A national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

National Renewable Energy Laboratory 

Innovation for Our Energy Future 

Energy Sector Market Analysis 

Sponsored by the Weatherization and 

Intergovernmental Program (WIP) at DOE 

D. Arent, R. Benioff, G. Mosey, L. Bird, 

J. Brown, E. Brown, L. Vimmerstedt, 

J. Aabakken, and K. Parks 

National Renewable Energy Laboratory 

M. Lapsa, S. Davis, M. Olszewski, D. Cox, 

K. McElhaney, and S. Hadley

Oak Ridge National Laboratory

D. Hostick, A. Nicholls, S. McDonald, and 

B. Holloman

Pacific Northwest National Laboratory

Technical Report 

NREL/TP-620-40541 

October 2006 

NREL is operated by Midwest Research Institute ● Battelle Contract No. DE-AC36-99-GO10337 
Energy Sector Market Analysis 

Sponsored by the Weatherization and 

Intergovernmental Program (WIP) at DOE 

D. Arent, R. Benioff, G. Mosey, L. Bird, 

J. Brown, E. Brown, L. Vimmerstedt, 

J. Aabakken, and K. Parks 

National Renewable Energy Laboratory 

M. Lapsa, S. Davis, M. Olszewski, D. Cox, 

K. McElhaney, and S. Hadley

Oak Ridge National Laboratory

D. Hostick, A. Nicholls, S. McDonald, and 

B. Holloman

Pacific Northwest National Laboratory

Prepared under Task No. IGST.5000 

Technical Report 

NREL/TP-620-40541 

October 2006 

National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393 

303-275-3000 • www.nrel.gov 

Operated for the U.S. Department of Energy 

Office of Energy Efficiency and Renewable Energy 

by Midwest Research Institute • Battelle 

Contract No. DE-AC36-99-GO10337
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TABLE OF CONTENTS 

Executive Summary...........................................................................................................1 

Cross-Sector Analytical Approach...................................................................................2 

Buildings Sector Analysis .................................................................................................3 

 Approach and Methodology ..........................................................................................3 

 Sector Characteristics.....................................................................................................3 

 Buildings Sector Appendix .........................................................................................10 

Clean-Energy Sector Analysis ........................................................................................17 

 Approach and Methodology ........................................................................................17 

 Sector Characteristics...................................................................................................17 

Industrial Sector Analysis ..............................................................................................29 

 Approach and Methodology ........................................................................................29 

 Sector Characteristics...................................................................................................30 

Transportation Sector Analysis......................................................................................36 

 Approach and Methodology ........................................................................................36 

 Sector Characteristics...................................................................................................37 

 iii
 1

EXECUTIVE SUMMARY 

This paper presents the results of energy market analysis sponsored by the Department of 

Energy’s (DOE) Weatherization and Intergovernmental Program (WIP) within the Office 

of Energy Efficiency and Renewable Energy (EERE). The analysis was conducted by a 

team of DOE laboratory experts from the National Renewable Energy Laboratory 

(NREL), Oak Ridge National Laboratory (ORNL), and Pacific Northwest National 

Laboratory (PNNL), with additional input from Lawrence Berkeley National Laboratory 

(LBNL). The analysis was structured to identify those markets and niches where 

government can create the biggest impact by informing management decisions in the 

private and public sectors. The analysis identifies those markets and niches where 

opportunities exist for increasing energy efficiency and renewable energy use. 

DOE’s Weatherization and Intergovernmental Program provides tools, information, and 

technical and financial assistance to help inform decisions by states, communities, tribal 

authorities, businesses, manufacturers, consumers, and international partners on the use 

of energy efficiency and renewable energy technologies and policies. This analysis took a 

fresh look at key decisions that are driving the use of renewable energy and energy 

efficiency in U.S. buildings, electricity, industrial, and transportation markets. For these 

markets, the analysis identified opportunities to increase the use of renewable energy and 

energy efficiency technologies and practices. 

The analysis attempted to answer the following questions: 

• What are the current markets that WIP programs are addressing and what impacts is 

WIP having on these markets? 

• What are the markets and segments within each market that hold the most potential 

for increased RE and EE deployment? 

• What are the major barriers for each of the markets and segments? 

• Who are the key customers and decision makers in each market and market segment, 

and what needs do they have for information or assistance. 

• Who are the other key actors in each market and market segment, what activities are 

they pursuing, and what opportunities exist for government to leverage resources and 

capabilities with these players? 

A common structure and format for the sectoral analyses was developed and performed 

for each of the target sectors: buildings, clean energy, industry, and transportation. Data 

were compiled and developed from existing information sources, including: 

• Total energy use in the sector 

• Energy use by major groups of energy users 

• Current levels of technology penetration and energy-savings technical potential 

• Market drivers and barriers 

• Key market actors and stakeholders 

• Investments in the sector 

Methodologies, assumptions, and detailed results for each sector are presented in the 

body of this report. 
CROSS-SECTOR ANALYTICAL APPROACH 

Analysis Team 

The analytic team included: 

Cross-Sector Analysis: 

Douglas J. Arent, NREL 

Ron Benioff, NREL 

Buildings: 

Donna Hostick, PNNL 

Andrew Nicholls, PNNL 

Sean McDonald, PNNL 

Brad Hollomon, PNNL 

John Brown, NREL 

Transportation: 

Gail Mosey, NREL 

Elizabeth Brown, NREL 

Laura Vimmerstedt, NREL 

Jørn Aabakken, NREL 

Melissa Lapsa, ORNL 

Stacy Davis, ORNL

Clean Energy: 

Lori Bird, NREL 

Elizabeth Brown, NREL 

Keith Parks, NREL 

Stan Hadley, ORNL 

Melissa Lapsa, ORNL 

Industrial: 

Mitch Olszewski, ORNL 

Karen McElhaney, ORNL 

Daryl Cox, ORNL

 2
Buildings Sector Analysis 

Approach and Methodology 

The buildings sector analysis was prepared using readily available sources, including 

Energy Information Administration (EIA) and other Department of Energy (DOE) data, 

Weatherization and Intergovernmental Program (WIP) project Web sites, and other 

reports prepared on energy efficiency and the buildings industry. More detailed 

information is contained in an appendix to this analysis. 

Sector Characteristics 

Baseline Market Assessment 

Building-related energy use was almost 40.3 quadrillion Btu1 (QBtu) in 2002, or 

approximately 40% of the total primary energy use in the United States. Of that, 

approximately 52% is consumed by the residential sector;2

 43% by the commercial 

sector;3

 and 5% by the industrial sector4 for non-process-related energy use associated 

with building energy services (e.g., lighting, HVAC). Of the total, almost 50% is due to 

electricity-related losses.5 Figure 1 illustrates the current and projected primary energy 

consumption for the residential and commercial sectors. 

0

10

20

30

40

50

60

2002 2005 2010 2020 2025

Energy Consumption, QBtu

Commercial

Residential

Figure 1. Buildings Sector Primary Energy Consumption6

In 2003, there were more than 112 million households and 70 billion square feet of 

commercial floor space. Each year, more than 1 million new housing units and 2 billion 

 

1

 Buildings Energy Data Book (BED), August 2004, Table 1.1.1, Table 1.3.11 

2

 BED, Table 1.2.1 

3

 BED, Table 1.3.4 

4

 BED, Table 1.3.11 

5

 BED, Table 1.1.1 

6

 BED, Tables 1.2.1 and 1.3.1 

 3
square feet of commercial floor space are built.7

 Of the new housing units built, 

approximately 10% built in 2004 qualified for Energy Star,8 and approximately 26,700 

homes have been built to date by participating Building America research partners.9

Additionally, almost 2,000 commercial and institutional buildings (representing about 

400 million square feet) have qualified for the Energy Star label. This is equivalent to 

about 0.04% of all commercial buildings and 0.56% of commercial square footage.10

Within the residential sector, the greatest energy uses among equipment are gas furnaces, 

incandescent lights, central air conditioning, refrigeration, and electric and gas water 

heating.11 Refrigerator energy efficiency, measured in kWh/unit/year, has greatly 

improved during the past 15 years.12 Gas furnace sales are now 30%-plus in the 90%- 

Annual Fuel Utilization Efficiency (AFUE)-and-over condensing range, with DOE/EPA 

Energy Star actively labeling these condensing units as Energy Star. Absent heat-pump 

breakthroughs, gas heating technology is at its physical-efficiency limit, as units nearing 

100% efficiency are commercially available.13

By contrast, the efficiency picture for incandescent lights, and electric and gas water 

heating use, offers less evidence of success to date. Incandescent lights use more than 2 

QBtu and have a low luminous efficacy – about 75% lower than fluorescent lighting, for 

example. Lighting offers large opportunities for efficiency gains, both because replacing 

incandescents with fluorescents saves a lot of energy, and also because the first cost 

differential continues to fall. Water heaters, which use roughly the same amount of 

energy, by contrast, do not offer consumers much in the way of efficiency/technology 

choice, and consequently there are no current Energy Star labels for water heating. 

Within the commercial sector, the largest equipment energy users are fluorescent and 

incandescent lighting, rooftop and unitary air-conditioning, nonpersonal computer (PC) 

office equipment, supply/return fans, and packaged heating.14 EIA anticipates only 

moderate growth in energy consumption for space heating, space cooling, and lighting 

due to current and proposed appliance standards.15 However, as lighting consumes more 

than 3.8 QBtu of energy, it represents a great area of opportunity with the introduction of 

“Super T-8s”; lighting controls; compact fluorescents (CFLs); and, in the future, lightemitting diodes (LEDs). 

 

7 Annual Energy Outlook 2005, Tables A.4 and A.5 8

 EPA Press Release, “Nearly One in 10 2004 Homes Qualified for Energy Star.” EPA Press Release 

#R037, March 3, 2005. 

9

 http://www.eere.energy.gov/buildings/building_america/cfm/project_locations.cfm 

10 EPA Press Release, “EPA’s List of Most Energy Efficient Buildings Nears 2000.” EPA Press Release 

#R012, January 25, 2005. 

11 DOE/BT, “The Revised FY 2006 AOP Process,” Guide for Evaluation of Energy Savings Potential, 

January 19, 2005. Table B. 

12 McMahon, et al. Impacts of U.S. Appliance Standards to Date. LBNL-45825. 13 ACEEE Consumer Guide: Top-Rated Energy Efficient Appliances: Gas and Oil Furnaces, accessed at 

http://www.aceee.org/consumerguide/topfurn.htm 

14 DOE/BT, “The Revised FY 2006 AOP Process,” Guide for Evaluation of Energy Savings Potential, 

January 19, 2005. Table D. 

15 Annual Energy Outlook 2005 

 4
Market Drivers 

At the most aggregate level of resolution, there are five primary markets within the 

buildings sector: new and retrofit homes, new and retrofit commercial buildings, and 

equipment/appliance sales (see Figure 2). The primary factors that influence (or drive) 

decision-making within the building market as a whole include interest rates, financing 

institutions, demographic factors, architects/engineers, general contractors, the demand 

for amenities, advertisers, and codes and standards. Additional drivers of decisionmaking in the equipment and appliance market include retailers, manufacturers, and 

Energy Star. 

New Buildings

• Architects

• Contractors

• ASHRAE/local codes

• HVAC engineers

New Homes

• Interest rates

• Tax treatment 

• Demographic factors

• Teleworking

• Demand for amenities

• Advertising

New Buildings

• Architects

• Contractors

• ASHRAE/local codes

• HVAC engineers

New Homes

• Interest rates

• Tax treatment 

• Demographic factors

• Teleworking

• Demand for amenities

• Advertising Equipment/Appliance Sales

• Buyers

• Builders/designers/developers

• Retailers

• HVAC installers

• Fed Standards/Energy Star

• Manufacturers

• Energy Service Providers

Homes - Retrofit Buildings - Retrofit

• Owners

• Assessors/Appraisers

• Advertisers/retailers

• General contractors

• Fed Govt – standards/Energy Star

• HVAC installers

• Energy Service Providers

• Capital providers

• Owners

• Real Estate Investment 

Trust

• Local codes

• Tenants 

• Energy service providers

Figure 2. Key Market Drivers 

Market Barriers16, , , 17 18 19

Market barriers in the buildings industry include a lack of information between the use of 

energy for a specific technology and its associated cost; the tradeoff between a lower lifecycle cost versus a lower first cost; principal-agent problems where the purchasing party 

 

16 Office of Policy and International Affairs, July 1996. Policies and Measures for Reducing Energy 

Related Greenhouse Gas Emissions: Lessons from Recent Literature. Office of Policy and International 

Affairs, U.S. Department of Energy. 

