Climate Change: Costs and Benefits of S. 2191/S. 3036
Congressional research reportMay 15, 2008
Ask Donna
What actually matters in this document.
Text
ȱ
ȱDZȱȱȱȱȱ
ǯȱŘŗşŗȦǯȱřŖřŜȱ
¢ȱȱ
ȱȱ¢ȱȱȱ¢ȱ
ȱǯȱȱ
ȱȱ¢ȱȱȱ¢ȱ
¢ȱŗśǰȱŘŖŖŞȱ
ȱȱȱ
ŝȬśŝŖŖȱ
ǯǯȱ
řŚŚŞşȱ
ȱȱȱ
Prepared for Members and Committees of Congress
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
¢ȱ
This report examines six studies that project the costs of S. 2191 (S. 3036) to 2030 or 2050. It is
difficult to project costs up to the year 2030, much less beyond. The already tenuous assumption
that regulatory standards will remain constant becomes more unrealistic, and other unforeseen
events loom as critical issues which cannot be modeled. Long-term cost projections are at best
speculative, and should be viewed with attentive skepticism. Despite models’ inability to predict
the future, cases examined here do provide insights on the costs and benefits of S. 2191.
First, the ultimate cost of S. 2191 would be determined by the response of the economy to
the technological challenges presented by the bill. The potential for technology to reduce S.
2191‘s costs is not fully analyzed by any of the cases, nor can it be. Technology development is
not sufficiently understood currently for models to replicate with confidence. Likewise, it is
difficult to determine if available incentives are directed in an optimal manner. The cases suggest
that S. 2191‘s Carbon Capture and Storage (CCS) bonus allowances would encourage
deployment of CCS, accelerating development by 5-10 years.
Second, a considerable amount of low-carbon generating capacity will have to be built
under S. 2191 in order to meet the reduction requirement. How much capacity will be
necessary depends on new and replacement capacity needs, along with consumer demand
response to rising prices and incentives contained in S. 2191.
Third, offsets could be a valuable tool not only to potentially reduce costs, but also to buy
time to permit further development of new, more efficient technologies. Cost could be
lowered further by greater availability of offsets and international credits and with a broader
definition of eligible international credits.
Fourth, the Carbon Market Efficiency Board could have an important effect on the cost of
S. 2191 through its power to extend the availability of offsets and international credits. In
this sense, the Board’s powers could mesh with the previous insight about the potential effect of
offsets on the bill’s overall costs.
Fifth, the Low Carbon Fuel Standard could significantly raise fuel prices and limit supply.
The effects will depend on what fuels are included, the emissions reductions achieved by
alternatives, and the ability to produce those alternatives.
Finally, S. 2191’s climate-related benefit is best considered in a global context and the desire to
engage the developing world in the reduction effort. The United States and other developed
countries agreed both to reduce their own emissions to help stabilize atmospheric concentrations
of greenhouse gases (GHGs) and to take the lead in reducing GHGs when they ratified the United
Nations Framework Convention on Climate Change (UNFCCC). This context raises two issues
for S. 2191: (1) whether S. 2191‘s GHG program would be considered sufficiently credible
by developing countries so that schemes for including them in future international
agreements become more likely, and (2) whether S. 2191‘s reductions meet U.S.
commitments under the UNFCCC.
ȱȱȱ
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
Overview of the Major Provisions of S. 2191 (S. 3036) ................................................................. 3
Earlier Versions of the Bill ........................................................................................................ 5
Bill as Introduced................................................................................................................ 5
Bill as Reported by Subcommittee...................................................................................... 6
As Ordered Reported by Committee................................................................................... 9
Deficit Reduction Amendment ........................................................................................... 9
Introduction: Models Cannot Predict the Future Costs of a Climate Change Program................... 9
Lessons from SO2 Cap and Trade Program............................................................................... 9
An Illustrative Example from Analyses of S. 2191..................................................................11
Likelihood for More Noise in Greenhouse Gas Reduction Cost Estimates .................................. 15
Complexity of the Problem ..................................................................................................... 15
Flexibility of Cap-and-Trade Program .................................................................................... 16
Importance of Technology to Future Results .......................................................................... 16
Increasing Problems with Ceteris Paribus Analysis ................................................................ 17
Changing Baselines By Changing Laws........................................................................... 17
Changing Baselines By Changing Regulation.................................................................. 18
Measuring the Noise: A Web of Cost Measures ............................................................................ 19
Three Perspectives: Getting Out of the Noise......................................................................... 22
Results for S. 2191 ........................................................................................................................ 25
Impact on Greenhouse Gas Emissions .................................................................................... 25
Impact on Non-Greenhouse Gas Emissions............................................................................ 28
Impact on GDP Per Capita ...................................................................................................... 28
Allowance Price Estimates...................................................................................................... 34
Auction Revenue Estimates .................................................................................................... 37
Issues Raised by the Models.......................................................................................................... 40
Technology Issues ................................................................................................................... 40
Electric Power Sector........................................................................................................ 40
Transportation Sector........................................................................................................ 48
Impact on Fuel Prices........................................................................................................ 50
Economic Issues...................................................................................................................... 52
Availability of Offsets....................................................................................................... 52
Impact of Banking............................................................................................................. 53
Impact of Carbon Market Efficiency Board...................................................................... 54
Impact of Revenue Recycling........................................................................................... 54
International Leakage........................................................................................................ 54
Ecological Issues..................................................................................................................... 55
Climate Change Benefits .................................................................................................. 55
Non-Climate Change Air Quality Benefits ....................................................................... 61
Impact on Behavior........................................................................................................... 62
Conclusion..................................................................................................................................... 65
ȱȱȱ
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
Figure 1. Predicted Impacts of Carbon Abatement on the U.S. Economy (162 Estimates
from 16 Models)......................................................................................................................... 21
Figure 2. Total Estimated Greenhouse Gas Emissions Under S. 2191.......................................... 26
Figure 3. Total Estimated Greenhouse Gas Emissions from Each Model Under S. 2191............. 27
Figure 4. GDP per Capita (2005$) Under S. 2191 ........................................................................ 29
Figure 5. GDP per Capita (2005$) from Each Model Under S. 2191 ........................................... 30
Figure 6. Percentage Change in GDP per Capita Under S. 2191 .................................................. 32
Figure 7. Percentage Change in GDP per Capita from Each Model Under S. 2191 ..................... 33
Figure 8. Projected Allowance Prices Under S. 2191.................................................................... 35
Figure 9. Projected Allowance Prices from Each Model Under S. 2191 ...................................... 36
Figure 10. Estimated Annual Revenues from Allowance Auctions Under S. 2191 ...................... 38
Figure 11. Global Mean Surface Air-Temperature Increase in Six Scenarios Using the
MIT IGSM.................................................................................................................................. 61
Figure 12. Energy Price Change: Recent History Versus the S. 2191 Core Case......................... 65
ȱ
Table 1. Allocation of Allowances Under S. 2191 .......................................................................... 7
Table 2. Allocation of Auction Revenue (excluding Deficit Reduction Fund) Under S.
2191.............................................................................................................................................. 8
Table 3. Representative Sample of 1990 Estimates of Annual Compliance Cost for SO2
Cap-and-Trade Program ..............................................................................................................11
Table 4. Reference Case and S. 2191 Analyses for 2050 .............................................................. 12
Table 5. Reference Case Scenarios for 2020 and 2030 ................................................................. 14
Table 6. Influence of Climate Change Perspectives on Policy Parameters ................................... 23
Table 7. General Perspective of CATF and ACCF/NAM Cost Assumptions................................ 24
Table 8. Selected Results from CATF and ACCF/NAM Analyses................................................ 24
Table 9. EPA/IPM Reduction of Conventional Air Pollutants from Electric Utilities................... 28
Table 10. Allocation of Estimated Annual Auction Revenue from S. 2191 Using
EPA/ADAGE-TECH Case ......................................................................................................... 39
Table 11. Assumptions about the Construction of Generating Capacity Under S. 191 to
2030............................................................................................................................................ 41
Table 12. Assumptions about the Availability of CCS .................................................................. 44
Table 13. Estimated Incremental Annual Combined Public and Private Funding Needs to
Achieve EPRI’s Full Portfolio.................................................................................................... 46
Table 14. Total Public Funding Needs for 2007 CURC-EPRI Clean Coal Technology
Roadmap over 18 Years (2008-2025)......................................................................................... 47
Table 15. Matrix of Climate Risks................................................................................................. 57
ȱȱȱ
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
Table 16. The Stern Review Estimates of Social Cost of Carbon for Three Emissions
Paths ........................................................................................................................................... 58
ȱ
Author Contact Information .......................................................................................................... 67
ȱȱȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
A
s Congress continues the debate on an appropriate response to the climate change issue,
multiple bills have been introduced to begin reducing greenhouse gas (GHG) emissions.
Of these, S. 2191 (the Lieberman-Warner Climate Security Act of 20081) has received
particular attention. Introduced by Senator Lieberman, S. 2191 was ordered reported by the
Senate Committee on Environment and Public Works on December 5, 2007.2 Numerous analyses
have been done on its impacts, and as of April 2008, six studies had been released.
The most comprehensive analysis has been conducted by the U.S. Environmental Protection
Agency (EPA). The report is entitled: EPA Analysis of the Lieberman-Warner Climate Security
Act of 2008: S. 2191 in 110th Congress (March 14, 2008).3 The analysis employs a suite of models
and basecases, along with some useful sensitivity analyses. This report will focus on three of the
models, two basecases, and sensitivity analysis as appropriate.
•
The first model is ADAGE: a computable general equilibrium (CGE) model
developed by RTI International.4 The case employing the reference basecase is
designated EPA/ADAGE-REF in this report, while the case employing the high
technology basecase is designated EPA/ADAGE-TECH.
•
The second model is IGEM: a CGE model developed by Dale Jorgenson
Associates.5 The case employing the reference basecase is designated
EPA/IGEM-REF in this report, while the case employing the high technology
basecase is designated EPA/IGEM-TECH.
•
The third model is IPM: a dynamic, deterministic linear programming model of
the U.S. electric power sector developed by ICF Resources. The case employing
the IPM model is designated EPA/IPM in this report.6
A second analysis has been conducted by the Energy Information Administration (EIA). The
report is entitled Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner
Climate Security Act of 2007 (April 2008). The analysis employs EIA’s NEMS model: a
macroeconomic forecasting model with extensive energy technology detail.7 In addition to
conducting a “core” analysis of S. 2191 using its preliminary 2008 Annual Energy Outlook (AEO)
Baseline, EIA also conducts some useful sensitivity analyses that focus on the upside risk of
increased energy prices under S. 2191 which are discussed as appropriate. The core S. 2191
analysis is designated EIA/NEMS in this report.
A third analysis has been conducted by the Massachusetts Institute of Technology (MIT) Joint
Program on the Science and Policy of Global Change. The report is an appendix to a more
comprehensive analysis of cap-and-trade programs released in 2007.8 The appendix is titled:
1
Originally titled America’s Climate Security Act of 2007.
As of May 14, 2008, the Ordered Reported version of the bill was available at Senator Lieberman’s website:
http://lieberman.senate.gov/documents/lwcsa.pdf.
3
The report and supporting model runs are available at http://www.epa.gov/climatechange/economics/
economicanalyses.html
4
For more information on the ADAGE model, see http://www.rti.org/adage.
5
For more information on the IGEM model, see http://post.economics.harvard.edu/faculty/jorgenson/papers/
papers.html.
6
For more information on the IPM model, see http://www.epa.gov/airmarkets/progsreg/epa-ipm/index.html.
7
For more on the NEMS model, see http://www.eia.doe.gov/oiaf/aeo/overview/index.html.
8
Sergey Paltsev, et al., Assessment of U.S. Cap-and-Trade Proposals, MIT Joint Program on the Science and Policy of
(continued...)
2
ȱȱȱ
ŗȱ
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
Appendix D: Analysis of the Cap and Trade Features of the Lieberman-Warner Climate Security
Act (S. 2191). The appendix employs MIT’s EPPA CGE model and presents some useful
sensitivity analyses of S. 2191‘s offset and carbon capture and storage (CCS) bonus allowance
provisions. The case that includes S. 2191‘s 15% international offset and CCS subsidies
provisions is designated MIT/EPPA in this report.9
A fourth analysis has been conducted for the Clean Air Task Force (CATF) by OnLocation. The
report is titled The Lieberman-Warner Climate Security Act—S. 2191: A Summary of Modeling
Results from the National Energy Modeling System (February 2008). Employing EIA’s NEMS
model, the CATF analysis is designated CATF/NEMS in this report.
A fifth analysis has been conducted for the American Council for Capital Formation (ACCF) and
National Association of Manufacturers (NAM) by Science Applications International
Corporation. The report is entitled Analysis of The Lieberman-Warner Climate Security Act (S.
2191) Using The National Energy Modeling System (NEMS). Employing NEMS, ACCF/NAM
employs two basic cases: (1) a high cost case using the most constrained and high cost
assumptions of any of the analyses presented here (designated as ACCF/NAM/NEMS-HIGH) and
(2) a low cost case using the second most constrained and high cost assumptions of any of the
analyses presented here (designated as ACCF/NAM/NEMS-LOW).
A sixth analysis has been conducted for the National Mining Association (NMA) by CRA
International. The report is entitled Economic Analysis of the Lieberman-Warner Climate Security
Act of 2007 Using CRA’s MRN-NEEM Model (April 8, 2008). The analysis employs CRA’s
MRN-NEEM macroeconomic model with extensive electric power sector detail.10 The case
employing the NMA analysis is designated NMA/CRA.
It should be noted that several of the studies examined in this report are published as
presentations with limited documentation, making comparative analysis difficult. Each
presentation has selected features or impacts it is particularly interested in highlighting. The
more comprehensive analyses are the work by EPA, EIA, and MIT. In order to increase the
comparability of the various cases examined here, CRS has converted all publicly available data
presented by the cases to 2005 dollars (where appropriate) and interpolated missing data where
possible. Likewise, where studies have stated they used specific projections as a base case (such
as EIA’s Annual Energy Outlook 2007 or preliminary 2008 projections), CRS has assumed those
assumptions have not been altered except as specifically stated by the study. This analysis
considers the bill as ordered reported by the Senate Committee on Environment and Public
Works, incorporating the proposed deficit reduction amendment—S. 3036 is identical to that
version, including the deficit amendment. Other proposed amendments are likely if the bill moves
to the floor, and these amendments, if adopted, could affect the costs and benefits of the overall
bill.
(...continued)
Global Change, Report No. 146 (April 2007).
9
The primary scenario used for this report—the S. 2191, 15% Offsets and CCS Subsidy case—is summarized on p.
D21. For more information on the EPPA model, see http://web.mit.edu/globalchange/www/eppa.html.
10
For more information on the MRN-NEEM model, see http://www.crai.com/uploadedFiles/
RELATING_MATERIALS/Publications/BC/Energy_and_Environment/files/MRNNEEM%20Integrated%20Model%20for%20Analysis%20of%20US%20Greenhouse%20Gas%20Policies.pdf.
ȱȱȱ
Řȱ
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱȱȱȱȱȱǯȱŘŗşŗȱ
ǻǯȱřŖřŜǼȱ
S. 2191, The Lieberman-Warner Climate Security Act of 2008, was introduced October 18, 2007,
by Senator Lieberman. On December 5, 2007, the Senate Committee on Environment and Public
Works ordered reported an amended version of the bill that would establish a mandatory cap-andtrade system to reduce greenhouse gas emissions from most sectors of the economy.11 As ordered
reported, S. 2191‘s emissions cap is estimated by its sponsors to require a 71% reduction from
2005 levels by 2050 from covered entities (estimated by the sponsors to account for 87% of total
U.S. greenhouse gas emissions). Overall, the sponsors estimate that S. 2191 would reduce total
U.S. greenhouse gas emissions by up to 66% from 2005 levels by 2050.
S. 2191 would establish an absolute cap on the emissions from covered sectors and would allow
trading of emissions permits (“allowances”) among covered and non-covered entities.12 The bill
achieves its broad coverage through an upstream compliance mandate on petroleum, natural gas,
and fluorinated gas producers and importers, and a downstream mandate on coal consumers,
such as electric generators. Specifically, the bill would limit greenhouse gas emissions from all
petroleum producers/importers, all natural gas processors, all facilities that use more than 5,000
tons of coal per year, and entities that produce or import more than 10,000 tons annually (carbon
dioxide equivalent) of fluorinated gases and other greenhouse gases.
