Climate Change: Costs and Benefits of S. 2191

Climate Change:
Costs and Benefits of S. 2191/S. 3036
May 15, 2008
Larry Parker and Brent D. Yacobucci
Specialists in Energy and Environmental Policy
Resources, Science, and Industry Division

Climate Change: Costs and Benefits of S. 2191/S. 3036
Summary
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.

Contents
Overview of the Major Provisions of S. 2191 (S. 3036)....................3
Earlier Versions of the Bill......................................7
Bill as Introduced..........................................7
Bill as Reported by Subcommittee.............................7
As Ordered Reported by Committee..........................10
Deficit Reduction Amendment..............................10
Introduction: Models Cannot Predict the Future Costs of
a Climate Change Program.....................................10
Lessons from SO² Cap and Trade Program.........................10
An Illustrative Example from Analyses of S. 2191...................12
Likelihood for More Noise in Greenhouse Gas Reduction Cost Estimates.....16
Complexity of the Problem.....................................16
Flexibility of Cap-and-Trade Program.............................17
Importance of Technology to Future Results........................18
Increasing Problems with Ceteris Paribus Analysis...................18
Changing Baselines By Changing Laws.......................18
Changing Baselines By Changing Regulation...................20
Measuring the Noise: A Web of Cost Measures.........................21
Three Perspectives: Getting Out of the Noise.......................24
Results for S. 2191................................................28
Impact on Greenhouse Gas Emissions.............................28
Impact on Non-Greenhouse Gas Emissions.........................31
Impact on GDP Per Capita......................................32
Allowance Price Estimates.....................................37
Auction Revenue Estimates.....................................40
Issues Raised by the Models........................................43
Technology Issues............................................43
Electric Power Sector......................................44
Transportation Sector......................................53
Impact on Fuel Prices......................................55
Economic Issues..............................................58
Availability of Offsets.....................................58
Impact of Banking........................................59
Impact of Carbon Market Efficiency Board.....................60
Impact of Revenue Recycling...............................60
International Leakage......................................60
Ecological Issues.............................................61
Climate Change Benefits...................................61
Non-Climate Change Air Quality Benefits.....................69
Impact on Behavior.......................................69
Conclusion ......................................................72

List of Figures
Figure 1. Predicted Impacts of Carbon Abatement on the
U.S. Economy (162 Estimates from 16 Models).....................23
Figure 2. Total Estimated Greenhouse Gas Emissions
Under S. 2191...............................................29
Figure 3. Total Estimated Greenhouse Gas Emissions from
Each Model Under S. 2191.....................................30
Figure 4. GDP per Capita (2005$) Under S. 2191.......................32
Figure 5. GDP per Capita (2005$) from Each Model
Under S. 2191...............................................33
Figure 6. Percentage Change in GDP per Capita Under S. 2191............35
Figure 7. Percentage Change in GDP per Capita
from Each Model Under S. 2191.................................36
Figure 8. Projected Allowance Prices Under S. 2191.....................38
Figure 9. Projected Allowance Prices from Each Model Under S. 2191......39
Figure 10. Estimated Annual Revenues from
Allowance Auctions Under S. 2191...............................41
Figure 11. Global Mean Surface Air-Temperature Increase in
Six Scenarios Using the MIT IGSM..............................68
Figure 12. Energy Price Change: Recent History Versus the
S. 2191 Core Case............................................72
List of Tables
Table 1. Allocation of Allowances Under S. 2191........................8
Table 2. Allocation of Auction Revenue (excluding
Deficit Reduction Fund) Under S. 2191............................9
Table 3. Representative Sample of 1990 Estimates of Annual
Compliance Cost for SO² Cap-and-Trade Program...................12
Table 4. Reference Case and S. 2191 Analyses for 2050..................13
Table 5. Reference Case Scenarios for 2020 and 2030....................15
Table 6. Influence of Climate Change Perspectives on
Policy Parameters.............................................25
Table 7. General Perspective of CATF and ACCF/NAM Cost
Assumptions .................................................26
Table 8. Selected Results from CATF and ACCF/NAM Analyses..........27
Table 9. EPA/IPM Reduction of Conventional Air Pollutants from
Electric Utilities..............................................31
Table 10. Allocation of Estimated Annual Auction Revenue from
S. 2191 Using EPA/ADAGE-TECH Case..........................42
Table 11. Assumptions about the Construction of Generating Capacity
Under S. 2191 to 2030.........................................45
Table 12. Assumptions about the Availability of CCS....................48
Table 13. Estimated Incremental Annual Combined Public and
Private Funding Needs to Achieve EPRI’s Full Portfolio..............51

Clean Coal Technology Roadmap over 18 Years
(2008-2025) .................................................52
Table 15. Matrix of Climate Risks....................................63
Table 16. The Stern Review Estimates of Social Cost of Carbon for
Three Emissions Paths.........................................64

Climate Change:
Costs and Benefits of S. 2191/S. 3036
As 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 110^th 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

¹ Originally titled America’s Climate Security Act of 2007.
² 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/economicanalys es.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/j orgenson/papers/papers.html ].

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: 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).

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 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/gl oba lchange/www/ eppa.html ].

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.
Overview of the Major Provisions
of S. 2191 (S. 3036)
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-and-trade 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

¹⁰ For more information on the MRN-NEEM model, see [http://www.crai.com/
uploadedFiles/RELAT ING_MAT ERIALS/Publications/ BC/Energy_and_E n vi r o n ment/
files/MRN-NEEM%20Int e gr a ted%20M odel%20for%20Analysis%20of%20US%20
Greenhouse%20Gas%20Policies.pdf].
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^th
the 110 Congress, by Larry Parker and Brent D. Yacobucci.

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 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.

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 Committee on Environment and
Public Works, dated May 13, 2008.

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 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

¹⁵ For a further discussion of Title VI, see Jeanne Grimmett and Larry Parker, Whether
Import Requirements Contained 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 Peter Folger.

carbon fuel standard (LCFS) requiring per-unit-energy reductions in
greenhouse gas emissions from transportation fuels.¹⁷
Earlier Versions of the Bill
Bill as Introduced.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 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.
Bill as Reported by Subcommittee. 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).

¹⁷ This LCFS provision is discussed in more detail in the section below under
“Transportation Sector.”

CRS-8
Table 1. Allocation of Allowances Under S. 2191
2012 2020 2030 2040 2050
Total Allowances (millions)Sec. 120157754924386027961732
Deficit Reduction FundSec. 3101 (as amended)6.10%8.40%14.43%15.99%15.99%
Remainder Allowances (millions)Sec. 3101 (as amended)54234510330323491455
Share of Remainder Allowances
ⁱ^{ki/CRS-RL34489}Early AuctionSec. 31015%0%
^g/wAuctionSec. 310221.5%36.5%62.8%69.5%69.5%
^s^.orEarly ActionSec. 32015%0%
^le^ak
StatesSecs. 3301-330410.5%10.5%10.5%10.5%10.5%
^://wikiTribal CommunitiesSec. 3303(d)0.5%0.5%0.5%0.5%0.5%
^h^ttpLow/Middle-Class Electricity Consumers Sec. 34019%9%9%9%9%
Low/Middle-Class Natural Gas ConsumersSec. 35012%2%2%2%2%
CCS Bonus AllowancesSec. 36014%4%4%0%
Domestic Agriculture and ForestrySec. 37015%5%5%5%5%
International Forest ProtectionSec. 38032.5%2.5%2.5%2.5%2.5%
Transition Assistance
Fossil Fueled Electric PlantsSec. 390119%16%1%
Rural Electric CooperativesSec. 39011%1%1%
Pilot Program for VA and MTSec. 3903(a)(2)0.2%0.2%0%
Energy-Intensive Manufacturing FacilitiesSec. 390110%8%0%
Petroleum Production/Import FacilitiesSec. 39012%2%0.25%
HFC Producers/ImportersSec. 39012%2%0.25%
Landfill and Coal Mine Methane ReductionSec. 39071%1%1%1%1%

CRS-9
Table 2. Allocation of Auction Revenue (excluding Deficit Reduction Fund) Under S. 2191
2012 2020 2030 2040 2050
Off-the-Top Allocation of Auction Proceeds
BLM Emergency Firefighting FundSec. 4302(b)(1)SSANSSANSSANSSANSSAN
Forest Service Emergency Firefighting FundSec. 4302(b)(2)SSANSSANSSANSSANSSAN
CSA Management FundSec. 4302(b)(3)SSANSSANSSANSSANSSAN
Percentage of Remaining Proceeds
Technology DeploymentSec. 4302(b)(4)(B)52%52%52%52%52%
ⁱ^{ki/CRS-RL34489}Energy Independence Acceleration FundSec. 4302(b)(4)(C)2%2%2%2%2%
^g/wEnergy Assistance FundSec. 4302(b)(4)(D)18%18%18%18%18%
^s^.orClimate Change Worker Training FundSec. 4302(b)(4)(E)5%5%5%5%5%
^le^akAdaptation FundSec. 4302(b)(4)(F)18%18%18%18%18%
^://wikiClimate Change and National Security FundSec. 4302(b)(4)(G)5%5%5%5%5%
^h^ttp
^No^te:^S^S^A^N^{= “}^s^u^c^h^s^u^m^s^asⁿ^eces^s^a^ry^{.” F}^o^{r i}^t^s^an^al^y^sⁱ^s^of^S^.^2191,^EPA^es^tⁱ^m^at^ed^t^o^t^a^l^prog^ram^cos^t^s^(“^C^S^A^Man^a^g^e^m^eⁿ^t^F^uⁿ^d^”)^at^1%^o^f^th^{e to}^{tal v}^a^lu^e
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As Ordered Reported by Committee. 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.
Deficit Reduction Amendment. 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.
Introduction: Models Cannot Predict
the Future Costs of a Climate Change Program
Lessons from SO² Cap and Trade Program
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 (1990-2000), 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 this¹⁹
far into the future are based more on philosophy than analysis. [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 EPA-ICF low estimate — the

¹⁸ See CRS Report 90-63, Acid Rain Control: An Analysis of Title IV of S. 1630, by Larry
Parker (January 31, 1990), p. 13. (Available from the author.)
¹⁹ Ibid., p. 16.

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 over-estimates 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 both²³

overstated for Title IV.
²⁰ The only 2010 national utility cost estimate mentioned in the summary of findings is for
the High Case: “Longer-term 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 Larry Parker
(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
(continued...)

Table 3. Representative Sample of 1990 Estimates
of Annual Compliance Cost for SO² Cap-and-Trade Program
^(billion^s^{, 2005 dollars}⁾
2000 2010
EPA-ICF $2.7-$3.6 $3.4-$8.0
NCAC-Pechan$4.4-$4.6 no estimate
(for 2000-2009)
E E I-T B S^a $7.1-$8.7 $7.9-$11.2
Estimated Actual Costs$1.9$2.2

2000-2007: Ellerman, et al.(for 2000-2007)

2010: EPA
^Source:^EP^A^-^IC^{F: IC}^{F R}^e^s^o^u^r^ces^In^corporated,^C^o^m^parⁱ^s^{on of}^t^h^{e Econom}ⁱ^c^Im^pact^s^of^t^h^{e Aci}^d
^{Rain Pr}^ovis^ions^{of the Senat}^e^B^{ill (}^S^{. 1630)}^{and the Hous}^{e Bill (}^S^{. 1630)}^{, P}^r^{epared f}^o^{r th}^{e U.S.}
^En^vⁱ^ron^m^en^t^a^l^Prot^ectⁱ^oⁿ^A^g^en^cy^(Ju^l^y¹⁹⁹⁰⁾^;^Pech^an^:^E.H^.^Pech^an^&^A^s^s^o^ci^at^es^,^C^l^{ean Air}^Act
^Am^endm^ent^C^o^s^t^s^{and Econom}ⁱ^c^Ef^f^ect^s^:^{A Revi}^ew^of^Publⁱ^s^hed^St^udi^es^{, P}^r^{epared f}^o^{r th}^{e Nation}^a^l
^C^l^ean^Aⁱ^r^C^o^alition^,^Nation^a^{l C}^l^ean^Aⁱ^{r Fu}ⁿ^d^{(October 1990); T}^B^{S: T}^e^m^p^{le, B}^a^rk^{er &}^Sloan^e^{, In}^c.,
^{Economic Evaluation of}^H.R.^{3030/S. 1490 “C}^{lean Air}^{Act Amendm}^ents^{of 1989”: Title}^V,^The^Acid
^Raiⁿ^C^ont^r^o^l^Pr^ogr^am^,^P^r^e^p^a^r^e^d^fo^r^the^E^d^isoⁿ^E^l^e^c^t^rⁱ^c^Iⁿ^stitute⁽^A^{ugust 3}⁰^,^1989).^E^s^ti^m^a^ted
^{2000-2007 actu}^al^cos^t^fr^o^m^A^.^D^e^nny^E^l^l^e^r^m^aⁿ^,^P^a^ul^L.^J^o^sko^w^,^aⁿ^d^D^a^vi^d^H^a^r^rⁱ^so^n,^J^r^.^,^E^m^issioⁿ^s
^T^r^adi^{ng i}ⁿ^t^h^{e U}^.^{S.: Exper}ⁱ^ence,^L^e^s^s^ons^{, and C}^onsⁱ^d^er^atⁱ^ons^f^o^r^G^r^eenhous^{e G}^a^s^e^s^{, prepared f}^o^r
^t^h^{e Pew}^C^eⁿ^t^{er on}^G^l^obal^C^lⁱ^m^at^{e C}^h^an^g^e^(N^ov^em^{ber 2007) p. 15.}^Estⁱ^m^a^t^e^d²^{010 actu}^al^cos^t
^fr^o^m^:^E^P^A,^Acid^Raⁱⁿ^Pro^g^r^a^m^Ben^e^{fits Exceed}^Exp^ecta^tioⁿ^s^{, F}ⁱ^g^u^r^e⁴^,^{p. 4. A}^v^ai^l^a^bl^{e at}
^[^h^t^t^p^:^/^/^www.^e^p^a^.^g^o^v^/^aⁱ^r^m^a^r^k^e^t^s^/^c^a^p^-^t^r^a^d^e^/^d^o^c^s^/^b^eⁿ^e^fⁱ^t^s^.^p^d^f^]^.^A^l^l^e^s^tⁱ^m^a^t^e^s^c^oⁿ^v^e^r^t^e^d^t^o^{2005 dol}^l^a^rs
^u^sⁱⁿ^g^th^{e GDP}^im^p^{licit p}^r^{ice d}^e^f^l^ato^r^.
^a.^Aⁿ^aly^s^is^of^origⁱⁿ^{al A}^d^mⁱⁿⁱ^s^t^ration^bill.^EP^A^es^tim^{ated th}^{at th}^{e f}ⁱⁿ^a^{l bill w}^a^s^{$400 m}^illion⁽¹⁹⁸⁸
^d^o^l^l^a^r^s⁾^a^nnua^l^l^y^m^o^r^e^e^x^p^eⁿ^sⁱ^v^e^t^h^aⁿ^t^h^e^o^rⁱ^gⁱⁿ^a^l^p^r^opos^al^{. S}^{ee EPA}^,^O^f^fⁱ^{ce of}^Aⁱ^{r an}^d
^Radⁱ^atioⁿ^,^C^l^{ean Ai}^r^Am^endm^ent^s^{: C}^o^s^t^C^o^m^parⁱ^s^on^(Jan^u^a^ry^{23, 1990).}
An Illustrative Example from Analyses of S. 2191
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 (Intensity^ghg) = Emissions^ghg
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

