This chapter provides further evidence suggesting that Appalachian Region 

investments in residential programs can generate benefits to the Appalachian Region‘s residents that 

more than exceed their public and private investment costs. Based on the residential program and 

policy bundles described in this chapter, building energy codes and efficient retrofits have the largest 

potential for energy savings (Figure 3.11). Together they account for approximately three-fourths of 

the 374 trillion Btu of residential savings that are projected to occur in the year 2030. 
Energy Efficiency in Appalachia, SEEA 

44

Figure 3.12 Residential Primary Energy Consumption 

With and Without Policy Packages (Quads), 2006-2030

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

3

3.2

2006 2009 2012 2015 2018 2021 2024 2027 2030 Quadrillion Btu

AEO 2007 AEO 2008 With Policy Packages

Residential 

Building Codes

98.66

26%

Expanded Weatherization

53.00

14%

Existing Home 

Retrofits

180.20

49%

Super-Efficient 

Appliance 

Deployment

41.65

11%

Figure 3.12 shows that implementation 

of these policy bundles could 

significantly curb the growth in 

residential energy consumption forecast 

for the Appalachian Region to 2030, with 

nearly flat consumption from 2028 to 

2030. These four policies generate 

savings of 15.1 percent of AEO 2008 

forecast consumption in 2030 (EIA, 

2008a). 

These estimated savings are generally 

lower, but not dramatically different, 

than other studies for states in the 

Region. Efficiency potential studies 

completed for Georgia Power and the 

Georgia Environmental Facilities 

Authority found maximum achievable 

electric efficiencies of nine percent over 10 years and 9.4 percent over five years, respectively (ICF, 

2005; Nexant, 2007). A study for North Carolina found a maximum achievable potential for 

residential electric efficiency of 16.9 percent over a 10 year horizon (GDS Associates, 2006). An 

efficiency potential study for Kentucky modeled minimally and moderately aggressive scenarios with 

residential savings of 2.7 percent and 8.2 percent, respectively, over 10 years (KPPC, 2007). More 

recently, a report by ACEEE et al. (2008) modeled residential savings for Virginia at 26 percent of 

their forecast electricity consumption in 2025. 

Figure 3.11 Residential Energy Savings by Policy Bundle, 2030 (trillion Btu)
Chapter 3: Residential Buildings 

45

Figure 3.13 Comparison of Appalachian Residential Delivered Energy 

Consumption Forecast Under Four Cases (Quads), 2006-2030

(EIA, 2008a)

0.00

0.20

0.40

0.60

0.80

1.00

1.20

2006 2009 2012 2015 2018 2021 2024 2027 2030 Quadrillion Btu (delivered)

AEO 2008 Reference High Technology

Frozen Building Technology Model Savings

0

50

100

150

200

250

300

350

400

2010 2015 2020 2025 2030 Trillion BTU

0

100

200

300

400

500

600

700

800 Million 2006$

Energy Savings Public Investment

Participant Investment

In addition to comparing our modeled 

savings case with the AEO 2008‘s 

Reference case, we can also compare 

the savings forecast with two of the 

AEO 2008 alternative cases: one 

representing the ―Frozen Building 

Technology‖ option and another 

reflecting the ―High Technology‖

alternative (Figure 3.13). It is clear 

from this figure that our estimated 

savings from the reference case are much 

greater when compared to the ―Frozen 

Building Technology‖ case and still 

noticeably more energy-efficient than the 

―High Technology‖ case. 

When considering this package of 

residential policies from the 

participants‘ perspective, the simple payback declines from about 4.2 years to about two years over 

the study horizon. The shortened payback period is largely driven by the gains in new residential 

building efficiency without a corresponding increase in incremental costs; it also follows an 

increasing cost of energy (a Btu saved in 20 years is worth more than a Btu saved today). It is 

assumed that materials, technology, and practice will improve over time to reduce the incremental 

cost of efficient buildings. The Existing Home Retrofit Program and Super-Efficient Appliance 

Deployment Program maintain paybacks of around six years and one year, respectively, throughout 

the study period. Participants do not make an investment in the expanded weatherization program as 

modeled, so the payback period is not considered. 

Figure 3.14 shows how public and 

private investments and energy savings 

change over the study horizon. The 

energy savings continue to accumulate 

at a persistent pace across the 20-year 

planning horizon. In contrast, the 

public investment drops in 2020 when 

the subsidies for retrofitting existing 

housing stock and incentives for superefficient appliances are sunset, while 

the participants costs increase in a 

compensatory manner. 

The economic feasibility of policy 

packages is a function of how the 

policy costs and benefits are 

distributed over time and across 

customer classes and residential subgroups. For the residential energy-efficiency policies modeled, 

the super-efficient appliance deployment, and building codes are the most cost-effective see Table 

3.13. However, consideration of non-energy benefits often drive adoption of policies such as 

Figure 3.14 Annual Investment and Energy Savings from 

Combined Residential Policy Packages, 2010-2030
Energy Efficiency in Appalachia, SEEA 

46

expanded weatherization, which has the lowest total resource cost test across the four policies, but 

offers substantial health, comfort, and safety benefits to low-income households. Similarly, 

consumers often adopt more efficient retrofit measures to remove drafts or reduce noise in addition 

their energy benefits. Measures are only counted for benefits over their useful lives; in this analysis, 

we have considered retrofit and weatherization savings to accrue for 20 years. For building codes 

and efficient appliances, we make the conservative assumption that the market would have caught up 

to our program by 2030, so we do not consider benefits after this time. 

Table 3.13 Results of Economic Tests for Residential Policies

Residential 

Building 

Codes

Expanded 

Weatherization

Existing 

Home 

Retrofits

Super-Efficient 

Appliance 

Deployment

Total

Participants Test

NPV Benefits 

(billion 2006$) 3.17 3.37 6.72 0.64 13.90

NPV Costs 

(billion 2006$) 0.92 1.60 4.36 0.09 6.98

Net BenefitsCosts (billion 

2006$)

2.25 1.77 2.36 0.54 6.92

B/C Ratio 3.44 2.10 1.54 6.75 1.99

Total Resource Cost Test

NPV Benefits 

(billion 2006$) 4.69 2.80 9.36 0.90 17.75

NPV Costs 

(billion 2006$) 1.26 2.23 5.52 0.13 9.14

Net BenefitsCosts (billion 

2006$)

3.43 0.57 3.84 0.78 8.61

B/C Ratio 3.72 1.26 1.70 7.05 1.94

The combined residential policy package is cost-effective with a benefit-to-cost ratio of about 2.0 for 

participants and about 1.9 for total resource costs. With $5.1 billion in program spending and an 

additional $12.3 billion in customer investments over the 2010-2030 period, the Appalachian Region 

could cost-effectively reduce its energy consumption by 6.0 quads, saving $60.4 billion in energy 

bills. The savings in 2030 represent the equivalent of 18.7 percent of the EIA‘s forecast consumption 

or 71.7 percent of forecast growth (EIA, 2008a). 
Chapter 4: Commercial Buildings 

47

Electricity

25%

Electricity 

Related 

Losses

55%

Natural Gas

15%

Liquid Fuels 

Subtotal

4%

Coal

0%

Renewable 

Energy

1%

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

2006 2009 2012 2015 2018 2021 2024 2027 2030 Quadrillion Btu

AEO 2007 AEO 2008

4 ENERGY EFFICIENCY IN COMMERCIAL BUILDINGS

4.1 INTRODUCTION TO COMMERCIAL BUILDINGS IN APPALACHIA 

The Appalachian commercial sector 

consumed about 1.47 quads of energy in 2006, 

while total expenditures were more than $10 

billion (2006 dollars).16

 Electricity (55 

percent) and natural gas (32 percent) were the 

dominant forms of delivered energy, 

excluding electricity related losses (EIA, 

2008a). When losses are included, the 

contribution from electricity and natural gas 

drops to 25 and 15 percent, respectively. 

From 2008 to 2030, commercial energy 

consumption in the Appalachian Region is 

expected to increase between 45 percent and 

63 percent up to between 2.26 and 2.55 quads, 

Figure 4.2 (EIA, 2007a; 2008a). According to 

these forecasts, energy consumption is growing 

more rapidly in commercial buildings than in any other sector. 

Commercial buildings offer significant 

energy-efficiency potential – mostly through 

upgrades to existing building stocks. Due to 

the longevity of commercial buildings and an 

economy that is ever more service based, 

new commercial buildings represent even 

less of the total stock of commercial 

buildings than new homes represent of the 

residential stock each year. See Table 4.1 for 

median commercial building lifetimes. 

 

16 Costs include those for distillate fuel oil, residual fuel oil, natural gas, and electricity based on population-weighted 

average Appalachian prices. Other fuels, such as liquid propane gas, kerosene, coal, and renewable energy were also used 

by Appalachian commercial buildings in 2006 but excluded from the cost given. 

Figure 4.2 Commercial Energy Consumption Forecast 

for the Appalachian Region (Quads), 2006-2030

(EIA, 2007a; 2008a)

Figure 4.1 Commercial Sector Energy 

Sources by Fuel, 2006

(EIA, 2008a)
Energy Efficiency in Appalachia, SEEA 

48

Table 4.1 Median Lifetimes for Selected Building Types

(EIA, 2008b, Table 12)

Median Expected 

Lifetime (years) Gammaa

Assembly 80 1.8

Education 80 2.6

Food Sales 65 2.5

Food Service 65 2.5

Health Care 65 2.3

Lodging 69 2

Large Office 73 2

Small Office 73 2

Mercantile/Service 65 1.8

Warehouse 80 1.6

Other 75 2.5

a

 ̳Gamma‘ is the ―rate at which buildings retire near their median 

expected lifetime‖ (EIA, 2008b).

Types of commercial buildings and end uses in the Appalachian Region differ slightly from national 

averages. The most significant differences in floorspace are in three building types: the Region has 

more floorspace in ―Assembly‖ and less in ―Food Sales‖ and ―Warehouse.‖

17

 The Appalachian 

Region does not differ significantly from national averages in commercial end-uses. 

4.2 POLICY OPTIONS FOR COMMERCIAL ENERGY EFFICIENCY 

This study models four policies, coupled with incentives, to encourage energy efficiency in 

commercial buildings: Commercial Building Energy Codes with Third Party Verification, Support 

for Commissioning of Existing Commercial Buildings, Efficient Commercial HVAC and Lighting 

Retrofit Incentive, and Tightened Office Equipment Standards with Efficient Use Incentive. There 

are several other kinds of policies that could be used to encourage more efficient use of energy in 

commercial buildings. Table 4.2 lists examples of policy actions, including those modeled; listed 

policies could be used as substitutions or complementary actions. The actual form of policies 

adopted within the Appalachian Region will depend on the specific goals and capacity of each policy 

making body. 

 

17 Religious worship buildings are included in assembly. 
Chapter 4: Commercial Buildings 

49

Table 4.2 Policy Actions that Support Commercial Energy Efficiency

Actions Commercial 

Building Codes

Existing Building 

Commissioning

HVAC and Lighting 

Retrofits

Office Equipment 

Standards

Research, 

Development, 

and 

Demonstration

Support for research 

and development in 

advanced building 

processes and 

materials

Development of new 

insulation, heating, and 

cooling technologies 

useful for the local 

climate

Development of new 

lighting, heating, 

cooling, and ventilation 

technologies useful for 

the local conditions

Support for research 

and development for 

innovation in 

appliance performance

Financing

Low or no-interest 

loans for incremental 

cost of improvements 

for new construction

Enable performance 

contracting

N/A

Low or no-interest 

loans for incremental 

cost of improvements 

for existing buildings

Enable performance 

contracting

Low or no-interest 

loans for ENERGY 

STAR equipment

Enable performance 

contracting

Financial 

Incentives

Incentives to builders 

for exceeding codes

Rebates or lower fees 

for builders for 

LEED or ENERGY 

STAR ratings

Incentives for cost of 

commissioning study 

and necessary repairs 

or replacements

Incremental cost 

incentives for efficient 

retrofits

Tax credits for efficient 

purchases

Incentives to use 

efficiency features and 

lower consumption

Tax credits for 

efficient purchases

Equipment buyback 

programs

Pricing N/A N/A N/A N/A

Voluntary 

Agreements

Agreement between 

major builders in the 

area to meet or 

exceed code

N/A N/A N/A

Regulations

Model Building 

Energy Code 

Legislation

Allowing third-party 

compliance 

inspection

N/A

Tighter lighting and 

HVAC equipment 

standards

Tighter office 

equipment standards

Standby efficiency 

standards

Information 

Dissemination & 

Training

Training Architects, 

Builders, 

Contractors, and 

Code Enforcement 

Officials

Public Awareness 

campaigns to inform 

consumers of the 

benefits of 

conservation and 

efficiency measures

Training Architects, 

Builders, Contractors, 

and Building Managers

Public Awareness 

campaigns to inform 

consumers of the 

benefits of conservation 

and efficiency measures

Training Architects, 

Builders, Contractors, 

and Building Managers

Awareness campaigns 

to inform executives of 

the benefits of 

efficiency measures

Advanced metering or 

billing methods 

Awareness campaigns 

to inform executives of 

the benefits of 

conservation and 

efficiency measures

Advanced metering 

and billing

Procurement

Government lead by 

example procurement 

programs

Government lead by 

example procurement 

programs

Government lead by 

example procurement 

programs

Government lead by 

example procurement 

programs

Market Reforms N/A N/A N/A N/A
Energy Efficiency in Appalachia, SEEA 

50

Table 4.2 Policy Actions that Support Commercial Energy Efficiency

Actions Commercial 

Building Codes

Existing Building 

Commissioning

HVAC and Lighting 

Retrofits

Office Equipment 

Standards

Planning 

Techniques

Low or No-Interest 

Loans for 

Incremental Cost of 

Improvements for 

New Construction

N/A N/A N/A

Capacity 

Building

Centers for energy 

efficiency to train 

next generation of 

architects, builders, 

retrofitters

Centers for energy 

efficiency to train next 

generation of architects, 

builders, retrofitters

Centers for energy 

efficiency to train next 

generation of architects, 

builders, retrofitters

N/A

This table describes policy actions available that could further the savings from the policy packages modeled in this study. The policy 

actions shown in italics are modeled in this study, while the others are not.

4.2.1 Research, Development, and Demonstration 

Developing advanced building processes and technologies can help to improve the performance of 

commercial buildings. Building materials and construction and renovation processes are especially 

important in commercial buildings where ownership changes hands – leading to renovations – many 

times in the course of the building‘s lifetime. Supporting advanced building materials and process 

research can help a state or locality to keep talented researchers and money for new technology from 

leaving the area. Research and development programs can work hand-in-hand with capacity building 

measures. Demonstration projects can be integrally linked to state procurement programs to help 

pull new technologies out of the research pipeline. Some states and localities create lead-by-example 

programs specifically requiring that government buildings must meet stricter standards or achieve 

year over year savings; these types of procurement programs help generate a demand pull that can 

keep innovations coming to the marketplace. 

4.2.2 Financing 

Financing policies can help to reduce the ―first cost‖ burden, making efficient investments more 

affordable. Loans available for incremental costs to builders allow them to invest in more efficient 

equipment and materials; commercial building owners/buyers (who are passed the cost through 

builders) and commercial building lessees then benefit from lower energy consumption and greater 

comfort levels. Many commercial buildings are plagued with the economic reality of the principle 

(owner) not being responsive to the agent‘s (lessees) needs; if owners do not pay the energy bills, 

they have less of an incentive to invest in equipment with a higher cost. 

Mississippi‘s Energy Investment Loan program is broad-based and geared towards helping to drive 

innovation and offers loans from $15,000 to $300,000 at three percent below prime for capital 

improvements or design and development of innovative energy conservation practices. North 

Carolina also has a broad-based loan program based on a service contract structure; the Energy 

Improvement Loan Program (EILP) provides loans, secured with a letter of credit for nongovernment borrowers, with interest rates of one percent or three percent, depending on the project, 

for renewable energy, recycled energy, or energy savings. A more targeted loan program, the New 
Chapter 4: Commercial Buildings 

51

York Energy Smart Loan Fund, provides for reduced interest rates compared to the lender‘s normal 

rate for certain renewable or energy efficiency improvements.18

State agency performance contracting is a popular financing mechanism. To illustrate, in New York, 

the New York Power Authority offers performance contracting for state-owned buildings and 

schools.19 Some states do not allow agencies to enter into contracts that cover more than the current 

fiscal year, which can prevent cost effective energy-efficient improvements. While performance 

contracting may present fiscal complications, it can save energy and taxpayer money in the long run. 

4.2.3 Financial Incentives 

Tax credits, rebates, reduced fees, and grants can all be used as financial incentives to encourage 

efficient technologies and practices for commercial buildings. Incentives help to reduce the burden 

of high costs and may help drive capital towards energy-efficiency improvements. There are many 

examples of financial incentives already in place throughout Appalachia and the rest of the nation. 

For example, Maryland‘s Green Building Tax Credit program encourages more efficient new large 

(more than 20,000 square feet) commercial buildings in targeted areas by offering a credit up to eight 

percent of the cost of building construction. In New York, there is both a prequalified cost-reduction 

program and a tax credit available for certain retrofits and new commercial buildings.20

Financial incentives are also used for transfers within state government: for example, Virginia‘s 

Technical Assistance Grant Program offers grants for technical assistance to reduce energy 

consumption in support of Executive Order 48, which calls for reducing energy consumption in state 

government facilities. Similarly, West Virginia‘s Lighting Grants Program provides 50/50 matching 

grants to state and local government facilities and schools; nonprofit hospitals, and public libraries 

for lighting improvements having a payback less than five years based on an EPA ENERGY STAR 

lighting audit. 

4.2.4 Voluntary Agreements 

Voluntary agreements, where builders agree to work towards greater efficiency, can have significant 

savings. For example, the Model Conservation Code in the northwest was gradually adopted by 

states as codes after builders innovated through voluntary standards (EPA, 2006). The Building 

America and Rebuild America programs are good examples of national voluntary innovation 

programs, with many leading builders, states, and national laboratories partnered together to improve 

building materials, technologies, and process. 

4.2.5 Regulations 

Regulating minimum efficiency levels through promulgation of standards and codes can reduce 

proliferation of poorly performing equipment, buildings, and practices. Model energy codes, subject 

to review and update, set a new minimum level of efficiency; this ensures that the average new 

 

18 The interest rate reduction for New York‘s Energy Smart Loan Fund for most of the state is up to 4.0 percent (400 basis 

points). Con Edison customers may be eligible to receive an interest rate reduction up to 6.5 percent (650 basis points) less 

than a Participating Lender's or Lessor's normal market rate. See http://www.nyserda.org/loanfund.default.asp 

19 Details about New York Power Authority‘s performance contracting program can be found at: 

http://www.nypa.gov/services/esp.htm 

20 See http://www.nyserda.org/programs/Existing_Facilities/default.html and http://www.dec.ny.gov/regs/4475.html 
Energy Efficiency in Appalachia, SEEA 

52

commercial building doesn‘t fall too far behind the leading new commercial buildings as materials, 

practices, and habits change. Regulation and standards-setting can be done at all levels of 

government; however, local and state standards and regulations are subject to pre-emption by the 

federal government. 

Some regulations set the foundation for other processes to work; for example, regulating contractors 

enables third party verification of savings and labeling while regulating lenders or utility actions 

enables performance contracting or other favorable financing options.21 With a third-party 

verification program, builders or retrofitters would be required to contract with a state or locally 

certified third party to verify compliance; any expenses associated with inspection and verification 

would be undertaken by the builder or contractor rather than the jurisdiction. California, New York, 

and Washington already use this type of program for building energy codes, with high compliance 

rates (EPA, 2006; Smith and McCullough 2001; Vine 1996). However, allowing for third party 

contracting takes time; for example, from adoption to effectiveness, the state of Washington spent 

three years (with utility funding) setting up training and certification programs to move their nonresidential code to a system allowing for third party inspection (Kunkle, 1997). 

4.2.6 Information Dissemination & Training and Capacity Building 

Lack of sufficient, trusted information is almost always a barrier to energy efficiency; there is simply 

not enough time for people and organizations to learn about energy savings measures and figure out 

how to incorporate the measures in a beneficial way (Brown et al., 2008). Training and information 

as well as capacity building programs can support the goals of improved energy performance by 

ensuring that there is a knowledgeable workforce in place to produce efficient homes. Most states 

offer programs to provide information to consumers through websites, printed brochures or flyers, 

and seminars or workshops. An example of capacity building is West Virginia‘s Building Energy 

Use Centers which provide the state with educational centers and the technical expertise to support 

programs like Saving Energy in West Virginia Schools.

4.3 MODELED SAVINGS IN APPALACHIAN COMMERCIAL BUILDINGS

4.3.1 Commercial Building Energy Codes with Third Party Verification 

Building codes lay out requirements for new building construction. For commercial buildings, model 

building energy codes, like the International Energy Conservation Code (IECC), are developed by 

either the ASHRAE or the International Code Council (ICC). One of the requirements for 

modifications to the code is that they be cost effective. 

A problem that arises with modeling building energy code savings is dealing with under- or overcompliance. In some cases, the newer code recommendations are such an improvement that there is 

compliance with the newer code before its adoption (over-compliance); therefore, the assumption 

that existing buildings do not already meet the newer code (built into an efficiency model) would 

lead to an over-estimation of savings. In other cases, promulgated codes are not enforced, and 

under-compliance with the existing codes could lead to an under-estimation of savings. 

 

21 See section 4.2.1 for examples of financing mechanisms 
Chapter 4: Commercial Buildings 

53

As shown in Table 4.3, three of the 13 Appalachian states do not have mandatory commercial 

building energy codes: Alabama, Mississippi, and Tennessee. Four of the ten states with mandatory 

codes have dated vintages from 2003. 

Table 4.3 State Commercial Building Energy Codes

State Commercial Energy Code Mandatory

Alabama1 ASHRAE/IESNA 90.1-2001 No

Georgia ASHRAE/IESNA 90.1-2004 Yes

Kentucky 2006 IECC and 2006 IBC Yes

Maryland 2006 IECC Yes

Mississippi2 ASHRAE 90-1975 No

New York 2006 IECC Yes

N. Carolina3

2003 IECC (ASHRAE 90.1-2004) Yes

Ohio 2006 IECC (ASHRAE 90.1-2004) Yes

Pennsylvania 2006 IECC (ASHRAE 90.1-2004) Yes

S. Carolina 2003 IECC Yes

Tennessee4 ASHRAE 90A-1980 and 90B-1975 No

Virginia 2006 IECC Yes

W. Virginia 2003 IECC No

1 Alabama‘s code is state specific – the Alabama Building Energy Conservation Code is mandatory for state 

government buildings and recommended for other commercial buildings (ADECA, 2005). There are also 

local adoptions of 2003 and 2006 IECC.

2 Mississippi‘s commercial code is mandatory for state government buildings, public buildings, and high-rises 

(BCAP, 2008).

3 North Carolina‘s code is state specific, but it is based on the 2003 IECC with reference to the ASHRAE 

90.1-2004.

4

Tennessee is scheduled to update to the 2003 IECC on January 1, 2009 (TNleg, 2008).

The policy package modeled here assumes that all states within the Appalachian Region adopt the 

2006 IECC commercial code or equivalent in 2009 with an effective date of 2010. The initial 

savings are assumed to come exclusively from lighting with this change, as the major difference 

between the 2001 and 2003 IECC (and 2004 ASHRAE 90.1) commercial code was in lighting 

density requirements. If the shell efficiencies are significantly different from the current practice in 

Mississippi and Tennessee (with codes based on ASHRAE 1975 and 1980 versions), savings for 

space heating and cooling in new buildings will be underestimated. Documentation of the 

methodology for calculating savings from improved commercial building energy codes can be found 

in Appendix C.1. These savings are assumed to be made through 80 percent compliance with model 

building code legislation enabled by third-party verification of code compliance. 
Energy Efficiency in Appalachia, SEEA 

54

Energy savings from Commercial Building Codes are expected to be about 2.3 percent of forecast 

commercial consumption by 2030 (Table 4.4). For comparison, this is about half the percent of 

energy savings estimated for residential building codes with third-party verification (see Chapter 3). 

Table 4.4 Energy Savings from Commercial Building Codes

Year

Electricity 

Savings

Natural Gas 

Savings

Fuel Oil 

Savings

Total 

Primary 

Energy Saved

% of 

Sector 

Primary 

(GWh) (trillion Btu) (trillion Btu) (trillion Btu) Energy

2010 84 0.00 0.00 0.92 0.06

2013 391 0.05 0.01 4.34 0.27

2020 1,551 0.73 0.11 18.48 0.98

2030 3,993 3.12 0.00 51.09 2.26

Costs for administering the commercial building energy codes are less than $2 million, annually, and 

includes two training personnel per state (apportioned) and one verification liaison per 10 million 

new square feet of commercial floorspace. Investment costs reflect the incremental cost of meeting 

the codes and increase from around $37 million in 2010 to nearly $47 million in 2030. Energy bill 

savings grow from almost $8 to $388 million over the study‘s time horizon (Table 4.5). 

Table 4.5 Costs and Savings from Commercial Building Codes

Year

Energy Savings Admin Costs Investment 

Costs

(million 2006$) (million 2006$) (million 2006$)

2010 7.95 1.60 37.47

2013 36.03 1.63 38.80

2020 144.65 1.72 42.39

2030 387.87 1.83 46.74
Chapter 4: Commercial Buildings 

55

Figure 4.3 Annual Investment and Energy Savings 

from Commercial Building Codes, 2010-2030

0

10

20

30

40

50

60

2010 2015 2020 2025 2030 Trillion BTU

0

5

10

15

20

25

30

35

40

45

50 Million 2006$

Energy Savings Public Investment Private Investment

Figure 4.3 illustrates the time-line of 

expenditures and savings for 

commercial building codes. Public 

investment (administrative costs and 

incentives) remains less than $2 

million throughout the program 

while private investment increases to 

cover the incremental costs of more 

efficient commercial buildings. 

Annual savings grow steadily 

throughout the study horizon 

because of the long lifetime of 

building stock and a growing 

number of commercial buildings are 

meeting energy codes. 

The Commercial Building Energy Codes 

with Third Party Verification is costeffective with a benefit-to-cost ratio of about 2.5 for participants and about 2.8 for society. With 

$36.2 million in program spending and an additional $887.7 million in customer investments over the 

2010-2030 period, the Appalachian region could see net cumulative savings of 441.5 trillion Btu, 

saving $3.4 billion in energy bills by 2030. This is the equivalent of about 2.3 percent of the EIA‘s 

forecast consumption or 7.3 percent of forecast growth (EIA, 2008a). 

4.3.2 Support for Commissioning of Existing Commercial Buildings 

Building a commercial structure is a complicated process, combining architectural design and 

construction as well as building systems design (e.g., HVAC) and installation. Often many different 

designers, contractors, and subcontractors are a part of the process, and as the complexity of the 

project increases, so does the probability of incorrect installation. In addition to problems with 

construction or installation, as a building matures, equipment can become obsolete or be altered to 

operate off-design. 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. According to the 

New York State Energy Research and Development Authority (NYSERDA, 2004) the 

commissioning of existing buildings should include four steps: planning, investigation, 

implementation, and handoff. Through measurement and inspection, a building‘s envelope, HVAC 

equipment, and other systems are evaluated against initial design documentation and corrective 

actions are taken, often with resulting energy savings. 

Commissioning existing buildings in the Appalachian Region could lead to immediate energy 

savings. In a meta-analysis conducted by Lawrence Berkeley National Laboratory, up to 57 percent 

of the total building energy was saved, though the median value for buildings that did not purchase 

thermal products was 10 percent (Mills et al., 2004). 

One policy that currently supports commissioning of existing buildings in the Appalachian Region is 

the NYSERDA Existing Facilities Program (NCSC/IREC, 2008). This program provides financial 

incentives for energy-efficiency improvements to most non-single family structures. Actions 

undertaken to correct off design performance that cost more than $10,000 are eligible for energy rate 
Energy Efficiency in Appalachia, SEEA 

56

rebates as well as reduction in demand rate charges. A public benefit fund is the source of funds for 

this program. 

Findings from the LBNL meta-analysis (Mills et al., 2004) and a study by Portland Energy 

Conservation and Oak Ridge National Laboratory (Haasl and Sharp, 1999) were used to estimate the 

energy savings potential in the ARC Region. The modeled program consists of commissioning 

incentives for the first ten years of the program as well as program administration to lead publicawareness campaigns, evaluate private-sector commissioning companies, study program 

effectiveness, and suggest program improvements as needed. Details of the modeling methodology 

are provided in Appendix C.2. 

Energy savings from commissioning of existing buildings are shown in Table 4.6. Modeled savings 

from commissioning grow from about three trillion Btu in the first year of the program to 391 trillion 

Btu by 2030. Energy savings grow from 7.8 percent of forecast commercial energy consumption in 

2020 to 17 percent in 2030. 

Table 4.6 Energy Savings from Commissioning of Existing Commercial Buildings

Year

Electricity 

Savings

Natural Gas 

Savings

Fuel Oil 

Savings

Total 

Primary 

Energy Saved

% of 

Sector 

Primary 

(GWh) (trillion Btu) (trillion Btu) (trillion Btu) Energy

2010 213 0.69 0.38 3.25 0.21

2013 1,868 5.84 1.50 28.11 1.72

2020 9,886 30.54 5.82 148.15 7.82

2030 26,611 76.51 11.46 390.77 17.26

The costs and savings related to the commissioning of existing commercial buildings can be found in 

Table 4.7. Energy savings are based on savings by fuel and forecast energy prices (EIA, 2008a). 

Administrative costs include costs of personnel to distribute and monitor incentives as well as 

provide information to increase awareness of the benefits of commissioning. Investment costs reflect 

the full cost of commissioning, both public incentive and private expenditure. 
Chapter 4: Commercial Buildings 

57

Figure 4.4 Annual Investment and Energy 

Savings for Commissioning, 2010-2030

0

50

100

150

200

250

300

350

400

450

2010 2015 2020 2025 2030 Trillion BTU

0

20

40

60

80

100

120

140

160 Million 2006$

 Energy Savings Public Investment Private Investment

Table 4.7 Costs and Savings from Commissioning of Existing 

Commercial Buildings

Year

Energy Savings Admin Costs Investment 

Costs

(million 2006$) (million 2006$) (million 2006$)

2010 22.67 1.33 22.68

2013 183.16 5.02 72.57

2020 971.08 8.44 129.70

2030 2,759.95 9.70 151.39

The public investment, including 

administrative costs and an incentive, 

rises and falls over the first ten years, 

as a reflection of the declining 

incentive rate. Private investment 

grows to a steady state while annual 

energy savings increase the entire 

study horizon (Figure 4.4). 

Support for Commissioning of 

Existing Commercial Buildings is 

cost-effective with a benefit-to-cost 

ratio of about 8.1 for participants and 

about 9.5 for total resource costs. 

With $522 million in program spending and an 

additional $2.4 billion in customer investments 

over the 2010-2030 period, the Appalachian 

Region could see net cumulative savings of 3.5 quads, saving $23.4 billion in energy bills by 2030. 

This is the equivalent of about 17.3 percent of the EIA‘s forecast consumption or 56.1 percent of 

forecast growth (EIA, 2008a). 

4.3.3 Efficient Commercial HVAC and Lighting Retrofit Incentive

Retrofits are designed as improvements to existing buildings. Major structural renovations to 

commercial buildings may occur only once or twice during its lifetime. In contrast, commercial 

lighting and HVAC equipment are replaced more frequently. 

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. High demand for the most 

efficient products can drive investment in commercialization of even better equipment, which in turn 

pushes the market to a more efficient average and can reduce the costs of efficiency as a product 

attribute. The modeling methodology for commercial retrofits is detailed in Appendix C.3. 
Energy Efficiency in Appalachia, SEEA 

58

Figure 4.5 Percent of ARC Commercial Floorspace Lit When Buildings

are Open and Closed, 2003 (million square feet)

(Source: EIA, 2007b)

Lighting represents about 13 percent of commercial energy consumption in the Appalachian Region 

(EIA, 2008a). Savings in lighting have a short payback and offer great opportunities – especially for 

buildings designed before computers were commonplace. For example, computer workstations 

require less ambient lighting than paper workstations. In addition, newer lights give off greater 

lighting, so fewer lights are needed even in areas where non-computer work is occurring. Savings 

come through upgrading magnetic to basic or premium electronic ballasts, greater lumens per watt 

for newer lamps, and fewer lamps overall. 

Retrofitting lighting assumes that lighting as an end-use will become 60 percent more efficient than 

forecast by 2030. Of this 60 percent, 14 percent is for replacement of ballasts, the other 46 percent is 

an assumed technology development and decrease in lamps per square foot. This model reflects 

adoptions of efficient technology without changing the standards for lighting; if standards are 

tightened, savings expected from an incentivized retrofit would need to be reduced in comparison to 

a new forecast stock efficiency. Reductions in lighting energy use have been shown to increase 

heating loads while reducing cooling loads; however, there is less of an increase in heating because 

this usually occurs at night (Sezgen and Koomey, 1998). The present study does not model this effect 

for the Appalachian Region; future research may examine this effect and adjust forecast savings. 

In addition to savings discussed here via retrofit of lighting equipment, some energy used for lighting 

could be saved through conservation and management practices. About half of the commercial 

floorspace in the ARC is reported to be at least partially lit when the building is closed (Figure 4.5). 

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

5500

Lit When Open Lit When Closed Million Square Feet

Zero percent 1 to 50 percent

51 to 99 percent 100 percent

In addition to lighting, we model savings from retrofit of heating, ventilation, and cooling (HVAC) 

equipment – which accounts for about 28 percent of commercial consumption in the Appalachian 

Region. Most of the commercial floorspace in the Region is heated or cooled (Figure 4.6). Table 4.5 

shows several types of polices that could complement or substitute for the modeled program of 

incentivizing highly efficient replacements for lighting and HVAC retrofits. 
Chapter 4: Commercial Buildings 

59

Figure 4.6 Percent of ARC Commercial Floorspace 

Heated and Cooled, 2003 (million square feet)

(EIA, 2007b)

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

5500

Heated Cooled Million Square Feet

Not Heated/Cooled 1 to 50 percent

51 to 99 percent 100 percent

Modeled energy savings from HVAC and Lighting Retrofits exceed 10 percent of forecast 

commercial consumption by 2020 and reach almost 20 percent by 2030 (Table 4.8). 

Table 4.8 Energy Savings from HVAC and Lighting Retrofits

Year

Electricity 

Savings

Natural Gas 

Savings

Fuel Oil 

Savings

Total 

Primary 

Energy Saved

% of 

Sector 

Primary 

(GWh) (trillion Btu) (trillion Btu) (trillion Btu) Energy

2010 712 1.50 0.41 9.44 0.60

2013 3,489 7.46 1.28 46.66 2.85

2020 14,129 29.70 4.46 194.55 10.27

2030 31,661 61.40 8.33 447.08 19.74

Energy bill savings are based on savings by fuel and forecast energy prices (EIA, 2008a). The 

administrative costs reflect personnel to distribute incentives, monitor performance, and provide 

information. Investment costs represent the total incremental cost of the efficient technologies, 

including incentives. Table 4.9 illustrates these costs and savings for select years. 
Energy Efficiency in Appalachia, SEEA 

60

Figure 4.7 Annual Investments and Energy Savings 

from HVAC and Lighting Retrofits, 2010-2030

0

50

100

150

200

250

300

350

400

450

500

2010 2015 2020 2025 2030 Trillion BTU

0

50

100

150

200

250

300

350

400

450 Million 2006$

Energy Savings Public Investment Private Investment

Table 4.9 Costs and Savings from HVAC and Lighting Retrofits

Year

Energy Savings Admin Costs Investment 

Costs

(million 2006$) (million 2006$) (million 2006$)

2010 87.88 0.47 217.75

2013 403.56 1.93 225.31

2020 1,607.94 5.78 245.08

2030 3,688.81 11.04 79.17

Figure 4.7 shows the annual public 

investment, including administrative 

costs and incentives, rising slowly 

during the incentive period (2010 to 

2020) and then dropping to a 

maintenance level that covers 

administrative costs for continuing 

education and outreach efforts. 

Private investment, incremental 

costs minus incentives until 2020, 

continues to grow until it plateaus 

and drops. The drop is caused by a 

decrease in the rate of retrofit – 

rather than five additional percent of 

buildings retrofit, only one percent are 

retrofit in the last years of the study 

horizon; this is due to reaching a large 

percentage of buildings over the aggressive first years of the program. Savings grow rapidly until 

2027 when their growth is slowed by the same phenomenon. 

The Efficient Commercial HVAC and Lighting Retrofit Incentive is cost-effective with a benefit-tocost ratio of about 4.8 for participants and about 5.9 for total resource costs. With $1.2 billion in 

program spending and an additional $5.4 billion in customer investments over the 2010-2030 period, 

the Appalachian Region could see net cumulative savings of 9.4 quads, saving $83.8 billion in 

energy bills by 2050. This is the equivalent of about 19.7 percent of the EIA‘s forecast consumption 

or 64.2 percent of forecast growth (EIA, 2008a).
Chapter 4: Commercial Buildings 

61

Box 4.1 Research and Development Example: Solid State Lighting (SSL)

Solid-state lighting (SSL) is a form of lighting technology that is dramatically more efficient than conventional 

lighting technologies, such as incandescent and fluorescent bulbs. Light emitting diodes (LEDs) are the form 

of SSL that hold the most market potential. Colored light LEDs have been on the market for several years –

they are often used in traffic lights, exit signs, and other lights that remain on almost constantly. As research 

has progressed, costs have gone down steadily. However, the development of white light LEDs is a recent 

technological breakthrough. 

Figure 4.8 Diagram of an LED

Unlike other lighting technologies, LEDs use electric current passed through semiconductors to produce light. 

Different colors of light are created by using different materials in the diode. Technology improvements are 

expected to bring brighter white light LEDs that provide light as good as or better than existing fluorescent 

fixtures with 25 to 45 percent less electricity usage. With successful R&D in these products, energy savings 

nationwide over all sectors could be as high as three to four quads (Navigant, 2006). As SSL technology 

advances, it is likely to become better suited to a broader array of applications. Future R&D goals include 

improving the light quality, increasing efficiency, and reducing prices. The potential energy savings will 

depend on how quickly and to what extent these developments occur. SSL was originally chosen to represent 

the technology‘s potential for the commercial sector, and although it will have the greatest impact there, SSL is 

also expected to transform residential and industrial lighting demand. Therefore, our analysis of LEDs 

potential includes all three sectors.

Under the aggressive research and development agenda being pursued by the U.S. Department of Energy, 

these substantial energy savings are very possible. A recent study on the market potential of SSL technology 

by Navigant (2006) determined that by 2027, LEDs could completely replace incandescent lighting and 

substantially replace most other forms of lighting in all sectors – residential, commercial, and industrial. 

Because incandescent bulbs are the least efficient form of lighting currently on the market, replacement of 

these bulbs with LEDs translates into tremendous savings of electricity. Table 4.10 illustrates the savings that

could be achieved from LEDs in comparison to lighting retrofits. Total market penetration potential by 2027 is 

estimated at 89 percent to 95 percent, depending on the sector (Navigant, 2006). With this level of the market 

switching to LEDs, other forms of lighting would be rendered almost obsolete.

Table 4.10 Projected Lumens per Watt

2007 2012 2017 2022 2027

LED 53.1 111 155.3 175 183.1

Lighting Retrofitsa

40.9 66.2 80.3 95.4 107.8

AEO 2008 (EIA, 2008a) 40.9 43.7 45.0 45.8 46.4

a Lighting retrofit efficiency only applicable to commercial sector; AEO 2008 forecast for 

commercial sector lighting efficiency is included for comparison
Energy Efficiency in Appalachia, SEEA 

62

Overall, the Navigant (2006) study estimates that LEDs could lead to 30 percent or more electricity saved 

annually. Following AEO 2008 estimates, in 2006 lighting accounted for approximately 26 percent of 

electricity usage in the Appalachian Region. By 2030 its share is expected to drop to 19 percent, in part due to 

recent Congressional action on lighting standards. Applying these percentages to our projections of electricity 

use in Appalachia, we estimate the amount of electricity consumed for lighting purposes was estimated, and 

the results are presented in Table 4.11. 

Table 4.11 Electricity and Lighting Demand in the Appalachian Region

2006 2013 2020 2030

Electricity Demand (Quads) 1.14 1.25 1.37 1.56

Percent Electricity Used in Lighting 25.8 21.2 21.2 19.0

Electricity Used in Lighting (Quads) 0.29 0.27 0.29 0.30

Figure 4.9 shows the estimated savings of primary energy for national energy consumption. The reference case 

in this analysis does not account for the changes in lighting standards beginning in 2008, so the energy savings 

attributable to LEDs estimated by Navigant are larger than possible with the updated base case scenario.