17 U.S. Congress, Office of Technology Assessment. May 1992. Building Energy Efficiency, OTA-E-518 

(Washington, D.C.: U.S. Government Printing Office). 

18 Hassell, S., A. Wong, A. Houser, D. Knopman, M. Bernstein. 2003. “Building Better Homes: 

Government Strategies for Promoting Innovation in Housing.” Science and Technology Policy Institute, 

RAND, for U.S. Department of Housing and Urban Development. 

19 National Energy Policy Development Group. May 2001. National Energy Policy, Chapter 4: Using 

Energy Wisely. Office of the President of the United States. 

 5
is not responsible for paying the energy bills; time/availability barriers where users make 

emergency purchases due to equipment failure and are then limited to stock on hand; risk 

barriers where users tend to purchase familiar products; and the belief held by builders 

that consumers are relatively unwilling to invest in energy efficiency (e.g., luxury items 

such as marble countertops are more attractive than increased air-conditioner efficiency) 

(see Figure 3). 

Additionally, the way in which the buildings industry is set up creates inherent barriers to 

the diffusion of energy efficiency. Because the industry is highly competitive, innovative 

measures are seen as more risky. Boom-bust cycles and small- to medium-size home 

builders equate too few resources for training and innovation; and multiple independent 

parties (e.g., architects/designers, contractors, developers) make it difficult to coordinate 

innovation and share information. 

New Buildings

• Energy not significant portion of most building 

expenditures

• 1-2 year payback vs. LCC focus

• If energy not “in” at financing stage, it’s “out”

• Granite entryway vs. AC 

• Lack of credible info re best ROI for efficiency

• Speculative building

New Homes

• Decisions made by builders, not owners 

who pay bills 

• Fragmented & risk-averse industry 

• Stick built with untrained labor force

• Lack of energy consciousness 

• Financing mechanisms for efficiency

• Renters/Owners

• No national rating system

New Buildings

• Energy not significant portion of most building 

expenditures

• 1-2 year payback vs. LCC focus

• If energy not “in” at financing stage, it’s “out”

• Granite entryway vs. AC 

• Lack of credible info re best ROI for efficiency

• Speculative building

New Homes

• Decisions made by builders, not owners 

who pay bills 

• Fragmented & risk-averse industry 

• Stick built with untrained labor force

• Lack of energy consciousness 

• Financing mechanisms for efficiency

• Renters/Owners

• No national rating system Equipment/Appliance Sales

• Purchase in emergency when unit

fails

• Household income

• First cost focus

• Despite Energy Star, lack of 

information about cost/performance 

• Commodity not attribute

• Efficiency generally not tax 

advantaged

Homes - Retrofit Buildings - Retrofit

•Lack of understanding on our 

part about this sector

• As built vs intended design

• Lack of energy awareness

• Lack of objective energy ROI

info

• Household income

• Granite countertop vs.AC

• Perceptions about resale ROI 

• No nationally known MPG 

analog

• See above

• Renovations rarely energy

driven

• Competition with other 

uses of scarce capital

• Cost/difficulty – window 

change out in 30 story

building, e.g.

Figure 3. Key Market Barriers 

Key market actors and stakeholders 

Key market actors and stakeholders in the buildings industry include buyers/owners, 

Realtors, general contractors, developers, code officials, banks/lenders, energy service 

providers, property management firms, retailers, manufacturers, and advertisers (see 

Figure 4). 

 6
New Buildings

• Capital providers, investors

• Users 

• Developers, contractors

• Property management firms

• Architects

• Codes officials/ASHRAE

New Homes

• Buyers

• Advertisers

• Realtors

• Homebuilders

• Code officials

• Banks/lenders

New Buildings

• Capital providers, investors

• Users 

• Developers, contractors

• Property management firms

• Architects

• Codes officials/ASHRAE

New Homes

• Buyers

• Advertisers

• Realtors

• Homebuilders

• Code officials

• Banks/lenders

Equipment/Appliance Sales

• Buyers

• Builders/designers/developers

• Retailers

• HVAC installers

• Fed Standards/Energy Star

• Manufacturers

• Energy Service Providers

Homes - Retrofit Buildings - Retrofit

• Owners

• Assessors/Appraisers

• Advertisers/retailers

• General contractors

• Fed Govt – standards/Energy Star

• HVAC installers

• Energy Service Providers

• Capital providers

• Owners

• Real Estate Investment Trust

• Local codes

• Tenants

• Energy service providers

Figure 4. Key Market Actors 

Current activities and impacts in the sector20

WIP funds a number of activities in the buildings sector, spending more than $260 

million annually in FY03-FY05. These activities include low-income weatherization, 

which has provided weatherization services to more than 5.3 million low-income 

families,21 with an average savings of 15% of household energy bills amounting to 

approximately $274 per year;22 State Energy Grants, with an annual energy savings of 

42.3 million Btu and annual cost savings of $256 million;23 Rebuild America, with 

annual savings of nearly 9 trillion Btu and $131 million in energy cost savings;24 and 

Building Codes Training and Assistance, with energy savings of nearly $1 billion per 

year and improvements to the energy efficiency of nearly 3 billion square feet of new 

commercial floor space and nearly 4 million new households.25

Other federal, state and local, and industry programs that are influencing this market 

include Energy Star; building codes – e.g., American Society of Heating, Refrigerating 

and Air-Conditioning Engineers (ASHRAE) standards, and the International Energy 

Conservation Code (IECC); appliance and equipment standards; state energy programs, 

 

20 Following the completion of this analysis in FY05, responsibility for Rebuild America and Building 

Codes Training and Assistance was transferred to EERE’s Building Technologies Program in FY06. 

21 http://www.eere.energy.gov/weatherization/about.html 

22 http://www.eere.energy.gov/weatherization/improving.html 

23 http://www.eere.energy.gov/state_energy_program/goals_metrics.cfm#metrics 

24 http://www.rebuild.gov/aboutus/overview.asp 

25 http://www.energycodes.gov/whatwedo/ 

 7
including California Energy Commission (CEC) and New York State Energy Research 

and Development Authority (NYSERDA); and utility rebate or Demand-Side 

Management (DSM) programs. These programs invest close to $2 billion, including $201 

million from the Low-Income Home Energy Assistance Program (LIHEAP),26 almost 

$1.6 billion from State Energy Efficiency Programs,27 and more than $100 million from 

other EERE offices (within Building Technologies, Solar, and Distributed Energy 

programs).28

Market Impact Assessments 

Energy-Savings Technical Potential 

Some of the most promising technologies and practices for the buildings sector are shown 

in Table 1. These technologies and practices were identified by the American Council for 

an Energy-Efficient Economy (ACEEE)29 as new technologies and practices that would 

be well-suited for market transformation activities, given their current low penetration 

rates (<2%) in the market. Note that some activities, such as the High Performance 

Building Deployment and 30% Above Code efforts, are not mutually exclusive because 

they target many of the same buildings. Therefore, the potential savings cannot be added. 

Table 1. Potential Buildings Energy-Savings Potential 

Action Type Clients Savings Potential 2020 

(Source TBtu)30

Whole Building Retrofit Consumers (Single Family), 

Corporations (Mercantile/ 

Service, Office), Institutions 

(Education, Health Care) 

Technical potential ~3,500 TBtu31 

Building America 

Deployment (Zero net 

energy homes) 

Consumers (Single Family) ~200 (technical potential ~2,500 TBtu 

at 100% market penetration) 

30% Above Code in New 

Commercial Construction 

Corporations, Institutions (New 

Construction only) 

~600 (technical potential ~900 TBtu at 

100% market penetration) 

High Performance 

Building Deployment 

(Ultra Low Energy 

Commercial Building 

Designs) 

Corporations 

(Mercantile/Service, Office), 

Institutions (Education, Health 

Care) 

~200 (technical potential ~2,000 TBtu 

at 100% market penetration) 

 

26 http://www.eere.energy.gov/weatherization/source_fund.html 

27 Telephone interviews conducted by CEE, Northeast Energy Efficiency Partnerships and Kushler, York, 

& Witte 2004 

28 Based on the DOE/EERE FY 2005 Budget Request. 

29 Sachs, H., S. Nadel, J. Thorne Amann, M. Tuazon, E. Mendelsohn, L. Rainer, G. Todesco, D. Shipley, 

and M. Adelaar. Emerging Energy-Saving Technologies and Practices for the Buildings Sector as of 2004. 

October 2004. ACEEE, Report Number A042.

30 Ibid. 

31 PNNL calculations based on residential and commercial energy consumption, documented in the 

Appendix to this section. 

 8
Increase CFL 

penetration in homes 

Consumers ~600 (technical potential ~1,200 TBtu 

at 100% market penetration) 

Advanced Residential 

Water Heating 

Consumers ~400 (technical potential ~800 TBtu at 

100% market penetration) 

Commercial Lighting 

High-Efficacy Options 

Commercial, Institutional ~500 (technical potential ~500-750 

TBtu at 100% market penetration) 

Potential for Regional Activities 

In 2003, almost half (46%) of new single-family homes were built in the south census 

region,32 where more than 36% of the existing residential stock is located.33 The south 

census region also presents opportunities in the commercial sector, with approximately 

35% of the commercial square footage located in that region.34 This region accounted for 

39% of residential energy use and 37% of commercial energy use in 2001.35 Targeted 

efforts in these states36 would represent an efficient use of scarce resources. 

 

32 BED, Table 2.1.8 

33 BED, Table 2.1.3 

34 BED, Table 2.2.4 

35 Energy Information Administration. State Energy Data 2001: Consumption. Table R.1. 

36 The south census region is comprised of Delaware, Maryland, District of Columbia, Virginia, West 

Virginia, North Carolina, South Carolina, Georgia, Florida, Kentucky, Tennessee, Mississippi, Alabama, 

Arkansas, Louisiana, Oklahoma, and Texas. 

 9
Buildings Sector Appendix 

Table A-1. Energy Use by Energy-User Group 

Sector - Major Groups of 

Energy Users 

Current Energy Use – 

Primary QBtu 

Residential37 20.91 

 Single Family 16.57 

 Multifamily 3.05 

 Mobile Home 1.29 

Commercial38 17.40 

 Office 3.86 

 Mercantile 2.61 

 Education 1.72 

 Health Care 1.44 

 Warehouse/Storage 1.32 

 Food Service 1.27 

 Lodging 1.24 

 Public Assembly 1.08 

 Service 0.99 

 Food Sales 0.78 

 Other 0.52 

 Vacant 0.35 

 Public Order/Safety 0.24 

Industrial39 1.96 

Table A-2. Residential and Commercial Sector Energy Consumption by State40

State Residential Sector 

Consumption (TBtu) 

Commercial Sector 

Consumption (TBtu) 

Alaska 53.2 65.4 

Alabama 379.9 253.5 

Arkansas 218.5 148.1 

Arizona 344.0 311.9 

California 1,445.7 1,508.8 

Colorado 302.8 287.1 

Connecticut 266.5 215.0 

District of Columbia 34.2 104.0 

Delaware 62.1 51.6 

Florida 1,192.6 958.4 

Georgia 642.0 502.9 

Hawaii 34.9 38.6 

Iowa 229.3 179.0 

Idaho 104.8 94.5 

 

37 Total energy use from Buildings Energy Data Book (BED), August 2004, Table 1.2.1; percentage 

primary consumption by building type from 2001 RECS, Table CE1-4c 

38 BED, August 2004, Tables 1.3.4 and 1.3.7 

39 BED, August 2004, Table 1.3.11 

40 Energy Information Administration. State Energy Data 2001: Consumption 

 10
Illinois 928.2 829.1 

Indiana 504.0 397.4 

Kansas 215.4 192.0 

Kentucky 339.0 246.1 

Louisiana 347.8 263.5 

Massachusetts 460.7 379.4 

Maryland 391.0 372.3 

Maine 110.9 73.8 

Michigan 789.5 598.2 

Minnesota 380.7 335.6 

Missouri 495.9 389.1 

Mississippi 234.1 162.8 

Montana 69.8 59.8 

North Carolina 640.6 513.1 

North Dakota 61.0 55.5 

Nebraska 152.3 129.8 

New Hampshire 86.7 65.3 

New Jersey 572.8 554.3 

New Mexico 107.1 122.4 

Nevada 147.1 107.9 

New York 1,194.3 1,303.2 

Ohio 892.0 682.2 

Oklahoma 297.8 232.5 

Oregon 251.7 207.6 

Pennsylvania 930.9 709.0 

Rhode Island 72.6 63.2 

South Carolina 321.7 235.4 

South Dakota 60.3 50.3 

Tennessee 500.0 369.2 

Texas 1,569.9 1,356.0 

Utah 139.6 140.1 

Virginia 548.9 533.8 

Vermont 47.8 32.7 

Washington 471.2 376.8 

Wisconsin 400.8 312.6 

West Virginia 156.6 110.6 

Wyoming 39.3 50.8 

United States 20,240.5 17,332.4 

 11
Table A-3. Technology Penetration by Energy-User Group, Existing Stock 

Residential % of Households 

199041 199742 200143

Heating 

Heat Pump 7.76% 9.56% 9.72% 

Furnace 58.28% 59.31% 62.80% 

Ind. Space Heat 11.28% 12.02% 9.35% 

Boiler 14.49% 12.32% 11.50% 

Cooling 

Res Central Air 31.06% 50.14% 55.73% 

Indiv/Room AC 26.60% 34.24% 28.11% 

Heat Pump 12.22% 14.40% 13.63% 

Water Heating 

Centralized 100.00% 99.61% 100.00% 

Note: Percentages represent equipment present in household. 