S. 2191 does not have a “safety valve”—an alternative compliance option that permits covered
entities to pay an excess emissions fee instead of reducing emissions. Instead, the bill creates a
Carbon Market Efficiency Board with authority to temporarily adjust the availability of
allowances through borrowing and other techniques; however, it is a zero-sum game. Allowances
borrowed must be repaid, so the emissions cap is maintained. The bill limits the availability of
domestic offsets to 15% of the allowance requirement, with allowances bought in an eligible
international allowance market also limited to 15%. Both percentages may be increased by the
Carbon Market Efficiency Board if market conditions suggest such action. The bill would permit
banking of allowances.
For each year 2012 through 2050, the bill specifies the total number of allowances available, then
explicitly states the percentage of those allowances that will go to covered and non-covered
sectors,13 as well as the share that will be auctioned. (See Table 1.) Over time, an increasing
share of the allowances are auctioned, while the allowances to covered sectors decrease to zero.
Auction proceeds are allocated for various purposes, including technology development and
deployment, transition assistance, adaptation, and program administration.14 (See Table 2.) Under
11
For more a more detailed discussion of S. 2191 provisions, and a comparison with other proposals, see CRS Report
RL33846, Greenhouse Gas Reduction: Cap-and-Trade Bills in the 110th Congress, by (name redacted), (name red
acted), and (name redacted).
12
See “Common Terms” box for definitions. For more detailed definitions, see CRS Report RL33846.
13
In addition to allowances given at no cost to covered sectors, the bill also allocates allowances to states and tribes for
various policy objectives, to local energy distribution companies to reduce costs to low- and middle-income energy
consumers, to the U.S. Department of Agriculture to fund sequestration projects, and other purposes. Non-covered
entities must sell their allowances (for “fair market value”) within one year of receipt and use the proceeds from those
sales for specified purposes.
14
For a more detailed description of the allocation of allowances and auction revenues under S. 2191, see CRS General
Distribution Memo Allocations of Carbon Allowances and Auctions under S. 2191 as Ordered Reported by the Senate
(continued...)
ȱȱȱ
řȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
a proposed amendment to make the bill revenue neutral, a percentage of allowances (starting at
6.1%, increasing to 15.99%) would be auctioned off-the-top for deficit reduction (“Deficit
Reduction Fund”). After the Deficit Reduction allowances are allocated, the rest of the
allowances (“remainder allowances”) are allocated according to the bill as reported. For example,
in 2012, 6.1% of the total number of allowances are auctioned for deficit reduction, and an
additional 21.5% of the “remainder allowances” are auctioned for program management,
technology deployment, adaptation, and other purposes.
Common Terms
Allowance. A limited authorization by the government to emit 1 metric ton of carbon dioxide equivalent. Although
used generically, an allowance is technically different from a credit. A credit represents a ton of pollutant that an entity
has reduced in excess of its legal requirement. However, the terms tend to be used interchangeably, along with
others, such as permits.
Auctions. Auctions can be used in market-based pollution control schemes to allocate some, or all of the
allowances. Auctions may be used to: (1) ensure the liquidity of the credit trading program; and/or (2) raise
(potentially considerable) revenues for various related or unrelated purposes.
Banking. The limited ability to save allowances for the future and shift the reduction requirement across time.
Cap-and-trade program. An emissions reduction program with two key elements: (1) an absolute limit (“cap”) on
the emissions allowed by covered entities; and (2) the ability to buy and sell (“trade”) those allowances among
covered and non-covered entities.
Coverage. Coverage is the breadth of economic sectors covered by a particular greenhouse gas reduction program,
as well as the breadth of entities within sectors.
Emissions cap. A mandated limit on how much pollutant (or greenhouse gases) an affected entity can release to the
atmosphere. Caps can be either an absolute cap, where the amount is specified in terms of tons of emissions on an
annual basis, or a rate-based cap, where the amount of emissions produced per unit of output (such as electricity) is
specified but not the absolute amount released. Caps may be imposed on an entity, sector, or economy-wide basis.
Greenhouse gases. The six gases recognized under the United Nations Framework Convention on Climate Change
are carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), sulfur hexafluoride (SF6), hydrofluorocarbons (HFC),
and perfluorocarbons (PFC).
Leakage. The shift in greenhouse gas (GHG) emissions from an area subject to regulation (e.g., cap-and-trade
program) to an unregulated area, so reduction benefits are not obtained. This would happen, for example, if a GHG
emitting industry moved from a country with an emissions cap to a country without a cap.
Offsets. Emission credits achieved by activities not directly related to the emissions of an affected source. Examples
of offsets would include forestry and agricultural activities that absorb carbon dioxide, and reductions achieved by
entities that are not regulated by a greenhouse gas control program.
Revenue recycling. How a program disposes of revenues from auctions, penalties, and/or taxes. Revenue recycling
can have a significant effect on the overall cost of the program to the economy.
Sequestration. Sequestration is the process of capturing carbon dioxide from emission streams or from the
atmosphere and then storing it in such a way as to prevent its release to the atmosphere.
In addition to the cap-and-trade program, S. 2191 has other key provisions to reduce greenhouse
gas emissions.
•
Title VI imposes an “international reserve allowance” requirement on certain
“covered” imported goods as a prerequisite for entry into the country.15 Unlike
(...continued)
Committee on Environment and Public Works, dated May 13, 2008.
15
For a further discussion of Title VI, see Jeanne Grimmett and (name redacted),
Whether Import Requirements Contained
(continued...)
ȱȱȱ
Śȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
importers of covered fuels that create greenhouse gases when used (which are
directly controlled as covered facilities under S. 2191), Title VI would affect
certain bulk goods manufactured in processes that generate greenhouse gases
(e.g. iron, paper, etc.) that would not be allowed into the country if the allowance
requirement were not met. The amount and allocation of international reserve
allowances would be determined by EPA, and a separate allowance trading
system could be established (international reserve allowances could not be used
for domestic compliance).
•
Title VIII on carbon sequestration16 requires: (1) EPA to amend regulations under
the Safe Drinking Water Act to allow commercial-scale underground injection of
carbon dioxide for sequestration, and to monitor such activity to reduce adverse
impacts from such injection; (2) the Department of the Interior to assess U.S.
capacity for geological sequestration; (3) the Department of Energy to assess the
feasibility of CO2 pipelines; and (4) EPA to establish a task force to study the
issues related to federal assumption of liability for sequestration sites.
•
Title IX permits the President to temporarily adjust or waive any regulations
promulgated under the bill if a “national security emergency exists,” and it is in
the “paramount interest of the United States” to modify the requirements in
response to that emergency.
•
In addition to the limits under the cap-and-trade program, Title X requires EPA to
establish a program limiting U.S. consumption of hydrofluorocarbons under a
separate HFC allowance program.
•
Title XI amends the Clean Air Act in three ways: (1) it requires EPA to establish a
program to limit emissions of greenhouse gases not covered under the program;
(2) it limits the sale and use of certain motor vehicle air conditioning fluids; and
(3) it establishes a low carbon fuel standard (LCFS) requiring per-unit-energy
reductions in greenhouse gas emissions from transportation fuels.17
ȱȱȱȱȱ
ȱȱ ȱ
S. 2191 (originally titled America’s Climate Security Act of 2007), as introduced October 18,
2007, by Senator Lieberman, would cap greenhouse gas emissions from the electric generation,
industrial, and transportation sectors (for facilities that emit more than 10,000 metric tons of
carbon dioxide equivalent). As introduced, the cap was estimated by the sponsors to reduce
emissions to 15% below 2005 levels in 2020, declining steadily to 63% below 2005 levels in
2050. The program would be implemented through an expansive allowance trading program to
maximize opportunities for cost-effective reductions. Credits obtained from increases in carbon
(...continued)
in Title VI of S. 2191, the Lieberman-Warner Climate Security Act of 2008, as Ordered Reported, Are Consistent with
U.S. WTO Obligations, Congressional Distribution Memorandum (March 27, 2008). Available from the authors.
16
For more information on carbon sequestration, see CRS Report RL33801, Carbon Capture and Sequestration (CCS),
by (name redacted).
17
This LCFS provision is discussed in more detail in the section below under “Transportation Sector.”
ȱȱȱ
śȱ
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
sequestration and acquisition of allowances from foreign sources could be used to comply with
30% of reduction requirements. The bill also establishes a Carbon Market Efficiency Board to
observe the allowance market and implement cost-relief measures if necessary.
ȱȱȱ¢ȱȱ
On November 1, 2007, the Senate Committee on Environment and Public Works’ Subcommittee
on Private Sector and Consumer Solutions to Global Warming and Wildlife Protection reported
out a revised version of S. 2191. As reported from subcommittee, S. 2191 was estimated to reduce
greenhouse gas emissions 19% below 2005 levels by 2020 (up from 15% as introduced) and 63%
below 2005 levels by 2050. The increase in the estimated reductions in 2020 is the result of
amended text that includes greenhouse gases from all natural gas uses under the overall emissions
cap. Other amendments approved included modifications to eligibility requirements for the
advanced technology vehicles manufacturing incentive program and the advanced coal generation
technology demonstration program. Modifications were also made to the proposed allocation of
allowances to help tribal communities respond to climate change and to encourage international
forest carbon activities, along with 1% of allowances reserved for rural cooperatives and a
corresponding reduction in allowances allocated to the rest of the electric power industry. The
revised bill also added two new recipients of auction revenues: a Bureau of Land Management
Emergency Firefighting Fund ($300 million) and a Forest Service Emergency Firefighting Fund
($800 million).
ȱȱȱ
Ŝȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
. Allocation of Allowances Under S. 2191
Table 1
2012
2020
2030
2040
2050
Total Allowances (millions)
Sec. 1201
5775
4924
3860
2796
1732
Deficit Reduction Fund
Sec. 3101 (as amended)
6.10%
8.40%
14.43%
15.99%
15.99%
Remainder Allowances (millions)
Sec. 3101 (as amended)
5423
4510
3303
2349
1455
Sec. 3101
Sec. 3102
Sec. 3201
Secs. 3301-3304
Sec. 3303(d)
Sec. 3401
Sec. 3501
Sec. 3601
5%
21.5%
5%
10.5%
0.5%
9%
2%
4%
0%
36.5%
0%
10.5%
0.5%
9%
2%
4%
62.8%
69.5%
69.5%
10.5%
0.5%
9%
2%
4%
10.5%
0.5%
9%
2%
0%
10.5%
0.5%
9%
2%
Sec. 3701
5%
5%
5%
5%
5%
Sec. 3803
2.5%
2.5%
2.5%
2.5%
2.5%
Sec. 3901
19%
16%
1%
Sec. 3901
1%
1%
1%
Sec. 3903(a)(2)
0.2%
0.2%
0%
Sec. 3901
10%
8%
0%
Sec. 3901
2%
2%
0.25%
Sec. 3901
2%
2%
0.25%
Sec. 3907
1%
1%
1%
1%
1%
Share of Remainder Allowances
Early Auction
Auction
Early Action
States
Tribal Communities
Low/Middle-Class Electricity Consumers
Low/Middle-Class Natural Gas Consumers
CCS Bonus Allowances
Domestic Agriculture and Forestry
International Forest Protection
Transition Assistance
Fossil Fueled Electric Plants
Rural Electric Cooperatives
Pilot Program for VA and MT
Energy-Intensive Manufacturing Facilities
Petroleum Production/Import Facilities
HFC Producers/Importers
Landfill and Coal Mine Methane Reduction
Ȭŝȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
. Allocation of Auction Revenue (excluding Deficit Reduction Fund) Under S. 2191
Table 2
2012
2020
2030
2040
2050
SSAN
SSAN
SSAN
SSAN
SSAN
SSAN
SSAN
SSAN
SSAN
SSAN
SSAN
SSAN
SSAN
SSAN
SSAN
Off-the-Top Allocation of Auction Proceeds
BLM Emergency Firefighting Fund
Forest Service Emergency Firefighting Fund
CSA Management Fund
Sec. 4302(b)(1)
Sec. 4302(b)(2)
Sec. 4302(b)(3)
Percentage of Remaining Proceeds
Technology Deployment
Sec. 4302(b)(4)(B)
52%
52%
52%
52%
52%
Energy Independence Acceleration Fund
Sec. 4302(b)(4)(C)
2%
2%
2%
2%
2%
Energy Assistance Fund
Sec. 4302(b)(4)(D)
18%
18%
18%
18%
18%
Climate Change Worker Training Fund
Sec. 4302(b)(4)(E)
5%
5%
5%
5%
5%
Adaptation Fund
Sec. 4302(b)(4)(F)
18%
18%
18%
18%
18%
Climate Change and National Security Fund
Sec. 4302(b)(4)(G)
5%
5%
5%
5%
5%
Note: SSAN = “such sums as necessary.” For its analysis of S. 2191, EPA estimated total program costs (“CSA Management Fund”) at 1% of the total value of allowances in
a given year.
ȬŞȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
ȱȱȱ¢ȱȱ
On December 5, 2007, the full committee ordered reported a revised version of S. 2191 by an 11
to 8 vote. The revised bill expands the greenhouse gas reduction program coverage by replacing
the previous definition of covered facility based on the electric power, transportation, and
industrial sectors with an upstream definition for oil refineries and natural gas processing plants,
and a downstream definition for coal consumers. Among the amendments agreed to by the full
committee were a new Low Carbon Fuel Standard (LCFS) that would require the carbon intensity
of transportation fuel to be frozen in 2011 and then reduced by 5% in 2015 and 10% in 2020.
Other amendments agreed to would increase incentives for states to modify their utility regulatory
structures to encourage energy efficiency, and would broaden the ability of states to use their
allowance allocations to mitigate adverse economic impacts resulting from the bill’s
implementation.
ȱȱȱ
Finally, in April 2008, a proposed amendment to S. 2191 was submitted by the committee to the
Congressional Budget Office (CBO) to be included in the scoring of the bill. The amendment
would provide for some of the auctioned revenues to be put aside for deficit reduction purposes.
DZȱȱȱȱȱȱ
ȱȱȱȱȱȱ
ȱȱŘȱȱȱȱȱ
During the Clean Air Act debate in 1990 on the Title IV sulfur dioxide (SO2) cap-and trade
program, CRS found it difficult to analyze the cost of the bill beyond the first 10 years (19902000), and considered any breakdown of even 2000 data on a state-by-state basis as “not useful
for any more than illustrative purposes.”18 As stated in 1990:
It is difficult (and some would consider it unwise) to project costs up to the year 2000, much
less beyond. The already tenuous assumption that current regulatory standards will remain
constant becomes more unrealistic, and other unforeseen events (such as electric utility
deregulation) loom as critical issues which can not be modeled. Hence, cost projections
beyond the year 2000 are at best speculative, and are more a function of each model’s
assumptions and structure than they are of the details of proposed legislation. Projections
this far into the future are based more on philosophy than analysis.19 [emphasis in
original]
The history of resulting SO2 cap-and-trade program costs has proven illuminating. As indicated in
Table 3, the 2010 cost estimates for the SO2 cap-and-trade program made in 1990 proved to be
substantially higher than what is now estimated to be the program’s actual costs. Indeed, the EPA18
See CRS Report 90-63, Acid Rain Control: An Analysis of Title IV of S. 1630, by (name redacted) (January 31, 1990),
p. 13. (Available from the author.)
19
Ibid., p. 16.
ȱȱȱ
şȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
ICF low estimate—the estimate closest to the projected actual number—is both 50% higher than
the actual number, and the estimate least focused-on in the original ICF report.20 It is interesting
that none of the analyses were willing to “speculate” with assumptions that would have created a
2010 cost estimate lower than EPA’s current projection.21
Equally interesting is that the “best” 2000 estimate was off by almost the same 50% that the 2010
estimate was.22 Like the 2010 estimates, the assumptions either underestimated the ingenuity and
creativity of companies in responding to the SO2 requirements, or mis-read the economics of the
cap-and-trade process. As explained below by Chestnut and Mills in 2005, the gross overestimates are essentially the product of the models’ failure both to fully incorporate the flexibility
that the cap-and-trade program provided participants and to employ sufficient imagination to
explore the potential for technological breakthroughs and enhancements:
Costs are lower than originally predicted primarily because flexibility occurred in areas that
were thought to be inflexible and technical improvements were made that were not
anticipated. Factors contributing to the lower costs included lower transportation costs for
low-sulfur coal (attributed to railroad deregulation), productivity increases in coal production
leading to favorable prices for low-sulfur and mid-sulfur coal, cheaper than expected
installation and operation costs for smokestack scrubbers, and new boiler adaptations to
allow use of different types of coal. It appears that Title IV has worked as expected to
provide the flexibility and incentives for producers to find low-cost compliance options.
