Technical Report

NREL/TP-6A2-47213

June 2010

Energy Efficiency and 

Renewable Energy Research, 

Development, and Deployment 

in Meeting Greenhouse Gas 

Mitigation Goals: The Case of 

the Lieberman-Warner Climate 

Security Act of 2007 (S. 2191)

Sharon Showalter and Frances Wood

OnLocation, Inc. / Energy Systems Consulting

Vienna, Virginia

Laura Vimmerstedt

National Renewable Energy Laboratory

Golden, Colorado
National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393

303-275-3000 • www.nrel.gov

NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

Contract No. DE-AC36-08-GO28308

Technical Report

NREL/TP-6A2-47213

June 2010

Energy Efficiency and 

Renewable Energy Research, 

Development, and Deployment 

in Meeting Greenhouse Gas 

Mitigation Goals: The Case of 

the Lieberman-Warner Climate 

Security Act of 2007 (S. 2191)

Sharon Showalter and Frances Wood

OnLocation, Inc. / Energy Systems Consulting

Vienna, Virginia

Laura Vimmerstedt

National Renewable Energy Laboratory

Golden, Colorado

Prepared under Task No. SAO9.1051
NOTICE

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iii

Table of Contents

Executive Summary.........................................................................................................................v

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

2 National Energy Modeling System ...........................................................................................5

3 EIA Analysis of S. 2191............................................................................................................7

4 Our Analysis of S. 2191 ..........................................................................................................10

5 Results .....................................................................................................................................21

6 Conclusions and Insights.........................................................................................................34
iv

List of Figures

Figure 1. Energy reductions by program.......................................................................................14

Figure 2. Cellulosic ethanol investment cost.................................................................................16

Figure 3. Covered greenhouse gas emissions less offsets.............................................................21

Figure 4. CO2 allowance prices.....................................................................................................22

Figure 5. Delivered energy prices in 2030 ....................................................................................23

Figure 6. Energy-related CO2 emission reductions from EIA base...............................................24

Figure 7. Energy-related CO2 emissions by end-use sector in 2030 .............................................25

Figure 8. Primary energy consumption by fuel.............................................................................25

Figure 9. Delivered energy demand by sector...............................................................................26

Figure 10. Growth in delivered energy demand in years 2010-2030............................................26

Figure 11. Electricity sales by sector in 2030................................................................................27

Figure 12. Electric generating capacity, including distributed generating capacity......................28

Figure 13. Renewable electric capacity, including distributed generating capacity .....................28

Figure 14. Electricity generation, including distributed generating capacity................................29

Figure 15. Buildings delivered energy consumption.....................................................................29

Figure 16. Industrial delivered energy consumption.....................................................................30

Figure 17. Transportation energy consumption by mode..............................................................31

Figure 18. Fuel efficiency for new light-duty and heavy-duty vehicles........................................31

Figure 19. Advanced technology LDV stock in 2030...................................................................32

Figure 20. Ethanol production by source ......................................................................................33

List of Tables

Table 1. Modeling Methodology for Each EERE Program ..........................................................13

Table 2. Comparison of NEMS-EERE and EIA Vehicle Assumptions in 2030...........................15

Table 3. Comparison of NEMS-EERE and EIA Renewable Generation Technology 

Assumptions.....................................................................................................................18

Table 4. EIA Fossil Generation Technology Assumptions...........................................................19
v

Executive Summary

The U.S. Congress and the Obama administration are considering legislative and 

administrative actions that would limit nationwide greenhouse gas emissions. Renewable 

energy and energy efficiency (RE/EE) technologies could play an important role in efforts to 

reduce greenhouse gas emissions from energy use in the United States. Accordingly, the cost 

of these technologies could significantly influence the overall cost of reducing greenhouse gas

emissions. This paper, which presents an analysis of the Lieberman-Warner Climate Security 

Act of 2007 (S. 2191), examines the potential for (RE/EE) technology improvements to reduce

the cost of greenhouse gas mitigation.

Senators Joseph Lieberman (I-Conn.) and John Warner (R-Va.) introduced S. 2191 in October 

2007. If passed, it would have established a nationwide cap-and-trade allowance system for 

greenhouse gas emissions. The Act had a goal of reducing emissions to 72 percent below

2006 emissions by 2050. Senators Lieberman and Warner subsequently requested that the 

U.S. Department of Energy’s Energy Information Administration (EIA) analyze the effects of 

their proposed legislation on the U.S. energy sector. Results of the EIA analysis include the 

projected cost of compliance and the effect of different energy technologies on market share.

In this report, we present results of an analysis of S. 2191 conducted for the U.S. Department 

of Energy’s Office of Energy Efficiency and Renewable Energy (EERE). Our analysis was 

similar to the EIA analysis but used modified technology assumptions to reflect EERE 

program goals for EERE’s portfolio of federally funded research, development, and 

deployment efforts that target further cost reductions in and accelerated adoption of a variety 

of renewable energy and energy efficiency technologies.

Both our analysis and the EIA analysis examine the impacts of S. 2191; both use versions of 

the National Energy Modeling System; and both use the same assumptions for all model 

inputs except the RE/EE technology characteristics and EERE program impacts. Our results 

show that achieving EERE program goals could reduce greenhouse gas allowance prices (and 

electricity prices) needed to meet target emission reductions and reduce energy intensity. 

EERE programs are also projected to reduce the cost of renewable electricity generation and 

transportation biofuels, allowing greater adoption of these technologies. Successful 

achievement of EERE goals thus could enhance the ability of the U.S. economy to thrive 

while simultaneously mitigating greenhouse gas emissions.

Although S. 2191 did not pass into law, several subsequent proposed “climate bills” have 

been patterned after this legislation. By studying the potential effects of the bill with and 

without EERE’s Research, Development and Deployment portfolio, we can gain insight into 

the potential energy and economic savings that the EERE programs may provide in a carbonconstrained economy. Similarly, the EERE program results represented here reflect a variety 

of assumptions related to program success and funding levels, all of which will likely change 

over time. Therefore, the results are presented here as representative of the impact that a 

greater emphasis on energy efficiency and renewable energy research and deployment could 

have on the cost of greenhouse gas mitigation.
1

1 Introduction 

On October 18, 2007, Senators Joseph Lieberman (I-CT) and John Warner (R-VA) introduced 

Senate bill S. 2191 in the 110th Congress.

1 Titled “America’s Climate Security Act of 2007,” 

this legislation, if passed, would have established a nationwide cap-and-trade allowance 

system for greenhouse gas emissions with a goal of reducing emissions to levels 70 percent

below 2005 emissions by 2050. The Senate bill was referred to the Committee on 

Environment and Public Works, which issued a substitute amendment in December 2007. 

Although Senator Barbara Boxer (D-Ca.) introduced a subsequent amendment in May 2008, 

the bill was never put to a vote. However, this bill marked the first comprehensive climate bill 

to be introduced for debate in the Senate, and it created a starting point for future proposed 

climate change legislation such as the Waxman-Markey and Boxer-Kerry bills introduced in 

2009 and ongoing legislative efforts in 2010.2

In November 2007, Senators Lieberman and Warner requested that the U.S. Energy 

Information Administration (EIA) analyze the effects of the proposed S. 2191 bill on the U.S. 

energy sector. In April 2008, EIA published the resulting report,

3

Our analysis is similar to the EIA analysis in many respects: both analyze the potential 

economic impacts of S. 2191; and both use versions of the National Energy Modeling System 

(NEMS).

which focused on the 

Committee’s December 2007 version of the bill.

4 Both analyses also share the same energy market and technology assumptions used 

in the EIA Annual Energy Outlook (AEO) 20085 except for some renewable energy and 

energy efficiency (RE/EE) technology characteristics that were modified for our analysis. 

Unlike the EIA analysis, our analysis does not include sensitivity analyses but instead focuses 

on the EIA Core S. 2191 Case that assumes low-carbon technologies such as nuclear and 

carbon capture and storage (CCS), as well as international offsets, are available in a timely 

manner to meet the bill’s requirements. In addition, our analysis uses a version of NEMS that 

has been modified to reflect program goals of the U.S. Department of Energy’s Office of 

Energy Efficiency and Renewable Energy (EERE).

6

 1 S. 2191: Lieberman-Warner Climate Security Act of 2007. For more information, see 

These program goals reflect the 

quantitative objectives of federally funded research, development, deployment, and 

http://www.govtrack.us/

congress/bill.xpd?bill=s110-2191. 2 The text of S. 2191, the Waxman-Markey bill (H.R. 2454, the “American Clean Energy and Security Act of 

2009”), and the Boxer-Kerry bill (S.1733, the “Clean Energy Jobs and American Power Act”) can be found on 

the Library of Congress’ THOMAS Web site (http://thomas.loc.gov/). 3 “Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner Climate Security Act of 2007,” 

Energy Information Administration Office of Integrated Analysis and Forecasting, SR/OIAF/2008-01, April 

2008.

4 Model documentation can be found on EIA’s Web site (http://www.eia.doe.gov/oiaf/forecasting.html) 5 For a complete description of these assumptions, refer to “Assumptions to the Annual Energy Outlook 2008,” 

Energy Information Administration Office of Integrated Analysis and Forecasting, DOE/EIA-0554(2008), June 

2008.