Household may contain more than one type of equipment per category. 

Commercial % of Commercial Floorspace 

199244 199545 199946

Heating 

Heat Pump 13.1% 10.2% 13.6% 

Furnace 26.0% 25.8% 23.3% 

Ind. Space Heat 35.9% 30.5% 27.5% 

Boiler 33.4% 30.8% 30.8% 

Pkg Heating 26.8% 31.4% 41.6% 

Other 2.7% 11.0% 4.8% 

Cooling 

Res Central Air 15.0% 17.7% 13.4% 

Indiv/Room AC 28.3% 24.4% 23.5% 

Heat Pump 14.3% 13.9% 14.6% 

Central Chillers 19.6% 23.5% 20.7% 

Pkg AC 49.1% 52.7% 62.0% 

Other 0.0% 0.7% 1.0% 

Lighting 

Incandescent 61.1% 63.8% 59.0% 

Std Fluorescent 95.2% 95.4% 93.8% 

Compact Fluorescent 11.7% 25.1% 31.8% 

High Intensity Discharge 25.2% 26.3% 29.4% 

Halogen 16.4% 26.7% 

Water Heating 

Centralized 54.3% 61.4% 63.4% 

Distributed 50.3% 31.5% 18.1% 

Combined 4.9% 15.8% 

Note: Percentages represent equipment present in building. 

Building may contain more than one type of equipment per category. 

 

41 1990 Residential Energy Consumption Survey, EIA 

42 1997 Residential Energy Consumption Survey, EIA 

43 2001 Residential Energy Consumption Survey, EIA 

44 1992 Commercial Buildings Energy Consumption Survey, EIA 

45 1995 Commercial Buildings Energy Consumption Survey, EIA 

46 1999 Commercial Buildings Energy Consumption Survey, EIA 

 12
WIP investments in the sector 

Table A-4. WIP Appropriations Summary for Buildings ($ Million) 

Project Activity FY03 FY04 FY05 

Grants 220.2 223.8 224.8 Weatherization 

Training & TA 3.4 3.4 3.4 

Rebuild America 11.0 10.0 8.7 

Building Codes Training 4.6 4.4 5.6 

Energy Star 4.2 3.7 0.0* 

SEP Buildings Allocation 23.5 21.8 21.9 

Buildings Total 266.9 267.1 264.4 

 Note: Energy Star has been moved to the Building Technologies Program within EERE. 

Other Resource Investments in the Sector 

Table A-5. 2004 Energy Efficiency Budgets (1)47

State Estimated Funding 

(million $) 

 State Estimated Funding 

(million $) 

Arizona 2 New Hampshire 15 

California 540 New Jersey 108 

Colorado 25 New York 278 

Connecticut 49 Ohio 3 

Illinois 9 Oregon 40 

Iowa 54 Rhode Island 15 

Maine 8 Texas (2) 68 

Massachusetts 132 Vermont 16 

Michigan 10 Washington 31 

Minnesota (2) 57 Wisconsin 39 

Montana 9 BPA Programs 35 

Nevada 11 TOTAL 1.584 Billion

Notes: 

1. Budgets are for 2004 unless otherwise indicated. In all cases, budgets are moving targets. Budgets 

for some of the states are estimates based on the prior year. 

2. 2003 estimated budget 

3. Programs administered by Bonneville Power Administration in the Pacific Northwest. The BPA 

figure is understated. 

 

47 Telephone interviews conducted by Consortium for Energy Efficiency (CEE), Northeast Energy 

Efficiency Partnerships and Kushler, York, & Witte 2004 

 13
Energy-Savings Potential 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Gas Furnace Incan Lights Central A/C Refrigerator Elec WH Gas WH Elec

Furnace

Clothes

Washer

Total Primary Energy Use (QBtu)

Figure A-1. Largest Equipment Uses – Residential

echnical Potential Calculations 

lculated as follows: 

uilding America Deployment

48

0.0

0.5

1.0

1.5

2.0

2.5

Fluor Lights

Incan Lights

Rooftop & Unitary A/C

Non-PC Office Equip

Supply/Return Fans

Packaged Heating

Elec WH

HID Lights

Total Primary Energy Use (QBtu)

Figure A-2. Largest Equipment Uses – Commercial49

T

Technical potential savings were ca

B : The ACEEE report calculated savings information for 

number to represent 100% penetration (199 TBtu / 8% * 100% = 2,487 TBtu). 

 

50

“Zero (Net) Energy Houses, Including Houses with > 50% Energy Savings” of 199 

source TBtu in 2020, assuming a feasible penetration of 8%. PNNL extrapolated this

48 DOE/BT, “The Revised FY 2006 AOP Process,” Guide for Evaluation of Energy Savings Potential, 

January 19, 2005. Table B. 

49 DOE/BT, “The Revised FY 2006 AOP Process,” Guide for Evaluation of Energy Savings Potential, 

January 19, 2005. Table D. 

50 Sachs, H., S. Nadel, J. Thorne Amann, M. Tuazon, E. Mendelsohn, L. Rainer, G. Todesco, D. Shipley, 

and M. Adelaar. Emerging Energy-Saving Technologies and Practices for the Buildings Sector as of 2004. 

October 2004. ACEEE, Report Number A042. Pages 158-159. 

 14
Whole-Building Retrofits: No similar activity was represented in the ACEEE report. 

NNL, therefore, calculated technical potential using the Buildings Energy Data Book51

roximately 14.3 QBtu were used in the residential 

•

nd lighting (BED Table 

nergy use in those 

High-P

P

to develop rough estimates as follows: 

• Residential: Approximately 80% of residential energy use occurs in single-family 

housing (BED Table 1.2.6); app

sector for space heating, space cooling, water heating, and lighting (BED Table 

1.2.3); and the whole-building retrofit goal would be to reduce energy use in those 

end uses by 20%, yielding approximately 2,300 TBtu. 

Commercial/Institutional: Approximately 10.0 QBtu were used in the commercial 

sector for space heating, space cooling, water heating, a

1.3.3); approximately 60% of commercial energy use occurs in 

mercantile/service, office, education, and health-care buildings (BED Table 

1.3.7); and the whole building retrofit goal would be to reduce e

end uses by 20%, yielding approximately 1,200 TBtu. 

erformance Building Deployment: The ACEEE report52 calculated savings 

formation for “Ultra Low Energy Commercial Building Designs (50% > Codes)” of 

ted 

in

199 source TBtu in 2020, assuming a feasible penetration of 10%. PNNL extrapola

this number to represent 100% penetration (199 TBtu / 10% * 100% = 1,990 TBtu). 

30% Above Code: The ACEEE report53 calculated savings information for “Integrated 

Commercial Building Design (30% > Code)” of 620 source TBtu in 2020, assuming a 

feasible penetration of 68%. PNNL extrapolated this number to represent 100% 

penetration (620 TBtu / 68% * 100% = 912 TBtu). 

Increase CFL Penetration in Homes: Currently, incandescent lighting uses more than 2.0 

QBtu annually. Assuming that CFLs save about 60% over incandescent lighting, the 

technical potential is about 60% of the incandescent consumption. 

Advanced Residential Water Heating: The ACEEE report54 calculated savings 

formation for “Residential Condensing Water Heaters” of 217 source TBtu in 2020, 

eaters” 

d 

 

in

assuming a feasible penetration of 70% and for “Residential Heat Pump Water H

of 158 source TBtu in 2020, assuming a feasible penetration of 30%. PNNL extrapolate

these numbers to represent 100% penetration ((217 TBtu / 70% * 100%) + (158 TBtu / 

30% * 100%) = 837 TBtu). 

51 Buildings Energy Databook, August 2004 

52 Sachs, H., S. Nadel, J. Thorne Amann, M. Tuazon, E. Mendelsohn, L. Rainer, G. Todesco, D. Shipley, 

and M. Adelaar. Emerging Energy-Saving Technologies and Practices for the Buildings Sector as of 2004. 

October 2004. ACEEE, Report Number A042. Pages 152-153. 

53 Sachs, H., S. Nadel, J. Thorne Amann, M. Tuazon, E. Mendelsohn, L. Rainer, G. Todesco, D. Shipley, 

and M. Adelaar. Emerging Energy-Saving Technologies and Practices for the Buildings Sector as of 2004. 

October 2004. ACEEE, Report Number A042. Pages 154-155. 

54 Sachs, H., S. Nadel, J. Thorne Amann, M. Tuazon, E. Mendelsohn, L. Rainer, G. Todesco, D. Shipley, 

and M. Adelaar. Emerging Energy-Saving Technologies and Practices for the Buildings Sector as of 2004. 

October 2004. ACEEE, Report Number A042. Pages 46-50. 

 15
Commercial Lighting High-Efficacy Options: Currently, commercial fluorescent lighting

uses almost 2.5 QBtu annually. Assuming that higher-efficacy fluorescents (such as 

“super T-8s” and scotopic lighting) save 20%-30% over standard fluorescent lighting, the 

technical potential would range from 20%-30% of the standard fluorescent consumption. 

 16
Clean-Energy Sector Analysis 

Approach and Methodology 

The following analysis examines the market for renewable energy and energy efficiency 

in the electric sector from 1990 through 2025 and assesses potential opportunities for 

future market expansion. The analysis traces the historical factors that have influenced 

the growth and adoption of renewable energy-generating sources and presents estimates 

of the potential future market size in light of key market drivers and barriers. 

The analysis relies on readily available information, including data from EIA and other 

Department of Energy (DOE) sources, as well as other reports prepared on renewable 

energy and demand-side management. 

Sector Characteristics 

Baseline Data and Recent Market Trends 

Trends in the Electric Sector 

In 2002, the base year for this analysis, U.S. electric-generating capacity totaled 876,700 

MW and total annual generation was 3,678 billion kWh. Primary energy consumption in 

the electric power sector was 38.19 quadrillion Btus (quads) annually. Delivered 

electricity, accounting for plant efficiencies and transmission and distribution losses, was 

equivalent to 13.11 

quads. Of the 

delivered electricity, 

50% was generated 

from coal, followed 

by nuclear (20%), 

natural gas (18%), 

renewables (9%), and 

petroleum (2%) 

(Figure 5). E

consumptio

use sector was fairly 

evenly distributed 

among residential (36%), 

commercial (34%),

industrial (28%) users, 

with less than 1% used by the transportation sector.

lectricity 

n by end-

 and 

 

55

0

5

10

15

20

25

30

35

40

45

50

2002 2020 2002 2020

Delivered Electricity Primary Energy

Consumption

Quadrillion btu

Electricity Imports

Other

Renewable 

Nuclear

Natural Gas

Petroleum

Coal 

Figure 5. Primary and Delivered Energy Consumption in 

the Electric Sector

55 EIA Annual Energy Outlook 2005, With Projections to 2025, DOE/EIA-0383(2005), preliminary release 

January 2005. http://www.eia.doe.gov/oiaf/aeo/index.html

 17
During the early to mid-1990s, energy consumption per capita increased with declining 

prices and strong economic growth. To meet growing demand, the vast majority of 

capacity additions were natural gas plants, because of their ability to meet air pollutant 

emissions standards and 

the relatively low cost 

of fuel. However, rec

increases in the price of

natural gas have led 

renewed interest in 

wind and coal-fired 

capacity. 

ent 

to a 

 

Looking forward, the 

Energy Information 

Administration (EIA) 

projects that electricity 

sales will grow at an 

annual average rate of 

1.9% through 2025 to 

5,220 billion kWh in 

2025, with sales to the commercial sector growing faster than in the residential and 

industrial sectors. With 43 GW of inefficient, older generating capacity projected to be 

retired, EIA estimates that 281 GW of new generation will be needed by 2025, bringing 

total installed capacity to 1,145 GW by 2025.56 

Fuel Prices to electricity generators, 1990-2025

0

1

2

3

4

5

6

7

1990 1995 2000 2005 2010 2015 2020 2025

$2003/million btu

Oil Natural Gas

Coal Nuclear

History Projection

Figure 6. EIA Projections of Fuel Prices to Electric 

Generators (1990-2025) 

Despite high fuel prices, more than 60% of the new capacity additions are projected to be 

natural gas-fired combined-cycle, combustion turbine, or distributed generation facilities. 