²³ (...continued)
US acid rain program,” Journal of Environmental Management 77 (2005) p. 255.

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 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.
Table 4. Reference Case and S. 2191 Analyses for 2050
^Difference^Difference^GH^G^Difference
^from^GD^P^p^e^r^from^Int^e^nsit^y^from
^Mo^d^e^l^P^o^pula^t^ioⁿ⁽^m^illio^ns)^lo^w^e^st^t^o^capita^lo^w^e^st^t^o^(G^HG^/^lo^w^e^st^t^o
^hig^h^est^(2005$)^hig^h^est^GD^P⁾^a^hig^h^est
^mo^d^e^l^mo^d^e^l^mo^d^e^l
^{Reference Cas}^e^Scenario
EPA/⁴⁰⁰^9%^$106,800^17%²⁴²^24%
ADAGE-
REF
EPA/⁴³⁴^$95,400²⁶⁹
IGEM-
REF
MIT /³⁹⁷^$111,300³⁰⁰
EPPA
^S^.^{2191 S}^cen^ari^o
EPA/⁴⁰⁰^9%^$104,300^24%¹²⁷^48%
ADAGE-
REF
EPA/⁴³⁴^$88,800¹⁰⁷
IGEM-
REF
MIT /³⁹⁷^$110,500⁸⁶
EPPA
^Source:^A^D^A^G^E^an^d^{IGEM m}^{odel as}^s^u^m^p^tion^s^f^r^om^th^{e “}^D^{ata A}ⁿⁿ^e^x^”^av^{ailable on}^th^e^EP^A^w^e^bs^ite
^a^t^[^h^t^t^p^:^/^/^www.^e^p^a^.^g^o^v^/^c^lⁱ^m^a^t^e^c^h^aⁿ^g^e^/^e^c^onomⁱ^c^s^/^econ^o^mⁱ^can^al^y^s^es^.h^t^m^l^]^{. T}^h^{e EPPA}^m^odel
^as^s^u^m^p^tion^s^f^r^om^Serg^ey^P^a^lts^ev^{, et al., “}^A^ppen^d^ix^{D” of}^P^a^lts^ev^{et al.,}^As^s^e^s^s^m^ent^of^U^.^{S. C}^ap-and-
^T^r^{ade Pr}^opos^al^s^,^M^I^T^Joiⁿ^t^Prog^ram^on^t^h^{e S}^cⁱ^eⁿ^{ce an}^{d Pol}ⁱ^c^y^of^G^l^obal^C^h^an^g^e^{(2007). A}^l^l
^es^tⁱ^m^at^es^con^v^e^rt^{ed t}^o^{2005 dol}^l^a^rs^u^sⁱⁿ^g^t^h^{e G}^D^Pⁱ^m^plⁱ^cⁱ^t^pri^{ce def}^l^at^or.
^{a. Measu}^r^edⁱⁿ^m^e^tr^{ic to}ⁿ^s^o^f^g^r^een^h^o^u^s^{e g}^a^{s em}^issioⁿ^{s p}^e^r^m^illioⁿ^d^o^llar^s^o^f^GDP^.
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 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 uses²⁵
the EIA baseline published in its Annual Energy Outlook 2007 for its analysis. 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

²⁴ Currently, EIA makes projections only to the year 2030.
²⁵ 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.

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
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.
Table 5. Reference Case Scenarios for 2020 and 2030
^Difference^Difference^GH^G^Difference
^from^GD^P^p^e^r^from^Int^e^nsit^y^from
^Mo^d^e^l^P^o^pula^t^ioⁿ⁽^m^illio^ns)^lo^w^e^st^t^o^capita^lo^w^e^st^t^o^(G^HG^/^lo^w^e^st^t^o
^hig^h^est^(2005$)^hig^h^est^GD^P⁾^a^hig^h^est
^mo^d^e^l^mo^d^e^l^mo^d^e^l
^Referen^{ce Cas}^e^S^cen^{ario (2030}⁾
^EP^A^/³⁶⁴^4%^$72,700^19%³⁴⁴^12%
^AD^AG^E^-
^RE^F
^EP^A^/³⁷²^$70,400³⁶³
^IGEM-^R^EF
^MIT^/^EPPA³⁵⁹^$73,700³⁷⁴
^CA^T^F^/^b³⁶⁵^$69,000³⁸⁴
^NEMS
^EIA^/³⁶⁶^$62,000³⁷²
^NEMS
^Referen^{ce Cas}^e^S^cen^{ario (2020}⁾
^EP^A^/³³⁶^2%^$59,000^12%⁴¹⁷^5%
^AD^AG^E^-
^RE^F
^EP^A^/³⁴²^$58,000⁴²⁸
^IGEM-^R^EF
^MIT^/^EPPA³³⁴^$59,200⁴³⁵
^CA^T^F^/^b³³⁷^$56,700⁴³⁸
^NEMS
^EIA^/³³⁸^$53,000⁴³¹
^NEMS
^Source:^A^D^A^G^{E an}^{d IGEM m}^{odel as}^s^u^m^p^tion^s^f^r^om^th^{e “}^D^{ata A}ⁿⁿ^e^x^”^av^ailable^on^th^e^EP^A^w^e^bs^ite
^a^t^[^h^t^t^p^:^/^/^www.^e^p^a^.^g^o^v^/^c^lⁱ^m^a^t^e^c^h^aⁿ^g^e^/^e^c^oⁿ^o^mi^c^s^/^e^c^oⁿ^o^mi^c^aⁿ^a^l^y^s^e^s^.^h^t^m^l^]^.^T^h^e^E^P^P^A^mo^d^e^l
^as^s^u^m^p^tion^s^f^r^om^Serg^ey^P^a^lts^ev^{, et al., “}^A^ppen^d^ix^{D” of}^P^a^lts^ev^{et al.,}^As^s^e^s^s^m^ent^of^U^.^{S. C}^ap-and-
^T^r^{ade Pr}^opos^al^s^{, MIT}^Joiⁿ^t^Prog^ram^on^t^h^{e S}^cⁱ^eⁿ^{ce an}^d^Polⁱ^c^y^of^G^l^obal^C^h^an^g^e^{(2007). T}^h^{e A}^E^O
^{2007 as}^s^u^m^p^tⁱ^oⁿ^s^f^r^om^En^erg^y^In^f^o^rm^atⁱ^oⁿ^A^d^mⁱⁿⁱ^s^t^ratⁱ^oⁿ^,^Ener^{gy M}^a^r^ket^{and Econom}ⁱ^c^Im^pact^s
^of^{S. 1766, t}^h^{e L}^o^w^C^a^r^{bon Econom}^{y Act}^of²⁰⁰⁷^(Jan^u^a^ry^{2007). T}^h^{e A}^E^O^{2008 ec}^oⁿ^o^mⁱ^c^an^d
^popu^lation^as^s^u^m^p^tion^s^f^r^om^EIA^’^s^w^e^bs^{ite at [h}^ttp://^www.^eⁱ^a^.^d^o^e^.^g^o^v^/^oⁱ^a^f^/^a^e^o^/ⁱⁿ^d^e^x^.^h^t^ml^]^.^T^h^e

^EIA^/^NEMS^as^s^u^m^p^tion^s^f^r^om^EIA^,^Ener^{gy M}^a^r^ket^{and Econom}ⁱ^c^Im^pact^s^of^{S. 2191, t}^h^{e L}ⁱ^eber^m^an-
^W^a^rⁿ^er^C^lⁱ^m^at^{e Secur}ⁱ^t^y^Act^of²⁰⁰⁷^(A^pri^l^{2008). A}^l^l^es^tⁱ^m^at^es^con^v^e^rt^{ed t}^o^{2005 dol}^l^a^rs^u^sⁱⁿ^g^t^h^e
^GDP^im^{plicit price def}^l^{ator w}^h^{ere n}^eces^s^a^ry^.
^{a. Measu}^r^edⁱⁿ^m^e^tr^{ic to}ⁿ^s^o^f^g^r^een^h^o^u^s^{e g}^a^{s em}^issioⁿ^{s p}^e^r^m^illioⁿ^d^o^llar^s^o^f^GDP^.
^{b. Bas}^e^{d on}^t^h^e^report^’^s^s^t^at^em^en^t^t^h^atⁱ^t^u^s^es^t^h^{e 2007}^AEO^b^a^selin^{e p}^r^o^j^ectioⁿ^f^o^r^{its an}^aly^s^{is. A}^l^l
^es^tⁱ^m^at^es^con^v^e^rt^{ed t}^o^{2005 dol}^l^a^rs^u^sⁱⁿ^g^t^h^{e G}^D^Pⁱ^m^plⁱ^cⁱ^t^pri^{ce def}^l^at^or.
Likelihood for More Noise in Greenhouse Gas
Reduction Cost Estimates
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 modeling²⁷
exercise and four pages of additional “qualitative” considerations. 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.
Complexity of the Problem
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

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.

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, 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.
Flexibility of Cap-and-Trade Program
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.

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.

Importance of Technology to Future Results
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:
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 and³⁰
development is a very significant policy instrument in the portfolio of options.
Increasing Problems with Ceteris Paribus³¹ Analysis
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 impact³²
resulting from the full implementation of a given policy response.
Changing Baselines By Changing Laws. That the policy baseline for
greenhouse gas emissions can be shifted significantly through new initiatives has

³⁰ John Houghton, “Introduction,” Energy Economics 28 (2006), p. 535.
³¹ 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.

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.
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.

³³ For more information on EISA, see CRS Report RL34294, Energy Independence and
Security Act of 2007: A Summary of Major Provisions, by Fred Sissine.
³⁴ American Council for an Energy-Efficient Economy, Energy Bill Savings Estimates as
passed by the Senate (December 14, 2007).

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.
Changing Baselines By Changing Regulation.³⁵ 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 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.

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).

Measuring the Noise: A Web of Cost Measures
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). GDP³⁷
measures the total value of goods and services produced within a nation’s borders.
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 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.

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.

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$.

Figure 1. Predicted Impacts of Carbon Abatement on the U.S. Economy
(162 Estimates from 16 Models)
^Source:^R^obert^R^e^pet^t^o^an^{d D}^uⁿ^can^A^u^s^tⁱⁿ^,^{The C}^o^s^t^s^{of C}^{limate Pr}^otecti^{on: A Guide for}^{the Per}^p^lexed^{, World}
^R^e^s^o^u^r^ces^In^s^titu^{te, 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 the³⁹
economy.
Generally, the cases examined here have not chosen to separate the 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).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.
Three Perspectives: Getting Out of the Noise
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

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.
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.