6 EERE leads federally funded research, development, and deployment efforts that target further cost reductions 

in and accelerated adoption of a variety of renewable energy and energy efficiency technologies. EERE 

programs are projected to increase the efficiency of the energy system and decrease the cost of renewable 

generation. In combination with climate policy, these improvements can reduce the cost of meeting a greenhouse 

gas emissions cap. For more information about EERE programs, visit http://www.eere.energy.gov/.
2

demonstration at budget levels proposed in the DOE FY2010 budget request for Congress 

published in May 2009.7

Differences in results between our analysis and the EIA analysis show the potential effect of 

the EERE program portfolio on reducing the cost of greenhouse gas mitigation. Renewable 

energy and energy efficiency technologies could play an important role in mitigating 

greenhouse gas emissions from energy use in the United States. Accordingly, EERE programs 

that reduce the cost of these technologies could reduce the cost of greenhouse gas mitigation. 

Our results suggest if EERE programs achieve their goals, the costs of compliance with 

proposed greenhouse gas mitigation goals would be lower, thereby reducing the consumer 

energy cost increases associated with greenhouse gas mitigation. Our results also suggest that 

reaching these goals could alter the relative market share of different energy technologies 

under the legislatively proposed emission constraint and could reduce the energy intensity of 

the energy system over the next 20 years.

EERE leads federally funded research, development, and 

deployment efforts that target further cost reductions in these technologies and accelerate 

adoption of RE/EE technologies. Estimated technology costs and characteristics used in the 

analysis reflect the expected outcome of EERE program activities from FY2010 forward and 

assume that program goals are met but do not reflect any technology development or 

deployment efforts outside the scope of EERE programs such as state or international efforts 

that are already reflected in the EIA AEO Reference Cases. Therefore, by design, this study 

isolates the effect of RE/EE technologies, rather than exploring the more general effect of 

improved energy technology performance and adoption. Also, our study focuses on a single 

set of RE/EE improvements as reflected in EERE program goals and does not broadly explore 

implications of diverse RE/EE improvement paths.

Although S. 2191 did not pass into law, several subsequent proposed climate bills have been 

patterned after this legislation. Due to the length of time needed to prepare this analysis and 

the similarities in the overall climate goals of the bills subsequent to S. 2191, we chose to 

focus on the S. 2191 instead of shifting to a moving target. By studying the potential effects of 

the bill with and without EERE’s Research, Development and Deployment (RD&D) portfolio, 

we can gain insight into the potential energy and economic savings that the EERE programs 

may provide in a carbon-constrained economy. Similarly, the EERE program results 

represented here reflect a variety of assumptions related to program success and funding 

levels, all of which will likely change over time. Therefore, the results are presented here as 

representative of the impact that a greater emphasis on RE/EE research and deployment could 

have on the cost of greenhouse gas mitigation.

As a computer model, NEMS cannot predict the future nor represent all aspects of 

comprehensive climate legislation like S. 2191. Analysts’ judgment plays a major role in 

determining the technology and policy assumptions that will drive the results of any analysis 

of energy markets, including the analyses discussed here.

 7 The budget documents can be viewed at the Web site of the DOE Office of the Chief Financial Officer 

(http://www.cfo.doe.gov/budget/10budget/start.htm#Summary Budget Documents)
3

S. 2191 Provisions

Senators Lieberman and Warner introduced S. 2191 to mitigate the effects of U.S. greenhouse 

gas emissions on the global climate. If passed, S. 2191 would have established a cap-andtrade allowance system for greenhouse gas emissions, reducing covered emissions to meet 

these goals:

• 7 percent below 2006 levels starting in 2012

• 39 percent below 2006 levels by 2030

• 72 percent below 2006 levels by 2050.

S. 2191 proposed regulation of approximately 87 percent of total greenhouse gases, including 

most sources of carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and three classes 

of fluorinated gases: hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur 

hexafluoride (SF6). Covered entities were primarily coal-fired power plants and industrial 

facilities, natural gas producers, petroleum refiners, and producers and importers of 

fluorinated gases. Sources exempt from meeting the cap included most non-CO2 agricultural 

emissions and methane from coal mines and landfills. Most HFC emissions were covered 

under a separate cap established in the bill, but in this analysis were treated as exempt.

In a cap-and-trade allowance system, such as that proposed under S. 2191, covered entities 

would be required to report their greenhouse gas emissions annually and submit a matching 

number of government-issued allowances, offset credits, or both (each allowance and credit is 

equivalent to one metric ton of carbon dioxide equivalent or CO2e.

8 Some tradable allowances 

would be distributed free of charge to covered entities, and the remainder would be auctioned 

by the government. Distribution of allowances for free is used to achieve objectives such as 

smoothing the transition to a carbon constraint for covered entities and providing rate relief 

for consumers. In S. 2191, the share of allowances that would be auctioned (including early 

auction incentives) starts at about 26 percent in 2012 and increases to about 63 percent by 

2030, as specified in Title III of the bill.9

The funds raised through allowance auctions would primarily be used to fund deployment of 

emissions reduction technologies, to mitigate economic impacts of the policy on energy 

consumers through rebates and subsidies for energy-efficient appliances, and to strengthen 

building efficiency codes. 

Covered entities would be able to meet up to 30 percent of their allowance obligation with 

tradable offset credits that are generated from government certified emissions reductions from 

non-covered domestic sources and from non-U.S. sources of greenhouse gases. Covered 

entities that hold allowances or offset credits in excess of the annual requirement can bank 

them for future use. “Banking” like this allows covered entities to over-comply in early years 

when the requirements are less stringent and use excess allowances in future years.

 8 Emissions of non-CO2 greenhouse gases are commonly measured in carbon dioxide equivalent units, calculated 

by multiplying their emissions (in metric tons) by their estimated global warming potential. For more 

information, see the U.S. Environmental Agency’s glossary of climate change terms (http://www.epa.gov/ 

climatechange/glossary.html). 9 A chart showing the allowance allocation shares may be found in EPA’s presentation on S. 2191 at 

http://www.epa.gov/climatechange/downloads/s2191_EPA_Analysis.pdf.
4

S. 2191 was approved by the Senate Committee on Environment and Public Works in 

December 2007 and was debated in the Senate in May 2008 with amendments added by 

Senator Boxer. Although the bill never went to a vote in the Senate, it was the first climate 

bill to be approved by a Senate committee, and it inspired similar legislation. This report 

remains of interest for studying the general effects of RD&D programs on GHG emission 

reductions and their costs, even as the specific provisions of climate legislation evolve.

Section 2 describes the National Energy Modeling System (NEMS), which was used in both 

our analysis and in the EIA analysis of S. 2191. Section 3 describes the EIA analysis of 

S. 2191 using NEMS. Our analytic methods are presented in Section 4, and results of our 

analysis compared to the EIA S. 2191 analysis are covered in Section 5. Section 6 summarizes 

our conclusions and insights.
5

2 National Energy Modeling System

The National Energy Modeling System (NEMS)10 is a publicly available model developed 

and maintained by EIA. It simulates the behavior of U.S. energy markets for use in evaluating 

midterm forecasts and policy impacts. NEMS can be used to estimate U.S. energy, economic, 

and environmental impacts of alternative energy policies and of different assumptions about 

energy markets and technologies. EIA uses NEMS to produce its AEO projections and to 

respond to congressional and agency requests for energy and environmental policy analysis. 

For this study, the National Renewable Energy Laboratory subcontracted with OnLocation, 

Inc./Energy Systems Consulting to perform modeling and analysis using NEMS. OnLocation 

is able to acquire the model and its updates from EIA and modify NEMS’ structure, 

assumptions, or both, in order to analyze proposed energy and environmental policies. 

OnLocation also modifies the model to estimate RD&D benefits for offices within DOE,

including for EERE. The model modifications and assumptions needed to create the model for 

EERE, referred to here as NEMS-EERE,

11 are described in detail in Section 4.

NEMS balances energy supply and demand in each year through energy prices, accounting 

for the economic competition between the various energy fuels and sources. NEMS is 

organized as a modular system, with a separate module representing each of the domestic fuel 

production markets, fuel conversion sectors, and end-use energy consuming sectors:

• Four supply modules (oil and gas, natural gas transmission and distribution, coal, and 

renewable fuels)

• Two conversion modules (electricity and petroleum refineries)

• Four end-use demand modules (residential, commercial, transportation, and industrial)

• Macroeconomic Activity Module12

• International energy module to simulate world oil markets

to simulate energy/economy interactions

• Integrating module that provides the mechanism to achieve a general market equilibrium 

among all the other modules

• Environmental module that tracks greenhouse gas emissions and the revenues associated 

with CO2 cap-and-trade policies.

The primary flows of information between each of these modules are the delivered prices of 

energy and the quantities supplied and consumed each year by region and by sector. The 

delivered fuel prices include the cost to produce, import, and transport fuels to the end user. 

End-use consumption is primarily a function of macroeconomic activity, technology choice, 

 10 For model documentation and detailed information about the model structure, see http://www.eia.doe.gov/

oiaf/aeo/overview/. 11 EIA approves the use of the name NEMS to describe only an official AEO version of the model without any 

modification to code or data. Because our analysis entailed minor code modifications and ran the model under 

various policy scenarios that deviate from AEO assumptions, the name NEMS-EERE refers to the model as used 

here.