EIA projects that natural gas prices to drop to $4.3/mmBtu ($2003) by 2010 and then rise 

again gradually through 2025 (Figure 6).57

Coal prices are expected to remain relatively flat over time in constant dollars. As natural 

gas prices rise later in the forecast, new coal-fired capacity becomes increasingly 

competitive, accounting for nearly one-third of the capacity expansion. EIA assumes that 

new nuclear facilities will not be economical. Of total generating capacity by 2025, EIA 

projects that 34% will be coal, 46% natural gas and other fossil, 9% nuclear, and 9% 

renewable. Retail electricity prices are projected to increase slightly or stay at the current 

historic highs through 2025 (Figure 7). 

56 EIA (2005). 

57 EIA Renewable Energy Annuals, 1990-2003, 

http://www.eia.doe.gov/cneaf/solar.renewables/page/pubs.html. 

 18
Renewable Energy 

Historic Trends Average US Retail Electricity Prices 1970-2025

0

2

4

6

8

10

12

1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025

2003 cents/kWh

Historical Projection In 2002, non-hydro 

renewables accounted for 

about 2% of total electric 

generating capacity and 

generation. Non-hydro 

generation totaled 86 

billion kWh, of which 

17% was from 

geothermal, followed by 

26% municipal solid 

waste, 45% other 

biomass, 1% solar, and 

12% wind. 

Figure 7. EIA Projection of Retail Electricity Prices 

During the 1990s, renewables experienced both a period of growth and decline. For 

example, from 1990 through 2002, non-hydro renewable capacity grew from 13,700 MW 

to 16,800 MW, with 5,000 MW of new capacity offset by losses of nearly 2,000 MW 

(Figure 8).58

0

5,000

10,000

15,000

20,000

25,000

1990 1995 2000 2005

Cumulative MW

Wind

Wood and other

biomass

MSW/Landfill

Gas

Solar

Geothermal

During the early 1990s, 

most new capacity 

(primarily geothermal and 

biomass) was spurred by 

long-term contracts under 

Public Utilities Regulatory 

Policy Act (PURPA) and a 

federal production tax 

incentive for landfill gas 

facilities. In the mid- to 

late 1990s, this trend 

reversed to some degree 

and some biomass and 

geothermal facilities ceased 

operation when their PURPA 

contracts expired. On the other hand, renewables also gained some ground during this 

period as a result of state policies and retail choice. In the late 1990s, a number of states 

adopted policies to encourage renewable energy development, typically to maintain 

public benefits during the movement toward retail competition. 

Figure 8. Cumulative Non-hydro Renewable Generating 

Capacity, 1990-2005 

 

58 EIA Renewable Energy Annuals, 1990-2003, 

http://www.eia.doe.gov/cneaf/solar.renewables/page/pubs.html.

 19
Figure 9. Recent Market Drivers for Renewables 

Since then, most renewable capacity additions (primarily wind) have been driven by state 

renewable portfolio standards (RPS) or renewable energy mandates, voluntary purchases 

of renewable energy by consumers, integrated resource planning (particularly in western 

states with cost-effective wind resources), and system benefits funds. Figure 9 shows the 

fraction of renewable 

generating capacity 

attributed to each of the 

major policy or market 

drivers, based on a 

number of existing 

studies by NREL, 

Lawrence Berkeley 

National Laboratory 

(LBNL), and EIA.59 We 

adjusted the estimates to 

account for doublecounting among the 

drivers. In addition, the 

federal PTC has been important for improving the economics of wind projects. While the 

extension of the PTC is uncertain, RPS, voluntary markets, and IRP are expected to be 

the most significant market drivers for renewables going forward. 

By the end of 2005, EIA expects non-hydro renewable energy capacity to reach 21,080 

MW and generate 115 billion kilowatt-hours (kWh), or 3% of total generation. To date, 

2005 is on track to be a record setting year for wind energy development, with the 

American Wind Energy Association expecting up to 2,500 MW of new wind energy 

capacity additions. If this occurs, the EIA projections will be exceeded. 

Market Segmentation: Renewables Penetration by Utility Type 

Most renewable energy facilities are owned by independent power producers. In 2003, 

94% of U.S. renewable energy generating capacity was owned by nonutility generators, 

with 4% owned by public utilities, and 2% by IOUs. 

 

59 Bird, L. and B. Swezey, 2004. Estimates of New Renewable Capacity Serving Green Power Markets

(2003), National Renewable Energy Laboratory, Golden, CO, March. 

http://www.eere.energy.gov/greenpower/resources/tables/new_gp_cap.shtml; Bolinger, M., R. Wiser, L. Milford, M. Stoddard, and K. Porter, 2000. Clean Energy Funds: An Overview 

of State Support for Renewable Energy, Lawrence Berkeley National Laboratory, Berkeley, California, 

LBNL-47705, April. 

Bird, L., B. Parsons, T. Gagliano, M. Brown, R. Wiser, and M. Bolinger, 2003. Policies and Market 

Factors Driving Wind Power Development in the United States, National Renewable Energy Laboratory, 

Golden, CO, NREL/TP-620-34599, July. 

Petersick, T, 2004. State Renewable Energy Requirements and Goals: Status Through 2003, DOE/EIA 

http://www.eia.doe.gov/oiaf/analysispaper/rps/pdf/rps.pdf

0

2,000

4,000

6,000

8,000

10,000

12,000

1990 1995 2000 2003 2005

Renewable Capacity (MW)

RPS

IRP

SBC

Voluntary Markets

PURPA/Financial

Incentives

Mostly IOUs

Mixed, but mainly Publics, IOUs

 20
0

2000

4000

6000

1995 2000 2005

Estimated Renewables 

8000

10000

12000

Capacity (MW)

Competitive Suppliers

Tribes

Federal

Co-op

Public

IOU

Figure 10 shows the fraction of renewables capacity of total generating capacity owned 

by utilities, according to 

utility type.60 However, 

nonutility generators 

typically sell the output of 

renewable energy projects 

to local utilities through 

power purchase agreements. 

Data are not widely 

available on the entity 

purchasing the output, 

making it difficult to track 

purchases or investments in 

renewable energy 

generation among investorowned, municipal, and 

cooperative utilities. 

Figure 10. Renewables as Percent of Total Generating 

Capacity by Utility Ownership 

An alternative approach to determining the magnitude of investment in renewables by 

utility type is to look at the primary drivers for renewable energy development, as some 

drivers apply only to particular 

utility types. For instance, RPS 

and system benefits charges 

typically pertain only to investorowned utilities (IOUs) and 

competitive electricity suppliers, 

with some exceptions (Figure 9). 

And while all types of utilities 

can use Integrated Resource 

Planning (IRP), IOUs are 

responsible for most of the largescale renewable capacity 

additions planned under recent 

IRPs. All types of utilities 

offer voluntary green power 

programs; although 

municipal utilities, in particular, have had significant success in implementing voluntary 

renewable energy programs. We present a rough estimate of the renewables capacity 

attributed to each of the major utility types, based on capacity additions resulting from 

key drivers and their applicability across utility types (Figure 11). Although this is a very 

rough estimate, it shows that IOUs, followed by public utilities, are responsible for most 

of the new capacity added from 1995 through 2005. 

0%

10%

20%

30%

40%

50%

60%

Cooperatives Federal IOUs Public Power Non-Utility

Generators

Percent (%)

Figure 11. Estimated Capacity Additions by Type of Utility 

Purchaser 

 

60 Source: Energy Information Administration, Form EIA-860, as of December 31, 2003 

 21
It is also important to note that IOUs represent only 7% of utilities in the United States, 

but represent 66% of electricity sales (Table 2).61 Thus, the largest opportunities for 

renewables may be with IOUs. However, there may be opportunities to work with publics 

and co-ops, because they are not the target of most existing policy drivers and may have 

greater interest in the public benefits that renewables provide. 

Table 2. Number of Utilities and Annual Electric Sales by Utility Type, 2002 

Number of 

Utilities

% Electric Sales 

(million kWh) 

% 

IOU 217 7% 2,151,889 66% 

Publics 1870 59% 516,475 16% 

Co-ops 889 28% 333,745 10% 

Federal 12 0% 35,787 1% 

Power Marketers 47 1% 201,640 6% 

Total 3035 100% 3,239,536 100% 

Forecasts of Renewable Energy Market Penetration 

Looking ahead, EIA projects slow growth for renewable energy technologies “both 

because of the relatively low costs of fossil-fired generation and because competitive 

electricity markets favor less capital-intensive technologies.”62 EIA estimates that nonhydro renewable energy capacity will grow to 28,350 MW by 2020 and 33,780 MW by 

2025 (Figure 12).63 This represents 7,280 MW of new capacity from 2005 to 2020 and 

12,700 new MW by 2025. Non-hydro renewable generation is projected to grow from 

115 billion kWh in 2005 to 182 billion kWh in 2025, or 2.3% annually. 

0

5000

10000

15000

20000

25000

30000

2005 2010 2015 2020

Renewables Capacity (MW)

Wind

Solar

Geothermal

MSW/Landfill Gas

Biomass/wood

In its forecast, EIA 

assumes that the 

federal Production 

Tax Credit (PTC) will 

expire at the end of 

2005. EIA also takes 

into account state 

mandates and 

renewable energy 

policies. EIA projects 

that state policies will 

lead to 1,600 MW of 

new non-hydro 

renewable capacity 

through 2025. This 

2005 renewable energy projection is more conservative than its 2004 estimate, because of 

increased uncertainty regarding federal and state policy implementation. EIA research 

Figure 12. EIA Projection of Cumulative Renewable Capacity 

by 2020 

 

61 EIA Electric Sales and Revenue, 2002 http://www.eia.doe.gov/cneaf/electricity/esr/esr_sum.html 62 EIA, 2005, p. 6. 

63 AEO, 2005 

 22
has indicated that RPS implementation is proceeding at a slower rate than assumed under 

earlier projections.64 Therefore, its AEO 2005 projections assume that utilities do not 

fully comply with established RPS policies in future years. The projection assumes that 

the rest of the renewables capacity additions will be driven by commercially viable 

projects. 

Other estimates offer more optimistic projections of renewable energy penetration. For 

example, Platts (2003) forecasts that renewable electric generating capacity will total 

60,000 MW by 2015 (five years earlier than the EIA projection); and will be driven 

mostly by RPS and, to a lesser extent, voluntary markets.65 

Further, a number of other projections are based on individual market drivers, such as 

RPS policies, system benefits charges, voluntary markets, and IRP. Summing these 

independent forecasts yields a projection similar to Platts. 

The Union of Concerned Scientists (UCS) estimates the new renewable energy 

generating capacity that will be needed to meet state renewable portfolio standards 

(RPS), and that will result from investments by state renewable energy funds. At the time 

we obtained the data, 18 states had adopted renewable portfolio standards and 15 states 

had established renewable energy funds, typically collected through a system benefits 

charge (SBC), or an adder on customers’ electricity bills. By 2017, UCS estimates that 

23,237 MW of new renewable capacity will be needed to meet existing state RPS 

policies, or 92.2 million kWh. UCS assumes that SBC funds will not bring on any 

additional capacity, because most renewable energy projects that are supported through 

SBC funds are used to comply with RPS policies. Therefore, all of the capacity is 

attributed to the RPS, to prevent double-counting.66 

Voluntary markets in which customers make voluntary purchases of renewable energy to 

match their own electricity consumption provide another market for renewable energy 

generation. A 2001 forecast by NREL and LBNL found that, by 2010, voluntary markets 

could support 596 average MW (aMW) of new renewables, or 5.2 million MWh 

annually, under a low-growth scenario and as much as 3,890 aMW (or 34.1 million MWh 

annually), under favorable market conditions (Table 3). The wide range of estimates of 

potential market size reflects the relatively immature nature of voluntary markets and the 

uncertainty surrounding issues such as the pace of restructuring (which was very 

uncertain at the time of the study), achievable market penetration rates, consumer access 

to green power, and the impact of green power marketing on supporting new renewables 

development.67 There is some potential for overlap between renewable energy used to 

meet RPS policies and that used to supply voluntary purchasers, but typically utilities and 

 

64 Petersick, 2004 

65 Platts, 2003. Renewable Power Outlook 2003, Platts Research & Consulting Renewable Power Service, 

Boulder, CO, October. 