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.
Table 6. Influence of Climate Change Perspectives on Policy
Parameters
^Appro^a^c^h^Se^rⁱ^o^u^sne^{ss o}^f^pro^b^lem^Risk^{in dev}^e^lo^ping^mⁱ^ti^gati^oⁿ^p^r^ogram^Co^sts
^T^e^c^hno^l^o^gy^I^s^a^gno^stⁱ^c^oⁿ^t^h^e^B^e^liev^e^{s an}^y^r^e^d^u^ctioⁿ^View^{ed f}^r^om^th^{e bottom}^-
^m^e^ri^t^s^of^t^h^{e probl}^em^.^prog^ram^s^h^ou^l^d^be^up^.^T^e^nd^{s t}^o^se^e
^T^h^{e f}^o^cu^s^is^on^d^e^si^gne^d^t^o^m^a^xi^mⁱ^z^e^sⁱ^gⁿⁱ^fⁱ^can^{t en}^erg^y
^d^e^ve^l^o^pⁱ^{ng ne}^w^opportuⁿ^ities^f^o^{r n}^e^wⁱⁿ^ef^fⁱ^cien^ciesⁱⁿ^th^e
^techⁿ^o^log^y^th^{at can}^be^techⁿ^o^lo^g^y^{. Risk}^{lies in}^cu^rren^{t econ}^o^mⁱ^{c s}^y^s^t^em
^j^u^stif^ied^f^r^o^m^m^u^ltip^le^no^t^d^e^ve^l^o^pⁱ^ng^th^{at cu}^r^r^eⁿ^tly^av^ailab^l^e
^cr^iterⁱ^{a, in}^clu^dⁱⁿ^g^t^e^c^hno^l^o^gy^b^y^t^h^e^{(or proj}^ected)
^econ^o^mⁱ^c,^ap^p^r^o^p^r^{iate tim}^{e. Fo}^cu^s^techⁿ^o^logⁱ^es^can
^e^nvi^r^o^nm^eⁿ^t^a^l^,^aⁿ^d^on^res^earch^,^elimⁱⁿ^{ate at little o}^rⁿ^o
^s^o^{cial pers}^pectiv^es^.^dev^e^l^opm^en^t^,^an^d^o^v^er^{all co}^{st to}^th^e
^dem^oⁿ^s^t^r^atⁱ^oⁿ^;^an^{d on}^econ^o^m^y^.
^r^e^m^o^vi^{ng b}^a^r^rⁱ^e^r^s^t^o
^co^m^m^e^r^c^ializatioⁿ^o^fⁿ^e^w
^t^e^c^hno^l^o^gy^.
^Ecoⁿ^o^m^ic^Un^d^e^r^s^tan^d^{s issu}^{e in}^B^e^liev^e^{s th}^{at eco}ⁿ^o^mⁱ^c^View^{ed f}^r^om^th^{e top-}
^ter^m^{s o}^f^q^u^an^tif^iab^l^e^cos^t^s^s^h^ou^{ld be ex}^amⁱⁿ^ed^d^o^w^n.^T^e^nd^{s t}^o^se^e^a
^cos^t^-^b^en^ef^{it an}^aly^s^is^.^ag^ain^s^{t econ}^o^mⁱ^{c ben}^e^f^its^g^r^ad^u^a^{l im}^p^r^o^v^em^en^{t in}
^Ge^ne^r^a^lly^a^ssum^e^{s the}ⁱⁿ^d^e^ter^mⁱⁿⁱⁿ^g^an^y^en^erg^y^ef^fⁱ^cien^cyⁱⁿ^th^e
^s^t^at^u^s^qu^{o i}^s^t^h^e^s^p^ecifⁱ^{c redu}^ction^econ^o^m^y^{, bu}^{t s}ⁱ^gⁿⁱ^fⁱ^can^t
^b^a^selin^{e f}^r^o^m^w^h^ich^p^r^o^g^r^a^m^.^Risk^{lies in}^co^{sts (}^u^su^ally^q^u^an^tif^ied
^cos^t^s^an^{d ben}^e^f^its^are^im^posⁱⁿ^g^cos^t^sⁱⁿ^ex^ces^sⁱⁿ^ter^m^{s o}^f^GDP^lo^ss)
^m^eas^u^r^ed.^of^ben^e^fⁱ^t^s^{. A}ⁿ^y^ch^os^en^r^e^sul^tⁱ^{ng fr}^o^m^gl^o^b^a^l
^Un^q^u^an^tif^iab^l^e^redu^ctⁱ^oⁿ^g^o^al^s^h^ou^l^d^be^clim^{ate ch}^an^g^e^con^t^rol
^uⁿ^cer^tain^ty^ten^d^{s to}^b^e^im^p^l^em^en^ted^th^r^o^u^g^h^prog^ram^s^{. T}^y^pi^cal^l^o^s^s
ⁱ^gno^r^e^d^.^econ^o^mⁱ^{c m}^eas^u^r^es^s^u^ch^es^tim^ates^ran^g^e^f^r^om^0-
^as^{tradeable perm}^its^or^4%^of^G^D^P^.
^e^m^issioⁿ^ta^xe^s.
^Ecol^ogⁱ^cal^Uⁿ^ders^t^aⁿ^d^sⁱ^s^s^u^esⁱⁿ^R^a^th^{er th}^an^econ^o^mⁱ^c^Vie^w^{s c}^o^{sts fr}^o^m^aⁿ
^ter^m^{s o}^f^th^eir^p^o^t^en^tial^cos^t^s^an^{d ben}^e^fⁱ^t^s^or^ethⁱ^{cal pers}^pectiv^{e in}
^th^{reat to bas}ⁱ^{c v}^a^lu^es^,^t^e^c^hno^l^o^gi^c^a^l^term^s^of^th^{e ecolog}ⁱ^cal
ⁱⁿ^clu^dⁱⁿ^g^ecologⁱ^cal^opport^uⁿⁱ^t^y^{, ef}^f^ectⁱ^v^e^v^a^lu^{es th}^{at g}^l^o^b^a^l
^vⁱ^ab^ility^an^d^th^{e w}^e^ll-^prot^ectⁱ^oⁿ^of^t^h^{e pl}^an^et^’^s^clim^{ate ch}^an^g^e^th^reaten^s^.
^beiⁿ^g^of^f^u^t^u^re^e^c^o^sy^ste^m^{s sho}^u^ld^b^e^the^B^e^liev^e^{s th}^{at v}^a^lu^{es su}^ch
^ge^ne^r^a^tⁱ^o^ns.^Suc^h^p^r^im^ar^y^cr^iterⁱ^oⁿⁱⁿ^asⁱⁿ^terg^en^eration^a^l
^v^a^lu^es^ref^l^ect^d^e^ter^mⁱⁿⁱⁿ^g^th^{e sp}^ecifⁱ^cs^eq^u^ity^sh^o^u^ldⁿ^o^{t b}^e
^ecologⁱ^{cal an}^{d eth}ⁱ^cal^of^an^y^redu^ctⁱ^oⁿ^prog^ram^.^coⁿ^s^id^er^ed^co^m^m^o^d^ities
^con^s^ideration^s^;^F^o^cu^s^of^prog^ram^s^h^ou^l^d^t^o^b^e^b^o^ught^,^so^l^d^,^o^r
^adh^e^ren^t^s^s^{ee attem}^p^ts^b^e^oⁿ^a^l^t^e^rⁱ^{ng va}^l^u^e^s^aⁿ^d^dⁱ^sc^o^unte^d^.^Co^{sts a}^r^e
^to^coⁿ^v^e^r^t^th^emⁱⁿ^to^b^r^o^a^d^eⁿⁱ^{ng c}^o^nsum^e^r^defⁱⁿ^e^{d broadl}^y^t^o
^co^m^m^o^d^{ities to}^b^e^ch^oices^.ⁱⁿ^clu^d^{e aes}^th^{etic an}^d
^b^o^ught^aⁿ^d^so^l^d^a^s^e^nvi^r^o^nm^eⁿ^t^a^l^va^l^u^e^s
^tr^iv^ializin^g^th^{e issu}^e.^th^{at econ}^o^mⁱ^{c an}^aly^s^is
^canⁿ^o^{t r}^ead^ily^q^u^an^tif^y
^an^{d m}^oⁿ^e^tize.

⁴² CRS Report 98-738, Global Climate Change: Three Policy Perspectives, by Larry Parker
and John Blodgett.

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.
Table 7. General Perspective of CATF and
ACCF/NAM Cost Assumptions
CATF ACCF / NAM - L ow ACCF / NAM - H i g h
TechnologyAssumes noAssumes significantAssumes substantial
constraints onconstraints onconstraints on
technology technology technology
availability beyondavailability andavailability and
those embedded inhigher costs thanhigher costs than
NEMS those embedded inthose embedded in
NEMSNEMS
EconomicAssumes efficientAssumes short-termAssumes short-term
decision-making viadecision-making withdecision-making with
banking and offsetsno banking; amountno banking; offsets
(30%) as allowed inof offsets allowedconstrained to 15%-
S. 2191“greater than 20%”20%
EcologicalAssumes decisionsNone — total GHGNone — total GHG
made in favor ofemissions reductionemissions reduction
efficiency over priceestimates notestimated not
because of S. 2191presentedpresented
incentives and
regulations
^Source:^C^R^S^an^aly^s^is^of^{: J}^oⁿ^a^th^an^B^aⁿ^k^s^{, C}^l^ean^Aⁱ^{r T}^a^s^k^Force,^{The Lieber}^man-W^a^rⁿ^e^r^C^lⁱ^m^ate
^Securⁱ^t^y^Act^{— S. 2191: A Sum}^m^a^r^y^of^M^odelⁱ^{ng Res}^u^l^t^s^f^r^om^t^h^e^N^a^tⁱ^onal^Ener^gy^M^odelⁱ^ng^Sys^t^em
^(F^ebru^ary^2008);^S^cⁱ^eⁿ^ce^A^pplⁱ^catⁱ^oⁿ^s^In^t^e^rn^atⁱ^oⁿ^a^l^C^o^rporatⁱ^oⁿ^,^Analys^is^{of The Lieber}^man-W^a^rⁿ^er
^C^lⁱ^m^at^{e Secur}ⁱ^t^y^Act⁽^S^{. 2191)}^U^sⁱ^{ng t}^h^{e N}^a^tⁱ^onal^Ener^{gy M}^odelⁱ^{ng Sys}^t^em⁽^N^EM^S)^{, a report}^by^t^h^e
^A^m^eri^can^C^o^uⁿ^cⁱ^l^f^o^{r C}^a^pi^t^a^l^F^o^rm^atⁱ^oⁿ^an^{d t}^h^{e N}^a^tⁱ^oⁿ^a^l^A^s^s^o^ci^atⁱ^oⁿ^of^Man^u^f^act^u^r^ers^(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.
Table 8. Selected Results from CATF and ACCF/NAM Analyses
CATF ACCF / NAM - L ow ACCF / NAM - H i g h
GDP per capitanot discernable0.8%1.1%
Reduction 2020^afrom graph
GDP per capita^a0.9%2.6%2.7%
Reduction 2030
Allowance Priceabout $21$52$61

2020 (2005$)

Allowance Priceabout $45$216$258

2030 (2005$)

Greenhouse Gasabout 5.5 (notnot publishednot published
Emissions 2020including set-
(MMT CO2e) asides)
Greenhouse Gasabout 5.4 (notnot publishednot published
Emissions 2030including set-
(MMT CO2e) asides)
^Source:^J^oⁿ^a^th^an^B^aⁿ^k^s^{, C}^l^ean^Aⁱ^r^T^a^s^k^Force,^The^Lieber^man-W^a^rⁿ^er^C^{limate Secur}^{ity Act — S.}
^{2191: A Sum}^m^a^r^y^of^M^odelⁱ^ng^Res^u^l^t^s^f^r^om^t^h^{e N}^a^tⁱ^onal^Ener^{gy M}^odelⁱ^{ng Sys}^t^em^(F^ebru^ary^2008);
^Scien^{ce A}^p^p^licatioⁿ^s^Iⁿ^terⁿ^atioⁿ^a^{l Co}^r^p^o^r^atioⁿ^,^Analys^is^{of the Lieber}^man-W^a^rⁿ^er^C^{limate Secur}^ity
^Act⁽^S^.²¹⁹¹⁾^U^sⁱ^{ng t}^h^{e N}^a^tⁱ^onal^Ener^{gy M}^odelⁱ^{ng Sys}^t^em⁽^N^EM^S)^{, a report by}^th^{e A}^m^erican^C^o^uⁿ^c^il
^f^o^{r C}^a^pi^t^a^l^F^o^rm^atⁱ^oⁿ^an^{d t}^h^{e N}^a^tⁱ^oⁿ^a^l^A^s^s^o^ci^atⁱ^oⁿ^of^Man^u^f^act^u^r^ers^{(2008). A}^l^l^es^tⁱ^m^at^es^con^v^e^rt^ed
^t^o^{2005 dol}^l^a^rs^u^sⁱⁿ^g^t^h^{e G}^D^Pⁱ^m^plⁱ^cⁱ^t^pri^{ce def}^l^at^or.
^{a. R}^e^du^ctⁱ^oⁿⁱ^s^rel^a^tⁱ^v^{e t}^o^t^h^{e m}^odel^’^s^ref^e^ren^{ce cas}^{e bas}^e^lⁱⁿ^{e f}^o^{r 2020 an}^{d 2030.}
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

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
(continued...)

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.
Results for S. 2191
Impact on Greenhouse Gas Emissions
Figures 2 and 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 (...continued)
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).

