UNITED STATES ENVIRONMENTAL PROTECTION AGENCY

WASHINGTON, D.C. 20460

OFFICE OF

AIR AND RADIATION

June 10, 2010

Memorandum

SUBJECT: Peer Review of ADAGE and IGEM

FROM: Eric Smith,

Climate Economics Branch

TO: EPA Council for Regulatory Environmental Modeling

The Climate Change Division (CCD) of the U.S. EPA Office of Air and Radiation is responsible

for economy-wide analysis of greenhouse gas mitigation bills and proposals for the Agency, the

Administration and Congress. CCD uses two computable general equilibrium (CGE) models to

conduct this analysis:

 “Applied Dynamics of the Global Economy” (ADAGE), developed and maintained

by Resource Triangle Institute of Research Triangle Park, North Carolina.

(See http://cfpub.epa.gov/crem/knowledge_base/crem_report.cfm?deid=198000

for additional information on the ADAGE model)

 “Intertemporal General Equilibrium Model” (IGME), developed and maintained by

Dale Jorgenson Associates of Cambridge, Massachussetts.

(See http://cfpub.epa.gov/crem/knowledge_base/crem_report.cfm?deid=198005

for additional information on the IGEM model)

In accordance with the Office of Management and Budget Information Quality Bulletin for Peer

Review and the U.S. EPA Peer Review Handbook, CCD has undertaken a peer review of the

models by a panel of external experts.

The Peer Review was coordinated by Industrial Economics, Incorporated, of Cambridge,

Massachussetts. IEc selected the panel of experts in economic modeling who conducted the

review. Please see the attached memo from IEc describing the process of the peer review.

The Review by the panel of external reviewers is attached. The Review Panel summarized their

findings in the following paragraph:

On balance, these are two excellent models that provide useful information to

EPA as it goes about its work of evaluating climate policies. While we may

have issues with elements of the models, in general we find these to be firstrate and state-of-the-art. These models are essential tools for the analysis of

environmental policy in general and climate policy in particular.
The panel made a number of suggestions for improvement. The IGEM and ADAGE modeling

teams each prepared responses to the peer review report addressing the findings and discussing

planned model improvements. The responses are each attached.

Attachments:

1. IEc Peer Review Documentation Memorandum

2. Report of the Peer Review Panel: “Peer Review of Computable General Equilibrium

Models for Climate Change Analysis”

3. Response to Peer Review Panel prepared by Dale Jorgenson Associates regarding

IGEM

4. Response to Peer Review Panel prepared by Resource Triangle Institute regarding

ADAGE
Attachment 1

IEc Peer Review Documentation Memorandum
1

MEMORANDUM | June 9, 2010 

TO Eric Smith, USEPA/CCD/CEB 

FROM James Neumann 

SUBJECT Documentation of Peer Review Process for the IGEM and ADAGE Computable General 

Equilibrium Models for Climate Change Analysis 

1. OVERVIEW 

Industrial Economics, Inc. (IEc) was contracted to manage the external peer review of 

two economic tools currently being used by the Climate Economics Branch (CEB) of the 

Climate Change Division (CCD) of the Office of Atmospheric Programs, USEPA: 1) the 

Intertemporal General Equilibrium Model (IGEM) developed by Dale Jorgensen and 

Associates; and 2) the Applied Dynamic Analysis of the Global Economy (ADAGE) 

model developed by RTI International. This memorandum summarizes the results of the 

peer review. The first section provides a brief description of the process for selecting 

individuals to serve on the review panel. The second section provides a summary of 

information provided to reviewers to support their activities. The full text of the peer 

review panel’s report is provided separately. 

This independent, external peer review was conducted in compliance with EPA’s Peer 

Review Guidelines. The peer review will assist CEB in supporting their analytical 

responsibilities to provide long-run economic and integrated assessment modeling 

support to CCD, EPA, and the US Government. These long-run analyses inform nearterm policy development with feasible alternative projections that provide insights for 

policy making. For example, ADAGE and IGEM have been used extensively by EPA for 

the purpose of analyzing climate change bills in both the U.S. Senate and the House of 

Representatives. 

Results of the peer review will be used to help CEB evaluate the insights that can be 

gleaned from the different models’ results and their relative strengths and limitations. The 

review will also help to identify opportunities for improvements to the models and 

suggest research directions that strengthen the credibility of model results. Reviewers are 

being asked to comment on the economic methodology and utility of model results. 

While the models will be deployed to support policy decision-making, no specific policy 

questions were asked of the reviewers. 

2. PROCESS FOR SELECTING PEER REVIEW PANELISTS 

The peer review process began in April 2009 and is now complete. IEc recruited four 

reviewers for the panel; one of these reviewers was recruited to serve as chairperson for 

the panel. The work of the peer review panel began in October 2009 and ended in 

February 2010. The reviewers were compensated for approximately ten days of effort and 

provided with an honorarium sufficient to attract a high-quality review panel. The chair, 

who was also asked to oversee the review of the models and engage particularly in the 

technical and substantive nature of the economic model review, was compensated for 

approximately 11 days of time. 
2

IEc initially provided to EPA a list of candidate peer reviewers – the list is provided in 

Attachment A to this memo. While we consulted with EPA staff to clarify the expertise 

necessary to perform this review, and ensure that candidates met the stated requirements, 

IEc independently selected all of the reviewers, consistent with the guidelines of the EPA 

Peer Review Handbook. (US EPA, 2006). IEc discussed conflict of interest and 

independence issues with each reviewer, and each signed a statement confirming that 

they had no financial or personal conflicts of interest (included as Attachment B). In 

addition all four reviewers signed a contract with IEc that included a requirement to 

immediately report any potential personal or organizational conflict of interest, should 

one arise during the course of completing the review. 

IEc selected peer reviewers for independence, economic expertise and knowledge, and 

modeling expertise. Each of the four peer reviewers ultimately selected are nationally 

recognized experts in the field of CGE modeling and climate economics and have a 

general knowledge of environmental economics issues, as well as the specific knowledge, 

expertise and experience required to adequately and authoritatively respond to the charge 

for the review. One of the key features of the IGEM and ADAGE models that makes 

them useful for CEB is that they capture economy-wide and sector specific economic 

activity at a regional, national, or international level, and, as they capture the interactions 

among sectors (and over time) they have advantages in analyzing complex dynamic 

responses to climate policy that jointly affect energy use, economic growth, and 

greenhouse gas (GHG) emissions. Each of the reviewers has expertise at the interface of 

energy and economics, and each is expert at assessing the implications of market-based 

approaches to regulating GHG emissions. 

Exhibit 1 below provides a brief description of the four reviewers and their relevant 

expertise. Full curriculum vitae (CVs) for each of the four reviewers are included in 

Attachment C to this memo. 
3

EXHIBIT 1. SUMMARY OF QUALIFICATIONS OF PEER REVIEW PANEL 

PEER REVIEWER BRIEF DESCRIPTION OF QUALIFICATIONS 

Dr. Charles Kolstad, 

University of 

California – Santa 

Barbara 

Economist with extensive background in energy and climate change 

modeling. Lead author for the Intergovernmental Panel on Climate 

Change and a member of the Academy of National Sciences 

committee charged with evaluating the U.S. Climate Change 

Research Program. Has published widely in peer-reviewed energy, 

climate, and science journals. 

Dr. Gilbert Metcalf, 

Tufts University 

Specializes in taxation, energy, and environmental economics, with 

a current research focus on policy evaluation and design in the area 

of energy and climate change. Member of the National Academy of 

Sciences Committee on Health, Environmental, and Other External 

Costs and Benefits of Energy Production and Consumption. 

Dr. Ian Sue Wing, 

Boston University 

Applied general equilibrium modeler who has studied induced 

technical change and the impact of carbon taxes, the energy 

intensity of U.S. production, accumulation of capital and knowledge 

in dynamic general equilibrium, energy technology detail in CGE 

simulations for energy and climate policy analysis, and the effect of 

uncertainty on the cost of U.S. climate policies 

Dr. Roberton Williams, 

University of Maryland 

Specializes in the general equilibrium effects of taxes in 

environmental policy, with a focus on energy taxation and optimal 

design of climate policies. Authored numerous publications 

examining the impact of climate policies on the US economy. 

3. MATERIALS PROVIDED TO PEER REVIEW PANELISTS 

Exhibit 2 provides a copy of the cover letter sent to the peer reviewers. As indicated in 

the letter, peer reviewers were provided a list of charge questions developed by the EPA 

sponsors; detailed documentation of each of the models; and summaries of the results of 

recent EPA efforts to apply the models to proposed GHG regulatory policies. The charge 

provided to the panel is included as Attachment D, and Attachment E provides an 

inventory of the materials provided for the reviewers. Note that some materials were 

provided in response to questions of clarification from the panelists to the EPA sponsors. 