Figure 4.9 U.S. Energy Consumption for Lighting Through 2007 (Quads)

Lighting demand for the Appalachian Region is expected to increase by little more than 10 percent between 

2006 and 2030 (EIA, 2008a). However, under the LED scenario lighting demand would be expected to 

decrease by more than 20 percent. If in 2030 the Region used 20 percent less energy for lighting than 

projected, a savings of 0.06 quads, or 3.8 percent of the projected BAU electricity consumption, would be 

expected for that year alone. Although the significant savings will not begin before 2015, the long term effects 

of LEDs on the electricity consumption in the Appalachian Region are substantial for all sectors.
Chapter 4: Commercial Buildings 

63

4.3.4 Tightened Office Equipment Standards with Efficient Use Incentive 

Appliance and equipment standards have been successfully applied since 1977 in the United States to 

set minimum efficiency levels for new appliances and equipment. However, standards have been 

developed more for residential appliances than commercial equipment. Rosenquist et al. (2006) use 

a national model of energy savings through appliance and equipment standards and find that the 

commercial sector standards have greater net present value than standards in the residential sector, in 

general. Standard-setting for office equipment – especially standby power rates – could provide 

savings in this sector with lower public costs than an incentive. Currently, end-uses like cooking and 

office equipment are not covered by Federal standards for commercial and industrial equipment; 

however, the EPA has developed ENERGY STAR guidelines for many of these products.22

Our current model focuses only on office equipment, including: computers, copiers, printers, 

monitors, multi-function devices, fax machines, and scanners. Savings are based on the forecast for 

―office equipment‖ as an end-use and not on proposed savings by technology, like copiers, because 

the EIA forecast, on which this model is based, does not provide more technology specific data. 

Methodology for estimating savings from commercial equipment standards can be found in 

Appendix C.4. 

Energy savings from Office Equipment Standards are expected to be almost three percent of forecast 

commercial consumption by 2020 and more than six percent in 2030 (Table 4.12). 

Table 4.12 Energy Savings from Office Equipment Standards

Year

Electricity 

Savings

Natural Gas 

Savings

Fuel Oil 

Savings

Total 

Primary 

Energy Saved

% of 

Sector 

Primary 

(GWh) (trillion Btu) (trillion Btu) (trillion Btu) Energy

2010 387 0.00 0.00 4.21 0.27

2013 1,060 0.00 0.00 11.62 0.71

2020 4,673 0.00 0.00 53.20 2.81

2030 12,017 0.00 0.00 143.16 6.32

The administrative and investment costs and energy bill savings related to office equipment standards 

and incentives can be found in Table 4.13. Energy bill savings are based on modeled savings and 

forecast electricity prices (EIA, 2008a). Administrative costs reflect personnel to promulgate 

standards, distribute incentives, and provide public information. Investment costs are the total 

incremental cost of more efficient equipment compared to stock efficiency, including incentives. 

 

22 Chapter 7 of the Buildings Energy Data Book provides details on Federal standards: 

http://buildingsdatabook.eren.doe.gov/ChapterView.aspx?chap=7#2). ENERGY STAR guidelines for office equipment 

can be found at: 

http://www.energystar.gov/index.cfm?fuseaction=find_a_product.showProductCategory&pcw_code=OEF 
Energy Efficiency in Appalachia, SEEA 

64

Figure 4.10 Annual Investment and Energy Savings 

from Office Equipment Standards, 2010-2030

0

20

40

60

80

100

120

140

160

2010 2015 2020 2025 2030 Trillion BTU

0

10

20

30

40

50

60 Million 2006$

Energy Savings Public Investment Private Investment

Table 4.13 Costs and Savings from Office Equipment Standards

Year

Energy Savings Admin Costs Investment 

Costs

(million 2006$) (million 2006$) (million 2006$)

2010 37.83 0.53 49.77

2013 96.71 0.33 25.75

2020 420.04 1.71 28.01

2030 1,098.05 3.11 31.67

The code adoption cycle is evident in 

the changing annual costs over the 

program horizon (Figure 4.10). In 

2010, the costs are high as standards 

become effective and the newer 

equipment is still expensive to adopt. 

However, the cost ―bumps‖ are smaller 

for subsequent standards changes; this 

is expected because the standard has a 

―built-in‖ increase that can be 

anticipated and met at lower cost than 

the first standard. Energy savings (all 

electric) rise over the study horizon 

with larger jumps associated with each 

new standard. 

These results suggest that tightening standards on other equipment – especially cooking equipment 

which is a large end-use – could also significantly reduce commercial energy consumption. The 

Tightened Office Equipment Standards with Efficient Use Incentive is cost-effective with a benefitto-cost ratio of about 8.6 for participants and about 9.9 for total resource costs. With $96.8 million in 

program spending and an additional $612.6 million in customer investments over the 2010-2030 

period, the Appalachian Region could see net cumulative savings of 1.3 quads, saving $10.7 billion 

in energy bills by 2035. This is the equivalent of 6.3 percent of the EIA‘s forecast consumption or 

20.6 percent of forecast growth (EIA, 2008a). 

4.4 SUMMARY OF EFFICIENCY POTENTIAL FOR COMMERCIAL BUILDINGS

Our analysis of commercial building policies suggests that moving existing buildings and equipment 

to the best practice produces much greater energy savings in the Appalachian Region than improving 

new buildings (Figure 4.11). 
Chapter 4: Commercial Buildings 

65

Figure 4.12 Cumulative Savings by Policy Package with 

Commissioning Included in Retrofits (trillion Btu), 2030

Commercial 

Building Codes

51.1

5%

Commissioning

391

38%

Lighting 

Retrofits

148

14%

HVAC Retrofits

299

29% Office 

Equipment 

Standards

143

14%

However, a policy that promotes accelerated turnover of HVAC and lighting equipment would likely 

be used by those buildings under commissioning. Therefore, we do not expect savings from 

commissioning and retrofits to be completely additive. Figure 4.12 shows the relative efficiency 

contribution with commissioning considered as wholly included in retrofits of lighting and HVAC. 

Commercial 

Building Codes

51.1

8%

Lighting 

Retrofits

148

23%

HVAC Retrofits

299

47%

Office 

Equipment 

Standards

143

22%

Figure 4.11 Commercial Primary Energy 

Savings by Policy Package (trillion Btu), 2030
Energy Efficiency in Appalachia, SEEA 

66

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

3

3.2

2006 2009 2012 2015 2018 2021 2024 2027 2030 Quadrillion Btu

AEO 2007 With Policy Packages

AEO 2008 With Policy Packages excluding Commissioning

0

200

400

600

800

1000

1200

2010 2015 2020 2025 2030 Trillion BTU

0

100

200

300

400

500

600

700 Million 2006$

Energy Savings

Energy Savings without Commissioning

Public Investment

Public Investment without Commissioning

Private Investment

Private Investment without Commissioning

If the four policy packages presented here 

provide additive results, the forecast 

growth would be more than completely 

offset by efficiency, over 200 trillion 

Btu less than 2006 consumption (Figure 

4.13).23 Even with commissioning 

considered a subset of retrofits, there 

are significant savings (28 percent of 

forecast commercial consumption in 

2030) – with efficiency offsetting about 

92 percent of forecast growth in commercial 

sector consumption. Since the AEO 2008 

forecast already includes new efficiency 

requirements from EISA 2007, it is clear that 

these policies provide even greater 

savings. 

Figure 4.14 shows how investments 

and energy savings change over 

time. Public investment 

(administrative costs and incentives) 

peaks around 2015, declines as 

incentive percentages decrease to 

commissioning programs, and drops 

off to a maintenance level to support 

education and outreach after 2020; 

when commissioning is included in 

retrofits, the peak disappears. 

Private investment continues to grow 

and peaks in 2026. The drop in 

private investment does not reflect 

an end to the market transformation 

effect of these programs; rather, it 

demonstrates the saturation of commercial 

buildings with retrofit or new buildings 

and continues at a much slower pace to continuously update this vintage of buildings to better 

performance. 

Table 4.14 shows the net present value through the participants and total resource cost tests. These 

reported figures are aggregate, individual costs and benefits will vary greatly – especially at the 

participant level where complexities of commercial buildings will favor some structures and uses 

much more than others. These estimated savings are in line with contemporary electricity efficiency 

studies for states in the Region. Efficiency potential studies completed for Georgia Power and the 

Georgia Environmental Facilities Authority found maximum achievable electric efficiencies of 11 

percent over 10 years and 9.6 percent over five years, respectively (ICF, 2005; Nexant, 2007). A

 

23 While the AEO 2007 (EIA, 2007a) and AEO 2008 (EIA, 2008a) forecasts are shown, the savings are based on and 

subtracted from the AEO 2008. 

Figure 4.13 Commercial Consumption With and Without 

Policy Packages (trillion Btu, Primary), 2006-2030

Figure 4.14 Annual Investments and Energy Savings 

for the Commercial Policy Package, 2010-2030
Chapter 4: Commercial Buildings 

67

study for North Carolina found a maximum achievable potential for commercial electric efficiency of 

12 percent over a 10 year horizon (GDS Associates, 2006). An efficiency potential study for 

Kentucky modeled minimally and moderately aggressive scenarios with commercial savings of 1.5 

percent and 6.8 percent, respectively, over 10 years (KPPC, 2007). More recently, a report by 

ACEEE et al. (2008) presented commercial savings for Virginia at 28 percent of their forecast 

electricity consumption in 2025. 

Table 4.14 Results of Economic Tests for Commercial Policies

Building 

Codes Commissioning Retrofit EquipmentStandards Total

Participants Test

NPV Benefits 

(billion 2006$) 0.89 8.48 13.41 2.65 25.44

NPV Costs 

(billion 2006$) 0.35 1.05 2.78 0.31 4.49

Net BenefitsCosts (billion 

2006$)

0.54 7.43 10.63 2.34 20.95

B/C Ratio 2.54 8.08 4.83 8.62 5.67

Total Resource Cost Test

NPV Benefits 

(billion 2006$) 1.29 13.61 20.60 3.81 39.31

NPV Costs 

(billion 2006$) 0.46 1.43 3.50 0.39 5.78

Net BenefitsCosts (billion 

2006$)

0.82 12.18 17.09 3.42 33.52

B/C Ratio 2.78 9.52 5.88 9.89 6.80

The commercial policy package modeled in this study is cost-effective with a benefit-to-cost ratio of 

about 5.7 for participants and about 6.8 for total resource costs. With $1.9 billion in program 

spending and an additional $9.3 billion in customer investments over the 2010-2030 period, the 

Appalachian region could see net cumulative savings of 19.3 quads, saving $156.9 billion in energy 

bills by 2050. This is the equivalent of about 46 percent of the EIA‘s forecast consumption in 2030 

or 148 percent of forecast growth from 2010-2030 (EIA, 2008a). 

The above discussion includes all four policies as additive. As discussed previously, commissioning 

and retrofit of HVAC and lighting may not be additive. If commissioning results are not considered, 

the policy package is still cost-effective with a benefit-to-cost ratio of about 4.9 for participants and 

about 5.9 for total resource costs. With $1.3 billion in program spending and an additional $6.9 

billion in customer investments over the 2010-2030 period, the Appalachian Region could see net 

cumulative savings of 11.1 quads, saving $100.1 billion in energy bills by 2050. This is the 

equivalent of about 28.3 percent of the EIA‘s forecast consumption in 2030 or 92.1 percent of 

forecast growth from 2010-2030 (EIA 2008a). 
Energy Efficiency in Appalachia, SEEA 

68
 Chapter 5: Industry 

69

Figure 5.1 Energy Consumption Forecast for Industry (Quads)

(EIA, 2007a; 2008a)

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

2006 2009 2012 2015 2018 2021 2024 2027 2030 Quadrillion Btu

AEO 2007 AEO 2008

5 ENERGY EFFICIENCY IN INDUSTRY 

5.1 INDUSTRIAL ENERGY USE IN APPALACHIA

The industrial sector currently 

comprises about 30 percent of overall 

energy use within the Appalachian 

Region. According to the EIA‘s 

2008 Annual Energy Outlook, 

industrial consumption will remain 

large, though its market share will 

decrease slightly to 28 percent by 

2030 (EIA, 2008a). The full 

baseline forecasts of industrial energy 

consumption in the Appalachian Region 

are illustrated in Figure 5.1. The 

difference seen in the forecast beyond 

2022 reflects a projection of slower growth in 

energy-intensive industry nationwide, 

which is estimated to be ―0.7 percent, 

relative to the 1.9 percent growth of 

less energy-intensive industry‖ (EIA, 

2008a). This nationwide trend is expected to also occur in the Appalachian Region. 

Industrial users consume a wide variety of energy 

sources and use them as heat and work sources. 

Primary fuels are also used as feedstock chemicals in 

the manufacture of good such as plastics. Figure 5.2 

illustrates the variety of energy used by Appalachian 

industry. Electricity and its related generation, 

transmission, and distribution losses account for 45 

percent of the energy used by industry while liquid 

fuels and natural gas comprise 23 and 15 percent of 

industrial energy use, respectively; note that site use is 

not dominated by electricity as it is in the residential 

and commercial sectors. 

The Appalachian Region is home to a wide 

variety of industries, employing residents in 

all of the industrial North American 

Classification System (NAICS) code 

categories. The top eight industrial employers are shown in Table 5.1. Energy-intensive industries 

in the Region include pulp and paper, chemical manufacturing, and mining. 

Figure 5.2 Industrial Energy Sources by Fuel, 2006

(EIA, 2008a)

Liquid Fuels 

Subtotal

23%

Natural Gas

16%

Coal

7% Renewable 

Energy

9%

Electricity

14%

Electricity 

Related Losses

31%
Energy Efficiency in Appalachia, SEEA 

70

Table 5.1 Top Industrial Employers in the Appalachian Region

(IMPLAN, 2006)

NAICS Division Percent of Industrial 

Employment

Wholesale trade 19

Transportation equipment 7

Fabricated metal products 7

Food products 6

Furniture and related products 5

Machinery manufacturing 5

Plastics and rubber products 5

Wood products 5

While accounting for more energy consumption than any other sector, industry benefits from having 

fewer unique users; therefore, education and information dissemination can occur more rapidly and 

with less cost. In addition, action at one industrial site can have more impact on energy consumption 

than action at one residence or commercial enterprise. 

Because industrial energy-efficiency improvements are often process or plant specific, it is difficult 

to characterize the potential for energy savings in this sector. Nevertheless, some policies can be 

discussed at a high level of aggregation. In particular, three policies are investigated in this study 

with regards to the industrial sector and are described below. 

5.2 POLICY OPTIONS FOR INDUSTRIAL ENERGY EFFICIENCY

This study in industrial energy efficiency investigates three policies: expansion of industrial 

assessment centers (IACs), energy savings assessment (ESA) training, and combined heat and power 

(CHP) incentives. Many types of policies could be used to encourage more efficient use of energy in 

industry. Examples of policy actions are shown in Table 5.2. The policies and programs listed could 

be used as substitutions for or complementary actions to the ones that were modeled in this study. 

The actual form of policies adopted within the Appalachian Region will depend on the critical 

barriers and market failures that inhibit the market uptake of energy-efficient technologies and 

practices, which vary across industries and subregions of Appalachia. The specific choice of policies 

will also reflect the goals and capacity of state and local agencies. 
 Chapter 5: Industry 

71

Table 5.2 Policy Actions that Support Industrial Energy Efficiency

Actions Expansion of Industrial 

Assessment Centers

Energy Savings 

Assessments

Industrial Combined Heat 

and Power

Research, 

Development, and 

Demonstration

Increased equipment and 

system performance; 

reduced installed cost

Increased equipment and 

system performance; reduced 

installed cost

Increased equipment and 

system performance; reduced 

installed cost

Financing Low or no interest loans 

for capital improvements 

Low or no interest loans for 

capital improvements

Low or no-interest loans for 

CHP equipment purchase

Financial 

Incentives

Assistance with energy 

audit costs; grants and 

tax credits

Grants and tax credits Grants and tax credits

Pricing – –

Reduced rates for natural gas

for CHP users

Voluntary 

Agreements N/A N/A N/A

Regulations Equipment standards Equipment standards Net metering and feed-in tariffs; 

equipment standards

Information 

Dissemination & 

Training

Campaigns to inform 

small- to medium-sized 

industrial sites of 

potential for energy and 

cost savings 

Training for on-site 

personnel during first 

assessment;

Software tools to perform 

future assessments;

Campaign to inform large 

industrial sites of the 

potential for energy and cost 

savings

Assessments to evaluate CHP 

feasibility at site;

Campaign to inform industrial 

sites of the potential for energy 

and cost savings

Procurement

Assistance with 

equipment procurement 

to lessen lead times

Assistance with equipment 

procurement to lessen lead 

times

Assistance with equipment 

procurement to lessen lead 

times

Market Reforms Public assistance fund Public assistance fund Public assistance fund

Planning 

Techniques

Outage management to 

facilitate energyefficiency upgrades; 

zoning and land use 

planning

Outage management to 

facilitate energy-efficiency 

upgrades; zoning and land 

use planning

Outage management to 

facilitate energy-efficiency 

upgrades; zoning and land use 

planning

Capacity Building

Increase the number of 

industrial assessment 

personnel 

Software development N/A

This table describes policy actions available that could further the savings from the policy packages modeled in this study. The policy 

actions shown in italics are modeled in this study, while the others are not.
Energy Efficiency in Appalachia, SEEA 

72

5.2.1 Research, Development, and Demonstration

Research, development, and demonstration of energy-efficient technologies are necessary to 

continually improve performance and reduce the cost of advanced equipment and practices, both of 

which affect adoption rates. The West Virginia and Maryland Industries of the Future Programs and 

the North Carolina Combined Heat and Power Center are examples of Appalachian organizations 

that are encouraging innovation in industrial energy efficiency. 

The Maryland Industries of the Future Program (IFP) has several goals. Its goals related to research 

are to help establish relationships between universities and develop funding for research and 

development that supports industry in the state of Maryland. These goals could directly aid in the 

development of future energy-efficient technologies while also providing economic development to 

the Region. The North Carolina Combined Heat and Power Center supports efforts in the 

development and implementation of combined heat and power (CHP) systems, which can reduce 

energy consumption. This center partners with several other centers throughout the Appalachian 

Region to promote the installation of CHP systems in the Southeast and the development of 

improved systems for future use. 

5.2.2 Financing

Though industrial energy-efficiency improvements can often pay for themselves within a few years, 

they also can require large capital investment to implement. Opportunities for loans in order to 

finance improvements can increase penetration of energy-efficient technologies into industry; loan 

programs are attractive because these loans can be repaid with the money saved by reduced energy 

consumption. An example of a loan program applicable to the industrial sector in the Appalachian 

Region is the North Carolina Energy Improvement Loan Fund (EILF). Under this program, with a 

bank letter of credit, an industrial site can receive a one percent loan for energy recycling or 

renewable energy projects or a three percent loan on projects that reduce energy demand, yield 

energy cost savings, or are energy-efficient. 

5.2.3 Financial Incentives

Reducing the cost of energy-efficiency improvements through financial incentives can increase the 

participation in new programs, yielding energy savings for the industrial site and Appalachia. As the 

program grows, it may be possible to lessen the incentives once the program‘s impact is 

demonstrated. 

Currently there are several state financial incentive programs that aid in the reduction of energy 

consumption in the Appalachian Region, two of which are the Kentucky Sales Tax Exemption for 

Manufacturing Facilities and the Ohio Energy Loan Fund (ELF) grants for energy-efficiency projects 

in manufacturing. Under the Kentucky program, an industrial site can receive a rebate on sales tax 

paid on an energy-efficiency project that maintains or increases the site‘s productivity while reducing 

its energy consumption by 15 percent or more. Under the Ohio ELF project, energy efficiency, 

distributed generation (including CHP), and renewable energy projects are eligible to apply for a 

grant to cover a portion of project expenses. 
 Chapter 5: Industry 

73

5.2.4 Regulations

Regulations can have a large impact on the availability and affordability of energy-efficient 

equipment. Without regulation, the availability of energy-efficiency products and equipment is 

dependent on market conditions. While markets could drive manufacturers to produce more 

efficiency components due to demand, regulations at the state or national level ensure these 

technologies are available to the public and provide a more secure market to those companies 

producing the equipment, reducing the risk of research, development, and introduction to the market. 

Affordability of an efficient device is impacted not only by its purchase price; it is also greatly 

affected by the utility framework under which it operates. Regulations pertaining to the ―buy-back‖

of electricity are critical to systems that generate electricity on-site, such as CHP systems. If a site 

produces more electricity than it uses at that location, the following could occur: (1) the electricity is 

not returned to the electrical grid (wasted), (2) low feed-in tariff where the electricity is returned to 

the grid, and the site is paid a set rate that is less than the rate it pays to buy from the grid, (3) the 

electricity is returned to the grid, and the meter runs backwards (i.e., the electricity is bought by the 

utility at the same rate it sells electricity to the customer, called ―net metering‖), or (4) high feed-in 

tariff where the electricity is returned to the grid, and the utility pays a premium price for it (e.g., 

photovoltaic power in Germany). Example scenarios (3) and (4) offer the site higher compensation, 

and, therefore, could aid in adoption of power-producing technologies. Table 5.3 lists net metering 

programs in the Appalachian Region. 
Energy Efficiency in Appalachia, SEEA 

74

Table 5.3 Current Industrial Net Metering Programs in Appalachian Region

(NCSC/IREC, 2008)

State Size Limit Applicable Technologies

Georgia Up to 100 kW PV, Wind, Fuel Cells 

New York Up to 2 MW Photovoltaics, Wind

North Carolina Up to 100 kW Photovoltaics, Landfill Gas, Wind, Biomass, 

Anaerobic Digestion, Small Hydroelectric

Pennsylvania Up to 3 MW

Solar Thermal Electric, Photovoltaics, Landfill Gas, 

Wind, Biomass, Hydroelectric, Fuel Cells, Municipal 

Solid Waste, CHP/Cogeneration, Waste Coal, CoalMine Methane, Anaerobic Digestion, Other 

Distributed Generation Technologies

Ohio

Must be sized to

meet some or all of 

customer‘s load

Solar Thermal Electric, Photovoltaics, Landfill Gas, 

Wind, Biomass, Hydroelectric, Fuel Cells, 

Microturbines

South Carolina Up to 100 kW Photovoltaics, Landfill Gas, Wind, Biomass, Small 

Hydroelectric

Virginia Up to 500 kW

Solar Thermal Electric, Photovoltaics, Wind, 

Biomass, Hydroelectric, Geothermal Electric, 

Municipal Solid Waste, Tidal Energy, Wave Energy

If high feed-in tariffs for energy-efficient systems were expanded to encompass all energy-efficient 

and renewable power production, distributed generation could have a large impact on reducing the 

Region‘s fossil energy consumption. 

5.2.5 Information Dissemination and Training

As shown in Table 5.2, information dissemination and training are important to all three of the policy 

bundles investigated in this study. Educating the industrial owners and workforce is key to 

propagating adoption of energy-efficient equipment and practices. An example of an information 

dissemination and training program active in the Appalachian Region is the North Carolina Energy 

Management Program Industry Extension. This organization develops and implements educational 

material and holds workshops in the area of industrial energy efficiency. The group also conducts 

industrial surveys and gathers information related to current system configurations and operations to 

provide guidance to those interested in improving site energy efficiency. 

 
 Chapter 5: Industry 

75

In addition to state-specific programs, another organization that could provide assistance with public 

education is the Hollings Manufacturing Extension Partnership (MEP). The groups participating in 

this partnership are publically-funded, not-for-profit state or university entities that assist 

manufacturing facilities in a wide variety of ways, from streamlining processes to implementing 

energy-savings programs (NIST, 2008). This well-established partnership could aid in information 

dissemination and training throughout the Appalachian Region. 

5.3 MODELED SAVINGS IN APPALACHIAN INDUSTRY

The following sections describe each of the modeled policies in more detail and estimate potential 

energy savings as well as the costs associated with implementation of each policy. At the end of the 

chapter, aggregated results for the sector are reported along with a discussion of the findings. 

Greater detail on the modeling methodology used to estimate the potential for industrial energyefficiency improvements can be found in Appendix D. 

5.3.1 Expanded Industrial Assessment Center Initiative (IACs)

Currently, there are 26 DOE Industrial Assessment Centers (IACs) located throughout the U.S. 

(DOE/EERE, 2008a). These centers are university-based, and teams comprised of both faculty and 

students perform thorough analyses at small to medium-sized industrial facilities24 within their local 

region. These assessments suggest savings improvements in energy efficiency, waste minimization, 

pollution prevention, and productivity. Table 5.4 illustrates the activities of this program in the 

Appalachian states, including number of assessments and implementation rate of recommendations. 

 

24 less than $2.5 million in energy expenditures per year (Soderlund, 2008) 
Energy Efficiency in Appalachia, SEEA 

76

Table 5.4 IAC Assessments to Date

(DOE/EERE, 2008a)

State Number of 

Assessments

Recommended 

Actions

Average

Payback 

(years)

Implemented

Actions

Average

Payback 

(years)

Implementation Rate

(%)

Alabama 116 849 1.5 334 1.3 39

Georgia 648 4,401 1.6 1,905 1.6 43

Kentucky 202 1,269 1.2 462 1.0 36

Maryland 42 361 1.0 181 0.9 50

Mississippi 300 1,971 1.1 701 0.8 36

North 

Carolina 319 2,488 1.0 1,187 0.7 48

New York 498 3,552 1.1 1,727 0.9 49

Ohio 853 5,808 1.1 2,968 1.0 51

Pennsylvania 341 2,933 1.1 1,343 0.9 46

South 

Carolina 92 668 1.5 308 1.4 46

Tennessee 468 2,989 1.0 1,367 0.8 46

Virginia 258 1,708 1.2 775 1.2 45

West 

Virginia 110 1,147 1.6 622 1.9 54

Most of the recommended improvements have corresponding energy savings. For example, it was 

recommended to an aircraft parts manufacturer in West Virginia that it should switch to a more 

efficient light source. This switch would save an estimated 686 MW-hr of electricity per year, which 

is 6.6 percent of the site‘s annual electricity use. The replacement would pay for itself in little over a 

year (DOE/EERE, 2008a). Other projects, such as improving logistics within each site, primarily 

yield financial savings; however, energy savings could be a secondary benefit. 

Expanding the capacity of Industrial Assessment Centers in Appalachia, through added personnel at 

existing locations and increasing the number of affiliated universities in the Region, could greatly 

improve the energy efficiency of industry in the Region. In 2007, the states that comprise the 

Appalachian Region benefitted from 163 industrial assessments from 11 centers. Based on 

population-weighting, approximately 40 of those occurred within the boundaries of ARC. 

To support the expansion of industrial assessment within the Appalachian Region, several programs 

were investigated. These policy components are shown in Table 5.2. These three components will 

aid in reaching nearly 100 percent of sites by 2030. To reach as many small- to medium-sized 

locations as possible, advertising, and information will be needed. In addition, program personnel 

may need to travel to sites for in-person visits to discuss the benefits of industrial assessment. Once 

sites request an assessment, personnel should be available to act. In order to increase the number of 

industrial assessments within the Region, additional personnel will be added at current industrial 

assessment centers located within the Region. If needed, additional universities could be asked to 

join a center to keep up with demand. 
 Chapter 5: Industry 

77

In order to model the potential benefits of increasing IAC capacity, findings from recent industrial 

assessments were compiled for each NAICS code in each Appalachian subregion. The resulting 

information was used with Appalachian Region employment statistics and population growth 

estimates to determine potential energy savings. 

The results of implementing increased IAC capacity are shown in Tables 5.5 and 5.6. Details of the 

IAC modeling, including base data, assumptions, and methodology are detailed in Appendix D.1. 

Table 5.5 Energy Savings from IACs

Year

Electricity 

Savings

Natural Gas 

Savings

Fuel Oil 

Savings

Total 

Primary 

Energy Saved

% of 

Sector 

Primary 

(GWh) (trillion Btu) (trillion Btu) (trillion Btu) Energy

2010 10 0.06 0.00 0.17 0.01

2013 631 3.27 0.00 10.45 0.42

2020 3,243 16.75 0.00 53.63 2.18

2030 7,261 37.37 0.00 119.95 4.87

Table 5.6 Costs and Savings from IACs

Year

Energy Savings Admin Costs Investment 

Costs

(million 2006$) (million 2006$) (million 2006$)

2010 0.84 0.68 1.81

2013 46.94 2.45 43.30

2020 238.45 2.96 55.08

2030 582.80 3.15 59.31
Energy Efficiency in Appalachia, SEEA 

78

Figure 5.3 Annual Investment and Energy 

Savings from IACs, 2010-2030

0

20

40

60

80

100

120

140

2010 2015 2020 2025 2030 Trillion BTU

0

10

20

30

40

50

60

70 Million 2006$

Energy Savings Public Investment Private Investment

These savings figures assume that 

IACs are able to increase from 

approximately 40 assessments per 

year in the Appalachian Region to 

having assessed nearly all small- to 

medium-sized facilities by 2030 

through an increase in workforce and 

number of centers located in or near 

the 13-state Region. In 2010, the 

increase in IAC capacity is minimal; 

however, by 2020, it is estimated that 

the total energy used by industry in 

Appalachia could be reduced by 2.2 

percent. By 2030, the energy savings 

could increase to 4.9 percent of the projected 

sector use. This represents only part of the 

energy-efficiency gains possible in the Region 

and is additive to the other industrial policy efficiency gains. 

The Expanded Industrial Assessment Centers Initiative is cost-effective with a benefit-to-cost ratio of 

about 5.0 for participants and about 5.9 for society. With $57.3 million in program spending, which 

includes the cost of each assessment, and an additional $1 billion in customer investments for capital 

improvements over the 2010-2030 period, the Appalachian Region could see net cumulative savings 

of 2.5 quads, cutting $11.9 billion from energy bills by 2050. This is the equivalent of about 4.9 

percent of the EIA‘s forecast consumption in 2030, or 29.6 percent of forecast growth (EIA, 2008a). 

5.3.2 Increasing Energy Savings Assessments 

Like industrial assessments, energy savings assessments (ESAs) can provide plant and facility 

managers with the tools they need to take control of their energy use; however, these assessments 

take place at large industrial sites and only on one system at a time.25 The impact of energy savings 

assessments on energy and economic savings has been documented by the U.S. DOE‘s Save Energy 

Now program. Save Energy Now assessments conducted in 2006 included identification of ways to 

reduce natural gas use in steam and process heat as well as on-site training of appropriate personnel 

to use the Save Energy Now software. Approximately 16 assessments were performed in the 

Appalachian Region during this time. These assessments were focused and quick (three days) and 

integrally involved the plant personnel to achieve buy in and capacity building for future in-house 

assessments. While only considering natural gas consumption in steam and process heat, the 200 

assessments, which occurred nationwide, found an average of 8.8 percent energy savings annually 

with a payback of less than two years for most recommendations (Wright et al., 2007). An example 

of the results of one site energy savings assessment is shown in Table 5.7. 

 

25 Large industrial sites are defined by DOE as those having greater than $2.5 million in energy expenditures per year 

(Soderlund, 2008) 
 Chapter 5: Industry 

79

Table 5.7 Example of Save Energy Now Energy Savings Assessments

(DOE/EERE, 2008b)

Shaw Industries (Flooring Manufacturing), Dalton, GA

System Assessed: Steam

Recommendations Implemented: Boiler control optimization, installation of waste water 

heat exchanger, stack economizer

Annual Energy Savings: 93,000 MMBtu

Annual Energy Cost Savings: $872,000

Simple Payback of Projects: 1.7 years

The programs that support energy savings assessment and training are shown in Table 5.2. These 

components are similar to the ones described above; however, two additional pieces aid in reaching 

the targeted number of systems: training of on-site personnel and software tools for second 

generation (and beyond) assessments. When the first assessment is conducted at a site, plant 

personnel are trained to perform future assessments on other large, energy-intensive systems within 

the plant and given software tools to aid them with this work. Once successful training has taken 

place, a site is self-sufficient and can continue to discover energy savings as resources allow. 

While not modeled in the current study, adhering to standards such as ANSI/MSE 2000:2005 is one 

way to insure proper prioritization of energy-efficiency projects and sustained benefits of systems 

already implemented. An initial ESA could be the springboard for a manufacturing facility to get 

started on the ANSI/MSE 2000:2005 path. This standard provides a framework for industrial sites to 

continuously improve energy efficiency while maintaining accountability for past, current, and future 

projects through a feedback loop between technical personnel and management (Meffert, 2007). 

Though equivalent benefits of following ANSI/MSE 2000:2005 can be achieved through continuing 

to conduct ESAs by onsite personnel, following a standard may make it easier for a facility to 

achieve maximum energy savings. 

Information gathered by the Save Energy Now program was used as a basis to estimate the potential 

of energy-savings assessments under various policy scenarios. The results of Increasing Energy 

Savings Assessments are shown in Tables 5.8 and 5.9. The policy bundle is estimated to cut the 

Region‘s industrial consumption by five percent in 2020, growing to 16.8 percent in 2030 when 413 

trillion Btu are estimated to be saved. Details of the ESA program modeling, including data, 

assumptions, and methodology are shown in Appendix D.2. 
Energy Efficiency in Appalachia, SEEA 

80

Figure 5.4 Annual Investment and Savings from 

Increased Assessments, 2010-2030

0

50

100

150

200

250

300

350

400

450

2010 2015 2020 2025 2030 Trillion BTU

0

200

400

600

800

1000

1200 Million 2006$

Energy Savings Public Investment Private Investment

Table 5.8 Energy Savings from Increasing Assessments and Training

Year

Electricity 

Savings

Natural Gas 

Savings

Fuel Oil 

Savings

Total 

Primary 

Energy Saved

% of 

Sector 

Primary 

(GWh) (trillion Btu) (trillion Btu) (trillion Btu) Energy

2010 106 1.06 0.00 2.26 0.09

2013 914 7.67 0.00 18.07 0.73

2020 6,422 50.14 0.00 123.18 5.00

2030 21,344 170.34 0.00 413.08 16.77

Table 5.9 Costs and Savings from Increasing

Assessments and Training

Year

Energy Savings Admin Costs Investment 

Costs

(million 2006$) (million 2006$) (million 2006$)

2010 11.65 0.88 14.74

2013 84.38 0.88 55.88

2020 565.60 1.15 395.16

2030 2,103.57 1.33 1,137.71

Figure 5.4 shows low public 

investment (administrative costs and 

incentives). Unlike the IAC 

program, the financial costs of each 

assessment are incurred by each 

industrial site; therefore, the public 

investment for the ESA program is 

lower, averaging about $1 million 

each year. In contrast, private levels 

of investment grow to $1.1 billion in 

2030, while the value of energy 

savings is nearly twice as great – at 

$2.1 billion. 

Increasing Energy Savings Assessments is 

cost-effective with a benefit-to-cost ratio of 

about 2.8 for participants and about 3.3 for 

society. With $23 million in program spending and an additional $8 billion in customer investments 

over the 2010-2030 period, the Appalachian Region could see net cumulative savings of 8.7 quads, 
 Chapter 5: Industry 

81

saving $43.3 billion in energy bills by 2030. This is the equivalent of about 16.8 percent of the 

EIA‘s forecast consumption in 2030, or 101.8 percent of forecast growth (EIA, 2008a). 

Box 5.1 Industrial R&D: Super Boiler

A combination of enhanced design features could increase industrial package boiler efficiency from 85 percent 

to 95 percent fuel-to-steam efficiency (Madgett, 2008). For improved heat transfer, super boilers use advanced 

firetubes with extended surfaces that help achieve a compact design, which reduces size, weight, and footprint. 

The advanced heat recovery system combines compact economizers, a humidifying air heater, and a patented 

transport membrane condenser. Many boilers used today are more than 40 years old, suggesting a large 

energy-savings opportunity (Gemmer, 2007). This technology provides compelling economic benefits to 

accelerate replacement of aging boilers.

Figure 5.5 Laboratory Prototype Boiler

(Rabovister and Knight, 2005)

The super boiler is estimated to be six to12 percent more efficient than a conventional boiler. The first 

commercial demonstrations have been installed and sales are expected to begin around 2009 or 2010. There is 

not yet a complete study on the market penetration potential of this technology, but its target market is 

approximately 53 percent of the total boiler market. Boilers have traditionally been replaced at an average of 

about one percent per year (Energetics, 2008). However, with the current opportunities presented by the large 

number of aging boilers, we can expect a higher replacement rate than this over the next decade.

In addition to the need to replace aging boilers, increasing energy costs can accelerate boiler replacement. With 

incentives such as tax credits or rebates for companies purchasing super boilers, replacement of conventional 

boilers could be increased even further. The Appalachian Region, particularly the southern portion, makes 

heavy use of boilers, and widespread installation of a more efficient boiler could mean tremendous savings, in 

energy and financial cost. The first demonstration super boiler has a projected payback of less than two years 

and saves thousands of dollars in energy costs annually (Energetics, 2008).
Energy Efficiency in Appalachia, SEEA 

82

0

20

40

60

80

100

120

140

160

2010 2015 2020 2025 2030 Trillion Btu

Super Boiler AEO 2008 Baseline

Figure 5.6 Comparison of Estimated Energy Savings from Super Boiler to

Industrial Baseline Consumption (trillion Btu), 2010-2030

Currently, about 32 percent of all primary energy used in the industrial sector goes to powering boilers within 

the Appalachian Region (EIA, 1998; 2002). Assuming that super boilers replace one percent of all boilers 

within the Appalachian region annually, and the average improvement in efficiency with the super boiler is 10 

percent. This new technology could save a total of 172 trillion Btu between 2010 and 2030, in addition to 

projected baseline savings. 

A 10 percent improvement in energy efficiency over a conventional new boiler can mean thousands of dollars 

saved in energy costs for an industrial site. Replacing only one percent of conventional boilers annually, the 

super boiler would deliver significant industry-wide savings. Still, policies that support the purchase and 

installation of energy-efficient technologies like the super boiler could result in even greater savings for 

industry. 

5.3.3 Supporting Combined Heat and Power with Incentives 

Combined heat and power (CHP) can offer significant energy use reductions by avoiding energy 

waste through heat loss. Many CHP systems consist of a prime mover, which produces electricity. 

The prime mover is coupled with one or more thermally-activated technologies, and these thermal 

systems use the prime mover‘s hot exhaust as an energy input to create a useful product such as 

steam or hot water that would otherwise be generated by using other high-value energy sources such 

as electricity or natural gas. The systems considered in the current study are of this type. Other types 

of systems could make use of fluids compressed to aid in transport (e.g., district steam used for space 
 Chapter 5: Industry 

83

heating) that, instead of being throttled down to a site‘s required system pressure, is coupled with a 

turbine, which generates power while also reducing pressure. These types of systems are not 

modeled in this analysis; however, they do have the potential to yield additional energy savings for 

the Appalachian Region. Other forms of recycled energy systems recover heat from an industrial 

process stream (e.g., a coking plant) and reuse it to drive another, lower temperature process (e.g., a 

drying operation). Such recycled energy systems are evaluated in industrial and energy savings 

assessments; therefore, they were not included in the CHP portion of this study. 

To determine the savings industrial CHP systems could yield, the current state of these systems in the 

Region must be established. It is estimated that there are currently six GW of installed CHP prime 

mover capacity at 198 sites within the ARC Region (EEA, 2007). CHP system performance and cost 

information were used to model CHP systems in order to quantify energy savings and financial costs 

for the Region in today‘s market. The information gained from these models, coupled with current 

industrial installation figures and growth projections, led to an estimation of the potential for savings 

for the Region under various policy regimes. 

The policies and programs evaluated in support of industrial CHP are shown in Table 5.2. Facilities 

may need assistance in identifying where CHP makes the most sense in their processes; training and 

information or audit programs could be helpful with this process. Also, managers may not be able to 

identify funds to cover the up-front cost of an upgrade. Grants and tax credits can reduce the first 

cost while low-interest loan programs, which can be paid back through energy cost savings, can 

reduce the financial hurdle of the investment without creating a large public burden.26

 

Currently few states in the Appalachian Region have energy policies that support CHP installations; 

however, other states have aggressive incentive programs and other financial assistance to aid in 

increasing energy efficiency through the use of waste heat. Connecticut is one such state. A 

summary of Connecticut‘s energy programs related to CHP is provided in Table 5.10. 

 

26 This is the sort of program offered by Energy Service Companies (ESCOs) which are not always trusted by industry due 

to their process specific needs. Industrial managers may require training and financial assistance in lieu of ESCO services 

to allow for protection of what might be a trade secret. 
Energy Efficiency in Appalachia, SEEA 

84

Table 5.10 Summary of CHP-Supportive Policies in Connecticut

(Energetics, 2006)

Type of Assistance Applicability and Amount Requirements and Limits

Grants for Customer-side 

Distributed Generation (DG)

Based-loaded systems: 

$450/kW

85 percent Capacity Factor 

during Peak Loads, max of 65 

MW

Back-up Electricity Rates

Reduced electricity rates for 

customer-side DG projects by 

eliminating backup rates and 

demand ratchets for DG 

projects.