Figure 2. Total Estimated Greenhouse Gas Emissions
Under S. 2191

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^{and Econom}ⁱ^c^Im^pact^s^of^S.^2191,^t^h^e^Lⁱ^eber^m^an-W^a^rⁿ^er^C^lⁱ^m^at^{e Secur}ⁱ^t^y^Act^of²⁰⁰⁷^(A^pri^l^2008).
^C^A^T^F^{/NEMS: J}^oⁿ^a^th^an^B^aⁿ^k^s^{, C}^l^ean^Aⁱ^{r T}^a^s^k^Force,^{The Lieber}^man-W^a^rⁿ^er^C^{limate Secur}^{ity Act}
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^NMA^/^CRA^{: CRA}^Iⁿ^terⁿ^atioⁿ^a^l,^Economⁱ^c^Anal^ysⁱ^s^of^t^h^{e L}ⁱ^eber^m^an-W^a^rⁿ^er^C^lⁱ^m^at^e^Securⁱ^t^y^Act
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CRS-30
Figure 3. Total Estimated Greenhouse Gas Emissions from Each Model Under S. 2191

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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.
Impact on Non-Greenhouse Gas Emissions
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
NO^x 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
S. 2191 Reduction fromCumulative Reduction
Reference Case: 2025 from Reference Case 2010-
(short tons)2025 (short tons)
Sulfur Dioxide 1,064,0003,000,000
Nitrogen Oxides 848,0004,900,000

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 Robert Meltz and James E. McCarthy.

Impact on GDP Per Capita
Figures 4 and 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.
Figure 4. GDP per Capita (2005$) Under S. 2191

^$^110,⁰⁰⁰
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CRS-33
Figure 5. GDP per Capita (2005$) from Each Model Under S. 2191

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To sort the situation out a little further, Figures 6 and 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 Figures 6 and 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.

Figure 6. Percentage Change in GDP per Capita Under S. 2191

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CRS-36
Figure 7. Percentage Change in GDP per Capita from Each Model Under S. 2191

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Allowance Price Estimates
Figures 8 and 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, MRN-NEEM, and EPPA models assume discount rates that tend to⁵³
encourage banking. 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 Figures 8 and 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).
⁵³ 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.

Figure 8. Projected Allowance Prices Under S. 2191

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CRS-39
Figure 9. Projected Allowance Prices from Each Model Under S. 2191

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Auction Revenue Estimates
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 International⁵⁵
Climate Change Assistance, combined.
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 $400⁵⁶
million for advanced vehicle and hydrogen fuel R&D. 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 Funding and Tax Incentives, by Jane A. Leggett.
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 Brent D. Yacobucci.

CRS-41
Figure 10. Estimated Annual Revenues from Allowance Auctions Under S. 2191

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Table 10. Allocation of Estimated Annual Auction Revenue from
S. 2191 Using EPA/ADAGE-TECH Case
^(m^illion^s^of^2005$)
Value of Auction Revenue20122020203020402050
Deficit Reduction Fund$6,531$11,705$25,622$33,352$33,455
General Auction Revenue$21,616$46,590$95,341$121,784$122,160
Off-the-Top Allocation of Auction Proceeds
BLM Emergency Firefighting Fund$150$150$150$150$150
Forest Service Emergency Firefighting Fund$430$430$430$430$430
CSA Management Fund$1,071$1,393$1,776$2,086$2,092
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
T echnology $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
Weatheriza tion $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
Ac tivities $359 $803 $1,674 $2,144 $2,151
Department of Commerce Adaptation
Ac tivities $359 $803 $1,674 $2,144 $2,151
Climate Change and National Security Fund$998$2,231$4,649$5,956$5,974
^No^tes:^C^R^{S es}^tim^ates^bas^e^{d on}^EP^A^/^A^D^A^G^E-^T^E^C^H^cas^{e allow}^aⁿ^{ce price proj}^ection^s^{. Hig}^h^e^r^a^l^l^o^w^aⁿ^c^e^p^rⁱ^c^e^e^s^tⁱ^m^a^t^es
^w^o^uld^lead^to^higher^auctioⁿ^p^r^o^ceed^s.^Fo^r^exam^p^l^e,^MI^T^/^E^P^P^A^allo^w^a^{nce p}^r^{ice p}^r^o^j^ectio^{ns r}^e^{sult in annual r}^e^venues
^r^o^ughl^y⁵⁰^%^t^o^100%^hⁱ^g^h^e^{r, depen}^dⁱⁿ^g^on^t^h^{e y}^ear.

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.
Issues Raised by the Models
Technology Issues
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.

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.

Electric Power Sector. Most of the analyses examined here focus on
technological alternatives in the electric power sector.
Availability of Technology. 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 coal-fired 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 technology-availability 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. 2191 to 2030
NuclearRenewableNaturalCoal with
Pow er Pow er Gas-f i red CCS
ACCF/NAM/NEMS-10 GW6 GW/yearabout 28425 GW
HIGH(limit)(limit)GW (built)(limit)
ACCF/NAM/NEMS-25 GW6 GW/yearabout 26950 GW
LOW(limit)(limit)GW (built)(limit)
MIT/EPPAabout 3-4about 26 GWabout 71 GWabout 236
GW (built)(built)(built)GW (built
with subsidy)
NMA/CRA40 GW130.5 GWabout 33 GW107 GW
(limit) (limit) (built) (limit)
EPA/IPM (for 2025)44 GW61.3 GW5.5 GW80 GW (built
(limit)(built)(built)with subsidy)
CATF/NEMS104 GW54 GW wind0133 GW
(built) power (built(built with
with subsidy)subsidy)
Biomass
(constrained)
EPA/ADAGE-REFabout 71 GWabout 58 GWlittleabout 165
(built)(built)GW (built
with subsidy)
EPA/ADAGE-TECHabout 70 GWabout 61GWlittleabout 89 GW
(built)(built)(built with
subsidy)
EIA/NEMS264 GW112 GW77 GW64 GW
(built) (built) (built) (built)
AEO 2007 baseline12.5GW12.4 GW88.2 GW145 GW (no
CCS)
^Source:^EP^A^/^A^D^A^G^{E an}^{d EP}^A^/^IP^{M: “}^D^{ata A}ⁿⁿ^e^x^”^av^{ailable on}^th^{e EP}^A^w^e^bs^{ite at}
^[^h^t^t^p^:^/^/^www.^e^p^a^.^g^o^v^/^c^lⁱ^m^a^t^e^c^h^aⁿ^g^e^/^e^c^oⁿ^o^mi^c^s^/^e^c^oⁿ^o^mi^c^aⁿ^a^l^y^s^e^s^.^h^t^m^l^]^M^I^T^/^E^P^P^A^:^S^e^r^g^e^y^P^a^l^t^s^e^v^,
^{et al., “}^A^p^p^e^ndix^{D” of}^P^a^lts^ev^{et al.,}^As^s^e^s^s^m^ent^of^U^.^{S. C}^ap-and-T^r^{ade Pr}^opos^al^s^{, MIT}^J^oⁱⁿ^t
^Prog^ram^on^t^h^{e S}^cⁱ^eⁿ^{ce an}^{d Pol}ⁱ^c^y^of^G^l^obal^C^h^an^g^e^{(2007). EIA}^/^N^E^MS^:^EIA^,^Ener^{gy Mar}^{ket and}
^Economⁱ^c^Im^pact^s^of^{S. 2191, t}^h^{e L}ⁱ^eber^m^an-W^a^rⁿ^er^C^lⁱ^m^at^{e Secur}ⁱ^t^y^Act^o^f²⁰⁰⁷^(A^pri^l^2008).
^C^A^T^F^{/NEMS: J}^oⁿ^a^th^an^B^aⁿ^k^s^{, C}^l^ean^Aⁱ^{r T}^a^s^k^Force,^{The Lieber}^man-W^a^rⁿ^er^C^{limate Secur}^{ity Act}
^—^S.^2191:^{A Sum}^m^a^r^y^of^M^odelⁱ^{ng Res}^u^l^t^s^f^r^om^t^h^{e N}^a^tⁱ^onal^Ener^{gy M}^odelⁱ^ng^Sys^t^em^(F^ebru^ary
^{2008). A}^C^C^F^/^N^A^M^S^/^N^E^MS^:^S^A^IC^,^Analys^is^{of the Lieb}^e^r^m^aⁿ^-^W^a^rⁿ^er^C^{limate Secur}^{ity Act (}^S^.
²¹⁹¹⁾^U^sⁱ^{ng T}^h^{e N}^a^tⁱ^onal^Ener^{gy M}^odelⁱ^{ng Sys}^t^em⁽^N^EM^S)^,^report^by^t^h^{e A}^C^C^F^an^{d N}^A^{M (2008).}
^NMA^/^CRA^{: CRA}^Iⁿ^terⁿ^atioⁿ^a^l,^Economⁱ^c^Anal^ysⁱ^s^of^t^h^{e L}ⁱ^eber^m^an-W^a^rⁿ^er^C^lⁱ^m^at^e^Securⁱ^t^y^Act
^of^{2007 U}^sⁱ^{ng C}^RA’s^M^R^N^-^N^EEM^M^odel^(A^pri^l^{8, 2008).}
^No^te:^“^Limⁱ^t^”^{is th}^{e m}^a^xⁱ^m^u^m^th^{at th}^{e m}^o^d^e^{l assu}^m^e^{s can}^b^e^b^u^{ilt — it is n}^o^{t n}^ecessarily^th^e^am^o^uⁿ^t
^th^{e m}^o^d^e^{l d}^e^ter^mⁱⁿ^ed^w^o^u^l^d^b^e^b^u^ilt.^“^B^uilt^”^{is th}^{e am}^o^uⁿ^t^th^{e m}^o^d^e^{l d}^e^ter^mⁱⁿ^edⁿ^eed^ed^to^b^e^b^u^ilt.
^“^A^bo^ut^”^{is an}^estim^{ate b}^y^{CRS o}^f^th^{e ad}^d^itioⁿ^a^{l cap}^acityⁿ^ecessary^f^o^{r th}^{e in}^cr^e^a^s^e^d^e^l^e^c^t^rⁱ^city
^produ^ctⁱ^oⁿ^proj^ect^{ed by}^t^h^{e m}^odel^bet^w^een²⁰¹⁰^an^d²⁰³⁰^uⁿ^d^er^S^.^{2191 i}ⁿ^t^h^{e abs}^eⁿ^{ce of}^capaci^t^y
^data^bein^g^prov^{ided. T}^h^{e ex}^ception^is^w^h^ere^th^eⁿ^a^tu^{ral g}^a^s^-^fⁱ^{red capacity}^w^a^s^es^tim^{ated f}^r^om^{a ch}^art.
^T^h^{e es}^tim^ates^w^e^{re calcu}^{lated as}^s^u^mⁱⁿ^g^an^{80% cap}^aci^t^y^f^act^or^f^o^{r bi}^om^as^s^,^90%^f^o^{r n}^u^c^l^{ear pow}^er
^an^{d coal}^{, 48%}^f^o^{r ren}^e^w^a^bl^es^{, an}^{d 85%}^f^o^{r n}^a^t^u^ral^g^a^s^.

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 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

⁶⁰ Compiled from EIA’s Reactor Status List available from EIA’s website.
⁶¹ Energy Information Administration, Fuel Choice in Steam Electric Generation: Historical
Overview, DOE/EIA-0472 (August 1985), pp. 5 and 7.
⁶² Environmental Protection Agency, EPA Analysis of the Low Carbon Economy Act of

2007: S. 1766 in the 110^th Congress (January 15, 2008) p. 49.

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.

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.