The panel conducted one group conference call, facilitated by IEc, on October 21, 2009, 

shortly after receipt of the review materials. A second call was conducted on November 

6, 2009, also facilitated by IEc, where panelists asked clarifying questions of the EPA 

sponsors. Reviews were originally requested to be complete by December 10, 2009, but 

because of some delays in receipt of clarifying materials and in light of the volume of 

materials reviewers were requested to be familiar with, the deadline was extended. A 

completed draft review report was delivered to EPA on January 25, 2010. The EPA 

review of the draft review report was solely to assess and suggest correction to any 

potential factual errors or clarifications. No such errors were found, and the report was 

finalized in April 2010, with no substantive changes from the draft. 
4

EXHIBIT 2. COVER LETTER FOR PEER REVIEWERS 
5
Attachment 2

Report of the Peer Review Panel: “Peer Review of

Computable General Equilibrium Models for

Climate Change Analysis”
Peer Review of 

Computable General 

Equilibrium Models for 

Climate Change 

Analysis 

By: 

Charles D. Kolstad+ 

Gilbert E. Metcalf 

Ian Sue Wing 

Robert C. Williams III 

Work performed under contract from the U.S. 

Environmental Protection Agency 

through 

Industrial Economics, Incorporated 

James E. Neumann, Program Manager 

+ Review Panel Chair 

April 2010 
Peer Review of Computable General Equilibrium 

Models for Climate Change Analysis 

Kolstad, Metcalf, Sue Wing & Williams April 2010 

1

I. INTRODUCTION 

The EPA contracted with Industrial Economics, Incorporated to 

conduct an expert review of two computable general equilibrium 

(CGE) models in the context of EPA’s work in evaluating proposed 

federal legislation for greenhouse gas mitigation. This is the result 

of that review. The EPA posed very specific charge questions to 

the expert panel and our review follows the structure of those 

questions. Furthermore, the EPA provided us with documentation 

of the models and several supplementary documents.1

The EPA uses two CGE models to evaluate prospective 

greenhouse gas policies: IGEM and ADAGE. IGEM 

(Intertemporal General Equilibrium Model) has been used for 

decades (though it is constantly being updated and refined) and 

was developed by Prof. Dale Jorgenson and colleagues. The 

ADAGE Model (Applied Dynamic Analysis of the Global Economy) 

is of more recent vintage, though it is adapted from MIT’s EPPA 

model (in part). 

Two recent and prominent analyses illustrate how these models 

are used by EPA. One is an analysis of the climate legislation 

which passed the US House of Representatives on June 26, 2009 

(H.R. 2454 – the Waxman-Markey bill).2

 Another is an analysis of 

proposed senate legislation intended to parallel HR 2454 (Senate 

bill S. 1733—the Kerry-Boxer bill).3

In our review of the two models, we considered model 

documentation provided to us, these two analyses of legislation 

using the two models, as well as a number of other sources which 

are cited in this report. 

II. GENERAL COMMENTS 

The EPA posed four general questions to the reviewers regarding 

the adequacy of the two CGE models. Before addressing these 

charge questions individually, it is appropriate to review our sense 

of the types of information EPA would want to obtain from the 

models, in order to be responsive to policymakers considering 

adopting legislation such as the two bills considered during 2009. 

Energy is of course the source of most greenhouse gas 

emissions. One would expect to be able to forecast energy 

consumption and production as a consequence of legislation. In 

 

1

 Primary documents reviewed: Ross (2009), Goettle et al. (2009), EPA (2009a,b). 

2

 US EPA (June 23, 2009). 

3

 US EPA (Oct. 23, 2009). 
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particular, one should be able to understand how electricity supply 

(by fuel) and use might be affected by legislation. How the use of 

low carbon fuels might change is also important. It is also 

important to be able to estimate the effect on electricity and other 

energy prices as a consequence of proposed legislation. 

CGE models are particularly good in capturing the 

interconnectedness of the sectors of an economy and in tracing 

throughout the economy the indirect effects of a regulatory 

intervention. One should be able to measure the effect of 

proposed legislation on household consumption and aggregate 

consumption. And one should be able to measure the effect on 

specific narrow sectors of the economy which may bear a 

significant burden from proposed regulations (such as energy 

intensive industries). Furthermore, the competitiveness 

implications for these sectors (in terms of leakage or losing market 

to foreign producers) are politically important. 

In the case of a cap and trade program (which appears to be 

central to most if not all proposed legislation), a model should be 

able to estimate a time-path for allowance prices, as well as a 

trajectory for emissions (and the extent to which allowances are 

banked or borrowed). With offsets (which are in both bills), it is 

important to estimate the use of international and domestic 

offsets. 

For all of the issues mentioned above, it is important to 

understand the incidence of regulations, broadly defined. How are 

the various issues raised above manifest in different geographic 

regions of the country? How are different income classes of the 

population affected by regulations? 

Generally, it is important that models be able to represent the wide 

variety of complex regulations which may need analyzing. Most 

legislation contains a set of complex rules, intended to address 

political dimensions of bill passage. There may be subsidies to 

certain sectors, free allocation of permits to certain consumers of 

electricity, incentives for R&D, and the list goes on. It is important 

that a model be flexible enough so that such diverse regulations 

can be analyzed. 

Finally, it is important that any model be able to capture 

uncertainties in parameter values as well as the structure of the 

problem. 

We now turn to the four general charge questions raised by EPA. 
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1. Given the scope and intended purpose of the model, are the 

analytical framework, assumptions, and application of data 

appropriate? 

Both the ADAGE and IGEM models are sophisticated computable 

general equilibrium models in which great care has been taken to 

model aspects of the economy particularly important for analyzing 

environmental policy in general and climate policy in particular. 

Both the ADAGE and IGEM models are comparable to other toptier economic models used to analyze climate policy (see, for 

example, the recent evaluation of models in Stanford University's 

Energy Modeling Forum 22 exercise).4

 Furthermore, in those 

comparisons with other models, ADAGE and IGEM are not 

outliers in the measures presented (emissions pathways and 

carbon prices). Although having results that are consistent with 

other models is in no way an indication that a model is correct, 

having a model which is always an outlier would be grounds for 

concern. 

ADAGE and IGEM are complimentary models in a sense. 

ADAGE is an international model that uses GTAP data to calibrate 

to a base year and has particular strengths in considering the 

interaction of U.S. policy with policy actions in other countries. 

IGEM, in contrast, has no detail on other countries but has 

considerable detail on industry sectors in the United States. Also 

in contrast to ADAGE's non-statistical approach to calibrating their 

model, IGEM is an econometrically estimated model building on 

extensive high-quality statistical work over the years by Dale 

Jorgenson and numerous co-authors. 

There are still major uncertainties even with the best models, so 

the models’ predictions should be regarded as rough estimates, 

particularly when projecting far into the future. Estimates from 

large-scale dynamic CGE models such as these will still likely be 

much more accurate than those from other approaches. And 

having even a rough estimate of the economic effects of policy is 

far better than having no information at all. But it is important to 

recognize those uncertainties. And neither model’s 

documentation does a particularly good job of identifying the key 

uncertainties. 

For important parameters, it would be useful to know both how 

uncertain the parameter estimate is and how sensitive the model’s 

results are to changes in that parameter. A quantitative 

assessment of the amount of uncertainty in a given parameter 

estimate is fundamentally very difficult; one can report an 

 

4

 For more information on EMF 22 and comparisons of IGEM, ADAGE and four other CGE models applied to 

climate change policy, see http://emf.stanford.edu/research/emf22/ and in particular, documentation of the June 

2009 workshop (available at the site). 
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estimated standard error (as the IGEM documentation does5), but 

that only expresses the uncertainty within a given estimation 

approach, not the additional uncertainty over what estimation 

approach is best. In general, neither model’s documentation 

identifies how much disagreement there is in the literature over 

the value of any given parameter. 

Identifying and quantifying the sensitivity of the model’s results to 

changes in a given parameter is much easier, and such sensitivity 

analysis is commonly used to help evaluate CGE models. Neither 

model’s documentation includes much sensitivity analysis (though 

presentations of the results from these models may well include 

much more sensitivity analysis). 

One notable (and very useful) exception is Chapter 7 of the IGEM 

documentation, which focuses on the role of the consumptionleisure tradeoff in determining the model’s results. This section 

clearly identifies the labor supply elasticity as a parameter that is 

relatively uncertain, and presents sensitivity analysis that shows it 

can have major effects on the model’s results. Similar analyses 

for other key parameters would be very helpful in pointing out and 

assessing key uncertainties. 

2. Are the methods applied in this model appropriate for 

quantifying the economic impacts of climate policies for the 

US and global economies? If not, what methods would you 

recommend? 