Natural Gas Rates Rebate of customer‘s natural 

gas delivery charge

Streamlined Interconnection <65 MW

Renewable Portfolio Standard Includes CHP as a technology 

to meet requirements

Grants for New Technologies Five awards of $10,000 each CT resident or CT business 

with less then 30 employees

Long-term Loans for 

Customer-side DG $150 million available

The suite of policies listed in Table 5.10 includes grants, loans, special rates, and ease of 

interconnection with the electrical grid. Any or all of these programs could be implemented to 

ensure the viability of a CHP program in the Appalachian Region. 

The results of increasing CHP capacity within the Appalachian Region are shown in Tables 5.11 and 

5.12. These results suggest that supporting CHP with incentives would generate less energy savings 

than either of the other two policy bundles. Specifically, 1.6 percent of the Region‘s industrial 

energy consumption is estimated to be cut in 2020, rising to nearly four percent in 2030. Details of 

the CHP modeling, including base data, assumptions, and methodology are shown in Appendix D.3. 
 Chapter 5: Industry 

85

Figure 5.7 Annual Investment and Energy 

Savings from Supported CHP, 2010-2030

0

10

20

30

40

50

60

70

80

90

100

2010 2015 2020 2025 2030 Trillion BTU

0

50

100

150

200

250

300 Million 2006$

Energy Savings Public Investment Private Investment

Table 5.11 Energy Savings from Supported CHP

Year

Electricity 

Savings

Natural Gas 

Savings

Fuel Oil 

Savings

Total 

Primary 

Energy Saved

% of 

Sector 

Primary 

(GWh) (trillion Btu) (trillion Btu) (trillion Btu) Energy

2010 0.00 0.00 0.00 0.00 0.00

2013 2,793 -20.10 0.00 11.67 0.47

2020 9,655 -69.46 0.00 40.35 1.64

2030 21,081 -151.66 0.00 88.10 3.58

Table 5.12 Costs and Savings from Supported CHP

Year

Energy Savings Admin Costs Investment 

Costs

(million 2006$) (million 2006$) (million 2006$)

2010 0.00 0.61 0.00

2013 28.07 0.88 180.04

2020 104.03 1.51 194.78

2030 128.14 2.95 238.67

Figure 5.7 shows the annual 

investments by private and public 

entities and the energy savings from 

supporting CHP. This policy bundle is 

supported by a large public cost-share 

throughout the study horizon. These 

incentives rise rapidly to more than 

$100 million in 2011 and continue to 

increase throughout the 20-year time 

frame. Reducing and eventually 

eliminating these incentives would 

perhaps represent a more defensible 

public policy for the Region. 

Supporting Combined Heat and Power 

with Incentives is not cost-effective as 

modeled with a benefit-to-cost ratio of about 

0.6 for participants and about 0.3 for total resource costs; low forecast electricity prices drive this 

result. CHP is cost-effective for many individual industrial and commercial facilities. With $2.5 
Energy Efficiency in Appalachia, SEEA 

86

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

3

3.2

2006 2009 2012 2015 2018 2021 2024 2027 2030 Quadrillion Btu

AEO 2007 AEO 2008 With Policy Packages

billion in program spending and an additional $4 billion in customer investments over the 2010-2030 

period, the Appalachian Region could see net cumulative savings of 1.9 quads, saving $3.3 billion in 

energy bills by 2050. This is the equivalent of 3.6 percent of the EIA‘s forecast consumption in 

2030, or 21.7 percent of forecast growth (EIA, 2008a). 

5.4 SUMMARY OF RESULTS FOR INDUSTRIAL ENERGY EFFICIENCY

Based on the industrial program and policy bundles described above, ESAs have the largest potential 

for energy savings (Figure 5.8) 

Figure 5.9 shows that implementation of these policy bundles could eliminate the growth in industrial 

energy consumption forecast, actually reducing consumption to levels below those in 2006, for the 

Appalachian Region to 2030. Savings with these three policies are estimated to be 27 percent of 

forecast consumption in 2030. 

Savings in 2030 (Trillion Btu, Delivered)

IAC

62.14

20%

ESA

243.16

80%

CHP offsets 79.73 

Trillion Btu of 

these delivered 

savings (due to 

switching)

Savings in 2030 (Trillion Btu, Primary)

IAC, 119.95, 19%

CHP, 88.10, 14%

ESA, 413.08, 

67%

Figure 5.8 Industrial Energy Savings by Policy Package (trillion Btu), 2030

Figure 5.9 Industrial Energy Consumption With and 

Without Energy Efficiency (2010-2030)
 Chapter 5: Industry 

87

Figure 5.10 shows how investments and energy savings change over time for the industrial policy 

packages. Although the public contribution to the CHP policy is quite large, private investment is 

much larger. 

0

100

200

300

400

500

600

700

2010 2015 2020 2025 2030 Trillion BTU

0

200

400

600

800

1000

1200

1400

1600 Million 2006$

Energy Savings Public Investment Private Investment

These estimated savings are similar to other efficiency studies for states in the Region. A recent 

report by ACEEE et al. (2008) presented an industrial potential for Virginia of 25 percent of their 

forecast electricity consumption in 2025 without CHP (they combined commercial and industrial 

CHP in their analysis). Efficiency potential studies completed for Georgia Power and the Georgia 

Environmental Facilities Authority found maximum achievable electric efficiencies of 10 percent 

over 10 years and 6.6 percent over five years, respectively (ICF, 2005; Nexant, 2007). Similarly, a 

study for North Carolina found a maximum achievable potential for industrial electric efficiency of 

12 percent over a 10 year horizon (GDS Associates, 2006). An efficiency potential study for 

Kentucky modeled cost effective industrial electricity savings of 15.5 percent and natural gas savings 

of 10.3 percent over 10 years in that state (KPPC, 2007). 

A summary of the economic tests performed on the various industrial policies is shown in Table 

5.13. 

Figure 5.10 Annual Investment and Energy Savings 

from Industrial Policy Package, 2010-2030
Energy Efficiency in Appalachia, SEEA 

88

Table 5.13 Summary of Economic Tests for Industrial Policy Bundles

IAC ESA CHP Total

Participants Test

NPV Benefits 

(billion 2006$) 1.86 5.57 1.53 8.96

NPV Costs 

(billion 2006$) 0.37 1.96 2.38 4.70

Net BenefitsCosts (billion 

2006$)

1.49 3.61 -0.84 4.26

B/C Ratio 5.03 2.84 0.65 1.91

Total Resource Cost Test

NPV Benefits 

(billion 2006$) 3.00 9.53 0.97 13.51

NPV Costs 

(billion 2006$) 0.51 2.89 3.09 6.50

Net BenefitsCosts (billion 

2006$)

2.49 6.64 -2.12 7.01

B/C Ratio 5.85 3.30 0.31 2.08

The Industrial Policy Package is cost-effective with a benefit-to-cost ratio of about 1.9 for 

participants and about 2.1 for society. With $2.5 billion in program spending and an additional $13.1 

billion in customer investments over the 2010-2030 period, the Appalachian Region could see net 

cumulative savings of 13.0 quads, saving $58.5 billion in energy bills by 2050. This is the equivalent 

of about 27.9 percent of the EIA‘s forecast consumption in 2030, or 153 percent of forecast growth 

(EIA, 2008a).
Chapter 6: Transportation 

89

1.2

1.4

1.6

1.8

2.0

2.2

2.4

2.6

2.8

3.0

3.2

2006 2009 2012 2015 2018 2021 2024 2027 2030 Quadrillion Btu

AEO 2007 AEO 2008

6 ENERGY EFFICIENCY IN TRANSPORTATION 

6.1 TRANSPORTION ENERGY USE IN APPALACHIA

The Appalachian transportation sector consumed 11.6 billion gallons of gasoline and 2.8 billion 

gallons of diesel in 2006. The transportation sector offers significant energy-efficiency potential 

through the implementation of stricter standards, enforcement, and a variety of policies. This analysis 

estimates the energy savings potential from both light- and heavy-duty highway vehicles.27

Heavy-duty consumption of diesel fuel is expected to grow along with population to reach 3.5 billion 

gallons by 2030 while gasoline consumption will reach 14.8 billion gallons by 2030. However, the 

passage of new Corporate Average Fuel Economy (CAFE) standards by the 2007 Energy 

Independence and Security Act (EISA, 2007) will significantly reduce gasoline consumption in 

Appalachia by 2030. 

Light duty gasoline consumption is also 

projected out to 2030 in Figure 6.1. 

Forecast growth in consumption falls 

steeply when the savings that result from 

the new CAFE standards are considered. 

The 2007 projection estimates that 

transportation energy consumption will 

reach 2.98 quads by 2030 while the 2008 

AEO ARC adjusted projection estimates 

consumption of 2.54 quads by 2030.28

EISA established a CAFE of at least 35 

miles per gallon for cars and light trucks 

by 2020, a 40 percent increase over 

today's fuel economy standard. Fuel 

savings documented in the transportation 

policy options that follow are estimated 

based on a reference case that 

incorporates the effect of new CAFE standards. Our estimates of gasoline savings in Appalachia due 

to the increase in fuel economy to 35 mpg by 2020 are shown in detail in Table 6.1. 

 

27 Throughout this analysis, we have considered gasoline only in light-duty analysis, and we have considered diesel only in 

heavy-duty analysis. We recognize that these segments of the sector are not exclusively using these fuels, but they 

represent the largest percentage of consumed fuel. Savings would also be expected in other fuels used in the sector, but 

they are not modeled. 

28 Please note that energy savings in Table 6.1 were obtained by running a stock model and do not match the consumption 

figures shown in Figure 6.1. 

Figure 6.1 Appalachian Region Transportation 

Consumption Forecast (quads), 2006-2030

(EIA, 2007a; 2008a)
Energy Efficiency in Appalachia, SEEA 

90

Table 6.1 CAFE Fuel Savings

Year

Motor 

Gasoline

Total Primary 

Energy Saved % of Sector Primary 

(million Energy

gallons) (trillion Btu)

2010 22 3 0.1

2013 337 42 1.8

2020 1,843 229 9.5

2030 4,222 524 20.9

The bulk of energy consumed in the transportation sector comes from motor gasoline (63 percent); 

diesel is another important source (23 percent) while propane, natural gas, ethanol blends, electricity, 

and other fuels are relied on less (see Figure 6.2). 

Motor 

Gasoline

63%

Diesel

23%

Other 

14%

6.2 POLICY OPTIONS FOR TRANSPORTATION ENERGY EFFICIENCY

The policy bundles described in detail in this report include pay-as-you-drive (PAYD) Insurance, 

Clean Car Standards, low-interest loans for heavy-duty efficiency improvements, and a stricter 

enforcement of speed limits. 

Progress has been made around the country on many of the policies discussed. Several experimental 

PAYD programs are already in place: GMAC offers a mileage-based discount in Arizona, Indiana, 

Illinois, and Pennsylvania; and Progressive Insurance has a pilot program in Minnesota and is 

launching one in Texas, where the Legislature has passed a bill allowing companies to offer mileagebased coverage. California recently approved regulations increasing the mileage-based component of 

insurance rates, and Oregon is providing tax credits to insurers offering pay-as-you-drive policies. 

Figure 6.2 Transportation Sector Energy Sources by Fuel, 2006

(EIA, 2008a)
Chapter 6: Transportation 

91

Georgia is conducting a PAYD study, and Washington a pilot project, both funded by the Federal 

Highway Administration.29 Similarly, several states have taken action with regards to fuel economy 

standards. Fourteen states have currently signed on to California‘s Clean Car Standard, vowing to 

reduce greenhouse gas emissions by 30 percent of 2002 levels by 2016. These states are still awaiting 

approval from the EPA to continue with implementation. 

Table 6.2 highlights the supporting policies that will ensure successful implementation of the options 

discussed in this chapter. 

Table 6.2 Policies That Support Transportation Energy Efficiency

Actions PAYD Clean Car 

Standards

Heavy-Duty 

Efficiency Loans

Improved Speed limit 

Enforcement

Research, 

Development, and 

Demonstration

Demonstration or pilot 

programs for PAYD 

insurance schemes

Vehicle R&D to 

encourage 

development of lowemissions vehicles

N/A N/A

Financing N/A N/A

Low-interest loans 

for the purchase of 

new high-efficiency 

vehicles or the 

retrofit of existing 

trucks with approved 

energy-efficiency 

technologies

N/A

Financial 

Incentives

Monetary incentives to 

insurance companies 

for each PAYD policy 

issued

Tax incentives and 

feebates for efficient 

vehicles support 

Clean Car Standards

Rebates and tax 

incentives to 

encourage fleet-wide 

adoption of efficient 

technologies for 

heavy trucks

N/A

Pricing PAYD Insurance N/A N/A N/A

Voluntary 

Agreements

Agreement from 

insurers to participate 

on a voluntary basis at 

the outset of the 

program

N/A N/A N/A

Regulations

Removal of regulatory 

obstacles to allow 

implementation of 

mileage-base insurance 

premiums

Clean Car Standards

Fuel economy 

standards for heavyduty vehicles

Reduced state speed 

limits, Improved Speed 

Limit Enforcement

 

29 Federal Highway Administration, Value Pricing Project Quarterly Report October-December 2006, 

http://ops.fhwa.dot.gov/tolling_pricing/value_pricing/quarterlyreport/qtr4rpt06/; Sightline Institute, Pay-as-You-Drive 

Pilot in Washington, http://daily.sightline.org/daily_score/archive/2007/03/29/pay-as-you-drive-pilot-in-washington

(2007) 
Energy Efficiency in Appalachia, SEEA 

92

Table 6.2 Policies That Support Transportation Energy Efficiency

Actions PAYD Clean Car 

Standards

Heavy-Duty 

Efficiency Loans

Improved Speed limit 

Enforcement

Information 

Dissemination & 

Training

Industry-wide 

workshops on PAYD 

insurance schemes.

N/A N/A

Consumer educational 

resources targeting 

road safety and fuel 

economy savings 

derived from driving at 

the speed limit

Procurement N/A

Purchase of fleet 

vehicles that 

individually meet the 

GHG standard

N/A N/A

Market Reforms N/A N/A N/A N/A

Planning 

Techniques N/A N/A N/A N/A

Capacity Building N/A N/A N/A N/A

This table describes policy actions available that could further the savings from this policy; those in italics are modeled in this study 

while the others are not.

6.3 MODELED SAVINGS IN THE TRANSPORTATION SECTOR

6.3.1 Enabling Pay-As-You-Drive Insurance

One reason that people use their vehicles as much as they do is that a high percentage of total driving 

costs are ―fixed‖ costs, i.e., they are independent of the number of miles driven. The impacts of 

driving, however, are very dependent on how much people drive. One approach to reducing miles 

driven is to convert a largely fixed cost, such as insurance, to a variable cost. ―Pay-as-you-drive‖

(PAYD) insurance accomplishes this by having the rate paid by an individual depend heavily on the 

number of miles driven. Drivers would pay a portion of their premiums up front, and the remainder 

would be charged in proportion to mileage, as determined by a mileage tracking device or periodic 

odometer readings. In principle, this makes sense from the insurance industry‘s perspective as well, 

because those who drive fewer miles have lower accident exposure, on average. 

The 2005 Federal transportation funding law ―SAFETEA-LU‖ includes a $3 million per year setaside for experimental, market-based incentive programs like PAYD insurance. Several states have 

already applied for funding.30

A PAYD program could be an insurance company policy or product, but some action on the part of 

states may be required to remove regulatory obstacles to changing the basis for premiums or to 

promote the program. The policy proposed here is to phase in PAYD insurance in the Appalachian 

Region, starting with a pilot program. For three years beginning in 2009, Appalachian states would 

offer incentives for insurance policies based largely on miles driven. More specifically, the Region 

(or entities within the Region) would grant $200 to insurance agencies for each one-year policy they 

 

30 Environmental Defense, Pay-As-You-Drive Auto Insurance,

http://www.environmentaldefense.org/article.cfm?contentid=2205 
Chapter 6: Transportation 

93

write for which 80 percent or more of the pre-program policy cost is scaled by the ratio of miles 

driven to the state average miles driven. The incentive is necessary so long as PAYD is optional; 

without it, insurance companies may be concerned about losing revenues from the low-mileage 

customers who would choose such a policy without being able to offset these costs with higher 

premiums for high-mileage customers. Should the pilot program prove successful, we recommend 

phasing in a mandatory PAYD insurance program over the next ten years. 

Insurance companies would be responsible for converting a percentage of their policies to PAYD, 

with the percentage increasing each year until PAYD is universal in 2020. Along with implementing 

PAYD insurance, the state should educate vehicle owners on how they can reduce their insurance 

payments by driving less. 

The program proposed here begins with a three-year pilot program subsidized by the Region. The 

Region would offer insurance companies a $200 incentive per PAYD policy, with goals of 2,000 

policies in 2009, 10,000 policies in 2010, and 20,000 policies in 2011. A mandatory program would 

then be phased in over the next ten years. Miles driven would be monitored using the odometer or an 

added tracking device. Numbers would periodically be reported back to insurance companies to 

ensure compliance with regulations. This program would be expected to result in a four percent 

reduction in driving and vehicle miles traveled (VMT), and consequently light-duty vehicle energy 

use, by 2020. Table 6.3 presents the projected impacts by year. 

Table 6.3 Estimated Impacts of a Mileage Pay-As-You Drive

Insurance Program

Year

Diesel Motor Gasoline Total Primary 

Energy Saved % of Sector 

Primary Energy

(million gallons) (million gallons) (trillion Btu)

2010 0.00 0.65 0.08 0.00

2013 0.00 130.80 16.24 0.69

2020 0.00 540.45 67.11 2.77

2030 0.00 595.54 73.95 2.91

Insurance companies could incur substantial per-vehicle monitoring costs during the pilot phase of 

the proposed program for distribution of mileage tracking devices and data collection expenses. 

However, once the program becomes mandatory, per-vehicle costs would decline as tracking device 

costs decline and data collection and analysis is spread over a large number of vehicles.31 We assume 

a per-vehicle cost of $40 per vehicle per year in the pilot phase and $10 per vehicle; thereafter these 

costs are represented as administrative costs in Table 6.4. While we have considered these costs as 

administrative costs in this analysis, they will not necessarily be borne by the public office 

administrating the enabling program; rather, they will not necessarily be borne by the insurance 

companies and likely passed on to consumers, who will benefit. 

 

31 Bordoff and Noel, 2008 
Energy Efficiency in Appalachia, SEEA 

94

0

10

20

30

40

50

60

70

80

2010 2015 2020 2025 2030 Trillion BTU

0

50

100

150

200

250 Million 2006$

Energy Savings Public Investment Private Investment

Table 6.4 Energy Savings and Costs a Pay-As-You Drive

Insurance Program

Year

Energy Savings Admin Costs Investment 

Costs

(million 2006$) (million 2006$) (million 2006$)

2010 1.60 4.69 2.00

2013 311.57 55.04 0.00

2020 1,298.31 217.44 0.00

2030 1,506.42 232.83 0.00

Although many states and several 

insurance companies have shown an 

interest in PAYD insurance, 

implementation to date has been limited. 

In a voluntary program, companies 

already in the market could lose lowmileage customers to new companies 

that can afford to offer these drivers 

mileage-based premiums without having 

to subsidize coverage for high-mileage 

drivers. To avoid this potential source of 

opposition to the program, the state 

could offer incentives only for policies 

that insurance companies write for their existing 

customers or for drivers new to the state. When the 

program becomes mandatory, this concern disappears, because reduced premiums for low-mileage 

drivers will be offset by increased premiums for high-mileage drivers. 

Like other pricing policies designed to reduce miles driven and promote alternative travel modes, 

PAYD insurance may raise questions of equity, especially in rural areas, where alternatives to 

driving are not readily available. Insurance premiums are generally lower in rural areas than in urban 

areas, however, so high-mileage premiums would be smaller there. Moreover, in a PAYD program, a 

rural driver's annual mileage would be compared to that of other rural drivers for purposes of 

determining the insurance premium. Also, low-income drivers generally drive less than higherincome drivers, and low-income drivers as a group consequently would be net beneficiaries of payas-you-drive insurance programs.32

Objections to PAYD insurance have also been raised based on privacy concerns. This is particularly 

the case when the proposed mileage verification system relies upon GPS-based information. A 

system based on periodic odometer readings will probably be adequate for such a program, however. 

 

32 Bordoff and Noel, 2008 

Figure 6.3 Pay-As-You-Drive Public 

Investment and Returns
Chapter 6: Transportation 

95

An alternative approach to reduce VMT through monetary incentives would be increasing the state 

gas tax. The average gas tax in the Appalachian states stands at $0.22 per gallon.33 As noted above, 

PAYD insurance would in effect increase the variable cost of driving by $0.048 per mile. Achieving 

the same result by raising the gas tax would require an increase of roughly $1.33 per gallon in the gas 

tax, something that would not be popular with the general public.34 Also, a gas tax increase, unlike 

PAYD insurance, would increase the tax burden in aggregate unless offset by reductions in other 

taxes such as income tax. 

PAYD insurance is one of many measures that could be adopted to reduce vehicle miles traveled, 

which include fees to enter metropolitan areas, parking pricing, enhanced transit, and improved land 

use policies to promote compact development. PAYD insurance is used here to signal the importance 

of a comprehensive approach to energy efficiency in the transportation sector, which must include 

system efficiency as well as vehicle efficiency. 

Supporting PAYD insurance is cost-effective with a benefit-to-cost ratio of more than 1,000 for 

participants and about 7.3 for total resource costs. With $3.4 billion in program spending over the 

2010-2030 period, the Appalachian Region could see net cumulative savings of 1.6 quads, saving 

$32.0 billion in energy bills by 2044. This is the equivalent of about 2.9 percent of the EIA‘s 

forecast consumption in 2030, or 27.9 percent of forecast growth (EIA 2008a). 

6.3.2 Clean Car Standards

The energy efficiency of automobiles relates directly to their emissions of carbon dioxide, the 

dominant greenhouse gas (GHG). States developing plans to reduce GHG emissions are eager to 

include cars and light trucks, which contribute 27 percent to U.S. GHG emissions.35 Fourteen states 

have adopted a Clean Car standard, introduced by California, that will reduce greenhouse gas 

emissions from new vehicles by 30 percent in 2016 while cutting emissions of traditional pollutants 

as well. These states are: Arizona, California, Connecticut, Maine, Maryland, Massachusetts, New 

Jersey, New Mexico, New York, Oregon, Pennsylvania, Rhode Island, Vermont, and Washington. 

Legislation has been introduced in Minnesota, Nevada, Tennessee, and Texas as well. 

Measures available to meet these states‘ GHG requirements include increased use of alternative fuels 

and improved air conditioners, for example. But in practice, the primary pathway to meeting the 

standard will be improvements in vehicle fuel efficiency. Thus, the adoption of the Clean Car 

Standard throughout the ARC Region could greatly improve vehicle fuel efficiency, thereby helping 

to meet the energy-efficiency goal. This would come about through accelerated penetration of 

technologies that are already entering the market, such as variable valve timing, cylinder 

deactivation, and five-speed transmissions, as well as increased sales of hybrid and diesel vehicles. 

Despite the recent passage of higher light-duty CAFE standards, many consumers and policymakers 

are still eager to see the more stringent California emissions criteria implemented nationwide. In 

addition to increasing efficiency faster in the near term than the new CAFE standards do, California‘s 

 

33 Federal Highway Administration, http://www.fhwa.dot.gov/ohim/mmfr/dec07/trmfuel.cfm

34 A gas tax increase of $1.33 per gallon would in fact reduce fuel consumption by more than a PAYD policy in the longterm because it would affect not only the amount people drive but also their choice of vehicle. We are proposing other 

mechanisms to increase vehicle efficiency, however. 

35 U.S. EPA, ―Greenhouse Gas Emissions from Transportation and Other Mobile Sources,‖

http://www.epa.gov/otaq/greenhousegases.htm
Energy Efficiency in Appalachia, SEEA 

96

standards will continue to rise after 2016. California has indicated its intention of extending the 

GHG standards to levels that would raise average fuel economy to at least 40 mpg by 2020.36

Based on these developments and the existence of cost-effective technologies to further increase fuel 

economy after 2020, we propose going above and beyond the California program to aim for a 

combined standard of 50 miles per gallon by 2030. 

Appalachia‘s adoption of Clean Car Standards means that new vehicles sold in the Region by each 

manufacturer would need to meet the requirements shown in Table 6.5 for GHG emissions, on 

average and also the fuel economy goals that we propose highlighted in Table 6.6. The standards 

divide vehicles into two categories; larger vehicles are allowed higher emissions than smaller 

vehicles. 

Table 6.5 Proposed Clean Car Standards for Greenhouse Gases, 2009-2020a

Year

CO2-equivalent emissions standard (g/mi)

Passenger cars and small 

trucks/SUVs Large trucks/SUVs

Near-term

2009 323 439

2010 301 420

2011 267 390

2012 233 361

Mid-term

2013 227 355

2014 222 350

2015 213 341

2016 205 332

Long-term 

2017 195 310

2018 185 285

2019 180 270

2020 175 265

a CARB, 2008

 

36 Comparison of Greenhouse Gas Reductions under CAFE Standards and ARB Regulations Adopted Pursuant to 

AB1493, CARB, 2008 
Chapter 6: Transportation 

97

Table 6.6 Proposed Fuel Economy Standards 2020-2030

Year

Fuel Economy Standards (mpg)

Combined (Passenger cars, small trucks/SUVs and

large trucks/SUVs)

2021 42.1

2022 42.9

2023 43.8

2024 44.7

2025 45.6

2026 46.5

2027 47.4

2028 48.2

2029 49.1

2030 50.0

Implementing the Clean Car Standard in the Appalachian Region would result in the energy savings 

and costs, beyond those of the Federal CAFE program, shown in Tables 6.7 and 6.8. Table 6.7 

shows that the savings, modeled only for gasoline and not diesel fueled vehicles, would amount to 

just 2.6 percent of forecast consumption in 2020, but more than 13 percent in 2030. 

Table 6.7 Light-Duty Vehicle Fuel Savings in Appalachia from

Adoption of Clean Car Standards

Year

Diesel Motor 

Gasoline

Total Primary 

Energy Saved % of Sector 

(million Primary Energy

gallons)

(million 

gallons)

(trillion Btu)

2010 0.00 0.00 0.00 0.00

2013 0.00 87.24 10.83 0.46

2020 0.00 516.19 64.09 2.65

2030 0.00 2,720.23 337.76 13.48

Table 6.8 shows the costs and monetary savings for select years in the study period; private 

investment grows from just about $50 million in 2010 to more than $2 billion in 2030 to keep up 

with the standard. Administrative costs remain low. 
Energy Efficiency in Appalachia, SEEA 

98

0

50

100

150

200

250

300

350

2010 2015 2020 2025 2030 Trillion BTU

0

400

800

1200

1600

2000

2400 Million 2006$

Energy Savings Public Investment Private Investment

Table 6.8 Energy Savings and Costs from Adoption

of Clean Car Standards

Year

Energy Savings Admin Costs Investment 

Costs

(million 2006$) (million 2006$) (million 2006$)

2010 0.00 4.29 51.60

2013 207.82 4.29 376.65

2020 1,240.03 4.29 1,235.41

2030 6,880.83 4.29 2,207.77

Figure 6.4 shows the growth in 

energy savings is rapid over the 

study horizon with a large private 

investment. 

States adopting Clean Car Standards 

have done so through their Federal 

Clean Air Act compliance programs. 

The Clean Air Act allows states to 

choose between the Federal vehicle 

pollution control program overseen 

by the U.S. Environmental 

Protection Agency (EPA) and the 

Low Emission Vehicle program devised 

by the California Air Resources Board 

(CARB). The latter program is the Clean 

Car Standards discussed here. However, in order for the standards to come into effect, the State of 

California must obtain a waiver from the EPA. This waiver was denied by the EPA in February 2008, 

but California and the 14 other states that have adopted the Clean Car Standards have filed a suit to 

overturn this denial. 

The New Clean Car Standard is cost-effective with a benefit-to-cost ratio of about 2.1 for participants 

and about 2.5 for total resource costs. With $90.1 million in program spending and an additional 

$24.8 billion in customer investments over the 2010-2030 period, the Appalachian Region could see 

net cumulative savings of 5.4 quads, saving $108.2 billion in energy bills by 2044. This is the 

equivalent of about 11.3 to 13.3 percent of the EIA‘s forecast consumption in 2030, or 46.9 to 127.4 

percent of forecast growth (EIA, 2007a; 2008a). 

6.3.3 SmartWay Heavy Truck Efficiency Loan Program

The Appalachian Development Highway System (ADHS) is the first highway system authorized by 

Congress for the purpose of stimulating economic development and will eventually be a complex 

Figure 6.4 Annual Investment and Energy Savings 

from a New Clean Car Standard, 2010-2030
Chapter 6: Transportation 

99

network that connects the Appalachian Region to national markets. Vehicle miles traveled by freight 

trucks on the ADHS is expected to grow by 400 percent by 2035.37

Due to the level of commercial activity that takes place on the ADHS, heavy trucks make up a 

significant portion of total vehicle miles traveled in the Appalachian Region. Tractor-trailers 

dominate heavy-duty fuel usage due to their high annual mileage and relatively low fuel economy. 

Trucking companies are sensitive to fuel costs, which are typically second only to labor among their 

business expenses; a tractor-trailer may consume well in excess of $50,000 of fuel annually. Truck 

manufacturers may therefore be more aggressive in improving the fuel economy of their products 

than are light-duty vehicle manufacturers. Yet substantial barriers to efficiency do exist in the truck 

market, including the rapid turnover of trucks from first to second owner and the absence of 

standards for heavy-duty fuel economy, or even a standardized test procedure to measure it. 

Consequently, there are numerous technologies and strategies available to improve fuel economy that 

are not fully utilized. Indeed, average fuel economy for new tractor-trailers could be raised by over 

50 percent through a variety of cost-effective existing and emerging technologies, including 

aerodynamics, engine improvements, transmission enhancements, and weight reduction. 38

Our proposal is to establish a low-interest loan program, beginning in 2009, to promote the purchase 

of new trucks or the retrofit of existing trucks with approved energy-efficiency technologies and 

equipment. In particular, equipment in the efficiency package identified by U.S. EPA‘s SmartWay 

Transport Partnership would be eligible. This SmartWay upgrade kit, which includes aerodynamic 

add-ons for trailers, efficient tires, and auxiliary power units (APUs) allowing long-distance truckers 

to dramatically reduce idling, has been found to reduce fuel consumption by 15 percent or more 

while reducing emissions. 

We estimate savings from the loan program for truck efficiency equipment, beginning with its 

application to the improvements identified by SmartWay. Determining what trucks are likely 

candidates for the program requires a breakdown of the heavy-duty truck stock. By far the biggest 

consumers of diesel fuel in the aggregate are ―heavy-heavy‖ trucks (those having Gross Vehicle 

Weight of at least 26,000 pounds), primarily tractor-trailers. Estimated diesel savings are shown in 

Table 6.9. 

Table 6.9 Savings from Low-Interest Loans for Heavy Truck Efficiency 

Improvements

Year

Diesel Motor 

Gasoline

Total Primary 

Energy Saved % of Sector 

(million Primary Energy

gallons)

(million 

gallons)

(trillion Btu)

2010 29.19 0.00 4.03 0.18

2013 73.75 0.00 10.18 0.44

2020 128.87 0.00 17.79 0.73

2030 138.96 0.00 19.19 0.77

 

37 Cambridge Systematics, 2008 

38 Langer, 2004 
Energy Efficiency in Appalachia, SEEA 

100

0

5

10

15

20

25

2010 2015 2020 2025 2030 Trillion BTU

0

50

100

150

200

250

300

350

400 Million 2006$

Energy Savings Public Investment Private Investment

Table 6.10 shows energy cost savings resulting from reduced consumption of diesel through this loan 

program; the savings are based on forecast diesel prices (EIA, 2008a). Administrative costs are flat 

over the program horizon while investment costs decline over time. 

Table 6.10 Energy Savings and Costs from Heavy Truck

Efficiency Improvements

Year

Energy Savings Admin Costs Investment 

Costs

(million 2006$) (million 2006$) (million 2006$)

2010 74.93 4.29 337.90

2013 183.62 4.29 287.85

2020 321.16 4.29 21.35

2030 371.53 4.29 25.01

Figure 6.5 shows that a constant 

public investment yields dramatically 

increasing energy savings in the first 

years of the program but plateaus 

around 2017. Private investment is 

expected to be much higher in the first 

years of the program. 

The heavy truck efficiency loan 

program is likely to be welcomed by 

trucking companies, especially those 

with small- to medium-size fleets. With the 

projected expansion of commercial trucking 

activity that is expected to take place in the Appalachian Region by 2035, fuel efficiency will be 

crucial to the operation of trucking fleets, and smaller companies will have more difficulty in 

affording the SmartWay upgrades described here without financial assistance. 

The SmartWay Heavy Truck Efficiency Loan Program is cost-effective with a benefit-to-cost ratio of 

about 1.3 for participants and about 1.6 for total resource costs. With $90.1 million in program 

spending over the 2010-2030 period, the Appalachian Region could see net cumulative savings of 0.5 

quads, saving $10.1 billion in energy bills by 2044. This is the equivalent of about 0.8 percent of the 

EIA‘s forecast consumption in 2030, or 7.2 percent of forecast growth (EIA, 2008a). 

6.3.4 Speed Limit Enforcement

At high speeds, vehicle efficiency falls off rapidly with further increases in speed, as aerodynamic 

drag begins to dominate vehicle energy requirements. The speed at which fuel economy is highest 

varies from vehicle to vehicle, but is typically below 60 miles per hour (mph) for a light-duty 

Figure 6.5 Annual Investments and Energy 

Savings from SmartWay Loans
Chapter 6: Transportation 

101

vehicle.39 Federal Highway Administration tests of nine light-duty vehicles in 1997 found that fuel 

economy declined on average by 3.1 percent when speed increased from 55 mph to 60 mph and by 

8.2 percent increasing from 65 to 70 mph.40 For a heavy truck such as a tractor trailer, fuel economy 

declines by about two percent per mph at highway speeds.41 Thus, slowing high-speed driving would 

be one means of improving the real-world efficiencies of cars and trucks. This could be 

accomplished either by reducing the maximum speed limit or by more stringently enforcing the 

existing speed limits. 

Rather than lowering current speed limits, this policy proposes more stringently enforcing the 

existing speed limits for vehicles in the Appalachian Region. Doing so could both increase highway 

safety and provide fuel savings. Given demands on the time of police and highway patrol, additional 

enforcement would best be approached through other means, including increased use of radar, lasers 

and speed cameras, and education. 

In many states across the country, recommended practice is to set speed limits at the 85th percentile 

of driving that occurs on the roadway. In reality, speed limits are set lower than this for most roads; 

on average, over half of all traffic travels over the speed limit. The energy savings that result from an 

improved enforcement of speed limits for both light- and heavy-duty vehicles are shown below in 

Table 6.11. 

Table 6.11 Estimated Benefits of Improved Speed Limit Enforcement –

Light-Duty and Heavy-Duty Vehicles

Year

Diesel Motor 

Gasoline

Total Primary 

Energy Saved % of Sector 

(million Primary Energy

gallons)

(million 

gallons)

(trillion Btu)

2010 33.03 140.13 21.96 0.97

2013 34.78 145.33 22.85 0.98

2020 36.46 150.12 23.67 0.98

2030 39.32 165.43 25.97 1.02

Table 6.12 shows that no private investment is expected to occur for reducing speed limits; however, 

a constant administrative cost is assumed. 

 

39 ―Drive more efficiently.‖ U.S.DOE and U.S. EPA, http://www.fueleconomy.gov/feg/driveHabits.shtml. 

40 Davis and Diegel, 2006 

41 ―Factors Affecting Truck Fuel Economy,‖ Goodyear Tire, 

http://www.goodyear.com/truck/pdf/radialretserv/Retread_S9_V.pdf
Energy Efficiency in Appalachia, SEEA 

102

19

20

21

22

23

24

25

26

27

2010 2015 2020 2025 2030 Trillion BTU

0

1

2

3

4

5

6

7

8

9

10 Million 2006$

Energy Savings Public Investment Private Investment

Table 6.12 Energy Savings and Costs from

Speed Limit Enforcement

Year

Energy Savings Admin Costs Investment 

Costs

(million 2006$) (million 2006$) (million 2006$)

2010 430.79 4.29 0.00

2013 432.78 4.29 0.00

2020 451.51 4.29 0.00

2030 523.57 4.29 0.00

Figure 6.6 shows that a constant 

public investment (administrative 

cost) can drive increased energy 

savings over time by reducing 

instances of speeding. 

While reducing the speed limit is 

generally difficult politically, better 

enforcing of current law should be 

less controversial, and may be 

politically viable primarily on the 

basis of enhanced public safety and 

the reduction in serious injuries and deaths 

from vehicular accidents. On the other hand, if 

a large percentage of drivers regularly exceed the speed limit, as assumed above, much of the traffic 

engineering community would take this as an indication that existing speed limits are set too low. 

Most proposals to enforce speed limits are controversial, especially with regards to speed 

requirements for heavy trucks. The American Trucking Association recently endorsed a proposal to 

limit the speed of heavy trucks to 68 miles per hour; the Owner-Operator Independent Drivers 

Association opposed the idea.42

Enforcing speed limits for both light- and heavy-duty vehicles is cost-effective with a benefit-to-cost 

ratio of 64.3 for total resource costs; because there is no assumed private investment cost, the 

participant ratio is undefined. With $180 million in program spending over the 2010-2030 period, 

the Appalachian Region could see net cumulative savings of 0.9 quads, saving $16.8 billion in 

energy bills by 2044. This is the equivalent of about 1.0 percent of the EIA‘s forecast consumption 

in 2030, or 9.8 percent of forecast growth (EIA, 2008a). 

 

42 See http://www.truckline.com/NR/exeres/CB4D4AAD-27EB-4801-8F4A-B82F45E03D70.htm and 

http://www.landlinemag.com/Special_Reports/2007/Jan07/SR%2001-29-

07%20OOIDA%20speed%20limiters%20by%20JJ.htm 

Figure 6.6 Speed Limit Enforcement
Chapter 6: Transportation 

103

6.4 SUMMARY OF RESULTS FOR TRANSPORTATION POLICY BUNDLES

Transportation efficiency is rarely modeled in efficiency potential studies, partly because states and 

local governments, who usually commission such studies, do not historically regulate transportation 

energy distribution in the same way that they address electricity and natural gas. There have been 

recent state efforts in this sector, following California, to regulate vehicle efficiency through clean 

car standards. Our model suggests that standards could provide substantial savings, nearly threefourths of our total transportation savings in 2030 come from new clean car savings (see Figure 6.7). 

Transportation Energy Savings in 2030, by Policy (Trillion Btu, 

Primary)

Clean Car 

Standard

338

74%

Pay-as-youDrive

73.9

16%

SmartWay

19.2

4%

Speed Limits

26.0

6%

Figure 6.8 shows that the combined transportation package reduces consumption to less than 2006 

levels by 2022 with savings roughly doubling what is estimated by the EIA to come from the new 

efficiency requirements in EISA 2007. 

1.2

1.4

1.6

1.8

2

2.2

2.4

2.6

2.8

3

3.2

2006 2009 2012 2015 2018 2021 2024 2027 2030 Quadrillion Btu

AEO 2007 AEO 2008 With Policy Packages

Figure 6.9 shows that public investment remains low for the entire study horizon while private 

investment climbs quickly from 2010 to 2016 and steadily thereafter; the bumps in the private 

Figure 6.7 Primary Energy Savings by Policy Package 

(trillion Btu), 2030

Figure 6.8 Transportation Primary Energy Consumption 

With and Without Policy Packages (quads), 2006-2030
Energy Efficiency in Appalachia, SEEA 

104

investment line represent peaks in investment in the Pay-as-you-Drive and SmartWay programs. 

Annual energy savings grow, in an exponential fashion, for the entire period. 

0

50

100

150

200

250

300

350

400

450

500

2010 2015 2020 2025 2030 Trillion BTU

0

500

1000

1500

2000

2500 Million 2006$

Energy Savings Public Investment Private Investment

Table 6.13 shows the benefit/cost ratios for each transportation policy bundle as well as test results 

for combined policy implementation. 

