Projected Benefits of 

Federal Energy Efficiency and 

Renewable Energy Programs

FY 2007 Budget Request

March 2006 NREL/TP-620-39684

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Projected Benefits of Federal 

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FY 2007 Budget Request 

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March 2006 

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Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

March 2006 – Page i 

Contents 

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

Preface ......................................................................................................................................P-1 

 Role of Benefits Analysis in Performance Management....................................................P-1 

 Benefits Framework............................................................................................................P-2 

 Benefits Analysis Process...................................................................................................P-6 

 Step 1: Baseline Case..........................................................................................................P-7 

 Step 2: Program and Market Inputs ..................................................................................P-10 

 Step 3: Individual Program and Integrated EERE Portfolio Benefits Estimates .............P-12 

 Scenario Analysis .............................................................................................................P-13 

 Future GPRA Benefits Development ..............................................................................P-14 

 Report Organization..........................................................................................................P-15 

Chapter 1 – FY 2007 Benefits Estimates.................................................................................. 1-1 

 Outcomes and Benefits Metrics.......................................................................................... 1-1 

 Portfolio Benefits................................................................................................................ 1-3 

 Program Benefits .............................................................................................................. 1-16 

Chapter 2 – Midterm Benefits Analysis of EERE’s Programs.............................................. 2-1 

 Introduction......................................................................................................................... 2-1 

 Biomass Program................................................................................................................ 2-2 

 Building Technologies Program ......................................................................................... 2-4 

 Federal Energy Management Program ............................................................................... 2-7 

 Hydrogen, Fuel Cells, and Infrastructure Technologies Program ...................................... 2-8 

 Industrial Technologies Program........................................................................................ 2-9 

 Solar Energy Technologies Program ................................................................................ 2-10 

 Vehicle Technologies Program......................................................................................... 2-13 

 Weatherization and Intergovernmental Program.............................................................. 2-18 

 Wind Technologies Program ............................................................................................ 2-20 

Chapter 3 – Long-Term Benefits Analysis of EERE’s Programs ......................................... 3-1

 Introduction......................................................................................................................... 3-1 

 Biomass Program................................................................................................................ 3-2 

 Building Technologies Program ......................................................................................... 3-4 

 Federal Energy Management Program ............................................................................... 3-8 

 Hydrogen, Fuel Cells, and Infrastructure Technologies Program ...................................... 3-8 

 Industrial Technologies Program...................................................................................... 3-12 

 Solar Energy Technologies Program ................................................................................ 3-13 

 Vehicle Technologies Program......................................................................................... 3-15 

 Weatherization and Intergovernmental Program.............................................................. 3-17 

 Wind Technologies Program ............................................................................................ 3-18 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

March 2006 – Page ii 

Appendix A – GPRA07 NEMS and MARKAL Baseline Descriptions .................................... A-1 

Appendix B – GPRA07 Hydrogen, Fuel Cells, and Infrastructure Technologies Program 

Documentation...................................................................................................................B-1 

Appendix C – GPRA07 Biomass Technologies Program Documentation.................................C-1 

Appendix D – GPRA07 Solar Energy Technologies Program Documentation ........................ D-1 

Appendix E – GPRA07 Wind Technologies Program Documentation......................................E-1 

Appendix F – GPRA07 Vehicle Technologies Program Documentation ..................................F-1 

Appendix G – GPRA07 Building Technologies Program Documentation ............................... G-1 

Appendix H – GPRA07 Industrial Technologies Program Documentation.............................. H-1 

Appendix I – GPRA07 Federal Energy Management Program Documentation.........................I-1 

Appendix J – GPRA07 Weatherization and Intergovernmental Program Documentation .........J-1 

Appendix K – GPRA07 Scenarios ............................................................................................ K-1 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

March 2006 – Page iii 

Tables 

ES.1. EERE FY 2007 Benefits Metrics...................................................................................ES-3 

ES.2. Summary of EERE Integrated Portfolio Benefits for FY 2007 Budget Request...........ES-2 

ES.3. U.S. Department of Energy Office of Energy Efficiency and Renewable Energy: 

FY 2006 Funding Summary and Selected 2025 and 2050 Benefits by Program ........ES-10 

P.1. Summary of Baseline Changes from the AEO2005 ........................................................P-9 

1.1. Annual EERE Portfolio Benefits for FY 2007 Budget Request for Selected Years ....... 1-4 

2.1. Program Benefits Modeling by Primary Type of Model Used and Activity Area .......... 2-1 

2.2. FY07 Annual Benefits Estimates for Biomass Program (NEMS-GPRA07)................... 2-3 

2.3. Modeling Approach for Building Technologies Program Activities............................... 2-4 

2.4. Building Technologies Program Delivered Energy Savings by End Use........................ 2-6 

2.5. FY07 Annual Benefits Estimates for Building Technologies Program 

(NEMS-GPRA07)............................................................................................................ 2-7 

2.6. FY07 Annual Benefits Estimates for FEMP (NEMS-GPRA07)..................................... 2-8 

2.7. FY07 Benefits Estimates for Hydrogen, Fuel Cells, and Infrastructure Technologies 

Program (NEMS-GPRA07)............................................................................................. 2-9 

2.8. FY07 Annual Benefits Estimates for Industrial Technologies Program 

(NEMS-GPRA07).......................................................................................................... 2-10 

2.9. NEMS-GPRA07 Projected Solar Capacity.................................................................... 2-12 

2.10. FY07 Annual Benefits Estimates for Solar Energy Technologies Program 

(NEMS-GPRA07).......................................................................................................... 2-13 

2.11. FY07 Benefits Estimates for Vehicle Technologies Program (NEMS-GPRA07) ........ 2-18 

2.12. FY07 Annual Benefits Estimates for Weatherization and Intergovernmental Program 

(NEMS-GPRA07).......................................................................................................... 2-19 

2.13. Wind Capacity ............................................................................................................... 2-21 

2.14. FY07 Benefits Estimates for Wind Technologies Program (NEMS-GPRA07)............ 2-22 

3.1. Long-Term Benefits Modeling by Primary Type of Model Used and Activity Area ..... 3-2 

3.2. Projected Ethanol Production and Use ............................................................................ 3-3 

3.3. Cellulosic Biorefinery Cogeneration Capacity and Net Generation................................ 3-4 

3.4. Annual Benefits Estimates for Biomass Program (MARKAL-GPRA07)....................... 3-4 

3.5. Residential Delivered Energy Savings by Demand and Fuel .......................................... 3-6 

3.6. Commercial Delivered Energy Savings by Demand and Fuel ........................................ 3-7 

3.7. Electricity Savings from Distribution Transformer and Electric Motor Standards ......... 3-7 

3.3. Cellulosic Biorefinery Cogeneration Capacity and Net Generation................................ 3-4 

3.8. Annual Benefits Estimates for Building Technologies Program 

(MARKAL-GPRA07)...................................................................................................... 3-7 

3.9. LDV and Commercial Light-Truck Vehicle Miles Traveled .......................................... 3-9 

3.10. Light-Duty Vehicle Market Shares for the Hydrogen Case........................................... 3-10 

3.11. Calculation of Petroleum Savings ................................................................................. 3-11 

3.12. Calculation of Carbon Emission Reduction................................................................... 3-11 

3.13. Hydrogen Production by Feedstock............................................................................... 3-12 

3.14. Annual Benefits Estimates for Hydrogen, Fuel Cells, and Infrastructure Technologies 

Program (MARKAL-GPRA07)..................................................................................... 3-12 

3.15. Industrial Program Integration Factors .......................................................................... 3-13 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

March 2006 – Page iv 

3.16. Solar-Generation Capacity by Case and Type............................................................... 3-14 

3.17. Solar Generation by Case and Type............................................................................... 3-14 

3.18. Annual Benefits Estimates for Solar Energy Technologies Program 

(MARKAL-GPRA06).................................................................................................... 3-15 

3.19. Projected Vehicle Miles Traveled by Vehicle Class ..................................................... 3-15 

3.20. Light-Duty Vehicle Market Shares for the Vehicles Technologies Individual 

Program Goal Case ........................................................................................................ 3-16 

3.21. Petroleum Consumption by Vehicle Class and Case..................................................... 3-17 

3.22. FY07 Benefits Estimates for Vehicle Technologies Program (MARKAL-GPRA07) .. 3-17 

3.23. Total Wind Capacity and Generation ............................................................................ 3-19 

3.24. Annual Benefits Estimates for Wind Technologies Program (MARKAL-GPRA07) ... 3-20 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

March 2006 – Page v 

Figures 

ES.1. U.S. Nonrenewable Energy Consumption, 1949-2005, and Projections to 2050: 

Baseline and Portfolio Cases .........................................................................................ES-4 

ES.2. U.S. Total Energy Expenditures, 1965-2005, and Projections to 2025: Baseline and 

Portfolio Cases...............................................................................................................ES-5 

ES.3. U.S. Energy-Related Carbon Emissions, 1949-2005, and Projections to 2050: Baseline 

and Portfolio Cases ........................................................................................................ES-6 

ES.4. U.S. Oil Consumption, 1949-2005, and Projections to 2050: Baseline and 

Portfolio Cases...............................................................................................................ES-7 

ES.5. U.S. Natural Gas Consumption, 1949-2005, and Projections to 2050: Baseline and 

Portfolio Cases...............................................................................................................ES-8 

ES.6. Proposed FY 2007 Budget Request for Technology Development...............................ES-9 

P.1. Generalized EERE Logic Model ....................................................................................P-3 

P.2. Holistic View of the Role of Benefits Analysis in EERE Performance Management ....P-4 

P.3. FY 2007 Benefits Metrics Reported ................................................................................P-5 

P.4. EERE Program and Portfolio Benefits-Analysis Process................................................P-7 

1.1. U.S. Nonrenewable Energy Consumption, 1949-2005, and Projections to 2050: 

Baseline, Individual Program Goal Cases, and EERE Portfolio Case............................. 1-5 

1.2. U.S. Total Energy Consumption versus Nonrenewable Energy Consumption, 

1949-2005, and Projections to 2050: Baseline and Portfolio Cases ................................ 1-7 

1.3. U.S. Total Nonrenewable Energy Savings and Total Renewable Energy Replacement 

Projections to 2050: EERE Portfolio Case ...................................................................... 1-7 

1.4. U.S. Total Energy Expenditure, 1970-2001, and Projections to 2025: Baseline 

 and Portfolio Cases......................................................................................................... 1-8 

1.5. U.S. Total Energy-System Cost Savings 2030 to 2050 ................................................... 1-9 

1.6. U.S. Carbon Emissions, 1980-2003, and Projections to 2050: Baseline and 

Portfolio Cases............................................................................................................... 1-10 

1.7. U.S. Carbon Emissions, 1980-2003, and Projections to 2050: Baseline and 

Portfolio Cases............................................................................................................... 1-10 

1.8. U.S. Oil Consumption, 1949-2004, and Projections to 2050: Baseline and 

Portfolio Cases............................................................................................................... 1-11 

1.9. U.S. Oil Savings Projections to 2050: Baseline and Portfolio Cases ............................ 1-11 

1.10. Foreign Oil Consumption, 1949-2004, and Projections to 2050: Baseline and 

Portfolio Cases............................................................................................................... 1-12 

1.11. U.S. Natural Gas Consumption, 1949-2004, and Projections to 2050: Baseline and 

Portfolio Cases............................................................................................................... 1-13 

1.12. U.S. Natural Gas Savings, Projections to 2050: Baseline and Portfolio Cases ............. 1-13 

1.13. Avoided Conventional Central Generating Capacity, Projections to 2025 ................... 1-14 

1.14. U.S. Renewable Energy Capacity, 1949-2004, and Projections to 2050: Baseline and 

Portfolio Cases............................................................................................................... 1-15 

1.15. EERE Program FY 2007 Budget Requests.................................................................... 1-16 

1.16. Individual Program Goal Cases Nonrenewable Energy Savings: Annual 

and Cumulative .............................................................................................................. 1-17 

1.17. Cumulative Individual Program Goal Cases Nonrenewable Energy Savings 

for 2015, 2025, 2050...................................................................................................... 1-18 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

March 2006 – Page vi 

1.18. Annual Individual Program Goal Cases Consumer Energy-Expenditure Savings: 

Projections to 2025 ........................................................................................................ 1-19

1.19. Annual Program Goal Cases Energy-System Net Cost Savings: Projections to 2050 .. 1-19 

1.20. Individual Program Goal Cases Carbon Emission Avoided: Annual and Cumulative . 1-20 

1.21. Cumulative Individual Program Goal Cases Carbon Emissions Avoided, 2015, 

2025, 2050...................................................................................................................... 1-20 

1.22. Individual Program Goal Cases Oil Savings ................................................................. 1-21 

1.23. Cumulative Individual Program Goal Cases Oil Savings.............................................. 1-21 

2.1. Commercial-Sector DG Adoption Rates ....................................................................... 2-11 

2.2. Vehicle Technology Sales Share in 2025 ...................................................................... 2-16 

2.3. Vehicle Miles Traveled in 2025..................................................................................... 2-17 
EXECUTIVE SUMMARY 

The Office of Energy Efficiency and Renewable Energy (EERE) of the U.S. Department of 

Energy (DOE) leads the Federal Government’s efforts to provide clean, reliable, and affordable 

energy for America, through its nine research, development, demonstration, and deployment 

(RD3

) programs. EERE invests in high-risk, high-value research and development (R&D) that—

conducted in partnership with the private sector and other government agencies—accelerates the 

development and facilitates the deployment of advanced clean energy technologies and practices. 

EERE designs its RD3

 activities to improve the Nation’s readiness for addressing current and 

future energy needs. 

Key Findings 

Under a business-as-usual energy future, realization of these goals and the associated projected 

market outcomes would: 

• Reduce the expected increase in U.S. demand for nonrenewable energy by 28% in 2025 and 

78% in 2050. 

• Reduce nonrenewable energy consumption starting in 2030. (Figure ES.1) 

• Reduce the expected increase in U.S. consumer energy expenditures by 51% in 2025. 

(Figure ES.2)

• Save more than $200 billion per year in U.S. energy system net costs in 2050. (Table ES.2)

• Reduce the expected increase in annual U.S. carbon emissions by 31% in 2025 and 68% in 

2050. (Figure ES.3) 

• Reduce the expected increase in U.S. oil consumption (most of which is expected to originate 

from outside the United States) by 28% in 2025 and 120% in 2050. 

• Result in declining oil consumption after 2025. (Figure ES.4) 

• Reduce the expected increase in U.S. natural gas consumption, much of which is expected to 

originate outside the United States, by 10% in 2025 and 20% in 2050. (Figure ES.5) 

• Avoid 118 gigawatts of additions to central conventional power in 2025. (Table ES.2)

Why Measure Benefits? 

EERE develops benefits projections annually to maintain compliance with the Government 

Performance and Results Act (GPRA) of 1993 and the President’s Management Agenda (PMA). 

GPRA requires Federal Government agencies to develop and report on output and outcome 

measures for each program. This analysis helps meet GPRA requirements by identifying the 

potential outcomes and benefits of realizing EERE program goals (outputs). The benefits 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Executive Summary – Page ES-1 
estimates do not reflect the technical risks or probabilities of realizing these goals, which are 

being addressed separately.1

The reported benefits reflect only the net annual improvement from 2005 to 2050 of program 

activities included in EERE’s FY 2007 Budget Request (including subsequent-year funding) and 

do not include the benefits from past work. The benefits estimates assume continued funding for 

program activities consistent with multiyear program plans.2 By basing estimated benefits on 

budget levels, the analysis addresses the performance-budget integration goal of the PMA. 

Modeling the Market Outcomes of EERE’s Technology Portfolio 

EERE uses two energy-economy models—NEMS-GPRA07 and MARKAL-GPRA07—to 

estimate the impacts of EERE programs on energy markets. The NEMS-GPRA07 model is a 

modified version of the National Energy Modeling System (NEMS), the midterm energy model 

used by the Department of Energy’s Energy Information Administration (EIA). The MARKALGPRA07 model is a modified version of the MARKet ALlocation (MARKAL) model, 

developed by Brookhaven National Laboratory and used by numerous countries worldwide. 

EERE uses NEMS-GPRA07 to estimate the midterm benefits of its programs, and MARKALGPRA07 to estimate the long-term benefits of its programs. 

Choosing Metrics for EERE’s Technology Portfolio 

EERE has adopted a benefits framework developed by the National Research Council (NRC)3

 to 

represent the various types of benefits resulting from the energy efficiency technology 

improvements and renewable energy technology development supported by EERE programs. 

Specifically, EERE’s benefits analysis focuses on three main categories of energy-linked 

benefits—economic, environmental, and security. The specific measures or metrics of these 

benefits estimated for FY 2007 are identified in Table ES.1. These metrics are not a complete 

representation of the benefits or market roles of efficiency and renewable technologies, but 

provide an indication of the range of benefits provided. EERE is continuing to take steps to more 

fully represent the NRC framework. 

Assessing the Integrated Portfolio versus the Individual Programs 

Analysts assess the impacts of EERE’s technology development programs in two ways: 1) as an 

integrated portfolio, and 2) as a set of individual program goal cases. The integrated portfolio 

assessment involves running NEMS-GPRA07 and MARKAL-GPRA07 with all programs 

simultaneously represented. This provides a picture of the overall EERE portfolio that takes into 

account synergy and competition among the different technologies offered by each program. The 

individual program goal cases measure the isolated impact of technology development and 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

 

1

 A standard approach to teatment of risk is being developed for EERE’s multiyear program plans. 2

 Funding levels may increase, decrease, or remain constant, depending on the program. See Appendices B through M for 

information on individual multiyear program plans. 3 Energy Research at DOE: Was It Worth It? Energy Efficiency and Fossil Energy Research 1978 to 2000, National Research 

Council (2001). The NRC is the principal operating agency of the National Academy of Sciences (NAS) and the National 

Academy of Engineering (NAE), providing services to the government, the public, and the scientific and engineering 

communities. 

Executive Summary – Page ES-2 
deployment success for each program. For these cases, each program is represented by itself in 

NEMS-GPRA07 and MARKAL-GRPA07 (in the absence of the other EERE programs). 

Table ES.1. EERE FY 2007 Benefits Metrics 

Primary Outcome 

 Energy displaced • Reductions in nonrenewable energy consumption (quadrillion Btu/yr) 

Resulting Benefits 

 Economic • Reductions in consumer energy expenditures (NEMS-GPRA07 - billion 2002 

dollars/yr) 

• Reductions in energy-system costs (MARKAL-GPRA07 - in billion 2002 dollars/yr) 

 Environmental • Reductions in carbon dioxide emissions (mmtc equivalent/yr) 

 Security • Reductions in oil consumption (mbpd) 

• Reductions in natural gas consumption (quadrillion Btu/yr) 

• Avoided additions to central conventional power (cumulative gigawatts) 

The Annual Impacts of EERE’s Technology Portfolio 

Table ES.2 shows the estimated energy displaced and resulting benefits to the Nation of 

realizing the EERE program goals associated with the FY 2007 budget request. These impacts 

are the benefits expected in the reported year—that is, the benefits are annual, not cumulative 

(with the exception of avoided additions to conventional central power). 

Table ES.2. Summary of Annual EERE Integrated Portfolio Benefits for FY 2007 Budget Request 

EERE Midterm Benefits (NEMS-GPRA07) 2010 2015 2020 2025 

Energy Displaced 

• Primary nonrenewable energy savings (quadrillion Btu/yr) 0.35 1.4 4.4 7.8

Economic 

• Energy-expenditure savings (billion 2003 dollars/yr)* 2.1 18 70 107

Environment 

• Carbon dioxide emission reductions (mmtce/yr) 8 26 86 166 

Security 

• Oil savings (mbpd) 0.03 0.43 1.07 1.69 

• Natural gas savings (quadrillion Btu/yr) 0.07 0.35 1.04 0.82 

• Avoided additions to central conventional power 

(cumulative gigawatts) 

0.53 11 54 118 

EERE Long-Term Benefits (MARKAL-GPRA07) 2030 2040 2050

Energy Displaced

• Primary nonrenewable energy savings (quadrillion Btu/yr) 14 25 32 

Economic

• Energy-system net cost savings (billion 2003 dollars/yr)* 63 138 207 

Environment

• Carbon dioxide emission reductions (mmtce/yr) 279 527 648 

Security

• Oil savings (mbpd) 3.9 8.0 11 

• Natural gas savings (quadrillion Btu/yr) 2.0 2.0 2.8 

* Midterm energy-expenditure savings only include reductions in consumer energy bills, while long-term energysystem cost savings also include the incremental cost of the advanced energy technology purchased by the 

consumer. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Executive Summary – Page ES-3 
Figure ES.1. U.S. Nonrenewable Energy Consumption, 1949-2005, and Projections to 2050: 

Baseline and Portfolio Cases

Note: The percentage change in the chart shown for 2025 and 2050 is the difference between the Baseline Case and 

the Portfolio Case, compared to the difference between the values of the Baseline Case in 2025 (or 2050) versus 

2005 Data Source: 1949-2005, Energy Information Administration, Annual Energy Review 2004, DOE/EIA-0384 

(2004) (Washington, D.C., August 2005), Table 1.1, Web site http://www.eia.doe.gov/emeu/aer/contents.html. 

The portfolio of EERE technologies avoids 28% of the anticipated growth in 

annual U.S. nonrenewable energy demand in 2025. 

By 2050, EERE's technology portfolio avoids almost 80% of the anticipated 

growth in annual U.S. nonrenewable energy demand. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Executive Summary – Page ES-4 
Figure ES.2. U.S. Total Energy Expenditures, 1965-2005, and Projections to 2025: 

Baseline and Portfolio Cases 

Note: The percentage change in the chart shown for 2025 and 2050 is the difference between the Baseline Case and 

the Portfolio Case, compared to the difference between the values of the Baseline Case in 2025 (or 2050) versus 

2005. Data Source: 1970-2001, Energy Information Administration, Annual Energy Review 2004, DOE/EIA-0384 

(2004) (Washington, D.C., August 2005), Table 3.5 and Table D1, Web site 

http://www.eia.doe.gov/emeu/aer/contents.html. 

. The portfolio of EERE technologies reduces the anticipated growth in annual 

U.S energy expenditures by 51% in 2025. 

By 2050, EERE's technology portfolio provides annual U.S. energy-system net 

savings of more than $200 billion (see Table ES.2). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Executive Summary – Page ES-5 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Figure ES.3. U.S. Energy-Related Carbon Emissions, 1949-2005, and Projections to 2050: 

Baseline and Portfolio Cases 

Note: The percentage change in the chart shown for 2025 and 2050 is the difference between the Baseline Case and 

the Portfolio Case, compared to the difference between the values of the Baseline Case in 2025 (or 2050) versus 

2005. Data Source: 1980-2000, Energy Information Administration, Annual Energy Review 2004, DOE/EIA-0384 

(2004) (Washington, D.C., August 2005), Table 12.2, Web site http://www.eia.doe.gov/emeu/aer/contents.html. 

The portfolio of EERE technologies avoids 31% of the anticipated growth in 

annual energy-related carbon emissions in 2025. 

By 2050, EERE's technology portfolio avoids 68% of the anticipated growth in 

annual energy-related carbon emissions. 

Executive Summary – Page ES-6 
Figure ES.4. U.S. Oil Consumption, 1949-2005, and Projections to 2050: 

Baseline and Portfolio Cases 

Note: The percentage change in the chart shown for 2025 and 2050 is the difference between the Baseline Case and 

the Portfolio Case, compared to the difference between the values of the Baseline Case in 2025 (or 2050) versus 

2005. Data Source: 1949-2000, Energy Information Administration, Annual Energy Review 2004, DOE/EIA-0384 

(2004) (Washington, D.C., August 2005), Table 1.3, Web site http://www.eia.doe.gov/emeu/aer/contents.html. Data 

were converted from quads per year to mbpd using conversion factor of 5.8 million Btus per barrel of crude oil. 

The portfolio of EERE technologies avoids 28% of the anticipated growth in 

annual U.S. oil demand in 2025. 

By 2050, EERE's technology portfolio avoids 120% of the anticipated growth 

in annual U.S. oil demand in 2050. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Executive Summary – Page ES-7 
Figure ES.5. U.S. Natural Gas Consumption, 1949-2005, and Projections to 2050: 

Baseline and Portfolio Cases 

Note: The percentage change in the chart shown for 2025 and 2050 is the difference between the Baseline Case and 

the Portfolio Case, compared to the difference between the values of the Baseline Case in 2025 (or 2050) versus 

2005. Data Sources: 1980-2000, EIA, Annual Energy Review 2004, DOE/EIA-0384 (2004) August 2005, Table 1.3, 

Web site http://www.eia.doe.gov/emeu/aer/contents.html; 2005-2025, NEMS-GPRA07; 2030-2050, MARKALGPRA07.

The portfolio of EERE technologies avoids 10% of the anticipated growth in 

annual U.S. natural gas demand in 2025. 

In the long run, EERE's technology portfolio avoids 20% of the anticipated 

growth in annual U.S. natural gas demand in 2050 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Executive Summary – Page ES-8 
Individual Program Budgets and Benefits 

Figure ES.6 and Table ES.3 summarize individual program budgets and the results of the 

benefits analysis for individual program goal cases. Individual program benefits are shown for 

the midterm (2025) and for the long term (2050). The largest program budget is $225 million for 

the Weatherization and Intergovernmental Program (WIP), which includes $164 million for 

Low-Income Weatherization Assistance. 

Figure ES.6. Proposed FY 2007 Budget Request for Technology Development 

Data Sources: FY 2007 proposed budget information is available at 

http://www1.eere.energy.gov/ba/pba/budget_07.html 

The picture that emerges from the individual program benefits presented here is one of 

robustness. Different technologies are positioned to dominate in the mid- and long term. Some 

technologies are best-suited to improving energy security by reducing our dependence on foreign 

oil. In addition, different programs emerge as important contributors to consumer energy savings 

versus those that emerge as important contributors to total energy system net cost savings. 

While incomplete (because the estimates of the individual program goal cases are not based on 

integrated runs), the results indicate both the range and approximate level of benefits available to 

the Nation from funding the efficiency and renewable investments in EERE’s portfolio of 

programs. They indicate a potential for making better use of existing technologies and for 

accelerating technological advances to make significant changes in our energy markets, which 

can drive the Nation to a period of level energy consumption. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Executive Summary – Page ES-9 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Executive Summary – Page ES-10 

Table ES.3. U.S. Department of Energy (DOE) Office of Energy Efficiency and Renewable Energy (

FY 2007 Funding Summary and Selected 2025 and 2050 Benefits by Program4

Program 

FY 2007 

Request 

(thousands $) 

Nonrenewable 

Energy Displaced 

(quads/yr) 

Energy 

Expenditure 

Savings 

(billions 2002$/yr) 

Energy System 

Cost Savings 

(billions 2002$/yr) 

Carbon 

Dioxide 

 Emissions 

Reductions 

(million mtce/

2025 2050 2025 2050 2025 2050 2025 2

Biomass 149,687 0.39 2.8 5.4 N/A N/A 2.3 6.8 5Building Technologies 77,329 1.99 5.4 17.3 N/A N/A 130.0 44.7 12Federal Energy Management $16,906 0.02 0.1 0.2 N/A N/A 0.0 0.4 Hydrogen, Fuel Cells, and Infrastructure Technologies 195,801 0.22 7.7 2.4 N/A N/A 27.5 5.8 10Industrial Technologies 45,563 ns ns ns N/A N/A 0.3 0.0 Solar Energy Technologies 148,372 1.07 5.2 7.9 N/A N/A 9.2 28.8 11Vehicle Technologies5 166,024 2.32 13.5 49.3 N/A N/A 67.5 41.5 26Weatherization and Intergovernmental 225,031 0.20 0.1 2.3 N/A N/A 2.1 3.8 Wind 43,819 3.10 3.9 17.6 N/A N/A 2.1 69.1 10Facilities and Infrastructure 5,935 N/A N/A N/A N/A N/A N/A N/A Program Direction and Management Support 104,954 

N/A N/A N/A N/A N/A N/A N/A 

Total EERE Integrated Portfolio Benefits** 

1,179,421 7.80 32 107 N/A N/A 207 166 

** The total benefits of the EERE integrated portfolio differ from the sum of the individual program benefits, because interactions among 

accounted for in the individual programs 

4 Data Sources: FY 2007 proposed budget information is available at http://www1.eere.energy.gov/ba/pba/budget_07.html5 The Vehicle Technologies Program is run by the Office of FreedomCAR and Vehicle Technologies. 
Scenario Analysis 

In prior years, benefits estimates were reported for a single future energy scenario. Because of 

the uncertainties of energy and economic projections, this view of our energy future has limited 

value, especially in assessing the benefits of the full suite of technologies in the EERE portfolio. 

Assessing only one possible future may be particularly misleading for programs in which a 

significant part of the worth of the program may lie as a hedge against less likely, but possible, 

futures. Evaluating EERE’s portfolio for a variety of possible futures offers insight about the 

robustness of the portfolio. 

 

This year, we have taken the first step toward introducing scenario analysis for the EERE 

portfolio. Two scenarios were evaluated: 1) a high oil-price case, and 2) a carbon-constrained 

future. Because this is EERE’s first foray into scenario analysis for GPRA benefits, we report the 

results as an appendix to this report (see Appendix K). Given the recent and sustained increases 

in crude oil and natural gas prices, the high fuels-price case is particularly relevant in 

understanding the value of EERE’s portfolio, in what is likely to be the Base Case in future 

years. Similarly, understanding the impact of these programs under different carbon emissions 

scenarios is an increasingly important topic. We will evaluate our methodology for scenario 

analysis this year; and we expect that scenario analysis will be a part of the main benefits report 

for the FY 2008 budget request. 

Future GPRA Benefits Development 

As part of DOE’s continuing efforts to implement the President’s Management Agenda—and to 

be responsive to the advice offered by the National Academy of Sciences/National Research 

Council—DOE is in the process of integrating its GPRA benefits analyses across the offices of 

Energy, Science, and Environment (ESE). This integration process is expected to be fully 

completed for the FY 2010 budget request, but significant and important steps and progress will 

be evident along the way. The GPRA benefits analysis for the FY08 budget request will show a 

DOE-wide portfolio case, in which all offices’ RD3 programs are combined. Further, EERE 

technologies’ benefits will be evaluated relative to an ESE-wide baseline (as opposed to a 

baseline in which only EERE advanced technologies are removed from the AEO reference). 

Moreover, the inputs to the integrating models will be developed using common methodologies 

across all ESE offices. The result will be a much clearer picture of the benefits of the full DOE 

portfolio than has been represented to date. 

Another major development afoot in DOE’s benefits analysis is the treatment of risk and 

uncertainty. As in prior years, the benefits in this report are shown for Programs and the Portfolio 

assuming that RD3 goals are achieved and that they are achieved on time. It is also assumed that 

RD3 funding is continued as required. These assumptions represent a considerable simplification 

in a number of ways. First, for R&D there is considerable technical risk in what the actual output 

of the program activities might be. In fact, the output in a given year could be greater or less than 

the specified goal; or, alternatively, a specified goal may be achieved earlier or later than 

scheduled. Moreover, for a given output, the outcome is not known with certainty, because it will 

be affected by market risk considerations. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Executive Summary – Page ES-11 
PROJECTED BENEFITS OF FEDERAL ENERGY EFFICIENCY AND 

RENEWABLE ENERGY PROGRAMS (FY 2007-FY 2050)

This report summarizes program benefits analysis undertaken by experts in energy technology programs, energy 

markets, and energy-economic modeling. The primary team members and areas of responsibility are listed below. 

Report Managers, Writers, and Editors

• EERE 

o Report Coordinator: Michael Leifman 

o Biomass: Tien Nguyen 

o Buildings: Brian Card and Charlie Hemmeline 

o Federal Energy Management: David Boomsma 

o Hydrogen, Fuel Cells, and Infrastructure Technologies: Jeff Dowd 

o Industry: Peggy Podolak 

o Solar: Michael York 

o Vehicle Technologies: Phil Patterson 

o Weatherization and Intergovernmental: Cathy Short 

o Wind: Linda Silverman 

• Contractors 

o Project Manager: John Sheehan, National Renewable Energy Laboratory (NREL) 

o Guidance and Review: Doug Arent (NREL), Thomas Jenkin (NREL), Lynn McLarty (TMS), 

Tobey Winters (NREL) 

o Editorial: Michelle Kubik (NREL) 

• Chapter Authors

o Preface and Chapter 1: John Sheehan (NREL) 

o Energy-Economic Integration–NEMS: Frances Wood (OnLocation Inc). 

o Energy-Economic Integration–MARKAL: Chip Friley, Brookhaven National Laboratory (BNL) 

o Biomass: David Andress, David Andress and Associates (DAA) 

o Buildings: Donna Hostick, Pacific Northwest National Laboratory (PNNL) 

o Federal Energy Management: Donna Hostick (PNNL) 

o Hydrogen and Fuel Cells: Margaret Singh, Argonne National Laboratory (ANL) 

o Industry: Jim Reed (Energetics) 

o Solar: Robert Margolis (NREL), Jim McVeigh, Princeton Energy Resources International (PERI) 

o Vehicle Technologies: Margaret Singh (ANL) 

o Weatherization and Intergovernmental Programs (WIP): Donna Hostick (PNNL) 

o Wind: Joe Cohen (PERI) 

In all cases, these lead analysts drew from the studies and expertise of many others. Other members of the Benefits 

Analysis Team include: 

EERE: Mark Bailey, Darrell Beschen, Jack Cadogan, Jerry Dion, Joan Glickman, Scott Hassell, Fred Joseck, 

Quonnie Laughlin, Neil Rossmeissl, John Ryan, Lee Slezak, Randy Steer, Glenn Strahs 

ANL: Don Hillebrand, Steve Plotkin 

Navigant Consulting: Ed Barbour 

NREL: Ian Baring-Gould, Kelly Ibsen, John Jechura, Cindy Riley, Laura Vimmerstedt 

OnLocation: John Holte, Laura Train, Will Georgi 

PERI: Jim McVeigh 

PNNL: David Anderson, David Belzer, Daryl Brown, Jim Dirks, Doug Elliott, Sean McDonald, Nancy Moore, 

Andrew Nicholls, Joe Roop 

Sentech; Bill Zwack 

TA Engineering: Jim Moore 

TMS: Brian Lavoie, Doug Norland 
PREFACE 

The Office of Energy Efficiency and Renewable Energy (EERE) develops—and encourages 

consumers and business to adopt—technologies that improve energy efficiency and increase the 

use of renewable energy. This report describes analysis undertaken by EERE to better understand 

the extent to which the Research, Development, Demonstration, and Deployment (RD3) 

technology and market improvements funded by its fiscal year (FY) 2007 Budget Request1

 will 

make energy more affordable, cleaner, and more reliable. 

This benefits analysis helps EERE meet the provisions of the Government Performance and 

Results Act (GPRA) of 1993 and the President’s Management Agenda (PMA). GPRA requires 

Federal Government agencies to develop and report on output and outcome measures for each 

program.2

 This EERE benefits analysis supports these GPRA requirements by developing an 

assessment of the benefits that may accrue to the Nation if the performance goals (outputs) of 

EERE’s programs are realized. The estimates of consumer energy-expenditure savings, energysystem cost savings,3

 carbon emission savings, and reduced reliance on fossil fuels (outcomes) 

that are reported here result from the increased use of energy-efficient technologies and 

increased production and use of renewable energy resources—which are supported by the 

technology advances and market-adoption activities pursued by EERE programs. 

Shortly after GPRA was enacted, EERE initiated a corporate approach to benefits analysis that 

examined the energy, economic, and environmental impacts of program efforts. Through the 

1990s, EERE program offices continued to refine their benefits-analysis methodologies and 

assumptions. Although the benefits analysis has changed since it was initiated 12 years ago, the 

amount of energy saved or displaced continues to be a key measure of the EERE program 

impact. Other key metrics include measurement of economic and environmental benefits, as well 

as increasingly important security and dependency metrics, such as oil and natural gas saved. 

This benefits analysis also supports the President’s Management Agenda. The analysis 

summarized in this report is based on modeling the impact of meeting program performance 

goals (or outputs). EERE’s programs develop these goals based on the following key 

assumptions:4

 

1

 See http://www.eere.energy.gov/office_eere/budget.html. 2

 See the Government Performance and Results Act (GPRA) of 1993 at http://www.whitehouse.gov/omb/mgmtgpra/gplaw2m.html and http://www.whitehouse.gov/omb/circulars/a11/02toc.html 3

 Our integrating energy model for calculating midterm benefits (through 2025), NEMS-GPRA07, and our integrating energy 

model for calculating long-term benefits (through 2050), MARKAL-GPRA07, report different economic measures. NEMSGPRA07 estimates consumer-expenditure savings, which are the gross savings from avoiding purchased energy. They do not 

include all incremental investment required to achieve these savings. MARKAL-GPRA07 estimates energy-system cost savings, 

which includes both the savings from avoiding purchased energy and the incremental investment required for the advanced 

energy technology. In future GPRA reports, it is intended that both models will report the same economic metric. 4

 Achieving program goals is generally not dependent on a single technical pathway, but instead encompasses a number of 

alternative approaches, of which some may fall short without jeopardizing realization of the final goal. The pursuit of multiple 

pathways can increase the likelihood of achieving program goals, thereby reducing the risk of the program. Risk is being 

addressed in a separate EERE effort to develop a standard approach to risk assessment. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Preface – Page P-1 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Preface – Page P-2 

 

• Programs will be funded at levels consistent with DOE’s FY 2007 Budget Request. 

• Funding levels will remain constant in inflation-adjusted dollars or increase to 

accommodate key initiatives in particular cases, as indicated. 

• Funding is assumed to be in place until goals are achieved. 

Role of Benefits Analysis in Performance Management 

EERE employs a widely used logic model5 as the foundation for managing its portfolio of 

efficiency and renewable investments, and for ensuring that these investments provide energy 

benefits to the Nation. In its simplest form, a logic model identifies budget and other inputs to a 

program, activities conducted by the program, and the resulting outputs and outcomes of those 

activities. The logic model employed by EERE (Figure P.1) provides an integrated approach 

that explicitly links requested budget levels to performance goals and estimated benefits—and 

helps ensure that estimated benefits reflect the funding levels requested. The elements of the 

logic model, which are specified in GPRA, are included in the annual budget request. 

Multiyear Program Plans (MYPPs), developed by each of EERE’s nine programs, address the 

inputs required, the activities that will be undertaken with their requested budget, the 

performance milestones they expect to achieve as they pursue these activities, and the resulting 

products or outputs of the RD3 effort.6

 Inputs may include cost-shared or leveraged funds, as 

well as EERE program dollars—and may also include advances by others on which the program 

builds. Performance milestones capture intermediate points of discernable progress toward 

outputs and are used by program managers, DOE, OMB, and others to track program progress 

toward their outputs. Outputs, often referred to as “program goals” or “program performance 

goals,”7 are the resulting products or achievements of an overall area of activity. EERE’s R&D 

programs typically specify their outputs in terms of technology advances (e.g., reduced costs, 

improved efficiency), while deployment programs develop outputs related to their immediate 

market impacts (e.g., number of homes weatherized). Outputs evolve over time as the program 

pursues increasing levels of technology performance or market penetration.8

 

This benefits analysis links these program outputs to their market impacts or outcomes. EERE’s 

programs have discernable effects on energy markets, both by reducing the level of energy 

demand (through efficiency improvements) and by changing the mix of our energy supplies 

(through increased renewable and distributed energy production). The program goals or outputs 

5 The logic model is a fundamental program planning-and-evaluation tool. For more information on logic models, see: Wholey, J. 

S. (1987). Evaluability assessment: developing program theory. Using Program Theory in Evaluation. L. Bickman. San 

Francisco, Calif., Jossey-Bass. 33. Jordan, G. B. and J. Mortensen (1997). "Measuring the performance of research and 

technology programs: a balanced scorecard approach." Journal of Technology Transfer 22(2). McLaughlin, J. A. and G. B. 

Jordan (1999). "Logic models: a tool for telling your program's performance story." Evaluation and Program Planning 22(1): 65-

72. 

6 Appendices B through J provide more information on each program’s multiyear program plan and the inputs, activities, 

milestones, and outputs contained therein. 

7 Some programs derive their outputs through technology-cost simulation models to develop the specific requirements to meet 

overall program cost and performance goals. Specific details of the representation of the program outputs in NEMS-GPRA07, 

MARKAL-GPRA07, and the underlying program analysis and documentation are found in Chapters 2 and 3 of this report and 

Appendices B through J. 

8

 The level of risk for the programs is assessed qualitatively as part of the Office of Management and Budget (OMB) R&D 

Investment Criteria. EERE is developing a standard approach to assessing technology and program risk. 
are therefore often used as input to the integrating energy models. Further, the changed energy 

mix has environmental and economic implications. EERE incorporates these effects in its 

outcomes—the displacement of conventional energy demand, the avoidance of carbon emissions, 

and the energy expenditure or net cost savings. 

Figure P.1. Generalized EERE Logic Model 

These changes in energy use provide the basis for the economic, environmental, and security 

benefits estimated here. The extent to which a new technology or a deployment effort changes 

energy markets will depend on a variety of external factors. The future demand for energy, its 

price, the development of competing technologies, and other market features (such as consumer 

preferences) all will contribute to the marketability and total sales of a new technology. 

While the logic model discussed here shows the linkage between resources and benefits for each 

program, it does not show the full scope of how benefits analysis fits in the overall process of 

performance management. Figure P.2 shows a more holistic perspective on the role of benefits 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Preface – Page P-3 
analysis in performance management. When used appropriately, benefits analysis serves as an 

important feedback loop at two levels: 1) individual program planning, and 2) EERE 

management assessment of its technology development and deployment portfolio. In the first 

case, this analysis can help individual program managers make better choices about the suite of 

activities and technology options that will maximize their program’s benefits to the Nation. 

Looking at the benefits available from the entire suite of EERE programs in an integrated 

portfolio can help decision-makers maximize the overall return on government investment in 

energy efficiency and renewable energy technologies. Results of benefits analyses represent just 

one of many important criteria that must be weighed in prioritizing spending across the portfolio. 

Figure P.2. Holistic View of the Role of Benefits Analysis in EERE Performance Management 

Benefits Framework 

The EERE Benefits Framework addresses the link between program outputs and their resulting 

outcomes—and, hence, benefits. EERE uses these R&D outputs to estimate its outcomes 

(benefits) by comparing the future U.S. energy system and its associated costs and environmental 

impacts with and without the contributions of its program outputs. The market impacts of each of 

the nine programs are first assessed separately and then combined to assess the benefits of 

EERE’s overall portfolio.9 The integrated portfolio benefits are not simply the sum of the 

individual program benefits, because the portfolio benefits reflect the interaction and interplay 

among the various programs. 

EERE—along with the offices of Fossil Energy (FE), Nuclear Energy (NE), Electricity Delivery 

 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Preface – Page P-4 

9 EERE’s benefits analysis, which measures final outcomes due to EERE programs and a host of other external factors as shown 

in Figure P.1, is distinct from impacts analysis, which determines the portion of outcomes having a causal relationship with 

EERE’s actions.
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Preface – Page P-5 

and Energy Reliability (OE), and Science (Sc)—is in the process of adopting a common 

framework, building on work initially developed by the National Research Council (NRC) to 

assess the benefits associated with past DOE research efforts.10 EERE’s annual estimates of 

prospective benefits have been incorporated into an integrated framework addressing the benefits 

of both existing and future program activities. The framework can be represented by a matrix, in 

which the rows distinguish among four types of benefits, and the columns represent different 

elements of time and uncertainty. 

This report addresses the three shaded cells of the matrix, reflecting benefits under a business-asusual energy future (Figure P.3). EERE, FE, NE, OE, and Sc currently are developing methods 

for assessing the value to the country of developing technologies that prepare the Nation for 

unexpected energy needs. DOE and EERE metrics are still evolving, especially with regard to 

how knowledge benefits and real options11 benefits are represented. 

Realized 

Benefits and 

Costs 

Expected 

Prospective Benefits 

and Costs 

Options 

Benefits and 

Costs 

Economic Benefits and Costs 3 

Environmental Benefits and Costs 3

Security Benefits and Costs 3

Knowledge Benefits and Costs 

Figure P.3. FY 2007 Benefits Metrics Reported 

Completing the cells of this matrix in ways that provide comparable results across programs (and 

DOE offices) poses a number of analytical challenges, especially in light of the varied portfolio 

that EERE maintains: 

• Standard baseline(s) and methodological approaches. EERE uses the Energy 

Information Administration’s (EIA) Annual Energy Outlook 2005 (AEO2005) Reference 

Case as a consistent starting point for analysis of all of its programs.12 A standard set of 

methodological approaches (guidance) is used to assess the incremental improvements to 

energy efficiency and renewable energy production, resultant from realization of EERE 

 

10 See Energy Research at DOE: Was It Worth It? Energy Efficiency and Fossil Energy Research 1978 to 2000, National 

Research Council (2001) for the original framework. DOE’s offices of Energy Efficiency and Renewable Energy, Fossil Energy, 

Nuclear Energy, and Science cosponsored DOE’s “Estimating the Benefits of Government-Sponsored Energy R&D” conference 

in March 2002 to explore ways of extending this framework to include the prospective benefits of program activities. As a result 

of the conference, the matrix was revised by placing knowledge as a benefit and explicitly showing expected prospective benefits 

and costs in addition to realized benefits and costs. The conference report is available at www.esd.ornl.gov/benefits_conference. 11 For its retrospective study, the NRC defined an option as a technology that is fully developed—but for which existing market 

or policy conditions are not favorable for commercialization. Because current technology choices are known, noncommercial 

(but developed technologies) are options, by default. 

12 See The Annual Energy Outlook 2005 with Projections to 2025, February 2005, DOE/EIA-0383 (2005), available at 

http://www.eia.doe.gov/oiaf/archive/aeo05/index.html. The timing of the release of the Annual Energy Outlook reports is such 

that we are always working with the prior year’s outlook. Thus, in 2006, we developed benefits estimates for the proposed FY 

2007 budget using AEO2005. In most years, this lag in the availability of the energy forecast poses little problem, because the

changes in the energy outlook from year to year are relatively small. This year, however, the recently released AEO2006 

reference case shows a dramatic increase in oil and gas prices relative to the AEO2005 forecast. When the new, higher, oil prices 

of AEO2006 are used next year, benefits estimates for the EERE programs will be substantially different. This year, we have 

added two new scenarios beyond the reference case to look at the impacts of higher oil prices and of possible constraints on 

carbon emissions. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Preface – Page P-6 

program goals (outputs). This guidance is applicable to all of EERE’s program activities 

and markets. 

• Varied markets. Program activities target all end-use markets (buildings, industry, 

transportation, and government) and energy-supply markets (use of renewable energy as 

new sources of liquid and gaseous fuels, and electricity). Because these markets vary 

enormously in structure, regulation, and consumer preferences, a fairly detailed, marketspecific analysis often is needed to gain sufficient understanding of the size and potential 

receptivity of each market to EERE’s activities. EERE strives to incorporate these unique 

market features that are likely to have a significant impact on the resulting benefits. 

• Varied time frames. The analytical time frame extends from a few years to the decades 

that are required for the development of new energy sources, infrastructure, market 

penetration, and product life cycle. This expansive time frame requires a baseline and 

analytical tools that can address energy markets in the short, mid-, and long term. This 

report addresses midterm (5-20 years) and long-term (20-50 years) time frames. 

Numerous market feedbacks. EERE technology and deployment efforts can have large enough 

effects on their respective energy markets that they generate supply or price feedbacks. EERE’s 

technologies also can interact with each other across their respective energy markets. For 

example, efficiency improvements in end-use markets can be large enough to forestall the 

development of new electricity-generating plants, reducing the potential growth of wind and 

other renewable electricity sources. Past EERE experience indicates that failure to reflect market 

responses tends to overestimate benefit levels. EERE utilizes integrated energy-economic models 

to produce final benefit estimates that consider these feedbacks and interactions at the program 

and portfolio levels. 

Benefits Analysis Process

EERE’s benefits-analysis process involves three major steps (Figure P.4). In Step 1, EERE’s 

Office of Planning, Budget, and Analysis (PBA) develops a standard baseline and 

methodological approach (guidance) to help ensure consistency in estimates across programs. In 

Step 2, EERE’s programs develop specific technology and market information, which is 

necessary to understanding the potential roles of each program in its target markets. In Step 3, 

PBA uses this program and market information to assess the impacts of each EERE program (as 

well as the overall EERE portfolio) on energy markets in the United States using integrated 

energy-economic models. 

The process by which the FY04 benefits estimates were developed largely reflects EERE’s prior 

organization, although a few changes in net benefits estimation were adopted in the FY04 

analysis, including an initial reflection of the benefits framework recommendations of the 

National Academy of Sciences (NAS). 
 

STEP 2 – Program and Market Inputs 

• Review the baseline projections of the timing and rate of adoption of EERE technologies. 

• Assess the potential roles of each program’s performance goals in these future energy 

markets. 

• Develop inputs to Step 3. 

STEP 3 – Individual Program and Integrated EERE Portfolio Benefits 

Estimates 

• Develop estimates of individual program benefits (the Program Cases). 

• Develop estimates of the combined benefits of all programs (the Portfolio Case). 

STEP 1 – Baseline Case and Guidance 

• Create a Baseline Case without EERE RDD&D. 

• Make any necessary updates to EERE’s guidelines on estimating benefits. 

Figure P.4. EERE Program and Portfolio Benefits-Analysis Process

Step 1: Baseline Case 

Baseline Case 

The first step in the benefits analysis process is to establish an appropriate Baseline Case. The 

EERE Baseline Case is a projection intended to represent the future U.S. energy system without 

the effect of EERE programs. This Baseline Case ensures that program benefits are estimated 

based on the same initial forecasts for economic growth, energy prices, and levels of energy 

demand. It also ensures that these initial assumptions are consistent with each other; e.g., that the 

level of electricity demand expected under the economic growth assumptions could be met at the 

electricity price assumed. It provides a basis for assessing how well renewable and efficiency 

technologies might be able to compete against future, rather than current, conventional energy 

technologies (e.g., more efficient central power generation). Finally, it helps ensure that 

improvements in efficiency and renewable energy, which may occur absent EERE’s RDD&D 

efforts, are not counted as part of the benefits of the EERE programs. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Preface – Page P-7 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Preface – Page P-8 

 

The most recent13 Annual Energy Outlook Reference Case is used as the starting point for 

developing the Baseline Case. The Energy Information Administration (EIA) Annual Energy 

Outlook (AEO) Reference Case provides an independent representation of the likely evolution of 

energy markets. This forecast reflects expected changes in the demand for energy (e.g., to reflect 

the availability of new appliances), technology improvements that might improve the efficiency 

of energy use, and changes in energy resource production costs, including renewable energy. 

Current energy market policies, such as state renewable portfolio standards (RPS)—which 

facilitate the development and adoption of these technologies—are included in the Baseline 

Case. This approach ensures that EERE’s benefits estimates do not include expected impacts of 

such policies. Neither the EIA Reference Case nor the EERE Baseline Case includes any 

changes in future energy policies.14

In establishing its Baseline Case, EERE makes a number of modifications to the AEO2005

Reference Case (see Table P.1)

15. Modifications are made to the same model—the National 

Energy Modeling System (NEMS)—used by EIA in developing the AEO2005. To distinguish it 

from EIA’s version, the model is referred to as NEMS-GPRA07. The AEO2005 Reference Case 

is also the starting point for the long-term (to 2050) benefits modeling using MARKALGPRA07. The Baseline Cases for both NEMS-GPRA07 and MARKAL-GPRA07 are aligned as 

closely as possible, but the two models are different in their internal design.16

13 Benefits analysis for the proposed FY 2007 budget began in January 2006. The most recent outlook available at the time was 

AEO2005. Final benefits estimates are submitted to OMB as part of the FY 2007 Budget Request in January 2007, before the 

new AEO2006 reference case was available. See Annual Energy Outlook 2005 with projections to 2025, February 2005, 

DOE/EIA-0383 (2005) for Reference Case projections. Available at 

http://www.eia.doe.gov/oiaf/archive/aeo04/pdf/0383(2004).pdf. 14 At the publication date of the AEO2005, the Energy Policy Act of 2005 (as well as the extension of the production tax credit) 

had not yet been affected. Because our GPRA benefits analysis is built off of AEO2005, our estimates are not reflective of these 

policies. While other changes are made to AEO’s baseline case, a change of this magnitude was not within the scope of our 

analysis, given the available time. 

15 More detail on baseline construction may be found in Appendix A. 16 See Box 2.1 in Chapter 2 for an overview of NEMS and Box 3.1 in Chapter 3 for an overview of MARKAL. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Preface – Page P-9 

Table P.1. Summary of Baseline Changes from the AEO2005 

AEO2005 GPRA07 Baseline Case 

Removal of EERE Programs

 Million Solar Roofs 0.3 GW installed 2007 to 2025 Removed 

 Photovoltaic system costs Significant improvement Slower rate of improvement 

 Residential high efficiency shell packages Small penetration Removed 

 Cellulosic ethanol production Commercially available by 2015 Not commercially available by 

2025 

Greater Technology Improvement in Base

 Solid-state lighting Very small improvement Much greater improvement

 Onshore wind performance 33% to 44% capacity factors, 

depending on wind class and 

year 

35% to 53% capacity factors, 

depending on wind class and 

year 

 Onshore wind capital costs 1% reduction over 20 years 12% to 15% reduction 

(depending on wind class) over 

20 years 

 Conventional corn ethanol production Yield of 2.65 gallons per bushel Yield of 2.80 gallons per bushel 

 Corn ethanol production with residual 

starch 

Not included Available in 2011 

 Hybrid Electric Vehicles Sales share at 6% by 2025 Sales share at 11% by 2025 

Energy Market Updates

 PV system size 2 kW residential, 25 kW 

commercial 

4 kW residential, 100 kW 

commercial 

 PV maximum market share 30% for both residential and 

commercial 

60% for residential and 55% for 

commercial 

 California PV subsidy Not included Included for residential systems 

 Solar water heat Maximum 20% replacement 

market 

New and up to 50% replacement 

market 

 Corn ethanol maximum production 5.7 billion gallons 10.0 billion gallons 

Structural Changes 

 Offshore wind No offshore wind technology Offshore wind 

 Commercial shell efficiency Index Technology representation 

 Commercial DG algorithms Market share and stock 

accounting modified 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Preface – Page P-10 

Step 2: Program and Market Inputs 

In Step 2, program goals and salient target-market characteristics are developed as inputs to 

modeling the benefits estimation in Step 3. The effort required under Step 2 varies, depending 

on the form in which programs specify their output or performance goals and how NEMSGPRA07 and MARKAL-GPRA07 utilize this information. It ranges from the compilation of 

technology goals to detailed market analyses that produce technology-penetration rates—and, in 

some cases, delivered energy savings. 

NEMS-GPRA07 and MARKAL-GPRA07 contain detailed technology representations of 

electricity markets, most residential and commercial end uses, and vehicle choice—but use 

trends to represent industrial efficiency improvements and existing residential shell retrofits. For 

programs that address these markets, this step simply requires (1) confirming the adequacy of the 

target-market representation in the Baseline Case and (2) providing the program goals in a 

format consistent with the model. Any updated market-characteristic information is used to 

adjust NEMS-GPRA07 and MARKAL-GPRA07 for both the Baseline Case and the Program 

Case to avoid ascribing external factors as benefits. Analysts use the program goal information to 

adjust the commercialization date, technology characteristics, or market-penetration rate for the 

Program Case. The comparison of market technology introduction and market-penetration rates, 

with and without the program goal—and the calculation of the energy displaced—occur within 

NEMS-GPRA07 and MARKAL-GPRA07. 

For much of EERE’s portfolio, additional “off-line” analyses are needed to translate information 

about program technology and market characteristics into usable modeling inputs. This off-line 

Step 2 analysis can range from spreadsheet calculations to the use of market-specific models for 

assessing technology or market features that cannot be adequately represented in a broad energyeconomic model, or to translate program goals into the variables used in the modeling. In 

general, analysts perform the most detailed off-line analyses for the Industrial Technologies 

Program , Weatherization and Intergovernmental Program (WIP), Federal Energy Management 

Program (FEMP), and portions of the Building Technologies Program. Analysts tailor these offline analytical approaches to the characteristics of the program and target market being analyzed; 

but, in all cases, they are conducted within the overall guidance provided through the GPRA 

benefits-estimation process. 

The market applications for EERE technologies are often very specific, and resulting energy 

savings for a given technology can vary from one application to another. For example, the 

impact of upgrading building codes can vary, due to differences in climate and in existing 

building-code standards, and therefore require analysis at the State level. The Building, 

Industrial, and WIP programs are most likely to require tailored analytical approaches that 

address these submarkets. 

Where NEMS-GPRA07 and MARKAL-GPRA07 do not include technology-by-technology 

information (e.g., cost, date of availability), or specific market-penetration rates, it is often 

necessary to translate program goals into the more general rates of technology improvement used 

by the models. This is true for the Industrial Technologies Program and some elements of the 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Preface – Page P-11 

 

Building Technologies Program, where numerous specific technology advances or marketdeployment efforts will accelerate overall efficiency improvements in buildings or factories 

specified in the Baseline Case. 

Off-line analysis also can be required for targeted submarkets that are simply not included in 

NEMS-GPRA07 or MARKAL-GPRA07—or for which the resulting technology use is not fully 

market-driven. Examples include the Federal sector (addressed by FEMP) and the Low-Income 

Weatherization Assistance Program, in which the Federal Government directly purchases home 

efficiency improvements. 

Because estimating the benefits of achieving program performance goals requires the ability to 

realistically assess the extent to which future energy markets might adopt the technology and 

market improvements developed by EERE programs, analysts explore the following features in 

these off-line analyses: 

Target Markets. New technologies will not necessarily be well-suited to all applications 

served by existing markets. Technologies may occupy niche markets, especially in early 

years. In some cases, initial markets are geographically limited as well. Where integrated 

models do not represent these submarkets explicitly, it may be necessary to develop off-line 

estimates of the applicable market share for the technology being developed, at least in the 

early years. 

Stock Turnover. Modeling stock turnover is crucial to estimating benefits for both new 

technologies and deployment programs. Analyses of the market adoption of new 

technologies must consider the rate at which the specific type of energy-using or -producing 

capital equipment is replaced, in addition to the growth rate of the overall market. Even when 

a technology is suitable and cost-effective for a percentage of a market, it may take a decade 

or more for the capital stock in that portion of the market to retire and be replaced. 

Particularly attractive new technologies might accelerate that turnover. EERE includes this 

potential for early retirement only when market evidence suggests that the technology 

improvement is significant enough to overcome typical hurdle rates to new investment. 

Although stock turnover fluctuates with business cycles, EERE does not incorporate business 

cycles into its Baseline or Program cases. As a result, nearer-term estimates of benefits, in 

particular, do not take into account year-to-year fluctuations in energy use attributable to 

business cycles. 

 

Market Penetration. Over time, new technologies typically make their way into markets—

and, therefore, affect energy use—gaining their share of new sales as consumers learn about 

the availability of the product. Manufacturing capacity then grows, and product prices fall 

with economies of scale and learning.17 While price helps determine whether a product is 

cost-effective, on average, energy prices vary by type of customer and region, so that new 

products may be cost-effective for some customers (a niche market) before they are generally 

cost-effective. Price, or cost-effectiveness, is often not the only aspect of the new technology 

or deployment program that shapes its rate of market uptake. Many non-price or cost factors 

affect consumer behavior. 

17 See Adam B. Jaffe, Richard G. Newell, and Robert N. Stavins, “Energy-Efficient Technologies and Climate Change Policies: 

Issues and Evidence,” Climate Issue Brief No. 19, Resources for the Future, Washington, D.C. (December 1999).
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Preface – Page P-12 

 

As an example, the off-line analysis for the Industrial Technologies Program uses a 

spreadsheet model that provides several possible market-penetration curves. The analyst 

chooses a curve, based on specific information from possible R&D partners, comparison of 

the new technology to similar technologies, or his or her expert judgment. The benefits 

guidance for industrial benefits estimation includes historic penetration curves for 11 

technologies and offers the analyst five choices of penetration-curve shapes. The five choices 

are accompanied by detailed data on technology equipment, financial, industry, regulatory, 

and impact characteristics to aid in making the choice. In addition to choosing the shape of 

the penetration curve, the analyst chooses the year—after all pilot testing and demonstration 

phases—that the new technology is expected to enter the market. 

Through the use of specialized spreadsheets or other models,18 program analysts produce 

estimates of market penetration and direct energy savings associated with these market sales. 

However, these “off-line” estimates of direct energy savings are not benefits estimates, 

because they do not account for market interactions. Analysts integrate these off-line 

estimates within the NEMS-GPRA07 and MARKAL-GPRA07 models as the final part 

(Step 3) of the process. 

Step 3: Individual Program and Integrated EERE Portfolio Benefits Estimates 

The final step for estimating the impacts of EERE’s FY 2007 Budget Request is to analyze all of 

EERE’s programs in a consistent economic framework and to account for the interactive effects 

among the various programs. Estimates of individual EERE program energy savings cannot be 

simply summed to create a value for all of EERE, because there are feedback and interactive 

effects resulting from (1) changes in energy prices resulting from lower energy consumption and 

(2) the interaction among programs affecting the mix of generation sources and those affecting 

the demand for electricity. 

The process begins by modeling each EERE program individually within NEMS-GPRA07 and 

MARKAL-GPRA07. In each NEMS-GPRA07 and MARKAL-GPRA07 Program Case, only the 

modeling assumptions related to the outputs of the program being analyzed are changed. The 

modeling assumptions related to the other EERE programs remain as they were in the EERE 

Baseline Case. Analysts model each program separately to derive estimated energy savings 

without the interaction of the other programs. They then compare the results from the NEMSGPRA07 and MARKAL-GPRA07 Program Cases to the Baseline Case to measure the individual 

benefits of the EERE program being analyzed. This process, while explicitly ignoring the 

potential market interactions of one EERE technology or program with all others, does provide a 

useful data point. Specifically, the Program Case represents neither the technical potential of a 

program (absent all market interactions) nor the full economic potential (with all market 

interactions), but somewhere in between those two points. It is admittedly unrealistic to assume 

that one program would meet its goals while all other programs fail to meet theirs. Nevertheless, 

the “Program Cases” allow the programs, analysts, and readers to examine the total potential 

benefits of each technology suite alone. 

18 In one case (the Building Technologies Program), a portion of NEMS (the buildings module) was used for off-line analysis.
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Preface – Page P-13 

 

For programs modeled using NEMS-GPRA07 and MARKAL-GPRA07 directly, analysts 

compute the Individual Program Goal Case by changing the assumptions representing the 

program outputs; i.e. the goals or performance targets of the program, such as reducing low 

wind-speed turbine costs and improving their performance. The R&D programs are represented 

in NEMS-GPRA07 and MARKAL-GPRA07 through changes in technology characteristics that 

represent the program goals, to the extent possible. Activities designed to stimulate additional 

market penetration of existing technologies generally are modeled through changes in consumer 

hurdle rates or other appropriate market-penetration parameters, with the goal of representing the 

market share targeted by the program. 

In cases where program goals cannot be easily modeled using NEMS-GPRA07 and MARKALGPRA07, analysts estimate benefits using a variety of off-line tools, as described in Step 2. 

These supporting analyses typically provide either estimates of market penetration and per-unit 

energy savings, or total site energy savings, which are then used as inputs to NEMS-GPRA07 

and MARKAL-GPRA07. In cases where the off-line analyses produce a direct estimate of site 

energy savings, analysts adjust this information by an “integration factor” and incorporate it in 

NEMS-GPRA07 and MARKAL-GPRA07, in order to calculate primary energy savings. The 

amount of the integration factor is based on how much program overlap or “integration” was 

captured by the off-line tools. The revision is based on the expert judgment of the benefits 

analysis team. See Chapters 2 and 3 for discussion of program-by-program benefit estimates, 

including such reductions. 

Once each of the programs (or group of programs) is represented individually within NEMSGPRA07 and MARKAL-GPRA07, the benefits of EERE’s portfolio are estimated by combining 

all of the program goals into one EERE Portfolio Case. The portfolio case is not equal to the sum 

of the individual program cases, because the former accounts for various market interactions. 

Some of EERE’s technologies and programs complement each other; others are competitive 

substitutes. The program cases do not capture the complementarity and substitutability inherent 

in the portfolio case. Detailed projections from the EERE Baseline and Portfolio Benefits Case 

are presented in Chapters 1, 2 and 3. 

Scenario Analysis 

In prior years, benefits estimates were reported for a single future energy scenario. Because of 

the uncertainties of energy and economic projections, this view of our energy future has limited 

value, especially in assessing the benefits of the full suite of technologies in the EERE portfolio. 

Assessing only one possible future may be particularly misleading for programs in which a 

significant part of the worth of the program may lie as a hedge against less likely, but possible, 

futures. Evaluating EERE’s portfolio for a variety of possible futures offers insight about the 

robustness19 of the portfolio. 

 

19 One measure of a portfolio’s “robustness” is the degree to which its composite parts become more or less important under a 

given future. In other words, the portfolio as a whole may be said to be robust, if it is resilient against a range of futures, even if 

the individual parts (or programs, in our case) may play differing roles. Note that portfolio robustness may be measured in a 

variety of ways, including how much redundancy there is in the portfolio – if contingency planning is valued; the degree to which 

the portfolio is subject to various risks; the expected performance of the portfolio alignment versus other possible alignments, etc. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Preface – Page P-14 

This year, we have taken the first step toward introducing scenario analysis for the EERE 

portfolio. Two scenarios were evaluated: 1) a high oil-price case, and 2) a carbon-constrained 

future. Because this is EERE’s first foray into scenario analysis for GPRA benefits, we report the 

results as an appendix to this report (see Appendix K). Given the recent and sustained increases 

in crude oil and natural gas prices, the high fuels-price case is particularly relevant in 

understanding the value of EERE’s portfolio in what is likely to be the base case in future years. 

Similarly, understanding the impact of these programs under different carbon emissions 

scenarios is an increasingly important topic. We will evaluate our methodology for scenario 

analysis this year; and we expect that scenario analysis will be a part of the main benefits report 

for the FY 2008 budget request. 

Future GPRA Benefits Development 

As part of DOE’s continuing efforts to implement the President’s Management Agenda—and to 

be responsive to the advice offered by the National Academy of Sciences/National Research 

Council—DOE is in the process of integrating its GPRA benefits analyses across the offices of 

Energy, Science, and Environment (ESE). This integration process is expected to be fully 

completed for the FY 2010 budget request, but significant and important steps and progress will 

be evident along the way. The GPRA benefits analysis for the FY08 budget request will show a 

DOE-wide portfolio case, in which all offices’ RD3 programs are combined. Further, EERE 

technologies’ benefits will be evaluated relative to an ESE-wide baseline (as opposed to a 

baseline in which only EERE advanced technologies are removed from the AEO reference). 

Moreover, the inputs to the integrating models will be developed using common methodologies 

across all ESE offices. The result will be a much clearer picture of the benefits of the full DOE 

portfolio than has been represented to date. 

Another major development afoot in DOE’s benefits analysis is the treatment of risk and 

uncertainty. As in prior years, the benefits in this report are shown for Programs and the Portfolio 

assuming that RD3 goals are achieved and that they are achieved on time. It is also assumed that 

RD3 funding is continued as required. These assumptions represent a considerable simplification 

in a number of ways. First, for R&D there is considerable technical risk in what the actual output 

of the program activities might be. In fact, the output in a given year could be greater or less than 

the specified goal, or alternatively a specified goal may be achieved earlier or later than 

scheduled. Moreover, for a given output, the outcome is not known with certainty, because it will 

be affected by market risk considerations. 

EERE and DOE, in coordination with NRC, are in the process of integrating the treatment of risk 

into the benefit process as part of a multiyear activity. This work will include the explicit 

treatment of technical risk and improved treatment market risk to lead to better estimates of both 

program outputs and outcomes. These refinements will also address the potential use of discount 

rates. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Preface – Page P-15 

Report Organization 

In addition to the Executive Summary and this Preface, this report contains three chapters. 

Chapter 1 presents the overall results of the benefits and savings estimates from both the 

individual programs and the overall EERE portfolio. Chapter 2 describes, in detail, the 

estimated midterm benefits (to 2025) of each program area using NEMS-GPRA07. Chapter 3

describes, in detail, the estimated long-term benefits (to 2050) of each program area using 

MARKAL-GPRA07. 

Eleven appendices are included. Appendix A provides the Baseline Cases and their 

implementation in NEMS-GPRA07 and MARKAL-GPRA07. Appendices B through J provide 

program-analysis team inputs for EERE’s programs. Appendix K describes the results of new 

scenario analyses conducted this year to look at the effect of high fuel prices and a carbon 

constraint. 
CHAPTER 1 

FY 2007 BENEFITS ESTIMATES 

The Office of Energy Efficiency and Renewable Energy (EERE) estimates expected benefits for 

its overall portfolio and for each of its nine programs. Benefits for the FY 2007 budget request1

are estimated for the midterm (2010-2025) and long term (2030-2050). Two separate models 

suited to these periods are employed—NEMS-GPRA07 for the midterm and MARKALGPRA07 for the long term. 

Benefits estimates are intended to reflect the value of program activities from 2007 forward. 

They do not include the impacts of past program success, nor technology development or 

deployment efforts outside EERE’s programs. This distinction is difficult to implement in 

practice, because many research and deployment activities provide continuous improvements 

that build on past success; and because EERE programs are leveraged with private-sector and 

other government efforts (e.g., in addition to the Baseline Case, private-sector improvements). 

Outcomes and Benefits Metrics 

The energy efficiency improvements and additional renewable energy production facilitated by 

EERE’s programs reduce the consumption of traditional energy resources. Reducing energy 

consumption affords the Nation a number of economic, environmental, and energy security 

benefits.2 The extent of these benefits depends on numerous factors including which energy 

sources are reduced, the costs of the new technologies, and the emissions performance of the 

energy technologies used. Different EERE portfolios would produce a different mix of benefits, 

even if the overall level of primary energy savings were the same. 

The public benefits resulting from these reductions in the use of traditional energy resources take 

many forms. Environmental improvements, for instance, can include reductions in local, 

regional, or global air emissions; reduced water pollution; noise abatement, etc. These public 

benefits are typically difficult to measure directly, and some aspects are not quantifiable. EERE 

has developed a set of indicators intended to provide a sense of the magnitude and range of the 

benefits its programs provide to the Nation. EERE estimates benefits for the following defined 

metrics: 

Energy Displaced - the difference in nonrenewable energy consumption with and 

without the technologies and market improvements developed by EERE programs.

Analysts measure energy savings on a primary basis, accounting for the energy consumed in 

 

1

EERE budget-request materials may be accessed at http://www1.eere.energy.gov/ba/pba/budget_formulation.html 2

 This is a categorization of EERE’s benefits estimates, based on the framework developed by a National Research Council 

(NRC) committee. The framework is described in more detail in the Preface. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-1
producing, transforming, and transporting energy to the final consumer. Energy savings from 

underlying private-sector improvements in technologies are not counted. Energy displaced is 

reported in quadrillion Btus per year (quads/yr). 

Economic Benefits: Economic benefits are the potential for EERE technologies to make 

energy more affordable by reducing expenditures on energy and energy services, increase 

economic productivity and GDP through more efficient production processes, reduce the 

impact of energy price volatility on the U.S. economy by providing more efficient 

technologies and providing alternative energy sources, and improve the balance of trade by 

exporting energy technologies. Of these, EERE currently estimates two aspects of 

affordability—energy-expenditure savings and total system net cost savings:3

Energy-expenditure savings – The difference in total consumer energy bills with and 

without the availability of technologies and market improvements developed by 

EERE technologies. This is an estimate of energy bill savings4

 and does not include all 

incremental costs to end users of acquiring the new technology. The NEMS model does 

not currently have the capability to directly calculate net cost savings.5 Energyexpenditure savings are reported in billions of 2003 dollars per year. 

Total system net cost savings – The difference in total system costs with and without 

the availability of technologies and market improvements developed by EERE 

technologies. Total system cost represents the economic cost to society to produce, 

import, convert, and consume energy. It is calculated as the sum of domestic resourceextraction costs, imported fuel costs, and the annualized capital and operating and 

maintenance costs of energy technologies (including end-use demand devices). Total 

system net cost savings is a net estimate of system costs generated by MARKALGPRA07; which, unlike the energy expenditure savings estimates generated by NEMSGPRA07, includes the incremental costs of end-use technologies. Total system net cost 

savings are reported in billions of 2003 dollars per year. 

Environmental Benefits: Environmental benefits that can result from use of EERE 

technologies include, among many others, lower carbon, SOx, NOx, and other air emissions.6

Of these, EERE currently estimates only the impacts of its programs on carbon emissions: 

Carbon savings (i.e. emission reductions) – The difference in the level of U.S. 

energy-related carbon emissions with and without the availability of EERE 

technologies and associated market improvements. Carbon emission reductions result 

from the reductions in fossil fuel consumption when these new supply (renewables) and 

 

3

 Energy-expenditure savings are calculated through 2025 using the NEMS-GPRA07. Total system net cost savings are 

calculated through 2050 using MARKAL-GPRA07. 

4

 Energy efficiency improvements and increased use of nonfuel renewable energy (e.g., renewable-generated electricity) reduce 

energy bills in two ways. Consumers who make energy efficiency or renewable energy investments benefit directly through 

reduced purchases of energy (quantity component). In addition, the lower demand for energy reduces the price of energy for all 

consumers (price component).

5

 In future GPRA benefits reports, we expect the NEMS-GPRA model to show a net economic metric, in addition to the 

consumer expenditures it currently reports. 

6 Because the level of emissions of many air pollutants is “capped” by the U.S. Environmental Protection Agency, in some cases 

EERE technologies may make compliance with the caps more cost-effective or less costly, but may not actually lower emissions. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-2
demand (energy-efficient) technologies are used in the market. As with the energysavings metric, emission reductions count the effect of upstream energy savings in 

producing, transforming, and transporting energy to the end user. Carbon savings are 

reported in million metric tons of carbon equivalent (mmtce) per year. 

Security Benefits: Security benefits include improvements in the reliability of fuel and 

electricity deliveries, reduced likelihood of supply disruptions, and reduced impacts from 

potential energy disruptions. EERE contributes to these security gains by reducing U.S. 

reliance on imported fuels, increasing the diversity of domestic energy supplies, increasing 

the flexibility and diversity of the Nation’s energy infrastructure, and reducing peak demand 

pressure on that infrastructure. Of these aspects of energy security, EERE has developed 

indicators related to concerns about fuel supplies and the reliability and diversity of 

electricity supplies:7

Oil savings – The difference in total U.S. oil consumption with and without EERE 

technologies and market improvements. Oil savings are reported in million barrels per 

day (mbpd). 

Natural gas savings – The difference in total U.S. natural gas consumption with and 

without EERE technologies and market improvements. Natural gas savings are 

reported in quadrillion Btu per year (quads/yr). 

Avoided additions to central conventional power – The difference in central 

conventional power additions with and without EERE technologies and market 

improvements. Avoided central conventional power additions result from electricity 

capacity displaced by efficiency improvements, and central renewable power-generating 

capacity.8

 Avoided capacity additions are reported in cumulative gigawatts (GW). 

In interpreting these metrics, it is important to remember that while the benefits of efficiency and 

renewable technologies are multifaceted, they are not always distinct or additive. Improvements 

in balance-of-trade or economic productivity, for instance, are contributory to improved GDP 

and not additional to improved GDP. Nonetheless, identifying the various types of economic or 

other contributions can help relate EERE’s portfolio to various economic or other policy 

concerns. 

Portfolio Benefits 

Table 1.1 shows the estimated economic, environmental, and security benefits of EERE’s 

overall portfolio of investments in improved energy-efficient technologies, renewable energy 

technologies, and assistance to consumers in adopting these technologies. Data by five-year 

 

7

 The inclusion of reliability improvements within the security category was part of the NRC suggestions on how to structure the

types of EERE benefits. 

8

 These measures are not additive and are not the same as a measure of peak-load reduction for conventional electricity or of 

improved reliability. Renewable capacity additions are not equivalent to capacity additions avoided because of differences in 

capacity factors and coincidence of renewable generation at system peak (i.e. peak electricity-generation output of wind, for 

example, may not coincide with the peak demand of the utility system to which it supplies power).

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-3
increments (2010 to 2025) are shown for NEMS-GPRA07 and by 10-year intervals (2030 to 

2050) for MARKAL-GPRA07.9

 

Table 1.1. Annual EERE Portfolio Benefits for FY 2007 Budget Request for Selected Years10,11

EERE Midterm Benefits (NEMS-GPRA07) 2010 2015 2020 2025 

Energy Displaced 

• Primary nonrenewable energy savings (quadrillion Btu/yr) 0.35 1.4 4.4 7.8

Economic 

• Energy-expenditure savings (billion 2003 dollars/yr)* 2.1 18 70 107

Environment 

• Carbon dioxide emission reductions (mmtce/yr) 8 26 86 166 

Security 

• Oil savings (mbpd) 0.03 0.43 1.07 1.69 

• Natural gas savings (quadrillion Btu/yr) 0.07 0.35 1.04 0.82 

• Avoided additions to central conventional power 

(cumulative gigawatts) 

0.53 11 54 118 

EERE Long-Term Benefits (MARKAL-GPRA07) 2030 2040 2050

Energy Displaced

• Primary nonrenewable energy savings (quadrillion Btu/yr) 14 25 32 

Economic

• Energy-system net cost savings (billion 2003 dollars/yr)* 63 138 207 

Environment

• Carbon dioxide emission reductions (mmtce/yr) 279 527 648 

Security

• Oil savings (mbpd) 3.9 8.0 11 

• Natural gas savings (quadrillion Btu/yr) 2.0 2.0 2.8 

* Midterm energy-expenditure savings only include reductions in consumer energy bills, while long-term energysystem net cost savings also include the incremental cost of the advanced energy technology purchased by the 

consumer. 

Energy Displaced: In 2005, Americans consumed 95 quadrillion Btus of nonrenewable 

energy. Absent the results of EERE’s programs,12 annual consumption of nonrenewable 

energy could grow by 28 quads from 2005 to 2025, to about 123 quadrillion Btus of energy 

per year; and by 41 quads from 2005 to 2050, to about 136 quadrillion Btus of energy per 

year. If the goals of EERE’s investment portfolio are achieved and the corresponding market 

outcomes realized, it will reduce nonrenewable energy consumption by 8 quadrillion Btus by 

2025, or about 28% of the expected incremental growth in energy demand over this time 

period; and by 32 quadrillion Btus by 2050, or about 78% of the expected incremental 

 

9

 NEMS-GPRA07 runs using one-year intervals, while Markal-GPRA07 runs using five-year intervals. 10 Estimates reflect the annual benefits in each year associated with program activities from FY 2007 to the benefit year, or to 

program completion (whichever is nearer), and are based on program goals developed in alignment with assumptions in the 

President’s Budget. Midterm program benefits were estimated using the GPRA07-NEMS model, based on the Energy 

Information Administration’s (EIA) National Energy Modeling System (NEMS) and using the EIA’s Annual Energy Outlook 

2005 (AEO2005) reference case. Long-term benefits were estimated using the GPRA07-MARKAL model developed by 

Brookhaven National Laboratory. Results can differ among models, due to structural differences. The models used in this 

analysis estimate economic benefits in different ways, with MARKAL reflecting the cost of additional investments required to 

achieve reductions in energy bills. 

11 For some metrics, the benefits estimated by MARKAL-GPRA07 do not align well with those reported by NEMS-GPRA07. 

Every attempt is made in the integrated modeling to use consistent baselines, input data, and assumptions in both models to 

produce consistent results. However, NEMS and MARKAL are, in some respects, fundamentally different models (see Boxes 2.1 

and 3.1). Discrepancies in the estimated benefits often occur simply because of these model differences. 12 See the Preface, and Appendix A for information on how EERE’s “no-program” Baseline Case is developed. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-4
growth in annual energy demand over this time period (see Figure1.1). This results in a 

declining demand for nonrenewable energy consumption starting in 2030, despite a growing 

economy. 

Figure 1.1. U.S. Nonrenewable Energy Consumption, 1949-2005, and Projections to 2050: 

Baseline, Individual Program Goal Cases and EERE Portfolio Case 

Data Source: 1949-2005, Energy Information Administration, Annual Energy Review 2004, DOE/EIA-0384 

(2004) (Washington, D.C., August 2005), Table 1.1 Web site http://www.eia.doe.gov/emeu/aer/contents.html. 

Individual Program versus Portfolio Benefits

As discussed in the Preface, two sets of benefits are determined: a set of individual program 

goal cases and a portfolio case. The individual program goal cases are based on modeling the 

impact of each EERE program on its own, without the potential overlap or synergies that 

occur in the portfolio case. While some program activities reinforce each other to produce 

larger benefits than would be evident from each program’s individual efforts, programs 

compete for the same markets in other cases. For example, the various renewable technology 

programs compete in the electricity-generation market. In addition, activities being funded by 

some programs reduce the potential market for technologies being developed in other 

programs. As an example, reductions in electricity demand due to efficiency improvements 

reduce the size of the generation market and, therefore, the market opportunity for 

renewable-generation technologies. A comparison of the “Sum of Program” and “Portfolio” 

curves shown in Figure 1.1 illustrates the overall effect of these interactions among the 

programs. Estimated energy savings of the portfolio case are almost 2 quads less in 2025, 

compared to the sum of the individual program benefits; and almost 7 quads less in 2050, 

compared to the sum of the individual program benefits. 

 

Supply Side and Demand Side Effects of EERE’s Portfolio 

To understand the relative contributions of EERE’s portfolio on supply and demand, one 

needs to consider the total primary energy consumption changes associated with EERE’s 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-5
portfolio, and not just the nonrenewable energy savings (see Figure 1.2). Total annual U.S. 

primary energy consumption—without the benefits of EERE’s portfolio—increases by 32

quads over the period of 2005 to 2025 to almost 134 quads per year, eventually increasing by 

48 quads to a level of 150 billion quads per year in 2050. Accomplishment of the goals and 

associated market outcomes of EERE’s technology portfolio reduces total primary energy 

consumption in 2025 by 5 quads per year, or about 15% of the incremental growth over that 

period; and by 24 quads per year in 2050, or 50% of the incremental growth over that period. 

By 2025, total primary energy consumption actually begins to decline slightly. As Figure 1.2

shows, the rate of decline in nonrenewable energy consumption is greater than the rate of 

decline in total energy demand. The difference reflects the supply-side impacts of replacing 

nonrenewable energy resources with renewable energy resources. 

In 2025, increased use of renewable energy accounts for 37% (or 3 out of 8 quads) of the 

annual nonrenewable energy savings generated by the EERE portfolio. About 25% of the 

annual nonrenewable energy savings (or 8 out of 32 quads) in 2050 is accounted for by 

increased use of renewable energy resources (see Figure 1.3a). Over the period of 2008 to 

2025, EERE’s portfolio adds a cumulative total of 25 quads of renewable energy over the 

amount that would have been used in the United States without these programs (see Figure 

1.3b). Cumulative additions to use of renewable energy amount to just more than 170 quads 

by 2050. Cumulative savings in nonrenewable energy are 147 quads over the period of 2008 

to 2025 and almost 600 quads by 2050. The differences between nonrenewable energy 

savings and use of renewable energy represent improvements in energy efficiency (that is, 

reductions in total primary energy demand). 

While some of the technologies in EERE’s portfolio focus strictly on energy efficiency or 

renewable energy production and use, many address both. Vehicle technologies, for example, 

reduce nonrenewable energy consumption through improvements in vehicle efficiency and 

through the introduction of vehicles capable of utilizing alternative fuels. Likewise, building 

technologies integrate the use of renewable energy and energy-efficient technologies. 

Renewable energy technologies in the electric sector can also lead to total primary energydemand savings, because of their greater efficiency in converting primary renewable energy 

into electricity—compared to conventional electricity production technologies. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-6
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-7

Figure 1.2 U.S. Total Energy Consumption versus Nonrenewable Energy Consumption, 1949-

2005, and Projections to 2050: Baseline and Portfolio Cases 

Data Source: 1949-2005, Energy Information Administration, Annual Energy Review 2004, DOE/EIA-0384 

(2004) (Washington, D.C., August 2005), Table 1.1 Web site http://www.eia.doe.gov/emeu/aer/contents.html. 

Figure 1.3 U.S. Total Nonrenewable Energy Savings and Total Renewable Energy Replacement 

Projections to 2050: EERE Portfolio Case 

Economic Benefits: The NEMS-GPRA07 model estimates that energy savings to the 

consumer, resulting from these efficiency and renewable energy contributions, will reduce 

annual consumer energy expenditures in 2025 by $130 billion (expressed in real 2003 

dollars) relative to the baseline projection of $1,050 billion (Figure 1.4), or about 12% of the 

Nation’s expected energy bill. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-8

While these energy bill savings appear to be large, they represent both reduced energy 

purchases and lower energy prices resulting from reductions in demand. They also exclude 

incremental costs to end users of acquiring the new technologies, because the NEMS model 

does not currently have the capability to calculate this measure directly. Lower energy 

demand dampens fuel costs and reduces the need for expensive new energy infrastructure 

expenditures. Lower energy prices improve affordability for all consumers, including those 

who make no additional efficiency or renewable investments as a result of EERE’s activities. 

Figure 1.4. U.S. Total Energy Expenditure, 1970-2001, and Projections to 2025: 

Baseline and Portfolio Cases 

Data Source: 1970-2001, Energy Information Administration, Annual Energy Review 2004, 

DOE/EIA-0384 (2004) (Washington, D.C., August 2005), Table 3.5 and Table D1, Web 

site http://www.eia.doe.gov/emeu/aer/contents.html. 

The EERE portfolio also will reduce annual total system energy costs by more than $200 

billion (in real 2003 dollars) in 2050 (Figure 1.5). This longer-term analysis is done using 

MARKAL-GPRA07, which includes the incremental costs to end users of acquiring the new 

technology.
Figure 1.5 U.S. Total Energy-System Cost Savings 2030 to 2050 

Environmental Benefits: Annual carbon emissions are projected to be 166 mmtce less than 

the 2025 baseline projection of 2,173 million metric tons—a reduction of about 8% (Figure 

1.6 and Figure 1.7a), avoiding 32% of the expected increase from 2005 to 2025 in the 

absence of EERE’s technology programs. Annual carbon emissions are projected to be 606 

million metric tons (carbon equivalent) less than the 2050 baseline projection of 2,532 

million metric tons—a reduction of about 24%, or 68% of the expected increase from 2005 to 

2050 without the benefits of EERE’s technology programs. During the period of 2008 to 

2025, the EERE portfolio of energy efficiency and renewable energy technology avoids 

cumulative emissions of carbon to the atmosphere of 1 billion metric tons of carbon 

equivalent (see Figure 1.7b). From 2008 to 2050, cumulative avoided additions to the 

atmosphere are 12 billion metric tons of carbon equivalent.

The portfolio also provides State and local governments with additional options for meeting 

Clean Air Act ambient air quality standards. For instance, the Clean Cities activity in the 

Weatherization and Intergovernmental Program facilitates local purchases of alternative-fuel 

vehicles. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-9
Figure 1.6. U.S. Carbon Emissions, 1980-2003, and Projections to 2050: 

Baseline and Portfolio Cases 

Data Source: 1980-2000, Energy Information Administration, Annual Energy 

Review 2004, DOE/EIA-0384 (2004) (Washington, D.C., August 2005), Table 

12.2, Web site http://www.eia.doe.gov/emeu/aer/contents.html. 

Figure 1.7. U.S. Carbon Emissions, 1980-2003, and Projections to 2050: 

Baseline and Portfolio Cases

Security Benefits: The largest relative impact of the EERE portfolio is on reducing the 

Nation’s reliance on oil. The portfolio is expected to reduce annual oil consumption by 1.7 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-10
mbpd from the 2025 baseline of 25 million barrels per day (mbpd), or about 28% of expected 

growth in oil demand between 2005 and 2025 (Figure 1.8 and Figure 1.9a). 

Figure 1.8. U.S. Oil Consumption, 1949-2004, and Projections to 2050: 

Baseline and Portfolio Cases 

Data Source: 1980-2000, Energy Information Administration, Annual Energy 

Review 2004, DOE/EIA-0384 (2004) (Washington, D.C., August 2005), Table 1.3, 

Web site http://www.eia.doe.gov/emeu/aer/contents.html. Data were converted 

from quads per year to mbpd using conversion factor of 5.8 million Btus per barrel 

of crude oil. 

Figure 1.9. U.S. Oil Savings Projections to 2050: 

Baseline and Portfolio Cases 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-11
The portfolio is expected to reduce oil consumption by 11.5 mbpd from the 2050 baseline of 

29 mbpd (about 120% of expected growth in oil demand between 2005 and 2050). This 

results in significantly declining oil consumption starting in 2030. Under the Portfolio Case, 

U.S. demand for oil would drop to levels not seen since the late 1970s. Over the period of 

2008 to 2025, EERE’s portfolio of technologies is projected to save a total 4.3 billion barrels 

of oil. From 2008 to 2050, cumulative oil savings would reach 67 billion barrels. 