Perhaps the biggest criticism of the two models for use in climate 

policy analysis is that they are generic models, for broad 

application, and for that reason do not necessarily adequately 

answer all of the climate-specific questions posed at the 

introduction to this section. For instance, the electricity sector is 

key in determining the allowance price as well as the effects on 

other sectors. In particular, fuel switching in electricity should be 

well represented. That is not a strength of either model.6

In addition, the price of allowances appears to be largely 

exogenous, dependent on assumptions about availability and cost 

of foreign offsets. That has been the reason offered for why the 

allowance price generated by both models is virtually identical 

 

5

 Because ADAGE’s parameters are generally calibrated based on values from the literature, rather than estimated 

as in IGEM, it doesn’t have standard error estimates for parameters. 

6

 In the EPA analysis of H.R. 2454 the differences between the CGE models and the more detailed electricity sector 

model (IPM) are clear: compare page 17 (ADAGE) with page 26 (IPM). The two models come up with strikingly 

different forecasts of fuel use in electric power under H.R. 2454. This is not to suggest IPM is more correct than 

ADAGE/IGEM, only that the differences are of concern. 
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through 2050 in the analysis of HR 2454.7 This is due to the fact 

that the supply curve for international offsets is exogenous to the 

models and it is that supply that is very important in HR 2454. 

However, because of the significance of the allowance price to 

any analysis of climate policy, we would strongly suggest that the 

EPA consider developing a more sophisticated representation of 

the international allowance market so that the price information 

coming from these two models is more robust. 

Incidence is also poorly represented in the models – how are 

different income classes and regions differentially affected by 

policies? IGEM is taking steps to be able to answer this question. 

For future model development, one fundamental structural aspect 

that might be worth changing about these models would be to 

move away from the representative-agent assumption and include 

more heterogeneous households. This would yield more realistic 

estimates of household-level economic responses, and would 

permit the models to examine the distribution of policy costs and 

benefits across different households. 

In terms of specific recommendations for improvement for the two 

models, some of those are incorporated into our response to 

specific charge questions. With respect to IGEM, we suggest the 

developers evaluate the efficiency of their solution algorithm.8

 We 

also suggest remedying the lack of non-marginal GHG-saving 

technological change by incorporating discrete alternative energy 

supply and demand technologies, which come on line 

endogenously in response to price. 

Due to the limitations of ADAGE’s documentation we are not able 

to make as specific recommendations for improvements to this 

model. However, in the spirit of IGEM’s econometric calibration, 

we suggest designing and conducting a program of testing to 

compare ADAGE’s baseline with available economic data, with 

the objective of establishing the congruence between the model 

base year and reality, and identifying and implementing 

adjustments to calibration procedures that reduce the most 

serious errors. This is admittedly not a simple exercise given the 

way in which ADAGE is calibrated, but for this very reason it is 

appropriate in order to have confidence in the initial conditions that 

form the basis for its projections. 

 

7

 Refer to page 12 of the EPA analysis of H.R. 2454. 

8

 There are alternative solution methodologies available, such as replacing the current Fair-Taylor solution algorithm 

with Dirkse and Ferris’ (1995) PATH algorithm based on Lau et al’s (2002) formulation of the intertemporal pseudoexcess demand correspondence as a mixed complementarity problem. 
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3. What are the overall major strengths and weaknesses of the 

model for EPA’s intended purpose? 

The main strength of both models is their internal consistency and 

ability to capture complex interactions among different sectors of 

the economy in response to climate policy. Both also have 

considerable detail about energy production and industry sectors 

that are particularly sensitive to higher energy prices from climate 

policy. 

Both models only focus on the economic costs of climate policy 

and cannot measure policy benefits. In fact, it is not the purpose 

of these models to measure policy benefits. This is appropriate 

given the greater confidence that economists have in modeling 

economic outcomes in contrast to our more limited understanding 

of the economic impacts of climate change.9

 

Both models – like nearly all CGE models – are full employment 

models. This is probably a reasonable assumption as these 

models should be viewed as modeling scenarios out forty or more 

years for which economic fluctuations should be viewed as 

deviations around a full-employment trend. 

As we discuss below, the IGEM model does not incorporate 

capital adjustment costs. Given the interest by policymakers in 

understanding the transitional costs of climate policy, this seems 

an important limitation and has the potential to significantly 

understate costs for particular subsections of the economy. 

IGEM’s strengths are its econometric calibration to historical data, 

ability to capture detailed sectoral impacts, and its sophisticated 

representation of endogenous incremental technological change. 

ADAGE’s strength is its ability to consistently characterize the 

incidence of climate policy at the sub-national level, based on its 

formulation of the U.S. economy as a set of interacting regions 

through movements of goods and factors, set in an international 

framework. Both models have weaknesses in the representation 

of capital frictions, household transportation, and induced 

technological change. 

4. Are all of the essential elements included in the model 

documentation? Is the document clear and well-written? 

What additional documentation, if any, do you feel is needed 

to ensure transparency? 

 

9

 Some integrated assessment models have made progress on measuring greenhouse gas concentrations and 

damages in response to climate policy (eg, DICE, RICE and MERGE). Credibly estimating the damage from climate 

change remains a difficult challenge that has yet to be successfully addressed in any model. 
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ADAGE’s documentation is simple, clear, and well organized, so it 

is easy to find the relevant sections on any particular topic. And it 

provides at least basic detail on the entire model structure, so 

there are no major sections that need to be added. However, it 

could be improved by adding more detail throughout, particularly 

on the details of how particular aspects of the model are 

implemented. Some examples are provided below, in our 

response to specific charge questions. 

Also, since ADAGE incorporates significant elements of MIT’s 

EPPA model (e.g., much of the structure and parameterization of 

production and utility functions), it would be useful to add a 

summary of how those elements of the EPPA model are 

parameterized (e.g., where do the substitution elasticities come 

from?) in the ADAGE documentation itself, along with a reference 

to the EPPA documentation in case more detail is necessary. 

Furthermore, a discussion of how ADAGE differs from EPPA 

would be appropriate. 

The documentation of IGEM is exactly the opposite of ADAGE. 

IGEM’s documentation is exhaustive, but can make for difficult 

reading, even for a professional modeler attempting to get their 

arms around this model. When presenting that much information, 

it is important that the information be very well organized in order 

for users to be able to find the information they need. The IGEM 

documentation is not that well organized. It is spread across two 

volumes and a set of appendices and it’s not always obvious even 

which volume to consult. 

Moreover, some of the information provided was superfluous to 

establishing the quality of the methods employed by IGEM, even 

as important gaps remained. A key area for improvement is 

describing in detail the procedure by which non-CO2 GHG 

emission inventories are constructed and the corresponding 

abatement costs are obtained. 

IGEM’s documentation, however, is generally quite complete, 

containing essentially all of the relevant detail about the model’s 

structure and parameterization (though there seem to be a handful 

of exceptions; for example, the estimated value of , the elasticity 

of intertemporal substitution, doesn’t seem to be reported 

anywhere in the documentation). However, the IGEM 

documentation isn’t that well organized. And the clarity of the 

IGEM documentation varies widely. Many sections are very 

clearly written, but others are very difficult to follow (for example, 

the explanation on p. 8-5 of the rationale for targeting nominal 

government expenditures is nearly incomprehensible). 

In short, the IGEM documentation contains (nearly) any detail of 

the model one might want to know, but finding that detail is often 
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difficult, and understanding it once you’ve found it is sometimes 

much harder than it ought to be. This may be inevitable for a 

model that has been around as long as IGEM and has had so 

many contributors. But nonetheless, it would be worthwhile to put 

some effort into organizing the documentation better (even very 

simple changes such as putting page numbers in the table of 

contents would help) and into writing more clearly. 

One other feature that would be nice to add to the documentation 

would be some sensitivity analysis of the model’s results with 

respect to key parameters. Chapter 7 of IGEM’s documentation 

does this for the labor supply elasticities, and it was quite helpful. 

But there is no similar analysis of other key parameters. As is, 

ADAGE’s documentation doesn’t include any such results, much 

less any sensitivity analysis. Both models would benefit from 

attention to this issue.10

III. SPECIFIC COMMENTS 

1. Quality of the Data Inputs

a) Are the underlying data used to benchmark the model 

(e.g. input-output table or Social Accounting Matrix) 

appropriate? 

As Donald Rumsfeld might have said, you develop a model based 

on the data you have, not what you might like to have. However, 

even given this caveat, economic data used in these models 

appears to be adequate and certainly the best that is readily 

available. ADAGE uses IMPLAN social accounting data, widely 

recognized as some of the best subregional economic data. 

ADAGE also used GTAP data for international information, 

similarly the best that is available. 