Table 12. Assumptions about the Availability of CCS
⁽ⁱ^{n G}ⁱ^ga^w^a^t^t^s^[^G^W^]⁾
2015 2020 2025 2030 Total
ACCF/ NAM / not not not not 25
NEMS-HIGH (buildpresentedpresentedpresentedpresented
limits)
ACCF/ NAM / not not not not 50
NEMS-LOW (buildpresentedpresentedpresentedpresented
limits)
MIT/EPPA0about 10about 10about 42about 63
(no subsidy)
MIT/EPPA (subsidy)about 10about 17about 59about 148about 236
NMA/CRA 2153060107
(build limits)
EPA/IPM 0070n/a70
(no subsidy)
EPA/IPM 5570n/a80
(subsidy)
CATF/NEMSabout 1about 8about 51about 73133
(subsidy)
EPA/ADAGE-REF0about 23about 47about 94about 165
(subsidy)
EPA/ADAGE-TECH0about 23about 9about 56about 89
(subsidy)
EIA/NEMS (subsidy)about 8about 16about 24about 1664
^Source:^EP^A^/^A^D^A^G^{E an}^{d EP}^A^/^IP^{M: “}^D^{ata A}ⁿⁿ^e^x^”^av^ailable^oⁿ^th^{e EP}^A^w^e^bs^{ite at}
^[^h^t^t^p^:^/^/^www.^e^p^a^.^g^o^v^/^c^lⁱ^m^a^t^e^c^h^aⁿ^g^e^/^e^c^oⁿ^o^mi^c^s^/^e^c^oⁿ^o^mi^c^aⁿ^a^l^y^s^e^s^.^h^t^m^l^]^M^I^T^/^E^P^P^A^:^S^e^r^g^e^y^P^a^l^t^s^e^v^,
^{et al., “}^A^ppen^d^ix^D”^o^f^P^a^lts^ev^{et al.,}^As^s^e^s^s^m^ent^of^U^.^{S. C}^ap-and-T^r^{ade Pr}^opos^al^s^{, MIT}^J^oⁱⁿ^t
^Prog^ram^on^t^h^{e S}^cⁱ^eⁿ^{ce an}^{d Pol}ⁱ^c^y^of^G^l^obal^C^h^an^g^e^{(2007). EIA}^/^N^E^MS^:^EIA^,^Ener^{gy Mar}^{ket and}
^Economⁱ^c^Im^pact^s^of^{S. 2191, t}^h^{e L}ⁱ^eber^m^an-W^a^rⁿ^er^C^lⁱ^m^at^{e Secur}ⁱ^t^y^Act^of²⁰⁰⁷⁽^A^pri^l^2008).
^C^A^T^F^{/NEMS: J}^oⁿ^a^th^an^B^aⁿ^k^s^{, C}^l^ean^Aⁱ^{r T}^a^s^k^Force,^{The Lieber}^man-W^a^rⁿ^er^C^{limate Secur}^{ity Act}
^{— S.}^2191:^A^Sum^m^a^r^y^of^M^odelⁱ^ng^Res^u^l^t^s^f^r^om^t^h^{e N}^a^tⁱ^onal^Ener^{gy M}^odelⁱ^{ng Sys}^t^em^(F^ebru^ary
^{2008). A}^C^C^F^/^N^A^M^S^/^N^E^MS^:^S^A^IC^,^An^a^l^{ysis o}^f^th^{e Li}^e^b^e^r^m^aⁿ^-^W^a^rn^{er Clima}^t^{e S}^ecu^{rity Act (}^S^.
²¹⁹¹⁾^U^sⁱ^{ng t}^h^{e N}^a^tⁱ^onal^Ener^{gy M}^odelⁱ^{ng Sys}^t^em⁽^N^EM^S)^{, report}^by^t^h^e^A^C^C^F^an^{d N}^A^{M (2008).}
^NMA^/^CRA^{: CRA}^Iⁿ^terⁿ^atioⁿ^a^l,^Economⁱ^c^Anal^ysⁱ^s^of^t^h^{e L}ⁱ^eber^m^an-W^a^rⁿ^er^C^lⁱ^m^at^{e Secur}ⁱ^t^y^Act
^of^{2007 U}^sⁱ^{ng C}^RA’s^M^R^N^-^N^EEM^M^odel^(A^pri^l^{8, 2008).}
^No^te:^GW^es^tim^ates^f^o^{r MIT}^/^EP^P^A^an^{d A}^D^A^G^{E calcu}^{lated as}^s^u^mⁱⁿ^g^{a 90% capacity}^f^actor.
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:
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 investment⁶⁷
in other areas of the economy, and thus increases the welfare cost of the policy.
Such retrofitable post-combustion technologies are in development. For
example, an ammonia-based, 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, chilled-ammonia-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 saline⁷⁰
aquifers on site. Once commercial viability is demonstrated at Mountaineer, AEP
plans to install the technology at its 450 MW Northeastern Station in Oologah, OK,⁷¹
early in the next decade. 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.
Effectiveness of Research, Development, Demonstration, and
Deployment Efforts. 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.

⁶⁶ EIA, Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner Climate
Security Act of 2007 (April 2008) p. iii.
⁶⁷ 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).

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 low-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

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.

Table 13. Estimated Incremental Annual Combined Public and
Private Funding Needs to Achieve EPRI’s Full Portfolio
⁽^m^illio^{ns o}^f^d^o^lla^r^s^a^nnua^lly⁾

2005-

2005- 2010- 2015- 2020- 2025- 2030
2009 2014 2019 2024 2030 Average
Annual
Distribution-enabled
technologies $250 $220 $140 $240 $240 $220
T r ansmission-enabled
technologies $100 $130 $120 $70 $60 $100
New/Extended Nuclear
Power $500 $520 $370 $370 $400 $430
Advanced coal and
Carbon Capture and
Storage $830 $800 $800 $620 $400 $690
Annual Totals$1,700$1,700$1,400$1,300$1,100$1,400
^Source:^Electrⁱ^{c P}^o^w^e^r^Resear^ch^Iⁿ^stitu^te,^The^Pow^e^r^{to Reduce C}^O²^Emissioⁿ^s^:^Th^{e Fu}^{ll Po}^rtfo^lio
⁽^A^ugust^2007).
^No^te:^“^D^istr^ib^u^tioⁿ^-^en^ab^led^techⁿ^o^lo^gⁱ^{es” r}^e^f^e^r^s^to^d^e^p^l^o^yⁱⁿ^g^sm^ar^{t d}ⁱ^strⁱ^b^u^tioⁿ^g^r^id^{s an}^d
^com^m^uⁿⁱ^catⁱ^oⁿ^sⁱⁿ^f^r^as^t^r^u^c^t^u^res^t^o^s^u^pport^com^m^e^r^cⁱ^a^lⁱ^z^a^tⁱ^oⁿ^o^f^eⁿ^d-^u^s^{e en}^erg^y^ef^fⁱ^ci^en^cy^{, di}^s^t^ri^bu^t^e^d
^en^erg^y^res^o^u^r^ces^{, an}^{d plu}^g^-ⁱⁿ^h^y^{brid electric v}^e^hⁱ^cles^.
^“T^r^a^nsmⁱ^ssi^oⁿ^-e^na^b^l^e^d^t^e^c^hno^l^o^gi^e^s^”^r^e^fe^r^s^t^o^d^e^pl^oyⁱⁿ^g^t^r^an^s^mⁱ^s^sⁱ^on^g^rⁱ^d^s^an^{d en}^erg^y^s^t^orag^e
ⁱⁿ^f^r^as^tru^c^tu^{re to s}^u^{pport as}^m^u^ch^as^20%-^{30% in}^term^itten^t^ren^e^w^a^blesⁱⁿ^s^p^ecifⁱ^{c reg}ⁱ^on^s^.
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.

Table 14. Total Public Funding Needs for 2007 CURC-EPRI
Clean Coal Technology Roadmap over 18 Years (2008-2025)
⁽^m^illioⁿ^s^o^f^d^o^llar^s⁾
Research andDemonstration
Development (80%Projects (50%Totals
Government Share)Government Share)
Integrated Gasification$2,100$2,000$4,100
Combined-Cycle (IGCC)
Combustion $580 $2,240 $2,820
Innovations for Existing$310$480$790
Plants (IEP)
Sequestration (Storage $180$740$920
— high CO2 scenario)
Fuel Cells$580$430$1,010
T urbines $360 $160 $520
T otals $4,110 $6,050 $10,160
^Source:^Co^{al Utilizatio}ⁿ^Resear^ch^Co^uⁿ^c^il,^T^h^{e C}^U^RC^{-EPRI C}^l^{ean C}^oal^T^echnol^{ogy Roadmap}^,
^a^v^aⁱ^l^a^b^l^e^a^t^[^h^t^t^p^:^/^/^www.^c^o^a^l^.^o^r^g^/^u^s^e^r^fⁱ^l^e^s^/^Fⁱ^l^e^/^U^p^d^a^t^e^d^_^C^U^R^C^_^E^P^R^I^_^C^l^e^aⁿ^_^C^o^a^l^_^_
^T^echⁿ^o^l^o^g^y^_R^oa.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.
Effectiveness of Economic and Regulatory Incentives. In addition to
the CCS bonus allowance provision, S. 2191 contains funding for zero- or low-
carbon 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

⁷⁴ See John A. Bewick, “Cultivating Clean Tech: New Models for Energy RD&D,” Public
Utilities Fortnightly (May 2008) pp. 42-48.

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.
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”).
Transportation Sector. 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 increasing⁷⁶
fuel economy for new passenger cars and light trucks. The renewable fuel standard
(RFS), also amended by EISA, requires an increasing amount of renewable

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 Brent D. Yacobucci and Robert Bamberger.

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).
Low Carbon Fuel Standard. 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 fuel⁷⁹
should not be included in the standard. 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

⁷⁷ For more information on the RFS, see CRS Report RL33290, Fuel Ethanol: Background
and Public Policy Issues, by Brent D. Yacobucci.
⁷⁸ Governor Arnold Schwarzenegger, Executive Order S-01-07: the Low Carbon Fuel
Standard, January 18, 2007.
⁷⁹ 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.

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 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.
Impact on Fuel Prices. Given the divergent projections by the various cases
about future electric generating capacity illustrated in Tables 11 and 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.
Natural Gas Prices. Some of the most confusing results presented by the
cases are for natural price prices. Besides different baselines, indices, and target

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 MRN-NEEM Model (April 8, 2008), p. 29.

⁸⁴ Ibid., p. 22.

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. The potential modest impact on natural gas prices would be
consistent with a future generation mix that is not heavily reliant on natural gas.
The three cases that project some additional natural gas-fired capacity —
MIT/EPPA, EIA/NEMS and NMA/CRA — vary in their results. For the two cases
that do not include allowance costs, the MIT/EPPA case projects a substantial decline
in natural gas prices through 2050, while NMA/CRA projects wellhead natural gas
prices increasing over basecase levels about 20% by 2020, then declining to no
increase by 2035 and declining steadily afterwards to about 25% below baseline
projections by 2050. For delivered natural gas, NMA/NEMS projects prices with
allowance costs at about 20% above basecase levels around 2025 and accelerating
rapidly after 2040. For the EIA/NEMS case that includes allowance costs, prices for
natural gas delivered to electric generators are projected to increase about 23% in
2020 and 40% in 2030; the price for natural gas delivered to residential consumers
increases about 14% in 2020, increasing to 26% in 2030 (compared with basecase).
In contrast to the cases above, the two ACCF/NAM/NEMS cases (which project
substantial increases in natural gas-fired capacity) estimate natural gas prices with
allowance costs increasing 108% (Low case) and 146% (High case) by 2030 for both
residential and industrial consumers. This result is consistent with the assumptions
used by ACCF/NAM in its analysis, as identified in Tables 7, 8, and 11.
Petroleum Prices. With the exception of NMA/CRA, the cases examined
here do not model the transportation sector in a detailed manner. As noted in the
“Transportation Sector” discussion, perhaps the most important impact on petroleum
prices under S. 2191 may come from the Low Carbon Fuels Standard (LCFS), at least
in the short term. For the cases that did not model the LCFS, three cases — the two
EPA/ADAGE cases and the MIT/EPPA case — project either modest increases or
declines in petroleum prices compared with basecase projections (a substantial
decline in the case of MIT/EPPA). These models are all global in scope, with the
petroleum price reflective of what they see as occurring in the international oil
market, and do not include the increased cost of carbon allowances.
The other four cases — the two ACCF/NAM/NEMS cases, EIA/NEMS and
CATF/NEMS — focus on gasoline prices and the price increases from the allowance

requirement. The CATF/NEMS case estimates gasoline price increases reaching
about a quarter ($0.25) per gallon by 2030, while EIA/NEMS estimates a 2020
gasoline price increase of about $0.22 per gallon and a 2030 price increase of about
$0.40 per gallon. EIA/NEMS also provides estimates for other transportation fuels,
including diesel and jet fuel. The two ACCF/NAM/NEMS cases project more
dramatic gasoline price increases of about $3.25 (High case) and about $1.70 (Low
case) per gallon by 2030 (2005$).
Electricity Prices. Electricity price calculations by the various cases include
allowance prices. However, like the presentation of natural gas prices, the cases use
a confusing array of different baselines, indices values, and target categories
(residential, industrial, average, etc.). This can lead to some misleading conclusions
when comparing different cases. For example, the MIT/EPPA case estimates 2030
electricity prices under S. 2191 at 57% above 2005 prices, compared with
EPA/ADAGE-REF’s 2030 estimate under S. 2191 of 29% above 2005 prices.
However, reflecting the critical role of basecase assumptions, the MIT/EPPA
basecase 2030 electricity price estimate is 39% above 2005 prices — thus the
incremental difference between the basecase and the S. 2191 estimate is 18
percentage points. In contrast, the EPA/ADAGE-REF basecase 2030 electricity price
estimate is an 11% decline below 2005 prices — thus the incremental difference
between the basecase and the S. 2191 estimate is 40 percentage points.
The only metric for which the cases provided sufficient data was percentage
increases from basecase levels. For the EPA/ADAGE cases, the basecase assumes
a decline in electricity prices from 2005 levels in 2030 (11%-15%). This compares
with a 29% increase in basecase prices for MIT/EPPA, a 5.6% increase for the
EIA/NEMS, NMA/CRA, and ACCF/NAM/NEMS cases, and a 0.5% decline for
CATF/NEMS relative to 2005 prices. It should be noted that several cases do not
state precisely what the 2005 electricity prices refers to — residential, industrial, all
users, or something else.⁸⁵
Relative to their respective baselines, three cases estimate electricity price
increases under 15%: CATF/NEMS, EIA/NEMS, and MIT/EPPA, and three cases
estimate price increases between 35%-45%: the two EPA/ADAGE cases and the
NMA/CRA case. In contrast, the two ACCF/NAM/NEMS cases project prices
substantially higher: 101% (Low case) and 129% (High case) in 2030.

⁸⁵ Electricity prices vary substantially by sector and region. For example, in 2006, EIA
reports residential rates of $30.52 per million BTU compared with industrial rates of $17.97
(2006$).