The oil savings projected under the Portfolio Case are nearly equivalent to reductions 

specifically in the projected demand for foreign oil. This is because almost all of the new 

U.S. demand for petroleum is projected to be met by foreign oil imports (see Figure 1.10). 

Figure 1.10. Foreign Oil Consumption, 1949-2004, and Projections to 2050: 

Baseline and Portfolio Cases

In the baseline projection, oil imports increase to 19 mbpd in 2025—equal to the total U.S. 

demand for oil in 2005. EERE’s portfolio of technologies reduces foreign oil demand by 1.8 

mpbd in 2025 (about 30% of expected growth in foreign oil imports in the baseline from 

2005 to 2025). By 2050, projections for oil imports increase to 24 mbpd. EERE’s programs 

would provide significant reductions in oil imports by 2050—cutting U.S. oil imports in half 

and eliminating all of the new growth in oil import demand for the period of 2005 to 2050. 

While EERE’s portfolio has elements that increase (as well as decrease) natural gas 

consumption; on balance, EERE’s portfolio is expected to reduce annual natural gas 

consumption by about 0.8 quadrillion Btu from the baseline of 31 quadrillion Btu in 2025 

and by 2.8 quadrillion Btu from the baseline of 38 quadrillion Btu in 2050 (Figure 1.11 and 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-12
Figure 12a). Over the period of 2008 to 2025, EERE’s portfolio of technologies provides 9 

quads of cumulative savings of natural gas (Figure 1.12b).

Figure 1.11. U.S. Natural Gas Consumption, 1949-2004, and Projections to 2050: 

Baseline and Portfolio Cases 

Data Source: 1949-2004, Energy Information Administration, Annual Energy Review 2004, 

DOE/EIA-0384 (2004) (Washington, D.C., August 2005), Table 1.3, Web site 

http://www.eia.doe.gov/emeu/aer/contents.html.

Figure 1.12. U.S. Natural Gas Savings, Projections to 2050: 

Baseline and Portfolio Cases 

Cumulative savings of natural gas through 2050 is 73 quads. While EERE does not estimate 

the portion of natural gas savings attributed to imported natural gas supplies, supplies from 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-13
countries other than the United States and Canada may be the marginal sources of natural gas 

for meeting any future growth in demand. 

EERE’s technology programs also contribute to the security of the Nation’s electricity supply 

by reducing central conventional power plant capacity additions (Figure 1.13). As shown in

Figure 1.14, renewable energy capacity additions (central and distributed) are projected to 

grow by an additional 73 GW, compared with the Baseline Case in 2025; and 332 GW, 

compared with the Baseline Case in 2050. 

Figure 1.13. Avoided Conventional Central Generating Capacity 

Projections to 2025 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-14
 

Figure 1.14. U.S. Renewable Energy Capacity, 1949-2004, 

and Projections to 2050: Baseline and Portfolio Cases 

Data Source: 1948-2004, Energy Information Administration, Annual Energy 

Review 2004, DOE/EIA-0384 (2004) (Washington, D.C., August 2005), Table 

8.11a, Web site http://www.eia.doe.gov/emeu/aer/contents.html. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-15
Program Benefits 

The remainder of this chapter is devoted to program-specific information, including program 

budget requests and benefits (see Chapters 2 and 3 for more specific program-level analysis). 

Figure 1.15 displays the EERE program budget requests for FY 2007. The largest program 

budget is $225 million for the Weatherization and Intergovernmental Program (WIP), which 

includes $164 million for Low-Income Weatherization Assistance. 

Figure 1.15. EERE Program FY 2007 Budget Requests

Source: Budget request from FY 2005 Budget-in-Brief, U.S. Department of Energy Office of Energy Efficiency and 

Renewable Energy, http://www1.eere.energy.gov/ba/pba/budget_07.html

Individual program benefits are not—as indicated in the earlier discussion on primary 

nonrenewable energy savings—in sum, representative of the total benefits of the integrated 

EERE technology portfolio. That is because individual programs can compete with or be 

synergistic with other programs in the portfolio—and the individual program benefits presented 

here represent how each program’s technologies can compete by themselves, without the 

presence of any other programs in the EERE portfolio. Still, the individual program benefits 

presented here serve as a proxy for understanding the relative strengths of each program’s 

technology. 

The picture that emerges from the individual program benefits presented here is one of 

robustness. Different technologies are positioned to dominate in the near, mid- and long term. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-16
Some technologies are best-suited to improving energy security by reducing our dependence on 

foreign oil. In addition, different programs emerge as important contributors to consumer energy 

savings versus those that emerge as important contributors to total energy system net cost 

savings. 

While incomplete, the results indicate both the range and approximate level of benefits available 

to the Nation from funding the efficiency and renewable investments in EERE’s portfolio of 

programs. They indicate a potential for making better use of existing technologies and for 

accelerating technological advances to make significant changes in our energy markets, which 

can drive the Nation to a period of level energy consumption. 

Energy Displacement: Figures 1.16 a and b show the time profile of each program’s savings 

(both annual and cumulative). The relative cumulative impact of the individual program cases 

is shown in Figure 17 for three different time frames (2015, 2025, and 2050). The Industry, 

WIP, and FEMP programs have their greatest influence in the near term (through 2015). The 

Building Technologies Program has the largest impact in the near term, followed closely by 

the Vehicle Technologies Program, the Biomass Program, and Weatherization. In the midterm 

(through 2025), the Wind Technologies Program shows the greatest relative impact on energy 

savings. In the long term, advanced transportation technologies (from Hydrogen, Vehicles, 

and Biomass) become the dominant potential impacts. In the meantime, solar technologies 

show continuous growth in relative impact throughout the period of 2008 through 2050. By 

2050, Vehicles, Buildings, Wind, Solar, Hydrogen, and Biomass (in descending order) each 

have significant potential impacts on energy displacement. 

Figure 1.16 Individual Program Goal Cases Nonrenewable Energy Savings: 

Annual and Cumulative 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-17
Figure 1.17. Cumulative Individual Program Goal Cases Nonrenewable Energy Savings for 2015, 

2025, 2050 (Quadrillion Btu) 

Economic Benefits: Figure 1.18 shows the time profile of the individual program impacts 

on consumer energy spending through 2025. As with energy displacement, energyexpenditure savings are dominated in the near term by the Building Technologies Program, 

followed closely by savings impacts from the Vehicle Technologies Program. By 2025, the 

largest individual program savings are associated with the Vehicles Program. Buildings, 

Wind, Solar, and Biomass are also positioned to have significant impacts. The Hydrogen 

Program is just beginning to show potential impact. Figure 1.19 shows the time profile of the 

individual program impacts on total energy system cost. Here, the relative strengths of the 

different programs play out very differently. Total energy cost, as opposed to consumer 

spending, is most heavily influenced by energy efficiency technologies. Thus, in the nearand midterm, the Buildings Program dominates the savings. In the long term, energy savings 

from the Vehicles Program and the Buildings Program dominate. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-18
Figure 1.18. Annual Individual Program Goal Cases Consumer Energy-Expenditure Savings: 

Projections to 2025 

Figure 1.19. Annual Program Goal Cases Energy-System Net Cost Savings: Projections to 2050 

Environmental Benefits: The time profiles and relative impacts of each of the programs on 

carbon emissions follows very closely the trends described for total nonrenewable energy 

savings (see Figures 1.20 and 1.21). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-19
Figure 1.20. Individual Program Goal Cases Carbon Emissions Avoided: 

Annual and Cumulative 

Figures 1.21. Cumulative Individual Program Goal Cases Carbon Emissions Avoided, 2015, 2025, 

2050 (Million Metric Tons Carbon Equivalent) 

Energy Security Benefits: Oil savings are dominated by the three main transportation 

related technologies—Vehicles, Hydrogen, and Biomass (see Figures 22 and 23). In the near 

term, the Vehicles and Biomass programs are equally positioned to dominate oil savings in 

the portfolio. In the long term, Hydrogen steps forward as the third major technology 

positioned to contribute to oil savings. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-20
Figure 1.22 Individual Program Goal Cases Oil Savings (mbpd) 

Figures 1.23: Cumulative Individual Program Goal Cases Oil Savings 

(Millions of Barrels)

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

FY 2007 Benefits Estimates (Chapter 1) – Page 1-21
CHAPTER 2 

MIDTERM BENEFITS ANALYSIS OF EERE’S PROGRAMS 

Introduction 

The anticipated outputs from the Office of Energy Efficiency and Renewable Energy (EERE) 

Research, Development, Demonstration, and Deployment (RD3) programs are represented in 

NEMS-GPRA07 in the Individual Program Goal Cases and Portfolio Cases to estimate the 

midterm (to 2025) benefits for each program and for EERE’s overall portfolio. This chapter 

describes the NEMS-GPRA07 analyses for each program. The appendices provide additional 

information on the inputs provided by each program. 

Table 2.1 shows a breakdown by program of the two types of analytical tools employed in its 

benefits analyses—specialized “off-line” tools and NEMS-GPRA07. A description of the Energy 

Information Administration’s (EIA) National Energy Modeling System (NEMS) model is 

provided in Box 2.1 at the end of this chapter.1

 Off-line tools are those used to develop input for 

NEMS and, in some cases, to estimate benefits for program activities outside of the scope of 

NEMS. Descriptions of the off-line tools are provided in the related program appendices. 

Table 2.1. Program Benefits Modeling by Primary Type of Model Used and Activity Area 

Program Activity Area Off-Line Tool NEMS-GPRA07

Biomass Ethanol from Corn Fiber and Residual Starch 3

Cellulosic Ethanol 3

Technology R&D 3 3 

Regulatory Actions 3 3

Building Technologies 

Market Enhancement 3

FEMP FEMP 3

Hydrogen, Fuel Cells, and Fuel Cells 3

Infrastructure Technologies Production and Delivery 3

Industrial Technologies Industrial programs 3

Solar Energy Technologies Photovoltaic Systems 3 3

Concentrated Solar Power 3 

Light Vehicle Hybrid and Diesel 3

Light-weight Materials for LDVs 3 

Vehicle Technologies 

Heavy Vehicles 3

Weatherization and Weatherization 3

Intergovernmental Domestic Intergovernmental 3

Wind and Hydropower Technologies Wind 3

Required off-line analysis using specialized off-line tools can range from simple verification of 

program goals to an initial calculation of energy savings, depending on the treatment of the target 

market in NEMS-GPRA07 and the nature of the program. The activity areas listed in Table 2.1

 

1

 For more detailed information about NEMS, see http://www.eia.doe.gov/bookshelf/docs.html for individual reports 

documenting the NEMS modules. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-1 
are groupings of activities within each program that share either technology or market features—

they do not represent actual program-management categories. 

Biomass Program 

The goal of the Biomass Program is the development of biomass refineries (biorefineries), which 

produce multiple products, including at least one energy product. Energy products include 

ethanol, other fuels, and electricity. Non-energy products include chemicals and materials. The 

biorefinery concept allows the cost of production to be reduced through synergies associated 

with feedstock handling and processing, and the allocation of capital and fixed O&M costs 

across multiple products. The current analysis is based on biorefineries that produce ethanol fuel 

as a major output along with specialized bioproducts.2 

Corn-based ethanol: The primary thrusts of the R&D related to corn-based ethanol production 

are the use of corn kernel fiber and residual starch in dry mills that will increase ethanol yields 

per bushel of corn, and the development of bio-based chemical coproducts. These goals are 

represented within the NEMS-GPRA07 framework through modifications in the corn ethanol 

yields, and per unit O&M and capital costs. The production of bio-based chemicals is treated as a 

revenue credit for the ethanol. The Biomass Program assumes that these same improvements 

would occur without the EERE R&D, but would be delayed by seven years. The program’s goal 

is to begin to deploy the technology in 2012 and assumes a seven-year phase-in for 

implementation. Therefore, 2019 is the first year of deployment in the Base Case. 

There were also several modifications made to the NEMS-GPRA07 representation of corn 

ethanol production with NEMS-GPRA07. The NEMS supply curves from the AEO2005 were 

expanded to allow up to 10 billion gallons of production by assuming the same slope as in the 

AEO’s feedstock corn prices and raising the last step of production. In addition, the base ethanol 

yields and credit for distillers’ dry grains (DDG), an animal feed material that is the coproduct of 

dry mills, were increased from those in the AEO2005 and are closer to those in the just-released 

AEO2006.3

 Note that, because NEMS-GPRA07 is based on the AEO2005 reference case, the 

Baseline Case does not include new policies from EPACT that are reflected in AEO2006, such as 

the implementation of a renewable fuel standard that mandates increased use of ethanol up to a 

level of 7.4 billion gallons per year in 2012. 

Cellulosic ethanol from biorefineries dedicated to the production of ethanol and ligninderived electricity: EERE is sponsoring research aimed at reducing the cost of producing 

ethanol from cellulosic biomass.4

 The cellulosic biorefineries modeled in this analysis are ones 

that focus on producing ethanol and lignin-derived electricity.5 The program goal, as alluded to 

 

2

 Future analyses could include additional fuels that the program may identify in the longer term. In addition, the research 

undertaken to improve the harvesting of agricultural residue feedstocks has not been included in the GPRA analysis. 3 Unfortunately, the timing of AEO’s release does not afford our program analysts and energy modelers the time to run our 

GPRA benefits analyses with the most recent AEO. 

4

 Cellulose and hemicellulose that can be converted to ethanol (and other chemicals, materials, and biofuels) are found in biomass 

such as agricultural residues (corn stover, wheat, and rice straw), mill residues, organic constituents of municipal solid wastes, 

wood wastes from forests, future grass, and tree crops dedicated to bio-energy production. 5

 In the future, when designs of alternative biorefinery configurations (e.g., those producing ethanol, electricity, and bio-based 

chemicals) are available, the benefits analysis will include such concepts as well. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-2 
in the President’s Advanced Energy Initiative,6

 is to achieve a production cost of $1.07 per 

gallon of ethanol by 2012, with cost reductions in subsequent years. In NEMS-GPRA07, the 

commercialization date was set to 2015 to allow for three years from pilot to start-up of a full 

commercial facility. Cellulosic production capacity is assumed to be able to expand at a rate of 

500 million gallons, or by 25% per year (whichever is greater), consistent with growth 

constraints based on historical data of the highest existing corn ethanol industry and gasolinerefinery capacity expansion rates. 

In NEMS-GPRA07, the capital costs, non-fuel operating costs, and conversion efficiencies for 

cellulosic ethanol were modified to reflect the program targets for the Individual Program Goal 

Case. The biomass feedstock curves in NEMS-GPRA07 are used to determine the feedstock 

price by region and year. In the Baseline, cellulosic ethanol production is assumed to penetrate 

after the NEMS time horizon to 2025. 

The refinery model within NEMS-GPRA07 evaluates the use of ethanol as a blending agent for 

gasoline, taking into account its chemical properties as well as its energy value. As ethanol 

becomes less expensive due to advanced technologies, more ethanol is used. In both the Baseline 

and Individual Program Goal Cases, corn ethanol reaches its peak of 10 billion gallons per year 

by 2025. Cellulosic ethanol grows from its introduction in 2015 to 7.3 billion gallons in 2025. 

The refinery model also produces E85, for which production levels are dependent on the relative 

attractiveness of its use primarily in flex-fuel vehicles. 

The Biomass Program benefits shown in Table 2.2 are the reductions in energy use and carbon 

emissions in the Individual Program Goal Case compared with the Baseline Case.7

Table 2.2. FY07 Annual Benefits Estimates for Biomass Program (NEMS-GPRA07)

 Benefits 2010 2015 2020 2025 

Energy Displaced 

Primary Nonrenewable Energy Savings (quadrillion 

Btu/yr) ns 0.27 0.36 0.39 

Economic 

 Energy-Expenditure Savings (billion 2003 dollars/yr) ns ns 7.7 5.4 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) ns 6 6 7 

Security 

 Oil Savings (million barrels per day) ns 0.20 0.27 0.22 

 Natural Gas Savings (quadrillion Btu/yr) ns -0.14 -0.10 ns 

 Avoided Additions to Central Conventional Power 

 (cumulative gigawatts) ns ns ns ns 

Other Program Metrics 

 Incremental Ethanol Production (billion gallons/yr) 0.00 5.2 7.8 7.3 

 

6

 For more details on the Advanced Energy Initiative, see http://www.whitehouse.gov/stateoftheunion/2006/energy/index.html 7

 Note that in the Biomass Individual Program Goal Case, the advanced transportation technologies available in Freedom Car and 

Vehicle Technologies Individual Program Goal Case are unavailable, despite the market synergies of the two suites of 

technologies. In the EERE portfolio case, both suites are modeled.

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-3 
More information about the assumptions underlying the Biomass Program’s benefits analysis can 

be found in Appendix C.

8

 

Building Technologies Program 

The activities of the Building Technologies Program can be classified into three general types: 

technology R&D, regulatory actions, and market enhancement. The modeling approach and 

applicable end uses for the activities that comprise the Building Technologies Program are 

shown in Table 2.3. Analysts model the technology R&D activities by modifying costs and 

efficiencies of the equipment and shell-technology slates. Market-enhancement activities and 

some regulatory activities (such as buildings codes) are modeled using penetration rates and 

energy-savings estimates. 

Table 2.3. Modeling Approach for Building Technologies Program Activities 

Building Technology Project List Resd Comm Heat Cool Water 

Heating Lighting Other

Energy Savings 

and Penetration 

Rates

Equipment 

Technology 

Costs and 

Efficiencies

Shell 

Technology 

Costs and 

Efficiencies

Residential Buildings Integration

 Research and Development (Building America) 3 33 3 3 3 3

 Residential Building Energy Codes 3 33 3

Commercial Buildings Integration

 Commercial Research and Development 333 3

 Commercial Building Energy Codes 333 3 3

Analysis Tools and Design Strategies 333 3

Refrigeration/Space Conditioning R&D

 Thermotunneling Based Cooling 33 3 3 3

 HyPak-MA 3 3 3

 Integrated Heat Pump 3 33 3 3

Building Envelope R&D

 Electrochromic Windows 333 3 3

 Superwindows 3 33 3 3

 Low-E Market Accpetance 33 3 3 3

 Advanced Wall Systems 3 33 3

 Next Generation Attic Systems 3 33 3

 Next Generation Envelope Materials 3 33 3

Lighting Research and Development

 Lighting Controls 3 3 3

 Solid State Lighting 3 3 3 3

Appliances and Emerging Technologies

 SSL Market Acceptance 3 3 3

Standards

 HID lamps 33 3

 Electric Motors, 1-200 HP 3 3 3

 Distribution Transformers 3

Rebuild America 3 3

Energy Star

 Compact Fluorescents 3 3 3

 Windows 3 33 3

 Refrigerators 3 3 3

 Dishwashers 3 3 3

 Clotheswashers 3 3 3

 Room AC 3 3 3

 Home Performance 3 33 3

Sector End-Use Modeling Approach

 

 

8 More information about the relevant NEMS modules may be found at 

http://tonto.eia.doe.gov/FTPROOT/modeldoc/m059(2005)-1.pdf (Petroleum Market Module, volume 1), 

http://tonto.eia.doe.gov/FTPROOT/modeldoc/m059(2005)-2.pdf (Petroleum Market Module, volume 2), 

http://tonto.eia.doe.gov/FTPROOT/modeldoc/m070(2005).pdf (Renewable Fuels Module) and 

http://tonto.eia.doe.gov/FTPROOT/modeldoc/m069(2005).pdf (Transportation Sector Demand Module). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-4 
Technology R&D: The technology R&D activities seek to develop new or improved 

technologies that are more energy efficient and more cost-effective than the alternatives currently 

available. The projected benefits for these are measured by modifying the technology slates from 

those that are available in the Baseline Case to reflect the program goals. Building technologies 

in NEMS-GPRA07 are represented by end use. For most end uses, there are conversion 

technologies (e.g., furnaces and water heaters) that use different fuels and that have several 

different levels of energy efficiency. The Baseline Case incorporates EIA’s estimation of future 

technology improvement. The rate of technology improvement is modified in the Individual 

Program Goal Case. 

Residential shell technologies (such as windows or insulation) for new buildings are represented 

by several combinations or “packages” of technologies with different levels of improvements. 

Each package is characterized by a capital cost, and heating and cooling load reductions. The 

commercial-sector shell measures are represented by window and insulation technologies that 

can be selected individually. EIA developed the residential methodology for the AEO2001, while 

OnLocation developed the commercial methodology for EERE. 

The residential and commercial sectors are each represented by several building types within 

nine Census divisions. NEMS-GPRA07 computes the end-use technology choice for each of 

these building types and geographic regions, based on the relative economics and estimations of 

consumer behavior for the technologies. The latter is important to replicate current technology 

market shares. 

In a few cases where NEMS-GPRA07 has insufficient detail for explicit technology 

representation, analysts computed market penetration using off-line tools, and the results were 

implemented with NEMS-GPRA07 through efficiency factors. 

Regulatory activities: Regulatory activities include setting new appliance standards—based on 

the legislatively mandated schedule—and encouraging state adoption of more stringent building 

codes. Modeling appliance standards is straightforward. In the year that the program expects the 

new standard to be implemented, all technologies that are less efficient than the standard are 

removed from the market and unavailable for consumer choice. The resulting energy savings 

depend on the difference in the level of efficiency of the standard compared to the technology 

that had been selected in the Baseline Case. The exception are distribution transformers that are 

not explicitly represented in the model, so off-line estimates of electricity savings are used to 

decrease the transmission and distribution losses. 

Market enhancement: Building-code development is primarily a regulatory activity, although it 

also involves outreach to encourage the various states to adopt new and stricter standards. 

Analysts make a spreadsheet computation of average savings using off-line estimates for the 

fraction of buildings within areas that adopt more stringent codes, as well as the heating, cooling, 

and lighting load reductions associated with the new levels of codes. For residential buildings, 

the savings are based on increased compliance with existing codes, accelerated adoption of the 

2000 edition of the International Energy Conservation Code, and the future development of more 

stringent building codes. For commercial buildings, savings are based on increased stringency 

from the combined impact of the latest forthcoming ASHRAE code and the next-generation code 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-5 
assumed to be published in 2007. These analyses were performed at the State level to reflect the 

current variation in building codes and climate factors. The resulting savings were then 

represented in NEMS-GPRA07 through modification of the building shell efficiencies. 

Energy Star aims to accelerate the market penetration of existing high efficiency technologies by 

providing greater information to consumers about their benefits and life-cycle operating savings. 

This is equivalent to lowering consumers’ hurdle rates for investment in energy-efficient 

appliances. Therefore, analysts represented the Energy Star activities by modifying the NEMSGPRA07 consumer-behavior coefficients, indicating how consumers trade first-cost expenditures 

for annual energy savings. The program goals for market penetration were used to determine the 

degree of change of these parameters. For most Energy Star appliances, the program goal is to 

reach a 20% market share for the more efficient Energy Star appliances.9

The Building Technologies Program results in energy savings primarily in four end-use 

categories: space heating, space cooling, water heating, and lighting. Table 2.4 demonstrates the 

level of delivered energy savings (excluding losses from electricity generation) from each 

category. In 2025, space heating and lighting end uses have the highest delivered energy savings 

in residential buildings; while the lighting energy-use reduction is the largest in commercial 

buildings. 

Table 2.4. Building Technologies Program Delivered Energy Savings by End Use

Energy Savings by 

End-Use (Quads) Residential Sector Commercial Sector 

 2010 2015 2020 2025 2010 2015 2020 2025 

Space Heating 0.02 0.13 0.22 0.33 0.00 0.02 0.04 0.08 

Space Cooling 0.00 0.03 0.06 0.11 0.00 0.02 0.04 0.11 

Water Heating 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Lighting 0.01 0.02 0.05 0.17 0.00 0.02 0.05 0.21 

Other 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

Total 0.04 0.18 0.32 0.60 0.01 0.06 0.13 0.40 

The Building Technologies Program benefits (Table 2.5) are estimated within the integrated 

NEMS-GPRA07, so that the electricity-generation primary energy savings are directly 

computed. In addition, the estimates include any feedbacks in the buildings or other sectors 

resulting from changes in energy prices that result from the reduced energy consumption. 

 

9

 Energy Star is a cooperative effort between DOE and the Environmental Protection Agency. There is a division of 

responsibilities with respect to specific technologies, and EERE claims benefits for the penetration of the technologies for which 

it is responsible. Nevertheless, some of the general campaigns and marketing strategies are joint efforts between the agencies, and 

attribution of the benefits to DOE or EPA is difficult. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-6 
Table 2.5. FY07 Annual Benefits Estimates for Building Technologies Program (NEMS-GPRA07)

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.10 0.41 0.81 1.99 

Economic 

 Energy-Expenditure Savings (billion 2003 dollars/yr) 1.2 7.7 16.5 17.3 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 2 8 17 45 

Security 

 Oil Savings (million barrels per day) ns 0.02 0.09 0.04 

 Natural Gas Savings (quadrillion Btu/yr) 0.05 0.25 0.23 0.48 

 Avoided Additions to Central Conventional Power (gigawatts) ns 9 26 62 

Other Program Metrics 

 Total Electricity Capacity Avoided (cumulative gigawatts) ns 13 32 76 

More detail about the assumptions underlying the Building Technologies Program’s benefits 

analysis can be found in Appendix G.

10

Federal Energy Management Program 

The Federal Energy Management Program (FEMP) is an implementation program to increase the 

energy efficiency of Federal Government buildings, which account for about 5% of U.S. 

commercial-building energy consumption. FEMP activities support the installation of a variety 

of existing technologies, rather than focusing on the development of specific technologies, as do 

many other EERE programs. Because it encompasses a broad technological scope—while, at the 

same time, targets a specific market segment—FEMP is difficult to model in an integrated 

framework such as NEMS-GPRA07. However, there is also less uncertainty associated with 

achieved energy savings, because the program tracks changes in Federal energy consumption. 

Delivered energy savings (estimated off-line) are used as inputs for the integrated modeling. 

These projected savings are subtracted from the Baseline Case for commercial-building energy 

consumption. Analysts use the model to compute the other benefits metrics of primary energy 

savings, carbon emission reductions, and energy-expenditure savings (Table 2.6). 

 

10 More details about the relevant NEMS modules may be found at: 

http://tonto.eia.doe.gov/FTPROOT/modeldoc/m067(2005).pdf (Residential Sector Demand Module) and 

http://tonto.eia.doe.gov/FTPROOT/modeldoc/m066(2005).pdf (Commercial Sector Demand Module). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-7 
Table 2.6. FY07 Annual Benefits Estimates for FEMP (NEMS-GPRA07) 

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.01 0.01 0.02 0.02 

Economic 

 Energy-Expenditure Savings (billion 2003 dollars/yr) 0.1 0.1 0.1 0.2 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 0.2 0.2 0.3 0.4 

Security 

 Oil Savings (million barrels per day) ns ns ns ns 

 Natural Gas Savings (quadrillion Btu/yr) ns 0.01 0.01 0.01 

 Avoided Additions to Central Conventional Power 

 (cumulative gigawatts) ns ns ns ns 

More detail on the Federal Energy Management Program’s benefits analysis can be found in 

Appendix I.

11

Hydrogen, Fuel Cells, and Infrastructure Technologies Program 

The Hydrogen, Fuel Cells, and Infrastructure Technologies Program is targeted toward the 

introduction of fuel cells for both stationary and vehicular applications, as well as the production 

and delivery of hydrogen at a reasonable price. NEMS-GPRA07 does not have a representation 

of hydrogen-supply options.12 Therefore, we employ a simplifying assumption that all hydrogen 

produced through 2025 would be derived from natural gas. The hydrogen conversion process is 

assumed to be 75% efficient and yield a hydrogen price of $2 per gallon of gasoline equivalent 

(excluding taxes) when the natural gas price is $5 per MMBtu. 

The stationary fuel cell research is focused on distributed proton-exchange membrane (PEM) 

fuel cells. The program goals for their capital costs and efficiencies were taken from the 

multiyear program plan (MYPP). The MYPP provides goals through 2010, and no further 

improvements were assumed. This conservative assumption most likely understates the benefits 

of these fuel cells. Analysts converted program technology goals into installed costs for 

combined heat and power systems in residential and commercial buildings. 

The fuel cell vehicles were modeled along with the Vehicle Technologies Individual Program 

Goal Case. The success of fuel cell vehicles is predicated on some of the vehicular improvements 

being developed under the Vehicle Technologies Program, so the fuel cell vehicles could not be 

treated in isolation. Analysts modified the gasoline and hydrogen fuel cell vehicle costs and 

efficiencies to reflect the program goals (see the Vehicle Technologies Program description for 

more detail about the modeling of vehicle choice). In addition, hydrogen was assumed to be 

available for vehicle refueling at 10% of vehicle refueling stations by 2020 and available at 25 

percent of refueling stations by 2025. The benefits associated with fuel cell vehicles were 

derived by comparing the number of fuel cell vehicles projected in the case in which both 

Hydrogen and Vehicle Technologies were evaluated to the number of fuel cell vehicles projected 

 

11 More details about the relevant NEMS module may be found at http://tonto.eia.doe.gov/FTPROOT/modeldoc/m066(2005).pdf

(Commercial Sector Demand Module). 

12 Hydrogen is represented within the refinery model of NEMS-H2, but for internal use only. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-8 
in the case with Vehicle Technologies only. Analysts computed energy savings, oil savings, and 

carbon emission reductions, based on the relative fuel and carbon emissions per mile of the 

incremental fuel cell vehicles relative to those in the Baseline. This approach leads to greater 

savings than would a simple difference between the Baseline and Individual Program Goal 

Cases, while still yielding smaller savings than would be derived by comparing a fuel cell 

vehicles case with the Baseline Case. Table 2.7 presents the overall benefits. 

Table 2.7. FY07 Annual Benefits Estimates for Hydrogen, Fuel Cells, and Infrastructure 

Technologies Program (NEMS-GPRA07) 

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) ns ns 0.02 0.22 

Economic 

 Energy-Expenditure Savings (billion 2003 dollars/yr) ns ns ns 2.4 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) ns ns ns 6 

Security 

 Oil Savings (million barrels per day) ns ns 0.03 0.28 

 Natural Gas Savings (quadrillion Btu/yr) ns ns -0.03 -0.33 

 Avoided Additions to Central Conventional Power (gigawatts) ns ns ns ns 

Other Program Metrics 

 Program-Specific Electric Capacity Additions 

 (Cumulative gigawatts) ns ns ns ns 

More details about the HFCIT Program’s benefits analysis can be found in Appendix B.

13

Industrial Technologies Program 

The Industrial Technologies Program seeks to increase energy efficiency in the energy-intensive 

basic materials processing industries, as well as some key technologies that are common across 

most industries. The heterogeneity of the program makes it difficult to represent the program 

activities explicitly through technologies in the NEMS-GPRA07 framework. Therefore, analysts 

perform an off-line analysis using detailed spreadsheet models, and use the resulting energy 

savings by fuel type to provide inputs into the integrated model. Analysts then run the fully 

integrated NEMS-GPRA07 to compute the benefits metrics of primary energy savings, carbon 

emission reductions, and energy-expenditure savings that are associated with the fuelconsumption reductions. 

At the time of publication of the Congressional Budget request, out-year funding profiles for a 

number of programs within DOE's FY 2007 Congressional Budget Request were not yet 

complete. In such instances, EERE assumed “steady-state” funding trajectories to calculate 

benefits estimates, pending further information. Now that “target” funding allocations have been 

finalized, the estimates shown here for the Industrial Technologies Program reflect DOE's 

decision to conclude this program after FY 2008. The benefits decline after 2010, due to the 

 

13 More details about the relevant NEMS modules can be found at http://tonto.eia.doe.gov/FTPROOT/modeldoc/m070(2005).pdf

(Transportation Sector Demand Module), http://tonto.eia.doe.gov/FTPROOT/modeldoc/m067(2005).pdf (Residential Sector 

Demand Module), and http://tonto.eia.doe.gov/FTPROOT/modeldoc/m066(2005).pdf (Commercial Sector Demand Module). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-9 
cessation of EERE R&D and the assumption that the program accelerates the adoption of 

efficient technologies—but that the private sector will eventually adopt at a later time even in the 

absence of the program. 

Table 2.8. FY07 Annual Benefits Estimates for Industrial Technologies Program 

(NEMS-GPRA07)

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.03 0.03 ns ns 

Economic 

 Energy-Expenditure Savings (billion 2003 dollars/yr) 0.4 0.2 ns ns 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 0.7 0.5 ns ns 

Security 

 Oil Savings (million barrels per day) ns ns ns ns 

 Natural Gas Savings (quadrillion Btu/yr) 0.01 0.01 ns ns 

More details about the Industrial Technologies Program’s benefits analysis can be found in 

Appendix H.

14

Solar Energy Technologies Program 

The Solar Energy Technologies Program develops two electric-solar technologies. Photovoltaics 

(PVs) are being improved for both distributed and central electricity generation applications, and 

the program is working to accelerate PV adoption through the Solar America Initiative. The 

concentrated solar power (CSP) R&D activity develops better technology for large-scale central 

electricity generation facilities that concentrate solar energy to produce electricity through a 

thermal process. 

Photovoltaic Systems: Several changes were made to the representation of distributed PV 

systems in the Baseline. The size of the typical distributed PV installation was increased to 4 kW 

per home (from 2 kW) and to 100 kW per commercial building (from 25 kW) to reflect literature 

on recent installations. The California renewable energy credit program, which provides a PV 

credit of $4,000/kW in 2003 (declining by $400/kW per year), was included for the Pacific 

region. The recently passed Federal tax credit was not included, because the legislation occurred 

after this analysis was performed. 

In addition, the adoption rates of distributed technologies in commercial buildings were modified 

to reflect market data gathered by the EERE on consumer adoption of energy efficiency projects 

as a function of payback time (Figure 2.1).15 The NEMS-GPRA07 framework uses a cash-flow 

model to evaluate the distributed energy (DE) technologies—combined heat and power (CHP) 

and photovoltaic (PV) systems—within the building sectors. For commercial buildings, debt and 

interest payments are computed over a loan period of 15 years, along with associated taxes and 

 

14 Details about the relevant NEMS module can be found at: http://tonto.eia.doe.gov/FTPROOT/modeldoc/m064(2005).pdf

(Industrial Demand Sector Module). 

15 Market Trends in the U.S. ESCO Industry: Results from the NAESCO Database Project. Goldman, C., J. Osborn and N. 

Hopper, LBNL, and T. Singer, NAESCO, May 2002, LBNL-49601. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-10 
tax benefits and assuming a 25% down payment. Annual fixed maintenance costs also are 

included. The value of the electricity produced is then subtracted from these costs to determine 

the cash flow. The number of years until positive cash flow is reached determines the market 

share in new buildings. The annual market share for existing buildings is assumed to be a 

fraction of the share for new. 

Figure 2.1. Commercial-Sector DG Adoption Rates

Under both the EIA and program assumptions, market share in new buildings decreases sharply 

as the number of years required to achieve positive cash flows increases. This reflects the high 

rates of return generally expected for energy-related projects by commercial-building owners. 

These shares apply to the fraction of commercial buildings assumed to be eligible for an 

installation of PV systems. The fraction of eligible buildings was increased from 30% to 60% for 

homes and to 55% for commercial buildings. These adoption-rate changes were made in the 

Baseline Case as well as the Individual Program Goal Case. In addition, the average-size 

building for commercial PV installations was modified from being four times the average size 

(as in the AEO2005) to being only twice as large. At this size, the PV-produced electricity is 

roughly equivalent to the annual electricity demand of the building. 

The AEO2005 Reference Case includes significant PV technological advancement. The GPRA07 

Baseline was developed assuming that private industry would continue to improve firstgeneration PV (crystalline silicon) technology, but would not invest significantly on its own in 

second- or third-generation PV (thin-film, etc.) technologies in the absence of continued EERE 

programs. For the Individual Program Goal Case, the capital and O&M costs were modified to 

reflect the program’s goals. The regional capacity factors in the Baseline Case were similar to 

those in the program’s goals, so they were left unchanged. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-11 
In addition to competing on an economic basis with other electricity-generation technologies, PV 

may be constructed for its environmental benefits. For example, the Solar Program’s Solar 

America Initiative goals were incorporated as planned distributed-PV capacity additions in 

NEMS-GPRA07. 

Table 2.9. NEMS-GPRA07 Projected Solar Capacity (GW)

Solar Generation Technologies 

2010 2015 2020 2025

GPRA Base 

Solar CSP 0.5 0.5 0.5 0.5 

Central PV 0.1 0.2 0.3 0.4

Distributed PV 0.5 0.5 0.5 1.3 

Total 1.1 1.2 1.3 2.2 

Solar Individual Program Goal Case 

Solar CSP 0.5 0.5 0.5 3.2 

Central PV 0.1 0.2 0.3 0.4 

Distributed PV 1.4 5.6 30.4 65.2 

Total 2.0 6.3 31.2 68.9 

Incremental Capacity 

Solar CSP 0.0 0.0 0.0 2.7 

Central PV 0.0 0.0 0.0 0.0 

Distributed PV 0.8 5.0 29.8 63.9 

Total 0.8 5.0 29.8 66.6 

Incremental Generation (BkWh) 

Solar CSP 0 0 0.0 18 

Central PV 0 0 0.0 0.0 

Distributed PV 2 10 60 129 

Total 2 10 60 147 

Concentrated Solar Power: The improved concentrated solar power (CSP) technology was 

represented by declining capital costs over time and higher capacity factors. The capital costs 

goals are higher than those used in the Baseline but represent systems with significantly more 

storage and, therefore, higher electrical output. A set of capacity factors by time periods within a 

year were computed by analysts to optimize the timing of solar output for each region within the 

bounds of the storage potential. The capacity factors and capital costs vary by region, due to 

differences in solar insolation and resulting storage costs. 

Primary energy, oil, and carbon emissions savings result from PV and CSP generation. These 

savings depend on which types of generating plants were built and operated in the Baseline Case. 

Over time, the mix of fuels and efficiencies of power generation vary; and, therefore, the energy 

savings will as well. Energy-expenditure savings are measured as the reduction in consumer 

expenditures for electricity and other fuels. Lower-cost renewable generation options reduce the 

price of electricity directly and reduce the pressure on natural gas supply, both of which benefit 

end-use consumers. Overall benefits of the Solar Energy Technologies Program are shown in 

Table 2.10. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-12 
Table 2.10. FY07 Annual Benefits Estimates for Solar Energy Technologies Program 

(NEMS-GPRA07) 

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) ns 0.06 0.35 1.07 

Economic 

 Energy-Expenditure Savings (billion 2003 dollars/yr) 1.1 2.3 8.1 7.9 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) ns 1 8 29 

Security 

 Oil Savings (million barrels per day) ns ns 0.03 ns 

 Natural Gas Savings (quadrillion Btu/yr) ns 0.05 0.09 Ns 

 Avoided Additions to Central Conventional Power (gigawatts) ns 3 20 54 

Other Program Metrics 

 Program-Specific Incremental Generation (gigawatt-hours/yr) 2 10 60 147 

 Program-Specific Electric Capacity Additions 

 (cumulative gigawatts) 1 5 30 67 

More details about the Solar Energy Technologies Program’s benefits analysis can be found in 

Appendix D.

16

Vehicle Technologies Program 

The Vehicle Technologies Program consists of research on light-duty vehicle hybrid and diesel 

technologies, heavy-vehicle engine/drivetrain and parasitic loss-reduction technologies, and 

lightweight materials for engines and vehicles. The program includes research in advanced 

petroleum and renewable fuels, the benefits of which are not modeled. In addition, Clean Cities, 

a deployment program to stimulate greater use of alternative fuels and efficient vehicles, is 

included within the Vehicle Technologies Program. 

Light-duty vehicle hybrid and diesel technologies: This research aims to improve engine 

technologies in light-duty vehicles, which include passenger cars and light-duty trucks. NEMSGPRA07 is used to compute benefits estimates for these activities through a process that 

estimates the penetration (sales) of the various technologies in the market for light-duty vehicles 

over time. The amount that each technology penetrates into the market determines the stock of 

these vehicles and the vehicle miles traveled (VMT) associated with each technology. 

In the NEMS-GPRA07 integrating model, the light-duty vehicle (LDV) market consists of six 

car classes—mini-compact, subcompact, compact, midsize, large, two-seater—and six light-duty 

truck classes—small and large pickup, small and large van, small and large sport utility vehicle 

(SUV)—in nine Census divisions. For each vehicle type and class and for each region, a number 

of LDV technologies compete against each other in the market for vehicle sales. These include 

conventional gasoline, advanced combustion diesel, gasoline hybrids, diesel hybrids, gasoline 

 

16 Details about the relevant NEMS modules are available at: http://tonto.eia.doe.gov/FTPROOT/modeldoc/m067(2005).pdf

(Residential Sector Demand Module), http://tonto.eia.doe.gov/FTPROOT/modeldoc/m066(2005).pdf (Commercial Sector 

Demand Module), http://tonto.eia.doe.gov/FTPROOT/modeldoc/m068(2004).pdf (Electricity Market Module), and 

http://tonto.eia.doe.gov/FTPROOT/modeldoc/m069(2005).pdf (Renewable Fuels Module). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-13 
fuel cell, hydrogen fuel cell, electric, natural gas, and alcohol. The plug-in HEV (PHEV) activity 

was added in FY06, but the capability to model the market acceptance of this new vehicle (which 

uses both electricity and a liquid fuel) has not yet been developed. 

Each vehicle technology is represented by a number of characteristics that can change over the 

forecast time horizon and that influence the technology’s acceptance in the marketplace and its 

sales. These characteristics include the vehicle cost, the fuel cost per mile (a combination of the 

fuel price and the vehicle efficiency), the vehicle range, the operating and maintenance cost, the 

acceleration, the luggage space, the fuel availability, and the make and model availability. The 

NEMS-GPRA07 model also includes “calibration” coefficients to calibrate the model to 

historical sales data. The associated characteristics for all the alternative technologies are 

specified as relative to those for the conventional gasoline vehicle. 

The model estimates the sales-penetration share of each technology in all of the vehicles, classes, 

and regions in each year of the forecast. The various characteristics of the technologies 

determine the technology’s value to consumers and its acceptance in the marketplace, but each 

characteristic has a differing degree of influence. The vehicle cost is generally the most 

influential of the characteristics, certainly having a much stronger influence than luggage space, 

for example. The values of all the characteristics are combined together to create an overall 

value. The technologies are competed against each other, based on the overall values, using a 

nested logit formulation. In a logit formulation, the relative size of the overall value for each 

technology determines the relative penetration share for that technology. Technologies that have 

higher values are given greater sales shares, resulting in a distribution of consumer preferences 

rather than the technology with the highest value receiving 100% of the market. 

 

In the FY 2007 benefits analysis, the Baseline Case for transportation programs includes some 

additional penetration of hybrids above the level in the AEO2005 Reference Case—sales of 

hybrids are roughly 11% by 2025, compared to only 6% in the AEO2005. This reflects the 

program’s view that the AEO2005 hybrid penetration is too low, due to the roughly constant 

hybrid vehicle efficiencies and costs over time. For the Baseline Case, the hybrid cost 

differentials relative to conventional gasoline vehicles were reduced so that they were 

approximately halfway between the AEO2005 Reference Case and the Individual Program Goal 

Case. The model calibration coefficient was also phased out over 20 years to represent a gradual 

increase in consumer acceptance of hybrids. The effect of the higher hybrid sales in the Baseline 

is to reduce the incremental benefits credited to the Vehicle Technologies Program. 

The Individual Program Goal Case uses the program technology characteristics, along with a 

variety of other assumptions relating to behavioral responses, in the underlying logit formulation 

of the NEMS-GPRA07 model. These include modeling an increase in the consumer acceptance 

of HEVs relative to gasoline internal-combustion engines17 more rapidly than in the Baseline, 

and reworking the manner in which the make and model availability coefficients are used. 

Lightweight materials for engines and vehicles: The lightweight materials developed under 

this R&D activity are used in both light and heavy vehicles and are represented in the NEMS-

 

17 Modelers, based on the expert judgment of the benefits analysis team, decrease the “calibration coefficients” over time to zero - 

faster in the Individual Program Goal Case than in the Base Case. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-14 
GPRA07 model. For light-duty vehicles, the effect of these materials for hybrids and advanced 

diesel is included in the projection of vehicle attributes described above, and is not modeled 

separately. However, for light-duty conventional vehicles, the effect of these materials is 

modeled using the Manufacturers' Technology Choice (MTC) submodule within NEMSGPRA07, where an economic decision is made based on the costs and efficiency of the 

technology. The costs and efficiencies are provided as attributes for an advanced conventional 

vehicle and transformed for use in existing lightweight technology slots in the MTC. For heavy 

vehicles, the effect of these materials is included in the projections of penetrations and 

efficiencies. 

Clean Cities: This deployment subprogram is represented through an increase in alternativefuel vehicles and an increase in dedicated ethanol (E85) vehicles and fuel use. For the increase in 

alternative-fuel vehicles, analysts used off-line analysis to determine the cumulative number of 

expected vehicles participating in Clean Cities. These were converted to annual vehicle sales and 

used as inputs into NEMS-GPRA07. The largest share of vehicles are compressed natural gas, 

ethanol, and liquefied petroleum gas—electric and methanol vehicle shares are small. For the 

portion of the program that encourages greater ethanol use, analysts determined the change in the 

fraction of vehicles using E85 over time and an increasing fraction of E85 use per vehicle. These 

were converted to overall fractions of E85 use and were then used as inputs to NEMS-GPRA07. 

Heavy-vehicle engine/drivetrain and parasitic loss reduction technologies: Heavy vehicles 

are those that have a gross weight (the weight when fully loaded) of 10,000 pounds or more. This 

program researches multiple technologies including engines/drivetrains, parasitics/accessories, 

aerodynamics, and hybrids. The benefits of this R&D activity are derived from penetration rates 

estimated by the Heavy Truck Energy Balance and TRUCK 2.0 models (developed for the 

Vehicle Technologies Program), using efficiency and technology cost assumptions. The 

penetration rates and efficiencies are then used in the NEMS-GPRA07 freight model to increase 

the efficiency of new vehicles. NEMS-GPRA07 performs the stock accounting for the fleet and 

determines the overall change in consumption. 

Using the integrated NEMS-GPRA07 model, the overall sales share for gasoline light-duty 

vehicles in 2025 falls from 77% in the Baseline Case to 37% in the Individual Program Goal 

Case (Figure 2.2). This decrease in share is due to the penetration of the alternative technologies. 

The overall share in 2025 for advanced combustion diesel increases from 5% to 18%; for 

gasoline hybrids, from 10% to 24%; and, for diesel hybrids, from 1% to 16%. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-15 
Figure 2.2 Vehicle Technology Sales Share in 2025 

These larger vehicle sales shares for advanced technology vehicles in 2025, however, translate 

into much smaller shares of overall vehicle stocks and overall shares of vehicle miles traveled 

(VMT) for each technology. The stock shares depend on the share of sales over time, which only 

gradually increases for the alternative-technology vehicles, and the rate of vehicle replacement 

and growth. The total VMT for gasoline vehicles falls from 3,311 billion miles in 2025 to 2,563 

(just more than 60 percent of the VMT) between the two cases (Figure 2.3). The total VMT for 

advanced-combustion diesel increases from 165 to 378 billion miles (9%); for diesel hybrids, 

from 25 to 291 billion miles (almost 7%); and, for gasoline hybrids, from 266 to 769 billion 

miles (18%). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-16 
Figure 2.3 Vehicle Miles Traveled in 2025 

 

The miles per gallon (MPG) for advanced-combustion diesel and for hybrid vehicles is much 

greater than the MPG for conventional gasoline vehicles. In addition, the conventional gasoline 

vehicles are more efficient, due to adoption of lower-cost lightweight materials. As a 

consequence of the advanced-technology vehicles substitution for the conventional gasoline 

vehicles and improved conventional vehicles, there is a considerable amount of fuel savings. 

 

In these integrated NEMS-GPRA07 model runs, the savings are typically somewhat less than 

what they would be if they were estimated in a transportation-only model, because of feedback 

effects that come through the integration with other sectors. The primary feedback effect occurs 

through lower fuel prices. In this case, reduced gasoline demand causes lower gasoline prices, 

which leads to an increase in travel and less-efficient vehicle purchases than would otherwise 

have occurred absent the price change. The rebound of gasoline consumption reduces the 

program savings. At the same time, energy-expenditure savings are greater. The small decreases 

in price apply to the total amount of fuel consumed and contribute significant additional 

expenditure savings. In addition, the “rebound” effect is also influenced by the fact that vehicles 

are more efficient, thereby reducing the cost to drive, causing more miles to be driven. The total 

effect is that light-duty VMT in 2025 is roughly 4% higher in the Individual Program Goal Case 

than in the Baseline. Table 2.11 presents the total program benefits, including those of heavy 

trucks and Clean Cities. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-17 
Table 2.11. FY07 Benefits Estimates for Vehicle Technologies Program (NEMS-GPRA07)18

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.04 0.38 1.15 2.32 

Economic 

 Energy-Expenditure Savings (billion 2003 dollars/yr) ns 4.4 26.2 49.3 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 1 7 20 41 

Security 

 Oil Savings (million barrels per day) 0.02 0.18 0.52 1.07 

 Natural Gas Savings (quadrillion Btu/yr) ns ns 0.18 0.15 

 Avoided Additions to Central Conventional Power 

 (cumulative gigawatts) ns ns ns ns 

More details about the Vehicle Technologies Program’s benefits analysis can be found in 

Appendix F.

19

Weatherization and Intergovernmental Program 

The Weatherization and Intergovernmental Program (WIP) encompasses several marketenhancement activities, rather than R&D. The major components include: International, Native 

American Renewable Initiative (also referred to as Tribal Energy), the Renewable Energy 

Production Incentive (REPI), Weatherization (Assistance), and State Energy Program Grants. 

The FY 2007 benefits estimate methodologies vary by activity. The International activities are 

currently outside the scope of the integrated modeling framework. 

Weatherization and State Energy Program Grants are implementation programs that lead to 

greater adoption of energy efficiency. The projected energy savings are based on the program’s 

evaluations of past experience for these programs. Weatherization is aimed primarily at 

achieving heating and cooling energy reductions in homes of low-income households. T o 

determine the annual energy savings, the number of homes projected to be weatherized is 

combined with the expected savings per household. The State Energy Program provides financial 

assistance to States and encompasses a number of types of activities including codes and 

standards, energy audits, retrofits, labeling, workshops and training, incentives, loans and grants, 

and technical assistance. Energy savings are estimated for each of these activities based on 

evaluations of prior-year efforts. The Weatherization and SEP energy savings are represented in 

NEMS-GPRA07 by reducing energy consumption in the residential and commercial sectors, 

based on the program goals. 

The Native American renewable initiative offers assistance for renewable energy feasibility 

studies and shares the cost of renewable energy projects on tribal lands. The goal is the 

electrification of currently nonelectrified occupied housing and the offsetting of more 

 

18 Note that in the Vehicle Technologies Individual Program Goal Case, the advanced ethanol production technologies available 

in the Biomass Program’s Case are unavailable, despite the market synergies of the two suites of technologies. In the EERE 

portfolio case, both suites are modeled.

19 Details about the relevant NEMS modules are available at: http://tonto.eia.doe.gov/FTPROOT/modeldoc/m070(2005).pdf

(Transportation Sector Module). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-18 
expensively provided electricity on tribal lands. Analysts made projections of central station 

wind and biomass capacity that would be stimulated by the program, as well as home-installed 

PV systems, based on the program’s goals. The wind and biomass capacities were added as 

“planned additions” 20 within the NEMS-GPRA electricity sector. The additional PV capacity is 

counted in the benefits for added program capacity, but is not included in the modeling as 

displacing conventional generation and fuel consumption, because the systems provide 

electrifications to those who would not have it otherwise. 

REPI provides payments to publicly owned utilities, such as municipal utilities or rural electric 

cooperatives, for electricity generation from renewable energy sources. These payments are the 

public power equivalent of the production tax credit for investor-owned renewable generators. 

Analysts projected the amount of new renewable generation that is likely to be stimulated by 

future REPI payments based on the requested budget levels and historical patterns of payments. 

Almost all the new generation is expected to be wind, based on the eligibility criteria and past 

experience. Some of the wind capacity added as planned builds to represent WIP displaces 

economic wind builds in NEMS-GPRA07, so the incremental is less than that calculated off-line. 

Overall benefits for WIP are shown in Table 2.12. 

Table 2.12. FY07 Annual Benefits Estimates for Weatherization and Intergovernmental Program

(NEMS-GPRA07) 

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.06 0.13 0.15 0.20 

Economic 

 Energy-Expenditure Savings (billion 2003 dollars/yr) ns 1.2 2.9 2.3 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 1 3 3 4 

Security 

 Oil Savings (million barrels per day) ns 0.01 0.04 0.01 

 Natural Gas Savings (quadrillion Btu/yr) 0.02 0.07 0.07 0.11 

 Avoided Additions to Central Conventional Power (gigawatts) ns 2 1 2 

Other Program Metrics 

 Program-Specific Incremental Generation (gigawatt-hours/yr) 1 11 7 17 

Program-Specific Electric Capacity Additions 

(cumulative gigawatts) 1 4 3 5

More details on the Weatherization and Intergovernmental Program’s benefits analysis can be 

found in Appendix J.

21

 

20 In NEMS, there are two ways that generation capacity is added to the energy system. “Builds” are capacity additions that the 

model endogenously calculates based on energy supply and demand. “Planned additions” are specific plants that are included in 

the model's capacity expansion plan based on modeler knowledge. These can represent capacity under construction at the time 

the forecast is made, capacity that is anticipated to meet local requirements (such as State renewable portfolio standards or State 

incentives), or capacity that may be built for site- or institution-specific reasons that are not reflected in the model's endogenous 

decision framework. The planned additions will displace capacity that the model would have otherwise built. Because there are 

supply curves for biomass and wind resources, the planned builds may, in part, offset endogenous builds of these resources. 21 More details on the relevant NEMS module are available at: http://tonto.eia.doe.gov/FTPROOT/modeldoc/m067(2005).pdf

(Residential Sector Demand Module), http://tonto.eia.doe.gov/FTPROOT/modeldoc/m066(2005).pdf (Commercial Sector 

Demand Module), and http://tonto.eia.doe.gov/FTPROOT/modeldoc/m068(2004).pdf (Electricity Market Model). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-19 
Wind Technologies Program 

The wind component of the Wind Technologies Program seeks to reduce the cost—and improve 

the performance—of wind generation. The FY 2007 benefits are based primarily on projecting 

the market share for wind technologies, based on their economic characteristics. 

Representation of Wind: The NEMS-GPRA07 electricity-sector module performs an economic 

analysis of alternative technologies in each of 13 regions. Within each region, new capacity is 

selected based on its relative capital and operating costs, its operating performance (i.e., 

availability), the regional load requirements, and existing capacity resources. NEMS-GPRA07 

characterizes wind by three wind classes, each with its own capital costs and resource cost 

multipliers. The regional resource cost multipliers act to increase costs as more of a wind class is 

developed in a region, and development may move to the next most cost-effective wind class. 

NEMS-GPRA07 assumes that the capacity value of wind diminishes with greater wind capacity 

in a region. Finally, another constraint on the growth of wind-resource development is how 

quickly the wind industry can expand before costs increase due to manufacturing bottlenecks. As 

in the AEO2005, the Individual Program Goal Case22 (see Table 2.13) assumes that a cost 

premium is imposed when new orders in a year are 20% higher than in the highest of the 

previous 10 years. 

The baseline characterizations of wind capital costs and capacity factors were modified to reflect 

a more consistent view relative to the program goals. The Baseline costs were reduced over time 

so that, by 2050, the onshore cost remains below the offshore costs by a ratio equivalent to that 

of that ratio in the Individual Program Goal Case. In addition, the capacity factors were increased 

for all three wind classes. The effect of these changes is to increase onshore wind capacity in the 

Baseline relative to the AEO2005, which reduces the benefits attributed to the program, but 

presents a better representation of the impact of the program’s R&D. 

NEMS-GPRA07 also includes a representation of offshore wind that is not in the AEO2005 

version of the NEMS model. The offshore wind is represented as a distinct technology that 

competes with all other generation technologies. It is characterized in a manner similar to 

onshore wind, with three wind classes—but also has a distinction between shallow and deeper 

water (transitional) sites. The constraints on intermittent generation and rapid growth apply 

similarly to offshore as to onshore wind development. The offshore wind does not have the 

regional resource cost multipliers, because there is insufficient data on how they might apply. 

The Baseline technology characteristics assume that improvements would occur without EERE 

R&D, but at a slower pace of roughly 10 years later. 

Analysts represented the Wind Program R&D activities by reducing the capital and O&M costs 

and increasing the performance of wind capacity to match the program cost goals. 

Table 2.14 provides the estimates of primary energy, oil, and carbon emissions savings 

stemming from wind and hydropower displacing fossil-fueled generation sources. Analysts 

measure the energy-expenditure savings as the reduction in consumer expenditures for electricity 

 

22 In the AEO2005, all generation technologies face similar premiums associated with rapid growth. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-20 
and other fuels. Lower-cost renewable generation options reduce the price of electricity directly 

and reduce the pressure on natural gas supply, both of which benefit end-use consumers. 

Table 2.13. Wind Capacity (GW) 

 2010 2015 2020 2025 

AEO Base 8.9 9.3 10.5 11.3 

GPRA Baseline 

Onshore Class 6 4.0 4.8 8.1 8.8 

 Class 5 4.4 7.8 15.7 20.7 

 Class 4 0.5 0.6 0.7 2.9 

 Subtotal 8.9 13.2 24.5 32.3 

Offshore Class 7 0.0 0.0 0.0 0.9 

 Class 6 0.0 0.0 0.0 2.9 

 Class 4&5 0.0 0.0 0.0 0.0 

 Subtotal 0.0 0.0 0.0 3.8 

Total Total 8.9 13.2 24.5 36.2 

Wind Individual Program 

Goal Case 

Onshore Class 6 4.0 5.5 8.4 8.7 

 Class 5 4.4 8.8 21.8 24.1 

 Class 4 0.5 4.6 28.8 47.0 

 Subtotal 8.9 18.9 59.0 79.8 

Offshore Class 7 0.0 0.0 0.9 3.5 

 Class 6 0.0 0.1 17.7 47.4 

 Class 4&5 0.0 0.0 0.0 5.2 

 Subtotal 0.0 0.1 18.7 56.1 

Total Total 8.9 19.0 77.6 135.8 

Incremental Capacity Onshore Class 6 0.0 0.7 0.3 0.0 

 Class 5 0.0 1.0 6.1 3.4 

 Class 4 0.0 4.0 28.1 44.1 

 Subtotal 0.0 5.7 34.5 47.5 

Offshore Class 7 0.0 0.0 0.9 2.6 

 Class 6 0.0 0.1 17.7 44.5 

 Class 4&5 0.0 0.0 0.0 5.2 

 Subtotal 0.0 0.1 18.7 52.2 

Total Total 0.0 5.8 53.1 99.7 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-21 
Table 2.14. FY07 Benefits Estimates for Wind Technologies Program 

(NEMS-GPRA07)

 Benefits 2010 2015 2020 2025 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) ns 0.14 1.60 3.10 

Economic 

 Energy-Expenditure Savings (billion 2003 dollars/year) ns 1.2 10.5 17.6 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/year) ns 3 34 69 

Security 

 Oil Savings (million barrels per day) ns ns 0.11 0.09 

 Natural Gas Savings (quadrillion Btu/year) ns 0.10 0.48 0.83 

 Avoided Additions to Central Conventional Power (gigawatts) ns 1 14 17 

Other Program Metrics 

 Program-Specific Incremental Generation (gigawatt-hours/yr) 0 23 225 429

 Program-Specific Electric Capacity Additions 

 (cumulative gigawatts) 0 6 53 100

 

More information about the Wind Program’s benefits analysis can be found in Appendix E.

23

 

23 Details about the relevant NEMS modules are available at: http://tonto.eia.doe.gov/FTPROOT/modeldoc/m068(2004).pdf

(Electricity Market Module) and http://tonto.eia.doe.gov/FTPROOT/modeldoc/m069(2005).pdf (Renewable Fuels Module). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-22 
Box 2.1—EIA’s National Energy Modeling System (NEMS)* 

The National Energy Modeling System (NEMS) is an energy-economy modeling system of U.S. energy markets for the 

midterm period through 2025. NEMS projects the production, imports, conversion, consumption, and prices of energy, 

subject to assumptions on macroeconomic and financial factors, world energy markets, resource availability and costs, 

behavioral and technological choice criteria, cost and performance characteristics of energy technologies, and demographics. 

NEMS was designed and implemented by the Energy Information Administration (EIA) of the U.S. Department of Energy 

(DOE). As described in the GPRA Baseline section, the NEMS-GPRA07 version of the model used for the EERE GPRA 

analysis includes minor modifications to the standard EIA NEMS. 

NEMS is designed as a modular system. Four end-use demand modules represent fuel consumption in the residential, 

commercial, transportation, and industrial sectors—subject to delivered fuel prices, macroeconomic influences, and 

technology characteristics. The primary fuel supply and conversion modules compute the levels of domestic production, 

imports, transportation costs, and fuel prices that are needed to meet domestic and export demands for energy—subject to 

resource base characteristics, industry infrastructure and technology, and world market conditions. The modules interact to 

solve for the economic supply and demand balance for each fuel. Because of the modular design, each sector can be 

represented with the methodology and the level of detail (including regional detail) that is appropriate for that sector. 

A key feature of NEMS is the representation of technology and technology improvement over time. Five of the sectors—

residential, commercial, transportation, electricity generation, and refining—include extensive treatment of individual 

technologies and their characteristics, such as the initial capital cost, operating cost, date of availability, efficiency, and other 

characteristics specific to the sector. Technological progress results in a gradual reduction in cost and is modeled as a 

function of time in these end-use sectors. In addition, the electricity sector accounts for technological optimism in the capital 

costs of first-of-a-kind generating technologies and for a decline in cost as experience with the technologies is gained both 

domestically and internationally. In each of these sectors, equipment choices are made for individual technologies as new 

equipment is needed to meet growing demand for energy services or to replace retired equipment. In the other sectors—

industrial, oil and gas supply, and coal supply—the treatment of technologies is more limited, due to a lack of data on 

individual technologies. In the industrial sector, only the combined heat and power and motor technologies are explicitly 

considered and characterized. Cost reductions resulting from technological progress in combined heat and power 

technologies are represented as a function of time as experience with the technologies grows. Technological progress is not 

explicitly modeled for the industrial motor technologies. Other technologies in the energy-intensive industries are 

represented by technology bundles, with technology possibility curves representing efficiency improvement over time. In the 

oil and gas supply sector, technological progress is represented by econometrically estimated improvements in finding rates, 

success rates, and costs. Productivity improvements over time represent technological progress in coal production. 

* Most of this description is taken from The National Energy Modeling System: An Overview 2003, DOE/EIA-0581(2003), 

March 2003. The document is available at http://tonto.eia.doe.gov/FTPROOT/forecasting/05812003.pdf . 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Midterm Benefits Analysis of EERE’s Programs (Chapter 2) – Page 2-23 
CHAPTER 3 

LONG-TERM BENEFITS ANALYSIS OF EERE’S 

PROGRAMS 

Introduction 

This chapter provides an overview of the modeling approach used in MARKAL-GPRA07 to 

evaluate the benefits of the Office of Energy Efficiency and Renewable Energy (EERE) R&D 

programs and technologies.1

 The program benefits reported in this section result from 

comparisons of each Individual Program Goal Case to the Baseline Case, as modeled in 

MARKAL-GPRA07. 

The Baseline Case used to evaluate the impact of the EERE portfolio was benchmarked to EIA’s 

Annual Energy Outlook 2005 (AEO2005) for the period between 2005 and 2025. To the extent 

possible, the same input data and assumptions were used in MARKAL-GPRA07 as were used to 

generate the AEO2005 Reference Case. For example, the macroeconomic projections for GDP, 

housing stock, commercial square footage, industrial output, and vehicle miles traveled were 

taken from the AEO2005. At the sector level, both supply-side and demand-side technologies 

were characterized to reflect the AEO2005 assumptions where the representation of technologies 

is similar between MARKAL (MARKet ALlocation) and the National Energy Modeling System 

(NEMS). The resulting projections track closely with the AEO2005 at the aggregate level, 

although they do not match exactly at the end-use level. For the period after 2025, various 

sources were used to compile a set of economic and technical assumptions. For instance, the 

primary economic drivers of GDP and population were based on the real GDP growth rate from 

the Congressional Budget Office’s Long-Term Budget Outlook and population growth rates from 

the Social Security Administration’s 2005 Annual Report to the Board of Trustees. Appendix A 

provides a more complete discussion of the MARKAL-GPRA07 Baseline Case.2

For each EERE RD3 program, analysts make modifications to the characteristics of the 

technologies involved to generate an Individual Program Goal Case. Individual Program Goal 

Cases also may include technologies not available in the Baseline Case. The modifications made 

to the model parameters and attributes of a technology depend on the nature of the program. 

They directly affect the technology’s competitiveness and market deployment presented in the 

model. 

 

1

 For three programs—Weatherization and Intergovernmental Activities (WIP), Federal Energy Management Program (FEMP), 

and the Industrial Technologies program—EERE did not report long-term benefits in the FY 2007 Congressional Budget request, 

but were nonetheless modeled in MARKAL-GPRA ’07. For consistency with the budget submission, this benefits report will not 

show the individual contributions of those three programs beyond 2025. Nevertheless, the programs’ long-term benefits are 

embedded in EERE’s aggregate long-term benefits. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-1 

2

 For a detailed documentation of the standard MARKAL model, please see http://www.etsap.org/MrklDoc-I_StdMARKAL.pdf . 
Table 3.1 provides a breakdown by program of the two types of analytical methods employed in 

EERE’s long-term benefits analyses—specialized “off-line” tools and MARKAL-GPRA07. For 

the long-term analysis, off-line tools are those that are used to provide input to MARKALGRPA07 and to estimate benefits for technologies outside the scope of MARKAL-GRPA07. 

The activities listed are groupings of activities within each program that share either technology 

or market features. They do not represent actual program-management categories. A description 

of the MARKAL model is provided in Box 3.1 at the end of this chapter. Descriptions of the offline models are provided in the related program appendix.3

 The indication that a particular 

program was modeled using off-line tools should not be interpreted to mean that the program 

was not included in the MARKAL-GPRA07 modeling, or that the results of the program analysis 

are not impacted by the MARKAL-GPRA07 modeling. 

Table 3.1. Long-Term Benefits Modeling by Primary Type of Model Used and Activity Area

Program Activities Off-Line Tools MARKAL-GPRA07

Ethanol from Corn Fiber & Residual 

Starch 

Biomass 3

Cellulosic Ethanol 3

Technology R&D 3 3

Regulatory Actions 3 3 

Buildings Technologies 

Market Enhancement 3

FEMP FEMP 3

Hydrogen, Fuel Cells, and Fuel Cells 3

Infrastructure Technologies Production 3

Industrial Technologies Industrial Programs 3

Solar Energy Technologies Central Solar Power 3

Photovoltaics 3 3

Light Duty Vehicle Hybrid and 

Diesel 

Vehicle Technologies 3

Heavy Trucks 3

Weatherization and Intergovernmental Weatherization 3

Domestic Intergovernmental 3

Wind Technologies Wind 3

The following sections summarize how each EERE program is formulated in MARKALGPRA07. In many cases, analysts convert the technological data and their projected market 

potentials in each program directly to MARKAL-GPRA07 input. When this is not feasible, the 

quantitative analyses undertaken in the program and market analyses are used, in part, to 

generate the Individual Program Goal Cases. 

Biomass Program 

The goal of the Biomass Program is the development of biomass-based refineries (biorefineries), 

which produce a range of products including cellulosic ethanol and/or other fuels, chemicals, 

materials, and/or electricity. The biorefinery concept allows the cost of production to be reduced 

through synergies associated with feedstock handling and processing, and the allocation of 

capital and fixed O&M costs across multiple products. The current analysis is based on 

 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-2 

3

 It is important to note that the off-line analyses were used to feed appropriate parameters and other factors into MARKALGPRA07, which was then run for all the programs. 
biorefineries that produce ethanol fuel as a primary output along with specialized bio-based 

products. Future analyses could include additional fuels that the program may identify in the 

longer term. Additionally, the program is working on increasing the yields of corn ethanol plants 

through the conversion of the fiber in corn kernels and residual (recalcitrant) kernel starch left 

over after conventional corn ethanol processing. The research undertaken to improve the 

harvesting of agricultural residue feedstocks has not been included in the GPRA analysis. 

Corn and cellulosic ethanol: EERE is sponsoring research aimed at reducing the cost of 

producing ethanol from corn and cellulosic biomass.4

 In the Biomass Individual Program Goal 

Case, the conversion of corn fiber and residual starch to ethanol becomes available for dry mills 

beginning in 2012 and yields a 20% increase in a dry mill's ethanol output. The projected 

revenue from producing bio-based products was treated as a cost credit toward producing ethanol 

in dry mills. Cellulosic biorefineries that produce ethanol, electricity, and bioproducts become 

available in 2015 in the Individual Program Goal Case and in 2033 in the Baseline Case. The 

cellulosic biorefineries are assumed to include a cogeneration unit, which will convert residual 

biomass to process heat and electricity. 

Table 3.2 depicts the production and use of corn and cellulosic ethanol projected by MARKALGPRA07, for both the Baseline Case and the Individual Program Goal Case, which reflects 

ethanol’s penetration, if program cost goals are met. Note that these scenarios are based on the 

AEO2005 Reference Case and do not include any of the incentives for biofuels from the Energy 

Policy Act of 2005. Table 3.3 shows the cellulosic ethanol plant cogeneration capacity and net 

electric generation that would be available for sale to the grid. 

Table 3.2. Projected Ethanol Production and Use 

(billion gallons/year) 

2030 2040 2050 

Corn 

Baseline Case 5.3 5.8 6.1 

Individual 

Program Goal 

Case 5.9 6.0 6.1 

Incremental 0.5 0.2 0.0 

Cellulosic 

Baseline Case 0.0 1.9 4.5 

Individual 

Program Goal 

Case 20.1 28.0 30.9 

Incremental 20.1 26.1 26.4 

Total Ethanol 

Baseline Case 5.3 7.7 10.6 

Individual 

Program Goal 

Case 25.9 33.9 37.0 

Incremental 20.6 26.2 26.4 

 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-3 

4 Cellulose and hemi-cellulose that can be converted to ethanol (and other chemicals, materials, and biofuels) are found in 

biomass such as agricultural residues (corn stover, wheat, and rice straw), mill residues, organic constituents of municipal solid 

wastes, wood wastes from forests, future grass, and tree crops dedicated to bio-energy production. 
Table 3.3. Cellulosic Biorefinery Cogeneration Capacity 

and Net Generation 

 2030 2040 2050 

Capacity (GW)

Baseline Case 0 1 2 

Individual Program Goal 

Case 10 13 14 

Incremental 10 12 12 

Generation (Bill. kWh)

Baseline Case 0 6 14 

Individual Program Goal 

Case 86 112 114 

Incremental 86 106 100 

The benefits of the Biomass Program derived in MARKAL-GPRA07 (Table 3.4) are the results 

of direct substitution of biomass-based energy for fossil fuels. Ethanol displaces an increasing 

fraction of the gasoline used in light-duty vehicles (LDVs), while the cogeneration of electricity 

at cellulosic biorefineries displaces coal and natural gas-fired power generation. The reduction in 

fossil fuel consumption at high marginal cost generates savings both in carbon emissions and 

energy-system costs. 

Table 3.4. Annual Benefits Estimates for Biomass Program (MARKAL-GPRA07)5

 Annual Benefits 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 2.3 2.9 2.8 

Economic 

 Energy-System Cost Savings (billion 2003 dollars/yr) 2 2 2 

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 45 60 57 

Security 

 Oil Savings (mbpd) 0.9 1.1 1.1 

 Natural Gas Savings (quadrillion Btu/yr) 0.2 ns ns 

More details on the Biomass Program’s benefits analysis can be found in Appendix C. 

Building Technologies Program 

MARKAL-GPRA07 models technologies and activities in the Buildings Program, based on three 

general types of activities: technology R&D, regulatory actions, and market enhancement. 

 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-4 

5

 Note that in the Biomass Individual Program Goal Case, the advanced transportation technologies available in Freedom Car and 

Vehicle Technologies Individual Program Goal Case are unavailable, despite the market synergies of the two suites of 

technologies. In the EERE portfolio case, both suites are modeled. 
Technology R&D: New and improved technologies are introduced into MARKAL-GPRA07 by 

modifying the technology slates that are available in the Baseline Case. These modifications are 

accomplished by changing any (or all) of the following three parameters to reflect program 

goals: the date of commercialization, capital cost, and efficiency. Building technologies for 

which these parameters can be characterized to meet specific building service demands include 

end-use devices such as furnaces, air conditioners, heat pumps, and water heaters. 

Technologies that lower service demand (e.g., building-shell technologies and lighting controls) 

are modeled in MARKAL-GPRA07 as steps in a conservation supply curve. Each supply step is 

characterized by capital cost, load-reduction potentials expressed as upper bounds of market 

penetration, consumer’s hurdle rate, and technology lifetime. These conservation steps reduce 

the market size or load demand for end-use devices. In the Buildings Individual Program Goal 

Case, these newly introduced technologies compete with the baseline technologies for market 

share. For example, in future time periods, the size of the market for commercial air-conditioning 

capacity is the projected total heat in trillion Btus to be removed from the service areas. The new 

investment opportunity in that time period is the difference between the projected service 

demands in that period and the capacity of capital stock carried over from the previous period. 

Technologies such as solid-state lighting, although available in the Baseline Case, do not have a 

significant market share initially because of their high consumer hurdle rate (44%). These hurdle 

rates are lowered to 18% when running the Buildings Technology Case to reflect consumer 

acceptance of these products with improved performance.6

 The 18% is an empirical value based 

on observed consumer responses, but is much higher than would be observed if consumers were 

minimizing life-cycle costs. Although the future market potential of new lighting technologies is 

great, due to the relatively short life of the equipment, the penetration of these technologies 

modeled in MARKAL-GPRA07 is limited to a sustainable growth path that generates a potential 

market penetration path consistent with the program goals. 

Regulatory activities: Analysts represent new appliance standards and building codes in 

MARKAL-GPRA07 as either new technologies or energy-conservation supply steps. In the time 

period that a new standard becomes effective, the model removes technologies with efficiency 

below the set standard from the market. Regulatory activities primarily affect the performance of 

new energy products for a specific end-use product purchased by consumers in future markets. 

The overall impact of the Buildings Program, therefore, depends on the size of these markets. 

MARKAL-GPRA07 determines the size of these markets by dynamically keeping track of the 

turnover of capital equipment and deriving the new investment needed to meet projected energyservice demands. Because some end-use devices (e.g., heating equipment) have a long service 

lifetime, the stock turnover constraints modeled in MARKAL-GPRA07 limit near-term energy 

savings. 

 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-5 

6 The hurdle rates in MARKAL-GPRA07 include factors to reflect both the interest rate available to consumers, as well as 

behavioral and risk premiums that are implicit in consumer decisions. Behavioral premiums would reflect a documented 

consumer bias toward choosing reduced up-front investment costs over longer-term operating cost savings. The behavioral 

premium also incorporates agency issues where the decision-maker would not benefit from long-term operating costs and, thus, 

would make decisions based primarily on initial capital costs. Risk premiums would apply to new, unfamiliar products that are 

presumed to be less desirable to consumers, due to the lack of familiarity or a track record of successful application. Also, risk 

premiums would be appropriate for modeling situations where technologies may appear to be cost-effective on paper, but are not 

chosen by consumers for reasons such as convenience, styling, or lack of availability.
Deployment activities: Deployment programs, such as the Energy Star Program, which is aimed 

at promoting individual technologies, were either modeled by adjusting the technologies discount 

rate or by applying lower bounds on the technology investment, based on off-line analysis.

In MARKAL-GPRA07, energy savings are achieved when a more efficient and economic (on a 

life-cycle basis) end-use device is selected to substitute for a conventional device competing in 

the same market. For example, a 20 Watt (W) compact fluorescent light bulb (CFL) can replace a 

75W incandescent light bulb and provide the same level of lighting service, but uses much less 

electricity. The total market potential for this substitution in a future time period, however, is 

constrained by the investment opportunity established in MARKAL-GPRA07. 

Tables 3.5 and 3.6 depict the projected delivered energy savings in residential and commercial 

buildings by demand and fuel generated from the use of more efficient end-use devices and costeffective conservation measures covered under the Buildings Program. Additional savings accrue 

from new standards for distribution transformers, and commercial and industrial electric motors 

up to 200 hp. The electricity savings from these activities are shown in Table 3.7. 

In addition to the reduction in delivered primary energy, the reduction in electricity demand in 

buildings also leads to the reduction in gas-fired generation capacity, as well as fuel used for 

generation. Furthermore, building code and envelope improvements reduce both the demand for 

delivered energy and the required output capacity of end-use devices, such as furnaces or air 

conditioners. Thus, consumers see both a reduction in their energy bills, as well as reduced 

capital costs for end-use appliances. This is another factor attributable to the overall reduction in 

energy-system cost, in addition to direct energy savings. 

Table 3.5. Residential Delivered Energy Savings by 

Demand and Fuel (Quadrillion Btu/year) 

2030 2040 2050 

Reduction by Service Demand 

Space Heating 0.575 0.877 1.221 

Space Cooling 0.132 0.164 0.134 

Water Heating 0.025 0.063 0.146 

Lighting 0.301 0.640 0.742 

Other 0 0 0 

Total 1.033 1.744 2.243 

Reduction by Fuel 

Petroleum 147 638 1,015 

Natural Gas 568 486 568 

Coal 10 22 25 

Electricity 308 598 621 

Total 1,033 1,744 2,243 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-6 
Table 3.6. Commercial Delivered Energy Savings by 

Demand and Fuel (Quadrillion Btu/year)

2030 2040 2050 

Reduction by Service Demand 

Space Heating 0.117 0.080 0.101 

Space Cooling 0.130 0.177 0.162 

Water Heating ns ns ns 

Lighting 0.234 0.507 0.781 

Other ns ns ns 

Total7 0.471 0.754 1.034 

Reduction by Fuel 

Petroleum 0.080 -0.018 -0.018 

Natural Gas 0.011 0.069 0.111 

Coal 0 0 0 

Electricity 0.380 0.703 0.941 

Total 0.471 0.754 1.034 

Table 3.7. Electricity Savings from Distribution 

Transformer and Electric Motor Standards

(billion kWh/year)

2030 2040 2050 

Distribution Transformers 45.9 49.1 51.9 

Electric Motors 43.3 43.3 43.3 

Table 3.8. Annual Benefits Estimates for Building Technologies Program 

(MARKAL-GPRA07) 

 Annual Benefits 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 2.6 5.0 5.4

Economic 

 Energy-System Cost Savings (billion 2003 dollars/yr) 57 103 135

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 56 117 124

Security 

 Oil Savings (mbpd) 0.2 0.3 0.5

 Natural Gas Savings (quadrillion Btu/yr) 0.8 0.5 0.7

 Electricity Capacity Avoided (gigawatts) 67 103 118

More details on the Building Technologies Program’s benefits analysis are available in 

Appendix G. 

 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-7 

7

 The total service demand reduction does not sum to the constituent parts of Table 3.6, because of the model’s “ns” (not 

significant) results. There are minor changes in parts of the energy system unrelated to the Buildings Program RD3, and the 

magnitude of these changes are deemed be in the “noise” of the model results. 
Federal Energy Management Program 

The Federal Energy Management Program (FEMP) aims to improve the overall energy 

efficiency in Federal Government buildings. As a deployment program, FEMP utilizes a broad 

spectrum of existing technologies and practices for achieving its goal. Therefore, it does not 

provide specific technological information in relating costs and energy savings under its 

activities. The program, which has a well-documented track record, provided estimates of future 

savings based on past results and current budgets. 

In order to quantify the broader benefits of these savings in MARKAL-GPRA07, a single 

energy-conservation supply curve was modeled in the FEMP Case to reduce the energy service 

demands in “miscellaneous” commercial energy demand. The conservation curve was set to 

reflect the program’s estimated delivered energy savings. Further adjustments were made to the 

case to roughly match the level of delivered energy savings for each fuel type. 

The reduction in commercial energy demand effectively leads to lower investment in future 

capacity of demand devices servicing the Federal buildings, resulting in lower energy use in 

these devices. The reduction in electricity demand also leads to a slight drop in the electric 

generation by gas-fired power plants. FEMP also directly reduces fossil fuels used in commercial 

(government) buildings. 

The activities of the Federal Energy Management Program are more “midterm” in nature. Thus, 

the long-term annual benefits estimates, which are calculated by MARKAL-GPRA07, were not 

included in the EERE budget submission or in this section of the report. However, the program’s 

activities were modeled for the EERE Portfolio Scenario and included in the long-term annual 

benefits of the EERE Portfolio, as shown in Chapter 1. 

More details on the Federal Energy Management Program’s benefits analysis can be found in 

Appendix E. 

Hydrogen, Fuel Cells, and Infrastructure Technologies Program 

The Hydrogen, Fuel Cells, and Infrastructure Technologies (HFCIT) Program conducts research 

and development activities in hydrogen production, storage, and delivery; and transportation and 

stationary fuel cells. On the demand side, the program’s activities focus on the introduction of 

fuel cells for both stationary and mobile applications. On the supply side, the program goal is to 

lower the production cost of hydrogen to a competitive level against petroleum products. 

The representation of the Hydrogen, Fuel Cells, and Infrastructure Technologies Program in 

MARKAL-GPRA07 requires representation of fuel cell vehicles and transportation markets, 

hydrogen production and distribution infrastructure, and stationary fuel cell applications. 

Fuel cell vehicles and transportation markets: Fuel cell vehicles are projected to compete with 

traditional petroleum and hybrid-electric vehicles for market share in the light-duty vehicle and 

commercial light-truck markets. In MARKAL-GPRA07, analysts measure energy-service 

demands for road transportation in vehicle miles traveled (VMT). Projected VMTs are taken 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-8 
directly from the Annual Energy Outlook 2005 and extended past 2025, based on historical 

relationships between passenger and commercial VMTs and population and economic growth. 

Projected VMTs for light-duty vehicles and commercial light trucks are shown in Table 3.9. 

Table 3.9. LDV and Commercial Light-Truck Vehicle 

Miles Traveled (billion VMTs/year) 

2030 2040 2050 

Light-Duty Vehicles 4,420 5,156 5,628 

Commercial Light Trucks 118 140 159 

For each time period, these demands are met by a mix of vehicle types selected by the model on 

the basis of total life-cycle costs. These life-cycle costs include initial vehicle cost, annual 

maintenance costs, and annual fuel costs. The vehicle type is characterized for each model year it 

is available for purchase. The Baseline Case cost and efficiencies of these vehicles were derived 

from the AEO2005 assumptions, although hybrid vehicle costs were reduced from AEO2005 

levels in accordance to the Vehicle Technologies Program’s view of likely market developments 

exclusive of program R&D activities. The effect of this baseline change is to increase the market 

share of hybrid vehicles in the Baseline Case and, thus, reduce the level of benefits attributed to 

the Vehicle Technologies and HFCIT Programs. For the Hydrogen Individual Program Goal 

Case, capital costs, operation and maintenance costs, and fuel efficiency goals were provided by 

the HFCIT Program for hydrogen fuel cell vehicles from 2020 to 2050. 

Hydrogen production and distribution infrastructure: The HFCIT Program conducts 

research on developing cost-effective hydrogen production technologies from distributed natural 

gas reformers, as well as a variety of renewable sources, including biomass. For the Hydrogen 

Individual Program Goal Case, analysts modeled nine hydrogen production technologies: 

distributed natural gas reformers, central natural gas reformers, central coal gasification (with 

and without cogeneration), central biomass gasification, distributed ethanol reformers, central 

electrolytic production (both grid electricity and wind-dedicated electrolysis), and distributed 

electrolytic production. Other renewable hydrogen-production technologies were not modeled, 

due to a greater degree of uncertainty in their costs. Nuclear hydrogen production technologies 

were also not represented in the MARKAL-GPRA07 model. We expect that more hydrogen 

production technologies will be modeled in future GPRA analyses, as the data become available. 

Carbon sequestration pathways were available for central coal and natural gas hydrogen 

production. However, because no carbon policies were assumed in the GPRA07 Baseline Case, 

producers would not have an economic incentive to incur the incremental cost to sequester 

carbon generated from hydrogen production activities and, thus, no carbon was sequestered in 

this Individual Program Goal Case. 

HFCIT Program goals were used to estimate capital and O&M costs and production efficiencies 

for distributed natural gas reformers, central biomass gasifiers, distributed ethanol reformers, and 

central and distributed electrolytic production technologies. Assumptions for central coal and 

natural gas production technologies were adapted from H2A analysis results. The infrastructure 

requirements and operating costs for the widespread distribution of hydrogen vary widely by 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-9 
distance and method. As a simplifying assumption, a flat cost of $5.28 per MMBtu—or 65 cents 

per gallon of gasoline equivalent (gge)8

—was assumed for hydrogen distribution costs, based on 

published data from NREL.9 We will be enhancing the representation of the distribution and 

fueling costs for hydrogen in future analysis, as data becomes available. 

Unlike other Individual Program Goal Cases, analysts ran the Hydrogen Individual Program 

Goal Case with both HFCIT and Vehicle Technologies Programs’ assumptions. The rationale for 

this change is that the hydrogen fuel cell vehicle assumptions provided by the HFCIT Program 

assume that the Vehicle Technologies Program’s hybrid systems and materials technologies 

R&D activities are successful. The market penetration of hydrogen fuel vehicles is somewhat 

limited by the increased competition from more-advanced hybrid vehicles. The market shares for 

LDVs are shown in Table 3.10. 

Table 3.10. Light-Duty Vehicle Market Shares for 

the Hydrogen Case (% of VMT)

2030 2040 2050 

Gasoline 40% 5% 0% 

Advanced Gasoline 17% 10% 0% 

Gasoline Hybrid 21% 49% 60% 

Diesel Hybrid 7% 7% 0% 

Hydrogen 2% 13% 37% 

Diesel and Other 13% 16% 3% 

Because the Hydrogen Individual Program Goal Case was run with both Hydrogen and Vehicle 

Technologies Programs’ assumptions, analysts could not perform the calculation of benefits 

through the direct comparison of the Hydrogen Individual Program Goal Case and the Baseline 

Case. Instead, analysts based the calculation of oil and carbon benefits for the Hydrogen Program 

by multiplying the average Baseline Case LDV and commercial light-truck fleet fuel/carbon 

intensities per vehicle miles traveled (VMTs) by the Individual Program Goal Case VMTs of 

hydrogen fuel cell vehicles. 

To determine petroleum savings, analysts calculated the average consumption of petroleum 

products per billion vehicle miles traveled (oil intensity) for light-duty vehicles and commercial 

light trucks in the Baseline Case. Analysts then multiplied the Baseline Case oil intensity by the 

VMTs traveled by hydrogen fuel cell vehicles in the Hydrogen Individual Program Goal Case to 

estimate how much oil would be consumed if these VMTs were traveled by traditional gasoline 

vehicles. These calculations are shown in Table 3.11. 

 

8

 One kilogram of hydrogen is roughly equivalent in energy content to one gallon of gasoline, and is often referred to as a gallon 

of gasoline equivalent (gge). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-10 

9

 Amos W.A., Lane J.M., Mann M.K., and Spath P.L. Update of hydrogen from biomass – determination of the delivered cost of 

hydrogen, NREL, 2000. 
Table 3.11. Calculation of Petroleum Savings

 2030 2035 2040 2045 2050 

Baseline Case Oil Intensities 

(TBtu/billion VMT)

Light-Duty Vehicles 5.56 5.35 5.11 5.09 4.90 

Light Trucks 8.46 8.34 8.18 8.00 7.87 

Hydrogen Vehicle (VMTs/yr)

Light-Duty Vehicles 109 325 674 1,240 2,101 

Light Trucks 8 17 38 69 115 

Petroleum Savings (TBtu/yr)

Light-Duty Vehicles 605 1,741 3,442 6,307 10,299 

Light Trucks 68 143 307 554 901 

Total 673 1,884 3,750 6,862 11,200 

Total (million barrels per day) 0.32 0.89 1.77 3.24 5.29 

Carbon emission reductions accounted for both the reduced carbon emissions from burning 

gasoline, as well as increases in carbon emissions from the production of hydrogen, assuming no 

sequestration. If the hydrogen is produced at central facilities and the resulting carbon is 

sequestered, then the carbon savings will be accordingly larger in the projections below. These 

calculations are shown in Table 3.12. 