Figure 6.9 Annual Investments and Energy Savings 

from Transportation Policy Packages, 2010-2030
Chapter 6: Transportation 

105

Table 6.13 Results of Economic Tests for Transportation Policy Bundles

Clean Car 

Standard

Pay-AsYou-Drive 

Insurance

SmartWay Speed Limits Total

Participants Test

NPV Benefits 

(billion 2006$) 14.94 6.87 2.37 4.39 28.57

NPV Costs 

(billion 2006$) 7.11 0.01 1.78 0.00 8.89

Net BenefitsCosts (billion 

2006$)

7.83 6.86 0.60 4.39 19.68

B/C Ratio 2.10 1,340 1.34 – 3.21

Total Resource Cost Test

NPV Benefits 

(billion 2006$) 25.49 10.20 3.37 5.98 45.04

NPV Costs 

(billion 2006$) 10.00 1.39 2.05 0.09 13.54

Net BenefitsCosts (billion 

2006$)

15.49 8.80 1.32 5.89 31.49

B/C Ratio 2.55 7.31 1.64 64.32 3.33

The transportation sector policy package is cost-effective with a benefit-to-cost ratio of about 3.2 for 

participants and about 3.3 for total resource costs. With $3.7 billion in program spending and an 

additional $27.6 billion in customer investments over the 2010-2030 period, the Appalachian Region 

could see net cumulative savings of 8.3 quads, saving $167.1 billion in energy bills by 2044. This is 

the equivalent of about 30.1 percent of the EIA‘s forecast consumption in 2030, or 172.4 percent of 

forecast growth (EIA, 2008a).
Energy Efficiency in Appalachia, SEEA 

106

Box 6.1 Intermodal Improvements

Several opportunities have been identified to improve the freight transportation network in the 

Appalachian Region through intermodal investments. Such improvements have been driven first and 

foremost by the Region's goal of economic growth, based on better integration with the rest of the 

country. Tapping into areas of growth in the national and global economies will require up-to-date 

approaches to moving goods, which today increasingly implies the use of energy-efficient 

technologies and modes. Consequently, energy-efficient freight movement, while perhaps not the 

driver of new transportation investment, could be a key element of the Region‘s economic 

development program. At the same time, to the extent that modern intermodal services draw freight 

from adjacent Regions, they could cause energy consumption within the Appalachian Region to 

increase, despite reducing energy consumption overall. 

Among the intermodal projects in progress or under consideration to serve the Appalachian Region 

are: 

 the Central Corridor Doublestack Initiative, to allow stacked intermodal containers to travel 

by rail along the Norfolk Southern route from Norfolk to Columbus;

 the South Carolina Inland Port, which would alleviate space and congestion pressures at the 

Port of Charleston by sending containers by rail from the port to an inland processing and 

transfer facility; and

 the Port of Pittsburgh Container-on-Barge, to provide a water alternative to trucking for highvalue goods traveling throughout the inland waterway system of the Appalachian Region and 

the Gulf Coast. (Rahall Transportation Institute and Wilbur Smith Associates, 2004)

Each of these three examples represents an opportunity to increase the efficiency of the goods 

movement system while reducing energy use by diverting freight from trucks to other modes. Rail 

and barge operations generally consume far less energy per ton-mile than trucks. While these projects 

are designed to attract the rapidly-growing and lucrative container flows associated with international 

trade, is it important to note that projected growth in freight traffic is largely tied to intraregional 

activity (Rahall Transportation Institute and Wilbur Smith Associates, 2004). Much of this shorterdistance freight will necessarily travel by truck, but if alternative modes can serve some intraregional 

traffic, freight energy use will decline. 

Aside from highways, freight infrastructure has historically been funded by the private sector. Today 

there is a crucial role for regional and national governments in the development and funding of this 

infrastructure, particularly for intermodal freight, given the large public costs and benefits of the 

goods movement system, the need for multi-state cooperation, the need for freight facilities serving 

multiple, competing carriers, and constraints on freight carriers‘ ability to invest in innovative 

projects. 
Chapter 7: Macroeconomic Results 

107

7 MACROECONOMIC RESULTS: 

EMPLOYMENT, INCOME, AND ECONOMIC ACTIVITY 

Up to this point in the analysis we have examined the potential costs and benefits of implementing 

policies that might stimulate greater levels of energy efficiency within the Appalachian Region. The 

evidence suggests that smart policies and programs can drive more productive investments in energyefficient technologies, and they can do so in ways that reduce the Region‘s total energy bill. But the 

question remains, what does this mean for the Regional economy? Do the higher gains in energy 

productivity – that is, do the increased levels of efficiency investment with their concomitant 

reduction in the need for conventional energy resources – create a net economic boost for 

Appalachia? Or, does the diversion of revenues away from energy-related industries negatively 

impact the Regional economy? In this chapter, we explore those issues and we present the analytical 

results of an economic model used to evaluate the impact of efficiency investments on jobs, income, 

and the overall size of the economy. 

A recent meta-review of some past 48 energy policy studies done within the United States suggests 

that if investments in more efficient technologies are cost-effective, the impacts on the economy 

should be small but net positive (Laitner and McKinney, 2008). As shown elsewhere in the report, it 

turns out that from a total resource cost perspective, the benefits (i.e., the energy bill savings) 

outweigh both the policy costs and investments by about two and one-half times. In other words, the 

energy-efficiency policy recommendations highlighted in the ―Region-at-Risk‖ scenario result in a 

substantial savings for households and businesses compared to the costs of implementing the 

policies. As we also discuss below, this consumer energy bill savings can drive a significant increase 

in the number of net new jobs within the Appalachian Region.43

 In fact, continued investments in 

energy-efficiency resources would maintain the energy resource benefits for many years into the 

future, well beyond the period of analysis examined in this report.44 The Region therefore has the 

opportunity to transition its energy markets to a more sustainable pattern of energy production and 

consumption in ways that benefit consumers. 

7.1 METHODOLOGY

The macroeconomic evaluation that we report in this chapter is undertaken in three separate steps. 

First, we calibrate ACEEE‘s economic assessment model called DEEPER (Dynamic Energy 

Efficiency Policy Evaluation Routine) to reflect the economic profile of the Appalachian economy 

(Laitner and McKinney, forthcoming). This is done for the period 2006 (the base year of the model) 

through 2030 (the last year of the analysis). In this respect, we incorporate the anticipated 

investment and spending patterns that are suggested by the standard forecast modeling assumptions. 

These range from typical spending by businesses and households in the analytical period to the 

anticipated construction of new electric power plants and other energy-related spending that might 

also be highlighted in the forecast. Second, we transform the set of key efficiency scenario results 

 

43 As we use the term here, the word ―consumer‖ refers to any one who buys and uses energy. Thus, we include both 

households and businesses as among the consumers who benefit from greater investments in energy efficiency. 

44 As we note elsewhere, the policy analysis ends in the year 2030. Yet, many of the investments we describe have a 

technology of perhaps 15 years. This means that investments made in 2030 would continue to pay for themselves through 

perhaps the year 2044 and beyond; and none of those ongoing energy bill savings are reflected in the analysis described in 

this chapter. 
Energy Efficiency in Appalachia, SEEA 

108

from the policy analysis into the direct inputs which are needed for the economic model. The 

resulting inputs include such parameters as: 

1. The level of annual policy and/or program spending that drives the key policy scenario 

investments; 

2. The capital and operating costs associated with more energy-efficient technologies; 

3. The energy bill savings that result from the various energy-efficiency policies described in 

the main body of the report; and 

4. Finally, a set of calibration or diagnostic model runs to check both the logic and the internal 

consistency of the modeling results. 

So that we can more fully characterize the analysis that was completed for this report, we next 

provide a simplified working example of how the modeling is done. We first describe the financial 

assumptions that underpin the analysis. We then highlight the analytical technique by showing the 

kinds of calculations that are used and then summarize the overall results in terms of net job impacts. 

Following this example, we then review the net impacts of the various policies as evaluated in our 

DEEPER model. A more detailed description of the economic model is presented in Appendix F. 

7.2 ILLUSTRATING THE METHODOLOGY: APPALACHIAN JOBS FROM 

EFFICIENCY GAINS

To illustrate how a job impact analysis might be done, we will use the simplified example of 

installing one hundred million dollars of efficiency improvements within large office buildings 

throughout the Appalachian Region. Office buildings (traditionally large users of energy due to 

heating and air-conditioning loads, significant use of electronic office equipment, and the large 

numbers of persons employed and served) provide substantial opportunities for energy-saving 

investments. The results of this example are summarized in Table 7.1. 

The assumption used in this example is that the investment has a positive benefit/cost ratio of 2.0. In 

other words, the assumption is that for every dollar of cost used to increase a building‘s overall 

energy efficiency, the upgrades might be expected to return a total of two dollars in reduced 

electricity and natural gas costs over the useful life of the technologies. This ratio is similar but 

generally lower to those cited elsewhere in this report. At the same time, if we anticipate that the 

efficiency changes will have an expected life of roughly 15 years, then we can establish a 15-year 

period of analysis. In this illustration, we further assume that the efficiency upgrades take place in 

the first year of the analysis, while the electricity bill savings occur in years one through 15. 
Chapter 7: Macroeconomic Results 

109

Table 7.1 Job Impacts from Commercial Building Efficiency Improvements

Expenditure Category Amount

(million $)

Employment 

Coefficient

Job

Impact

Installing Efficiency Improvements in Year One $100 13 1,300

Diverting Expenditures to Fund Efficiency 

Improvements $-100 12 -1,200

Energy Bill Savings in Years One through 15 $200 12 2,400

Lower Utility Revenues in Years One through 15 $-200 5 -1,000

Net 15-Year Change $0.0 1,500

Note: The employment multipliers are adapted from the appropriate sector multipliers found in Appendix F. The 

benefit/cost ratio is assumed to be 2.0. The jobs impact is the result of multiplying the row change in expenditure 

by the row multiplier. The sum of these products yields a working estimate of total net job-years over the 15-year 

time horizon. To find the average annual net jobs in this simplified analysis we would divide the total job-years 

by 15 years which, of course, gives us an estimated net gain of 100 jobs per year for each of the 15 years. For 

more details, see the text that follows. 

The analysis assumes that we are interested in the net effect of employment and other economic 

changes. This means we must first examine all changes in household and business expenditures – 

both positive and negative – that result from a movement toward greater levels of energy efficiency. 

Although more detailed and complicated within the DEEPER model, for this heuristic exercise we 

then multiply each change in expenditures by the appropriate sector employment coefficient (adapted 

from the values shown in Appendix F). The sum of these products will then yield the net result for 

which we are looking. 

In our example above, there are four separate changes in expenditures, each with their separate 

impact. As Table 7.1 indicates, the net impact of the scenario suggests a cumulative gain of 1,500 

jobs in each of the 15-year period of analysis. This translates into an average net increase of 100 jobs 

each year for 15 years. In other words, the $100 million efficiency investment made in Appalachian 

office buildings is projected to sustain an average of 100 jobs each year over a 15-year period 

compared to a ―business-as-usual‖ scenario. 

The economic assessment of the alternative energy scenarios was carried out in a very similar 

manner as the example described above. That is, the changes in energy expenditures brought about 

by investments in energy-efficiency and renewable technologies were matched with their appropriate 

employment multipliers. There are several modifications to this technique, however. 

First, it was assumed that only 72 percent of both the efficiency investments and the savings are 

spent within the Appalachian Region. We based this initial value on the Minnesota IMPLAN Group, 

Inc. (IMPLAN, 2008) dataset as it describes local purchase patterns that typically now occur in the 
Energy Efficiency in Appalachia, SEEA 

110

Region. We anticipate that this is a conservative assumption since most efficiency and renewable 

energy installations are likely (or could be) carried out by local contractors and dealers. If the set of 

policies encourages greater local participation so that the share was increased to 90 percent, for 

example, the net jobs might grow another 15 percent compared to our standard scenario exercise. At 

the same time, the scenario also assumes Appalachia provides only 40 percent of the manufactured 

products consumed within the Region. But again, a concerted effort to build manufacturing capacity 

for the set of clean energy technologies would increase the benefits from developing a broader instate energy efficiency and renewable energy manufacturing capability. 

Second, an adjustment in the employment impacts was made to account for assumed future changes 

in labor productivity. As outlined in the Bureau of Labor Statistics Outlook 2006–2016, productivity 

rates are expected to vary widely among sectors (BLS, 2008). For instance, drawing from the BLS 

data we would expect that electric utilities might increase labor productivity by 1.8 percent annually 

while the business and personal service sectors of the economy might increase productivity by 2.2 

percent per year. This means, for example, that we might expect a one million dollar expenditure for 

utility services in the year 2030 would support only 68 percent of the jobs that the same expenditure 

would have supported in 2008, while other services sectors of the economy would support only 62 

percent of the jobs as in 2008. 

Third, for purposes of estimating energy bill savings, it was assumed that all energy prices within 

Appalachia would follow the same growth rate as those published by the Energy Information 

Administration in its Annual Energy Outlook (EIA, 2008a).45 Fourth, it was assumed that 

approximately 80 percent of the efficiency investments‘ upgrades are financed by bank loans that 

carry an average eight percent interest rate over a five-year period. To limit the scope of the analysis, 

however, no parameters were established to account for any changes in interest rates as less capitalintensive technologies (i.e., efficiency investments) are substituted for conventional supply strategies, 

or in labor participation rates – all of which might affect overall spending patterns. Fortunately, 

however, it is unlikely that these sensitivities would greatly impact the overall outcome of this 

analysis. 

While the higher cost premiums associated with the energy-efficiency investments might be expected 

to drive up the level of borrowing (in the short term), and therefore interest rates, this upward 

pressure would be offset to some degree by the investment avoided in new power plant capacity, 

exploratory well drilling, and new pipelines. Similarly, while an increase in demand for labor would 

tend to increase the overall level of wages (and thus lessen economic activity), the job benefits are 

small compared to the current level of unemployment or underemployment in the Region. Hence the 

effect would be negligible. 

Fifth, as described in the previous chapters for the buildings, industrial, and transportation end-use 

sectors it was assumed that a program and marketing expenditure would be required to promote 

market penetration of the efficiency improvements. Since these vary significantly by policy bundle 

we don‘t summarize them here but payment for these policy and program expenditures were treated 

as if new taxes were levied on the Region commensurate with the level of energy demands within the 

Region. Hence, the positive program spending impacts are offset by reduced revenues elsewhere in 

the economy. 

 

45 In fact, we used a population-weighted average of regional energy prices referenced in the Annual Energy Outlook as 

they overlapped with the states and counties found within the jurisdiction of the Appalachian Regional Commission. 
Chapter 7: Macroeconomic Results 

111

Sixth, it should be noted that the full effects of the efficiency investments are not accounted for since 

the savings beyond 2030 are not incorporated in the analysis. Nor does the analysis include other 

benefits and costs that can stem from the efficiency investments. Non-energy benefits can include 

increased worker productivity, comfort and safety, and water savings, while non-energy costs can 

include aesthetic issues associated with compact fluorescent lamps and increased maintenance costs 

due to a lack of familiarity with new energy-efficiency equipment (NAPEE, 2007b, p. 3-8). 

Productivity benefits, for example, can be substantial, especially in the industrial sector. Industrial 

investments that increase energy efficiency often result in achieving other economic goals such as 

improved product quality, lower capital and operating costs, increased employee productivity, or 

capturing specialized product markets (see, for example, Worrell et al., 2003). To the extent these 

―co-benefits‖ exceed any non-energy costs, the economic impacts of an energy-efficiency initiative 

in Appalachia would be more favorable than those reported here. Finally, although we show how the 

calculations would look from an employment perspective, we don‘t show the same kind of data or 

assumptions for either income or for impacts on the Gross Regional Product (the sum of value-added 

contributions to the Appalachian Regional economy). Nonetheless, the approach is very similar to 

that described for net job impacts. 

7.3 IMPACTS OF RECOMMENDED ENERGY-EFFICIENCY POLICIES

For each year in the analytical period, the given change in a sector spending pattern (relative to the 

reference scenario) was matched to the appropriate sectoral impact coefficients. Two points are 

worth special note: first, it was important to match the right change in spending to the right sector of 

the Appalachian economy; and second, these coefficients change over time. For example, labor 

productivity changes mean that there may be fewer jobs supported by a one million dollar 

expenditure today compared to that same level of spending in 2030. Both the negative and positive 

impacts were summed to generate the estimated net results shown in the series of tables that follow. 

Presented here are two basic sets of macroeconomic impacts for the benchmark years of 2010, 2013, 

2020, and 2030. These include the financial flows that result from the policies described in the 

previous chapters. They also include the net jobs, income, and GRP impacts that result from the 

changed investment and spending patterns. 

Table 7.2 presents the changes in consumer expenditures that result from these policies. While the 

first row in the table presents the full cost of the energy-efficiency policies, programs, and 

investments, the utility customers will likely borrow a portion of the money to pay for these 

investments. Thus, ―annual consumer outlays,‖ estimated at about $1,083 million 2010, rise to 

$6,165 million (or nearly $6.2 billion) in 2030. These outlays include actual ―out-of-pocket‖

spending for programs and investments, along with money borrowed to underwrite the larger 

technology investments. The annual energy bill savings reported in Table 7.1 are a function of 

reduced energy purchases from the many Appalachian utilities and other energy providers within the 

Region. 

As we further highlight in the table that follows, the annual energy bill savings begins with a modest 

first year benefit of $708 million. As more and more investments are directed toward the purchase of 

more energy-efficient technologies, the annual consumer energy bill savings rise to about $27.6 

billion by 2030. 
Energy Efficiency in Appalachia, SEEA 

112

Table 7.2 Financial Impacts from Energy-Efficiency Policy Scenario

(Millions of 2006 $) 2010 2013 2020 2030

Annual Consumer Outlays 1,083 2,734 4,564 6,165

Annual Energy Savings 788 2,577 9,944 27,567

Annual Net Consumer Savings (295) (157) 5,380 21,402

Cumulative Net Energy Savings (295) (1,230) 15,226 150,809

 ̳Annual‘ refers to the total that is reported in the benchmark year while  ̳Cumulative‘ is the total from previous 

years beginning in 2010 through the benchmark year.

Annual consumer outlays include administrative costs to run programs, incentives provided to consumers, 

investments in energy-efficiency devices and interest paid on loans needed to underwrite the needed efficiency 

investments. 

Annual energy savings is the reduced energy bill expenditures that benefit both households and businesses 

within a given year. The net savings is the difference between savings and outlays. The numbers in 

parentheses are losses in that specific year.

Perhaps the critical element that jumps out in Table 7.2 is that, in the early years and especially as the 

policies ramp up quickly to simulate a greater level of efficiency improvements, the consumer 

outlays outweigh the energy bill savings. In 2010, the net costs begin at $295 million and rise 

through 2013. Although not shown in the table, this remains the case through the year 2014 when 

the savings show a small net return of $11 million. These savings mount steadily through the year 

2030 by when they nearly reach an estimated $21.4 billion for the Region as a whole. The last row 

of the table highlights cumulative impacts with losses peaking in 2013 and ending in 2015 (not 

shown). By 2030 the net cumulative savings over the period 2010 through 2030 are strongly net 

positive at just under $151 billion. 

While the annual net consumer savings first turn positive in 2014 – four years after the policy 

initiatives are in operation – the simple payback period to participants is much shorter, ranging from 

1.54 to 3.75 years, from 2010 to 2030 or 2.24 to 3.75 from 2011 to 2030, depending on the year of 

participation. The shortest payback is for investments made in 2010 and the longest in 2011, in the 

aggregate. The benefits, from a TRC perspective, outweigh costs by about 3.3 times. 

At this point we then have the financial flows estimated as they are distributed across the end-use 

sectors described earlier in the report. The question then becomes what might be the impacts on the 

Regional economy as we‘ve been able to evaluate them for a given year using the DEEPER model. 

The modeling then evaluates impact on jobs and wages sector-by-sector, and evaluates their 

contribution to Appalachia‘s Gross Regional Product (GRP), which is a sum of the net gain in valueadded contributions provided by the energy productivity gains throughout all sectors of the Regional 

economy. As with the previous table on financial impacts, Table 7.3 highlights the net impacts for 

the benchmark years 2010, 2013, 2020, and 2030. 
Chapter 7: Macroeconomic Results 

113

Table 7.3 Economic Impact of Energy-Efficiency Investment in Appalachia

Macroeconomic Impacts 2010 2013 2020 2030

Jobs (Actual) 16,231 15,466 37,268 77,378

Wages (million $2006) 517 450 1,169 3,018

GRP (million $2006) 763 444 1,197 3,056

Given both the financial flows and the modeling framework, the analysis suggests a net contribution 

to the Appalachian employment base as measured by full-time jobs equivalent. In the year 2010 we 

see a small net increase of 16,231 jobs which increases to a significantly larger total of 77,378 net 

jobs by 2030. This significantly positive impact might seem to provide us with a counterintuitive 

result. The early years of the policy scenarios show small net cost to the economy. Yet we continue 

to see a net increase in jobs. How is this possible? 

In Appalachia, the electric utility and the natural gas service sectors directly and indirectly employ 

about 5.3 and 3.7 jobs, respectively, for every $1 million of spending (as highlighted in the multiplier 

table in Appendix F). But, sectors vital to energy-efficiency improvements, like construction and 

manufacturing, utilize 13.3 and 8.3 jobs per $1 million of spending. Once job gains and losses are 

netted out in each year, and following a similar logic shown in Table 7.1, the analysis suggests that, 

by diverting expenditures away from non-labor intensive energy sectors, the cost-effective energy 

policies can positively impact the larger Appalachia economy – even in the early years, but 

especially in the later years of the analysis as the energy savings continue to mount. 

To highlight the results of this analysis in a little more detail, Figure 7.1 provides year-by-year 

impacts on net jobs within the Appalachia Region. Figure 7.2 highlights the anticipated net gain to 

the state‘s wage and salary compensation and GRP, both measured in millions of 2006 dollars. 

0

10

20

30

40

50

60

70

80

2010 2015 2020 2025 2030 Jobs (thousands)

Figure 7.1 Net Job Impacts for Appalachia (2008-2030)
Energy Efficiency in Appalachia, SEEA 

114

The end result of this policy analysis, then, suggests that an early program stimulus which drives a 

higher level of efficiency investments can actually increase economic impact, creating an average of 

16,000 net new jobs each year in the first five years of the study, and rising to an estimated average 

of 60,000 net new jobs over the last decade of the analysis. This is roughly equivalent to the 

employment that would be directly and indirectly supported by the construction and operation of 480 

small manufacturing plants within Appalachia. As indicated by Figure 7.2, these investments also 

increase both wages and Gross Regional Product throughout Appalachia. 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

2010 2015 2020 2025 2030 Billion 2006 $

Wages GSP

In short, the more efficient use of energy resources provides a cost-effective redirection of spending 

away from less labor-intensive sectors into those sectors that provide a greater number of jobs within 

Appalachia. Similarly, cost-effective energy productivity gains also redirect spending away from 

sectors that provide a smaller rate of value-added into those sectors with slightly higher levels of 

value-added returns per dollar of revenue. The extent to which these benefits are realized will 

depend on the willingness of business and policy leaders to implement the recommendations that are 

at the heart of this report and found earlier in this assessment. 

Figure 7.2 Wages and Gross Regional Product Impacts for Appalachia

(million 2006$), 2008-2030
Chapter 8: Summary and Conclusions 

115

7.0

7.5

8.0

8.5

9.0

9.5

10.0

10.5

11.0

11.5

2006 2009 2012 2015 2018 2021 2024 2027 2030 Quadrillion Btu

AEO 2007 AEO 2008 With Policy Packages

8 SUMMARY AND CONCLUSIONS

8.1 ECONOMY-WIDE RESULTS

An aggressive package of energyefficiency policies implemented 

throughout Appalachia beginning in 

2010 could deliver significant costeffective energy savings. According to 

the latest EIA ―business-as-usual‖

forecast, Appalachia will require 9.2 

quads of energy in 2020 and 10.1 

quads in 2030. In contrast, a bold 

energy-efficiency initiative could cut that 

consumption by between 9 and 12 percent 

to 8.2 quads in 2020 and by between 23 

and 28 percent to 7.7 quads in 2030. Such a 

bold and aggressive initiative could shrink the 

energy budget required by the Region in 

2030 to less than the Region consumed in 

2006 – more than offsetting the forecast 

growth in energy use (Figure 8.1). 

In 2030, most of the energy-efficiency resources (42 percent) come from efficiency programs for 

commercial buildings. The remaining energy savings come mostly from industrial energy uses (25 

percent), followed by transportation (18 percent) and residences (15 percent). Accounting for 28 

percent of energy use in Appalachia but only 18 percent of the energy efficiency resources, 

transportation has a relatively low energy-efficiency potential. This conclusion is understandable 

given the newly legislated energy-efficiency standards that require 40 percent stricter vehicle fuel 

economy standards by 2020. These savings are incorporated in the AEO 2008 baseline. 

Primary Energy Savings, by Sector (2030, Trillion Btu)

Residential

374

15%

Industrial

621

25%

Transportation

457

18%

Commercial

1030

42%

Figure 8.1 Potential Displacement of Appalachian Energy 

Consumption by Cost-Effective Efficiency Resources

Figure 8.3 Share of Cost-Effective Efficiency 

Resources by Sector, Excluding Commercial 

Commissioning (Primary Energy in trillion Btu, 

2030)

Figure 8.2 Share of Cost-Effective Efficiency 

Resources by Sector

(Primary Energy in trillion Btu, 2030)

Primary Energy Savings (excluding Commercial Commissioning), 

by Sector (2030, Trillion Btu)

Residential

374

18%

Commercial

641

30% Industrial

621

30%

Transportation

457

22%
Energy Efficiency in Appalachia, SEEA 

116

Consider the case where the energy-savings potential from the commissioning of commercial 

buildings is excluded from the estimation of energy efficiency resources (because of its unknown 

degree of overlap with the commercial building retrofit incentive program). In this situation, the total 

energy-efficiency potential in 2030 would be 0.4 quads smaller. At the same time, these energy 

efficiency resources would be much more evenly distributed across the four end-use sectors (Figure 

8.3). 

Examining the potential energy-efficiency impacts of individual policies draws attention to five 

policy instruments that substantially ―move the market‖ (Table 8.1). The efficient commercial 

HVAC and lighting retrofit incentive has the largest impact, reducing commercial energy 

consumption by 10 percent in 2020 and by almost 20 percent in 2030. Support for commissioning of 

existing commercial buildings is the next most significant policy in terms of saving energy in the 

year 2030. It cuts commercial energy consumption in 2030 by an additional 17 percent. 

The expanded industrial assessment centers policy, which targets small- to medium-sized industrial 

sites, has the third largest impact in 2030. By that year, it is assumed that all of the eligi 

ble sites have had an energy assessment and 80 percent of the cost-effective recommendations are 

adopted, producing a 17 percent reduction in the Region‘s industrial energy consumption. 

Clean car standards offer the fourth largest potential for saving energy and are by far the most 

influential of the four transportation policy packages modeled here. By accelerating the fuel economy 

improvements of combined passenger cars, small trucks/SUVs and large trucks/SUVs to 50 mpg in 

2030, 13 percent of the transportation energy use forecast for that year can be reduced. 

Finally, providing retrofit incentives for existing homes in combination with resale energy labeling 

has the fifth largest energy impact in 2030; it cuts the residential sectors projected energy 

consumption by four percent in 2020 and by seven percent in 2030. 
Chapter 8: Summary and Conclusions 

117

Table 8.1 Energy Savings from Individual Policies

(trillion Btu of primary energy saved per year)*

Primary 

Energy 

Savings: 2013

Primary 

Energy 

Savings: 2020

Primary 

Energy 

Savings: 2030

Residential Buildings

Improved Building Energy Code with Third Party 

Verification and Compliance Incentive 5.3 (0.3%) 40.5 (1.9%) 123.3 (5.0%)

Expanded Weatherization Assistance Programs 9.2 (0.5%) 26.2 (1.2%) 53.0 (2.1%)

Residential Retrofit Incentive with Resale Energy 

Labeling and Incremental Cost Incentives 32.8 (1.7%) 91.5 (4.2%) 180.2 (7.3%)

Super-Efficient Appliance Deployment 6.1 (0.3%) 28.5 (1.3%) 97.0 (3.9%)

Commercial Buildings

Commercial Building Energy Codes with Third 

Party Verification and Compliance Incentives 5.4 (0.3%) 23.1 (1.2%) 63.9 (2.8%)

Support for Commissioning of Existing 

Commercial Buildings 28.1 (1.7%) 148.1 (7.8%) 390.8 (17.3%)

Efficient Commercial HVAC and Lighting Retrofit 

Incentive 46.7 (2.9%) 194.5 (10.3%) 447.1 (19.7%)

Tightened Office Equipment Standards with 

Efficient Use Incentives 11.6 (0.7%) 53.2 (2.8%) 143.2 (6.3%)

Industry

Expanded Industrial Assessment Centers 18.1 (0.7%) 123.2 (5.0%) 413.1 (16.8%)

Increasing Energy Savings Assessments 10.4 (0.4%) 53.6 (2.2%) 119.9 (4.9%)

Supporting Combined Heat and Power (CHP) with 

Incentive 11.7 (0.5%) 40.3 (1.6%) 88.1 (3.6%)

Transportation

Pay-as-You-Drive Insurance 16.2 (0.7%) 67.1 (2.8%) 73.9 (2.9%)

Clean Car Standards 10.8 (0.5%) 64.1 (2.6%) 337.8 (13.3%)

SmartWay Heavy Truck Efficiency Loan Program 10.3 (0.4%) 17.9 (0.7%) 19.3 (0.8%)

Speed Limit Enforcement 22.8 (1.0%) 23.7 (1.0%) 26.0 (1.0%)

*Also expressed as a percent of the energy to be consumed in that year based on the ―business-as-usual‖ forecast.
Energy Efficiency in Appalachia, SEEA 

118

Most of the policies modeled in this study spur innovation and technology improvement, as would be 

the case, for instance, with clean car standards of 50 mpg. In order to illustrate the potential for a 

concerted energy-efficiency RD&D initiative to expand clean energy opportunities in Appalachia, we 

analyzed three transformational technologies that appear to hold great promise for the region. The 

results are summarized in Box 8.1, and the background details are presented in Chapters 3, 4, and 5. 

Box 8.1 The Promise of RD&D to Expand the Efficiency Potential of

Three Transformational Technologies

 Air-source integrated heat pump

Accelerated RD&D is assumed to result in the commercialization of a single system based on heat 

pumping technology that provides space heating and cooling, water heating, ventilation and 

dehumidification, and humidification. It is still in the development stages and is not yet commercially 

available; however, it could provide substantial energy savings to homes is the Appalachian Region. 

Within an average Appalachian home, installation of the AS-IHP would save approximately 37 million 

Btu per year. If this heat pump system were installed in 30 percent of all new homes built in Appalachia 

between 2010 and 2030, energy consumption in the residential sector could be reduced by 35 trillion Btu 

in the year 2030.

 Solid State Lighting

Accelerated RD&D is expected to produce technology improvements that bring brighter LEDs and 

provide light equivalent to existing fluorescent fixtures with 25 to 45 percent less electricity usage. 

Lighting demand for the Appalachian Region is expected to increase by little more than 10 percent 

between 2006 and 2030 (EIA, 2008a). However, under the LED scenario lighting demand would be 

expected to decrease by more than 20 percent. If by 2030, LED lighting fully penetrated the Appalachian 

Region, the Region would use 20 percent less energy for lighting than projected, saving 60 trillion Btu in 

that year.

 Industrial super boiler

A combination of enhanced design features could increase industrial package boiler efficiency from 75 

percent to 95 percent. Many boilers used today are more than 40 years old, suggesting a large energysavings opportunity. The Appalachian Region, particularly the southern portion, makes heavy use of 

boilers and widespread installation of a more efficient boiler could mean tremendous savings, in energy 

and financial cost. Assuming that super boilers replace one percent of all boilers within the Appalachian 

Region annually, and the average improvement in efficiency with the super boiler is 10 percent, this new 

technology could save almost 20 trillion Btu in 2030 and a total of 172 trillion Btu between 2010 and 

2030.

Dividing the cost-effective energy-efficiency resources fostered by the entire policy portfolio into 

fuel types and sectors highlights the prominence of the potential for reduced electricity consumption. 

Taking into account the energy lost in the generation and transmission of electricity as well as losses 

from ―end-use‖ equipment such as motors, lighting, and air conditioning, 69 percent of the energyefficiency potential in Appalachia resides in the electricity system (i.e., 1.7 quads of the 2.5 quads of 

energy-efficiency potential in 2030). The next largest wedge of energy-savings potential comes from 

motor gasoline consumption by vehicles (17 percent), followed by savings potentials from natural 

gas, fuel oil, and diesel end-uses (14 percent). 
Chapter 8: Summary and Conclusions 

119

The 1.7 quads of electricity-efficiency potential in 2030 represent a potential savings of 33 percent 

relative to the projected consumption of electricity in 2030 (that is, 5.2 quads of primary energy). It 

represents 106 percent of the anticipated 1.6 quads of load growth in 2030. 

0

500

1000

1500

2000 Trillion Btu Transportation 0.0 0.0 0.0 24.8 432

Industrial 565 56.0 0.0 0.0 0.0

Commercial 871 141 20.5 0.0 0.0

Residential 274 85.1 13.9 0.0 0.0

Electric 

Savings

Natural Gas 

Savings

Fuel Oil 

Savings

Diesel 

Savings

Motor 

Gasoline 

1710

282

34 24.8

432

Tables 8.2 and 8.3 provide additional detail on the cost-effective efficiency resources estimated to be 

available in the Appalachian Region. Table 8.2 shows savings with the commercial building 

commissioning program included, and Table 8.3 shows savings assuming that the savings from 

commercial building commissioning are included in retrofit savings for the commercial sector as a 

whole. These results suggest that overall efficiency resources could save between 9 and 12 percent of 

estimated energy consumption in 2020, and between 23 and 28 percent of estimated energy 

consumption in 2030. The most significant savings are in electricity overall; between 11 and 15 

percent in 2020 rising to between 27 and 33 percent in 2030. Motor gasoline consumption is reduced 

by almost as much: 11 percent in 2020 and 33 percent in 2030. Natural gas savings are next in order 

of magnitude, saving between 5 and 7 percent of the forecast consumption in 2020, and between 14 

and 20 percent of the forecast consumption in 2030. 

Again, this illustrates the ability of an aggressive energy efficiency initiative tp shrink the energy 

budget required by the Region in 2030 to less than the Region consumed in 2006 – thereby more than 

offsetting the forecast growth in energy use. 

Figure 8.4 Share of Cost-Effective Efficiency Resources by Fuel Type and Sector 

(Primary Energy in trillion Btu, 2030)
Energy Efficiency in Appalachia, SEEA 

120

Table 8.2 Cost-Effective Efficiency Resources as a Percent of Projected Primary 

Energy Consumption in the Appalachian Region in 2020 and 2030

2020

Residential

%

Commercial

%

Industrial

%

Transportation

%

Total

%

Electricity 5.4 22.3 18.0 0.0 15.4

Natural Gas 10.9 22.9 -0.7 0.0 6.8

Fuel Oil 11.0 21.4 0.0 0.0 1.9

Diesel 0.0 0.0 0.0 3.6 3.6

Gasoline 0.0 0.0 0.0 10.7 10.7

All Fuels 7.6 21.9 8.2 7.1 11.9

2030

Electricity 11.1 46.2 42.4 0.0 33.1

Natural Gas 22.6 47.6 14.5 0.0 19.5

Fuel Oil 23.9 46.6 0.0 0.0 3.5

Diesel 0.0 0.0 0.0 3.3 3.3

Gasoline 0.0 0.0 0.0 33.1 33.1

All Fuels 15.1 45.6 21.7 18.0 27.8

Several states in the region have commissioned estimates of their cost-effective electricity-efficiency 

potential – including Georgia, New York, North Carolina, and Virginia. Each of these state studies 

uses a distinct planning horizon and set of methodological assumptions, resulting in divergent results. 

For example, Georgia focuses on the 2005-2010 time frame, and estimates that 56 percent of load 

growth, or nine percent of the forecast electricity consumption in 2010 could be met with costeffective electric-efficiency improvements. North Carolina focuses on 2007-2017 and concludes that 

85 percent of load growth could be met, or 14 percent. Virginia examines the period 2007-2025, and 

concludes that 58 percent of load growth could be met, or 25 percent of the forecast load in 2025. 

Energy Efficiency in Appalachia estimates a higher level of electricity-efficiency potential, but it also 

has a longer planning horizon. 
Chapter 8: Summary and Conclusions 

121

Table 8.3 Cost-Effective Efficiency Resources as a Percent of Projected Primary 

Energy Consumption in the Appalachian Region in 2020 and 2030

(Excluding Commercial Building Commissioning)

2020

Residential

%

Commercial

%

Industrial

%

Transportation

%

Total

%

Electricity 5.4 8.2 18.0 0.0 10.5

Natural Gas 10.9 11.4 -0.7 0.0 4.7

Fuel Oil 11.0 9.4 0.0 0.0 1.3

Diesel 0.0 0.0 0.0 3.6 3.6

Gasoline 0.0 0.0 0.0 10.7 10.7

All Fuels 7.6 8.5 8.2 7.1 8.8

2030

Electricity 11.1 30.2 42.4 0.0 27.2

Natural Gas 22.6 21.8 14.5 0.0 14.2

Fuel Oil 23.9 20.3 0.0 0.0 2.3

Diesel 0.0 0.0 0.0 3.3 3.3

Gasoline 0.0 0.0 0.0 33.1 33.1

All Fuels 15.1 28.3 21.7 18.0 23.4

At a national scale, the Scenarios for a Clean Energy Future (Brown et al., 2001) estimated that 

advanced policies to promote clean energy technologies, if implemented in 2000 could cut U.S. 

electricity consumption in 2020 by 24 percent, with no net cost to the economy. More recently, 

McKinsey & Company (2007) identified a significant amount of ―negative cost‖ carbon abatement 

opportunity, primarily from cost-saving energy-efficiency investments. Findings of Energy Efficiency 

in Appalachia are consistent with these two major national assessments. 

According to the macroeconomic analysis reported in Chapter 7, cumulative net benefits from the 

Appalachian energy-efficiency initiative modeled here take time to materialize. In the early years 

before the policies have had a chance to ramp up, the consumer outlays outweigh the energy bill 

savings. Overall, the value of cumulative energy savings does not exceed cumulative costs until the 

year 2017 – the year the initiative breaks even. However, from the participants‘ perspective, the 

payback is much quicker, ranging from 2.0 to 2.9 years depending on the year of participation 

between 2011 and 2030. The time required to recover participant expenses is generally shortest in the 

commercial sector and longest in the industrial sector. 
Energy Efficiency in Appalachia, SEEA 

122

0

1

1

2

2

3

3

2010 2015 2020 2025 2030 Quadrillion BTU

0

1

1

2

2

3

3

4

4

5

5 Billion 2006$

Primary Energy Savings Public Investment Private Investment

As Figure 8.5 illustrates, energy 

savings expand at a slightly 

increasing pace over the 20-year 

period. In contrast, public 

investments drop from approximately 

$650 million per year during the first 

decade to slightly less than $450 

million in the second decade, 

reflecting the sun setting of several 

program subsidies and incentives in 

the year 2020. The public 

expenditures are much smaller than 

the private investment of nearly $5 

billion in 2030. 

As Table 8.4 shows, the economy-wide 

package of energy-efficiency policies is highly cost-effective. From the participants‘ perspective, the 

benefit/cost ratios range from 1.9 in the industrial sector to 5.7 in the commercial sector, with an 

overall average of approximately 3.1. Based on the total resource cost test, the benefit/cost ratios 

range from 1.9 in the residential sector to 6.8 for commercial buildings, with an overall average of 

3.4. 

As the result of $5.8 billion in program spending (in net present value terms, $10.8 billion 

cumulative), supplemented by an NPV of $25 billion in customer investments ($59.7 billion 

cumulative) over the 2010 to 2030 period, program participants in the Appalachian Region could see 

energy bill savings worth a net present value of $84 billion by 2030. Most of the public investment 

supports residential (NPV $3.2 billion) policies, followed by commercial (NPV $1.4 billion), and 

industrial (NPV $1.2 billion) policies, while much less is expended on programs in the transportation 

sector (NPV $238 million). The costs to participants are greatest in the transportation sector and least 

in the commercial sector. 

With $10.8 billion in program spending and an additional $59.7 billion in customer investments over 

the 2010-2030 period, the Appalachian Region could see net cumulative savings of 23.2 quads, 

saving $218.4 billion in energy bills by 2030. This is the equivalent of 23 to 28 percent of the EIA‘s 

forecast consumption in 2030 or more than 100 percent of forecast growth (EIA, 2008a). 