12 The Macroeconomic Activity Module is developed and maintained by IHS Global Insight, Inc.
6

capital stock turnover, and short-term demand elasticities that reflect price responses (such as 

adjusting thermostats).

NEMS includes regional detail to represent the regional differences in energy markets, to 

estimate policy impacts at the regional level, and to model the costs and limitations in 

transportation flows of fuel from supply regions to demand regions. The level of regional 

detail for each module reflects the unique structure of each sector: nine U.S. Census divisions 

for the end-use demand modules; production and consumption regions specific to oil, natural 

gas, and coal supply and distribution; 13 North American Electric Reliability Council (NERC) 

regions and sub-regions for electricity; and five Petroleum Administration for Defense 

Districts for refineries.

A key feature of NEMS is the representation of technology and its improvement over time. 

Five of the sectors—residential, commercial, transportation, electricity generation, and 

refining—include extensive treatment of individual technologies and their characteristics, 

such as the initial capital cost, operating cost, date of availability, energy efficiency, and other 

sector-specific characteristics. Endogenous learning functions are used for several 

technologies to model the effect of “learning by doing”, with costs improving as installed 

capacity increases. The cost and characteristics of most other technologies are specified as a 

function of time.

In each of these five sectors, equipment choices are made for individual technologies based 

primarily on economic criteria, as new equipment is needed to meet growing demand for 

energy services or to replace retired equipment. In the other sectors—industrial, oil and gas 

supply, and coal supply—the treatment of technologies is more limited due to a lack of data 

on individual technologies and because of the complex nature of energy-related decisions that 

may incorporate non-energy factors (such as labor productivity in industry).

In a greenhouse gas cap-and-trade scenario, the model calculates an allowance price per ton of 

CO2e in each year that reflects the system-wide cost of meeting the required reduction in 

emissions, subject to allowance banking requirements. If banking is available, the allowance 

price increases at a real discount rate of 7.4 percent to reflect the assumed rate of return 

required for large capital investments such as power plants. The allowance price is reflected in 

delivered energy prices to covered entities.
7

3 EIA Analysis of S. 2191

On November 9, 2007, Senators Lieberman and Warner asked the EIA to analyze the 

potential economic impact of S. 2191 on the U.S. energy sector. Results of the EIA analysis—

which were published in April 200813

EIA performed its analysis using the version of the NEMS model that was used for AEO 

2008.

and which reflect the version of the bill approved by 

the Senate Committee in December 2007—include the projected cost of compliance and 

effect on market share of different energy technologies. The version of NEMS used for the

EIA analysis and many of the modeling assumptions used for the EIA analysis are the basis 

for our analysis, and the EIA results are used as a point of comparison.

14 EIA developed modeling assumptions such as projected technology costs and 

characteristics, fuel prices and availability, and market behavior for the AEO 2008, and EIA 

used these in their analysis of S. 2191. These assumptions are described in detail in the AEO 

assumptions document.15

For their analysis, EIA developed an S. 2191 Core Case that assumed that both low-carbon 

technologies (such as nuclear and CCS) as well as international offsets were available in a 

timely manner to meet the bill’s requirements. In addition, EIA was asked to do sensitivity 

cases to vary the availability of low-carbon technologies and international offsets, but these 

cases are not discussed in this paper. Key policy assumptions that EIA used to model the S. 

2191 Core Case, and that we adopted for our analysis, include:

• Covered entities encompass all sources of energy-related CO2 including electricity 

generation, transportation (through refineries), and the residential, commercial, and 

industrial sectors. Covered entities must obtain allowances equivalent to their emissions. 

The model calculates allowance price based on the cost and availability of options to 

mitigate greenhouse gases and reflects compliance costs in the delivered price of fossilbased energy (coal, oil and gas) consumed by these sectors.

• Covered gases are assumed to include energy-related carbon dioxide, methane from 

natural gas and oil systems, methane and nitrous oxide emissions from stationary 

combustion and mobile sources, and fluorinated gases except most HFCs that are treated 

in the bill under a separate cap.

• Offset credit limits are defined as 15 percent of allowances for domestic offsets (noncovered entities and domestic biogenic carbon sequestration) and 15 percent for 

international offsets for a total limit of 30 percent.

• The supply and pricing of all non-CO2 gas reductions and offset projects, which include 

domestic carbon sequestration and international offset projects, were derived by EIA from 

 13 “Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner Climate Security Act of 2007,” 

Energy Information Administration Office of Integrated Analysis and Forecasting, SR/OIAF/2008-01, April 

2008.

14 Energy Information Administration, “Annual Energy Outlook 2008 with Projections to 2030,” DOE/EIA0383(2008), June 2008.

15 Energy Information Administration, “Assumptions to the Annual Energy Outlook 2008,” DOE/EIA0554(2008), June 2008.
8

data developed by the U.S. Environmental Protection Agency and discounted to reflect 

EIA’s views such as exclusion of forestry projects from developing countries.

• The NEMS model projects energy supply, demand, and prices through 2030, and the 

analysis did not address the impact of S. 2191 provisions thereafter. EIA assumed that the 

allowance bank balance in 2030 should be about 5 billion allowances in order to satisfy 

the need for allowances in the years after 2030. A real discount rate of 7.4 percent was 

used for banking to reflect the assumed rate of return required for large capital 

investments such as power plants.

• The value of allowances, allocated each year by distribution or auction as specified in 

Title III, is represented in the model as follows:

o The value of free allowances allocated to generators is reflected in the model 

as reduced electricity rates in regulated cost of service regions. The share of 

these allowances declines from 19 percent in 2012 to 1 percent in 2030. 

o The value of free allowances allocated to local electricity distribution 

companies and electric cooperatives is reflected in the model as a reduction in 

the distribution portion of the delivered electricity price to consumers in all 

regions. Ten percent of the allowances are allocated this way. 

o Bonus allowances available to fossil generators with CCS technology are 

represented in the model as payments calculated per ton of sequestered carbon 

and constitute an initial 4 percent of allowances that are phased out by 2035.

o Allowance auction funds that are used to provide consumer rebates for energy 

efficient appliances are represented in the model as reduced incremental costs 

of the most efficient technologies. 

o Incentives for strengthening building codes are represented in the EIA S. 2191 

report by making some building efficiency packages obsolete; however, the 

building code improvements were not included in NEMS-EERE to avoid 

double counting with the shell improvement assumptions attributed to EERE 

programs. 

o Remaining auction proceeds are modeled in the macroeconomic module of 

NEMS and they are used to fund research for low-carbon technologies such as 

CCS (reflected as increased government spending) to reduce the economic 

impact on businesses and consumers (through lump-sum payments) and to 

maintain the federal deficit at baseline levels.

Other policy assumptions for the EIA Core S. 2191 Case, along with the assumptions and 

results of the sensitivity cases, are described in detail in the EIA S. 2191 report. The same 

assumptions were adopted for our analysis except where otherwise noted.

There are uncertainties in technology and policy assumptions that have a significant impact on 

the model results. Our study did not address sensitivity analyses to explore these uncertainties. 

Some of the key areas of uncertainty are:

• The future cost and availability of low-carbon technologies such as nuclear and CCS are 

unclear. The next generation of nuclear plants and commercial-scale CCS plants has not 
9

yet been built in the United States. Issues related to siting, public acceptance, and 

technological progress can affect the expected cost and timing of these technologies.

• The cost and availability of carbon offsets, especially international offsets, can be affected 

by the regulatory requirements that have yet to be determined, as well as by international 

agreements with eligible suppliers.

• The assumption that covered entities will need to bank 5 billion tons by 2030 to meet 

post-2030 emission reductions is highly speculative. For example, a technological 

breakthrough could result in an inexpensive carbon-free solution that would be readily 

available and would reduce or eliminate the need for banked allowances. Assumptions 

related to banking can have a significant impact on the expected allowance price and the 

cost of the policy.
10

4 Our Analysis of S. 2191

Each year, EERE performs a prospective benefits analysis of its programs—as part of its 

overall planning, budget, and analysis cycle—to support implementation of the requirements 

of the Government Performance and Results Act (GPRA) of 1993.

16

• Benefits are estimated across EERE’s entire portfolio and for each of the individual 

programs.

Major features of 

EERE’s program benefits analysis include:

• Analysis supports EERE’s annual Congressional Budget Request.

• Two different models are used as integrating frameworks for analysis: NEMS-EERE for 

the mid-term (to 203017) and MARKAL18

• Economic, environmental, and energy security metrics are quantified.

for the long-term (to 2050).

• Benefits estimates reflect only the potential future benefits that would be attributable to 

the proposed EERE portfolio. Neither historical advances due to EERE programs nor 

advances due to non-DOE efforts are included in the benefits estimates.

For the GPRA FY2010 benefits analysis completed in May 2009, model modifications were 

made by OnLocation to the EIA NEMS AEO 2008 model in order to represent key aspects of 

the EERE program goals. The resulting NEMS-EERE model was used as an integrated 

modeling framework for the GPRA FY2010 analysis, and it is the model used in this study. 

NEMS-EERE includes these model enhancements:

• Addition of a hydrogen production and delivery module along with modifications to the 

representation of fuel cell, light-duty vehicle markets in the transportation module

• Enhancements to the distributed generation technology representation especially for solar 

photovoltaics19

• Expansion of wind resource characterization to include Class 3 wind

• Modifications to the transportation module to allow the user to specify light-duty vehicle 

attributes over time for each vehicle type and class

• Minor modifications to the residential, commercial, industrial and transportation modules 

that allow easier implementation of EERE program assumptions.