66 Union of Concerned Scientists, 2005. E-mail received from Jeff Deyette, February 5, 2005, containing 

estimates of renewables capacity planned under RPS policies through 2017. 

67 Wiser, R., M. Bolinger, E. Holt, and B. Swezey, 2001. Forecasting the Growth of Green Power Markets 

in the United States, NREL/TP-620-30101, LBNL-48611, October. 

http://www.eere.energy.gov/greenpower/resources/pdfs/30101.pdf

 23
marketers are careful to supply voluntary customers renewable energy above and beyond 

what is used to meet the RPS. 

Table 3. Estimates of Renewables Development to Serve Green Power Markets (2010) 

Low Growth (aMW) High Growth (aMW) 

Existing Renewables 309 3,082 

New Renewables 596 3,890 

Total Renewables 905 6,971 

 Source: Wiser et al 2001 

Another driver for new renewable energy-generating capacity is the concept of Integrated 

Resource Planning (IRP). An IRP is defined as overall least-cost, long-term plan for a 

utility to meet its projected power needs. While the use of IRP declined during the 

movement toward restructuring, it is again becoming an important policy tool for 

developing renewables, particularly as wind generation has become more competitive 

with conventional generating technologies. Wiser (2005) collected data on renewable 

energy capacity planned through IRP for a number of large utilities in the West. Table 4

shows that just seven western utilities plan to add about 3,800 MW of renewable capacity 

by 2020, according to their least-cost plans.68 

Table 4. Renewable Capacity Planned Under IRP 

Utility Total IRP Additional 

Capacity (MW) 

PacifiCorp 1,420 

Idaho Power 450 

Avista 75 

Portland General Electric 195 

Puget Sound Energy 980 

Northwestern 150 

PSCO 500 

Total 3,770 

 Source: Wiser (2005) 

Summing the estimates of renewable energy market penetration resulting from RPS, state 

renewable energy funds, IRP, and voluntary markets yield about 35,000 MW of new 

renewable capacity additions by 2015, bringing total renewable energy capacity to 

approximately 55,000 MW, which is generally consistent with the Platts estimate (Figure 

13). We derived this estimate by using the average of the voluntary market high and lowgrowth scenarios for 2010, assuming 5% annual growth through 2015. The original 

NREL/LBNL forecast used an annual growth rate of 7% for the low-growth scenario; 

thus, our assumed growth rate is intended to be conservative. The UCS and Wiser (2005) 

estimates are used directly. Although there is potential for overlap in these estimates (i.e., 

that some capacity will be brought online for IRP and used to meet an RPS), we do not 

account for any double-counting among the different studies. We believe this assumption 

 

68 Wiser, R., 2005. E-mail dated January 1, 2005, containing estimates of renewable capacity planned under 

Integrated Resource Planning. 

 24
is reasonable, because generally sales of renewable energy to voluntary consumers are 

not counted toward RPS compliance. And, while projects brought online for IRP can 

often be sold into 

voluntary markets or used 

to meet an RPS, we have 

very limited data on IRP 

commitments, making the 

estimate very 

conservative, and most of 

the utilities listed in 

Table 4 are not currently 

subject to an RPS. 

Furthermore, this e

is intended only to 

provide a rough esti

of future market 

penetration given current 

market conditions and 

drivers. 

0

5000

10000

15000

20000

25000

30000

35000

40000

2005 2010 2015

MW

Voluntary Markets

IRP

SBC

RPS

Figure 13. Estimated Renewable Capacity Additions by Key 

Driver, 2005 through 2015 

xercise 

mate 

Using this approach, we find that, by 2015, 61% of the new renewable energy capacity 

additions would be attributed to RPS policies, 24% to voluntary markets, 13% to IRP, 

and 2% to system benefits funds (UCS, 2005; Wiser et al. 2001; Wiser 2005) (Figure 

13). 

To provide some indication of the level of market penetration that could be achieved 

given favorable market conditions or increased support for clean energy technologies, we 

assume that the “technical 

potential” or maximum achievable 

penetration for renewables by 2020 

is approximately 20% of total U.S. 

electric generating capacity. 

Although the true technical 

potential is larger, there is general 

consensus among industry 

participants that there may be 

significant challenges associated 

with integrating intermittent 

renewables, such as wind, into the 

grid at penetration levels beyond 

20%. Figure 14 compares our 

assumed maximum achievable 

renewables penetration of 20% 

with EIA’s forecast of renewable 

penetration by 2020 and the Platts forecast. WIP action in the clean-energy sector would 

likely lead to additional renewables above EIA’s market forecast, but would not likely 

0

50000

100000

150000

200000

250000

2005 2010 2015 2020

Capacity (MW)

20% of

Capacity by

2020

EIA

Projection

Platts

Projection

Figure 14. Market Forecasts for Renewables 

vs. 20% Penetration 

 25
exceed the 20% threshold. Therefore, WIP action should be targeted to maximize its 

impact within these theoretical upper and lower bounds. 

Key Barriers to Renewable Resource Development

Of course, the ability for renewables to overcome current barriers will play a role in 

determining future market penetration levels. For example, lack of access to transmission 

is a key barrier to the development of many of the best renewable sites today. In addition, 

uncertainties related to short-term policies or ineffective policy implementation have 

affected renewable resource development. The most striking examples of this are the 

short-term extensions of the federal PTC, which has created a boom-bust cycle in the 

wind industry in recent years, and the inability of public utilities to use the Renewable 

Energy Production Incentive (REPI) for capital financing, because it is subject to annual 

congressional appropriations. Further, although RPS policies provide a long-term market 

for renewables, they have proven to be insufficient in some regions to encourage 

electricity providers to enter into long-term contracts for renewables. Other important 

barriers are the intermittency of some renewable energy sources, siting issues (such as 

bird and bat mortality and aesthetic issues for wind), interconnection obstacles, and the 

higher costs (or perceived costs) of some renewable energy technologies. And finally, 

there are information barriers, such as a lack of knowledge among states, utilities, and 

end-use consumers regarding the costs and benefits of renewable energy technologies. 

Future renewable energy penetration levels depend on the success of current efforts to 

address these barriers. 

Energy Efficiency in the Electric Sector

With respect to energy 

efficiency in the electric 

sector, demand-side 

management (DSM) 

programs are used to 

curtail peak energy use, 

conserve natural resources, 

and delay transmission and 

distribution (T&D) 

infrastructure investments. 

Included in these programs 

are energy efficiency 

measures such as home 

winterization and incentive 

programs for consumers to 

purchase more energyefficient appliances, as w

as incentives for on-site 

generation of power. 

Because of the wide 

breadth of the energy 

efficiency measures, some 

Source: EIA (2005) 

Figure 15. Utility Investment and Energy Savings Resulting 

from Utility DSM Programs 

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

19891990199119921993 199419951996 1997 199819992000 2001 2002

Energy Savings (mKwH)

0

500

1,000

1,500

2,000

2,500

3,000

Utility Investment ($M)

Energy Savings

Investment

ell 

 26
of the resulting benefits from DSM are captured in the sectors toward which the programs 

are targeted: buildings or industrial. 

In 1993, utility investment in DSM programs reached its peak and then began to decline, 

in part because of electric restructuring and the initiation of system benefits programs. 

However, the decreasing utility funding is not necessarily a reflection of decreasing 

demand-side electricity reduction program activity or impacts; rather, in some cases, it 

may be that utility investments were supplanted by investments by state system benefit 

funds. While annual savings peaked in 1996 (Figure 15), energy savings in subsequent 

years have still been 

significant, 

indicating program 

activity using 

alternate funding 

mechanisms. 

Historically, IOUs 

made the largest 

investments in D

programs. Table 5

shows utility 

investments in DSM

by utility type 

during 2003. IOUs were responsible for 78% of total DSM expenditures.

SM 

 

69 While the 

largest opportunities for energy savings through DSM measures may exist at the IOU 

level, both public and cooperative utilities also offer opportunities. Municipal and 

cooperative utilities have been slow to participate in DSM programs, often because of 

limitations in staffing and budgets, resulting in extensive opportunities for energy 

efficiency improvements. Furthermore, municipal and cooperative utilities may be more 

open to DSM opportunities, because they do not have the same pressures that IOUs have 

to derive profits for their shareholders from the sale of electricity. 

0.50

0.01

0.00

0.01

0.03

0.00

0.02

0.00

0.42

**Energy 

Savings from 

All DSM in 

2003 (Quads*)

Total 3,282 442 1,341 0.030

State 26 4 72 0.000 

Municipal Mrkt Auth 19 5 3 0.000 

Political Subdivision 124 21 40 0.001

Municipal 1846 178 90 0.003

Federal 9 1 20 0.001

Cooperative 885 151 66 0.001 

Power Marketer 150 1 0 -

Private 223 81 1,050 0.024

Energy Savings 

from DSM 

Added in 2003 

(Quads*)

2003 DSM 

Expenses 

(M$)

Number with 

DSM 

Expenditures 

in 2003

Total 

Number

0.50

0.01

0.00

0.01

0.03

0.00

0.02

0.00

0.42

**Energy 

Savings from 

All DSM in 

2003 (Quads*)

Total 3,282 442 1,341 0.030

State 26 4 72 0.000 

Municipal Mrkt Auth 19 5 3 0.000 

Political Subdivision 124 21 40 0.001

Municipal 1846 178 90 0.003

Federal 9 1 20 0.001

Cooperative 885 151 66 0.001 

Power Marketer 150 1 0 -

Private 223 81 1,050 0.024

Energy Savings 

from DSM 

Added in 2003 

(Quads*)

2003 DSM 

Expenses 

(M$)

Number with 

DSM 

Expenditures 

in 2003

Total 

Number

Table 5. Utility Investment in DSM Measures, 2003 

Recent Investments by WIP and Others in Electric Sector 

In FY05, WIP invested $13 million in renewable and clean-energy programs in the 

electricity sector under the Tribal Energy Program ($5.5 million), the Renewable Energy 

Production Incentive ($5 million), and the State Energy Program ($2.3 million). WIP 

investments were similar in previous years, totaling $13 million in FY03 and $11 million 

in FY04 (Table 6). In addition, a number of state, federal, and private-sector entities are 

making significant investments in the electric sector to develop clean and renewable 

energy sources. 

69 EIA-861 Form; * Primary energy savings based on 34% average electric efficiency; ** Energy savings 

from all active DSM programs, including earlier years’ activities70 Technology Roadmap: Energy Loss 

Reduction and Recovery in Industrial Energy Systems, prepared by Energetics, Inc. for the U.S. Department 

of Energy Office of Energy Efficiency and Renewable Energy Industrial Technology Programs, Draft 

 27
For example, state renewable energy funds are currently investing about $300 million 

annually in various forms of support for renewables. At the federal level, incentives such 

as the PTC, REPI, accelerated depreciation, and investment tax credits are available, as 

well as farm bill incentives ($23 million). Many states also offer incentives such as 

personal, sales, or property tax incentives. Private-sector investments include an 

estimated $2.8 billion in capital and operation and maintenance expenditures in 2005. 

While information is not 

available on all other 

investments, Table 6

shows that private and 

other state/federal 

investments currently 

outweigh WIP 

investments by nearly 

250 to 1. 

Table 6. Electric Sector Investments in Renewables 

A variety of organizations currently plays a role or makes investments in clean-energy 

markets or technologies (see Table 7). 

Table 7. Key Market Actors/Potential Partners 

Private 

 Utilities 

 Project developers 

State and Regional 

 Public utility commissions 

 SBC funds administrators 

 State energy offices 

 Governors/Western Governors Assoc. 

 RTOs 

 Northeast Regional Greenhouse 

 Gas Initiative 

Federal 

 FERC 

 DOE Technology Programs 

 EPA Clean Energy Program 

 U.S. Department of Agriculture 

Nongovernmental Organizations Consumers 

 Residential and C&I 

 28
 29

Industrial Sector Analysis 

Approach and Methodology 

Because manufacturing represents, by far, the largest energy consumption in the 

industrial sector, this analysis will focus on U.S. manufacturing energy consumption. 

Savings in the remaining areas of the industrial sector are likely to be proportional (based 

on energy consumption). 

The energy footprint provides a blueprint of the energy flows within industry and 

individual industrial sectors.70 The energy footprint shown in Figure 16 details the 

energy flows for the 24.7 quadrillion Btus (quads) of annual energy use associated with 

U.S. manufacturing (EIA 2004, EIA 2003). The total energy use shown in Figure 16

includes: 

• On-site energy losses, i.e., the energy that is lost in energy systems from 

equipment inefficiencies, thermodynamic operating limitations, during 

distribution of energy throughout the plant, and in the conversion of energy to 

useful work; and 

• Off-site losses, i.e., energy losses incurred off-site at the utilities providing the 

electricity and fuels that are purchased by the industrial sector. Off-site losses 

occur primarily in the generation and transmission of electricity. 