[footnote omitted] Banking opportunities also induced early reductions in emissions for
some facilities. Harrington et al (2000) compared estimates of actual costs of many large
regulatory programs to predictions of those costs made while the regulatory programs were
being developed and found a tendency for predicted costs to overstate the actual
implementation costs, especially for market-based programs such as the SO2 trading
program. They cite technological innovation and unanticipated efficiency gains as key
factors leading to lower than predicted costs. They noted that unit costs are often more
accurately predicted than total costs because predicted emission reductions are sometimes
overstated, but they report that predicted unit costs and total costs were both overstated for
Title IV.23
20
The only 2010 national utility cost estimate mentioned in the summary of findings is for the High Case: “Longerterm costs reach about $5 billion [1988 dollars] per year by 2010 under both the High House and Senate cases, due to
the provisions requiring new source emissions to be offset.” The Low House and Senate cases for 2010 are not
mentioned. See EPA-ICF: ICF Resources Incorporated, Comparison of the Economic Impacts of the Acid Rain
Provisions of the Senate Bill (S. 1630) and the House Bill (S. 1630), Prepared for the U.S. Environmental Protection
Agency (July 1990), p. 21.
21
The implementation of the SO2 provisions of the Clean Air Interstate Rule (CAIR) will significantly increase the
stringency of the SO2 cap for 23 states and the District of Columbia and will likely prevent EPA from estimating actual
Title IV compliance costs in 2010 because of program interaction.
22
In its 1990 analysis, CRS agreed with the range of estimates provided by the EPA-ICF analysis for 2000. As
suggested above, CRS did not estimate the costs for 2010. See CRS Report 90-63, Acid Rain Control: An Analysis of
Title IV of S. 1630, by (name redacted) (January 31, 1990), p. 56. (Available from the author.)
23
Lauraine G. Chestnut and David M. Mills, “A fresh look at the benefits and costs of the US acid rain program,”
Journal of Environmental Management 77 (2005) p. 255.
ȱȱȱ
ŗŖȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
Table 3. Representative Sample of 1990 Estimates
of Annual Compliance Cost for SO2 Cap-and-Trade Program
(billions, 2005 dollars)
EPA-ICF
NCAC-Pechan
EEI-TBSa
Estimated Actual Costs
2000-2007: Ellerman, et al.
2010: EPA
2000
2010
$2.7-$3.6
$4.4-$4.6
(for 2000-2009)
$7.1-$8.7
$1.9
(for 2000-2007)
$3.4-$8.0
no estimate
$7.9-$11.2
$2.2
EPA-ICF: ICF Resources Incorporated, Comparison of the Economic Impacts of the Acid Rain Provisions of
the Senate Bill (S. 1630) and the House Bill (S. 1630), Prepared for the U.S. Environmental Protection Agency (July
1990); Pechan: E.H. Pechan & Associates, Clean Air Act Amendment Costs and Economic Effects: A Review of Published
Studies, Prepared for the National Clean Air Coalition, National Clean Air Fund (October 1990); TBS: Temple,
Barker & Sloane, Inc., Economic Evaluation of H.R. 3030/S. 1490 “Clean Air Act Amendments of 1989”: Title V, The
Acid Rain Control Program, Prepared for the Edison Electric Institute (August 30, 1989). Estimated 2000-2007
actual cost from A. Denny Ellerman, Paul L. Joskow, and David Harrison, Jr., Emissions Trading in the U.S.:
Experience, Lessons, and Considerations for Greenhouse Gases, prepared for the Pew Center on Global Climate
Change (November 2007) p. 15. Estimated 2010 actual cost from: EPA, Acid Rain Program Benefits Exceed
Expectations, Figure 4, p. 4. Available at http://www.epa.gov/airmarkets/cap-trade/docs/benefits.pdf. All estimates
converted to 2005 dollars using the GDP implicit price deflator.
a. Analysis of original Administration bill. EPA estimated that the final bill was $400 million (1988 dollars)
annually more expensive than the original proposal. See EPA, Office of Air and Radiation, Clean Air
Amendments: Cost Comparison (January 23, 1990).
Source:
ȱ ȱ¡ȱȱ¢ȱȱǯȱŘŗşŗȱ
There is no reason to believe that cost estimates for greenhouse gas reductions will be any
more accurate than the 1990 SO2 estimates; indeed, they are likely to be more unreliable.
This is not to say that they will be too high; they may be too low. To illustrate, CRS examines
some results of the modeling efforts with respect to the costs of S. 2191. To frame this
illustration, we focus on the three primary drivers of greenhouse gas emissions: (1) population
growth, (2) incomes (measured as per capita gross domestic product [GDP]), and (3) intensity of
greenhouse gas emissions relative to economic activities (measured as metric tons of greenhouse
gas emissions per million dollars of GDP). As shown in the following formula, a country’s annual
greenhouse gas emissions are the product of these three drivers:
(Population) x (Per Capita GDP) x (Intensityghg) = Emissionsghg
This is the relationship for a given point in time; over time, any effort to change emissions alters
the exponential rates of change of these variables. This means that the rates of change of the three
left-hand variables, measured in percentage of annual change, sum to the rate of change of the
right-hand variable, emissions.
Using the three drivers, Table 4 provides the essential assumptions from three analyses of S. 2191
for the year 2050. Examining the “business-as-usual” reference cases, a range of assumptions are
employed by the models. As suggested by the formula above, the differing assumptions result in
very different 2050 baseline GHG emissions: 10.3 billion metric tons for EPA/ADAGE-REF, 11.1
ȱȱȱ
ŗŗȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
billion metric tons for EPA/IGEM-REF, and 13.3 billion metric tons for MIT/EPPA—a 29%
difference from the lowest to the highest. Interestingly, major sources of disagreement in the
reference cases include per capita GDP and population projections—two variables that are
generally not the focus of greenhouse gas reduction strategies.
. Reference Case and S. 2191 Analyses for 2050
Table 4
Model
EPA/
ADAGEREF
EPA/
IGEMREF
MIT/
EPPA
Population
(millions)
Difference
Difference
from lowest
GDP per from lowest
to highest
capita
to highest
model
(2005$)
model
Reference Case Scenario
GHG
Intensity
(GHG/
GDP)a
Difference
from lowest
to highest
model
$106,800
242
24%
400
9%
17%
434
$95,400
269
397
$111,300
300
S. 2191 Scenario
EPA/
ADAGEREF
EPA/
IGEMREF
MIT/
EPPA
400
9%
$104,300
24%
127
434
$88,800
107
397
$110,500
86
48%
ADAGE and IGEM model assumptions from the “Data Annex” available on the EPA website at
http://www.epa.gov/climatechange/economics/economicanalyses.html. The EPPA model assumptions from Sergey
Paltsev, et al., “Appendix D” of Paltsev et al., Assessment of U.S. Cap-and-Trade Proposals, MIT Joint Program on
the Science and Policy of Global Change (2007). All estimates converted to 2005 dollars using the GDP implicit
price deflator.
a. Measured in metric tons of greenhouse gas emissions per million dollars of GDP.
Source:
Moving to the S. 2191 scenario as modeled, the variability in the results widens for two of the
three drivers (the 2050 reference case population remains constant in the three models). Not
surprisingly, the range widens for the projected 2050 greenhouse gas emissions estimates: 5.3
billion metric tons for EPA/ADAGE-REF, 4.1 billion metric tons EPA/IGEM-REF, and 3.8 billion
metric tons for MIT/EPPA—a 40% difference. In particular, the models’ assumptions about the
flexibility and responsiveness of the U.S. economy resulted in some interesting reversals: (1) The
MIT/EPPA model, which has the closest relationship between GHGs and GDP in the reference
case, has the most responsive assumptions resulting in the greatest reduction in GHG and GHG
intensity under S. 2191; (2) In contrast, the EPA/ADAGE-REF model, which has the lowest GHG
intensity assumption in its reference cases, has the highest GHG intensity result under S. 2191.
The MIT/EPPA model assumes more economic growth per capita and more responsiveness by the
economy to GHG constraints; the EPA/ADAGE model assumes the most GHG-efficient
ȱȱȱ
ŗŘȱ
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
economy, but the least amount of flexibility to respond to GHG constraints; and the EPA/IGEM
model assumes the fastest growth in population.
The result of these different views of the economy is that the economic impact is less than the
differences in the models’ reference case assumptions. As indicated in Table 4, the MIT/EPPA
model projection of the country’s 2050 GDP per capita under S. 2191 is greater than the basecase
projections of either of the other models. According to the MIT/EPPA model, the 2050 GDP per
capita of the country is reduced by only 0.75% under S. 2191. The reduction under the other two
models is 6.9% for EPA/IGEM-REF and 2.4% for EPA/ADAGE-REF—well within the
variability of the reference cases.
The result is not significantly more consistent for projections for 2030, particularly with the
addition of the EIA baselines.24 The CATF/NEMS analysis uses the EIA baseline published in its
Annual Energy Outlook 2007 for its analysis.25 The EIA/NEMS analysis uses a preliminary
version of EIA’s upcoming 2008 AEO baseline.26 As indicated in Table 5, the basecase
assumptions for per capita GDP vary by a greater percentage for 2030 than they do for 2050. The
introduction of the EIA 2008 baseline is responsible for much of the increase in GDP per capita
variability (it would be 7% without it). Similarly, the inclusion of the 2007 and 2008 EIA baseline
increases the variability of the greenhouse gas intensity driver (it would be 9% without it).
Likewise, the GDP per capita impact of S. 2191 is within the noise of the reference cases as the
estimated GDP per capita reduction under S. 2191 is only 0.3% for EIA/NEMS, 0.37% for EPPA,
0.90% for ADAGE, and 3.8% for IGEM.
The situation is more constant in the 2020 reference cases, although the impact of S. 2191 is still
within the noise of the per capita GDP assumptions, with S. 2191 GDP per capita impact
estimated at 0.3% for EIA/NEMS, 0.69% for EPA/ADAGE-REF, 0.78% for MIT/EPPA, and
2.6% for EPA/IGEM-REF.
The uncertainty about the future direction of the basic drivers of greenhouse gas emissions
and the economy’s responsiveness (economically, technologically, and behaviorally)
illustrate the inability of models to predict the ultimate macroeconomic costs of reducing
greenhouse gases. Policy relevant analysis is analysis that provides insight into the features
and design of proposals that increase or reduce compliance cost and under what economic,
technological, and behavior conditions, and that identify potential intended and unintended
consequences on the economy. Models cannot predict the future, but they can indicate the
sensitivity of a program’s provisions to varying economic, technological, and behavioral
assumptions that may assist policymakers in designing a greenhouse gas reduction strategy.
24
Currently, EIA makes projections only to the year 2030.
EIA, Annual Energy Outlook 2007 With Projections to 2030, DOE/EIA-0383 (2007), (February 2007).
26
Available at http://www.eia.doe.gov/oiaf/aeo/index.html EIA/NEMS and the two ACCF/NAM/NEMs cases also use
the preliminary 2008 baseline. The NMA/CRA case is also based on the preliminary 2008 basecase, but CRA does not
explain how it extends EIA’s baseline beyond 2030 to 2050.
25
ȱȱȱ
ŗřȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
Table 5. Reference Case Scenarios for 2020 and 2030
Model
EPA/
ADAGEREF
EPA/
IGEM-REF
MIT/EPPA
CATF/
NEMSb
EIA/
NEMS
Population
(millions)
Difference
Difference
from lowest
GDP per
from lowest
to highest
capita
to highest
model
(2005$)
model
Reference Case Scenario (2030)
364
4%
$72,700
19%
GHG
Intensity
(GHG/
GDP)a
Difference
from
lowest to
highest
model
344
12%
372
$70,400
363
359
365
$73,700
$69,000
374
384
366
$62,000
372
Reference Case Scenario (2020)
EPA/
ADAGEREF
EPA/
IGEM-REF
MIT/EPPA
CATF/
NEMSb
EIA/
NEMS
336
2%
$59,000
12%
417
342
$58,000
428
334
337
$59,200
$56,700
435
438
338
$53,000
431
5%
ADAGE and IGEM model assumptions from the “Data Annex” available on the EPA website at
http://www.epa.gov/climatechange/economics/economicanalyses.html. The EPPA model assumptions from Sergey
Paltsev, et al., “Appendix D” of Paltsev et al., Assessment of U.S. Cap-and-Trade Proposals, MIT Joint Program on
the Science and Policy of Global Change (2007). The AEO 2007 assumptions from Energy Information
Administration, Energy Market and Economic Impacts of S. 1766, the Low Carbon Economy Act of 2007 (January
2007). The AEO 2008 economic and population assumptions from EIA’s website at http://www.eia.doe.gov/oiaf/
aeo/index.html. The EIA/NEMS assumptions from EIA, Energy Market and Economic Impacts of S. 2191, the
Lieberman-Warner Climate Security Act of 2007 (April 2008). All estimates converted to 2005 dollars using the
GDP implicit price deflator where necessary.
a. Measured in metric tons of greenhouse gas emissions per million dollars of GDP.
b. Based on the report’s statement that it uses the 2007 AEO baseline projection for its analysis. All estimates
converted to 2005 dollars using the GDP implicit price deflator.
Source:
ȱȱȱ
ŗŚȱ
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱȱȱȱȱ ȱ ȱ
ȱȱȱ
The potential for noise is greater in estimating the costs of a GHG program than the simple three
driver illustration presented above. In its analysis of S. 2191, EPA presents eight pages of bullets
identifying various limitations on its modeling exercise and four pages of additional “qualitative”
considerations.27 This is a good indicator of the modeling complexity in attempting to estimate
the impact of a greenhouse gas reduction bill. These modeling limitations reflect the inherent
complexity of such strategies that cannot be quantified or predicted.
¡¢ȱȱȱȱ
Compared with the complexity of implementing a greenhouse gas cap-and trade scheme, the SO2
program was trivial. Conceptually, a CO2 tradeable permit program could work similarly to the
SO2 program. However, significant differences exist between acid rain and possible global
warming that affect current abilities to model responses. For example, the acid rain program
involves up to 3,000 new and existing electric generating units that contribute two-thirds of the
country’s SO2. This concentration of sources makes the logistics of allowance trading
administratively manageable and enforceable. The imposition of the allowance requirement is
straightforward. The acid rain program is a “downstream” program focused on the electric utility
industry. The allowance requirement is imposed at the point of SO2 emissions so the participant
has a clear price signal to respond to. The basic dynamic of the program is simple, although not
necessarily predictable.
A comprehensive greenhouse gas cap-and-trade program would not be as straightforward to
implement. Greenhouse gas emissions sources are not concentrated. Although over 80% of the
greenhouse gases generated comes from fossil fuel combustion, only about 33% comes from
electricity generation. Transportation accounts for about 26%, direct residential and commercial
use about 8%, agriculture about 6%, and direct industrial use about 16%.28 Thus, small dispersed
sources in transportation, residential/commercial, agriculture, and the industrial sectors are far
more important in controlling greenhouse gas emissions than they are in controlling SO2
emissions. This greatly increases the economic sectors and individual entities that may be
required to reduce emissions.
It also affects the operation of a cap-and-trade program, as the diversity of sources creates
significant administrative and enforcement problems for a tradeable permit program if it is meant
to be comprehensive. A downstream approach is impractical for a comprehensive greenhouse gas
program where the transportation sector and dispersed residential, commercial, and agricultural
sources emit almost half the total emissions. One alternative is to move the imposition point more
“upstream” in those sectors, as is done by S. 2191. This complicates the economics of the
program as the price signal has to work its way through multiple paths to the particular entities—
utilities, consumers, industry—that are the ultimate sources of the greenhouse gases. Arguably,
27
U.S. Environmental Protection Agency, EPA Analysis of the Lieberman-Warner Climate Security Act of 2008
(March 14, 2008), pp. 96-102, 108-115.
28
U.S. Environmental Protection Agency, U.S. Inventory of Greenhouse Gas Emissions and Sinks: 1990-2006
(April 2008), p. ES-8.
ȱȱȱ
ŗśȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
the primary purpose of an economic mechanism, such as a cap-and-trade program, is to put a
price on greenhouse gas emissions. In the case of a comprehensive cap-and-trade program, the
impact of that price signal will not be simple or straightforward, with unintended consequences
likely.29 In addition, attempts by analysts to capture the general equilibrium effects of the
program’s interaction with the overall economy add a layer of assumptions and opaqueness to the
analysis that can hide insights the analysis may have on program design and implementation.
¡¢ȱȱȬȬȱȱ
The flexibility envisioned by most cap-and-trade programs exceeds that of the SO2 program. Acid
rain is a regional problem that resulted in independent responses by the United States and Canada.
The United States chose a cap-and-trade program that included important flexibility mechanisms
like banking; Canada chose a variety of approaches and the entire process was later codified by
treaty. Offsets (emission reductions made by entities not directly covered by the program) are not
a major component of the SO2 program. Uncovered industrial entities that want to participate in
the program must become covered entities with their own baselines and monitoring equipment.