Economic Issues
Availability of Offsets. Along with technology development, the availability
and price of offsets is one of the critical factors determining the costs of S. 2191,
particularly in the short- to mid-term. As noted by EIA: “the highest prices in the
first 5 years of the cap-and-trade program occur when international offsets are not
assumed to be available.”⁸⁶ As stated more forcefully by EPA:
From the various scenarios analyzed, the use or limitation of offsets and
international credits has a larger impact on allowance prices than the modeled⁸⁷
availability or constraint of key enabling technologies.
However, this conclusion is not obvious from a first read of the cases. In its
heavily constrained cases, ACCF/NAM/NEMS notes that “the purchase of relatively
inexpensive offsets significantly constrains allowance prices until the early 2020s...”
when the available offsets run up against the limits contained in the bill or in the
model’s assumptions.⁸⁸ In contrast, the NMA/CRA finds no such relief, stating that
“since the limit on domestic offsets is projected not to be reached until after 2025,
allowing greater use of domestic offsets does not reduce near term costs.”⁸⁹
Obviously, there is significant disagreement on the availability and cost-effectiveness
of domestic and international offsets.⁹⁰
A critical factor in this uncertain situation is the availability of international
credits. The EPA/ADAGE, EPA/IGEM, and EIA/NEMS cases assume the
availability of substantial international credits at reasonable prices. Sensitivity
analysis by EPA indicates that if domestic and international credit availability were
unlimited, 2050 allowance prices would fall by 71%. It should be noted that, subject
to changes in the international framework for international commitments and trading,
S. 2191 would not allow U.S. companies to obtain credits via mechanisms such as
the Clean Development Mechanism (CDM), either directly or through a secondary
market as currently written (Section 2502).⁹¹ Instead, participation would be indirect

⁸⁶ EIA, Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner Climate
Security Act of 2007 (April 2008), p. xi.
⁸⁷ EPA, EPA Analysis of the Lieberman-Warner Climate Security Act of 2008: S. 2191 in

110th Congress (March 14, 2008), p. 3.

⁸⁸ 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 National Association of
Manufacturers (2008), p. 9.
⁸⁹ 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. 12
⁹⁰ For more information on the role of offsets in a cap and trade system, see CRS Report
RL34436, The Role of Offsets in a Greenhouse Gas Emissions Cap-and-Trade Program:
Potential Benefits and Concerns, by Jonathan L. Ramseur.
⁹¹ Because the United States has not ratified the Kyoto Protocol, it is not eligible to
(continued...)

via substitutions of CDM-style credits for eligible allowances (such as those used by
the EU-ETS) by other controlled countries and then sold to U.S. companies. The
EPA/ADAGE, EPA/IGEM, and EIA/NEMS cases assume this interpretation of
Section 2502.
Of course, given the long time frame of S. 2191, projecting the availability and
prices of international credits is an uncertain business. For example, the European
Commission (EC) has not decided on the status of credits from the Clean
Development Mechanism (CDM) for the post-Kyoto period. Given the indirect
arrangement necessary for those credits to impact S. 2191 compliance costs, this
uncertainty can not be readily resolved.
NMA/CRA disagrees that substantial international credits would be available
at reasonable prices. NMA/CRA argues that since the countries involved must have
programs of “comparable stringency,” the allowance prices are likely to be similar
to U.S. prices and excludes them from its analysis. As suggested by sensitivity
analysis conducted by EPA, EIA, and MIT, restrictions on international credits
substantially increase the cost of S. 2191. EIA estimates that the unavailability of
international credits would increase allowance prices 39% in 2030; for 2050, MIT
estimates an allowance price increase of 15% while EPA projects that increase at

34%.

The impact of domestic offsets in reducing costs is projected to be less dramatic
than for international credits, although the incentives available for domestic offsets
could alter this. EPA sensitivity analysis indicates that unlimited domestic offsets
would reduce allowance prices by 26% (p. 6).⁹²
Impact of Banking. Experience with the acid rain program strongly indicates
that participants bank allowances in the face of price uncertainty. In the case of
greenhouse gas reductions, the availability of offsets and international credits also
interacts with the banking provisions. As noted earlier, the ACCF/NAM/NEMS cases
do not include banking: this fact helps explain their dramatically increasing
allowance prices. All other cases include banking.
The models suggest two important results from banking. First, as noted earlier,
banking has a flattening effect on allowance prices as participants buy more than they
need early, raising prices, and use them later, lowering prices from the levels they
would be otherwise. Second, and perhaps more critically, banking allows participants
more control over the scheduling of reduction efforts. Given the pivotal nature of
technology development to the ultimate success of any greenhouse gas reduction
program, the ability to delay making major capital investments is very important. In
the EPA/ADAGE and EPA/IGEM cases, entities bank allowances until around 2030
(depending on the scenario). This is possible because of the availability of offsets
and international credits. In the EIA/NEMS case, it is modeled in a manner to ensure

⁹¹ (...continued)
participate in the CDM.
⁹² This estimate includes reductions associated with allowance set-asides in Title III,
Subtitle G (agriculture and forestry) and Title III, Substitle J (landfill and coal methane).

a 5 billion allowance bank remains at the end of 2030 as a proxy to reflect allowance
needs in the post-2030 period (EIA/NEMS does not project beyond 2030).
Impact of Carbon Market Efficiency Board. The models generally do not
consider the Carbon Market Efficiency Board (established by Title I, Subtitle F).
However, one can infer from the models’ results that the most important power that
the Board may have is the ability to increase the availability of domestic offsets and
international credits. As noted, increasing the availability of domestic offsets and
international credits could have a significant effect on overall costs. If the Board
chose to “loosen” the limitation on offset and international credit availability, the cost
reductions could be substantial as indicated by the EPA cases. In addition, if the
Board determined that technology development was not occurring on schedule and
causing volatility in the allowance markets, loosening constraints on offsets and
international credits could allow covered entities to bank more, allowing more time
for technology development, if necessary. However, the Board is primarily designed
to deal with short-term volatility due to episodic events in the allowance market and
has only short-term powers. Whether it could coordinate a longer term strategy, if
necessary, with its proposed authority is not known.
Impact of Revenue Recycling. As indicated in the “Auction Revenue
Estimates” section, S. 2191 could redirect hundreds of billions of dollars annually
through the economy. Only the NMA/CRA case states it captured the effects of this
redirection. The other models generally assume the effect on the economy is similar
to a lump-sum adjustment to taxes designed to keep S. 2191 deficit- and revenue-
neutral.
International Leakage. International leakage is the shift in GHG emissions
from a country subject to regulation (e.g., cap-and-trade program) to an unregulated
country, 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. Only the EPA/ADAGE cases looked at the international trade aspect
of Title VI of S. 2191. EPA’s sensitivity analysis indicates that if countries without
legally binding commitments to reduce greenhouse gases commit to maintaining their
2015 levels beginning in the year 2025, and to returning their emissions to 2000
levels by 2050, no international emission leakage occurs (p. 82). Imports of energy-
intensive goods are projected to fall under this scenario, while exports expand as
developing countries cope with their new emission limits.
In a worst case scenario, EPA’s sensitivity analysis looked at a no-international-
actions-to-2050 scenario. In this scenario, the International Reserve Allowance
provisions of Title VI are assumed to be triggered because of the lack of international
action. Emissions from countries without legally binding commitments are estimated
to rise by 350 million CO²e by 2030 and 385 million by 2050 — less than 1% of their
basecase levels under ADAGE. It would be equivalent to U.S. emission leakage
rates of approximately 11% in 2030 and 8% in 2050. These emissions compare with

increases of 361 million and 412 million for 2030 and 2050 respectively if Title VI
is not implemented. EPA describes the impact of Title VI on leakage as “minimal.”⁹³
The impact on imports is more significant. Without the International Reserve
Allowance Requirement, imports from countries without legally binding
commitments are projected to increase 5.4% in 2030, rising to 7% in 2050. In
contrast, under Title VI, imports are estimated to increase about 1% in 2030 and
decline about 5% in 2050. U.S. exports decline in both cases as countries use more
of their domestic manufacturing (p. 85).
If the EPA projections are reasonable, the differential effect of Title VI on trade
versus emissions leakage could present problems if the title is brought before the
World Trade Organization (WTO).⁹⁴
Ecological Issues
Climate Change Benefits. None of the cases examined here attempt to
quantify or monetize the benefits of reducing greenhouse gases. Indeed, with the
exception of MIT’s overall study of cap-and-trade proposals, the environmental⁹⁵
benefits of reducing greenhouse gases are generally not discussed. This hole in
reports designed to discuss the impacts of S. 2191 is not surprising. Like the cost
estimates discussed above, benefit estimates are fraught with uncertainty. Thus, this
discussion should be considered illustrative — more research and resources devoted
to benefits analysis are necessary before more comprehensive reports will be
available.
Monetizing Benefits: Some Illustrations. Monetizing benefits from
reducing air pollutants has been attempted for decades. For example, during the
debate in the 1980s on controlling sulfur dioxide, EPA conducted an illustrative
analysis of the health benefits of promulgating a 1-hour sulfur dioxide National
Ambient Air Quality Standard (NAAQS) as part of its Regulatory Impact Analysis⁹⁶
(RIA). Based on partial analysis of health impacts, EPA’s illustrative exercise put
the potential health benefits from stringent sulfur dioxide control at between zero and
$385 billion (1984$) annually. These health-based benefits were in addition to a CRS

⁹³ EPA, EPA Analysis of the Lieberman-Warner Climate Security Act of 2008: S. 2191 in

110th Congress (March 14, 2008) p. 84.

⁹⁴ For a further discussion of S. 2191, Title VI and WTO, see Jeanne Grimmett and Larry
Parker, Whether Import Requirements Contained 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.
⁹⁵ 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. 45-55.
Available at [http://mit.edu/globalchange].
⁹⁶ Office of Air and Radiation, Environmental Protection Agency. Regulatory Impact
Analysis on the National Ambient Air Quality Standards for Sulfur Oxide (Draft). Research
Triangle Park (1987), appendix B.

partial estimate of welfare benefits from reducing sulfur dioxide that exceeded $4
billion (1985$) annually.⁹⁷
Because climate change is a global problem, monetizing benefits from reducing
greenhouse gases is difficult. Indeed, some consider the effort impossible, bordering
on the unethical. The complexity of the global response is magnified by the need to
value benefits that accumulate over 100 years or more. Discount rates — economics’
approach to valuing time — used in attempts to value long-term damage from
climate change range from 0 to 4-5% in the literature.⁹⁸ Indeed, the effect of
discounting is so great on a long-term marginal damage estimate of climate change
that “using standard assumptions about discounting [i.e., 4-5%] and aggregation, the
marginal damage costs of carbon dioxide emissions are unlikely to exceed $50 tC
[$14 tCO²], and probably much smaller.”⁹⁹ Indeed, estimates of the Social Cost of
Carbon (SCC) — the marginal damage resulting from the addition of one more ton
of CO² — span over three orders of magnitude: from zero to over a $500 a ton.¹⁰⁰
However, most current attempts to monetize environmental benefits are
incomplete. The matrix presented in Table 15 illustrates the problem. Most studies
that attempt to monetize benefits focus on the market impact of predictable, average
changes in climate (the “easiest to measure” box of Table 15). Only a few attempt
to value non-market impacts or extreme events and fewer still consider catastrophes
or socially contingent impacts.¹⁰¹ In reviewing 28 studies the UK Government had
analyzed in re-examining its estimate of an appropriate Social Cost of Carbon,
Ackerman and Stanton observed:
That is, all of the studies that estimate the social cost of carbon base their
numbers on an incomplete picture of climate risks — often encompassing only
the simplest and most predictable corner of the vast, troubling canvas that has
been painted by climate science. There is, of course, no way to assign monetary
values to the global response to the possibility of widespread droughts across
large parts of Asia, or an increase in the probability of a sudden change in ocean
currents that would make the UK as cold as Canada, but in the understandable
absence of such impossible monetary values, it is important to remember the
disclaimer from the DEFRA [Department for Environment, Food & Rural
Affairs] review: all estimates of the SCC [Social Cost of Carbon] omit some of

⁹⁷ The Clean Air Standards Attainment Act: An Analysis of Welfare Benefits From S. 1894,
CRS Report 88-298 ENR (April 15, 1988) by Larry Parker (available from the author). For
a further discussion of benefits, see CRS Report 90-72 ENR, Potential Benefits of Enacting
Clean Air Act Amendments by John Blodgett. (Available from the author).
⁹⁸ Richard S.J. Tol, “The Marginal Damage Costs of Carbon Dioxide Emissions: An
Assessment of the Uncertainties,” Energy Policy (2005), pp. 2064-2074.
⁹⁹ Ibid., p. 2064.
¹⁰⁰ For a discussion of SCC uncertainty and the UK shadow cost of carbon, see Simon Dietz,
Review of DEFRA paper: “The Social Cost of Carbon and the Shadow Price of Carbon;
What They Are, and How to Use Them in Economic Appraisal in the UK” Review
Comments (September 2007).
¹⁰¹ Frank Ackerman and Elizabeth Stanton, Climate Change — the Costs of Inaction, Report
to Friends of the Earth England, Wales and Northern Ireland (October 11, 2006), p. 26.