The IGEM model is also closely tied to data. Given that the IGEM 

model has been developed over several decades, and has used 

the best national data that is available, IGEM cannot be faulted in 

terms of economic data used to estimate the model. 

It is unclear what data are used by the models for carbon 

abatement costs. IGEM appears to adopt the reasonable 

assumption that it is only substitution away from carbon intensity 

 

10 As already indicated, the EPA does appear to be conducting sensitivity analysis though that work has not yet 

been made publically available, as far as we are aware. 
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that results in a reduction in emissions. Thus abatement is 

endogenous to the model, though there is a recognition that some 

abatement is exogenous (such as soil carbon sequestration and 

CCS). There is very little discussion of the source of abatement 

cost estimates in ADAGE. Furthermore, because ADAGE is 

calibrated, not estimated, it is unclear how confident we should be 

in the model’s representation of energy substitution opportunities. 

b) Do the parameters of the model (eg, elasticities, 

emission coefficients, etc) have an appropriate basis 

(eg, econometric estimation, support from existing 

literature)? 

IGEM’s parameters are very carefully estimated (econometrically). 

ADAGE’s parameters are chosen so that the model behaves 

realistically. IGEM’s estimation approach is much more 

sophisticated, but each approach has its advantages and 

disadvantages and neither is unambiguously better than the other. 

One suggestion, in the spirit of IGEM’s econometric calibration, is 

to design and conduct a program of testing to compare the 

ADAGE benchmark with available economic data, with the 

objective of establishing the congruence between the model base 

year and reality, and identifying and implementing adjustments to 

calibration procedures that reduce the most serious errors. This is 

admittedly not a simple exercise given the way in which ADAGE is 

calibrated, but for this very reason it is absolutely necessary if we 

are to have confidence in the initial conditions that form the basis 

for its projections. 

c) Are the reference data used to construct the baseline 

(e.g., GDP and emissions baselines of EIA’s 2009 

Annual Energy Outlook) sound? 

The authors of both models appear to have used sound energy 

and economic data as the basis for their baseline. 

2. Theoretical and Analytical Quality

a) Activities Accounts 

i) Sectoral disaggregation. The degree of sectoral 

disaggregation of ADAGE and IGEM are both adequate. IGEM in 

particular incorporates many more industries than ADAGE, whose 

sectoral resolution is just about the minimum necessary to 

undertake detailed analyses of the effects of climate mitigation 

policies. Given the central cost-moderating role envisaged for 

domestic and international emission offsets under H.R. 2454, as 

well as the importance of the agriculture and forestry sectors as 
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sources of such credits, disaggregation of these sectors in both 

the GTAP and IMPLAN data inputs to ADAGE is perhaps 

worthwhile.

ii) Firm behavior. IGEM and ADAGE follow the standard 

approach of modeling each producing sector as a representative 

competitive firm. The zero- profit condition for production is a 

CRTS cost function, and the input demands derived by 

Shephard’s Lemma are the building blocks of the models’ 

commodity and factor market clearance conditions. Both models 

adopt fairly complicated nested production structures. In IGEM the 

structure of the nest is the same for all sectors and differs only in 

its parameterization, while in ADAGE both the structure and 

parameters of the nesting hierarchy differs from one sector to 

another. 

iii) Production functions (functional form and 

parameters). As is now standard CGE modeling practice, both 

IGEM and ADAGE are formulated and solved in terms of the dual, 

with sectors’ production modeled as cost functions. In IGEM these 

take the form of a system of econometrically estimated 

hierarchical Translog cost share equations, while ADAGE employs 

a more traditional hierarchical CES production structure. 

Each of these modeling choices has its pros and cons. The fact 

that IGEM’s production structure is econometrically estimated on a 

sequence of input-output tables would seem to make it superior to 

traditional calibration techniques which rely on a single year of 

data and elasticities of substitution whose values are assumed, 

and tuned to the modeler’s sense of plausibility or the general 

economic literature, as is the case with ADAGE. The somewhat 

ad-hoc character of the latter approach has long been the subject 

of criticism (e.g., Jorgenson, 1984; McKitrick, 1998). Nevertheless, 

the use of econometric calibration implicitly assumes that the 

future will be a continuation of past trends, which may not 

necessarily be the case with a policy such as climate change 

mitigation which is sectorally wide-ranging and capable of 

profoundly transforming the structure of the energy system and 

the economy. While it is undoubtedly good that one’s model 

reproduces the historical record, this is no guarantee that the 

resulting calibrated numerical scheme is any better at projecting 

the far future than one that relies more heavily on assumptions. 

A concern about IGEM is its exclusive reliance on a functional 

form which is flexible but not globally regular, in the sense that the 

estimated Translog share equations are not guaranteed to map an 

arbitrary vector of positive prices into a vector of positive input 

shares (e.g., Lutton and LeBlanc, 1984; Perroni and Rutherford, 
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1998). Ad-hoc numerical countermeasures have been developed 

to deal with this issue (Wilcoxen, 1988, p. 127—especially 

footnote 2), but there is no discussion of how much use IGEM 

makes of them—particularly in situations where a stringent policy 

regime is being modeled, with attendant energy prices that lie far 

outside the range of the historical record on which the 

econometrically estimated share equations are based. This 

problem does not arise with nested CES functions. 

A possible solution is to develop an alternative specification of 

production for IGEM that is based on the type of nested CES 

functions employed by ADAGE, but parameterize the substitution 

elasticities, distribution parameters, and technical bias trends 

using nonlinear econometric estimation techniques. 

iv) Capital investment. Both models’ representations of 

capital dynamics may lead to biased estimates of the transition 

costs associated with abatement of GHG emissions. Production in 

IGEM is perfectly reversible, with capital services being 

frictionlessly reallocated among sectors within each period so as 

to earn the highest possible return, and increments to the 

aggregate capital stock facing no adjustment costs from one 

period to the next. This would suggest a downward bias in welfare 

costs. 

ADAGE’s formulation is somewhat more realistic, specifying an 

intertemporal pure putty-clay scheme in which each sector initially 

has only fixed-proportions “clay” capital, but the latter depreciates 

away over time, replaced by reversible “putty” capital as 

investment proceeds over the simulation horizon. This scheme 

does not resolve retrofit or retirement of the existing capital 

stock—which would likely generate more rapid adjustment in the 

energy-using characteristics of the capital stock, and leads to an 

expansion of substitution possibilities over time as the aggregate 

capital stock becomes increasingly malleable. Since a higher 

aggregate elasticity of substitution lowers the welfare costs of a 

price or quantity distortion, in an intertemporal model this 

formulation will likely imply an unintended additional inducement 

(over and above discounting) for the simulated regional 

representative agents to postpone costly abatement to future 

periods. 

These representations of capital dynamics are ostensibly 

employed because of the conceptual difficulty of calibrating 

intertemporal GE models with as many capital stocks as there are 
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industries, and the computational difficulty of solving for the 

equilibrium of the resulting excess demand correspondence. 

v) Technologies. A key limitation of IGEM in comparison to 

other CGE models for climate policy analysis is that it does not 

include disaggregated representations of key energy supply and 

demand technologies that reasonably might be anticipated to 

become economically viable over the coming century. The latent 

technology trends in IGEM’s cost functions do capture the longrun influence of run-of the-mill disembodied technical progress on 

economic growth and emissions. But there is no process within 

the model that is able to adequately reflect the potential impact of 

radical technical change as a result of, e.g., cost-reducing or 

performance-enhancing breakthroughs in “backstop” energy 

supply options that facilitate their rapid penetration once the costs 

of conventional fossil fuels rise sufficiently high. 

This is not an issue for the analysis of policies such as H.R. 2454, 

in which the marginal cost of GHG abatement are significantly 

moderated by a large influx of low-cost emission offsets from 

abroad. But it is easy to envision more costly scenarios such as 

global GHG concentration stabilization in which allowance prices 

in the early periods would be much higher, creating incentives for 

energy suppliers to undertake substantial investments in 

alternative, low-carbon technologies such as carbon capture and 

sequestration in the 2040-2060 timeframe. The anticipated 

reductions in abatement costs from the operation of these 

technologies would then be missed by IGEM in its current form. 

ADAGE does an adequate job of representing alternative 

electricity supply technologies within the nested CES cost function 

framework, though the input cost share and markup assumptions 

are subject to considerable uncertainty—especially if one takes 

into consideration the potential cost-reducing effects of learning by 

doing (as the authors imply on p. 27 of the documentation). 