 

Table 3.12. Calculation of Carbon Emission Reduction

 2030 2035 2040 2045 2050 

Decreased CO2 Emissions from Decline in 

Gasoline Consumption 

Decrease in Gasoline Consumption (TBtu/yr) 673 1,884 3,750 6,862 11,200 

Carbon Intensity of Gasoline (MT of Carbon per 

MMBtu) 19.3 19.3 19.3 19.3 19.3 

Decline in Carbon (MMT/yr) 13.0 36.4 72.5 132.7 216.6 

CO2 Emissions from Hydrogen Production 

Production of Hydrogen (TBtu/yr) 255 695 1,383 2,432 3,920 

Carbon Intensity of Hydrogen (MT of Carbon 

per MMBtu) 30.5 32.2 32.2 27.0 30.0 

Increase in Carbon (MMT/yr) 7.8 22.3 43.9 64.5 116.2 

Net decrease in Carbon Emissions (MMT/yr) 5.2 14.2 28.6 68.2 100.4 

The carbon intensity of hydrogen varies significantly, because of the varying carbon content and 

market shares of the feedstocks used to produce hydrogen. Hydrogen production by feedstock is 

shown in Table 3.13. It should be noted that this analysis was conducted with a single-region 

MARKAL-GPRA07 model, and that the price of feedstocks and distribution costs are based on 

national averages. There is significant variation in regional fuel costs in the United States, and it 

is likely that during the development of a hydrogen infrastructure, these differences would lead 

to a greater diversity of hydrogen-production technologies than shown below. Furthermore, this 

analysis was conducted with only a subset of the full range of hydrogen-production technologies. 

Thus, this analysis may be biased toward hydrogen production from coal. Future efforts are 

planned to correct for these modeling limitations. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-11 
 

Table 3.13. Hydrogen Production by Feedstock (% of total hydrogen production)

2030 2035 2040 2045 2050 

Central Coal - No Co-Product 17% 6% 3% 2% 1% 

Central Coal - With Electric Co-Product 34% 46% 55% 46% 53% 

Remote Natural Gas 50% 48% 26% 25% 25% 

Central Natural Gas 0% 0% 0% 0% 0% 

Central Biomass 0% 0% 16% 27% 21% 

Distributed Biomass 0% 0% 0% 0% 0% 

Central Electrolytic H2 – Grid 0% 0% 0% 0% 0% 

Central Electrolytic H2 – Wind 0% 0% 0% 0% 0% 

Distributed Electrolytic H2 0% 0% 0% 0% 0% 

Overall, the Hydrogen, Fuel Cells, and Infrastructure Technologies Program reduces gasoline 

consumption in the transportation sector through the deployment of hydrogen fuel cell LDVs and 

commercial light trucks. Furthermore, the reduction in petroleum consumption leads to reduced 

carbon emissions. However, as noted above, these reductions in carbon emissions are partly 

offset due to carbon emissions from the production of hydrogen. The reductions in total energysystem costs arise from both the reduction in petroleum imports, as well as associated refining 

and distribution capacity. However, this is offset somewhat by the cost of establishing the 

hydrogen-production and -distribution infrastructure. 

Table 3.14. Annual Benefits Estimates for Hydrogen, Fuel Cells, and 

Infrastructure Technologies Program (MARKAL-GPRA07) 

 Annual Benefits 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 0.4 2.6 7.7

Economic 

 Energy-System Cost Savings (billion 2003 dollars/yr) 0 4 28

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 5 29 100

Security 

 Oil Savings (mbpd) 0.3 1.8 5.3

 Natural Gas Savings (quadrillion Btu/yr) 0.0 -0.3 -0.6

More details about the Hydrogen, Fuel Cells, and Infrastructure Technologies Program’s benefits 

analysis can be found in Appendix B. 

Industrial Technologies Program 

The Industrial Technologies Program (ITP) covers a wide range of technologies, industries, and 

end-use applications. The overall goal of this program is to increase energy efficiency through 

R&D, as well as the deployment of new and improved technologies. The heterogeneity of the 

program’s R&D activities makes it difficult to represent program activities explicitly in the 

MARKAL-GPRA07 framework. Instead, the projected ITP goals by various industries were 

aggregated into MARKAL-GPRA07 industrial energy-use demand categories as a set of 

conservation supply curves. Because this approach does not reflect economic competition nor 

interaction among program technologies, analysts reduced the off-line energy savings by an 

“integration factor” before these supply curves were constructed and input into the model (Table 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-12 
3.15). The amount of the integration factor is based on how much program overlap or 

“integration” was captured by the off-line tools. The reduction is based on the expert judgment of 

the benefits analysis team. 

Table 3.15. Industrial Program Integration Factors 

Subprogram Integration Factor 

Industries of the Future 0% 

Crosscutting R&D 10% 

Industrial Assessment Centers 10% 

Best Practices 0% 

The potential savings represented in these conservation measures yield an overall reduction in 

delivered energy consumption. Furthermore, the reduction in electricity demand also leads to the 

reduction in coal, gas, and wind-based generation. Both conservation and reduction in electricity 

demand result in less investment in end-use devices and electric-generation capacity on the 

supply side. 

The activities of the Industrial Technologies Program are more “midterm” in nature. Thus, the 

long-term annual benefits estimates, which are calculated by MARKAL-GPRA07, were not 

included in the EERE budget submission or this section of the report. However, the program’s 

activities were modeled for the EERE Portfolio Scenario and included in the long-term annual 

benefits of the EERE Portfolio, as shown in Chapter 1.

More details about the Industrial Technology Program’s benefits analysis can be found in 

Appendix H. 

Solar Energy Technologies Program 

The Solar Energy Technologies Program covers photovoltaic (PV)-based electricity generation 

and central solar-thermal generation with energy storage. The program goal is to lower the cost 

and improve performance of these technologies. 

Analysts modeled both centralized and decentralized PV power and central solar-thermal 

systems. The capital cost and O&M costs for both units are reduced to reflect program 

technology goals. In addition, analysts set the discount rates of these technologies at 8% (instead 

of the industrial average of 10%) to reflect the Modified Accelerated Cost Recovery System 

(MACRS)-accelerated depreciation schedule available for solar, wind, and geothermal 

generation technologies. The total installed capacity of the decentralized units reflects the Solar 

America installation goals for reducing end-use electricity demand from the central grid. 

Analysts model the centralized PV-generating systems to compete with conventional fossil fuelbased power plants. 

Solar photovoltaic capacity increases dramatically over the Baseline Case (Table 3.16). By 

2050, the Solar Energy Technologies Individual Program Goal Case shows an additional 238 

GW of photovoltaic capacity over the Baseline Case. Additionally, the Solar Energy 

Technologies Individual Program Goal Case shows an additional 26.5 GW of central solarthermal generation. By 2050, the improved PV and thermal technologies generate an incremental 

698.8 billion kWh of generation over the Baseline Case (Table 3.17). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-13 
 

Table 3.16. Solar-Generation Capacity by 

Case and Type (gigawatts)

2030 2040 2050 

Baseline Case 

Central PV 0.4 0.4 0.4 

Distributed PV 5.3 11.0 17.4 

Central Thermal 1.3 0.8 0.5 

Total 7.0 12.2 18.4 

Individual Program 

Goal Case 

Central PV 0.2 0.0 0.0 

Distributed PV 68.6 149.0 255.4 

Central Thermal 11.6 22.3 27.0 

Total 80.4 171.3 282.4 

Increase 

Central PV -0.2 -0.4 -0.4 

Distributed PV 63.3 138.0 237.9 

Central Thermal 10.3 21.5 26.5 

Total 73.4 159.1 264.0 

Table 3.17. Solar-Generation by Case and 

Type (Billion kWh)

2030 2040 2050 

Baseline Case 0.5 0.8 0.8 

Central PV 11.9 24.6 39.1 

Distributed PV 3.9 2.4 1.6 

Central Thermal 16.2 27.9 41.6 

Total 0.5 0.8 0.8 

Solar Individual 

Program Goal Case 

Central PV 0.3 0.0 0.0 

Distributed PV 154.0 334.5 573.3 

Central Thermal 68.3 136.6 167.1 

Total 222.6 471.1 740.4 

Increase 

Central PV -0.2 -0.8 -0.8 

Distributed PV 142.1 309.9 534.2 

Central Thermal 64.4 134.2 165.5 

Total 206.4 443.3 698.8 

Central and distributed PV and central thermal generation technologies in the Solar Energy 

Technologies Individual Program Goal Case directly displace central gas and coal-fired 

generation capacity. However, because of the PV technologies’ lower availability factor and 

reduced contribution to peak power supply, the total gas and coal capacity replaced is less than 

the installed solar capacity. Benefits estimates for the Solar Energy Technologies Program are 

shown in Table 3.18. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-14 
Table 3.18. Annual Benefits Estimates for Solar Energy Technologies Program 

(MARKAL-GPRA07) 

 Annual Benefits 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 1.7 3.2 5.2

Economic 

 Energy-System Cost Savings (billion 2003 dollars/yr) 3 6 10

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 40 65 111

Security 

 Oil Savings (mbpd) ns ns ns

 Natural Gas Savings (quadrillion Btu/yr) 0.2 1.4 2.1

 Capacity (gigawatts) 73 159 264

More details about the Solar Energy Technologies Program’s benefits analysis can be found in 

Appendix D. 

Vehicle Technologies Program 

The Vehicle Technologies Program10 consists of Hybrid Systems R&D, Advanced Combustion 

R&D, Heavy Systems R&D, and Materials Technologies R&D. The general goal of these R&D 

activities is to improve the efficiency and lower the cost of road vehicles. 

Energy-service demands for road transportation are measured in vehicle miles traveled (VMT). 

Projected VMTs are taken directly from the Annual Energy Outlook 2005 (AEO2005) and 

extended past 2025, based on historical relationships between passenger and commercial VMTs, 

and population and economic growth. Projected VMTs for light duty vehicles11, commercial 

light trucks,12 and heavy trucks are shown in Table 3.19. 

Table 3.19. Projected Vehicle Miles Traveled by 

Vehicle Class (billion VMTs/year) 

Vehicle Class 2030 2040 2050 

Light-Duty Vehicles 4,420 5,156 5,628 

Commercial Light Trucks 118 140 159 

Heavy Trucks 414 484 544 

For each time period, these demands are met by a mix of vehicle types, selected by the model on 

the basis of total life-cycle costs. The vehicle type is characterized for each model year that it is 

available for purchase. The Baseline Case cost and efficiencies of these vehicles were derived 

 

10 The Vehicle Technologies Program is run by the Office of FreedomCAR and Vehicle Technologies. 11 Light-duty vehicles include passenger cars and light trucks with a gross vehicle weight under 8,500 pounds and may include 

pickups, vans, or light trucks. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-15 

12 Commercial light trucks are light trucks with a gross vehicle weight between 8,500 and 10,000 pounds and may include 

pickups, vans, or light trucks. 
from the AEO2005 assumptions, although hybrid-vehicle costs were reduced from AEO2005 

levels in accordance to the Vehicle Technologies Program’s view of likely market developments 

exclusive of program R&D activities. The effect of this baseline change is to increase the market 

share of hybrid vehicles in the Baseline Case and, thereby, reduce the level of benefits attributed 

to the Vehicle Technologies Program. 

For the Vehicle Technologies Individual Program Goal Case, the costs and efficiencies for 

hybrid-electric vehicles (“hybrids” or HEV) and advanced diesel vehicles were changed for 

passenger cars, light trucks, commercial light trucks, and commercial heavy trucks. These 

changes reflect the results of the fuel combustion, hybrid systems, and materials R&D activities. 

Alternate cost and efficiency assumptions were provided for gasoline and diesel hybrid vehicles, 

as well as advanced diesel engines for use in passenger cars, light trucks, and commercial light 

trucks for the period 2010 to 2050. Cost and efficiency assumptions for advanced diesel and 

diesel hybrid Class 3-6 trucks and advanced diesel Class 7-8 trucks also were provided for the 

period 2010 to 2050. The cost and efficiency assumptions were provided from the off-line 

analysis as ratios to conventional gasoline or diesel internal combustion engine-powered vehicles 

of that vintage. 

The oil savings generated from the Vehicle Technologies Program are attributable to the market 

penetration of more efficient LDVs, commercial trucks, and heavy trucks. Table 3.20 shows the 

market shares for traditional gasoline and alternative light-duty vehicles for the Vehicle 

Technologies Individual Program Goal Case, while Table 3.21 shows transportation-sector 

petroleum consumption for the Baseline and Vehicles Technologies Individual Program Goal 

Case. 

The reduction in transportation-sector petroleum consumption (Table 3.22) is due to both 

increased market share and fuel efficiency of alternative vehicles, particularly hybrid-electric 

vehicles. The reductions in total energy-system costs arise from both the reduction in petroleum 

imports, as well as associated refining and distribution capacity. 

Table 3.20. Light-Duty Vehicle Market Shares for the 

Vehicles Technologies Individual Program Goal Case 

(% of total fleet) 

2030 2040 2050 

Gasoline 38% 4% 0% 

Advanced Gasoline 17% 12% 0% 

Gasoline Hybrid 25% 58% 96% 

Diesel Hybrid 7% 10% 2% 

Adv. Diesel & Other 14% 16% 3% 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-16 
Table 3.21. Petroleum Consumption by Vehicle Class and 

Case (trillion Btu/year) 

2030 2040 2050 

Baseline Case 

Light-Duty Vehicles 24,367 25,868 27,063 

Commercial Light Trucks 1,002 1,141 1,253 

Heavy Trucks 7,779 8,849 9,681 

Total Transportation Sector 40,426 43,625 46,107 

Individual Program Goal Case 

Light-Duty Vehicles 19,422 16,889 16,382 

Commercial Light Trucks 819 894 927 

Heavy Trucks 7,192 7,529 8,126 

Total Transportation Sector 34,711 33,080 33,546 

Savings 

Light-Duty Vehicles 4,945 8,978 10,681 

Commercial Light Trucks 183 247 326 

Heavy Trucks 587 1,320 1,555 

Total Transportation Sector 5,715 10,545 12,561 

Table 3.22. FY07 Benefits Estimates for Vehicle Technologies Program 

(MARKAL-GPRA07)13

 Annual Benefits 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 6.2 11.4 13.5

Economic 

 Energy-System Cost Savings (billion 2003 dollars/yr) 4 37 70

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 117 217 260

Security 

 Oil Savings (mbpd) 2.9 5.4 6.5

 Natural Gas Savings (quadrillion Btu/yr) ns ns ns

More details about the Vehicle Technologies Program’s benefits analysis are available in 

Appendix F. 

Weatherization and Intergovernmental Program 

The Weatherization and Intergovernmental Program (WIP) Case formulated in MARKALGPRA07 focuses on deployment programs that have an impact on the energy consumption in the 

residential and commercial sectors. Projected program goals of the Weatherization Assistance 

Program and State Energy Program are transformed into conservation-supply curves that reduce 

the heating and cooling loads in households and commercial buildings benefiting from these 

programs. The Tribal Energy Program provides assistance in preparing feasibility studies for 

 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-17 

13 Note that in the Vehicle Technologies Individual Program Goal Case, the advanced ethanol production technologie available 

in the Biomass Program’s Case are unavailable, despite the market synergies of the two suites of technologies. In the EERE 

portfolio case, both suites are modeled. 
renewable generation projects on tribal lands. The impact of this program was modeled by 

placing lower bounds on the penetration of wind turbines and biomass-fired power generation, 

which are projected to be developed on tribal lands as a result of this program. The Renewable 

Energy Production Incentive (REPI) provides payments to publicly owned utilities for renewable 

power generation. Off-line estimates of the amount of additional renewable generation was made 

and implemented in the MARKAL model through lower bounds on new wind-generation 

capacity investment. 

The reduction in electricity demand in residential space conditioning and lighting also leads to 

the reduction in gas-based generation in the long run. Both conservation and reduction in 

electricity demand result in fewer investments in end-use devices and electric-generation 

capacity on the supply side. This is another factor attributable to the overall reduction in energysystem cost and carbon emissions, in addition to direct energy savings. 

The activities of the Weatherization and Intergovernmental Program are more “midterm” in 

nature. Thus, the long-term annual benefits estimates, which are calculated by MARKALGPRA07, were not included in the EERE budget submission or in this section of the report. 

However, the program’s activities were modeled for the EERE Portfolio Scenario and included 

in the long-term annual benefits of the EERE Portfolio, as shown in Chapter 1. 

More detail about the WIP Program’s benefits analysis can be found in Appendix J.

Wind Technologies Program 

The Wind Technologies Program R&D aims to reduce capital and O&M costs and improve 

capacity factors for both onshore and offshore wind turbines. The program goals are represented 

in the MARKAL-GPRA07 model by changing the capital and O&M costs and capacity factors 

for wind turbines. 

The discount rate for wind generators is set at 8% (instead of the utility average of 10%) to 

reflect the accelerated depreciation schedule available for renewable-generation technologies. 

Wind generators are modeled as centralized plants to compete with fossil fuel-based plants. 

 

The improvements in wind turbines result in a significant increase in installed wind-generation 

capacity over the Baseline Case. Total wind generation increases, due to both the increase in 

total installed capacity and the increase in capacity factors. The resulting generation capacity is 

different from the NEMS results described in Chapter 2, due to differences in model structure 

and the treatment of offshore wind resources. As with the treatment of onshore wind in both 

NEMS and MARKAL, a “resource” multiplier is applied to MARKAL’s treatment of offshore 

wind turbine costs. These resource-cost multipliers increase the installed cost of wind turbines as 

the most suitable wind sites are taken. Furthermore, because the current MARKAL model is a 

single-region model, offshore and onshore wind technologies compete directly, although they are 

expected to supply different markets. The change in wind capacity and generation is shown in 

Table 3.23. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-18 
Table 3.23. Total Wind Capacity and Generation

2030 2040 2050 

Wind Capacity (GW) 

Baseline Case 

Onshore 20.3 20.6 28.7 

Offshore 4.7 11.1 24.7 

Total 25.1 31.8 53.4 

Individual Program Goal Case 

Onshore 75.0 97.6 107.1 

Offshore 16.2 32.9 72.9 

Total 91.2 130.5 180.0 

Increase 

Onshore 54.7 77.0 78.4 

Offshore 11.5 21.8 48.2 

Total 66.1 98.8 126.6 

Wind Generation (Billion kWh)

Baseline Case 

Onshore 80 87 129 

Offshore 21 50 110 

Total 101 137 239 

Individual Program Goal Case 

Onshore 316 414 457 

Offshore 73 149 330 

Total 389 563 787 

Increase 

Onshore 236 327 328 

Offshore 52 99 219 

Total 288 426 548 

When the MARKAL model dispatches electric generation capacity, wind generation displaces 

the generation from the dispatchable unit with the highest marginal cost. This is normally a gasfired combustion turbine. However, MARKAL also determines new generation capacity 

additions over the full projection period. Natural gas price forecasts have increased during the 

past several years in many energy models’ forecasts of the U.S. economy. As a consequence, 

these same models have often forecast more base-load coal-fired capacity. MARKAL is included 

in this group, and the MARKAL-GPRA07 Baseline Case projects more base-load coal than in 

past projections. Thus, coal is increasingly becoming the marginal capacity to be built. As such, 

for capacity builds on the margin, wind is actually competing with coal, not with gas. Because 

wind is an intermittent power source and much of the coal technology is non-rampable, gas-fired 

turbines are installed with wind generation to provide backup and peaking. Toward the end of the 

forecast horizon in the Individual Program Goal Case, wind and gas-fired capacity are installed 

in place of coal technology, resulting in lower overall coal capacity. 

This difference in marginal capacity has implications for the competition for dispatch. 

Specifically, the Baseline Case increase in coal, combined with the Individual Program Goal 

Case increase in wind, forces the model to dispatch more natural gas when wind is not available 

or to meet peak demands, thus increasing natural gas consumption over the Baseline Case in the 

out years. We will be examining this result in further detail over the coming year. The estimated 

benefits for the Wind Program are shown in Table 3.24. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-19 
Table 3.24. Annual Benefits Estimates for Wind Technologies Program 

(MARKAL-GPRA07) 

Annual Benefits 2030 2040 2050 

Energy Displaced 

 Primary Nonrenewable Energy Savings (quadrillion Btu/yr) 2.1 3.6 3.9

Economic 

 Energy-System Cost Savings (billion 2003 dollars/yr) 2 2 2

Environmental 

 Carbon Savings (million metric tons carbon equivalent/yr) 47 95 101

Security 

 Oil Savings (mbpd) ns ns ns

 Natural Gas Savings (quadrillion Btu/yr)14 0.6 -0.3 -0.2

 Capacity (gigawatts) 66 99 127

 

More details on the Wind Program’s benefits analysis are available in Appendix E. 

 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-20 

14 The net increase in natural gas consumption in “out” years is due to a shift from installed marginal coal capacity in the 

Baseline Case to wind supported by natural gas in the Individual Program Goal Case. 
Box 3.1—The MARKAL Model 

The U.S. MARKAL model is a technology-driven linear optimization model of the U.S. energy system that runs in five-year 

intervals over a 50-year projection period. MARKAL provides a framework to evaluate all resource and technology options 

within the context of the entire energy/materials system, and captures the market interaction among fuels to meet demands 

(i.e. competition between gas and coal for electric generation). The model explicitly tracks the vintage structure of all capital 

stock in the economy that produces, transports, transforms, or uses energy. 

In MARKAL, the entire energy system is represented as a network, based on the reference energy system (RES) concept. The 

RES depicts all possible flows of energy from resource extraction, through energy transformation, distribution, and 

transportation; to end-use devices that satisfy the demands of useful energy services (e.g., vehicle miles traveled, lumensecond in lighting). Figure 3.1 illustrates a simplified RES in graphical form. The U.S. MARKAL has detailed technical 

representations of four end-use sectors (residential, commercial, industrial, and transportation), as well as fossil fuel and 

renewable resources, petroleum refining, power generation, hydrogen production, and other intermediate conversion sectors. 

Cross comparisons of MARKAL outputs provide detailed technical and economic information to use in estimating the 

programs’ benefits. 

Technology choice in the MARKAL framework is based on the present value of the marginal costs of competing technologies 

in the same market sector. On the demand side, the marginal cost of demand devices is a function of levelized capital cost, 

O&M cost, efficiency, and the imputed price of the fuel used by these devices. For a specific energy-service demand and time 

period, the sum of the energy-service output of competing technologies has to meet the projected demand in that period. The 

relative size of the energy-service output (market share) of these technologies depends not only on their individual 

characteristics (technical, economic, and environmental), but also on the availability and cost of the fuels (from the supply 

side) they use. The actual market size of a demand sector in a future time period depends on the growth rate of the demand 

services and the stock turnover rate of vintage capacities. MARKAL dynamically tracks these changes and defines future 

market potentials. Another factor considered in MARKAL, which affects the market penetration of a specific demand device, 

is the sustainability of the expansion in the implied manufacturing capacity to produce these devices. For EERE R&D 

programs that have independently projected the market potentials of their technologies, an initial market penetration 

(combined with an annual growth rate limit) was imposed in MARKAL to replicate these potentials for assessing the benefits 

of these technologies. 

On the supply side, technology choice made in MARKAL is based on the imputed price of the energy products and the 

marginal cost of using these products downstream in the demand sectors. The cost of resource input for production 

(exogenously projected in MARKAL) such as imported oil prices and cost of biomass feedstock, together with the 

characteristics of supply technologies (including electricity generation) determine the market share of a particular fuel type 

(including renewables) and the technology that produces it. The supply-demand balance achieved for all fuels under the least 

energy-system cost represents a partial equilibrium in the energy market. 

Figure 3.1 An Illustrative Reference Energy System . 

Resource

Extraction

Refining

and

Conversion

Transport Generation

Transmission

and

Distribution

Utilizing

Device End Use

Misc. Electric -Building

Misc. Electric - Industrial

Office Equipment

Air Conditioning

Water Heat

Space Heat

Electric

Process Heat

Petrochemicals

Renewables

Nuclear

Crude Oil

Natural Gas

Resource

Extraction

Refining

and

Conversion

Transport Generation

Transmission

and

Distribution

Utilizing

Device End Use Resource

Extraction

Refining

and

Conversion

Transport Generation

Transmission

and

Distribution

Utilizing

Device End Use

Misc. Electric -Building

Misc. Electric - Industrial

Office Equipment

Air Conditioning

Water Heat

Space Heat

Electric

Process Heat

Petrochemicals

Renewables

Nuclear

Crude Oil

Natural Gas

Coal

Biomass

Passenger Travel

Other Transportation

Hydrogen Fuel Cell

Hydrogen Fuel Cell LDV

Refined Products

X

X

x 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Long-Term Benefits Analysis of EERE’s Programs (Chapter 3) – Page 3-21 
Appendix A – GPRA07 Benefits Estimates: NEMS-GPRA07 

and MARKAL-GPRA07 Baseline Cases 

Overview 

The Office of Energy Efficiency and Renewable Energy (EERE) programs use integrated energy 

models to analyze the benefits expected from successful implementation of individual programs 

and the EERE portfolio as a whole. The use of integrated models provides a consistent economic 

framework and incorporates the interactive effects among the various programs. Feedback and 

interactive effects result from (1) changes in energy prices resulting from lower energy 

consumption, (2) the interaction between supply programs affecting the mix of generation 

sources and the end-use sector programs affecting the demand for electricity, and (3) additional 

savings from reduced energy production and delivery. 

A modified version of the National Energy Modeling System (NEMS)1 was one of the models 

used for this benefits analysis. NEMS is an integrated energy model of the U.S. energy system 

that was developed by the Energy Information Administration (EIA) for forecasting and policy 

analysis purposes. NEMS provides projection capability to the year 20252 and so is used for the 

midterm benefits analysis. The latest version of NEMS available at the time of the benefits 

analysis was used as the starting point. This is a slightly updated version from Annual Energy 

Outlook 2005 (AEO2005) that was setup by EIA at the request of the DOE R&D offices for use 

in GPRA scenarios3

. Several modifications were subsequently made to the model by EERE to 

enhance its ability to represent the EERE programs. The modified version of the model is 

referred to as NEMS-GPRA07. 

For projections beyond 2025, a modified version of the MARKAL (MARket Allocation) model 

was employed, referred to here as MARKAL-GPRA07. To the extent possible, the same input 

data and assumptions were used in MARKAL-GPRA07 as were used to generate the AEO2005 

Reference Case. MARKAL-GPRA07 was “benchmarked” to NEMS-GPRA07. While the 

models have some similarities, there are basic structural differences and parameter differences 

that will yield slightly different model results, even under the same reference conditions. 

 

1

 The National Energy Modeling System: An Overview 2003, March 2003, DOE/EIA-0581(2003). 2

 For the AEO2006, NEMS projects to the year 2030. 

3

 The request for the slightly modified Base Case was made under an initiative to coordinate and integrate the GPRA analyses 

undertaken by the various offices within DOE’s Office of Energy, Science, and Environment. Formally, the request was 

transmitted to EIA through the Office of Fossil Energy and the National Energy Technology Laboratory’s NEMS modelers. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-1
This appendix describes the changes made to the NEMS and MARKAL models’ baselines to 

derive the NEMS-GPRA07 and MARKAL-GPRA07 baselines, the rationale for those changes, 

and the resulting energy and economic projections for each model’s forecast horizon. While the 

first section of this appendix nominally pertains to NEMS, it is relevant to MARKAL as well, 

because MARKAL is benchmarked to NEMS—and because the fundamental baseline changes 

affect both models. The second section of the appendix pertains only to the MARKAL baseline. 

The MARKAL section focuses on the energy and economy assumptions and projections beyond 

2025. 

NEMS Baseline Case Assumptions and Projections 

GPRA 2007 Baseline 

The first step in the benefits analysis process is to establish an appropriate Baseline Case. The 

EERE Baseline Case is a projection intended to represent the future U.S. energy system without 

the effect of EERE Programs. This Baseline Case assures that program benefits are estimated, 

based on the same initial forecasts for economic growth, energy prices, and levels of energy 

demand. It also assures that these initial assumptions are consistent with each other; e.g., that the 

level of electricity demand expected under the economic growth assumptions could be met at the 

electricity price assumed. It provides a basis for assessing how well renewable and efficiency 

technologies might be able to compete against future, rather than current, conventional energy 

technologies (e.g., more efficient central power generation). Finally, it helps ensure that 

underlying improvements in efficiency and renewable energy are not counted as part of the 

benefits of the EERE programs. 

The most recent Annual Energy Outlook Reference Case is used as the starting point for 

developing the base case.4

 The Energy Information Administration (EIA) Annual Energy 

Outlook (AEO) Reference Case provides an independent representation of the likely evolution of 

energy markets. This forecast reflects expected changes in the demand for energy (e.g., to reflect 

the availability of new appliances), technology improvements that might improve the efficiency 

of energy use, and changes in energy resource production costs, including renewable energy. 

Energy market policies that are current at the time of the AEO’s publication are included in the 

Base Case.5

 This approach ensures that EERE’s benefits estimates do not include expected 

impacts of such policies. Neither the EIA Reference Case nor the EERE Base Case includes any 

changes in future energy policies. 

Removal of EERE programs. Several adjustments are made to remove EERE programs from 

the EIA Reference Case. For example, EIA’s estimate of rooftop photovoltaic installations 

 

4

As described above, the updated NEMS produces similar reference case projections as the Annual Energy Outlook 2005 with 

Projections to 2025, January 2005, DOE/EIA-0383 (2005). See http://www.eia.doe.gov/oiaf/archive/aeo05/pdf/0383(2005).pdf. 

The Energy Information Administration’s recently released Annual Energy Outlook 2006 indicates significantly higher oil and 

fuels prices for much of the forecast horizon than does the previous forecast (AEO 2005) on which this benefits analysis is based. 

All else equal, higher fuels prices would be expected to increase the market penetration of renewable energy and energy 

efficiency measures undertaken irrespective of DOE programs, as these technologies become more price competitive. As such, 

some of the nonrenewable energy savings, cost savings, and emissions reductions attributable to DOE programs might be 

reduced. 

5

 At the publication date of AEO 2005, EPACT 2005 had not passed, nor had the extension of the production tax credit incentive 

for renewable energy generation. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-2
resulting from the Million Solar Roofs Initiative was removed for the EERE Baseline. The 

improvement in distributed photovoltaic system costs was reduced. The most efficient shell 

improvement packages for new residential buildings were removed as well, although the impact 

was minimal, because they received very small market share in the AEO. Cellulosic ethanol 

production was assumed to not become available until after 2025 without EERE’s R&D efforts. 

Table A-1. Summary of Baseline Changes from the AEO2005 

AEO2005 GPRA07 Baseline Case 

Removal of EERE Programs 

 Solar America 0.3 GW installed 2007 to 2025 Removed 

 Photovoltaic system costs Significant improvement Slower rate of improvement 

 Residential high efficiency shell packages Small penetration Removed 

 Cellulosic ethanol production Commercially available by 2015 Not commercially available by 

2025 

Greater Technology Improvement in Base

 Solid-State Lighting Very small improvement Much greater improvement 

 Onshore Wind Performance 

33% to 44% capacity factors, 

depending on wind class and 

year 

35% to 53% capacity factors, 

depending on wind class and 

year

 Onshore Wind Capital Costs 1% reduction over 20 years 

12% to 15% reduction 

(depending on wind class) over 

20 years 

 Conventional Corn ethanol production Yield of 2.65 gallons per bushel Yield of 2.80 gallons per bushel 

 Corn ethanol production with starch Not included Available in 2011 

 Hybrid Electric Vehicles Sales share at 6% by 2025 Sales share at 1% by 2025 

Energy Market Updates 

 PV system size 2 kW residential, 25 kW 

commercial 

4 kW residential, 100 kW 

commercial 

 PV maximum market share 30% for both residential and 

commercial 

60% for residential and 55% for 

commercial 

 California PV subsidy Not included Included for residential systems 

 Corn ethanol maximum production 5.7 billion gallons 10.0 billion gallons 

Structural Changes 

 Offshore wind No offshore wind technology Offshore wind 

 Commercial shell efficiency Index Technology representation 

 Commercial DG algorithms Market share and stock 

accounting modified 

Greater Technology Improvement in the Baseline

There are a few EERE technologies that are either not represented in the AEO2005 or their 

improvement is less than anticipated by the program in the absence of EERE programs. These 

technology assumptions were also modified for the GPRA07 Baseline. 

• In commercial lighting, solid-state lighting characteristics were assumed to improve 

more than the very minimal improvement in the AEO2005. 

• Offshore wind technology characteristics were added, and the onshore wind 

characteristics were modified. The onshore capital costs were assumed to decline more 

rapidly over time. In addition the capacity factors for each wind class were assumed to 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-3
be higher than in the AEO2005, although lower than the program goals. Both of these 

changes for onshore wind raise the projected market penetration of wind in the Baseline 

and shrink the benefits attributed to the EERE R&D. 

• The representation of hybrid electric vehicles was modified to lower their costs over time 

and to gradually increase the consumer preference for hybrids. Corn ethanol with residual 

starch conversion was added to the EERE base that is not in the AEO. This leads to 

increases in ethanol yields and decreases in unit costs over time. In addition the supply 

curves were extended to allow up to 10 billion gallons of corn ethanol production. As a 

result of these changes, the EERE base has more ethanol production that the AEO. 

Energy Market Updates 

A few other modifications were made to reflect EERE program assumptions or updated 

information about energy markets. These changes affect both the Baseline and the Benefits 

Cases. The size of typical PV systems was increased to 4 kW in residential and 100 kW in 

commercial buildings to reflect recent PV installation experience and trends. The maximum 

market for PV systems was increased from 30% to 55% in the commercial sector and to 60% for 

residential PVs. Similarly, the maximum market share for gas-fired distributed generation 

technologies was increased from 30% to 50% in the commercial sector. California PV credits 

were incorporated in the Pacific region. 

Structural Changes 

In a few cases, we made structural changes to improve the model’s representation of markets 

important to EERE technologies. 

• Offshore wind was added as another technology option with resources available in the 

coastal regions and the regions around the Great Lakes. 

• The shell indices in the commercial module were replaced with a technology choice 

algorithm necessary for later representation of EERE shell technologies. In addition, 

alterations to the distributed generation algorithm in the building modules were made to 

reflect market adoption data gathered by Lawrence Berkeley National Laboratory, to 

account for buildings that have already installed a DG technology in prior years, and to 

allow greater than an annual 0.5% adoption in existing buildings. 

A summary of these modifications is provided in Table A-4. Greater detail can be found in the 

individual program appendices. 

GPRA07 Baseline 

In the Baseline projections, oil prices are projected to fall and then gradually increase through 

2025, as shown in Figure A-1. Natural gas prices follow a similar pattern. Coal prices, on the 

other hand, are projected to be relatively constant in real terms, with a very slight decline. 

Electricity prices are projected to experience a decrease through 2010 and then increase 

gradually. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-4
0

2

4

6

8

10

1985 1990 1995 2000 2005 2010 2015 2020 2025

2002$ per MMbtu or Cents per kWh

Average Electricity

World Oil

Natural Gas 

W llh d

Minemouth Coal

Projected

Figure A-1. Projected Energy Prices

The resulting Baseline Case projects a 30% increase in conventional energy demand from 2005 

to 2025.6

 Energy efficiency and renewable energy improvements, however, contribute toward a 

28% reduction in conventional energy intensity (energy used per dollar of GPD produced) over 

the same period (Figure 2).7 Between 2005 and 2025, renewable energy technology 

improvements result in increases in renewable electric generation in central and distributed 

applications of roughly 180 billion kWh, which is an almost 50 % increase in nonhydroelectric 

generation. 

50

60

70

80

90

100

110

120

130

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

Conventional Energy Demand (Quadrillion Btu) 

0

2

4

6

8

10

12

14

16

Conventional Energy Intensity 

(1000 Btu/2000$ GDP)

Figure A-2. U.S. Conventional Energy Demand and Energy Intensity 

 

6

 Very similar to the AEO2005. 7

 Energy intensity changes result from a mix of structural changes in the economy (e.g., growing service sector) and efficiency 

improvements. Two recent EERE-sponsored studies provide additional background on understanding the sources of changes to 

our energy intensity: Ortiz and Sollinger, Shaping Our Future by Reducing Energy Intensity in the U.S. Economy; Volume 1: 

Proceedings of the Conference (2003, Rand Corporation); and Bernstein, Fonkych, Loeb, and Loughran, “State-Level Changes in 

Energy Intensity and their National Implications” (2003, Rand Corporation). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-5
NEMS-GPRA07 Baseline Tables A-1 Through A-6 

 Table A-1. Total Energy Supply and Disposition Summary

 (Quadr

Pr

 Cr

 Natur

 Dr

 Coal

 Nuc

 Rene

 O

 T

Im

 Cr

 Petr

 Natur

 O

 T

Ex

 Petr

 Natur

 Coal

 T

Dis

Cons

 Petr

 Natur

 Coal

 Nuc

 Rene

 O

 T

Net Im

Pr

 W

 G

 Coal M

 Elec

 1/ Inc

m

fr

gas

elec

 2/ Inc

illion Btu per Year, Unless Otherwise Noted)

2010 2015 2020 2025

oduction

ude Oil & Lease Condensate 12.74 11.63 10.99 10.00

al Gas Plant Liquids 2.65 2.66 2.78 2.79

y Natural Gas 20.90 21.23 22.36 22.29

25.12 25.54 26.86 29.48

lear Power 8.46 8.59 8.64 8.64

wable Energy 1/ 6.81 7.23 8.11 9.14

ther 2/ 1.02 0.83 0.83 1.28

otal 77.72 77.70 80.56 83.61

ports

ude Oil 3/ 24.69 29.00 32.49 35.08

oleum Products 4/ 6.01 6.23 6.61 7.53

al Gas 5.77 8.02 8.82 9.68

ther Imports 5/ 0.92 1.07 1.15 1.23

otal 37.39 44.32 49.07 53.51

ports

oleum 6/ 2.15 2.21 2.26 2.32

al Gas 0.65 0.81 0.86 0.83

1.06 0.88 0.89 0.67

otal 3.86 3.89 4.01 3.81

crepancy 7/ 0.02 -0.11 -0.06 -0.10

umption

oleum Products 8/ 44.85 48.05 51.29 54.23

al Gas 26.10 28.61 30.50 31.33

24.98 25.68 27.10 30.04

lear Power 8.46 8.59 8.64 8.64

wable Energy 1/ 6.82 7.23 8.11 9.14

ther 9/ 0.03 0.08 0.05 0.04

otal 111.23 118.24 125.68 133.42

ports - Petroleum 28.55 33.02 36.84 40.28

ices (2003 dollars per unit)

orld Oil Price ($ per bbl) 10/ 25.00 26.75 28.50 30.31

as Wellhead Price ($ / Mcf) 11/ 3.68 4.17 4.55 4.75

inemouth Price ($ / ton) 17.32 16.85 17.13 18.60

tricity (cents / Kwh) 6.63 6.94 7.20 7.26

ludes grid-connected electricity from conventional hydroelectric; wood and wood waste; landfill gas;

unicipal solid waste; other biomass; wind; photovoltaic and solar thermal sources; non-electric energy

om renewable sources, such as active and passive solar systems, and wood; and both the ethanol and

oline components of E85, but not the ethanol components of blends less than 85 percent. Excludes

tricity imports using renewable sources and nonmarketed renewable energy. 

ludes liquid hydrogen, methanol, supplemental natural gas, and some domestic inputs to refineries.

 3/ Includes imports of crude oil for the Strategic Petroleum Reserve.

 4/ Includes imports of finished petroleum products, unfinished oils, alcohols, ethers, and blending components.

 5/ Includes coal, coal coke (net), and electricity (net).

 6/ Includes crude oil and petroleum products.

 7/ Balancing item. Includes unaccounted for supply, losses, gains, net storage withdrawals,

heat loss when natural gas is converted to liquid fuel, and heat loss when coal is converted to liquid fuel.

 8/ Includes natural gas plant liquids, crude oil consumed as a fuel, and nonpetroleum-based liquids for

blending, such as ethanol.

 9/ Includes net electricity imports, methanol, and liquid hydrogen.

 10/ Average refiner acquisition cost for imported crude oil.

 11/ Represents lower 48 onshore and offshore supplies.

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-6
 Table A-2. Energy Consumption by Sector and Source

 (Quadrillion Btu per Year, Unless Otherwise Noted)

2010 2015 2020 2025

Energy Consumption

 Residential

 Distillate Fuel 0.89 0.87 0.82 0.77

 Kerosene 0.09 0.09 0.09 0.09

 Liquefied Petroleum Gas 0.57 0.61 0.64 0.67

 Petroleum Subtotal 1.56 1.57 1.56 1.52

 Natural Gas 5.68 5.90 6.05 6.16

 Coal 0.01 0.01 0.01 0.01

 Renewable Energy 1/ 0.40 0.39 0.39 0.38

 Electricity 5.02 5.40 5.79 6.18

 Delivered Energy 12.66 13.28 13.79 14.25

 Electricity Related Losses 10.79 11.29 11.83 12.46

 Total 23.45 24.57 25.62 26.71

 Commercial

 Distillate Fuel 0.61 0.65 0.70 0.75

 Residual Fuel 0.07 0.07 0.08 0.08

 Kerosene 0.03 0.03 0.03 0.03

 Liquefied Petroleum Gas 0.10 0.10 0.11 0.11

 Motor Gasoline 2/ 0.04 0.04 0.04 0.04

 Petroleum Subtotal 0.85 0.90 0.95 1.00

 Natural Gas 3.47 3.69 3.96 4.27

 Coal 0.10 0.10 0.10 0.10

 Renewable Energy 3/ 0.09 0.09 0.09 0.09

 Electricity 4.99 5.62 6.29 7.07

 Delivered Energy 9.50 10.39 11.38 12.52

 Electricity Related Losses 10.74 11.74 12.87 14.25

 Total 20.23 22.13 24.25 26.77

 Industrial 4/

 Distillate Fuel 1.04 1.08 1.14 1.19

 Liquefied Petroleum Gas 2.30 2.44 2.59 2.73

 Petrochemical Feedstocks 1.48 1.52 1.55 1.56

 Residual Fuel 0.34 0.38 0.38 0.37

 Motor Gasoline 2/ 0.31 0.33 0.35 0.37

 Other Petroleum 5/ 4.68 4.69 5.01 5.20

 Petroleum Subtotal 10.16 10.44 11.02 11.43

 Natural Gas 8.12 8.50 8.91 9.43

 Lease and Plant Fuel 6/ 1.20 1.22 1.32 1.30

 Natural Gas Subtotal 9.32 9.73 10.22 10.73

 Metallurgical Coal 0.55 0.48 0.42 0.37

 Steam Coal 1.42 1.42 1.42 1.42

 Net Coal Coke Imports 0.06 0.05 0.05 0.05

 Coal Subtotal 2.03 1.95 1.89 1.83

 Renewable Energy 7/ 2.07 2.19 2.34 2.49

 Electricity 3.78 3.98 4.19 4.40

 Delivered Energy 27.36 28.28 29.67 30.89

 Electricity Related Losses 8.13 8.31 8.58 8.87

 Total 35.49 36.59 38.24 39.75

 1/ Includes wood used for residential heating. 

 2/ Includes ethanol (blends of 10 percent or less) and ethers blended into gasoline.

 3/ Includes commercial sector consumption of wood and wood waste, landfill gas, municipal solid waste, and other 

biomass for combined heat and power. 

 4/ Includes energy for combined heat and power plants, except those whose primary business is to sell electricity,

or electricity and heat, to the public.

 5/ Includes petroleum coke, asphalt, road oil, lubricants, still gas, and miscellaneous petroleum products.

 6/ Represents natural gas used in the field gathering and processing plant machinery.

 7/ Includes consumption of energy from hydroelectric, wood and wood waste, municipal solid waste, and other biomass.

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-7
 Table A-2. Energy Consumption by Sector and Source (Continued)

2010 2015 2020 2025

 Transportation

 Distillate Fuel 8/ 6.95 7.66 8.33 9.02

 Jet Fuel 9/ 4.04 4.45 4.74 4.89

 Motor Gasoline 2/ 19.16 20.81 22.28 23.98

 Residual Fuel 0.56 0.57 0.58 0.58

 Liquefied Petroleum Gas 0.06 0.07 0.08 0.09

 Other Petroleum 10/ 0.26 0.27 0.29 0.31

 Petroleum Subtotal 31.02 33.83 36.30 38.87

 Pipeline Fuel Natural Gas 0.70 0.73 0.82 0.84

 Compressed Natural Gas 0.06 0.08 0.10 0.12

 Renewable Energy (E85) 11/ 0.00 0.00 0.00 0.00

 Liquid Hydrogen 0.00 0.00 0.00 0.00

 Electricity 0.09 0.10 0.11 0.12

 Delivered Energy 31.87 34.74 37.34 39.95

 Electricity Related Losses 0.19 0.21 0.22 0.24

 Total 32.06 34.95 37.56 40.19

 Electric Power 14/

 Distillate Fuel 0.40 0.40 0.48 0.45

 Residual Fuel 0.86 0.92 0.98 0.96

 Petroleum Subtotal 1.26 1.32 1.46 1.41

 Natural Gas 6.87 8.47 9.35 9.22

 Steam Coal 22.84 23.63 25.10 28.11

 Nuclear Power 8.46 8.59 8.64 8.64

 Renewable Energy/Other 15/ 4.26 4.56 5.29 6.17

 Electricity Imports 0.03 0.08 0.05 0.04

 Total 43.72 46.64 49.88 53.58

 Total Energy Consumption

 Distillate Fuel 9.89 10.66 11.47 12.18

 Kerosene 0.14 0.14 0.14 0.13

 Jet Fuel 9/ 4.04 4.45 4.74 4.89

 Liquefied Petroleum Gas 3.04 3.22 3.42 3.60

 Motor Gasoline 2/ 19.51 21.18 22.67 24.39

 Petrochemical Feedstocks 1.48 1.52 1.55 1.56

 Residual Fuel 1.84 1.94 2.01 1.99

 Other Petroleum 12/ 4.92 4.94 5.29 5.49

 Petroleum Subtotal 44.85 48.05 51.29 54.23

 Natural Gas 24.20 26.65 28.36 29.20

 Lease and Plant Fuel 6/ 1.20 1.22 1.32 1.30

 Pipeline Natural Gas 0.70 0.73 0.82 0.84

 Natural Gas Subtotal 26.10 28.61 30.50 31.33

 Metallurgical Coal 0.55 0.48 0.42 0.37

 Steam Coal 24.37 25.15 26.62 29.63

 Net Coal Coke Imports 0.06 0.05 0.05 0.05

 Coal Subtotal 24.98 25.68 27.10 30.04

 Nuclear Power 8.46 8.59 8.64 8.64

 Renewable Energy 16/ 6.82 7.23 8.11 9.14

 Liquid Hydrogen 0.00 0.00 0.00 0.00

 Electricity Imports 0.03 0.08 0.05 0.04

 Total 111.23 118.24 125.68 133.42

 2/ Includes ethanol (blends of 10 percent or less) and ethers blended into gasoline.

 6/ Represents natural gas used in the field gathering and processing plant machinery.

 8/ Diesel fuel containing 500 parts per million (ppm) or 15 ppm sulfur.

 9/ Includes only kerosene type.

 10/ Includes aviation gasoline and lubricants.

 11/ E85 refers to a blend of 85 percent ethanol (renewable) and 15 percent motor gasoline (nonrenewable). To address

cold starting issues, the percentage of ethanol actually varies seasonally. The annual average ethanol content of 74

percent is used for this forecast.

 12/ Includes unfinished oils, natural gasoline, motor gasoline blending components, aviation gasoline, lubricants,

 still gas, asphalt, road oil, petroleum coke, and miscellaneous petroleum products.

 13/ Includes electricity generated for sale to the grid and for own use from renewable sources, and non-electric energy from

renewable sources. Excludes nonmarketed renewable energy consumption for geothermal heat pumps, buildings

photovoltaic systems, and solar thermal hot water heaters.

 14/ Includes consumption of energy by electricity-only and combined heat and power (CHP) plants whose primary business

is to sell electricity, or electricity and heat, to the public. Includes small power producers and exempt wholesale generators.

 15/ Includes conventional hydroelectric, geothermal, wood and wood waste, municipal solid waste, other biomass,

petroleum coke, wind, photovoltaic and solar thermal sources. Excludes net electricity imports.

 16/ Includes hydroelectric, geothermal, wood and wood waste, municipal solid waste, other biomass, wind, photovoltaic

 and solar thermal sources. Includes ethanol components of E85; excludes ethanol blends (10 percent or less)

 in motor gasoline. Excludes net electricity imports and nonmarketed renewable energy consumption for geothermal

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-8
 Table A-3. Energy Prices by Sector and Source

 (2003 Dollars per Million Btu, Unless Otherwise Noted)

2010 2015 2020 2025

 Residential 14.38 15.00 15.66 16.09

 Primary Energy 1/ 8.38 8.75 9.20 9.60

 Petroleum Products 2/ 10.41 10.76 11.34 11.94

 Distillate Fuel 8.24 8.48 8.84 9.12

 Liquefied Petroleum Gas 14.23 14.44 14.98 15.62

 Natural Gas 7.84 8.23 8.66 9.04

 Electricity 23.03 23.66 24.16 24.16

 Commercial 13.83 14.85 15.63 16.00

 Primary Energy 1/ 6.84 7.21 7.54 7.79

 Petroleum Products 2/ 7.11 7.28 7.56 7.84

 Distillate Fuel 6.26 6.48 6.77 7.06

 Residual Fuel 4.26 4.52 4.80 5.08

 Natural Gas 6.91 7.34 7.68 7.92

 Electricity 20.01 21.22 22.06 22.23

 Industrial 3/ 6.87 7.25 7.74 8.08

 Primary Energy 5.55 5.83 6.24 6.60

 Petroleum Products 2/ 7.23 7.42 7.81 8.34

 Distillate Fuel 6.75 7.18 7.40 7.73

 Liquefied Petroleum Gas 9.99 10.23 10.64 11.33

 Residual Fuel 3.87 4.10 4.33 4.62

 Natural Gas 4/ 4.41 4.83 5.25 5.43

 Metallurgical Coal 1.82 1.76 1.75 1.68

 Steam Coal 1.56 1.55 1.56 1.62

 Electricity 13.91 14.65 15.52 15.67

 Transportation 10.90 10.94 11.15 11.44

 Primary Energy 10.88 10.92 11.12 11.41

 Petroleum Products 2/ 10.88 10.92 11.13 11.41

 Distillate Fuel 5/ 10.71 10.69 10.70 10.84

 Jet Fuel 6/ 6.23 6.29 6.56 6.93

 Motor Gasoline 7/ 12.25 12.26 12.50 12.78

 Residual Fuel 3.74 4.01 4.28 4.56

 Liquefied Petroleum Gas 8/ 15.23 15.28 15.59 16.19

 Natural Gas 9/ 8.59 9.11 9.44 9.66

 Ethanol (E85) 10/ 16.86 16.90 17.14 16.88

 Electricity 18.92 19.66 20.04 19.92

 1/ Weighted average price includes fuels below as well as coal.

 2/ This quantity is the weighted average for all petroleum products, not just those listed below.

 3/ Includes energy for combined heat and power plants, except those whose primary business is to sell

 electricity, or electricity and heat, to the public.

 4/ Excludes use for lease and plant fuel.

 5/ Diesel fuel containing 500 parts per million (ppm) or 15 ppm sulfur. Price includes Federal and

State taxes while excluding county and local taxes.

 6/ Kerosene-type jet fuel. Price includes Federal and State taxes while excluding county and local taxes.

 7/ Sales weighted-average price for all grades. Includes Federal, State, and local taxes.

 8/ Includes Federal and State taxes while excluding county and local taxes.

 9/ Compressed natural gas used as a vehicle fuel. Price includes estimated motor vehicle fuel taxes.

 10/ E85 refers to a blend of 85 percent ethanol (renewable) and 15 percent motor gasoline (nonrenewable).

To address cold starting issues, the percentage of ethanol actually varies seasonally. The annual average 

ethanol content of 74 percent is used for this forecast.

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-9
 Table A-3. Energy Prices by Sector and Source (Continued)

2010 2015 2020 2025

 Average End-Use Energy 10.55 10.95 11.41 11.77

 Primary Energy 8.59 8.83 9.16 9.51

 Electricity 19.44 20.35 21.12 21.26

 Electric Power 11/

 Fossil Fuel Average 2.07 2.28 2.43 2.43

 Petroleum Products 4.55 4.77 5.12 5.43

 Distillate Fuel 5.34 5.52 5.96 6.34

 Residual Fuel 4.19 4.44 4.71 5.00

 Natural Gas 4.32 4.81 5.20 5.38

 Steam Coal 1.25 1.23 1.24 1.32

 Average Price to All Users 12/

 Petroleum Products 2/ 9.87 10.00 10.27 10.64

 Distillate Fuel 9.49 9.70 9.80 10.03

 Jet Fuel 6.23 6.29 6.56 6.93

 Liquefied Petroleum Gas 10.97 11.21 11.65 12.32

 Motor Gasoline 7/ 12.24 12.25 12.49 12.77

 Residual Fuel 3.99 4.25 4.52 4.80

 Natural Gas 5.56 5.94 6.32 6.56

 Coal 1.27 1.25 1.26 1.33

 Ethanol (E85) 10/ 16.86 16.90 17.14 16.88

 Electricity 19.44 20.35 21.12 21.26

Non-Renewable Energy Expenditures by Sector

(billion 2003 dollars)

 Residential 176.3 193.3 209.8 223.2

 Commercial 130.1 153.0 176.6 199.0

 Industrial 140.1 152.6 169.9 185.1

 Transportation 339.7 372.2 407.2 447.2

 Total Non-Renewable Expenditures 786.2 871.1 963.5 1054.4

 Transportation Renewable Expenditures 0.0 0.0 0.1 0.1

 Total Expenditures 786.3 871.2 963.5 1054.5

 2/ This quantity is the weighted average for all petroleum products, not just those listed below.

 7/ Sales weighted-average price for all grades. Includes Federal, State, and local taxes.

 10/ E85 refers to a blend of 85 percent ethanol (renewable) and 15 percent motor gasoline (nonrenewable).

To address cold starting issues, the percentage of ethanol actually varies seasonally. The annual average 

ethanol content of 74 percent is used for this forecast.

 11/ Includes electricity-only and combined heat and power plants whose primary business is to sell

 electricity, or electricity and heat, to the public.

 12/ Weighted averages of end-use fuel prices are derived from the prices shown in each sector and the

corresponding sectoral consumption.

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-10
 Table A-4. Electricity Supply, Disposition, Prices, and Emissions

 (Billion Kilowatthours, Unless Otherwise Noted)

2010 2015 2020 2025

Generation by Fuel Type

Electric Power Sector 1/

 Power Only 2/

 Coal 2171 2248 2418 2786

 Petroleum 111 117 128 123

 Natural Gas 3/ 635 844 1000 1000

 Nuclear Power 810 823 827 827

 Pumped Storage/Other -9 -9 -9 -9

 Renewable Sources 4/ 386 408 462 527

 Distributed Generation (Natural Gas) 0 0 1 3

 Total 4106 4432 4828 5258

 Combined Heat and Power 5/

 Coal 33 33 33 33

 Petroleum 6 7 8 7

 Natural Gas 187 201 193 183

 Renewable Sources 4 4 4 4

 Total 230 245 238 227

 Total Net Generation 4336 4676 5067 5486

 Less Direct Use 66 65 65 65

Net Available to the Grid 4270 4611 5001 5420

Commercial and Industrial Generation 6/

 Coal 21 21 21 21

 Petroleum 9 10 12 12

 Natural Gas 101 121 150 186

 Other Gaseous Fuels 7/ 4 5 5 5

 Renewable Sources 4/ 43 45 49 54

 Other 8/ 10 10 10 10

 Total 188 212 247 288

 Less Direct Use 140 153 173 198

 Total Sales to the Grid 48 59 74 90

Total Electricity Generation 4523 4888 5314 5774

Total Net Generation to the Grid 4318 4670 5076 5511

 1/ Includes electricity-only and combined heat and power (CHP) plants whose primary business is to sell 

electricity, or electricity and heat, to the public.

 2/ Includes plants that only produce electricity.

 3/ Includes electricity generation from fuel cells.

 4/ Includes conventional hydroelectric, geothermal, wood, wood waste, municipal solid waste, landfill gas,

other biomass, solar, and wind power.

 5/ Includes combined heat and power plants whose primary business is to sell electricity and heat to the public

(i.e., those that report NAICS code 22).

 6/ Includes combined heat and power plants and electricity-only plants in the commercial and industrial sectors;

and small on-site generating systems in the residential, commercial, and industrial sectors

used primarily for own-use generation, but which may also sell some power to the grid.

 7/ Other gaseous fuels include refinery and still gas.

 8/ Other includes batteries, chemicals, hydrogen, pitch, purchased steam, sulfur, and miscellaneous technologies.

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-11
 Table A-4. Electricity Supply, Disposition, Prices, and Emissions (Continued)

2010 2015 2020 2025

Net Imports 10 22 16 11

Electricity Sales by Sector

 Residential 1470 1584 1696 1812

 Commercial 1463 1646 1845 2072

 Industrial 1107 1166 1229 1290

 Transportation 26 29 32 35

 Total 4067 4424 4801 5208

Direct Use 205 218 238 264

Total Consumption 4273 4643 5039 5472

End-Use Prices 9/ (2003 cents per kilowatthour)

 Residential 7.9 8.1 8.2 8.2

 Commercial 6.8 7.2 7.5 7.6

 Industrial 4.7 5.0 5.3 5.3

 Transportation 6.5 6.7 6.8 6.8

 All Sectors Average 6.6 6.9 7.2 7.3

Prices by Service Category 9/

(2003 cents per kilowatthour)

 Generation 4.1 4.5 4.7 4.8

 Transmission 0.6 0.6 0.7 0.7

 Distribution 2.0 1.9 1.8 1.8

Electric Power Sector Emissions 1/

 Sulfur Dioxide (million tons) 9.3 9.0 9.0 8.9

 Nitrogen Oxide (million tons) 4.0 4.1 4.2 4.3

 Mercury (tons) 54.4 55.2 55.7 55.5

 1/ Includes electricity-only and combined heat and power (CHP) plants whose primary business is to sell

electricity, or electricity and heat, to the public.

 9/ Prices represent average revenue per kilowatthour.

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-12
 Table A-5. Electricity Generating Capacity

 (Gigawatts)

2010 2015 2020 2025

Electric Power Sector 2/

 Power Only 3/

 Coal Steam 305.1 310.3 331.6 383.1

 Other Fossil Steam 4/ 119.4 98.8 97.8 97.2

 Combined Cycle 136.2 146.9 172.6 185.5

 Combustion Turbine/Diesel 132.5 141.4 164.7 183.8

 Nuclear Power 5/ 100.6 102.2 102.7 102.7

 Pumped Storage 20.9 20.9 20.9 20.9

 Fuel Cells 0.0 0.0 0.0 0.0

 Renewable Sources 6/ 95.0 100.0 113.1 128.1

 Distributed Generation (Natural Gas) 7/ 0.4 1.0 2.7 6.2

 Total 910.0 921.6 1006.0 1107.5

 Combined Heat and Power 8/

 Coal Steam 5.1 5.0 5.0 5.0

 Other Fossil Steam 4/ 1.1 1.1 1.1 1.1

 Combined Cycle 33.5 33.5 33.5 33.5

 Combustion Turbine/Diesel 5.1 5.1 5.1 5.1

 Renewable Sources 6/ 0.3 0.3 0.3 0.3

 Total 45.1 45.0 45.0 45.0

 Cumulative Planned Additions 9/

 Coal Steam 1.8 1.8 1.8 1.8

 Other Fossil Steam 4/ 0.0 0.0 0.0 0.0

 Combined Cycle 28.3 28.3 28.3 28.3

 Combustion Turbine/Diesel 3.9 3.9 3.9 3.9

 Nuclear Power 0.0 0.0 0.0 0.0

 Pumped Storage 0.0 0.0 0.0 0.0

 Fuel Cells 0.0 0.0 0.0 0.0

 Renewable Sources 6/ 2.7 2.8 2.9 3.0

 Distributed Generation 7/ 0.0 0.0 0.0 0.0

 Total 36.7 36.8 36.9 37.0

 Cumulative Unplanned Additions 9/

 Coal Steam 0.0 5.8 27.0 78.5

 Other Fossil Steam 4/ 0.0 0.0 0.0 0.0

 Combined Cycle 3.3 14.6 40.3 53.2

 Combustion Turbine/Diesel 5.8 19.1 44.7 65.2

 Nuclear Power 0.0 0.0 0.0 0.0

 Pumped Storage 0.0 0.0 0.0 0.0

 Fuel Cells 0.0 0.0 0.0 0.0

 Renewable Sources 6/ 0.1 5.0 18.0 33.0

 Distributed Generation 7/ 0.4 1.0 2.7 6.2

 Total 9.7 45.6 132.7 236.1

 Cumulative Electric Power Sector Additions 46.3 82.3 169.6 273.0

 Cumulative Retirements 10/

 Coal Steam 1.9 2.5 2.5 2.5

 Other Fossil Steam 4/ 9.3 29.8 30.8 31.4

 Combined Cycle 0.1 0.7 0.7 0.7

 Combustion Turbine/Diesel 1.9 6.3 8.6 9.9

 Nuclear Power 0.0 0.0 0.0 0.0

 Pumped Storage 0.0 0.0 0.0 0.0

 Fuel Cells 0.0 0.0 0.0 0.0

 Renewable Sources 6/ 0.1 0.1 0.1 0.1

 Total 13.3 39.4 42.7 44.6

Total Electric Power Sector Capacity 955.1 966.6 1051.0 1152.5

 Table 5. Electricity Generating Capacity (Continued)

Commercial and Industrial Generators 11/

 Coal 4.1 4.1 4.1 4.1

 Petroleum 1.5 1.5 1.6 1.7

 Natural Gas 17.5 20.2 24.1 29.0

 Other Gaseous Fuels 1.5 1.6 1.6 1.7

 Renewable Sources 6/ 7.0 7.4 8.0 9.4

 Other 0.7 0.7 0.7 0.7

 Total 32.3 35.5 40.3 46.6

 Cumulative Capacity Additions 9/ 5.1 8.3 13.0 19.3

 1/ Net summer capacity is the steady hourly output that generating equipment is expected to supply to

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-13
 Table 6. Renewable Energy Generating Capacity and Generation

 (Gigawatts, Unless Otherwise Noted)

2010 2015 2020 2025

Electric Power Sector 1/

 Net Summer Capacity

 Conventional Hydropower 0.8 0.9 0.8 0.6

 Geothermal 2/ 5.2 4.6 4.4 4.1

 Municipal Solid Waste 3/ 11.3 13.3 14.9 16.1

 Wood and Other Biomass 4/ 11.3 13.3 14.9 16.1

 Solar Thermal 0.0 0.0 0.0 0.0

 Solar Photovoltaic 5/ 0.0 0.0 0.0 0.0

 Wind 17.3 18.8 20.1 20.8

 Total 0.0 0.0 0.0 0.0

 Generation (billion kilowatthours)

 Conventional Hydropower 2.0 1.8 1.8 2.1

 Geothermal 2/ 0.6 0.8 0.9 1.0

 Municipal Solid Waste 3/ 0.5 0.5 0.6 0.6

 Wood and Other Biomass 4/ 1.0 1.0 1.1 1.1

 Dedicated Plants 1.1 1.4 1.5 1.6

 Cofiring 0.6 0.5 0.5 0.9

 Solar Thermal 0.0 0.0 0.0 0.0

 Solar Photovoltaic 0.0 0.0 0.0 0.0

 Wind 0.0 0.0 0.0 0.0

 Total 0.0 0.0 0.0 0.0

End Use Sector

 Net Summer Capacity

 Combined Heat and Power

 Municipal Solid Waste 4.7 5.1 5.5 5.8

 Biomass 0.8 0.8 0.9 0.9

 Total 5.3 5.4 5.7 6.0

 Other End-Use Generators 6/

 Conventional Hydropower 7/ 23.0 24.7 26.3 27.8

 Geothermal 0.0 0.0 0.0 0.0

 Solar Photovoltaic 5/ 0.0 0.0 0.0 0.0

 Total 0.0 0.0 0.0 0.0

 Generation (billion kwh)

 Combined Heat and Power

 Municipal Solid Waste 0.6 0.6 0.7 0.6

 Biomass 23.0 24.7 26.3 27.8

 Total 0.0 0.0 0.0 0.0

 Other End-Use Generators 6/ 0.0 0.0 0.0 0.0

 Conventional Hydropower 7/ 0.0 0.0 0.0 0.0

 Geothermal 25.0 26.8 28.5 30.3

 Solar Photovoltaic 0.6 0.6 0.6 0.7

 Total 0.0 0.0 0.0 0.0

 1/ Includes electricity-only and combined heat and power (CHP) plants whose primary business is to sell electricity,

or electricity and heat, to the public.

 2/ Includes hydrothermal resources only (hot water and steam).

 3/ Includes landfill gas.

 4/ Includes projections for energy crops after 2010.

 5/ Does not include off-grid photovoltaics (PV). EIA estimates that another 76 megawatts of remote

electricity generation PV applications were in service in 1999, plus an additional 205 megawatts

in communications, transportation, and assorted other non-grid-connected applications.

 6/ Includes small on-site generating systems in the residential, commercial, and industrial sectors used

primarily for own-use generation, but which may also sell some power to the grid.

 7/ Represents own-use industrial hydroelectric power.

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-14
MARKAL Baseline Case Assumptions and Projections 

Economic and Demographic Assumptions 

The Baseline Case used to evaluate the impact of the EERE portfolio was benchmarked to EIA’s 

Annual Energy Outlook 2005 (AEO2005) for the period between 2005 and 2025.8 To the extent 

possible, the same input data and assumptions were used in MARKAL-GPRA07, as were used to 

generate the AEO2005 Reference Case. For example, the macroeconomic projections for GDP, 

housing stock, commercial square footage, industrial output, and vehicle miles traveled were 

taken from the AEO2005. At the sector level, both supply-side and demand-side technologies 

were characterized to reflect the AEO2005 assumptions where the representation of technologies 

is similar between MARKAL (MARKet ALlocation) and the National Energy Modeling System 

(NEMS). The resulting projections track closely with the AEO2005 at the aggregate level, 

although they do not match exactly at the end-use level. For the period after 2025, various 

sources were used to compile a set of economic and technical assumptions. For instance, the 

primary economic drivers of GDP and population were based on the real GDP growth rate from 

the Congressional Budget Office’s Long-Term Budget Outlook and population growth rates from 

the Social Security Administration’s 2005 Annual Report to the Board of Trustees low-cost 

assumptions. 9

In the reference case, GDP is projected to increase at an average annual rate of 3% from 2005 to 

2025, and then slow to an average annual rate of 2.2% from 2025 to 2050. The population 

growth rate is projected to decline from an average annual rate of 0.8% between 2005 and 2025 

to 0.5% from 2025 to 2050. The reference case macroeconomic assumptions are shown in 

Table A-7. 

Table A-7. Reference Case Macroeconomic and Demographic Assumptions 

Annual Growth Rates

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 '05-'25 25-'50 '05-'50

GDP (Bill. 2001$) $11,490 $13,398 $15,581 $18,057 $20,779 $23,625 $26,534 $29,584 $32,651 $35,961 3.0% 2.2% 2.6%

Population (Million) 296.8 310.1 323.5 337.0 350.6 362.6 373.3 381.8 387.7 393.1 0.8% 0.5% 0.6%

Total Households (Million) 115.0 122.0 129.1 135.8 142.5 145.0 149.3 152.7 155.1 157.2 1.1% 0.4% 0.7%

Commercial Floorspace (Bill. sq ft) 74.7 81.2 88.4 96.2 104.8 112.9 120.8 128.7 136.3 144.1 1.7% 1.3% 1.5%

Industrial Production (2000=100) 96 108 120 133 148 167 185 205 225 245 2.2% 2.0% 2.1%

Light Duty Vehicle Miles Traveled 

(Bill. VMT) 2,667 3,017 3,354 3,680 4,053 4,377 4,680 4,929 5,106 5,272 2.1% 1.1% 1.5%

 

8

 The Energy Information Administration’s recently released Annual Energy Outlook 2006 (Early Release) indicates significantly 

higher oil and fuels prices for much of the forecast horizon than does the previous forecast (AEO 2005), on which this benefits 

analysis is based. All else equal, higher fuels prices would be expected to increase the market penetration of renewable energy 

and energy efficiency measures undertaken irrespective of DOE programs, as these technologies become more price competitive. 

As such, some of the nonrenewable energy savings, cost savings, and emissions reductions attributable to DOE programs might 

be reduced. 

9 The Long-Term Budget Outlook, Congressional Budget Office, December 2003. 

The 2005 Annual Report of the Board of Trustees of the Federal Old Age and Survivors Insurance and the Federal Disability 

Insurance Trust Funds, March 2005. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-15
Assumptions on Energy Prices

Table A-8 shows projected energy prices for the reference case. Real natural gas prices are 

projected to drop between 2005 and 2010, and then increase at nearly 1.7% per year from 2010 

to 2025 before increasing amounts of arctic gas and LNG imports limit the average annual 

increase to 0.8% from 2025 to 2050. Real crude oil prices are also projected to decrease between 

2000 and 2005, increase at average annual rates of 1.3% between 2010 and 2025, and 1% per 

year thereafter. 

Average real mine mouth coal prices are projected to continue to decline by about 1% a year 

between 2005 and 2015, due to increasing productivity gains and a continued shift to less labor 

intensive Western coal production. However, coal prices are projected to increase at an average 

rate of 1% per year after 2015, due to increased demands, gradually increasing mine depths, and 

a saturation of labor productivity gains. 

Table A-8. Reference Case Energy Prices 

Annual Growth Rates

2003 $s 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 '05-'25 25-'50 '05-'50

World Oil Price ($/bbl) $33.65 $24.73 $26.42 $28.11 $29.94 $31.89 $33.91 $34.75 $36.51 $38.40 -0.6% 1.0% 0.3%

Natural Gas Wellhead Price 

($/Mcf) $5.10 $3.63 $4.31 $4.52 $4.66 $4.41 $4.63 $4.87 $5.12 $5.68 -0.4% 0.8% 0.2%

Coal Minemouth Price ($/short 

ton) $18.49 $17.17 $16.72 $17.22 $18.17 $19.45 $20.95 $22.57 $23.49 $24.00 -0.1% 1.1% 0.6%

Primary Energy Consumption

As a result of slightly increasing energy prices relative to technology improvements and shifts 

within the economy, energy demand is projected to increase more slowly than GDP. As shown in 

Table A-9, total primary energy use is projected to increase at a rate of 1.3% per year from 2005 

to 2025, and at an average annual rate of 0.5% between 2025 and 2050. By 2050, total primary 

energy consumption is projected to reach just under 150 quadrillion Btus (quads). Overall, the 

energy consumption to GDP ratio is projected to decline by 1.7% per year from 2005 to 2050, 

while total carbon emissions increase by 1.1% per year over the same period. 

Table A-9. Primary Energy Consumption, Energy Intensity, and Carbon Emissions 

Annual Growth Rates

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 '05-'25 25-'50 '05-'50

Petroleum 40.6 44.5 47.2 49.5 52.1 54.0 55.4 56.9 58.5 60.7 1.3% 0.6% 0.9%

Natural Gas 23.4 27.4 29.1 31.1 32.7 33.0 33.7 35.3 36.7 37.7 1.7% 0.6% 1.1%

Coal 24.0 23.9 26.1 28.1 30.2 33.0 35.3 35.5 35.9 35.1 1.2% 0.6% 0.8%

Nuclear 8.4 8.4 8.6 8.6 8.6 8.6 6.6 6.0 4.2 2.7 0.1% -4.5% -2.5%

Renewables 7.2 7.7 8.3 9.1 9.8 10.2 11.2 11.6 12.4 13.4 1.5% 1.3% 1.4%

Total Primary Energy 103.6 111.8 119.3 126.4 133.5 138.8 142.3 145.3 147.7 149.7 1.3% 0.5% 0.8%

Energy/GDP (Thos. Btu/ '01$ GDP) 9.0 8.3 7.7 7.0 6.4 5.9 5.4 4.9 4.5 4.2 -1.7% -1.7% -1.7%

Carbon Emissions (MMT) 1,657 1,835 1,983 2,130 2,274 2,347 2,454 2,549 2,634 2,714 1.6% 0.7% 1.1%

Crude oil’s share of total energy consumption is projected to increase from 39% in 2005 to 

nearly 41% in 2050. The natural gas share is projected to grow from 23% to 25% over the same 

period. Coal generation is projected to increase slightly from a 23% share in 2005 to nearly 24% 

in 2050. Almost all existing nuclear generation capacity is assumed to retire between 2025 and 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-16
2050.10 However, 29 GW of new nuclear capacity is projected to be added between 2025 and 

2050. The share of renewable energy is also projected to increase from 7% and 9% throughout 

the projection period. 

End-Use Energy Demand 

The sectoral breakout of energy use, shown in Figure A-3, demonstrates that commercial 

buildings and transportation energy demand is projected to increase most rapidly, at 1.2% and 

1.1% per year respectively, from 2005 to 2050; while residential and industrial energy demand 

increases most slowly, at 0.4% and 0.6% per year, respectively. The growth rates in energy 

consumption are a function of the opposing trends of increasing end-use energy-service demand 

and improvements in the efficiency of technologies that satisfy this demand, as well as 

macroeconomic shifts toward less energy-intensive industries. This phenomenon is best 

illustrated by examining the energy intensity of the economy. Figure A-4 shows the relative 

energy intensity for different end-use and conversion sectors and the economy as a whole. 

0

20

40

60

80

100

120

140

160

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Quad. Btus

Transportation

Industrial

Commercial

Residential

Note: consumption totals include electric generation and distribution losses 

Figure A-3. Energy Consumption by Sector

 

10 The nuclear generation retirement schedule was derived by examining reactor-license expiration dates and applying one 20-

year extension where applicable. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-17
Relative Energy Intensity

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Residential Commercial Industry Transportation Electric Generation Economy

Index (2005 = 1)

2000

2010

2020

2030

2040

2050

Figure A-4. Relative Energy Intensity by Sector 

Note: Residential index is primary energy excluding misc. use per household; Commercial index is primary energy use excluding office 

equipment and misc. appliances per square foot; Industrial index is total primary energy per unit output; Transportation index is light 

duty vehicle primary energy per mile traveled; Electricity index is non-renewable average heat rate; and Economy index is total

primary energy per unit GDP. 

As shown in Figure A-4, our Reference Case projection indicates that the energy intensity of the 

economy (which we’ve defined as total primary energy consumption per $ of GDP) is projected 

to fall by more than half by 2050. This decrease reflects both a continued shift toward a servicebased economy, as well as increases in energy technology efficiency. End-use efficiencies are 

projected to increase throughout the economy over the projection period as new, more efficient 

capital stocks are purchased to replace existing equipment and to meet new demand. The 

Reference Case technology database includes technologies that are expected to become available 

in the future, as well as those that are currently on the market. For example, more efficient 

electric heat pumps and light-duty vehicles are assumed to become available throughout the 

projection period. The technical and economic data associated with these technologies are 

derived from a variety of sources, but rely most heavily on the NEMS database. 

The residential energy intensity index shows significant improvements in energy use per 

household. However, the residential index excludes “miscellaneous demands,” the fastest 

growing segment of residential energy demand. The miscellaneous demand category includes 

electric devices such as home computers, TVs, microwave ovens, as well as devices such as gas 

lamps and swimming pool heaters. Because these service demands are growing faster than the 

sector as a whole, their energy use per household actually increases over time. Thus, the 

inclusion of miscellaneous demands in the calculation of residential energy intensity would 

obscure the efficiency gains being made in other residential service demands. While these 

miscellaneous demands are excluded from the chart, they are modeled within MARKAL. 

The commercial energy intensity index shows significant improvements in energy use per square 

foot. However, as with the residential sector, this calculation excludes the fastest-growing 

demand categories; office equipment and miscellaneous commercial appliances. The inclusion of 

these demand categories would result in relatively constant commercial energy demand per 

square foot. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-18
The industrial-sector efficiency index shows dramatic declines in energy intensity, due to a shift 

from energy-intensive industries to nonenergy-intensive manufacturing, as well as improvements 

in process efficiency. Between 2005 and 2050, nonenergy-intensive manufacturing output is 

expected to grow at twice the rate as energy-intensive industrial output. This shift in output 

exaggerates the decline in energy intensity. However, in the transportation sector, consumer 

preferences for more powerful engines, and a continued shift from passenger cars to SUVs, limit 

gains in overall efficiency. 

In the power-generation sector, the efficiency of nonrenewable generation is expected to increase 

as older, less-efficient fossil steam units retire and new high efficiency gas combined cycle and 

IGCC capacity is built. Electric generation by type is shown in Figure A-5. Natural gas-fired 

generation is projected to increase its share of total generation from about 19% to 26% over the 

projection period. Coal-fired generation remains the largest source of electricity at 51% to 59% 

of total generation. Due to significant retirements of existing nuclear capacity, the share of 

nuclear generation falls from 20% to 4% of generation in the projection period. Renewable 

generation is relatively constant at 10% to 11% of total generation. 

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Billion kWh

Renewable

Nuclear

Natural Gas & Oil

Coal

Figure A-5. Electricity Generation by Type: Reference Case

While both natural gas and coal-fired generation show increased efficiency, fossil fuel use for 

electric generation increases by 60% during the projection period. Such an increase in coal and 

natural gas demand for power generation is dependent on the availability of these resources. 

However, potential reduction in supply, such as changes in the outlook in natural gas supply, 

would necessitate a significant change in fuels used for electric generation. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix A – NEMS and MARKAL – Page A-19
Appendix B – GPRA07 Hydrogen, Fuel Cells, and 

Infrastructure Technologies (HFCIT) Program Documentation 

1. Introduction 

The target markets for the Office of Hydrogen, Fuel Cells, and Infrastructure Technologies

(HFCIT) program include transportation (cars and light trucks) and stationary (particularly 

residential and commercial) applications. The two main markets will be discussed separately 

below. 

1.1 Target Market: Fuel Cell Vehicle Market

The market for fuel cell vehicles (FCVs) includes all cars and light trucks sold for both personal 

and business use. Today, the size of this market is approximately 17 million vehicle sales per 

year. Total car and light-truck stock is about 220 million vehicles. EIA projects both sales and 

stock to grow to more than 20 million and 300 million respectively by 2025. Additional growth 

is expected post-2025, as explained in Chapter 3. The vehicle miles of travel are projected to 

grow from 3.28 trillion in 2020 to 5.63 trillion in 2050. 

1.2 Key Factors in Shaping the Market Adoption of FCVs 

Key factors associated with the adoption of new vehicle technologies include how the new 

vehicle technologies compare with the baseline vehicle technologies in terms of the following 

vehicle attributes: 

• Vehicle Price 

• Fuel Economy 

• Range 

• Maintenance Cost 

• Acceleration 

• Top Speed 

• Luggage Space 

Of these, vehicle price and fuel economy are the most important. 

Nonvehicle attributes that are important factors in a consumer’s decision to purchase new vehicle 

technologies include the following: 

• Fuel Price 

• Fuel Availability 

1.3 Methodology and Calculations 

The factors listed above include the factors used in the modeling of new vehicle technology 

penetration by the NEMS and MARKAL models. FCV attributes and other factors are discussed 

below. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix B – Hydrogen, Fuel Cells, and Infrastructure Technologies Program – Page B-1
1.3.1 FCV Attributes 

FCV attributes were developed based on the HFCIT program goals, discussions with HFCIT 

program managers, Powertrain Systems Analysis Toolkit (PSAT) modeling, and payback 

analysis (Refs. 1-5). The PSAT model is a simulation model used by DOE to evaluate the fuel 

economy and performance of light vehicles using various technologies. (See Section 1.3.2 of 

Appendix F for a discussion of how the fuel economies of FCVs and other advanced technology 

vehicles are estimated in GPRA 07). 

Payback analysis was used to estimate what the incremental price of FCVs would be when they 

become cost competitive with conventional vehicles, a goal of the program. (The incremental 

price equals the present value of the energy cost reduction achieved by FCVs over three years, 

assuming a hydrogen price of $1.50/gallon gasoline equivalent and 7.5% discount rate. See 

Section 1.3.3 of Appendix F for additional discussion of how the incremental prices of FCVs and 

other advanced technology vehicles are estimated in GPRA 07.) Other attributes were based on 

a review of past GPRA characterizations (e.g., Ref. 6). 

Because the NEMS and MARKAL models require different levels of detail, two separate vehicle 

characterizations are provided. In both cases, most of the attributes are provided as ratios to the 

vehicle attributes of conventional vehicles. (For NEMS, the $ value of the price increments were 

provided.) The attributes are for new vehicles in the year listed. The conventional vehicles to 

which the FCVs are compared are the conventional vehicles of the AEO 2004 Reference Case 

extended to 2050 with modest increases in fuel economy. (See Appendix A for the description 

of the GPRA 07 Baseline.) 

Table 1 contains the vehicle attributes for FCVs provided for input to the NEMS model. 

Attributes are provided for six car size classes and six light-truck classes. Table 2 contains 

vehicle attributes for FCVs provided as input to the MARKAL model. MARKAL uses only 

vehicle price and fuel economy attributes. MARKAL does not disaggregate cars and light trucks 

into various classes. 

1.3.2 Hydrogen Price 

HFCIT Program goals were used to estimate capital and O&M costs and production efficiencies 

for distributed natural gas reformers, central biomass gasifiers, distributed ethanol reformers, and 

central and distributed electrolytic production technologies. Assumptions for central coal and 

natural gas production technologies were adapted from H2A analysis results. The infrastructure 

requirements and operating costs for the widespread distribution of hydrogen vary widely by 

distance and method. As a simplifying assumption, a flat cost of $5.28 per MMBtu—or $0.65 

per gallon of gasoline equivalent (gge)1—was assumed for hydrogen distribution costs based on 

published data from NREL.2 We will be enhancing the representation of the distribution and 

fueling costs for hydrogen in future analysis as data becomes available. Table 3 shows projected 

 

1

 One kilogram of hydrogen is roughly equivalent in energy content to 1 gallon of gasoline, and is often referred to as a gallon of 

gasoline equivalent (gge). 

2

 Amos W.A., Lane J.M., Mann M.K., and Spath P.L. Update of hydrogen from biomass – determination of the delivered cost of 

hydrogen, NREL, 2000. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix B – Hydrogen, Fuel Cells, and Infrastructure Technologies Program – Page B-2
hydrogen costs by cost component for the Hydrogen Program Case. Due to market factors 

affecting feedstock costs, the projected costs do not always match HFCIT Program goals. 

1.3.3 Hydrogen Availability at Stations

An availability factor for hydrogen refueling stations is required by the NEMS model. The 

assumptions used are as follows: 1) hydrogen (H2) will not be available at any stations until 

sometime between 2015 and 2020; 2) in 2020, H2 will be available at 10% of all U.S. service 

stations and 3) H2 will be available at 25% of all U.S. service stations by 2025. These 

assumptions were provided by the HFCIT program. 

1.3.4 FCV Market Penetration Methodology 

Brief descriptions of how the NEMS and MARKAL models project new vehicle technology 

penetration using these vehicle attributes can be found in Chapter 2 (NEMS) and Chapter 3

(MARKAL). 

1.4 Sources 

1. “Hydrogen, Fuel Cells & Infrastructure Technologies Program: Multi-Year Research, 

Development and Demonstration Plan” (Draft), U.S. Department of Energy, Energy Efficiency 

and Renewable Energy (June 3, 2003). 

2. PSAT (POWERTRAIN SYSTEM ANALYSIS TOOLKIT): see 

http://www.transportation.anl.gov/software/PSAT/ 

3. Phillip Sharer and Aymeric Rousseau, “PSAT Results for GREET and GPRA – FE Adjusted 

081705.xls,” August 17, 2005. 

4. Rousseau, Aymeric, “Number Associated with Presentation,” July 6, 2005. 

5. Payback model developed by Jim Moore, TA Engineering (2003) and expanded by Margaret 

Singh, ANL (2005). 

6. “Program Analysis Methodology: Office of Transportation Technologies, Quality Metrics 

2003 Final Report,” prepared by OTT Analytic Team, for Office of Transportation Technologies, 

U.S. Department of Energy (March 2002). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix B – Hydrogen, Fuel Cells, and Infrastructure Technologies Program – Page B-3
Table 1. FCV Attributes Input to NEMS 

(All units are ratios to the conventional gasoline vehicles of the specific year, 

except for the incremental price which is in 2003$)

2-SEATER MINI-COMPACT SUB-COMPACT 

Fuel Cell (H2) 2022 2025 2022 2025 2022 2025 

Incremental Vehicle Price ($) 2392 1611 2311 1559 1964 1325 Range 0.90 0.96 0.90 0.96 0.90 0.96 

Maintenance Cost 1.05 1.02 1.05 1.02 1.05 1.02 

Acceleration 1.00 1.06 1.00 1.06 1.00 1.06 

Top Speed 0.90 0.93 0.90 0.93 0.90 0.93 

Luggage Space 0.80 0.86 0.80 0.86 0.80 0.86 

Fuel Economy (a) 2.37 2.43 2.37 2.43 2.37 2.43 

MEDIUM CAR LARGE CAR 

Fuel Cell (H2) 2018 2023 2025 2018 2023 2025

Incremental Vehicle Price ($) 2251 1661 1613 2415 1775 1722Range 1.00 1.00 1.00 1.00 1.00 1.00

Maintenance Cost 1.05 1.00 0.97 1.05 1.00 0.97

Acceleration 1.00 1.00 1.04 1.00 1.00 1.04

Top Speed 0.85 0.9 0.92 0.85 0.9 0.92

Luggage Space 0.90 1.00 1.00 0.90 1.00 1.00

Fuel Economy (a) 2.28 2.38 2.42 2.28 2.38 2.42 (a) Gasoline gallon equivalent 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix B – Vehicle Technologies Program – Page B-4
 Table 1 (continued). 