the most important unpriced risks of climate change. The same disclaimer¹⁰²
applies to virtually any quantitative economic estimate of climate impacts.
Table 15. Matrix of Climate Risks
Type of ImpactMarketNon-marketphysicalSociallycontingent
(to the right)ImpactsImpactsImpacts
PredicatabilityExamples ofAgriculturalDeaths,Migration,
(below)impacts (to theoutput, healthextinctions,response to
right and below)costs,ecosystemfood & water
property lossdamagesshortages
AveragesTemperature, sea(Easiest to
levels,measure)
atmospheric CO2
steadily rising
Extremes Increased
frequency and
strength of heat
waves, storms,
droughts, floods
CatastrophesPolar ice sheets(Hardest to
melting, “turningmeasure)
off” major ocean
currents
^Source:^T^o^m^Dowⁿⁱⁿ^g^an^d^P^a^u^l^W^a^tk^is^s^,^Ov^ervⁱ^ew^:^T^h^{e Marg}ⁱⁿ^{al Social C}^o^s^t^of^C^a^rbonⁱⁿ^P^o^licy
^Makⁱⁿ^g^:^A^pplication^s^{, Un}^certain^ty^{, an}^{d a P}^o^s^sⁱ^{ble R}ⁱ^s^k^B^a^s^e^{d A}^pproach^,^{DEFRA Int}^e^r^natⁱ^onal
^Semⁱ^nar^{on t}^h^{e Soci}^a^l^C^o^s^t^s^of^C^a^r^bon^{(2003), as}^adapt^e^d^by^F^r^an^k^A^c^k^e^rm^an^an^{d El}ⁱ^zabet^h^S^t^an^t^oⁿ^,
^C^lⁱ^m^at^{e C}^{hange — t}^h^{e C}^o^s^t^s^of^Inactⁱ^on,^Rep^o^r^t^to^Fr^ien^d^s^o^f^th^{e Ear}^t^h^En^g^l^an^d^,^W^a^{les an}^d^No^r^t^h^e^rⁿ
^Irel^aⁿ^d^(O^ct^{ober 11, 2006).}
The matrix also indicates the moral dilemma presented by efforts to monetize
benefits — a dilemma magnified by the issue of intergenerational discounting. The
notion that deaths, extinctions, and other such potential impacts are less important
because they occur in some future generation is, for some, morally problematic.
Criticizing the UK government attempt to put a price on climate change, the UK
House of Commons Select Committee on Environmental Audit stated:
Furthermore, given the inherent difficulties in putting a price on climate change,
the Government’s first priority in deciding on the merits of potential policies and
construction projects ought to be deciding how they affect UK carbon budgets,
and only secondly on what the monetary value of resulting carbon emissions¹⁰³

would be.
¹⁰² Ibid., p. 26.
¹⁰³ The United Kingdom Parliament, Select Committee on Environmental Audit, Third
(continued...)

Besides moral considerations, one’s valuation of the social cost of carbon is
dependent on one’s assumptions about the emissions path the world is on.¹⁰⁴ This is
due to the relationship between atmospheric concentrations of GHGs and radiative
forcing (i.e., the higher the atmospheric concentration, the less the effect of one more
ton on warming), the relationship between climate change and economic impacts
(i.e., the higher the damage, the less the effect of one more ton on that damage), and
discounting (impacts occurring earlier are valued more than impacts occurring
later).¹⁰⁵ This phenomenon is illustrated in The Stern Review on the economics of
stabilizing climate change.¹⁰⁶ As shown in Table 16, the SCC declines as the path
of emissions is projected to result in less severe damages. Such estimates would
increase over time as the damage got closer and closer.
Table 16. The Stern Review Estimates of Social Cost
of Carbon for Three Emissions Paths
Stabilization ScenarioSocial Cost of Carbon(per metric ton, 2005$)
Business-as-usual (no effort to stabilize$95
emissions beyond basecase levels)
On a path to stabilize GHG$34
concentrations at 550 ppm
On a path to stabilize GHG$28
concentrations at 450 ppm
^Source:^{Sir Nich}^olas^Stern^,^T^h^{e Econom}ⁱ^c^s^of^C^lⁱ^m^at^{e C}^{hange: Th}^e^S^t^e^r^{n Revi}^ew^{(2006) p. 304.}
^Es^tⁱ^m^at^es^con^v^e^rt^{ed t}^o^{2005$ u}^sⁱⁿ^g^t^h^{e G}^D^Pⁱ^m^plⁱ^cⁱ^t^pri^{ce def}^l^at^or.
In an attempt to respond to the implications of climate change and The Stern
Review, the UK Government has instituted a shadow price for carbon to be used in
official cost-benefit analyses.¹⁰⁷ A shadow price is a little different from a Social Cost
of Carbon value. The latter is an attempt to determine the marginal damage resulting
from the addition of one more ton of CO² — it indicates what people should be
willing to pay now to avoid the future damage caused by more carbon emissions. In
contrast, a shadow price represents a cost or benefit from a good when the market

¹⁰³ (...continued)
Report (February 26, 2008), in press.
¹⁰⁴ Simon Dietz, Review of DEFRA paper: “The Social Cost of Carbon and the Shadow
Price of Carbon; What They Are, and How to Use Them in Economic Appraisal in the UK”
Review Comments (September 2007) pp. 5-10.
¹⁰⁵ Ibid., p. 6.
¹⁰⁶ Sir Nicholas Stern, The Economics of Climate Change: The Stern Review (2006).
¹⁰⁷ UK Department for Environment, Food, and Rural Affairs, The Social Cost of Carbon
And The Shadow Price of Carbon: What They Are, And How To Use Them In Economic
Appraisal In The UK (December 2007).

price is a poor indicator of economic value or there is no market at all. The UK
shadow price of carbon is based on the Social Cost of Carbon of a 550 ppm
stabilization goal as determined in The Stern Review, plus consideration of abatement
costs and the value of UK leadership in encouraging global participation and from
being out front in developing new technology. The result is a shadow price of about
$43 a ton in 2012 (2005$), rising 2% annually thereafter in real terms.
Using this shadow price of carbon and the UK Green Book discount rates of

3.5% for the first 30 years and 3.0% afterward, the net present value (NPV) of S.

2191’s estimated reductions would range from $4.2 trillion (ADAGE-REF case) to
$5.5 trillion (MIT/EPPA case) in 2005 dollars. To complete this illustrative exercise,
NMA/CRA case estimates the net present value of the total cost of S. 2191
(presumably not including general equilibrium effects) of about $4.5 trillion (2005$)
(p. 18). NMA/CRA did not disclose the discount rate used in making this estimate.
Not surprisingly, the estimates illustrated here have been criticized by some
(including the UK Parliament) for being too low and incomplete. Likewise, others
have criticized the estimates as too large and inflated. For example, in its recent
assessment of new average fuel economy standards, the U.S. National Highway
Traffic Safety Administration (NHTSA) chose to value carbon reductions at $7 a ton
and employ a 7% discount rate.¹⁰⁸ Applied to the reduction estimated under S. 2191,
the resulting NPV would be about one order of magnitude lower than the UK shadow
price-based estimates. Thus, reminiscent of EPA’s illustrative calculation of the
health benefits of a 1-hour sulfur dioxide standard, the illustration here results in a
range of climate-related benefits from reducing greenhouse gases under S. 2191 at
between zero and $200-$260 billion annually (2005$).¹⁰⁹
As illustrated with the long-term cost estimates presented in this report, attempts
to monetize climate-related benefits currently reflect much about the philosophies
and assumptions of the people doing the estimating. As stated in The Stern Review:
“It is very important ... to stress that such estimates [NPV of climate change policy

¹⁰⁸ For a discussion of NHTSA’s rationale for its estimate, see Department of
Transportation, National Highway Traffic Safety Administration, Average Fuel Economy
Standards: Passengers and Light Trucks: Model Years 2011-2015, Notice of Proposed
Rulemaking (April 2008) pp. 216-222.
¹⁰⁹ A zero or near-zero estimate could result from one of three lines of thought: (1) denial
that climate change is occurring; (2) belief that the potential benefits of a warmer climate
cancel out the damages from that change; or (3) the damages will not be great (at least for
the United States) and are far in the future — justifying a low damage evaluation and a high
discount rate. It appears that NHTSA employed the final line of thought in its proposed
rulemaking. As stated by NHTSA: “Although no estimates of benefits to the U.S. itself that
are likely to result from reducing CO² emissions are currently available, NHTSA expects
that if such values were developed, the agency would employ those rather than global
benefit estimates in its analysis. NHTSA also anticipates that if such values were
developed, they would be lower than comparable global values, since the U.S. is likely to
sustain only a fraction of total global damages resulting from climate change.” Department
of Transportation, National Highway Traffic Safety Administration, Average Fuel Economy
Standards: Passengers and Light Trucks: Model Years 2011-2015, Notice of Proposed
Rulemaking (April 2008) p. 220.

benefits] reflect a large number of underlying assumptions, many of which are very
tentative or specific to the ethical perspectives adopted.”¹¹⁰
Putting Emission Reductions under S. 2191 into Context. It is
difficult to put the actions of one country’s emissions reduction plan in the context
of a fragmented global effort to address climate change. One useful perspective is¹¹¹
provided by MIT’s general study of cap and trade bills. Using the MIT Integrated
Global System Model (IGSM), MIT explored the climate response to different
stabilization goals being discussed in the international community. It developed
parameterizations of IGSM that represented each of three major atmosphere-ocean
general circulation models (AO GCMs) that would help illustrate the uncertainty in
translating emission trends into an estimate of climate change: those of the Goddard
Institute for Space Studies (CISS-SB), the Geophysical Fluid Dynamics Laboratory
(GFDL-2.1), and the National Center for Atmospheric Research (CCSM3).
MIT simulated the climate effects of six different policy scenarios through 2100.
Four of these are of interest in exploring S. 2191: (1) a reference scenario that
assumes no specific global climate policy (Reference); (2) a global participation
scenario (Global Participation, 203 bmt case), (3) a global participation scenario
where abatement efforts in developing countries are delayed until 2050 (Developing
Countries Delayed); and (4) a partial participation scenario where no abatement
efforts occur in developing countries (Developed Only). Under scenarios 2, 3, and
4, developed countries (including the United States) are assumed to have reduced
emissions by 50% below 1990 levels by 2050 (and held them there through 2100).
This assumption is in the ballpark of U.S. reductions anticipated under S. 2191. For
developing countries, scenario 2 assumes their emissions reductions begin in 2025,
with emissions returning to their 2015 levels, and with additional reductions
beginning in 2035 with emissions returning to their 2000 levels, and are held there;
scenario 3 assumes emissions reductions are delayed until 2050, at which point they
return to 2000 levels; and scenario 4 assumes developing country emissions are not
stabilized at all.
The climate effects of these scenarios as simulated by MIT IGSM replication of
the three AO GCMs identified above is shown in Figure 11. As indicated by the red
line, the impact of S. 2191, combined with that of the other developed countries (all
of which have ratified the Kyoto Protocol), is to reduce by 0.5 degrees C the
projected 3.5 degrees C to 4.5 degrees C increase in global mean temperatures
suggested by the simulations. If the United States chose not to reduce, the impact
would be to move the red, green, and blue lines closer to the reference case line.
With respect to the red line, it should be noted that, in 2000, the United States’
greenhouse gas emissions were about 40% of the developed world’s total emissions.
In terms of the effect of any U.S. reductions on global mean temperatures, that is
about all that can be said in isolation. As noted by MIT:

¹¹⁰ Sir Nicholas Stern, The Economics of Climate Change: The Stern Review (2006) p. 304.
¹¹¹ 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). Readers are
urged to consult the report for details on the analysis discussed here.

...it is not possible to connect specific U.S. policy targets with a particular global
concentration or temperature target, and therefore the avoided damages, because
any climate gains depend on efforts in the rest of the world.... If a cooperative
solution is at all possible, therefore, a major strategic consideration in setting
U.S. policy targets should be their value in leading other major countries to take¹¹²
on similar efforts.
Instead, S. 2191’s climate-related environmental benefit must be considered in
a global context and the desire to engage the developing world in the reduction effort.
It is in this context that the United States and other developed countries agreed both
to reduce their own emissions to help stabilize atmospheric concentrations of
greenhouse gases and to take the lead in reducing greenhouse gases when they
ratified the 1992 United Nations Framework Convention on Climate Change
(UNFCCC). This global context raises two issues for S. 2191: (1) whether S.
2191’s greenhouse gas reduction program and other provisions 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 to stabilization under the
UNFCCC and occur in a timely fashion so that global stabilization may occur
at an acceptable level.

¹¹² ibid., p. 55.

Figure 11. Global Mean Surface Air-Temperature Increase
in Six Scenarios Using the MIT IGSM

^Source:^Serg^ey^P^a^lts^ev^{, et al.,}^As^s^e^s^s^m^ent^of^U^.^{S. C}^ap-and-T^r^{ade Pr}^opos^al^s^{, MI}^T^J^oⁱⁿ^{t P}^r^o^g^r^a^m^oⁿ
^t^h^{e S}^cⁱ^eⁿ^{ce an}^{d Pol}ⁱ^c^y^of^G^l^obal^C^h^an^g^e^{, R}^e^port^N^o^{. 146 (A}^pri^l^{2007) p. 51. A}^v^ai^l^a^bl^{e at}
^[^h^ttp^://m^it.ed^u^/^g^l^o^b^a^lch^aⁿ^g^e^]^.^Used^b^y^p^e^r^m^issioⁿ^{. Read}^er^{s ar}^{e u}^r^g^e^d^to^coⁿ^s^u^lt^th^e^r^e^p^o^r^t^f^o^r^d^e^tails
^on^th^{e an}^aly^s^is^dis^c^u^s^s^e^{d h}^e^re.