 16 This section draws from the FY2010 detailed budget justification as described in U.S. DOE (2009). “FY2010 

Congressional Budget Request Volume 3: Energy Efficiency and Renewable Energy, Electricity Delivery and 

Energy Reliability, Energy Transformation Acceleration Fund, Nuclear Energy.” U.S. DOE, Office of the Chief 

Financial Officer, DOE/CF-037, http://www.cfo.doe.gov/budget/10budget/Content/Volumes/Volume3.pdf 17 The analysis period has been extended to 2035 in AEO 2010. 18 MARKAL, a model developed by the International Energy Agency, is tailored to support DOE’s GPRA 

analysis of energy technology development beyond the time horizon of the AEO. 19 The fraction of eligible buildings for PV systems was increased from 30 percent in the AEO 2008 reference 

case to 55 percent for commercial buildings and 60 percent for residential buildings in NEMS-EERE. In 

addition, PVs are allowed to compete in multi-family as well as single family buildings in the NEMS-EERE 

analysis. The California PV subsidy was also represented.
11

The NEMS-EERE model provides the framework for the following GPRA FY2010 basic 

scenarios:

• Business-As-Usual (BAU) Baseline: Any identifiable future effects of the EERE 

portfolio are removed from the AEO 2008 Reference Case, and RE/EE technologies 

reflect only improvements anticipated by EERE in the absence of continued EERE 

program activities (which in some cases are more optimistic than EIA’s assumptions). All 

other assumptions, including fossil and nuclear technology assumptions, are the same as 

those in the EIA AEO 2008 Reference Case. The GPRA BAU Baseline and the AEO

2008 Reference Case are both intended to represent only those policies that have been 

enacted into law, not possible future policies.

• Program Portfolio: NEMS-EERE is modified to estimate expected future program 

effects integrated across all EERE programs. This includes both modification of 

technology cost and performance assumptions in NEMS-EERE to reflect achievement of 

program goals and incorporation of external results related to specific technologies from 

more-detailed models. All other assumptions are the same as the BAU baseline. In this 

integrated scenario, individual EERE program technologies compete with each other to 

meet market demand.

• Individual Programs: Projected effects of individual EERE programs are represented in 

the absence of other programs.

These scenarios and the program-specific assumptions are described in more detail in EERE’s 

forthcoming documentation of the GPRA FY2010 benefits analysis.

The GPRA benefits analysis requires that overall program goals be quantified as specific 

improvements. For example, the EERE program impacts on technology cost reduction, 

technology performance improvements, deployment barrier reduction or removal, or other 

improvements must be quantitatively expressed. These effects are associated with the 

proposed budget level. Overall, the benefits analysis quantifies the expected result of the 

public investment. If EERE programs are funded at the proposed budget level and achieve 

their stated cost, performance, and energy reduction goals, then they are expected to result in 

technology and system improvements (reflected in the program assumptions) that will create 

future economic, environmental, and energy security benefits (as quantified).

EERE programs are intended to change energy technologies and markets, and for this reason, 

their goals may involve substantial changes from business-as-usual conditions. The program 

assumptions involve the advancement of technologies and accelerated adoption rates for 

energy efficiency in all energy demand sectors and for renewable energy used primarily for 

power and liquid fuels substitution.
12

All EERE programs except the relatively new Hydropower portion of the Wind and 

Hydropower Technologies Program were modeled for the FY2010 budget analysis:

• Biomass Program

• Building Technologies Program

• Federal Energy Management Program (FEMP)

• Fuel Cells Technologies Program

• Geothermal Technologies Program

• Hydrogen, Fuel Cells and Infrastructure Technologies Program

• Industrial Technologies Program

• Solar Energy Technologies Program

• Vehicle Technologies Program

• Weatherization and Intergovernmental Program (WIP)

• Wind and Hydropower Technologies Program

With an integrated model such as NEMS-EERE, interaction among programs and with the 

rest of the energy system can be considered. For example, increased energy efficiency and 

lower energy consumption lead to lower energy prices, which in turn can lead to a loss of 

some of the energy savings through either “rebound effects” (such as consumers raising 

thermostats) or lower market share for high efficiency technologies. At the same time, 

individual programs may compete with or be synergistic with other programs in the portfolio. 

Several programs seek to improve efficiency, which will reduce electricity demand and lead 

to reduced need for new generation capacity, while other programs aim to improve renewable 

generation technologies and increase their deployment. As a result, the net impact of the 

integrated EERE portfolio is not equal to the sum of the individual program impacts.

A summary of the major program activities and primary modeling methodology for each of 

the programs is shown in Table 1. For some programs, off-line analysis (i.e., analysis outside 

NEMS-EERE) is necessary either because the model lacks the technological detail to 

adequately represent the program R&D and deployment activities or because the program 

goals are based on historical or other criteria not found in NEMS-EERE. Specialized off-line 

tools can range from tools that estimate market share to those that perform an initial 

calculation of energy savings, depending on the treatment of the target market in NEMSEERE and on the nature of the program.
13

Table 1. Modeling Methodology for Each EERE Program*

EERE Program Activity Area Off-Line

Tool

NEMSEERE

Programs Focused on Energy Efficiency 

Building Technologies Market enhancement  

Regulatory actions  

Technology RD&D   

FEMP Federal energy management  

Fuel Cells 

Technologies

Fuel cells for CHP** and vehicles  

Fuel cells for niche applications  

Hydrogen production and delivery  

Industrial Technologies Industrial programs  

WIP Weatherization  

Domestic intergovernmental activities  

Programs Focused on Renewable Energy 

Biomass Cellulosic ethanol  

Geothermal 

Technologies

Hydrothermal power and enhanced geothermal systems 

(EGS)

  

Solar Energy 

Technologies

Photovoltaic systems  

Concentrated solar power  

Vehicle Technologies Light-duty vehicles (LDVs): Diesel vehicles, hybrid vehicles, 

and plug-in hybrid electric vehicles (PHEVs) 

  

Light-weight materials for LDVs  

Heavy-duty vehicles   

Wind and Hydropower 

Technologies

Wind  

*Activity areas listed group activities within each program that share either technology or market features; 

they do not represent actual program-management categories.

** Combined heat and power

EERE Programs Focused on Energy Efficiency

For each of the programs that is generally focused on energy efficiency (buildings, industry, 

vehicle, FEMP, and WIP), we describe its objectives and the method we used to represent the 

program in NEMS-EERE. The Buildings, Industrial, and Vehicle Technologies programs are 

primarily involved in developing and increasing deployment of technologies that lead to 

greater energy efficiency, reduced environmental impact, and reduced petroleum use. FEMP 

facilitates implementation of efficiency and renewable energy technologies in federal 

facilities and equipment as part of its overall support for federal leadership in energy 

management and cost-effective energy investment. WIP’s mission is to accelerate deployment 

of energy efficiency, renewable energy, and oil displacement technologies, with a focus on 

activities of nonfederal government jurisdictions as well as other stakeholders.

The Building Technologies program is represented in the model by a combination of specific 

technology improvements (such as cost and efficacy of solid-state lighting) and off-line 

estimates of energy savings from more detailed modeling (such as for commercial shell 
14

efficiency improvements), while the Industrial Technologies Program analysis is performed 

outside of NEMS-EERE.

FEMP and WIP estimates are based on historical data linking EERE budgets to energy 

savings and so are also estimated outside of NEMS-EERE. The off-line analyses provide 

energy savings that are used as inputs to NEMS-EERE.

Figure 1 illustrates the energy savings for each of the energy efficiency programs when 

analyzed individually in a business-as-usual scenario. These savings are slightly different 

when combined, as they are under the EERE Base Case (due to interactive effects across the 

portfolio) or EERE S. 2191 Case (due to interaction between the policy and the technology 

improvements).

Figure 1. Energy reductions by program

BT = Building Technologies Program;

ITP = Industrial Technologies Program

The Vehicle Technologies Program, on the other hand, is modeled primarily by representing 

improvements to vehicle characteristics. For light-duty vehicles, the cost, fuel economy, and 

performance attributes are provided by advanced vehicle type (e.g., advanced diesels, hybrids, 

and plug-in hybrids) for each of six size classes for both cars and light trucks (see Table 2 for 

a sample of the vehicle price and fuel economy assumptions for two size classes).20

 20 Conventional vehicles are less efficient in the EERE case due to greater adoption of advanced vehicle 

technology, which alleviates the need for conventional technology improvements in meeting Corporate Average 

Fuel Economy standards.

In 

addition, consumer biases against advanced vehicles are assumed to be mitigated by the 

program’s activities. The model is then used to project the adoption (sales) of the various 

technologies in the market for light-duty vehicles over time, the stock of these vehicles and 

the vehicle miles traveled associated with each technology. For heavy trucks, the market 

adoption of advanced vehicles is estimated off-line and used as an input to NEMS-EERE for 

the stock accounting and energy savings estimates.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Delivered Quadrillion Btu

Other

Oil

Gas

Electric

2020 2030
15 

The Fuel Cells Technologies Program seeks to advance fuel cell technologies through 

research, development, and demonstration, in order to achieve cost and functional 

competitiveness. The attributes of hydrogen fuel cell, light-duty vehicles are modified within 

NEMS-EERE—similarly to the method used for the Vehicles Technologies Program—to 

represent the program’s RD&D efforts.