Figure 16. U.S. Manufacturing Energy Footprint (El 2003) 

The energy losses occurring on-site in manufacturing facilities represent immediate 

targets for energy efficiency improvements. As shown in Figure 16, of the 24.7 quads of 

energy used by manufacturing, approximately 5.5 quads (22%) are lost within the 

industrial plant boundary prior to reaching the process. An additional 20%-50% of the 

energy finally delivered to processes can also be lost in the form of waste heat, flares, 

byproducts, and other sources. These downstream losses, which were not estimated for 
the footprint, are more complex and depend on the nature of the process and specific site 

conditions. In general, on-site energy losses can be reduced by adoption of more efficient 

technology, better integration of heat sinks and sources within the plant, increased 

utilization of waste energy, and improved operating and maintenance practices. 

Reduction of on-site losses has a compound effect, because less primary source energy is 

required to be produced. 

ITP has successfully targeted on-site losses through its BestPractices program by using 

plant-wide assessments (PWA) and other tools and training programs targeting large 

facilities and through the Industrial Assessment Centers (IAC) for medium-size 

manufacturing facilities. Historical data from the BestPractices and IAC programs is used 

to establish a baseline of energy reduction opportunities. 

Sector Characteristics 

EERE’s BestPractices program (~$8.5M/yr DOE investment) for industrial plants 

classified as “large” (i.e., with annual energy costs greater than $2M; Figure 17) has 

been shown to have a significant impact on industrial energy consumption and associated 

cost savings. Figure 18 shows that the 6,802 large-size plants account for 53% of energy 

consumed in U.S. manufacturing. Therefore, concentrated deployment efforts targeted at 

these relatively few plants will show a substantial return on investment per plant. 

1998 EIA MECS

Number of US Manufacturing Plants Within Energy Cost Categories

104,299

115,696

6,802

226,797

0

50,000

100,000

150,000

200,000

250,000

Small Plants Medium Plants Large Plants Total All US Plants

Plant Category

Number of Plants in Category

Small Plants: Energy Costs <$100K

Medium Plants: $100K < Energy Costs < $2M

Large Plants: Energy Costs > $2M

 Source: ORNL analysis of 1998 MECS data 

Figure 17. Number of U.S. Manufacturing Plants Within Energy Cost Categories 

 30
1998 Total Energy Consumption for U.S. Manufacturers

by Energy Cost Category

25,000

1,500

10,200

13,300

0

5,000

10,000

15,000

20,000

25,000

30,000

Small Plants Medium Plants Large Plants Total All US Plants

Plant Category

Annual source Fuel Consumption, TBtu/yr 

6%

41%

Small Plants: Energy Costs <$100K

Medium Plants: $100K < Energy Costs < $2M

Large Plants: Energy Costs > $2M

Consumption estimates derived from 1998 EIA-MECS

53%

 Source: ORNL analysis of 1998 MECS data 

Figure 18. Total Energy Consumption for U.S. Manufacturing Plants by Energy Cost 

Category

Based on experience with approximately 50 PWAs of energy efficiency opportunities, 

ORNL has estimated that an average savings of 16% of a plant’s annual energy costs can 

be achieved via deployment of existing technologies and best operating practices. In 

terms of potential industrial sector market impact, column 2 in Table 8 represents the low 

range of potential savings based on projected 16% PWA savings alone (i.e., additional 

savings attributed to other BestPractices tools/services not included). This estimate is 

based on 6,802 large-size plants with an average per plant energy consumption of 1,960 

BBtu/yrb

. If the BestPractices program were expanded to help reduce other on-site losses 

by one-half, column 3 in Table 8 represents an upper range of potential savings. These 

estimates assume a 100% market penetration. 

Table 8. Estimated Impact for Large Plants at 100% Market Penetration 

(1) Impact Through 

FY0371

(2) Potential Industrial 

Market Impact Based 

on BP PWA Savings 

Only72

(3) Potential Industrial 

Market Impact Based 

on BP PWA Savings + 

Half of Other Onsite 

Losses Saved 

TBtu/yr Savings 200 2,132 3,599 

ITP’s IAC program for “medium”-size plants (i.e., with annual energy costs greater than 

$100K but less than $2M; Figure 17) has also significantly contributed to industrial 

 

71 Source: ORNL analysis for ITP’s annual Impacts report. Cumulative annual savings from BestPractices 

program inception in 1998 through 2003. 

72 Source: ORNL analysis of 1998 MECS data (see Figure 17) 

 31
energy savings. Between assessments and the professional activities of the “army of 

energy engineers” generated by the program, the IAC program has resulted in cumulative 

industrial energy savings of 1,027 TBtu/yr over its 27-year life. DOE invests about 

$7M/yr for the IAC program. 

According to DOE, the average IAC assessment saves $55,000.73 With average per plant 

energy consumption for medium-size plants of 88,000 MMBtu/yrb, and assuming an 

energy cost of $5/MMBtu, this results in an average 12.5% savings per assessment. If the 

IAC program were augmented with additional resources to help reduce other on-site 

losses by one-half, column 3 in Table 9 represents an upper range of potential savings. 

These estimates assume a 100% market penetration. The 115,636 medium-size plants 

account for another 41% of overall manufacturing energy consumption, so additional 

efficiency improvement activities should be targeted at these plants, as well. 

Table 9. Estimated Impact for Medium Plants at 100% Market Penetration 

(1) IAC Impact 

Through FY0374 (2) Potential Industrial Market Impact Based 

on IAC Savings Only 

(3) Potential Industrial 

Market Impact Based 

on IAC Savings + Half 

of Other Onsite 

Losses Saved 

TBtu/yr Savings 1,027 1,272 2,391 

The combined impacts for large and medium plants are given in Table 10, assuming a 

100% market penetration. 

Table 10. Estimated Impact for Medium and Large Plants at 100% Market Penetration 

(1) BP + IAC Impact 

Through FY0375 (2) Potential Industrial 

Market Impact Based 

on BP + IAC Savings 

Only 

(3) Potential Industrial 

Market Impact Based 

on BP + IAC Savings 

+ Half of Other Onsite 

Losses Saved 

TBtu/yr Savings 1,227 3,404 5,990

Market Penetration 

To date, BestPractice’s PWA activities have impacted about 60 plants (< 1% of the large 

plant population); IAC assessments have been conducted at slightly more than 13,000 

plants (11% of the medium-size plant population). 

Follow-up studies have shown that, for most large plants participating in PWA activities, 

nearly 100% of the identified savings will eventually be captured. In addition, some large 

 

73 Source: www.oit.doe.gov/iac/ 

74 Source: ORNL analysis of annual Impacts report. Cumulative annual savings from IAC inception in 1977 

through 2003. 

75 Source: ORNL analysis for Impacts report. 

 32
corporations have used PWA and training activities as impetus to establish in-house 

energy teams and have replicated activities at the majority of their facilities. 

Other Potential Industrial Areas for Reducing Energy Use 

Water/Wastewater 

Nationally, water and wastewater systems have been estimated to account for up to 4% of 

total electricity demand (~54 to 75 billion kWh/yr) (EPRI, 2002). This load is expected to 

increase 20% over the next 15 years (EPRI, 2004). In fact, as Table 11 shows, electricity 

use in the water/wastewater industry is on the same order of magnitude as the pulp and 

paper, chemical, and petroleum industries. 

Table 11. Industrial Electricity-Use Comparison 

Industry Electricity Use (% of total U.S. Load) 

Water/Wastewater ~3%

Pulp and Paper ~3%

Chemical ~6%

Petroleum Refining ~ < 2%

Source: DOE, 2004 

Pumping costs to deliver the water supply to the customer comprise the largest 

component of energy use (~ 68-80%) in water/wastewater operations. Waste treatment 

comprises approximately 24% of energy use. The ITP BestPractices program already has 

tools and training in place to efficiently address pumping systems. DOE-sponsored 

targeted assessments and plant-wide assessments have shown that a 25% decrease in 

pumping system energy use is routinely achievable. 

If this average energy savings is achieved at all water/wastewater plants, the potential 

energy savings of about 28 to 38 billion kWh/yr (280 to 380 trillion Btu/yr of primary 

energy) could be attained. ITP BestPractices does not currently consider water/wastewater 

operations to be included in their targeted industries

Industrial Buildings 

As indicated in the Buildings section of this document, energy use in industrial buildings 

accounts for almost 2 quads of energy use annually. The ITP BestPractices Program 

focuses exclusively on process and plant utility energy consumption and does not 

currently address the building aspects of energy consumption in the sector. Major 

opportunities exist in lighting, HVAC, and building shell areas. Based on the discussion 

in the Buildings section, an average savings of 10% is reasonably anticipated in this area. 

Mining 

Figure 19 shows that for NAICS code 212 (coal, metal ore, and nonmetallic mineral 

mining), total annual energy input is 1,273 trillion Btu (1.3 quads). Approximately 311 

trillion Btu are lost within the mining operation boundary before reaching the process. An 

opportunity exists to reduce these on-site losses by one-half, annual savings of 156 

trillion Btu could be achieved. 

 33
 Source: DOE, 2004 

Figure 19. NAICS Coal, Metal Ore, and Nonmetallic Mining Total Energy Input: 

1,273 Trillion Btu 

Potential for Regional Activities 

U.S. energy consumption by state for the Industrial Sector is listed in Table 12. The table 

shows that 48% of all industrial energy consumption is accounted for in only seven 

states: Texas, Louisiana, California, Ohio, Indiana, Pennsylvania, and Illinois. Texas 

alone accounts for 20% of the U.S. industrial energy consumption. Targeted efforts in 

key states would be an efficient method of deployment. A 10% reduction in energy use in 

these top states would represent a potential 1,567 trillion Btu/yr in overall savings. 

 34
Table 12. Industrial Sector Energy Consumption by State 

 

State Trillion Btu 

Texas 6,426.3 

Louisiana 2,134.8 

California 1,927.9 

Ohio 1,428.9 

Indiana 1,295.9 

Pennsylvania 1,285.6 

Illinois 1,173.4 

Michigan 928.3 

Georgia 876.2 

Alabama 863.2 

Kentucky 845.9 

Tennessee 745.5 

North Carolina 743.2 

Wisconsin 728.5 

New York 667.4 

South Carolina 609.1 

Florida 598.2 

Washington 586.0 

Virginia 547.0 

Oklahoma 543.5 

Minnesota 526.1 

New Jersey 490.9 

Iowa 472.3 

Arkansas 461.9 

Mississippi 427.3 

Alaska 412.5 

Kansas 384.8 

Missouri 374.3 

West Virginia 311.4 

Oregon 298.3 

Colorado 293.8 

Massachusetts 261.4 

Maryland 251.8 

Wyoming 238.1 

Utah 232.6 

Arizona 221.3 

New Mexico 219.5 

North Dakota 202.5 

Maine 198.8 

Nebraska 182.4 

Idaho 180.2 

Nevada 169.1 

Connecticut 133.6 

Montana 128.4 

Delaware 113.2 

Hawaii 76.7 

New Hampshire 68.3 

South Dakota 54.2 

Vermont 31.2 

Rhode Island 25.8 

District of Columbia 4.2 

United States 32,430.9 

Source: EIA, 2001 

 35
Transportation Sector Analysis 

Approach and Methodology 

The transportation sector comprises 27% of 

overall energy consumed in the United States 

(Figure 20), second only to the industrial s

Because it is primarily a petroleum-based 

market, the transportation sector consum

nearly 70% percent of petroleum in the United 

States,

ector. 

es 

ment. 

 

76 lending itself to having the largest 

potential impacts on petroleum displace

The purpose of this analysis is to identify those 

displacement opportunities and outline the 

potential for petroleum reductions in the 

transportation sector. 

The objective of this summary is to describe the 

transportation market, and identify opportunities for government to reduce petroleum 

consumption in the transportation sector. The first section outlines the market and 

identifies the most petroleum-intensive transportation subsectors, as well as summarizes 

the projections of future petroleum use. The second section describes the key end-use 

market segments and reviews the market drivers and barriers related to those segments. 

Commercial

18%

Residential 

Transport 22%

27%

Industrial

33%

Source: EIA-AER 2004 

Figure 20. Energy Use by Sector

 

Understanding the transportation market is a challenge because of the diversity of the 

market, as well as the diversity of data sources tracking the market. The variety of 

vehicles in the sector makes accurately estimating the average petroleum use per vehicle 

difficult, and an average with such high variation has limited usefulness in effective 

program design. As a result, research in this area has organically grown to focus on a 

number of smaller market segments such as light-duty vehicles or heavy-duty vehicles. 