SMALL SUV LARGE SUV SMALL TRUCK CARGO (Incl. 2b) TRUCK 

Fuel Cell (H2) 2020 2025 2018 2023 2025 2020 2025 2024 2025 

Incremental Vehicle Price ($) 2626 1879 3281 2350 2242 2342 1685 2802 1861 

Range 0.90 1.00 1.00 1.00 1.00 0.90 1.00 0.90 0.93Maintenance Cost 1.10 1.00 1.05 1.00 0.97 1.10 1.00 1.05 1.04

Acceleration 1.00 1.10 1.10 1.10 1.10 1.00 1.10 1.00 1.00

Top Speed 0.90 0.95 0.90 0.95 0.95 0.90 0.95 0.90 0.90Luggage Space 0.90 0.95 0.95 1.00 1.00 0.90 0.95 0.90 0.91

Fuel Economy (a) 2.37 2.35 2.36 2.36 2.35 2.16 2.16 2.16 2.16

MINIVAN LARGE VAN

Fuel Cell (H2) 2020 2025 2018 2023 2025 

Incremental Vehicle Price ($) 2535 1821 3178 2267 2158 

Range 0.90 1.00 1.00 1.00 1.00

Maintenance Cost 1.10 1.00 1.05 1.00 0.97Acceleration 1.00 1.10 1.10 1.10 1.10Top Speed 0.90 0.95 0.90 0.95 0.95 

Luggage Space 0.90 0.95 0.95 1.00 1.00 

Fuel Economy (a) 2.37 2.35 2.36 2.36 2.35 

(a) Gasoline gallon equivalent 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix B – Vehicle Technologies Program – Page B-5
Table 2. FCV Attributes for Input to MARKAL 

Ratios to Conventional Vehicles 

2010 2020 2025 2030 2035 2050 

CARS MPG 2.12 2.32 2.42 2.54 2.67 2.95 

Incremental 

Price 1.061 1.036 

LIGHT TRUCKS MPG 2.21 2.30 2.29 2.27 2.26 2.73 

Incremental 

Price 1.063 1.036 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix B – Hydrogen, Fuel Cells, and Infrastructure Technologies Program – Page-B6
Table 3. Hydrogen Production Costs by Technology and Component 

(2003 $/gge) 

Central Coal - No Coproduct

Unit Costs 2025 2030 2035 2040 2045 2050 

Capital Costs 0.65 0.65 0.65 0.65 0.65 0.65 

O&M 0.23 0.23 0.23 0.23 0.23 0.23 

Feedstock Costs 0.20 0.24 0.26 0.27 0.29 0.32 

Plant Gate 1.08 1.12 1.14 1.14 1.17 1.20 

Distribution, Storage & Tax 1.23 1.23 1.23 1.23 1.23 1.23 

Total 2.30 2.34 2.36 2.37 2.39 2.42 

Central Coal - with Elec 

Co-product

Unit Costs 2025 2030 2035 2040 2045 2050 

Capital Costs 1.11 1.11 1.11 1.11 1.11 

O&M -0.24 -0.23 -0.19 -0.20 -0.21 

Feedstock Costs 0.31 0.34 0.34 0.38 0.41 

Plant Gate 1.18 1.21 1.26 1.28 1.31 

Distribution, Storage & Tax 1.23 1.23 1.23 1.23 1.23 

Total 2.40 2.44 2.48 2.51 2.54 

Remote Gas Reformer 

Unit Costs 2025 2030 2035 2040 2045 2050 

Capital Costs 0.58 0.58 0.58 0.58 0.58 0.58 

O&M 0.46 0.46 0.46 0.46 0.46 0.46 

Feedstock Costs 0.94 0.91 0.94 0.97 1.02 1.13 

Plant Gate 1.98 1.95 1.98 2.01 2.07 2.18 

Distribution, Storage & Tax 0.46 0.46 0.46 0.46 0.46 0.46 

Total 2.44 2.41 2.44 2.47 2.53 2.64 

Central Gas Reformer 

Unit Costs 2025 2030 2035 2040 2045 2050 

Capital Costs 0.21 0.21 0.21 0.21 0.21 0.21 

O&M 0.10 0.10 0.10 0.10 0.10 0.10 

Feedstock Costs 1.02 0.98 1.02 1.06 1.13 1.25 

Plant Gate 1.33 1.29 1.33 1.37 1.44 1.56 

Distribution, Storage & Tax 1.23 1.23 1.23 1.23 1.23 1.23 

Total 2.56 2.52 2.56 2.59 2.66 2.79 

Central Biomass 

Unit Costs 2025 2030 2035 2040 2045 2050 

Capital Costs 0.42 0.42 0.42 0.41 0.41 0.41 

O&M 0.26 0.26 0.26 0.26 0.26 0.26 

Feedstock Costs 0.35 0.35 0.35 0.35 0.40 0.52 

Plant Gate 1.04 1.04 1.04 1.03 1.08 1.20 

Tax 1.23 1.23 1.23 1.23 1.23 1.23 

Total 2.26 2.26 2.26 2.25 2.31 2.42 

Distributed Ethanol 

Unit Costs 2025 2030 2035 2040 2045 2050 

Capital Costs 0.44 0.44 0.44 0.44 0.44 0.44 

O&M 0.50 0.50 0.50 0.50 0.50 0.50 

Feedstock Costs 2.44 2.45 2.43 2.33 2.35 2.35 

Plant Gate 3.39 3.39 3.38 3.28 3.29 3.30 

Distribution, Storage & Tax 0.46 0.46 0.46 0.46 0.46 0.46 

Total 3.85 3.85 3.84 3.74 3.75 3.76 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix B – Hydrogen, Fuel Cells, and Infrastructure Technologies Program – Page-B7
Central Electrolytic H2 - 

Grid or Wind

Unit Costs 2025 2030 2035 2040 2045 2050 

Capital Costs 0.23 0.23 0.23 0.23 0.23 0.23 

O&M 0.10 0.10 0.10 0.10 0.10 0.10 

Feedstock Costs 2.94 2.18 2.16 2.02 2.05 2.22 

Plant Gate 3.28 2.52 2.50 2.36 2.39 2.56 

Distribution & Storage* 1.23 1.23 1.23 1.23 1.23 1.23 

Total 4.51 3.75 3.72 3.58 3.62 3.78 

Distributed Electrolytic 

H2 

Unit Costs 2025 2030 2035 2040 2045 2050 

Capital Costs 0.66 0.66 0.66 0.66 0.66 0.66 

O&M 0.50 0.50 0.50 0.50 0.50 0.50 

Feedstock Costs 3.16 2.35 2.32 2.17 2.21 2.39 

Plant Gate 4.33 3.51 3.49 3.34 3.37 3.55 

Distribution, Storage & Tax 0.46 0.46 0.46 0.46 0.46 0.46 

Total 4.79 3.97 3.95 3.80 3.83 4.01 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix B – Hydrogen, Fuel Cells, and Infrastructure Technologies Program – Page-B8
2.1 Stationary Fuel Cell Market 

Stationary fuel cells are one of a variety of distributed electricity-generation technologies. 

The particular market sectors in which stationary fuel cells are most applicable include 

residential and commercial applications. 

2.2 Key Factors in Shaping the Market Adoption of Stationary Fuel Cells 

Key factors associated with the market penetration of stationary fuel cells include the 

energy efficiency (electrical and combined heat and power), installed cost, and 

maintenance cost of the fuel cells relative to other distributed and traditional electricitygeneration technologies. 

2.3 Methodology and Calculations 

2.3.1 Baseline Assumptions for Stationary Fuel Cells 

There were no changes in the technology assumptions for distributed generation, 

including stationary fuel cells, from AEO 2004 to AEO2005. There remain a few 

definitional differences in how the HFCIT Program goals are stated and how the 

technology characterizations are used within NEMS. There also remains a difference in 

the view of current (or nearly current) technology that might reflect different trade-offs of 

efficiency and costs or may reflect differences in development. In either case, the same 

2005 values should be used for the GPRA Baseline and Program cases so the Baseline 

was modified to reflect the Program view of 2005. As described below, the Program 

values were first adjusted to the same definitions as used in NEMS. By 2010, the 

Baseline returns to the AEO2005 values, with higher efficiencies and also higher costs 

than the values for 2005. Because of their relatively high costs, fuel cells are not costeffective in the early years regardless of which source of data is used. 

Residential 5kW PEMFC Baseline 

AEO2005 Reference Case 

CHP System Electrical Installed Cost Maint. Cost

Year Efficiency Efficiency (2003 $/kW) (2003$/kW-yr)

2005 0.690 0.300 5500 264

2010 0.700 0.320 3800 184

2015 0.710 0.335 3000 168

2020 0.720 0.350 2200 152

2025 0.725 0.355 1750 140

GPRA07 Baseline 

2005 0.675 0.288 2300 264

2010 0.700 0.320 3800 184

2015 0.710 0.335 3000 168

2020 0.720 0.350 2200 152

2025 to 2050 0.725 0.355 1750 140

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix B – Hydrogen, Fuel Cells, and Infrastructure Technologies Program – Page-B9
Commercial 200kW Fuel Cell Baseline 

AEO2005 Reference Case 

CHP System Electrical Installed Cost Maint. Cost

Year Efficiency Efficiency (2003 $/kW) (2003$/kW-yr)

2005 0.750 0.360 5200 232

2010 0.720 0.490 2500 128

2015 0.720 0.500 2150 124

2020 0.720 0.510 1800 120

2025 0.735 0.520 1450 112

GPRA07 Baseline 

2005 0.675 0.288 1930 232

2010 0.720 0.490 2500 128

2015 0.720 0.500 2150 124

2020 0.720 0.510 1800 120

2025 to 2050 0.735 0.520 1450 112

2.3.2 Program Case Assumptions for Stationary Fuel Cells 

Assumptions for distributed PEM fuel cells are based on the multiyear program plan 

(Ref.1). Capital costs and efficiencies were provided in the MYPP for the years 2005 and 

2010. The costs are assumed to be in year 2003 dollars. No values were listed for 

maintenance costs, so the AEO2005 values are used The costs and efficiencies assumed 

for NEMS by 2025 were held constant through 2050 in MARKAL. 

The program goal capital costs were increased to account for the installation cost that is 

assumed in the Baseline fuel cells costs from the NREL report. In addition, the 

efficiencies in the multiyear plan are expressed in lower heating values and were 

converted to higher heating value efficiencies for use in NEMS. 

Residential 5kW PEMFC Program Case 

HFCIT Goals from Multiyear Plan 

CHP System Electrical Equip. Cost Maint. Cost

Year Efficiency* Efficiency* (2003 $/kW) (2003$/kW-yr)

2005 0.75 0.32 1500 n/a

2010 0.80 0.35 1000 n/a

* based on LHV on input fuel 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix B – Hydrogen, Fuel Cells, and Infrastructure Technologies Program – Page-B10
Model Inputs for HFCIT Goals 

CHP System Electrical Installed Cost Maint. Cost

Efficiency Efficiency (2003 $/kW) (2003$/kW-yr)

Year

2005 0.675 0.288 2300 264

2010 0.720 0.315 1800 184

2015 0.720 0.315 1800 168

2020 0.720 0.315 1800 168

2025 to 2050 0.720 0.315 1800 168

Commercial 200kW Fuel Cell Program Case 

HFCIT Goals from Multiyear Plan 

CHP System Electrical Equip. Cost Maint. Cost

Efficiency* Efficiency* (2003 $/kW) (2003$/kW-yr)

Year

2005 0.75 0.32 1250 n/a

2010 0.80 0.40 750 n/a

* based on LHV on input fuel 

Model Inputs for HFCIT Goals 

CHP System Electrical Installed Cost Maint. Cost

Efficiency Efficiency (2003 $/kW) (2003$/kW-yr)

Year

2005 0.675 0.288 1930 232

2010 0.720 0.360 1430 128

2015 0.720 0.360 1430 128

2020 0.720 0.360 1430 128

2025 to 2050 0.720 0.360 1430 128

2.4 Sources 

1. “Hydrogen, Fuel Cells & Infrastructure Technologies Program: Multi-Year Research, 

Development and Demonstration Plan” (Draft), U.S. Department of Energy, Energy 

Efficiency and Renewable Energy (June 3, 2003). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix B – Hydrogen, Fuel Cells, and Infrastructure Technologies Program – Page-B11
Appendix C – GPRA07 Biomass Technologies Program 

Documentation 

Background 

This appendix discusses the assumptions and methods employed in the biomass benefits 

analysis, which is part of the fiscal year 2007 GPRA benefits analysis for all of the Department 

of Energy’s Energy Efficiency and Renewable Energy (EERE) research and deployment 

programs. The biomass benefits analysis focuses on the benefits of future achievements by the 

Biomass Program and excludes retrospective benefits, as well as benefits resulting from 

industry’s own initiative and funding. 

The major program focus is to enable integrated biorefineries that produce ethanol as the main 

output and, where possible, limited volumes of coproducts such as chemicals, materials, and/or 

heat and power. Biorefineries process biomass into these products using biochemical processes 

(such as hydrolysis of biomass to sugars followed by fermentation of sugars to fuels and/or 

chemicals) or thermochemical processes (such as gasification of biomass to syngas followed by 

conversion of syngas to fuels or chemicals). Biorefinery configurations may vary as a function 

of site-specific conditions, including feedstock availability and price, local and regional market 

demand, and other factors. Heat and power produced within the biorefinery can be used for 

internal biorefinery power requirements, with excess electricity production sold externally. 

As an interim step leading to future biorefineries, the program is working with corn ethanol 

production plants on near-term technologies aimed at increasing the ethanol production from 

traditional feedstocks (corn kernels) and enhancing the value of nonethanol coproducts such as 

animal feed additives. Only the starch portion of the corn kernels is currently converted to 

ethanol. Ethanol yield increases can be achieved with technology that converts the fibrous 

component of the corn kernels, which is composed of cellulosic material, to ethanol. This 

process also exposes the starch that is tightly bound by this fiber (“residual starch”), which is not 

currently available for conversion to ethanol in existing corn ethanol plants. A further benefit of 

this technology is the production of a higher-protein animal feed that may be more suitable for 

the poultry and swine markets. Cellulosic ethanol technology will be used to convert the fibrous 

component of kernels and expose the residual starch to fermentation. The advantage of 

introducing cellulosic ethanol technology into existing corn biorefineries is that the plants can 

utilize existing facilities for processing corn kernels, energy services, and fermenting and 

distilling ethanol, thus reducing capital expenditures and financial risks. Operators of corn 

biorefineries have expressed an interest in this technology as a means of expanding corn ethanol 

yields and increasing profits, and DOE has entered into partnership agreements with several 

plant operators. Positive experience with kernel fiber and residual starch conversion could 

facilitate the companies’ decisions to investigate the possibility of converting additional 

cellulosic feedstock such as agricultural residues. 

The GPRA07 benefits analysis is based on the concept that enhanced corn ethanol plants—with 

the added production of corn fiber ethanol, residual starch ethanol, and eventually stover-based 

ethanol—will lead to the development of mature stand-alone lignocellulosic ethanol plants. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix C – Biomass Technologies Program – Page C-1
Ligno-cellulosic biomass includes challenging feedstock such as corn stover, wheat straw, rice 

straw, woody forest wastes, and future energy crops, e.g., fast-growing trees such as hybrid 

poplars, and fast-growing grasses such as switchgrass. The time-based progression is as follows: 

a. “Starch Biorefineries” (SBs) that use corn kernels or other grains to produce ethanol and a 

small volume of coproducts (chemicals and materials). Advanced starch biorefineries will 

convert some of the fiber in the corn kernels to ethanol using cellulosic ethanol technology. 

b. “Cellullosic Biorefineries” (CBs) that use corn stover and/or other cellullosic biomass to 

produce ethanol. 

A biorefinery industry is expected to result in biomass displacing petroleum feedstocks 

traditionally used in the production of fuels, chemicals, and materials. The biorefinery concept 

allows the cost of production to be reduced through synergies associated with feedstock handling 

and processing, and the allocation of capital and fixed operating and maintenance costs across 

multiple products. While the current analysis assumes that ethanol is the major output of 

biorefineries, future analyses could include additional fuels that the program may identify in the 

longer term. 

When processing lignocellulosic biomass, the biorefinery will process the cellulosic portion of 

the biomass into ethanol and use the remaining lignin residues to generate electricity.a

 Excess 

electricity will be sold externally, thereby reducing the net ethanol cost. The program assumed 

the coproduction of a small quantity of nonfuel chemicals or materials in corn ethanol plants. 

These bio-based products were modeled as a “credit” that reduces the ethanol production cost. 

Analysts in EERE’s Office of Planning, Budget and Analysis (PBA) used ethanol 

supply/demand data with biorefinery synergy credit—values for each year provided by the 

Office of the Biomass Program (OBP) —and “current law” tax incentives to estimate market 

penetration for ethanol. 

A variety of chemicals and materials could potentially be coproduced at biorefineries. Sugar and 

starch products derived through fermentation and thermochemical processes include alcohols, 

acids, starch, xanthum gum, and other products. Some of these chemicals and materials 

represent end products, while others represent “intermediates” used in the production of other 

products. The potential target markets are even more diverse than the list of potential products. 

Therefore PBA analysts did not characterize or analyze specific target markets for bio-based 

products in this benefits analysis. Instead, this analysis represented the bio-based product as a 

“generic/composite” product. OBP assumptions for the “generic/composite” product were 

derived from several OBP-sponsored studies that assessed a wide range of possible bio-based 

products (polymers, solvents, etc.).

 

a

 The structural materials that plants produce to form the cell walls, leaves, stems, stalks, and woody portions of biomass consists 

mainly of three biobased chemicals called cellulose, hemicellulose, and lignin. This composite material called lignocellulose is 

composed of rigid cellulose fibers embedded in a cross-linked matrix of lignin and hemicellulose that bind the fibers. 

Lignocellulose material is resistant to physical, chemical, and biological attack. In a biorefinery, the cellulose and hemicellulose 

can be broken down to produce fermentable, simple sugars through a process called hydrolysis. Cellulose is a very large polymer 

molecule composed of many hundreds or thousands of glucose molecules (polysaccharide).. Hemicellulose consists of short, 

highly branched, chains of sugars. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix C – Biomass Technologies Program – Page C-2
The discussion in this section will focus on describing target markets and technical 

characteristics used in the analysis of the market penetration and benefits attributable to the three 

types of biorefineries referenced above. 

Target Markets for Biorefineries

Corn ethanol plants, both dry mills and wet mills,b

 currently use corn kernels (no cellulosic 

feedstock) to produce ethanol and some coproducts such as animal feed additives (both dry and 

wet mills), or corn oil and high-fructose corn syrup (wet mills). 

In 2004, U.S. ethanol fuel production reached approximately 3.4 billion gallons, an increase of 

21% from the previous year. As of January 2006, the estimated 2005 production is 3.9 billion 

gallons. According to the Renewable Fuels Association, in January 2006, the operating ethanol 

plants in the United States had a total production capacity of 4.37 billion gallons, with an 

additional capacity of 1.75 billion gallons under construction or in expansion. Ethanol competes 

in transportation fuel markets for light-duty vehicles. In 2004, the U.S. prime supplier sales 

volume of motor gasoline was approximately 140 billion gallons.1 

In 2004, approximately 99% of the ethanol consumed in the United States was for the gasoline 

additive market and 1% was for use as a gasoline substitute.2 In 2004, the majority of the 

ethanol consumed was used as an oxygenate component for gasoline, and the remainder is used 

as a gasoline additive to improve octane in conventional gasoline. Within the oxygenate market, 

in early 2004, methyl-tertiary-butyl-ether (MTBE) and ethanol each provided approximately 

50% of the volume. However, ethanol has taken a much larger share of this market, because 

MTBE has been or is being phased out in many states due to environmental concerns (see 

discussion of MTBE later in this section for additional detail). 

The original Clean Air Act required a minimum level of oxygen content in both reformulated 

gasoline (RFG) and oxygenated gasoline. The Energy Policy Act of 2005 repealed the oxygen 

content requirement for RFG to take place 270 days after enactment. However, the GPRA 2007 

analysis was completed prior to the approval of the Energy Policy Act of 2005 and was based on 

current law at the time of this analysis. RFG is required in ozone nonattainment areas, and 

oxygenated gasoline is required in carbon monoxide (CO) nonattainment areas. Ethanol 

competed with MTBE in both of these oxygenate market segments. Most of the MTBE (and an 

increasing share of ethanol) used are used in RFG, which is the most important market segment 

for oxygenates. The Energy Policy Act did not enact a nationwide ban on MTBE,c

 but many 

states have already banned the use of MTBE in gasoline sold within their states. In addition, 

several major refiners have since announced their intent to discontinue the use of MTBE. Some 

refiners have indicated that, because of ethanol’s desirable properties and restrictions on MTBE 

use, they will continue using ethanol in a substantial portion of their RFG after the repeal of the 

RFG oxygen requirement. 

 

b

 To learn more about these plants, see http://www.ethanolrfa.org 

c

 MTBE is currently the subject of environmental concern in several communities, due to its leakage and contamination of 

groundwater. It imparts a turpentine odor to water at low concentrations. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix C – Biomass Technologies Program – Page C-3
Both ethanol and MTBE are used in the smaller oxygenated gasoline market segment, with 

ethanol being the dominant oxygenate. In a third market segment, ethanol is blended with 

conventional gasoline to make gasohol, which is primarily marketed in the Midwest. Gasohol 

consists of 90% gasoline and 10% ethanol by volume, with the ethanol serving as an octane 

enhancer and gasoline extender. 

After adjusting for its Federal excise tax exemption, the price of ethanol has historically tracked 

with the price of gasoline, whereas MTBE has normally been priced at a premium relative to 

gasoline. MTBE had been the oxygenate of choice in RFG for most refiners outside the Midwest 

because of its wider availability, more favorable blending characteristics for summer Reid Vapor 

Pressure, and ease of distribution. When blended into gasoline, ethanol raises the vapor pressure 

of the mixture; adding MTBE to gasoline has only a minor effect on vapor pressure. Because 

ethanol absorbs water, which is typically present in small quantities in the U.S. petroleum 

products pipeline system, ethanol and ethanol blends are not routinely shipped via pipeline. 

Consequently, ethanol is shipped by rail, truck, and/or barge to distribution terminals, where it is 

blended into gasoline. MTBE is blended into gasoline at the refinery, and MTBE blends do not 

require any special handling compared with gasoline that has no MTBE. 

The consumption of MTBE in 2002 was approximately 4 billion gallons, but MTBE 

consumption has been declining as California, New York, Connecticut, and other states 

transitioned from MTBE to ethanol. A national ban on MTBE would increase the demand for 

ethanol because ethanol, like MTBE, is a high-octane content, virtually sulfur-free additive that 

reduces toxic air emissions. Ethanol also will help solve the problem of fuel volume loss that 

would accompany an MTBE ban, because oxygenates such as MTBE (or ethanol or other 

oxygenates), when blended in gasoline, also are used by the automobile engine as a fuel. 

Reformulated gasoline containing MTBE typically contains 11% MTBE. Outside of California, 

reformulated gasoline containing ethanol typically contains 10% ethanol. In California, the 

current blend level is typically 5.7% ethanol. 

Vehicle fleets provide additional demand for ethanol fuel. These include alternative-fuel 

vehicles that have been either modified or manufactured to accommodate the use of E85, i.e. 

85% ethanol and 15% gasoline (the number of E95 vehicles is negligible at this time). The E85 

vehicles are flexible-fuel vehicles that can use either gasoline or E85. The vehicle fleet market is 

dominated by government agencies, but also includes fleets owned by corporate entities and 

other organizations (taxi cabs, utilities, airport authorities, etc.). The use of green fuels in 

Federal Government fleets is driven largely by the alternative-fuel vehicle requirements under 

the Energy Policy Act (EPACT) of 1992. 

The market penetration of E85 has been much lower than for E10 because (1) only a limited 

number or vehicles can use E85 (and fleet rules under EPACT do not necessarily require the use 

of alternative fuels in these vehicles), (2) E85 has generally been more costly than gasoline on an 

energy-equivalent basis, (3) the availability of E85 refueling stations is limited, and (4) the 

required investment for refueling infrastructure is greater for E85 than for E10. In the longer 

term, once production technology improvements achieve cost parity between ethanol and 

gasoline, ethanol will compete directly with gasoline in broader automotive fuel markets. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix C – Biomass Technologies Program – Page C-4
Baseline Technology Improvements 

The degree to which this technology would progress in the absence of EERE’s biomass R&D has 

not been studied in detail. Instead, EERE adopted the methodology recommended by the 

National Research Council (NRC) to estimate how EERE RD&D funding would accelerate 

technology improvements.3

 The NRC recommended using an N-year rule, in which technology 

deployment would be accelerated by N years with EERE R&D, or conversely delayed by N 

years in the absence of EERE R&D. PBA used a multitiered approach to recognize differences 

between shorter- and longer-term goals. PBA analysts assumed that without Federal investment 

in RD&D, technological advances would be delayed 7 years for corn fiber/recalcitrant starch, 12 

years for bio-based products technologies for dry mills, and 15 years for ethanol production 

technologies using cellulosic feedstock. 

Compared to the program case, PBA assumed a delay of 7 years is used for baseline parameters 

for dry mill, plus corn fiber and residual starch conversion. The reason for a moderate delay is 

that industry has shown interest and willingness to cost-share R&D in this area, and the 

estimated development time is short compared to that for lignocellulosic ethanol technology. 

OBP has already catalyzed work in this area, as indicated by several projects that are underway. 

It seems reasonable that, absent any further OBP involvement, industry would continue to build 

on work already accomplished, albeit at a slower rate. The rationale for assuming a 15-year delay 

for stand-alone cellulosic ethanol biorefineries is industry’s reticence to underwrite cellulosic 

ethanol research, because of its greater risk and cost. 

Baseline Market Acceptance for Ethanol Biorefineries

Gasoline is a mix of both high- and lower-value petroleum-based components, with the highvalue components comprising only a small fraction of the total volume. With current ethanol tax 

incentives and ethanol’s value to refiners due to its environmental and octane characteristics, 

corn-based ethanol is competitive with the small fraction of high-value petroleum-based 

constituents of gasoline that give gasoline acceptable octane and emissions levels. Therefore, a 

small amount of ethanol (10% or less) can be blended with 90% or more gasoline to produce a 

fuel that is competitive with conventional gasoline. Blending ethanol with gasoline in higher 

concentrations becomes less competitive, because a gallon of ethanol has only two-thirds the 

energy of a gallon of gasoline, which historically has made it difficult for ethanol to compete 

with gasoline on an energy-equivalent basis. In 2005, however, the price of oil reached new 

highs, and the cost of producing corn ethanol compared favorably with the cost of producing 

gasoline on an energy equivalent basis. Production capacity constrains the amount of ethanol 

that can enter the market, and the current production capacity for ethanol is much less than that 

of gasoline. 

Ethanol is already widely used in gasoline and accepted as a component of transportation fuel in 

the target market. As the technology for producing cellulosic ethanol matures in the longer term, 

the retail value of cellulosic ethanol will become competitive with gasoline on an energy basis. 

At that point, fuel markets will likely accept nearly pure ethanol such as E85,because of its 

environmental characteristics and indigenous supply basis. In Brazil, for example, both E22 and 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix C – Biomass Technologies Program – Page C-5
E100 are readily available,and most new cars sold are flex-fueled vehicles that can use either 

fuel. Increases in market penetration for ethanol will also be affected by competition from other 

alternative transportation fuels and success in overcoming the lack of an established nationwide 

E85 transportation and distribution infrastructure. Eventually, increases in market penetration 

may be constrained by the availability of feedstock, rather than market demand. 

Biomass Program Technology Outputs for Corn Ethanol Biorefinery 

Table C-1 summarizes ethanol production cost targets for corn ethanol dry mill biorefineries. 

Table C-1. EERE Ethanol Production Costs - Targets in 2003$ per Gallon for Dry Mills 

Before Adding Feedstock Costs 

Year

Operating 

$/gal EtOH 

Capital 

$/gal EtOH 

Denatured 

Ethanol 

Yield 

gal/bu 

DDG 

Yield 

Lb/bu

DDG 

Enrichment

Factor 

Electricity

Usage 

kWh/gal 

Natural Gas 

Usage 

MMBtu/gal 

Bio-based 

Product 

Credit 

$/gal EtOH

2007 $ 0.220 $ 0.152 2.80 18.50 1.00 0.77 0.03 $ - 

2008 $ 0.220 $ 0.151 2.80 18.50 1.00 0.77 0.03 $ - 

2009 $ 0.219 $ 0.151 2.81 18.50 1.00 0.77 0.03 $ - 

2010 $ 0.219 $ 0.151 2.81 18.50 1.00 0.76 0.03 $ (0.01)

2011 $ 0.219 $ 0.151 2.82 18.50 1.00 0.76 0.03 $ (0.01)

2012 $ 0.218 $ 0.149 2.89 17.41 1.06 0.76 0.03 $ (0.02)

2013 $ 0.218 $ 0.146 2.96 16.43 1.13 0.75 0.03 $ (0.03)

2014 $ 0.218 $ 0.144 3.03 15.56 1.19 0.75 0.03 $ (0.03)

2015 $ 0.217 $ 0.142 3.10 14.78 1.25 0.75 0.03 $ (0.04)

2016 $ 0.217 $ 0.140 3.17 14.07 1.31 0.74 0.03 $ (0.05)

2017 $ 0.217 $ 0.138 3.24 13.43 1.38 0.74 0.03 $ (0.05)

2018 $ 0.216 $ 0.136 3.31 12.85 1.44 0.73 0.03 $ (0.06)

2019 $ 0.216 $ 0.134 3.38 12.31 1.50 0.73 0.03 $ (0.06)

2020 $ 0.215 $ 0.134 3.39 12.31 1.50 0.73 0.03 $ (0.07)

2021 $ 0.215 $ 0.133 3.39 12.31 1.50 0.73 0.03 $ (0.08)

2022 $ 0.215 $ 0.133 3.40 12.31 1.50 0.73 0.03 $ (0.08)

2023 $ 0.214 $ 0.133 3.40 12.31 1.50 0.72 0.03 $ (0.09)

2024 $ 0.214 $ 0.133 3.41 12.31 1.50 0.72 0.03 $ (0.10)

2025 $ 0.214 $ 0.132 3.41 12.31 1.50 0.72 0.03 $ (0.10)

2030 $ 0.212 $ 0.131 3.44 12.31 1.50 0.72 0.03 $ (0.14)

2035 $ 0.210 $ 0.130 3.47 12.31 1.50 0.71 0.03 $ (0.17)

2040 $ 0.209 $ 0.129 3.50 12.31 1.50 0.71 0.03 $ (0.20)

2045 $ 0.207 $ 0.128 3.53 12.31 1.50 0.70 0.03 $ (0.20)

2050 $ 0.205 $ 0.127 3.55 12.31 1.50 0.69 0.03 $ (0.20)

Source: John Jechura , Adv Dry Mill Curve 8-25-2005 - DA changes.xls , National Renewable Energy Laboratory

Dry mills process corn into ethanol, distillers dried grain solubles (DDGS), and carbon dioxide 

(CO2). DDGS is sold into the animal feed market. Some dry mill operators are able to sell their 

CO2 production, but the CO2 market is limited and therefore not considered in this analysis. As 

dry mill plants begin to deploy the technology to convert the fiber and residual starch to ethanol, 

the yield of the weight DDGS coproduct decreases, but the protein component (weight) in the 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix C – Biomass Technologies Program – Page C-6
DDGS remains constant. DDGS is valued in the marke place primarily for its protein. The 

relative protein content of the DDGS is computed by multiplying the DDGS weight by the 

DDGS enrichment factor, both of which are shown in the table. Most of the DDGS is now used 

in the cattle feed market, but it is expected that the higher-protein (percent) DDGS will be 

suitable for the poultry and swine markets. 

The operating costs in the table do not include the energy costs. The table lists the per-gallon 

energy requirements for natural gas and electricity separately. NEMS and MARKAL use their 

endogenously computed energy prices to calculate the energy costs to produce corn-based 

ethanol (unit price of energy times energy consumption per gallon of ethanol). The Biomass 

Program assumed a bio-based coproduct credit of 1 cent per gallon of ethanol beginning in 2010, 

increasing gradually to 20 cents per gallon by 2040 for a dry mill processing corn kernels. The 

coproduct credit represents a generic bio-based product coproduced with ethanol and was 

provided by the National Renewable Energy Laboratory (NREL). The program’s draft MultiYear Program Plan of August 31, 2005, was not yet available when this analysis was conducted. 

PBA analysts assumed that commercialization of technologies for the conversion of corn kernel 

fiber and recalcitrant starch would begin in 2012, based on preliminary planning information 

developed by NREL. The successful technology will cause the industry-wide ethanol yield 

(gallons of ethanol per bushel of corn) to increase by 23% between 2012 and 2050(2). 

Rather than assuming “instantaneous” deployment by all dry mills, the corn fiber and recalcitrant 

starch technology was assumed to be deployed by 100% of the dry mills by 2050, with a rampup beginning in 2012. The conversion of the corn fiber and recalcitrant starch was modeled as 

an increase in the conversion efficiency in terms of gallons of ethanol per bushel of corn. The 

ramp-up includes technology improvements and an estimate of the overall rate of adoption by 

ethanol plant operators, as shown in Denatured Ethanol Yield Column in Table C-1. 

References: 

John Jechura (NREL) provided the cost and conversion targets in an EXCEL spreadsheet titled 

Adv Dry Mill Curve 8-25-2005 - DA changes.xls. NREL report: Evaluating progressive technology 

scenarios in the development of the advanced dry mill biorefinery, Kelly Ibsen (NREL), Robert Wallace 

(NREL), Sue Jones (PNNL), Todd Werpy (PNNL), 3/04/05 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix C – Biomass Technologies Program – Page C-7
Biomass Program Technology Outputs for Cellulosic Ethanol Biorefinery 

Table C-2 summarizes ethanol production cost targets for cellulosic ethanol biorefineries as 

originally submitted to OMB as part of the original budget submission for the Biomass Program 

in October 2005. These targets represent an assumption of level funding for the Biomass 

Program, reflecting the level of funding experienced in recent budget cycles. 

Table C-2. Cellulosic Ethanol Production Costs and Conversion Efficiency Targets. 

 Costs are in 2003$ per Gallon and do not Include Feedstock Costs 

Source: John Jechura , Adv Dry Mill Curve 8-25-2005 - DA changes.xls , National Renewable Energy Laboratory

Annualized Ethanol Electricity Nat Gas 

Operating Capital Yield Usage Usage 

Year $/gal EtOH $/gal EtOH gal/ton kWh/gal MMBtu/gal 

2018 $ 0.45 $ 0.46 83.8 -3.70 0

2019 $ 0.39 $ 0.44 86.8 -3.69 0

2020 $ 0.34 $ 0.43 89.8 -2.08 0

2021 $ 0.32 $ 0.42 89.9 -2.07 0

2022 $ 0.31 $ 0.41 90.1 -2.06 0

2023 $ 0.30 $ 0.40 90.2 -2.06 0

2024 $ 0.29 $ 0.39 90.3 -2.05 0

2025 $ 0.28 $ 0.38 90.5 -2.04 0

2030 $ 0.23 $ 0.34 91.2 -2.01 0

2035 $ 0.19 $ 0.30 91.9 -1.97 0

2040 $ 0.15 $ 0.27 92.6 -1.94 0

2045 $ 0.13 $ 0.24 93.3 -1.91 0

2050 $ 0.10 $ 0.22 94.0 -2.03 0

Conversion of corn stover to ethanol is assumed to begin in 2018, based on preliminary planning 

information developed by NREL.(3). NREL supplied the non-feedstock capital and operating 

costs on a per gallon of ethanol. A real capital cost recovery factor of 15% is used to calculate 

the per-gallon capital costs. Cellulosic ethanol plants combust the lignin portion of the 

lignocellulosic feedstock to produce heat and electricity. The plants produce excess electricity 

that is sold into the grid. The negative numbers in the electricity use column represent the sale of 

the excess electricity. The electricity credit is computed by multiplying the price of electricity 

times the excess electricity production. Electricity prices are determined endogenously in 

NEMS and MARKAL. 

Reference: 

The methodology used for the cellullosic cost estimates was documented in NREL Report

Determining the Cost of Producing Ethanol from Corn Starch and cellulosic Feedstocks, NREL/TP-580-

28893, Andrew McAloon, Frank Taylor, Winnie Yee, Kelly Ibsen and Robert Wooley, October 2000. 

John Jechura of NREL provided the cost and conversion targets in an EXCEL spreadsheet titled 

Adv Dry Mill Curve 8-25-2005 - DA changes.xls. NREL report: Estimated Ethanol Cost Curves 

from Advanced Dry Mills to 2050, John Jechura, 6/2/05

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix C – Biomass Technologies Program – Page C-8
Corn ethanol growth is based on our latest assessment of the industry that was done prior to the 

enactment of the Renewable Fuels Standard. For cellulosic ethanol based on corn fiber 

conversion, a near-term technology being developed by the program and industry partners, we 

assumed success for dry mills only. While the other type of ethanol plants, wet mills, may also 

succeed in deploying this technology, the benefits from wet mills are not considered in order to 

make the estimates more conservative. The volume of cellulosic ethanol from corn fiber includes 

the ethanol resulting from converting the fiber in the corn kernel and the residual starch that can 

be converted once liberated from the fiber. The total increment in ethanol output would equal 

20% of the current dry mill’s ethanol output. As previously stated, future biorefineries are 

assumed to use cellulosic biomass such as corn stover and energy crops, and not the corn kernel 

or its fiber as feedstock. The cellulosic ethanol estimates from corn stover, other cellulosic 

wastes, and energy crops resulted from market equilibrium analyses that compete ethanol with 

petroleum constituents in the low-blend fuel market (E10) and versus corn ethanol. 

 

Technical Characteristics - The cellulosic biorefinery analysis is based on a plant whose main 

product is fuel ethanol with coproduction electricity. Excess electricity is sold to the grid and is 

modeled as a reduction in the cost of producing ethanol. The analysis is for a biorefinery with a 

total throughput of 2,000 dry metric tons of feedstock per day and with a conversion efficiency 

increasing from approximately 83 gallons of ethanol per dry U.S. ton of feedstock in 2018 to 94 

in 2050, as a result of technological advances. Corn ethanol plants produce a more uniform sugar 

stream. Therefore EERE is targeting these plants for the production of new, high-valued, biobased products that will compete favorably with petroleum product counterparts. The market 

for these products is limited compared to the fuels market, and the final choice of which products 

will be produced is still in the formative stages. Consequently, the economics of new bio-based 

products are represented as credits to the corn biorefineries, similar to the way credits for animal 

feed co-products are handled. 

Technical Potential - The biomass feedstock resources discussed here do not include wood 

waste and black liquor waste from paper mills, an important but captive resource—these 

resources are typically used within the forest and paper products industry. Under favorable R&D 

outcome and market scenarios, the upper bound for ethanol supply from U.S. biomass is 

estimated at 35 billion gallons per year from cellulosic feedstock and 15 billion gallons per year 

from starch crops. The farm-gate price and supply relationship for biomass used in the market 

analysis are presented in Table C-3. 

While forest residues and some of the “other wastes” may not be optimal for sugar-based ethanol 

production, we recognize that future syngas-based fuels production may use forest residues and 

certain “other wastes” as feedstock. Therefore, this analysis is not deemed to be overly 

optimistic, in spite of the assumption that biorefineries are sugar-based. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix C – Biomass Technologies Program – Page C-9
Table C-3. Farm-gate Biomass Quantities Supplied vs. Price Range 

Excluding Mill Residues and Black Liquor Near Term 

(million dry tons per year) 

Feedstock 

up to 

$21.20/dt

up to 

$31.80/dt

up to 

$42.40/dt 

up to 

$53.00/dt

Forest Residues 0 12 20 70

Agricultural Crops Residues 0 1 65 80

Potential Energy Crops 0 0 80 187

Other Wastes 0 17 25 35

Total 0 30 190 372

Transportation costs ranging from $7.50 to $15.0 per dry ton (depending on hauling distance) 

were added to farm-gate prices to account for hauling to the biorefinery, assuming no difference 

between the case without EERE and the case with EERE (this assumption will be improved in 

future analyses). After adding these costs and applying the factors shown in Table 5, the nearterm supply as a function of price per dry ton at the biorefinery gate is shown in Table 6. 

Because the models do not represent all competing uses of biomass, e.g., for biopower or fiber 

uses which are competitors to OBP’s biorefineries. The fraction of the total feedstock assumed 

to be available to biorefineries is used as a proxy for reserving some of feedstock for competing 

uses. The fractions in Table C-4 are assumed values. 

Table C-4. Fraction of Total Feedstock Assumed To Be Available To Biorefineries 

Feedstock Fraction 

Forest Residues 0.70

Agricultural Crops Residues 0.70

Potential Energy Crops 0.66

Other Wastes 0.60

Table C-5. Biorefinery-gate Quantities Supplied vs. Price Range 

Excluding Mill Residues and Black Liquor Near Term 

(million dry tons per year)

Feedstock 

Up to 

$29.15/dry ton

Up to 

$42.40/dry ton

Up to 

$55.65/dry ton 

Up to 

$68.90/dry ton

Agricultural Crops Residues 0 0.7 45 56

Potential Energy Crops 0 0 53 123

Forest and Other Wastes 0 15 23 49

Total 0 16 121 228

The annual quantity available for ethanol production, at up to $68.90 per dry ton (including costs 

of transportation to the biorefinery), has been reduced from 372 million to 228 million dry tons 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix C – Biomass Technologies Program – Page C-10
after applying the reduction factors from Table C-5. In the longer term (2040, for example), crop 

yields increasing at the rate of 1% per year will result in additional feedstock as shown in Table 

C-6, assuming no difference between the case without EERE and the case with EERE (this 

assumption will be improved in future analyses). 

Table C-6. Long-term Biorefinery-Gate Supply vs. Prices 

Excluding Mill Residues and Black Liquor, Year 2040 

(Million dry tons per year and 2000$. Costs include transportation costs 

from farm to biorefinery)

Feedstock 

Up to $29/dry 

ton

Up to $42/dry 

ton

Up to $56/dry 

ton 

Up to $69/dry 

ton

Agricultural Crops Residues 0 1 68 83

Potential Energy Crops 0 0 78 184

Forest and Other Wastes 0 19 29 70

Total 0 20 175 337

At approximately 93 gallons of ethanol per dry ton of feedstock, the potential supply in the long 

term is 31 billion gallons in 2040. This potential would increase significantly with appropriate 

incentives such as those aimed at increasing feedstock availability. 

Expected Market Uptake - This analysis was done prior to the enactment of the Energy Policy 

Act of 2005 and was limited to policies existing at that time. We will include the biofuel specific 

policies in Energy Policy Act of 2005 in the GPRA08 analysis. The GPRA07 analysis did not 

include the RFS enacted in the Energy Policy Act. Corn ethanol is projected to continue to 

expand in GPRA07 as a result of various states’ phase-outs of MTBE, but only to approximately 

4 billion gallons/year by 2012 compared with an RFS requirement of 7.5 billion gallons/year in 

2012. Although GPRA07 did not include the RFS, the impact on benefits estimates is mitigated 

by the fact that in the longer term, where most of the program benefits accrue, ethanol 

consumption easily exceeds the RFS requirement. Future cellulosic ethanol capacity will slowly 

replace corn ethanol capacity as the new technology becomes more competitive relative to corn 

ethanol. The tables show corn ethanol continuing at a fairly constant level through 2050. 

The Biomass Program estimates that, beginning in 2012, corn ethanol plants will deploy the 

technologies for processing corn fiber, a cellulosic feedstock, into ethanol. This would be in 

addition to their continuing production of ethanol from corn starch. Beginning in 2018, a 

number of the ethanol plants will also convert corn stover to ethanol if R&D is successful. 

NEMS and MARKAL were used to estimate ethanol market penetration for cellulosic ethanol 

from corn stover, energy crops, and other cellulosic residues; but excluding corn fiber and 

residual starch. The market penetration of corn fiber and residual starch-based ethanol, small 

quantities in comparison with the other cellulosic ethanol, were modeled as increases in the 

ethanol yield per bushel of corn in corn ethanol plants. The following section describes 

ELSASBioref and its use for this analysis. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix C – Biomass Technologies Program – Page C-11
Methodological Approach - Biomass ethanol market penetration analysis was accomplished 

through the integration of the results of various analyses conducted primarily by national 

laboratory personnel and their subcontractors. NEMS and MARKAL served as the integrating 

tools. 

Cellulosic Feedstock Supply - Oak Ridge National Laboratory (ORNL) developed cellulosic 

feedstock supply curves with the aid of BIOCOST,4

 POLYSYS,5 and other regionally detailed 

models. The feedstock supply-curve information shows quantities of different categories of 

cellulosic feedstocks available at different prices and time periods. The current GPRA case uses 

ORNL data reported by Arthur D. Little Inc.6

 These data were modified, based on more recent 

ORNL work on agricultural residue availability and cost7

. 

Cellulosic feedstock costs are adjusted to include transportation charges from the farm gate to the 

conversion facility, and feedstock supplies are allocated among different competing uses as 

described above in the Technical Potential section. In addition, the analysis assumes that 

agricultural residues and bio-energy crops will increase at an annual rate of 1% during the 

analysis period, due to increasing agricultural productivity. This assumption yields a total U.S. 

feedstock supply in 2040 approaching 337 million dry tons of agricultural residues, forest 

wastes, energy crops and other biomass wastes, after excluding potential competing uses. 

Ethanol Conversion Costs - NREL, which is partnering with industry and universities to develop 

competitive ethanol production technologies, provided estimates of cellulosic ethanol production 

costs (other than feedstock-related costs) on a per-gallon basis. The NREL estimate of the 

efficiency of converting feedstock into ethanol is input as a function of date, namely the number 

of gallons per dry ton of feedstock increases in the future as a result of R&D success. This allows 

the ORNL-provided feedstock costs to be presented on a per-gallon basis and added to the NREL 

non-feedstock costs to obtain the cost of producing a gallon of cellulosic ethanol. Corn mills 

may also produce other high value bio-based products from the sugar streams in addition ethanol 

and animal feeds. Because of the large number of potential products, each with relatively small 

markets, that have been identified, a detailed analysis of the markets for potential bio-based 

products was not feasible. Instead, NREL estimated a generic credit for bio-based product to 

corn ethanol mills. The GPRA analysis did include the extra demand for corn used to produce 

bio-based products in calculating the market price for corn.

Benefits Estimation - In both NEMS-GPRA07 and MARKAL-GPRA07, reductions in fossil 

energy use and carbon emissions attributable to EERE R&D (Program case) were calculated by 

computing the fossil energy use and carbon emissions in the Program and Bases cases, and 

taking their differences. Fossil energy use includes the fossil energy embedded in the final 

product, e.g., in gasoline, as well as the upstream fossil energy consumption, e.g., the fossil 

energy used to extract and transport oil, refine the oil into gasoline, and to transport the gasoline 

to retail service stations. Fossil energy requirements to produce both corn and cellulosic ethanol 

are input into NEMS-GPRA07 and MARKAL-GPRA07. Both models calculate carbon 

emissions by multiplying fossil fuel consumption by carbon emission factors, which depend on 

the fossil fuel type. Both models calculate energy costs endogenously in the process of solving 

for market equilibrium conditions. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix C – Biomass Technologies Program – Page C-12
For GPRA07, the analysis only considers reductions in fossil fuel use and carbon emissions from 

the production and consumption of ethanol. It does not include that benefits that accrue from 

bio-based products that displace petrochemical products, because the analysis assumes a generic 

bio-based product. As the amount of bio-based products that are produced is small compared 

with the amount of ethanol produced, the GPRA benefits estimates are not materially 

understated. 

Update to the GPRA07 Inputs Based on President’s Initiative 

In his State of the Union Address, the president described new initiatives for developing 

alternative fuels that will break the nation’s “addiction to oil.” Among these was increased 

funding for the development of ethanol from cellulosic biomass as a substitute for gasoline. This 

led to a major rethinking of the goals of the Biomass Program. 

Corn ethanol technology

The original inputs for Biomass involved improvements to corn ethanol production technology 

as well as inputs for introduction of cellulosic ethanol technology. In this revised set of inputs, 

we have made no changes to the assumptions about the impacts of the program on the existing 

corn ethanol technology. 

Cellulosic ethanol technology

The president’s new initiative for biomass accelerates cellulosic ethanol technology 

development—achieving a nominal ethanol selling price of $1.07 per gallon of ethanol in 2012. 

See Figure C-1 for a comparison of the revised nominal cost trajectory for cellulosic ethanol. 

The nominal selling price is a proxy for cost performance that reflects: 

• A minimum rate of return of 10% on capital investment 

• A nominal feedstock cost of $40 per dry U.S. ton 

• A plant gate price (no costs for fuel distribution, marketing and taxes) 

We interpret the goal of $1.07 in 2012 to reflect projected nominal cost of ethanol for technology 

that has been proven at the pilot scale. Therefore, we assume that fully commercially available 

technology at this nominal cost is available by 2015, allowing three years for commercial 

demonstration and design, construction, and start-up of a full commercial-scale facility. 

In NEMS and MARKAL, we do not use the nominal cost, but rather, break this cost down into 

annualized cost of capital, operating expenses excluding feedstock cost, and a credit for excess 

electricity sold to the grid (Table C-7). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix C – Biomass Technologies Program – Page C-13
Figure C-1. Revised cost trajectory for nominal ethanol cost 

($2003 per gallon of ethanol) 

Table C-7. Revised Inputs to NEMS and MARKAL for Cellulosic Ethanol 

Year Annualized Cost of 

Capital ($2003/gal) 

Non-biomass 

Operating Costs 

($2003/gal) 

Electricity Use 

(kWh per gal) 

Ethanol Yield 

(Gal/ton) 

Orig Rev Orig Rev Orig Rev Orig Rev 

2015 $0.41 $0.31 -2.06 90.10 

2016 $0.40 $0.30 -2.06 90.20 

2017 $0.39 $0.29 -2.05 90.30 

2018 $0.46 $0.38 $0.45 $0.28 -3.7 -2.04 83.8 90.50 

2019 $0.44 $0.34 $0.39 $0.23 -3.69 -2.01 86.8 91.20 

2020 $0.43 $0.30 $0.34 $0.19 -2.08 -1.97 89.8 91.90 

2021 $0.42 $0.27 $0.32 $0.15 -2.07 -1.94 89.9 92.60 

2022 $0.41 $0.24 $0.31 $0.13 -2.06 -1.91 90.1 93.30 

2023 $0.40 $0.22 $0.30 $0.10 -2.06 -2.03 90.2 94.00 

2024 $0.39 $0.22 $0.29 $0.10 -2.05 -2.03 90.3 94.00 

2025 $0.38 $0.22 $0.28 $0.10 -2.04 -2.03 90.5 94.00 

2030 $0.34 $0.22 $0.23 $0.10 -2.01 -2.03 91.2 94.00 

2035 $0.30 $0.22 $0.19 $0.10 -1.97 -2.03 91.9 94.00 

2040 $0.27 $0.22 $0.15 $0.10 -1.94 -2.03 92.6 94.00 

2045 $0.24 $0.22 $0.13 $0.10 -1.91 -2.03 93.3 94.00 

2050 $0.22 $0.22 $0.10 $0.10 -2.03 -2.03 94 94.00 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix C – Biomass Technologies Program – Page C-14
 

We have taken the numbers from Jechura’s original analysis that correspond to $1.07 per gallon 

in 2022 and shifted those to start in 2015. In addition, we have taken remaining improvements 

that Jechura shows occurring through 2050 and accelerated them so that they reach their 

endpoint by 2020 instead of 2050. This corresponds to an aggressive R&D effort aimed at 

bringing the technology to its mature state more rapidly. After 2020, no further improvements in 

cost are assumed. 

Biomass Supply Curves 

Biomass supply curves have not been changed from the original inputs provided by the Biomass 

Program. Updated supply curves—currently used in the Biomass Transition Model—are 

available, but were not used in updating the GPRA models. This may have the effect of 

constraining biomass availability below the level available in the Biomass Transition Model. In 

the upcoming cycle for GPRA 08, we should consider updating the NEMS-GPRA08 and 

MARKAL-GPRA08 models to make them consistent with the Biomass Transition Model. In the 

case of NEMS, we will need to regionalize the supply curves. 

Energy Markets

 

In the past, projections have changed little from year to year. Not so this year. The AEO 2006 

projections were officially published in February 2006. They show what is nothing short of a 

“sea change” in EIA’s perspective on energy prices. On average, for example, the new oil price 

projections are more than double the prices projected in AEO 2005. 

The GPRA 07 benefits estimates are based on energy projections reported by the Energy 

Information Administration in their Annual Energy Outlook 2005 (AEO 2005) report. The timing 

and level of effort involved in putting together the GPRA benefits forces us to use projections for 

energy markets that are roughly one year behind what is available by the time we publish our 

benefits estimates. While we realize that updating to AEO 2006 energy prices would make a big 

difference in projected market penetration of biomass technology and other energy efficiency 

and renewable energy technologies, we simply do not have time to completely redo the benefits 

estimates for the entire EERE technology portfolio. To remain consistent with the rest of the 

portfolio, the new inputs for the Biomass Program also use last year’s lower energy prices. 

Adjusting Industry Growth Constraints 

In the NEMS-GPRA07 and MARKAL-GPRA07 models, we have increased the constraints on 

industry growth to make them consistent with assumptions in the Biomass Transition Model. 

Thus, bioethanol capacity now has a maximum growth rate of 25% per year, up to a limit of 

around 5 billion gallons per year of new capacity. These higher growth rates are based on 

historical data for growth of the existing corn ethanol industry and growth of U.S. gasoline 

refining capacity. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix C – Biomass Technologies Program – Page C-15
Sources 

 

1

 Petroleum Marketing Annual 2004, U.S. Energy Information Administration, Table 48. 

2

 Davis, S.C., and S.W. Diegel, “Transportation Energy Data Book.” Oak Ridge National 

Laboratory, Edition 24, December 2004. 

3 Energy Research at DOE: Was It Worth It? Energy Efficiency and Fossil Energy Research 

1978 to 2000, National Research Council, http://www.nap.edu/catalog/10165.html 

4

 Oak Ridge National Laboratory. BIOCOST: A Tool to Estimate Energy Crops on a PC. 

http://bioenergy.ornl.gov/papers/misc/biocost.html. 

5

 Daryll E. Ray, Daniel G. De La Torre Ugarte, Michael R. Dicks, and Kelly H. Tiller, "The 

Polysis Modeling Framework: A Documentation." Agricultural Policy Analysis Center, 

University of Tennessee, May 1998, http://apacweb.ag.utk.edu/polysys.html 

6

 “Aggressive Growth in the Use of Bioderived Energy and Products in the U.S. by 2010” 

Unpublished report prepared by Arthur D. Little Inc. for U.S. Department of Energy, Oct. 2001. 

7

 Graham, R.L, "Key Findings of the Corn Stover Supply Analysis," Oak Ridge National 

Laboratory, unpublished paper, October 15, 2003. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix C – Biomass Technologies Program – Page C-16
Appendix D – GPRA07 Solar Energy Technologies Program 

Documentation 

1. Introduction 

This appendix provides detailed information on the assumptions and methods employed to 

estimate the benefits of EERE’s Solar Energy Technologies Program. The benefits analysis for 

the Solar Program utilized both NEMS and MARKAL as the analytical tools for estimating the 

program’s benefits. As will be discussed below, a number of assumptions and structural 

modifications to the models were made in order to represent the suite of solar technologies 

funded by the program as accurately as possible—photovoltaics (PVs) and concentrating solar 

power (CSP). Many of the assumptions used in the FY07 analysis are the same as or similar to 

those employed in the FY06 analysis; however, two key changes are important to highlight upfront. First, the program case cost targets for photovoltaics included here are considerably more 

aggressive than the GRPA06 targets. This reflects anticipated changes in FY07 in the solar 

program’s structure and funding as included in the President’s Solar America Initiative. Second, 

the FY07 analysis does not include Solar Hot Water (SHW) technology benefits which were 

included in the FY06 analysis. This change is based on zeroing out funding for SHW as 

reflected in the president’s proposed in FY07 budget. 

The body of this appendix contains two sections. The first discusses the assumptions used to 

construct the GPRA07 Solar Program baseline scenario. The second discusses the modifications 

that were made to this baseline to construct the GPRA07 solar program scenario. 

2. GPRA07 Solar Program Baseline Assumptions 

Several changes from the AEO2005 Reference Case were incorporated into the GPRA07 

Baseline. These changes include the following: 

Revising projected PV cost. The residential and commercial PV system characteristics in the 

AEO2005 were based on a recent Navigant Consulting report (Navigant 2003). This report lays 

out a projection of future PV system costs, but does not explicitly distinguish between Federal 

R&D and private activity effects. However, the projections are very similar to the program’s 

FY06 targets as laid out in its recent draft Multi-Year Program Plan (DOE 2005). Thus, the 

AEO2005 targets do appear to include R&D. As such, they are not appropriate for use as a 

Baseline from which the program’s impacts are to be measured. Therefore, an alternative 

Baseline was developed assuming that private industry would continue to improve firstgeneration PV (crystalline silicon) technology, but would not invest significantly on its own in 

second- or third-generation PV (thin-film, etc.) technologies. As shown in Figure 1, changes in 

the program’s structure and funding levels are expected to result in accelerated cost reductions 

through 2015 under the GPRA07 Program case. In constructing the GPRA07 baseline, the 

following approach was used. Between 2005 and 2015, the costs of PV are assumed to decline 

more slowly than in the AEO2005 targets, leading to a five-year lag between the GPRA07 

baseline and AEO2005 targets by 2015. Beyond 2015, the GPRA07 baseline and GPRA07 

program numbers are assumed to continue to diverge. This approach captures the notion of 

technological lock-in (Cowan and Kline 1996). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix D – Solar Energy Technologies Program – Page D-1
PV Commercial System Cost

0

1,000

2,000

3,000

4,000

5,000

6,000

2010 2015 2020 2025 2030 2035 2040 2045 2050

Installed System Cost (2003 $ per Wp)

GPRA07 Base

AEO2005

GPRA07 Program

PV Residential System Cost

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

2010 2015 2020 2025 2030 2035 2040 2045 2050

Installed System Cost (2003 $ per Wp)

GPRA07 Base

AEO2005

GPRA07 Program

Figure 1. Projected PV System Costs 

Increasing the average commercial building system size from 25kW to 100kW. A sample of 

data from 14 PV systems installed by PowerLight Corporation, between July 1999 and March 

2003, reveals that the average commercial system installed by PowerLight during this period was 

381kW (Table 1). 

Table 1. Commercial System Size and Surface-Area Requirements 

PowerLight System Installation Location 

Date 

Completed

System Peak 

Capacity (kW)

PV Surface 

Area (sq. ft.) W/sq.ft.

Santa Rita Jail – Alameda County, California Apr-02 1,180 130,680 9.0

Cypress Semiconductor – San Jose, California Jul-02 335 26,100 12.8

Fala Direct Marketing – Farmingdale, New York Nov-02 1,010 102,700 9.8

Fetzer Vineyards – Hopland, California Jul-99 41 3,750 10.9

Franchise Tax Board – a Sacramento, California Aug-02 470 50,000 9.4

Greenpoint Manufacturing – Brooklyn, New York Mar-03 115 11,500 10.0

Mauna Lani Resort – Kohala Coast, Hawaii Jan-02 528 43,330 12.2

Naval Base – Coronado, California Sep-02 924 81,470 11.3

Neutrogena Corp. – Los Angeles, California Aug-01 229 30,154 7.6

Parker Ranch – Kameula, Hawaii Jan-01 209 20,000 10.5

PSGA/Ortho-McNeil Facility – Pennsylvania Apr-02 75 17,500 4.3

U.S. Coast Guard – Boston, Massachusetts Sep-99 37 3,800 9.7

U.S. Postal Service – Marina del Rey, California Nov-01 127 15,000 8.5

Yosemite National Park - Yosemite, California Oct-01 47 4,500 10.4

Total 5,327 540,484 

Average 381 38,606 10

Source: PowerLight Case Study data sheets, Downloaded from www.powerlight.com, 5/21/03. 

Note: Some of the locations shown in this table have multiple installations. In these cases, the total installed 

capacity is shown above, and the most recent installation date is shown in the date-completed column. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix D – Solar Energy Technologies Program – Page D-2
The average space required for these systems was 0.1 sq. ft/W., based on a U.S. average 

commercial building size in 2000 of 14,500 square feet (AEO2003), and assuming a ratio of 

usable roof space to floor space of 0.7. This ratio of usable roof space to floor space was based 

on the “architecturally suitable area” in an International Energy Agency (IEA) report, Table 2, 

examining the potential for integrated photovoltaics in buildings (IEA 2001). Using this 

approximation, the average commercial building could easily accommodate a 100 kW PV 

system, i.e., a 0.7*14,500 sq. ft. = 10,100 sq. ft. PV array. Thus, setting the average system size 

at 100kW is a conservative assumption based on industry trends, as well as the available roof 

space on a large share (50+%) of the commercial building stock. This is a very conservative 

assumption because it does not reflect expectations that the efficiency of PV cells will increase; 

the space requirements for a PV system will decrease; and, as system costs decline, facades and 

other spaces (such as parking lots) also could be utilized for PV systems. 

Increasing the maximum share of commercial buildings with solar access from 30% to 

55%. Similar to the preceding ratio of usable roof space to floor space, the share of roof space 

suitable for PV installations was based on the published IEA report on integrated photovoltaics 

in buildings (IEA 2001). This report indicates that a reasonable estimate for the share of roof 

space suitable for PV installations is 55%. This estimate includes shading and other factors that 

would limit the use of roof space for PV systems (IEA 2001). 

Increasing the average residential building system size from 2kW to 4kW. A couple of 

years ago, a typical residential rooftop PV system was a 2kW system—this is most likely the 

source for EIA’s 2kW system size in the AEO2005 reference case. However, residential rooftop 

systems being installed in Japan, Europe, and the United States have been growing larger. For 

example, the average Japanese rooftop system size in 2002 was 3.7 kW (Ikki 2003) and the 

average rooftop system size in California in 2004 was 3.6 kW.1

 The average home in the United 

States has 1,700 square feet of floor space (this is expected to increase in the future). Using data 

from EIA’s residential energy-consumption survey (EIA 1999, Table HC1-2a) one can estimate 

a floor- to roof-space ratio of 0.7 (based on distribution of one-story, two-story, and three-story 

single-family homes). This is a conservative estimate—most homes have pitched roofs, which 

would increase the total available roof space (yet may make a significant portion of the roof 

oriented away from the sun). If a typical system can accommodate 10 W/sq.ft (as above), then a 

4kW system would require roughly 400 square feet of roof space, which is well below the 

average available space allowing for multiple floors and pitched roofs. Thus, roof space is not a 

constraint for installing residential rooftop PV systems in the 4kW range. Because the efficiency 

of PV cells is likely to improve, a trend toward larger systems on rooftops is likely to continue. 

Thus, based on available roof space and what is happening in the marketplace, setting the 

average system size at 4kW is a conservative assumption. 

Increasing the maximum share of residential buildings with solar access from 30% to 60%. 

A maximum share of 60% for residential buildings with solar access was used. This estimate 

accounts for the fact that some homes will not be suitable for PV systems due to shading, 

 

1

 This estimate was based on data from the California Energy Commission’s Emerging Renewables Program, downloaded on 

1/27/05 from www.energy.ca.gov/renewables/emerging_renewables.html. Data on small PV systems (i.e., with a system size 

under 10kW) were extracted from the full dataset. It indicated that, during 2004, a total of 15.9 MW of PV was installed in 4,372 

small PV systems in California, with an average system size of 3.6kW. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix D – Solar Energy Technologies Program – Page D-3
building orientation, roof construction, or other factors. This value was calculated from a 

combination of single-family homes (70%) and multifamily homes (30%), using a 75%-25% 

split between single-family and multifamily homes (EIA 2003, Table A4). Thus, the average 

maximum share was set at 0.7*0.75 + 0.3*0.25 = 0.6. 

Including a declining PV buy-down program in California. This baseline is constructed 

under the assumption that the California renewable energy credit program that provided a PV 

credit of $4,000/kW in 2003 will continue to be available, but will decline by $400/kW per year. 

This credit is roughly in-line with the declining subsidy included in the recently past California 

Solar Initiative. This credit was included for the entire Pacific region. Given that a number of 

other local credits were not included in the GPRA baseline, applying the California state-level 

credit to the whole Pacific region is likely to be a reasonable approximation. 

Modifying the adoption rate of distributed generation technologies. The modification to the 

adoption rate was based on information provided by the DER program (Figure 2). This applies 

to PV as well as gas-fired CHP technologies. 

0.0

0.2

0.4

0.6

0.8

1.0

1 3 5 7 9 11 13 15 17 19

Number of Years to Positive Cash Flow

Fraction Adopting

AEO

Alternative

Figure 2. Commercial-Sector DG Adoption Rates 

These changes lead to increased adoption of PV systems in the baseline. However, the AEO2005 

assumptions about PV installations through the Million Solar Roofs program were removed, so

that there would not be double-counting when these were introduced in the Program Case.

3. GPRA07 Solar Program Scenario Assumptions 

Three key sets of assumptions were modified to generate the GPRA07 Solar Program scenario. 

Green power additions. Green power additions by region, from Princeton Energy Resources 

International (PERI), were added back into the Solar Program scenario (Table 2). These 

projections take into account the Baseline assumptions of noneconomic capacity additions. This 

capacity is added in NEMS-GPRA07 as exogenous additions in residential and commercial 

buildings. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix D – Solar Energy Technologies Program – Page D-4
Table 2. Incremental Green Power PV Capacity Additions (MW) 

Incremental Green Power PV Capacity Additions (MW) 

2007-2010 2011-2015 2016-2020 2021-2025 Total 

ECAR 64 183 140 41 428 

ERCT 58 167 129 38 392 

MAAC 56 159 122 35 372 

MAIN 16 47 36 11 110 

MAPP 4 12 9 3 28 

NY 12 35 27 8 81 

NE 16 47 36 10 109 

FL 75 214 164 47 500 

STV 225 641 491 142 1,500 

SPP 61 173 133 40 406 

NWPP 11 31 23 7 72 

RA 19 54 42 13 128 

CNV 0 0 0 1 1 

Total 618 1,761 1,350 396 4,125 

Technology Characteristics. More aggressive technology targets were used for the range of 

solar technologies: concentrating solar power (CSP), central PV systems, and distributed PV 

systems. The CSP technology characteristics were based on the Solar Program’s most recent 

draft Multi-Year Technical Plan (DOE 2005). The PV targets were based on anticipated changes 

in the Program’s structure and funding. 

In order to define a consistent set of long-term targets going out to 2050, a multi-lab, multitechnology team was assembled in 2003. This team produced technology cost projections for 

use in NEMS that are consistent with the Solar Program’s Draft Multi-Year Program Plan (DOE 

2005) through 2025 and extended the Solar Program’s targets to 2050 (for details, see Margolis 

and Wood 2004). In setting the targets used for PV technology in the GPRA07 analysis, we also 

drew on the U.S. PV Industry Roadmap (SEIA 2004). Thus the targets shown in Tables 3 and 4 

are consistent with the Program’s Draft Multi-Year Program Plan (DOE 2005), Margolis and 

Wood (2004), and SIEA (2004). It is important to note that beyond 2025, the targets are 

increasingly uncertain and are likely to be revised as the Solar Program continues to analyze the 

long-term prospects for technology cost reductions. Note that, on an annual basis, costs are 

assumed to decline linearly between the years shown in the tables below. 

While the technology assumptions for commercial rooftop PV systems are shown above in 

Figure 1, detailed data for PV systems in the three markets modeled is provided in Table 3. 

Although the costs shown below are for specific years, the costs decline annually between the 

years shown. Note that in both the GPRA baseline and program scenarios, the AEO2005 

Reference Case assumptions for solar insolation and capacity factors were used. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix D – Solar Energy Technologies Program – Page D-5
Table 3. PV Systems 

Central Generation Residential Buildings Commercial Buildings 

Year 

Installed 

Price 

(2003$/kW) 

O&M 

(2003$/kW)

Installed 

Price 

(2003$/kW)

O&M 

(2003$/kW)

Installed 

Price 

(2003$/kW) 

O&M 

(2003$/kW)

2005 5,500 40 8,500 100 7,000 40 

2010 3,700 10 5,600 40 4,800 20 

2015 2,100 4 2,800 20 2,600 10 

2020 1,680 3 2,240 16 2,080 8 

2025 1,428 2.7 1,904 13.6 1,768 7 

2030 1,285 2.0 1,714 12.0 1,591 6 

2050 1,221 2.0 1,628 12.0 1,512 6 

Note: Installed costs do not include the impact of the 10% investment tax credit. 

The data for CSP technology shown in Table 4 are for California. The CSP costs are up to 13% 

higher in other regions with less solar insolation to account for greater capacity and storage 

requirements. The annual capacity factors by 2020 range from 49% in MAPP (the Upper 

Midwest) to 74% in the Southwest. The capacity factors by time period were computed by 

Sandia analysts to optimize the timing of solar output for each region within the bounds of the 

storage potential. Note that the AEO2005 Reference Case assumptions include lower-cost CSP 

systems, but with significantly less storage and therefore lower electrical output. 

The future cost assumptions for CSP technology in the Solar Program scenario are based on a 

funding level consistent with the FY07 budget request for FY07 and a funding level 

commensurate with those outlined in the Draft CSP Technology Transition Plan for years 

beyond FY07 (DOE 2005). 

Table 4. Concentrating Solar Power 

Year 

Installed 

Price 

(2003$/kW)

O$M 

(2003mills/kWh)

Capacity 

Factor 

2010 3,510 7.8 65%

2020 2,462 4.0 72%

2025 2,199 3.6 72%

2030 1,993 3.2 72%

2035 1,879 3.1 72%

2040 1,826 3.0 72%

2050 1,797 2.9 72%

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix D – Solar Energy Technologies Program – Page D-6
4. Sources 

Cowan, Robin, and David Kline. 1996. The Implications of Potential “Lock-In” in Markets for 

Renewable Energy. National Renewable Energy Laboratory, Golden, Colorado. NREL/TP-460-

22112. 

Department of Energy (DOE). 2005. Solar Energy Technologies Program – Multi-Year 

Program Plan 2006-2011 – Draft (September). Office of Energy Efficiency and Renewable 

Energy, U.S. Department of Energy, Washington, D.C. 

DOE. 2004. CSP Technology Transition Plan – Draft. Internal DOE draft document. 

Energy Information Administration (EIA). 1999. A Look at Residential Energy Consumption in 

1997. Energy Information Administration, U.S. Department of Energy, Washington, D.C. 

EIA. 2004. Annual Energy Outlook 2004. Energy Information Administration, U.S. Department 

of Energy, Washington, D.C. 

EIA. 2003. Annual Energy Outlook 2003. Energy Information Administration, U.S. Department 

of Energy, Washington, D.C. 

IEA (International Energy Agency). 2001. Potential for Building Integrated Photovoltaics. St. 

Ursen, Switzerland. Report No: IEA - PVPS T7-4. 

Ikki, Osamu. 2003 (May). "PV Activities in Japan.” Resources Total Systems Co. Ltd., Tokyo, 

Japan. 

Margolis, Robert M. and Frances Wood. 2004. “The Role for Solar in the Long-Term Outlook 

of Electric Power Generation in the U.S.” Paper presented at the IAEE North American 

Conference in Washington, D.C., July 8–10. 

Navigant Consulting. 2003. The Changing Face of Renewable Energy. Navigant Consulting, 

Burlington, MA. Available from www.navigantconsulting.com. 

SEIA (Solar Energy Industries Association). 2004. Our Solar Power Future: The U.S. 

Photovoltaic Industry Roadmap Through 2030 and Beyond. Solar Energy Industries 

Association, Washington, D.C. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix D – Solar Energy Technologies Program – Page D-7
Appendix E – GPRA07 Wind Technologies Program 

Documentation 

Introduction 

GPRA benefits for the Wind Technologies Program are estimated primarily from model 

projections of the market share for wind technologies, based on their economic characteristics. 

Two models are utilized for this purpose: NEMS-GPRA07 (a modified version of the National 

Energy Modeling System), and MARKAL-GPRA07 (a modified version of standard 

MARKAL). This document describes the inputs and assumptions that are used by the models to 

calculate those benefits. 

FY07 Program Goals Assessed 

Program Objective 

The mission of the Wind Technologies Program is to “lead the Nation's research and 

development efforts to improve wind energy technology through public/private partnerships that 

enhance domestic economic benefit from development, and to address the barriers to the use of 

wind energy in coordination with stakeholders, resulting in greater energy security through 

more diverse, clean, reliable, affordable and secure domestic supply." To achieve the mission, 

the Wind Program portfolio includes both short-term and long-term research and outreach to 

solve technology and institutional issues. Balancing this portfolio effectively will help maintain 

U.S. wind industry momentum. 

Program Performance Goals 

The Wind Program’s Multi-Year Program Plan [6] contains the following goals1

: 

• By 2012, reduce the cost of electricity (COE) from large wind systems in Class 4 winds 

to 3.6 cents/kWh for onshore systems (from a baseline of 5.5 cents/kWh in 2002) 

• By 2014, reduce the COE from large wind systems in Class 6 winds to 5 cents/kWh for 

shallow water (depths up to 30 meters) offshore systems (from a baseline of 9.5 cents in 

FY 2005) 

• By 2016, reduce the COE from large wind systems in Class 6 winds to 5 cents/kWh for 

transitional (depths up to 60 meters) offshore systems (from a baseline of 12 cents in FY 

2006) 

 

1

 Onshore system COEs are stated in 2002 dollars for consistency with other Wind Program documents. However, to be 

consistent with AEO 05 assumptions used in NEMS, the onshore COE figures should be converted to 2003 dollars using the 

GDP deflator of 1.018312. Likewise, offshore COE figures above are stated in 2005 dollars, and a deflator of 0.960338 should 

be used to convert those to 2003 dollars. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix E – Wind Technologies Program – Page E-1 
• By 2007, reduce the COE from distributed wind systems to 10-15 cents/kWh in Class 3 

wind resources, from a baseline of 17-22 cents/kWh in 2002. 

• By 2010, facilitate the installation of at least 100 MW of wind in at least 30 states from a 

baseline of eight states in 2002. 

Resource Assumptions 

The Fiscal Year 2007 budget request2 for Wind Energy is $43.8 million, a nearly $5 million 

increase over the Fiscal Year 2006 Appropriation. A summary of the recent and requested 

budget, by major activity area, is shown in Table 1. The table shows a large portion of the 

appropriated FY 2006 budget for congressionally directed activities ($12.87 million). 

Table 1. FY 2007 Budget Request for Wind Energy Program

Funding ($ in thousands) 

FY 2005 

Approp. 

FY 2006 

Approp. 

FY 2007 

Request Activity 

Technology Viability........................... 25,961 18,353 35,905

Technology Application...................... 10,111 7,634 7,914

Congressionally Directed Activities.... 4,559 12,870 0

TOTAL............................................... 40,631 38,857 43,819

An estimated breakout of the FY 2007 requested budget by program performance goal categories 

is shown Table 2. Figures are based on preliminary assessments. 

Table 2. Estimated FY 2007 Budget By Performance Goal Category 

Performance Goal Category Estimated 2007 

Budget ($ million) 

Low Wind Speed Technology 19.4 

Offshore Wind Technology 15.0 

Distributed Wind Technology 1.0 

Wind Grid Integration/Systems Integration 4.1 

Technology Application/Technology Acceptance and Coordination 3.9 

Small Business Innovative Research (not a specific category) 0.9 

Funding for congressionally directed activities for FY 2007 and beyond is assumed to be zero. 

Future program funding is assumed to remain level at the FY 2007 request level through 

completion of offshore wind turbine R&D in 2016. The program estimates the annual industry 

cost-sharing level for all private/public partnerships to be approximately 50%. Figure P.1 in the 

Preface to the main report depicts the logical flow of all generalized aspects of the program. 

 

2

 EERE’s FY07 “Budget in Brief” may be accessed at http://www1.eere.energy.gov/ba/pba/budget_07.html. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix E – Wind Technologies Program – Page E-2 
Significant Changes 

The program’s 2012 goal for onshore low wind-speed technology (LWST) was revised in FY06 

from 3.0 cents/kWh to 3.6 cents/kWh in Class 4 sites.3

 The leading factor for this revision was 

the reduction in discretionary FY06 funding, which caused a large reduction in research and 

industry subcontracts for onshore technology development. 

As a result of funding reductions, several full system and component development projects had 

to be rescoped or terminated. Even if full funding were to be restored to these projects in future 

years, significant project momentum has been lost, thus reducing the likelihood of timely COE 

impacts. A closely related factor is the balance between the Wind Program onshore and offshore 

activities in a constrained funding environment. The values used for the wind technology cost 

and performance projections in the GPRA benefits analysis are consistent with this new goal. 

The assessment of current status and future trends for offshore wind energy technology, and the 

formulation of R&D goals, has been under development for the past two years. The Wind 

Program continues to develop data and analysis toward that end. For this year’s GPRA analysis, 

cost and performance estimates for offshore wind technology include a combination of shallow 

water technology (depths of 30 meters and less), which is competitive in near term; and 

transitional water technology (depths of 30-60 meters), which will be competitive beginning in 

the midterm, have been determined that are consistent with program goals. This is a change 

from the FY 2006 GPRA analysis, which used a combination of shallow and deep water 

technology, the latter in water from 60 to 900 meters. These depth figures were developed by 

the program. The program views this revised strategy as one of incremental technology 

development—moving from the better understood shallow water technologies to the transitional 

depth; and, finally, utilizing the accumulated knowledge base from those two applications for 

eventual deep water technology development. 

Target Markets (The Base Case) 

Target market Description 

Large-scale wind energy is expected to penetrate in two market segments: the least-cost 

(competitive bulk power) power market and the segment comprising a combination of voluntary 

(green power) and mandatory (green power or renewable portfolio standards) market programs 

or requirements. Because of the geographic diversity of the resource, wind energy is also 

available in any combination of grid-integration scenarios, including large or small plants at long 

or short distances from transmissions and distribution tie-in points. For instance, large amounts 

of offshore wind energy is available near load centers in the Northeast Region, whereas the wind 

resource in the Southeast region is relatively far from the largest load centers in the western part 

of that. 

 

3

 COEs are stated in constant 2002 dollars, to be consistent with other program documents. To be consistent with 

the AEO 05 assumptions used in NEMS modeling for this GPRA report, the onshore COE figures should be 

converted to 2003 dollars using the GDP inflator of 1.018312. Thus, 3 and 3.6 cents/kWh become 3.05 and 3.67 in 

2003 dollars. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix E – Wind Technologies Program – Page E-3 
Currently, wind turbines in Class 6 wind sites (6.4 – 7.0 m/s at 10 m height) compete well 

against conventional power producers such as gas, oil, and hydro; and the costs are becoming 

increasingly competitive with coal-fired power production. However, as the industry grows, the 

areas with excellent Class 6 wind resources located close to load centers are dwindling; and wind 

growth is hampered as it expands to the more remote, windy regions of the country, such as the 

Great Plains. In many of these windier (Class 6) locations, grid connection is problematic 

because they are so far from load centers and because of capacity constraints on existing 

transmission lines. A recent study illustrates this for North and South Dakota. [8] 

Class 4 wind sites (5.6 – 6.0 m/s at 10 m height), covering a much broader area of the nation, are 

on average five times closer to load centers and represent 20 times more wind resource. [7] 

Modeling for the FY 2006 GPRA report showed that with successful implementation of the 

Wind Program activities that provide industry with the means to develop Class 4 sites, the annual 

generating capacity for land based wind applications could be more than 90 gigawatts (GW) by 

2025. However; the only way wind can currently take advantage of Class 4 sites economically is 

with the support of the Federal production tax credit (PTC). The PTC has been available only 

intermittently. Through 2005, the PTC has been extended for no more than two years at a time, 

and there have been periods of uncertainty when the PTC has lapsed, which retards the 

development of a solid manufacturing base in the United States. The Energy Policy Act of 2005 

extended the PTC through 2007, but it is unclear whether it will be extended beyond that time. 

The uncertain availability of the tax credit forces the wind industry into a boom or bust cycle, 

reducing efficiency and increasing costs. Reducing wind energy cost to levels that are 

competitive without dependence upon tax incentives is one of the drivers of the wind program. 

The shallow water technology goal of 5 cents/kWh in class 6 winds by 2014 would achieve 

commercial costs at approximately 10% of U.S. sites between 5 and 50 nautical miles from 

shore, specifically in the constrained electricity markets along the east coast. Estimates place 

these resources at approximately an additional 90 GW for regions that have been surveyed. [6] 

A paper that further examines these estimates is due to be published in FY 07. [10] Gaining 

access to the shallow offshore market will allow wind technologies to supply low-cost energy to 

this congested region. 

In the midterm, offshore technology development will focus on turbine support structures for 

installations at depths up to approximately 60 meters and technologies to offset inherent 

adversities such as increased distance from shore, decreased accessibility, and more severe 

environmental conditions. This technology development pathway is planned to begin in FY 2007 

with a goal of 5 cents/kWh in Class 6 winds by 2016. If this technology is fully developed, then 

a total of 25% of surveyed resources between 5 and 50 nautical miles from shore would be 

available for wind deployment. Estimates of these resources add approximately 180 GW to the 

available development potential in the surveyed regions. 

Distributed and small wind applications have also played a key, although smaller, role within the 

DOE’s Wind Program. Focusing primarily on wind turbines rated less than 100 kilowatts (kW) 

in size, the needs of this market are expected to be met by approximately 13,000 units worldwide 

in 2005, of which about half will come from U.S.-based suppliers. Continued downward trends 

in the cost of energy (COE) of these turbines and expanded state-based subsidies are expected to 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix E – Wind Technologies Program – Page E-4 
greatly expand this market through 2011. Distributed Wind Technologies are currently not 

assessed as part of the GPRA benefits analysis. 

Baseline Technology Improvements 

The GPRA FY 07 baseline trajectory is based on the assumption that wind energy technology 

will continue to improve over time without EERE-sponsored R&D. The wind energy industry is 

comprised of several major international manufacturers and many smaller manufacturers, 

consultants, and government and university researchers. In addition to the United States, the 

primary expertise currently lies in Europe. Additionally, Japan—and, increasingly, India and 

China—will also provide expertise for future technology development. The baseline projections 

for onshore technology include only incremental improvements for higher wind-speed 

technology. The assumption for low wind-speed technology is that somewhat more R&D will be 

applied by non-EERE entities to continue to bring cost of energy down. Europe has much less 

land available in all wind classes than the United States, but especially in the higher classes. 

Therefore, they may be expected to focus some R&D on lower wind-speed technology. 

Additionally, since low wind-speed technology increases the international market potential, 

manufacturers should be interested in continuing improvements. Finally, because past R&D has 

focused on higher wind-speed technology, there is more potential for technical improvements to 

low wind-speed technology. However, current market trends demonstrate more interest among 

European turbine manufactures in considering shallow water offshore technologies operating in 

higher wind resource areas in place of further investments in low wind-speed technologies. 

Additionally, the European renewable electricity sector has a large environmental component, 

which allows wind technologies to be cost-competitive at a higher cost than would be acceptable 

in the U.S. market. Both of these factors indicate that although technology improvement in 

Europe will impact the U.S. market, they are unlikely to address several issues specific to the 

U.S. market. 

More than 700 megawatts of offshore wind energy capacity is operating in shallow waters off the 

shores of several European countries, and some of these countries are pursuing plans for major 

expansions of offshore wind power. [6] Offshore turbines have been operating in Europe for 

more than 10 years, primarily using marinized versions of onshore wind turbines installed on 

monopile tube towers in shallow waters (under 20 meters). The primary drivers have been the 

limited availability of suitable land-based sites in Northern Europe and favorable wind energy 

pricing. Early efforts to develop offshore wind energy were not considered relevant to the United 

States, because of widely available U.S. onshore wind resources. However, the lack of low-cost 

environmentally friendly energy supply options, especially in the Northeast; positive market 

incentives; and the scarcity of excellent wind sites in proximity to load centers along the coasts, 

have made offshore wind technologies an increasingly economically competitive electric power 

generation technology. 

European offshore conditions are fairly dissimilar to those in the United States. The continental 

shelf typically drops off much faster from our coasts. Without R&D support from EERE, 

European offshore technology can only be used for shallow water sites in the United States. The 

GPRA baseline assumes that there would be a 10-year lag in technology development for 

transitional depth technologies because, without the need for that technology in Europe, its 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix E – Wind Technologies Program – Page E-5 
development would be dependent on manufacturers, developers, installers, and operators first 

obtaining substantial experience with shallow water technology in the United States. The Wind 

Program based that estimate on expectations for a shallow water market to develop over the next 

10 years. Given the difficulties faced by offshore projects in overcoming a variety of barriers to 

market acceptance during the past several years, and those projected for at least the next few 

years, the 10-year estimate may be a slight underestimate.

Baseline Market Acceptance 

The U.S. large turbine wind energy market has been characterized by boom and bust cycles 

driven by the instability of the federal production tax credit (PTC). Table 3 shows the 

incremental installed wind capacity since 2000 and illustrates the sensitivity of annual installed 

capacity levels to the PTC, which was in place in 2001, 2003, and 2005. It also demonstrates the 

mainstream acceptance of wind energy technology in the current market. The American Wind 

Energy Association is predicting several more years of installation rates above 2,000 MW/year. 

Table 3. U.S. Installed Wind Energy Capacity 2000–2006 

Annual Installed 

Capacity (MW) 

Cumulative Installed 

Capacity (MW) 

2000 67 2,578 

2001 1,697 4,275 

2002 446 4,685 

2003 1,687 6,372 

2004 389 6,740 

2005 2,431 9,149 

2006 3,000+ 12,150+ 

References: Press Releases, American Wind Energy 

Association, May 12, 2005, and January 24, 2006 

Key Factors in Shaping Market Adoption 

Price 

Through program-sponsored research, wind technology is projected to improve significantly 

over the next decade. This improvement is represented in the GPRA07 modeling effort by a 

declining capital cost trajectory, lower O&M costs, and increased performance. These 

projections match the program’s performance goals, as described above. The Wind Energy 

Program forms its goals using a probabilistic modeling technique.4

 The projected COEs 

 

4 The technique first requires a reference set of performance and capital and operating cost characteristics for wind plants, using a 

composite of leading-edge technology for the reference year. It next defines a set of Technology Improvement Opportunities 

(TIOs) that may lead to lower levelized cost of energy (COE). A set of quantitative estimates of improvements to COE equation 

inputs (e.g., turbine cost, net annual energy) are then made for each TIO. A wind plant COE spreadsheet model is then run using 

Monte Carlo simulation add-on software to obtain a probabilistic evaluating of COEs for possible turbine technology 

configurations, or “pathways,” resulting from successful implementation of all possible combinations of those improvements. 

This approach captures the uncertainty of both R&D outcomes (potential sizes of various improvements) and the probability of 

achieving any improvement, (R&D "success"), regardless of the improvement size. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix E – Wind Technologies Program – Page E-6 
resulting from the cost and performance trajectories therefore represent figures that are close to 

the mean expected value, not the most optimistic or most conservative possible. 

Although there is a standard mathematical formula for characterizing cost reductions in 

manufactured goods from “learning effects,” there is no standard definition of the term, i.e. what 

effects it includes; nor is there an accepted single set of assumptions and overall methodological 

approach for calculating or predicting learning curve (sometimes referred to as “experience 

curve”) impacts. While some cost reductions may result from “learning” that is dependent on 

cumulative volume levels, other cost reductions may be obtained from economies of scale due to 

levels of annual volume of production. Therefore, the program’s analysis reflects the potential, 

on a probabilistic basis, for corresponding cost reductions that would result from both learning 

curve effects and economies of scale, the latter including discounts for large- volume purchase of 

materials, parts and components. 

The Wind Program’s “pathways analysis” assumes that there is at least a chance that the annual 

level of wind turbine manufacturing output will increase over time, along with cumulative 

volume. The program represents cost reductions from both annual and cumulative volume in a 

single number, for which an estimated range is discussed in the remainder of this section. The 

bottom end of that range is low enough (2%) to represent reductions from any combination of 

annual or cumulative volume increases. A complete discussion of cost-reduction potential from 

learning effects and economies of scale can be found in “Wind Energy Technology Pathways 

Analysis Methodology and Baseline Report” to be published by NREL in FY 2006. 

Among the parameters affecting the magnitude of the learning rate for a global technology are: 

exchange rates, choice of inflators to correct for inflation, use of production costs versus market 

prices, choice of market boundaries and subsequent inclusion or exclusion of imports or exports 

from cumulative production levels, definition of production units (e.g., energy production, 

capacity or number of turbines), and cost or price (e.g., $/turbine, $/kW, $/wind plant, $/kWh 

produced). In addition, off-the-shelf components of wind energy plants that are already massproduced will tend to show much less cost decrease over time than lower volume, customdesigned and -built components, because the former have already “come down” the learning 

curve.[4] The assumed mix of these two different types of components will impact the learning 

rate. There is also uncertainty concerning whether learning rates remain constant over time or 

tend to decrease, causing cost reductions to diminish as market diffusion increases. There are 

arguments to support the possibility of either case occurring.[5] 

Although the application of learning curves to wind energy cost contains a large number of 

uncertainties, there have been many recent attempts to construct such curves from the growing 

set of empirical market data. Those data shows that most reductions in cost for the various 

markets studied have been from 2% to 15% for every doubling of cumulative installed capacity. 

Despite the difficulties in applying learning curve theory to projection of future costs, the 

relatively narrow range of results across those many studies can be used to develop a reasonable 

range of estimates for potential cost reductions from learning. Accordingly, the Wind Program 

chose a range of 2% to 15% cost reduction for overall capital cost-reduction potential from 

learning by 2012 for onshore wind plants, with the expected value of 5% chosen to skew the 

distribution of values toward the conservative side. In addition, lower rates of cost reduction 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix E – Wind Technologies Program – Page E-7 
were chosen for balance of station costs, O&M costs, and replacement costs, because it was 

assumed that a larger percentage of leaning from onshore experience transfers in these areas than 

in the specialized platforms that contribute heavily to the initial capital cost. 

The program’s projected cost reduction from learning and increased economies of scale can 

result from a wide range of assumptions for the combination of the learning and market diffusion 

rates (i.e. doublings of wind turbine production and increase in annual production levels). Even 

the maximum level of cost reduction estimated by 2012, 15%, can be met by quite conservative 

combinations of those factors. In addition, the small, incremental cost reductions beyond 2012 

for onshore wind plants, and in the later years for offshore plants (i.e. in years past, the point 

where the have met the program goals), can be easily justified by conservative assumptions 

regarding learning effects and economies of scale. 

Nonprice Factors 

In addition to competing on an economic basis with other electricity generation technologies, 

wind capacity may be partly valued for its environmental attributes. Renewable energy credit 

markets, green power programs, and renewable portfolio standards are all examples of ways such 

value is beginning to be recognized in the market. 

Electricity produced from offshore locations is expected to be of higher value than many onshore 

locations in many cases, because proximity of several major load centers to the coasts could 

reduce transmission constraints and costs facing large-scale onshore power generation. 

Methodology and Calculations

Inputs To Base Case 

The GPRA07 Baseline is a modification of the AEO2005 Reference Case for onshore 

technologies. Offshore wind technology currently is not included in the AEO reference case, and 

so the program decided to use the technology characteristics (capital and operating costs, and 

energy production) equivalent to the preliminary program case values developed in June 20055

, 

but lagged by 10 years. In other words, progress in offshore wind technology in the absence of 

program R&D was assumed to be slower but eventually achieve the program goals. The onshore 

wind technology representation was modified to reflect the fact that the Wind Program has a 

different view of the characteristics of current technology than that in the AEO, as well as the 

trajectory over time. The program estimates of wind capacity factors are 12% to 13% higher 

than EIA’s (e.g. 0.47 for Class 6 versus 0.41), and their 2005 capital costs are just slightly lower. 

Justification for Wind Program estimates for both current and future technology characteristics 

for turbines in Class 4 sites are contained in a report expected to be published by NREL in FY 

2006, tentatively titled, “Low Wind Speed Turbine Pathways Analysis Report,” which updates 

earlier, preliminary documentation.[1] 

 

5

 Note, as a result of the emerging nature of the offshore program, its goals were later modified; but the GPRA Baseline was not

revised due to timing constraints. The revised program cost and performance goals are less aggressive than the original ones. 