Table 2. Comparison of NEMS-EERE and EIA Vehicle Assumptions in 2030

Midsize Cars Large SUVs

EIA NEMSEERE EIA NEMSEERE

Fuel Economy (miles per gallon)

Gasoline 39.4 35.0 26.9 25.7

Ethanol-Flex 40.0 35.3 27.2 26.0

Diesel 50.2 49.9 34.3 35.1

Gasoline-Electric Hybrid 53.0 72.2 35.0 46.6

Diesel-Electric Hybrid 56.8 79.9 40.9 51.9

Plug-in Gasoline Hybrid 72.0 96.7 50.3 60.2

Fuel Cell Hydrogen 54.1 104.0 34.3 66.9

Plug-in Fuel Cell n/a 130.7 n/a 84.7

Vehicle Prices (thousand 2006$)

Gasoline 26.4 25.5 37.0 36.8

Ethanol-Flex 26.7 25.9 37.4 37.1

Diesel 27.6 28.8 38.3 39.2

Gasoline-Electric Hybrid 28.0 29.0 40.1 40.5

Diesel-Electric Hybrid 32.6 30.0 45.8 41.2

Plug-in Gasoline Hybrid 32.2 28.7 45.2 40.2

Fuel Cell Hydrogen 51.5 28.6 72.9 41.0

Plug-in Fuel Cell n/a 28.8 n/a 41.8

EERE Programs Focused on Renewable Sources for Fuels and Electricity 

Generation 

The programs that focus on fuels and electricity generation are described here in terms of their 

objectives and representation of resource and technology in the model. The Biomass 

Technologies Program seeks to facilitate development of biofuels, bioproducts, and biopower. 

It is modeled in NEMS-EERE for the benefits analysis through the improvement of cellulosic 

ethanol production characteristics—lower costs and higher yields.
16

As shown in Figure 2, our analysis uses a more optimistic assumption about improvements in 

cellulosic technology even without future DOE RD&D than EIA does in the updated AEO 

2008 Reference Case. The private sector is assumed to continue biofuels research but at a 

slower pace without DOE participation; over the long-term, it is assumed to achieve similar 

cost reductions and improved yields.

Figure 2. Cellulosic ethanol investment cost

The geothermal, solar, and wind power programs conduct RD&D to accelerate 

commercialization of renewable generation technologies. The Geothermal Technologies 

Program focuses on EGS. The Solar Technologies Program focuses on electricity generation 

but includes solar heating activities not analyzed in the GPRA benefits analysis. The wind 

portion of the Wind and Hydropower Technologies Program emphasizes development and 

deployment along with low wind-speed turbine improvements. The hydropower portion 

includes both marine and hydrokinetic technologies. The RD&D goals for each of these 

programs are represented within NEMS-EERE by improvements in capital costs and capacity 

factors for these technologies. Geothermal, concentrated solar and wind technologies are 

primarily large, centralized generation technologies and compete with conventional, advanced 

fossil and nuclear technologies for market share.

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

2010 2015 2020 2025 2030

2007 $/ Gallon Capacity

EIA Base

EERE Base

EERE Goal
17 

NEMS-EERE is able to characterize the regional variability of renewable resources and 

therefore the attractiveness of these technologies in the model’s 13 NERC regions and 

subregions. We include updates to the geothermal resource to reflect new information about 

hydrothermal resources and EGS.

21

For comparison, 

Solar photovoltaic (PV) systems are represented as utility 

scale as well as distributed generation where homes or businesses evaluate the costeffectiveness of installing PV systems to reduce their purchases of electricity. The costs used 

in the model of a sample of renewable generation technologies and their operating 

characteristics are shown in Table 3.

Table 4 illustrates the costs of select fossil generation options, including 

advanced nuclear, integrated gasification combined cycle (IGCC) coal with and without CCS 

technology, advanced gas combined cycle, and IGCC biomass technologies. Although power 

plant costs have escalated in recent years, the values shown in Table 3 and Table 4 are 

designed to be consistent between technologies and therefore are not unduly biased by these 

cost escalations. However, higher power plant costs, such as EIA’s more recent estimates in 

the AEO2010 Reference Case, would penalize all capital intensive technologies (such as coal, 

nuclear, and most renewables) and would also likely increase the overall cost of meeting caps 

in CO2 emissions.

 

21 Petty, S.; Porro, G. (2007). Updated U.S. Geothermal Supply Characterization. 24 pp.; NREL Report No. CP640-41073.
18

Table 3. Comparison of NEMS-EERE and EIA Renewable Generation Technology Assumptions*

Class 4 

Onshore Wind

Class 6 

Onshore Wind

Class 6 

Offshore Wind

Concentrated

Solar Power Residential PV

EIA NEMS-EERE EIA NEMS-EERE EIA NEMS-EERE EIA NEMS-EERE EIA NEMS-EERE

Overnight Capital Costs (2006$/kW)**

2010 1423 1422 1423 1422 n/a 2812 3379 4803 6165 5027

2015 1415 1193 1415 1300 2741 1984 3122 4159 5477 3317

2020 1411 1100 1411 1225 2658 1700 2846 3836 4773 2819

2025 1407 1048 1407 1136 2574 1600 2638 3478 4367 2396

2030 1403 1016 1403 1118 2490 1468 2430 3037 3961 2037

Capacity Factors

2010 0.31 0.39 0.39 0.46 0.39 0.41 0.38 0.41 varies by region

2015 0.32 0.44 0.40 0.48 0.40 0.53 0.39 0.41 same in both

2020 0.33 0.45 0.41 0.49 0.41 0.53 0.39 0.68

2025 0.33 0.45 0.41 0.49 0.42 0.53 0.39 0.74

2030 0.34 0.45 0.42 0.49 0.43 0.53 0.38 0.79

*Only a sample of wind classes and PV systems is shown here for illustrative purposes. Geothermal is not shown due to its highly site specific 

characteristics in NEMS.

** Overnight capital costs do not include transmission costs, interest during construction, or investment tax credits.
19

Table 4. EIA Fossil Generation Technology Assumptions

Advanced

Nuclear

IGCC

with CCS

IGCC

without CCS

Advanced Gas Combined 

Cycle

IGCC

Biomass

Overnight Capital Costs (2006$/kW)*

2010 n/a n/a n/a 702 n/a

2015 n/a 2,428 1,723 690 2,695

2020 2,310 2,347 1,684 677 2,610

2025 2,241 2,266 1,646 664 2,525

2030 2,172 2,185 1,607 651 2,439

* Overnight capital costs do not include transmission costs or interest during construction. Also excludes effect of any investment tax credits.
20

Modeling Scenarios

To examine the potential impact of EERE programs if S. 2191 were enacted, we ran two new 

scenarios:

• EERE Base Case: AEO 2008 Reference Case with EERE program goals achieved and no 

CO2 cap-and-trade policy, using the NEMS-EERE integrated model. This case is 

equivalent to the EERE GPRA FY2010 Program Portfolio Case.

• EERE S. 2191 Case: S. 2191 CO2 cap-and-trade policy case with EERE program goals 

achieved.

We compared these results to the EIA S. 2191 scenarios:

• EIA Base Case: AEO 2008 Reference Case developed by the EIA, representing a 

business-as-usual scenario without EERE program goals

• EIA S. 2191 Case: S. 2191 CO2 cap-and-trade core policy case without EERE programs. 

Although EIA ran sensitivity cases to test key assumptions in the core policy case, these 

will not be discussed in this paper.

We will focus on the differences between the EERE S. 2191 Case and the EIA Base and S. 

2191 Cases as a means to explore the ability of EERE programs to help meet the requirements 

of the CO2 cap in a cost-effective way. These differences will include the effects of both the 

EERE programs and the price response related to the cap-and-trade policy. In most cases, we 

will not attempt to isolate the program effect from the policy effect when discussing the 

results of the analysis.

EIA used an updated AEO 2008 version of NEMS for its original analysis of S. 2191. We 

recreated the EIA S. 2191 scenarios using the NEMS-EERE model described here (but 

without the modifications made to represent EERE programs as described in Section 4), 

which was created for use in analyzing the EERE FY2010 programs. Use of the NEMS-EERE 

model for all four scenarios ensures that all results are comparable. However, the results of 

the EIA S. 2191 scenarios presented in Section 5 may differ slightly from EIA’s published 

results.
21

5 Results

CO2 Allowance Prices and Emissions

Figure 3 compares annual covered greenhouse gas emissions for each of the four scenarios 

(two of which have identical emissions—the S. 2191 Cases), and it displays the S. 2191 cap 

level, which is not one of the scenario emission levels. The EERE Base Case without a CO2

cap-and-trade policy reduces covered emissions by 14 percent by 2030 relative to EIA’s Base 

Case. The S. 2191 Case with EERE programs has essentially the same covered emissions as 

the S.2191 Case without EERE programs, due to the cap. There are a few minor differences 

in annual banking of allowances between these two S. 2191 cases, but emissions from both 

appear as a single line in Figure 3. The S. 2191 Cases reduce emissions about 55 percent from 

the EIA Base Case by 2030 in order to meet the bill’s requirements. Emission reductions 

include offset credits that can be used in place of allowances to meet the requirement up to the 

bill’s specified offset limit of 30 percent of the emissions cap.