The bill also sets up a small reserve of allowances to reward reductions through conservation and
renewable energy efforts. With the sulfur dioxide cap-and-trade system being limited to the
United States, there is no international trading in the acid rain program.
In contrast, most GHG cap-and-trade proposals expand the supply of available allowances by
permitting offsets from a wide variety of sources, including agricultural practices, forestry
projects, sequestration activities, and alternative energy projects. These diverse sources multiply
as the trading extends globally and as other non-CO2 greenhouse gases are included in the supply
mix. Finally, the interaction of these various supply sources and the demand of other countries
also reducing emissions (or who may decide to reduce in the future) provide for an almost infinite
number of possible scenarios. Crucially, the availability of offsets may have a significant impact
on compliance costs, particularly in the short-term.
ȱȱ¢ȱȱȱȱ
The three driver analysis illustrated the importance of reducing the greenhouse intensity of the
economy to reducing overall greenhouse gas emissions. The other two drivers, population and
economic growth, are generally not elements targeted for reduction under greenhouse gas
reduction programs (indeed, by any federal program).
The key factor in reducing the intensity driver over the long run is technology development. This
is recognized in most greenhouse gas reduction bills, including S. 2191, with substantial funding,
incentives, and price signals to encourage both accelerated deployment and the initiation of
efforts to develop new generations of technology. The effectiveness of these initiatives and
price signals would be pivotal to the ultimate cost of any reduction strategy, particularly in
the long term. As stated by Houghton:
29
This is particularly true if allowances are allocated to upstream entities at no cost. See Sergey Paltsev, et al.,
Assessment of U.S. Cap-and-Trade Proposals, MIT Joint Program on the Science and Policy of Global Change
(April 2007), p. 5.
ȱȱȱ
ŗŜȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
Technology change is a particularly critical component of the climate change debate. For
example, the cost of meeting stabilization levels is very sensitive to assumptions about future
technologies. If assumed technology improvements lead to relatively low emissions, then it
is relatively inexpensive to meet stabilization levels, and vice versa. Furthermore, technology
research and development is a very significant policy instrument in the portfolio of options.30
ȱȱ ȱȱȱ¢řŗȱ
As was the case with analyses of the SO2 cap-and-trade program, current studies of greenhouse
gas reduction proposals assume that in the absence of new legislation EPA would take no action
in this area between now and the year 2050, and no future initiatives would be enacted in related
areas, such as energy policy. This seems unlikely. Indeed, the potential for a future requirement to
reduce greenhouse gas emission may already be having an effect on decisions by industry and
consumers. As noted by EIA:
While forecasting policy change is beyond EIA’s mandate, an argument can be made that, all
else being equal, public and industry awareness of climate change as a major policy issue can
potentially impact energy investment decisions even if no specific policy change actually
occurs. Any adjustment to reflect the influence of climate change as an unresolved policy
issue, while raising costs in the Reference Case, would generally reduce the estimated
incremental impact resulting from the full implementation of a given policy response.32
ȱȱ¢ȱȱ ȱ
That the policy baseline for greenhouse gas emissions can be shifted significantly through new
initiatives has already been illustrated by enactment of the 2007 Energy Independence and
Security Act (EISA).
On December 19, 2007, President Bush signed EISA (P.L. 110-140). EISA contains many energy
provisions that could lead to reductions in greenhouse gas emissions, including33
•
more stringent fuel economy (CAFE) standards for passenger cars and light
trucks;
•
higher efficiency standards for appliances and lighting;
•
higher efficiency requirements for government buildings; and
•
research and development on renewable energy.
The American Council for an Energy-Efficient Economy estimates that the efficiency provisions
in EISA will save roughly 700 million metric tons of carbon dioxide annually by 2030.34 Most of
this savings would come from tighter CAFE standards.
30
John Houghton, “Introduction,” Energy Economics 28 (2006), p. 535.
From Latin, roughly meaning all else being held the same. In analysis, this refers to the practice of holding certain
variables constant to isolate the effect of the variable being analyzed.
32
Energy Information Administration, Energy Market and Economic Impact of S. 2191, the Lieberman-Warner
Climate Security Act of 2007 (April 2008) p. viv.
33
For more information on EISA, see CRS Report RL34294, Energy Independence and Security Act of 2007: A
Summary of Major Provisions, by (name redacted).
31
ȱȱȱ
ŗŝȱ
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
In addition to these indirect reductions, EISA also directly addresses climate change issues in
several ways.
First, EISA expands the renewable fuel standard (RFS) established in P.L. 109-58. The EISA
amendments to the RFS significantly expand the mandated level. Further, the new law requires
that an increasing share of the RFS be met with “advanced biofuels,” defined as having 50%
lower lifecycle greenhouse gas emissions than petroleum fuels. Further, conventional biofuels
from new refineries must have at least 20% lower lifecycle emissions. This is the first time that
Congress has enacted national policy addressing the carbon content of motor fuels.
Second, Title VII of the new law focuses on research, development, and demonstration of
technologies to capture and store carbon dioxide. DOE carbon storage R&D is expanded and is to
include large-scale demonstration projects. The Department of the Interior must develop a
methodology to assess the national potential for geologic and ecosystem storage of carbon
dioxide, and must recommend a regulatory framework for managing geologic carbon
sequestration on public lands.
In addition to the above programs, EISA also requires the establishment of an Office of Climate
Change and Environment in the Department of Transportation (DOT). This office is to plan,
coordinate, and implement research at DOT on reducing transportation-related energy use,
mitigating the causes of climate change, and addressing the impacts of climate change on
transportation.
The practical result of this is the necessary re-working of EIA’s AEO 2008 baseline to reflect the
energy and environmental impact of the new laws. More changes are likely over the 40-year time
frame of S. 2191.
ȱȱ¢ȱȱřśȱ
The stringency of the SO2 cap-and-trade is being changed by EPA’s Clean Air Interstate Rule
(CAIR). The baseline may also be influenced by future EPA initiatives not requiring new
authority. The Clean Air Act is a powerful tool that could be used to regulate emissions of
greenhouse gases from mobile sources of all kinds, their fuels (with the exception of jet fuel), and
both large and small stationary sources. The possibility for regulation through existing Clean Air
Act authority was recently outlined by EPA in congressional testimony.36
The key to such regulation is that the EPA Administrator issue appropriate findings on whether
greenhouse gases “contribute to air pollution that is reasonably anticipated to endanger public
health or welfare.” It is difficult, bordering on impossible, to determine where such a finding
would lead. The Administrator has substantial discretion in defining what emission limits
should be set once he or she makes such a finding, and what sections of the act he or she might
(...continued)
34
American Council for an Energy-Efficient Economy, Energy Bill Savings Estimates as passed by the Senate
(December 14, 2007).
35
This section prepared by James McCarthy, Specialist in Environmental Policy.
36
Robert J. Meyers, Principal Deputy Assistant Administrator, Office of Air and Radiation, U.S. Environmental
Protection Agency, Testimony before the Subcommittee on Energy and Air Quality, Committee on Energy and
Commerce, U.S. House of Representatives (April 10, 2008).
ȱȱȱ
ŗŞȱ
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
use. Greenhouse gases could be defined as criteria air pollutants, or not. They could be
controlled in mobile sources of all kinds. They could be subject to New Source Performance
Standards (NSPS), Prevention of Significant Deterioration (PSD), or Maximum Available
Control Technology (MACT) requirements. Each of these has its own standard-setting process
and criteria.
To some extent, the important question may be how an Administrator would define the source
categories. If all power plants were considered in the same category, then the act’s authority could
be used to require the use of natural gas or cleaner fuels (or at least to set emission standards
based on the emissions from plants using such fuels). If coal-fired plants were their own category
or a technological approach were taken, the best technology could be carbon capture and storage
(CCS). How the sources would be categorized would be at the discretion of the Administrator.
The Administrator would also get to make technical judgments concerning whether technologies
were “available” or “achievable.” These judgments could be crucial in determining how much
technology-forcing the regulations would do.
ȱȱDZȱȱȱȱȱȱ
Because of the economic complexities and interactions noted above, analysts have generally
chosen to focus on estimating the macro-economic effects of proposals, such as GDP impacts.
There are two components of macro-economic cost measures: (1) the direct abatement (or
compliance) cost of a greenhouse gas reduction program, and (2) the general equilibrium effects
of a greenhouse gas reduction program (i.e., the interactions of the direct abatement costs with the
rest of the economy).
The most common measure presented is Gross Domestic Product (GDP). GDP measures the total
value of goods and services produced within a nation’s borders.37 Although it is commonly used
as a measure of quality of life, this application is problematic. Generally, it includes only those
items for which there is a value defined in a market, and does not take into account some
activities that have economic value, but no market valuation (e.g., leisure time, environmental
quality, etc.). GDP is intended to be a measure of economic activity, not quality of life.
A second measure sometimes presented is consumption effects (sometimes called welfare
effects). Unfortunately, the models do not measure consumption or welfare effects in a consistent
fashion (the primary advantage of measuring GDP). For example, the MIT/EPPA analysis
presents “welfare effects” in terms of changes in aggregate market consumption plus leisure.
Measured as “equivalent variation,” the change in welfare represents the amount of income
needed to compensate for the change. In contrast, the EIA/NEMS model presents “real
consumption impacts” in terms of consumer expenditures. This makes comparisons difficult and
lessens the utility of the measure. For example, when analyzing proposed legislation, the “welfare
effects” of legislation under the MIT/EPPA are usually less than the GDP effects, while the “real
consumption impacts” under EIA/NEMS are usually greater than the GDP effects on a percentage
37
It has four basic components: private consumption (including most personal expenditures of households);
investments by business and households in capital (including new house purchases); government expenditures on goods
and services (but not transfer payments, such as Social Security); and net imports.
ȱȱȱ
ŗşȱ
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
basis. In addition, like GDP, none of the definitions of consumption or welfare currently
employed quantify any environmental effects.
A third measure generally presented is allowance prices. These generally reflect to some degree
the aggregate marginal cost of the program as estimated by the models. Marginal cost is the cost
of reducing the last ton (and, therefore, the most expensive) of greenhouse gases required by the
program at a specific point in time. Marginal costs are very useful to affected entities in choosing
what reduction strategy would be the most cost-effective in achieving their assigned reduction
requirement. They are not an average cost and therefore cannot be simply multiplied by the
greenhouse gases reduced to estimate total compliance cost. They also need to be put into the
context of the overall reduction achieved at the given point and time being examined.
However, allowance prices in most analyses are actually different from marginal costs because of
program provisions, such as banking. Banking activity reflects the assumed foresight of affected
entities to the likelihood of increasing allowance prices (in real terms) as the cap tightens. As
indicated by the experience with the SO2 program, entities will bank substantial allowances early
and use them later as the program’s requirements tighten. This results in allowance prices being
higher than marginal costs in the early years of the program, and lower in later years. For
example, the NMA/CRA International analysis of S. 2191 has a 2050 allowance price of about
$352 under “no banking” assumptions, but an allowance price of about $195 with banking. In
contrast, 2015 allowance prices are estimated at $51 for the “banking” scenario, but only $38
under the no banking scenario.38 This ability to time-shift reduction requirements and compliance
costs means that allowance price projections reflect the assumed foresight of affected entities as
much as they do actual marginal costs.
In presenting cost measures, most analyses over-emphasize aggregate welfare indicators, such as
GDP. As illustrated above, aggregate, macroeconomic cost results for S. 2191 fall into the noise
of uncertainty about future conditions. In addition, aggregate macroeconomic measures reduce
the transparency of the analyses’ compliance strategies, and as a result, make them easier to
dismiss. For example, Figure 1 below shows a 1997 scatter-plot by World Resources Institute
(WRI) of 162 predicted impacts estimates from 16 different economic models of the U.S.
economy as a result of a CO2 abatement program. As indicated, the vast majority of estimates fall
with a range of 0%-4% of GDP, regardless of the reduction requirement. Over-emphasis on GDP
or other aggregate cost measures can obscure fundamental technological, economic, or
behavioral insights the analyses may have in helping policymakers craft legislation. Instead,
the analysis becomes a “black box” exercise with little enlightenment function.
38
W. David Montgomery and Anne E. Smith, Economic Analysis of the Lieberman-Warner Climate Security Act of
2007 Using CRA’s MRN-NEEM Model, CRA International (April 8, 2008) p. 18. Prices are in 2007$.
ȱȱȱ
ŘŖȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
Figure 1. Predicted Impacts of Carbon Abatement on the U.S. Economy
(162 Estimates from 16 Models)
Source:
Robert Repetto and Duncan Austin, The Costs of Climate Protection: A Guide for the Perplexed, World
Resources Institute, 1997.
This “fog” is inherent when analysts choose to include the general equilibrium effects of a
program in their cost measure—a fog that can limit the explanatory value of the analysis. While
supporting use of aggregate welfare cost measures, MIT notes:
GE [general equilibrium] effects can stem from interactions with pre-existing distortions
(e.g., taxes), from externally induced terms-of-trade effects, from the fact that the domestic
policy itself creates terms-of-trade effects, and from other rigidities in the economy. Many
aspects of model structure produce GE effects that are not easy to separately measure
because of the inherent interactions in the economy.39
39
Sergey Paltsev, et al., Assessment of U.S. Cap-and-Trade Proposals, MIT Joint Program on the Science and Policy of
Global Change (April 2007), p. 27.
ȱȱȱ
Řŗȱ
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
Generally, the cases examined here have not chosen to separate the two components of macroeconomic cost measures: (1) the direct abatement (or compliance) cost of a greenhouse gas
reduction program, and (2) the general equilibrium effects of a greenhouse gas reduction program
(i.e., the interactions of the direct abatement costs with the rest of the economy).40 The
availability of compliance cost estimates would allow policymakers to put current greenhouse gas
reduction proposals in the context of other environmental initiatives—be they acid rain or toxic
air pollutants—and, indeed, to the overall environmental agenda, and greatly increase the
transparency of the analyses’ insights. It would also help relieve confusion between compliance
costs, average costs (per ton reduced), and the other commonly presented costs, such as
allowance prices.41 It is argued that an aggregate macroeconomic cost measure provides a more
complete view of the economic impact of proposed legislation, and helps identity potential
unintended economic effects of compliance strategies. This may be true, particularly if, for
example, auction revenues are being recycled via a reformed tax code. However, as indicated
here, aggregated macroeconomic cost measures, such as GDP, can also be interpreted to
merely show that the United States has a massive economy that can absorb substantial
shocks with limited long-term effect.
ȱDZȱ ȱȱȱȱȱ
Breaking through the fog of analyses and cost indicators, cost estimates to reduce CO2 emissions
vary greatly and focus attention on an estimator’s basic beliefs about the problem and the future,
in addition to simple, technical differences in economic assumptions. In a previous report,
CRS identified three “lenses” through which people can view the global climate change issues,
and their influence on cost analysis.42 These are summarized in Table 6. None of these
perspectives is inherently more “right” or “correct” than another; rather, they overlap and to
varying degrees complement and conflict with one another. People generally hold to each of the
lenses to some degree.
40
The compliance cost estimates provided by EPA in its analyses are flawed. As noted by EPA, they are overestimates
of actual costs. Worse, the overestimation increases as the tonnage reduction requirement and marginal costs increase.
NMA/CRA provides an estimate of the net present value of S. 2191’s total costs.
41
For a good discussion of the confusion that can arise from mixing cost measures, see Anne E. Smith, Jeremy Platt,
and A. Denny Ellerman, “The Cost of Reducing SO2 (It’s Higher Than You Think),” Public Utilities Fortnightly (May
15, 1998), pp. 22-29.
42
CRS Report 98-738, Global Climate Change: Three Policy Perspectives, by (name redacted) and (name redacted).
ȱȱȱ
ŘŘȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
Table 6. Influence of Climate Change Perspectives on Policy Parameters
Approach
Seriousness of problem
Technology
Is agnostic on the merits
of the problem. The focus
is on developing new
technology that can be
justified from multiple
criteria, including economic,
environmental, and social
perspectives.
Economic
Understands issue in terms
of quantifiable cost-benefit
analysis. Generally assumes
the status quo is the baseline
from which costs and benefits
are measured. Unquantifiable
uncertainty tends to be ignored.
Ecological
Understands issues in terms
of their potential threat to
basic values, including ecological
viability and the well-being of
future generations. Such values
reflect ecological and ethical
considerations; adherents
see attempts to convert
them into commodities to
be bought and sold as
trivializing the issue.