The macroeconomic analysis suggests that the energy-efficiency policy bundles would have a net 

contribution to the Appalachian employment base as measured by full-time jobs equivalent. In the 

year 2010 we see a small net increase of 16,231 jobs, which increases to a significantly larger total of 

77,378 net jobs by 2030. 

Figure 8.5 Annual Investment and Energy Savings: 

2010-2030
Chapter 8: Summary and Conclusions 

123

0

1

2

3

4

5

6

7

8 Residential Commercial Industrial Transportation Total Residential Commercial Industrial Transportation Total Benefit-to-Cost Ratio

Participants Cost Test Total Resource Cost Test

Table 8.4 Results of Economic Tests for Four Sectors and All Sectors

Residential Commercial Industrial Transportation Total

Participants Test

NPV Benefits 

(billion 2006$) 13.90 25.44 8.96 28.57 76.87

NPV Costs 

(billion 2006$) 6.98 4.49 4.70 8.89 25.06

Net BenefitsCosts (billion 

2006$)

6.92 20.95 4.26 19.68 51.81

B/C Ratio 1.99 5.67 1.91 3.21 3.07

Total Resource Cost Test

NPV Benefits 

(billion 2006$) 17.75 39.31 13.51 45.04 115.60

NPV Costs 

(billion 2006$) 9.14 5.78 6.50 12.17 33.59

Net BenefitsCosts (billion 

2006$)

8.61 33.52 7.01 32.87 82.01

B/C Ratio 1.94 6.80 2.08 3.70 3.44

The benefit/cost ratios displayed in Table 8.4 are a function of numerous assumptions, including 

particular discount rates and avoided costs used to value the energy saved. A sensitivity analysis was 

completed to examine the impact of alternative discount rates and avoided cost assumptions. 

For example, ten and seven percent 

discount rates are used to calculate the 

time value of benefits and costs in the 

participants cost test and in the total 

resource cost test, respectively. In this 

sensitivity analysis, these discount 

rates are allowed to vary between 

four, seven, and 10 percent to reflect 

different ―time values of money.‖ The 

resulting benefit/cost ratios for the 

participants cost test are considerably 

higher with the lower discount rates 

(Figure 8.6). For the policy portfolio 

as a whole, the benefit/cost ratio 

increases from 3.3 to 4.2. Discounting 

the total resource cost (TRC) test 

Figure 8.6 Sensiti results by four and 10 percent instead vity Analysis of Benefit/Cost Ratios 

Based on Alternative Discount Rates
Energy Efficiency in Appalachia, SEEA 

124

0

2

4

6

8

10 Benefit-to-Cost Ratio 50% Lower than Energy Prices 0.97 3.40 1.04 1.66 1.65

25% Lower than Energy Prices 1.46 5.10 1.56 2.49 2.48

Retail Energy Prices 1.94 6.80 2.08 3.33 3.31

Energy Prices with $25/MtC Tax 2.15 6.59 2.67 3.41 3.45

Energy Prices with $50/MtC Tax 2.35 7.42 3.26 3.49 3.58

Energy Prices with $100/MtC Tax 2.75 9.09 4.45 3.65 3.84

Residential Commercial Industrial Transportation Total

of seven does not have a large impact on the benefit/cost ratios. The overall TRC benefit/cost ratio 

for the portfolio package varies between 3.5 and 4.0. 

Similarly, the principal estimate 

used in this project to monetize 

the avoided costs of potential 

energy savings is the retail price 

of energy. Figure 8.7 shows what 

would happen to the costeffectiveness tests if the value of 

the avoided cost was cut in half 

(as might happen, for instance, if 

power plant costs were to decline 

significantly or if new 

inexpensive fuels were to flood 

the market). It also examines the 

benefit/cost ratios if avoided costs 

were inflated (e.g., in conjunction 

with the promulgation of a 

nationwide carbon cap-and-trade 

system). 

The lowest avoided costs drive the residential and industrial programs to borderline cost-effective. In 

contrast, inflating energy costs by $25 to $100 per metric ton of carbon dioxide significantly raises 

the benefit/cost ratios of the policy packages targeting the commercial, residential, and industrial 

sectors. There is a much smaller effect on the cost-effectiveness of the transportation sector energysavings potential – due to the lower carbon content of gasoline and diesel. Across all of the sectors, 

the carbon-inflated avoided costs make investments in energy efficiency more cost-effective 

compared with the business-as-usual scenario. 

8.2 RESULTS BY SECTOR

8.2.1 Residential Buildings

From 2008 to 2030, Appalachian residential energy consumption is forecast to increase 30-32 

percent, to approximately 2.5 quads (EIA, 2007a; 2008a). Numerous barriers to efficiency 

improvements are expected to limit the Region‘s adoption of more efficient technologies and 

practices. Foremost among these is the first-cost, or incremental cost, of more efficient products and 

materials; while high costs are a barrier across the nation, the higher rates of poverty and lower 

average incomes in Appalachia magnify this issue. Each of the policies proposed for the residential 

sector include an anticipated ―incentive‖ portion to help overcome this barrier. 

Energy Efficiency in Appalachia demonstrates the potential for residential efficiency to curb the 

Region‘s growing energy demand, by focusing on building efficient new homes, improving the 

performance of existing homes, and pulling the appliance stock towards best available technology. 

Home construction practices and lighting are expected to improve, roughly doubling the efficiency of 

homes built today, through tighter building codes and stricter enforcement – enforced through thirdparty compliance verification. A two-tiered retrofit program, providing no-cost weatherization 

services to low-income households and a cost-share for other households, is expected to significantly 

Figure 8.7 Sensitivity Analysis of Benefit/Cost Ratios for the Total 

Resource Cost Test With Carbon “Adders”
Chapter 8: Summary and Conclusions 

125

improve the heating and cooling efficiency of about 40 percent of the existing building stock. 

Appliances are targeted through an incentive program for super-efficient appliances; due to appliance 

lifetimes, this method improves the stock appliance efficiency over the study horizon. 

Considering energy benefits alone, these policies are cost-effective with a benefit-to-cost ratio of 

about 2.0 for participants and about 1.9 for society. Improving home performance is assumed to be 

even more cost-beneficial when non-energy benefits are quantified, especially improved health and 

safety for the elderly and most vulnerable children in the community. These three focus areas (new 

homes, thermal envelopes, and appliances), modeled here through four policy packages, are 

estimated to provide cumulative annual savings of about 370 trillion Btu in 2030. This represents 

more than half of the estimated growth in consumption over the study horizon. 

With $5.1 billion in program spending and an additional $12.3 billion in customer investments over 

the 2010-2030 period, the Appalachian Region could see net cumulative savings of 6.0 quads, saving 

$60.4 billion in energy bills by 2050. This is the equivalent of about 18.7 percent of the EIA‘s 

forecast consumption in 2030 or 71.7 percent of forecast growth (EIA, 2008a). 

8.2.2 Commercial Buildings

Commercial energy consumption in Appalachia is forecast to increase 45-63 percent from 2006 to 

2030, to approximately 2.4 quads (EIA, 2007a; 2008a). More efficient technologies and practices are 

especially important in existing commercial building stock, which is not expected to be replaced over 

the study horizon. Ensuring that retiring equipment is replaced by the most efficient technology can 

be a challenge due to high relative costs, low management priority for energy costs, and principal 

agent issues. The policies suggested by this study for the commercial sector address these barriers. 

Commercial building savings are anticipated in this study through efficient new building 

construction, commissioning and retrofit of existing building stock, and adoption with proper use of 

efficient office equipment. New building construction practices and lighting methods are expected to 

improve, roughly doubling the efficiency of buildings constructed today, through tighter building 

codes and third-party compliance verification. Commissioning generally ensures that design 

efficiencies are achieved; modifications based on commissioning studies achieve about 10 percent 

savings over non-commissioned buildings (Mills et. al, 2004). Addressing compliance and 

commissioning help to overcome principle agent issues in that builders (and often the owners) of 

commercial buildings will not be responsible for energy costs. Ensuring that retrofit HVAC and 

lighting equipment are completed with the most efficient technologies and methods presents higher 

first costs that can be overcome with incentives; these retrofits are cost-effective without incentives, 

but limited access to capital and financing for energy-efficiency improvements can hinder their 

adoption (Brown and Chandler, 2008). While adoption of efficient office equipment is increasing, 

use of efficiency features is still low because of a generally low management priority for efficiency; 

tightening standards while offering incentives for application of the technology can significantly 

reduce electricity consumption at a small cost. 

The Commercial Policy Package is cost-effective with a benefit-to-cost ratio of about 5.7 for 

participants and about 6.8 for society. With $1.9 billion in program spending and an additional $9.3 

billion in customer investments over the 2010-2030 period, the Appalachian region could see net 

cumulative savings of 9.5 quads, saving $72.5 billion in energy bills by 2030. This is the equivalent 

of about 45.6 percent of the EIA‘s forecast consumption in 2030 or 148.3 percent of forecast growth 

from 2010-2030 (EIA, 2008a). 
Energy Efficiency in Appalachia, SEEA 

126

8.2.3 Industry

Industry in Appalachia currently comprises 30 percent of overall energy use within the Region. 

According to the EIA‘s 2008 Annual Energy Outlook, industry is expected to continue to use a large 

proportion of energy in Appalachia though its market share will decrease slightly down to 28 percent 

by 2030 (EIA, 2008a). Energy costs for industry can be substantial, and, though many are keenly 

aware of the impact energy efficiency can have on the ―bottom line,‖ hurdles such as large initial 

capital costs and down time needed to install updated equipment often prevent action in industry. 

Through structured programs that help mitigate these challenges, a large impact in the rate of energy 

consumption could be achieved. 

The industrial portion of the current study investigates three programs, which, together, would audit 

industrial sites and make recommendations for energy-efficiency improvements; help support the 

installation of new equipment; train industrial site personnel to continue to make improvements; and 

promote the use of systems that utilize waste heat to provide a useful product, such as steam. 

Through expansion of current programs, small- to medium-sized industrial sites can take advantage 

of government-funded energy auditing to help them make decisions on cost-effective, high-impact 

changes that can be made to improve their energy consumption. Large industrial facilities can 

receive training on how to pinpoint improvements in systems throughout their complex to improve 

energy efficiency, on average reducing overall site consumption by roughly nine percent (Wright et 

al., 2007). In plants that require thermal inputs, systems that produce electric power can be placed 

on-site, and the hot exhaust can be used as ―free‖ energy to drive equipment that would ordinarily 

require natural gas, saving total energy for the Region. These types of programs are a large step in 

the direction of reduced energy demand. 

No policy or program comes without some financial costs; however, based on the energy saved 

through the policy bundles modeled in the current study, the financial benefits far outweigh the costs. 

The four actions achieved through three programs are estimated to provide an annual energy savings 

of 670 trillion Btu by 2030, which reduces the sector‘s total energy consumption below 2007 levels. 

The Industrial Policy Package is cost-effective with a benefit-to-cost ratio of about 1.9 for 

participants and about 2.1 for society. With $2.5 billion in program spending and an additional $13.1 

billion in customer investments over the 2010-2030 period, the Appalachian Region could see net 

cumulative savings of 5.2 quads, saving $22.6 billion in energy bills by 2030. This is the equivalent 

of about 21.7 to 27.9 percent of the EIA‘s forecast consumption in 2030, or 90.0 to 153.1 percent of 

forecast growth (EIA, 2007a; 2008a). 

8.2.4 Transportation

The transportation sector in the Appalachian Region shows enormous energy savings potential. 

Appalachian transportation energy consumption is forecast to increase to 2.54 quads by 2030 (EIA, 

2008a). The stricter CAFE standards promulgated in 2007 alone should reduce primary energy 

consumption for the sector by at least 21 percent by 2030. If the standards are combined with other 

policies modeled in this report, total primary energy savings could reach 35 percent by 2030. Future 

investments targeted at moving Appalachia‘s freight more efficiently could bring about additional 

fuel savings not modeled in this analysis. The biggest contributor to total savings outside of the new 

fuel economy regulations is the extension of the Clean Car Standard program we propose in this 

analysis, which would bring about gasoline savings of 2.72 billion gallons a year by 2030 – 
Chapter 8: Summary and Conclusions 

127

approximately 13.5 percent of total primary transportation energy consumption in the Appalachian 

Region. 

Numerous barriers to efficiency improvements are expected to limit the Region‘s adoption of more 

efficient technologies and practices. Foremost among these is the first-cost, or incremental cost, of 

more efficient products and materials; while high costs are a barrier across the nation, the higher rates 

of poverty and lower average incomes in Appalachia magnify this issue. Each of the policies 

proposed for the residential sector include an anticipated ―incentive‖ portion to help overcome this 

barrier. 

The transportation policy options presented here are a cost-effective way of realizing energy-savings 

potential in Appalachia. The benefit/cost ratios indicate that the high cost of implementation 

associated with some of the transportation policies is far outweighed by the total benefits incurred. If 

all policy options were to be implemented in combination, participants would receive approximately 

$2 for every additional $1 spent. Similarly, every additional $1 spent on implementation would reap 

$3.14 in total benefit. 

While implementation of such policies might depend on a variety of economic and political 

conditions, this report demonstrates that there is significant opportunity in Appalachia for large 

energy savings in the transportation sector. Carefully crafted policies that target vehicles fuel 

economy, vehicle-miles traveled (VMT) and driving behavior will lead to increased energy 

efficiency in the transportation sector. 

The transportation sector policy package is cost-effective with a benefit-to-cost ratio of about 3.2 for 

participants and about 3.7 for total resource costs. With $0.4 billion in program spending and an 

additional $27.6 billion in customer investments over the 2010-2030 period, the Appalachian Region 

could see net cumulative savings of $80.4 billion in energy bills by 2030 and $167.1 billion by 2044. 

This is the equivalent of about 30.1 percent of the EIA‘s forecast consumption in 2030 or 172.4 

percent of forecast growth from 2010-2030 (EIA, 2008a). 

8.3 CONCLUSIONS

Policy action aimed at exploiting the energy-efficiency potential described in this report would set 

Appalachia on a course toward a sustainable and prosperous energy future. The Region‘s energyefficiency resources could go a long way toward meeting its future energy needs while ensuring its 

continued economic and environmental health. 

The problem is that energy-efficiency upgrades require consumer and business investment and they 

take time away from other priorities. With so many demands on financial and human capital, energyefficiency upgrades tend to be given a low priority. Through a combination of information 

dissemination and education, financial assistance, regulations, and capacity building, consumers, 

businesses, and industry can be encouraged to take advantage of energy-efficiency opportunities. In 

addition, expanded research, development, and demonstration is needed to innovate and deploy 

transformational technologies that expand the efficiency potential. 

By exploiting the Region‘s substantial energy-efficiency resources, Appalachia can cut the energy 

bills of its households, businesses and industries, create ―green‖ jobs, and grow its economy. The 

ability to convert this vision into a reality will depend on the willingness of business and policy 

leaders to implement the recommendations that are at the heart of this report. 
Energy Efficiency in Appalachia, SEEA 

128
Chapter 9: References 

129

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Acknowledgements 

137

ACKNOWLEDGEMENTS 

Funding for this project was provided by the Appalachian Regional Commission (ARC) to the 

Southeast Energy Efficiency Alliance (SEEA), through a competitive solicitation led by Ben Taube 

(Executive Director of SEEA). The guidance provided by the project‘s ARC and SEEA managers 

(Greg Bischak, Katie Whiteman, and Ben Taube) was invaluable. 

The authors also wish to express their appreciation to the project‘s Advisory and Stakeholders 

Committees. 

The Advisory Committee included: 

Carl Bauer, Director, National Energy Technology Laboratory (Pamala Stasia, Administrative 

Specialist) 

Raymond Boeman, Director, Transportation Program and NTRC User Facility, Oak Ridge National 

Laboratory 

Tom Congdon, Assistant Secretary for Energy, State of New York 

Richard Hirsh, Professor, History and Science & Technology Studies, Virginia Polytechnic Institute 

and State University 

Carl Irwin, Professor, West Virginia University and Director, WVU National Research Center for 

Coal and Energy 

Richard G Newell, Gendell Associate Professor of Energy and Environmental Economics, Duke 

University, Nicholas School of the Environment and Earth Sciences 

Tony Wright, Director, Industrial Technologies Program, Oak Ridge National Laboratory 

The Stakeholders Committee included: 

John Davies, Director, Division of Renewable Energy and Energy Efficiency, Kentucky Governor’s 

Office of Energy Policy 

Joe Hoagland, Vice President, Energy Efficiency and Demand Response, Tennessee Valley Authority 

Joel Yudken, Principal, High Road Strategies, Arlington, Virginia 

Finally, the authors wish to thank Charles M. Zeh (National Energy Technology Laboratory); Etan 

Gumerman (Duke University); Youngsun Baek and Frank Southworth (Georgia Tech); Steve 

Capanna, Jeff Harris, and Nils Petermann (Alliance to Save Energy); and Neal Elliott 

(ACEEE) for their insights and review comments on various technical aspects of the report. 

In addition, we are indebted to Charlotte Franchuk for her careful editing of the report. 

ENERGY EFFICIENCY IN APPALACHIA 

HOW MUCH MORE IS AVAILABLE, AT WHAT COST, 

AND BY WHEN? 

Appendices 

APPENDIX A ARC ENERGY-EFFICIENCY POLICY INVENTORY 

APPENDIX B RESIDENTIAL POLICY AND METHODOLOGY DETAIL 

APPENDIX C COMMERCIAL POLICY AND METHODOLOGY DETAIL 

APPENDIX D INDUSTRIAL SITES

APPENDIX E TRANSPORTATION

APPENDIX F DEEPER MODEL

APPENDIX G SENSITIVITIES AT SECTOR LEVEL

Appendix A: ARC Energy Efficiency Policy Inventory

A-1

APPENDIX A: ARC ENERGY EFFICIENCY POLICY INVENTORY

A policy inventory was developed to better assess the current status and approach of the Appalachian 

Region to energy efficiency. The inventory was compiled by Georgia Institute of Technology 

researchers and sent to state energy offices for review. As of September 2008, ten states have replied 

with corrections and additions to the inventory (AL, GA, KY, MS, NY, OH, PA, SC, TN, VA, and 

WV) while two states have yet to respond (MD, and NC). All thirteen state energy offices have been 

made aware of the purpose and intent of this inventory and will be provided a copy of the complete 

inventory for their own reference. 

The Appalachian Region has over 200 policies 

promoting energy efficiency identified in this 

inventory. However, they differ in scope, intent, and 

level of support. Policies are even nested in different 

levels: locally, state, region, and Federal. There are 

very few local level policies identified in Appalachia; 

however, there may be policies in place that were not 

identified. Figure A.1 shows that 91 percent of 

policies identified in the region are at the state level. 

Federal policies are not included in this policy 

inventory, and no applicable policies were identified 

at the Regional level. 

Policies can be organized into 12 distinct categories as 

described by Geller (2002); Figure A.2 shows how current policies in the region fit within these 

categories. A description of each policy option along with some examples from current programs in 

Appalachia follows. 

0

5

10

15

20

25

30

35

40

45

50

Capacity Building Financial Incentives Financing Information Dissemination & Training Market Obligations Planning TechniquesPricing Procurement RDD Regulations Voluntary Agreements Market Reforms

Figure A.1 Breakdown of Policy 

Level for Policies in Inventory

Figure A.2 Policy Options (count) in the Appalachian Region

State

192

91%

Local

20

9%
Energy Efficiency in Appalachia, SEEA

A-2

Some policies are designed to fit into more than one of these categories, like market obligation 

policies that also require state lead-by-example procurement. In this case, policies are not double 

counted; rather they appear under just one option. 

RESEARCH, DEVELOPMENT, AND DEMONSTRATION 

These policies generally are used to get technologies and practices from the lab to the marketplace. 

Throughout the region, there are three identified policies supporting research, development, and 

demonstration for energy efficiency. Kentucky, South Carolina, and Virginia have targeted programs 

for innovation, including energy efficiency; they are all designed as competitive grant programs. 

New York has an office of energy research, and has been active in efficiency research and 

development for more than three decades. 

Kentucky’s Energy Research and Development Grants for Renewable Energy and Energy Efficiency, 

operated by the Kentucky Office of Energy Policy since 2005, awarded five research and 

development grants in 2007 via competitive solicitation for renewable energy and energy efficiency 

initiatives for a total of over $518,000. 

Perhaps the most well known research effort in the Appalachian region is NYSERDA. Since 1975, 

NYSERDA has conducted a multifaceted energy and environmental research and development 

program. NYSERDA’s R&D Program supports the development and commercialization of 

innovative energy and environmental products, technologies, and processes that improve the quality 

of life for New York's citizens and help New York businesses to compete and grow in the global 

economy. NYSERDA R&D activities are organized into seven primary program areas: Energy 

Resources; Transportation and Power Systems; Energy and Environmental Markets; Industry; 

Buildings; Transmission and Distribution; and Environmental Research. 

FINANCING 

Governments offer financing programs to increase adoption rates for technologies that may have 

longer paybacks or may not qualify for traditional financing. Financing programs include no interest 

loans, low interest loans, access to standard rate loans, and the ability to utilize performance 

contracting (for government agencies). 

There are 23 financing programs to advance energy efficiency in Appalachia (Figure A.3). Seven of 

these allow government agencies to contract for energy services, through performance contracting or 

Energy Service Contracting Organizations (ESCOs), or allow special lease financing, on similar 

terms, from a government fund. Sixteen are loan programs: three are loans with technical assistance, 

two are no-interest loan programs, and eleven are low-interest loan programs. 
Appendix A: ARC Energy Efficiency Policy Inventory

A-3

0

2

4

6

8

10

12

ESCO Loans Low Interest

Loans

No Interest Loan Lease Financing

Financing

West Virginia, Ohio, and Georgia do not have financing programs for energy efficiency in this policy 

inventory. Table A.1 shows the number of financing policies by state for those that use this type of 

policy to promote energy efficiency. 

Table A.1 Financing Policies by State

State Number of Policies

AL 2

KY 1

MD 3

MS 4

NC 2

NY 3

PA 2

SC 2

TN 2

VA 2

Mississippi’s Energy Investment Loan Program provides low interest (three percent below prime) 

loans “to individuals, partnerships, or corporations, for either capital improvements, or in the design 

and development of innovative energy conservation processes.” The broad scope of this policy 

makes it stand out, as most financing programs target one sector or technology. 

In South Carolina, the ConserFund is a low-interest revolving loan program administered by the SC 

Energy Office for energy-efficiency improvements in state agencies, public colleges or universities, 

school districts, local governments, and private nonprofit organizations.

Figure A.3 Financing Mechanisms Used for Energy Efficiency in Appalachia
Energy Efficiency in Appalachia, SEEA

A-4

FINANCIAL INCENTIVES 

Financial incentives help to encourage early and wider adoption of efficient technologies and 

practices by reducing or eliminating the incremental cost of adoption of energy-efficient technologies 

and practices (Geller, 2002, p.55). These incentives come in the form of grants, tax credits, tax 

exemptions, and rebates. 

Forty-six programs for energy efficiency in Appalachia come in the form of financial incentives. 

Twenty-three are grants, while nine are tax credits; five are tax exemptions or tax holidays. Of seven 

types of rebates, three programs are designed as rebates/reimbursements, two as tax rebates, and two 

as fee rebates. Single programs are designated as ―tax‖ and ―mix.‖ Figure A.4 shows the frequency 

of these programs. 

0

5

10

15

20

25

Fee Rebate Grants Rebate Tax Tax Credit Tax

Exemption

Tax Rebate Mix

Financial Incentives

The program labeled “tax” provides an interesting example for energy-efficiency financial 

incentives. In 2007, the Virginia legislature passed a bill that allows local governments to create a 

new real estate classification for buildings that achieve efficiencies at least 30 percent greater than 

the Virginia Uniform Building Code; with this new classification, local governments could offer 

incentivized tax rates for efficient buildings. One city, Roanoke, VA, appears to have taken this 

option up already. 

Efficiency Long Island, labeled “mix,” is set to offer a combination of financial incentives; this 

program is intended to succeed and expand upon Long Island Power Authority’s (LIPA) Clean 

Energy Initiative that expires at the end of 2008. Efficiency Long Island is a 10-year, $924 million 

energy-efficiency program that will make a wide array of incentives, rebates and initiatives available 

to LIPA’s residential and commercial customers to assist them in reducing their energy usage and 

thereby lowering their bills. 

Figure A.4 Financial Incentives for Energy Efficiency Used in Appalachia
Appendix A: ARC Energy Efficiency Policy Inventory

A-5

The breakdown by location can be seen in Table A.2. Alabama and Tennessee have no identified 

financial incentive programs for energy efficiency. Within North Carolina, two financial incentives 

are at the local level in Asheville, NC. 

Table A.2 Financial Incentives Policies by State

State Number of Policies

GA 1

KY 2

MD 4

MS 1

NC 3

NY 19

OH 2

PA 4

SC 5

VA 4

PRICING

Pricing policies include added taxes and fees and net-metering programs that encourage energy 

efficiency. Besides net-metering programs, pricing policies have not been employed by governments 

in Appalachia. However, states do have varying levels of utility and fuel taxes. These taxes could 

have some implications for consumption behaviors. Previous research efforts by the ARC identified 

Regional price elasticities of electricity to be -0.15, -0.17, and -0.55 for residential, commercial, and 

industrial users, respectively (CBER, 2006). While pricing consumers out of the market for energy 

could be a concern with the region‘s large proportion of population in poverty, pricing mechanisms 

could be used with other programs to reduce peak demand. Pricing measures could also be used to 

discourage excessive use without impacting the average customer bill (when much higher prices are 

set at levels above typical consumption); a grim trigger policy like this can keep consumption from 

reaching the trigger point, but will not incentivize reductions below that point. 

Georgia, Kentucky, Maryland, New York, North Carolina, Ohio, and Pennsylvania have state-wide 

net-metering programs. In addition, Tennessee‘s Tennessee Valley Authority also sponsors a netmetering type program which uses dual-metering and directly purchasing renewable generated power 

at 10 cents per kWh. These net-metering programs have varying rules on maximum installation, 

forms of generation, and payback structures. 

For example, Pennsylvania’s net-metering policy requires investor-owned utilities to offer the 

program to residential customers with systems up to 50 kilowatts (kW) in capacity; nonresidential 

customers with systems up to three megawatts (MW) in capacity. In addition, customers (of any 

sector) with system capacities between three and five MW must be allowed to participate in netmetering if they make their systems available to the grid during emergencies, or if a microgrid 

system is in place in order to maintain critical infrastructure. Pennsylvania allows many sources to 
Energy Efficiency in Appalachia, SEEA

A-6

be eligible for net metering: photovoltaics (PV), solar-thermal energy, wind energy, hydropower, 

geothermal energy, biomass energy, fuel cells, combined heat and power (CHP), municipal solid 

waste, waste coal, coal-mine methane, other forms of distributed generation (DG), and certain 

demand-side management technologies. 

VOLUNTARY AGREEMENTS

Voluntary Agreements can be faster than regulations at achieving goals set by government officials. 

Throughout Appalachia, however, voluntary agreements are mainly found as partnerships between 

state and Federal government organizations. 

About half (six) of Appalachian states are partners with the U.S. Environmental Protection Agency 

(EPA) in the Clean Energy Environment program; of these states, Georgia, New York, Ohio, and 

Pennsylvania were charter state partners, and North Carolina joined shortly after the publication of 

the Clean Energy-Environment guide to action in 2005. Virginia joined in February of 2008. 

Several Appalachian states are partners with the Building America and Rebuild America programs. 

An illustrative voluntary agreement intrastate is Georgia’s Earthcraft house partnership between 

Southface Energy Institute and the Greater Atlanta Home Builders Association that seeks to provide 

builders with greater information about comfort and energy efficiency that can be achieved through 

design and construction practices. 

REGULATIONS

Regulations can set a minimum standard for efficiency to discourage waste. In Appalachia, most 

states or local governments have building codes for new residential and commercial buildings that 

meet or exceed the standards of the 2003 International Energy Code. However, only New York and 

Maryland have efficiency standards for appliances; most of these were preempted by Federal 

appliance standards in 2005. 

INFORMATION DISSEMINATION AND TRAINING 

These programs seek to distribute information, raise public knowledge, provide accessible 

information, or train persons for particular tasks. Many information programs are packaged with 

financial assistance; for example, in New York, seven programs are grants with technical assistances. 

Only two of these are grouped in the information section, while the other five are counted under 

financial incentives. 
Appendix A: ARC Energy Efficiency Policy Inventory

A-7

0

5

10

15

20

25

30

Audits Information Training Technical Assistance

Information Dissemination & Training

Most states in Appalachia use Information Dissemination and Training to promote energy efficiency. 

The number of programs within states can be seen in Table A.3. 

Table A.3 Information Dissemination and 

Training Policies by State

State Number of Policies

AL 4

GA 4

KY 6

MD 6

MS 1

NC 3

NY 4

OH 1

SC 2

TN 2

VA 2

WV 2

PROCUREMENT

Procurement programs establish demand for particular products through government acquisition. 

These programs can also be used to demonstrate the applicability of products or practices. 

Procurement programs are sometimes combined with market reform or market obligations or state 

agency financing mechanisms. Seven programs across Appalachia were identified as requiring 

government agencies to adopt or purchase certain types of equipment. 

Figure A.5 Information Dissemination and Training Program Types Used in Appalachia
Energy Efficiency in Appalachia, SEEA

A-8

By January 1, 2008, all Tennessee state agencies, universities, and community colleges having more 

than 10 state-owned vehicles in their fleet are required to develop and implement plans to increase 

the state’s use of alternative fuels and hybrid electric or other fuel-efficient or low-emission vehicles. 

Specifically, they must incorporate a goal to reduce or displace at least 20 percent of the fleet’s 

consumption of petroleum by January 1, 2010. The goal is reduced to a minimum of 10 percent 

reduction if the fleet includes vehicles modified for educational, emergency, or public safety purposes 

or vehicles used for emergency or law enforcement purposes. 

MARKET REFORMS 

Sweeping changes to the market for energy can be considered market reforms. These could be 

privatization, deregulation, reregulation, or other structural changes. Not all market reforms are 

designed to promote efficiency; this policy inventory does not include deregulation of natural gas or 

electric utilities as efficiency policies. Surcharges, like public benefits funds, can also be used as 

market reforms. New York and Pennsylvania have implemented Public Benefits Funds as surcharges 

on energy bills. The collected funds are used to provide for low income energy bill relief and to 

incentivize greater renewable energy and energy efficiency. 

Pennsylvania has its public benefits funds program created through individual settlements with the 

state’s five major distribution utilities: Metropolitan Edison Company (Met-Ed), Pennsylvania 

Electric Company (Penelec), PECO Energy (PECO), PP&L (PPL), and Allegheny Power/West Penn 

Power Company (WPP). These utilities created individual “Sustainable Energy Funds” with the 

goals of promoting (1) the development and use of renewable energy and advanced clean-energy 

technologies, (2) energy conservation and efficiency, and (3) sustainable-energy businesses. Each 

utility has established an oversight board and designated a fund administrator. [Because of this setup, financial incentives are in the utility service areas rather than state-wide or locality based 

policies.] 

MARKET OBLIGATIONS

Obligations to have particular fuel or efficiency mixes, such as renewable or sustainable energy 

portfolio standards, fall under the category of market obligations. New York, North Carolina, Ohio, 

and Pennsylvania have portfolio standards that include energy efficiency as a qualified source. 

Virginia has a targeted goal for renewable energy and energy efficiency. 

North Carolina’s Renewable Energy and Energy Efficiency Portfolio Standard requires investorowned utilities in the state to supply 12.5 percent of 2020 retail electricity sales in the state from 

eligible energy resources by 2021. Up to 25 percent of the requirements can be met through energy 

efficiency technologies, including combined heat-and-power systems powered by non-renewable 

fuels. After 2018, up to 40 percent of the standard can be met through energy efficiency. The 

requirements are less stringent for municipal utilities and electric cooperatives, which must meet a 

target of 10 percent renewables by 2018 and are subject to slightly different rules. 

Within Ohio’s Alternative Energy Resource Standard (effective January 1, 2009), utilities are 

required to implement energy efficiency and peak demand reduction programs that achieve a 

cumulative energy savings of 22 percent by the end of 2025, and reduce peak demand by 1.0 

percent in 2009 and 0.75 percent annually thereafter through 2018.
Appendix A: ARC Energy Efficiency Policy Inventory

A-9

CAPACITY BUILDING

Capacity Building includes developing centers for energy efficiency that work with the private sector 

to carry out demonstrations, provide information and training, offer financing, and promote 

efficiency. Capacity building also allows for the production of local skill building; creating local 

capacity to carry out necessary energy related activities. Thirteen energy efficiency programs with 

capacity building focus have been identified in five states (Table A.4). Within Appalachia, most 

capacity building seems to be achieved through funding of centers at state colleges and universities. 

Table A.4 Capacity Building Policies by State

State Number of Policies

KY 3

MD 1

NC 2

NY 5

WV 2

The Southeastern Combined Cooling, Heating and Power Regional Application Center 

(CHPCenterSE), is directed by the Mississippi Development Authority-Energy Division, Mississippi 

State University’s Micro-CHP Application Center and North Carolina State University’s NC+CHP 

Application Program. The new Regional center seeks to double the installed CHP capacity in the 

Southeast by the year 2010. They will also coordinate and conduct education and outreach activities 

to stimulate market development as guided by a CHP Center Roadmap, which will be developed in 

the first three months of the project. While this center will be located in North Carolina, it 

demonstrates how effective interstate cooperation can produce meaningful savings. 

New York’s Saratoga Technology + Energy Park is a unique venture by the New York State Energy 

Research and Development Authority (NYSERDA) to provide an “integrated knowledge community.” 

The park actively recruits innovative energy technology firms to do business within the area.

PLANNING TECHNIQUES

Integrated planning methods that take into account interactions as well as differential short and long 

term impacts of policies or practices are considered planning techniques. Five policies in Appalachia 

were identified as planning for energy efficiency, from North Carolina, New York, and Virginia. 

In North Carolina, the Division of Bicycle and Pedestrian Transportation (DBPT) works with 

localities to create a four-year schedule of projects using the locality’s priority listing of needs along 

with the adopted project selection criteria. All project requests are documented and distinguished as 

independent or incidental (part of a highway project). Independent project requests are evaluated by 

DBPT using project selection criteria. A prioritized list of these projects is presented to the North 

Carolina Bicycle Committee, which reviews the list, makes revisions and recommendations, and 
Energy Efficiency in Appalachia, SEEA

A-10

adopts a four-year schedule of projects. The adopted schedule is sent to the North Carolina Board of 

Transportation for approval and inclusion in the state’s TIP. 

An example of a local planning effort that is broader in scope is PlaNYC. PlaNYC was developed for 

New York City as a sustainability plan for the city that identifies energy planning and energy 

efficiency as significant action objectives. 

Beyond these policies set in place by state and local governments, there are a number of nationwide 

programs that are available in Appalachia. These nationwide programs are sponsored by the Federal 

government or non-profit organizations. 

REFERENCES

Center for Business and Economic Research (CBER). 2006. Energy Efficiency and Renewable 

Energy in Appalachia: Policy and Potential. 

http://www.arc.gov/images/reports/2006/energy/arc_renewable_energy_full.pdf

Geller, Howard. 2002. Energy Revolution: Policies For A Sustainable Future. Island Press. 
Appendix B: Residential Policy and Methodology Detail

B-1

APPENDIX B: RESIDENTIAL POLICY 

AND METHODOLOGY DETAIL 

RESIDENTIAL MODEL BUILDING ENERGY CODES

Introduction

Residential model building energy codes prescribe the minimum level of efficiency that must be 

achieved in new residential single and multi-family (less than three stories) construction. 

Manufactured (and mobile) homes are covered under Federal manufactured housing efficiency 

requirements. Residential building energy codes focus on shell efficiency and construction/design; 

HVAC and appliances are not specifically addressed. This study assumes that Appalachian counties 

all adopt the 2006 IECC by 2009 and subsequently more efficient codes every three years thereafter; 

codes are assumed to become effective the year following adoption. In 2015, this study assumes that 

the Region ―catches up‖ to the current code cycle; therefore, the code adopted in 2015 is assumed to 

be a modeled 2015 code rather than the modeled 2012 code. 

The 2006 IECC simplified codes and compliance by reducing the number of climate zones and 

separating climate zones by geography rather than just heating degree days (HDD). Compliance may 

be achieved through meeting prescriptive ―R‖ values, whole house ―UA‖ values, or through building 

energy simulation software, not including appliances (BECP, 2006). The differences by climate zone 

can be seen for the four climate zones in the Appalachian Region in Table B.1, and the breakdown of 

climate zones by state for the Appalachian Region is in Table B.2. 

Table B.1 2006 IECC Prescriptive Requirements by Climate Zone

(ICC, 2006)

Climate 

Zone

Fenestration 

U-Factor

Skylight 

U-Factor

Glazed 

Fenestration 

SHGC

Ceiling 

RValue

Wood 

Frame 

Wall 

RValue

Mass 

Wall 

RValue

Floor 

RValue

Basement 

Wall RValue

Slab 

RValue 

and 

Depth

Crawl 

Space 

Wall 

RValue

3 .65 .65 .4 30 13 5 19 0 0 5/13

4 .4 .6 N.R. 36 13 5 19 10/13 10, 2 ft 10/13

5 .35 .6 N.R. 36 19 or 

13+5 13 30 10/13 10, 2 ft 10/13

6 .35 .6 N.R. 49 19 or 

13+5 15 30 10/13 10, 4 ft 10/13

In the northern climate zones (5 and 6), a vapor retarder is required; in southern climates zones (3), 

the solar heat gain coefficient must be less than 0.40 (BECP, 2006). 
Energy Efficiency in Appalachia, SEEA

B-2

Table B.2 Climate Zones for Appalachian Portions of States

(ICC, 2006)

Climate 

Zone AL GA KY MS MD NC NY OH PA SC TN VA WV

3 X X X X

4 X X X X X X X X

5 X X X X X X

6 X X

Codes adopted in later years are expected to continue to increase efficiency through reduced heat 

loss, more efficient windows, duct sealing measures, and passive solar design. 

Methodology

Forecasts of housing starts are available by census division as part of the EIA‘s Annual Energy 

Outlook 2008 (EIA, 2008). These forecasts are population weighted to the Appalachian portion of 

each census division to construct a housing start forecast unique to the Region (REMI, 2007). The 

portion of new housing construction that is single or multi-family homes is set to have a reduced 

consumption relative to the forecast without building codes. 

Building codes are only anticipated 

to reduce consumption for space 

heating, space cooling, and water 

heating loads by improving shell 

efficiency. The three major fuels 

used for these purposes (electricity, 

natural gas, and distillate fuel oil) 

are modeled for savings. Reduced 

consumption for other fuels (e.g., 

wood, kerosene, and LPG) and for 

other end-uses, such as lighting, 

may also follow building energy 

code changes, but they are not 

included in this assessment. Figure B.1 shows how energy savings from residential building codes 

are determined. 

Energy Savings

Energy savings for building energy codes are determined by changing the anticipated efficiency of 

newly constructed homes. The modeled efficiencies are shown in Table B.3. 

Figure B.1 Residential Model Building Energy Code Methodology

Housing Stock by Type by 

Census Division

ARC Percentage

by Census Division

Housing Starts by Type by 

Census Division

Percent of ARC Housing Stock by 

Type Constructed under Building Code 

by Census Division

Consumption by End-Use by 

Fuel by Census Division

ARC Consumption by End-Use 

by Fuel by Census Division

Building Code 

Relative Efficiency 

by End-Use by Fuel

ARC Building Code Savings by 

End-Use by Fuel by Census 

Division
Appendix B: Residential Policy and Methodology Detail

B-3

Table B.3 Achievable Efficiencies if Building Codes are 

Adopted and Enforced

Year 

Operationala

%

Savings Relative 

to Baselineb

Potential

Code

Homes Built 

to This Codec

2010 6 2006 IECC 330,105

2013 18 2009 IECC 335,092

2016 30 2015 IECC 329,656

2019 36 2018 IECC 312,876

2022 42 2021 IECC 301,258

2025 48 2024 IECC 303,090

2028 54 2027 IECC 292,890

a

The ―Year Operational‖ is in three year increments because we assume a three year 

code adoption cycle.

b Relative savings are to year 2005 RECS estimates for the end-uses modeled to be 

impacted by building codes: Space Heating, Space Cooling, and Water heating. 

This is not whole house efficiency. This number should be interpreted to mean that 

the 418,865 single and multi-family homes built from 2013 to 2015 use 18 percent

less energy for space heating, space cooling, and water heating than they would 

have if built with NEMS assumed efficiencies for the same years.

c

Single Family and Multi-Family Homes are included. Mobile Homes (about 12

percent of ARC housing units) are not included. Number reflects 80 percent 

compliance.