Figure 3. Covered greenhouse gas emissions less offsets

Banking is measured as the difference between the emissions cap and the actual covered 

emissions for the Core Cases as shown in Figure 3. Banking allows covered sources to set 

aside excess allowances and offsets for use in meeting requirements in future years. In the

S. 2191 Cases, emitters are projected to bank excess emission allowances starting in 2019 in 

order to build a cumulative bank of 5 billion allowances by 2030 for use in years beyond 2030 

when the bill’s requirements become more stringent. EIA chose the 2030 cumulative bank 

target for its S. 2191 analysis; for consistency, we used the same target in the EERE S. 2191 

Case. These banking assumptions can have a significant impact on the cost of the policy by 

changing the total cumulative emissions reductions required by 2030. In our analysis, a 2030 

bank balance of 5 billion allowances adds about 13 percent to the required emissions 

reductions in both S. 2191 Cases. To explain their choice of 5 billion allowances, EIA states 

“This level of allowance banking is consistent with the greater difficulty complying with the 

0

2,000

4,000

6,000

8,000

10,000

2010 2015 2020 2025 2030

Million Metric Tons CO2e

EIA Base

EERE Base

S2191 Cap

S2191 Cases

banking
22 

post-2030 targets under continued growth in population and the economy, yet balanced by the 

technological progress likely to help mitigate the economic cost of abatement.”22

Figure 4

 

illustrates that if EERE program benefits are realized, CO2 allowance prices required 

to meet future S. 2191 policy goals are projected to be about $41 per ton by 2030 (measured 

in real 2006 dollars) or about 34 percent lower than the EIA projected allowance price of $62 

per ton.23 

Figure 4. CO2 allowance prices

Lower allowance prices generally translate into lower energy prices for consumers because 

energy producers can usually include some or all of their allowance costs in the price of their 

products. The NEMS scenarios assume full cost recovery in all non-electric energy markets 

and partial recovery in electricity markets due to competitive, marginal cost pricing in some 

regions.

Figure 5 compares a sample of national average, delivered energy prices in the S. 2191 Cases 

compared with prices in the EIA Base Case in 2030. The allowance price is evident in all of 

these prices, but the magnitude of the allowance price effect varies, depending upon the 

carbon content of the fuel and the ability of producers to recover costs. Coal prices (with 

carbon cost included) rise the most in a carbon-constrained scenario, increasing from almost 

$2 per million Btu (mmBtu) in the base case to more than $7 per mmBtu in the EIA S. 2191 

Case, which is more than triple the base case forecasted price. Because the EERE S. 2191 

Case results in lower allowance prices, coal prices are much lower than in the EIA S. 2191 

Case but still more than double the base case price by 2030. In addition, the EERE programs 

result in lower demand and provide improved technologies that lead to lower energy prices. 

The impact on gasoline prices is less severe due to the fuel’s lower carbon content: the EIA 

 

22 “Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner Climate Security Act of 2007,” 

Energy Information Administration Office of Integrated Analysis and Forecasting, SR/OIAF/2008-01, April 

2008, page 7.

23 As described in Section 2, the EIA S. 2191 allowance prices are assumed to increase at 7.4 percent per year in 

real terms. The EERE case adopts the same growth rate beginning in 2019 when banking starts. Prior to 2019, 

there is insufficient incentive for banking in this case so the model determines the allowance price needed to 

meet the annual emissions cap without banking.

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

2012 2015 2018 2021 2024 2027 2030

2006$ per ton CO2e

EIA S2191 EERE S2191
23 

2191 Case is 18 percent or roughly $0.45 per gallon higher than the base price whereas the

EERE S. 2191 Case is only 6 percent higher than the base case in 2030.

S.

Electricity prices are affected less by allowance prices for a number of reasons: the mix of 

fuels used to generate electricity (and their carbon content) changes over time; free allowance 

allocations to electric generators and electric load-serving entities provide some rate relief, 

especially in the early years; and competitive pricing in some regions reduces the allowance 

price impact.24 Average electricity prices under a carbon scenario are about 11 percent or 1.0 

cents per kWh higher comparing EIA S. 2191 to EIA Base Case in 2030 and about 3 percent 

higher comparing EERE S. 2191 to EIA Base Case. 

Figure 5. Delivered energy prices in 2030

Sectoral Results

As shown in Figure 6, the electric power sector is projected to reduce the largest sectoral 

share of energy-related emissions in both climate policy scenarios. Reductions are calculated 

from the EIA Base Case in order to illustrate the combined effect of the EERE programs and 

the climate policy in reducing emissions in the EERE S. 2191 Case. Between 80 percent and 

90 percent of total reductions by 2030 are related to electricity and are due to a combination 

of fuel switching to low-carbon sources of electricity and energy efficiency improvements by 

the end-use sectors. Because the electric power industry depends heavily on carbon-rich coal 

and is the primary consumer of coal in the United States, it is able to reduce emissions 

substantially by switching from coal to other fuels through a combination of plant dispatch 

and capacity investment decisions. These scenarios assume that low-carbon options such as 

nuclear and CCS are cost-effective and are available for fuel switching; if these options are 

not available or are more expensive, emission reductions from the electricity sector would be 

more limited and the price of carbon allowances would be higher. The residential, 

commercial, and industrial sectors contribute to electric sector reductions 1) by increasing

 

24 The cases assume electricity prices are determined differently, depending on whether the regional market is a 

competitive market or a regulated one. In portions of the country with competitive markets, high-carbon 

electricity producers cannot pass full allowance costs on to consumers, whereas in parts of the country with 

regulated markets, producers are assumed to be allowed to recover average costs of service, including allowance 

costs. Whether this holds true is contingent on regulators’ decisions.

0.0

5.0

10.0

15.0

20.0

25.0

30.0

Electric Steam Coal Industrial Natural Gas Motor Gasoline Average Electricity

2006$ per million BTU

EIA Base EIA S2191 EERE S2191
24

purchases of energy efficient electricity-using devices—many of which are enhanced by 

EERE programs—and 2) by reducing their demand for energy services. The transportation 

sector depends on carbon-rich petroleum products and currently has limited cost-effective 

options for fuel switching. However, the EERE Vehicle Technologies and Biomass Programs 

are working to expand transportation options for advanced vehicles and fuels. These results 

are based on the assumption that current regulations and consumer behavior do not change 

over time. However, future regulatory changes—such as stronger Corporate Average Fuel 

Economy (CAFE) standards for vehicles or enhanced appliance standards—or other changes 

that may significantly alter consumer attitudes toward energy use, may provide greater 

emissions reductions from sources other than electricity generation than these results 

illustrate.

Figure 6. Energy-related CO2 emission reductions from EIA base;

End-use emissions are non-electric only; electricity-related reductions are included in the electric power sector.

Figure 7 illustrates each sector’s share of emissions in 2030 in the EIA Base Case and in the 

EERE S. 2191 Case. Despite reductions from the EERE efficiency programs (buildings, 

FEMP, fuel cells, industry, and WIP), the share of total energy-related CO2 emissions from 

non-electric sectors increases from 57 percent in the EIA Base Case in 2030 to more than 80 

percent in the EERE S. 2191 Case because of significant reductions from the electric power 

sector. The buildings and industry share of total emissions show a modest increase of 3 

percent to 5 percent of total emissions by 2030, while transportation’s share of emissions 

increases dramatically compared to the base case. Transportation-related emissions are 

projected to be half of total emissions in the S. 2191 Case compared to one-third in the EIA 

Base Case. Current shares of emissions are similar to the EIA Base Case 2030 emissions.

0%

20%

40%

60%

80%

100%

Percentage of Total Reductions

EIA S2191 EERE S2191 EIA S2191 EERE S2191

Buildings Industry Transportation Elec Power

2020 2030

 

 
25

Figure 7. Energy-related CO2 emissions by end-use sector in 2030

Economy-wide primary energy consumption by fuel tells a similar story, as shown in

Figure 8. Liquid fuels are the dominant fuel source in the U.S. economy, especially in the

transportation sector where about 70 percent of liquid fuels are currently consumed. Both of 

the S. 2191 Cases are projected to reduce fossil fuel consumption by about 30 percent by 

2030, with most of the reductions in coal and natural gas used in electricity generation. In the 

EIA S. 2191 Case, nuclear generation increases the most in response to fossil generation 

reductions, while in the EERE S. 2191 Case, demand reduction and renewable generation 

provide the corresponding increase. Much of the demand reduction occurs in the EERE Base 

Case without the climate policy, whereas the change in fuel mix from fossil fuels to 

renewable generation is primarily a function of the policy. Liquid fuels consumption in 2030 

is reduced 5 percent in the EIA S. 2191 Case compared to the base case, and 8 percent in the 

EERE S. 2191 Case. These results may differ under different assumptions about relative costs 

of nuclear, energy efficiency, and renewable generation technologies.