This discrepancy will be addressed in the FY 2008 GPRA analysis. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix E – Wind Technologies Program – Page E-8 
In addition, the AEO2005 assumed that any cost improvement over time results only from a 

learning (or experience) effect that lowers cost proportional to the increase in cumulative 

installed volume, but not from R&D advances. Because the AEO projects a small amount of 

penetration (additional cumulative volume), the capital cost decrease in the AEO projections is 

negligible. Under that assumption, the onshore costs eventually become greater than the Wind 

Program’s projected offshore costs because the rate of offshore improvements is higher than for 

the onshore. Although such a relative cost relationship in the long-term is not yet intuitively 

understood by researchers, the assumption used for this GPRA analysis is that offshore costs 

should remain higher than those for onshore. Studies initiated by the program are currently 

addressing this area in detail, and assumptions will be revisited for the FY 2008 GPRA analysis.6 

A new baseline onshore cost trajectory was constructed to address those issues. The initial point 

in 2005 reflects an average of the program and AEO points. The levelized cost of energy (COE) 

trajectory then declines, so that by 2050 the onshore cost remains below the offshore costs by a 

ratio equivalent to that of the Program Case. Capital costs, O&M costs, and capacity factors 

were modified proportionally to achieve the target COE. The resulting COEs for Class 6 are 

illustrated in Figure 1. Table 4 provides the baseline values for the all the wind classes and 

technology types required for NEMS and MARKAL modeling. 

Class 6 COE

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

2010 2015 2020 2025 2030 2035 2040 2045 2050

2002 $/Mwh

Offshore Base

Shallow Offshore

AEO2005

Onshore Base

Onshore Program

Figure 1. Class 6 Baseline and Program COE Trajectories 

 

6

 While cost characteristics for offshore wind turbine components will differ somewhat from their offshore counterparts, due to 

potential size differences and to unique requirements for reliability, durability, and serviceability in the marine environment, 

many aspects of the two systems will continue to be similar and are expected to track each other in terms of future cost 

reductions. However, the platform and anchoring components for offshore systems are unique to that application, and will be 

subject to steeper cost reductions from learning, relative to the rest of the plant equipment, as designs enter the market and 

subsequent cumulative volume increases. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix E – Wind Technologies Program – Page E-9 
Table 4. GPRA07 Baseline Technology Characteristics (Model Inputs) 

2003 Dollars 2005 2010 2015 2020 2025 2030 2040 2050

Capital Costs* 

Onshore Class 5&6 1069 1026 985 946 907 872 802 739

 Class 4 1123 1087 1051 1016 984 951 890 833

Offshore Shallow 2132 2132 1691 1334 934 905 818 771

 Transitional 2519 2519 2203 1905 1143 953 905 858

O&M Costs 

Onshore All Classes 25.5 23.6 21.9 20.3 18.7 17.4 15.0 12.8

Offshore Shallow 45.2 45.2 45.2 41.9 36.5 33.8 32.1 30.3

 Transitional 67.9 67.9 67.9 73.9 44.5 35.6 32.1 30.3

Capacity Factors 

Onshore Class 6 0.440 0.448 0.457 0.466 0.476 0.485 0.504 0.525

 Class 5 0.402 0.412 0.423 0.434 0.446 0.458 0.482 0.508

 Class 4 0.348 0.356 0.364 0.372 0.381 0.389 0.407 0.425

Offshore Class 6 0.391 0.405 0.428 0.467 0.500 0.500 0.500 0.500

Shallow Class 5 0.358 0.380 0.389 0.403 0.430 0.430 0.430 0.430

 Class 4 0.264 0.317 0.332 0.356 0.400 0.400 0.400 0.400

Offshore Class 7 0.468 0.484 0.485 0.486 0.549 0.556 0.600 0.600

Transitional Class 6 0.382 0.394 0.396 0.399 0.421 0.463 0.500 0.500

 Class 5 0.340 0.358 0.357 0.355 0.394 0.398 0.430 0.430

*Includes 1.05 contingency factor for onshore systems and 1.07 for offshore systems. [1] [2] [6] 

Onshore cost were converted from 2002 dollars using GDP inflator of 1.018312. Offshore costs were 

converted from 2005 dollars using GDP deflator of 0.960338 

Technical Characteristics 

Description of Key Elements of the National Energy Modeling System (NEMS) Approach 

to Modeling Wind

The electricity-sector module performs an economic analysis of alternative technologies in each 

of 13 regions. Within each region, new capacity is selected based on its relative capital and 

operating costs, its operating performance (i.e. capacity factor, which reflects energy conversion 

efficiency, and both resource and plant availability), the regional load requirements, and existing 

capacity resources. NEMS-GPRA07 characterizes wind by three wind classes, each with its own 

capital costs and resource cost multipliers. Different wind classes are used for different 

technology applications—classes 4, 5 and 6 for land-based technologies, and 5, 6, and 7 for 

offshore technologies. The regional resource cost multipliers increase capital costs as increasing 

portions of a wind class are developed in a given region to reflect 1) declining natural resource 

quality, 2) required transmission network upgrades, 3) competition with other market uses, 

including aesthetic or environmental concerns. As the cost in that region increases, it may be 

more cost-effective to consider installing wind turbines in areas of lesser wind resource, but with 

lower ancillary costs and less costly access to the grid, as reflected in the model by the capital 

cost multipliers. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix E – Wind Technologies Program – Page E-10 
Other key assumptions that can affect projections include a limit on the share of generation in 

each region that can be met with intermittent technologies. The AEO2005 assumption that wind 

may provide only a maximum of 20% of a region’s generation was maintained, even though the 

program disagrees with that characterization. NEMS-GPRA07, as in the AEO2005, also assumes 

that the capacity value of wind diminishes with increasing levels of installed wind capacity in a 

region. Finally, another constraint on the growth of wind resource development is how quickly 

the wind industry can expand before costs increase due to manufacturing bottlenecks. The 

AEO2005 assumption that a cost premium is imposed when new orders exceed 50% of installed 

capacity was maintained for the benefits analysis. 

As part of the development efforts for the offshore wind energy activates, the program is 

currently working to upgrade the NEMS software to more accurately model offshore wind 

technologies. The first stage of these efforts is represented in the FY2007 version of the 

software, and additional improvements are expected to be made in the FY2008 GPRA analysis. 

Further detail on the representation of wind power in NEMS may be found in Chapter 2. 

Wind Program Case Assumptions 

The assumptions about capital costs, capacity factors, and O&M costs—which are used as inputs 

into the NEMS-GPRA07 model for the Program Case—are provided in Table 5. These 

projections match the program’s performance goals, as described above. Projections for onshore 

wind plants are consistent with the analysis described in [6]. The capital costs include a 

contingency factor of 5% for onshore wind and 7% for offshore wind, similar to other electricgenerating technologies. The current technology characteristics in Table 5 represent leadingedge technology available in the market. The projected characteristics for low wind speed 

onshore wind plants result from the probabilistic path analysis approach described on Page 7 of 

this appendix. Estimates for future technology characteristics are consistent with mean values 

from that analysis or values that are between the mean and the best. However, they are always 

significantly below the best (at least 30% in the worst case). As the program develops further 

data on offshore technology, a similar path analysis is expected to be conducted in FY06 or 

FY07. 

The Program Case wind capital costs were updated in December 2005 to reflect the impact of 

earmarks on existing and planned projects related to meeting the program goal. Long-term costs 

were also increased by 5% over FY 2006 values to reflect higher estimates of developer fees, 

based on analysis of confidential market data. It was too late to change the Baseline as well, so 

the long-term capital costs are slightly lower in the Baseline than the program case. However, 

because technologies compete on the basis of cost of energy in the market models, and the higher 

capacity factors in the program case dominate the difference in COE between the two cases, the 

impact on the benefits estimate for the program R&D is small. 

Program analysis and documentation for offshore technology characteristics is an evolving 

process as offshore R&D activities ramp up. To develop the offshore cost and performance 

inputs shown in Table 5, program analysts scaled capital costs over six periods from 2006 to 

2025, using learning rates (i.e., capital cost reductions for each period corresponding to a 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix E – Wind Technologies Program – Page E-11 
doubling of installed capacity) typical of wind industry experience, and that are assumed to 

include improvements in technology, production volume, learning curve effects, and 

improvements in operational proficiency. The doubling periods and learning rates used in the 

cost calculations were derived from IEA and European reports [3]. The learning rate was 

augmented by a one-time additional 10% reduction in capital cost in year 2015 due to technology 

R&D. The resulting levels of improvements to wind plant COE served as an upper boundary for 

Program Case estimates. That is, the Program Case projections in Table 5 are all within the 

bounds established by the cost-scaling exercise. The next analytic step for the program will be to 

apply its Wind Energy Technology Pathways Analysis methodology to transitional water depth 

offshore technology to obtain probabilistic data for technology characteristic projections. 

Table 5. Program Projections for Capital Costs, Capacity Factors, and O&M Costs for Onshore and 

Offshore Wind Plants

2002 Dollars 2005 2010 2015 2020 2025 2030 2040 2050

Capital Costs* 

Onshore Class 5&6 1050 982 893 872 866 840 819 798

 Class 4 1103 1034 971 945 919 893 872 851

Offshore Shallow 2220 1816 1009 969 941 916 866 842

 Transitional 2623 2321 1211 1059 1015 990 941 916

O&M Costs 

Onshore All Classes 25.0 20.0 16.0 15.0 14.2 13.8 13.2 12.8

Offshore Shallow 47.1 47.1 38.7 35.8 34.9 33.9 32.1 30.2

 Transitional 70.7 66.0 47.1 37.7 34.9 33.9 32.1 30.2

Capacity Factors 

Onshore Class 6 0.440 0.475 0.500 0.511 0.517 0.519 0.523 0.525

 Class 5 0.402 0.445 0.470 0.482 0.490 0.492 0.497 0.500

 Class 4 0.348 0.400 0.460 0.469 0.472 0.474 0.479 0.480

Offshore Class 6 0.405 0.435 0.500 0.505 0.510 0.511 0.513 0.515

Shallow Class 5 0.380 0.400 0.430 0.435 0.440 0.441 0.443 0.445

 Class 4 0.317 0.355 0.400 0.405 0.410 0.411 0.413 0.415

Offshore Class 7 0.484 0.486 0.516 0.544 0.556 0.574 0.574 0.574

Transitional Class 6 0.381 0.387 0.458 0.478 0.494 0.511 0.513 0.515

 Class 5 0.304 0.356 0.394 0.412 0.426 0.441 0.443 0.445

*Includes 1.05 contingency factor for onshore systems and 1.07 for offshore systems. [2] [3] 

It was necessary to make one major modification to the offshore wind resource inputs for the 

NEMS model. The current NEMS-GPRA07 projections include very high offshore wind 

penetration in the Southeastern Electric Reliability Council (SERC) region. The NEMS model 

splits the United States electricity market into 13 North American Electric Reliability Council 

(NERC) regions. The SERC region includes the Virginia-Carolinas sub-region (VACAR), the 

TVA sub-region (Tennessee and adjacent portions of Alabama, Georgia, Kentucky, and 

Mississippi), and the Southern sub-region (Georgia, Alabama, part of Mississippi, and the 

panhandle of Florida), and is the largest of the NERC regions in terms of electricity sales (almost 

23% of total U.S. sales7

). All electricity technologies represented in NEMS compete within 

these 13 regions for market share. 

 

7

 Energy Information Administration, Annual Energy Outlook 2005 Supplemental Tables (Tables 60-72). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix E – Wind Technologies Program – Page E-12 
Updated resource curves for the SERC region are being used for the FY 2007 GPRA NEMS 

analysis. The resource curves provided by NREL for the SERC region account for offshore wind 

classes 5 and above that are located off the shores of Virginia, North Carolina, and South 

Carolina. Because SERC includes many inland areas far from these offshore wind resources, 

one can argue that there might be significant portions of the region where the transmission of 

electricity produced by these wind resources would be cost-prohibitive. However, because the 

NEMS model treats the region as one market, transmission costs are assumed to be equal 

throughout the region. This explains why the model tends to produce offshore wind penetration 

levels in the SERC region much higher than expected. 

In order to address this issue within the current GPRA cycle, a short-term solution was 

developed; namely, to adjust the SERC offshore wind data to reflect the portion of the region 

that is in close enough proximity to the resources for cost-effective transmission. The latest 

electricity sales data by state from EIA8

 indicates that the three states nearest to the offshore 

wind resources (Virginia, North Carolina, and South Carolina) account for roughly 38% of total 

sales in the SERC region. Because a small portion of southwestern Virginia is located in the 

East Central Area Reliability Council (ECAR) region, it was assumed that only 95% of 

Virginia’s sales are included in SERC. This 38% market share then is used to adjust the SERC 

offshore wind resource data. 

MARKAL 

The program goals are represented in the MARKAL-GPRA07 model by changing the capital and 

O&M costs and capacity factors for wind turbines to match the program goals as represented in 

Table 5. 

The discount rate for wind generators is set at 8% (instead of the utility average of 10%) to 

reflect the accelerated depreciation schedule available for renewable generation technologies. 

Wind generators are modeled as centralized plants to compete with fossil fuel-based plants. The 

potential contribution of wind systems to meeting peak power demand is limited to 40%, 

reflecting the intermittent nature of the technology. As with PV systems, this disadvantages wind 

generators, as additional reserve capacity is needed to meet peak power requirements. However, 

this disadvantage is offset by the reduction in capital cost and performance improvements 

projected for wind technologies by the program. As a result, wind generators near the central 

grid can be competitive with fossil fuel-based power plants. 

 

8

 Energy Information Administration, Electric Power Annual 2003 – Spreadsheets (sales_state.xls). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix E – Wind Technologies Program – Page E-13 
Sources 

1. Cohen, Joseph M., “Assessment Of Potential Improvements In Large-Scale Low Wind Speed 

Technology,” proceedings of Global Windpower 2004, March 29, 2004, American Wind Energy 

Association, Washington, D.C. 

2. Musial, W., and Butterfield, S., “Future For Offshore Wind Energy in the United States,” 

June 2004, NREL.CP-500-46413 http://www.nrel.gov/docs/fy04osti/36313.pdf

3. Milborrow, D., “Offshore Wind Rises to the Challenge,” Windpower Monthly, April 2003. 

4. Brock, Frank, Koontz, Robert, and Cohen, Joseph. (December 21, 1995). The Effects of 

Increased Production on Wind Turbine Costs. Princeton Economic Research Inc. Prepared for 

the National Renewable Energy Laboratory, Golden, CO. 

5. Junginger, Martin. (May 13, 2005). Learning in Renewable Energy Technology 

Development. Ph.D. thesis. The Netherlands: Utrecht University. http://www.chem.uu.nl/nws -

> publications -> theses 

6. Department of Energy (DOE) Wind and Hydropower Technologies Program, Wind Energy 

Multi-Year Program Plan (MYPP) for fiscal years 2007-2011, Draft, February 9, 2006, Final to 

be available at www.nrel.gov/wind in FY 2006. 

7. Goldman, P., et. al. (April, 2002). “Advanced Low Wind Speed Technology Research and 

Development in the U.S. Department of Energy Wind Program,” Proceedings of the 2002 Global 

Windpower Conference, Paris, France, April 2002. (Available on request) 

8. Martin, Don. ABB, Inc., Electric Systems Consulting. (October 3, 2005). Dakotas Wind 

Transmission Study, Prepared for the Western Area Power Authority’s Great Plains Regional 

Office. http://www.wapa.gov/ugp/study/DakotasWind/FinalReports/

9. Parsons, B., Wan, Y, Elliot, D. (March 26-30, 1995). “Estimates of Wind Resource Land 

Area and Power Potential In Close Proximity to Existing Transmission Lines.” Proceedings of 

WindPower ’95. p. 295. American Wind Energy Association. Washington, D.C. 

10. Musial, W. et.al. (expected 2006). A Path Toward Large-Scale Offshore Wind Power in the 

United States, National Renewable Energy Laboratory, Golden, CO. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix E – Wind Technologies Program – Page E-14 
Appendix F – GPRA07 Vehicle Technologies Program 

Documentation 

1. Introduction 

The target markets for the Office of FreedomCAR and Vehicle Technologies (FCVT) program 

include light vehicles (cars and light trucks) and heavy vehicles (trucks more than 10,000 pounds 

Gross Vehicle Weight). Each will be discussed separately below. 

1.1 Target Market: Alternate Technology Light Vehicle (ATV) Market 

The alternate technology light vehicles (ATVs) included in the FCVT program are gasoline 

hybrid vehicles, diesel hybrid vehicles, advanced diesel, and advanced gasoline vehicles. The 

market for these technologies includes all cars and light trucks sold for both personal and 

business use. Today, the size of this market is approximately 17 million vehicle sales per year. 

Total car and light truck stock is about 220 million vehicles. EIA projects both sales and stock to 

grow to more than 20 million and 300 million, respectively, by 2025. Additional growth is 

expected post-2025, as explained in Chapter 2. The vehicle miles of travel are projected to grow 

from 3.28 trillion in 2020 to 5.63 trillion in 2050. 

1.2 Key Factors in Shaping the Market Adoption of ATVs 

Key factors associated with the adoption of new vehicle technologies include how the new 

vehicle technologies compare with the baseline vehicle technologies in terms of the following 

vehicle attributes: 

• Vehicle Price 

• Fuel Economy 

• Range 

• Maintenance Cost 

• Acceleration 

• Top Speed 

• Luggage Space 

Of these, vehicle price and fuel economy are the most important. 

Nonvehicle attributes that are important factors in a consumer’s decision to purchase new vehicle 

technologies include the following: 

• Fuel Price 

• Fuel Availability 

1.3 Methodology and Calculations 

The factors listed above include the factors used in the modeling of new vehicle technology 

penetration by the NEMS and MARKAL models. ATV attributes and other factors are discussed 

below. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-1
1.3.1 ATV Attributes: General 

ATV attributes were developed based on the FCVT program goals, discussions with FCVT 

program managers, Powertrain Systems Analysis Toolkit (PSAT) modeling and payback 

analysis (Refs. 1-5). The PSAT model is a simulation model used by DOE to evaluate the fuel 

economy and performance of light vehicles using various technologies. Section 1.3.2 below 

discusses the fuel economy estimates developed in this analysis. Payback analysis was used to 

estimate what the incremental price of ATVs would be (given the fuel economies from the PSAT 

model) when they become cost competitive with conventional vehicles, a goal of the program. 

Section 1.3.3 below discusses the price estimates in further detail. Other attributes were based 

on a review of past GPRA characterizations (e.g., Ref. 6). 

Because the NEMS and MARKAL models require different levels of detail, two separate vehicle 

characterizations are provided. In both cases, most of the attributes are provided as ratios to the 

vehicle attributes of conventional vehicles. (For NEMS, the $ value of the price increments were 

provided.) The attributes are for new vehicles in the year listed. Table F-1 contains the vehicle 

attributes for ATVs provided for input to the NEMS model. Attributes are provided for all six car 

size classes and six light truck (LT) classes that NEMS uses.

Table F-2 contains vehicle attributes for ATVs provided as input to the MARKAL model. 

MARKAL uses only vehicle price and fuel economy attributes. MARKAL does not disaggregate 

cars and light trucks into various classes. 

1.3.2 Estimation of ATV MPG Estimates 

PSAT model results underlie the fuel economy and cost estimates that serve as input to the 

GPRA benefits models. This section explains how PSAT results have been used to develop the 

fuel economy inputs to the GPRA models. While the discussion mentions FCVs (because the 

same methodology was applied to estimate FCV fuel economy), we do not present the FCV 

MPG estimates in this appendix. 

1. There are two GPRA models: NEMS-GPRA07 and MARKAL-GPRA07. The NEMSGPRA07 model requires characterization of six cars and six LTs for each technology to 2025. 

The MARKAL-GPRA07 model requires characterization of an average car and an average LT 

for each technology to 2050. Table F-3 summarizes the vehicle classes used in both models. 

2. The PSAT model itself only provides fuel economy estimates for 4 of the 12 vehicle classes 

required by NEMS. The four classes in PSAT are also presented in Table 3. They include 

compact and midsize cars, a SUV and a pickup. PSAT results for those four classes thus must be 

adjusted in order to develop the fuel economy estimates required by the GPRA models. This 

adjustment is made as discussed below using a simple spreadsheet model. 

3. Two sets of PSAT results were used in this analysis. One set of PSAT results (new vehicle 

fuel economies) was provided for five vehicle technologies (advanced gasoline, gasoline HEV, 

advanced diesel, diesel HEV and FCV) in 3 vehicle classes (midsize car, SUV, and pick-up) in 2 

years (2010 and 2020) (3). “Low,” “high,” and “average” results were provided. The “high” 

results are the only one of the three sets of results that represent achievement of the goals of the 

FCVT (and HFCIT) program to 2020 for these three vehicle types. Therefore, we used the 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-2
“high” results in our analysis. Because PSAT results were not available for the compact car, we 

assumed that the “high” results of the midsize cars also apply to the compact cars. We do not 

use the same fuel economies, but instead use the same ratio or “X” factor of ATV fuel economy 

relative to the baseline gasoline vehicle fuel economy. 

4. For GPRA, estimates need to be developed to 2050. The PSAT results discussed above only 

extend to 2020. Another set of PSAT results were provided for two vehicle technologies 

(gasoline HEV and FCV) in 3 vehicle types (compact, midsize car and SUV) in 4 years (2010, 

2020, 2035, and 2050) (4). Again, “low” and “high” results were provided. Using the “high” 

results, we estimated the improvement rate in fuel economy from 2020 to 2035, and 2035 to 

2050 for the midsize car and SUV for these two technologies. We then applied the improvement 

rates for the gasoline HEV to the 2020 estimates developed in No. 3 (midsize car to midsize and 

compact car and SUV to SUV and pickup) to generate new vehicle fuel economy estimates to 

2050 for all the technologies (except the FCV). 

5. The payback analysis discussed below uses on-road vehicle fuel economy. We assume a 20% 

degradation factor between the new vehicle fuel economy estimates generated by PSAT and the 

fuel economies actually achieved “on-road.” (The NEMS and MARKAL models also make this 

assumption.) This fuel economy degradation factor is then applied to the new vehicle fuel 

economies developed in No. 3 and No. 4 for ATVs. 

6. The PSAT results are developed relative to current gasoline vehicles. EIA projects 

improvements in conventional gasoline vehicles. The NEMS-GPRA07 and MARKAL-GPRA07 

models assume such improvements. We applied EIA’s rate of improvement to the current 

gasoline vehicles modeled in PSAT and developed new vehicle and on-road fuel economies for 

the four conventional vehicle types characterized so far (midsize car, SUV, pickup and compact 

car.) For 2025 to 2050, we used EIA’s 2020-2025 improvement rate. 

7. Given the new vehicle fuel economies developed for advanced technologies in No. 5 and for 

comparable conventional vehicles in No. 6, the final fuel economy ratios (X factors) for those 

five technologies (advanced gasoline, gasoline HEV, advanced diesel, diesel HEV and FCV) in 

four vehicle types (compact -car, midsize car, SUV, and pick-up) in several years (2010, 2020, 

2025, 2030, 2035, and 2050) are estimated. 

8. For the NEMS model, the new vehicle fuel economy X factors of the compact cars are 

assumed to apply to the mini-compact, subcompact and two-seater as well as the compact. The 

new vehicle fuel economy X factors of the midsize cars apply to medium and large cars. The 

new vehicle fuel economy X factors of the SUV (which is a large SUV according to the NEMS 

classification) are assumed to apply to large and small SUVs and all vans. The new vehicle fuel 

economy X factors of the pickup (which is a large pickup according to the NEMS classification) 

are assumed to apply to both small and large pickups. 

9. The fuel economy estimates finalized in No. 7 and No. 8 are for the years 2010, 2020, and 

2025. For the NEMS model we need to provide estimates for intervening years. For those 

intervening years, we use linear interpolation to estimate the X factors. 

 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-3
10. As stated above, the MARKAL model uses only one car and one light truck. We examined 

current sales volumes of the six different car and six different LT types. Based on that 

examination, we weighted the compact and midsize cars 50-50 to estimate the fuel economy X 

factors of an average car and we weighted the SUV and pickup 67-33 to estimate the fuel 

economy of an average LT. 

1.3.3 Incremental Vehicle Price Estimates 

As indicated above, payback analysis was used to estimate what the incremental price of ATVs 

would be when they become cost competitive with conventional vehicles, a goal of the program. 

The incremental price equals the present value of the energy cost reduction achieved by ATVs 

over three years, assuming a fuel price of $1.50/gallon gasoline equivalent and 7.5% discount 

rate. Incremental prices are higher in the early years of market introduction. In fact, we develop 

three sets of prices for each class of vehicle for input to NEMS. Prices are developed for a 

“market introduction” date, a “price success” date and a “price maturity” date. The price at 

“price maturity” is the “final” incremental price; the price at “market introduction” is 50% higher 

than it would be if the technology were “mature” and the price at “price success” is 10% higher 

than it would be if the technology were “mature.” These dates vary for the different technologies. 

For MARKAL, we weight the incremental prices estimated for each technology in 2025 in the 

same manner that we weighted the fuel economy estimates as described in No. 10 of Section 

1.3.1. We then assume a gradually declining incremental price to 2050 for each technology. 

1.3.4 ATV Market-Penetration Methodology 

Brief descriptions of how the NEMS and MARKAL models each project new vehicle technology 

penetration using these vehicle attributes can be found in Chapter 2 (NEMS-GPRA07) and 

Chapter 3 (MARKAL-GPRA07). 

1.4 Sources 

1. “Strategic Plan,” U.S. Department of Energy Efficiency and Renewable Energy, DOE/GO102002-1649 (October 2002). 

2. PSAT (POWERTRAIN SYSTEM ANALYSIS TOOLKIT): see 

http://www.transportation.anl.gov/software/PSAT/ 

3. Phillip Sharer and Aymeric Rousseau, “PSAT Results for GREET and GPRA – FE Adjusted 

081705.xls”, August 17, 2005. 

4. Rousseau, Aymeric, “Number Associated with Presentation”, July 6, 2005. 

5. Payback model developed by Jim Moore, TA Engineering (2003) and expanded by Margaret 

Singh, ANL (2005). 

6. “Program Analysis Methodology: Office of Transportation Technologies, Quality Metrics 

2003 Final Report”, prepared by OTT Analytic Team, for Office of Transportation Technologies, 

U.S. Department of Energy (March 2002). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-4
Table F-1. ATV Attributes Input to NEMS 

All units are ratios to the conventional gasoline vehicles of the specific year, 

except for the incremental price (which is in 2003 dollars) 

2-SEATER MINI-COMPACT SUB-COMPACT 

Market 

Intro 

Price 

Success 

Price 

Maturity

Market Intro 

Price Success

Price Maturity

Market Intro 

Price Success

Price Maturity

Market Intro 

Advanced Diesel 2014 2019 2024 2025 2018 2023 2025 N/A 2012 2017 2022 2025 2011 Incremental Vehicle Price ($) 1266 984 900 902 1280 956 925 1003 788 738 742 1001 

Range 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 

Maintenance Cost 1.00 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 

Acceleration 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

Top Speed 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

Luggage Space 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

Fuel Economy 1.41 1.47 1.49 1.49 1.45 1.49 1.49 1.39 1.44 1.48 1.49 1.38 

Diesel Hybrid 2016 2021 2025 N/A 2020 2025 N/A N/A 2016 2021 2025 N/A 2014 

Incremental Vehicle Price ($) 1843 1414 1303 1871 1360 1509 1160 1072 1480 

Range 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 

Maintenance Cost 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 

Acceleration 1.00 1.00 1.00 0.90 0.90 0.90 0.90 0.90 0.90 

Top Speed 1.00 1.00 1.00 0.90 0.90 0.90 0.90 0.90 0.90 

Luggage Space 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 

Fuel Economy 1.75 1.86 1.87 1.86 1.87 1.75 1.86 1.87 1.70 

Gasoline Hybrid 2013 2018 2023 2025 2011 2016 2021 2025 2010 2014 2019 2025 2007 

Incremental Vehicle Price ($) 1370 1116 1042 1043 1245 1035 1010 1009 1023 840 847 858 1057 

Range 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 

Maintenance Cost 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 

Acceleration 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

Top Speed 1.00 1.00 1.00 1.00 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 

Luggage Space 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95 

Fuel Economy 1.45 1.56 1.61 1.61 1.41 1.52 1.60 1.61 1.39 1.47 1.58 1.61 1.39 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-5
Table F-1 (continued) 

MEDIUM CAR LARGE CAR 

Market 

Intro 

Price 

Success 

Price 

Maturity

Market 

Intro 

Price 

Success 

Price 

Maturity 

Advanced Diesel 2010 2015 2020 2025 2009 2014 2019 2025

Incremental Vehicle Price ($) 1113 882 851 848 1216 935 903 905Range 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20

Maintenance Cost 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90

Acceleration 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Top Speed 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Luggage Space 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Fuel Economy 1.36 1.42 1.48 1.49 1.36 1.41 1.46 1.49

Diesel Hybrid 2014 2019 2024 2025 2014 2019 2024 2025

Incremental Vehicle Price ($) 1682 1324 1205 1205 1808 1419 1287 1286

Range 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25

Maintenance Cost 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05

Acceleration 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90

Top Speed 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90

Luggage Space 0.95 0.95 0.95 0.95 0.95 0.95 0.95 0.95

Fuel Economy 1.70 1.83 1.87 1.87 1.70 1.83 1.87 1.87Gasoline Hybrid 2006 2011 2016 2025 2009 2014 2019 2025

Incremental Vehicle Price ($) 1200 888 917 983 1281 1037 1042 1048

Range 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25

Maintenance Cost 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05

Acceleration 1.00 0.90 0.90 0.90 0.90 0.90 0.90 0.90

Top Speed 0.90 0.90 0.90 0.90 1.00 0.90 0.90 0.90

Luggage Space 0.85 0.95 0.95 0.95 0.85 0.95 0.95 0.95

Fuel Economy 1.39 1.41 1.51 1.61 1.39 1.47 1.58 1.61

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-6
Table F-1 (continued) 

SMALL SUV LARGE SUV SMALL TRUCK CA

Market 

Intro 

Price 

Success 

Price 

Maturity

Market 

Intro 

Price 

Success

Price 

Maturity

Market 

Intro 

Price 

Success

Price 

Maturity

Market 

Intro 

Advanced Diesel 2008 2013 2018 2025 2007 2012 2017 2025 2008 2013 2018 2025 2006 

Incremental Vehicle Price ($) 2018 1455 1293 1298 2518 1827 1615 1607 1346 1057 1049 1113 1701 

Range 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20 

Maintenance Cost 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 Acceleration 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Top Speed 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

Luggage Space 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 

Fuel Economy 1.75 1.75 1.74 1.78 1.75 1.75 1.74 1.78 1.43 1.48 1.56 1.64 1.43 Diesel Hybrid 2011 2016 2021 2025 2015 2020 2025 N/A 2012 2017 2022 2025 2016 

Incremental Vehicle Price ($) 2314 1704 1553 1555 2897 2116 1926 2034 1528 1419 1426 2537 

Range 1.20 1.20 1.20 1.20 1.20 1.20 1.20 0.90 0.90 0.90 0.90 0.90 

Maintenance Cost 1.05 105 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 

Acceleration 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 

Top Speed 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.90 0.90 0.90 0.90 0.90 

Luggage Space 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.80 0.90 0.90 0.90 0.80 

Fuel Economy 1.99 2.02 2.06 2.10 2.01 2.05 2.10 1.83 1.90 1.97 2.00 1.89 

Gasoline Hybrid 2007 2012 2017 2025 2008 2013 2018 2025 2010 2015 2020 2025 2010 

Incremental Vehicle Price ($) 1984 1479 1374 1401 2530 1858 1714 1735 1568 1250 1211 1236 1918 

Range 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25 

Maintenance Cost 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05 Acceleration 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90 Top Speed 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.90 0.90 0.90 0.90 0.90 

Luggage Space 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.80 0.95 1.00 1.00 0.80 

Fuel Economy 1.75 1.77 1.82 1.89 1.75 1.78 1.83 1.89 1.54 1.63 1.71 1.76 1.54 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-7
Table F-1 (continued) 

MINIVAN LARGE VAN

Market 

Intro 

Price 

Success 

Price 

Maturity 

Market 

Intro 

Price 

Success 

Price 

Maturity 

Advanced Diesel 2008 2013 2018 2025 2006 2011 2016 2025

Incremental Vehicle Price ($) 1914 1393 1245 1258 2538 1775 1574 1547

Range 1.20 1.20 1.20 1.20 1.20 1.20 1.20 1.20

Maintenance Cost 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90

Acceleration 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Top Speed 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Luggage Space 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Fuel Economy 1.75 1.75 1.74 1.78 1.75 1.75 1.74 1.78

Diesel Hybrid 2013 2018 2023 2025 2012 2017 2022 2025

Incremental Vehicle Price ($) 2221 1642 1505 1508 2804 2051 1861 1854

Range 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25

Maintenance Cost 1.09 1.05 1.05 1.05 1.09 1.05 1.05 1.05

Acceleration 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90

Top Speed 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90

Luggage Space 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90

Fuel Economy 2.00 2.03 2.08 2.10 1.99 2.03 2.07 2.10

Gasoline Hybrid 2009 2014 2019 2025 2010 2015 2020 2025

Incremental Vehicle Price ($) 1903 1432 1335 1358 2416 1813 1668 1670

Range 1.25 1.25 1.25 1.25 1.25 1.25 1.25 1.25

Maintenance Cost 1.05 1.05 1.05 1.05 1.05 1.05 1.05 1.05

Acceleration 0.90 0.90 0.90 0.90 0.90 0.90 0.90 0.90

Top Speed 0.75 0.90 0.90 0.90 0.90 0.90 0.90 0.90

Luggage Space 0.90 1.00 1.00 1.00 1.00 1.00 1.00 1.00

Fuel Economy 1.75 1.79 1.84 1.89 1.75 1.80 1.85 1.89

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-8
Table F-1 (continued) 

2-SEATER MINI-COMPACT SUB-COMPACT 

Advanced Gasoline 2010 2015 2020 2025 2010 2015 2020 2025 2010 2015 2020 2025 2010 

Incremental Vehicle Price ($) 283 449 581 588 271 432 561 569 230 367 477 484 233 

Range

Maintenance CostAcceleration

Top Speed

Luggage Space

Fuel Economy 1.07 1.16 1.26 1.27 1.07 1.16 1.26 1.27 1.07 1.16 1.26 1.27 1.07 

MEDIUM CAR LARGE CAR 

Advanced Gasoline 2010 2015 2020 2025 2010 2015 2020 2025

Incremental Vehicle Price ($) 258 419 546 551 278 450 584 588Range

Maintenance Cost 

Acceleration

Top Speed 

Luggage Space 

Fuel Economy 1.07 1.16 1.26 1.27 1.07 1.16 1.26 1.27

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-9
Table F-1 (continued) 

SMALL SUV LARGE SUV SMALL TRUCK CA

Advanced Gasoline 2010 2015 2020 2025 2010 2015 2020 2025 2010 2015 2020 2025 2010 

Incremental Vehicle Price ($) 991 759 714 705 1242 946 887 873 627 649 732 730 767 

Range

Maintenance CostAcceleration

Top Speed

Luggage Space

Fuel Economy 1.27 1.29 1.31 1.31 1.27 1.29 1.31 1.31 1.16 1.25 1.34 1.34 1.16 

MINIVAN LARGE VAN 

Advanced Gasoline 2010 2015 2020 2025 2010 2015 2020 2025

Incremental Vehicle Price ($) 944 729 690 684 1201 917 858 841

Range

Maintenance Cost 

Acceleration

Top Speed 

Luggage Space 

Fuel Economy 1.27 1.29 1.31 1.31 1.27 1.29 1.31 1.31

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-10
Table F-2. ATV Attributes for Input to MARKAL 

(Units are ratios to the conventional gasoline vehicles of the specific year. Prices are in 2003 

Ratios to Conventional Vehicles

2010 2020 2025 2030 2035 2050

CARS Advanced Gasoline MPG 1.07 1.26 1.27 1.29 1.31 1.30

Incremental Price 1.022 1.019

Diesel MPG 1.36 1.48 1.49 1.51 1.53 1.64

Incremental Price 1.033 1.029

Gasoline HEV MPG 1.39 1.60 1.61 1.64 1.66 1.77

Incremental Price 1.039 1.020

Diesel HEV MPG 1.59 1.86 1.87 1.90 1.93 2.06

Incremental Price 1.047 1.030

LIGHT TRUCKS Advanced Gasoline MPG 1.23 1.32 1.32 1.32 1.32 1.25

Incremental Price 1.027 1.024

Diesel MPG 1.64 1.69 1.73 1.77 1.81 1.82

Incremental Price 1.045 1.028

Gasoline HEV MPG 1.68 1.80 1.85 1.89 1.94 1.94

Incremental Price 1.05 1.025

Diesel HEV MPG 1.92 2.01 2.07 2.12 2.17 2.17

IncrementalPrice 1.056 1.029

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-11
Table F-3. Vehicle Classes Used in Various Models 

Car Classes Light Truck Classes 

MARKAL Cars Light TrucksNEMS 2-

seater 

Minicompact Sub- compact Compact Medium Large Small SUV Large SUV Small Truck Cargo Truck Minivan Lar

Van 

PSAT Compact Midsize SUV Pickup 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-12
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-13

2.0 Heavy Vehicle Benefits Analysis Introduction 

The following sections describe the approach to estimating the fuel economies, incremental 

costs, and market penetration of heavy vehicles resulting from the Heavy Vehicle Technologies 

activities of the FreedomCAR and Vehicle Technologies Program of EERE, which are then 

provided as inputs to the NEMS and MARKAL models. It also describes how the oil savings 

benefits of these activities are estimated at a detailed level that the NEMS and MARKAL 

models cannot provide. The scope of the effort includes: 

• Characterizing baseline and advanced technology vehicles for Class 3–6 and Class 7 

and 8 trucks. Gross Vehicle Weights for these vehicle classes are as follows (Ref. 1): 

o Class 310,001 – 14,000 lbs 

o Class 4: 14,001 – 16,000 lbs 

o Class 5: 16,001 – 19,500 lbs 

o Class 6: 19, 501 – 26,000 lbs 

o Class 7 : 26,001 – 33,000 lbs 

o Class 8: 33,001 lbs and up. 

• Identification of technology goals associated with the DOE EERE programs, 

• Estimating the market potential of technologies that improve fuel efficiency and/or use 

alternative fuels, 

• Determining the petroleum savings associated with the advanced heavy vehicle 

technologies. These estimates are developed at the program element level to assist project 

prioritization by the FCVT program. These savings are slightly different from the savings 

generated by NEMS and MARKAL. 

In FY05, the Heavy Vehicles program activity expanded its technical involvement to more 

broadly address various sources of energy loss as compared to focusing more narrowly on engine 

efficiency and alternative fuels. This broadening of focus has continued in the activities planned 

for FY07. These changes are the result of a planning effort that occurred during FY04 and FY05 

(Ref. 2). 

This narrative describes characteristics of the heavy truck market as they relate to the analysis 

and provides a description of the analysis methodology—including a discussion of the models 

used to estimate market potential and benefits. The market penetration of advanced heavy 

vehicle technologies estimated here is then modeled as part of the EERE-wide integrated 

analysis (using NEMS and MARKAL) to provide final benefit estimates reported in the FY07 

Budget Request. 

2.1 Target Market: Heavy Vehicle Target Market 

“Heavy Vehicles” are defined in this analysis as including Classes 3 through 6 (Medium Trucks) 

and Classes 7 and 8 (Heavy Trucks). The Heavy Truck classes are further subdivided by end-use 

types: i.e., Long-Haul, Intermediate, and Local Use. Vehicle Inventory and Use Survey (VIUS) 

data were examined for all vehicles in use and vehicles two years old or less (Ref. 3). The Heavy 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-14

Truck vehicle market was then disaggregated into these three end-use types. The specific vehicle 

configurations grouped in each of the three types have similar patterns of travel and annual 

vehicle mile usage patterns. The vehicle type segments are made up of the vehicle configurations 

listed below: 

• Local Use (Type 1) – multistop, step van, beverage, utility, winch, crane, wrecker, 

logging, pipe, garbage collection, dump, and concrete delivery; 

• Intermediate Use (Type 2) – platform, livestock, auto transport, oil-field, grain, and tank; 

• Long-Haul (Type 3) – refrigerated van, drop frame van, open top van, and basic enclosed 

van. 

The lower speed and “stop and start” duty characteristics of Type 1 trucks greatly reduce the 

potential efficiency benefits in that sector compared to Types 2 and 3. For similar reasons, fuel 

economy improvements due to other speed-dependent measures such as improved tires will have 

lower benefit here than in the other two types. 

As compared to long distance, over the road travel, Type 2 vehicles tend to be used in a mix of 

local and regional delivery; and, as a result, will also realize greater fuel economy benefit from 

aerodynamic improvements than Type 1, but not as great as Type 3. Distances traveled by Type 

2 vehicles are typically greater than Type 1, which infers that the typical speeds are higher. 

These characteristics make them a somewhat better market sector for measures that perform in 

relation to speed such as advanced tires. In general, Type 3 vehicles are the best candidates for 

technologies that reduce drivetrain or vehicle losses. 

Refueling characteristics; i.e. central-source refueling or non-central source also are considered 

in the market characteristics, as centrally refueled vehicles would find an alternative fuel source 

more practical than vehicles that always refuel at road-side facilities. 

Eleven travel distance categories for medium trucks and heavy trucks are represented in the 

model. These categories were determined using travel distributions developed with the VIUS 

data by Oak Ridge National Laboratory (ORNL) (Refs. 3, 4). 

Exhibit 1 shows the distribution of annual travel for Class 3 through 6 and the three types of 

Class 7 and 8 vehicles. Type 3 vehicles display the greatest amount of annual travel of all heavy 

vehicle classes as is evidenced in part by the curve’s peaking in the 120,000- to 139,000-mile 

segment. 

Exhibit 2 shows the vehicle use pattern for Local or Type 1 Heavy trucks. The distributions 

based both on vehicles and vehicle-miles traveled are indicated. 

The contrast in distribution by type is evident when Exhibits 2 and 3 are compared. Exhibit 3 

shows the same information as Exhibit 2, but for Type 3 trucks. For Type 1, the distribution 

peaks in the 20,000- to 39,000-mile segment. For Type 3, the peak distribution shows annual 

travel of 100,000 miles greater than Type 1: 120,000 to 139,000 miles. 

Centrally refueled and non-centrally fueled vehicle use characteristics also have been analyzed. 

Centrally refueled vehicles travel less per year than non-centrally refueled vehicles. In the noncentrally refueled vehicle segment, the majority of travel occurs from 100,000 to 140,000 miles 

per year. In the central refueling segment, the majority of travel occurs in a more even 

distribution between 20,000 and 140,000 miles per year. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-15

Heavy vehicle market characteristics that are pertinent to the analysis are summarized in 

Exhibit 4. In the medium truck market segment (Classes 3 through 6), all vehicle types, with the 

exception of auto transport, travel about 20,000 miles per year on average. Heavy trucks, 

depending on type, travel an average of 40,000 miles to 92,000 miles per year. The base fuel 

economy for all 3 truck types was updated using VIUS 2002 data (Ref. 5). 

2.2 Key Factors Shaping Market Adoption of Technology 

Based on a survey conducted by the American Trucking Associations in 1997, energyconservation purchase decisions for this sector are significantly affected by economic viability—

specifically the payback of the investment (Ref. 6). The survey of 224 motor carriers revealed 

that paybacks of one to four years were acceptable for energy-conserving technologies. Based on 

those findings, we model the market acceptance of the various technologies based on payback 

performance. 

2.2.1 Effects of Lower Emissions on Heavy Vehicle Fuel Economy 

The Environmental Protection Agency (EPA) has initiated regulation of emissions from Heavy 

Trucks. This is changing engine technology and diesel fuel refining. Some reduction in fuel 

economy with the new engines is also expected as the combustion process optimization is 

addressing reduction of emissions. Normally, a requirement for reduced emissions will cause a 

decline in fuel economy. These changes will impose both operating and capital costs on truck 

operators. 

One such EPA rule addressed Ultra-low-Sulfur Diesel (ULSD). The ULSD rule is designed to 

lower the sulfur content of transportation diesel fuel produced by refineries by 2007. The 

content of other pollutants, including Nitrogen Oxides (NOX), Particulate Matter (PM) and 

Hydrocarbons (HC) are being reduced as well. 

These new standards have started to go into effect with 2004 engines and will continue on for 

model years 2007 and 2010 for highway vehicles, and later for other applications. Major 

elements of these rules include the following: 

• Reduce nitrogen oxides (NOx) and fine particulate matter PM2.5 from new heavy-duty 

highway diesels (e.g., trucks and buses) by about 90%, effective in 2007 for PM, and 

2007-2010 for NOx. 

• Reduce the sulfur content in highway diesel fuel to 15 ppm ("ultra-low sulfur diesel" fuel, 

or "ULSD" fuel) beginning in late 2006. 

• Reduce nitrogen oxides (NOx) and fine particulate matter PM2.5 from new heavy-duty 

nonroad diesels (e.g., construction, farming and logging equipment) by about 90%, 

effective in the 2011-2014 time frame depending on the pollutant and the size of engine. 

• Reduce the sulfur content in diesel fuel used in stationary engines in two steps, to 500 

ppm in 2007 and 15 ppm beginning in 2010. 

• Reduce the sulfur content in diesel fuel used in new locomotive and many marine engines 

in two steps, to 500 ppm in 2007 and 15 ppm beginning in 2012. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-16

The EPA rule-making process includes a cost analysis for the technologies required to meet the 

new standards. The costs for the new emission control technologies for the 2004 models 

assumed that fuel injection and turbocharger improvements would happen without the new 

standards. So in estimating increases in engine costs, the EPA excluded 50% of the technology 

cost from the total estimated cost. The incremental costs for heavy-duty engines were estimated 

at $803 in 2004, decreasing to $368 in 2009. The EPA also estimates the increase in annual 

operating cost for heavy-duty engines to be $104 for the maintenance of the exhaust gas 

recirculation (EGR). 

The effect of additional equipment that is used for treating emissions was also considered. The 

added weight of the equipment requires additional horsepower output from the engine, which 

results in a reduction in fuel efficiency. The EPA expects NOx adsorbers to be the most likely 

emission control technology applied by the industry. NOx adsorber regeneration will require 

small injections of diesel fuel for “light off” and desorption of stored NO for downstream 

catalysis under rich-burn conditions. This could result in additional fuel use beyond combustion 

for propulsion of 2-4%, depending on system maturity. The majority of the reduction in 

efficiency is associated with the control of sulfur-containing emissions (Ref. 7-9). 

2.3 Methodology and Calculations: Overview 

The analysis of the benefits expected from achieving the Heavy Vehicle technologies program 

goals was developed based on four primary reference sources: 

• Technology energy efficiency and fuel-use characteristics—as provided by the managers 

of the technology programs; 

• Vehicle characteristics and use information—as obtained from the 1997 VIUS. This 

provides information on both vehicle performance characteristics, such as fuel economy, 

and vehicle-use patterns such as miles traveled per year (Ref. 3); 

• Truck operator investment requirements—as provided by a survey of Owner-Operators 

performed by the American Trucking Associations in 1995 (Ref. 6); 

• Important “background” information such as energy prices and baseline technology fuel 

economies—as provided in the Annual Energy Outlook (Reference Case) prepared by the 

Energy Information Administration (Ref. 10). This information is used in the market 

penetration methodology which is needed to estimate future fuel economies. 

The methodology involves the definition of the energy conservation or displacement and cost 

attributes of the advanced technologies being fostered by the program, the characterization of the 

markets affected, and the estimation of the benefits. Several models are used. Specifically, initial 

benefits estimates are generated through the linkage of four spreadsheet models: (Refs 11-12). 

• HTEB - Heavy Truck Energy Balance Model (Version 2.0) 

• TRUCK 2.0 - Heavy Vehicle Market Penetration Model 

• VISION 2005, and 

• Heavy Truck Summary (HVS) report generator. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-17

 

The relationship of these four models is indicated in Exhibit 5.1

 Cost estimates are developed 

separately. 

The Heavy Truck Energy Balance Model (HTEBM) was developed to assess the overall fuel 

economy effect of several changes to the vehicle involving both the engine and other elements of 

the vehicle. It takes into account energy losses based on user selected inputs of vehicle use. It is a 

steady-state model. It was required as a result of the lack of existence of publicly available 

vehicle simulation tool. The fuel economies of new advanced heavy vehicle technologies 

estimated with the HTEB model are presented in Exhibit 6. 

The price estimates for these vehicles are also presented in Exhibit 6. All prices are in 2003 

dollars. Technology cost is not really estimated, any assumed added cost is selected to have a 

two year payback. As an example, the price schedule for the Exhibit 6 technologies in the Long 

Haul vehicle application is indicated in Exhibit 7. This process was replicated for Medium 

Trucks to develop similar cost estimates. 

The values for fuel economy improvement from HTEBM and cost are then input to TRUCK 2.0.

This model was developed to estimate the potential market impacts of new technologies on the 

medium and heavy truck market. The results generated by this model are: 

• Market penetrations, in units of percent of new vehicles sold for each type and class of 

vehicle, and 

• Composite fuel economy rating (new mpg) of the vehicles sold, for each truck type. 

As discussed, the TRUCK 2.0 model estimates market penetration based on the costeffectiveness of the new technology. Cost-effectiveness is measured as the incremental cost of 

the new technology less the expected energy savings of that technology over a specified time 

period in relation to specified payback periods. 

Exhibit 8 shows the payback distribution assumed in the TRUCK model. This payback 

distribution was generated from the American Trucking Association’s survey described above 

(Ref. 6). The survey found that, for example, 16.4% of the truck operators responding require a 

payback of one year on an investment. The TRUCK model market penetration calculation 

method for Class 7 and 8, Type 1 vehicles is described in Exhibit 9. 

The market penetration results are supplied through a link to the VISION model (Ref. 11). The 

VISION model is used to estimate preliminary or first order oil/energy use and CO2 emissions 

from highway vehicles through 2050 by program element. It contains a baseline estimate of 

heavy vehicle energy use to 2050. Through 2025 that baseline is the same as that of the AEO. 

1 The HTEB was developed by William Shadis and James Moore of TA Engineering. The TRUCK (2.0) Model was developed as 

a collaborative effort, initially by John Maples of Oak Ridge National Laboratory (ORNL), with assistance from James Moore, of 

TA Engineering, Inc. Subsequent enhancements have been performed by Shadis and Moore (TA Engineering). The Vision 

model was developed by Maples, Anant Vyas and Margaret Singh of ANL. The Heavy Truck Summary Model is a 

report generating spreadsheet. It was initially developed by Maples, and has subsequently been modified by TA 

Engineering.
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-18

For the period from 2026 to 2050 the baseline energy use is very similar to that of MARKAL. 

By inputting the market penetration and fuel economy of the advanced heavy vehicle 

technologies into the model, an alternative estimate of future heavy vehicle energy use is 

generated and benefits relative to the baseline can be estimated. 

Since VISION does not disaggregate Types 1-3 Heavy Trucks or Hybrid-Non-hybrid Medium

Trucks, the fuel economy multipliers generated by Truck 2.0 are aggregated on both a sales and 

VMT-weighted basis for input to VISION. These aggregated fuel economy multipliers are 

provided in Exhibit 10. They are also adjusted to take into account differences in baseline fuel 

economies provided in VIUS (used in TRUCK 2.0) and the AEO (used in VISION). These 

factors and the market penetration estimates also presented in Exhibit 10 are the factors 

ultimately used in the EERE-wide integrated analysis. More specifically, the factors in cells 

that are highlighted in yellow are provided for input to the NEMS and MARKAL models.

Finally, the Heavy Truck Summary report generator summarizes the first order benefits for the 

period covering 2000 through 2050. Benefits (that are used by the FCVT program) include the 

following: 

• Heavy Truck Petroleum Use and Savings, by Class 3-6 and Class 7-8, Million BPD 

• Heavy Truck Petroleum Savings - % 

• Class 7&8 Truck Savings by Program Element (Technology), Million BPD 

• Local Use Truck Savings by Program Element (Technology), Million BPD 

• Intermediate Truck Savings by Program Element (Technology), Million BPD 

• Long-Haul Truck Savings by Program Element (Technology), Million BPD. 

These first order benefits have been generated and will be reported in a forthcoming report. The 

benefits by FreedomCAR Program Element can not be generated by the NEMS and MARKAL 

models, and are, therefore, generated by the TRUCK and VISION models. 

2.4 Heavy Truck Energy Use Models: Workbooks, Inputs and Outputs 

Specific workbooks used in the modeling system are listed below. Exhibit 11 provides a detailed 

view of the relationships among the four principal models. In practice, calendar dates indicating 

times of use are added to the file names for specific Energy Benefits analysis exercises, but these 

are omitted in this discussion. 

1. Heavy Truck Energy Balance Model (HTEBM)-Version 2.0 

• Energy Balance Workbook-Baseline Model 

• Energy Balance Workbook-Technology Model(s) (copied from the Baseline 

Model) 

• Combined –Effects (used to allocate fuel savings among several technologies). 

2. TRUCK (Market Penetration) Models 

• TRUCK-2 Type 1 (projects market penetration of Class 7&8, Type 1 heavy trucks to 

2050). 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-19

• TRUCK-2 Type 2 (projects market penetration of Class 7&8, Type 2 heavy trucks to 

2050). 

• TRUCK-2 Type 3 (projects market penetration of Class 7&8, Type 3 heavy trucks to 

2050). 

• TRUCK-2 Type M (projects market penetration of Classes 3-6 Type heavy trucks to 

2050). 

• TRUCK-2 Composite (combines all Type 1, 2, 3, M results to obtain summary 

market penetrations and fleet average fuel economies). 

3. VISION MODELS 

• VISION 2005 AEO ICE MPG Base Case (projects energy use of baseline truck fleet 

to 2050). 

• VISION GPRA0 7Veh.Mi-1 (projects energy use of improved truck fleet to 2050).

4. HvyTrkSum-GPRA-V1 mkt pen veh mi (calculates energy and carbon savings-total heavy 

truck fleet, classes 3-8, to 2050). 

All workbooks should be copied into the same hard-drive subdirectory and all should be loaded

so that all of the links are active during the data entry-calculation process. 

2.4.1 HTEBM (Heavy Truck Energy Balance Model) Version 2.0 

The Heavy Truck Energy Balance Model is based on a simplified calculation of average road 

loads experienced by typical heavy trucks. It calculates an average fuel economy that balances 

the truck engine output with the needs to meet engine friction, accessory loads, auxiliary loads 

and road loads (rolling resistance, aerodynamic resistance, and vehicle braking loads). The 

model is a method to match baseline vehicles with actual road-load fuel economy results and 

then to estimate the variations in fuel economy that will occur when various engine and vehicle 

operational characteristics are changed. Therefore, it is important that actual, simulation-based, 

or program goals for road-load vehicle fuel economy values be available. 

Fuel savings are caused by a combination of technologies-load reducing technologies and engine 

efficiency-increasing technologies. Each technology under consideration and each analysis year 

requires a separate run of HTEBM. Since each run includes both input assumptions and results, 

they need to be maintained for adequate support and documentation. 

Engine/Vehicle improvements that lead to reduced fuel use can be categorized under the 

following headings. 

• Increased engine cycle efficiency 

• Increase compression ratio 

• Reduced engine thermal losses 

• Reduced engine internal friction loads 

• Air-Breathing Losses 

• Pistons & Piston Rings 

• Rod and crankshaft bearings 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-20

• Valve train/camshaft 

• Reduced engine accessory loads 

• Fuel Injector 

• Power Steering 

• Oil Pump 

• Coolant Pump

• Engine fan 

• Reduced drive-train parasitic loads 

• Transmission 

• Driveshaft 

• Axle/Transaxle 

• Differential 

• Axle & Wheel bearings 

• Brake Drag 

• Reduced vehicle auxiliary system loads 

• Alternator 

• Air Conditioner 

• Air Brake Compressor 

• Reduced road-loads 

• Aerodynamic loads 

• Rolling resistance loads 

• Braking loads.

For the Government Performance and Results Act (GPRA), vehicle characteristics to support 

fuel economy goals at 10-year increments are developed (2010, 2020, 2030, 2040, and 2050). 

 “Combined Effects” Workbook

The results of the multiple runs of HTEBM are collected in this summary workbook. Whereas 

HTEBM permits only one set of conditions per-run, “Combined Effects” can store any number 

of HTEBM results. 

The Combined Effects Submodel is used to allocate the fuel savings among the several 

technologies included in the Truck Technology option. This is done by assuming that the 

percentage of fuel savings attributable to each separate technology will be proportional to the 

relative fuel economy improvement of each separate technology, taken separately. 

Currently, “Combined Effects” includes four individual heavy vehicle technologies (accessory 

loads reduction, engine efficiency increase, vehicle weight reduction, and aerodynamic drag 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-21

reduction). These can be varied to other technologies or Technology Program definitions by the 

user, if desired.

2.4.2 TRUCK 2.0 Market Penetration Models 

The fuel-saving technologies under analysis are characterized in the TRUCK 2.0 models in terms 

of the projected fuel economy improvement ratio (new fuel economy divided by the baseline fuel 

economy), the installed cost of the improvement ($ per vehicle), and the cost of the fuel type 

being used. Market penetration occurs for technologies that meet payback values of 4 years or 

less. If technology cost information is not available, cost equivalent to a two-year payback is 

assumed. TRUCK 2.0 can be set to assume the following heavy truck fuels: diesel fuel, gasoline, 

liquefied propane gas (LPG), ethanol, compressed natural gas (CNG), or electricity (battery 

storage). 

The output from the TRUCK 2.0 Models for each truck Type is a projection of market 

penetration rates (percent of new vehicle sales) by class and type over the future time from 

current through year 2050 (or shorter if modeled for a shorter time period). The absolute number 

of trucks projected to be equipped with the new technology is calculated in the VISION model 

(see below). 

• “TRUCK Composite” Submodel 

This model collects the market penetration data from the four TRUCK models. It was created as 

a separate workbook since the TRUCK models are all driven by macros and with distinct inputs. 

The market penetration and fuel economy results for each of the truck types are linked to this 

workbook.

2.4.3 VISION Models 

• VISION Base Case Model

The VISION models accept average new fleet MPG values for Class 3-6 and Class 7 & 8 

vehicles and calculate the amount of fuel used each year as these vehicles mature, age and 

eventually wear out within the operating fleet. Calculations are made for the years 2000 to 2050. 

• VISION Enhanced Case Model 

This version of VISION calculates the fleet energy use assuming that the proposed technologies 

(fuel savings technologies) are introduced into the new vehicle fleet as calculated by the TRUCK 

models. Fuel economy and market penetration results from the TRUCK models are consolidated 

into a single value (for each year to 2050) for Class 7 and 8, and a single value for Classes 3 

through 6, using VMT data to weight the fuel economies of each truck Type.

2.4.4 Heavy Truck Summary Submodel (HvyTrkSum) 

Key inputs and results of the Truck Model analysis are summarized in the HvyTrkSum 

workbook. The format used here is intended to meet the needs and requirements of the 

FutureCar and Vehicle Technologies program, as well as the Planning and Evaluation Office. 

HvyTrkSum results form the basis of the GPRA and related reports generated annually 

presenting the benefits of the Heavy Truck program elements. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-22

2.5 Sources 

1. TRANSPORTATION ENERGY DATA BOOK:EDITION 24. Stacy C. Davis, Susan W. 

Diegel. Oak Ridge National Laboratory. December 2004 

2. FreedomCAR and Vehicle Technologies R & D Plan (Draft), August 22, 2003. 

3. “1997 Vehicle Inventory and Use Survey,” EC97TV-US U.S. Bureau of the Census, 

Washington, D. C., 1999. 

4. Personal Communication with Stacy Davis, ORNL, November 2001 

5. Personal Communication with Stacy Davis, ORNL, June 2005 

6. “1997 Return on Investment Survey,” American Trucking Association, Arlington Va., 1997. 

7. Diesel – Clean Air Task Force (CATF): http://www.catf.us/projects/diesel/rulemaking.php

8. The Transition to Ultra-Low-Sulfur Diesel Fuel: Effects on Prices and Supply 2. Efficiency 

and Cost Impacts of Emission Control Technologies: 

http://www.eia.doe.gov/oiaf/servicerpt/ulsd/chapter2.html

9. The Potential Effect of Future Energy-Efficiency and Emissions-Improving Technologies on 

Fuel Consumption of Heavy Trucks. A. Vyas, C. Saricks, and F. Stodolsky. Argonne 

National Laboratory. August 2002. 

10. “Annual Energy Outlook 2004, With Projections to 2030,” Energy Information Agency, 

Department of Energy, Washington, D. C., (Web site address: 

http://www.eia.doe.gov/bookshelf.html Library/Archives-Forecasting). 

11. Singh, M.; A. Vyas, and E. Steiner, “VISION Model: Description of Model Used to Estimate 

the Impact of Highway Vehicle Technologies and Fuels on Energy Use and Carbon 

Emissions to 2050,” ANL/ESD/04-1 (Dec. 2003). 

12. FreedomCAR and Vehicle Technologies Heavy Vehicle Program FY 2006 Benefits 

Analysis: Methodology and Results -- Final Report. (ANL Report No. 05/60) James Moore, 

Bill Shadis. TA Engineering, Inc. November 2005. 
Exhibit 1: Annual Miles Traveled for Four Truck Categories, 1997 

-10%

0%

10%

20%

30%

40%

50%

60%

0-19.9 20-39.9 40-59.9 60-79.9 80-99.9 100-

119.9

120-

139.9

140-

159.9

160-

179.9

180-

199.9

200+

1,000 mi/yr per vehicle

Percent of Vehicles

Type M (Medium-Class 3-6)

Type 1 (Class 7&8)

Type 2 (Class 7&8)

Type 3 (Class 

7&8)

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-23
Exhibit 2: Type 1 Vehicle Use 

Distribution of Type 1 Vehicles and VMT by Annual Miles Per Year

0%

5%

10%

15%

20%

25%

30%

35%

40%

0-19.9 20-39.9 40-59.9 60-79.9 80-99.9 100-

119.9

120-

139.9

140-

159.9

160-

179.9

180-

199.9

200+

Miles Per Year (survey data)

Sample Precentage

Vehicle Miles Distribution

Type 1

Vehicle Distribution

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-24
Exhibit 3: Type 3 Vehicle Use 

Distribution of Type 3 Vehicles and VMT by Annual Miles Per Year

0%

5%

10%

15%

20%

25%

30%

0-19.9 20-39.9 40-59.9 60-79.9 80-99.9 100-

119.9

120-

139.9

140-

159.9

160-

179.9

180-

199.9

200+

Miles Per Year (survey data)

Sample Precentag

e

Vehicle-Miles Distribution

Type 3 

Vehicle Distribution

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-25
Exhibit 4: Heavy Vehicle Characteristics (1997) 

Vehicle Type Class 7 & 8,

Type 1

Class 7 & 8,

Type 2

Class 7 & 8,

Type 3

Class 3 

through 6 Comments

Body Types Note 1 Note 2 Note 3

Fuel Economy 

(Baseline) 5.60 5.60 5.90 8.90

Fuel Economy 

Improvement, % 146% 164% 179% 170%

Combined effect of FCVT 

Technologies, 2020; 

Class 3 thru 6 is w/o Hybrid

Average Miles 

Traveled, miles 40,043 74,066 92,434 20,126

Portion of Heavy 

Truck Fuel Use, % 11.2% 19.6% 52.9% 4.3% Estimated--Year 2005

Portion of Vehicle 

Travel < 50 k Miles, 47% 14.3% 1.6% 73.6%

Portion of Vehicle 

Travel 50 k to 100 k 44% 43.5% 35.7% 24.4%

Portion of Vehicle 

Travel >100 k 9% 42.2% 53.8% 2.0%

Note 1:

Note 2:

Note 3:

Local Use (Type 1) – multi-stop, step van, beverage, utility, winch, crane, wrecker, 

logging, pipe, garbage collection, dump, and concrete delivery

Intermediate Use (Type 2) – platform, livestock, auto transport, oil-field, grain, and 

tank;

Long-Haul (Type 3) – refrigerated van, drop frame van, open top van, and basic 

enclosed van.

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-26
Exhibit 5: Heavy Truck Benefits Analysis Models 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-27

VISION 2.0

Heavy Truck 

Summary 

(Benefits Report 

Generator)

HTEB

(Heavy Truck

Energy Balance)

Truck 2.0

(Heavy Truck

Market 

Penetration)
Exhibit 6: Advanced Heavy Vehicle Characterization - New Vehicles 

(prices are in 2003 dollars) 

Characteristic 2010 2020 2030 2040 2050

1

Fuel Economy Class 7-8, 

Local Travel (Type 1)

mpg Multiplier

1.20 1.51 1.53 1.54 1.54 

2

Fuel Economy Class 7-8, 

Intermediate Travel (Type 

2)

mpg Multiplier

1.21 1.53 1.56 1.57 1.57 

3

Fuel Economy Class 7-8, 

Long Haul Travel (Type 3)

mpg Multiplier

1.25 1.59 1.63 1.63 1.63 

4 Fuel Economy Class 3-6-

Hybrid, mpg Multiplier 1.41 1.61 1.61 1.61 1.61 

5 Fuel Economy Class 3-6-

Non-hybrid, mpg Multiplier 1.20 1.46 1.48 1.48 1.48 

6 Class 7-8, Iincremental 

Cost, $ $ 40,000 $ 20,000 $ 10,000 $ 7,000 $ 7,000 

7 Class 3-6 Hybrid, 

Incremental Cost, $ $ 19,000 $ 5,400 $ 2,700 $ 2,700 $ 2,700 

8 Class 3-6 Nonhybrid, 

Incremental Cost, $ $ 5,400 $ 1,700 $ 1,700 $ 1,700 $ 1,700 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-28
Exhibit 7: Example Price and Efficiency Schedule for Advanced Technologies 

(2003 dollars) 

Non-Hybrid Technologies

Diesel Fuel (only)

Year

Baseline 

Vehicle Cost 

($)

Gross 1st 

Cost ($) Efficiency Ratio

2000 150,000 0 1.000

2005 150,000 45,000 1.200

2010 150,000 40000 1.250

2015 150,000 30000 1.350

2020 150,000 20000 1.590

2025 150,000 15000 1.610

2030 150,000 10000 1.630

2035 150,000 7,600 1.630

2040 150,000 7,000 1.630

2045 150,000 7,000 1.630

2050 150,000 7,000 1.630

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-29
Exhibit 8: ATA Survey Payback Preference Distribution 

Number of 

Years

Percent of 

Motor Carriers

1 16.4%

2 61.7%

3 15.5%

4 6.4%

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-30
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-31

Exhibit 9: Truck Payback Algorithm—Type 1 Trucks

Spreadsheet Location Description Comments

Column A Year Identifies year for which values, calcuations and results are representative.

Columns B - F Fuel Economy by Technology Values are developed based on baseline technology mpg assumptions and 

efficiency ratios for advanced technologies.

Column G Cost of Alternative Fuel in $/GGE Links to Fuel Prices Page

Columns H - I Calculates annual savings for 2 alternative 

technologies

For Advanced Diesel: 

(VMT(C10)x$/GGE/Baseline MPG - VMT x $/GGE/Adv. Diesel MPG)

Columns J - M Calculates Net Present Value of Savings for 

'Advanced Diesel' Column J: 1 Year, K: 2 years, L: 3 years; M: 4 years

Columns N - Q Calculates Net Present Value of Savings for 

'Alternative Fuel Technology' Column N: 1 Year, O: 2 years, P: 3 years; Q: 4 years

Columns R - U If-then Statement to determine 'Cost Effectiveness 

Factor' (CEF)

If NPV of savings is > Cost of Technology, cell value is (cost - 

NPVSavings)/Cost; Otherwise cell value is 0. Columns are for paybacks of 1, 2, 

3, and 4 years.

Column V Technology purchase cost 'Alternative Fuel 

Technology' Values are linked to Cost values on 'Inputs' page.

Column W - Z Repeats calcuations in Columns R through U for 

'Alternative Fuel Technology'

Column AA If-then Statement to determine 'Technology 

Adoption Factor' (TAF) for 'Advanced Diesel'

If 'Cost Effectiveness Factor' for Year 1 PB is 0, cell value = 100; Otherwise (100-

((exp(1995 CE Factor-Current Yr. Factor) - 1)/10 x 100)

Column AB Continuation of TAF Calculation for Year 1 Payback 

market If AA<0, cell value is 1; Otherwise the Value is the same as AA.

Columns AC + AD Repeat AA and AB for 2 year payback market

Columns AE + AF Repeat AA and AB for 3 year payback market

Columns AG + AH Repeat AA and AB for 4 year payback market

Columns AI - AP Repeat Columns AA through AH methodology for 

'Alt. Fuel Technology'

Column AQ If-then statement. Start of Market Penetration for 

'Advanced Diesel'

If AB = 100, then cell value is 0; Otherwise cell value is 

(1/(1+Abvalue/exp(-2 x Col. R CEF for 1 Year PB))

Column AR Same as AQ, but for 2 year PB market.

Column AS Same as AQ, but for 3 year PB market.

Column AT Same as AQ, but for 4 year PB market.

Column AU Final, Step 1; Weighted average market penetration 

for year 1 through year 4 markets weighting factors

Weighting factors are based on ATA survey results and are listed at the top of 

Columns AQ-AT.

Column AV

Final, Step 2: Reduces Market Penetration to 

account for market penetration of 'Atl. Fuel 

Technology' and stay below 100% share.

=+(AU+(1-BA)*AU)/2

Columns AW - AZ Same as columns AQ - AT for 'Alterntive fuel 

technology'.

Column BA

Final, Step 1; For 'Alt. Fuel Tech.', weighted average 

market penetration for year 1 through year 4 

markets weighting factors

Column BB

Final, Step 2: Reduces Market Penetration to 

account for market penetration of 'Atl. Fuel 

Technology' and stay below 100% share.

Columns BD - BN Macro Results Array-Centrally Refueled Advanced 

Diesels Central1 Macro results are printed in this part of spreadsheet

BO

Final Step 3: 'Advanced Diesel' (Centrally Refueled) 

Summation of %VMT that is centrally refueled for 

the VMT range (e.g. 0-19.9k)* % Market penetration 

for BD - BN array. 

Results are linked to Market Penetration Page

Columns BQ - CA Macro Results Array-Centrally Refueled Alternative 

Fuels

Macro results are printed in this part of spreadsheet. Alt Fuel technology only 

competes in Centrally Refueled Segment

CB

Final Step 3: 'Alt. Fuel' Summation of %VMT that is 

centrally refueled for the VMT range (e.g. 0-19.9k)* 

% Market penetration for BD - BN array. 

Results are linked to Market Penetration Page

Columns CD - CN Macro Results Array-Non Centrally Refueled 

Advanced Diesels Macro results are printed in this part of spreadsheet

CO

Final Step 3: 'Advanced Diesel' (Non-centrally 

refueled) Summation of %VMT that is centrally 

refueled for the VMT range (e.g. 0-19.9k)* % Market 

penetration for BD - BN array. 

Results are linked to Market Penetration Page
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-32

Exhibit 10: Advanced Heavy Vehicle Market Penetration 

and Fuel Economy Results for NEMS Modeling

Class 3 - 6

Year

Combined 

Market 

Penetration,

% VMT

Base MPG

(VISION) in 

gasoline 

equivalent 

gallons

Fuel 

Economy for 

All New 

Technology 

Sales,

mpg

Fuel 

Economy 

Multiplier 

only for 

trucks with 

new 

techology 

which 

achieve the 

market 

penetration 

shown in 

Column 2

Estimate of 

fuel economy 

for all new 7-8 

trucks

Estimate of X 

factor to input 

to VISION 

(only those 

for 2010, 

2020, 2030, 

2040 + 2050 

are input)

Efficiency, % 

VMT

Hybrid, % 

VMT

Combined 

Market 

Penetration, 

% VMT

Base MPG

(VISION) in 

gasoline 

equivalent 

gallons

Fuel 

Economy for 

All New 

Technology 

Sales,

mpg

Fuel 

Economy 

Multiplier 

only for 

trucks with 

new 

techology 

which 

achieve the 

market 

penetration 

shown in 

Column 6

Estimate of 

fuel 

economy for 

all new 3-6 

trucks

Estimate of 

X factor to 

input to 

VISION 

(only those 

for 2010, 

2020, 2030, 

2040 + 2050 

are input)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

2010 0% 5.64 6.97 1.24 5.639 1.00 0% 0% 0% 8.54 10.27 1.20 8.54 1.00

2011 0% 5.69 7.15 1.27 5.69 1.00 0% 0% 0% 8.55 10.49 1.22 8.55 1.00

2012 0% 5.76 7.34 1.30 5.76 1.00 0% 0% 0% 8.55 10.72 1.25 8.55 1.00

2013 0% 5.85 7.53 1.34 5.85 1.00 0% 0% 0% 8.55 10.94 1.28 8.55 1.00

2014 0% 5.96 7.71 1.37 5.97 1.00 0% 0% 0% 8.55 11.17 1.30 8.56 1.00

2015 1% 6.08 7.90 1.40 6.09 1.00 1% 0% 1% 8.56 11.40 1.33 8.58 1.00

2016 2% 6.12 8.08 1.43 6.15 1.00 1% 0% 1% 8.57 11.62 1.35 8.60 1.00

2017 2% 6.18 8.27 1.47 6.22 1.01 3% 0% 3% 8.58 11.85 1.38 8.65 1.01

2018 4% 6.25 8.46 1.50 6.31 1.01 4% 0% 4% 8.58 12.07 1.41 8.69 1.01

2019 7% 6.25 8.64 1.53 6.36 1.02 13% 0% 13% 8.58 12.30 1.43 8.93 1.04

2020 12% 6.25 8.83 1.57 6.48 1.04 15% 0% 15% 8.59 12.53 1.46 9.02 1.05

2021 14% 6.26 8.85 1.57 6.53 1.04 17% 0% 17% 8.59 12.54 1.46 9.08 1.06

2022 18% 6.28 8.87 1.57 6.63 1.06 22% 0% 22% 8.59 12.56 1.46 9.24 1.08

2023 21% 6.30 8.89 1.58 6.72 1.07 24% 0% 24% 8.59 12.57 1.47 9.30 1.08

2024 24% 6.31 8.91 1.58 6.79 1.08 23% 1% 24% 8.59 12.59 1.47 9.31 1.08

2025 31% 6.33 8.93 1.58 6.96 1.10 23% 2% 25% 8.59 12.60 1.47 9.34 1.09

2026 32% 6.34 8.95 1.59 7.00 1.10 25% 2% 27% 8.59 12.62 1.47 9.41 1.09

2027 47% 6.36 8.97 1.59 7.37 1.16 29% 3% 32% 8.59 12.64 1.47 9.56 1.11

2028 48% 6.38 8.99 1.60 7.42 1.16 35% 6% 40% 8.59 12.65 1.47 9.87 1.15

2029 59% 6.39 9.01 1.60 7.70 1.21 35% 7% 41% 8.59 12.67 1.48 9.91 1.15

2030 61% 6.41 9.03 1.60 7.78 1.21 40% 7% 47% 8.59 12.68 1.48 10.13 1.18

2031 62% 6.43 9.04 1.60 7.83 1.22 40% 8% 47% 8.59 12.69 1.48 10.14 1.18

2032 68% 6.44 9.04 1.60 8.00 1.24 36% 12% 48% 8.60 12.69 1.48 10.18 1.18

2033 68% 6.46 9.04 1.60 8.02 1.24 36% 13% 48% 8.60 12.70 1.48 10.19 1.19

2034 70% 6.48 9.05 1.60 8.07 1.25 34% 17% 51% 8.60 12.70 1.48 10.28 1.20

2035 70% 6.49 9.05 1.61 8.11 1.25 29% 25% 54% 8.60 12.71 1.48 10.43 1.21

2036 71% 6.51 9.05 1.61 8.12 1.25 29% 28% 57% 8.60 12.71 1.48 10.55 1.23

2037 71% 6.53 9.06 1.61 8.13 1.25 29% 28% 57% 8.60 12.72 1.48 10.57 1.23

2038 71% 6.54 9.06 1.61 8.15 1.25 29% 29% 58% 8.60 12.72 1.48 10.59 1.23

2039 71% 6.56 9.06 1.61 8.16 1.24 30% 29% 59% 8.60 12.73 1.48 10.63 1.24

2040 71% 6.58 9.07 1.61 8.18 1.24 33% 29% 62% 8.60 12.73 1.48 10.76 1.25

2041 71% 6.60 9.07 1.61 8.19 1.24 48% 27% 76% 8.60 12.73 1.48 11.39 1.32

2042 72% 6.61 9.07 1.61 8.20 1.24 43% 35% 78% 8.60 12.73 1.48 11.51 1.34

2043 72% 6.63 9.07 1.61 8.21 1.24 41% 38% 79% 8.60 12.73 1.48 11.58 1.35

2044 72% 6.65 9.07 1.61 8.23 1.24 40% 39% 79% 8.61 12.73 1.48 11.58 1.35

2045 72% 6.66 9.07 1.61 8.24 1.24 40% 39% 79% 8.61 12.73 1.48 11.59 1.35

2046 73% 6.68 9.07 1.61 8.27 1.24 40% 39% 79% 8.61 12.73 1.48 11.59 1.35

2047 73% 6.70 9.07 1.61 8.29 1.24 40% 40% 80% 8.61 12.73 1.48 11.59 1.35

2048 74% 6.72 9.07 1.61 8.30 1.24 40% 40% 80% 8.61 12.73 1.48 11.60 1.35

2049 74% 6.73 9.07 1.61 8.31 1.24 40% 40% 80% 8.61 12.73 1.48 11.61 1.35

2050 74% 6.75 9.07 1.61 8.33 1.23 41% 40% 81% 8.61 12.73 1.48 11.65 1.35
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2006-FY 2050) 

Appendix F – Vehicle Technologies Program – Page F-33

Exhibit 11: Heavy Truck Energy Modeling System Details 

Inputs

Outputs from each 

TRUCK model 

Types 1,2,3,M

Outputs

Market 

Penetration Rates 

(% of new vehicle sales) 

New Fleet MPG

“TRUCK Models

Types 1, 2, 3, M”

“TRUCK 

Composite”

Inputs

Miles/yr/truck

Fuel Cost

MPG base & Enhanced

Enhancement cost $

Outputs

Market 

Penetration Rates 

(% of new vehicle sales) 

New Fleet MPG 

Each Type

“Heavy Truck

Energy Balance 

Models” (HTEBM)

“Combined

Effects”

Inputs

Engine Power

Thermal Efficiency

Engine Parasitics

Vehicle Parasitics

Braking Loads

Rolling Resistance

Aero Resistance

Outputs

Fuel Economy

Inputs

Fuel Economy of 

individual TEBM runs.

Outputs

Summary of many 

TEBM runs

“VISION”

Base Case

Inputs

Vehicle production 

rate/yr, miles/veh/yr, 

Fuel Type 

MPG

Outputs

Total Annual 

Fuel Use + 

Environmental 

Factors

Inputs

Vehicle production 

rate/yr, miles/veh/yr, 

Fuel Type 

MPG

Outputs

Total Annual 

Fuel Use + 

Environmental 

Factors

“VISION”

Enhanced Case

HvyTruckSum

Heavy Truck Summary

(Report Generator)
Appendix G – GPRA07 Building Technologies (BT) Program 

Documentation 

1.0 Residential Buildings Integration G-3

1.1 Residential Building Energy Codes G-3

1.2 Research and Development: Building America G-6

2.0 Commercial Buildings Integration G-11

2.1 Commercial Building Energy Codes G-11

2.2 Research and Development G-14

3.0 Equipment Standards and Analysis G-18

3.1 Electric Motors, 1-200 HP G-18

3.2 HID Lamps G-21

3.3 Distribution Transformers G-23

4.0 Emerging Technologies G-26

4.1 Analysis Tools and Design Strategies G-26

4.2 Appliances and Emerging Technologies R&D: Solid State Lighting Market Acceptance G-28

4.3 Envelope Research and Development: Windows G-30

4.4 Envelope Research and Development: Thermal G-41

4.5 Lighting Research and Development G-47

4.6 Space Conditioning and Refrigeration R&D G-51

5.0 Technology Validation and Market Introduction G-55

5.1 Rebuild America G-55

5.2 Energy Star Program G-57

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-1
Introduction 

The mission of the Building Technologies Program is to develop technologies, techniques, and 

tools for making residential and commercial buildings more energy efficient, productive, and 

affordable. Table G-1 outlines the activities characterized for the GPRA07 Building 

Technologies Program. Characterizations and inputs for these activities were provided to the 

Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy (EERE) 

as inputs to EERE’s integrated modeling effort. Between the time that the original activity list 

was developed for the integrated modeling process and the time that the final budget request 

was submitted, a small number of activities were added into, subtracted from, or moved within 

the request; however, these changes were not reflected within the modeling process. The 

specific impact of these changes on the integrated benefits estimates is not known, but would be 

expected to be minimal. 

Table G-1. Building Technologies Subprograms, Projects, and Activities 

Subprogram Project Activity 

Research & Development: 

Building America Research & Development: Building America 

Residential Buildings Integration Residential Building Energy 

Codes Residential Building Energy Codes 

Research & Development Research & Development 

Commercial Buildings Integration Commercial Building 

Energy Codes Commercial Building Energy Codes 

Lighting R&D Lighting R&D: Solid-State Lighting 

Lighting R&D: Controls * 

Refrigeration R&D: Hy-Pak MA *

Refrigeration R&D: Thermotunneling 

Based Cooling 

Space Conditioning & 

Refrigeration R&D

Refrigeration R&D: Integrated Heat Pump 

Appliances & Emerging 

Technologies R&D 

Appliances & Emerging Tech R&D: SolidState Lighting Market Acceptance ** 

Window Technologies: Electrochromic 

Windows 

Window Technologies: Superwindows 

Window Technologies: Low-E Market 

Acceptance 

Thermal Technologies: Advanced Wall 

Systems 

Thermal Technologies: Next Generation 

Attic Systems 

Building Envelope R&D 

Thermal Technologies: Next Generation 

Envelope Materials 

Emerging Technologies 

Analysis Tools and Design 

Strategies 

Analysis Tools and Design Strategies 

Standards: Electric Motors, 1-200 HP 

Standards: HID Lamps Equipment Standards and 

Analysis 

Equipment Standards and 

Analysis # 

Standards: Distribution Transformers 

Rebuild America Rebuild America 

Energy Star: Clothes Washers 

Technology Validation and Market 

Introduction Energy Star 

Energy Star: Refrigerators 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-2
Energy Star: Room Air Conditioners 

Energy Star: Dishwashers 

Energy Star: CFLs 

Energy Star: Windows 

Energy Star: Home Performance 

* activities that were not funded in the final FY07 budget request 

** activity that was moved to Lighting R&D 

# excludes other Standards activities that were added following the passage of the Energy Policy Act of 2005 

Often such analysis requires the development and use of enabling or simplifying assumptions. 

In many cases, no citable sources exist for substantiating assumptions. Therefore, assumptions 

are developed through an iterative process with project managers, project contractors, and 

GPRA analysts. Often, we base these assumptions on project knowledge and experience, as 

there are varying degrees of corroborative studies available on which project information can be 

substantiated, depending on the maturity of the project. Enabling assumptions are sometimes 

relatively crude and should be revisited annually as new and better data are developed. 

1.0 Residential Buildings Integration 

The long-term goal of Residential Buildings Integration is to develop cost-effective 

technologies and building practices that will enable the design and construction of net Zero 

Energy Buildings (ZEB) – houses that produce as much energy as they use on an annual basis – 

by 2020. 

1.1 Residential Building Energy Codes 

Project Description. The Residential Building Energy Codes project improves the minimum 

or baseline energy efficiency of new federal and model residential building codes. The project 

promulgates upgraded standards for Federal residential buildings. The project works with the 

International Code Council to upgrade the energy-efficiency requirements of its model energy 

codes. State, and local jurisdictions then adopt and implement these upgraded model energy 

codes. The long-term goal is to improve the minimum energy efficiency by 20% to 25% in new 

low-rise residential building construction. 

1.1.1 Significant Changes from FY06 

No significant changes were made to this program for the FY07 effort. In previous years, all of 

the building codes activities have been modeled together, independent of funding source, with 

code development funded activities funded under BT and codes training and deployment 

activities funded as part of the Weatherization and Intergovernmental Activities Program (WIP). 

The impact of the individual codes activities (residential, commercial, and training and 

assistance) has been allocated to the individually funded activities based on the presumed 

impacts of greater compliance of existing codes as well as future code development and 

adoption. The FY 2006 activity within WIP to provide incentive funding and technical 

assistance to aid in the adoption, compliance, and enforcement of codes was discontinued in FY 

2007, although the State Energy Program Grants program within WIP is expected to continue to 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-3
fund similar activities, as they have done historically. The expected impact on the BT-funded 

portion of the codes activities is not anticipated to be significant. 

1.1.2 Target Market 

Market Description. The market includes new and renovated residential low-rise buildings, 

three stories or less in height, requiring code permits. 

Size of Market. In recent years approximately 1.6 million single-family residential building 

permits have been issued(2). Although not all jurisdictions currently have energy efficiency 

building codes in place, the Pacific Northwest National Laboratory (PNNL) estimates that about 

80 percent all new residential construction comes under building energy code requirements. 

Also, consumers spend several billion dollars a year on remodeling and renovating projects in 

private residences, about half of which are estimated to be covered by an energy code. One 

market not covered by codes is manufactured homes, which fall under Housing and Urban 

Development (HUD) jurisdiction and regulations. 

Baseline Technology Improvements. Initial compliance with new codes was assumed to be 

lower in the base case, i.e., without the Building Energy Codes project, than with the project. 

For FY07, the percentage of potential savings, in the first year of implementation of the single 

future code, was assumed to be approximately 35% for heating and cooling measures without 

the project. 

Baseline Market Acceptance. Under the baseline scenario, 23 states were assumed to have 

adopted the International Energy Conservation Code (IECC 2000 or IECC 2003) standard by 

the end of 2005. The GPRA estimates were partly based on states' accelerated schedule of 

adoption of the IECC 2000 and IECC 2003 codes. Through the efforts of the Building Energy 

Codes project, 31 states were assumed to have adopted the 2000 or 2003 standard by the end of 

2005. The project was assumed to accelerate the adoption of the standard by an average of 

three years nationwide. 

1.1.3 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. BT assumed a five-year payback period on investment to develop incremental 

investment costs (i.e., an annual energy cost savings of $1 implies an initial investment of $5). 

Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered in developing savings estimates. 

• Improved environment and more comfortable buildings. 

• Lower home maintenance and repair activities 

• Reduced pollution due to the reduced burning of fossil fuels and electricity generation, 

which improves air quality and mitigates the negative impacts of global warming. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-4
1.1.4 Methodology and Calculations 

Inputs to Base Case. With respect to codes, it is indeterminate as to whether, and to what 

extent potential future code improvements are incorporated into the National Energy Modeling 

System (NEMS) base case. The NEMS-GPRA07 base case includes some improvements to the 

building shell efficiency; however, the basis for these improvements (e.g., general building 

practice improvements, changes in codes requirements, improvements in materials) is not 

specified by the Energy Information Administration (EIA). Codes that have been issued (but 

that have not gone into effect) may be included in the NEMS-GPRA07 base case, but would not 

be included in the GPRA forecast of savings for the code development activity, because it no 

longer would be funded. The GPRA estimates include only an estimate of savings due to 

potential future codes. 

Technical Characteristics. The FY 2007 GPRA estimates are based on the future 

development of more stringent building codes. The energy-savings methodology was applied at 

a state level to better link changes in the national codes (e.g., IECC 2006) with variations in 

climate by states (and differences among states) in their adoption and enforcement of codes. 

The IECC's ongoing activities are expected to lead to more stringent residential standards in the 

future. The Department of Energy (DOE) is assumed to play a major role in developing the 

analytical and economic basis for such standards. For the GPRA process, these activities were 

subsumed in a single upgrade of the IECC standard projected to become available in the latter 

part of the current decade. BT estimated that the results of these upgrades were to reduce 

heating and cooling loads in new residential structures by 10%. Without these activities, BT 

assumed that an equivalent national (IECC) standard would not be developed within the time 

frame of the analysis. 

Expected Market Uptake. The project's activities were assumed to improve future building 

codes. The analysis assumed that when states first adopt the new standard (assumed to become 

available in the 2006-2007 time frame), the potential energy savings from moving to the new 

standard would be 84% at the time of adoption, increasing to 90% with the effect of the project 

after the first 10 years. a

 

1.1.5 Sources 

(1) “Building Technologies Program: 2006 Multi-Year Program Plan.” U.S. Department of 

Energy Buildings Technology Program, March 2005. 