The current study‘s estimates of savings relative to the year 2005 RECS estimates are intentionally 

conservative. Lucas (2006) estimated annual savings of 16-17 percent in West Virginia by adopting 

the 2003 IECC (unamended) in place of the 2003 IRC with amendments. The 2003 IECC and 2006 

IECC are similar in efficiency requirements as the major changes are in ease of compliance and 

structure. Further, our assumptions are not as aggressive as the ―30 Percent Solution‖ that would set 

the 2009 IECC for residential buildings at 30 percent more efficient than current model energy code; 

supporters include many national, regional, utility, and trade organizations (EECC, 2008). 

Because building codes accelerate the adoption of advanced building materials and technologies that 

would be adopted in the future anyway, savings beyond 2030 are not included in this analysis. 

Policy Implementation Cost

This policy assumes a change in the way energy codes are handled; therefore, high compliance rates, 

modeled at 80 percent, do not require significantly higher public administration costs. Because 

compliance is verified with third parties paid by the construction firms, public administration of the 

program requires: maintaining a certification program for third-party verifiers, training of 

construction and verification firms, and periodic review of the competence of third-party verifiers. 

This study assumes that two training administrators, at $75,000 each, will be required per state (only 

that portion of the state that is in the ARC is counted as a cost), and one additional verification 

liaison will be required per every 10,000 homes built in the Appalachian Region per year at $75,000. 
Energy Efficiency in Appalachia, SEEA

B-4

A business roundtable study, which included survey results from building code offices around the 

country concluded that it is ―almost impossible to develop a meaningful relationship between the 

number of staff, professional competence, or level of service provided by a code enforcement agency 

and its allocated financial resources‖ (Business Roundtable, 1982). As such, it is unclear what level 

of program costs will be necessary to achieve successful code compliance in any particular 

jurisdiction. 

Incremental investment costs vary by climate zone and current practice. Because current practice is 

unknown, it is assumed that current homes are built to the prescriptive envelope of each state‘s 

building energy code. A handful of estimates of compliance costs in areas with similar climate zones 

to the Appalachian Region are shown in Table B.4. The present study assumes that each home has 

an incremental cost of $1,000 which likely overestimates costs in climate zone 4 and 5, which makes 

up most of the Appalachian Region, but may underestimate costs in climate zone 3 and 6. 

Table B.4 Incremental Cost Estimates for Building Code Compliance

State Code Studied Climate 

Zonea

Cost Estimate 

(per home) Reference

Illinois none to 2006 

IECC 4 573-1715 Lucas, 2007

Illinois none to 2006 

IECC 5 1173-3062 Lucas, 2007

Iowa 1992 MEC to 

2003 IECC 5,6 0-500 Lucas, 2003

Kentucky 1992 MEC to 

2000 IECC 4 0-300 Lucas, 2001

West Virginia

2003 IRC 

amended to 2003 

IECC

4 639 Lucas, 2006

West Virginia

2003 IRC 

amended to 2003 

IECC

5 659 Lucas, 2006

a Climate zone is the 2006 IECC climate zone. Previous code cycles had more zones.

This study does not include an incentive for meeting or exceeding the building energy code; 

however, there are options for incentivizing highly efficiency construction, described below. 

The actual program or measure used for distributing these incentive funds may have a significant 

impact on compliance. Several program design options exist. An example would be requiring new 

residences to have a Home Energy Rating Score (HERS) score below a certain level to get a tax 
Appendix B: Residential Policy and Methodology Detail

B-5

credit or rebate. New Mexico passed efficiency legislation in 2007 allowing 

for builder tax rebates for new homes with a HERS rating of 60 or less (NM, 

2007). 

Another option would be to support utility residential new construction 

programs. For example, PG&E (California) operates a residential new 

construction program that provides an incentive of $400 or $500 to builders 

per ENERGY STAR home; it also provides incentives for outfitting compliant 

homes with energy efficient appliances (P&GE, 2008). Vine (1996) presented 

compliance levels from California, Oregon, and Washington and found that 

utility residential new construction programs achieved near 100 percent 

compliance from builders while residences built outside of the program were 

found to be six percent (or more) less efficient than the code prescribed.1

Market Penetration 

The purpose of this study is to estimate cost-effective energy savings; since building energy codes 

are required to be cost-effective before adoption, we assume compliance at 80 percent. Total homes 

affected by each code are given in Table B.3 (above). 

EXPANSION OF WEATHERIZATION ASSISTANCE

Introduction

The Weatherization Assistance Program (WAP) was created by the Energy Conservation and 

Production Act of 1976 (Title IV) to reduce heating bills for low-income families and seniors. 

Weatherization programs specifically target heating loads; they have a dual purpose of reducing 

energy consumption and corresponding bills while improving resident comfort (Schweitzer and 

Tonn, 2002). 

This study assumes that each year an additional one percent of homes are weatherized over and 

above those that would have been weatherized based on current levels. About 19 percent (1,750,093) 

of Appalachian single family and manufactured homes (15 percent of all homes) are weatherized 

under this expanded program between 2010 and 2030.2

The present study‘s one percent additional weatherization measure represents an ambitious effort to 

reduce heating consumption and improve comfort within Appalachian homes. While a 

disproportionate number of these homes are expected to be in the 410-county Appalachian Region 

due to higher poverty levels, weatherization data are not available at this level for all states (see 

Table B.5 for an example from Ohio). Of the 40,391 homes weatherized in the 2007 Program Year 

in the thirteen Appalachian states, a population weighted estimate assumes 8,856 were in 

Appalachian Region counties (about one-tenth the annual effort required to meet one percent). 

 

1

 Utility residential new construction programs offer incentives to builders to meet or exceed model energy codes. 

2

 Weatherization assistance is also available to certain small multi-family units, but multi-family structures are not 

included for this study. 
Energy Efficiency in Appalachia, SEEA

B-6

Table B.5 Appalachian Region Households Eligible and Served by 

Weatherization Assistance Program in Ohio, 2003

(OOEE, 2006)

Region

%

Households Eligible 

for HWAP

%

Eligible Households 

Served

%

Households 

Served

Adams 38.10 12.50 5

Athens 40.10 6.70 3

Belmont 35.80 7.20 3

Brown 26.60 9.90 3

Carroll 29.10 3.70 1

Clermont 18.60 4.30 1

Columbiana 30.50 5.60 2

Coshocton 28.80 15.00 4

Gallia 36.80 7.30 3

Guernsey 35.40 7.50 3

Harrison 33.80 7.40 3

Highland 30.30 8.40 3

Hocking 31.00 6.70 2

Holmes 37.70 3.90 1

Jackson 35.80 9.80 4

Jefferson 33.80 5.70 2

Lawrence 38.10 8.30 3

Meigs 39.90 10.50 4

Monroe 34.60 5.70 2

Morgan 38.30 8.10 3

Muskingum 29.60 6.50 2

Noble 33.00 8.70 3

Perry 30.10 8.40 3

Pike 35.70 10.90 4

Ross 28.30 6.60 2

Scioto 38.80 6.40 2

Tuscarawas 27.60 5.00 1

Vinton 37.20 11.20 4

Washington 29.50 6.50 2

The Department of Energy provides funding to states for WAP, although states can also use a portion 

of LIHEAP funds for weatherization, and states also seek other sources of funding, such as utility 

partnerships (WAPTAC, 2008). Ohio was the first state to legislatively set aside the maximum of 15 

percent of LIHEAP funding for weatherization efforts in 1981 (OOEE, 2006). 
Appendix B: Residential Policy and Methodology Detail

B-7

Many states also partner with gas and electric utilities to offer more weatherization services; 

weatherization improves the client‘s ability to pay for energy bills, so the utilities also benefit 

(McCold et al., 2008). DeRamos (2002) and OOEE (2006) found that savings and performance were 

better (up to an additional 30 percent) in homes that received joint utility and weatherization program 

service. Total funding for each of the Appalachian states and a population weighted Appalachian 

Region is shown in Table B.6. 

Table B.6 WAP Funding from All Sources ($million), 1997-2007

(Census, 2005; WAPTAC, 2008)

State 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

AL 1.77 2.32 1.96 2.30 2.42 3.12 3.12 3.45 3.46 3.83 3.28

GA 6.82 6.79 6.42 4.79 4.98 6.75 6.85 6.41 6.42 10.33 7.43

KY 10.09 4.91 4.84 6.93 7.61 8.06 7.89 7.32 8.68 10.28 8.96

MD 3.71 1.87 3.04 3.17 3.56 4.54 2.83 4.28 5.46 6.02 5.60

MS 1.02 1.02 1.08 0.98 1.11 1.11 1.62 3.82 1.66 1.85 1.48

NY 33.75 30.16 36.68 59.44 52.39 62.43 62.16 61.22 60.65 65.31 63.60

NC 9.31 8.22 6.81 9.44 8.17 8.85 10.54 9.58 9.92 14.59 9.90

OH 24.88 26.83 31.94 33.47 35.18 32.72 50.41 48.60 49.24 56.64 52.44

PA 17.83 19.11 19.63 25.70 27.02 32.92 32.49 33.72 34.76 43.09 33.12

SC 2.97 2.74 2.39 2.82 3.39 3.62 2.98 3.63 3.63 3.98 3.64

TN 3.83 4.74 4.24 5.50 4.86 6.55 5.98 6.69 6.35 7.24 5.99

VA 4.87 7.44 7.43 9.27 9.74 10.53 9.73 10.82 11.16 15.59 9.97

WV 3.63 2.79 3.98 3.68 5.24 5.90 5.77 5.75 5.75 7.39 7.27

ARCa

28.20 26.61 27.93 33.27 34.44 39.21 41.57 43.01 42.98 54.18 44.50

a ARC Population weights for years 1997-2007 are based on the relative populations in 2005 using Census estimates of 

population for 2005 and REMI estimates of ARC county populations for 2005. This funding approximation does not 

claim that any particular WAP funding was distributed in the ARC counties.

Methodology

Housing forecasts are available by 

census division as part of the EIA‘s 

Annual Energy Outlook 2008 (EIA, 

2008). Weatherization savings are 

calculated by summing the end-use 

savings by fuel for one percent of 

single family and manufactured 

homes each year. 

Figure B.2 Expanded Low Income Weatherization Program Methodology

Housing Stock by Type by 

Census Division

ARC Percentage

by Census Division

ARC Housing Stock by Type 

Weatherized by Census Division

Consumption by End-Use by 

Fuel by Census Division

ARC Consumption by End-Use 

by Fuel by Census Division

Weatherization 

Relative Efficiency 

by End-Use by Fuel

ARC Weatherization Savings by 

End-Use by Fuel by Census 

Division

Percent of Homes 

Weatherized
Energy Efficiency in Appalachia, SEEA

B-8

Energy Savings

Energy savings for weatherization are based on Berry and Schweitzer‘s (2003) meta-evaluation of 

state weatherization studies; savings percentages are shown in Table B.7. 

Table B.7 Achievable Efficiencies for Weatherization

Fuel End-Use

Savings

(Post vs. Pre)

Percent

Natural Gas Heating 30.8

Natural Gas Whole House 21.9

Electricity Heating 26.7

Electricity Whole House 10.5

Berry and Schweitzer (2003) found savings for heating oil and propane to be slightly lower than 

natural gas; however, Shen et al. (1996) found heating oil and propane savings not significantly 

different from natural gas savings. In the present study, heating oil savings are set at the same levels 

as natural gas (30.8 percent for heating and 21.9 percent total) and propane savings are not modeled. 

In hot and humid climates in the southern portion of the Appalachian Region, improvements in space 

cooling are also made (McCold et al., 2008). This study assumes a 10 percent savings in space 

cooling energy use. In many weatherization programs, it is standard practice to install a water heater 

blanket; at a cost of just $10 to $20, these blankets can save four percent to nine percent in water 

heating costs (DOE/EERE, 2008). 

Energy savings are modeled to last 20 years. For examination of costs and benefits of this program, 

savings out to 2050 (for those homes weatherized in 2030) are included. Since the EIA forecast for 

energy prices ends at 2030, this study assumes that prices and avoided costs are constant at 2030 

levels until 2050. This assumption likely underestimates the value of energy saved during the period 

from 2030 to 2050. 

Policy Implementation Cost

Investment costs are assumed to be $2,300 per home (2006 dollars), as allowed by the Department of 

Energy program, but funds from other sources (such as utility programs) are assumed to be leveraged 

to provide greater service levels to clients. 

An additional 10 percent is assumed to be spent on administration of the program. Weatherization is 

currently a program with no participant cost-share; the modeled program assumes no private 

investment. 
Appendix B: Residential Policy and Methodology Detail

B-9

Market penetration

This study assumes that about 15 percent of Appalachian homes are weatherized under this program 

expansion from 2010 to 2030. Since this is an extension of the existing weatherization program that 

we assume will continue over the study horizon, the total number of homes weatherized should be 

higher. Between the original and extended program, a significant number of qualified homes will be 

provided weatherization assistance. 

RETROFIT OF EXISTING HOMES 

Introduction

Retrofitting of existing homes seeks to reduce energy consumption in homes that do not qualify for 

the Weatherization Assistance Program. It is hoped that, in combination, these two programs could 

improve the energy consumption levels of most homes in the Region to at least the performance of 

the 2006 IECC by 2030. 

Existing home retrofits already occur; however, they do not always target energy-efficient 

improvements. This study assumes that energy-efficiency improvements are encouraged when 

homeowners seek to remodel and also whenever siding, roofing, wiring, or heating and cooling 

equipment would need to be replaced. 

The retrofit program would probably come in the form of a tax credit, grant, or rebate program. It is 

designed as an incentive measure to accompany two other policies – home energy disclosure and onbill financing. 

Methodology

Housing forecasts are available by 

census division as part of the EIA‘s 

Annual Energy Outlook 2008 (EIA, 

2008). Retrofit savings are 

calculated by summing the end-use 

savings by fuel. The model assumes 

retrofit of two percent of single 

family homes each year. 

Baseline Energy Savings

While it can be compared to the weatherization program, the retrofit program includes more 

measures, at a greater expense, and should yield higher savings. The assumed efficiency 

improvements are 50 percent for heating, and 40 percent for cooling, lighting, and water heating 

loads better than forecast. 

Figure B.3 Retrofit of Existing Homes Methodology

Housing Stock by Type by 

Census Division

ARC Percentage

by Census Division

ARC Housing Stock by Type 

Retrofitted by Census Division

Consumption by End-Use by 

Fuel by Census Division

ARC Consumption by End-Use 

by Fuel by Census Division

Retrofit Relative 

Efficiency by EndUse by Fuel

ARC Retrofit Savings by EndUse by Fuel by Census Division

Percent of Homes 

Retrofitted
Energy Efficiency in Appalachia, SEEA

B-10

Energy savings are modeled to last 20 years. For examination of costs and benefits of this program, 

savings out to 2040 (for those homes undergoing retrofit in 2020) are included. Since the EIA 

forecast for energy prices ends at 2030, this study assumes that prices and avoided costs are constant 

at 2030 levels until 2040. This assumption likely underestimates the value of energy saved during 

the period from 2030 to 2040. 

Policy Implementation Cost

Investment costs are assumed to be $3,400 per home. Retrofits are expected to include items like 

those suggested by the Energy Efficient Rehab Advisor, which is an online retrofit savings estimator 

provided by the U.S. Department of Housing and Urban Development (Table B.8 – Table B.10). 

Table B.8 Suggested Retrofits, Costs, and Savings for Homes in Central States

(HUD, 2005)

Retrofits Cost

($)

Annual Energy 

Savings

($)

Payback 

(Years)

Programmable Thermostat 115 195 0.6

Seal Air Leaks 540 401 1.3

Heating and Cooling System Tune-up 191 137 1.4

Seal Duct Leaks 432 285 1.5

Insulate Walls to R11 1015 348 2.9

Upgrade Gas Water Heater 115 21 5.4

Insulate Ceilings to R38 (R30 in 

warmer climates) 950 169 5.6

Total 3358 1556 2.2

Note: Costs and Savings based on a typical 2000 sq ft home in Nashville, TN, with a finished basement 

and natural gas heating
Appendix B: Residential Policy and Methodology Detail

B-11

Table B.9 Suggested Retrofits, Costs, and Savings for Homes in

Southeastern States

(HUD, 2005)

Retrofits Cost

($)

Annual Energy 

Savings

($)

Payback 

(Years)

Programmable Thermostat 115 146 0.8

Heating and Cooling System Tune-up 191 113 1.7

Insulate Ducts 388 108 3.6

Seal Duct Leaks 443 131 3.9

High Efficiency Central Air 

Conditioner 486 121 4

Seal Air Leaks 554 66 8.4

Insulate Floors to R19 776 85 9.1

Total 2953 770 3.8

Note: Costs and Savings based on a typical 2000 sq ft home in Houston, TX, with a finished basement 

and natural gas heating

Table B.10 Suggested Retrofits, Costs, and Savings for Homes in

Northeastern States

(HUD, 2005)

Retrofits Cost

($)

Annual Energy 

Savings

($)

Payback 

(Years)

Programmable Thermostat 115 183 0.6

Heating System Tune-up 96 122 0.8

Seal Duct Leaks 443 311 1.4

Windows (U-factor 0.35) and 

Skylights (U-factor 0.6 or less) 744 341 2.2

Insulate Ceilings to at least R49 –

install vapor retarders 643 268 2.4

Seal Air Leaks 554 66 8.4

Insulate Ducts to R6 443 183 2.4

Total 3038 1474 2.1

Note: Costs and Savings based on a typical 2000 sq ft home in Burlington, VT, with a finished basement 

and fuel oil heating
Energy Efficiency in Appalachia, SEEA

B-12

These investment costs are expected to be born primarily by the homeowner (participant). A public 

incentive of 20 percent is assumed until 2020. After 2020, the program is only providing public 

awareness of energy-efficiency measures, aiding the administration of the residential home energy 

labeling program, and supporting on-bill financing. 

Administrative costs are expected to include one administrator at $150,000 and one employee at 

$75,000 per state, proportioned to ARC counties, for a total of $.99 million per year. After 2020, 

these administrative costs are reduced to $330,000 per year. 

Market Penetration

This study assumes that about 38 percent of Appalachian single family homes are retrofitted between 

2010 and 2030 under this program (accounts for 28 percent of all Appalachian housing units in 

2030). 

RESIDENTIAL EFFICIENT APPLIANCE INCENTIVE

Introduction

The present study assumes that appliances are improved over time with greater improvements after 

cyclical updates to the ENERGY STAR guidelines. Thus, the most efficient appliances are modeled 

to improve to efficiencies three percent better than forecast stock efficiency each five years. For 

example, modeled end-use appliances consume 97 percent energy relative to stock appliances in 

2010; with improvements every five years until they consume 86 percent relative to stock in 2030. 

This assumption is made because ENERGY STAR guidelines are not available for the period under 

evaluation (2010-2030). In some cases, appliances are more disaggregate than forecast end-use data 

allow. For example, ―cooking‖ and ―microwave‖ as end-uses are modeled, while specific ovens and 

cooktops are not. 

The present study does not evaluate the secondary market for appliances. When equipment is 

replaced, it is assumed to be replaced at the current efficiency or the high efficiency level. Reducing 

the availability of outdated appliances to secondary markets may be necessary to achieve the 

modeled savings. The best known type of policy for this purpose has been ―bounty‖ programs for 

refrigerators due to the great technical improvements in energy consumption. Refrigerator recycling 

programs are in place in 14 states across the nation – no statewide efforts were identified in 

Appalachia; such programs prevent older less-efficient refrigerators from entering the secondary 

market (ENERGY STAR, 2008).3

 

3 New York Power Authority does run a refrigerator recycling program for public housing residents, see 

http://www.nypa.gov/services/esprograms2.htm
Appendix B: Residential Policy and Methodology Detail

B-13

Methodology

Energy Savings

Energy savings are represented by a percentage of end-use consumption. We multiplied the percent 

savings projected by year to the AEO forecast of consumption for that end-use by turnover for the 

end-use based on an average equipment lifetime. 

Table B.11 Modeled End-Uses for Super-Efficient Appliance Deployment by Fuel

End-use Lifetime

Savings Modeled

Electricity Natural Gas Distillate

Cooking 15 X

Microwave 6 X

Clothes Washer 14 X

Dryer 14 X X

Freezer 19 X

Refrigerator 19 X

Security System 10 X

DVD Players 8 X

Home Audio 5 X

PC 8 X

TV 8 X

Figure B.4 Residential Super-Efficient Appliance Deployment Methodology
Energy Efficiency in Appalachia, SEEA

B-14

Policy Implementation Cost

We assume administrative costs similar to a standards program of $0.13 per MBtu based on 1995 

cost of .095/MBtu from the Clean Energy Future study and a 1.32 inflation factor (Interlaboratory 

Working Group, 2000 [Appendix E]; BLS, 2008). 

Investment costs vary widely by appliance, brand, model, and location. In order to generalize, we 

assumed a cost of $30 per MBtu (delivered) saved, representing a three year payback with energy 

prices at $10 per MBtu. This cost overestimates the incremental cost of some end-use appliances and 

underestimates others. 

This program is expected to be accompanied by an incentive program that will cover 40 percent of 

the incremental costs of adopting the higher efficiency models from 2010 to 2015 and 20 percent 

from 2015 to 2020. In effect, this market transformation program helps to accelerate the adoption of 

efficient appliances that would be adopted anyway at some later point in the future. As such, the 

savings beyond 2030 are not included in this analysis. 

Market Penetration

This study assumes that 50 percent of new purchases or replacement products from 2010 to 2020 and 

40 percent from 2020 to 2030 are more efficient than forecast stock efficiency in the Annual Energy 

Outlook which already assumes some efficiency gains (EIA, 2007; 2008). 

REFERENCES

Berry, Linda and Martin Schweitzer. 2003. Metaevaluation of National Weatherization Assistance 

Program Based on State Studies, 1993–2002, ORNL/CON 488.

http://weatherization.ornl.gov/pdf/CON_488.pdf

Building Energy Codes Program (BECP). 2006. ―Residential Requirements of the 2006 

International Energy Conservation Code.‖

http://www.energycodes.gov/training/presentations/2006iecc_residential.ppt

Business Roundtable, The. 1982.‖ Administration and Enforcement of Building Codes and 

Regulations: A Construction Industry Cost Effectiveness Project Report.‖ Washington, D.C. 

http://curt.org/pdf/155.pdf

De Ramos, Kevin Monte. 2002. ―Lessons Learned from Columbia Gas of Ohio‘s WarmChoice 

Program.‖ KMDR Research, Inc. 

Energy Efficient Codes Coalition (EECC). 2008. Thirty Percent Solution Supporter List. 

http://www.thirtypercentsolution.org/modules/smartpartner/

Energy Information Administration (EIA). 2007. Annual Energy Outlook 2007. 

http://tonto.eia.doe.gov/ftproot/forecasting/0383(2007).pdf

———. 2008. Annual Energy Outlook 2008. http://www.eia.doe.gov/oiaf/aeo/
Appendix B: Residential Policy and Methodology Detail

B-15

ENERGY STAR 2008. The ENERGY STAR® Recycle My Old Fridge Campaign Website. 

http://www.recyclemyoldfridge.com/findaprogram.aspx

Interlaboratory Working Group. 2000. Scenarios for a Clean Energy Future. Oak Ridge, TN; Oak 

Ridge National Laboratory and Berkeley, CA; Lawrence Berkeley National Laboratory, 

ORNL/CON-476 and LBNL-44029. 

International Code Council (ICC). ―2006 International Energy Conservation Code®.‖

http://www.iccsafe.org/e/prodshow.html?prodid=3800S06

Lucas, R.G. 2001. Assessment of Impacts from Updating Kentucky’s Residential Energy Code to 

Comply with the 2000 International Energy Conservation Code. PNNL 13525. 

http://www.energycodes.gov/implement/pdfs/kentucky_res.pdf 

———. 2003. Assessment of Impacts from Updating Iowa’s Residential Energy Code to Comply 

with the 2003 International Energy Conservation Code. PNNL 14431. 

http://www.energycodes.gov/implement/pdfs/iowa_2003_iecc_report.pdf

———. 2006. Energy Efficiency of the 2003 International Energy Conservation Code in West 

Virginia. PNNL 16284. http://www.energycodes.gov/implement/pdfs/wv_res_2003_iecc.pdf 

———. 2007. Assessment of Impacts from Adopting the 2006 International Energy Conservation 

Code for Residential Buildings in Illinois. PNL 16266. 

http://www.energycodes.gov/implement/pdfs/il_2006_resfinal.pdf

McCold, Lance, Richard Goeltz, Mark Ternes, and Linda Berry. 2008. Texas Field Experiment: 

Performance of the Weatherization Assistance Program in Hot-Climate, Low-Income Homes. 

ORNL CON 499. http://weatherization.ornl.gov/pdf/CON%20499.pdf

New Mexico State Legislature (NM). 2007. SB 0543. 

Ohio Office of Energy Efficiency (OOEE). 2006. ―Ohio Home Weatherization Assistance Program 

Impact Evaluation.‖

http://www.odod.state.oh.us/cms/uploadedfiles/CDD/OEE/200463%20Impact%20Eval%20Final

%20070606.pdf

Pacific Gas and Electric (PG&E). 2008. ―Residential New Construction Program.‖

http://www.pge.com/mybusiness/energysavingsrebates/incentivesbyindustry/newconstruction/en

ergystar/index.shtml

Regional Economics Models, Inc. (REMI). 2007. Data provided to the authors by the 

Appalachian Regional Commission on Dec 21, 2007.

Shen, Lester, David Bohac, Karen Linner, and Timothy Dunsworth. 1996. ―Development of 

Production Based Tools and Protocols for the Weatherization Program Assessment: Final Report 

for Minnesota.‖ http://www.mncee.org/pdf/tech_pubs/96-2.pdf
Energy Efficiency in Appalachia, SEEA

B-16

Schweitzer, Martin and Bruce Tonn. 2002. ―Non energy benefits from the Weatherization 

Assistance Program: A Summary of Findings from the Recent Literature.‖

http://weatherization.ornl.gov/download_files/Con-484-april02.pdf

U.S. Bureau of Labor Statistics (BLS). 2008. Inflation Calculator. http://data.bls.gov/cgibin/cpicalc.pl

U.S. Census Bureau (Census). 2005. There is a ref in Ch 3 for Census 2008 that is a table of data thru 

2005 – is this the same ref?

U.S. Department of Energy Efficiency and Renewable Energy (DOE/EERE). 2008. ―EERE 

Consumer‘s Guide: Insulate Your Water Heater Tank for Energy Savings.‖

http://apps1.eere.energy.gov/consumer/your_home/water_heating/index.cfm/mytopic=13070

U.S. Environmental Protection Agency (EPA). 2006. Clean Energy-Environment Guide to Action: 

Policies, Best Practices, and Action Steps for States. 

http://www.epa.gov/cleanenergy/documents/gta/guide_action_full.pdf

U.S. Housing and Urban Development (HUD). 2005. Energy Efficient Rehab Advisor. 

http://www.rehabadvisor.pathnet.org/ (last update October 2005)

Vine, Edward. 1996. ―Residential Building Code Compliance: Implications for Evaluating the 

Performance of Utility Residential New Construction Programs.‖ 

http://www.osti.gov/bridge/servlets/purl/418459-Bsrs72/webviewable/418459.pdf 

Weatherization Assistance Program Technical Assistance Center (WAPTAC). 2008. WAP PY 07 

Funding Survey. http://www.waptac.com/si.asp?id=1198
Appendix C: Commercial Policy and Methodology Detail

C-1

APPENDIX C: COMMERCIAL POLICY AND 

METHODOLOGY DETAIL 

COMMERCIAL BUILDING MODEL BUILDING ENERGY CODES

Introduction

Commercial model building energy codes prescribe the minimum level of efficiency that must be 

achieved in new commercial buildings.1 The present study assumes that all counties in the 

Appalachian Region adopt and enforce the 2006 IECC effective by 2010 and update to new building 

codes every three years. The 2006 IECC reduces the number of climate zones for different 

requirements; it also reduces the allowed glazing area to 40 percent (from 50 percent). See Table C.1 

for details of the 2006 IECC for the climate zones in the Appalachian Region; a breakdown of 

climate zones by Appalachian state is given in Table C.2. Codes adopted in later years are expected 

to continue to increase efficiency through reduced heat loss, more efficient windows, duct sealing 

measures, and passive solar design. 

 

1 A commercial building is anything other than a low-rise (1-3 stories) house, condominium, or apartment (R-2, R-3, and 

R-4).
Energy Efficiency in Appalachia, SEEA

C-2

Table C.1 2006 IECC Commercial Envelope Prescriptive

Requirements by Climate Zone

(ICC, 2006, Table 502.3)

Climate Zone 3 4 5 6

Vertical Fenestration (40 percent maximum

of above grade wall)

Framing materials other than metal 

with or without metal reinforcement 

or cladding

U-factor 0.65 0.4 0.35 0.35

Metal framing with or without 

thermal break 

Curtain Wall/Storefront U-factor 0.6 0.5 0.45 0.45

Entrance Door U-factor 0.9 0.85 0.8 0.8

All Other U-factor10.65 0.55 0.55 0.55

Metal SHGC-All Frame Types 

SHGC: PF < 0.25 0.25 0.4 0.4 0.4

SHGC: 0.25 ≥ PF < 0.25 0.33 NA NA NA

SHGC: PF ≥ 0.5 0.4 NA NA NA

Skylights (3% maximum) 

Glass; U-factor 0.9 0.6 0.6 0.6

Glass; SHGC 0.4 0.4 0.4 0.4

Plastic; U-factor 1.3 1.3 1.3 0.9

Plastic; SHGC 0.35 0.62 0.62 0.62

1All others includes operable windows, fixed windows and non-entrance doors.

PF: Projection factor

Table C.2 Climate Zones for ARC Portions of States

(ICC, 2006)

Climate 

Zone AL GA KY MS MD NC NY OH PA SC TN VA WV

3 X X X X

4 X X X X X X X X

5 X X X X X X

6 X X
Appendix C: Commercial Policy and Methodology Detail

C-3

While some Appalachian jurisdictions may not currently have the capacity to administer and enforce 

building codes, regional, national training, or grant programs could help; spillover effects between 

residential and commercial energy code activities are expected. On July 9, 2008, H.R. 4461 

―Community Building Code Administration Grant Act (CBCAG)‖ passed the house. If passed by the 

senate (S 2458), enacted, and funded, CBCAG Act would authorize a grant program through the U.S. 

Department of Housing and Urban Development (HUD) to provide competitive matching funds 

grants to local jurisdictions to build-up their local building code administration and enforcement 

capabilities. 

Methodology

New commercial floorspace forecasts are 

available by building type, by census 

division as part of the EIA‘s Annual 

Energy Outlook 2008 (EIA, 2008). We 

used population weights to the 

Appalachian portion of each census 

division to construct a new commercial 

construction floorspace by building type 

forecast unique to the Region (REMI, 

2007). 

Building codes are only anticipated to 

reduce consumption for space heating, 

space cooling, and lighting loads by 

improving shell efficiency and 

reducing/modifying lighting fixtures. The three major fuels used for these purposes, electricity, 

natural gas, and distillate fuel oil, are modeled for savings. Reduced consumption for other fuels 

(e.g. wood, kerosene, and LPG) and for other end-uses may also follow building energy code 

changes, but they are not included in this assessment. 

Energy Savings

Energy savings for building energy codes are determined by changing the anticipated efficiency of 

newly constructed floorspace. The modeled efficiencies are shown in Table C.3. 

Figure C.1 Commercial Model Building Energy 

Codes Methodology

Percentage of 

Floorspace Subject 

to Building Code, 

by type

ARC Commercial 

Building Energy Use

ARC New Commercial 

Floorspace, by type

ARC Commercial 

Floorsp Floorspace, by type ace, by type

Program 

Penetration 

Rate (100%)

Program 

Penetration 

Rate (80%)

Compute Building Code 

Energy Savings

Percentage of Total 

Sector Energy Savings

Building Code 

Energy Savings, 

by end-use 

Enduse -

consumption, by 

fuel, by building 

type
Energy Efficiency in Appalachia, SEEA

C-4

Table C.3 Achievable Efficiencies if Building Codes are Adopted and Enforced

Year 

Operational1

Lighting Savings 

Relative to 

Baseline2

%

Space Heating/ 

Cooling Savings 

Relative to 

Baseline2

%

Potential

Code

Floorspace 

built to this 

code3

(million sq ft)

2010 20 0 2006 IECC 303

2013 30 3 2009 IECC 315

2016 33 6 2012 IECC 328

2019 36 9 2015 IECC 340

2022 39 12 2018 IECC 350

2025 42 15 2021 IECC 361

2028 45 18 2024 IECC 371

1

The ―Year Operational‖ is in three year increments because we assume a three year code adoption cycle.

2 Relative savings are to AEO 2008 forecast of consumption estimates for the end-uses modeled to be impacted by 

building codes: Space Heating, Space Cooling, and Lighting.

3 All 11 types of commercial buildings are included. Square footage listed is 80 percent of the forecast constructed 

floorspace during the time the code would be in force; this relates to our assumed compliance rate of 80 percent.

The current study‘s estimates of savings relative to the AEO 2008 (EIA, 2008) estimates are 

intentionally conservative. Generally untouched by building codes is ―wasted energy‖ from unused 

energy services in transitional spaces – such as lobbies and hallways. Pitts and Bin Saleh (2007) 

show that transitional spaces make up 10-40 percent of commercial buildings; further, they model 

several building designs and conclude that space conditioning savings would be about 13 percent if 

buildings were designed with a non-cooled, but ventilated, transitional buffer surrounding the main 

building space. 

Policy Implementation Cost

Administrative costs are assumed to include two training administrators per state, proportioned, at 

$75,000 each and one additional verification liaison at $75,000 for each 10 million square feet of 

new floorspace per year. 

Investment costs are assumed to be $0.30 per square foot of new floorspace. There is no incentive 

for compliance included; however, enabling and then requiring third-party verification of code 

compliance can increase compliance. Trainers/verifiers working with code officials can spot-check 

third-party verification reports and offer guidance to construction and verification firms. 

Market Penetration

The purpose of this study is to estimate cost-effective energy savings; since building energy codes 

are required to be cost-effective before adoption, we assume compliance at 80 percent. This 

compliance rate reflects less than total participation because some shirking is expected despite having 
Appendix C: Commercial Policy and Methodology Detail

C-5

a third-party verification procedure. Total commercial floorspace affected by each code are given, 

above, in Table C.3. 

COMMISSIONING OF EXISTING BUILDINGS

Introduction

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. According to the New York 

State Energy Research and Development 

Authority (NYSERDA, 2004) the 

commissioning of existing buildings 

should include four steps: planning, 

investigation, implementation, and 

handoff. Through measurement and 

inspection, a building‘s envelope, 

HVAC equipment, and other systems are 

evaluated against initial design 

documentation and corrective actions are 

taken, often with resulting energy savings. 

As with the residential buildings stock, the retention rate for commercial structures in the ARC 

Region was assumed to be 98 percent. This assumed, coupled with values for the commercial 

building floorspace and energy consumption in the ARC yielded both the total building area that 

would be subject to the commissioning program as well as a baseline commercial building energy 

intensity value. The energy intensity is then multiplied by the program square footage to yield the 

baseline energy consumption for existing buildings. 

Energy Savings

Data related to commissioning of existing commercial buildings are generally limited to individual 

case studies or broad averages made from many different building types. Since data specifically 

related to the ARC Region were not available, the latter approach was used. In a meta-analysis 

performed by Evan Mills of Lawrence Berkeley National Laboratory (Mills et al., 2004), the median 

energy savings yielded after commissioning was 10 percent of site energy for buildings that did not 

purchase thermal products from outside sources. This value was applied to the applicable building 

housing stock, and a near linear increase in the total commissioned square footage was assumed. By 

2030, all of the floorspace under this program is assumed to be commissioned. All buildings 

constructed after 2010 were excluded as they are subject to other policies in this assessment. These 

newer buildings, which may have been commissioned upon construction, could be re-commissioned 

at a later date; however, building re-commissioning is not addressed under this program. 

To estimate the total program energy savings, the site energy savings of 10 percent was applied to the 

square footage to be commissioned during each year. These numbers were then compared with the 

Figure C.2 Commissioning of Existing Buildings 

Energy Savings Methodology
Energy Efficiency in Appalachia, SEEA

C-6

total sector energy consumption. To make this comparison, it was assumed that energy savings were 

proportional to the baseline energy consumption by energy/fuel source. Estimates of these values 

were supplied by the U.S. Energy Information Administration (2007). The site electricity was 

converted to primary energy, which includes generation, transmission, and distribution losses, and 

the total energy saved by the program was compared with the sector energy consumption. 

Policy Implementation Costs

Investment costs for the commissioning of 

existing buildings were derived from Mills et 

al. (2004). In their meta-analysis, it was found 

that the median, inflation-adjusted 

commissioning costs were $0.30 per square 

foot (Mills et al., 2004). Figure C.32

illustrates the components of commissioning 

investment costs and their relative importance. 

For the 55 buildings that reported 

commissioning costs breakdowns, the majority 

of costs were used to investigate and plan 

commissioning. 

In the same study, incentives from utilities played a 

large role in overall project funding. Of 48 projects 

reporting utility incentives, the median level of support was 82 percent of the total project cost and 

was covered by the utility in order to decrease energy demand. This value was used as a program 

starting point, steadily decreasing to no support by year 2020. 

Much of the personnel costs related to this program are covered in the investment cost required of the 

building owner, which may or may not be subsidized by an outside entity such as a utility. It was 

assumed that the program would require a director for each state in the Appalachian Region at 

$150,000 per year, population-weighted to only include costs associated with the Region, with 

supporting staff members added at $90,000 per year for every 10,000,000 square feet commissioned 

per year. 

COMMERCIAL RETROFITS IN LIGHTING AND HVAC

Introduction

Commercial retrofits offer an opportunity to improve end-use consumption to the best available 

technology. A program for commercial retrofits is expected to encourage investment by offering a 

matching incentive in the form of a tax credit, grant, or rebate. Amman and Mendelsohn (2005) 

reviewed commercial retrofit programs and suggested that an integrated ―whole building‖ approach 

would likely achieve even greater savings especially in areas where systems overlap. 

Due to the variant nature of buildings and their circumstances, we have chosen to model only 

targeted retrofits of lighting and HVAC to best available technology – although a policy or measure 

developed for commercial retrofits could also use a systems approach. The modeled efficiencies are 

 

2

 Adapted from Figure 13 ―Commissioning Cost Allocation‖ (Mills et al., 2004, p. 35) 

Figure C.3 Breakdown of Existing Building 

Commissioning Investment Costs

(Mills et al., 2004)

Verification and 

Persistance 

Tracking

2%

Implementation

27%

Investigation and 

Planning

69%

Reporting

2%
Appendix C: Commercial Policy and Methodology Detail

C-7

about what would be required, independent of savings in other end-uses, to meet the Building 

Owners and Manager‘s Association‘s (BOMA) challenge to reduce commercial building 

consumption by 30 percent by 2012 (BOMA, 2007). 

Methodology

The present study uses a consumption 

reduction figure for the four end-uses 

affected (heating, cooling, ventilation, 

and lighting). There may be some 

overlap between savings characterized in 

the Commissioning of Existing 

Buildings program and this retrofit 

program because both target existing 

building stock. DeCanio and Watkins 

(1998) found that characteristics of firms 

are important in their adoption of these 

cost-effective retrofits. However, data 

on firm characteristics are not available 

for the Region, so the present study assumes 

constant adoption over all types. 

Energy Savings

Energy savings for commercial building retrofits of HVAC and lighting equipment are determined by 

changing the anticipated efficiency of heating, cooling, ventilation, and lighting end-uses. The 

modeled efficiencies compared to the stock equipment efficiencies from the AEO 2008 are shown in 

Table C.4 (EIA, 2008). 

Savings for duct-sealing alone have been shown to be around 15 percent with a payback of 1.1 years 

(Jump, Walker, and Modera, 1996; Franconi, Delp, and Modera, 1998). Similarly, Woods (2006) 

 

Figure C.4 Example of Improved Lighting Appearance with Energy Saving Lighting Retrofits

(Efficient Lighting, 2008)

Figure C.5 Commercial Retrofit of HVAC 

and Lighting Methodology

Percentage of 

Floorspace Subject to 

Retrofit, by type

ARC Commercial Building 

Energy Use

ARC Commercial 

Floorspace, by type

Program 

Penetration 

Rate

Compute Retrofit Energy 

Savings

Percentage of Total Sector 

Energy Savings

Retrofit Energy 

Savings, by enduse, by fuel

HVAC End-use 

consumption, by fuel, by 

building type

Lighting End-use 

consumption, by fuel, by 

building type
Energy Efficiency in Appalachia, SEEA

C-8

describes several cases of air sealing (rooftops, doors, windows) where savings were between 19 and 

35 percent with paybacks from less than half a year to around five years. 