Figure 8. Primary energy consumption by fuel

9%

16%

32%

43%

EIA Base Case

(Total = 6,861 mmtCO2)

14%

19%

50%

17%

EERE S.2191 Case

(Total = 4,065 mmtCO2)

Buildings

Industry

Transportation

Elec Power

0

25

50

75

100

125

EIA Base EIA Base EIA S2191 EERE Base EERE S2191

Quadrillion Btu

Liquid Fuels Natural Gas Coal Nuclear Power Renewables/Other

2010 2030 2030 2030 2030
26

The next two figures illustrate the change in delivered energy demand by sector between 2010 

and 2030. In Figure 9, total delivered energy in 2010 is projected to be 75 quadrillion Btu 

(quads), of which about 20 quads will be consumed by the residential and commercial sectors, 

26 quads by the industrial sector (including refineries), and 29 quads by the transportation 

sector. Delivered energy demand is projected to increase 10 quads or about 13 percent by 

2030 in the EIA Base Case but only 5 quads or 7 percent in the EIA S. 2191 Case; it is 

expected to decrease 1.5 quads or 2 percent in the EERE S. 2191 Case. Shown a different 

way, Figure 10 illustrates that EERE programs can dramatically reduce the projected growth 

in delivered energy demand over the next 20 years. Demand growth actually declines in the 

residential and industrial sectors and in total delivered energy demand in the EERE S. 2191 

Case from 2010 levels, and growth is reduced significantly in the commercial and 

transportation sectors. This is greater than the changes in behavior and efficiency adoption 

that occur with carbon pricing alone in the EIA S. 2191 Case. 

Figure 9. Delivered energy demand by sector

Figure 10. Growth in delivered energy demand in years 2010-2030

0

20

40

60

80

100

EIA Base EIA Base EIA S2191 EERE Base EERE S2191

Quadrillion Btu

Residential Commercial Industrial Transportation

2010 2030 2030 2030 2030

-40%

-20%

0%

20%

40%

60%

80%

EIA Base EIA S2191 EERE S2191

Residential Commercial Industrial Transportation
27

The following discussion explores the sources of these sectoral emission reductions in more 

detail for the electric power sector, the buildings and industrial sectors, and the transportation 

sector.

Electric Power Sector

As shown in Figure 11, the EERE cases result in an average of 18 percent to 23 percent 

reduction in electricity sales by 2030 relative to the EIA Base Case, primarily due to EERE 

energy efficiency programs. Reductions related to the climate policy are about 5 percent of 

sales in both the EIA and EERE S. 2191 Cases compared to their respective base cases. 

Reductions in electricity sales result in reduced emissions from the electric power sector. 

Electricity demand from electric vehicles increased from 9 billion kWh in the EIA Base Case 

to 18 billion kWh in the EERE cases, but this represents less than one percent of total sales.

Figure 11. Electricity sales by sector in 2030

Other emission reductions from the power sector come from fuel switching to low-carbon or 

non-carbon fuels such as renewable energy, both by changing investment decisions for new 

capacity and by changing the dispatch of existing plants. Figure 12 illustrates the capacity 

investment decisions that are projected to occur under a climate scenario with and without 

EERE RD&D programs.

Conventional coal capacity is reduced more than 65 percent in both of the S. 2191 Cases

compared to the EIA Base Case due to retirements, and other conventional fossil capacity

such as natural gas is reduced more than 20 percent. These capacity changes are due to a 

combination of fewer fossil-fueled plants being built under a carbon scenario, and existing 

fossil capacity being retired, although some investment is made in new CCS technologies. 

Nuclear capacity increases dramatically in the EIA S. 2191 Case to reduce carbon emissions, 

with 270 gigawatts of new capacity built (or about 270 new plants), whereas the EERE

S. 2191 Case builds a more modest 63 new nuclear plants by 2030. Renewable capacity 

increases in both S. 2191 Cases, but as expected, the increase is more dramatic in the EERE 

S. 2191 Case.

0

1000

2000

3000

4000

5000

EIA Base EIA Base EIA S2191 EERE Base EERE S2191

Billion KWh

Residential Commercial Industrial Transportation

2010 2030 2030 2030 2030
28

Figure 12. Electric generating capacity, including distributed generating capacity

Figure 13 illustrates the change in total renewable electric capacity as well as the mix of 

renewable technologies under each scenario. Renewable capacity in 2030 in the EERE

S. 2191 Case is almost 75 percent greater than the capacity in the EIA S. 2191 Case, 

increasing from 239 GW (20 percent of total capacity) to 413 GW (37 percent of total 

capacity). In the EIA S. 2191 Case, biomass and land-based wind are the dominant-growth 

renewable technologies, whereas the EERE S. 2191 Case favors land-based wind and 

distributed PV.25 These results depend heavily upon the technology assumptions, but EERE 

RD&D programs aim to reduce the cost of these technologies and improve their performance,

making them more attractive especially in a carbon-constrained scenario.

Figure 13. Renewable electric capacity, including distributed generating capacity

Figure 14 provides a complete picture of the ability of EERE programs to both reduce 

electricity demand through efficiency programs and increase renewable generation by 

lowering the costs and improving the availability of these technologies. While the EIA

S. 2191 Case relies heavily on the construction of large-scale nuclear plants to meet the bill’s 

requirements, the combination of demand reductions and low-cost renewables greatly reduces 

the need for new nuclear facilities in the EERE S. 2191 Case. At the same time, the share of 

renewable generation increases from 19 percent of total generation in EIA S. 2191 to 39 

 25 Distributed PV is rooftop solar panels purchased by homes and businesses.

0

250

500

750

1,000

1,250

EIA Base EIA Base EIA S2191 EERE Base EERE S2191

Gigawatts

Renewable/Other

Nuclear

Natural Gas with CCS

Conv Gas/Other Fossil

Coal with CCS

Conventional Coal

 

2010 2030 2030 2030 2030

0

100

200

300

400

500

EIA Base EIA Base EIA S2191 EERE Base EERE S2191

Gigawatts

Offshore Wind

Land-Based Wind

Distributed PV

Concentrating Solar

Biomass

MSW/Landfill Gas

Geothermal

Hydro/Pumped 

Storage/Other

 

2010 2030 2030 2030 2030
29 

percent in EERE S. 2191. Renewable generation is currently about 11 percent of total 

generation.

Figure 14. Electricity generation, including distributed generating capacity

Residential and Commercial Buildings and Industrial Sectors

As shown in Figure 15, delivered energy consumption in residential and commercial buildings 

(primarily natural gas and electricity) in the EERE S. 2191 Case is almost 18 percent below 

the EIA Base Case by 2030, and is 1.5 percent below current consumption levels, due to 

efficiency improvements attributed to the Building Technologies in conjunction with the 

higher energy prices of the climate policy. Energy consumption in the EERE Base Case alone 

is about 13 percent below the EIA Base Case. Space heating and cooling contributed about 30

percent of total savings; water heating and lighting contributed another 23 percent of savings; 

the remaining savings come from a variety of other devices such as televisions, clothes 

washers and dryers, personal computers, and battery chargers. 

Figure 15. Buildings delivered energy consumption

Delivered energy consumption in the industrial sector declined in both of the EERE cases 

relative to the EIA Base Case by 2030, as shown in Figure 16. Most of the reductions are 

from savings in natural gas consumption that are due to EERE improvements in energy 

0

1,000

2,000

3,000

4,000

5,000

6,000

EIA Base EIA Base EIA S2191 EERE Base EERE S2191

Billion KWh

Renewable/Other

Nuclear

Natural Gas with CCS

Conv Gas/Other Fossil

Coal with CCS

Conventional Coal

 

2010 2030 2030 2030 2030

0

5

10

15

20

25

30

EIA Base EIA Base EIA S2191 EERE Base EERE S2191

Quadrillion Btu

Space Heating Space Cooling Water Heating Lighting Televisions Other Uses

2010 2030 2030 2030 2030
30

efficiency. Energy consumption in the EERE cases is also below current levels by 2030, 

whereas consumption continues to increase over time in the EIA cases. Industrial 

electrification (coupled with electric sector decarbonization) has been proposed as a carbon 

mitigation strategy, but this study did not explicitly examine this strategy.

Although coal represents a small share of industrial energy consumption, industrial coal use 

declines, mainly because of reductions in coal-fired boilers and coal-to-liquids production, by 

32 percent in the EIA S. 2191 Case and by almost 40 percent in the EERE S. 2191 Case 

compared to the EIA Base Case by 2030. About two-thirds of the industrial direct (i.e., nonelectric) emissions reductions are due to coal savings.

Figure 16. Industrial delivered energy consumption

Transportation Sector

Transportation energy consumption in the EERE S. 2191 Case declined about 1.8 quadrillion 

Btu (quads) or 6 percent from the EIA Base Case by 2030, primarily due to assumed 

improvements in light-duty vehicle (LDV) and heavy-duty vehicle energy efficiency, as 

shown in Figure 17 and Figure 18. Light-duty vehicles and heavy-duty vehicles (freight trucks 

and buses) consume almost 80 percent of total transportation sector energy (primarily liquid 

fuels). The S. 2191 Cases also showed reduced energy consumption from rail (due to less coal 

consumption by electric generators) and from natural gas pipelines, which is included in the 

“Other” category.