One technological arena in which both models fall short is the 

transport sector. IGEM and ADAGE appropriately distinguish 

between transportation that is purchased (i.e., transportation 

industries) versus supplied by households using purchased inputs 

of vehicle capital and fuel. But neither model seems to represent 

the potential for the substitution of electric vehicles and biofuels in 

the production of transport services. Given that these technologies 

currently exist only at negligibly small scale, it is a simple matter to 

include them as dormant cost functions that embody a premium 

over benchmark output prices which enables them to 
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endogenously switch on and produce output once the relative 

price of conventional transport increase sufficiently. However, 

parameterizing the cost functions might be challenging. 

vi) Technological innovation. CGE models for climate 

policy analysis typically incorporate a mix of exogenous and 

endogenous technical progress. Typically, exogenous 

technological change of the “economy-growing” kind (labor 

productivity or TFP) is included in part as a way of compensating 

for diminishing returns to both capital and labor as the former 

accumulates and the supply of the latter saturates with slowing 

rates of population expansion. Exogenous technological change of 

the “emission-saving” kind applies a secular declining trend to the 

coefficients on fossil fuel commodities in industries’ cost functions 

or households’ expenditure functions, popularly dubbed 

autonomous energy efficiency improvement (AEEI). The AEEI 

progressively decouples GHG emissions from economic growth, 

proxying for the attenuating impact on CO2 emissions of the nonprice-induced declines in the energy-GDP ratios historically 

observed in many countries. Price-induced endogenous 

technological change is more difficult to represent. One approach 

is to incorporate backstop energy supply technologies, which 

remain dormant until climate policy-induced relative price changes 

enable them to be profitably operated. 

ADAGE follows the foregoing conventional recipe, whose major 

limitations are (a) the lack of responsiveness of incremental 

energy-saving technical progress to emission limits—i.e., induced 

technological change (ITC), and, (b) uncertainty over the future 

rate of growth of the AEEI index.11 ADAGE calibrates the timepath of AEEI to approximate EIA projections of baseline energy 

use and emissions to 2030. Given the importance of this 

parameter, the documentation needs to be much more specific 

about the rates of future decline in the energy-GDP ratio 

(regionally and nationally) thus implied, and how they compare to 

historical trends. 

Against this backdrop, the procedure used by IGEM to estimate 

incremental energy-saving technical progress is nothing short of 

remarkable. The rate and biases of technical change are all 

modeled econometrically as latent variables in the sectoral cost 

functions, and structural parameters governing their evolution are 

estimated as the transition matrix in a vector auto regression 

(VAR). This last piece of information enables the latent trends in 

the rate and bias of technology to be propagated forward as 

 

11 There is a large literature on endogenous technological change, though less so in the case of CGE models. For a 

recent review of this literature, see Popp et al (2009). 
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functions of the equilibrium prices that are being solved for, 

effectively incorporating the state of technology as a variable 

within the determination of general equilibrium. 

Despite the impressive methodological advance that this 

represents, we question the value of the results for climate policy 

analysis on two counts. The first is econometric: with so many 

parameters to be estimated for each industry from a mere 46 

years of data, one worries about the capacity of the data to speak 

when so much structure has to be imposed to achieve 

identification, and about the standard errors given the degrees of 

freedom penalty that will likely attend latent variable estimation. 

The second point is a philosophical one, namely, that the 

representation of technology through trends is something the 

modeling community should be doing less, not more, of. 

Notwithstanding the sophistication of the present approach, it 

would be intuitively attractive to represent ITC in a way that is 

more faithful to the process of cumulative investment in the 

creation of knowledge, followed by industries employment of the 

services from the resulting stock of intangible capital. This “stock 

of knowledge” approach has proved attractive in theory but elusive 

in empirical applications, though the papers of Popp (2001, 2004, 

2006) are potentially useful in this context. We would encourage 

research to develop this further, marrying hard data on innovation 

(e.g., R&D, patents) with empirical models of production in the 

IGEM mold to create climate policy simulations with endogenous 

invention and diffusion of the subsequent innovations. We feel the 

issue is sufficiently important that resources should be devoted to 

research on improving the representation of innovation in CGE 

models. 

vii) Greenhouse gas emissions calculation. CO2 is handled 

in the standard way, which is straightforward given the 

stoichiometric relationship between emissions and the quantities 

of fossil fuel use. It is much more difficult to model non-CO2 GHGs 

and their abatement possibilities, because there is no fixed 

stoichiometric linkage between these emissions and the economic 

activities resolved within the CGE model. The solution, developed 

by Hyman et al (2003) and incorporated within ADAGE, is to 

employ bottom-up non-CO2 marginal abatement cost (MAC) 

curves from EPA (which are constructed from engineering 

estimates) to calibrate the parameters of a constant elasticity of 

substitution composite of the benchmark quantity of emissions 

and the benchmark value of the activity with which they are 

associated. Non-CO2 GHGs are thereby transformed into an input 
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to production for which purchases of labor, capital or intermediate 

inputs can substitute, depending on the price of allowances 

relative to the latter inputs. 

Given IGEM’s representation of the aggregate economy and the 

availability of EPA’s detailed GHG emissions accounts for the US, 

it isn’t apparent why the authors could not distribute emissions 

amongst sectors and sub-tiers of the production and consumption 

hierarchies, and directly model abatement possibilities using the 

aforementioned state-of-the-art technique, instead of the now 

dated method of using MAC curves directly as a side-constraint on 

the general equilibrium problem. Turning to ADAGE, while the 

approach to modeling non-CO2 GHGs is adequate, the data are 

not. The dearth of state- and regional-level inventories of non-CO2

GHG emissions has compelled the authors to impute emissions 

data at the regional level based on the geographic distribution of 

output and consumption. Remedying this data gap is therefore a 

priority for model development. 

b) Household Sector (labor supply, consumption, and 

savings) 

ADAGE and IGEM each model consumer behavior using 

fundamentally the same approach. Both assume an infinitely lived 

household whose decision-making can be divided into several 

stages. At the top level, the household allocates full consumption 

(consumption of leisure, goods and services) among different time 

periods to maximize an intertemporal utility function. Within each 

period, the household then makes a labor-leisure decision, 

deciding how much to work and how much to spend on goods and 

services to maximize utility within that period. And then the 

household divides that spending across the range of goods and 

services. This approach is theoretically very sound, and is very 

widely used in dynamic CGE models. 

The differences come in how those utility functions are 

parameterized, and the resulting implications for household 

behavior. In IGEM, the utility function parameters are estimated 

using historical data and an estimation approach that is 

theoretically consistent with the structure of the model. This 

approach is different from the approach most CGE models take, 

and it has a number of advantages. The theoretical consistency is 

intellectually appealing, and avoids practical problems that can 

appear when parameters estimated under one set of assumptions 

are used in a model that makes different assumptions. 

ADAGE uses the much more common approach of taking 

elasticity parameters from the prior literature. Indeed, it takes both 
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those parameters and the form of the goods-and-services subutility function from MIT’s EPPA model. This approach is much 

simpler and easier to implement than the estimation approach 

used in IGEM. It also has the advantage that the values for each 

key parameter can be taken from an empirical study specifically 

focused on that parameter, which may yield more accurate 

estimates: the ideal empirical setting for estimating labor supply 

elasticities is probably not the same as the ideal setting for 

estimating an intertemporal elasticity of substitution. But it may 

also introduce inconsistencies between those parameters 

because they were estimated in different ways. Each approach 

has its advantages, and it is not at all clear which is better overall. 

The elasticity of labor supply is very important in determining the 

welfare effects of interactions between environmental policy and 

pre-existing distortions from taxes on labor. This elasticity comes 

out of the labor-leisure decision within a time period, and so it is 

determined largely by the parameters in that level of the utility 

function, and most importantly by the elasticity of substitution 

between consumption and leisure. 

ADAGE takes the labor supply elasticities from a survey by 

Russek (1996), using a value of 0.4 for the compensated and 0.15 

for the uncompensated labor supply elasticity. These values 

seem reasonable, and are consistent with those used in other 

CGE models. It might still be worthwhile for the ADAGE modelers 

also to consult other sources, such as the survey by Fuchs et al 

(1998) and the recent empirical literature on taxable income 

elasticities, but those more recent sources imply a very similar 

range of elasticities. 

ADAGE then sets the household time endowment and elasticity of 

substitution between consumption and leisure in order to achieve 

those values of the labor supply elasticities. Given that ADAGE is 

using a CES functional form, this is the right way to calibrate those 

parameters (see Ballard, 1999, for details). 

IGEM estimates values of the relevant utility function parameters 

as part of its larger estimation approach.12 Those parameter 

estimates imply a compensated labor supply elasticity in the range 

of 0.8 to 1 and an uncompensated elasticity of roughly zero. That 

 

12 IGEM also sets the time endowment to 14 hours per day. In a homothetic utility function, the time endowment 

directly determines the income elasticity of labor supply, so choosing an arbitrary value can cause problems. But 

this is not generally true for a non-homothetic function and the translog function is not homothetic, so the time 

endowment assumption is unlikely to have any significant effect. Nonetheless, it might be useful to try changing that 

assumed time endowment in the estimation and see how this changes the labor supply elasticities. 
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uncompensated elasticity is lower than most studies use, but is 

still within the range of estimates from the labor literature. 