Adding to the challenge of outlining the market is that some bodies of research 

concentrate only on a particular end-use segment, as in the case of research that focuses 

only on light-duty vehicles or on diesel-fueled vehicles, which make cross-sector 

comparisons difficult. The following market analysis synthesizes a large volume of 

available resources, including federal and state government, nonprofit, and private-sector 

data and research77 – all in an effort to create a comprehensive market description that 

defines market segments based on common petroleum-use characteristics. 

Following the market synopsis section, we analyze the potential for petroleum reduction 

in the transportation sector by market segment. This includes a discussion of market 

barriers to reducing petroleum use and the previous and potential role of government and 

other entities in lowering those barriers. 

76 Energy Information Administration (EIA), Department of Energy (DOE). 2005. Annual Energy Review 

2003. DOE/EIA-0384(2003) 77 Resources include the Energy Information Administration, state energy offices, Natural Resources 

Canada, and the 2004 Automotive Fleet Fact Book. Specific references to resources are noted in the text. 

 36
Overall, this analysis suggests opportunities in the transportation sector for improving 

energy efficiency and alternative-fuel use through consumer (personal vehicle) and 

corporate (fleet) information transfer, and the encouragement of local and state programs 

to voluntarily adopt mandates for increasing market penetration. Results of the benefits 

analysis show that government programs targeting these areas could have a significant 

impact on increasing market penetration with in the identified client groups. Future 

research into potential benefits would allow for refining the proposed programs and more 

accurate benefits estimation. 

Sector Characteristics 

Market Synopsis 

In 2002, the transportation sector 

accounted for more than 26 

quadrillion Btus or 27% of total 

energy consumption (Figure 20). 

Highway transportation 

accounted for 81% of 

transportation energy use, 

including light-, medium-, and 

heavy-duty vehicles as well as 

buses. The remaining energy use 

is primarily split between air and 

water transport (Figure 21).78 

Light-duty vehicles (cars and 

light trucks) made up about 75% 

of highway fuel consumption, or 

16 total quads.79 The 200 million light-duty vehicles account for the greatest proportion 

of transportation fuel use (Figure 22). The primary fuel of light-duty vehicles was 

gasoline, although more than 3 million – alternative fuel vehicles (AFVs) – are equipped 

to use alternative fuels, Of these AFVs, 99% are flexible fuel vehicles, meaning gasoline 

or the alternative fuel can be used for fuel. The remaining 1% are dedicated AFVs, only 

configured to use the alternative fuel. 

Source: Transportation Energy Data Book: Edition 

24, 2004, Table 2.5 

Figure 21. Transportation Energy Use 

by Mode

Air

8%

Med/

Heavy Trucks

19% Light vehicles

62%

Bus

0.19

Water

4%

Rail

2% Pipeline

4%

Total Quads = 26.36

The actual use of alternative fuels in vehicles is difficult to determine because of the 

majority of flex-fuel AFVs. Averaging the use of alternative fuels over the entirety of the 

fleet is also limited by regional differences in fuel infrastructure, availability, and price. 

As a result of these difficulties, alternative-fuel use is generally measured by alternativefuel sales, not actual usage per vehicle. In 2002, liquefied petroleum gas (LPG) and 

 

78 Energy Information Administration, Department of Energy. 2005. Annual Energy Outlook 2005 with 

Projections to 2025. DOE/EIA-0383(2005). Table 7. 79 Light-duty vehicles account for almost 16 quads out of almost 21 total quads of highway transportation 

energy consumption. Energy Information Administration, Department of Energy. 2005. Annual Energy 

Outlook 2005 with Projections to 2025. DOE/EIA-0383(2005)). 

 37
compressed natural gas (CNG), dominated the alternative-fuel market making up 47% 

and 46%, respectively.80 A further trend is the growth of electricity as an alternative 

vehicle fuel in the years leading up to 2002 (Figure 23).81 

0

5

10

15

20

25

30

35

1970 1980 1990 2000 2010 2020

Quadrillion Btu 

Auto

Light Trucks

Heavy 

Trucks

Buses

Historical Projection

Sources: EIA. Historical – Annual Energy Review 2003, 

Projection – AEO 2004 

Figure 22. Highway Transportation Energy 

Consumption by Mode 

Future use of alternative fuels is 

projected by the Energy 

Information Administration 

(EIA) in the Annual Energy 

Outlook (AEO).82 The 

projections are largely dependent 

on a set of assumptions about the 

future of the AFV market and 

infrastructure for alternative-fuel 

delivery systems. The 

assumptions that have the largest 

potential to change the 

projections are those associated 

with the Energy Policy Act of 

1992 (EPAct), which (among 

other things) requires that certain 

fleets increase their amount of 

annual purchases of AFVs (further 

discussed in the fleet-specific 

market review below). The EIA 

projections assume no change in 

the EPAct through 2025, and a 

constant share of sales for 

alternative vehicles based on 2000 

sales. For non-fleet purchases, EIA 

assumes that consumers will 

choose vehicles based on fuel type 

and availability, and then within 

the broader set of vehicle attributes 

(such as price, maintenance costs, 

and battery replacement).83 Based 

on these assumptions, projections 

for light-duty vehicle alternativefuel consumption show continued 

LPG

0.00

2001 2006 2011 2016 2021

CNG

E85

Electricity

Hydrogen

0.05

0.10

0.15

Quads

Source: AEO 2004 

Figure 23. Projected Growth in Alternative 

Fuels for the Transportation Sector

 

80 Energy Information Administration, Department of Energy. 2005. Annual Energy Outlook 2005 with 

Projections to 2025. DOE/EIA-0383(2005). Supplemental 81 EIA Annual Energy Review 2003. Table 10.7 82 Energy Information Administration, Department of Energy. 2005. Annual Energy Outlook 2005 with 

Projections to 2025. DOE/EIA-0383(2005). Supplemental Table 46. 83 For more detailed assumptions, see EIA. 2005. Assumptions to the Annual Energy Outlook 2005. 

 38
growth84 for CNG and LPG, while electricity comes on in later years. Total alternativefuel use is projected to increase from 0.16% of the light-duty vehicle market to 0.66% 

between 2002 and 2025.85

Medium- and heavy-duty vehicles are defined as those highway vehicles weighing more 

than 10,000 pounds. Diesel fuel is the dominant fuel in this market, comprising 94% of 

total use. Gasoline, LPG, and CNG are also used, making up 5%, 0.2%, and 0.05% of the 

market, respectively. EIA projects changes in the heavy-duty alternative-fuel vehicle 

market, primarily assuming changes to federal environmental laws regulating air and 

particulate emissions.86 This methodology leads to an increase in alternative 

(nonpetroleum) fuels in the medium- and heavy-duty transportation sectors overall to 

0.88%, with CNG being the primary alternative fuel at 0.69% of the market.87 

Investments in the Transportation Sector 

Increasing market share of nonconventional vehicles involves a combination of public 

and private investment in technology development and consumer education and 

understanding. Two primary difficulties exist in the categorization and quantification of 

publicly and privately funded programs in the transportation sector. The first is that the 

designers of the various programs have differing priorities and, therefore, the focus of the 

program and the available measures of success vary. This, combined with the highly 

competitive nature of the automobile industry – which limits access to development and 

R&D investment information – make identifying the current level of funding for 

increasing market share of alternative-fuel vehicles challenging. A primary role of the 

private sector is largely to meet or exceed customer expectations profitably, including the 

use of new technologies for improved performance, fuel economy, and price/value. While 

deployment and marketing are also important aspects of automotive investment in 

advanced technology, information on investments made by industry in specific vehicletypes (AFVs, for example) is very limited, and therefore useful information is not readily 

available. 

The opportunities for energy efficiency improvement and alternative-fuel vehicles in this 

sector are heavily weighted toward consumer education, where there is some data on 

previous investments from the governmental and nonprofit sector. The Department of 

Energy’s (DOE) Office of Energy Efficiency and Renewable Energy (EERE) 

incorporated the goal of increasing market share for advanced vehicle technology and 

deployment in many of its programs, including the Hydrogen, Fuel Cells, and 

Infrastructure Program and the FreedomCAR and Vehicle Technologies Program. 

Programs focusing primarily on deployment include those housed within the 

 

84 Energy Information Administration, Department of Energy. 2005. Annual Energy Outlook 2005 with 

Projections to 2025. DOE/EIA-0383(2005). Supplemental Table 34 85 Energy Information Administration, Department of Energy. 2005. Annual Energy Outlook 2005 with 

Projections to 2025. DOE/EIA-0383(2005). Electricity includes electricity used by electric vehicles, but 

not electricity used by hybrid (diesel or gas) vehicles. 

86 For more detailed assumptions, see EIA. 2005. Assumptions to the Annual Energy Outlook 2005. 87 Energy Information Administration, Department of Energy. 2005. Annual Energy Outlook 2005 with 

Projections to 2025. DOE/EIA-0383(2005). Supplemental Table 34. 

 39
Weatherization and Intergovernmental Program (WIP), including the Clean Cities88 and 

State Partnership grants for increasing market share of advanced technology. The 

investment that WIP makes in these two programs was approximately $11 million in 

Clean Cities and $4 million in State Partnership grants in 2004. This funding leverages 

almost 100% of its value in matching private and state and local government funds. 

Another program focusing on consumer education and information transfer is the Fuel 

Economy Guide Program, the centerpiece of which is a Web site allowing consumers to 

review the benefits of increased fuel economy and other attributes of vehicles. The 

program invests $700,000 in the information program and reports savings of more than 

65 million gallons of gasoline annually.89

Key Segments in the Light-Duty Transportation Market 

Personal-Vehicle Market 

Market Description 

Generally, data and information presented on the light-duty vehicle market does not 

differentiate between personal vehicles and fleet vehicles. For the purposes of designing 

WIP programs, however, this analysis needs to separate the fleet from the personal 

vehicle market to understand the needs of the market in terms of the key end-use market 

segments. Market data indicates that 95.6% of the total U.S. vehicle stock is comprised of 

personally owned vehicles.90 Because personal vehicles are, on average, driven less than 

average fleet-owned vehicles, the personal vehicle market accounts for a 

disproportionately smaller 90% of light-duty vehicle fuel consumed.91 The structure of 

this description of the personal-vehicle market, unless otherwise noted, reflects the data 

collected for all light-duty highway vehicles, less the portion of the market assumed to be 

fleet-related. 

 

The personal-vehicle market accounts for approximately 65% of highway fuel 

consumption92 and almost 48% of overall transportation energy consumption.93 There 

 

88 Following completion of this analysis in FY05, the Clean Cities activity was moved to EERE’s 

FreedomCAR and Vehicle Technologies Program in FY06. 

89 Taylor, K. 2005. (CTTS). Personal Communication to Elizabeth Brown via Ron Benioff. April 14, 2005. 

90 There were 9 million light-duty fleet vehicles (Transportation Energy Data Book, Edition 24, 2004) out 

of a total of 208 million light-duty vehicles. Energy Information Administration, Department of Energy. 

2004. Annual Energy Outlook 2004 with Projections to 2025. DOE/EIA-0383(2004). 91 Light-duty fleet vehicles account for 1.6 quadrillion Btu of energy consumption out of a total of 15.583 

quadrillion Btu light-duty vehicle consumption. Energy Information Administration, Department of 

Energy. 2004. Annual Energy Outlook 2004 with Projections to 2025. DOE/EIA-0383(2004). Table 50. 92 Light-duty vehicles consume 15,583 quadrillion Btus (75%) of energy out of 20,792 total highway 

consumption in 2002. Energy Information Administration, Department of Energy. 2005. Annual Energy 

Outlook 2005 with Projections to 2025. DOE/EIA-0383(2005). Sixty-five percent reflects the 14% that is 

attributable to fleets. Highway Use is defined as not air, water, pipeline and rail transport. 93 Light-duty vehicles consume 15,583 quadrillion Btus (58% ) of energy out of 26,922 total highway 

consumption in 2002. Energy Information Administration, Department of Energy. 2005. Annual Energy 

Outlook 2005 with Projections to 2025. DOE/EIA-0383(2005). Forty-eight percent reflects the 14% that is 

attributable to fleets. 