0

5

10

15

20

25

30

EIA Base EIA Base EIA S2191 EERE Base EERE S2191

Quadrillion Btu

Liquid Fuels Natural Gas Coal Renewables Electricity

2010 2030 2030 2030 2030
31

Figure 17. Transportation energy consumption by mode

Figure 18. Fuel efficiency for new light-duty and heavy-duty vehicles

As illustrated in Figure 18, average fuel efficiency for new LDVs improved 15 percent in the 

EERE S. 2191 Case by 2030, from an average of 37 miles per gallon (mpg) in the EIA

S. 2191 Case to 42 mpg with the EERE programs. Average fuel efficiency for new heavyduty vehicles increased about 8 percent between the two cases, from 7.0 mpg to 7.5 mpg in 

the EERE cases. Average LDV stock efficiency improved at a slower pace, from 28 mpg in 

the Base Case to 30 mpg by 2030, due to the rate of vehicle stock turnover. The current 

average fuel efficiency is 27 mpg for new light-duty vehicles, and the new CAFE standards 

passed by Congress as part of the Energy Independence and Security Act of 2007 (EISA)26

The EIA Base and S. 2191 Cases are virtually identical for vehicle fuel efficiency because 

gasoline and diesel fuel prices only increase roughly 50 cents per gallon by 2030 due to S. 

2191 and because CAFE standards mandated under EISA are binding in these cases. The 

increased fuel economy in the EERE cases is primarily due to improvements in and greater 

adoption of advanced diesel and hybrid vehicles (including plug-in hybrid electric vehicles or 

require that the combined average fuel efficiency of LDVs reach 35 mpg by 2020.

 26 The full text of EISA can be found on the Library of Congress’ THOMAS Web site (http://thomas.loc.gov/). 

0

5

10

15

20

25

30

35

EIA Base EIA Base EIA S2191 EERE Base EERE S2191

Quadrillion Btu

Other

Air Travel

Rail, Shipping

Freight Trucks, Buses

Light-Duty Vehicles

2010 2030 2030 2030 2030

0

10

20

30

40

50

2010 2015 2020 2025 2030

Miles Per Gallon

LDV-EERE S2191

LDV-EIA S2191

HDV-EERE S2191

HDV-EIA S2191
32

PHEVs) and advanced materials. PHEVs’ level of emissions depends not only on fuel 

efficiency but also on the carbon content of the electricity they use.

The share of advanced technology vehicles, shown in Figure 19, increased from 35 percent of 

total LDV stock in the EIA cases to 50 percent in the EERE cases by 2030, mainly due to 

increases in hybrid vehicles and advanced diesel. These results suggest that S. 2191 

provisions alone are not enough to increase the share of advanced technologies in LDVs, 

whereas EERE programs can do so.

Figure 19. Advanced technology LDV stock in 2030

The Renewable Fuel Standard (RFS) of EISA stimulates additional ethanol production, 

although in all scenarios, the total biofuels quantity falls short of the 36 billion gallon required 

by 2022 because assumptions in the model do not force this requirement to be met. The effect 

of EERE RD&D programs dramatically increases the share of cellulosic ethanol in the EERE

S. 2191 Case, from 29 percent of ethanol production in the EIA Base to 75 percent by 2030; it 

also increases total ethanol production. As shown in Figure 20, corn ethanol production in the 

EERE S. 2191 Case declines between 2020 and 2030 but remains constant at 15 billion 

gallons in both EIA cases as part of meeting the RFS. Total ethanol production increases 18 

percent in the EERE S. 2191 Case compared to the EIA Base Case by 2030 due to lower 

ethanol costs stimulating greater E85 demand.

27

 27 E85 is a blend of 85 percent ethanol and 15 percent gasoline by volume.

0

25

50

75

100

125

150

175

EIA Base EIA S2191 EERE Base EERE S2191

Millions of Vehicles

Other Fuels

Electric-Diesel Hybrid

Plug-in Gasoline Hybrid

Electric-Gasoline Hybrid

Ethanol Flex-Fuel

Advanced Diesel
33

Figure 20. Ethanol production by source

0

10

20

30

40

Billions of Gallons

Imports

Cellulose Based

Corn Based

2020 2030
34

6 Conclusions and Insights

This study analyzed the projected effects of EERE RD&D programs on energy consumption, 

technology choice, and associated greenhouse gas emissions in the United States under a 

carbon constraint. We compared cases in which EERE program goals were achieved to cases 

in which they were not achieved, in cases both with and without a cap on greenhouse gas 

emissions, using NEMS-EERE to model each case.

EERE programs, if successful, are expected to increase the efficiency of the energy system 

and decrease the cost of renewable generation and transportation biofuels. With our 

assumptions, EERE programs reduced greenhouse gas allowance prices by almost 35 percent 

by 2030, compared to EIA’s estimate of the impact of the S. 2191 proposal using business-asusual technology assumptions. Delivered energy consumption was reduced 9 percent, and 

renewable generation increased 70 percent by 2030 compared to the EIA S. 2191 Case; 

consumption was reduced 13 percent, and renewable generation more than doubled compared 

to the EIA Base Case.

The electricity sector accounted for the largest share of emissions reductions in both the EIA 

and EERE S. 2191 Cases due to a combination of fuel switching to low-carbon sources of 

electricity and energy efficiency improvements by the end-use sectors. Because the electric 

industry depends on carbon-rich coal and is the primary consumer of coal used in the United 

States, it is able to reduce emissions substantially by switching away from coal to other fuels 

through a combination of plant dispatch and capacity investment decisions. The residential, 

commercial, and industrial sectors contribute to electric sector reductions by purchasing 

efficient electrical equipment, and by reducing their demand for energy services. Achieving 

EERE program goals would reduce costs and improve adoption of energy efficient 

technologies and practices.

The transportation sector depends on carbon-rich petroleum products, and currently has 

limited cost-effective options for fuel switching. However, the EERE Vehicle Technologies 

and Biomass Programs are working to expand transportation options for advanced vehicles 

and fuels. Despite substantial penetration of advanced technology vehicles and biofuels in the 

EERE cases, transportation sector emissions reductions were more expensive to achieve than 

electric sector emissions reductions were under the assumptions and timeframe of this study. 

S. 2191 alone did not provide enough incentive to reduce transportation emissions much 

beyond what was achieved in the EERE Base Case. More targeted incentives are needed to 

substantially reduce emissions from this sector, and EERE programs can provide the 

necessary technologies to enable those incentives.

Implications of EERE programs are evident in the roles of biomass and PHEVs. With EERE 

RD&D success in the Biomass Program, biomass plays a more significant role in ethanol 

production and less in power production, with the reduced role in power production 

attributable in part to competition from other low-cost renewable generation technologies. 

PHEVs also play a role in reducing greenhouse gas emissions. The ability for plug-in hybrids 

to reduce emissions improves as the carbon content of electricity declines; this has only a 

modest benefit by 2030 but could become more important post-2030, especially in a carbonconstrained policy.
35

EERE Vehicle Technologies, Industrial Technologies, and Building Technologies Programs 

were shown to significantly reduce or even reverse the trend of energy demand growth in the 

next 20 years. The carbon policy alone was not enough to reverse these trends or improve 

vehicle efficiencies without the success of these programs.

EERE programs, if successful, will reduce the marginal cost of CO2 emissions reductions, 

reflected in the allowance price, which in turn will reduce fuel prices for households and 

businesses, notably electricity generators. Lower fuel prices will reduce the need for 

generators to retire existing power plants, while the electricity savings from the RD&D 

programs will reduce the need to build new generating capacity. When new capacity is 

needed, generators will have low-cost renewable technologies available to meet the emissions 

cap. Successful achievement of these RD&D goals thus enhances the ability of the U.S. 

economy to thrive while simultaneously mitigating greenhouse gas emissions.
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4. TITLE AND SUBTITLE

Energy Efficiency and Renewable Energy Research, Development, 

and Deployment in Meeting Greenhouse Gas Mitigation Goals: The 

Case of the Lieberman-Warner Climate Security Act of 2007 

(S.2191)

5a. CONTRACT NUMBER

DE-AC36-08-GO28308

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6. AUTHOR(S)

S. Showalter, F. Wood, L. Vimmerstedt

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SAO9.1051

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14. ABSTRACT (Maximum 200 Words)

The U.S. federal government is considering actions to reduce greenhouse gas emissions. Renewable energy and 

energy efficiency technologies could help reduce greenhouse gas emissions, so the cost of these technologies could 

significantly influence the overall cost of meeting greenhouse gas limits. This paper examines the potential benefit of 

reduced technology cost by analyzing the case of the Lieberman-Warner Climate Security Act of 2007 (S.2191). This 

act had a goal of reducing national carbon emissions in 2050 to levels 72 percent below 2006 emission levels. In 

April 2008, the U.S. Department of Energy, Energy Information Administration (EIA) published an analysis of the 

effects of S.2191 on the U.S. energy sector. This report presents a similar analysis: both analyses examined the 

impacts of S.2191, and both used versions of the National Energy Modeling System. The analysis reported here 

used modified technology assumptions to reflect U.S. Department of Energy, Office of Energy Efficiency and 

Renewable Energy (EERE) program goals. The results show that achieving EERE program goals could reduce the 

cost of meeting greenhouse gas limits, reduce the cost of renewable electricity generation and biofuels, and reduce 

energy intensity.

15. SUBJECT TERMS

Lieberman-Warner Climate Security Act of 2007; S.2191; Senate Bill 2191; carbon emissions; greenhouse gas 

emissions; emission reductions; greenhouse gas allowance prices; energy intensity; carbon cap; technological 

change; research and development; renewable energy; energy efficiency; federal government

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