However, the compensated elasticity is much higher than the 

values typically used in other CGE work, and is substantially 

above the range of values found in the labor literature (even in 

studies that include labor force participation and labor effort 

choices, not just decisions about how many hours to work). 

Moreover, because the uncompensated elasticity is relatively low 

and the compensated quite high, that implies that the income 

elasticity of labor supply (which is closely related to the difference 

between the compensated and uncompensated elasticities) must 

be much higher than in other studies. 

An empirical approach specifically designed to estimate labor 

supply elasticities seems likely to yield better estimates of those 

elasticities than an approach designed to estimate a broader 

range of utility function parameters. Moreover, the estimates in 

the prior literature come from a range of different data sets, time 

periods, and methodologies, and yet very few of them are as high 

as the estimate used in IGEM. 

To their credit, the IGEM modelers have included some sensitivity 

analysis with respect to the labor supply elasticity in the IGEM 

documentation. That analysis indicates that using a lower labor 

supply elasticity (a compensated elasticity of 0.3 instead of 0.8) 

makes a big difference in the estimated cost of a carbon cap-andtrade regulation: the estimated cost under the lower elasticity is 

roughly half of the estimated cost under the higher elasticity. A bit 

more detail in this section would help in interpreting this result and 

evaluating its implications. In particular, when the compensated 

elasticity was lowered to 0.3, what did that imply for the 

uncompensated elasticity? Theoretical work in the environmental 

tax-interaction literature suggests that both the compensated and 

uncompensated elasticities are important. So the interpretation of 

this sensitivity analysis is very different if the uncompensated 

elasticity dropped by the same amount (i.e., from 0 to -0.5, very 

strongly backward-bending and well outside the range of prior 

estimates) than if the uncompensated elasticity remained 

constant. 

Thus, without more information, it is difficult to know just how the 

use of more standard labor supply elasticities would affect IGEM’s 

results. And one cannot say for sure that IGEM’s elasticities are 

too high. Nonetheless, these results suggest that the labor supply 

elasticities used in IGEM will tend to overstate the costs of climate 

change policy. It would be good to run more sensitivity analyses 

(and provide more details on the current sensitivity analysis). And 

it might well be worth either changing the labor supply elasticity in 

IGEM’s central case or including a disclaimer that IGEM’s labor 
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supply elasticities are substantially higher than in other studies, 

and that this will tend to yield higher cost estimates. 

The key parameter for consumption and savings decisions is the 

elasticity of intertemporal substitution (the elasticity of substitution 

in the top level of the utility function), which controls how easy it is 

to substitute across different time periods. A higher elasticity 

means that consumption and savings will be more sensitive to 

changes in incentives to save. The elasticity of intertemporal 

substitution is notoriously difficult to estimate accurately, so 

estimates vary widely, but most credible estimates lie somewhere 

between zero and one. 

ADAGE uses a value of 0.5 for this elasticity, which is quite 

reasonable. The ADAGE documentation doesn’t explicitly say 

where this value came from, though it appears to have been taken 

from MIT’s EPPA model. 

IGEM estimates this elasticity using the standard Euler equation 

approach, which seems highly appropriate. But the 

documentation doesn’t seem to report the estimated value 

anywhere, making it impossible to confirm that the resulting 

elasticity estimate is reasonable. Nonetheless, given the wide 

range of estimates in the literature, it seems quite likely that IGEM 

is using a value that falls within that range. 

c) Government Sector 

i) Government behavior (consumption and investment). In 

general, the way government is modeled in both ADAGE and 

IGEM is quite reasonable and appropriate, and fairly standard in 

CGE modeling. However, some of their modeling assumptions 

may yield misleading results in some cases. 

The most important issue here is the assumption about the effects 

of government purchases (government spending used to 

purchase goods, as opposed to transfer spending), and how that 

assumption interacts with assumptions about what elements of 

government spending are held constant. 

Both ADAGE and IGEM assume that government purchases do 

not have any effect on household utility, nor do they affect 

production (though the taxes used to finance those purchases 

impose costs on firms and households, and distort economic 

decisions). In other words, both models assume that government 

purchases provide nothing of value, and thus are completely 
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wasteful.13 This is clearly a simplifying assumption, not any 

attempt at realism (there is widespread disagreement over the 

magnitude of benefits from government purchases, which would 

make them very difficult to model, but those benefits are clearly 

greater than zero). 

ADAGE assumes that government purchases of goods and 

services remain constant for any policy change. Under this 

assumption, any assumptions about the benefits from government 

purchases are irrelevant, and thus assuming that those benefits 

are zero causes no problem. 

IGEM, in contrast, does not hold government purchases constant. 

Combined with the assumption that government purchases 

provide no benefit, this assumption can cause substantial 

problems. The model will overestimate the net cost of any policy 

change that increases real government purchases, because it 

recognizes the cost of such an increase, but ignores any potential 

benefits. 

This problem shows up in the analysis in Chapter 8 of the IGEM 

documentation, which finds that using carbon permit revenues to 

reduce marginal tax rates on labor income actually raises the cost 

of the policy (relative to returning the revenue lump-sum to 

households), a result that is strongly at odds with the extensive 

literature on environmental tax interactions. That result arises 

because of an assumption that nominal government expenditures 

remain constant, and under the model’s numeraire assumption, 

lowering the labor tax lowers prices throughout the economy, so 

holding nominal expenditures constant implies higher real 

government purchases. Thus, the anomalous result is an artifact 

created by the combination of the numeraire assumption, the 

assumption that government purchases provide no benefits, and 

the assumption that nominal government expenditures remain 

constant. This illustrates how these assumptions can combine to 

yield very misleading results. 

One way to address this problem would be to allow benefits from 

government purchases (e.g., having those government purchases 

be used to provide a public good that would then enter the 

consumer utility function). But that would add substantial 

complexity, and would be very difficult to calibrate accurately. A 

simpler solution would be for IGEM to assume that (real) 

government purchases remain constant for policy changes (as 

ADAGE does). 

 

13 IGEM may allow some government purchases to provide benefits, though the documentation is very unclear on 

this point. Page 1-31 of the IGEM documentation says “Government spending affects household welfare directly 

through transfer payments and public health spending and indirectly through public capital that improves private 

sector productivity.” But there is no mention anywhere else in the documentation either of public health spending or 

of how public capital affects private sector productivity, and there do not appear to be any terms in the relevant 

equations that would represent those effects. 
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More generally, IGEM should avoid targeting any nominal variable 

(e.g., holding a variable constant in nominal terms, or assuming 

that it rises at a constant nominal rate). The value of any nominal 

variable depends on the choice of numeraire, which is inherently 

arbitrary. 

In other respects, both models’ treatment of government 

purchases seems appropriate and reasonable. Both have a solid 

basis for how they allocate government purchases across different 

goods and services, and for how these fit into the larger model. 

A distinct but closely related issue is how the models assume that 

government responds to a change in revenue. Both models 

assume that tax rates remain constant (except for cases in which 

the policy being analyzed explicitly includes changes in taxes), so 

when the tax bases change in response to a policy, government 

revenue also changes. Thus, some other part of the government 

budget constraint must also change. IGEM holds the government 

budget deficit and government transfer payments fixed, which 

means that government purchases change to offset any change in 

revenue. ADAGE also holds the government budget deficit fixed 

(indeed, it appears to assume that the deficit is always zero, 

though the ADAGE documentation doesn’t explicitly say this). 

But, as noted above, it keeps government purchases fixed, so 

government transfers adjust to offset changes in revenue. 

As noted above, IGEM’s assumption that government purchases 

change is clearly problematic when combined with other 

assumptions in the model. One could argue that ADAGE’s 

assumption that government purchases are fixed and government 

transfers adjust is preferable. But it might be better still for both 

models to make the assumption that all government expenditures 

are fixed (in real terms), and thus that tax rates adjust to hold 

government revenue fixed (also in real terms). This issue is 

similar to the point above about holding government purchases 

constant. In a representative-agent model, there is no reason for 

government to make transfers to the household, but such 

transfers do have a cost (the distortionary cost of raising tax 

revenue). But in a more sophisticated model, there would be a 

reason for those transfers. Because these models assume a 

representative agent for simplicity, they ignore the reasons for 

government transfers, and thus will give somewhat misleading 

results for cases in which lump-sum transfers change. 