(2) U.S. Bureau of Census. New Privately Owned Housing Units Authorized: Annual 2004 

Data. Accessed online on January 2006 at

http://www.census.gov/const/www/C40/table2.html#annual. 

 

a

 The 84% assumption is based upon two other assumptions: 1) 60% of new homes fully comply with the new code, and 2) for 

the other 40% of new homes, 60% of the potential energy savings is achieved. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-5
1.2 Research and Development: Building America 

Project Description(1,2). The project's long-term goal is to develop integrated cost-effective 

whole-building strategies to enable residential buildings to use up to 70 percent less total energy 

than current code-compliant buildings by 2020 and provide up to 30 percent in additional 

energy savings through the use of integrated onsite power systems.b BT also will develop 

techniques to integrate new home energy efficiency and onsite power technology into existing 

homes to improve the energy efficiency of existing homes by up to 30 percent. In addition, 

user-friendly residential control packages are expected to be designed that interconnect and 

drive all components and reduce summer peak energy consumption by 100 percent when 

needed and annual energy consumption by 10-20 percent, by 2025. 

1.2.1 Significant Changes from FY06 

Existing buildings were added to the target market for the input characterization for FY07. 

1.2.2 Target Market 

Market Description (1): The target market primarily includes all new residential homes. The 

new home energy conservation approaches will also be tested and demonstrated in existing 

homes beginning FY 2007. The impacts on existing homes from this program are modeled to 

begin in 2010. 

Size of Market(4): Each year about 1.6 million new single-family housing unit building permits 

are issued. 

Market Introduction: Initial penetration of zero-net energy designs began in the southwest in 

2003 and the design approach is anticipated to expand into the northern climate zones beginning 

in 2008(5). The renewable technologies supported by this project currently exist; however, 

penetration into the general market is expected to continue to be extremely low without DOE 

funding because the technology is currently unaffordable for production home builders. BT 

assumed that Building America activities would not occur without DOE funding; therefore, no 

acceleration of market acceptance was modeled. 

Baseline Technology Improvements. There are no technology improvements assumed apart 

from what appears in the Energy Information Administration (EIA) baseline. 

1.2.3 Key Factors in Shaping Market Adoption of EERE Technologies 

Price - Incremental Cost for each level of energy savings in new homes(2): 

• 40% whole house savings costs $1,850/household (HH) 

• 60% whole house savings costs $5,300/HH 

 

b Whole house energy savings are measured relative to the BA Research Benchmark Definition (Building America, Building 

America Research Benchmark Definition, Version 3.1, November 11, 2003, National Renewable Energy Laboratory) which 

consists of the 2000 IECC requirements plus lighting, appliances and plug load energy levels (www.buildingamerica.gov) 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-6
• 70% whole house savings costs $15,000/HH 

• By 2020, incremental costs are assumed to fall by 50%. 

• One hundred percent savings (including renewable resources) costs $31,000/HH 

declining to $9,100 by 2020. 

In developing the cost of solar technologies as part of the 70% incremental cost, BT assumed 

that the solar program meets it stated goal. BT assumed that Building America is credited for 

savings beyond 20% of the baseline (see the Methodology and Calculations section below). 

Incremental costs for existing buildings have not yet been determined. 

Key Consumer Preference/Values – Nonenergy Benefits.

 The following nonenergy 

characteristics were not considered in developing savings estimates: 

• Improved comfort, durability, and occupant health from better indoor air quality 

• Reduced on-site generated waste 

• Reduced maintenance. 

1.2.4 Methodology and Calculations 

New Residential Technical Characteristics and Market Uptake 

For any one year, the Building America project's energy savings are calculated by multiplying 

the number of homes built with Building America techniques that year multiplied by the percent 

savings per home. Added to this are the energy savings, accrued in that year, for Building 

America homes built in previous years, beginning in 2007. 

BT developed incremental costs for whole-building energy savings using Navigant Consulting's 

Residential Optimization Model (ROM, Version 5.7)(2). Cost increments were developed for 

three levels of percentage savings from the baseline: -40%, -60%, and -70%. BT assumed that 

half of the costs and corresponding savings for the first level (equivalent to 20% savings from 

the baseline) would occur as a result of other related programs in BT, namely appliance 

standards, building codes, and Energy Star homes. Thus, the net savings percentages with 

Building America are translated to 20%, 40%, and 50% of the baseline unit. The ROM model 

simulations and savings percentage assumptions formed the inputs for NEMS-GPRA07. 

The ROM simulations were conducted for four cities: Minneapolis, Boston, Atlanta, and 

Phoenix (see Table G-2). Each city represents a proxy for a climate region in the U.S. 

Population weights to develop a national average were assigned in rough fashion (see 

Table G-3). Because the NEMS shell module only treats heating and cooling, the energy 

savings from the inputs shown in Table G-2 will underestimate the potential savings from BT's 

Residential R&D program. NEMS does produce the number of new homes that are deemed to 

use one of the five shell packages available in the model. Assuming the same cost and 

performance of the technologies not modeled specifically in the shell module, the total savings 

are assumed to be roughly three times that shown in the model.c

 These additional savings 

beyond heating and cooling would occur in lighting, water heating, and other appliances in 

homes built to Building America criteria. The challenge for the integrated modeling effort is to 

 

c

 The factor of “three” is based on PNNL’s assumption that space conditioning energy use in new homes would be about onethird of the total end-use energy affected by these technologies. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-7
try to incorporate these additional savings which are due to the program, with a link to the 

number of homes using advanced shell packages four and five (as shown in Table G-4). 

Shell package #4 is assumed to represent a “baseline” scenario for high-efficiency homes, based 

upon current costs. Building America activities are designed to seek innovative methods to 

reduce those costs in the future. The impact of this aspect of the program is shown in Shell 

package #5. Starting in 2010, the overall cost of the package is assumed to be 20% lower than 

the baseline ($223 versus $278), and then falling an additional 10% relative to the baseline 

every five years. 

Table G-2. ROM Simulation Results for Representative Cities 

 Minneapolis Boston Atlanta Phoenix 

 Cost Impact 

 All 

Technologies 

Building 

America Total Cost 

 Delta 

Cost Total Cost 

 Delta 

Cost Total Cost 

 Delta 

Cost Total Cost 

 Delta 

Cost 

 Base $46,499 $25,164 $22,884 $28,384 

20% 

40% 20% $48,297 $899 $27,373 $1,105 $24,818 $967 $29,646 $631 

60% 40% $51,543 $5,044 $30,793 $5,629 $28,376 $5,492 $32,671 $4,287 

70% 50% $62,467 $15,968 $39,880 $14,716 $39,784 $16,900 $40,112 $11,728 

 

 Energy Use MMBtu/HH MMBtu/HH MMBtu/HH MMBtu/HH 

 Base 214.9 191.7 164.2 176.0 

20% 172.0 153.4 131.3 140.8 

40% 129.0 115.0 98.5 105.6 

60% 107.5 95.9 82.1 88.0 

70% 64.5 57.5 49.3 52.8 

Table G-3. Population Weights and Incremental Costs for Representative Cities 

Incremental Costs, Building America 

 City Weight 20% 40% 50% 

 Minneapolis 0.2 $899 $5,044 $15,968 

 Boston 0.3 $1,105 $5,629 $14,716 

 Atlanta 0.3 $967 $5,492 $16,900 

 Phoenix 0.2 $631 $4,287 $11,728 

 Average * $927 $5,203 $15,024 

 HVAC share ** 0.3 $278 $1,561 $4,507 

*Costs for percentage reduction in whole-building energy use 

**Costs for percentage reduction in heating and cooling consumption 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-8
Table G-4. Suggested Adjustments to NEMS Shell Factors 

Heating Shell Efficiency Adjustments (multiplicative factors) 

Package 2003 2005 2010 2015 2020 2025

4* 1.00 0.80 0.80 0.60 0.50 0.50

5* 1.00 1.00 0.80 0.60 0.50 0.50

 Cooling Shell Efficiency Adjustments (multiplicative factors) 

Package 2003 2005 2010 2015 2020 2025

4 1.00 0.80 0.80 0.60 0.50 0.50

5 1.00 1.00 0.80 0.60 0.50 0.50

 Shell Cost Adjustment Factors (Amount Subtracted) 

Package 2003 2005 2010 2015 2020 2025

4 0 -$278 -$278 -$1,561 -$4,507 -$4,507

5 -$223 -$1,093 -$2,704 -$2,254

* Packages 4 and 5 represent Building America 

** Costs are incremental, above the baseline 

The fundamental premise that leads to wide adoption of the technology is that existing 

technologies and DOE projects will eventually reduce energy use by about 70% and reduce 

summer peak loads to zero. This, in turn, will result in significantly less solar electric and solar 

thermal technology needed to supply the home’s load. The combination of lower building loads 

and onsite power will shave summer peak loads and thereby alleviate some of the need to 

expand the grid to accommodate system summer peaks. 

Existing Residential Technical Characteristics and Market Uptake 

The performance goal for existing residential is to reduce whole-house energy use by 20% by 

2010. The expected market uptake is based on U.S. Census renovated space estimates and 

project management input.(8) Estimated market penetration rates for whole house design for 

existing homes are found in Table G-5. 

Table G-5. Whole House Energy Efficient Design - 

Existing Residential Homes Market Penetration 

Year Percent of 

Existing Stock 

2007 0.0000

2008 0.0000

2009 0.0000

2010 0.0092

2011 0.0203

2012 0.0336

2013 0.0487

2014 0.0653

2015 0.0829

2016 0.1006

2017 0.1178

2018 0.1337

2019 0.1479

2020 0.1600

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-9
1.2.5 Sources 

(1) “Building Technologies Program: 2006 Multi-Year Program Plan.” Draft. U.S. DOE, 

March 2005. 

(2) Final Draft: Zero Energy Homes’ Opportunities for Energy Savings: Defining the 

Technology Pathways Through Optimization Analysis, U.S. Department of Energy 

Building Technologies Program, October 2003. 

(3) U.S. Department of Energy, Building America Research Benchmark Definition. Version 

3.1, November 11, 2003. Accessed online March 2004, at 

http://www.eere.energy.gov/buildings/building_america/benchmark_def.html. 

(4) U.S. Bureau of Census. New Privately Owned Housing Units Authorized: Annual 2004 

Data. Accessed online on January 2006 at 

http://www.census.gov/const/www/C40/table2.html#annual. 

(5) Information obtained in discussions with the project manager, Lew Pratsch, 

August/September 2003. 

(6) New Houses Sold, by Region, by Sales Price: Annual Data. U.S. Census Bureau, 

Manufacturing and Construction Division. www.census.gov/const/regsoldbypricea.pdf, 

accessed August 8, 2003. 

(7) Buildings Energy Databook (July 26, 2003), Table 5.1.1., “2001 Five Largest Residential 

Homebuilders.” 

(8) U.S. Census Bureau 2000. 1997 Economic Census Construction Geographic Area Series.” 

U.S. Department of Commerce, March 2000. Washington D.C. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-10
2.0 Commercial Buildings Integration 

The long-term goal of the Commercial Buildings Integration subprogram is to develop costeffective technologies and building practices that will enable the design and construction of net 

Zero Energy Buildings – commercial buildings that produce as much energy as they use on an 

annual basis – by 2025. 

2.1 Commercial Building Energy Codes 

Project Description. The Commercial Building Energy Codes project improves the minimum 

energy efficiency of new commercial and multifamily high-rise buildings and additions and 

alterations to existing buildings requiring code permits. The project promulgates upgraded 

energy-efficiency requirements for federal commercial and high-rise residential building types. 

Similarly, the project works with model energy code groups to upgrade the energy-efficiency 

requirements of their codes. These upgraded national energy standards are then adopted by 

federal, state, and local jurisdictions as part of their building codes. The project's long-term 

goal is to improve minimum energy efficiency by 30% to 35% in new commercial building 

construction. Energy use will be reduced by states and local jurisdictions widely adopting the 

national standards as building energy codes. 

2.1.1 Significant Changes from FY06 

No significant changes were made to this program for the FY07 effort. In previous years, all of 

the building codes activities have been modeled together, independent of funding source, with 

code development funded activities funded under BT and codes training and deployment 

activities funded as part of the Weatherization and Intergovernmental Activities Program (WIP). 

The impact of the individual codes activities (residential, commercial, and training and 

assistance) has been allocated to the individually funded activities based on the presumed 

impacts of greater compliance of existing codes as well as future code development and 

adoption. The FY 2006 activity within WIP to provide incentive funding and technical 

assistance to aid in the adoption, compliance, and enforcement of codes was discontinued in FY 

2007, although the State Energy Program Grants program within WIP is expected to continue to 

fund similar activities, as they have done historically. The expected impact on the BT-funded 

portion of the codes activities is not anticipated to be significant. 

2.1.2 Target Market 

Market Description. The market includes new commercial and multifamily high-rise (above 

three stories) buildings and all additions/renovations to commercial buildings requiring permits. 

Size of Market. The commercial market size is about 2 billion square feet of new commercial 

floor space each year. The Federal sector represents nearly 2.3% overall of new commercial 

building construction. 

Baseline Technology Improvements. Initial compliance with new codes was assumed to be 

lower in the base case, i.e., without the Building Energy Codes project. For FY07, the 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-11
percentage of potential savings, in the first year of the single future code, was estimated to be 

approximately 20% for envelope measures and 30% for lighting measures without the project. 

Baseline Market Acceptance. The FY 2007 GPRA estimates are based on the future 

development of more stringent building energy codes. 

2.1.3 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. BT developed incremental investment costs by assuming a five-year payback period on 

investment (i.e., an annual energy cost savings of $1 implies an initial investment of $5). 

Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered in developing savings estimates. 

• Improved environment and more comfortable buildings. 

• Lower building maintenance and repair activities 

• Reduced pollution due to the reduced burning of fossil fuels and electricity generation, 

which improves air quality and mitigates the negative impacts of global warming. 

2.1.4 Methodology and Calculations 

Inputs to Base Case. With respect to building codes, it is indeterminate the extent to which 

potential future code improvements are incorporated into the NEMS-GPRA07 base case. The 

NEMS-GPRA07 base case does include some improvements to the building shell efficiency; 

however, the basis for these improvements (e.g., general building practice improvements, 

changes in code requirements, and improvements in materials) is not specified by EIA. The 

impact of accelerated adoption and improved compliance by states of recently issued national 

building standards (e.g., ASHRAE 90.1-1999) is included in the GPRA forecast of savings. 

Therefore, BT did not provide inputs to change the base case assumptions for program markets. 

Technical Characteristics. Energy savings from this project result from some basic 

improvements to the overall energy efficiency of commercial buildings in their space-heating, 

space-cooling, and lighting loads. This project funds research analysis of cost-effective levels 

of energy codes for new commercial and multifamily high-rise buildings. 

Improvements to building codes are primarily supported by research efforts to review existing 

codes and specific targeted areas of building energy use, as well as the adoption of code 

modifications that promote cost-effective reductions in these energy-use areas. The adoption 

process for the research work has typically taken place in three areas: 

• Upgrading ASHRAE/IES Standard 90.1-1989, "Energy Efficient Design of New 

Buildings Except Low-Rise Residential Buildings"(1) 

• Upgrading the Federal commercial and multifamily high-rise building energy code, 10 

CFR 434, "Energy Code for New Federal Commercial and Multi-Family High Rise 

Residential Buildings"(2) 

• Upgrading the International Energy Conservation Code (IECC).(3)

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-12
The FY 2007 GPRA estimates are based on the future development of more stringent building 

energy codes. The energy-savings methodology was applied at a state level to better link 

changes in the codes with variations in climates by states and differences among states in their 

adoption and enforcement of building codes. The discussion below uses national averages of 

some of the key assumptions related to adoption and compliance to help summarize the 

methodology, but appropriate state averages were used in the analysis. 

The ongoing activities of the ASHRAE 90.1 committee were assumed to lead to more stringent 

commercial-building standards in the future. DOE was assumed to play a major role in 

developing the analytical and economic basis for such standards. For the GPRA process, these 

activities were subsumed in a single upgrade of the ASHRAE standard, estimated to become 

available in the latter part of the current decade. The GPRA analysis assumed that the overall 

result of these upgrades is to reduce electricity consumption by 10% and natural gas 

consumption by 10% in new commercial buildings. 

Expected Market Uptake. As part of work for an unpublished analysis of the historical 

impacts of Building Energy Codes in August 2003, the baseline assumptions regarding the 

acceleration effect of the overall program were modified (e.g., program training and assistance) 

activities leading to states adopting (the most recent national ASHRAE or IECC) codes more 

rapidly than they would have otherwise). In general, without the training and assistance 

elements of the building codes project, the states were classified into groups that: 1) 

immediately (one or two years) adopted the 90.1-1989 ASHRAE code, 2) would have adopted 

within five years or 3) would have adopted within 10 years..d

 These time periods were then 

reduced by one year for each successive major code cycle after the 1989 code. (For example, a 

five-year lag for 90.1-1989 is assumed to fall to four years for the 90.1-1999 code, three years 

for the ASHRAE 90.1-2004 code, and two years for the next major update of the code). The 

overall impact of this change was to decrease the average lag between the publication of a new 

standard and when it is adopted – without the project. For the scenario involving a new 

commercial code (circa 2009), states are assumed to adopt that code over a period extending 

from 2011 to 2022, with a mean adoption year of 2015. 

2.1.5 Sources 

(1)

(2)

(3)

(4)

 

ASHRAE/IES Standard 90.1-1989, "Energy Efficient Design of New Buildings Except 

Low-Rise Residential Buildings," American Society of Heating, Refrigeration, and AirConditioning Engineers and Illuminating Engineering Society. 

10 CFR 434, "Energy Code for New Federal Commercial and Multi-Family High Rise 

Residential Buildings," Code of Federal Regulations, as amended. 

International Energy Conservation Code. 2003. International Code Council, Falls Church, 

Virginia. 

ASHRAE/IES Standard 90.1-1999, "Energy Standard for Buildings Except Low-Rise 

Residential Buildings," American Society of Heating, Refrigeration, and Air-Conditioning 

Engineers. 

d

 The historical record for states adopting the 90.1-1989 standard was 1) two states adopted within the first two years of 

publication, and 2) 24 states had adopted by 1998. Three states are not considered in the analysis as they had had active code

development programs in their own states: California, Oregon, and Florida. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-13
(5)

(6)

ASHRAE/IES Standard 90.1-2001, "Energy Standard for Buildings Except Low-Rise 

Residential Buildings," American Society of Heating, Refrigeration, and Air-Conditioning 

Engineers.

U.S. Department of Energy. March 2002. “Commercial Buildings Determinations, 

Explanation of the Analysis and Spreadsheet (90_1savingsanalysis.xls).” 

http://www.energycodes.gov/implement/determinations_com.stm

2.2 Research and Development 

Project Description.(1) In order to reach net zero conventional energy buildings (ZEB) by 

2025, DOE will employ integrated whole-building strategies to enable commercial buildings to 

be designed and constructed to use 70% less energy. By 2010, the BT goal is to integrate 

design approaches, highly efficient component technologies and controls, improved 

construction and maintenance practices, and operating procedures that will make new and 

existing commercial buildings durable, healthy and safe for occupants, and will reduce energy 

use for new buildings by 50% and by 30% for existing buildings, relative to conventional 

practice. e

 

2.2.1 Significant Changes from FY06 

For FY07, BT changed the out-year performance and cost inputs for new buildings, which were 

held constant through the analysis period for the FY06 effort. Additionally, BT estimates that 

Commercial Technology R&D would accelerate the adoption of relevant energy-savings 

products, technologies and designs by 5 years. This estimate is a revision from a 10 year period 

(assumed in FY06). 

2.2.2 Target Market 

Market Description(1): Although this project does not explicitly exclude any particular 

building type, the types of commercial buildings that will most likely be impacted by the 

technologies developed by this project primarily include small commercial buildings with 

relatively high energy use intensities such as assembly, education, food service, food sales, 

lodging, mercantile and service, and office buildings. 

Baseline Technology Improvements. There are no technology improvements assumed apart 

from what appears in the Energy Information Administration (EIA) baseline. 

Baseline Market Acceptance. In 1998, PNNL conducted a study examining the historical 

market penetration for 10 energy-efficient products related to the buildings sector. The results 

of this study are documented in the PNNL report, Methodological Framework for Analysis of 

Buildings-Related Programs: The GPRA Metrics Effort (2004)(6). The study suggested several 

generic penetration curves based on the type of equipment of interest. BT used the curve related 

to design products to model this project. 

 

e

 Energy savings are measured relative to the 2001 International Energy Conservation Code (IECC). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-14
2.2.3 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. 

Total Building Cost of Conventional Technology(4): Average of $101/ft2

 for the targeted new 

commercial and multifamily; $0 for existing buildings. 

Total Building Cost of BT Technology(5): $103/ft2

 for new commercial and multifamily, 

increasing to $107/ft2

 in 2020f; $4/ ft2 for existing buildings. 

Incremental Cost(5): 2% above base for new buildings, increasing to 6% above base in 2020; 

$4/ft2

 for existing buildings. 

Key Consumer Preference/Values – Nonenergy Benefits. The following nonenergy 

characteristics were not considered in developing energy output estimates: 

• Reduced operation and maintenance expenses 

• Improved indoor environmental quality 

• Increased property asset value 

• Higher tenant satisfaction and retention rates 

• Increased technology sales. 

2.2.4 Methodology and Calculations 

Inputs to Base Case. BT did not provide inputs to change the base case assumptions for the 

program markets. BT’s calculations were based on a baseline that was developed from the 

Energy Information Administration’s (EIA’s) Commercial Buildings Energy Consumption 

Survey (CBECS), Residential Energy Consumption Survey (RECS), and the Annual Energy 

Outlook (AEO). 

Technical Characteristics (2,3). In concert with the Analysis, Tools, and Design Strategies 

project, the performance goals are to reduce heating and cooling loads by 50% in new small 

commercial construction as compared with ASHRAE 90.1-2004, increasing to 70% savings by 

2020. The goal is also to save 30% in existing buildings.g

Expected Market Uptake. The market penetration goal(3) is to accelerate the penetration of 

high-performance building designs, such that 55% of new commercial and multifamily 

construction (Figure G-1) and 20% of existing construction incorporates the products supported 

by this project by 2025 (Figure G-2). Penetration curves were developed based on market 

diffusion curves developed by PNNL(6). BT assumed that this project accelerates the adoption 

of relevant energy-savings products, technologies and designs by 5 years. 

 

f

 Cost estimates corresponding with 70% energy savings are based on escalation estimates associated with similar energy 

savings in residential sector. Cost escalation estimates are based on Navigant Consulting's Residential Optimization Model 

(Version 5.7) 

g

 The percentage of the load reduction attributed between Commercial R&D and Analysis Tools and Design Strategies is in 

proportion with their respective budget requests. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-15
0

10

20

30

40

50

60

70

80

90

100

2011

2016

2021

2026

% Market Share

Accelerated Penetration with DOE R&D

Penetration without DOE R&D

Net Penetration from DOE R&D

Figure G-1. Market-Penetration Curve for Commercial R&D Project Targeting New Buildings 

0

10

20

30

40

50

60

70

80

90

100

2005 2010 2015 2020 2025 2030

Market Share (%)

Accelerated Penetration with DOE R&D

Penetration without DOE R&D

Net Penetration from DOE R&D

Figure G-2. Market-Penetration Curve for Commercial R&D Project Targeting Existing Buildings 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-16
2.2.5 Sources 

(1) “Building Technology Program: Research, Development and Demonstration Plan, Planned 

Program Activities for 2004-2010.” Final Draft. U.S. DOE, January 9, 2004. 

(2) Torcellini, Paul, et. al. Lessons Learned from Field Evaluation of Six High-Performance 

Buildings, NREL/CP-550-36290, National Renewable Energy Laboratory, June 2004. 

(3) E-mail correspondence with project manager, Dru Crawley, June 2003. 

(4) RS Means Company, Inc. 2002. “RS MEANS Square Foot Costs.” 23rd Edition, Kingston, 

MA. 

(5) Kats, Greg (Capital E), et. al. “The Costs and Financial Benefits of Green Buildings,” A 

Report to California’s Sustainable Building Task Force. October 2003. 

(6) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics 

Effort. PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-17
3.0 Equipment Standards and Analysis 

The Equipment Standards and Analysis subprogram seeks to develop minimum energy 

efficiency standards that are technologically feasible and economically justified. 

3.1 Electric Motors, 1-200 HP 

Project Description(1). The Energy Policy Act of 1992 (EPAct) requires that general purpose, 

polyphase, single speed, squirrel-cage induction motors rated from 1-200 horsepower (HP) 

manufactured for sale in the U.S. from October 1997 onward meet minimum efficiency 

standards. The EPAct standard was adapted from earlier standards promulgated by the National 

Electrical Manufacturer’s Association (NEMA). NEMA maintains a more stringent voluntary 

standard known as NEMA PremiumTM. DOE is proposing to change the minimum 

requirements for motor efficiency to be comparable to the NEMA PremiumTM. The efficiency 

standard targets motors designed for use under usual service conditions without restriction to a 

particular application or type of application. Motors covered by the Energy Policy Act (EPAct) 

account for 50-70 percent of all integralh

 horsepower motors sold. 

3.1.1 Significant Changes from FY06 

This characterization represents a new activity for FY07. 

3.1.2 Target Market 

Market Description(1). Industrial motor systems are the largest single electrical end use in the 

U.S. economy. According to the United States Industrial Electric Motor Systems Market 

Opportunity Assessment, electric motors used in industrial processes consumed 679 billion 

kWh (approximately 7.5 quads of primary energy) in 1994. Commercial sector motors are 

much more numerous than industrial motors, and tend to be smaller. In 1995, there were 123 

million commercial sector motors in total. About 36 million commercial motors fall in the 

EPAct size range. 

 

Size of Market. There is an installed base of 12.3 million industrial sector units and 4.1 million 

commercial sector units, 1-200HP, that are subject to EPAct. There are about 1.5 million 

industrial motors shipped annually, and an additional 0.54 million commercial units. 

Baseline Technology Improvements. There are no technology improvements assumed apart 

from what appears in the Energy Information Administration (EIA) baseline. 

3.1.3 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. No price information was available or used to model this program. 

 

h

 Motors below 1 horsepower (HP) are known as fractional horsepower motors; those 1 HP and above are known as integral. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-18
Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered in developing energy output estimates: 

• Reduced CO2 and SOX emissions 

• Increased life of equipment operating at cooler temperatures 

• Reduced first costs that transform new technologies into commodities. 

3.1.4 Methodology and Calculations 

Technical Characteristics. 

Energy performance assumptions are based on DOE’s FY2005 Technical Support Document, 

Appendix A, and include the following: 

• Base Case: Average electric motor energy consumption: 25.61 thousand kWh/yr 

• Performance: Standard results in 2% average reduction in energy 

• Equipment lifetime: 15 years 

• Start Date: Effective date of standard is 2010. 

Expected Market Uptake. BT assumed that the entire stock of existing motors is replaced 

with motors meeting the standard by 2025 (the standard goes into effect in 2010, so within 15 

years, the estimated lifetime, all motors have been replaced). Subsequent increases in savings 

are only from increases in new sales (as opposed to replacement sales). 

Based on forecasted shipment data(1), Tables G-6 and G-7 contain the energy savings 

calculations. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-19
Table G-6. Annual Industrial Energy Savings 

Year 

In-Year Energy 

Savings from 

Sales (million 

kWh/yr Site)

Total Annual 

Energy 

Savings – 

Installed Base 

(million kWh/yr 

Site)

Annual Energy 

Savings – 

Installed Base 

(TBtu/yr Site) 

2007 0.0

2008 0.0

2009 0.0 - - 

2010 652.3 652.3 2.2 

2011 665.3 1,317.6 4.5 

2012 678.6 1,996.2 6.8 

2013 692.2 2,688.3 9.2 

2014 706.0 3,394.4 11.6 

2015 720.1 4,114.5 14.0 

2016 734.5 4,849.0 16.5 

2017 749.2 5,598.3 19.1 

2018 764.2 6,362.5 21.7 

2019 779.5 7,142.0 24.4 

2020 795.1 7,937.1 27.1 

2021 811.0 8,748.1 29.8 

2022 827.2 9,575.3 32.7 

2023 843.8 10,419.1 35.5 

2024 860.6 11,279.7 38.5 

2025 225.6 11,505.3 39.3 

2026 230.1 11,735.4 40.0 

2027 234.7 11,970.1 40.8 

2028 239.4 12,209.5 41.7 

2029 244.2 12,453.7 42.5 

2030 249.1 12,702.8 43.3 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-20
Table G-7. Annual Commercial Energy Savings 

Year 

In-Year Energy 

Savings from 

Sales (million 

kWh/yr Site) 

Total Annual 

Energy 

Savings – 

Installed Base 

(million kWh/yr 

Site)

Annual Energy 

Savings – 

Installed Base 

(TBtu/yr Site)

2007 0.0

2008 0.0

2009 0.0 - - 

2010 176.7 176.7 0.6 

2011 180.2 356.8 1.2 

2012 183.8 540.6 1.8 

2013 187.5 728.1 2.5 

2014 191.2 919.3 3.1 

2015 195.0 1,114.3 3.8 

2016 198.9 1,313.3 4.5 

2017 202.9 1,516.2 5.2 

2018 207.0 1,723.2 5.9 

2019 211.1 1,934.3 6.6 

2020 215.3 2,149.6 7.3 

2021 219.6 2,369.3 8.1 

2022 224.0 2,593.3 8.8 

2023 228.5 2,821.8 9.6 

2024 233.1 3,054.9 10.4 

2025 61.1 3,116.0 10.6 

2026 62.3 3,178.3 10.8 

2027 63.6 3,241.9 11.1 

2028 64.8 3,306.7 11.3 

2029 66.1 3,372.9 11.5 

2030 67.5 3,440.3 11.7 

3.1.5 Sources

(1) U.S. Department of Energy 2005. Appendix A: FY2005 Technical Support Document. 

Accessed online on January 2006 at 

http://www.eere.energy.gov/buildings/appliance_standards/pdfs/fy05_priority_setting_app_

a.pdf

3.2 HID Lamps 

Project Description. (1) High Intensity Discharge (HID) Lamps are electric lamps that produce 

light in a small arc tube under high internal pressure. Typical applications for these lamps are 

street and roadway lighting, area lighting such as parking lots and stadiums, industrial and 

commercial building interior lighting, commercial, industrial and residential security lighting, 

and landscape lighting. There are three HID lamp types: mercury vapor, metal halide, and high 

pressure sodium and the least efficient of these is mercury vapor. The Energy Policy and 

Conservation Act (EPCA, 42 U.S.C.6317(a)(1)) requires the Department of Energy (the 

Department) to undertake a determination to see if energy conservation standards for HID 

lamps would be technologically feasible and economically justified, and would result in 

significant energy savings. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-21
3.2.1 Significant Changes from FY06 

This characterization represents a new activity for FY07. 

3.2.2 Target Market 

Market Description: According the draft Technical Support Document published in 

December 2004,(2) mercury vapor lamps account for about one-sixth of the approximately 140 

TWh used by all high intensity sources. Thus mercury vapor lamps use about 75 TBtu of 

delivered electricity per year. 

Baseline Technology Improvements. There are no technology improvements assumed apart 

from what appears in the Energy Information Administration (EIA) baseline. 

Market Introduction. BT assumed that the effective date of the standard would be 2010. 

3.2.3 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. Costs as defined by the NEMS commercial model (file Ktech.txt) 

Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered in developing energy output estimates: 

• Reduced CO2 and SOX emissions 

• Increased life of equipment operating at cooler temperatures 

3.2.4 Methodology and Calculations 

From documents on the BT web site(1) related to the setting of efficiency standard for HID 

lamps, the basic impact will likely be the elimination of mercury vapor lamps via a federal 

efficiency standard. As stated in the documentation of expected impacts in the Technical 

Support Document, “... the focus is on a possible standard that could be met by lamps with 

efficacies above those typical of today’s MV lamps.” Subsequent events have confirmed that 

choice for modeling the standard. The EPAct 2005 requires that mercury vapor lamp ballasts 

shall not be manufactured or imported after January 1, 2008.

Technical Characteristics. 

The commercial model in NEMS contains several lighting segments that include mercury 

vapor, metal halide (MH), and high pressure sodium (HPS). The market segments considered 

by the NEMS model are low-bay applications (< 25 feet high) and high-bay applications (> 25 

feet high) for high-intensity discharge lamps. Table G-8 shows the efficacies used in the 

NEMS input files for these common lamp technologies and the typical wattages for those 

lamps. For purposes of modeling the HID standard in the GPRA integrated model, mercury 

vapor lamps were no longer one of lighting technology choices in these two market segments in 

the expected year of the standard. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-22
Table G-8. Efficacies for Common HID Lamp Types in NEMS (Lumens/Watt, lpw)i

Application Mercury Vapor Metal Halide High Pressure 

Sodium 

 Low-bay 34 lpw 

(175 watts) 

46 lpw (pulse) 

(100 watts) 

59 lpw 

(70 watts) 

 High-bay 37 lpw 

(400 watts) 

61 lpw (pulse) 

(250 watts) 

83 lpw 

(200 watts) 

Performance Parameters. 40 lumens per watt for mercury vapor; 70 lumens per watt for 

metal halide; and 90 lumens per watt for high pressure sodium. 

3.2.5 Sources 

(1) BT Appliances and Commercial Equipment Standards web site, accessible at: 

http://www.eere.energy.gov/buildings/appliance_standards/commercial/high_intensity_lamp

s.html. 

(2) High-Intensity Discharge Lamps Analysis of Potential Energy Savings, December 2004, 

accessible at: 

http://www.eere.energy.gov/buildings/appliance_standards/commercial/pdfs/hid_energy_sa

vings_report.pdf. 

3.3 Distribution Transformers 

Project Description. Distribution transformers convert high-voltage electricity from 

distribution centers to lower-voltage electricity for use at the household level. During this 

conversion process, a small fraction of heat is lost. The Energy Policy and Conservation Act 

(EPCA) of 1975 established an energy conservation program for major household appliances. 

The National Energy Conservation Policy Act of 1978 amended EPCA to add Part C of Title 

III, which established an energy conservation program for certain industrial equipment. The 

Energy Policy Act of 1992 amended EPCA to add certain commercial equipment, including 

distribution transformers. 

The department is currently conducting two rulemakings for Distribution Transformers: an 

energy conservation standard and a test procedure. 

3.3.1 Significant Changes from FY06 

For the FY07 effort, the effective date of the standard was changed from 2008 to 2010 based on 

the current rule-making schedule. 

3.3.2 Target Market 

Market Description(3). Over one million new distribution transformers are purchased annually. 

Utility distribution transformers account for an estimated 61 billion kWh of the annual energy 

 

i

 Source is the ktech.wk1 spreadsheet containing cost and performance characteristics for the NEMS commercial module used 

for the 2006 AEO reference projection. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-23
lost in the generation and delivery of electricity. Additional transformer losses in non-utility 

applications are estimated to be 79 billion kWh. 

Baseline Technology Improvements. There are no technology improvements assumed apart 

from what appears in the Energy Information Administration (EIA) baseline. 

3.3.3 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. BT assumed a 10-year payback period on investment to develop incremental investment 

costs (i.e., an annual energy cost savings of $1 implies an initial investment of $10). 

Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered in developing energy output estimates: 

• Reduced CO2 and SOX emissions 

3.3.4 Methodology and Calculations 

Inputs to Base Case. BT did not provide inputs to change the base case assumptions for the 

program markets. BT’s calculations were based on a baseline that was developed from the 

Energy Information Administration’s (EIA’s) Commercial Buildings Energy Consumption 

Survey (CBECS), Residential Energy Consumption Survey (RECS), and the Annual Energy 

Outlook (AEO). 

Technical Characteristics 

Performance Target. Savings estimates for a distribution transformer standard were based on 

the DOE Draft ANOPR Analysis for Distribution Transformers Rulemaking (January 6, 

2004).(1) The analysis assumed the following: 

• Average savings of 140 watts per unit 

• A transformer sales forecast (see Table G-9). 

• 0% sales complying with the new level without the standard (this was taken into account 

in calculating the 140 watts average savings) 

• 8,760 annual operating hours per unit 

• 30-year life of equipment. 

BT assumed that the distribution transformer standard would not go into effect until 2010, based 

on the BT 2006 Multi-Year Program Plan indicating that the final rule would be issued 

September 2007, with the standard going into effect three years later.(2) The savings estimate of 

140 watts per unit installed was multiplied by the estimated hours of operation and then by the 

forecasted number of units installed. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-24
Expected Market Uptake 

Table G-9. Distribution Transformer Market Penetration (# of units) 

Year 

Transformer Sales 

Forecast 

2010 1,582,000 

2011 1,614,000 

2012 1,646,000 

2013 1,673,000 

2014 1,701,000 

2015 1,729,000 

2016 1,756,000 

2017 1,782,000 

2018 1,810,000 

2019 1,840,000 

2020 1,870,000 

2021 1,898,000 

2022 1,929,000 

2023 1,960,000 

2024 1,994,000 

2025 2,025,000 

2026 2,058,000 

2027 2,090,000 

2028 2,124,000 

2029 2,158,000 

2030 2,192,000 

3.3.5 Sources 

(1) DOE Draft ANOPR Analysis for Distribution Transformers Rulemaking, January 6, 2004. 

(2) “Building Technologies Program: 2006 Multi-Year Program Plan.” Draft. U.S. DOE, March 

29, 2005. 

(3) Barnes, P.R., S. Das, B.W. McConnell, J.W. Van Dyke, 1997. Supplement to the 

“Determination Analysis” (ORNL-6847) and Analysis of the NEMA Efficiency Standard for 

Distribution Transformers. ORNL-6925, Oak Ridge National Laboratory, Oak Ridge, 

Tennessee. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-25
4.0 Emerging Technologies 

The Emerging Technologies subprogram seeks to develop cost effective technologies (e.g., 

lighting, windows, and space heating and cooling) for residential and commercial buildings that 

can reduce the total energy use in buildings by 60% to 70%. The improvement in component 

and system energy efficiency, when coupled with research to integrate onsite renewable energy 

supply systems into the commercial building, can result in marketable net zero energy designs. 

4.1 Analysis Tools and Design Strategies 

Project Description.(1) The Analysis Tools and Design Strategies project researches the 

interrelationship of energy systems and building energy performance, develops various building 

analysis tools to more accurately model energy use in new and existing buildings, and provides 

recommendations and strategies to cost effectively lower energy use and improve building 

performance. The project focuses on whole-building software tools for evaluating energy 

efficiency and renewable energy. The project also focuses on non-software solutions such as 

improved standards, guidelines, and performance measurements, all of which bring about 

excellence in designing new buildings. The project's long-term goal is to improve energy 

designs for all building types through a number of widely used analytical tools and guidance 

documents. 

4.1.1 Significant Changes from FY06 

BT estimates that Analysis Tools and Design Strategies would accelerate the adoption of 

relevant energy-savings products, technologies and designs by 5 years. This estimate is a 

revision from a 10 year period (assumed in FY06). 

4.1.2 Target Market 

Market Description: Although this project does not explicitly exclude any particular building 

type, the types of commercial buildings that most likely will be impacted by the technologies 

developed by this project include those with relatively higher energy use intensities such as 

assembly, education, health care, lodging, and office buildings. 

Market Introduction(1,3): BT assumed that this project accelerates the introduction and market 

penetration of the advanced building energy tools and design strategies by 5 years. Historically, 

there have been a number of building energy tools that have been developed privately; however, 

most of these tools use algorithms, code, and modules developed by DOE. BT estimated that a 

proportion of these activities (50%) would not occur without DOE funding. These assumptions 

are necessary in the absence of citable sources documenting DOE’s influence on building 

energy tool adoption and algorithm attribution. 

Baseline Technology Improvements. There are no technology improvements assumed apart 

from what appears in the Energy Information Administration (EIA) baseline. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-26
Baseline Market Acceptance. In 1998, Pacific Northwest National Laboratory (PNNL) 

conducted a study examining the historical market penetration for 10 energy-efficient products 

related to the buildings sector. The results of this study are documented by PNNL(5). The study 

suggested several generic penetration curves based on the type of equipment of interest. BT used 

the curve related to design products to model this project. 

4.1.3 Key Factors in Shaping Market Adoption of EERE Technologies 

Price(3,4). Although the tools supported by this project are distributed free of charge, users must 

invest a certain amount of time to learn the tools. Without a user-friendly interface, 

approximately one person-month is required to be come proficient with the tools. Analysis 

Tools and Design Strategies is currently developing energy-simulation tools without a userfriendly interface. This allows the private sector to contribute its knowledge of user needs and 

market competition to design their own user-friendly interface. 

Key Consumer Preference/Values – Nonenergy Benefits.

 The following nonenergy 

characteristics were not considered in developing energy output estimates: 

• Improved indoor environmental quality, such as thermal comfort and ventilation 

adequacy 

• Improved indoor air quality 

• Fire safety 

• Overall environmental sustainability (i.e., Green Buildings). 

4.1.4 Methodology and Calculations 

Inputs to Base Case. BT did not provide inputs to change the base case assumptions for the 

program markets. BT’s calculations were based on a baseline that was developed from the 

Energy Information Administration’s (EIA’s) Commercial Buildings Energy Consumption 

Survey (CBECS), Residential Energy Consumption Survey (RECS), and the Annual Energy 

Outlook (AEO). 

Technical Characteristics(2). In concert with Commercial Buildings R&D project, the 

performance goals are to reduce heating and cooling loads by 50% in new small commercial 

construction and by 30% in existing buildings.j

Expected Market Uptake(3). The market penetration goal is to accelerate the penetration of 

high-performance building design, such that 55% of new commercial and multifamily 

construction and 20% of existing construction incorporates the products supported by this 

project by 2025. BT assumes that this project accelerates the adoption of relevant energysavings products, technologies and designs by 5 years. 

 

j

The percentage of the load reduction attributed between Commercial R&D and Analysis Tools and Design Strategies is in 

proportion with their respective budget requests. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-27
4.1.5 Sources 

(1) “Building Technology Program: Research, Development and Demonstration Plan, Planned 

Program Activities for 2004-2010.” Final Draft. U.S. DOE, January 9, 2004. 

(2) Torcellini, Paul, et. al. Lessons Learned from Field Evaluation of Six High-Performance 

Buildings, NREL/CP-550-36290, National Renewable Energy Laboratory, June 2004. 

(3) E-mail correspondence with project manager, Dru Crawley, June 2003 and June 2004. 

(4) Kats, Greg (Capital E), et. al. “The Costs and Financial Benefits of Green Buildings,” A 

Report to California’s Sustainable Building Task Force. October 2003. 

(5) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics 

Effort. PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 

4.2 Appliances and Emerging Technologies R&D: Solid State 

Lighting Market Acceptance 

Project Description(1): The purpose of this program is to accelerate the market acceptance of 

solid-state lighting technologies. This will be accomplished through a variety of methods 

potentially including: 

• Competitive technology procurements 

• Late-stage technology refinement in conjunction with influential product users 

• Field performance evaluation and verification for the benefit of large-scale buyers 

• Product performance testing 

• Product design competitions in cooperation with major market actors 

• Voluntary product guidelines and conventions 

4.2.1 Significant Changes from FY06 

The activities modeled for Appliances and Emerging Technologies R&D changed for FY07. 

The only activity modeled for FY07 is Solid State Lighting Acceptance, which is a new activity. 

4.2.2 Target Market 

Market Description: The market is the entire market for solid-state lighting. 

Size of Market: Lighting consumes 26% (3.9 QBtu) of the primary energy used in commercial 

buildings, which had building stock of about 69 billion ft2 in 2000.k

4.2.3 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. Given that this is a market acceptance program there will be no direct cost borne by the 

consumers. 

 

k

 According to a report completed for DOE by Navigant Consulting (“U.S. Lighting Market Characterization, Volume I: 

National Lighting Inventory and Energy Consumption Estimate,” September 2002), the amount of energy used for lighting is 

greater than EIA has traditionally estimated. The report estimates that commercial lighting requires 4.2 QBtu and residential 

lighting requires 2.2 QBtu. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-28
Key Consumer Preferences/Values(1): This program recognizes that market acceptance is 

determined by the buyers’ perspective and needs. Price, reliability, and performance are key 

consumer values for lighting.

4.2.4 Methodology and Calculations 

As advanced appliance, equipment, and envelope technologies emerge, the AET program plays 

a key role in expanding and accelerating the market acceptance of technologies that are not only 

on the critical pathway to ZEB in the future but also relevant to the broader new and retrofit 

residential and commercial building sectors in the near term.(1) 

Market Introduction: It is projected that this program will accelerate the market penetration 

of the technology by 2 years. 

Expected Market Uptake: Figure G-3 (with largely hypothetical numbers) illustrates the 

market uptake concept. In the graph it is assumed that the Emerging Technologies R&D 

Program accelerates the penetration of SSL technologies by 2 years. Hence the benefit of the 

emerging technology program is captured by the yellow line (the difference between the purple 

line and the blue line). 

Site Electric Saving

0

100

200

300

400

500

600

700

800

2005 2010 2015 2020 2025 2030 2035

Year

TBtu/year

SSL Program

w/o Emerging

Tech R&D

SSL Program

w/ Emerging

Tech R&D

Emerging

Tech R&D

Contribution

Figure G-3. Solid State Lighting Market Uptake Concept 

4.2.5 Sources 

1) “Building Technologies Program: 2006 Multi-Year Program Plan.” Draft. U.S. DOE, March 

29, 2005. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-29
4.3 Envelope Research and Development: Windows 

Project Description(1). Windows typically contribute about 30 percent of overall building 

heating and cooling loads with an annual impact of about 3.7 quads, with an additional potential 

savings of 1 quad from daylight use. The BT approach is to first convert windows from their 

current role as significant thermal losses to the point where they are energy neutral, and then 

move to a higher level of performance, where they contribute to a net energy surplus in a ZEB, 

thus offsetting other energy costs. 

About 60 percent of window sales are to the residential sector and 40 percent to commercial, so 

that this program targets both sectors. Sales are evenly distributed between new construction 

and existing buildings, so both markets are included in the R&D program. Because the energy 

needs of residential users differ from commercial, and new construction and renovation/retrofit 

are different, and because all performance is strongly influenced by climate and orientation, the 

development of a single “silver bullet R&D solution” that solves all problems is not possible. 

Furthermore, window impacts on building energy use are linked to other building systems. 

Therefore the technical approach of the Windows activity is built around three themes: 

1. The need for a broad portfolio of cost-effective advanced technologies to address the 

disparate heating, cooling and daylighting needs of these different conditions; 

2. Recognition that these advanced glazing and façade technologies will perform best when 

they are optimized as part of fully integrated building systems to address competing 

performance needs as a function of time, climate, building type and orientation; and 

3. The need for decision-support infrastructure to rate and label products, and tools to 

select and optimize window selection and design solutions. For existing energy efficient 

products, rating and labeling an entire suite of products with a strong focus on 

commercial building applications will remove barriers for product specification and 

promotion by industry and non-profit organizations. 

4.3.1 Significant Changes from FY06 

No significant changes were made to this program for the FY07 effort. 

4.3.2 General Target Market 

Market Description: The market includes new and existing commercial and residential 

buildings in all climate zones. 

Size of Market: 500 million square feet of windows for commercial buildings and 

approximately 55 million manufactured units sold each year for residential and light 

commercial. 

Baseline Technology Improvements. There are no technology improvements assumed apart 

from what appears in the Energy Information Administration (EIA) baseline. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-30
4.3.3 Key Factors in Shaping Market Adoption of EERE Technologies 

Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered in developing energy output estimates: 

• Reduced utility and building peak loads 

• Reduced HVAC Requirements and first costs 

• Improved indoor comfort and aesthetics. 

4.3.4 Electrochromic Windows 

Project Description.

(1) Windows are capable of providing solar heat when it is needed, 

rejecting solar gain to reduce cooling loads, and offsetting most of a building’s lighting needs 

during daylight hours. To fully accomplish these functions, windows and skylights must 

continuously and dynamically control their transmittance of sunlight and daylight. In 

commercial buildings the dynamic tradeoffs between cooling load reductions and daylight 

utilization are particularly complex. Glazings whose solar optical properties can be varied 

rapidly over a wide dynamic range are needed to address these performance needs. Research 

activities include development of durable chromogenic coatings, emphasizing electrochromic 

technology for the first generation of products and the exploration of other switchable coating 

mechanisms with lower cost, faster switching and wider dynamic range over time. Work 

includes fundamental coating technology, characterization, durability testing, prototype testing, 

and controls integration and optimization including field-testing. 

4.3.4.1 Target Market 

Market Introduction: 2010; This project was assumed to accelerate the introduction of this 

technology into the marketplace by 10 years. 

4.3.4.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. Incremental Installed Cost over competing technology (Low-e Double-Pane Windows) 

• 2010: $54.42/ ft2

• 2011: $44.42/ ft2

• 2012: $34.42/ ft2

• 2013: $24.42/ ft2

• 2014: $19.42/ ft2

• 2015: $14.42/ ft2

• 2016: $9.42/ ft2

• 2017: $7.42/ ft2

• 2018: $5.42/ ft2

• 2019: $3.42/ ft2

• 2020: $1.42/ ft2

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-31
4.3.4.3 Methodology and Calculations 

Inputs to Base Case. BT did not provide inputs to change the base case assumptions for the 

program markets. BT’s calculations were based on a baseline that was developed from the 

Energy Information Administration’s (EIA’s) Commercial Buildings Energy Consumption 

Survey (CBECS), Residential Energy Consumption Survey (RECS), and the Annual Energy 

Outlook (AEO). Note that the base technology to which this technology is being compared is that 

of low-e double pane windows. 

Technology Characteristics 

Performance Parameters: Performance parameters for Electrochromic Windows are 

presented in Table G-10. 

Table G-10. Performance Parameters for Electrochromic Windows 

End Use Shading Coefficient U-Value 

Heating 0.6 0.25 Btu/ft2·°F 

Cooling 0.1 0.25 Btu/ft2·°F 

Performance Target: Performance characteristics vary by building type and climate zone. 

The estimated savings per building were determined by simulating residential and commercial 

buildings in all climate zones (see Table G-11). Commercial lighting savings are estimated to 

be 5% in all regions. 

Table G-11. Performance Targets for Electrochromic Windows 

Region Sector End Use New Building 

Savings 

Existing Building 

Savings 

Units 

Heating 1.83 1.61 MMBtu/ksf Northern Commercial Cooling 4.62 4.58 MMBtu/ksf 

Heating 1.88 1.66 MMBtu/ksf North Central Commercial Cooling 5.80 5.52 MMBtu/ksf 

Heating 3.91 4.38 MMBtu/HH Residential Cooling 11.16 11.30 MMBtu/HH 

Heating 0.94 0.88 MMBtu/ksf South Central 

Commercial Cooling 5.75 5.51 MMBtu/ksf 

Heating 3.00 3.61 MMBtu/HH Residential Cooling 7.51 7.76 MMBtu/HH 

Heating 0.56 0.53 MMBtu/ksf Southern 

Commercial Cooling 3.05 2.92 MMBtu/ksf 

Heating 3.65 4.16 MMBtu/HH Residential Cooling 10.13 10.28 MMBtu/HH 

Heating 1.43 1.28 MMBtu/ksf 

Weighted National 

Average (Southern 

and South Central 

for Residential) Commercial Cooling 4.96 4.81 MMBtu/ksf 

Note: MMBtu is millions of Btus; Ksf is thousand square foot; HH is household 

Window Lifetime: 20 years. 

Expected Market Uptake. The goal is to obtain 50% of window sales by 2020 in the 

commercial sector, and 20% of window sales by 2020 in the residential sector. Penetration 

curves were developed and documented based on market diffusion curves developed by 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-32
PNNL(2). The “Accelerated” penetration curve represents the percent of electrochromic window 

sales with the DOE project; the “Net” penetration curve represents the percent of sales 

attributable to DOE, as BT assumed that the DOE project would accelerate market acceptance 

by 10 years. See penetration curves in Figures G-4 through G-7. 

0%

10%

20%

30%

40%

50%

60%

70%

80%

2000 2005 2010 2015 2020 2025 2030 2035

Accelerated Penetration

from DOE R&D

Net Penetration with DOE

R&D

Figure G-4. Electrochromic Windows – New Commercial Buildings Percent of Sales 

0%

10%

20%

30%

40%

50%

60%

70%

2000 2005 2010 2015 2020 2025 2030 2035

Accelerated Penetration

from DOE R&D

Net Penetration with DOE

R&D

Figure G-5. Electrochromic Windows – Existing Commercial Buildings Percent of Sales 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-33
0%

5%

10%

15%

20%

25%

30%

35%

2000 2005 2010 2015 2020 2025 2030 2035

Accelerated Penetration

from DOE R&D

Net Penetration with DOE

R&D

Figure G-6. Electrochromic Windows – New Residential Buildings Percent of Sales 

0%

5%

10%

15%

20%

25%

30%

2000 2005 2010 2015 2020 2025 2030 2035

Accelerated Penetration

from DOE R&D

Net Penetration with DOE

R&D

Figure G-7. Electrochromic Windows – Existing Residential Buildings Percent of Sales 

4.3.5 Superwindows 

Project Description. (1) With heating loads being the largest end-use impact, improving winter 

performance has the potential for large energy savings. Low-E gas-filled windows introduced 

in the 1980s have now captured more than 40% of the residential market. But, heat loss rates for 

whole windows must be reduced by at least a factor of 2 to approach levels needed for zeroenergy buildings. Highly leveraged competitive R&D will be conducted towards achieving 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-34
these impacts. Research activities will include basic and exploratory research on advanced 

optical coatings, gas filled and evacuated cavities, microporous transparent insulating materials, 

improved edge and frame materials; and applied research to support rating, design tools, and 

implementation of efficient window technologies. 

4.3.5.1 Target Market 

Market Introduction: 2007; BT assumed that this project would accelerate the introduction of 

this technology into the marketplace by 10 years. 

4.3.5.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. Incremental Installed Cost Over competing technology (Low-e Double-Pane Windows) 

• 2007: $6.00/ft2

• 2020: $4.00/ft2

• 2030: $3.00/ft2

4.3.5.3 Methodology and Calculations 

Inputs to Base Case. BT did not provide inputs to change the base case assumptions for the 

program markets. BT’s calculations were based on a baseline that was developed from the 

Energy Information Administration’s (EIA’s) Commercial Buildings Energy Consumption 

Survey (CBECS), Residential Energy Consumption Survey (RECS), and the Annual Energy 

Outlook (AEO). Note that the base technology to which this technology is being compared is that 

of low-e double pane windows. 

Technical Characteristics 

Performance Parameters: Superwindows have maximum U-valuel and solar heat gain 

coefficient (SHGC)m for four climate zones. These climate zones do not directly correspond to 

the traditional climate zones used in CBECS or RECS; they also do not correspond to the 

census divisions used in NEMS. These new climate zones are based on the eight climate zones 

that were developed as part of the IECC 2003 code change cycle or Residential IECC Code 

Change (RICC). In general, the Superwindow zones map from the RICC zones (Table G-12). 

Table G-12. Mapping of RICC Zones to Superwindow Zones 

RICC Zone Superwindow Zone 

1 Southern 

2 Southern 

3 South/Central 

4 North/Central 

5 Northern 

6 Northern 

7 Northern 

8 Northern 

To construct the four Superwindow zones there was a fair amount of smoothing required due to 

geo-political boundaries, existing codes, and commercial regions. For example, a strict 

 

l

 U-Value is defined as the rate of heat loss, in Btu per hour, through a square foot of surface. m SHGC is the fraction of solar radiation admitted through a window. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-35
adherence of the eight RICC zones to four Superwindow zones shown above would have 

portions of California in all four Superwindow zones and would result in discontinuities in the 

zones across the country. The final result is that California is wholly within the South/Central 

zone and all four Superwindow zones are continuous across the country. Performance 

parameters are listed in Table G-13. 

Table G-13. Performance Parameter Maximums for Superwindows 

Region End Use Shading Coefficient U-Value 

Northern Heating 0.6087 0.10 Btu/ft2·°F 

Cooling 0.2609 0.10 Btu/ft2·°F 

North Central Heating 0.6807 0.10 Btu/ft2·°F

Cooling 0.2609 0.10 Btu/ft2·°F

South Central Heating 0.1304 0.20 Btu/ft2·°F

Cooling 0.1304 0.20 Btu/ft2·°F

Southern Heating 0.1304 0.20 Btu/ft2·°F

Cooling 0.1304 0.20 Btu/ft2·°F

Performance Target: Performance characteristics vary by climate zone. The estimated savings 

per building were determined by simulating residential buildings in all climate zones (see Table 

G-14). 

Table G-14. Performance Targets for Superwindows 

Region Sector End Use New Building 

Savings 

Existing Building 

Savings 

Units 

Heating 10.80 11.15 MMBtu/HH Northern Residential Cooling 4.29 4.31 MMBtu/HH 

Heating 8.83 9.18 MMBtu/HH North Central Residential Cooling 5.05 5.15 MMBtu/HH 

Heating -0.08 0.02 MMBtu/HH South Central Residential Cooling 10.10 10.32 MMBtu/HH 

Heating 1.64 1.90 MMBtu/HH Southern Residential Cooling 6.32 6.66 MMBtu/HH 

Weighted National Heating 6.24 6.51 MMBtu/HH 

Average Residential Cooling 6.34 6.44 MMBtu/HH 

Lifetime: 30 years 

Expected Market Uptake. The goal is to obtain 65% of window sales in new residential 

buildings and 33% in existing residential buildings by 2020. Penetration curves were developed 

based on market diffusion curves developed by PNNL and documented in the 2004 PNNL 

report, Methodological Framework for Analysis of Buildings-Related Programs: The GPRA 

Metrics Effort (Elliott, et. al). The “Accelerated” penetration curve represents the percent of 

superwindow sales with the DOE project; the “Net” penetration curve represents the percent of 

superwindow sales attributable to DOE, as BT assumed that the DOE project would accelerate 

market acceptance by 10 years. See penetration curves in Figures G-8 and G-9. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-36
0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

2000 2005 2010 2015 2020 2025 2030 2035

Accelerated Penetration

from DOE R&D

Net Penetration with DOE

R&D

Figure G-8. Superwindows – New Residential Buildings Percent of Sales 

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

2000 2005 2010 2015 2020 2025 2030 2035

Accelerated Penetration

from DOE R&D

Net Penetration with

DOE R&D

Figure G-9. Superwindows – Existing Residential Buildings Percent of Sales 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-37
4.3.6 Low-Emissivity Glass Acceptance

Project Description. (1) Low-e windows have at least one surface coated with a thin, nearly 

invisible, metal oxide or semiconductor film that reduces the heat transfer through windows. 

The conventional windows that they replace have no coating. Currently low-e windows 

represent less than 20% of the commercial market and are not the default product for builders in 

the residential market, constituting about 40% of that market. Additional research that supports 

industry and nonprofit energy efficiency programs from FY07 through FY09 can significantly 

increase the penetration of these energy-efficient products. The purpose of the program is to 

increase the penetration of low-e glass from 40% in the residential market and 10% in the 

commercial market to 100% in both markets by 2020. Two programs, Low-e Market 

Acceptance and Energy Star Windows, form the joint means to achieving the low-e penetration 

goal; hence, the savings will be split equally. The performance of the low-e glass is as 

described for the Electrochromic and Super Windows baseline. 

4.3.6.1 Target Market 

Market Introduction: The technology is commercially available. BT assumed that this 

project would accelerate the penetration in the marketplace by 10 years. 

4.3.6.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. Incremental Installed Cost over Conventional Double-Pane Windows 

• 2005: $1.00/ft2

• 2015: $0.50/ft2

4.3.6.3 Methodology and Calculations 

Inputs to Base Case. BT did not provide inputs to change the base case assumptions for the 

program markets. BT’s calculations were based on a baseline that was developed from the 

Energy Information Administration’s (EIA’s) Commercial Buildings Energy Consumption 

Survey (CBECS), Residential Energy Consumption Survey (RECS), and the Annual Energy 

Outlook (AEO). 

Technical Characteristics 

Performance Parameters: Low-e Windows have maximum U-value and SHGC for four 

different climate zones. These climate zones do not directly correspond to the traditional 

climate zones used in CBECS or RECS; they also do not correspond to the census divisions 

used in NEMS. These new climate zones are based on the eight climate zones that were 

developed as part of the IECC 2003 code change cycle or Residential IECC Code Change 

(RICC). In general the Low-e zones map from the RICC zones as follows in Table G-15. 

Table G-15. Mapping of RICC Zones to Low-e Zones 

RICC Zone Low-e Zone 

1 Southern 

2 Southern 

3 South/Central 

4 North/Central 

5 Northern 

6 Northern 

7 Northern 

8 Northern 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-38
To construct the four Low-e zones, there was a fair amount of smoothing required due to geopolitical boundaries, existing codes, and commercial regions. For example, a strict adherence of 

the eight RICC zones to four Low-e zones shown above would have portions of California in all 

four Low-e zones and would result in discontinuities in the zones across the country. The final 

result is that California is wholly within the South/Central zone and all four Low-e zones are 

continuous across the country. Performance parameters are listed in Table G-16. 

Table G-16. Performance Parameter Maximums for Low-e Windows 

Region Shading Coefficient U-Value 

Northern 0.60 0.35 Btu/ft2·°F 

North Central 0.55 0.40 Btu/ft2·°F 

South Central 0.40 0.40 Btu/ft2·°F 

Southern 0.40 0.65 Btu/ft2·°F 

Performance Target: Performance characteristics vary by building type and climate zone. The 

estimated savings per building were determined by simulating residential and commercial 

buildings in all climate zones (see Table G-17). 

Table G-17. Performance Targets for Low-e Windows 

Region Sector End Use New Building 

Savings 

Existing Building 

Savings 

Units 

Heating 8.17 8.30 MMBtu/HH Residential Cooling 0.06 0.19 MMBtu/HH 

Heating 6.24 5.73 MMBtu/ksf Northern 

Commercial Cooling -0.45 -0.58 MMBtu/ksf 

Heating 2.88 2.94 MMBtu/HH Residential Cooling 1.72 1.79 MMBtu/HH 

Heating 2.98 2.77 MMBtu/ksf North Central 

Commercial Cooling 0.74 0.68 MMBtu/ksf 

Heating 0.09 0.00 MMBtu/HH Residential Cooling 10.50 10.39 MMBtu/HH 

Heating 0.75 0.66 MMBtu/ksf South Central 

Commercial Cooling 5.91 5.62 MMBtu/ksf 

Heating -1.48 -1.77 MMBtu/HH Residential Cooling 9.18 8.77 MMBtu/HH 

Heating -0.14 -0.14 MMBtu/ksf Southern 

Commercial Cooling 5.21 4.98 MMBtu/ksf 

Heating 3.82 3.82 MMBtu/HH Residential Cooling 4.43 4.42 MMBtu/HH 

Heating 3.36 3.08 MMBtu/ksf 

Weighted National 

Average Commercial Cooling 2.25 2.07 MMBtu/ksf 

Expected Market Uptake. The purpose of the program is to increase the penetration of low-e 

glass from 40% in the residential market and 10% in the commercial market to 100% in the 

residential market by 2020 and in the commercial market by 2025. Both programs, Low-e 

Market Acceptance and Energy Star Windows, form the joint means to achieving the low-e 

penetration goal – the savings are to be split equally. Penetration curves were developed based 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-39
on market diffusion curves developed and documented by PNNL(2). The “Accelerated” 

penetration curve represents the percent of superwindow sales with the DOE project; the “Net” 

penetration curve represents the percent of sales attributable to DOE, as BT assumed that the 

DOE project would accelerate market acceptance by 10 years. The penetration rates are shown 

in Figures G-10 and G-11. For Low-e Market Acceptance/ Energy Star Windows, BT 

assumed that these projects would accelerate the acceptance of this technology in the 

marketplace by 10 years. 

0%

20%

40%

60%

80%

100%

120%

2000 2005 2010 2015 2020 2025 2030 2035

Accelerated Penetration from

DOE R&D

Net Penetration with DOE

R&D

Figure G-10. FY07 Low-e Windows – Commercial Buildings Percent of Sales 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-40
0%

20%

40%

60%

80%

100%

120%

2000 2005 2010 2015 2020 2025 2030 2035

Accelerated Penetration

from DOE R&D

Net Penetration with DOE

R&D

Figure G-11. FY07 Low-e Windows – Residential Buildings Percent of Sales 

4.3.7 Sources 

(1) “Building Technology Program: Research, Development and Demonstration Plan, Planned 

Program Activities for 2004-2010.” Final Draft. U.S. DOE, January 9, 2004. 

(2) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics 

Effort. PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 

4.4 Envelope Research and Development: Thermal 

The Building Technology Program’s long range goal of developing Zero Energy Buildings 

(ZEB) by 2025 will require more cost effective, durable and efficient building envelopes. 

Reducing envelope heat transfer will significantly facilitate attainment of a practical ZEB since 

a significant amount of space heating and cooling energy is lost through inefficient envelopes.(1)

To make ZEB affordable, efforts to reduce the energy required for the building are a necessary 

complement to efforts to reduce the cost of renewable, on-site power. Forty-three percent of the 

primary energy used in a residence is spent on space heating and cooling. (2)

4.4.1 Significant Changes from FY06 

The thermal activities are new for FY07. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-41
4.4.2 Advanced Wall Systems 

Project Description(1): Develop new types of regionally optimized wall systems that are 

inexpensive and are insensitive to moisture ingress. Additionally, invent and evaluate new 

techniques for window/wall interface. The goal for the advanced wall systems project is to 

make these systems constructed by 2010 twice as efficient as Building America’s regional 

benchmarks with no additional envelope failure risk. 

4.4.2.1 Target Market 

Market Description: The market is new single family residential home. 

Size of Market: In 2003, 1,386,300 new single-family homes were built. 

4.4.2.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. At market introduction: 30% above conventional insulation and window material costs 

or about $1980/single family housen

. At maturity (10 years): equal to conventional. 

Key Consumer Preferences/Values(1): A market resistance to increased wall thickness has 

jeopardized opportunities to improve the energy efficiency of this envelope component in many 

regions. Therefore, advanced materials and systems are needed that deliver significant 

improvements in energy performance without increasing wall thickness. 

4.4.2.3 Methodology and Calculations 

Technology Characteristics 

Market Introduction: 2010 

Performance Parameters: Performance and design parameters for baseline and Advanced 

Wall Systems are presented below. 

Baseline: Wall has R-value of 10 Btu/hr·ft2

·F° (U-value = 0.1 hr·ft2·F°/Btu) and includes 

fenestration. 

• Windows 

o 15% of wall area 

o Double pane wood or vinyl 

o U-value = 0.36 Btu/hr·ft2

·F° 

o Shading coefficient = 0.48 

• Opaque Wall 

o 85% of wall area 

o Wood siding on wood frame 

o U-value = 0.054118 Btu/hr·ft2·F° 

 

n

 Based on a new 2,349 ft2 house (NAHB—2004 single family home) with roughly 470 ft2 of window area (20% of floor area 

and 15% of wall area) and 1,666 ft2

 of insulated ceiling area (RECSS 2001—average number of stories is 1.41) 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-42
Advanced: Wall has R-value of 20 Btu/hr·ft2

·F° (U-value = 0.05 hr·ft2·F°/Btu) and includes 

fenestration. 

• Windows 

o 15% of wall area 

o Advance window 

o U-value = 0.18 Btu/hr·ft2·F° 

o Shading coefficient = 0.48 

• Opaque Wall 

o 85% of wall area 

o Wood siding on wood frame 

o U-value = 0.027059 Btu/hr·ft2·F° 

Performance Target: Tables G-18 and G-19 present the changes in heating and cooling loads 

by regions for Advanced Wall Systems. These data are presented in both absolute and 

percentage terms. 

Table G-18. Heating and cooling load decrease per household per year (MMBtu/year) 

New 

England

Middle 

Atlantic

East 

North 

Central

West 

North 

Central

South 

Atlantic

East 

South 

Central

West 

South 

Central Mountain Pacific National

MBtu/year Heat 13.1 13.1 15.0 15.6 5.8 7.5 5.2 9.4 5.2 9.2

MBtu/year Cool -1.2 -1.2 -1.1 -1.0 -0.9 -0.7 -0.3 0.8 -2.3 -0.7

Table G-19. Heating and cooling load decrease per household per year (% decrease) 

New 

England

Middle 

Atlantic

East 

North 

Central

West 

North 

Central

South 

Atlantic

East 

South 

Central

West 

South 

Central Mountain Pacific National

% Decrease Heat 30.4% 30.4% 28.2% 27.6% 38.2% 37.1% 43.7% 33.9% 45.8% 32.3%

% Decrease Cool -10.5% -10.5% -12.0% -9.8% -2.9% -2.5% -0.7% 2.7% -14.8% -2.8%

Lifetime: Same as baseline or longer. 

4.4.3 Next Generation Attic Systems 

Project Description(1): Develop and regionally optimize the next generation of attic systems 

(e.g., insulation, ventilation strategy, component location, ducts). Also investigate new attic 

structural systems that will allow for automated construction and develop reliable consensusbased rating methods to assess energy efficiency options for roofing systems. The goal for the 

next generation attic systems project is to make these systems constructed by 2013 twice as 

efficient as Building America’s regional benchmarks with no additional envelope failure risk. 

4.4.3.1 Target Market 

Market Description: The market is new single family residential homes. 

Size of Market: In 2003, 1,386,300 new single-family homes were built. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-43
4.4.3.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. At market introduction: 30% above conventional insulation material costs or about 

$165/single family house.o

 At maturity (10 years): equal to conventional. 

Key Consumer Preferences/Values: Consumers largely ignore attics and are mostly 

concerned about envelope failure. 

4.4.3.3 Methodology and Calculations 

Market Introduction: 2013 

Performance Parameters: Performance and design parameters for baseline and Next 

Generation Attic Systems are presented below. 

Baseline: Roof has R-value of 30 Btu/hr·ft2·F° (U-value = 0.0333 hr·ft2·F°/Btu). 

• Shingle or shake roof with attic 

• Unconditioned 

Next Generation: Roof has R-value of 45 Btu/hr·ft2

·F° (U-value = 0.0222 hr·ft2·F°/Btu). 

• Shingle or shake roof with attic 

• Conditioned 

Performance Target: Tables G-20 and G-21 present changes in heating and cooling loads by 

regions for Next Generation Attic Systems. These data are presented in both absolute and 

percentage terms. 

Table G-20. Heating and cooling load decrease per household per year (MMBtu/year) 

New 

England

Middle 

Atlantic

East 

North 

Central

West 

North 

Central

South 

Atlantic

East 

South 

Central

West 

South 

Central Mountain Pacific National

MBtu/year Heat 3.0 3.0 3.5 3.7 1.2 1.5 1.0 2.1 1.1 2.1

MBtu/year Cool 0.3 0.3 0.2 0.2 0.8 0.8 1.1 1.2 0.6 0.7

Table G-21. Heating and cooling load decrease per household per year (% decrease) 

New 

England

Middle 

Atlantic

East 

North 

Central

West 

North 

Central

South 

Atlantic

East 

South 

Central

West 

South 

Central Mountain Pacific National

% Decrease Heat 7.1% 7.1% 6.7% 6.5% 8.0% 7.6% 8.2% 7.6% 10.0% 7.3%

% Decrease Cool 2.4% 2.4% 1.7% 2.4% 2.4% 2.7% 2.9% 4.1% 3.6% 2.9%

Lifetime: Same as baseline or longer. 

 

o

 Based on a new 2,349 ft2 house (NAHB—2004 single family home) with roughly 1,666 ft2 of insulated ceiling area (RECSS 

2001—average number of stories is 1.41) 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-44
4.4.4 Next Generation Envelope Materials 

Project Description(1): Develop a portfolio of new insulation and membrane materials, 

including the exterior finishes, having residential and commercial application. The major 

components of strategy are: 

• Develop next generation of low density thermal insulation materials. 

• Develop reflective exterior wall finishes. 

• Develop smart membrane materials with climatically tuned properties. 

• Develop thermochromic roofing surfaces using microstructures down to the nanoscale. 

 

4.4.4.1 Target Market 

Market Description: The market is new single family residential home. 

Size of Market: In 2003, 1,386,300 new single-family homes were built. 

4.4.4.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. At market introduction: 30% above conventional insulation material costs or about 

$535/single family house.p

 At maturity (10 years): equal to conventional. 

Key Consumer Preferences/Values(1): Roofing products and wall finishes for cooling 

dominated climates need to be aesthetically pleasing to the consumer but reflect large 

percentages of solar radiation. 

4.4.4.3 Methodology and Calculations 

Market Introduction: 2015 

Performance Parameters: Performance and design parameters for baseline and Next 

Generation Envelope Materials are presented below. 

Baseline: Wall has R-value of 10 Btu/hr·ft2

·F° (U-value = 0.1 hr·ft2·F°/Btu) and includes 

fenestration. Roof has R-value of 30 Btu/hr·ft2

·F° (U-value = 0.0333 hr·ft2·F°/Btu). 

• Windows 

o 15% of wall area 

o Double pane wood or vinyl 

o U-value = 0.36 Btu/hr·ft2

·F° 

o Shading coefficient = 0.48 

• Opaque Wall 

o 85% of wall area 

o Wood siding on wood frame 

o U-value = 0.055118 Btu/hr·ft2·F° 

 

p

 Based on a new 2,349 ft2 house (NAHB—2004 single family home) with roughly 470 ft2 of window area (20% of floor area 

and 15% of wall area), 2,662 ft2

 of opaque wall area, and 1666 ft2 of insulated ceiling area (RECSS 2001—average number of 

stories is 1.41) 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-45
• Roof 

o Shingle or shake roof with attic 

o Unconditioned 

o Insulation R-value is 29.2 

Next Generation: Wall has R-value of 11.1 Btu/hr·ft2

·F° (U-value = 0.090031 hr·ft2·F°/Btu) and 

includes fenestration. Roof has R-value of 43.8 Btu/hr·ft2

·F° (U-value = 0.022834 

hr·ft2·F°/Btu). 

• Windows (unchanged) 

o 15% o f wall area 

o Double pane wood or vinyl 

o U-value = 0.36 Btu/hr·ft2·F° 

o Shading coefficient = 0.48 

• Opaque Wall 

o 85% of wall area 

o Wood siding on wood frame 

o U-value = 0.042389 Btu/hr·ft2

·F° 

• Roof 

o Shingle or shake roof with attic 

o Unconditioned 

Performance Target: Tables G-22 and G-23 present changes in heating and cooling loads by 

regions for Next Generation Envelope Materials. These data are presented in both absolute and 

percentage terms. 

Table G-22. Heating and cooling load decrease per household per year (MMBtu/year) 

New 

England

Middle 

Atlantic

East 

North 

Central

West 

North 

Central

South 

Atlantic

East 

South 

Central

West 

South 

Central Mountain Pacific National

MBtu/year Heat 5.5 5.5 6.4 6.7 2.3 2.9 1.9 3.9 2.2 3.8

MBtu/year Cool 0.2 0.2 0.1 0.2 0.9 0.9 1.4 1.8 0.5 0.9

Table G-23. Heating and cooling load decrease per household per year (% decrease) 

New 

England

Middle 

Atlantic

East 

North 

Central

West 

North 

Central

South 

Atlantic

East 

South 

Central

West 

South 

Central Mountain Pacific National

% Decrease Heat 12.7% 12.7% 12.0% 11.7% 15.2% 14.5% 16.3% 13.8% 19.0% 13.3%

% Decrease Cool 2.1% 2.1% 1.0% 2.1% 2.9% 3.2% 3.7% 5.9% 3.4% 3.5%

Lifetime: Same as baseline or longer. 

4.4.5 Sources 

1) “Building Technology Program: Research, Development and Demonstration Plan, Planned 

Program Activities for 2004-2010.” Final Draft. U.S. DOE, January 9, 2004. 

2) D&R International, Ltd., The 2005 Building Energy Databook,” Silver Spring MD, August 

2005 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-46
4.5 Lighting Research and Development 

4.5.1 Significant Changes from FY06 

No significant changes were made to this program for the FY07 effort. 

4.5.2 Solid-State Lighting 

Project Description. The Solid-State Lighting activity develops and accelerates the 

introduction of solid-state lighting and seeks to achieve the following for lighting: 

• Significantly greater efficacy than conventional sources, such as T8 fluorescents 

• Easy integration into building systems of the future 

• Ability to provide the appropriate color and intensity for any application 

• Ability to last 20,000 to 100,000 hours 

• Ability to readily supplement natural sunlight. 

4.5.2.1 Target Market 

Market Description: The market includes all commercial buildings, with some technologies 

being introduced into residential buildings. 

Size of Market(1): Lighting consumes 26% (3.9 QBtu) of the primary energy used in 

commercial buildings, which had building stock of about 69 billion ft2 in 2000.q

Baseline Technology Improvements. There are no technology improvements assumed apart 

from what appears in the Energy Information Administration (EIA) baseline. 

4.5.2.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered in developing energy output estimates: 

• Helps maintain U.S. semiconductor leadership 

• Develops U.S. leadership in lighting technology 

• Reduces pollution and contributes to U.S. climate-change goals 

• Improves U.S. productivity from better lighting in work environments 

• Coordinates with and receives technical advice from an industry consortium of forprofits companies representing the traditional lighting and semiconductor industries. 

4.5.2.3 Methodology and Calculations 

Technical Characteristics. Key assumptions concerning the likely dates of introduction and 

the expected efficacies were influenced by two sources: 1) “The Case for a National Research 

 

q

 According to a report completed for DOE by Navigant Consulting (“U.S. Lighting Market Characterization, Volume I: 

National Lighting Inventory and Energy Consumption Estimate,” September 2002), the amount of energy used for lighting is 

greater than EIA has traditionally estimated. The report estimates that commercial lighting requires 4.2 QBtu and residential 

lighting requires 2.2 QBtu. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-47
Program on Semiconductor Lighting,”(2) a white paper prepared by Hewlett-Packard and Sandia 

National Laboratories and presented in late 1999 at an industry forum; and 2) a more extended 

study(3) conducted by Navigant Consulting for BT in late 2003; the study presented price and 

performance improvement curves for solid-state lighting that were developed in close 

consultation with industry experts. 

NEMS characterizes each lighting technology by source efficacy level (lumens/watt), capital 

cost ($/1000 lumens or $/kLumen), and annual maintenance cost of lamps. For new 

technologies, the capital costs can be reduced along a logistic-shaped curve. The NEMS model 

divides the commercial lighting market into five major groups: 1) incandescent CFL (point 

source), 2) 4-foot fluorescent, 3) 8-foot fluorescent, 4) high-intensity discharge (HID) low bay 

and 5) HID high bay. Solid-state lighting was assumed to compete in all market groupings 

with different color rendition index lamps. 

Given the cost and efficacy assumptions, the NEMS model chooses among these technologies 

for each building type in each census division. For each group, the market is assumed to be 

further segmented, with each segment characterized by a different discount rate in its decisionmaking criteria. Within each segment, a lighting technology is selected based on minimum 

annualized cost. 

Solid-state lighting was also assumed to be available in the residential lighting market, where it 

competes with conventional incandescent and compact fluorescent options. 

Table G-24 summarizes the cost and performance inputs for the solid state lighting 

technologies used in NEMS-GPRA07 for FY 2007. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-48
Table G-24. Solid-State Lighting Cost and Efficiency Assumptions – FY 2007 GPRA 

Efficacy Price (2004$/klm) 

Low CRI Med CRI High CRI V.High CRI Low CRI Med CRI High CRI V.High CRI 

2005 55 39 24 8 $ 70.85 $ 132.15 $ 182.88 $ 288.17 

2006 60 44 28 11 $ 62.22 $ 123.45 $ 172.51 $ 269.09 

2007 65 49 32 14 $ 50.87 $ 110.25 $ 156.40 $ 240.15 

2008 70 54 37 17 $ 40.17 $ 95.23 $ 137.43 $ 207.21 

2009 75 59 41 20 $ 30.79 $ 79.33 $ 116.59 $ 172.35 

2010 79 65 45 23 $ 23.07 $ 63.75 $ 95.37 $ 138.20 

2011 85 71 49 25 $ 17.05 $ 49.60 $ 75.37 $ 107.17 

2012 90 77 54 28 $ 12.56 $ 37.60 $ 57.82 $ 80.85 

2013 96 84 60 31 $ 9.31 $ 28.00 $ 43.39 $ 59.81 

2014 102 90 65 37 $ 7.01 $ 20.69 $ 32.15 $ 43.78 

2015 107 96 72 42 $ 5.41 $ 15.31 $ 23.74 $ 32.00 

2016 113 102 78 47 $ 4.31 $ 11.47 $ 17.66 $ 23.58 

2017 118 108 84 53 $ 3.56 $ 8.78 $ 13.36 $ 17.68 

2018 123 114 91 58 $ 3.05 $ 6.93 $ 10.37 $ 13.61 

2019 129 118 97 65 $ 2.70 $ 5.65 $ 8.31 $ 10.82 

2020 133 123 103 72 $ 2.47 $ 4.79 $ 6.91 $ 8.92 

2021 137 128 109 80 $ 2.32 $ 4.20 $ 5.96 $ 7.64 

2022 141 133 114 87 $ 2.21 $ 3.81 $ 5.32 $ 6.77 

2023 144 136 119 94 $ 2.14 $ 3.54 $ 4.89 $ 6.19 

2024 147 140 124 101 $ 2.10 $ 3.36 $ 4.59 $ 5.80 

2025 150 143 129 107 $ 2.06 $ 3.24 $ 4.40 $ 5.54 

2026 152 146 132 113 $ 2.04 $ 3.16 $ 4.27 $ 5.36 

2027 155 149 135 120 $ 2.03 $ 3.11 $ 4.18 $ 5.24 

2028 158 151 139 123 $ 2.02 $ 3.07 $ 4.12 $ 5.16 

2029 159 152 142 127 $ 2.01 $ 3.05 $ 4.08 $ 5.11 

2030 160 153 145 131 $ 2.01 $ 3.03 $ 4.05 $ 5.07 

4.5.2.4 Sources 

(1) Annual Energy Outlook 2002. 2002. Energy Information Administration, Washington, 

D.C.. 

(2) Haitz, R., and F. Kish (Hewlitt-Packard Co) and J. Tsao and J. Nelson (Sandia National 

Laboratories). 1997. "Case for a National Research Program on Semiconductor Lighting," 

White paper presented at the 1999 Optoelectronics Industry Development Association 

forum in Washington D.C., October 6, 1999. 

(3) Navigant Consulting, 2003. Energy Savings Potential of Solid-State Lighting in General 

Illumination Applications. Prepared for DOE's Office of Building Technologies by 

Navigant Consulting, Washington D.C. 

4.5.3 Lighting Controls 

4.5.3.1 Target Market 

Market Description: The market includes all commercial buildings, with some technologies 

being introduced into residential buildings. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-49
Size of Market: Lighting consumes 26% (3.9 quadrillion Btu) of the primary energy used in 

commercial buildings, which had a building stock of about 69 billion ft2

 in 2000(1). 

Baseline Technology Improvements. There are no technology improvements assumed apart 

from what appears in the Energy Information Administration (EIA) baseline. 

4.5.3.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. BT assumed a 4-year payback period on investment to develop incremental investment 

costs (i.e., an annual energy cost savings of $1 implies an initial investment of $4). 

Key Consumer Preferences/Values. The following nonenergy characteristics were not 

considered in developing energy output estimates: 

• Develops U.S. leadership in lighting technology 

• Reduces pollution and contributes to U.S. climate-change goals 

• Improves U.S. productivity from better lighting in work environments 

4.5.3.3 Methodology and Calculations 

Inputs to Base Case. BT did not provide inputs to change the base case assumptions for the 

program markets. BT’s calculations were based on a baseline that was developed from the 

Energy Information Administration’s (EIA’s) Commercial Buildings Energy Consumption 

Survey (CBECS), Residential Energy Consumption Survey (RECS), and the Annual Energy 

Outlook (AEO). 

Technical Characteristics. Various field studies(2) have shown a very large energy savings 

potential for lighting controls, primarily using occupancy and daylighting controls. These 

studies have shown that aggressively implementing controls can save 20% to 40% of lighting 

energy use. BT supports the development of more advanced systems—through both research 

and field testing—that will further reduce energy used for lighting in commercial buildings. BT 

support of research to evaluate the interrelationship between human vision and efficient light 

use will also contribute to future energy savings. 

For FY 2007, the impact of the BT activities in lighting controls and efficient lighting practices 

was assumed to yield an incremental 5% reduction in lighting energy use compared with current 

practice. (By incremental, the BT activities are assumed to lead to further savings over and 

above the control technologies that the private sector offers now and are likely to offer.) 

Expected Market Uptake. BT assumed that up to 60% of new commercial buildings could 

incorporate these technologies and that 20% of the existing stock could be retrofitted with these 

systems by 2020. A time profile of penetration rates was based on the historical pattern of 

market penetration observed for electronic ballasts. An S-shaped penetration curve was fit to 

historical market shares for electronic ballasts and then applied to project future adoption of 

advanced lighting distribution systems and controls. This curve indicated that nearly 50% of 

the ultimate market penetration was achieved after nine years. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-50
4.5.3.4 Sources 

(1) Annual Energy Outlook 2002. 2002. Energy Information Administration, Washington, 

D.C. 

(2) See http://eande.lbl.gov/btp/450gg/publications.html and 

www.cmpco.com/services/pubs/lightingfacts/controls.html

(3) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics 

Effort. PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 

4.6 Space Conditioning and Refrigeration R&D 

The Building Technology Program’s long range goal of developing Zero Energy Buildings 

(ZEB) by 2025 will require more efficient and less expensive HVAC equipment if ZEBs are 

going to be widespread and affordable. Equipment integration (waste heat from one appliance 

is beneficially used by another) and new approaches to providing space conditioning are 

integral to this goal. 

4.6.1 Significant Changes from FY06 

For FY07, new activities for Space Conditioning R&D were characterized for the GPRA 

estimates. 

4.6.2 Hy-Pak MA 

To make ZEB affordable, efforts to reduce the energy required for the building are a necessary 

complement to efforts to reduce the cost of renewable, on-site power.(1) Eleven percent of the 

primary energy used in commercial buildings is spent on space cooling. (2)

Project Description: Develop a cost-effective, hydronic rooftop HVAC unit that reduces 

energy consumption 50% and delivers 0 to 100% ventilation air 

4.6.2.1 Target Market 

Market Description: The market is commercial buildings. Because of the evaporative nature 

of the device the market is limited to dry west coast climates only. 

Size of Market: The applicability varies by census region. This technology takes advantage of 

evaporative cooling and therfore is applicable only in dry and marine climates. Table G-25

contains the portion of the census region to which this technology could be applied: 

Table G-25. Percentage of census region to which Hy-Pak MA technology is applicable 

New 

England

Middle 

Atlantic

East North 

Central

West

North 

Central

South 

Atlantic

East

South 

Central

West

South 

Central Mountain Pacific National

0% 0% 0% 0% 0% 0% 8.87% 100% 97.31% 23.02%

Market Introduction: 2008 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-51
4.6.2.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. 

• Cost at market introduction: 2 times cost of conventional technology (Simple payback: 

2-3 years). 

• Cost at maturity (10 years): 1.5 times cost of conventional technology (Simple payback: 

1-1.5 years) 

Key Consumer Preferences/Values: Because this technology uses evaporative cooling in the 

condenser and indirect evaporative cooling of the ventilation air it is not likely to be accepted by 

consumers in areas of high relative humidity. 

4.6.2.3 Methodology and Calculations 

Performance Parameters: Performance parameters for baseline and rooftop AC and HyPakMA are presented below. 

• Baseline: Conventional rooftop air conditioning—11.2 EER 

• HyPak-MA: 16.8 EER 

Lifetime: Same as baseline. 

Expected Market Uptake: Anticipated market share in 2018 is 20% of rooftop AC market in 

applicable regions. 

4.6.3 Thermotunneling Based Cooling 

To make ZEB affordable, efforts to reduce the energy required for the building are a necessary 

complement to efforts to reduce the cost of renewable, on-site power.(1) Eleven percent of the 

primary energy used in buildings is spent on space cooling.(2) In addition, refrigeration uses 

seven percent of the primary energy used in buildings.(2) Savings associated with refrigeration 

are not considered here. 

Project Description: Develop high efficiency, compact, quiet, environmentally friendly, 

reliable cooling without the use of moving parts or refrigerants. Cooling using themortunneling 

technology involves the transport of hot electrons across a gap between two low work function 

electrodesr

, from the object to be cooled (the cathode) to the heat rejection electrode (the anode). 

4.6.3.1 Target Market 

Market Description: The market is all residential and commercial cooling. 

Size of Market: All commercial and residential air conditioning equipment in new and existing 

residential and commercial buildings. 

Market Introduction: 2010 

 

r

 A work function is the energy needed to extract an electron from a material; a low work function ≡ <1.0eV (where eV is 

electron-volt). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-52
4.6.3.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. 

• Cost at market introduction: 75% of the cost of conventional technology 

• Cost at maturity (10 years): 25% of the cost of conventional technology 

Key Consumer Preferences/Values: Quieter, more reliable air conditioning equipment should 

have a market advantage. 