Risk in developing mitigation
program
Believes any reduction
program should be designed
to maximize opportunities
for new technology. Risk lies
in not developing technology
by the appropriate time. Focus
on research, development, and
demonstration; and on removing
barriers to commercialization of
new technology.
Believes that economic
costs should be examined
against economic benefits
in determining any specific
reduction program. Risk lies
in imposing costs in excess of
benefits. Any chosen reduction
goal should be implemented
through economic measures
such as tradeable permits or
emission taxes.
Rather than economic costs
and benefits or technological
opportunity, effective
protection of the planet’s
ecosystems should be the primary
criterion in
determining the specifics of
any reduction program.
Focus of program should be
on altering values and
broadening consumer choices.
Costs
Viewed from the bottom-up.
Tends to see significant
energy inefficiencies in the
current economic system that
currently available (or
projected) technologies can
eliminate at little or no
overall cost to the economy.
Viewed from the top-down.
Tends to see a gradual
improvement in energy
efficiency in the economy, but
significant costs (usually
quantified in terms of GDP
loss) resulting from global
climate change control
programs. Typical loss
estimates range from 0-4%
of GDP.
Views costs from an ethical
perspective in terms of the
ecological values that global
climate change threatens.
Believes that values such as
intergenerational equity
should not be considered
commodities to be bought,
sold, or discounted. Costs are
defined broadly to include
aesthetic and environmental
values that economic analysis
cannot readily quantify and
monetize.
However, different combinations of these perspectives lead to very different cost estimates. A
classic example of this is the contrast between the S. 2191 results obtained by the Clean Air Task
Force (CATF) and the American Council for Capital Formation/National Association of
Manufacturers (ACCF/NAM) using the same model: EIA’s NEMS model. Table 7 summarizes
the general approach of the two analyses according to the three perspectives identified above. In
its analysis, CATF expresses confidence in S. 2191‘s various technology and efficiency
provisions and models the bill assuming EIA’s Best Available Technology (BAT) case, banking,
and offsets. In contrast, ACCF/NAM states that it is “unlikely” that technology, new energy
sources, and market mechanisms (e.g., carbon offsets, banking) will be sufficiently available to
achieve S. 2191‘s emission targets. Accordingly, ACCF/NAM’s assumptions differ substantially
from CATF’s and other studies by excluding banking, significantly capping the availability of
various technologies, and assuming higher construction costs.
ȱȱȱ
Řřȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
. General Perspective of CATF and ACCF/NAM Cost Assumptions
Table 7
CATF
ACCF/NAM-Low
ACCF/NAM-
Technology
Assumes no constraints
on technology availability
beyond those embedded
in NEMS
Economic
Assumes efficient decisionmaking via banking and offsets
(30%) as allowed in S. 2191
Ecological
Assumes decisions made in
favor of efficiency over price
because of S. 2191
incentives and regulations
Assumes significant
constraints on technology
availability and higher
costs than those embedded
in NEMS
Assumes short-term
decision-making with no
banking; amount of offsets
allowed “greater than 20%”
None—total GHG
emissions reduction
estimates not presented
Assumes substantial
constraints on technology
availability and higher costs
than those embedded in
NEMS
Assumes short-term
decision-making with no
banking; offsets constrained
to 15%-20%
None—total GHG
emissions reduction
estimated not presented
High
CRS analysis of: Jonathan Banks, Clean Air Task Force, The Lieberman-Warner Climate Security Act—
S. 2191: A Summary of Modeling Results from the National Energy Modeling System (February 2008); Science
Applications International Corporation, Analysis of The Lieberman-Warner Climate Security Act (S. 2191) Using the
National Energy Modeling System (NEMS), a report by the American Council for Capital Formation and the
Source:
National Association of Manufacturers (2008).
As indicated by Table 8, the widely different cost assumptions provided the expected results,
although all three analyses remained in the 0-4% GDP range common for greenhouse gas
reduction analysis. Allowance price estimates are widely different, but this cost measure tends to
exaggerate differences between results and should not be confused with average costs or program
costs. This is particularly true in this case, as ACCF/NAM did not publish its environmental
results in terms of greenhouse gases reduced; thus, one can not compare the allowance price with
what is being reduced over time. Unfortunately, the analyses do not present sufficient sensitivity
analysis and other information to determine whether it is the economic assumptions (e.g., banking
and offset availability), the behavioral assumptions (e.g., BAT), the technology assumptions (e.g.,
availability), or just the higher cost assumptions of the ACCF/NAM analysis that explains the
difference in allowance prices.
. Selected Results from CATF and ACCF/NAM Analyses
Table 8
GDP per capita Reduction 2020a
GDP per capita Reduction 2030a
Allowance Price 2020 (2005$)
Allowance Price 2030 (2005$)
Greenhouse Gas Emissions 2020
(MMTCO2e)
Greenhouse Gas Emissions 2030
(MMTCO2e)
CATF
ACCF/NAM-Low ACCF/NAM-High
not discernable
from graph
0.9%
about $21
about $45
about 5.5 (not
including
set-asides)
about 5.4 (not
including
set-asides)
0.8%
1.1%
2.6%
$52
$216
not published
2.7%
$61
$258
not published
not published
not published
Source: Jonathan Banks, Clean Air Task Force, The Lieberman-Warner Climate Security Act—S. 2191: A Summary
of Modeling Results from the National Energy Modeling System (February 2008); Science Applications International
ȱȱȱ
ŘŚȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
Analysis of the Lieberman-Warner Climate Security Act (S. 2191) Using the National Energy Modeling
System (NEMS), a report by the American Council for Capital Formation and the National Association of
Manufacturers (2008). All estimates converted to 2005 dollars using the GDP implicit price deflator.
a. Reduction is relative to the model’s reference case baseline for 2020 and 2030.
Corporation,
Some attempts have been made to sort out the importance of various assumptions in analyzing the
costs of greenhouse gas reduction proposals, beginning with Repetto and Austin’s effort for the
World Resources Institute (WRI) in 1997, with more recent efforts by Barker, Qureshi and Kohler
in 2006 and Barker and Jenkins in 2007.43 Indeed, Dr. Repetto has set up a website where people
may answer seven key questions about the cost and benefit assumptions they feel are most
reasonable and find out how their choices would affect GDP.44 Through meta-analysis of the
results from multiple independent studies, the role of various assumptions and methodologies are
quantified.45 In general, these studies found seven underlying assumptions affecting results: (1)
the efficiency of the economic response;46 (2) availability of non-carbon technology;47 (3)
availability of the Kyoto mechanisms;48 (4) method of revenue recycling; (5) method of
incorporating technological advancements; (6) inclusion of non-climate-related environmental
benefits; and (7) inclusion of climate-related benefits. As none of the models reviewed in this
report quantify any environmental benefits in their analyses, all models’ results can be
considered “worst-case” scenarios.
ȱȱǯȱŘŗşŗȱ
ȱȱ ȱ ȱȱ
Figure 2 and Figure 3 present greenhouse gas emissions under S. 2191 as estimated by the ten
cases, relative to their baseline assumptions. The range might seem surprising, given the emission
cap defined in the bill. The cause of the range is largely two-fold: (1) estimated emissions growth
in the 10%-15% of the economy not covered under the bill, (2) estimated use of international
credits to meet emission reduction requirements that do not reduce domestic emissions.
43
Robert Repetto and Duncan Austin, The Costs of Climate Protection: A Guide for the Perplexed, World Resources
Institute (1997); Terry Barker, Mahvash Saeed Qureshi, and Jonathan Kohler, The Costs of Greenhouse Gas Mitigation
with Induced Technological Change: A Meta-Analysis of Estimates in the Literature, Tyndall Centre for Climate
Change Research (July 2006); and Terry Barker and Katie Jenkins, The Costs of Avoiding Dangerous Climate Change:
Estimates Derived from a Meta-Analysis of the Literature, A Briefing Paper for the Human Development Report 2007
(May 2007).
44
http://www.climate.yale.edu/seeforyourself/.
45
As defined by Repetto on the “See For Yourself” website: “The meta-analysis was based on more than 1,400 policy
simulations performed with the various models. It used statistical regression analysis to ascribe differences among
models in the predicted economic cost of a given percentage reduction of greenhouse gas emissions to differences
among models in specific assumptions. Though some of the models related only to the U.S. economy, others to the
world economy, the meta-analysis found that both sets of models produced the same results.”
46
In this regard, Computable General Equilibrium Models (CGE) generally assume efficient economic responses to
programs while macroeconomic models allow time for the economy to adjust, resulting in higher short-term costs.
47
Some models include a “backstop” technology in unlimited amounts at a specified high price.
48
Credits from the Clean Development Mechanism (CDM) and Joint Implementation (JI).
ȱȱȱ
Řśȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
Figure 2.Total Estimated Greenhouse Gas Emissions Under S. 2191
13000
GHG Emissions (MMT CO2 eq.)
11000
9000
7000
5000
3000
2010
2020
2030
2040
Reference Cases
EPA/ADAGE-REF
EPA/ADAGE-TECH
EPA/IGEM-REF
EPA/IGEM-TECH
CATF/NEMS
EIA/NEMS
MIT/EPPA
NMA/CRA
S. 2191 Cases
EPA/ADAGE-REF
EPA/ADAGE-TECH
EPA/IGEM-REF
EPA/IGEM-TECH
CATF/NEMS
EIA/NEMS
MIT/EPPA
NMA/CRA
2050
EPA/ADAGE and EPA/IGEM: “Data Annex” available on the EPA website at http://www.epa.gov/
climatechange/economics/economicanalyses.html MIT/EPPA: Sergey Paltsev, et al., “Appendix D” of Paltsev et al.,
Assessment of U.S. Cap-and-Trade Proposals, MIT Joint Program on the Science and Policy of Global Change (2007).
EIA/NEMS: EIA, Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner Climate Security Act of 2007
(April 2008). CATF/NEMS: Jonathan Banks, Clean Air Task Force, The Lieberman-Warner Climate Security Act—S.
2191: A Summary of Modeling Results from the National Energy Modeling System (February 2008).
ACCF/NAM/NEMS: SAIC, Analysis of the Lieberman-Warner Climate Security Act (S. 2191) Using the National Energy
Modeling System (NEMS), report by the ACC. and NAM (2008). NMA/CRA: CRA International, Economic Analysis
of the Lieberman-Warner Climate Security Act of 2007 Using CRA’s MRN-NEEM Model (April 8, 2008). Estimates
extrapolated by CRS from available data where necessary.
Sources:
ȱȱȱ
ŘŜȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
.Total Estimated Greenhouse Gas Emissions from Each Model Under S. 2191
13000
13000
11000
11000
GHG Emissions (MMT CO2 eq.)
GHG Emissions (MMT CO2 eq.)
Figure 3
9000
7000
5000
3000
2030
2040
2050
5000
2010
2030
2040
EPA/ADAGE-REF
EPA/ADAGE-TECH
Reference Cases
EPA/IGEM-REF
EPA/IGEM-TECH
S. 2191 Cases
EPA/ADAGE-REF
EPA/ADAGE-TECH
S. 2191 Cases
EPA/IGEM-REF
EPA/IGEM-TECH
13000
13000
11000
11000
9000
7000
5000
2010
2020
Reference Cases
3000
ȬŘŝȱ
7000
3000
2020
GHG Emissions (MMT CO2 eq.)
GHG Emissions (MMT CO2 eq.)
2010
9000
2050
9000
7000
5000
3000
2020
2030
Reference Cases
CATF/NEMS
S. 2191 Cases
CATF/NEMS
2040
2050
2010
2020
2030
2040
EIA/NEMS
Reference Cases
MIT/EPPA
NMA/CRA
EIA/NEMS
S. 2191 Cases
MIT/EPPA
NMA/CRA
2050
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
The most stringent interpretation of S. 2191’s emissions cap is by NMA/CRA. The resulting
emissions estimates could be attributed to three factors: (1) NMA/CRA does not allow any
international credits to be used to achieve reductions, (2) NMA/CRA uses the preliminary AEO
2008 baseline, which may project lower emissions growth by non-covered sectors because of
EISA or other factors; and (3) NMA/CRA also analyzes the effect of the bill’s proposed Low
Carbon Fuel Standard, which reduces emissions further, as discussed later.
The highest emissions permitted under the bill are estimated by the two EPA/ADAGE cases. This
higher emissions level is probably the result of the substantial use of international credits and
percentage of uncovered entities assumed by ADAGE.
Interestingly, the two ACCF/NAM/NEMS cases do not present any estimates of their total
greenhouse gas emissions baseline, or the reduction calculated by their analysis. The closest they
come to presenting emissions reductions is a chart with assumed increases in energy-related CO2
emissions and their interpretation of the reductions S. 2191 would require on the energy sector.
ȱȱȬ ȱ ȱȱ
The only estimates of non-greenhouse gas emission reductions under S. 2191 are provided by
EPA/IPM. Those projections are for the electric power sector only, assume implementation of the
Clean Air Interstate Rule (CAIR) rule (currently in litigation), and only go to 2025. The
projections also reflect the interaction of CO2 reductions with the banking provisions of the Acid
Rain and CAIR rules. This interaction results in the short-term changes (to 2015) in emissions
being overstated. As indicated in Table 9 below, one-third of the SO2 reductions and one-sixth of
the NOx reductions are achieved in the last year of the projection. EPA/IPM also projected
mercury emissions reductions; however, they were done in the context of the now-vacated
mercury rule.49 This eliminated their utility for this analysis.
Table 9. EPA/IPM Reduction of Conventional Air Pollutants from Electric Utilities
Reduction from
Reference Case: 2025
(short tons)
Cumulative Reduction from
Reference Case 2010-2025
(short tons)
Sulfur Dioxide
1,064,000
3,000,000
Nitrogen Oxides
848,000
4,900,000
S. 2191
ȱȱ ȱȱȱ
Figure 4 and Figure 5 present the estimated GDP per capita in the baseline and S. 2191 scenarios
for the various cases. As suggested by the discussion of “noise” earlier, uncertainty about the
basecase assumptions absorbs the impact of S. 2191. Indeed, they are so intertwined as to make
the results nearly meaningless in one sense. In another sense, the figures indicate the models’
expectations that the economy continues to growth under S. 2191, albeit at a slower rate than
under their respective reference cases.
49
For more information on the court decision, see CRS Report RS22817, The D.C. Circuit Rejects EPA’s Mercury
Rules: New Jersey v. EPA, by (name redacted) and (name redacted).
ȱȱȱ
ŘŞȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
Figure 4. GDP per Capita (2005$) Under S. 2191
$110,000
GDP per Capita (2005$)
$100,000
$90,000
$80,000
$70,000
$60,000
$50,000
$40,000
2010
2020
2030
2040
Reference Cases
EPA/ADAGE-REF
EPA/ADAGE-TECH
EPA/IGEM-REF
EPA/IGEM-TECH
CATF/NEMS
ACCF/NAM/NEMS-HIGH
ACCF/NAM/NEMS-LOW
EIA/NEMS
MIT/EPPA
NMA/CRA
S. 2191 Cases
EPA/ADAGE-REF
EPA/ADAGE-TECH
EPA/IGEM-REF
EPA/IGEM-TECH
CATF/NEMS
ACCF/NAM/NEMS-HIGH
ACCF/NAM/NEMS-LOW
EIA/NEMS
MIT/EPPA
NMA/CRA
2050
EPA/ADAGE and EPA/IGEM: “Data Annex” available on the EPA website at http://www.epa.gov/
climatechange/economics/economicanalyses.html MIT/EPPA: Sergey Paltsev, et al., “Appendix D” of Paltsev et al.,
Assessment of U.S. Cap-and-Trade Proposals, MIT Joint Program on the Science and Policy of Global Change (2007).
EIA/NEMS: EIA, Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner Climate Security Act of 2007
(April 2008). CATF/NEMS: Jonathan Banks, Clean Air Task Force, The Lieberman-Warner Climate Security Act—
S. 2191: A Summary of Modeling Results from the National Energy Modeling System (February 2008).
ACCF/NAM/NEMS: SAIC, Analysis of the Lieberman-Warner Climate Security Act (S. 2191) Using The National Energy
Modeling System (NEMS), report by the ACC. and NAM (2008). NMA/CRA: CRA International, Economic Analysis
of the Lieberman-Warner Climate Security Act of 2007 Using CRA’s MRN-NEEM Model (April 8, 2008). Estimates
extrapolated by CRS from available data where necessary. Estimates converted to 2005$ using GDP implicit
price deflator.