Alternatively, one could keep the assumption that tax rates stay 

constant and transfers adjust, which is simpler than adjusting tax 

rates, but explicitly note that when describing the results and add 

the caveat that this may have significant distributional effects. 
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In addition, both models would benefit from better documentation 

of how they model government. ADAGE’s documentation on this 

issue is simple and well-organized, so it is easy to find and to 

understand the relevant sections, but more detail on 

implementation would be useful. For example, the documentation 

says that government purchases of goods and services are held 

constant. This suggests that the quantity of each good or service 

purchased by the government is held constant, not that the 

amount spent on each good is held constant, or that the total 

amount spent is held constant, but the description is so brief that 

one cannot be entirely sure. 

IGEM provides much more information, which is very helpful, but 

the documentation is not well organized, and it is difficult to find 

the relevant sections. Moreover, different sections of the 

documentation sometimes seem inconsistent.14 These may in fact 

be consistent, but not written clearly, or this may be the result of 

minor modifications to the model with different sections of the 

documentation being written at different times. 

ii) Taxes. ADAGE and IGEM each recognize and include the 

important distinction between marginal and average tax rates on 

labor, which differ substantially in a progressive tax system 

(IGEM, for example, uses a marginal labor tax rate of roughly 28% 

and an average rate of roughly 12%). The average tax rate is 

important for calculating tax revenue, whereas the marginal tax 

rate determines the size of the tax distortions in the labor market. 

Both models therefore include this distinction (though the ADAGE 

documentation could use a bit more detail on exactly how the two 

different rates are used in the model). 

Both models calculate average tax rates in essentially the same 

way, by dividing labor tax revenue by pre-tax labor income from 

national accounting data. But they take marginal tax rates from 

different sources. ADAGE uses the National Bureau of Economic 

Research’s TAXSIM model to calculate marginal tax rates. This 

model includes many details of the federal and state income tax 

systems, and is widely used for economic models that include 

income taxes. IGEM draws marginal tax rates from IRS data on 

income tax paid at different income levels. This approach is less 

precise and less detailed, but still sufficient for estimating a 

marginal tax rate for use in a representative-agent model. The 

two approaches yield very similar results for the marginal labor tax 

rate exclusive of FICA (Social Security) taxes: their estimated 

marginal rates differ by less than 1%, with both models using a 

rate of approximately 28%. 

 

14 For an example, refer to Footnote #13 of this report. 
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However, the two models differ in how they treat FICA taxes. 

ADAGE treats the FICA tax as a tax, which adds roughly 13% to 

the marginal labor tax rate (the full combined FICA tax rate is 

15.3%, but applies only on earnings up to a cap, so its effect on 

the average marginal tax rate is smaller). IGEM treats the FICA 

tax as a combination of household saving (to the extent that 

contributions are going into the Social Security trust fund) and 

transfers within the household sector, and thus does not include 

the FICA tax when calculating the marginal tax rate on labor. 

Thus, even though both models use very similar marginal rates 

exclusive of FICA tax, the total marginal tax rates differ 

substantially, with ADAGE using a rate of roughly 41% and IGEM 

using a rate of roughly 28%. 

The appropriate treatment of FICA taxes in calculating marginal 

tax rates remains something of an open question. The issue is 

that a marginal dollar of FICA-covered earnings is taxed, but may 

lead to a marginal increase in future Social Security benefits. 

Thus, the true effect on marginal labor tax rates is equal to the 

marginal FICA tax rate minus the expected discounted value of 

the marginal increase in future Social Security benefits. ADAGE, 

by treating the FICA tax purely as a tax, is ignoring the effect of 

future benefits. IGEM, in contrast, is effectively assuming that 

increase in benefits exactly offsets the tax. The truth is likely 

somewhere between those two assumptions: Feldstein and 

Samwick (1992) calculated effective marginal FICA tax rates 

taking into account changes in future benefits, and found that they 

vary widely, but in most (though not all) cases lie between zero 

and the full statutory tax rate. Some discussion of this issue in the 

documentation of both models would be useful so model 

consumers would understand what assumptions are being made 

in this context. 

Both models contain a relatively detailed set of capital tax rates 

(though even that level of detail represents a vastly simplified 

version of the actual system of capital taxation in the US). In each 

case, these capital tax rates and other relevant variables are used 

to calculate a user cost of capital. This is conceptually the right 

way to put capital taxes into the model. 

One key difference between the models, however, is that IGEM 

appears to be using the average effective tax rate on capital in its 

cost-of-capital calculations, whereas ADAGE is using the marginal

effective tax rate. The roles of the two tax rates are analogous to 

their roles in the labor tax case: the average tax rate is important 
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in calculating revenue, while the marginal tax rate determines the 

distortions in the capital market15

Of these two approaches, it is more important to get the marginal 

tax rate correct. Holding the marginal rate constant while the 

average rate changes (i.e., an inframarginal change in the tax), is 

equivalent to a lump-sum transfer between households and the 

government. This affects the accounting, but has no direct 

efficiency effect. In contrast, changing the marginal tax rate while 

holding the average rate constant (i.e., a compensated change in 

the tax rate) changes the magnitude of distortions in the capital 

market, and thus directly affects economic efficiency. Thus, if a 

model incorporates only a single capital tax rate, it is better to use 

the marginal rate than the average rate. However, it would be 

better still to explicitly represent both average and marginal rates 

in the model. ADAGE and IGEM both take this approach for labor 

taxes, but it appears that neither model takes this approach for 

capital taxes (though neither model’s documentation is completely 

clear on this point). 

d) International trade 

i) Commodity flows. These are for the most part handled 

appropriately by both models. One area in which there might be 

concern is international energy trade in ADAGE, given that trade 

is modeled using the widely used Armington approach which 

represents the aggregate supply of each good as a CES 

composite of imported and domestic varieties. The CRTS CES 

aggregator function conserves value—the value of output equals 

the value of inputs—but not quantities, so that the exajoules of, 

say, coal used will generally differ from the sum of the exajoules of 

coal that are domestically-produced plus imported from each trade 

partner. This is a common problem for CGE models using the 

Armington approach. 

A second issue is the ADAGE model’s use of a gravity scheme to 

calibrate benchmark bilateral state-to-state trade flows, which are 

not part of the IMPLAN dataset and must be imputed. The dearth 

of this kind of trade data is a well documented problem.16 This 

state of affairs raises two questions. First, how do ADAGE’s data 

reconciliation procedures measure up to alternatives? Second, 

given the broader context of ADAGE’s already heavily calibrated 

 

15 As in the labor tax case, the difference between the marginal and average rates appears to be substantial: the 

average effective capital tax rate in IGEM is given in Table G.1 as 13.44%, whereas the marginal effective capital 

tax rates in ADAGE (Table 4-7) for different industries range from 26.3% to 45.8%. There are many reasons why 

these rates differ between the models other than just the distinction between average and marginal rates, so it is not 

strictly correct to compare them, but the difference is illustrative nonetheless. 

16 Some recent work which addresses this issue include Canning and Wang (2005), Jackson et al. (2006), and Park 

et al., 2009). 
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base-year dataset and the considerable uncertainties involved in 

these imputation procedures, does all this effort really add up to 

an improvement in the accuracy of the simulations relative to a 

naive Armington formulation based on an aggregate supply pool? 

ii) Financial flows. The ADAGE model documentation yields 

few clues as to how international or interregional capital flows are 

handled. G-Cubed (McKibbin and Wilcoxen, 1998) appears to be 

the only CGE model that explicitly tracks flows of capital. The 

traditional scheme adopted by multiregional models based on the 

GTAP dataset is to specify a trade closure rule in which the 

current account balance moves toward zero at an exogenouslyspecified rate and the price of foreign exchange in each region 

adjusts endogenously in response, without regard to the origins 

and destinations of the underlying financial transfers among 

regions. Therefore, in an intertemporal GTAP model the only 

observable impact of such rules will be adjustments in the present 

value of aggregate income in each region. 

This is essentially the same procedure employed by IGEM, whose 

authors’ note the difficulty of pursuing the alternative approach of 

specifying the time path of the real exchange rate and allowing the 

import and export price indexes to adjust accordingly. However, 

the two approaches appear equally feasible. Trade-focused small 

open economy CGE models traditionally set the exchange rate as 

the numeraire price, and in an intertemporal setting it can be 

specified as declining at the steady-state rate of return on financial 

wealth, r*, for a reduction of 1 / (1 + r*) per annum. It is then a 

straightforward proposition to formulate a complementary 

slackness condition that allows the current account balance to 

adjust endogenously to satisfy this price path. 

e) Macroeconomic framework 

These are handled appropriately by both models, as they 

generally must be in any CGE model. 

f) General 

i) Resolution: are the model input and output scale resolutions 

appropriate and do they provide meaningful policy insights at 

these levels? 
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The ADAGE model has an international module that is built on the 

GTAP Version 6 database of SAMS for 87 countries. This allows 

considerable flexibility for the model to consider a variety of 

international configurations with which the US model can interact. 