Non-Climate Change Air Quality Benefits. As noted earlier, only the
EPA/IPM study included any estimates of emission reductions from non-greenhouse
gas air pollutants. Only two pollutants were analyzed, and the resulting estimates
reflect short-term interactions between S. 2191 and existing cap and trade programs.
However, it should be noted that values have been assigned to these pollutants from
time to time. For example, in the notice of proposed rulemaking for the new average
fuel economy standard, the Department of Transportation assigned emission damage
costs of $3,900 a short ton for nitrogen oxides, $16,000 a short ton for sulfur dioxide,
and $164,000 a short ton for particulate matter — all pollutants that are also emitted
from coal-fired generating facilities.¹¹³ This is an incomplete set of pollutants that
would be reduced by S. 2191. Other benefits may occur from reductions of pollutants
such as mercury and carbon monoxide.
Impact on Behavior. The impact of any price increases from S. 2191 on
households, industries, and businesses would depend on their responsiveness to the
price signal, the distribution of safety net funds under S. 2191, and the impact of
various other provisions of the bill that encourage, or could be used to encourage,
conservation and new technology development. Simple attempts by some
presentations to break down the cost by industrial sector or by state “should be
viewed with attentive skepticism”¹¹⁴ for at least two reasons. First, baseline forecasts
are even less accurate at a sector level than they are at an aggregate national level.
As noted by Winebrake and Sakva, sector level baseline forecasts have significantly
higher errors compared with aggregate estimates, nor have sector estimates improved
over the past two decades:
We find that low errors for total energy consumption are concealing much larger
sectoral errors that cancel each other out when aggregated. For example, 5-year
forecasts made between 1982 and 1998 demonstrate a mean percentage error for
total energy consumption of 0.1%. Yet, this hides the fact that the industrial
sector was overestimated by an average of 5.9%, and the transportation sector
was underestimated by an average of 4.5% We also find no evidence that¹¹⁵
forecasts within each sector have improved over the two decades studied here.
Second, particularly with respect to industry, the effect of S. 2191 is likely to be
very site-specific, particularly as the primary impact will be indirect in terms of
added energy costs, not direct compliance costs. An industry-by-industry approach
masks the interplay of companies that would be affected differently by S. 2191.
Most industries face a competitive market (sometimes international in scope)
both in terms of producers of the same products and producers of substitute products.

¹¹³ Department of Transportation, National Highway Traffic Safety Administration, Average
Fuel Economy Standards: Passengers and Light Trucks: Model Years 2011-2015, Notice
of Proposed Rulemaking (April 2008), p. 182.
¹¹⁴ As noted by CRS with respect to acid rain costs estimates in 1990. See CRS Report 90-
63, Acid Rain Control: An Analysis of Title IV of S. 1630, by Larry Parker. (Available from
the author.)
¹¹⁵ James J. Winebrake and Denys Sakva, “An Evaluation of Errors in US Energy Forecasts:

1982-2003,” Energy Policy 34 (2006), p. 3475.

Also, in some cases, an industry may face a fairly elastic demand for its product.
Thus, most industries are price sensitive, and therefore any increase in manufacturing
costs hurts the competitiveness of a firm. This complex situation is further
complicated for energy-intensive industries in the case of S. 2191 as competitors
within the same industry may experience different energy price increases (particularly
for electric power), depending on their individual energy needs and power
arrangements. Thus individual facilities within the same industry will be affected
differently by S. 2191 and other unforeseen events in the future. For example, an
aluminum plant receiving power from a hydro-electric facility may not be affected
the same way as a similar plant with a power contract with a coal-fired power
supplier.
This differential effect on individual companies under S. 2191 could have
several potential impacts. First, as noted above, it may affect the competitive balance
of specific facilities in the United States. Second, investment decisions by industries
could be affected, particularly with respect to technology. New, more efficient
technology is emerging for some processes. The combination of current price signals
being sent from the energy markets and potential ones from S. 2191 could speed their
development. If commercialized, new technology would reduce the impact of S. 2191
and, indeed, improve competitiveness. Not surprisingly, none of the cases presented
here have sufficient industry sector detail to examine this possibility, nor did any
attempt to develop proxies to explore the possibilities for industrial technology over
the next 40 years.
S. 2191 attempts to ameliorate these effects somewhat by providing a subsidy
for such industries facing international competitiveness issues. The degree to which
the subsidy could address the issue was not examined by any of the cases presented
here. Likewise, the sufficiency of the funds was not examined. Interestingly, such
an approach to exposed industry has some parallels to recommendations that have
been made with respect to carbon intensive industries in Europe facing reduction
requirements and increased fuel cost from the EU’s Kyoto Protocol, and post-Kyoto
commitments. For example, one such recommendation by the UK Carbon Trust
suggested the following:
For a very small number of carbon-intensive, internationally exposed activities
headed by steel and cement production, governments should establish a
transitional ‘compensating rate of free allocation’ on an activity-specific basis,
based upon the likely degree of cost pass-through given international trade
conditions. The scale of free allocation to electricity-intensive activities in the
EU-ETS (notably pulp and paper) should also take account of their electricity
consumption, whilst manufacturing of fertilisers and basic chemicals might
benefit from being brought into the EU-ETS on a similar basis. Together with
aluminium smelting these constitute four trade-exposed electricity-intensive
activities for which additional measures, linked to redistribution of auction
revenues or equivalent ‘downstream’ allocation of electricity-related allowances,
could be considered.... However, focused measures to facilitate direct, long-term

investment in low carbon electricity generation may offer the best long-term¹¹⁶
solution.
For households, the interplay of price signals, conservation, and regulations is
difficult to separate in the CGE models, such as ADAGE, IGEM, and EPPA. NEMS
does break down the residential sector into both residential energy consumption and
residential prices by fuel. However, any estimates from such a breakdown can only
be considered illustrative at best. For example, the CATF/NEMS analysis illustrates
that if S. 2191 results in only a moderate increase in electricity and natural gas prices,
then households could, on average, respond with sufficient conservation and
efficiency improvements to overcome the projected price increases and reduce their
monthly bill compared with business as usual levels. If allowance prices are higher,
this become more difficult. An effort by Keohane and Goldmark to estimate the
monthly increase in residential electric bills based on MIT/EPPA’s higher allowance
prices resulted in a 6% increase in those bills in 2030.¹¹⁷ Impacts on residential
monthly natural gas bills would follow a similar pattern. The CATF/NEMS analysis
indicates that an aggressive demand response by consumers almost eliminates the
projected modest increase in natural gas prices. In contrast, Keohane and Goldmark
calculations based on the higher allowance prices of the MIT/EPPA analysis result¹¹⁸
in a 14% increase in monthly natural gas prices in 2030.
As with the energy-intensive industries discussed above, S. 2191 attempts to
ameliorate the impact of projected energy price increases for low- and middle-income
households by providing funds for states to provide electricity and natural gas impact
assistance. The EIA/NEMS analysis breaks out the estimated impact of the
electricity impact assistance funds in its calculation of household impacts. Assuming
the value of the allowances allocated would be passed on to all consumers — not just
low-income — EIA/NEMS estimates the reduction at one-half cent per kilowatthour
(KWH) or about a 5% reduction in rates. If the money were directed toward low-
income consumers, the impact would be greater. Including the effects of the impact
assistance, EIA/NEMS estimates the average monthly household energy bill
(excluding transportation) under S. 2191 would increase about $3 a month in 2020,
rising to about $6 a month in 2030.
Overall, EIA/NEMS estimates that the Consumer Price Index (CPI) for energy
in 2030 would be 18% higher for residential consumers and 29% higher for industrial
consumers than basecase levels. To put these potential increased costs into context,
EIA/NEMS compared its estimated incremental consumer and industrial energy
prices increases under S. 2191 with those of the past 5 years. As indicated in Figure

12 and stated by EIA/NEMS, “if measured from 2008 energy prices, it takes 22 years

¹¹⁶ Carbon Trust, EU ETS Impacts on Profitability and Trade: A Sector by Sector Analysis
(January 2008) p. 8.
¹¹⁷ Nataniel Keohane and Peter Goldmark, What Will it Cost to Protect Ourselves from
Global Warming? The Impacts on the U.S. Economy of a Cap-and-Trade Policy for
Greenhouse Gas Emissions, Environmental Defense Fund (2007), p. 16.
¹¹⁸ Ibid, p. 17

in the S. 2191 Core Case to reach the same percentage change that current energy
prices have increased from 2003 to 2008.”¹¹⁹
Figure 12. Energy Price Change:
Recent History Versus the S. 2191 Core Case

^Source:^Eⁿ^e^r^gy^Iⁿ^fo^r^m^a^tⁱ^o^{n A}^d^mⁱⁿⁱ^st^r^a^tⁱ^o^n,^Ener^gy^M^a^r^ket^and^Economⁱ^c^Im^pact^s^of^{S. 2191, t}^h^e
^Lⁱ^eber^m^an-W^a^rⁿ^er^C^lⁱ^m^at^{e Secur}ⁱ^t^y^Act^of²⁰⁰⁷^(A^pri^l^{2008), p. 34.}
Conclusion
This report examines six studies that project the costs of S. 2191 to 2030 or
2050. It is difficult (and some would consider it unwise) to project costs up to the
year 2030, much less beyond. The already tenuous assumption that current
regulatory standards will remain constant becomes more unrealistic, and other
unforeseen events (such as technological breakthroughs) loom as critical issues
which cannot be modeled. Hence, long-term cost projections are at best speculative,
and should be viewed with attentive skepticism. In the words of the late Dr. Lincoln
¹¹⁹ EIA, Energy Market and Economic Impacts of S. 2191, the Lieberman-Warner Climate
Security Act of 2007 (April 2008) p. 32.

Moses, the first Administrator of the Energy Information Administration: “There are
no facts about the future.”¹²⁰
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. The various cases examined here do provide some important insights on the
costs and benefits of S. 2191 and its many provisions.
First, if enacted, the ultimate cost of S. 2191 would be determined by the
response of the economy to the technological challenges presented by the bill.
The bill provides numerous price, research and development, deployment, and
regulatory incentives for technology innovation. The potential for new technology to
reduce the costs of S. 2191 is not fully analyzed by any of the cases examined, nor
can it be. The process of technology development and dissemination is not
sufficiently understood at the current time for models to replicate with any long-term
confidence. In the same vein, it is difficult to determine whether the various
incentives provided by S. 2191 are directed in the most optimal manner.
Second, in some ways, the interplay between nuclear power, renewables,
natural gas, and coal-fired capacity with CCS among the cases is a proxy for the
need for a low-carbon source of electric generating capacity in the mid- to long-
term. A considerable amount of low-carbon generation will have to be built
under S. 2191 in order to meet the reduction requirement. The cases presented
here do not agree on the amount of new generating capacity necessary under S. 2191
or the mix of fuels and technologies that would be employed. The estimated amount
of capacity constructed depends on the cases’ assumptions about the need for new
capacity and replacement/retirement of existing capacity under S. 2191, along with
consumer demand response to the rising prices and incentives contained in S. 2191.
Third, the cases suggest that the CCS bonus allowance allocation under S.
2191 is effective in encouraging deployment of CCS, accelerating development
by 5-10 years. However, the cases disagree on whether the bonus amount provided
by S. 2191 is sufficient, or needs to be extended additional years.
Fourth, the cases generally indicate that offsets could be a valuable tool for
covered entities not only to potentially reduce costs, but perhaps more
importantly, to buy time to further develop new, more efficient technologies.
The availability of offsets could be complemented by the bill’s provisions permitting
banking, allowing companies more time to develop long-term investment and
strategic plans, and to pursue technology development. Cost could be lowered further
by allowing greater availability of offsets and international credits and with a broader
definition of eligible international credits. A more direct path for permitting
international credits from mechanisms such as the CDM would also reduce one of
the more important cost uncertainties revealed by the cases’ varying interpretations
of international credit eligibility requirements and their projected price.

¹²⁰ Lincoln E. Moses, Administrator, Energy Information Administration, Annual Report to
Congress — 1977, Volume 2, (1978).

Fifth, the Carbon Market Efficiency Board could have an important effect
on the cost of the program through its power to increase the availability of
offsets and international credits. The cases generally do not consider the Board in
their analyses, however, one can infer from the cases’ results that the most important
power that the Board may have is the ability to increase the availability of domestic
offsets and international credits (although not the authority to change the eligibility
requirements for domestic offsets and international credits). In this sense, the Board’s
powers could mesh with the previous insight about the importance of offsets and
banking to the cost-effectiveness of S. 2191. However, the Board is primarily
designed to deal with short-term volatility due to episodic events in the allowance
market and has only short-term powers. Whether it could coordinate a longer term
strategy, if necessary, with its proposed authority is not known.
Sixth, the Low Carbon Fuel Standard could significantly raise fuel prices
and limit supply. The effects will depend on what fuels are included in the LCFS,
the level of emissions reductions achieved by alternatives, and the ability of suppliers
to produce those alternatives. If plug-in hybrid vehicles or large amounts of
cellulosic biofuel are available early, or if certain fuels such as aviation fuel are
excluded from the mandate, the costs could be lower. Only one case provided any
analysis of the LCFS.
Seventh, S. 2191’s climate-related environmental benefit is best considered
in a global context and the desire to engage the developing world in the
reduction effort. It is in this context that the United States and other developed
countries agreed both to reduce their own emissions to help stabilize atmospheric
concentrations of greenhouse gases and to take the lead in reducing greenhouse gases
when they ratified the 1992 United Nations Framework Convention on Climate
Change (UNFCCC). This global scope raises two issues for S. 2191: (1) whether
S. 2191’s greenhouse gas reduction program and other provisions 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 to stabilization under the
UNFCCC and occur in a timely fashion so that global stabilization may occur
at an acceptable level.