AEO 

2008 Present

AEO 

2008 Present

AEO 

2008 Present

AEO 

2008 Present

AEO 

2008 Present

AEO 

2008 Present

AEO 

2008 Present

2010 42.90 61.29 1.18 1.68 2.96 4.23 0.25 0.35 0.77 1.10 0.92 1.32 0.78 1.11

2011 43.34 63.73 1.19 1.75 3.01 4.43 0.25 0.37 0.77 1.14 0.94 1.38 0.78 1.15

2012 43.70 66.21 1.20 1.81 3.06 4.64 0.25 0.38 0.77 1.17 0.96 1.45 0.78 1.18

2013 44.00 68.76 1.20 1.88 3.10 4.85 0.25 0.39 0.78 1.21 0.97 1.52 0.78 1.22

2014 44.27 71.41 1.21 1.95 3.14 5.07 0.25 0.41 0.78 1.26 0.99 1.59 0.78 1.26

2015 44.52 74.19 1.22 2.03 3.18 5.30 0.26 0.43 0.78 1.30 1.00 1.67 0.78 1.31

2016 44.74 77.14 1.22 2.11 3.22 5.55 0.26 0.44 0.78 1.35 1.01 1.74 0.78 1.35

2017 44.96 80.29 1.23 2.19 3.25 5.81 0.26 0.46 0.78 1.40 1.02 1.83 0.78 1.40

2018 45.16 83.62 1.23 2.28 3.29 6.09 0.26 0.48 0.78 1.45 1.04 1.92 0.79 1.45

2019 45.33 87.18 1.24 2.38 3.32 6.38 0.26 0.50 0.79 1.51 1.05 2.01 0.79 1.51

2020 45.49 90.99 1.25 2.49 3.35 6.70 0.26 0.53 0.79 1.57 1.06 2.11 0.79 1.57

2021 45.64 93.14 1.25 2.56 3.38 6.90 0.27 0.54 0.79 1.61 1.07 2.17 0.79 1.61

2022 45.77 95.35 1.26 2.62 3.41 7.10 0.27 0.56 0.79 1.64 1.07 2.24 0.79 1.64

2023 45.91 97.68 1.27 2.69 3.44 7.31 0.27 0.57 0.79 1.68 1.08 2.30 0.79 1.68

2024 46.04 100.09 1.27 2.76 3.46 7.53 0.27 0.59 0.79 1.72 1.09 2.37 0.79 1.72

2025 46.15 102.56 1.28 2.84 3.49 7.75 0.27 0.60 0.79 1.76 1.10 2.44 0.79 1.76

2026 46.26 105.13 1.28 2.92 3.51 7.98 0.27 0.62 0.79 1.80 1.10 2.51 0.79 1.80

2027 46.35 107.79 1.29 3.00 3.53 8.22 0.27 0.64 0.79 1.85 1.11 2.58 0.79 1.84

2028 46.43 110.54 1.29 3.08 3.56 8.47 0.27 0.65 0.80 1.90 1.11 2.65 0.79 1.89

2029 46.52 113.45 1.30 3.17 3.58 8.72 0.28 0.67 0.80 1.94 1.12 2.73 0.79 1.93

2030 46.59 116.49 1.31 3.26 3.59 8.98 0.28 0.69 0.80 1.99 1.12 2.81 0.79 1.98

Space Heating Space Cooling Space Heating

Electricity Natural Gas Distillate

Lighting (lumens 

per watt) Space Heating Space Cooling

Ventilation (cubic 

feet per minute per 

a

 The present study also considers savings for Ventilation provided by natural gas and distillate fuel oil. These are small 

portions of the equipment stock, and the AEO 2008 did not include stock efficiency data for these fuels in their forecast 

(EIA, 2008). 

Policy Implementation Cost

Administration of a retrofit incentive program (such as allowing for a tax credit or grant) is not 

expected to be burdensome. One administrator at $75,000 per 500 million square feet of retrofit 

space per year is assumed. 

Investment costs are set at $1.00 per retrofit square foot. This assumption is general because data 

were not available on equipment capacity and age at the level of detail required for modeling by 

equipment costs. If data are collected at this level in a future analysis, the costs could better 

approximate the true investment necessary to achieve these savings. We assume these costs are 

reasonable. For example, a 5,000 square foot building would have an incremental cost of $5,000. 

This assumes that the building was already undergoing replacement of equipment; the incremental 

costs represent the difference in cost for purchasing higher efficiency equipment. 

The present study includes an assumption of a significant 30 percent incentive to undergo 

commercial retrofits; the incentive is in place from 2010 to 2020. While this makes adoption more 

likely, it increases the public cost of the program. An incentive program at a lower level could be 

Table C.4 Modeled End-Use Efficiencies for Retrofit Equipment Compared

to AEO 2008 Stock Equipment

(EIA, 2008)a
Appendix C: Commercial Policy and Methodology Detail

C-9

designed to achieve a significant portion of the modeled savings. Amman and Mendelsohn (2005) 

reviewed several commercial retrofit programs and found varying incentive structures from fixed 

amounts to 100 percent of cost, depending on the provider and the program goals. 

Market Penetration

We assume to reach nearly all lighting and HVAC equipment within the Appalachian commercial 

floorspace with efficient retrofits. With heating and cooling equipment having lifetimes less than 20 

years and lighting having lifetimes less than 10 years, it is not unreasonable that all equipment could 

be replaced over the program lifetime. 

COMMERCIAL INCENTIVES FOR EFFICIENT OFFICE EQUIPMENT AND USE 

Introduction

Equipment standards set minimum efficiency levels for products to enter the market in a given state 

or nationwide. For those appliances not covered by Federal standards, states can set efficiency 

standards; however, Federal law preempts state action if the appliance is covered by Federal 

standards (EPA, 2006). This study considers improved equipment standards for office equipment, 

including computers. Greater savings could be achieved if cooking and miscellaneous plug-loads 

were addressed as well. 

The present study does not evaluate the secondary market for commercial equipment. Reducing the 

availability of outdated appliances to secondary markets may be necessary to achieve the modeled 

savings. The best known type of policy for this purpose has been for refrigerators due to the great 

technical improvements in energy consumption behavior. Refrigerator recycling programs are in 

place in 14 states; such programs prevent older less-efficient refrigerators from entering the 

secondary market (ENERGY STAR, 2008).

Methodology

ARC Commercial Building 

Energy Use

ARC Commercial 

Floorspace, by type

Equipment 

Replacement 

Rate

Compute Equipment Standards 

Energy Savings

Percentage of Total Sector 

Energy Savings

Equipment 

Standards by enduse

Computer End-use 

consumption, by building 

type

Office Equipment End-use 

consumption, by building 

type

Figure C.6 Office Equipment Incentives Methodology
Energy Efficiency in Appalachia, SEEA

C-10

Energy Savings

The present study assumes that Office Equipment (PC and other), as an end-use becomes 40 percent 

more efficient from 2010 to 2030. This is driven by the assumption that standards set the minimum 

efficiency at 20 percent better than the stock efficiency in 2010 and these standards increase every 

five years such that the standards average 40 percent better than stock efficiency in 2030. Adoption 

is set to five percent per year, based on office equipment lifetimes. Due to short lifetimes, 100 

percent of the equipment is assumed to be replaced over the study time horizon. The market already 

adopts ENERGY STAR labeled equipment with substantial savings potential, as shown in Table C.5. 

Table C.5 ENERGY STAR Market Share, Savings, and Lifetimes

(Sanchez et al., 2007; Koomey et al., 1995)

Equipment

2006 ENERGY STAR 

Market Share

%

2008 ENERGY STAR 

Device Savings

%

Equipment 

Lifetime 

(years)

Copiers 90 20 6

Fax 99 39 4

Printers 99 13 5

Scanners 85 10 4

MFDa98 22 6

CRTb0 47 4

LCDc37 44 4

Computers 98 20 4

a: MFD – Multi-Function Device

b: CRT – Cathode Ray Tube

c: LCD – Liquid Crystal Display

While this study models an improvement in efficiency standards to best available technology, it 

implies that efficiency features are used. Roberson et al. (2004) found a significant percentage of 

office equipment in commercial buildings not taking advantage of power saving features, which 

generally qualify this equipment for ENERGY STAR (Table C.6). Using the power saving features 

of equipment already purchased has an investment cost of zero, and a corresponding immediate 

payback. 
Appendix C: Commercial Policy and Methodology Detail

C-11

Table C.6 Turn Off and Power Management Status of

After-Hours Office Equipment, 2000 and 2003

(Roberson et al., 2004, Table 7 p. 16)

Category Type # in 2003

Turn-off 

Rate

%

Power 

Management Rate

%

2000 2003 2000 2003

Computers desktop + ICSa 1,498 44 37 5 7

desktop 1,453 36 6

ICSa 45 60 61

Monitors all 1,598 32 29 56 72

CRTb 1,329 32 71

LCDc 269 18 75

Printers all 353 25 23 44 31

monochrome 

laser 24 53

high-end color 15 61

laser 158 15 60

inkjet 123 31 30 3 0

impact 22 31 27 0 0

thermal 38 18 0

wide format 8 57 75 32 0

solid ink 4 0 75

MFDsdall 79 18 20 56 29

inkjet 16 19 31

laser 63 21 28

Copiers all 33 18 49 32 28

Fax machines 47 2 0 6

Scanners all 34 29 41 60

a

ICS- Integrated Computer Systems cLCD – Liquid Crystal Display

b CRT – Cathode Ray Tube d MFD – Multi-Function Device

Policy Implementation Cost

Administrative costs are relatively low for appliance and equipment standards. In general, standards 

do not have a consistent expense – administrative costs would be highest in the years of adoption and 

lowest between adoption cycles. We assume administrative costs of $75,000 per year per 500 

million square feet of floorspace. It is assumed that these administrators would be charged with 

promoting use of energy saving features on office equipment purchased with the program. 
Energy Efficiency in Appalachia, SEEA

C-12

Investment costs vary widely by equipment type, brand, model, and location. In order to generalize, 

we assumed a cost of $0.10 per square foot of floorspace in buildings adopting new equipment 

(based on an average of nine pieces of office equipment per 1000 gross square foot of building space 

(Roberson et al., 2004). The density of office equipment will lead to significant variations in costs 

and savings across building types and specific buildings. This cost overestimates the incremental 

cost of types of office equipment and underestimates others. 

This program is expected to be accompanied by an incentive program that will cover 20 percent of 

the incremental costs of adopting the higher efficiency models until 2020. 

Market Penetration

This study assumes that all office equipment will be replaced with high efficiency equipment 

(meeting an assumed ENERGY STAR standard) at the end of its lifetime. Savings are compared to 

the Annual Energy Outlook which already assumes some efficiency gains (EIA, 2007; 2008). 

REFERENCES

Amman, Jennifer Thorne and Eric Mendelsohn. 2005. ―Comprehensive Commercial Retrofit 

Programs: A Review of Activity and Opportunities.‖ Report A052. Washington DC: ACEEE. 

Building Owners and Managers Association, International (BOMA). 2007. ―Market Transformation 

Energy Plan and 7 Point Challenge.‖ http://www.boma.org/NR/rdonlyres/EE51F93D-822D4959-A82F-6A643F6242D1/0/Market_Trans_Web.pdf 

DeCanio, Stephen J. and William E. Watkins. 1998. Investment in Energy Efficiency: Do the 

Characteristics of Firms Matter? The Review of Economics and Statistics, Vol. 80, No. 1, pp. 95-

107. 

Efficient Lighting. 2008. Description of Sunwest Properties retrofit. 

http://www.efficientlightingco.com/projects/sunwest.htm

Energy Information Administration (EIA). 2007. Annual Energy Outlook 2007. 

http://tonto.eia.doe.gov/ftproot/forecasting/0383(2007).pdf

———. 2008. Annual Energy Outlook 2008. http://www.eia.doe.gov/oiaf/aeo/

ENERGY STAR. 2008. ―ENERGY STAR Qualified New Homes Market Indices for States.‖

http://www.energystar.gov/index.cfm?fuseaction=qhmi.showHomesMarketIndex 

Franconi, E., W.W. Delp, M.P. Modera. 1998. Impact of Duct Air-Leakage on VAV Distribution 

System Performance. Lawrence Berkeley National Laboratory, LBNL-42417.

Jump, David A. Iain S. Walker and Mark P. Modera. 1996. ―Field Measurements of Efficiency and 

Duct Retrofit Effectiveness in Residential Forced Air Distribution Systems‖ (Lawrence Berkeley 

National Laboratory, LBL-38537). Proc. 1996 ACEEE Summer Study, August 1996, Asilomar, 

CA.
Appendix C: Commercial Policy and Methodology Detail

C-13

International Code Council (ICC). 2006 International Energy Conservation Code®. ‖

http://www.iccsafe.org/e/prodshow.html?prodid=3800S06

Koomey, Jonathan, Michael Cramer, Mary Ann Piette and Joseph Eto, 1995. Efficiency

Improvements in U.S. Office Equipment: Expected Policy Impacts and Uncertainties. Lawrence

Berkeley Laboratory. LBL-37383.

Mills, Evan, Hannah Friedman, Tehesia Powell, Norman Bourassa, David Claridge, Tudi Haasl, 

Mary Ann Piette. 2004. The Cost Effectiveness of Commercial-Buildings Commissioning. 

Lawrence Berkeley National Laboratory. Berkeley, CA. 

http://eetd.lbl.gov/Emills/PUBS/PDF/Cx-Costs-Benefits.pdf

New York State Energy Research and Development Authority (NYSERDA). 2004. Guideline to the 

Commissioning Process for Existing Buildings, or “Retro-Commissioning.” Portland Energy 

Conservation, Inc. http://www.nyserda.org/programs/pdfs/retrocxhandbookfinal040704.pdf

Pitts, Adrian and Jasmi Bin Saleh. 2007. ―Potential for energy saving in building transition spaces.‖ 

Energy and Buildings 39 7: 815-822. 

Regional Economics Models, Inc. (REMI). 2007. Data provided to the authors by the 

Appalachian Regional Commission on Dec 21, 2007.

Roberson, Judy A., Carrie A. Webber, Marla C. McWhinney, Richard E. Brown, Margaret J. 

Pinckard, and John F. Busch. 2004. After-hours Power Status of Office Equipment and Inventory 

of Miscellaneous Plug-Load Equipment. Energy Analysis Department, Lawrence Berkeley 

National Laboratory, LBNL-53729. http://enduse.lbl.gov/Info/LBNL-53729.pdf 

Sanchez, Marla, Richard E. Brown, Gregory K. Homan, and Carrie A. Webber. 2007. DRAFT 2008 

Status Report Savings Estimates for the ENERGY STAR Voluntary Labeling Program. Lawrence 

Berkeley Laboratory. LBL-56380. 

U.S. Environmental Protection Agency (EPA). 2006. Clean Energy-Environment Guide to Action: 

Policies, Best Practices, and Action Steps for States. 

http://www.epa.gov/cleanenergy/documents/gta/guide_action_full.pdf

Woods, Tony. 2006. 30 year History proves energy savings from air barrier retrofit. 

http://www.buildingenvelopeforum.com/pdf/30energysavings.pdf 

Appendix D: Industrial Sites

D-1

APPENDIX D: INDUSTRIAL SITES

D.1 INDUSTRIAL ASSESSMENT CENTERS

Introduction

Industrial Assessment Centers (IACs) evaluate small- to medium-sized industrial sites (less than $2.5 

million in energy consumption per year) and identify savings opportunities in the form of energy 

efficiency, waste minimization, pollution prevention, and productivity improvement (DOE/EERE, 

2008). Expansion of these centers in the Appalachian Region could yield large improvements in 

energy efficiency at industrial facilities.

Examples of industrial assessment findings are shown in Table D.1. Each industrial assessment 

yields a list of recommendations, which includes a statement of the type of recommended 

improvement, its cost to implement, the potential energy savings, and a quantification of how much 

money would be saved per year if each recommendation were implemented.

Table D.1 Examples of Recommended Improvements in Energy Consumption

(DOE/EERE, 2008)

State Year Principal 

Product

Baseline Year 

Energy Cost

$

Potential 

Electricity Saved

%

Potential Natural 

Gas Saved

%

PA 2008 Chain Assemblies 602,793 26 50

WV 2008 Fiberglass Media 1,140,227 10 10

NC 2008 Cutting Tools 584,396 4 0

MS 2008 Wiring Harness 

Components 218,074 9 0
Energy Efficiency in Appalachia, SEEA

D-2

METHODOLOGY 

Energy Savings

Figure D.1 highlights the process 

implemented to determine the potential energy 

savings that could be realized if IACs were 

expanded in Appalachia. First, findings 

yielded from industrial assessment in the 

states that comprise the Appalachian Region 

were compiled. Information related to 

productivity improvement and waste 

minimization was excluded unless there was a 

stated energy savings associated with these 

actions. These data include such information 

as the North American Industry Classification 

System (NAICS) code, baseline energy usage, 

recommended changes, potential energy 

savings, and cost of recommended changes. 

After these data were compiled, they were 

averaged by ARC subregion (North, Central, and 

South) while maintaining the information by 

separate industrial NAICS codes. 

Employment figures for the ARC (IMPLAN, 2006) were provided and used along with information 

provided by the industrial assessments to estimate the number of industrial sites in the ARC Region. 

In addition, the employment figures and IAC data were used to compute a weighted-average energy 

savings number by energy type for each site. These averages take into account the weighting of each 

industry in the subregion. The average values used for analysis are found in Table D.2. Current 

implementation rates can be on the order of 50 percent. In order to quantify the full potential of this 

policy bundle, it was assumed that all of the recommended changes are implemented. This may be 

possible by addressing barriers to implementation through increased education and outreach, 

assistance with outage planning, and low-interest loans, as mentioned in Table 5.2. 

Table D.2 Average Site Energy Savings and Investment Costs for IAC Program

(Author‘s calculations; DOE/EERE, 2008)

ARC Subregion Electricity Savings

%

Natural Gas Savings

%

Implementation Cost

$

North 13.6 15.0 245,612

Central 4.9 13.2 121,461

South 11.7 13.4 195,643

Figure D.1 IAC Energy Savings Methodology
Appendix D: Industrial Sites

D-3

Savings in the North subregion were found to be greatest, and the costs required to implement the 

changes were also the greatest. The Central subregion had much lower savings values related to 

electricity; however, the costs for changes were also much lower. The differences between the 

subregions may be due to climate and weighting of various industries in each subregion as well as a 

limited sample size in the Central region. The Central region is the smallest ARC subregion by area. 

After estimating the number of sites in the Appalachian Region, the number of sites eligible for IAC 

assistance was determined. On average, approximately 90 percent of industrial sites are in the smallto medium-size range, and, therefore, eligible for industrial assessment (Soderlund, 2008). These 

sites represent approximately half of the total energy use in industry. The remaining 10 percent of 

sites would be eligible for other programs such as Energy Savings Assessments (ESAs) and/or 

combined heat and power systems. 

The extensive market penetration of industrial assessments was estimated based upon the success of 

the current IAC program. Currently, approximately 40 assessments are performed in the Region per 

year. With a large push, a goal of 100 percent assessment by 2030 in Appalachia was assumed, 

which would require an eight-fold increase in the current number of assessments per year. With the 

number of eligible sites determined and a program penetration assumed, the program energy savings 

by each energy type were determined. 

Policy Implementation Cost

As seen in Table D.2, the investment costs were determined from previous industrial assessment 

data. Since the IAC program is already in existence, administration costs were assumed to be similar 

to those already incurred. Each assessment requires approximately $9,000 in funds (Soderlund, 

2008). Additionally, in support of the program, each state was assumed to have an administrator in 

charge of promoting the program. Each program champion was budgeted at $150,000 and that total 

was then proportioned to the Appalachian Region based on population. 

D.2 ENERGY SAVINGS ASSESSMENTS 

Introduction

Energy Savings Assessments (ESAs) are evaluations of major energy systems at large industrial sites 

(greater than $2.5 million in annual energy expenditures). Each assessment only targets one system. 

The U.S. Department of Energy‘s Save Energy Now program was developed to use ESAs to aid in 

the reduction of natural gas consumption post hurricane Katrina (Wright et al., 2007). 

The results summarized by Wright et al. (2007) are for assessments that focused on steam and 

process heating; however, the program also trains on-site personnel to be able to conduct future 

ESAs at their location and provides them with tools, such as software, to aid in ESA completion. In 

addition, while one of the major focuses of the Save Energy Now program is reduction of natural gas 

usage, ESAs can also be applied to other energy-intensive processes at an industrial site. 
Energy Efficiency in Appalachia, SEEA

D-4

Methodology 

Energy Savings

The methodology used to determine ESA 

program energy savings is shown in Figure 

D.2. Due to confidentially issues, data, such 

as those available for industrial assessments, 

were not available. Data shared by Wright et 

al. (2007) were listed separately by two 

categories: NAICS code and state. These sets 

of data were combined with Appalachian 

Region employment information and IAC 

data for specific system savings to estimate 

weighted-average energy savings values by 

energy type (Table D.3). 

The number of industrial sites for which the 

ESA program would be applicable was 

determined through the IAC analysis and 

combined with subregion growth estimates to determine 

the number of sites available for future assessment. In 

addition, it was assumed that up to four high-impact 

assessments could take place at each physical site location. 

All of these ―virtual sites‖ would be able to be assessed, each increasing the sector energy savings. 

Table D.3 Average Site Energy Savings for ESA Program

(Author‘s calculations; Wright et al., 2007)

ARC Subregion Electricity Savings

%

Natural Gas Savings

%

North 11.5 17.5

Central 8.4 12.8

South 7.3 11.1

Once the number of ―virtual sites‖ (or potential assessments) and site energy savings were 

determined, a penetration rate of 100 percent was assumed for each first assessment. In 2006, it is 

estimated that only 16 ESAs were performed in Appalachia, based on population-weighting. This 

was, in part, due to the limited scope of the Save Energy Now program. By 2030, it is estimated that 

approximately 170 assessments would be completed each year throughout the Region, with almost 

all being performed by on-site personnel. It was assumed that subsequent assessments would lag the 

first assessment due to effort required to implement first assessment recommendations. Overall, over 

Figure D.2 ESA Energy Savings 

Calculation Methodology
Appendix D: Industrial Sites

D-5

60 percent of available assessments were completed by 2030. All recommendations have four-year or 

shorter payback period, and it is assumed that all recommendations were executed. 

Policy Implementation Costs

While the costs of assessing one system in a large-scale industrial plant is similar to the cost of a full 

industrial assessment for a small- to medium-sized facility, current program infrastructure is much 

smaller and would require the addition of more administrators. It was assumed that each state would 

have a program champion, funded at $150,000 per year and proportioned to the Appalachian Region 

based on population ratios in each state, with support personnel at $90,000 per year, one for every 10 

new sites assessed per year in order to continue outreach and education while also monitoring the 

progress of every site assessed. These personnel would be tasked with tracking the results of initial 

assessments and for encouraging future assessments at these locations as well as assisting with such 

tasks as site software updates. 

Based on the data available from the Save Energy Now program and scaling industrial assessment 

results yielded the average investment cost values shown in Table D.4. 

Table D.4 Average Investment Costs for 

ESA Program

(Author‘s calculations; Wright et al., 2007)

ARC Subregion Investment Cost

(million 2006$)

North 1.5

Central 2.1

South 2.3

D.3 INDUSTRIAL COMBINED HEAT AND POWER 

Introduction

The use of industrial combined heat and power (CHP) systems could have a large impact on 

industry‘s future consumption of primary energy. Many CHP systems consist of a prime mover, 

which produces electricity. The prime mover is coupled with one or more thermally-activated 

technologies, and these thermal systems use the prime mover‘s hot exhaust as an energy input to 

create a useful product such as steam or hot water that would otherwise be generated by using other 

high-value energy sources such as electricity or natural gas. The systems considered in the current 

study are of this type. Other types of systems could make use of fluids compressed to aid in transport 

(e.g., district steam used for space heating) that, instead of being throttled down to a site‘s required 

system pressure, is coupled with a turbine, which generates power while also reducing pressure. 

These types of systems are not modeled in this analysis; however, they do have the potential to yield 

additional energy savings for the Appalachian Region. When cost-effective for a particular site, socalled recycled energy systems, where heat is recovered from a process stream and reused to drive 

another, lower temperature process, are included in industrial and energy savings assessments; 
Energy Efficiency in Appalachia, SEEA

D-6

therefore, they were not included in the CHP portion of the study. Through the use of this otherwise 

wasted energy, CHP systems can yield total system thermal efficiencies of up to 80 percent (EPA, 

2008). 

Methodology 

Energy Savings

The methodology used to calculate the energy 

savings that could result from a CHP program in 

the Appalachian Region is detailed in Figure 

D.3. In order to assess the impact of expanding 

industrial CHP, it was necessary to determine 

the current level of installed CHP generation 

capacity in the Region. Information on current 

CHP installations is maintained in a database 

funded by the U.S. Department of Energy and 

administered by Energy and Environment 

Analysis, Inc. (EEA, 2007). Based upon the 

installed capacity in each ARC state, a capacity 

value was determined for the Region. 

According to this estimate, approximately four 

percent of the installed capacity in the ARC 

Region is part of a CHP system. 

In a study conducted by ACEEE, under an 

accelerated CHP scenario,1it is estimated that 

the share of installed capacity that is also part of 

a CHP system could reach 19 percent by 2030 

(Laitner, 2008). Lemar (2001) estimated that under a 

―moderate scenario‖ for CHP expansion and a 20-year 

time period, 15.7 percent of industrial electricity needs could be met by CHP. Under a much more 

aggressive scenario, 34.7 percent of industrial electricity could be generated by CHP systems. For 

the current study, it is assumed that by 2030, CHP‘s share of the Region‘s electricity generation 

capacity will reach 20 percent. 

In addition to market penetration, characteristics of grid electricity, CHP prime mover, and site 

energy needs must be defined. Electricity generated in centralized power plants is subject to direct 

generation inefficiencies as well as energy losses due to transmission and generation. The electric 

efficiency related to grid-supplied electricity was calculated from EIA energy data (EIA, 2007). 

Including losses from generation, transmission, and distribution, the energy efficiency of gridsupplied electricity in the Appalachian Region decreases from 30.3 percent in 2010 to 25.1 percent in 

2030. During the same time frame, the national average delivered electric efficiency increases from 

31.7 percent in 2010 to 33.4 percent in 2030. 

 

1

 An accelerated CHP scenario is one in which the rate of development of CHP in a Region approaches the rates seen in 

such countries as Finland and the Netherlands over the past 30 years. 

Figure D.3 Industrial CHP Energy 

Savings Methodology
Appendix D: Industrial Sites

D-7

CHP characteristics are highly dependent on the choice of prime mover. The prime mover 

characteristics used in this study are listed in Table D.5. 

Table D.5 Example CHP Prime Mover Characteristics

(Elliot, 2008)

Type Size

(kW)

Electric 

Efficiency

(%)

Thermal 

Output

(Btu/ Wh)

Installed 

Cost 

($/kW)

O&M 

Costs

($/k Wh)

After-treatment 

Cost

($/kW)

MT 60 24.6 6308 2,739 0.022 500

RE 100 28.4 6100 2,210 0.022 --

MT 250 26.0 4800 2,684 0.013 500

RE 800 35.0 2313 1,640 0.013 300

GT 3,000 26.0 5018 1,690 0.006 210

RE 3,000 35.9 3510 1,130 0.011 200

RE 5,000 39.0 3046 1,130 0.009 150

GT 10,000 29.0 4674 1,298 0.006 140

GT 40,000 37.0 2189 972 0.004 90

MT = Microturbine; RE = Reciprocating Engine; GT = Gas Turbine

In 2000, ONSITE SYCOM Energy Corporation (OSEC) published a study on the market and 

technical potential of industrial CHP (OSEC, 2000). A result of its analysis was a quantification of 

the CHP potential by SIC code,2 which also listed such information as size of prime mover as well a 

ratio of site power and steam requirements. By weighting information on site energy use compiled 

by OSEC by Appalachian Region industrial site information derived from IAC information, an 

overall average power to steam ratio for the Region was determined to be 0.87. Average CHP prime 

mover characteristics for the Region were also determined and are shown in Table D.6. 

Table D.6 Average CHP Prime Mover 

Characteristics for ARC Region

(Author‘s calculations)

Electric Efficiency 32.0%

Thermal Output 2,948 Btu/kWh

Installed Cost $1,227/kW

O&M Costs $0.0061/kWh

After-treatment Cost $150/kW

 

2

 SIC codes where converted to approximate NAICS codes in order to determine Region characteristics. 
Energy Efficiency in Appalachia, SEEA

D-8

While technological advances could improve prime mover performance, no such changes were 

assumed over the study time horizon. Additionally, raw material and manufacturing were assumed 

to be constant. 

Policy Implementation Costs

Investment costs are a combination of installed, after-treatment, and Operations and Maintenance 

(O&M) costs. Over the study period, this policy scenario results in the installation of an additional 

2.8 GW of CHP electric power generation capacity in the 2010-2030 period. At the average assumed 

price of $1,227 per kilowatt, over $2 billion dollars will be invested in equipment purchase and 

installation alone with an additional $580 million dollars in O&M spent. 

While industrial CHP systems save energy overall when grid generation, transmission, and 

distribution losses are taken into account, the prices an industrial site in the Appalachian Region will 

pay for natural gas versus electricity in the base case do not make CHP systems a highly-attractive 

choice on a cost basis alone. To aid in promoting CHP in the Region, preliminary estimates 

suggested incentives on the order of 60 percent of the total investment cost will likely be required to 

encourage adoption. This high incentive rate is due to the baseline energy prices assumed for the 

Region, which are based on EIA‘s Annual Energy Outlook 2008 values. These price values were 

population-weighted based upon the Appalachian portion of each census division (EIA, 2008). 

Discounted natural gas rates and premium pricing for electricity exported to the grid are two 

incentive mechanisms that could support CHP installation in the Region. As shown in Table 5.6, 

programs currently exist that assist CHP system owners with the high up-front costs of purchasing 

and installing these systems as well as guaranteeing discounted energy rates. California has a grant 

program which pays $600-$800/kW for the first 1,000 kilowatts of distributed generation capacity 

(Energetics, 2006). 

A program to promote CHP in the Appalachian Region will require both education and publicity, due 

to economic hurdles. Administration costs are assumed to include a program directorship for each 

Appalachian state at $150,000 per year, proportioned to the Region based on population ratios, and 

one supporting staff member added for every 100 MW of additional capacity at a rate of $90,000 per 

year to provide public education and outreach, assistance to sites and projects, and oversight in the 

use of public funds for financing CHP systems. 

REFERENCES

Elliott, R. Neal. 2008. American Council for an Energy Efficiency Economy (ACEEE). Personal 

Communication. 

Energetics. 2006. ―U.S. CHP Incentives List.‖ 

http://www.energetics.com/madri/pdfs/USCHPIncentiveslist(5-12-06).pdf

Energy and Environmental Analysis, Inc. (EEA). 2007. Combined Heat and Power Installation 

Database. http://www.eea-inc.com/chpdata/index.html

Energy Information Administration (EIA). 2007. Annual Energy Outlook 2007. 

http://tonto.eia.doe.gov/ftproot/forecasting/0383(2007).pdf
Appendix D: Industrial Sites

D-9

———. 2008. Annual Energy Outlook 2008. http://www.eia.doe.gov/oiaf/aeo/

Laitner, John A. ‖Skip‖. 2008. Amercian Council for an Energy-Efficiency Economy (ACEEE). 

Personal Communication.

Lemar, Paul, Jr. 2001. ―The potential impact of policies to promote combined heat and power in 

U.S. industry.‖ Energy Policy, 29, pp. 1243-1254.

Minnesota IMPLAN Group, Inc. (IMPLAN). 2006. A 2005 dataset for Appalachia (Alabama, 

Georgia, Kentucky, Maryland, Mississippi, New York, North Carolina, Ohio, Pennsylvania, 

South Carolina, Tennessee, and Virginia. Stillwater, Minn.: Minnesota IMPLAN Group, Inc.

ONSITE SYCOM Energy Corporation (OSEC). 2000. The Market and Technical Potential for 

Combind Heat and Power in the Commercial/Institutional Sector (Revision 1). 

http://www.eere.energy.gov/de/pdfs/chp_comm_market_potential.pdf

Soderlund, Matthew. 2008. Georgia Institute of Technology Industrial Assessment Center. Personal 

Communication. 

U.S. Department of Energy Energy Efficiency and Renewable Energy (DOE/EERE). 2008a. 

Industrial Technologies Program-IAC Database. http://iac.rutgers.edu/database/about.php

U.S. Environmental Protection Agency (EPA). 2008. Efficiency Benefits. 

http://www.epa.gov/chp/basic/efficiency.html

Wright, Anthony, Michaela Martin, Bob Gemmer, Paul Scheihing, and James Quinn. 2007. Results 

from the U.S. DOE 2006 Save Energy Now Assessment Initiative: DOE’s Partnership with U.S. 

Industry to Reduce Energy Consumption, Energy Costs, and Carbon Dioxide Emissions.

http://apps1.eere.energy.gov/industry/saveenergynow/partners/pdfs/sena_2006_report_final_09_

17_07.pdf

Appendix E: Transportation

E-1

APPENDIX E: TRANSPORTATION 

REFERENCE CASE ENERGY EFFICIENCY IN AEO 2008 (EIA, 2008) 

The 2008 Annual Energy Outlook (AEO) projects energy consumption and prices based on energyefficiency legislation enacted in the 2007 Energy Independence and Security Act (EISA, 2007). 

EISA established a Corporate Average Fuel Economy (CAFE) standard of at least 35 miles per 

gallon for cars and light trucks by 2020, a 40 percent improvement over today‘s fuel economy 

standard. Based on national fleet turnover data, we estimate that the new CAFE standard will reduce 

national light-duty vehicle fuel consumption by 22 percent by 2030. A host of additional policies are 

necessary to achieve an even more significant decrease in fuel consumption nationally and in the 

Appalachian Region. All policy-specific fuel savings documented in this analysis are additional to 

Reference Case fuel savings from the updated CAFE standard relative to a scenario with no change 

to fuel economy standards (Table E.1). All AEO projections cited here are national projections and 

have been scaled down by population proportion for the Appalachian Region. Please note that our 

estimate that gasoline savings will amount to 0.52 quads by 2030 differs slightly from AEO 2008

estimate that the new CAFE regulations will save 0.48 quads a year by 2030. 

Table E.1 2008 EISA CAFE Fuel Savings

Year

Gasoline 

Savings

%

Gasoline 

Savings

(million gallons)

Gasoline 

Savings

(quads)

2010 0.2 22 0.00

2011 1 81 0.01

2012 1 186 0.02

2013 3 337 0.04

2014 4 507 0.06

2015 5 661 0.08

2016 6 844 0.10

2017 7 1,056 0.13

2018 9 1,293 0.16

2019 10 1,556 0.19

2020 12 1,843 0.23

2021 14 2,125 0.26

2022 15 2,400 0.30

2023 16 2,665 0.33

2024 17 2,920 0.36
Energy Efficiency in Appalachia, SEEA

E-2

Table E.1 2008 EISA CAFE Fuel Savings

Year

Gasoline 

Savings

%

Gasoline 

Savings

(million gallons)

Gasoline 

Savings

(quads)

2025 19 3,164 0.39

2026 19 3,397 0.42

2027 20 3,620 0.45

2028 21 3,831 0.48

2029 22 4,032 0.50

2030 22 4,222 0.52

CURRENT TRANSPORTATION ENERGY CONSUMPTION IN THE APPALACHIAN 

REGION 

Gasoline consumption in the ARC Region in 2008 is estimated at 12.1 billion gallons. Gasoline use 

is expected to grow along with population and total vehicle miles travelled to reach 14.9 billion 

gallons by 2030. Similarly, consumption of diesel fuel will rise from 2.9 billion gallons in 2008 to 

3.5 billion gallons by 2030. This study considers energy consumption of highway modes only. 

PAY-AS-YOU-DRIVE INSURANCE

Introduction 

Pay-as-you-drive insurance programs convert some of the fixed costs associated with driving into 

variable costs dependent on the total distance driven by a vehicle over a given year. Drivers would 

pay a portion of their premiums up front, and the remainder would be charged in proportion to 

mileage, as determined by periodic odometer readings or a mileage tracking device. 

Methodology 

Energy Savings

To determine the reduction in total VMT 

driven in Appalachia that will result from a 

pay-as-you-drive insurance scheme, we 

followed the above methodology. Estimates of 

the reduction in vehicle-miles traveled 

(VMT), and therefore energy use, resulting 

from a PAYD policy depend upon the price 

elasticity of travel demand, i.e., the percent 

change in travel resulting from each percent 

increase in the cost of travel. Estimates of 

elasticity vary considerably among those who Figure E.1 Pay-As-You-Drive Insurance Energy 

Savings Methodology

Cost-elasticity of 

driving

Average cost of 

insurance in 

ARC

Average 

VMT/vehicle

Average fuel 

economy

Per gallon cost 

of gasoline

Compute 

insurance 

cost/mile

Compute 

gasoline 

cost/mile 

Compute % increase in 

variable costs

Compute % VMT 

reduction

Compute VMT 

under scenario

Compute gasoline 

consumption and 

savings using VMT 

and gallons/mile 

data 
Appendix E: Transportation

E-3

study them, and differ also according to the time elapsed between the change in price and the 

response to it. We use here a value of -0.1 for the long-term elasticity of driving with respect to 

gasoline price; that is, over 10-15 years, we assume there is a one percent reduction in driving for a 

10 percent increase in gasoline price.1 At $3.04 per gallon, gasoline for an automobile or light truck 

having the average on-road fuel economy of 20.2 miles per gallon costs 14 cents per mile.2 The 

average cost of an insurance policy in the Appalachian Region in 2005 was $785, and we assume that 

vehicles in Appalachia are driven 12,000 miles per year, roughly the national average.3 This means 

an average insurance cost of 6.5 cents per mile. 

If 80 percent of the cost of the insurance premium was charged on a per-mile basis, the average cost 

per mile would then be $0.052 per mile, about 37 percent of the per-mile cost of fuel.4 Variable 

driving costs would increase by 37 percent as a result. An elasticity of -0.1 implies a corresponding 

reduction in driving of about four percent. Thus 100 percent adoption of PAYD insurance would be 

expected to reduce car and light truck energy use in Appalachia by about four percent over 10-15 

years. 

Policy Implementation Costs

During the pilot stage of the pay-as-you-drive insurance option, the ARC will grant $200 to 

insurance agencies for each one-year policy that is mileage based. We assume that 2,000 such 

policies will be sold in 2009, 10,000 in 2010 and 20,000 in 2011. Therefore, ARC can expect to pay 

incentives of $400,000 in 2009, $1.7 million in 2010 and $3.3 million in 2020 (2006 dollars). 

Administrative costs were estimated based on the assumption that each Appalachian state will 

employ one administrator for the program at a cost of $150,000 and two program staff at the cost of 

$90,000. These costs were scaled by the Appalachian population in each state to estimate ARCspecific administrative costs. Also included in administrative costs is the cost of implementation 

incurred by insurance companies. Insurance companies could incur substantial per-vehicle 

monitoring costs during the pilot phase of the proposed program for distribution of mileage tracking 

devices and data collection expenses. However, once the program becomes mandatory, per-vehicle 

costs would decline as tracking device costs decline and data collection and analysis is spread over a 

large number of vehicles. We assume a per-vehicle cost of $40 per vehicle per year in the pilot phase 

and $10 per vehicle thereafter. 5

 

1

 Greene and Leiby, 2006; Litman, 2008 

2

 EIA, 2006

3

 Insurance rates from Bureau of Labor Statistics http://data.bls.gov; miles per year calculated from 2005 vehicle 

registration

4

 Bordoff and Noel (2008) assume that 84 percent of the insurance premium is charged on a per-mile basis. 