Sources:
ȱȱȱ
Řşȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
. GDP per Capita (2005$) from Each Model Under S. 2191
$110,000
$110,000
$100,000
$100,000
GDP per Capita (2005$)
GDP per Capita (2005$)
Figure 5
$90,000
$80,000
$70,000
$60,000
$50,000
$70,000
$60,000
$40,000
2010
2020
2030
2040
2050
2010
2020
2030
2040
Reference Cases
EPA/ADAGE-REF
EPA/ADAGE-TECH
Reference Cases
EPA/IGEM-REF
EPA/IGEM-TECH
S. 2191 Cases
EPA/ADAGE-REF
EPA/ADAGE-TECH
S. 2191 Cases
EPA/IGEM-REF
EPA/IGEM-TECH
$110,000
$110,000
$100,000
$100,000
GDP per Capita (2005$)
GDP per Capita (2005$)
$80,000
$50,000
$40,000
$90,000
$80,000
$70,000
$60,000
$50,000
2050
$90,000
$80,000
$70,000
$60,000
$50,000
$40,000
$40,000
2010
2020
2030
2040
Reference Cases
CATF/NEMS
ACCF/NAM/NEMS-HIGH
ACCF/NAM/NEMS-LOW
EIA/NEMS
S. 2191 Cases
CATF/NEMS
ACCF/NAM/NEMS-HIGH
ACCF/NAM/NEMS-LOW
EIA/NEMS
ȬřŖȱ
$90,000
2050
2010
2020
2030
2040
Reference Cases
MIT/EPPA
NMA/CRA
S. 2191 Cases
MIT/EPPA
NMA/CRA
2050
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
To sort the situation out a little further, Figure 6 and Figure 7 show percentage reductions in
GDP per capita from S. 2191 (relative to the models’ respective reference cases) according to the
ten cases presented here. With the exception of the IGEM model, all projections for all years
between 2020 and 2050 fell into a range between 0.3% (EIA/NEMS for 2020 and 2030) and 2.7%
(ACCF/NAM-HIGH for 2030). As indicated in Figure 6 and Figure 7, the EPA/IGEM cases
produced 2050 estimates that were more than twice those of the other models.
The high estimates for GDP per capita reduction by the EPA-IGEM cases result from its structure
and assumptions contained in the model. For example, the assumption about the relationship
between leisure and consumption in IGEM is quite different from the other models. Essentially,
as prices for goods and services increase, IGEM assumes a highly responsive relationship, with
people deciding to work less and buy less. As a result, a small increase in prices will produce a
relatively large loss of consumption, resulting in a larger impact on GDP and other cost measures.
In contrast, other models are less responsive, assuming people will absorb higher prices without
changing their work or consumption habits very much.50 Other factors influencing IGEM’s results
include (1) a somewhat higher emissions baseline, (2) the lack of some less carbon-emitting
technological alternatives, such as carbon capture and storage, (3) a U.S.-only context that affects
the model’s estimates of exports, and (4) elasticities that are calibrated based on historical data.
The only year for which GDP per capita estimates were presented for all cases is 2030.51 Once
again, the estimates from the IGEM model are substantially higher (3.6% and 3.8%) than the
seven other cases for reasons noted above. The other cases fall into two categories. The largest
category is six cases that estimate 2030 GDP effect at about 1% or less. These cases are:
EPA/ADAGE-REF, EPA/ADAGE-TECH, CATF/NEMS, EIA/NEMS, MIT/EPPA, and
NMA/CRA. The other category is the two ACCF/NAM/NEMS cases where the GDP effect is
2.6% and 2.7% in 2030. Thus, despite their restrictive assumptions, the ACCF/NAM/NEMS
cases do not exceed the 0-4% range of GDP effects common to reduction programs.
50
See Janet Peace and John Weyant, Insights Not Numbers: The Appropriate Use of Economic Models, Pew Center on
Global Climate Change (April, 2008), pp. 18-19. This is an additional warning to readers about understanding the
assumptions and limitations of models. As stated later by Peace and Weyant: “The sensitivity of modeling results to a
single assumption—in this case, the elasticity of substitution between consumption and leisure—also serves to illustrate
that important differences between models are not always obvious. Most casual users would never dive deep enough
into model documentation to ascertain that IGEM and ADAGE utilize a different assumption about the tradeoff
between consumption and leisure. For this reason, it is very important that model developers (a) make transparent their
assumptions and inputs (as Jorgenson, Goettle, and Poss do) and (b) to the extent possible, characterize principal
sources of uncertainty in the model design and identify limitations that influence model results.” p. 20.
51
For the 2010 and 2020 estimates presented in Figures 4 and 5, CRS extrapolated the data for some of the
presentations.
ȱȱȱ
řŗȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
Figure 6. Percentage Change in GDP per Capita Under S. 2191
0%
Change in GDP per Capita (%)
-1%
-2%
-3%
-4%
-5%
-6%
-7%
2010
2020
2030
2040
EPA/ADAGE-REF
EPA/ADAGE-TECH
EPA/IGEM-REF
EPA/IGEM-TECH
CATF/NEMS
ACCF/NAM/NEMS-HIGH
ACCF/NAM/NEMS-LOW
EIA/NEMS
MIT/EPPA
2050
NMA/CRA
Note:
Reductions are relative to each model’s reference case baseline.
EPA/ADAGE and EPA/IGEM: “Data Annex” available on the EPA website at http://www.epa.gov/
climatechange/economics/economicanalyses.html MIT/EPPA: Sergey Paltsev, et al., “Appendix D” of Paltsev et al.,
Assessment of U.S. Cap-and-Trade Proposals, MIT Joint Program on the Science and Policy of Global Change (2007).
EIA/NEMS: EIA, Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner Climate Security Act of 2007
(April 2008). CATF/NEMS: Jonathan Banks, Clean Air Task Force, The Lieberman-Warner Climate Security Act—
S. 2191: A Summary of Modeling Results from the National Energy Modeling System (February 2008).
ACCF/NAM/NEMS: SAIC, Analysis of the Lieberman-Warner Climate Security Act (S. 2191) Using The National Energy
Modeling System (NEMS), report by the ACC. and NAM (2008). NMA/CRA: CRA International, Economic Analysis
of the Lieberman-Warner Climate Security Act of 2007 Using CRA’s MRN-NEEM Model (April 8, 2008). Estimates
extrapolated by CRS from available data where necessary. Estimates converted to 2005$ using GDP implicit
price deflator.
Sources:
ȱȱȱ
řŘȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
. Percentage Change in GDP per Capita from Each Model Under S. 2191
0%
0%
-1%
-1%
Change in GDP per Capita (%)
Change in GDP per Capita (%
Figure 7
-2%
-3%
-4%
-5%
-6%
-7%
2020
2030
EPA/ADAGE-REF
2040
-4%
-5%
-6%
2050
2010
2020
EPA/ADAGE-TECH
2030
EPA/IGEM-REF
0%
0%
-1%
-1%
Change in GDP per Capita (%)
Change in GDP per Capita (%)
-3%
-7%
2010
-2%
-3%
-4%
-5%
-6%
-7%
2040
2050
EPA/IGEM-TECH
-2%
-3%
-4%
-5%
-6%
-7%
2010
CATF/NEMS
Ȭřřȱ
-2%
2020
ACCF/NAM/NEMS-HIGH
2030
2040
ACCF/NAM/NEMS-LOW
2050
EIA/NEMS
2010
2020
2030
MIT/EPPA
2040
NMA/CRA
2050
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱȱȱ
Figure 8 and Figure 9 present the estimated allowance prices for each of the ten cases examined
here. In addition, we have included the Congressional Budget Office’s estimates used in scoring
S. 2191.52 It is clear from the figures that the banking assumption of the different cases has a
fundamental influence on projected prices. For example, as noted earlier, the ACCF/NAM/NEMS
cases do not include banking—an expressed decision by ACCF/NAM and not an inherent part of
the NEMS model as evident by the CATF/NEMS and EIA/NEMS cases. This assumption has a
clear effect on the trajectory of their allowance prices. In contrast, the ADAGE, IGEM, MRNNEEM, and EPPA models assume discount rates that tend to encourage banking.53 As noted
earlier, banking tends to increase allowance prices in the early years of the program and lower
them in the out-years. This flattening effect results in the gentler slope of the allowance price
curves evident in Figure 8 and Figure 9 below for these cases.
Of the 2030 estimates for the eight cases that include S. 2191‘s banking provision, four cases
project allowance prices in the range of $45-$61 (CATF/NEMS, EIA/NEMS, and the two
EPA/ADAGE cases) while the other four cases project allowance prices in the $73-$86 range
(MIT/EPPA, NMA/CRA, and the two EPA/IGEM cases). The spread of allowance price estimates
expands after 2030, as evident in the figures.
52
Congressional Budget Office. Cost Estimate: S. 2191: America’s Climate Security Act of 2007 (April 10, 2008).
For a discussion of the models’ banking assumptions, see Congressional Budget Office, Cost Estimate: S. 2191:
America’s Climate Security Act of 2007 (April 10, 2008), pp. 21-23.
53
ȱȱȱ
řŚȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
Figure 8. Projected Allowance Prices Under S. 2191
Allowance Price (2005$/MMT CO2 eq.)
300
250
200
150
100
50
0
2010
2020
2030
2040
EPA/ADAGE-REF
EPA/ADAGE-TECH
EPA/IGEM-REF
EPA/IGEM-TECH
CATF/NEMS
ACCF/NAM/NEMS-HIGH
ACCF/NAM/NEMS-LOW
EIA/NEMS
MIT/EPPA
NMA/CRA
CBO
2050
EPA/ADAGE and EPA/IGEM: “Data Annex” available on the EPA website at http://www.epa.gov/
climatechange/economics/economicanalyses.html MIT/EPPA: Sergey Paltsev, et al., “Appendix D” of Paltsev et al.,
Assessment of U.S. Cap-and-Trade Proposals, MIT Joint Program on the Science and Policy of Global Change (2007).
EIA/NEMS: EIA, Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner Climate Security Act of 2007
(April 2008). CATF/NEMS: Jonathan Banks, Clean Air Task Force, The Lieberman-Warner Climate Security Act—
S. 2191: A Summary of Modeling Results from the National Energy Modeling System (February 2008).
ACCF/NAMS/NEMS: SAIC, Analysis of The Lieberman-Warner Climate Security Act (S. 2191) Using the National
Energy Modeling System (NEMS), report by the ACCF and NAM (2008). NMA/CRA: CRA International, Economic
Analysis of the Lieberman-Warner Climate Security Act of 2007 Using CRA’s MRN-NEEM Model (April 8, 2008). CBO:
Congressional Budget Office, Cost Estimate: S. 2191: America’s Climate Security Act of 2007 (April 10, 2008).
Estimates extrapolated by CRS from available data where necessary. Estimates converted to 2005$ using GDP
implicit price deflator.
Sources:
ȱȱȱ
řśȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
. Projected Allowance Prices from Each Model Under S. 2191
Figure 9
300
Allowance Price (2005$/MMT CO2 eq.)
Allowance Price (2005$/MMT CO2 eq.)
300
250
200
150
100
50
0
2010
2020
2030
EPA/ADAGE-REF
2040
150
100
50
2020
EPA/ADAGE-TECH
2030
EPA/IGEM-REF
2040
2050
EPA/IGEM-TECH
300
Allowance Price (2005$/MMT CO2 eq.)
Allowance Price (2005$/MMT CO2 eq.)
200
0
2010
2050
300
250
200
150
100
50
0
2010
CATF/NEMS
ȬřŜȱ
250
2020
ACCF/NAM/NEMS-HIGH
2030
2040
ACCF/NAM/NEMS-LOW
2050
EIA/NEMS
250
200
150
100
50
0
2010
2020
2030
MIT/EPPA
NMA/CRA
2040
CBO
2050
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱȱȱ
None of the analyses examined were conducted after the proposed deficit reduction amendment
was announced April 10, 2008.54 Therefore, CRS has provided the following estimates based on
two cases: a “high” revenue case based on the MIT/EPPA study, and a “low” revenue case based
on the EPA/ADAGE-TECH case (Figure 10). In each case, the auction revenue estimates are
calculated by multiplying the estimated allowance price in a given year by the number of
allowances auctioned by the program for deficit reduction (“Deficit Reduction Fund”) and the
number of “remainder allowances” allocated for auction (“General Auction”). As the number of
allowances for auction in a given year is set by the bill, the total auction revenue for that year
becomes a function of the allowance price. A higher allowance price will lead to higher auction
revenue. As shown in Figure 10, using the lower allowance prices in the EPA/ADAGE-TECH
case, total auction revenues start in the tens of billions of dollars (2005$) and increase to over
$100 billion before 2030. Using higher allowances prices, such as the MIT/EPPA case, total
auction revenues exceed $100 billion before 2020. In comparison, currently the federal
government spends roughly $5 billion annually for the Climate Change Science Program, the
Climate Change Technology Program, and International Climate Change Assistance, combined.55
As indicated in Table 10, after the firefighting, deficit reduction, administration expenses, and
other funds have been allocated, a substantial amount of auction revenue would remain available
annually for technology deployment even in the low revenue EPA/ADAGE-TECH case. For
example, the Advanced Technology Vehicles Manufacturing Incentive Program (Sec. 4405)
would provide grants to automakers and parts manufacturers to develop the capacity to build
plug-in hybrid and other advanced vehicles (and parts). Funds could be used for engineering
integration of vehicles and retooling old plants to produce advanced vehicles. Using the lower
allowance prices in the EPA/ADAGE-TECH case, this program would provide over $1 billion
(2005$) annually in 2012, increasing to more than $7 billion by 2040. In comparison, DOE
currently spends between $200 million and $400 million for advanced vehicle and hydrogen fuel
R&D.56 As noted in the next section, the effectiveness of these funds in accelerating technology
development and commercialization—as well as agencies’ and firms’ capacity to absorb (in some
cases) very large funding increases—could have a significant effect on the overall costs of S.
2191 and the ultimate success of the program.
54
Submitted to CBO April 9, 2008. CBO, S. 2191, America’s Climate Security Act, with an Amendment (April 10,
2008).
55
For more information on federal expenditures on climate change, see CRS Report RL33817, Climate Change:
Federal Program Funding and Tax Incentives, by (name redacted).
56
For more information on advanced vehicle R&D, see CRS Report RS21442, Hydrogen and Fuel Cell Vehicle R&D:
FreedomCAR and the President’s Hydrogen Fuel Initiative, by (name redacted).
ȱȱȱ
řŝȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
. Estimated Annual Revenues from Allowance Auctions Under S. 2191
Figure 10
EPA/ADAGE-TECH
MIT/EPPA
$300
$250
$200
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
$100 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
$50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
$150 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
$0
2012
2017
2022
2027
2032
2037
2042
2047
Estimated Annual Auction Revenue (billion 2005$)
Estimated Annual Auction Revenue (billion 2005$)
$300
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
$250 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
$200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
$150 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000 000
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
0
$100 0 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00
$50 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
$0
2012
2017
2022
2027
000
General Auction 0 0 0 Deficit Reduction Fund
Source:
ȬřŞȱ
CRS Analysis of S. 2191 using allowance price estimates from EPA and MIT.