 40
were 194 million personal light-duty vehicles on the road94 in 2002 and 15.3 million new 

personal vehicles sold.95 Of these vehicles, 189 million were conventional gasoline 

fueled vehicles.96

In 2001, more than 92% of households had at least one vehicle. The average number of 

vehicles per household was 1.9. Daily trips, primarily to work or school, averaged 14,500 

miles per person per year in 2001. The working-age (25-64) population averaged the 

largest amount of vehicle trips per day at 4.6. A total of 86% of those trips were taken in 

a personal vehicle.97 

Projected Trends 

According to the Energy Information Administration (EIA), personal vehicle sales show 

a movement toward more light trucks over cars in general, but with nonconventional98

technology vehicles reaching 2% of new vehicle sales by 2025. For the EIA estimates, 

gains in nonconventional technology vehicles result from high gas prices relative to other 

fuels, increasing cost-effectiveness for the manufacturer to produce the vehicle, and a 

variety of performance features integrated into the Transportation Demand Model of 

NEMS.99 

EIA estimates may be conservative, however, in terms of the increased market 

penetration of hybrid-technology vehicles. Recent new-vehicle sales numbers released by 

manufacturers have led to different projections stemming from the private sector. Sales of 

hybrid vehicles doubled between January 2004 and January 2005, and tripled between 

April 2004 and 2005.100 The overall sales of hybrid vehicles increased 81% between 

2003 and 2004.101 Assuming that hybrid sales consistently increase by a third of that, or 

25%, between 2005 and 2025, the market share for hybrids could increase to 16% of new 

car sales by 2025, well above the EIA projected 2%. 

 

94 Two hundred three million total light-duty vehicles in stock in 2002, less 4.4% attributable to fleets. 

Energy Information Administration, Department of Energy. 2004. Annual Energy Outlook 2004 with 

Projections to 2025. DOE/EIA-0383(2004). Table 45. 95 Sixteen million total light-duty vehicles sold in 2002, less 4.4% attributable to fleets. Energy Information 

Administration, Department of Energy. 2004. Annual Energy Outlook 2004 with Projections to 2025. 

DOE/EIA-0383(2004) Table 45. 

96 http://www.eia.doe.gov/oiaf/aeo/supplement/suptab_46.xls 

97 Bureau of Transportation Statistics. 2003. Highlights of the 2001 National Household Transportation 

Survey. BTS: Washington, D.C. 98 Hybrid and alternative-fuel vehicle. 

99 EIA 2005. Major Assumptions of the Annual Energy Outlook 2003. 

http://www.eia.doe.gov/oiaf/aeo/assumption/pdf/0554(2005).pdf 

100 R.L. Polk http://www.polk.com/ 101 http://www.gm.com 

 41
The EIA projection of 2

market penetration falls 

full percentage point 

under a J.D. Power and 

Associates projection of 

3% by 2010 (Figure 24).

The J.D. Power and 

Associates projection 

levels off after 2010 due 

to their assumption that 

conventional vehicle 

technology will improve 

to compete with the 

projected popularity of 

hybrids.

% 

a 

 

102 A 2004 Booz 

Allen Hamilton report 

projects a much larger 

share of the sales market to be saturated by hybrids, at 20% by 2010 and 80% by 2015.103

Figure 24 shows the range of these different projections of new vehicle sales, with the 

EIA projection being lowest. 

0

10

20

30

40

50

2002 2007 2012 2017 2022

HEVs as % of Total 

New Vehicle Sales 60

70

80

EIA 2005 Projection

25% Annual Growth

JD Power

Booz Allen

Source: Various (see text) 

Figure 24. Hybrid New Vehicle Sales as Percent of Total 

Changes in vehicle stock, 

however, require longer-term 

consumer choice changes 

because of the slow growth and 

turnover of vehicle stock.104 To 

illustrate the turnover in the 

market, Figure 25 shows the 

EIA projections for vehicle 

sales by technology type, and 

Figure 26 shows the resulting 

change in overall stock 

composition, and includes the 

projected impact of the 

increasing market for conventional 

light trucks. While the EIA numbers 

for hybrid growth are likely 

conservative as discussed above, the stock makeup projections are useful to show the 

complicated nature of the market and the length of time required to make changes to the 

market due to the long life cycle of vehicles. 

0

5,000

10,000

15,000

20,000

25,000

2001200320052007200920112013201520172019202120232025

Thousands

Conventional Cars

AFV Cars

Conventional Light Trucks

AFV Light Trucks

Source: AEO 2004

Figure 25. New Vehicle Sales by 

Technology Type 

102 http://www.jdpower.com/news/releases/pressrelease.asp?ID=2005013 103 http://www.businessweek.com/magazine/content/04_44/b3906051_mz011.htm 104 David Greene spreadsheet assumption 

 42
Consumer Market Drivers and Barriers 

The foremost market drivers for increasing vehicle efficiency and nonconventional 

vehicles in the consumer transportation sector are increasing fuel prices and increasing 

fuel price volatility. EIA data reflect seasonal volatility in gasoline prices.105 The longerterm energy outlook incorporates price volatility and increases resulting from domestic 

policies and changes in distribution costs, but does not account for decreasing supply 

(real or artificial) over time.106 Current and future political unrest in major petroleumproducing regions may 

have impacts on gasoline 

price and volatility not 

reflected by the DOE 

estimates. These 

uncertainties are the largest 

drivers for changes in 

petroleum consumption. 

Hybrids and alternativefuel vehicles may appeal to 

the consumer as a way to 

alleviate gasoline price 

uncertainty as evidenced by 

the increasing demand for 

hybrid vehicles. 

0

50

100

150

200

250

300

20012006201120162021

Millions

Conventional Cars

AFV Cars

Conventional Light Trucks

AFV Light Trucks

Source: AEO 2004 

Figure 26. Total U.S. Vehicle Stock Projection 

Canadian research shows that given level incentives and taxes, and removing the need for 

increased infrastructure, the cost of most alternative fuels is equal to or lower than 

gasoline-powered vehicles.107 Disproportionate fuel incentives and the high cost of fuel 

infrastructure are reflected in the real cost of fuels – that cost seen by consumers – and 

represent significant barriers to integration of alternative-fuel vehicles into the market. In 

addition, technical challenges still exist in the alternative-fuel market, as distance range 

and weight issues are being worked out for a variety of technologies.108 Finally, in the 

case of hybrids, consumers may not account for the life-cycle costs of the vehicle, leading 

to undervaluing the efficiency of the technology.109 

 

105 Monthly Energy Review, EIA 106 EIA assumptions (petroleum module) 107 NRCAN 

http://www.nrcan.gc.ca/es/etb/cetc/cetc01/htmldocs/factsheet_transportation_energy_technologies_ 

program_e.html 

108 Hydrogen and compressed natural gas, specifically. 109 Flynn, Peter, 2002. Commercializing an alternate fuel vehicle: lessons learned from natural gas for 

vehicles. 

 43
Fleet Market 

Market Description 

The fleet market of light-duty vehicles consisted of almost 9 million vehicles in 2003 

(4.4%) of the light-duty vehicle market.110 Light-duty fleet vehicles accounted for about 

1.6 quadrillion Btus of energy consumption (10%) of total light-duty consumption.111

The top light-duty fleet manufacturers in terms of number of cars are GM, Ford, and 

Chrysler.112 Car fleets are used for business, rental cars, government and police, and taxi; 

while light-duty truck fleets 

are primarily business and 

government. 

Medium- and heavy-duty 

fleet trucks consume 2.5 

quadrillion Btu of energy 

(about 60%) of mediumand heavy-duty fuel use.113

Four industries dominate 

medium- and heavy-duty 

fuel use: for-hire 

transportation and 

warehousing, construction, 

wholesale, and retail. 

Figure 27 shows the EIA 

projections for future fleet 

petroleum use. 

The light-duty fleet market can be divided into two primary market segments as a result 

of the Energy Policy Act of 1992 (EPAct): EPAct and Non-EPAct fleets. This act 

requires certain governmental fleets to purchase an increasing percent of their overall 

new-vehicle acquisitions as alternative-fuel vehicles. Fleets that must comply with EPAct 

include federal and state non-emergency government fleets comprised of more than 20 

light-duty vehicles. EPAct-regulated fleets are already mandated to comply with 

particular alternative-fuel requirements and, thus, fall outside the scope of this analysis. 

Fleets that are not covered by EPAct can be further segmented by organizational structure 

and decision-making behavior. The largest subsegment is corporate-owned fleets, 

 

110 Davis, S. and S. Diegal. 2004. Transportation Energy Data Book, Edition 24. ORNL-6973. ORNL: Oak 

Ridge, Tennessee. 

111 Energy Information Administration, Department of Energy. 2005. Annual Energy Outlook 2005 with 

Projections to 2025. DOE/EIA-0383(2005). Light-duty fleets consume 1.6 quadrillion Btus out of a total of 

15.583 for light-duty vehicle consumption. 

112 Source: Automotive Fleet Factbook, 2004. 113 Energy Information Administration, Department of Energy. 2005. Annual Energy Outlook 2005 with 

Projections to 2025. DOE/EIA-0383(2005). 

FreightMedium

Buses Light Trucks

Cars 

Commercial 

Light Trucks 

3

4

5

6

7

 (Quadrillion Btu)

Freight - 

Large

0

1

2

2001 2005 2010 2015 2020

Energy Use

Source: AEO 2004 

Figure 27. Projected Fleet Energy Use by 

Vehicle Type

 44
comprising 50% of the fleet market.114 A smaller subsegment is state and local 

government fleets that are not required to comply with EPAct (“non-EPAct government”) 

and institutional fleets, which account for a combined 10% of the market.115 Fleetpurchasing structure lends itself to programmatic targeting because of the opportunity to 

impact a large number of vehicles – an entire fleet – through one point of contact, which 

has potentially large effects on petroleum consumption. The market drivers and barriers 

differ across the two subsegments, but the decision-making criteria within each segment 

is well-defined, which makes it easier to structure a focused program. More detailed 

market information for these two types of fleets follows in the next two sections. 

Corporate Fleets 

Medium to large corporate 

fleets comprise roughly 50% 

of the fleet market. Thirteen 

companies account for 2.5% 

of the fleet market in terms o

number of vehicles (Fi

28).

f 

gure 

 

116 Factors that drive 

corporate fleet marketpurchasing decisions range 

from economic to image 

considerations.117 Corporate 

fleet managers and decisionmakers tend to be proactive, 

fiscally minded, and image 

conscious. Corporate fleet 

managers also tend to favor longer-term solutions and are driven by incentives, subsidies, 

and tax breaks. 

Source: Automotive Fleet Fact Book: Edition 24, 2004 

Figure 28. Large U.S. Corporate Fleets 

30,277

26,070

22,500

21,500

17,498

17,358

14,268

13,690

12,949

12,761

12,736

11,465

10,689

10,000 15,000 20,000 25,000 30,000 35,000

Coca-Cola 

Service Master

Tyco 

Comcast

State Farm 

Frito Lay

Interstate Brands

Aramark

Johnson & Johnson

Pfizer

Sears, Roebuck

Glaxosmithkline

Sprint

Number of Vehicles

The market barriers to purchasing alternative fuel vehicles and hybrids by corporate fleets 

fall primarily under infrastructure constraints and cost. Two infrastructure considerations 

are fuel and vehicle service availability. Cost considerations include the initial higher 

capital cost of alternative solutions, operating and maintenance costs, and any associated 

training and productivity costs. 

Non-EPAct Government and Institutional Fleets 

Non-EPAct government and institutional fleets account for about 10% of the fleet 

market. The drivers and barriers that impact this group differ from those of the corporate 

114 K. Nesbitt and D. Sperling, 2001. Fleet purchasing behavior: decision processes and implications for 

new vehicle technologies and fuels.

115 Ibid 116 Automotive Fleet Fact Book, Edition 24, 2004. As a point of interest, Wal-Mart is No. 30 on the list w/ 

3,703 total fleet vehicles (6% are heavy duty, Class 3-6). Wal-Mart likely contracts out most of its trucking. 

117 K. Nesbitt and D. Sperling, 2001. Fleet purchasing behavior: decision processes and implications for 

new vehicle technologies and fuels. 

 45
fleet, primarily because of organizational structure.118 Non-EPAct and institutional fleets 

tend to follow company policy and favor the status quo. Fleet policies are often 

established centrally and administered locally.119 

Market barriers in this group reflect the highly formalized, decentralized structure of 

these organizations. Non-EPAct and institutional fleet purchasing decisions may rely on 

precedent and can be veto-prone. This category of fleet manager tends to be more 

reactive and favor short-term solutions in their purchasing patterns.120

 

118 K. Nesbitt and D. Sperling, 2001. Fleet purchasing behavior: decision processes and implications for 

new vehicle technologies and fuels. 

119 Ibid 120 Ibid

 46
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This paper presents the results of energy market analysis sponsored by the Department of Energy’s (DOE) 

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additional input from Lawrence Berkeley National Laboratory (LBNL). The analysis was structured to identify those 

markets and niches where government can create the biggest impact by informing management decisions in the 

private and public sectors. The analysis identifies those markets and niches where opportunities exist for increasing 

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15. SUBJECT TERMS

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