5

 Bordoff and Noel, 2008 
Energy Efficiency in Appalachia, SEEA

E-4

Table E.2 Summary of Energy Savings Financial Costs

For Pay-As-You-Drive Insurance

Year

Reduction in 

VMT

%

Sector Primary 

Energy Savings

%

Investment 

Costs

(2006$)

Admn Costs

(2006$)

2009 0.00 0.0007 4,370,000

2010 0.01 0.0035 4,690,000

2011 0.01 0.0071 5,090,000

2012 0.60 0.4161 34,526,596

2013 1.00 0.6931 55,037,088

2014 1.40 0.9725 75,833,244

2015 1.80 1.2526 96,918,060

2016 2.20 1.5317 118,294,558

2017 2.60 1.8077 139,965,788

2018 3.00 2.0862 161,934,830

2019 3.40 2.3638 184,204,789

2020 4.00 2.7799 217,436,109

2021 4.00 2.7837 218,928,132

2022 4.00 2.7849 220,430,599

2023 4.00 2.7959 221,943,583

2024 4.00 2.8096 223,467,158

2025 4.00 2.8280 225,001,398

2026 4.00 2.8535 226,546,378

2027 4.00 2.8771 228,102,172

2028 4.00 2.9039 229,668,858

2029 4.00 2.9345 231,246,510

2030 4.00 2.9692 232,835,205

CLEAN CAR STANDARD 

Introduction

Appalachia‘s adoption of the Clean Car Standard means that new vehicles sold in the Region by each 

manufacturer would need to meet the requirements shown in Table E.3 for GHG emissions, on 

average. Our analysis of the energy savings that result from such a policy assumes that ARC will aim 

to achieve the long-term standards outlined below, standards that correspond to an average fleet fuel 

efficiency goal of 50 miles-per-gallon by 2030. 
Appendix E: Transportation

E-5

Table E.3 Proposed Clean Car Program

Greenhouse Gas Tailpipe Standards, 2009-2020

(California Air Resources Boarda)

Year

CO2-equivalent emissions standard (g/mi)

Combined (Passenger cars and small trucks/

SUVs and large trucks/SUVs)

Near-term

2009 375.2

2010 359.5

2011 322.4

2012 290.6

Mid-term

2013 284.6

2014 279.6

2015 270.6

2016 262.2

Long-term

2017 246.8

2018 230.0

2019 220.5

2020 215.5

a Comparison of Greenhouse Gas Reductions under CAFE Standards and ARB Regulations 

Adopted Pursuant to AB1493, CARB, 2008

Methodology 

Energy Savings

Figure E.2 highlights the methodology used to 

determine energy savings resulting from 

implementation of the Clean Car Standard. Annual 

average fuel economy figures under the policy 

scenario case were obtained by assuming that the 

proposed greenhouse gas standards are met entirely 

through an increase in new vehicle fuel economy, 

then running a stock model run to determine fuel 

consumption for the entire vehicle stock. National 

light-duty vehicle miles traveled were scaled by 

ARC population to approximate ARC-specific data. 

Results showed that implementation of the Clean 

Car Standard will save the Appalachian Region 

more than 18 percent in energy consumption by 

2030. Figure E.2 Clear Car Standard Energy 

Savings Methodology

Grams per mile emissions by 

year under Clean Car

Standard

Compute associated mile per 

gallon average by year for 

new vehicles

ARC population 

by year

Compute gasoline 

consumption by year 

using vehicle stock 

model

Compute energy 

savings from reference 

case under Clean Car 

Standard
Energy Efficiency in Appalachia, SEEA

E-6

Policy Implementation Cost

The California Air Resources Board (CARB) estimated the increase in the purchase cost of vehicles 

as the new standards are phased in over the period 2009-2016. The extra first cost is less than $100 in 

the early years (2009-2010), but then rises to about $277-367 by 2012 and about $1,000 by 2016. We 

then projected costs between 2016 and 2030 based on the assumption that the increase in purchase 

cost of vehicles would reach $1,500 by 2020 and $3,000 by 2030. Using these cost estimates, and 

assuming that vehicle sales per capita in the Appalachian Region remain constant through 2030, we 

estimate that the Clean Car Standard leads to an investment in vehicle efficiency totaling $11.83 

billion in the period 2009-2030, on a 2006 value basis. 

The resulting savings in fuel costs over the lifetime of these vehicles (on average 15 years) would 

equal about $19.8 billion (present value), assuming gasoline prices remain at their 2007 levels. This 

gives a net economic benefit of $7.9 billion (2006 dollars) over the life of the vehicles purchased in 

2009-2030. Here, fuel savings exclude the average gasoline tax amount for Appalachia (22.0 cents 

per gallon). Hence the net economic benefits reflect the loss to the Region in fuel tax revenues but 

not the wealth transfer from the state to consumers.6

 

Administrative costs were estimated based on the assumption that each Appalachian state will 

employ one administrator for the program at a cost of $150,000 and two program staff at the cost of 

$90,000. These costs were scaled by the Appalachian population in each state to estimate ARCspecific administrative costs. 

 

6

 It should also be noted that the loss in the state‘s gasoline tax receipts is in the context of growing gasoline consumption; 

adoption of the Clean Car Standard would slow the growth in gasoline use, and therefore gasoline tax receipts, but they 

would not actually decline. 
Appendix E: Transportation

E-7

Table E.4 Summary of Energy Savings and Financial Costs for Clean Car Standard

Year

Gasoline 

Savings

%

Sector Primary 

Energy Savings

%

Investment 

Costs

(2006$)

Admn Costs

(2006$)

2011 0.2 0.14 163,701,381 4,290,000

2012 0.5 0.37 262,023,382 4,290,000

2013 0.7 0.46 376,648,018 4,290,000

2014 0.8 0.56 473,497,511 4,290,000

2015 1.0 0.72 656,280,630 4,290,000

2016 1.3 0.88 842,672,776 4,290,000

2017 1.7 1.17 938,888,979 4,290,000

2018 2.3 1.57 1,036,410,917 4,290,000

2019 3.0 2.07 1,135,252,158 4,290,000

2020 3.8 2.65 1,235,409,919 4,290,000

2021 4.8 3.34 1,326,994,975 4,290,000

2022 5.9 4.13 1,419,801,686 4,290,000

2023 7.2 5.03 1,513,842,668 4,290,000

2024 8.6 6.02 1,609,130,654 4,290,000

2025 10.1 7.10 1,705,678,493 4,290,000

2026 11.6 8.26 1,803,499,154 4,290,000

2027 13.2 9.48 1,902,605,727 4,290,000

2028 14.9 10.77 2,003,011,420 4,290,000

2029 16.6 12.11 2,104,729,565 4,290,000

2030 18.3 13.49 2,207,773,617 4,290,000

SMARTWAY HEAVY-DUTY EFFICIENCY LOANS 

Introduction 

Despite the availability of numerous programs and technologies to increase the efficiency of heavy 

trucks, trucking companies do not consistently take advantage of such options to reduce their overall 

diesel consumption. Barriers to adoption include high first costs, the rapid turnover of commercial 

trucks, limited manufacturer technology development investment and lack of a standard fuel 

economy test for trucks. Nevertheless, average fuel economy for new tractor-trailers could be raised 

by over 50 percent through a variety of cost-effective existing and emerging technologies. The 

program we propose is to provide low-interest loans for the purchase of a specific package of 

equipment to improve the efficiency of new or existing trucks. This is EPA's SmartWay package, 
Energy Efficiency in Appalachia, SEEA

E-8

which consists of efficient tires, auxiliary power units, and aerodynamic devices. EPA estimates that 

the SmartWay package will typically improve tractor-trailer fuel economy by 12-15 percent. 

Methodology 

Energy Savings

About 2.1 billion miles are driven annually by longdistance trucks,7

i.e., those having a primary range 

of operation over 500 miles, of Class 7 and 8 (i.e., 

those having gross vehicle weight rating of 26,000 

lbs. or more) that are registered in the Appalachian 

states. These are the trucks that would use auxiliary 

power units, since they would frequently be away 

from their home bases at night. To determine the 

number of long distance trucks that would use 

auxiliary power units, we scaled national data from 

the 2002 Vehicle Inventory and Use Survey (VIUS) 

by a population ratio and thus estimated that the 

Appalachian Region has approximately 19,997 

long-distance trucks. Of these we estimate 

that 20 percent already have anti-idling 

technology, leaving 15,998 trucks eligible to 

acquire auxiliary power units. Fuel 

consumption at idle is roughly one gallon 

per hour, and typical annual hours of idling is 1,830 per year. A diesel-fueled APU uses on the order 

of 0.18 gallons per hour, resulting in net savings for these trucks of 1,500 gallons per year.8

The other efficiency equipment in the SmartWay upgrade kit, namely energy-efficient tires and 

trailer side skirts, is beneficial to the somewhat larger set of heavy-heavy trucks that travel largely at 

highway speeds. We assume that trucks typically driving 200 or more miles per day fall into this 

category; there are 34,731 such trucks registered in the Appalachian Region. We estimate, based on 

EPA‘s documentation, that the SmartWay package saves about 12-15 percent in fuel consumption, 

that the fuel savings from this equipment alone totals eight percent. 

The EPA has demonstrated that a low-interest loan program would allow truckers purchasing 

equipment in the SmartWay package to realize fuel cost savings that would exceed their monthly 

loan payments. We assume that usage of the loan program ramps up over five years, reaching 75 

percent of trucks eligible for the various types of equipment by 2012. This results in a four percent 

reduction in fuel consumption over the entire truck stock by 2030. 

Policy Implementation Costs

Administrative cost values are based on the assumption that each ARC state requires one 

administrator costing the state $150,000 and two additional staff costing $90,000 each. These costs 

are then apportioned by the percentage of total state population that resides within the ARC Region. 

 

7

 Scaled from national data obtained from VIUS 

8

 Stodolsky, Gaines, and Vyas, 2000

Figure E.3 SmartWay Heavy Duty Efficiency Loan 

Program Energy Savings Methodologya

a All national VIUS data was scaled to the Appalachian Region 

using Appalachia population estimates.

VIUS Data (# 

of long 

distance

trucks)

VIUS Data (# of 

medium-to-long 

distance trucks)

Trucks eligible 

for idling 

reduction

Hours per 

vehicle idling 

per year

APU fuel 

savings

Total diesel 

gallons 

saved per 

year

Average mpg

Average miles 

traveled per 

truck

Fuel savings 

from aero

technology

Fuel savings 

from efficient 

tires

Total diesel 

gallons 

saved per 

year

Total percent 

diesel savings
Appendix E: Transportation

E-9

Regarding investment costs, the typical SmartWay upgrade kit costs $16,500.9 Based on the fuel 

savings associated with that package and decline in truck miles per year over time, we estimate that 

the benefit/cost ratio for the package will be about two-to-one over the life of the truck. Private 

investment is expected to be higher at the beginning of the program effort, so the benefit/cost ratio 

varies year to year. For truck loans granted through 2020, fuel cost savings out to 2030 total $2.6 

billion (2006 dollars). If we assume the benefit/cost ratio for the SmartWay upgrades is two-to-one, 

then cost of the program through 2020 would be about $1.3 billion, giving a net savings of $1.3 

billion during 2009-2030. 

Results

Table E.5 Summary of Energy Savings and Financial Costs for

SmartWay Heavy Duty Efficiency Loans

Year

Diesel Savings

%

Sector Primary 

Energy Savings

%

Investment 

Costs

(2006$)

Admn Costs

(2006$)

2010 0.98 0.18 337,898,964 4,290,000

2011 1.47 0.27 355,138,295 4,290,000

2012 1.96 0.36 358,899,782 4,290,000

2013 2.36 0.43 287,852,837 4,290,000

2014 2.75 0.50 288,418,413 4,290,000

2015 3.14 0.58 297,185,679 4,290,000

2016 3.53 0.65 301,991,611 4,290,000

2017 3.93 0.73 295,200,513 4,290,000

2018 3.93 0.73 17,079,986 4,290,000

2019 3.93 0.73 17,997,312 4,290,000

2020 3.93 0.74 21,353,420 4,290,000

2021 3.93 0.74 22,010,579 4,290,000

2022 3.93 0.74 19,771,778 4,290,000

2023 3.93 0.75 21,844,172 4,290,000

2024 3.93 0.75 21,747,168 4,290,000

2025 3.93 0.75 22,722,709 4,290,000

2026 3.93 0.76 24,284,094 4,290,000

2027 3.93 0.76 25,236,861 4,290,000

2028 3.93 0.76 22,972,164 4,290,000

2029 3.93 0.76 21,304,921 4,290,000

2030 3.93 0.77 25,012,852 4,290,000

 

9

 U.S. EPA ―Innovative Financing – Frequently Asked Questions,‖ http://epa.gov/smartway/documents/420f07027.htm
Energy Efficiency in Appalachia, SEEA

E-10

SPEED LIMIT ENFORCEMENT

Introduction

At high speeds, vehicle efficiency falls off rapidly with further increases in speed, as aerodynamic 

drag begins to dominate vehicle energy requirements. The speed at which fuel economy is highest 

varies from vehicle to vehicle, but is typically below 60 miles per hour (mph) for a light-duty 

vehicle.10 Federal Highway Administration tests of nine light-duty vehicles in 1997 found that fuel 

economy declined on average by 3.1 percent when speed increased from 55 mph to 60 mph and by 

8.2 percent increasing from 65 to 70 mph.11 For a heavy truck such as a tractor trailer, fuel economy 

declines by about two percent per mph at highway speeds.12 Thus, slowing high-speed driving would 

be one means of improving the real-world efficiencies of cars and trucks. This could be 

accomplished either by reducing the maximum speed limit or by more stringently enforcing the 

existing speed limits. 

Methodology 

Energy Savings

To estimate energy savings from additional 

enforcement, we assume that: 

1. 50 percent of vehicles on highways in the 

Appalachian Region are exceeding speed 

limits; 

2. they are exceeding the limit by five miles per 

hour on average; and 

3. their fuel economy is consequently eight 

percent lower than it would be traveling at the 

speed limit. 

In Appalachia, we assume that 60 percent of all travel is conducted on highways (based on the 

national average13). This leads to an estimate of energy savings of up to 2.2 percent from improved 

enforcement of speed limits for all vehicles. If we assume the enforcement program leads to a 50 

percent reduction in speeding, estimated energy savings for both heavy and light-duty vehicles would 

be as shown in Tables E.6 and E.7. 

Policy Implementation Costs 

The cost to the Region for this effort could be paid for in full or in part from additional revenue from 

speeding fines collected in each state. Administrative costs associated with this policy option are 

 

10 ―Drive more efficiently,‖ U.S.DOE and U.S. EPA, http://www.fueleconomy.gov/feg/driveHabits.shtml

11 Davis and Diegel, 2006 

12 ―Factors Affecting Truck Fuel Economy,‖ Goodyear Tire, 

http://www.goodyear.com/truck/pdf/radialretserv/Retread_S9_V.pdf

13 Federal Highway Administration, Highway Statistics, 2006, http://www.fhwa.dot.gov/policy/ohim/hs06/index.htm 

Figure E.4 Speed Limit Enforcement Energy 

Savings Methodology

Proportion of total 

driving done on 

highways

Reduction in diesel and 

gasoline consumption 

for slower vehicles

Overall reduction in 

consumption w/ 

complete 

compliance

Overall diesel and 

gasoline savings 

given 50% 

compliance
Appendix E: Transportation

E-11

based on the assumption that the additional costs of enforcement will be recouped through increased 

fine revenue. 

Results 

Table E.6 Summary of Energy Savings and Financial Costs for

Enforcement of Speed Limits (Heavy-Duty)

Year Diesel Savings

%

Sector Primary 

Energy Savings

%

Investment 

Costs

(2006$)

Admn Costs

(2006$)

2010 1.1 0.19 4,290,000

2011 1.1 0.19 4,290,000

2012 1.1 0.19 4,290,000

2013 1.1 0.19 4,290,000

2014 1.1 0.19 4,290,000

2015 1.1 0.19 4,290,000

2016 1.1 0.19 4,290,000

2017 1.1 0.19 4,290,000

2018 1.1 0.19 4,290,000

2019 1.1 0.19 4,290,000

2020 1.1 0.19 4,290,000

2021 1.1 0.19 4,290,000

2022 1.1 0.19 4,290,000

2023 1.1 0.19 4,290,000

2024 1.1 0.19 4,290,000

2025 1.1 0.19 4,290,000

2026 1.1 0.19 4,290,000

2027 1.1 0.19 4,290,000

2028 1.1 0.19 4,290,000

2029 1.1 0.19 4,290,000

2030 1.1 0.19 4,290,000
Energy Efficiency in Appalachia, SEEA

E-12

Table E.7 Summary of Energy Savings and Financial Costs for

Enforcement of Speed Limits (Light-Duty)

Year

Gasoline 

Savings

%

Sector Primary 

Energy Savings

%

Investment 

Costs

(2006$)

Admn Costs

(2006$)

2010 1.1 0.76 4,290,000

2011 1.1 0.76 4,290,000

2012 1.1 0.76 4,290,000

2013 1.1 0.77 4,290,000

2014 1.1 0.77 4,290,000

2015 1.1 0.77 4,290,000

2016 1.1 0.77 4,290,000

2017 1.1 0.77 4,290,000

2018 1.1 0.77 4,290,000

2019 1.1 0.77 4,290,000

2020 1.1 0.77 4,290,000

2021 1.1 0.77 4,290,000

2022 1.1 0.77 4,290,000

2023 1.1 0.77 4,290,000

2024 1.1 0.78 4,290,000

2025 1.1 0.78 4,290,000

2026 1.1 0.79 4,290,000

2027 1.1 0.80 4,290,000

2028 1.1 0.80 4,290,000

2029 1.1 0.81 4,290,000

2030 1.1 0.82 4,290,000

REFERENCES

Bordoff, J.E. and P.J. Noel. 2008. Pay-as-you-drive Auto Insurance: A Simple Way to Reduce 

Driving-Related Harms and Increase Equity, The Brookings Institution. 

http://payasyoudrive.com.au/Resources/PDF/PAYDAutoInsuranceASimpleWayToReduceDrivin

gRelatedHarmsAndIncreaseEquity.pdf

California Air Resources Board (CARB). 2008. Comparison of Greenhouse Gas Reductions For All 

Fifty United States Under Cafe Standards And ARB Regulations Adopted Pursuant To AB1493 

http://www.arb.ca.gov/cc/ccms/reports/pavley-addendum.pdf
Appendix E: Transportation

E-13

Davis, S. and S. Diegel. 2006. Transportation Energy Data Book, Edition 25. Oak Ridge, TN: Oak 

Ridge National Laboratory. 

Energy Independence and Security Act of 2007, Public Law 110-140, 110th Congress (EISA). 2007. 

http://frwebgate.access.gpo.gov/cgibin/getdoc.cgi?dbname=110_cong_public_laws&docid=f:publ140.110.pdf

Energy Information Administration (EIA). 2006. Annual Energy Outlook 2006, p. 145. 

http://www.eia.doe.gov/oiaf/archive.html 

———. 2008. Annual Energy Outlook 2008. http://www.eia.doe.gov/oiaf/aeo/

Greene, D.L. and P.N. Leiby. 2006. The Oil Security Metrics Model.

http://cta.ornl.gov/cta/Publications/Reports/ORNL_TM_2006_505.pdf

Litman, T. 2008. Transportation Elasticities: How Prices and Other Factors Affect Travel Behavior, 

Online TDM Encyclopedia,Victoria Transportation Policy Institute. 

http://www.vtpi.org/tdm/tdm11.htm#_Toc119831339

Stodolsky, F., L. Gaines, and A. Vyas. 2000. Analysis of technology options to reduce the fuel 

consumption of idling trucks. Argonne, IL: Center for Transportation Research, Argonne 

National Laboratory. 

Appendix F: The DEEPER Model

F-1

APPENDIX F: THE DEEPER MODEL

The Dynamic Energy Efficiency Policy Evaluation Routine – or the DEEPER Model – is a 15-sector 

quasi-dynamic input-output impact model of the U.S. economy.1 Despite its more conventional 

input-output framework, it is in the tradition of so-called general equilibrium models following the 

logic of Hanson and Laitner (forthcoming) and Laitner and Hanson (2006). Although an updated 

model with a new name, the model actually has a 16-year history of use and development. See, for 

example, Geller, DeCicco, and Laitner (1992), Laitner, Bernow, and DeCicco (1998), and Laitner 

and McKinney (2008) for a review of past modeling efforts in this tradition. 

The DEEPER model is generally used to evaluate the macroeconomic impacts of a variety of energyefficiency and climate policies at both the state and national level. The national model now evaluates 

policies for the period 2008 through 2050. For the Appalachia specific analysis, however, the 

DEEPER Model covers the period between 2008 through 2030. As it is now designed, the model 

accepts policy inputs in the form of investments and expenditures as described throughout the report. 

It then evaluates the changed pattern of expenditures for the net direct and indirect impacts on the 

different sectors of the Regional economy. DEEPER is an Excel-based analytical tool that consists 

generally of six sets of key modules or groups of worksheets. These six sets of modules now 

include: 

Global data: The information in this module consists of the economic time series data and key 

model coefficients and parameters necessary to generate the final model results. The time series data 

includes the projected reference case energy quantities such as trillion Btus and kilowatt-hours, as 

well as the key energy prices associated with their use. It also includes the projected gross domestic 

product, wages and salary earnings, and levels of employment as well as information on key 

technology cost and performance characteristics. The sources of economic information include data 

from the Energy Information Administration, the Bureau of Economic Analysis, the Bureau of Labor 

Statistics, and Economy.com. The cost and performance characterization of key technologies is 

derived from available studies completed by ACEEE and others, as well as data from the Energy 

Information Administration‘s (EIA) National Energy Modeling System (NEMS). One of the more 

critical assumptions in this study is that alternative patterns of electricity consumption will change 

and/or defer the mix of investments in conventional power plants. Although we can independently 

generate these impacts within DEEPER, we can also substitute assumptions from the ICF Integrated 

Planning Model (IPM) and similar models as they may have different characterizations of avoided 

costs or alternative patterns of power plant investment and spending. 

Macroeconomic model: This set of modules contains the ―production recipe‖ for the Region‘s 

economy for a given ―base year‖ – in this case, 2006, which is the latest year for which a complete 

set of economic accounts are available for the Regional economy. The I-O data, currently purchased 

from the Minnesota IMPLAN Group (IMPLAN, 2008), is essentially a set of input-output accounts 

that specify how different sectors of the economy buy (purchase inputs) from and sell (deliver 

 

1

 There is nothing particularly special about this number of sectors. The goal is to provide sufficient detail to show key 

negative and positive impacts while maintaining a manageable sized model. If we choose to reflect a different mix of 

sectors and stay within the 15 x 15 matrix, that can be done easily. If we wish to expand the number of sectors, that would 

take some minor programming changes or adjustments to reflect the larger matrix. 
Energy Efficiency in Appalachia, SEEA

F-2

outputs) to each other. In this case, the model is now designed to evaluate impacts for 15 different 

sectors, including: Agriculture, Oil and Gas Extraction, Coal Mining, Other Mining, Construction, 

Manufacturing, Electric Utilities, Natural Gas Distribution, Transportation, and Other Public Utilities 

(including water and sewage), Wholesale & Retail Trade, Services, Finance, Government, and 

Households. 

Given the I-O assumptions mapped out for the Appalachian Region, the table that follows highlights 

the Type I Multipliers for each of the major sector activities within DEEPER. These multipliers refer 

to the direct and indirect job impacts per million dollars of revenue received by a particular sector, 

and the direct and indirect income and contributions to value-added (or Gross Regional Product) 

created by the expenditure of one dollar within each sector. 

Almost the first thing that jumps out from Table F.1 is the relative low values shown for energyrelated sectors compared to almost all other sectors of the economy. For example, electric utilities 

provide a total of 5.3 direct and indirect jobs per million dollars of revenue that they receive. All 

other sectors show a significantly higher impact in this regard. It is these differences that are 

underscored in the heuristic analysis shown in Table 7.1 in the main report. Although not quite as 

pronounced there is a similar pattern for both wages and salaries and contributions to the GRP. 

Hence, any cost-effective adjustment to the energy production patterns of the Appalachian Regional 

economy should leave to a small but net positive impact on the economy. 

Table F.1 Appalachia Economic Multipliers for Key Sectors

SECTOR

Type I Multiplier 

Jobs (per million

$ of final demand)

Type I 

Multiplier 

Compensation 

(per dollar of 

final demand)

Type I Multiplier 

Value-Added

(per dollar of final 

demand)

Agriculture 21.5 0.224 0.646

Oil and Gas Extraction 5.1 0.167 0.742

Coal Mining 6.0 0.335 0.712

Other Mining 7.6 0.365 0.740

Construction 13.3 0.429 0.720

Manufacturing 8.3 0.338 0.630

Petroleum Refining 2.6 0.095 0.311

Electric Utility Services 5.3 0.285 0.818

Natural Gas Utility Services 3.7 0.204 0.552

Transportation Other Public Utilities 11.0 0.418 0.747

Wholesale Trade 15.3 0.461 0.863

Services 14.1 0.415 0.834

Financial Services 8.6 0.385 0.828

Governmental Services 19.7 0.885 0.974
Appendix F: The DEEPER Model

F-3

Investment, expenditures and energy savings: Based on the scenarios mapped into the model, this 

worksheet translates the energy policies into a dynamic array of physical energy impacts, investment 

flows, and energy expenditures over the desired period of analysis. It estimates the needed 

investment path for an alternative mix of energy efficiency and other technologies (including 

efficiency gains on both the end-use and the supply side). It also provides an estimate of the avoided 

investments needed by the electric generation sector. These quantities and expenditures feed directly 

into the final demand module of the model which then provides the accounting that is needed to 

generate the set of annual changes in final demand (see the related module description below). 

Price dynamics: There are two critical drivers that impact energy prices within DEEPER. The first 

is a set of carbon charges that are added to retail prices of energy depending on the level of desired 

level of emission reductions and also depending on the available set of alternatives to achieve those 

reductions. The second is the price of energy as it might be affected by changed consumption 

patterns. In this case, DEEPER employs an independent algorithm to generate energy price impacts 

as they reflect changed demand. Hence, the reduced demand for natural gas in the end-use sectors, 

for example, might offset increased demand by utility generators. If the net change is a decrease in 

total natural gas consumption, the wellhead prices might be lowered. Depending on the magnitude of 

the carbon charge, the change in retail prices might either be higher or lower than the set of reference 

case prices. This, in turn, will impact the demand for energy as it is reflected in the appropriate 

modules. In effect, then, DEEPER scenarios rely on both a change in prices and quantities to reflect 

changes in overall investments and expenditures. For this analysis, however, we have used the 

conservative assumption that price dynamics remain unchanged. 

Final demand: Once the changes in spending and investments have been established and adjusted to 

reflect changes in prices within the other modules of DEEPER, the net spending changes in each year 

of the model are converted into sector-specific changes in final demand. This, in turn, drives the 

input-output model according to the following predictive model: 

X = (I-A)-1 * Y 

where: 

X = total industry output by sector 

I = an identity matrix consisting of a series of 0‘s and 1‘s in a row and column format for 

each sector (with the 1‘s organized along the diagonal of the matrix) 

A = the production or accounting matrix also consisting of a set of production coefficients 

for each row and column within the matrix 

Y = final demand, which is a column of net changes in final demand by sector 

This set of relationships can also be interpreted as 

∆X = (I-A)-1 * ∆Y 

which reads, a change in total sector output equals the inverted (I-A) matrix times a change in the 

final spending demand for each sector. Employment quantities are adjusted annually according to 

exogenous assumptions about labor productivity in each of the sectors (based on Bureau of Labor 

Statistics forecasts). Table 7.1 in the main report illustrates the approach suggested by this 

perspective. 
Energy Efficiency in Appalachia, SEEA

F-4

Results: For each year of the analytical time horizon (again out to 2030 for the Appalachia specific 

analysis), the model copies each set of results into this module in a way that can also be exported to a 

separate report. 

There are other support spreadsheets as well as routines in visual basic programming that support the 

automated generation of model results and reporting. For more detail on the model assumptions and 

economic relationships, again please refer to the forthcoming model documentation (Laitner and 

McKinney, forthcoming). And as alluded in the beginning of this appendix, for a review of how an 

I-O framework might be integrated into other kinds of modeling activities, see Hanson and Laitner 

(forthcoming). While not an equilibrium model we borrow from some key concepts of mapping 

technology representation into DEEPER using the general scheme outlined in Laitner and Hanson 

(2006). 

REFERENCES

Geller, Howard, John DeCicco, and John A. ―Skip‖ Laitner. 1992. Energy Efficiency and Job 

Creation: The Employment and Income Benefits from Investing in Energy Conserving 

Technologies," Washington, DC: The American Council for an Energy-Efficient Economy. 

Hanson, Donald A. and John ―Skip‖ Laitner. Forthcoming. ―Input-Output Equations Embedded 

within Climate and Energy Policy Analysis Models.‖ Forthcoming in Faye Duchin and Sangwon 

Suh, Editors., Input-Output Analysis Handbook. New York, N.Y.: Kluwer Academic Press. 

Laitner, John A. ―Skip‖ and Donald A. Hanson. 2006. ―Modeling Detailed Energy-Efficiency 

Technologies and Technology Policies within a CGE Framework.‖ Energy Journal, Special 

Edition, Hybrid Modeling of Energy-Environment Policies: Reconciling Bottom-Up and TopDown, pp. 151-69. 

Laitner, John A. ―Skip,‖ Stephen Bernow, and John DeCicco. 1998. ―Employment and Other 

Macroeconomic Benefits of an Innovation-Led Climate Strategy for the United States.‖ Energy 

Policy, 26(5), 425–433. 

Laitner, John A. ―Skip‖ and Donald A. Hanson. 2006. ―Modeling Detailed Energy-Efficiency 

Technologies and Technology Policies within a CGE Framework.‖ Energy Journal, Special 

Edition, ―Hybrid Modeling of Energy-Environment Policies: Reconciling Bottom-Up and TopDown,‖ pp. 151-69. 

Laitner, John A. ―Skip‖ and Vanessa McKinney. 2008. Positive Returns: State Energy Efficiency 

Analyses Can Inform U.S. Energy Policy Assessments. Washington, D.C.: American Council 

for an Energy-Efficient Economy. 

———. Forthcoming. Draft DEEPER Documentation. Forthcoming. Washington, DC: American 

Council for an Energy-Efficient Economy. 

Minnesota IMPLAN Group, Inc. (IMPLAN). 2008. A 2006 dataset for Appalachia (Alabama, 

Georgia, Kentucky, Maryland, Mississippi, New York, North Carolina, Ohio, Pennsylvania, 

South Carolina, Tennessee, and Virginia. Accessed January. Stillwater, Minn.: Minnesota 

IMPLAN Group, Inc.
Appendix G: Sensitivities at Sector Level

G-1

APPENDIX G: SENSITIVITIES AT SECTOR LEVEL 

SUPPLEMENTAL TABLES

Table G.1 Carbon Footprint and Consumption Data for 17 Appalachian Metropolitan Areas 

(Brown and Logan, 2008) 

% in ARC Subregion Residential 

Electricity Residential Fuel Light Duty Vehicle Freight Trucks Residential Electricity Residential Fuel Light Duty Vehicle Freight Trucks

Albany-Schenectady-Troy, NY 20% Northern 0.38 0.58 1.23 0.33 36.88 38.06 63.64 16.45

Allentown-Bethlehem-Easton, PANJ 

25% Northern 0.56 0.47 0.96 0.37 38.19 29.69 49.84 18.70

Cincinnati-Middletown, OH-KY-IN 13% Northern 1.25 0.45 1.14 0.44 59.46 31.50 58.91 21.86

Harrisburg-Carlisle, PA 33% Northern 0.62 0.53 1.32 0.72 42.54 33.38 68.24 36.17

New York-Northern New JerseyLong Island, NY-NJ-PA 4% Northern 0.23 0.44 0.66 0.16 21.78 27.43 34.33 8.07

Pittsburgh, PA 100% Northern 0.54 0.55 0.91 0.27 36.93 34.91 47.18 13.66

Scranton--Wilkes-Barre, PA 100% Northern 0.58 0.55 1.01 0.51 39.79 34.99 52.27 25.79

Washington-Arlington-Alexandria, 

DC-VA-MD-WV

5% Northern 1.61 0.35 0.98 0.17 62.02 23.76 50.86 8.72

Youngstown-Warren-Boardman, 

OH-PA

33% Northern 0.77 0.43 1.01 0.54 36.33 30.21 52.46 27.32

Lexington-Fayette, KY 17% Central 1.48 0.24 1.10 0.64 63.60 16.92 56.94 32.07

Nashville-Davidson--Murfreesboro, 

TN

23% Central 1.15 0.19 1.32 0.57 74.21 13.53 68.19 28.48

Atlanta-Sandy Springs-Marietta, GA 43% Southern 0.84 0.21 1.22 0.41 51.01 15.28 63.26 20.61

Birmingham-Hoover, AL 100% Southern 0.99 0.16 1.34 0.42 64.55 10.83 69.03 21.17

Chattanooga, TN-GA 100% Southern 1.05 0.20 1.27 0.59 68.00 14.43 65.77 29.42

Greenville, SC 67% Southern 0.57 0.14 0.87 0.28 56.56 10.30 45.16 13.92

Knoxville, TN 100% Southern 1.07 0.20 1.40 0.46 68.91 14.50 72.50 23.35

Memphis, TN-MS-AR 13% Southern 0.99 0.18 1.16 0.53 64.20 13.31 60.07 26.65

Total Top 100 Metros 0.61 0.31 1.00 0.31 41.35 21.14 51.93 15.32

Appalachian Metro Average 1.05 0.42 1.35 0.53 63.21 28.07 69.90 26.60

Annual metric tons of carbon emitted per person Annual Mbtu consumed per capita

DISCOUNT RATE SENSITIVITIES 

The discount rates used in analysis can have a significant impact on the results of cost effectiveness 

tests. Since this study seeks to identify policies that are cost-effective, the choice of discount rate has 

been conservatively selected for the base analysis at seven percent for the total resource cost test and 

10 percent for participants. Discount rates are generally accepted to be higher for private investment 

than public programs due to the greater societal concern for the future. This sensitivity analysis 

reports the benefit-to-cost ratio at discount rates of four, seven, and ten percent for both participants 

and the total resource costs. 

Figures G. 1 – G. 4 show the discount rate sensitivity for each of the policies within the four modeled 

sectors. 
Energy Efficiency in Appalachia, SEEA

G-2

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00 Residential Building Codes Expanded Weatherization Existing Home Retrofits Super-Efficient Appliance Deployment Total Residential Building Codes Expanded Weatherization Existing Home Retrofits Super-Efficient Appliance Deployment Total BC Ratio

Participants Cost Test Total Resource Cost Test

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

11.00

12.00

13.00

14.00 Commercial Building Codes Commissioning HVAC and Lighting Retrofits Office Equipment StandardsTotal Commercial Building Codes Commissioning HVAC and Lighting Retrofits Office Equipment StandardsTotal BC Ratio

Participants Cost Test Total Resource Cost Test

Figure G.1 Discount Rate Sensitivities for Residential Sector Policies

Figure G.2 Discount Rate Sensitivity for Commercial Sector Policies
Appendix G: Sensitivities at Sector Level

G-3

0

1

2

3

4

5

6

7

8

IAC ESA CHP Total IAC ESA CHP Total BC Ratio

Participants Cost Test Total Resource Cost Test

0

1

2

3

4

5

6

7

8

9 Clean Car Standard Pay-as-you-drive Insurance Smart Way Speed Limits Total Clean Car Standard Pay-as-you-drive Insurance Smart Way Speed Limits Total BC Ratio

Participants Cost Test Total Resource Cost Test

At this scale, the benefit-to-cost ratios cannot be seen for the policies of Supporting Pay as you Drive 

Insurance or Enforcing Speed Limits because they are too high to show on the same scale. 

AVOIDED COST SENSITIVITIES 

The avoided costs of producing the energy that will be saved by a particular policy or measure, or 

package of policies or measures, can have an enormous impact on the benefit-to-cost ratio on a total 

resource basis. In our base analysis, we assume that the avoided costs are equal to the energy price 

forecast; this assumption was driven by low forecast energy prices and general belief that these as 

Figure G.3 Discount Rate Sensitivities for the Industrial Sector Policies

Figure G.4 Discount Rate Sensitivities for the Transportation Sector Policies
Energy Efficiency in Appalachia, SEEA

G-4

well as forecast avoided costs are understated. However, there is much uncertainty about future 

avoided costs, as it will be dependent upon many factors. We have considered lower and higher 

costs for sensitivity analysis in this study. 

To model the cost effectiveness of lower avoided costs, we have used rates at 50 percent and 75 

percent of forecast retail energy prices. To model the cost effectiveness of higher avoided costs, we 

have considered the impact of carbon taxes of $25, $50, and $100 per metric ton of carbon (MtC) 

(Brown & Atamturk, 2008). 

Figures G.5 – G.8 show how sensitive the cost-effectiveness of the modeled policies are to avoided 

costs. 

0

2

4

6

8

10

12

Residential Policy Package BC Ratio

50% Lower than Energy Prices 1.86 0.63 0.85 3.52 0.97

25% Lower than Energy Prices 2.79 0.94 1.27 5.29 1.46

Retail Energy Prices 3.72 1.26 1.70 7.05 1.94

Energy Prices with $25/MtC Tax 3.96 1.40 1.89 8.32 2.15

Energy Prices with $50/MtC Tax 4.19 1.54 2.08 9.58 2.35

Energy Prices with $100/MtC Tax 4.67 1.84 2.47 12.12 2.75

Residential 

Building Codes

Expanded 

Weatherization

Existing Home 

Retrofits

Super-Efficient 

Appliance 

Deployment

Total

Overall, the residential policy package is cost-effective for a variety of avoided cost scenarios. If 

future avoided costs are lower than forecast energy prices, the two residential retrofit programs may 

not be cost-effective. 

Figure G.5 Avoided Cost Sensitivities for the Residential Sector
Appendix G: Sensitivities at Sector Level

G-5

0

2

4

6

8

10

12

14

16

18

Commercial Policy Package BC Ratio

50% Lower than Energy Prices 1.39 4.76 2.94 4.94 3.40

25% Lower than Energy Prices 2.08 7.14 4.41 7.41 5.10

Retail Energy Prices 2.78 9.52 5.88 9.89 6.80

Energy Prices with $25/MtC Tax 3.22 11.05 6.68 11.44 6.61

Energy Prices with $50/MtC Tax 3.66 12.55 7.47 12.99 7.43

Energy Prices with $100/MtC Tax 4.55 15.60 9.06 16.10 9.11

Commercial 

Building Codes Commissioning

HVAC and 

Lighting 

Retrofits

Office 

Equipment 

Standards

Total

For all modeled avoided costs, the commercial sector policies we have considered will be cost 

effective over their lifetime. 

0

2

4

6

8

10

12

Industrial Policy Package BC Ratio

50% Lower than Energy Prices 2.93 1.65 0.16 1.04

25% Lower than Energy Prices 4.39 2.47 0.24 1.56

Retail Energy Prices 5.85 3.30 0.31 2.08

Energy Prices with $25/MtC Tax 7.13 3.92 0.76 2.67

Energy Prices with $50/MtC Tax 8.37 4.52 1.24 3.26

Energy Prices with $100/MtC Tax 10.93 5.77 2.13 4.45

IAC ESA CHP Total

While most of the policies modeled are cost-effective at most avoided costs, the policy to support 

combined heat and power modifications for industrial facilities is only cost effective when a carbon 

tax, or other cause for higher energy prices, is considered. 

Figure G.6 Avoided Cost Sensitivities for the Commercial Sector

Figure G.7 Avoided Cost Sensitivities for the Industrial Sector
Energy Efficiency in Appalachia, SEEA

G-6

0

20

40

60

80

Transportation Policy Package BC Ratio 50% Lower than Energy Prices 1.27 3.66 0.82 32.2 1.66

25% Lower than Energy Prices 1.91 5.49 1.23 48.2 2.49

Retail Energy Prices 2.55 7.31 1.64 64.3 3.33

Energy Prices with $25/MtC Tax 2.61 7.49 1.69 65.8 3.41

Energy Prices with $50/MtC Tax 2.67 7.67 1.73 67.2 3.49

Energy Prices with $100/MtC Tax 2.79 8.03 1.82 70.1 3.65

Clean Car 

Standard

Pay-as-youdrive Insurance Smart Way Speed Limits Total

The benefit-to-cost ratios are extraordinarily high for some of our modeled transportation policies 

because the policies have low program costs. However, their cost effectiveness is still impacted by 

avoided costs. The SmartWay loan program, as modeled, would not be cost-effective from a total 

resource cost perspective if energy prices are much lower than forecast. 

References

Brown, Marilyn and Nilgun Atamturk. 2008. ―Potential Impacts of Energy and Climate Policies on 

the U. S. Pulp and Paper Industry,‖ Georgia Institute of Technology School of Public Policy 

Working Paper #40, June, 2008. http://www.spp.gatech.edu/faculty/workingpapers.php 

 Marilyn A. Brown and Cecelia (Elise) Logan. 2008. ―The Residential Energy and Carbon 

Footprints of the 100 Largest Metropolitan Areas,” Georgia Institute of Technology School of 

Public Policy Working Paper. http://www.spp.gatech.edu/faculty/workingpapers.php 

Figure G.8 Avoided Cost Sensitivities for the Transportation Sector