General Auction
2032
2037
2042
0 0 0 Deficit Reduction Fund
2047
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
. Allocation of Estimated Annual Auction Revenue from S. 2191 Using
EPA/ADAGE-TECH Case
Table 10
(millions of 2005$)
Value of Auction Revenue
2012
2020
2030
2040
2050
Deficit Reduction Fund
General Auction Revenue
$6,531
$21,616
$11,705
$46,590
$25,622
$95,341
$33,352
$121,784
$33,455
$122,160
$150
$430
$1,071
$150
$430
$1,393
$150
$430
$1,776
$150
$430
$2,086
$150
$430
$2,092
Off-the-
Top Allocation of Auction Proceeds
BLM Emergency Firefighting Fund
Forest Service Emergency Firefighting Fund
CSA Management Fund
Value of Remaining Proceeds
Technology Deployment
$10,382 $23,201 $48,352 $61,942 $62,134
Zero- or Low-Carbon Energy Technology
$3,322
$7,424 $15,473 $19,821 $19,883
Advanced Coal and Sequestration Technology
$2,595
$5,800 $12,088 $15,485 $15,533
Fuel from Cellulosic Biomass
$623
$1,392
$2,901
$3,717
$3,728
Adv. Tech. Vehicles Manufacturing Incentives
$1,246
$2,784
$5,802
$7,433
$7,456
Sustainable Energy Program
$2,595
$5,800 $12,088 $15,485 $15,533
Energy Independence Acceleration Fund
$399
$892
$1,860
$2,382
$2,390
Energy Assistance Fund
$3,594
$8,031 $16,737 $21,441 $21,508
LIHEAP
$1,797
$4,015
$8,369 $10,721 $10,754
Weatherization
$898
$2,008
$4,184
$5,360
$5,377
Rural Energy Assistance
$898
$2,008
$4,184
$5,360
$5,377
Climate Change Worker Training Fund
$998
$2,231
$4,649
$5,956
$5,974
DOE University Programs
$250
$558
$1,162
$1,489
$1,494
Adaptation Fund
$3,594
$8,031 $16,737 $21,441 $21,508
DOI - Wildlife Conservation and Restoration
$1,258
$2,811
$5,858
$7,504
$7,528
DOI - Adaptation Activities
$683
$1,526
$3,180
$4,074
$4,086
DOI - Cooperative Grant Programs
$180
$402
$837
$1,072
$1,075
DOI - Tribal Wildlife Grants
$36
$80
$167
$214
$215
Land and Water Conservation Fund
$359
$803
$1,674
$2,144
$2,151
DOI LWCF Sec. 6 Grants
$60
$134
$279
$357
$358
DOI LWCF Sec. 7 Acquisitions
$120
$268
$558
$715
$717
USDA Forest Legacy Program Sec. 7
$60
$134
$279
$357
$358
USDA LWCF Sec. 7 Acquisitions
$120
$268
$558
$715
$717
Forest Service Adaptation Activities
$180
$402
$837
$1,072
$1,075
EPA Adaptation Activities
$180
$402
$837
$1,072
$1,075
Army Corps of Engineers Adaptation Activities
$359
$803
$1,674
$2,144
$2,151
Department of Commerce Adaptation Activities
$359
$803
$1,674
$2,144
$2,151
Climate Change and National Security Fund
$998
$2,231
$4,649
$5,956
$5,974
Notes: CRS estimates based on EPA/ADAGE-TECH case allowance price projections. Higher allowance price
estimates would lead to higher auction proceeds. For example, MIT/EPPA allowance price projections result in
annual revenues roughly 50% to 100% higher, depending on the year.
ȱȱȱ
řşȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
CRS estimates of firefighting fund requirements are based on historic data.
The estimate of administration cost (“CSA Management Fund”) is based on EPA’s estimate of 1%
of total allowance value.
ȱȱ¢ȱȱȱ
¢ȱ ȱ
A frontier area in model development is creating fuller representations of technology
advancement. A substantial amount of technological change occurs within the economy without
direct policy intervention—the free enterprise system provides significant rewards for those who
develop cost-effective alternatives and introduce them into the market.57 However, technological
change is a very complex subject and can also be induced through a variety of policy levers,
including prices (such as allowance prices), subsidies, and technology mandates or standards,
along with both publicly and privately funded research and development.58 This “induced
technological change” (ITC) is not fully represented in the models used here, although it is a
critical part of S. 2191. Observing that no single source dominates the process of technology
change—a process that includes roles for research and development, learning-by-doing, and
spillovers from other industries engaged in these activities, L. Clarke, et al. states:
The lesson from these observations is to be cautious in interpreting the policy conclusions of
models that assume only a single source of technological progress or that neglect critical
factors such as spillovers. This includes virtually all formal models in use today, implying a
need both for more comprehensive treatments of technological change and more research to
understand the nature and magnitude of any distortions of policy conclusions from models
with limited representations of technological change.59
That models used to project GHG reductions costs are deficient in treating technology
change is a likely major source of error that will only become cognizable as the future
unfolds. S. 2191 includes numerous incentives for technology development—incentives for
which no model has (or could be expected to have) estimated the collective effect.
ȱ ȱȱ
Most of the analyses examined here focus on technological alternatives in the electric
power sector.
57
Generally expressed in terms of autonomous energy efficiency improvement (or AEEI), those effects are generally
estimated using historical data.
58
For an overview of induced technological change, see Lawrence H. Goulder, Induced Technological Change and
Climate Policy, Pew Center on Global Climate Change (October 2004).
59
Leon Clarke, John Weyant, and Alicia Birky, “On the Sources of Technological Change: Assessing the Evidence,”
Energy Economics 28 (2006) p. 593.
ȱȱȱ
ŚŖȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
¢ȱȱ¢ȱ
When and how quickly technology will be available is a difficult but critical issue. Indeed, the
models examined here do not agree on the availability of current electric generating technology,
such as nuclear or wind power, much less emerging technologies such as carbon capture and
storage (CCS), or the potential for breakthroughs over the next 40 years. The general lack of
detailed technology descriptions in the CGE models does not help in this regard. For example, the
EPA/IGEM’s presentation of the energy sector and technology options is too aggregated to be
analyzed in terms of technology development under S. 2191.
Current Technologies. Several currently available technologies emit less greenhouse gases (or
none) compared to a conventional coal-fired facility. Those technologies include electric
generation from wind, biomass, landfill gas, nuclear, geothermal, and natural gas. Some of these
sources, such as biomass and natural gas, have some repowering potential with respect to coalfired generation.
The models do not provide much insight on the likely mix of these technologies under S. 2191.
Some cases, like the ACCF/NAM/NEMS cases, strictly define the availability of these
technologies; while others, like the CATF-NEMS and EIA/NEMS cases, allow the model to meet
the requirements without any additional constraints. Table 11 identifies some of the technologyavailability limits assumed in the different model runs, along with the resulting capacity built to
meet electricity demand from 2010 to 2030. Because the ACCF/NAM/NEMS cases heavily
constrain the availability of most alternatives to natural-gas generation, it is not surprising that a
substantial amount of natural gas capacity is assumed to be built under these cases during this
time period. This result is confirmed by sensitivity analysis conducted by EIA that shows a
movement to natural gas if the availability of nuclear power, renewable power, and coal with CCS
are constrained. In contrast, the EPA/IPM, CATF/NEMS, and two EPA/ADAGE cases indicate
little or no new construction of natural gas. Instead, these models allow a mix of renewable power
(including wind and biomass), nuclear power, and coal-fired capacity with CCS to meet future
demand and to begin replacing coal-fired capacity without CCS. In these cases, each model
included the CCS subsidy contained in S. 2191. Finally, MIT/EPPA, EIA/NEMS, and NMA/CRA
cases show a moderate role for natural gas during this time frame.
Table 11. Assumptions about the Construction of Generating Capacity Under S. 191
to 2030
Nuclear
Renewable
Natural
Coal with
Power
Power
Gas-fired
CCS
ACCF/NAM/
NEMS-HIGH
ACCF/NAM/
NEMS-LOW
MIT/EPPA
10 GW
(limit)
25 GW
(limit)
about 3-4
GW (built)
6 GW/year
(limit)
6 GW/year
(limit)
about 26 GW
(built)
about 284
GW (built)
about 269
GW (built)
about 71
GW (built)
NMA/CRA
40 GW
(limit)
44 GW
(limit)
130.5 GW
(limit)
61.3 GW
(built)
about 33
GW (built)
5.5 GW
(built)
EPA/IPM
(for 2025)
ȱȱȱ
25 GW (limit)
50 GW (limit)
about 236 GW
(built with
subsidy)
107 GW (limit)
80 GW (built
with subsidy)
Śŗȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
Nuclear
Renewable
Natural
Coal with
Power
Power
Gas-fired
CCS
CATF/NEMS
104 GW
(built)
0
133 GW (built
with subsidy)
about 71
GW (built)
54 GW
wind power
(built with
subsidy)
Biomass
(constrained)
about 58 GW
(built)
EPA/ADAGEREF
little
EPA/ADAGETECH
about 70
GW (built)
about 61GW
(built)
little
EIA/NEMS
264 GW
(built)
12.5GW
112 GW
(built)
12.4 GW
77 GW
(built)
88.2 GW
about 165 GW
(built with
subsidy)
about 89 GW
(built with
subsidy)
64 GW (built)
AEO 2007
baseline
145 GW
(no CCS)
EPA/ADAGE and EPA/IPM: “Data Annex” available on the EPA website at http://www.epa.gov/
climatechange/economics/economicanalyses.html MIT/EPPA: Sergey Paltsev, et al., “Appendix D” of Paltsev et al.,
Assessment of U.S. Cap-and-Trade Proposals, MIT Joint Program on the Science and Policy of Global Change (2007).
EIA/NEMS: EIA, Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner Climate Security Act of 2007
(April 2008). CATF/NEMS: Jonathan Banks, Clean Air Task Force, The Lieberman-Warner Climate Security Act—
S. 2191: A Summary of Modeling Results from the National Energy Modeling System (February 2008).
ACCF/NAMS/NEMS: SAIC, Analysis of the Lieberman-Warner Climate Security Act (S. 2191) Using The National
Energy Modeling System (NEMS), report by the ACCF and NAM (2008). NMA/CRA: CRA International, Economic
Analysis of the Lieberman-Warner Climate Security Act of 2007 Using CRA’s MRN-NEEM Model (April 8, 2008).
Note: “Limit” is the maximum that the model assumes can be built—it is not necessarily the amount the
model determined would be built. “Built” is the amount the model determined needed to be built. “About” is
an estimate by CRS of the additional capacity necessary for the increased electricity production projected by the
model between 2010 and 2030 under S. 2191 in the absence of capacity data being provided. The exception is
where the natural gas-fired capacity was estimated from a chart. The estimates were calculated assuming an 80%
capacity factor for biomass, 90% for nuclear power and coal, 48% for renewables, and 85% for natural gas.
Source:
In some ways, the interplay between nuclear power, renewables, and coal-fired capacity with
CCS is a proxy for the need for a low-carbon source of electric generating capacity in the mid- to
long-term. As indicated, a considerable amount of low-carbon generation will have to be built
under S. 2191 to meet the reduction requirement. The amount of capacity constructed depends on
the models’ basecase assumptions about future supply and demand and need for capacity
replacement/retirement under S. 2191, along with the degree of consumer response to rising
prices and incentives contained in S. 2191.
To put these numbers into historical context, from 1963 to 1985, 78 GW of nuclear power were
ordered, constructed and began operation.60 For the 19-year period of 1966 through 1984, the
country added 464 GW of total generating capacity, including 210 GW of coal-fired capacity, 38
GW of hydropower, 27 GW of natural gas capacity (steam technology), 46 GW of oil-fired
capacity, and 54 GW of peaking capacity to improve system reliability after the 1965 blackout. In
addition to new additions, between 1965 and 1972, about 400 coal-fired generating units were
converted to oil to meet environmental requirements. After the 1973 oil embargo, this trend was
60
Compiled from EIA’s Reactor Status List available from EIA’s website.
ȱȱȱ
ŚŘȱ
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
reversed with 11GW of capacity converted back to coal by 1983.61 For a more recent time period,
from 2001 through 2005, the United States added about 180 GW of new capacity—almost all
natural gas-fired.62
Beyond construction of new facilities and repowering of existing ones, conservation is likely to
play an important role in reducing the need for new construction under S. 2191. In general, the
models estimate a 10%-30% reduction in projected demand for electricity from the 2030 basecase
level due to S. 2191.
Emerging Technologies. The emerging technology receiving the most attention in the models is
carbon capture and storage (CCS). This is not surprising. The models generally agree that the
long-term viability of coal-fired electric generation is dependent on developing a CCS system.
Indeed, the models’ various projections of coal consumption are a direct result of the models’
assumptions about the introduction and commercialization of CCS. Of the numerous provisions
in S. 2191 designed to promote emerging technologies, the CCS bonus allowance provision is the
only one that received substantial attention by the models.
Table 12 indicates the various assumptions and limits the models placed on CCS deployment
under S. 2191. As indicated, the cases that included the CCS subsidies contained in S. 2191
generally assumed that the technology would be available earlier and in increasing amounts over
the cases that did not include the subsidies. For example, the EPA/IPM sensitivity analysis on S.
2191‘s CCS bonus allowance subsidy indicates that the subsidy (along with sufficiently high
allowance prices) results in the technology emerging in the commercial market in 2015 with full
production (as limited by the models) being achieved in 2025. The MIT/EPPA subsidy case
agrees with a 2015 commercialization date while the EPA/ADAGE cases delay availability until
2020. EIA/NEMS states only that the subsidy makes the technology economical.
While the models agree that the CCS bonus allowance provisions are effective, they disagree on
whether they are sufficient. For example, EIA/NEMS noted that the subsidy improves CCS’s
relative economics; however, nuclear and renewable fuels are projected to still play a larger
role.63 In contrast, EPA/IPM states that by 2025, coal with CCS is economic even without the
subsidy. The advantage, according to EPA/IPM, is the earlier start-up resulting from the
subsidy that would result in even more CCS being installed if the subsidy weren’t capped and
eventually ran out.64 MIT/EPPA agrees that the bonus allowances would be over-subscribed for
almost all years.65
Among the no-subsidy scenarios, only NMA/CRA views CCS as available before 2025.
61
Energy Information Administration, Fuel Choice in Steam Electric Generation: Historical Overview, DOE/EIA0472 (August 1985), pp. 5 and 7.
62
Environmental Protection Agency, EPA Analysis of the Low Carbon Economy Act of 2007: S. 1766 in the 110th
Congress (January 15, 2008) p. 49.
63
EIA, Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner Climate Security Act of 2007 (April
2008) p. 23.
64
U.S. Environmental Protection Agency, EPA Analysis of the Lieberman-Warner Climate Security Act of 2008
(March 14, 2008), p. 40.
65
Sergey Paltsev, et al., Assessment of U.S. Cap-and-Trade Proposals, MIT Joint Program on the Science and Policy of
Global Change (April 2007), Appendix D, p. D11.
ȱȱȱ
Śřȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
Table 12.
Assumptions about the Availability of CCS
(in Gigawatts [GW])
ACCF/NAM/
NEMS-HIGH
(build limits)
ACCF/NAM/
NEMS-LOW
(build limits)
MIT/EPPA
(no subsidy)
MIT/EPPA
(subsidy)
NMA/CRA
(build limits)
EPA/IPM
(no subsidy)
EPA/IPM
(subsidy)
CATF/NEMS
(subsidy)
EPA/ADAGE-REF
(subsidy)
EPA/ADAGE-TECH
(subsidy)
IA/NEMS
(subsidy)
2015
2020
2025
2030
Total
not presented
not presented
not presented
not presented
25
not presented
not presented
not presented
not presented
50
0
about 10
about 10
about 42
about 63
about 10
about 17
about 59
about 148
about 236
2
15
30
60
107
0
0
70
n/a
70
5
5
70
n/a
80
about 1
about 8
about 51
about 73
133
0
about 23
about 47
about 94
about 165
0
about 23
about 9
about 56
about 89
about 8
about 16
about 24
about 16
64
EPA/ADAGE and EPA/IPM: “Data Annex” available on the EPA website at http://www.epa.gov/
climatechange/economics/economicanalyses.html MIT/EPPA: Sergey Paltsev, et al., “Appendix D” of Paltsev et al.,
Assessment of U.S. Cap-and-Trade Proposals, MIT Joint Program on the Science and Policy of Global Change (2007).
EIA/NEMS: EIA, Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner Climate Security Act of 2007
(April 2008). CATF/NEMS: Jonathan Banks, Clean Air Task Force, The Lieberman-Warner Climate Security Act—
S. 2191: A Summary of Modeling Results from the National Energy Modeling System (February 2008).
ACCF/NAMS/NEMS: SAIC, Analysis of the Lieberman-Warner Climate Security Act (S. 2191) Using the National Energy
Modeling System (NEMS), report by the ACCF and NAM (2008). NMA/CRA: CRA International, Economic Analysis
of the Lieberman-Warner Climate Security Act of 2007 Using CRA’s MRN-NEEM Model (April 8, 2008).
Note: GW estimates for MIT/EPPA and ADAGE calculated assuming a 90% capacity factor.
Source:
Future Technologies . The above discussion focuses on current perspectives on technological
alternatives—alternatives that mostly rely on the construction of new facilities, be they nuclear
power, biomass power, or coal-fired integrated gasification combined cycle (IGCC) with CCS.
Many existing coal facilities are assumed to be retired early because, in the words of EIA/NEMS,
retrofitting them with CCS technology “is generally impractical.”66 As suggested by MIT, this
points out both a need and a concern:
66
EIA, Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner Climate Security Act of 2007 (April
2008) p. iii.