4.6.3.3 Methodology and Calculations 

Performance Parameters: Performance and design parameters for baseline and 

Thermotunneling Based Cooling are presented below. 

• Baseline: COP ~ 40-45% of Carnot efficiency 

• Thermotunneling Based Cooling: 65% of Carnot efficiency; including ancillary 

equipment the net results is a 35% increase in cooling efficiency. 

Lifetime: Same as baseline or longer. 

Expected Market Uptake: Anticipate market share of 70% at maturity 

4.6.4 Integrated Heat Pump 

To make ZEB affordable, efforts to reduce the energy required for the building are a necessary 

complement to efforts to reduce the cost of renewable, on-site power. Thirty-one percent of the 

primary energy used in a residence is spent on space heating, cooling, and water heating.(2) 

Project Description: Develop an integrated, multifunction heat pump that provides space 

heating, cooling, water heating and dedicated dehumidification.(3)

 

4.6.4.1 Target Market 

Market Description: The market is new single family residential homes. 

Size of Market: In 2003, 1,386,300 new single-family homes were built. 

Market Introduction: 2010 

4.6.4.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. 

• Cost at market introduction: 2.5 times cost of conventional heat pump without water 

heating capability ($2700 versus $1100 for a 1.5 ton unit) 

• Cost in 5 years: 1.7 times cost of conventional heat pump without water heating 

capability ($1900 versus $1100 for a 1.5 ton unit) 

Key Consumer Preferences/Values: Dedicated dehumidification should enhance sales in high 

humidity markets. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-53
4.6.4.3 Methodology and Calculations 

Performance Parameters: Annual operating cost savings of $400/year over conventional unit 

due to higher efficiency and dual production (simultaneous cooling and hot water production) 

Lifetime: Same as baseline. 

Expected Market Uptake: Anticipate market share is 8% in 5 years and 30% ultimately. 

4.6.5 Sources 

1) “Building Technology Program: Research, Development and Demonstration Plan, Planned 

Program Activities for 2004-2010.” Final Draft. U.S. DOE, January 9, 2004. 

2) D&R International, Ltd., The 2005 Building Energy Databook,” Silver Spring MD, August 

2005 

3) Building Technologies Program Fiscal Year 2006 Annual Operating Plan: Project Proposal 

for Residential ZEB-Enabling Equipment. CEBT002 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-54
5.0 Technology Validation and Market Introduction 

This effort seeks to accomplish effective delivery of the full menu of efficiency and renewable 

resources aligned with community and customer focus. The activities focus on the end-user 

needs, rather than individual EERE programs, and provide easier access to EERE’s array of 

technologies and resources to ensure they are part of the economic solutions for communities 

across the country. 

5.1 Rebuild America 

Project Description. Rebuild America accelerates energy-efficient improvements in existing 

buildings through community-level partnerships and focuses on K-12 schools, colleges, and 

universities, State and local governments, public and multi-family housing, and commercial 

buildings. Rebuild America connects people, resources, proven ideas, and innovative practices 

to solve problems. The project provides one-stop shopping for information and assistance on 

how to plan, finance, implement, and manage retrofit projects to improve buildings energy 

efficiency and helps communities find other resources on renewable energy applications, 

efficient new building designs, energy education, and other innovative energy conservation 

measures. 

5.1.1 Significant Changes from FY06 

This project was previously included in the program structure under Weatherization and 

Intergovernmental Programs. For FY07, the project was moved under BT. For modeling 

purposes, the characterization of the project did not change. 

5.1.2 Target Market 

Market Description. The general target market includes new and existing multifamily 

housing; public/assisted single-family residential units; and commercial buildings, particularly 

new and existing assembly, health-care, lodging, office, and education buildings. 

Size of Market.(4) The primary market is the commercial-building sector, which includes 

nearly 68 billion square feet of building space; however, the five commercial building types that 

this project targets make up a total of nearly 32 billion square feet. The public assistance(5) and 

multifamily housing that this project also targets make up an additional 27 billion square feet. 

Baseline Technology Improvements. For this analysis, BT did not suggest any changes in 

technology improvements apart from the EIA baseline. 

5.1.3 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. 

Cost of Conventional Technology:

(4) Average of $101/ ft2

 for new commercial and 

multifamily; $0 for existing buildings. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-55
Cost of BT Technology:

(7) $103.00/ ft2

 for new commercial and multifamily; $3/ ft2 (2007 to 

2009), increasing to $4/ ft2

 (2010 to 2030) for existing buildings. 

Incremental Cost: 2% above base for new buildings; $3/ft2 (2007 to 2009), $4/ ft2 (2010 to 

2030) for existing buildings. 

Key Consumer Preference/Values -- Nonenergy Benefits.(5) The following nonenergy 

characteristics were not considered. 

• Revitalized neighborhoods and business districts 

• Improving school facilities 

• Better low-income housing 

• Positive economic impact from keeping dollars locally and increasing property values. 

5.1.4 Methodology and Calculations 

Inputs to Base Case. BT did not provide inputs to change the base case assumptions for the 

program markets. 

Technical Characteristics. The project displaces current design/building practices with the 

target of reducing heating, cooling, water heating, and lighting energy use in retrofitted and new 

buildings by 18%/ft2

 in 2007 (1) and 25%/ft2 by 2010 (3). 

Expected Market Uptake. BT assumed that this activity would not occur in the absence of 

DOE funding, therefore, no acceleration of market acceptance was modeled. The penetration 

rates shown in Table G-26 are based on project goals of committing 2.24 billion square feet by 

2010. 

Table G-26. Penetration Goals for Rebuild America (2,6) 

Penetration Rate % 

Building Type* 2007 2010 2020 2030 

Targeted 

Commercial 

Buildings & MultiFamily Existing 

0.7 1.9 2.3 2.3 

Targeted 

Commercial 

Buildings & MultiFamily New 

0.7 1.9 0.0 0.0 

Single-Family 

Existing 

0.0 0.02 0.04 0.04 

Single-Family 

New 

0.2 0.24 0.0 0.0 

 * Unless otherwise specified, the building vintage is both new (Post 2007) 

 and existing (2007 and prior construction). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-56
5.1.5 Sources 

(1) Weatherization and Intergovernmental Activities Funding Profile by Subprogram. FY 2006 

Corporate Review Budget, U.S.DOE, May 2004. 

(2) DRAFT Weatherization and Intergovernmental Program Multi-Year Program Plan, U.S.DOE, 

September 30, 2003. 

(3) Rebuild America 2002, Rebuild Annual Report, 2002, U.S.DOE, Washington D.C. 

(4) Commercial building and multifamily square footage numbers come from Energy Information 

Administration. 2001. Annual Energy Outlook 2002. DOE/EIA-0383 (2002). U.S. 

Department of Energy, Washington, D.C. 

(5) FY 2002 Budget Request – Data Bucket Report for Rebuild America Program (includes 

Energy Smart Schools and Competitively Selected Community Program) (internal WIP 

document). 

(6) Rebuild America Key Metric Totals from Oct 2003; Dec 2003; Mar 2004; April 2004; May 

2004, Spreadsheet used to document key metrics. (internal WIP document). 

(7) RS Means Company, Inc. 2002. “RS MEANS Square Foot Costs,” 23rd Edition. 

Kingston, MA. 

5.2 Energy Star Program 

Project Description. Energy Star was introduced by the Environmental Protection Agency in 

1992 as a voluntary labeling program designed to identify and promote energy efficient 

products, with the goal of reducing carbon dioxide emissions. Through its partnership with 

more than 7,000 private and public sector organizations, Energy Star delivers the technical 

information and tools that organizations and consumers need to choose energy-efficient 

solutions and best management practices. 

5.2.1 Significant Changes from FY06 

This project was previously included in the program structure under Weatherization and 

Intergovernmental Programs. For FY07, the project was moved under BT. The 

characterization of the project did not change. 

5.2.2 General Target Market 

Market Description. The market is determined by the project equipment. For FY 2007, the 

following residential equipment is characterized: 

o Clothes washers 

o Refrigerators 

o Room air conditioners 

o Dishwashers 

o Compact Fluorescent Lamps (CFLs) 

o Windows 

o Home Performance 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-57
Baseline Technology Improvements. There are no technology improvements assumed apart 

from what appears in the Energy Information Administration (EIA) baseline. 

5.2.3 Key Factors in Shaping Market Adoption of EERE Technologies 

Key Consumer Preferences/Values and Manufacturing Factors. The following nonenergy 

characteristics were not considered. 

• Increased comfort for residential homeowners 

• Decreased time spent changing incandescent lamps 

• Water and water-bill savings from higher efficiency dishwashers and clothes washers 

• Increased amenities with clothes washers, also decreased time required for dryer cycle 

• Higher profits for manufacturers. 

5.2.4 General Methodology 

Market transformation projects, such as Energy Star, attempt to accelerate market penetration of 

existing high-efficiency technologies. The information provided by these programs is designed 

to influence the consumer’s awareness of future energy cost savings as compared to the initial 

cost of the technology. From a modeling standpoint, these efforts are assumed to be represented 

by a reduction in the consumer’s implicit discount rate or hurdle rate. The implicit discount rate 

for a technology is assumed to capture the perceived risk in the purchase of new products. For 

Energy Star technologies, most of the costs are incurred at the time the technology is purchased, 

while most of the energy-saving benefits occur in the future. If the implicit discount rate for a 

given technology is particularly high, the value a consumer places on these future energy-saving 

benefits will be low relative to the weight the consumer places on present costs – reflecting the 

consumer’s uncertainty about future benefits. Therefore, to facilitate project modeling, one goal 

of the Energy Star project is to reduce implicit discount rates by providing additional 

information about the potential benefits to the consumer. 

Within NEMS-PNNLs

, the two modeling parameters determining the implicit discount rate are 

labeled Beta1 and Beta2(1). Beta1 is used as multiplicative factor with the initial cost of the 

appliance, and Beta2 is used to multiply the annual energy cost. The sum of the two products 

(i.e., Beta1 * initial cost + Beta2 * operating cost) is used in the logit specification to yield 

market shares for each technology. As a rough approximation, the ratio of Beta1/Beta2 can be 

interpreted as the consumer discount rate for a specific technology. In the residential NEMSPNNL module, the Beta1 and Beta2 coefficients vary among technologies, as do the resulting 

discount rates. For example, the implied discount rate for refrigerators is 16%, while the 

discount rate is estimated to be more than 80% for electric water heaters. Because the Beta 

parameters must be modified through an iterative process to achieve the discount rate goal for 

each technology, and because the Energy Star program goals have not changed significantly 

since the FY 2004 effort when the original NEMS-PNNL modifications were made, BT has not 

repeated this iterative process using the latest version of NEMS. References to AEO 2001 

reflect the original NEMS model inputs on which the Energy Star program inputs are based. 

 

s

 Any modification or alteration to the official NEMS model must be called out as such; for PNNL’s effort, the modified version 

used is referred to as NEMS-PNNL 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-58
The modifications to the NEMS input file (RTEKTY)—required to estimate energy savings in 

NEMS-PNNL for each technology in an Energy Star project—are described in the following 

sections. The assumed reduction in the discount rate (from Energy Star support) is modeled by 

reducing the Beta1 parameter. The baseline assumptions made by the EIA, the changes in the 

Beta1 coefficients, and the resulting changes in the market shares for the most energy-efficient 

products are documented by technology. 

General Expected Market Uptake. BT modeled clothes washers, refrigerators, electric water 

heaters, gas water heaters, room air conditioners, and dishwashers using input from EIA's 

Annual Energy Outlook 2001,(2) based on a project goal of Energy Star appliances achieving 

20% of the market share by 2010. 

5.2.5 Clothes Washers 

5.2.5.1 Target Market 

Market Description. This project targets new clothes-washer sales. 

5.2.5.2 Methodology and Calculations 

Inputs to Base Case and Technical Characteristics. Modeling the energy savings of clothes 

washers is complex, because energy can be saved by reducing the consumption of the motor, 

hot water use, or dryer energy use. The most efficient new technology is the horizontal-axis 

design, which achieves the bulk of its energy savings by reducing hot water use. 

The residential NEMS input file (RTEKTY) includes a column of factors that relate to hot 

water. The (unitless) factors can be used to adjust the hot water load associated with clothes 

washers and dishwashers. . 

Expected Market Uptake. With the support of the Energy Star project, the Beta1 parameter, 

which impacts the resulting market share of each clothes-washer technology, was modified 

from -0.03811 to -0.0101, based on this product's project goals. 

5.2.6 Refrigerators 

5.2.6.1 Target Market 

Market Description. This project targets new refrigerator sales. 

5.2.6.2 Methodology and Calculations 

Inputs to Base Case and Technical Characteristics. EIA uses four separate models to 

represent the range of energy efficiencies in the refrigerator market. The first three models are 

conventional top-mount freezer models with a total capacity of 18 cubic feet. The fourth is a 

through-the-door model (for water and ice) and does not compete with the first three models. . 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-59
Expected Market Uptake. The Annual Energy Outlook 2001(2) baseline parameters that 

determined the market share for high-efficiency refrigerators are described as follows: 

%19

1207.0

0229.0

2

1 ≈ = −

− = implicit discount rate

Beta

Beta

With the support of the Energy Star project, the parameters impacting market share were 

assumed by BT to change in the following manner, based on project goals: 

%3

1207.0

0040.0

2

1 ≈ = −

− = −

−

−

StarE

StarE

StarE

implicit discount rate

Beta

Beta

This was modified so that the implied discount rate was 6%. The resulting market share for the 

most efficient unit (400 kWh per year for the AEO2005) was roughly 17% greater than in the 

Baseline. 

5.2.7 Room Air Conditioners 

5.2.7.1 Target Market 

Market Description. This project targets sales of new room air conditioners. 

5.2.7.2 Methodology and Calculations 

Inputs to Base Case and Technical Characteristics. For 2005, EIA assumed that efficiencies 

of room air conditioners will range from a low of 2.83 COP (seasonal energy efficiency ratio) to 

a high of 3.52 COP. 

Expected Market Uptake. The baseline parameters that determined the market share for highefficiency room air conditioners are described as follows: 

%100

0120.0

0170.0

2

1 ≈ > −

− = implicit discount rate

Beta

Beta

With the support of the Energy Star project, the parameters impacting market share were 

assumed to change in the following manner, based on project goals: 

%58

0120.0

0070.0

2

1 ≈ = −

− = −

−

−

StarE

StarE

StarE

implicit discount rate

Beta

Beta

The lower hurdle rate was phased in over a 5 year period. 

5.2.8 Dishwashers 

5.2.8.1 Target Market 

Market Description. This project targets sales of new dishwashers. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-60
5.2.8.2 Methodology and Calculations 

Inputs to Base Case and Technical Characteristics. The NEMS baseline includes three 

levels of efficiency for dishwashers 

Expected Market Uptake. The Annual Energy Outlook 2001(2) baseline parameters that 

determined the market share for high-efficiency dishwashers are described as follows: 

%100

02413.0

02738.0

2

1 ≈ > −

− = implicit discount rate

Beta

Beta

With the support of the Energy Star project, the parameters impacting market share were 

assumed to change in the following manner, based on project goals: 

%55

02413.0

01338.0

2

1 ≈ = −

− = −

−

−

StarE

StarE

StarE

implicit discount rate

Beta

Beta

5.2.9 Energy Star CFLs 

5.2.9.1 Target Market 

Market Description. The target market for this technology is residential non-can and non-RLamp Edison socket lights, which would not otherwise switch to Compact Fluorescent Lamps 

(CFLs). 

5.2.9.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. BT assumed that the cost of the conventional incandescent technology is $0.75, and that 

there is no incremental cost associated with a comparable Energy Star CFL. 

Baseline market acceptance. In 1998, PNNL conducted a study examining the historical 

market penetration for 10 energy-efficient products related to the buildings sector. The results 

of this study are documented in the PNNL report, Methodological Framework for Analysis of 

GPRA Metrics: Application to FY04 Projects in BT and WIP (2003, PNNL-14231). The 

resulting data were used to develop a set of generic diffusion curves. These curves were used to 

generate market penetration estimates for projects that do not have a forecast of annual sales 

targets. For the Energy Star CFL activity, the lighting diffusion curve was used. 

5.2.9.3 Methodology and Calculations 

Technical Characteristics. Energy Star-qualified CFLs are assumed to be 66 percent more 

efficient than incandescent lamps (25 W compared to 75 W) 

Expected Market Uptake. Future market share growth for CFLs was extrapolated from 

historical sales data (see Table G-27). On average the CFLs are assumed to be used 4 hours per 

day and have a lifetime of 8000 hours. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-61
Table G-27. Estimated CFL Sales Share and Incremental Savings 

CFL Sales 

Share 

Incremental 

Savings (Billion 

kWh) 

2005 2.3% 0.0 

2010 3.5% 3.2 

2015 4.5% 9.9 

2020 5.5% 18.3 

2025 7.0% 27.5 

Due to their longer lifetimes and use in high use sockets, CFL’s provide roughly 20 percent of 

general residential lighting demand by 2025. 

5.2.10 Windows 

5.2.10.1 Target Market 

Market Introduction. The technology is commercially available. BT assumed that this 

project would accelerate the penetration in the marketplace by 10 years. 

5.2.10.2 Methodology and Calculations 

Performance Parameters: Energy Star Windows have maximum U-value and SHGC for four 

different climate zones. These climate zones do not directly correspond to the traditional 

climate zones used in CBECS or RECS; they also do not correspond to the census divisions 

used in NEMS. These new climate zones are based on the eight climate zones that were 

developed as part of the IECC 2003 code change cycle or Residential IECC Code Change 

(RICC). In general the Energy Star zones map from the RICC zones as follows in Table G-28. 

Table G-28. Mapping of RICC Zones to Energy Star Zones 

RICC Zone Energy Star Zone 

1 Southern 

2 Southern 

3 South/Central 

4 North/Central 

5 Northern 

6 Northern 

7 Northern 

8 Northern 

To construct the four Energy Star zones there was a fair amount of smoothing required due to 

geo-political boundaries, existing codes, and commercial regions. For example, a strict 

adherence of the eight RICC zones to four Energy Star zones shown above would have portions 

of California in all four Energy Star zones and would result in discontinuities in the zones 

across the country. The final result is that California is wholly within the South/Central zone 

and all four Energy Star zones are continuous across the country. Performance parameters are 

listed in Table G-29. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-62
Table G-29. Performance Parameter Maximums for Low-e Windows 

Region Shading Coefficient U-Value 

Northern 0.60 0.35 Btu/ft2·°F 

North Central 0.55 0.40 Btu/ft2·°F 

South Central 0.40 0.40 Btu/ft2·°F 

Southern 0.40 0.65 Btu/ft2·°F 

Performance Target: Performance characteristics vary by building type and climate zone. The 

estimated savings per building were determined by simulating residential and commercial 

buildings in all climate zones (see Table G-30). 

Table G-30. Performance Targets for Low-e Windows 

Region Sector End Use New Building 

Savings 

Existing Building 

Savings 

Units 

Heating 8.17 8.30 MMBtu/HH Residential Cooling 0.06 0.19 MMBtu/HH 

Heating 6.24 5.73 MMBtu/ksf Northern 

Commercial Cooling -0.45 -0.58 MMBtu/ksf 

Heating 2.88 2.94 MMBtu/HH Residential Cooling 1.72 1.79 MMBtu/HH 

Heating 2.98 2.77 MMBtu/ksf North Central 

Commercial Cooling 0.74 0.68 MMBtu/ksf 

Heating 0.09 0.00 MMBtu/HH Residential Cooling 10.50 10.39 MMBtu/HH 

Heating 0.75 0.66 MMBtu/ksf South Central 

Commercial Cooling 5.91 5.62 MMBtu/ksf 

Heating -1.48 -1.77 MMBtu/HH Residential Cooling 9.18 8.77 MMBtu/HH 

Heating -0.14 -0.14 MMBtu/ksf Southern 

Commercial Cooling 5.21 4.98 MMBtu/ksf 

Heating 3.82 3.82 MMBtu/HH Residential Cooling 4.43 4.42 MMBtu/HH 

Heating 3.36 3.08 MMBtu/ksf 

Weighted National 

Average Commercial Cooling 2.25 2.07 MMBtu/ksf 

Installed Cost:—Incremental Cost Over Conventional Double-Pane Windows 

• 2005: $1.00/ft2

• 2015: $0.50/ft2

Expected Market Uptake. The purpose of the program is to increase the penetration of low-e 

glass from 40% in the residential market and 10% in the commercial market to 100% in the 

residential market by 2020 and in the commercial market by 2025. Both programs, Low-e 

Market Acceptance and Energy Star Windows, form the joint means to achieving the low-e 

penetration goal – the savings are to be split equally. Penetration curves were developed based 

on market diffusion curves developed and documented by PNNL(10). The “Accelerated” 

penetration curve represents the percent of superwindow sales with the DOE project; the “Net” 

penetration curve represents the percent of sales attributable to DOE, as BT assumed that the 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-63
DOE project would accelerate market acceptance by 10 years. The penetration rates are shown 

in Figures G-13 and G-14. For Low-e Market Acceptance/ Energy Star Windows, BT 

assumed that these projects would accelerate the acceptance of this technology in the 

marketplace by 10 years. 

0%

20%

40%

60%

80%

100%

120%

2000 2005 2010 2015 2020 2025 2030 2035

Accelerated Penetration from

DOE R&D

Net Penetration with DOE

R&D

Figure G-13. FY07 Energy Star Windows – Commercial Buildings Percent of Sales 

0%

20%

40%

60%

80%

100%

120%

2000 2005 2010 2015 2020 2025 2030 2035

Accelerated Penetration

from DOE R&D

Net Penetration with DOE

R&D

Figure G-14. FY07 Energy Star Windows – Residential Buildings Percent of Sales 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-64
5.2.11 Energy Star Home Performance 

5.2.11.1 Target Market 

Home Performance with Energy Star is a joint effort with the Environmental Protection Agency 

to develop and support pilot projects that promote whole-house retrofits for existing homes in 

order to save energy. Home Performance’s three main components include whole-house 

inspections, marketing efforts, and quality assurance. 

5.2.11.2 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. BT assumed that the cost of Home Performance pilot projects (the average price per 

household) would be $5,000—in FY05, Pilot Project homeowners were spending between 

$4,000 and $6,000 in retrofits through the Pilot Project program.(9) 

5.2.11.3 Methodology and Calculations 

Inputs to Base Case. BT did not provide inputs to change the base case assumptions for the 

program markets. BT’s calculations were based on a baseline that was developed from the 

Energy Information Administration’s (EIA’s) Commercial Buildings Energy Consumption 

Survey (CBECS), Residential Energy Consumption Survey (RECS), and the Annual Energy 

Outlook (AEO). 

Technical Characteristics. BT assumed that Home Performance with Energy Star activities 

would primarily impact the space conditioning load of existing buildings, as most of the retrofit 

measures involve the building shell (e.g., insulation, windows); however, water heating and 

lighting loads are also reduced. Because these retrofits are occurring due to the programmatic 

builder certification, marketing efforts and financing options, BT assumed the activity would 

reap all benefits associated with the retrofits, roughly a 20% load reduction. 

Expected Market Uptake. The penetration rates for Home Performance with Energy Star was 

developed using a diffusion model based on Fisher and Pry (1971)(11). The equation for 

determining market diffusion over time is: 

))( )81ln( exp(1

)(

mtt

t

tN

− ∆−+

= κ

Where K = Maximum market share potential 

tm = year in which 50% of potential is reached 

∆t = time to grow from 10% to 90% of potential (years) 

For Home Performance with Energy Star, k=0.0002%, tm=17, and ∆t=20. These values were 

developed through trial and error to achieve the expected annual household impact in 2007 and 

in “out” years, based on discussions with the program manager. Table G-31 displays the 

resulting estimated number of homes impacted based on the penetration curve developed. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-65
Table G-31. FY 2007 Market Penetration for Energy Star Home Performance 

Year Annual No. Homes 

2007 700

2008 859

2009 1,052

2010 1,284

2011 1,562

2012 1,891

2013 2,279

2014 2,729

2015 3,245

2016 3,828

2017 4,474

2018 5,177

2019 5,927

2020 6,709

2021 7,503

2022 8,291

2023 9,053

2024 9,771

2025 10,434

2026 11,031

2027 11,557

2028 12,010

2029 12,395

2030 12,714

BT assumed that the portion of the Energy Star Home Performance activity funded by DOE 

would not occur without DOE funding, because it allocates money for builder training and 

certification, program marketing support, and program-specific financing options; therefore, no 

acceleration of market acceptance was modeled. 

5.2.12 Sources 

(1) Model Documentation Report: Residential Sector Demand Module of the National Energy 

Modeling System. 2003. Energy Information Administration, Washington, D.C. 

DOE/EIA-M067(2003) http://tonto.eia.doe.gov/FTPROOT/modeldoc/m067(2003).pdf

(2) Annual Energy Outlook 2001. 2001. Energy Information Administration, Washington, 

D.C. 

(3) “Clothes Washer Technical Support Document” source: 

www.eere.energy.gov/buildings/appliance_standards/residential/clwash_0900_r.html. 

(4) Arthur D. Little, Inc. (ADL). 1998. “EIA Technology Forecast Updates – Residential and 

Commercial Building Technologies, Reference Case.” 

(5) Vineyard, E.A. and J.R. Sand. 1998. “Fridge of the Future: Designing a One KilowattHour/Day Domestic Refrigerator Freezer.” In 1998 ACEEE Summer Study Proceedings. 

(6) National Appliance Energy Conservation Act of 1987, Public Law 100-12. 

(7) http://www.energystar.gov/products/cfls/EnergyStarCFLSpecification_Final_8.9.01.pdf p.5. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-66
(8) http://eetd.lbl.gov/btp/papers/43782.pdf Creating Markets For New Products To Replace 

Incandescent Lamps: The International Experience. Presented at the 1998 ACEEE 

Summer Study on Energy Efficiency in Buildings, August 23-28, 1998, Pacific Grove, CA, 

and published in the Proceedings. Figure 2. 

(9) Based on results documented in article, “Energy Star Tackles Existing Homes,” Energy 

Design Update, Volume 23, No. 8, August 2003 as well as discussions with Kyle Andrews, 

Project Manager, June 2004 and Lana Nirk, Project Manager, May, 2004. 

(10)Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics 

Effort. PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 

(11)Fisher, J.C., and R.H. Pry, (1971) “A Simple Substitution Model of Technological 

Change.” Technological Forecasting and Social Change, 3, 75-88. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix G – Building Technologies Program – Page G-67
Appendix H – GPRA07 Industrial Technologies Program 

Documentation 

1 Introduction 

The information provided in this appendix is consistent with the Industrial Technologies 

Program (ITP) draft report of the GPRA07 process, “GPRA07 Quality Metrics – Methodology 

and Results,” Energetics Incorporated, December, 2005. The draft report includes additional 

methodological details and the detailed fuel-specific off-line energy savings metrics results 

submitted to the Office of Energy Efficiency and Renewable Energy (EERE). 

The GPRA07 calculation of future program impacts was performed separately for each planning 

unit and summed to produce the total ITP program impact. The planning units are: technology 

development (described as R&D planning units) and technology deployment (described as 

Technology Delivery planning units). 

Within planning units, impacts were calculated differently for R&D planning units than for 

Technology Delivery planning units. Impacts for R&D planning units were calculated at the 

project level, using a uniform methodology embodied in a spreadsheet-based computer tool 

called the Technology Impact Projections Model. Impacts for Industrial Assessment Center 

(IAC) and Best Practices planning units were calculated for subprogram element activities using 

historical data, estimates, and assumptions documented in tabular format; and summed to 

produce the Technology Delivery planning unit impacts. ITP’s subprogram structure includes: 

R&D Planning Units 

1. Aluminum Industry Vision 

2. Chemicals Industry Vision 

3. Forest Products Industry Vision 

4. Glass Industry Vision 

5. Metal-Casting Industry Vision 

6. Steel Industry Vision 

7. Mining Industry Vision 

8. Supporting Industry Vision 

9. Industrial Materials Crosscut 

10. Sensors and Automation Crosscut 

11. Combustion Crosscut 

Technology Delivery Planning Units 

1. Industrial Assessment Center (IAC) Program 

2. Best Practices Program 

 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-1
1.1 Target Markets (The Baseline Case) 

• Target Market Description 

Advanced industrial energy efficiency technologies under development with program support 

will enter a variety of specialized markets for production equipment, plant energy conversion, 

distribution, heat recovery, and waste-reduction equipment. Underlying fuel prices, the 

electricity generation and distribution fuel mix and heat rates, and sector economic growth rates 

—which were used in the NEMS-GPRA07 runs that produced the ultimate results from ITP’s 

energy-savings inputs—were consistent with the reference case in the Department of Energy’s 

(DOE/EIA) Annual Energy Outlook 2005. ITP’s off-line calculation of fuel and electricity 

savings for individual projects and program-element activities did not refer explicitly to macrobaseline projection of energy consumption quantities; rather, a unique market growth rate was 

specified in each of the 105 Technology Impact Projections Model runs. This permitted the 

analysts to differentiate among highly varied market outlooks in the various industries. Except 

for several chemicals industry market targets with short-term growth rates of more than 5.0%, 

the range of these annual market growth rates was from -1.0% to 5.0%, with an average close to 

1.5%. 

Due to differences in the analytical framework of the NEMS-GPRA07 model and ITP’s bottomup energy-savings projection methodology, it was not possible to definitively match those 

models’ base-case assumptions with the implicit base case in the GPRA study. NEMS-GPRA07 

addresses the entire industry group in a top-down manner, assigning energy intensities to a 

comprehensive set of activities to project total industry energy use under alternative assumptions. 

The bottom-up ITP GPRA study specified the unit energy savings of a particular set of 105 

advanced technologies, each in comparison to a best-available commercial technology 

alternative. ITP GPRA savings are only those savings attributable to these technologies in their 

primary intended markets. The two approaches are not inconsistent. The NEMS-GPRA07 model 

provides the context for benefit measurement in the overall economic framework. 

The target market for each of 105 R&D technologies included in the ITP study was described 

qualitatively and quantitatively in a spreadsheet-based Technology Impact Projections Model 

run. The technologies were grouped based on common production activity Impact Targets. This 

specification of target markets was done to facilitate the identification of potentially overlapping 

markets. Where potentially overlapping markets were found, either the market was split between 

the two competing technologies or only one spreadsheet model run was used to represent both 

technologies. 

Markets were initially defined in terms of the total number of technology units potentially in use 

at the year of introduction. This number was then reduced to the fraction of those units 

considered technically and economically accessible, and then further reduced to the likely 

achievable technology market share accessible to the technology, as compared to other advanced 

technologies. And, finally, the target market was reduced to the savings potential attributable to 

the program. The market size was adjusted annually by the spreadsheet logic, based on the 

specified annual percentage market growth rate. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-2
• Baseline Technology Improvements 

Continued baseline improvement in energy productivity was accounted for in the ITP 

methodology. ITP’s method essentially subtracted a fixed “next best” baseline technology from a 

fixed advanced technology to obtain unit technology savings. The energy savings of a new 

technology were determined by the number of years the technology’s market introduction is 

accelerated by the Federal program involvement. The energy savings associated with the 

program were explicitly projected to occur without the EERE R&D after a period of years 

known as the “acceleration period.” Only the slice of energy savings attributable to the 

program’s effort to accelerate technology development was counted as GPRA savings. In this 

way, the methodology incorporated an assumption (consistent with NEMS GPRA07) that the 

energy intensity of industrial production will steadily improve, and that specific Federal 

interventions in cofunding R&D only temporarily accelerate the rate of improvement in the 

targeted production activities. Acceleration periods varying from one year to 42 years were 

found in the GPRA07 runs, with an average close to 9 years. 

Likewise, in the ITP off-line study, the conventional technology with which each new 

technology was compared was generally the best currently available technology—not a projected 

technology that might exist at the time of market introduction. Therefore, the comparison 

excludes future sales of the new technology, and the average technology in use, and uses only 

current best technology as the baseline for assessing impact. 

While the industry-level rate of improvement in production energy intensity tends to follow 

fairly smooth curves of monotonic improvement, it is very difficult to predict the future energy 

performance of as-yet unidentified technologies to perform specific functions. In addition, the 

best currently available technology is often not yet widely adopted in the market, so that when 

the ITP technology enters the market, the current best-available technology may still represent 

the next-best decision alternative for many cases. As a result, the use of next best technology as a 

comparison point for new technology investment would have the tendency to understate rather 

than overstate the impact of a new technology savings. As well, taking credit for only that slice 

of savings due to the presumed acceleration of the new technology’s market introduction date 

was intended to minimize any overestimation of savings due to the underlying rate of technology 

improvement. 

The commercial introduction of a technology normally occurs after a significant demonstration, 

use of an operating prototype, and after an adequate test and evaluation period along with 

allowances for the beginnings of production, dissemination of information, initial marketing and 

sales, and other “start-up” factors. To capture this lengthy process, users of the Technology 

Impact Projections Model were asked to indicate the timeline for developing and introducing the 

technology into the market. This timeline includes the years for when an initial prototype, refined 

prototype, and commercial prototype of the technology has or will be completed; and the year 

when the technology will be commercially introduced. An initial prototype is the first prototype 

of the technology. A refined prototype represents changes to the initial prototype, based on 

testing and field tests but not a commercially scaled-up version. A commercial prototype is a 

commercial-scale version of the technology. Commercial introduction occurs when the first unit 

beyond the commercial prototype is operating. Prototype and commercial introduction years, 

consistent with the technology development program plans, are estimated; and two values for a 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-3
commercial introduction year were requested. One estimate reflects when the technology is 

projected to be introduced, if the program proceeds as expected (“With ITP” case). The other 

year estimate reflects when the technology would have entered the market, if the program had 

not been involved (“Without ITP” case). The difference in commercial introduction years for the 

“With ITP” and “Without ITP” cases is referred to as the “acceleration period.” 

• Baseline Market Acceptance 

The rate of market penetration of novel technologies in industrial production markets is captured 

explicitly in the methodology. 

Based on historical data, new technologies normally penetrate a market following a familiar Scurve—the lower end representing the uncertainties overcome by “early adopters.” The curve 

tails off at the far future, where some may never adopt the new technology. The steepest portion 

of the S-curve is where the new technology is most rapidly penetrating the market and producing 

new savings. The rate at which technologies penetrate their markets varies significantly. The 

actual penetration rate varies due to economic, environmental, competitive, productivity, 

regulatory, and other factors. For example, penetrations of long-lived assets associated with 

industrial technologies generally occur over decades, while simple process or control changes 

can penetrate much more rapidly. 

In a 1998 study by Arthur D. Little Inc. (“Streamlining of OIT’s GPRA Process - Draft,” Arthur 

D. Little Inc., Reference 33550-01, May 27, 1998), data was presented on a number of actual 

penetration rates of past and present technologies. These penetration rates were analyzed, 

normalized, and grouped into five classes, based on a number of characteristics and criteria. 

These criteria were then used to classify technologies, based on their characteristics and the 

characteristics of the target markets where the technologies are expected to penetrate. Each 

technology was matched to the most similar one of five generic technology classifications to 

represent a specific technology’s market adoption profile. The process is described below. 

Users of the ITP Technology Impact Projections Model were asked to complete Table H-1 for 

each project by selecting a technology-impact value for each technology characteristic as a, b, c, 

d, or e in the right-hand column for those characteristics for which they could make a judgment. 

Based on the strength of these characteristic scores, the overall technology market-penetration 

curve selection was entered in the first row at the right under “Score.” The table was copied onto 

the spreadsheet model run at the “Background” tab. Note that the characteristics (rows) are 

relatively independent, and a given technology will likely fit best in different classes for different 

characteristics. By examining the pattern, however, it is possible (based on best judgment and 

experience) to select the most likely class (rate) at which the new technology may penetrate the 

market. This may be a “subjective average” of the characteristics, or it is possible that one or two 

characteristics are expected to dominate future adoption decisions that a particular class of 

penetration rate is justified. There also may be “windows of opportunity,” where significant 

replacements of existing equipment may be expected to occur in the future for other reasons. The 

user was asked to insert into the spreadsheet the class of penetration rate believed most likely—

all things considered—and provide a narrative of the rationale for selection, if not obvious from 

Table 1. The GPRA07 study included projects with curve selections a, b, c, and d, with b and c 

being the most common selections. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-4
For additional context, Table H-2 shows actual technologies and the class of their historical 

penetration rates. Comparison of the new technology, by analogy or similarity, with these 

examples provided additional insight into selecting the appropriate penetration rate that might be 

expected for the new technology. The actual technologies’ historical market penetrations are 

shown graphically in Figure 1, falling within the market-penetration rate classes used by the 

model. 

Table H-1. Selecting the Market-Penetration Rate Class 

Technology/project Score 

(a,b,c,d,e) 

Characteristic a b c d e 

Time to saturation 5 yrs 10 yrs 20 yrs 40 yrs >40 yrs 

Technology factors 

Payback discretionary <<1 yrs <1 yr 1-3 yrs 3-5 yrs >5 yrs 

Payback nondiscretionary <<1 yr <1 yr 1-2 yrs 2-3 yrs >3 yrs 

Equipment life <5 yrs 5-15 yrs 15-25 yrs 25-40 yrs >40 yrs 

Equipment 

replacement 

none minor unit 

operation 

plant 

section 

entire 

plant 

Impact on product 

quality 

$$ $$ $$ $ 0/- 

Impact on plant 

productivity 

$$ $$ $$ $ 0/- 

Technology 

experience 

new to 

U.S. only 

new to 

U.S. only 

new to 

industry 

new new 

Industry factors 

Growth (% per annum) >5% >5% 2-5% 1-2% <1% 

Attitude to risk open open cautious conservative averse 

External factors forcing forcing driving none none 

Gov’t regulation 

Other 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-5
Table H-2. Examples of Technologies 

Class A B C D E 

Aluminum Treatment of 

used cathode 

liners 

Strip casting, 

VOC 

incinerators 

Chemicals New series of 

dehydrogenati

on catalyst 

(incremental 

change) 

CFCs -> 

HCFCs, 

incrementally 

improved 

catalysts, 

membranebaed chloralkali 

Polypropylene 

catalysts, 

solvent to waterbased paints, 

PPE-based AN 

Synthetic 

rubber and 

fibers 

Forest 

Products 

Impulse drying, 

de-inking of 

waste 

newspaper 

Kraft pulping, 

continuous 

paper 

machines 

Glass Lubbers glass 

blowing, 

Pilkington float 

glass 

Particulate 

control, 

regenerative 

melters, 

oxygenase in 

glass furnaces 

Metals 

Casting 

New shop floor 

practice 

Petroleum New series 

HDS catalysts 

Alkylation 

gasoline 

Thermal 

cracking, 

catalytic 

cracking 

Residue 

gasification, 

flexicoking 

Steel Improved EAF 

operating 

practice (e.g. 

modify electric/ 

burner heating 

cycle to 

minimize dust 

generation) 

BOF steel 

making 

Oxyfuel burners 

for steel, Level II 

reheat furnace 

controls, 

continuous 

casting, 

particulate 

control on EAF, 

high-top 

pressure blast 

furnace 

Open-hearth 

technology, 

EAF 

technology 

Other Advanced 

refrigerator 

compressors, 

oxygen flash 

copper 

smelting, 

solvent 

extraction with 

liquid ion 

exchange 

Fluegas 

desulfurization 

(coal-fired 

utilities), low Nox 

industrial 

burners, 

industrial gas 

turbines, ore 

beneficiation 

Dry-kiln 

cement, 

industrial 

ceramic 

recuperators 

Industrial heat 

pumps 

 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-6
Data were fitted with formula Data were fitted with formula 

Figure 1. Market Penetration Rate Classes

)( 12 1

1

httc e f −− + =

A B CD E

0%

20%

40%

60%

80%

100%

0 20 40 60 80

Time (years)

Market Share (f)

Synthetic/Natural 

Rubber

Synthetic/Natural 

Fibers

Open Hearth

 Steels

BOF Steels,

Float glass

Elec Arc Steels

Cracked

 Gasoline

Dry Kiln Cement 

Production

Gasoline 

Alkanation

Glass Blowing

(Lubbers) Polypropylene

Production

Continuous 

Steel

Electronic 

Ballast

Advanced 

Compressor

1.2 Key Factors in Shaping Market Adoption of EERE Technologies 

• Price 

ITP methodology places little emphasis on cost-based estimation of market penetration, because 

useful cost information on industrial technologies in the R&D stage of development is, in nearly 

all cases, impossible to obtain. Instead, relative costs in the form of the expected payback period 

were one of numerous market-driving factors in matching the market-penetration schedule with 

each innovative technology (see previous section). These market-penetration schedules are 

typical of historical industrial-sector technology innovations, whose characteristic payback 

period, scale, equipment lifetime, impact on product quality, relevant experience level, market 

growth rate, attitude to risk, and other factors were matched to each innovative technology to 

select the best market-penetration schedule. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-7
• Nonprice Factors 

- Key Consumer Preferences/Values. 

Several consumer-preference/value issues were incorporated in the ITP market-penetration curve 

selection technique. These include factors such as technology scale, equipment lifetime, impact 

on product quality, etc. listed above. 

- Manufacturing Factors. 

The benefits-estimation approach requested the analyst to estimate the year in which the 

technology is expected to be successfully developed at the successive stages of (1) completion of 

initial R&D, (2) initial system prototype, (3) refined prototype, (4) commercial prototype, and 

finally (5) commercial introduction, given the push provided by the ITP program support. These 

estimates were documented as part of each spreadsheet model run. 

- Policy Factors. 

In the great majority of cases, no policy factors were considered significant to the market 

introduction and acceptance of ITP technologies. However, for cases where a regulation or other 

policy will drive the market to accept a new technology solution, the market-penetration curve 

selection procedure was set up to accept this information and allow it to play a role in the 

analysis. Any such influence was discussed in documentation provided in the spreadsheet model 

run. 

1.3 Methodology and Calculations 

• Changes in Inputs to Base Case 

ITP did not provide inputs that changed the base case assumptions for the industrial markets. 

• Technical Characteristics of the Program Case 

ITP did not provide specific changes to the NEMS-GPRA07 industrial-sector characteristics. 

ITP’s estimates of the energy savings of its advanced technologies were based on information 

provided to the analysts through the proposal review and contracting process, which includes 

industry participation and review, followed by program review of these estimates. ITP analysis 

by sector has focused on assessing the industrial processes where energy is actually consumed 

and understanding current and best practices for each proposed technology. The participation of 

industry experts in this process has been critical to helping refine the estimates. 

• Expected Market Uptake 

 - R&D Planning Units 

GPRA07 energy savings in the ITP off-line study were projected for individual projects within 

planning units and summed to total results for planning units and for ITP as a whole. Active 

projects were selected by the ITP program managers for GPRA07; thus, the FY 2006 program 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-8
portfolio was used as a surrogate for the (as-yet unknown) FY 2007 portfolio. The number of 

study projects in each planning unit was controlled to represent an aggregate nominal funding 

level not greater than 100% of the FY 2006 budget request. 

This prospective assessment was carried out with the aid of an experience-based marketpenetration model designed to estimate the national energy, economic, and environmental 

impacts of innovative industrial technologies. ITP’s off-line calculations for GPRA07 did not 

utilize the model’s capabilities to project environmental and cost impacts, so the results will 

focus only on energy savings. EERE guidance for GPRA07 was to project the energy impacts of 

the FY 2007 program, which subsequently were used by others to specify scenario projections by 

the NEMS-GPRA07. The resulting NEMS-GPRA07 runs (reported elsewhere) produce 

environmental and cost results using integrated demand and supply assumptions consistent 

across the demand sectors. 

The Technology Impact Projections Model was used to estimate the potential energy savings 

resulting from research, development, and demonstration projects funded by the Industrial 

Technologies Program (ITP). Benefit estimates are critical for evaluating projects and presenting 

the merits of both individual projects and the overall RD&D portfolio. 

The Technology Impact Projections Model has been used by proposers responding to a 

Solicitation or Request for Proposals to estimate program impacts. Where not provided in 

proposals, principal investigators were asked to provide inputs for their active projects. Use of 

the model across all projects allows ITP to estimate the impacts of its projects in a consistent 

manner. 

Users were asked to provide their best estimate for each piece of information required for the

spreadsheet model. A description of the advanced technology was required to provide an 

overview of the project/technology. This includes the project name, project number (once project 

is funded), estimates preparer, program manager, planning unit, lab and industry contacts, and 

data sources. A narrative summary of the technology on which benefit estimates are based was 

required. This described what constitutes a typical process unit for the technology, in terms of 

annual output (production capacity multiplied by duty factor). For simplicity, the analysis 

assumed that all units in the industry have the same capacity. An average, or typical, unit 

capacity was chosen, particularly for situations where the unit size may vary in different 

installations. By convention and to enable comparisons, units for the new technology and the 

current state-of-the-art were equal in output capacity; even if, in reality, the new technology 

might have a different unit capacity for various reasons. 

The new technology also might not be a physical item of hardware. Rather, it could be a process 

change, a computer model or control system, operational change, or other nonphysical technique. 

In such cases, a unit was defined as the typical or average process or plant that would utilize the 

new technique. The annual energy inputs, based on the expected energy consumption of the 

process or plant with the new technique, were then compared with annual energy consumption 

required by existing techniques. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-9
Key information was provided on the performance of single installed units or applications of the 

advanced technology. For comparison, information was required on the performance of the bestavailable technology for the application, not the average of all in-place technology units. 

Users were required to provide energy use per year for the new and conventional units, by fuel: 

Electricity - Includes direct electricity. 

Natural Gas - Includes pipeline fuel natural gas and compressed natural gas. 

Petroleum - Includes residual fuel, distillate fuel, and liquid petroleum gas. 

Coal - Includes metallurgical coal, steam coal, and net coal coke imports. 

Feedstock - Includes fossil fuels consumed in nonenergy uses such as process feedstocks. 

Biomass - Includes the use of biomass (for energy or as feedstock). 

Wastes - Includes the use of fuels that are generated as wastes or process byproducts. 

Examples of such fuels are refinery fuel gas, blast furnace gas, hog and bark fuel, and sewage 

sludge. 

Other - Includes any fuels that may not be included in those listed above. 

Total Primary Energy - Is calculated from individual energy inputs. The primary equivalent 

of direct electricity consumption includes losses in electricity generation and distribution. For 

GPRA07, fuel and electricity savings were used as inputs to specify NEMS-GPRA07 runs 

that themselves applied heat rates, etc., varying over time to produce primary energy savings. 

Energy use was entered in physical units (e.g., billion cubic feet of natural gas) or primary units 

(trillion Btu). The exception was electricity use, which has to be entered as site energy 

consumption (either in billion kWh or trillion Btu). 

To determine the potential impact of the new technology as it becomes adopted, it was necessary 

to estimate the total market for the technology, reduce that to the likely actual market, and 

estimate when—and the rate at which—the new technology will penetrate the market. 

Users were required to estimate the number of installed units in the U.S. market in a specified 

year. That market was defined as narrowly as possible: the smallest group of applications that 

covers all potential applications for which the user may have some data. Users could apply their 

own data on energy use of the state-of-the-art technology. Other potential data sources include 

ITP’s Energy and Environmental Profile for the relevant industry, EIA’s MECS (Manufacturing 

Energy Consumption Survey) data, or industry sources. 

The annual market growth rate was specified by the model user, based on an EIA or industry 

growth projection for the relevant industry and process. A source for the growth rate was called 

for in the comments section. 

Market share was specified as a function of the potential accessible market share and the likely 

market share. The Potential Accessible Market Share was defined as the market that the new 

technology could reasonably access given technical, cost, and other limitations of the 

technology. For example, certain technologies may be applicable only to a certain scale of plant, 

certain temperature-range processes, certain types of existing equipment or subsystems, or only 

certain segments of the industry. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-10
A further delimiting fraction was called the “Likely Market Share.” In some instances, in 

addition to technical and cost factors, the technology may compete with other new technology 

approaches (or with other companies) for the market. The user was asked to use current marketshare information or base their estimated market share on the number of competitors in the 

market, assuming they are using different technologies not resulting from this project. This 

market-share estimate is different than the possibility of “copycats,” which should not be 

considered as competing uses; the reason is, if others adopt essentially the same (or slightly 

modified) technology due to this new technology, that additional adoption was triggered by the 

project being described and that project should be “credited” with causing that trend. This 

mechanism encompasses the case for techniques where the intellectual property cannot be, or is 

not, protected and becomes general knowledge throughout the industry. 

In some instances, a program may be developing a technology in conjunction with another ITP, 

EERE, or DOE program. In these cases, the analysts were asked to provide an estimate of the 

percentage of savings that is attributed to the program. The attribution percentage should be 

similar to the percentage of Federal funds provided to the project by the program. A default 

value of 100% was entered in the model and the principal investigator could provide additional 

information. 

As previously described under Baseline Technology Improvements, the market penetration of the 

technology was projected based on two estimates: the technology development and 

commercialization timeline, and the market-penetration curve. The technology development 

and commercialization timelines were determined first. The commercial introduction of a 

technology normally occurs after a significant technology demonstration or by a lengthy field 

testing of an operating prototype and after an adequate test-and-evaluation period. To capture 

this lengthy process, the analyst indicated the timeline for developing and introducing the 

technology into the market. This includes the years for when an initial prototype, refined 

prototype, and commercial prototype of the technology has or will be completed, as well as the 

year when the technology will be commercially introduced. An initial prototype is the first 

prototype of the technology. A refined prototype represents changes to the initial prototype but 

not a commercially scaled-up version. A commercial prototype is a commercial-scale version of 

the technology. 

Commercial introduction occurs when the first unit beyond the commercial prototype is 

operating. Prototype and commercial-introduction years were estimated to be consistent with the 

technology-development program plans. Two values for a commercial introduction year were 

requested. One reflected when the technology is projected to be introduced, if the program 

proceeds as expected (“With ITP” case). The other reflected when the technology would have 

entered the market if the program had not been involved (“Without ITP” case). If the technology 

would not have been commercially introduced without the program, then a year of 2050 for the 

“Without ITP” case was entered. The difference in commercial introduction years for the “With 

ITP” and “Without ITP” cases is referred to as the acceleration period. Only the slice of energy 

savings attributable to the program’s effort to accelerate technology development was counted as 

GPRA savings. 

As previously described, new technologies are estimated to penetrate a market following a 

familiar S-curve, the lower end representing the above uncertainties overcome by “early 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-11
adopters.” The curve tails off where some may never adopt the new technology. The major 

portion of the S-curve that is most important is where the new technology is penetrating the 

market, and benefits are being reaped. The rate at which technologies penetrate their markets 

varies significantly. The actual penetration rate varies due to economic, environmental, 

competitive, productivity, regulatory, and other factors. Penetrations of new technologies with 

long-lived assets often associated with heavy industrial technologies generally take place over 

decades, while simple process or control changes can penetrate much more rapidly. 

Technology impact projections-model runs for individual R&D projects receiving R&D support 

were aggregated to obtain energy savings associated with each R&D planning unit. In 

aggregating the savings, market targets were examined explicitly to avoid double-counting the 

same potential savings in the infrequent instances when the same energy efficiency market is 

clearly addressed by multiple projects. Where possible, market overlaps were found; and, in 

these cases, the markets were either assigned to only one technology or divided among the 

competing technologies under development. This process increases confidence that any systemic 

double-counting within planning units has been minimized. Nevertheless, some double-counting 

across planning units within ITP or with other EERE programs is assumed to remain, and this 

residual was handled as described below. 

The approximate portion of the FY 2006 budget represented by the analysis for each planning 

unit was noted, but the results were not scaled to 100% of the FY 2006 budget. Typically, the 

projects analyzed represented 80% to 99% of the FY 2006 budget for the various planning units. 

Projected benefits for these planning units do not include the effects of R&D projects completed 

prior to the current year. 

The justification for assuming that all of the projects analyzed will succeed is twofold. First, 

projects that fail will likely be replaced with new projects using different technical approaches to 

achieve similar goals. However, this rationale requires that the program is continuously funded. 

Second, the projects analyzed do not comprise 100% of the FY 2006 budget. Therefore, although 

there is no explicit estimation of risk, the aggregate benefits are discounted, which is equivalent 

to an allowance for some risk. In addition, there are benefits that are excluded from the program 

estimates. These include: the knowledge benefits of ITP’s R&D portfolio; this scientific and 

technical knowledge can help to underpin additional production technology innovations in the 

future, and spin-off applications in both the near and longer terms. 

 - Technology Delivery Planning Units 

The Industrial Analysis Center program and the Best Practices program were assessed, based on 

retrospective analysis of performance data accumulated over a period of years. ITP’s off-line 

Quality Metrics study for these planning units is based on the premise that continuation of the 

programs will result in beneficial impacts proportional to documented experience at historical 

budget levels. These analyses did not count as “savings” any continuing contributions from prior 

program expenditures, but only assumed that future expenditures will produce results 

proportionate to those reported for past expenditures. 

The approaches for calculating the impacts of the IAC and best-practices planning units were 

similar. In each case, those program activities associated with historic documented energy 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-12
savings were projected into the future based on assumed continuation at the FY 2006 budget 

level. Outreach is measured in a number of ways including the numbers of assessments, Web site 

visitors, and trained individuals. The activity levels performed in each future year were used to 

arrive at the future energy savings attributable to the activity, given continued performance at 

historical levels of effectiveness. Each quantity and assumption was explicitly shown in a tabular 

format intended to show the contribution of each step of the calculation to the final result and to 

make the entire analytical process repeatable. 

The IAC program benefits were supported by more than 20 years of actual assessment and 

implementation data. Among other assumptions, the effects of assessments were projected to last 

for seven years. The effects of student training were projected to persist for 11 years. The effects 

of the Web site information activity were projected to last for seven years. 

Best Practices program benefits were based on findings of an Oak Ridge National Laboratory 

study of program effects, and on a FY 2004 peer review that focused on ORNL’s outcome 

evaluation study. The basic methodology used in each of four best-practices activity areas was 

very similar. First, the activity reach was estimated by calculating the number of individuals 

“touched” by best-practices information. This number was then scaled back to calculate the 

number of plants taking action, due to this information dissemination. The scale-back factors 

included accounting for duplicate “touches” within the same company, the percentage of 

companies actually taking action, and a reduction factor to discount program credit due to it 

being but one of multiple sources of influence. To obtain the total program energy savings, 

reported rates of energy savings were applied to the number of plants estimated to be affected by 

best-practices activities in each future year. 

Best Practices activity areas evaluated for GPRA07 were Plant-wide Assessments, Training, 

Software, and Qualified Specialists. Total annual energy savings attributed to Best Practices 

were the sum of the subtotals estimated for these four delivery channels. 

1.4 Sources 

“GPRA07 Quality Metrics – Methodology and Results,” Energetics Incorporated, draft, 

December, 2005. 

DOE/EIA, Annual Energy Outlook 2005. 

“Streamlining of OIT’s GPRA Process (Draft),”Arthur D. Little Inc. Reference 33550-01, 

May 27, 1998. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix H – Industrial Technologies Program – Page H-13
Appendix I – GPRA07 Federal Energy Management Program 

Documentation 

Project Description. The mission of the Federal Energy Management Program (FEMP) is to 

promote energy security, environmental stewardship, and cost reduction through energy 

efficiency and water conservation; report progress toward the Executive Order goals at Federal 

sites; and support energy management activities of the Department of Energy.1

Through the Federal Government’s own actions, FEMP’s target is to facilitate energy efficiency 

and renewable energy investments in FY 2007 that will result in life-cycle energy savings of 17.1 

trillion Btus. This target includes only those investments at Federal agencies that can be 

quantified and directly related to FEMP activities.2 

FEMP’s spreadsheet model is not integrated into the larger FY 2007 GPRA models (NEMSGPRA07 and MARKAL-GPRA07). However, the delivered energy savings are used as inputs 

for the integrated modeling. The projected savings are subtracted from the Baseline Case for 

commercial-building energy consumption. Analysts use NEMS-GPRA07 to compute the other 

benefits metrics of primary energy savings, carbon emission reductions, and energy expenditure 

savings. This appendix provides an outline of how the delivered energy savings estimates were 

calculated. The specific mathematical calculations are available in two spreadsheet files which 

are available through the FEMP program. 

Background: Note that the FY 2007 GPRA benefits were calculated during a transitional period 

in FEMP. In 2005, at the time the GPRA calculations had already been completed for the 

Corporate Review Budget, FEMP was in the midst of changing its metrics and benefits 

calculations as part of the OMB Program Assessment Rating Tool (PART) process. To ensure 

consistency between the GPRA benefits and the PART metrics, the final GPRA methodology for 

FY 2007 represents a somewhat disjointed approach, combining both a bottom-up approach to 

reflect PART and the existing GPRA top-down model. As part of the FY 2008 budget 

development process, FEMP will employ a more unified methodology to calculate GPRA 

benefits. 

Target Market 

Target Market Description. The target market is the Federal sector, the Nation’s 3.0 billion 

square feet of standard Federal buildings (e.g., military bases, post offices, hospitals, 

courthouses) and the Nation’s 300 million square feet of Federal energy-intensive operations 

(e.g., laboratories, check-processing facilities, and linear accelerators). The Federal 

Government’s actions—via leadership, awards, influence, and raw purchasing power—may well 

influence private-sector and state and local government decisions with respect to energy-related 

decisions, but any such “spillover” impact is not estimated in this GPRA process. 

 

1

 Department of Energy FY 2007 Congressional Budget Request. Vol. 3. p. 407. 

2

 Department of Energy FY 2007 Congressional Budget Request. Vol. 3. p. 408. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-1 
Key Factors in Shaping Market Adoption of EERE Technologies 

Policy Factors. FEMP’s mission is to assist the 32 Federal agencies in attaining the goals set 

by Executive Order and other legislation for the Federal government. Strictly speaking, these are 

not goals for FEMP but goals for each individual agency, and their involvement is essential. 

Executive Order 13123 establishes that the goal for all Federal agencies is to reduce energy 

intensity in “standard” Federal buildings by 35% by 2010 (relative to the 1985 statutory baseline 

level of 138,610 Btu per gross square foot).3

 Additionally, Executive Order 13123 contains a 

goal for energy-intensive operations, which is to reduce energy per square foot by 25% in 2010 

relative to a 1990 baseline. 

The Energy Policy Act of 2005 (EPACT 2005) establishes the following goals: to reduce energy 

consumption per square foot by 20% by 2015 compared to the baseline year of FY 2004 at a rate 

of 2% per year; and to ensure that at least 3% of Federal electricity consumption is generated by 

renewables in the years FY 2007 through FY 2009, by 5% in the years FY 2010 through FY 

2012, and by 7.5% in FY 2013 and each fiscal year thereafter.4

Methodology and Calculations 

FEMP used a combination of a “bottom-up” and “top-down” approach in calculating GPRA 

benefits that accrue due to its activities. For activities through FY 2010, FEMP estimated the 

energy savings that will result from only its quantifiable activities and then summed these 

savings to generate a comprehensive savings estimate. Quantifiable activities are limited to 

project financing projects, technical assistance projects, and departmental energy management 

projects. 

Note that the comprehensive savings estimate does not take into account additional savings that 

likely result from FEMP’s non-quantifiable activities (e.g., product specifications, outreach, 

training, reporting). Furthermore, the Federal Government’s actions—via leadership, awards, 

influence, and raw purchasing power—may well influence private-sector and state and local 

government decisions with respect to energy-related decisions, but any such “spillover” impact is 

not estimated in this GPRA process. 

For years FY 2011 through FY 2013, FEMP relied on a model and supporting research that 

estimates the government-wide energy savings for these years, and then approximated FEMP’s 

contribution to this savings based on prior performance. Finally, for years 2014 and beyond, 

FEMP assumed a 1% annual decrease in the overall energy intensity of Federal facilities. Then, 

as with years FY 2011 through FY 2013, FEMP approximated its contribution to this reduction 

based on past performance. 

Inputs to Base Case. FEMP did not provide inputs to change the Base Case assumptions for 

the program markets. FEMP’s calculations were based on a baseline that was developed from 

Federal building historical energy-use data, per Executive Order and legislation. 

 

3

 Department of Energy FY 2007 Congressional Budget Request. Vol. 3. p. 409. 

4

 Department of Energy FY 2007 Congressional Budget Request. Vol. 3. p. 409. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-2 
Technical Characteristics. FEMP maintains a database with information on all of the 

projects it assists—both through its technical assistance and project financing efforts. The 

database includes information regarding engineering estimates of energy and cost savings for 

individual projects among other important data. FEMP relied on this database, as well as written 

contracts, to develop annual energy savings estimates for projects it assisted in FY 2002, FY 

2003, and FY 2004. These engineering estimates were used to develop a savings projection for 

FY 2007. 

Annual energy savings projections attributable to quantifiable FEMP activities were calculated 

for five FEMP sub-programs using the following sources and assumptions. Life-cycle energy 

savings were estimated by multiplying the annual savings by 15 years, the average life span of 

installed energy-efficient equipment. 

Project Financing Activities: 

(1) Energy Savings Performance Contracts (ESPC) 

Annual savings for these contracts were obtained directly from FEMP Super ESPC Delivery 

Order schedules. Savings are assumed to begin accruing in the year of the delivery order 

award. In instances where annual savings were not available for a particular delivery order, 

the average savings per dollar of project investment (9,000 Btu/dollar) was used to estimate 

annual savings. 

(2) Utility Energy Savings Contracts (UESC) 

These savings were obtained directly from UESCs awarded with direct assistance from 

FEMP. 

(3) Energy Markets/Shared Energy Savings Support 

These estimates were derived from projects in which FEMP directly assisted Federal 

agencies in successfully applying for public benefit funds or other energy efficiency funds. 

Technical Assistance Activities: 

(4) Technical Assistance Projects (TA) 

These estimates reflect the savings potential from projects for which FEMP provided 

technical assistance, including both energy efficiency and renewable energy support. The 

estimates do not credit FEMP with the full energy savings potential by the projects, but rather 

for the incremental savings that would be accrued if FEMP’s technical recommendations 

were followed. In the case of renewable energy projects, energy savings are presumed to 

equal the amount of energy generated from the on-site renewable project and used by the 

federal facility. 

Departmental Energy Management Activities: 

(5) Departmental Energy Management (DEMP) 

These estimates were derived directly from engineering estimates of energy savings reported 

by DOE sites that received funding from FEMP for energy efficiency and renewable energy 

projects. 

FEMP Project Financing: Estimated Savings. FEMP Project Financing performance 

measures were derived from the average annual energy savings (in billion Btu) for projects 

signed in fiscal years 2002 through 2004. Table I-1 details the annual FEMP-facilitated savings 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-3 
for the three project financing programs: Super ESPCs, Utility Energy Service Contracting 

support, and Energy Markets, including support for the United States Postal Service’s shared 

energy savings projects. 

Table I-1. Annual Savings (Billion Btu) 

 2002 2003 2004 2002-2004 

Super ESPC 517 2,634 215 3,366 

UESC 204 163 140 507 

Energy Markets 0 66 142 208 

Project Financing Total 720 2,863 498 4,081 

FEMP divided the average annual energy savings by the total project financing annual budgets 

(Table I-2) for the three years to determine “Annual Energy Savings per FEMP Dollar of 

Funding” shown in Table I-3. 

Table I-2. Project Financing Dollars (Thousand $) 

2002 2003 2004 2002-2004

$8,700 $7,839 $7,830 $24,369

Table I-3. Annual Energy Savings per FEMP Dollar of Funding (Site-Delivered Btu/$) 

2002 2003 2004 2002-2004

82,813 365,224 63,550 167,469

The “Annual Energy Savings per FEMP Dollar of Funding” for 2002-2004 was multiplied by the 

approximate project financing budget request for FY 2007 ($6 million) to estimate annual 

savings from the project financing program for that year, yielding an estimate of 1,005 billion 

Btu. 

FEMP’s performance measure target for project financing in 2007 is 80% of the annual estimate 

for FY 2007 (or 804 billion Btu) based on the average performance of the fiscal years 2002 

through 2004. FEMP used the 80% multiplier to ensure that the projected savings estimates 

were conservative and attainable. 

FEMP calculated life-cycle energy savings by taking the estimated annual savings and 

multiplying by 15 to reflect an average project life of 15 years, for a total life-cycle energy 

savings of 12,060 billion site-delivered Btu. 

FEMP Technical Assistance: Estimated Savings. Program performance measures for 

these activities were derived first from the estimated annual savings from all TA projects 

facilitated by FEMP (Table I-4) whether or not those projects are ultimately implemented by the 

agency. The estimated annual energy savings in million Btu (MMBtu) for fiscal years 2001 

through 2004 were divided by the total TA budget for those years to arrive at “Identified Annual 

Savings from TA Projects per dollar of TA Funding” (200 MMBtu). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-4 
Table I-4. Technical Assistance Project Savings and Funding Levels 

 

 

2001 

 

2002

 

2003

 

2004

2001 - 2004 

4-Year Total 

 2007 

Estimate

Total TA Funding 

(Thousand $) $7,896 $7,000 $7,825 $8,140 $30,861 $6,591

Identified Savings from 

Recommended TA 

Projects (MMBtu) 824,019 865,590 3,695,862 776,670 6,162,141 1,316,052

Identified Savings per $ 

of TA funding (MMBtu) 104 124 472 95 200 200

FEMP multiplied the 200 MMBtu value (Identified Annual Savings per dollar of TA Funding) 

by the budget request for FY 2007 to estimate potential annual savings identified by all TA 

projects for FY 2007 (illustrated in the far right column of Table I-4). 

FEMP estimated “Implemented Savings” for FY 2007 by taking 30% of estimated potential 

annual savings identified by all TA projects, yielding 395 billion Btu. FEMP used the 30% 

multiplier to reflect that not all projects for which FEMP provides technical assistance are 

actually implemented. Based on historical implementation rates, FEMP determined the 30% 

figure to be a reasonable estimate of how many projects would be implemented in the future. 

FEMP calculated the target 2007 TA project target performance measure by taking 80% of 

estimated “Implemented Savings” from TA program facilitated projects yielding 316 billion Btu. 

FEMP used the 80% multiplier to ensure that the projected savings estimates were conservative 

and attainable. 

FEMP calculated life-cycle energy savings by taking the estimated annual savings and 

multiplying by 15 to reflect an average project life of 15 years, for a total life-cycle energy 

savings of 4,740 billion site-delivered Btu. 

FEMP Departmental Energy Management: Estimated Savings. FEMP DEMP 

performance measures were derived from the average annual energy savings (in billion Btu) for 

projects signed in fiscal years 2002 through 2004. Table I-5 details the annual FEMP-facilitated 

savings, the DEMP budget, and the resulting “Annual Energy Savings per FEMP Dollar of 

Funding.” 

Table I-5. DEMP Annual Savings and Funding 

 2002 2003 2004 2002-2004 

Annual Savings (Billion Btu) 26.9 27.2 35.4 89.5 

DEMP Budget (Thousand $) $1,421 $1,445 $1,963 $4,829 

DEMP Cost-Share from Sites 

(Thousand $) $1,097 $402 $555 $2,054 

Annual Energy Savings per 

Dollar of Funding (DEMP Budget 

plus Cost-Share) 10,683 14,727 14,059 13,003 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-5 
The “Annual Energy Savings per FEMP Dollar of Funding” for 2002-2004 was multiplied by the 

approximate Departmental Energy Management budget request for FY 2007 ($2 million) to 

estimate annual savings from the Departmental Energy Management activities for that year, 

yielding an estimate of 26.0 billion Btu. 

FEMP’s performance measure target for DEMP in 2007 is 80% of the annual estimate for FY 

2007 (or 20.8 billion Btu) based on the average performance of the fiscal years 2002 through 

2004. FEMP used the 80% multiplier to ensure that the projected savings estimates were 

conservative and attainable. 

FEMP calculated life-cycle energy savings by taking the estimated annual savings and 

multiplying by 15 to reflect an average project life of 15 years, for a total life-cycle energy 

savings of 312 billion site-delivered Btu. 

Total estimated annual savings for all quantifiable FEMP activities for FY 2007 is 1.14 trillion 

Btu, which is equivalent to 17.1 trillion Btu life cycle energy savings. FEMP assumed that this 

target level of savings would remain in effect through 2010, based on the Executive Order goal 

year. 

Projection of Estimated Savings through the Analysis Period. In order to project the 

estimated savings through the remainder of the analysis period (FY 2011 – FY 2030), FEMP 

developed an estimate of the reasonably attainable potential of the Federal sector. The method 

FEMP used to develop the Federal building retrofit potential is outlined in the next section on 

technical potential. Using this projection, FEMP calculated the amount of the total potential that 

is attributable to FEMP (based on FEMP’s target), and applied that percentage to the projected 

estimates to obtain out-year FEMP savings. By using the projected Federal building retrofit 

potential, FEMP could incorporate future baseline changes in energy use intensity, which affect 

the level of savings. 

FEMP used a weighted average for FY 2007 – FY 2010, equal to the sum of the target savings 

divided by the sum of the potential savings, as the attribution factor for FY 2011 through FY 

2030. Table I-6 provides the projected savings levels, the target levels, and the attribution 

percentage. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-6 
Table I-6. Development of Out-Year Energy Savings Estimates 

Year Potential Total 

Site Energy 

Displaced (TBtu)

FEMP Target, 

FY 2007 – FY 2010 

(TBtu)

Attribution 

Factor 

2007 6.68 1.14 17.08% 

2008 13.22 2.28 17.25% 

2009 19.63 3.42 17.43% 

2010 25.91 4.56 17.60% 

2015 50.45 17.42% 

2020 65.73 17.42% 

2025 80.26 17.42% 

2030 94.07 17.42% 

FEMP allocated the energy savings into savings by fuel type using historical fuel mix data from 

the Federal sector along with Energy Information Administration (EIA) forecasts, as outlined in 

the section, “Fuel Mix” within the Technical Potential section below. Energy savings by fuel 

type, measured in MMBtu, were converted to alternative units for reporting requirements via the 

conversion factors listed in Table I-7. 

Table I-7. Energy Conversion Factors5

Fuel Oil: 5.825 MMBtu/barrel 

Natural Gas: 1.027 MMBtu/1000 cubic feet 

Coal: 22.489 MMBtu/short ton 

Electricity: 3.412 MMBtu/MWh 

LPG: 3.603 MMBtu/barrel 

Energy Savings Results. Estimated annual and cumulative energy savings attributable to 

FEMP resulting from the FY 2007 Budget Request are summarized in Table I-8 and Table I-9. 

Table I-8. Annual Energy Metrics for Federal Standard Buildings and Energy-Intensive Operations 

 (FY 2007 Budget Request) 

Year Total Site 

Energy 

Displaced 

(TBtu) 

Direct 

Electricity 

Displaced 

(billion 

kWh) 

Direct 

Natural 

Gas 

Displaced 

(billion 

CF) 

Direct 

Petroleum 

Displaced 

(million 

barrels) 

Direct Coal 

Displaced 

(million 

short tons)

Direct 

Biomass 

Displaced 

(TBtu) 

Direct 

Energy 

Displaced 

from 

Feedstocks 

(TBtu) 

Direct 

Energy 

Displaced 

from 

Wastes 

(TBtu) 

Other 

Direct 

Energy 

Displaced 

(TBtu) 

2007 1.140 0.114 0.426 0.014 0.009 0 0 0 0

2008 2.280 0.228 0.817 0.037 0.018 0 0 0 0

2009 3.420 0.345 1.240 0.055 0.027 0 0 0 0

2010 4.560 0.459 1.641 0.080 0.035 0 0 0 0

2015 8.790 0.816 3.378 0.181 0.061 0 0 0 0

2020 11.452 0.995 4.540 0.247 0.080 0 0 0 0

2025 13.984 1.183 5.605 0.311 0.098 0 0 0 0

2030 16.392 1.359 6.679 0.365 0.114 0 0 0 0

 

5

 Source: Performance Planning Guidance (GPRA Data Call) FY2004-2008 Budget Cycle-Draft. April 1, 2002. 

U.S. Department of Energy. Office of Energy Efficiency and Renewable Energy. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-7 
Table I-9. Cumulative Energy Metrics for Federal Standard Buildings and Energy-Intensive 

Operations (FY 2007 Budget Request) 

Year Total Site 

Energy 

Displaced 

(TBtu) 

Direct 

Electricity 

Displaced 

(billion 

kWh) 

Direct 

Natural 

Gas 

Displaced 

(billion 

CF) 

Direct 

Petroleum 

Displaced 

(million 

barrels) 

Direct Coal 

Displaced 

(million 

short tons)

Direct 

Biomass 

Displaced 

(TBtu) 

Direct 

Energy 

Displaced 

from 

Feedstocks 

(TBtu) 

Direct 

Energy 

Displaced 

from 

Wastes 

(TBtu) 

Other 

Direct 

Energy 

Displaced 

(TBtu) 

2007 1.140 0.114 0.426 0.014 0.009 0 0 0 0

2008 3.432 0.344 1.248 0.052 0.028 0 0 0 0

2009 6.886 0.692 2.501 0.108 0.055 0 0 0 0

2010 11.516 1.159 4.167 0.189 0.090 0 0 0 0

2015 47.779 4.633 17.724 0.920 0.346 0 0 0 0

2020 99.769 9.253 38.086 2.026 0.711 0 0 0 0

2025 164.676 14.785 64.058 3.453 1.165 0 0 0 0

2030 241.867 21.226 95.332 5.172 1.704 0 0 0 0

Technical Potential. FEMP estimated the energy savings to the Federal sector that FEMP 

expects to be reasonably attainable within the analysis period. FEMP estimated the Federal 

building retrofit potential as one combined effect in the market, measured in terms of energy use 

per square foot per year. 

Actual historical and estimated future energy consumption are characterized in terms of fuel 

consumption (million Btu or MMBtu), fuel mix (the fractions of total fuel consumption by fuel 

type), and building floor space (thousand square feet or ksf). A critical derived figure is building 

energy intensity (MMBtu/ksf). The development of these measures is described in the sections 

that follow.

Historical Federal Agency Energy Consumption and Cost. Estimates of future Federal 

agency energy consumption start from the latest data available for actual energy consumption. 

For the analysis of impacts resulting from the FY 2007 Budget Request, the latest actual data 

were for FY 2004. These data were provided by the individual Federal agencies to McNeil 

Technologies, which has the responsibility for collecting and managing these data for FEMP. 

These data are eventually documented in the Annual Report to Congress on Federal Government 

Energy Management and Conservation Programs6

 for each fiscal year. As of September 2005, 

the most recent published version of this report covered fiscal year 2002 and was published 

September 29, 2004. 

The historical data available for analysis are energy consumption (MMBtu) by fuel type and 

building floor space (ksf). These data are reported by each agency. The fuel type categories are 

electricity, fuel oil, natural gas, liquefied petroleum gas (lpg), coal, purchased steam, and 

“other.” Building energy intensities (MMBtu/ksf) are calculated from these raw data. 

Future Federal Agency Energy Consumption. Future Federal energy consumption was 

estimated by combining estimates of future building energy intensity, fuel mix, and building 

 

6

 Available on FEMP’s Web site at http://www.eere.energy.gov/femp/pdfs/annrep02.pdf 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-8 
floor space. Total energy consumption (MMBtu) is the product of building energy intensity 

(MMBtu/ksf) and building floor space (ksf), as defined by Equation 1. Energy consumption by 

fuel type (MMBtu) is the product of total energy consumption and fuel-mix fraction for each fuel 

type, as defined by Equation 2. 

E = EIB × SFB Eqn. 1. 

Ef = E × F Eqn. 2. 

Where E = total energy consumption (MMBtu) 

 EIB = building energy intensity 

 SFB = building floor space 

 Ef = energy consumption by fuel type 

 F = fuel mix fraction 

The Department of Defense (DOD), DOE, General Services Administration (GSA), United 

States Postal Service (USPS), and Veterans Affairs (VA) were selected for specific metric 

development because they are the five largest agencies measured by annual energy use, 

consuming nearly 90% of the Federal total in FY 2004; DOD alone is nearly two-thirds of total 

Federal energy use (see Figure I-1). Reduction in MMBtu/ksf from FY 2003 through FY 2013 

was estimated for each of these five agencies and all other agencies (27 total) grouped together 

for standard buildings. Metrics for energy intensive operations were developed for the Federal 

government as a whole. The following subsections describe the development of building energy 

intensity, building floor space, and fuel-mix fraction assumptions. In addition, the resulting 

estimates of building energy intensity reductions are provided. 

VA 9% DOD 63%

USPS 8%

DOE 5%

GSA 4%

All Other 

Agencies 11%

Figure I-1. FY 2004 Federal Agency Standard Building Energy Consumption 

Building Energy Intensity. Estimates for agency-specific reductions in MMBtu/ksf by FY 

2013 relative to FY 2003 were aggregated from estimates due to a) cost-effective retrofits of 

building energy systems, b) replacement of equipment upon failure (with generally more 

efficient equipment), c) cost-effective retrofits of central energy plants and thermal distribution 

systems (DOD, DOE, and VA only), and d) improvements in O&M practices. These four 

categories have differing assumptions, and the assumptions for each agency can be different 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-9 
within a particular category. The assumptions are discussed in the text below, and are based on 

literature referenced in the text. Table I-10 presents the output estimates of energy intensity 

reductions derived from the spreadsheet model by category and agency. 

Table I-10. Energy Intensity Reduction Estimates 

Estimated Reduction in MMBtu/ksf by 2013 from 2003 

Agency 

Reduction Source DOD DOE GSA USPS VA Other 

Building Retrofit 5 8 6 6 6 6 

Replace on Failure 4 10 3 3 7 5 

CEP and Dist Retrofit 4 4 4 

Improved O&M 3 8 2 2 6 4 

Total 16 30 11 11 23 15 

FY 2003 MMBtu/ksf 102 238 69 68 186 124 

The reduction in MMBtu/ksf from building retrofit was previously based on data developed in 

two Pacific Northwest National Laboratory (PNNL) reports, Economic Energy Savings Potential 

in Federal Buildings,7 and An Assessment of Prospective FORSCOM Energy Intensities.8 The 

former was prepared for FEMP by D. Brown, J. Dirks, and D. Hunt; the latter was prepared for 

the U.S. Army’s Forces Command (FORSCOM) by D. Brown and J. Dirks. 

The report for FEMP specifically examined the retrofit potential based on government financing 

for all government agencies, while the report for FORSCOM examined the retrofit potential for 

their facilities based on either government or alternative-financing (i.e., private funding) 

mechanisms.9 The former report was used as the basis for civilian agencies while the latter was 

used for the military. The ratio of cost-effective savings found in the FORSCOM report for 

private and government funding was applied to the civilian results from the FEMP report to 

estimate civilian agency retrofit potential with private funding. Government-financed retrofit 

projects were assumed to be minimal, so the private funding potential was used for developing 

the energy intensity savings estimate. Finally, 50% of the potential was assumed captured over a 

10-year period, from 2000 to 2010. This was consistent with the rate of annual alternativefinancing investment and the ratio of energy savings per dollar invested from FY 1998 through 

FY 2000. The report for FORSCOM also looked at the impacts of the natural turnover of HVAC 

and service hot water (SHW) equipment (called “replace on failure” in Table I-10) and 

improvements to central energy plants (CEPs, i.e., boilers and/or chillers) and thermaldistribution systems. 

 

7

 D.R. Brown, J.A. Dirks, and D.M. Hunt. 2000. Economic Energy Savings Potential in Federal Buildings. PNNL13332. Pacific Northwest National Laboratory. Richland, Washington. 

8

 Distribution of the full report is limited by FORSCOM. The following paper, based on the full report, is publicly 

available. D.R. Brown and J.A. Dirks. 2002. “Prospective FORSCOM Energy Intensities.” Proceedings of the 25th

World Energy Engineering Conference. Association of Energy Engineers. Atlanta, Georgia. 9

 Alternative financing includes energy-saving performance contracts (ESPC) and utility energy service contracts 

(UESC). 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-10 
Battelle, Pacific Northwest Division, and PNNL have since conducted approximately two dozen 

assessments of energy efficiency retrofit potential at Army facilities.10 The Army facilities 

evaluated represent about 9% of total DOD floor space and have a mix of building types 

generally representative of DOD as a whole. The average retrofit potential via government 

funding was found to be 14.9 MMBtu/ksf, compared to 21.1 MMBtu/ksf in the prior FORSCOM 

study. The ratio of energy savings potential for private and government funding averaged 0.76, 

compared to 0.67 in the FORSCOM study. 

The decline in cost-effective retrofit potential from the previous study is believed to be the result 

of the following three factors. 

1. The recent Army results are based on a series of more thorough investigations than the 

previously cited work done specifically for FORSCOM. 

2. Declining building energy intensities generally imply less energy savings potential, 

following the economic law of diminishing returns. 

3. Privately financed projects in the past few years have dropped the Federal building 

energy intensity by about 3 MMBtu/ksf. 

The increase in the ratio of private to government-financed cost-effective retrofit potential is 

consistent with a greater drop in private real interest rates compared to the government real 

interest rate. The latter has been limited by statute for the interest rate prescribed for energy 

projects by NIST to a minimum of 3% even though long-term Treasury bond rates and inflation 

forecasts suggest a lower real cost of government financing.11

Assuming that interest rates rise back toward long-run averages, the prescribed government rate 

will rise relatively little compared to the private rate. Thus, the cost-effective retrofit potential 

with government financing will remain the same while the cost-effective potential with private 

financing will drop. Therefore, the prior (0.67) ratio of private-funded to government-funded 

retrofit potential was thought to better represent the long-term condition. 

The percentage decline in cost-effective retrofit potential collectively found in the more recent 

Battelle and PNNL studies for the Army was assumed to apply to the civilian agencies too 

because the latter two of the three explanatory factors cited above apply to both civilian and 

military agencies. The bottom line result was a 30% reduction in agency energy intensity via 

cost-effective building retrofits for the period 2004 through 2013 compared to the previous 

estimates developed for the period 2001 through 2010. 

Replacement of HVAC and SHW equipment occurs continuously as equipment ages, fails, and 

must be replaced. In general, the efficiency of HVAC and SHW equipment has substantially 

improved because of technology advances, stimulated in part by stricter equipment and appliance 

standards at the national level. Other factors include building energy codes and the forces of 

technological innovation. As a result, replacement equipment will usually consume less energy 

than the equipment being replaced; and, in some cases, much less energy (refrigerators and 

 

10 A complete listing of these references is presented at the end of this documentation. 11 S.K. Fuller and A.S. Rushing. 2005. Energy Price Indices and Discount Factors for Life-Cycle Cost Analysis – 

April 2005. NISTIR 85-3273-20. National Institute of Standards and Technology. Gaithersburg, MD. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-11 
chillers, for example). The estimated energy-intensity reduction from this mechanism was about 

4% over a 10-year period in the FORSCOM study; the estimated impact for civilian agencies 

was judged by FEMP to be the same, since the phenomenon of improving energy efficiency in 

new equipment and appliances is economy-wide and not restricted to just DOD. More 

specifically, the estimated impact was judged by FEMP to be similar on a percentage basis 

(proportional to current energy intensity) rather than similar on a fixed basis (the same 

MMBtu/ksf impact for all agencies). This latter assumption represents a change from prior 

year’s estimate for this mechanism. 

DOD sites often have large central energy plants (CEPs) and accompanying thermal distribution 

systems. Results from the recent PNNL studies conducted for the Army and cited above indicate 

a savings potential equivalent to about 8 MMBtu/ksf. Again, it is unlikely that 100% of the 

potential will be captured. A 50% capture fraction was assumed to be consistent with the 

building retrofit capture fraction assumption. Among the four civilian agencies considered 

explicitly, only DOE and VA have a significant number of sites with CEPs, so this projected 

savings was only applied to these two agencies, in addition to DOD. The estimated energy 

intensity reduction of 4 MMBtu/ksf is about 50% higher than the previous estimate. The 

increase can be attributed to consideration of decentralization from central boilers to buildinglevel boilers (eliminating all thermal distribution losses external to a building) as well as 

improvements in the efficiencies of central boilers and existing thermal distribution systems. 

The estimated decrease in MMBtu/ksf from improved O&M practices was previously developed 

from data presented in Using Targeted Energy Efficiency Programs to Reduce Peak Electrical 

Demand and Address Electric System Reliability Problems by S. Nadel (et al) of American 

Council for an Energy Efficient Economy (ACEEE); and Energy and Comfort Benefits of 

Continuous Commissioning in Buildings by D. Claridge (et al) of Texas A&M University. 

Specifically, Nadel estimated cost-effective energy savings via improved O&M practices to be 

between 5% and 15% of existing energy consumption, with a maximum penetration rate of 50%. 

A more recent PNNL study12 conducted for FEMP also concluded that the energy savings 

potential through improved O&M practices is approximately 10% of existing energy 

consumption. The authors of the PNNL study agreed that capturing one-third of the O&M 

potential by 2013 relative to a 2003 baseline were reasonable assumptions. Previously, 25% of 

the estimated O&M savings potential was assumed captured by 2010 relative to 2000. 

The FY 2013 building energy-intensity calculations are defined by Equation 3 for standard 

buildings. To calculate energy intensity for FY 2013, the estimated reductions in MMBtu/ksf 

shown in Table I-10 are subtracted from the actual energy intensities for each agency in FY 

2003. Although actual FY 2004 energy consumption data are currently available, the estimated 

energy intensities for FY 2013 are based on FY 2003 to be consistent with the references (reports 

for FEMP and the Army described above) supporting the figures in Table I-10. As described 

earlier, the FY 2010 energy intensity for energy-intensive operations was set at the value that 

exactly meets the energy-intensity goal for these types of facilities. 

 

12 W.D. Hunt and G.P. Sullivan. 2002. Assessing the Potential for a FEMP Operations and Maintenance (O&M) 

Program to Improve Energy Efficiency. PNNL-14076. Pacific Northwest National Laboratory. Richland, 

Washington. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-12 
 EIB in FY 2013 = EIB in FY 2003 – EIB Reduction Estimate Eqn. 3 

Where EIB = building energy intensity 

Energy intensities for years between FY 2004 and FY 2013 were geometrically interpolated 

between these two endpoints. Energy intensities beyond FY 2013 were assumed to continue 

declining, with each year 1% less than the previous year. This is a conservative assumption 

compared to the average compounded rate of decline from 1985 through 2004, which was 1.5%. 

Building Floor Space. As Federal floor space is not specifically tracked nor projected by 

EIA, future Federal building floor space was set equal to the FY 2004 value, i.e. no change in 

floor space was assumed through FY 2030. Total Federal floor space has been relatively 

constant since FY 1997 after declining from FY 1985 to FY 1997. The decline through FY 1997 

was driven mostly by reductions in DOD. Continued decline in DOD floor space since FY 1997 

has been offset by increases in other agencies. Most notably, USPS floor space has increased by 

85% from FY 1985 through FY 2004. It is not clear whether an increase or decrease in floor 

space is more likely during the next 5 years, let alone the next 25 years; therefore, floor space 

was assumed to remain constant for the duration of the analysis period. 

Fuel Mix. Since FY 1985, total site use of coal and fuel oil has declined significantly, while the 

use of electricity has remained nearly constant and the use of natural gas has declined slightly. 

As a consequence of these changes, the fractions of fuel use associated with electricity (and to a 

lesser extent, natural gas) have increased over time (See Figure I-2). EIA forecasts from the 

Annual Energy Outlook 2005 suggest that this trend will continue, with site use of electricity 

increasing relative to other energy forms. 

0

50

100

150

200

250

300

350

400

450

500

85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04

Fiscal Year

Trillion Btu

other

lpg

steam

coal

oil

ng

elec

Figure I-2. Historical Energy Use in Standard Federal Buildings 

Changes in the forecast fuel mix for the commercial sector from EIA’s Annual Energy Outlook 

2005 were applied to the actual Federal fuel mixes in FY 2004 to estimate future federal fuel 

mixes. Projected changes for the commercial-sector fuel mix were first normalized relative to 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-13 
the existing commercial-sector fuel mix in 2004. For example, the normalized electricity 

fraction in the commercial sector grew from 1.0 (by definition) in 2004 to 1.17 in 2030. In 

contrast, the normalized natural gas fraction in the commercial sector fell from 1.0 in 2004 to 

0.84 in 2030. The normalized fuel fractions for each fuel and each year were multiplied by the 

actual Federal fuel fractions in 2004 for each agency or agency group to estimate future Federal 

fuel mixes. 

This procedure was applied to standard buildings, but not to energy-intensive operations. There, 

it was not so clear what sector (commercial or industrial) would better represent energy-intensive 

operations or whether the year-to-year volatility in reported data for energy-intensive operations 

would invalidate the refined approach. Instead, future fuel mixes for energy-intensive operations 

were assumed to remain as they were in FY 2004. 

Federal Agency Energy Consumption Baseline. The estimated FY 2006 Federal agency 

energy consumption is used as the baseline Federal agency energy consumption. FY 2007 is the 

first possible year that could be affected by the FY 2007 budget, so FY 2006 is the logical 

baseline year. As previously described, the latest actual data are from FY 2004. Energy 

consumption by fuel type is estimated for each year after FY 2004, including the FY 2006 

baseline year, via the process described above in the section on Future Federal Agency Energy 

Consumption. 