ȱȱȱ
ŚŚȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
The need to phase out coal without CCS indicates the potential value of a CCS technology
that could be used to retrofit existing generation plants, extending the life of existing
investment and limiting the number of completely new plants that were needed. The capital
intensity of these technologies are a concern as we find that the investment demand needed
for such expansions crowds out investment in other areas of the economy, and thus increases
the welfare cost of the policy.67
Such retrofitable post-combustion technologies are in development. For example, an ammoniabased, regenerative process for CO2 capture from existing coal-fired facilities is being developed
by Powerspan.68 Called ECO2, two commercial demonstrations (125 MW and 120 MW) have
been announced with projected operations to begin in 2012 and 2011.69 A second, chilledammonia-based post-combustion capture process is being developed by Alstom. In collaboration
with American Electric Power (AEP) and RWE AG (largest electricity producer in Germany),
Alstom has announced plans to demonstrate the technology on a 20 MW slip stream at AEP’s
Mountaineer plant with the captured CO2 injected in deep saline aquifers on site.70 Once
commercial viability is demonstrated at Mountaineer, AEP plans to install the technology at its
450 MW Northeastern Station in Oologah, OK, early in the next decade.71 Other solvent-based
post-combustion processes are in the pilot stage.72 To the extent these and other future
retrofittable technologies become available, the mid- and long-term costs and capital investment
projected by the models could be significantly mis-stated.
ȱȱǰȱǰȱǰȱȱ¢ȱ
ȱ
One factor that will determine the availability of emerging and future technology is research,
development, demonstration, and deployment funding. The potential for such subsidies to
accelerate deployment is suggested by the previous discussion of CCS. However, S. 2191
contains numerous provisions with respect to technology. As noted in the previous discussion on
auction/allowance revenues, technology development will receive substantial funding under S.
2191. However, in general, only the bonus allowance incentives for CCS are explicitly modeled
in any of the cases. The exceptions to this are some innovative efforts by the CATF/NEMS and
EIA/NEMS cases to use various proxies to illustrate the potential of this funding. These are
discussed later. In addition, NMA/CRA states that S. 2191 deployment subsidies “would be fully
utilized by CRA’s projected technology investments.” NMA/CRA does not state whether they
assumed that the technology subsidies had any effect on deployment schedules or amounts.
A basic question about S. 2191 technology development funding is: How much is enough? The
amount provided by the bill dwarfs current efforts to develop and deploy reduction and low67
Sergey Paltsev, et al., Assessment of U.S. Cap-and-Trade Proposals, MIT Joint Program on the Science and Policy of
Global Change, Report No. 146 (April 2007), pp. 33-34.
68
Powerspan Corp. Carbon Capture Technology for Existing and New Coal-Fired Power Plants (April 15, 2008).
69
One is to be sited at NRG’s W.A. Parish plant in Texas and is to use a 125 MW slip stream. The second is to use a
120 MW slip stream from Basin Electric’s Antelope Valley Station. The captured CO2 is to be sold or used for
Enhanced Oil Recovery (EOR).
70
AEP News Release, RWE to Join AEP in Validation of Carbon Capture Technology, (November 8, 2007).
71
The captured gas is to be used for Enhanced Oil Recovery.
72
For a useful summary of carbon capture technology, see Steve Blankinship, “The Evolution of Carbon Capture
Technology Part 1,” Power Engineering (March 2008).
ȱȱȱ
Śśȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
carbon technologies. To put S. 2191‘s technology funding efforts into context, two proposed
research, development, and demonstration strategies are summarized below.
Table 13 presents the Electric Power Research Institute’s (EPRI’s) estimated combined public
and private research and development funding needs to obtain a “full portfolio” of electricity
technologies to meet greenhouse gas reduction targets. The technology targets for 2030 are (1)
30% reduction in load growth by efficiency improvements; (2) 70 GW of non-hydro renewables;
(3) 64 GW of new nuclear power; (4) new coal-plant efficiency of 49%; (5) CCS widely deployed
after 2020; (6) plug-in hybrids as 39% of new car sales; and (7) distributed energy resources at
5% of baseload.73
Table 13. Estimated Incremental Annual Combined Public and Private Funding
Needs to Achieve EPRI’s Full Portfolio
(millions of dollars annually)
Distribution-enabled technologies
Transmission-enabled technologies
New/Extended Nuclear Power
Advanced coal and Carbon Capture
and Storage
Annual Totals
20052009
20102014
20152019
20202024
20252030
20052030
Average
Annual
$250
$100
$500
$830
$220
$130
$520
$800
$140
$120
$370
$800
$240
$70
$370
$620
$240
$60
$400
$400
$220
$100
$430
$690
$1,700
$1,700
$1,400
$1,300
$1,100
$1,400
Electric Power Research Institute, The Power to Reduce CO2 Emissions: The Full Portfolio (August 2007).
Note: “Distribution-enabled technologies” refers to deploying smart distribution grids and communications
infrastructures to support commercialization of end-use energy efficiency, distributed energy resources, and
plug-in hybrid electric vehicles.
“Transmission-enabled technologies” refers to deploying transmission grids and energy storage infrastructure to
support as much as 20%-30% intermittent renewables in specific regions.
Source:
Table 14 presents the public funding needs for a strategy focused on commercializing various
“clean coal” technologies funded over 18 years (2008-2025). The strategy would provide for
several carbon capture and storage demonstration projects along with improvements to
combustion technology and development of CCS retrofit technology.
73
Electric Power Research Institute, The Power to Reduce CO2 Emissions: The Full Portfolio, Discussion Paper
(August 2007), p. 2-2. The targets do not reflect economic or potential regulatory and siting constraints.
ȱȱȱ
ŚŜȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
.Total Public Funding Needs for 2007 CURC-EPRI Clean Coal Technology
Roadmap over 18 Years (2008-2025)
Table 14
(millions of dollars)
Integrated Gasification
Combined-Cycle (IGCC)
Combustion
Innovations for Existing Plants
(IEP)
Sequestration (Storage—high
CO2 scenario)
Fuel Cells
Turbines
Totals
Source: Coal Utilization Research Council,
Research and
Development
(80%overnment
Share)
Demonstration
Projects (50%
Government
Share)
Totals
$2,100
$2,000
$4,100
$580
$310
$2,240
$480
$2,820
$790
$180
$740
$920
$580
$360
$4,110
$430
$160
$6,050
$1,010
$520
$10,160
The CURC-EPRI Clean Coal Technology Roadmap, available at
http://www.coal.org/userfiles/File/Updated_CURC_EPRI_Clean_Coal__Technology_Roa.pdf.
The “Technology Deployment” funds allocated by S. 2191, as shown in Table 10 (over $10
billion annually in 2012, nearing $50 billion annually by 2030) exceed the amounts estimated for
the strategies identified above in Table 13 and Table 14 (combined, roughly $2 billion annually).
Several organizations, including EPRI and the Pew Center for Global Climate Change, have
called for at least a doubling of DOE’s current funding of advanced coal options (2008 funding:
$438 million).74 This is not to say that S. 2191‘s allocations are optimal, only that S. 2191 funding
would appear to fill a projected need for public funds to promote technology milestones to
encourage the future availability of useful technology at the appropriate time.
ȱȱȱȱ¢ȱ ȱ
In addition to the CCS bonus allowance provision, S. 2191 contains funding for zero- or lowcarbon energy technology, advanced coal and sequestration technology, fuel from cellulosic
biomass, advanced technology vehicles (such as plug-in hybrids), and sustainable energy
technology, including distributed energy systems. In addition, the bill calls for new appliance and
building efficiency standards—some of which were included in EISA, as discussed earlier.
As noted earlier, the CATF/NEMS case attempted to model partially the effect of these incentives
through proxies. Specifically, CATF/NEMS simulated the incentives for low and no carbon power
technologies by using a production tax credit for CCS and extending the wind production tax
credit to 2030. CATF/NEMS also used EIA’s “Best Available Technology” case as a proxy for the
appliance and building standards included in the bill. The results are some of the lowest overall
cost estimates of any of the cases, along with substantial development of coal-fired CCS, nuclear
power and renewables.
74
See John A. Bewick, “Cultivating Clean Tech: New Models for Energy RD&D,” Public Utilities Fortnightly
(May 2008) pp. 42-48.
ȱȱȱ
Śŝȱ
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
Other innovative approaches were taken by EIA/NEMS, to attempt to mimic the impact of energy
efficiency incentives by reducing the incremental cost of the most energy-efficient residential
appliances by half—simulating a rebate for buying more efficient appliances. Likewise
EIA/NEMS mimicked the incentives for stronger building codes by tightening the residential
codes in the model by 30% in 2015 and 50% in 2025 compared with basecase levels. These
proxies come in addition to the EISA provisions that are contained in the preliminary AEO 2008
basecase used by EIA/NEMS. The proxies contribute to some of the lowest cost estimates of any
of the cases.
The only other model to incorporate these initiatives was MNA/CRA, which incorporated the
preliminary AEO 2008 baseline that includes the EISA provisions. However, the NMA/CRA
results do not separate out the efficiency standards from the new Corporate Average Fuel
Economy (CAFE) or renewable fuel standard (RFS) requirements (see next section on
“Transportation Sector”).
ȱȱ
The transportation sector presents particular problems for a cap-and-trade system. First, the sheer
number of motorized and aviation vehicles effectively necessitates an upstream regulation of
transportation fuels. It would be impracticable to place emissions monitors on the hundreds of
millions of cars, trucks, motorcycles, off-road vehicles, boats, trains, and aircraft in the United
States.75 Likewise, requiring each motorist to submit allowances for his or her fossil fuel use
would greatly increase the administrative costs of an emission reduction program.
Therefore, any regulation of transportation, especially motor vehicles, would likely occur
upstream of the emitting source, as is the case with S. 2191. Emissions reductions from
transportation generally must come in one of three ways: (1) reduce fuel consumption through
more efficient vehicles or through reduction in vehicle-miles traveled (e.g., mass transit,
carpooling, etc.); (2) reduce the carbon content of transportation fuels through the blending of
lower-carbon fuels in conventional fuels; (3) switch from conventional fuels to alternatives with
lower lifecycle emissions. Current federal policy attempts to address numbers 1 and 2. The
federal Corporate Average Fuel Economy (CAFE) standards, as amended by EISA, require
increasing fuel economy for new passenger cars and light trucks.76 The renewable fuel standard
(RFS), also amended by EISA, requires an increasing amount of renewable transportation fuel,
and that an increasing share of that fuel have lower greenhouse gas emissions.77 Both of these
programs should help reduce the number of allowances needed by the petroleum industry by
reducing the amount of fuel consumed, and the carbon content of the fuel supplied.
The cap-and-trade restrictions on petroleum would most likely be felt by transportation users
through higher prices. Users would receive the price signal and decide whether to invest in new
capital (e.g., purchase a new car), use less fuel (and drive less), or change fuels (if possible).
75
The European Union has proposed a downstream reduction program for the aviation industry, whereby airlines
would need to submit allowances to cover their own emissions. However, the number of aircraft is considerably smaller
than the number of passenger and freight vehicles in either the EU or the United States.
76
For more information on CAFE, see CRS Report RL33413, Automobile and Light Truck Fuel Economy: The CAFE
Standards, by (name redacted) and (name redacted).
77
For more information on the RFS, see CRS Report RL33290, Fuel Ethanol: Background and Public Policy Issues,
by (name redacted).
ȱȱȱ
ŚŞȱ
ȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱȱȱȱ
One key feature of S. 2191 and its impact on the transportation sector is the Low Carbon Fuel
Standard (LCFS) in Section 11003. The LCFS requires a 5% reduction in lifecycle greenhouse
gas emissions from transportation fuels from 2008 levels by 2015 and a 10% reduction from 2008
by 2020. This is similar to the proposed low carbon fuel standard established in California by
Governor Arnold Schwarzenegger.78
A major question on the effects of the LCFS is the definition of “transportation fuel.” In
discussions over the California program, most stakeholders, including California Air Resources
Board staff, argued that aviation fuel and bunker fuel should not be included in the standard.79
Simply put, the more fuels included in the program, and the greater the volume that must be
displaced, the more stringent the standard becomes. This is especially true for aviation fuel since
there are currently few or no options to reduce jet fuel lifecycle greenhouse gases.80 Therefore,
the more jet fuel included in the program, the greater the reductions necessary from other fuels.
For example, EIA projects 15.79 million barrels per day of transportation fuel demand in 2020, or
roughly 240 billion gallons annually.81 To meet a 10% reduction requirement, 24 billion gallons
of zero-carbon fuel would be needed, assuming equivalent energy content per gallon. However,
many low-carbon fuels have less energy per gallon than petroleum fuels, and all have some
associated carbon emissions. If cellulosic ethanol is found to have a 90% reduction in lifecycle
emissions, and the fuel has 2/3 the energy content of gasoline, then roughly 40 billion gallons
would be required. This is considerably more than the existing RFS mandate of 30 billion gallons
of renewable fuels82 in the same year. If, however, only motor gasoline and diesel fuel are
considered, then the total volume is reduced to 13.47 million barrels per day, or 206 billion
gallons annually. The equivalent amount of cellulosic ethanol required would be roughly 35
billion gallons, still a significant target.
The assumptions for the amount of low-carbon fuel available, the expected emission reductions
for that fuel, and the total amount of fuel subject to the requirements would significantly affect
the costs and feasibility of the LCFS program. The way the provisions are written in S. 2191, the
LCFS program is separate from the cap-and-trade program, and there is no way to purchase
credits or offsets from other sectors. If the necessary amount of low-carbon fuel is not available,
then under the program fuel providers must reduce the amount of fuel they sell, or pay civil
penalties. In its analysis of S. 2191, NMA/CRA states that in 2015 the LCFS “can only be met by
a decrease in gasoline consumption to allow the limited supplies of low carbon biofuel to meet
the averaging requirements of the standard.”83 Further, the model estimates that because of the
78
Governor Arnold Schwarzenegger, Executive Order S-01-07: the Low Carbon Fuel Standard, January 18, 2007.
See the California Air Resources Board page on the LCFS. http://www.arb.ca.gov/fuels/lcfs/lcfs.htm.
80
Further, EPA currently does not have the authority to regulate aviation fuels under the Clean Air Act; that authority
rests with the Federal Aviation Administration. Since this provision would amend the Clean Air Act, EPA may not
have the authority to include aviation fuel in the definition of transportation fuel.
81
EIA, Annual Energy Outlook. Table 11.
82
It should be noted, however, that the RFS mandates only require 15 billion gallons of “advanced biofuel” with a 50%
reduction in lifecycle emissions (as opposed to the 90% reduction in the example). The remaining 15 billion gallons of
the RFS mandate are not required to have any emissions reductions.
83
CRA International, Economic Analysis of the Lieberman-Warner Climate Security Act of 2007 Using CRA’s MRNNEEM Model (April 8, 2008), p. 29.
79
ȱȱȱ
Śşȱ
ȱDZȱȱȱȱȱǯȱŘŗşŗȦǯȱřŖřŜȱ
ȱ
decrease in supply, motor fuel prices increase 140% in 2015 over the baseline case.84 The
NMA/CRA analysis suggests that if the LCFS is construed to include all ground transportation
fuels without exception, then it may be difficult to achieve it without reducing fuel demand.
Depending on the design of the program and what fuels are included, the effects on fuel supply
and prices could be dramatic. However, if plug-in hybrid vehicles or large amounts of cellulosic
biofuel are available earlier than expected, or if certain fuels such as aviation fuel and non-road
fuels are excluded from the mandate, the costs could be lower.
ȱȱȱȱ
Given the divergent projections by the various cases about future electric generating capacity
illustrated in Table 11 and Table 12 and, with the exception of NMA/CRA, no detailed modeling
of the transport sector, it is not surprising that their estimates of the fuel price impacts of S. 2191
vary widely. Also, perhaps more than any other results, the cases were very selective in terms of
the results they chose to highlight in their studies and how they chose to present them. Hence,
CRS highlighted general themes coming out of the cases to focus on the insights this wide variety
of assumptions and calculations has to offer. A further discussion of the impact of energy costs on
households and energy-intensive industries is presented later.
ȱ ȱȱ
Some of the most confusing results presented by the cases are for natural price prices. Besides
different baselines, indices, and target categories (e.g., utility, industrial, residential, “average”),
some prices presented include allowance costs, while others do not. Likewise, some cases include
the “free” allowance allocations provided under S. 2191, others do not. In general the CGE
models present natural gas prices without the added cost of allowances; NEMS cases present
natural gas prices that include allowance costs.
In general, the incremental impact of S. 2191 on natural gas prices depends on the degree to
which natural gas-fired generation is used to back out existing coal-fired capacity and to meet
future demand. As discussed above, the cases fall into three categories with respect to future
natural gas-fired generation: (1) little or no increased generation; (2) modest increased generation;
or (3) substantial increased generation. Of the three cases included in the first category, the
EPA/ADAGE-REF and EPA/ADAGE-TECH cases project declining natural gas prices that do
not include any allowance costs. This compares with the CATF/NEMS case that projects natural
gas prices increasing only 3% in 2030 over baseline levels with allowance costs included. T
This text is long and has been trimmed here. Open the source document for the complete record.
This is a copy of a public record, reproduced as it was published. It is not legal advice, and it may not be the version a court would rely on. Check the official source before you cite it.