Future Federal Agency Energy Savings. Annual energy savings were calculated by 

subtracting the estimated energy consumption in FY 2006 from the estimated energy 

consumption for FY 2007 and each following year. These calculations were done for each fuel 

type. Implicitly, if not for activities conducted by FEMP and the Federal agencies, future energy 

consumption would remain as estimated for FY 2006, and there would be no energy savings. 

Energy savings were summed across agencies and fuel types to determine total energy savings. 

Equations 4 through 6 define these calculations. 

 ESf,A in FY20XX = Ef,A in FY20XX – Ef,A in FY2006 Eqn. 4. 

ESf,F in FY20XX = Σ ESf,A in FY20XX Eqn. 5. 

ESF in FY20XX = Σ ESf,F in FY20XX Eqn. 6. 

Where ESf,A = energy savings by fuel type and agency 

 Ef,A = energy consumption by fuel type and agency 

 ESf,F = Federal energy savings by fuel type 

 ESF = Federal energy savings 

The estimated Federal building retrofit potential energy savings are contained in Table I-6, in 

the column titled “Potential Total Site Energy Displaced.” 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-14 
Sources for Building Energy Retrofit Potential 

Battelle, Pacific Northwest Division Reports 

Brown, D.R., J.A. Dirks, D.L. Hadley, B. Liu, and S.A. Parker. 2004. Final Assessment Report 

for Fort Shafter, Hawaii: Western Power Grid Peak Demand and Energy Reduction Program. 

PNWD-3412. Battelle—Pacific Northwest Division, Richland, WA. 

Brown, D.R., J.A. Dirks, D.L. Hadley, B. Liu, and S.A. Parker. 2004. Final Assessment Report 

for Schofield Barracks, Hawaii: Western Power Grid Peak Demand and Energy Reduction 

Program. PNWD-3413. Battelle—Pacific Northwest Division, Richland, WA. 

Brown, D.R., J.A. Dirks, D.L. Hadley, B. Liu, A.P. Melendez, E.E. Richman, and S.A. Parker. 

2003. Final Assessment Report for Fort Huachuca, Arizona: Western Power Grid Peak Demand 

and Energy Reduction Program. PNWD-3411. Battelle—Pacific Northwest Division, Richland, 

WA. 

Dahowski, R.T., D.R. Dixon, D.L. Smith, and S.A. Parker. 2004. Final Assessment Report for 

Fort Drum, New York: Western Power Grid Peak Demand and Energy Reduction Program. 

PNWD-3368. Battelle—Pacific Northwest Division, Richland, WA. 

Hadley, D.L., D.R. Dixon, A.E. Solana, and S.A. Parker. 2004. Final Assessment Report for 

Fort Carson, Colorado: Western Power Grid Peak Demand and Energy Reduction Program. 

PNWD-3410. Battelle—Pacific Northwest Division, Richland, WA. 

Hadley, D.L., B. Liu, J.A. Dirks, and S. A. Parker. 2004. Final Assessment Report for the 

Presidio of Monterey, California: Western Power Grid Peak Demand and Energy Reduction 

Program. PNWD-3414. Battelle—Pacific Northwest Division, Richland, WA. 

Parker, G.B., D.L. Hadley, D.L. Smith, A.E. Solana, and S.A. Parker. 2004. Final Assessment 

Report for Sierra Army Depot, California: Western Power Grid Peak Demand and Energy 

Reduction Program. PNWD-3417. Battelle—Pacific Northwest Division, Richland, WA. 

Parker, G.B., B. Liu, E.E. Richman, A.P. Melendez, and S.A. Parker. 2004. Final Assessment 

Report for Parks Reserve Forces Training Area (RFTA), California: Western Power Grid Peak 

Demand and Energy Reduction Program. PNWD-3416. Battelle—Pacific Northwest Division, 

Richland, WA. 

Pacific Northwest National Laboratory Reports 

Brown, D.R., J.A. Dirks, D.R. Dixon, and G. B. Parker. 2003. Southeast Region 10-Year Energy 

Plans; Prospective Energy Savings; Hunter Army Airfield. PNNL-14607. Pacific Northwest 

National Laboratory. Richland, Washington. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-15 
Brown, D.R., J.A. Dirks, D.R. Dixon, and G. B. Parker. 2003. Southeast Region 10-Year Energy 

Plans; Prospective Energy Savings; Fort Stewart. PNNL-14608. Pacific Northwest National 

Laboratory. Richland, Washington. 

Brown, D.R., and J.A. Dirks. 2003. Southeast Region 10-Year Energy Plans; Prospective 

Energy Savings; Fort McPherson. PNNL-14606. Pacific Northwest National Laboratory. 

Richland, Washington. 

Brown, D.R., and J.A. Dirks. 2003. Southeast Region 10-Year Energy Plans; Prospective 

Energy Savings; Fort Gillem. PNNL-14605. Pacific Northwest National Laboratory. Richland, 

Washington. 

Brown D.R., J.A. Dirks, and R.W. Reilly. 2004. FORSCOM 10-year Energy Plans - Prospective 

Energy Savings at Fort Campbell . PNNL-14598. Pacific Northwest National Laboratory, 

Richland, WA. 

Brown, D.R., J.A. Dirks, and D.R. Dixon. 2005. Facility Energy Decision System (FEDS) 

Assessment Report for Anniston Army Depot, Alabama. PNNL-15216. Pacific Northwest 

National Laboratory. Richland, WA. 

Brown, D.R., J.A. Dirks, and D.R. Dixon. 2005. Facility Energy Decision System (FEDS) 

Assessment Report for Fort Bragg, North Carolina. PNNL-15100. Pacific Northwest National 

Laboratory. Richland, WA. 

Chvala, W.D., B. Liu, D.R. Brown, and D.R. Dixon. 2004. Facility Energy Decision System 

(FEDS) Assessment Report for Fort Benning, Georgia. PNNL-14885. Pacific Northwest 

National Laboratory. Richland, WA. 

Chvala, W.D., A.E. Solana, and D.R. Dixon. 2005. Facility Energy Decision System (FEDS) 

Assessment Report for Fort Buchanan, Puerto Rico. PNNL-15053. Pacific Northwest National 

Laboratory. Richland, WA. 

Chvala, W.D., D.R. Brown, and D.R. Dixon. 2004. Facility Energy Decision System (FEDS) 

Assessment Report for Redstone Arsenal, Alabama. PNNL-14905. Pacific Northwest National 

Laboratory. Richland, WA. 

Hadley, D.L., B. Liu, W.D. Chvala, A.E. Solana, and D.R. Dixon. 2004. Facility Energy 

Decision System (FEDS) Assessment Report for Fort Knox, Kentucky. PNNL-14827. Pacific 

Northwest National Laboratory. Richland, WA. 

Liu, B., G.P. Sullivan, G.B. Parker, E.E. Richman, A.E. Solana, and D.R. Dixon. 2005. Facility 

Energy Decision System (FEDS) Assessment Report for Military Ocean Terminal (MOT) Sunny 

Point, North Carolina. PNNL-15090. Pacific Northwest National Laboratory. Richland, WA. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-16 
Parker, G.B., A.E. Solana, D.L. Hadley, and D.R. Dixon. 2004. Facility Energy Decision 

System (FEDS) Assessment Report for Fort Jackson, South Carolina. PNNL-14781. Pacific 

Northwest National Laboratory. Richland, WA. 

Parker, G.B., A.E. Solana, and D.R. Dixon. 2005. Facility Energy Decision System (FEDS) 

Assessment Report for Blue Grass Army Depot, Kentucky. PNNL-15075. Pacific Northwest 

National Laboratory. Richland, WA. 

Sullivan, G.P., A.E. Solana, E.E. Richman, and D.R. Dixon. 2005. Facility Energy Decision 

System (FEDS) Assessment Report for Fort Gordon, Georgia. PNNL-15068. Pacific Northwest 

National Laboratory. Richland, WA. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix I – Federal Energy Management Program – Page I-17 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-1

Appendix J - GPRA07 Weatherization and 

Intergovernmental Activities Program (WIP) 

Documentation 

Introduction 

The Weatherization and Intergovernmental Activities Program (WIP) develops, 

promotes, and accelerates the adoption of energy efficiency, renewable energy and oil 

displacement technologies and practices by a wide range of stakeholders. These include 

State and local governments, weatherization agencies, communities, companies, fleet 

managers, building code officials, Native American Tribal Governments, and 

international partners. Table J-1 outlines the activities characterized for WIP for 

GPRA07. Characterizations and inputs for these activities were provided to the 

Department of Energy’s (DOE’s) Office of Energy Efficiency and Renewable Energy 

(EERE) as inputs to EERE’s integrated modeling effort. 

Table J-1. WIP Subprograms, Projects, and Activities 

Subprogram Project Activity 

State Energy Program Grants State Energy Program Grants 

Codes and Standards 

Energy Audits 

Rating and Labeling 

Workshops/Training 

Incentives 

Retrofits 

Loans and Grants 

Technical Assistance 

Traffic Signals 

Weatherization Assistance 

Program Grants Weatherization Assistance Weatherization Assistance 

Tribal Energy Activities Tribal Energy Activities 

Intergovernmental Activities International Renewable 

Energy Program 

International Renewable 

Energy Program 

1.0 State Energy Program Grants 

Project Description. The State Energy Program provides financial assistance to States, 

enabling State governments to target their own high priority energy needs and expand 

clean energy choices for their citizens and businesses. With these funds and the 

resources leveraged by them, the State and Territory Energy Offices develop and manage 

a variety of programs geared to increase energy efficiency, reduce energy use and costs, 

develop alternative energy and renewable energy sources, promote environmentally 

conscious economic development, and reduce reliance on imported oil.
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-2

1.1 State Energy Program Grants 

1.1.1 Significant Changes from FY06 

Inputs for the State Energy Program Grants were updated, based on more recent and 

more complete information. The FY06 inputs were derived from the 2003 report, 

Estimating Energy and Cost Savings and Emissions Reductions for the State Energy 

Program Based on Enumeration Indicators Data;

(1) the updated inputs are based on the 

2005 report, An Evaluation of State Energy Program Accomplishments: 2002 Program 

Year.

(2) For this report, all states and territories were contacted by the SEP program and 

asked to provide counts of specified SEP activities that were performed during the 2002 

program year. All 50 states and four of five territories provided information for activities 

that used SEP funds. For FY07, the WIP program added a new project area, Traffic 

Signals, to the analysis. 

1.1.2 Target Market 

Market Description. The market includes all markets (including buildings, 

transportation, industry, and power technologies), except new construction and all 

categories of energy end use. 

Baseline Technology Improvements. There are no technology improvements assumed 

apart from what appears in the Energy Information Administration (EIA) baseline. 

1.1.3 Methodology and Calculations 

Inputs to Base Case. The WIP program did not provide inputs to change the base case 

assumptions for the program markets. The WIP program’s calculations were based on a 

baseline that was developed from the Energy Information Administration’s (EIA’s) 

Commercial Buildings Energy Consumption Survey (CBECS), Residential Energy 

Consumption Survey (RECS), and the Annual Energy Outlook (AEO). 

Technical Characteristics. For the FY07 GPRA metrics, the State Energy Program 

(SEP) was characterized based on the budget request and leveraged funds. Based on the 

report, An Evaluation of State Energy Program Accomplishments: 2002 Program Year

(Schweitzer and Tonn 2005),(2) nine activities (referred to in the report as project areas) 

supported by SEP were selected to represent the project. These activities—Codes and 

Standards, Energy Audits, Rating and Labeling, Workshops/Training, Incentives, 

Retrofits, Loans and Grants, Technical Assistance, and Traffic Signals—comprised 

approximately 90% of the total estimated energy savings reported. 

In previous years, the SEP has administered funds on behalf of other EERE projects, 

through “Special Projects” funds. The WIP program has assumed that the energy savings 

resulting from these funds were captured in the originating project (the project that 

provided the funding). For FY07, funds previously budgeted through Special Project 

funds became part of SEP. The WIP program assumed that this new funding would be 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-3

administered to project areas based on the historical percentages reported in Schweitzer 

and Tonn. A key assumption in the program’s methodology is that benefits are directly 

proportional to funds expended. 

Codes and Standards. The purpose of the SEP Codes and Standards activity is to 

encourage the adoption of building codes and standards through training and 

implementation activities. Data was collected on three separate metrics related to 

building codes: name of new energy-efficiency building code adopted; name of old 

energy-efficiency building code replaced; and percentage of new construction in state 

covered by the new code.(2) The information provided by the states on all three metrics 

combined was used to calculate energy savings achieved by code activity.(2) For 

consistency, the WIP program based the estimated savings of the Codes and Standards 

activities funded by the SEP on the savings estimates produced for the Residential and 

Commercial Energy Codes projects within the Office of Building Technologies (BT). 

Historically, Codes and Standards activities accounted for almost 19% of SEP funding.(1) 

Based on the FY 2006 budget request, this would have equated to approximately $7.7 

million; with the inclusion of the former Special Projects money (of about $19 million for 

FY07), the budget for codes and standards would be anticipated to be about $11.3 

million, an increase of about $3.6 million. The WIP program assumes that this increase 

in budget corresponds to an allocation of about 50% of the estimated energy savings for 

Codes and Standards training/implementation activities. 

Energy Audits. The purpose of the SEP Energy Audits activity is to perform energy 

audits. Energy-audit calculations were based on three indicators: number of audits, 

square feet retrofit, and reported savings. (2) For this effort, the WIP program converted 

these three indicators to number of households and square footage of commercial floor 

space impacted. 

The WIP program assumed a savings per audit of 21.7 MMBtu per household and 0.0167 

MMBtu per square foot of commercial floor space. (2) The per-unit energy savings 

estimate for residential retrofits listed in the “An Evaluation of State Energy Program 

Accomplishments: 2002 Program Year” report (43.3 million source Btu per project) 

provides the base for the estimate of savings associated with energy audits in the 

residential sector. An adjustment factor of 0.50 was applied to the retrofit number, based 

on the conservative assumption that only half of the recommended measures would be 

installed. Based on Tables 1.2.3 and 1.2.4 of the Buildings Energy Databook,(2)

approximately 84 MMBtu/HH/yr are used by residential space heating and space cooling, 

yielding a load reduction attributable to the audits of 26% for residential space heating 

and cooling. Based on Tables 1.3.3 and 1.3.4 of the Buildings Energy Databook, 

approximately 121 kBtu/SF/yr are used by commercial space heating, space cooling, and 

lighting, yielding a load reduction attributable to the audits of 14% for commercial space 

heating, space cooling, and lighting. 

States reported to the WIP program a total of 581 residential audits performed, 1,878,809 

residential square feet retrofit, and 139,851 MMBtu projected residential source savings. 

To convert the residential indicators into an estimated number of households, the WIP 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-4

program assumed that each residential audit represented one household, divided the total 

residential square feet retrofit by 1,707, which is the average heated square footage for all 

residential units in the United States from the 2001 Residential Energy Consumption 

Survey, and divided the estimated reported annual savings by the 21.7 MMBtu/HH 

figure.(2) This yielded an estimate of approximately 8,100 households impacted by 

energy audits in any given year. 

 

In the categories of commercial, industrial, and institutional, States reported to the WIP 

program a total of 35 audits performed, 67,976,934 square feet retrofit, and 17,551,878 

MMBtu projected source savings. To convert the commercial/industrial/institutional 

indicators into an estimated commercial square footage, the WIP program assumed that 

each commercial audit represented one building multiplied by 14,500 square feet, which 

is the average building size taken from the 1999 Commercial Building Energy 

Consumption Survey, used the square footage reported, and divided the estimated annual 

savings by the 0.0167 MMBtu/SF figure.(2) This yielded an estimate of approximately 

1.1 billion square feet impacted by energy audits in any given year, or 1.6% of existing 

commercial floor space, in each year. 

The WIP program assumed that the number of energy audits performed would be in 

direct proportion to the funds available for energy audits. Therefore, the estimated 

penetration was adjusted upward by 46% to reflect the additional funds from the Special 

Projects monies that would be funded through SEP in FY07. 

Rating and Labeling. The energy savings in this project area describe the amount of 

energy saved (statewide) as a result of a state’s endorsement of rating and labeling 

systems for up to 15 different types of energy consuming devices. Because the Energy 

Star program is the biggest and most successful rating and labeling program operating at 

this time, and many states use SEP funds to encourage participation in the Energy Star 

program, savings associated with the Energy Star program were used to represent the 

savings achieved by all state rating and labeling efforts. The difference in annual energy 

use between an Energy Star unit and a typical unit for each type of device was 

identified.(2) The national savings for each type of energy-consuming device was 

adjusted downward by multiplying by an “attribution factor” of 0.10, which approximates 

the proportion of Energy Star purchases made as a result of state encouragement.(6)

Table J-2 contains the estimated energy savings from rating and labeling. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-5

Table J-2. Estimated Energy Savings from Rating and Labeling(2)

Device Energy 

Star 

savings 

per unit 

(MMBtu 

source) 

Number of 

Energy Star 

units sold in 

U.S., 2002 

National 

Savings, 

2002 

(MMBtu 

source) 

Adjusted 

national 

savings (using 

0.10 

“attribution 

factors” 

(MMBtu source)

Office Computer/Monitor 2.938 22,941,000 67,400,658 6,740,066 

Home Computer/Monitor 0.853 11,402,000 9,725,906 972,591 

Fax Machine 1.801 2,271,000 4,090,071 409,007 

Copier 3.033 209,000 633,897 63,390 

Multi-function Device 6.540 1,338,000 8,750,520 875,052 

Scanner 2.654 6,810,000 18,073,740 1,807,374 

Printer 2.085 7,369,000 15,364,365 1,536,437 

TV 0.360 10,446,000 3,760,560 376,056 

VCR 0.171 12,028,000 2,056,788 205,679 

TV/VCR 0.332 4,643,000 1,541,476 154,148 

Audio Equipment 0.171 3,687,000 630,477 63,048 

Room AC 0.663 2,195,000 1,455,285 145,529 

Dishwasher 0.569 2,262,000 1,287,078 128,708 

Refrigerator 1.137 1,956,000 2,223,972 222,397 

Clothes Washer 2.464 1,224,000 3,015,936 301,594 

Average Savings per Device 933,405

The WIP program used a national per-device estimate for rating and labeling of 

approximately 933,400 MMBtu per year. (2) While Schweitzer and Tonn allocated these 

savings to states (based on population) to determine an estimate of savings for states 

reporting estimates, the WIP program allocated the device savings equally across all 

states, because no forecast is available for determining which states would fund rating 

and labeling projects in the future. The equivalent savings per state is about 18,670 

MMBtu per device (the national estimate divided by 50). 

In 2002, seven states were promoting Energy Star; and 15 widely used energy-consuming 

devices were characterized in terms of their energy savings.(2) While data underlying the 

Schweitzer and Tonn report indicate that the seven states make up an average of only 

7.7% of sales of the profiled devices, the Energy Star Web site states that more than 40 

devices are labeled. There is no forecast available as to which of these 40 devices would 

be promoted as a result of a state’s endorsement of rating and labeling systems, 

Therefore, while savings per device (for states participating in 2002) are overstated using 

the averaging methodology that the WIP program used, potential savings from the other 

25 labeled devices are not included. The WIP program assumed that the average energy 

savings are therefore representative of the total potential rating/labeling package. To 

reflect the additional funds from the Special Projects monies that would be funded 

through SEP in FY07, the WIP program assumed that 10 states (instead of the reported 

seven) would provide rating and labeling support in any given year, covering a total of 

150 devices (10 × 15). The WIP program assumed that the savings would be effective 

for 15 years, and that they were attributable to electricity. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-6

Workshops/Training. The purpose of this SEP activity is to promote energy-efficiency 

measures through workshops and training. The approach to developing a residential 

sector energy-savings multiplier was to select a package of four common energyconservation measures that could easily be taught in workshops and training sessions. 

Consequently, the WIP program modeled the residential training measures as air 

infiltration sealing, resetting water heater thermostats, attic insulation, and CFLs; and 

assumed that the average annual savings per household for these four measures was 28.7 

source MMBtu, which was derived from the impacts of these measures in four 

representative cities (Schenectady, New York; Birmingham, Alabama; Moline, Illinois; 

and Eureka, California) using the Home Energy Saver System,a a Web-based energy audit 

system, which is driven by the DOE-2 building simulation program. (2) The WIP program 

assumed that 3.4 MMBtu of those savings resulted from CFLs; 5.5 MMBtu resulted from 

resetting water heater thermostats; and that the rest was attributable to space 

conditioning. (5) Based on the Building Energy Databook,

 (3) Tables 1.2.4 and 1.2.3, total 

primary household consumption for 2005 is 191.4 MMBtu/HH: 44.1% (or 84.4 MMBtu) 

is space conditioning, 12.7% (or 24.3 MMBtu) is water heating, and 11.8% (or 22.6 

MMBtu) is lighting. Therefore, the estimated savings resulting from residential 

workshops and training are 23.4% space-conditioning savings, 22.6% water heating 

savings, and 15% lighting savings. The WIP program assumed that 20% of attendees 

would implement the measures, based on the findings from three recent studies, (7, 8, 9) and 

that the average attendee would influence 1.75 homes based on U.S Census Bureau 

residential construction numbers and conservative estimates formulated in the Schweitzer 

and Tonn report. (2) There were approximately 49,000 residential workshop attendees in 

2002, (2) so the WIP program assumed that this number would continue, resulting in 

residential workshops/training impacting approximately 17,150 existing residential 

households, or 0.02% of existing residential homes per year. 

Schweitzer and Tonn provided an estimate for both commercial and institutional 

buildings. Because the savings coefficients reported for commercial (156.8 

MMBtu/attendee) and institutional (151 MMBtu/attendee) were within 5% of each other, 

the two were modeled together by the WIP program. The WIP program assumed 

estimated commercial savings of 5.25% for HVAC measures and 3.2% for lighting 

measures based on two reports(10, 11) that identified the percent energy savings possible 

from HVAC and lighting retrofits in large and small office buildings. (2) Because the 

buildings evaluated in those reports were selected for their unusually high savings 

opportunities, the reported savings were divided in half to better represent the potential 

savings achievable in more typical office buildings, and were then further adjusted by 

multiplying by an installation rate of 0.20 to reflect the finding noted above, that roughly 

20% of workshop attendees implement the measures. The WIP program assumed that 

HVAC savings equate to both space heating and space cooling. The WIP program used a 

weighted median number of buildings influenced by each trainee as four buildings per 

traineeb

. The total number of attendees in 2002 that had training for commercial 

 

a

 Accessible at http://hes.lbl.gov/

b

 U.S. Census Bureau 1997 indicates that the average residential construction firm builds an average of eight new 

homes per year. Schweitzer and Tonn applied the conservative assumption that a residential retrofitter will work on 

approximately 50% of the mean number of homes constructed annually by firms engaged in new construction 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-7

buildings was 19,000 and institutional was 25,000. (2) This is equivalent to 176,000 

buildings impacted. The WIP program assumed the average square feet per commercial 

building is 14,500, based on the 1999 Commercial Buildings Energy Consumption 

Survey,

(2) so commercial and institutional workshops/training impacts about 0.51 billion 

square feet of existing commercial floorspace, or 0.74% of existing commercial 

floorspace per year. 

The WIP program assumed that the number of workshops/training sessions performed 

would be in direct proportion to the funds available for workshops and training. 

Therefore, the estimated penetration was adjusted upward by 46% to reflect the 

additional funds from the Special Projects monies that would be funded through SEP in 

FY07. 

Technical Assistance. The WIP program assumed that technical assistance is credited 

with half the implementation of workshops, and half the savings achieved by workshop 

attendees (see discussion above for derivation of savings estimates). (2) Because the WIP 

program assumed that technical assistance savings were half the savings of workshops, (2)

the estimated savings resulting from residential technical assistance are 11.7% spaceconditioning savings, 11.3% water heating savings, and 7.5% lighting savings. The WIP 

program assumed that 10% of attendees would implement the measures. (2) This 

implementation rate is half that of the rate used for workshops and training, based on the 

assumption that the implementation rate would be substantially lower than workshops 

and training sessions because technical assistance is less intensive and personal 

interaction is more limited, providing less detailed instruction, and would therefore be 

expected to be less motivational. There were approximately 297,350 contacts for 

residential technical assistance in 2002, (2) so residential technical assistance impacts 

approximately 29,735 existing residential households, or 0.04% of existing residential 

homes per year. 

Because the WIP program assumed that technical-assistance commercial building savings 

would be half the savings of workshops, (2) this yielded estimated savings of 2.63% in 

space conditioning and 1.6% in lighting. The WIP program assumed that HVAC savings 

equate to both space heating and space cooling. The WIP program assumed that 10% of 

attendees would implement the measures. (2) The total number of technical assistance 

contacts in 2002 for commercial buildings was 67,000. (2) The WIP program assumed the 

average square feet per commercial building is 14,500, from the 1999 Commercial 

Buildings Energy Consumption Survey,

 (2) so commercial and institutional 

workshops/training impacts about 0.19 billion square feet of existing commercial 

floorspace, or 0.28% of existing commercial floorspace per year. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-8

The WIP program assumed that the amount of technical assistance provided would be in 

direct proportion to the funds available for technical assistance. Therefore, the estimated 

penetration was adjusted upward by 46% to reflect the additional funds from the Special 

Projects monies that would be funded through SEP in FY07. 

Financial Incentives. The purpose of this SEP activity is to provide financial incentives 

(or rebates) to encourage the installation of energy-efficient equipment. Defensible study 

results were cited on rebate payments and the associated energy savings for four 

programs: Anaheim Public Utilities Energy Efficiency Incentives Program, Pacific Gas 

and Electric (PG&E) Single Family Homes Energy Efficiency Rebate Program, Pacific 

Gas and Electric Multifamily Energy Efficiency Rebate Program, and Pacific Gas and 

Electric Express Efficiency Program.(2) These program results provide the basis for 

assumptions made by sector. The WIP program assumed the estimates of savings per 

rebate dollar by sector as reported in Table J-3.

 (2) The residential sector estimate is a 

simple average of the Anaheim, PG&E Single Family, and PG&E Multifamily programs. 

The commercial-, industrial-, and institutional-sector estimates are a simple average of 

the Anaheim and PG&E Express Efficiency programs. The agricultural-sector estimate 

was taken from the PG&E Express Efficiency program. 

In 2002, incentive funding of $34.7 million ($0.56 million of SEP funds and $34.1 

million in leveraged funds) provided for $21.5 million worth of rebates. (2) The WIP 

program therefore assumed that SEP leverages $60.87 for each program dollar, and that 

each dollar of total funding provides $0.62 in rebates. Incentive funding as a percent of 

total SEP funding reported for all project areas was 1.3% in 2002. (2) The WIP program 

assumed that this percentage would apply to FY07. The WIP program assumed that 

leveraged dollars per SEP dollar for incentives was $60.87. (2) Based on the FY 2007 

request, the WIP program assumed that approximately $48.2 million dollars (from both 

SEP and leveraged funds) would be spent on incentive activities, equating to about $29.9 

million in rebates. Using the rebate dollar amounts by sector from Schweitzer and 

Tonn’s underlying data, the percentage of the total rebate package per sector was 

calculated (see Table J-3) to determine the proportion of each sector’s savings, yielding a 

total annual savings of about 1.6 TBtu [(78.6% x $29.9M x 0.0281) + (14.9% x $29.9M x 

0.1558) + (3.1% x $29.9M x 0.1558) + (2.8% x $29.9M x 0.1558) + (0.5% x $29.9M x 

0.1455)]. The WIP program assumed that the savings would be in effect for 15 years.

Table J-3. Percentage of Total Rebate Amount and Savings per Rebate Dollar by Sector 

Residential Commercial Industrial Institutional Agriculture

% of rebate 78.6% 14.9% 3.1% 2.8% 0.5%

MMBtu/$ 

rebate 0.0281 0.1558 0.1558 0.1558 0.1455

Retrofits. Energy-savings estimates for retrofits were reported in residential and 

commercial structures, schools, health-care facilities, government buildings, and 

industrial applications.(2) Retrofit calculations were based on two indicators: number of 

retrofits and square feet retrofit. (2) For this effort, the WIP program converted these two 

indicators to number of households and square feet of commercial floor space impacted. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-9

The WIP program assumed a savings per retrofit of 43.4 MMBtu per household based on 

an unweighted, nationwide average energy savings for the residential sector. This 

number was based on primary energy savings per house from residential retrofits for four 

regions of the country, as developed for the Weatherization Assistance Program.(5) The 

WIP program assumed a savings per retrofit of 18.8% per square foot of commercial 

floor space. This number was based on the average savings in retrofits in commercial 

buildings reported in two studies.(12, 13) The WIP program also applied the commercial 

number to schools and hospitals. (2) Based on Tables 1.2.3 and 1.2.4 of the Buildings 

Energy Databook, approximately 84 MMBtu/HH/yr are used by residential space heating 

and space cooling, yielding a load reduction of 54% for residential space heating and 

cooling. The WIP program applied the 18.8% savings to commercial space heating, 

space cooling, and lighting. 

States reported to the WIP program a total of 683 residential building retrofits and 49.7 

million square feet of residential floor-space retrofit. To convert the residential indicators 

into an estimated number of households, the WIP program assumed that each residential 

retrofit represented one household, and divided the total residential square feet retrofit by 

the average square feet per household (1,707, which is the average heated square footage 

for all residential units in the United States from the 2001 Residential Energy 

Consumption Survey). This yielded an estimate of approximately 29,800 households 

impacted by retrofits in any given year, or 0.067% of existing residential single-family 

buildings in each year. 

States reported to the WIP program a total of 92 commercial/industrial/institutional 

building retrofits and 206.8 million square feet of commercial/industrial/institutional 

floor-space retrofit. To convert the indicators into an estimated commercial square 

footage, the WIP program assumed that each commercial retrofit represented one 

building multiplied by the average building size (14,500 square feet, from the 1999 

Commercial Buildings Energy Consumption Survey) and used the square footage 

reported. This yielded an estimate of approximately 0.021 billion square feet impacted 

by retrofits in any given year, or 0.302% of existing commercial floor space in each year. 

The WIP program assumed that the number of retrofits performed would be in direct 

proportion to the funds available for retrofits. Therefore, the estimated penetration was 

adjusted upward by 46% to reflect the additional funds from the Special Projects monies 

that would be funded through SEP in FY07. 

Loans and Grants. The WIP program found defensible study results on the amount of 

loans provided and estimated energy savings associated with those loans for the 

following three programs: Oregon Low-Interest Loan Program, Texas LoanStar Program, 

and Nebraska Dollar and Energy Savings Loan Program.(2) The WIP program also found 

defensible study results on the amount of grants provided and energy savings associated 

with those grants for the following five programs: Illinois Energy Efficient Affordable 

Housing Program, California Grants, Louisiana Institutional Conservation Program, 

Wisconsin Farm Save Energy Project, and New York State Variable Speed Drive 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-10

Program.(2) The WIP program assumed the estimates of savings per loan/grant by sector, 

as reported in Table J-4.

 (2) Because the estimates of savings resulting from loans are 

more conservative than the estimates of savings from grants, the savings from loans were 

used to represent the total loan and grant activity. The residential-sector estimate is a 

simple average of the Oregon and Nebraska programs. The commercial-sector estimate 

is a simple average of the Oregon, Texas, and Nebraska programs. The industrial-, and 

institutional-sector estimates are a simple average of the Oregon and Texas programs. 

The agricultural-sector estimate was based on the an average of the Wisconsin Farm Save 

Energy Project and New York State Variable Speed Drive Program, adjusted by the 

average ratio of loan to grant coefficients in all other sectors. 

Loan/grant funding as a percent of total SEP funding reported for all project areas was 

16.2% in 2002. (2) The WIP program assumed that this percentage would apply to FY07. 

In 2002, leveraged dollars per SEP dollar for loans/grants was $10.65. (2) Based on the 

FY 2007 request, the WIP program assumed that approximately $113.2 million dollars 

(from both SEP and leveraged funds) would be spent on loan/grant activities. Using the 

loan/grant dollar amounts by sector from Schweitzer and Tonn’s underlying data, the 

percentage of the total loan/grant package per sector was calculated (see Table J-4) to 

determine the proportion of each sector’s savings, yielding a total annual savings of about 

1.9 TBtu [(22.9% x $113.2M x 0.0148) + (9.1% x $113.2M x 0.0148) + (3.4% x 

$113.2M x 0.0178) + (63.3% x $113.2M x 0.0178) + (1.2% x $113.2M x 0.0161)]. The 

WIP program assumed that the savings would be in effect for 15 years. 

Table J-4. Percentage of Total Loan/Grant Amount and 

Savings per Loan/Grant Dollar by Sector 

Residential Commercial Industrial Institutional Agriculture 

% of loan 22.9% 9.1% 3.4% 63.3% 1.2% 

MMBtu/$ loan 0.0148 0.0148 0.0178 0.0178 0.0161 

Traffic Signals. The WIP program assumed that incandescent bulbs used in traffic 

signals would be replaced with LEDs. (2) The average traffic light serviced would save 

793.9 kWh or 8.64 million source Btu per year, and the total number of traffic signals 

replaced in 2002 was 94,824. (2) The WIP program assumed that this number would be 

replaced in FY07. The WIP program also assumed that the savings would be in effect for 

15 years. 

The WIP program assumed that the number of traffic signals replaced would be in direct 

proportion to the funds available for signal replacement. Therefore, the estimated 

penetration was adjusted upward by 46% to reflect the additional funds from the Special 

Projects monies that would be funded through SEP in FY07. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-11

1.1.4 Sources 

(1) Schweitzer, M., D.W. Jones, L.G. Berry, and B.E. Tonn. 2003. Estimating Energy and 

Cost Savings and Emissions Reductions for the State Energy Program Based on Enumeration 

Indicators Data. ORNL/CON-487, Oak Ridge National Laboratory, Oak Ridge, TN. 

(2) Schweitzer, M, and B. Tonn. June 2005. An Evaluation of State Energy Program 

Accomplishments: 2002 Program Year. ORNL/CON-492, Oak Ridge National Laboratory, 

Oak Ridge, TN. 

(3) 2005 Buildings Energy Databook. http://www.buildingsdatabook.eren.doe.gov. 

(4) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics 

Effort. PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 

(5) Schweitzer, M. and J.F. Eisenberg. 2002. Meeting The Challenge: The Prospect of 

Achieving 30 Percent Energy Savings Through the Weatherization Assistance Program. 

ORNL/CON 479, Oak Ridge National Laboratory, Oak Ridge, Tennessee. 

(6) Feldman, S. and B. Tannenbaum, 2000. “Swoosh!? Awareness and Effects of the Energy Star 

Brand in Wisconsin Appliance Efficiency Programs,” ACEEE Summer Study 2000, 6.107-

119, American Council for an Energy-Efficient Economy, Washington, DC. 

(7) Reed, J.M., M. O’Drain, and J. Chace, 1999. “Transforming Markets Through Educational 

Information: A Study of the Pacific Energy Center,” 1999 International Energy Program 

Evaluation Conference, Denver, CO, August 18-20. 

(8) Peters, J.S. and M.R. McRae, 2001. Market Progress Evaluation Report, Architecture + 

Energy Program, Final Report, Report #E01-084, Northwest Energy Efficiency Allowance, 

Portland, Oregon, June. 

(9) Tools of Change, 2004. Whitney Public School Case Study, www.toolsofchange.com

(10) McClain, H., S. Leigh, and J. McDonald, 1994. Analysis of Savings Due to Multiple 

Energy Retrofits in a Large Office Building, ORNL/CON-363, Oak Ridge National 

Laboratory, Oak Ridge, TN, May. 

(11) Abraham, M. and J. MacDonald, 1995. Energy and Conservation Opportunities in Small 

Commercial Buildings, ORNL/CON-414, Oak Ridge National Laboratory, Oak Ridge, TN, 

August. 

(12) Greely, K., J. Harris, and A. Hatcher, 1990. “Measured Energy Savings and CostEffectiveness of Conservation Retrofits in Commercial Buildings,” ACEEE 1990 Summer 

Study on Energy Efficiency in Buildings, American Council for an Energy-Efficient 

Economy, Washington, DC. 

(13) Coates, B., 1995. “Persistence of Energy Savings in Commercial Buildings,” 1995 

Energy Program Evaluation Conference, 649-655, Chicago, IL. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-12

 

2.0 Weatherization Assistance Grants 

Project Description. The Weatherization Assistance Project provides cost-effective 

energy-efficiency services to low-income households that otherwise could not afford the 

investment, but would benefit significantly from the cost savings of energy efficiency 

technologies. The project focuses on households that spend a disproportionate amount of 

their income for energy, giving priority to households with elderly members, persons 

with disabilities, and children. 

Weatherization Assistance provides technical assistance and formula grants to State and 

local weatherization agencies throughout the United States. A network of approximately 

970 local agencies provide trained crews to perform weatherization services for eligible 

low-income households in single-family homes, multifamily dwellings, and mobile 

homes. Of the homes weatherized annually, 49% are occupied by an elderly person with 

special needs. or a person with disabilities. All homes receive a comprehensive energy 

audit, which is a computerized assessment of a home’s energy use and an analysis of 

which energy-conservation measures are best for the home—and a combination of those 

energy-saving measures are installed. 

2.1 Weatherization Assistance 

2.1.1 Significant changes from FY06 

No significant changes were made to this program for the FY07 effort. 

2.1.2 Target Market 

Market Description. The market includes households that are eligible for Federal 

assistance. Households are categorized as eligible for federal assistance if the household 

income is below the federal maximum standard of 150% of the poverty line or 60% of 

Statewide median income, whichever is higher. Individual States can also set the 

standard at a lower level than the federal maximum.c

 Target measures include air 

sealing; caulking and weather stripping; furnace and boiler tune-up, repair, and 

replacement; cooling system tune-up and repair; replacement of windows and doors; 

addition of storm windows and doors; insulation of building shells; and replacement of 

air conditioners, whole-house fans, evaporative coolers, screening, and window films.(2) 

Weatherization Plus expands this strategy to include water heating, refrigeration, 

lighting, and cooling.(1) 

Size of Market. About 34 million eligible low-income homes are included in the market. 

Baseline Technology Improvements. There are no technology improvements assumed 

apart from what appears in the Energy Information Administration (EIA) baseline. 

c Eligibility requirements for Weatherization Assistance can be found at 

http://www.eere.energy.gov/weatherization/apply.html
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-13

2.1.3 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. The WIP program employed the average household weatherization cost of 

$1,830;(6) this estimate does not include training, technical assistance, and administrative 

costs. Incremental investment beyond this amount for Weatherization Plus homes, 

estimated at an average of $1,400 by the Weatherization project,(6) was assumed by the 

Weatherization Assistance Program to be provided by leveraging funds from other 

organizations. Table J-5 shows the estimated total costs by region for Plus homes. 

Table J-5. Estimated Regional Costs for Weatherization Plus Homes 

Region 

Cost per 

“Plus”Household 

South $2861 

Northeast $3674 

West $1814 

Midwest $3429 

2.1.4 Methodology and Calculations 

Inputs to Base Case. The WIP program did not provide inputs to change the base case 

assumptions for the program markets. The WIP program’s calculations were based on a 

baseline that was developed from the Energy Information Administration’s (EIA’s) 

Commercial Buildings Energy Consumption Survey (CBECS), Residential Energy 

Consumption Survey (RECS), and the Annual Energy Outlook (AEO). For more 

information about the methodology used by the WIP program, see Methodological 

Framework for Analysis of Buildings-Related Programs: The GPRA Metrics Effort

(2004)(7). 

Technical Characteristics. This project was characterized based on an estimated level 

of savings per household, cost to weatherize each household, budget request, leveraged 

funds, and an assumed life expectancy of 15 years for weatherization measures. The 

basic assumptions were derived from a spreadsheet provided by the Weatherization 

project in September 2001.(6)

Table J-6 shows the savings per household used for each region. 

Table J-6. Savings Per Household for the Weatherization Assistance Project 

Region 

Regular 

Household 

Savings 

(MMBtu/yr) 

“Plus” 

Household 

Savings 

(MMBtu/yr) 

South 22.25 24.23 

Northeast 31.20 46.04 

West 19.04 20.31 

Midwest 31.20 49.21 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-14

The figures in the table were calculated based on the 1997 ORNL meta-evaluation 

report,

(2) the ORNL Meeting the Challenge report,(3) and special tabulations from the 

1997 “Residential Energy Consumption Survey.”(4) 

Of the units weatherized in FY 2007, nearly 50% were assumed by the Weatherization 

Project(3) to have the higher savings rates associated with Weatherization Plus. In the 

Meeting The Challenge report,(3) these savings rates were calculated on a regional basis 

and multiplied by the expected number of Plus households in each region. 

To develop energy savings by building type, the WIP program evaluated historical 

Weatherization project data in the 1997 ORNL report(2) concerning the types of 

households weatherized (see Table J-7). 

Table J-7. Percent of Weatherized Households by Type 

Household 

Type 

% of Weatherized 

Households 

Single Family 64.0% 

Mobile Home 20.0% 

Multi Family 16.0% 

To develop energy savings by fuel type, the WIP program also used the historical 

primary fuel Weatherization project data in the 1997 ORNL report.(2) Because the GPRA 

metrics are reported for electricity, natural gas, and fuel oil (but not for LPG and 

kerosene), other fuels were allocated within those types based on similarities of 

emissions. Table J-8 shows the allocation approaches used. 

Table J-8. Percent of Weatherized Households by Fuel Type 

Primary Heating Fuel 

% of Weatherized 

Households Categorized As 

Natural Gas 

Liquid Propane Gas 

50.6 

13.2 

Natural Gas 

Fuel Oil 

Kerosene 

Other (includes wood and coal) 

16.0 

3.2 

7.5 

Fuel Oil 

Electricity 9.5 Electricity 

The Department of Energy (DOE) budget and leveraged funding forecasts were used to 

determine the number of households weatherized in each category (regular or Plus) for 

each of the four regions (South, Northeast, West, and Midwest) based on the 

weatherization costs per household and assumptions regarding the use of leveraged funds. 

Table J-9 shows the projection for regular and Plus households to be weatherized. The 

WIP program assumed that the number of households weatherized for each category 

would be constant from 2011 through 2030. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-15

Table J-9. Projected Regular and Plus Households to be Weatherized 

2007 2008 2009 2010 2011 

Total Households 188,286 186,942 185,618 184,267 182,983 

Regular South 18,907 18,758 18,610 18,460 18,318 

Regular Northeast 22,524 22,355 22,189 22,020 21,860 

Regular West 24,758 24,661 24,567 24,470 24,378 

Regular Midwest 27,955 27,697 27,442 27,183 26,936 

Plus South 18,907 18,758 18,610 18,460 18,318 

Plus Northeast 22,524 22,355 22,189 22,020 21,860 

Plus West 24,758 24,661 24,567 24,470 24,378 

Plus Midwest 27,955 27,697 27,442 27,183 26,936 

The number of households in each category was multiplied by the estimated savings level 

for each category. The estimated savings level for each household category was further 

divided by household type and then by fuel type. The WIP program assumed that savings 

from each household weatherized would last for 15 years; i.e. savings from households 

weatherized in 2007 were included in the annual total savings estimates for the years 

2007 through 2021. 

2.1.5 Sources 

(1) Weatherization Plus: Opportunities for the 21st Century, April 1999, Millennium Committee 

Strategy Report accessed at http://www.eere.energy.gov/weatherization/pdfs/mcsr.pdf 

(2) Berry, L.G., M.A. Brown, and L.F. Kinney. 1997. Progress Report of the National 

Weatherization Assistance Program, ORNL/CON-450, Oak Ridge National Laboratory, 

Oak Ridge, Tennessee. 

(3) Schweitzer, M. and J.F. Eisenberg. 2000. Meeting The Challenge: The Prospect of 

Achieving 30 Percent Energy Savings Through the Weatherization Assistance Program. 

ORNL/CON 479, Oak Ridge National Laboratory, Oak Ridge, Tennessee. 

(4) Eisenberg, J.F., Oak Ridge National Laboratory. 2001. Special tabulations for the 

Weatherization Population derived from the 1997 Residential Energy Consumption Survey. 

(5) Brown, M.A., L.G. Bery, R.A. Balzer, and E. Faby. 1993. National Impacts of the 

Weatherization Assistance Program in Single-Family and Small Multifamily Dwellings. 

ORNL/CON-326, Oak Ridge National Laboratory, Oak Ridge, Tennessee. 

(6) Eisenberg, J.F., Oak Ridge National Laboratory. 2001. Projections for the Weatherization 

Assistance Program, provided to the WIP program in file “Projections02d230.xls.” 

(7) Elliott, D.B., D.M. Anderson, D.B. Belzer, K.A. Cort, J.A. Dirks, D.J. Hostick. 2004. 

Methodological Framework for Analysis of Buildings-Related Programs: The GPRA Metrics 

Effort. PNNL-14697. Pacific Northwest National Laboratory, Richland, Washington. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-16

3.0 Intergovernmental Activities 

The Intergovernmental Activities promote the market transfer of clean energy 

innovations for sustainable development, trade, security, environment, and climate.

3.1 Tribal Energy Activities 

Tribal Energy Activities builds partnerships with Tribal governments to help assess 

Native American energy needs for residential, commercial, and industrial uses. 

Additionally, it provides technical and financial assistance in energy efficiency and 

renewable energy project development. Energy projects are competitively awarded on a 

cost-shard basis for Native American Tribes to implement comprehensive energy plans.

3.1.1 Significant changes from FY06 

This program was not modeled for GPRA benefits prior to the FY07 budget. The WIP 

program has not characterized this program in the past, because when viewed in the 

context of national-level energy supply or consumption, the Tribal Energy Program 

(TEP) would not be expected to either generate or save an amount of energy that would 

appear in the significant digits of a national number. However, for the sake of 

completeness, the WIP program characterized this program for the FY07 budget. 

3.1.2 Target Market 

Target market description. DOE provides enabling funding for tribes to conduct 

renewables feasibility studies and energy plans, which may lead to actual supply 

development projects—also funded in part by DOE. The program has the goal of 1 GW 

of renewables capacity development in Indian Country by 2012 (TEP 2004). The 

program also funds the development of off-grid solar electrification of reservation 

households. 

Baseline technology improvements. The stated (TEP 2005a) goal of the program is to 

promote tribal energy sufficiency, economic development and employment on tribal 

lands through the use of renewable energy and energy efficiency technologies. The TEP 

offers financial and technical assistance to tribes through government-to-government 

partnerships that: 

1) Allow tribal leaders to make informed decisions; 

2) Bring renewable energy and energy efficiency options to Indian Country; 

3) Enhance human capacity through education and training; 

4) Improve local tribal economies and the environment; and 

5) Make a difference in the quality of life of Native Americans. 

The program seeks to increase development of renewable energy supply. In 2003, the 

National Renewable Energy Laboratory (NREL) collaborated with the Bureau of Land 

Management to assess the public lands renewable resource potential (DOE/DOE 2003). 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-17

This information informs the planning of the Tribal Energy Program. The program will 

proceed with central station development of wind resources, followed by biomass 

resources. Biomass was found to show the most potential for central station development 

on tribal lands; and, thus, would be expected to reach an assumed parity with wind 

development in terms of capacity additions (TEP 2005b). 

Baseline market acceptance. The WIP program attributed the estimated outcome 

entirely to the success of this program. However, in many cases, the program funds are 

leveraged with many other sources such as tribal, State, other Federal, and local grants. 

The basis for this attribution is that were DOE not leading this activity, these 

development projects would never occur. There are no standard leverage formulas to 

apply uniformly. The WIP program did not analyze whether success of this program 

would eventually lead to the private-sector involvement in developing the new 

renewables capacity on tribal lands in later years, but such an outcome would be possible 

under the right pricing conditions. 

3.1.3 Key Factors in Shaping Market Adoption of EERE Technologies 

Price. To enable analysis, the WIP program assumed the cost of leased solar arrays and 

battery storage of electricity to be less than the consumer costs of extending electrical 

transmission from the nearest electrical utility. For central station development, the WIP 

program assumed the electricity resource produced from renewable resources would cost 

less than utility-supplied electricity provided to the immediate tribal land with 

jurisdiction. 

Nonprice factors.

• Key consumer preferences/values: This program seeks to establish electrical 

service for households currently without electricity on tribal lands. This is not a 

comparison of alternative electrical services or of using renewable fuels to 

provide electrical service, but rather a characterization of providing electrical 

service where none currently exists, using fuels and facilities that are within the 

control of tribal organizations. 

• Manufacturing factors: Based on program materials and TEP program Web site 

documents (TEP 2004, 2005a, 2005b), most current activities are focused on 

development of wind resources. EIA (2000) suggests that biomass provides the 

greatest potential for central station power at competitive prices; therefore, the 

WIP program assumed that an even mix of technology will develop over time. 

• Policy factors: Having renewable resources in the resource stack for utilities 

continues to increase in popularity with all customer classes, even at cost 

premiums. Central station facilities on tribal lands utilizing renewable fuels may 

generate value streams from off-reservation utility interests. 
Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-18

3.1.4 Methodology and Calculations 

To permit analysis of program success, the WIP program made several enabling 

assumptions in consultation with the Tribal Energy Program: 

• Achieving the program goal of 1,000 MW in new renewables capacity on tribal 

lands by 2012 would represent approximately 20% of the total potential capacity, 

or 5,000 MW. 

• Current development efforts are almost all wind projects, but the mix will likely 

shift over time to an even split between wind capacity and biomass capacity for 

central station development over the next 20 years. 

• New biomass plants would operate at a capacity factor of 80%, on average. 

• For solar electrification, EIA (2000) states that roughly 16,000 reservation 

households are without electricity access. As a reasonable assumption, the efforts 

of the program lead to a potential to electrify 10,000 of those households in 20 

years. 

Table J-10 provides the inputs needed to develop the benefit metrics in the integrated 

models. Based on the enabling assumption presented above, the viability of biomass 

versus wind as a renewable fuel on tribal lands will cause the biomass share of new 

capacity additions to overtake that of new wind resources over the next 20 years. Also, 

the WIP program assumed the capacity factor of new wind resources would increase 

from 15% currently to 30% within 20 years, while the new biomass capacity factor would 

increase from 80% to 90% over the same period. 

Table J-11 provides the development of the off-grid PV electrification of tribal 

households in the Desert Southwest. The WIP program assumed a capacity factor of 

20% for new PV systems deployed in that region, and also assumed the average system 

would be rated for 1.2 kW capacity. The Navajo tribe and other program material 

indicate that there are at least 18,000 Navajo reservation households without electricity 

access. An arbitrary assumption was made to facilitate analysis—that the actions of the 

program could lead to providing distributed solar/PV to 10,000 households. The default 

system was assumed to be 1.2 kW.
Table J-10. Development of Tribal Renewable Energy Capacity resulting from the FY2007 Budget Assum

Share Assumptions Added MW Cumulative MW Capacity factor 

Year Fraction of Potential 

MW 

capacity 

(cumulative) 

Added 

MW Wind 

Fraction 

Biomass 

Fraction Wind Biomass Total Wind Biomass Wind Biomass

2007 0.011 45 45 1.00 0.00 45 0 45 45 0 0.150 0.7502008 0.020 100 55 1.00 0.00 55 0 55 100 0 0.158 0.7582009 0.037 185 85 0.95 0.05 81 4 85 181 4 0.167 0.7672010 0.067 335 150 0.95 0.05 143 8 150 323 12 0.175 0.7752011 0.118 590 255 0.95 0.05 242 13 255 566 25 0.183 0.7832012 0.200 1000 410 0.50 0.50 205 205 410 771 230 0.200 0.7922013 0.319 1595 595 0.50 0.50 298 298 595 1,068 527 0.206 0.8002014 0.468 2340 745 0.50 0.50 373 373 745 1,441 900 0.211 0.8062015 0.622 3110 770 0.50 0.50 385 385 770 1,826 1,285 0.217 0.8112016 0.755 3775 665 0.50 0.50 333 333 665 2,158 1,617 0.222 0.8172017 0.852 4260 485 0.50 0.50 243 243 485 2,401 1,860 0.228 0.8222018 0.900 4500 240 0.25 0.75 60 180 240 2,461 2,040 0.233 0.8282019 0.911 4556 56 0.25 0.75 14 42 56 2,475 2,082 0.239 0.8332020 0.922 4611 55 0.25 0.75 14 41 55 2,488 2,123 0.244 0.8392021 0.933 4667 56 0.25 0.75 14 42 56 2,502 2,165 0.250 0.8442022 0.944 4722 55 0.25 0.75 14 41 55 2,516 2,206 0.260 0.8502023 0.956 4778 56 0.25 0.75 14 42 56 2,530 2,248 0.268 0.8682024 0.967 4833 55 0.25 0.75 14 41 55 2,544 2,289 0.276 0.8762025 0.978 4889 56 0.25 0.75 14 42 56 2,558 2,331 0.284 0.8842026 0.989 4944 55 0.25 0.75 14 41 55 2,572 2,373 0.292 0.8922027 1.000 5000 56 0.25 0.75 14 42 56 2,586 2,415 0.300 0.900

Note: Based on enabling assumptions indicated in the text. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-19
Table J-11. Development of Off-Grid Solar PV Capacity resulting from FY2007 

Budget Assumptions 

Year Cumulative 

Households MW Capacity MWh 

2007 110 0.13 231 

2008 200 0.24 420 

2009 370 0.44 778 

2010 670 0.80 1,409 

2011 1,180 1.42 2,481 

2012 2,000 2.40 4,205 

2013 2,533 3.04 5,326 

2014 3,067 3.68 6,447 

2015 3,600 4.32 7,569 

2016 4,133 4.96 8,690 

2017 4,667 5.60 9,811 

2018 5,200 6.24 10,932 

2019 5,733 6.88 12,054 

2020 6,267 7.52 13,175 

2021 6,800 8.16 14,296 

2022 7,333 8.80 15,418 

2023 7,867 9.44 16,539 

2024 8,400 10.08 17,660 

2025 8,933 10.72 18,781 

2026 9,467 11.36 19,903 

2027 10,000 12.00 21,024 

3.1.5 Sources 

1) Energy Information Administration (EIA 2000). Energy Consumption and Renewable 

Energy Development Potential on Indian Lands, SR/CNEAF/2000-01, available online at: 

http://www.eia.doe.gov/cneaf/solar.renewables/ilands/ilands_sum.html

2) DOE/DOI (2003). Assessing the Potential for Renewable Energy on Public Lands, Joint 

report from the Bureau of Land Management and the National Renewable Energy 

Laboratory, DOE/GO-102003-1704, February 2003. 

3) Tribal Energy Program (TEP 2004). FY 2004 Peer Review Meeting, Roadmap and 

Metrics presentation, available online at: 

http://www.eere.energy.gov/tribalenergy/pdfs/7_road_map_metrics.pdf 

4) Tribal Energy Program (TEP 2005a). DOE’s Tribal Energy Program, Program Review, 

October 17, 2005. Available at: 

http://www.eere.energy.gov/tribalenergy/pdfs/0510review_tep.pdf

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

5) Tribal Energy Program (TEP 2005b). Email communication (6/28/2005) with Thom 

Sacco, Native American and International Programs Office of Weatherization and 

Intergovernmental Programs. 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-20
3.2 International Renewable Energy Program 

The International Renewable Energy Program promotes market transformation in 

international energy markets to increase the installation of U.S.-developed technologies. 

The program states the goal of developing 1,000 MW of new renewables capacity 

worldwide by 2010. Even if all of this new generation displaced fossil generation, the 

savings are insignificant—especially on a world scale. In many instances, the new 

generation that would be created would serve to electrify currently unelectrified regions 

of the world—adding to world energy consumption. About 1,000 MW each five years 

would be equivalent to replacing one moderate-sized coal or oil-fired power plant each 

five years. 

The activities of the program are more consistent with information programs and other 

outreach activities. The difference being that these activities occur with foreign 

governments. These activities could have the effect of placing U.S. technologies in 

foreign countries for demonstration or deployment, which may lead to potential adoption 

in the United States as a result, but this linkage is tenuous at best. 

Based on these observations, analysis effort has been focused elsewhere. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix J –Weatherization and Intergovernmental Activities Program – Page J-21
Appendix K – GPRA07 Alternative Scenarios 

Background 

There is inherently considerable uncertainty in long term energy projections such as those 

underlying the EERE GPRA benefits analysis. In fact, uncertainty about future energy demand, 

supplies and prices is one of the motivations for Federal investment in R&D, so that the nation 

might be better prepared in the face of potentially adverse world energy markets. As a result, the 

benefits of some of EERE programs may be greater under other conditions than the reference or 

business-as-usual (BAU) projections used in the main report. The prospective benefits 

framework developed by the National Research Council (NRC)1 recognizes this in their 

recommendation of assessing benefits for several “global scenarios.” 

The Office of Fossil Energy (FE) and EERE have jointly developed alternative scenarios to 

capture two of the key uncertainties impacting future energy: energy prices and climate policy. 

The scenarios were used on a pilot basis, internally within EERE, with respect to its FY06 GPRA 

analysis, and were more fully implemented for FY07. As all of the R&D offices within DOE’s 

Office of Energy Science and Environment (ESE) work toward developing a common GPRA 

benefits analysis methodology, it is anticipated that these alternative scenarios, as well as 

potentially other scenarios, will be used. 

The use of alternative scenarios that project other possible futures address just one type of 

uncertainty; and, in this case, along two dimensions of market uncertainty. Model risk is still 

embedded in the scenarios in that energy models are abstractions of reality and may contain 

biases in their abstractions. 

 

Programmatic success is another major area of risk that is not addressed through these scenarios. 

All the EERE Portfolio cases assume that the programs meet their goals. Work is ongoing 

within EERE and other DOE R&D offices to account for technological and programmatic risk in 

the GPRA benefits process. 

The two alternative market scenarios that were developed for GPRA07 are a high fuel price case 

and a case with a constraint on national energy-related carbon emissions. Baseline cases, 

without EERE programs, and portfolio cases, with representation of all the EERE programs, 

were created for these alternative cases. The benefits of the programs are then evaluated as the 

difference between each pair of portfolio and base cases, using a similar methodology as for the 

BAU GPRA07 benefits. However, the resulting benefits are not always comparable to the BAU 

benefits. For one, the alternative scenarios have different underlying macroeconomic 

assumptions and demands for energy services (such as light, travel, industrial steam, etc.) due to 

the higher energy prices. In other words, the energy system is smaller; and, therefore all else 

equal, the benefits (of reduced energy or avoided emissions) in absolute terms would be smaller. 

In addition the definition of the climate policy as a cap on energy related carbon emissions leads 

 

1

 National Research Council, Prospective Evaluation of Applied Energy Research and Development at DOE (Phase 

One), 2005 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-1
to reduced emissions to the cap level in both the baseline and EERE portfolio cases. Hence there 

are no carbon emission reductions attributable to the EERE portfolio of programs. The primary 

benefit is that the EERE programs help reach that cap at lower cost.2

The off-line analyses that support the benefits analysis were not revised for the alternative 

scenarios. Thus for programs whose inputs to the integrating models are “outcomes” (such as 

market penetration rates or energy savings), this potentially leads to an overstatement of benefits, 

especially in the carbon constraint scenario, because there would likely be greater efficiency 

investments in the Base case and therefore reduced savings resulting from the EERE 

technologies. For all programs, the baseline was not revisited, except to the extent that the 

models will endogenously project reactions to the scenarios. The program analysts were not 

asked to consider how the sectors in which their programs’ technologies have an effect would 

react in absence of DOE research, development, and deployment (RD3) and in the presence of 

the scenario constraints. In other words, the baseline technology characteristics for EERE 

technologies for the most part remain the same under all the scenarios. 

As in the benefits analysis shown in the main body of the report, two integrated energy models 

were used for the scenarios analysis: NEMS-GPRA07 and MARKAL-GPRA07. The former 

provides the mid-term projections (to 2025), while the latter is used to extend them out to 2050. 

In most cases, the two models show similar results in the mid-term period, so both sets are not 

shown. However, there are inter-temporal dynamics in the carbon constraint scenario that 

necessitate showing the full projection period for MARKAL-GPRA07. 

High Fuel Price Scenario Definition 

The High Fuel Price scenario is predicated on a future in which the supply of natural gas and oil 

are more limited, and as a result significantly higher fuel prices occur.3 The world oil price 

follows the trajectory of the AEO2005 High B price case (the higher of the two high oil price 

cases). As seen in Figure 1, oil prices start off higher than the reference case in 2005 and rises to 

$48 (2003 dollars4) per barrel by 2025 and then to $70 by 2050. Natural gas supply in the GPRA 

High Fuel Price case was restricted in order to cause natural gas prices to reach and remain 

above $5 by 2015. They continue to rise and reach $8.25 by 2050. The restrictions on gas 

supply included limiting the ability of new LNG terminals to be constructed, delaying the 

Canadian and Alaska gas pipelines until after 2025, and reducing assumed Canadian resources. 

Coal price assumptions were not explicitly changed, but coal prices rise by up to 12 percent due 

to the increases in other prices. 

 

2

 One might argue that the presence of EERE technologies that allows for more rapid adoption of technologies that reduce carbon 

emissions might influence climate policy, however carbon caps, including the one modeled here, usually specify a time-path for 

reductions. The level of emissions reductions and the timing of the reductions are thus dictated by the scenario, and not 

influenced by the technologies in this construct. 

3

 In hindsight the oil price path turns out to be very similar to the more current view of prices as represented by the Annual 

Energy Outlook 2006. 4

 Unless otherwise stated, all prices are given in 2003 dollars. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-2
Figure 1. Projected World Oil and Wellhead Natural Gas Price 

Carbon Constraint Scenario Definition 

A second scenario was designed to examine the implications of EERE benefits under a cap on 

energy-related carbon emissions. The cap is imposed beginning in 2008 and drops to 1,580 

million metric tons of carbon equivalent (MMTCE) by 2017, which is roughly the level of 

emissions in 2003. After 2017, the cap is assumed to remain constant at that level. An 

economy-wide trading system is assumed where the lowest cost reductions will occur first. The 

price of the carbon allowances indicates the marginal cost of compliance. 

Figure 2. Carbon Emissions 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-3
Benefits Under High Fuel Prices 

Changes in Primary Non-Renewable Energy Consumption 

Figure shows the impacts of EERE’s technology portfolio under business-as-usual and high fuel 

price scenarios. 

Figure 3. Primary Nonrenewable Energy Use Under Alternative Fuel Price 

Scenarios 

The benefits of EERE’s portfolio of programs, as measured by the traditional GPRA metrics, are 

generally lower under the high fuel price conditions than under the reference conditions. One of 

the key determinants of how the benefits change under high fuel price conditions is the baseline 

projection, given that benefits are defined as the difference between the baseline and the 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-4
portfolio cases. Higher prices shrink the demand for energy. In part, this is due to lower demand 

for energy services as a result of slightly lower incomes and economic output, and behavioral 

responses to higher prices such as turning down thermostats and driving less. Higher energy 

prices also stimulate greater investment in energy efficient and renewable technologies even in 

the absence of technology improvements resulting from the EERE R&D and deployment 

programs. In the Portfolio case, with advanced technologies available, greater adoption of these 

technologies is expected. The net result is somewhat smaller primary non-renewable energy 

savings resulting from the EERE portfolio in most years as shown in Figure 3. 

Sector- Specific Effects 

Figure 4. Growth in End-Use Energy Consumption from 2005 to 2025: 

Business-as-Usual and High Fuel Price Scenarios 

The most significant reduction in energy consumption with high prices occurs in the 

transportation sector (see Figure 4), where higher gasoline and diesel prices lead to reduced 

vehicle miles traveled and higher vehicle efficiencies. Both of these factors reduce the 

opportunity for savings from the FreedomCar and hydrogen fuel cell vehicles, even though 

higher prices make these vehicles more attractive. 

Similarly, in the residential and commercial sectors, higher prices reduce energy consumption, 

primarily oil but also natural gas and electricity to a less extent, through behavioral changes as 

well as equipment efficiencies. Overall, the buildings energy demand reduction resulting from 

the EERE Portfolio is roughly 15 percent smaller with higher fuel prices in 2025. The industrial 

sector’s energy use actually increases in the high price case, due to increased consumption of 

coal for coal-to-liquids production. 

The base case reduction in service demands and increased efficiency in the High Fuel Price 

scenario relative to the BAU scenario leads to a reduction in incremental delivered energy 

consumption in the buildings sector of 9 percent and 4 percent in 2030 and 2050 respectively. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-5
The addition of EERE technologies in the Portfolio Case reduces delivered energy consumption 

in the buildings sector for both the BAU and high fuel price scenarios. This reduction in 

delivered energy consumption in the EERE Portfolio Cases is attributable to improved buildings 

shell packages, highly efficient space conditioning technologies and improved and highly 

efficient solid state lighting technologies. Figure 5 shows the incremental buildings delivered 

energy consumption for the Base and EERE Portfolio cases in the long term in both the BAU 

and High Fuel Price scenarios. 

Figure 5: Buildings-Sector Delivered Energy Consumption: 2030 and 2050 

In the transportation sector, service demands are lower and efficiency is increased. Light duty 

vehicle (LDV) vehicle miles traveled (VMT) fall by about 5 percent between the BAU and high 

fuel price scenario. Furthermore, the Base case average LDV stock efficiency increases by 9 

percent and 30 percent in 2030 and 2050 in the high fuels scenario relative to the BAU scenario. 

Over the long run, the increase in LDV stock efficiency is due to a dramatic increase in hybrid 

vehicle penetration in the High Fuel Price Base case. Also, with the higher oil prices coal-toliquids (CTL) technologies become cost-effective and ethanol consumption increases in the High 

Fuel Price Base case relative to the BAU Base case. The increased uses of these substitute fuels 

further reduce base case oil consumption in the High Fuel Price scenario. 

By 2050, both the BAU and High Fuel Price EERE Portfolio cases, the LDV fleet is comprised 

entirely of EERE hybrids and fuel cell vehicles, although the proportion of fuel cell vehicles 

increases in the high fuels scenario. The EERE Portfolio cases lead to reduced petroleum 

consumption due to the increased market share of highly efficient EERE hybrid and fuel cell 

vehicles. Also, increased production of cellulosic ethanol further displaces petroleum 

consumption. Figure 6 shows the LDV stock market shares. 

 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-6
Figure 6: Light Duty Vehicle Stock Market Shares, 2030 and 2050 

Electricity Generation and Capacity Effects 

The mix of electric generation capacity shifts away from natural gas fired technologies 

(combustion turbines and combined cycles) and toward coal (steam coal and integrated 

gasification (IGCC)) and renewable capacity in the High Fuel Price Base relative to the BAU 

Base. The EERE Portfolio stimulates a greater amount of renewable capacity in the High Fuel 

Price scenario and displaces a greater proportion of coal than occurs with the BAU scenario. 

The shifts become even more pronounced over the long term. By 2050, coal-fired capacity is 40 

percent higher in the high fuels base case than in the BAU base case, while renewable generation 

capacity shows a more modest 8 percent increase. With the inclusion of the EERE portfolio 

technologies, by 2050 IGCC and natural gas combined-cycle capacity decrease 63 percent and 

41 percent, respectively, relative to the High Fuel Price Base case. The decreases are attributable 

to both improved renewable generation technologies and end-use efficiency. The impacts for the 

mid and long-term are shown in 

There is a corresponding shift in the fuels used for electric generation with generally more coal 

and less oil and gas when fuel prices are higher. Total generation decreases in the EERE 

Portfolio Case due to greater adoption of energy efficient equipment. (Figure 8) 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-7
Figure 7. Gigawatts of Electric Generation Capacity in 2025 and 2050 

Business-as-Usual and High Fuel Price Cases

Figure 8. TWh of Electricity Generation in 2025: 

Business-as-Usual and High Fuel Price Cases 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-8
As in the BAU scenario, wind and solar PV technologies increase substantially in the High Fuel 

Price Portfolio case, reflecting the R&D success in lowering costs and improving performance. 

The effect is magnified with higher fuel prices, even though more is adopted in the base (no 

R&D) case as well. The mix of generation is similar, although PV and wind are intermittent 

technologies and therefore provide less generation than other technologies that can be operated 

all the time. 

In the long run, the most significant increase in both renewable capacity and generation in the 

High Fuel Price Portfolio case relative to the High Fuel Price Base case is for central solar 

thermal generation and distributed photovoltaics. These generation sources provide power 

during periods of peak electric demands and thus reduce the need for gas-fired combustion 

turbines. Figures 9 and 10 show non-hydro renewable generation and capacity for the mid and 

long-term. 

Figure 9. Gigawatts of Non-Hydro Renewable Generation Capacity, 

2030 and 2050, Business-as-Usual and High Fuel Price Cases 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-9
Figure 10. TWh of Non-Hydro Renewable Electric Generation in 2025 and 2050 

 in the Business-as-Usual and High Fuel Price cases

Carbon Emission Reductions 

In the mid-term, carbon emission reductions resulting from the EERE Portfolio under high prices 

are higher in some years and lower than others relative to the BAU. Because the baseline 

projects a greater reliance on coal-fired generation, electricity generation displaced by efficiency 

and renewable generation in the portfolio case is more carbon intensive. On the other hand, 

energy savings are slightly lower. In the long-term, the coal displacement effect dominates and 

results in greater carbon emission reductions. Thus, as increased buildings efficiency and 

improved and less costly renewable generation technologies penetrate the market, a higher 

proportion of carbon intensive coal-fired power generation being displaced in the high fuels 

EERE scenario. 

Economic Benefits 

Energy expenditure savings, as measured in the mid-term projections, are greater due to higher 

fuel prices. Each unit of energy saved has a higher value. In addition, the impact of reduced 

consumption has a greater impact of reducing prices, especially natural gas, in the high price 

case. Energy system cost savings, as measured in the long-term projections, also increase due to 

the reduction in increasingly more expensive fossil fuels. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-10
Benefits Summary 

Table 1. FY07 Annual Benefits Estimates for the EERE Portfolio 

Under High Fuel Prices (NEMS-GPRA07) 

 Benefits 2010 2015 2020 2025

Energy Displaced 

 Non-Renewable Energy Savings (quadrillion Btu/year) 0.3 1.2 4.3 7.5

Economic 

 Energy Expenditure Savings (billion 2003 dollars/year)* 1 26 89 152

Environmental 

 Carbon Savings (million metric tons carbon equivalent/year) 7 24 89 167

Security 

 Oil Savings (million barrels per day) 0.03 0.2 0.7 1.3

 Natural Gas Savings (quadrillion Btu/year) 0.1 0.4 0.9 0.7

Program-Specific Electric Capacity (gigawatts) 2 14 95 155

Program-Specific Generation (terawatt-hours/year) 2 39 314 515

Table 2. FY07 Annual Benefits Estimates for the EERE Portfolio 

Under High Fuel Prices (MARKAL-GPRA07) 

 Benefits 2030 2040 2050

Energy Displaced 

 Non-Renewable Energy Savings (quadrillion Btu/year) 13 28 31

Economic 

 Energy-system net cost savings (billion 2003 dollars/year)* 89 203 307

Environmental 

 Carbon Savings (million metric tons carbon equivalent/year) 286 626 698

Security 

 Oil Savings (million barrels per day) 6 9 13

 Natural Gas Savings (quadrillion Btu/year) 1 1 2

Program-Specific Electric Capacity (gigawatts) 160 337 479

Program Specific Generation (terawatt-hours/year) 525 1,066 1,396

* Midterm energy-expenditure savings only include reductions in consumer energy bills, while long-term energysystem net cost savings also include the incremental cost of the advanced energy technology purchased by the 

consumer. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-11
 Benefits Under a Carbon Emissions Constraint 

Changes in Primary Nonrenewable Energy Consumption 

Figure 11 shows the primary nonrenewable energy consumption for the base and EERE cases 

under both the BAU and Carbon Constraint scenarios as projected by NEMS-GPRA07 and 

MARKAL-GPRA07. The imposition of the carbon cap has a profound impact on the energy 

system. By 2025, the cap requires a 27 percent reduction in emissions from the BAU Baseline 

and a 21 percent reduction from the BAU Portfolio case. 

Figure 11. Primary Nonrenewable Consumption in the BAU and Carbon 

Constraint Cases, through 2050

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-12
These emission reductions are achieved through a combination of energy service demand 

reductions, increased efficiency, and fuel switching to less carbon intensive energy sources. In 

the Carbon Constraint Base case, without EERE R&D, primary fossil energy use drops in 2025 

by 21 percent relative to the BAU, and primary nonrenewable energy is reduced by 17 percent. 

Because meeting the cap requires such a significant shift in consumption, only slightly greater 

reductions occur in the EERE Portfolio case. 

The most dramatic changes occur during the transition period between 2010 and 2020 when the 

carbon cap is being phased-in, before it reached the cap level of 1580 MMTCE that is then held 

constant. During the transition period, a large amount of carbon-intensive steam coal electric 

generation capacity is replaced with carbon free nuclear and renewable generation. At the same 

time, lower service demands and increased end-use sector efficiency result in lower demands for 

electricity 

Carbon Reductions and Carbon Allowance Prices 

By definition, carbon emissions are at the specified cap in the carbon cases, and take place on a 

specified schedule, with or without EERE technologies.5

 Therefore no carbon emission 

reductions are expected that would be attributable to the EERE programs, unless the cap 

becomes non-binding as occurs in the very long term. Nevertheless, there is a significant 

reduction in the cost required to meet that cap when the EERE advanced technologies are 

available. 

In the midterm, the carbon allowance price that is necessary to reduce emissions to the cap level 

falls from a maximum of $200 per metric ton of carbon equivalent in the Base to $165 per ton in 

the Portfolio case. From 2015 to 2015, NEMS-GPRA07 projects an average price drop of $35 

per ton with the EERE portfolio relative to the Base case without the R&D. 

Carbon Allowance Prices

0

50

100

150

200

250

2005

2007

2009

2011

2013

2015

2017

2019

2021

2023

2025

2003 Dollars per Ton Carbon

Baseline

EERE Portfolio

Figure 12. Carbon Allowance Prices through 2025 in the Base and 

EERE Portfolio Cases 

 

5

 No banking of allowances was included in the scenario. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-13
The expenditures that consumers pay for energy are higher due to the carbon allowances that are 

embedded in energy prices. The reduction in the carbon allowance price in the EERE portfolio 

case, along with a reduction in energy consumption due to greater energy efficiency 

opportunities, leads to greater energy expenditure savings from the EERE portfolio than in the 

BAU case – 85 percent higher in 2020 and 51 percent greater in 2025. 

The longer term effects are driven in large part by the energy system’s reaction during the 

transition period. Specifically, the introduction of a large stock of highly efficient and/or carbon 

free capital stock during the transition period frees the energy system to make only incremental 

investments in additional efficiency and carbon free capital stock in future periods in order stay 

under the carbon cap while meeting increased demand for energy services. Thus, in the Carbon 

Constraint Base case, the marginal value of carbon falls dramatically from about $250 per metric 

ton of carbon in 2015 to stabilize at roughly $100 per metric ton after 2030. 

The differences between model projections for carbon allowance prices are attributable to at least 

three fundamental factors: 

• MARKAL’s design includes perfect foresight and thus “optimizes” over the entire model 

horizon, while different NEMS-GPRA07 modules have different foresight formulations 

(myopic and simulation-based for the demand sectors; close to perfect foresight and 

optimizing for the electric generation sector). Because the carbon cap is instituted in 

2012, MARKAL makes an optimal decision early, but with respect to the shape of the 

energy system through 2050. NEMS-GPRA07 only “sees” through 2025, and only its 

supply modules optimize over the time frame. 

• The version of MARKAL employed for GPRA07 does not include elastic service 

demands. As relative prices change, the demand sectors continue to demand the same 

amount of energy services as under the original prices.6 In NEMS-GPRA07, the demand 

sectors can lower their energy demands in the short-term in response to higher real 

energy prices; in MARKAL the only mechanism is through adoption of new 

technologies7

. 

• MARKAL runs in five-year time steps, while NEMS runs in one-year time steps. While 

this distinction is usually not very significant for long-term models, the timing of the 

carbon cap in our scenario makes the time-step issue quite important. The carbon cap 

starts in 2008 and becomes more stringent in 2017. In MARKAL, this translates into 

2010 and 2015, and the change occurs in one time step. In NEMS, the additional 

resolution of annual time periods allows the energy system to respond more gradually; in 

MARKAL, the new stringency is a one-period shock to the system. 

With the addition of the EERE technologies in the Carbon Constraint Portfolio Case, the energy 

system carbon emissions fall below the cap after 2030, and the value of carbon (expressed as the 

allowance price) drops to zero. There are several reasons for this result. As noted above, the 

 

6

 Energy demand is generally quite inelastic in the short term, reflecting the existing capital stock, but more elastic in the longterm as fuel switching and capital retirements and replacements become more economic and have time to occur. EERE expects 

to implement an elastic service demand version of MARKAL in future GPRA reports. 

7

 For these scenarios, some of the service demands, such as VMT, were adjusted to reflect the response from NEMS-GPRA07. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-14
version of MARKAL being used has fixed service demands, thus consumers are not responding 

to a drop in the value of carbon (and the resulting decrease in fuel prices) by demanding more 

energy services. The introduction of more efficient and low/no carbon capital stock during the 

transition period has a lasting effect on carbon emissions throughout the projection period. 

Finally, the introduction of EERE technologies further reduces the cost of reducing carbon 

emissions. In fact, in the BAU scenario without any climate policy, the EERE portfolio 

emissions drop to about 1890 MMT of carbon by 2050. This is about two-thirds of the required 

savings from the BAU baseline needed to meet the carbon cap. The marginal value of carbon 

(allowance prices) and total carbon emissions as projected by MARKAL-GPRA07 are shown in 

Figures 13 and 14. 

Marginal Cost of Carbon Emissions

0

50

100

150

200

250

300

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

2003$ per ton Carbon

CO2 - Base

CO2 - EERE

Figure 13. Carbon Allowance Prices through 2050 in the Base and EERE Portfolio 

Cases 

Total Energy System Carbon Emissions

1250

1450

1650

1850

2050

2250

2450

2650

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

MMT of Carbon

BAU - Base

CO2 - Base

BAU - EERE

CO2 - EERE

Figure 14. Carbon Emissions through 2050 in the BAU and Carbon Constraint 

Base and EERE Portfolio Cases 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-15
Generation and Capacity Effects 

In both time periods and models, the greatest shifts in energy use patterns occur in the electric 

power sector. A significant portion of existing coal-fired power plants retire, and nuclear, 

sequestered coal, and renewable capacities increase. With the EERE Portfolio, an additional 121 

GW of renewable capacity is added by 2025 compared to the carbon constraint baseline. This is 

very similar to the incremental amount that was added in the BAU Portfolio relative to the BAU 

Base although the mix of renewable types is different, as will be shown later. In the BAU 

scenario, the additional renewable capacity displaces a mix of gas and coal capacity. In the 

Carbon Constraint scenario, the displaced capacity includes nuclear and sequestered coal, as well 

as conventional gas and coal capacity. In 2025, total capacity is projected to be higher in the 

Carbon Constraint case despite lower electricity demand due to the increasing share of 

intermittent wind capacity that leads to greater capacity reserve requirements. 

In the long-term projections, the inclusion of the EERE Portfolio technologies lowers the total 

generation capacity. Here the impact of reduced electricity demand due to increased efficiency 

in the end-use sectors leads to lower capacity overall. There is a shift away from existing coal 

and towards carbon free generation capacity, such as nuclear or IGCC with sequestration. 

However, with the EERE Portfolio of technologies, there are more cost-effective opportunities 

for reducing carbon emissions and the carbon allowance price declines significantly (shown in 

Figure 13). Therefore, the system’s use of carbon-free generation sources declines and more 

unsequestered IGCC capacity is added at the end of the forecast period. The decline in the 

carbon allowance price also reduces the economic incentive to build large amounts biomass-fired 

and wind-powered generation capacity. However, the wind turbines built in the Portfolio Case 

have higher capacity factors than those built in the Base Case, and the decrease in wind 

generation between the Carbon Constraint Base case and EERE portfolio case is lower than the 

reduction in capacity. The decline in nuclear capacity occurs as no new nuclear plants are built 

after 2025 due to both the decline in the carbon allowance price, the reduction in electricity 

demand, and improvements in renewable generation technologies relative to the base case. Total 

electric generation capacity is shown in Figure 15 and nuclear generation capacity is shown in 

Figure 16. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-16
Figure 15. Electric Generation Capacity in 2025 and 2050 

Business-as-Usual and Carbon Constraint Cases

Nuclear Capacity Additions per 5 Year Period

0

10

20

30

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

GW

GPRA06 - BASE

CO2 - Base

CO2 - EERE

Nuclear Generation Capacity

0

20

40

60

80

100

120

140

160

180

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

GW

GPRA06 - BASE

CO2 - Base

CO2 - EERE

Figure 16: Nuclear Generation Capacity 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-17
In the Carbon Constraint case, the drop in coal generation is even greater than the decrease in 

capacity as existing coal plants become more expensive to operate. The EERE portfolio 

provides more cost-effective renewable generation, and nuclear and natural gas generation 

decline relative to the base. Total generation is slightly lower as well due to greater adoption of 

energy-efficient technologies. 

0

1000

2000

3000

4000

5000

6000

7000

Base Portfolio Base Portfolio

Billion kWh

Renewable

Nuclear

Natural Gas

Petroleum

Coal

 BAU Carbon Cap

Figure 17. Electricity Generation in 2025 in the BAU and Carbon Constraint cases

With the EERE portfolio technologies, non-hydro renewable generation capacity increases in 

both the BAU and Carbon Constraint scenarios. However, the composition of this capacity 

changes dramatically in the Carbon Constraint scenario. In the Carbon Base case, the largest 

increase is in biomass and wind generation and capacity. In the EERE portfolio case, wind and 

solar technologies make significant additional contributions to carbon emission reductions in the 

midterm period. Dedicated biomass power, which is not in the EERE R&D portfolio, is greater 

in the Carbon Base case, but shrinks as the wind and solar technologies improve in the EERE 

Portfolio case. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-18
Figure 18. Non-Hydro Renewable Electric Generation Capacity in 2025 and 2050 

Business-as-Usual and Carbon-Constraint cases 

Figure 19. Non-Hydro Renewable Electric Generation in 2025 and 2050 

Business-as-Usual and Carbon Constraint cases 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-19
In the long run, the largest increase is in solar generation relative to the carbon base case. In the 

carbon base case, biomass and wind generation were among the most cost effective options in 

reducing carbon emissions. However, with the introduction of the EERE technologies, end-use 

efficiency in both the buildings and transportation sectors are very cost effective in reducing 

carbon emissions. Furthermore, the EERE solar technologies show relatively higher cost and 

performance improvement over their base case levels. 

Sector- Specific Effects 

The growth in end-use consumption is reduced considerably in the Carbon Constraint base case 

even without the advanced EERE technologies. Part of the drop is due to lower demands for 

energy services, such as vehicle miles traveled or home heating and cooling, and partly due to 

greater efficiency. With the inclusion of EERE technologies, energy efficiency improves further, 

and energy consumption drops a bit more. The incremental reduction due to EERE is smaller in 

the carbon constraint case, because of overall reduced demand for services and greater efficiency 

in the base. 

Growth in Energy Use 2005 to 2025

0

5

10

15

20

25

Base Portfolio Base Portfolio

Delivered Quad Btu

Transport

Industry

Commercial

Residential

 BAU Carbon Cap

Figure 20. Growth in Energy Consumption from 2005 to 2025 in the BAU and 

Carbon Constraint cases 

In the long term, the carbon constraint continues to reduce delivered buildings energy 

consumption in the Carbon Constraint Base case relative to the BAU Base. Incremental 

delivered energy consumption in the buildings sector is roughly 40 percent lower in 2030 and 

2050. The inclusion of EERE portfolio technologies further reduces delivered energy 

consumption in the buildings sector in both the BAU and Carbon Constraint scenarios. The 

reduction in delivered energy consumption in the EERE Portfolio cases are attributable to 

improved buildings shell packages, highly efficient space conditioning technologies and 

improved and highly efficient solid state lighting technologies. Figure 21 shows the 

incremental buildings delivered energy consumption for the base and EERE portfolio cases 

in both the BAU and High Fuel Price scenarios. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-20
Incremental Buildings Delivered Energy Consumption

 Over 2005 Levels

0

2

4

6

8

10

12

14

BAUBaseBAUEERECO2-

Base

CO2-

EERE

BAUBaseBAUEERECO2-

Base

CO2-

EERE

Quad. Btus

2030 2050

Figure 21. Buildings-Sector Delivered Energy Consumption: 2030 and 2050 

The transportation sector reacts to the carbon constraint by reducing service demands and by 

increasing efficiency. LDV VMTs fall by about 6 percent between the BAU and Carbon 

Constraint scenario. Furthermore, in the Carbon Constraint scenario the Base case average LDV 

stock efficiency increases by 8 percent in 2030 and 18 percent in 2050 relative to the BAU 

scenario. The increase in LDV stock efficiency is due to an increase in hybrid vehicle 

penetration in the carbon constraint base case. Also, ethanol consumption increases in the 

Carbon Constraint Base case relative to the BAU Base. The increased use of ethanol further 

reduces Base case oil consumption in the Carbon Constraint scenario. 

By 2050, both the BAU and Carbon Constraint EERE Portfolio cases, the LDV fleet is 

comprised entirely of EERE hybrids and fuel cell vehicles. The EERE Portfolio cases lead to 

reduced petroleum consumption due to the increased market share of highly efficient EERE 

hybrid and fuel cell vehicles. Also, increased production of cellulosic ethanol further displaces 

petroleum consumption. Figure 22 shows the LDV stock market shares. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-21
LDV Stock Market Shares

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

BAUBaseBAUEERECO2-

Base

CO2-

EERE

BAUBaseBAUEERECO2-

Base

CO2-

EERE

% of LDV Stoc

k

Other

Hydrogen FC

Hybrid

Gasoline ICE

2030 2050

Figure 22: Light Duty Vehicle Stock Market Shares, 2030 and 2050 

As noted earlier, many of the benefits estimates for the Carbon Constraint scenario are lower 

than in the BAU scenario due to the massive changes in the energy system in the Base case that 

result from the imposition of a carbon cap. Furthermore, the reduction in service demands 

means that the energy system cost savings are not comparable to those in the BAU scenario. 

However, the increased buildings and transportation efficiency, as well as improved cost and 

performance of renewable technologies still lead to significant benefits relative to the base case. 

Benefits Summary 

Table 3. FY07 Annual Benefits Estimates Through 2025 for the EERE Portfolio 

Under a Carbon Constraint (NEMS-GPRA07) 

Benefits 2010 2015 2020 2025 

Energy Displaced 

 Non-Renewable Energy Savings (quadrillion Btu/year) 0.01 0.4 1.6 3.9

Economic 

 Energy Expenditure Savings (billion 2003 dollars/year)* 13 55 129 162

Environmental 

 Carbon Savings (million metric tons carbon equivalent/year) ns ns ns ns

Security 

 Oil Savings (million barrels per day) 0.1 0.2 0.5 0.9

 Natural Gas Savings (quadrillion Btu/year) 0.2 0.6 1.5 1.0

Program-Specific Electric Capacity (gigawatts) ns 25 116 163

Program-Specific Generation (terawatt-hours/year) ns 94 423 575

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-22
Table 4. FY07 Annual Benefits Estimates Through 2050 for the EERE Portfolio 

Under a Carbon Constraint (MARKAL-GPRA07) 

 Benefits 2030 2040 2050 

Energy Displaced 

 Non-Renewable Energy Savings (quadrillion Btu/year) 6 13 16

Economic 

 Energy-system net cost savings (billion 2003 dollars/year)* 67 130 185

Environmental 

 Carbon Savings (million metric tons carbon equivalent/year) ns 83 29

Security 

 Oil Savings (million barrels per day) 3 6 8

 Natural Gas Savings (quadrillion Btu/year) 3 3 4

Program-Specific Electric Capacity (gigawatts) 62 85 139

Program Specific Generation (terawatt-hours/year) 269 291 260

* Midterm energy-expenditure savings only include reductions in consumer energy bills, while long-term energysystem net cost savings also include the incremental cost of the advanced energy technology purchased by the 

consumer. 

Projected Benefits of Federal Energy Efficiency and Renewable Energy Programs (FY 2007-FY 2050) 

Appendix K – Alternative Scenarios – Page K-23
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Projected Benefits of Federal Energy Efficiency and Renewable 

Energy Programs - FY 2007-FY 2050 (FY 2007 Budget Request) 

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 NREL/TP-620-39684 

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This document summarizes the results of the benefits analysis of EERE’s programs, as described in the FY 2007 

Budget Request. EERE has adopted a benefits framework developed by the National Research Council (NRC) to 

represent the various types of benefits resulting from the energy efficiency technology improvements and renewable 

energy technology development prompted by EERE programs. EERE’s benefits analysis focuses on three main 

categories of energy-linked benefits—economic, environmental, and security. 

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benefits analysis; biomass program; building technologies program; distributed energy resources program; federal 

energy management program; hydrogen, fuel cells, and infrastructure technologies program; industrial technologies 

program; solar energy technologies program; vehicle technologies program; weatherization and intergovernmental 

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