As the documentation indicates, a reasonable grouping of 

countries for a global analysis – essential for thinking about 

climate policy – would include the United States, Europe, Japan, 

Australia/New Zealand, China/India/Russia and the Rest of World. 

ADAGE's clustering of countries is similar to other CGE models 

used for climate policy analysis including MIT's Emissions Policy 

and Prediction Analysis (EPPA) model. EPPA, for example, in a 

recent report focused on interactions between the United States 

and Canada, Japan, the European Union, Australia/New Zealand, 

the Former Soviet Union, Eastern European countries in 

transition, India, China, Indonesia, Higher Income East Asian 

countries, Mexico, Central and South American, the Middle East, 

Africa, and the Rest of World (Paltsev et al. (2009)). While this 

cited analysis using the EPPA model has greater detail in the 

countries, it appears that ADAGE has the capability to do 

comparably detailed modeling of international clusters of 

countries. 

Within the United States, ADAGE's US Regional model allows for 

any configuration of states as is appropriate for modeling. The 

documentation included for this peer review in Ross (2009) shows 

an illustrative grouping in Figure 1-3 of the US broken down into 

five regions: West, Plains, Midwest, South and Northeast. 

ADAGE's treatment of regions both internationally and within the 

United States is both appropriate and consistent with the regional 

resolution of other CGE models focusing on environmental issues 

including climate change. Other models that are in use to 

evaluate U.S. environmental and energy policy include CRA 

International's Multi-Region National (MRN) model, EPRI's 

MERGE model and Pacific Northwest National Lab's MiniCam 

model. 

ADAGE breaks down each region's economy at the country level 

into five broad industrial groups (agriculture, energy-intensive 

manufacturing, other manufacturing, services, and transportation) 

and five primary energy industries (coal, crude oil, electricity, 

natural gas and refined petroleum). Electricity is further broken 

down into various sources of generation. Again this is consistent 

with the sectoral breakdown in other CGE models used for 

environmental and energy policy analysis. Given the importance 

of the electricity sector for climate change – over half the 

electricity in the United States is generated from coal – an 

important feature of the ADAGE model is its handling of 
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renewable electricity sources (wind, solar, hydro, and biomass) as 

well as its inclusion of new fossil fuel technologies (IGCC with or 

without carbon capture and storage). The parameterization of 

these newer technologies is to some degree speculative. But 

given the importance of these new technologies for helping effect 

a shift to a carbon-free economy it is important to include them in 

any analysis of climate change policy. 

ADAGE is a representative agent model. That means that in each 

country income is received by and consumption undertaken by a 

representative utility maximizing household. The advantages of 

this approach are that it is easier for ADAGE to compute a full 

forward looking intertemporal general equilibrium in which the 

representative agent in each country (or sub-region within the 

United States) maximizes its utility fully anticipating future policy 

impacts. Saving and investment is endogenous and consistent 

with economic theory. The drawback to this approach is that the 

ADAGE model cannot provide any information on heterogeneous 

policy impacts across different household types within the United 

States (other than across region). Elsewhere in this review we 

have discussed the shortcomings of using a representative agent 

and made suggestions for improvement. 

Adding heterogeneity across income groups would be a valuable 

extension of the ADAGE model. With increased computing power 

it is becoming increasingly feasible to add this level of detail and 

keep the model tractable for computational purposes. MIT's 

EPPA model has recently been extended to allow for both regional 

and income heterogeneity in its US-REP model (see Rausch et al. 

(2009) for a discussion of this new model). That model is being 

extended in a dynamic-recursive framework along the lines of the 

standard EPPA model. 

IGEM is a highly detailed CGE model of the U.S. economy with 

great sectoral and household detail. It's strengths are the detailed 

modeling of industries in the U.S. economy (35 industries listed in 

Goettle et al. (2009)). The benefit of the detailed sectoral 

groupings in the IGEM model is the ability to track specific industry 

impacts with great specificity. For example, IGEM is capable of 

providing analysis for specific industries – the chemicals and allied 

products industry, for example, while ADAGE groups this industry 

with other sectors into a single energy-intensive manufacturing 

sector. 

One drawback of the IGEM model is its treatment of the energy 

sector. The model does an excellent job of treating existing 

energy production at a fairly high level: coal mining, oil and gas 

mining, petroleum refining, electric utilities, and gas utilities. This 

representation of energy precludes the model saying anything 
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about the introduction of new technologies and will not easily 

represent the expansion of newer technologies (e.g. wind and 

solar). This is due to the model's econometric approach to 

constructing and parameterizing the model with data from the 

period 1960 to 2005. Wind and solar, for example, have been 

growing rapidly. Focusing on wind, it accounted for 0.1 percent of 

generation by 1991 and had grown in importance to 1.3 percent 

by 2008.17 Using historic data to parameterize the model makes it 

difficult to add new technologies (or allow for more recent changes 

in technology for which a fundamental technological change has 

occurred that is not well represented by the translog functions). 

This approach also means that new technologies such as carbon 

capture and storage (CCS) simply cannot be incorporated into the 

analysis. Goettle and Fawcett (forthcoming), for example, 

estimate a 44 percent reduction in the output of the coal mining 

industry by 2030 in response to a U.S. policy that limits GHG 

emissions to 203 billion metric tons (bmt) between now and 2050. 

The analysis by Paltsev et al. (2009) finds little growth of CCS by 

2030 for a 203 bmt scenario but finds the growth of CCS sensitive 

to the relative cost differences between nuclear and CCS. 

Moreover the Paltsev et al analysis finds a greater role for CCS in 

the 167 bmt scenario. 

IGEM handles this issue (as well as the issue of domestic and 

international offsets in climate policy) by incorporating external 

marginal abatement curves for different options into the analysis. 

Through an iterative approach, IGEM merges external marginal 

abatement costs with its internally computed marginal abatement 

cost schedule to arrive at a mix of emission reductions determined 

from IGEM's general equilibrium in the face of policy constraints 

along with a mix of externally generated emission reductions. In 

general, this approach is an appropriate grafting of new 

technologies and policy options onto a model that has been 

developed from econometrically derived relations. 

IGEM is a domestic model. It uses an Armington (1969) 

assumption and treats imports as imperfect substitutes for 

domestic production. It is unclear how IGEM is able to handle 

different assumptions about climate policy by other countries in 

affecting US production, consumption and welfare outcomes, 

given this approach. 

A new version of IGEM (Version 3) allows for household 

heterogeneity across family size, region of the country in which 

the household lives, race and gender of household head and type 

 

17 Table 8.2a in Energy Information Administration (2008). 
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of residence. The peer review team received this new 

documentation late in the review process and we have not done a 

detailed analysis of it. We found the reporting of household 

welfare effects in the documentation sent to us intriguing but 

difficult to interpret (e.g. Figure 9.9, Household Welfare Effects by 

Region). We would expect that as the new IGEM model is further 

developed the modelers will find more useful ways to report 

results taking household heterogeneity into account. 

One issue for any analysis reporting results across regions is the 

treatment of household migration. It appears that implicit in the 

ADAGE and IGEM results is the assumption that households do 

not ever move to a new region. This is an assumption made by 

others including the MIT US-REP model, the U.S. regional variant 

of the EPPA model. 

ii) Sensitivity to alternate specifications or data. Neither the 

ADAGE nor the IGEM model reports results from alternative 

specifications or data. It is thus impossible for the Peer Review 

Team to assess the sensitivity of the models to alternative 

specifications or data. 

iii) Uncertainty: how does the representation of uncertainty 

compare with others in this class of models, and are there 

recommendations for better representation of uncertainty? 

Many environmental problems are associated with considerable 

uncertainty over the benefits and costs of policy response and/or 

inaction. 

Considerable uncertainty exists over the likelihood of the 

development of new technologies to help shift the U.S. and other 

economies away from reliance on fossil fuels. Some sensitivity 

analysis of the ADAGE and IGEM marginal abatement cost curves 

for new technologies would be helpful for understanding how 

important assumptions about new technology development are in 

driving results. This can be done to a great extent by considering 

various scenarios about the availability of new technology as well 

as its cost. 

As a class, CGE models for climate policy analysis generally do 

not have structural representations of uncertainty (as in, e.g., a 

dynamic programming simulation), nor do their outputs generally 

do a good job of capturing uncertainty, in terms of their input data, 

parameter values or random shocks. IGEM and ADAGE are as 

bad at this as their peers (e.g., MIT-EPPA). That said, there is 

enormous scope for conducting parametric uncertainty analysis 

with either of these models, but the key challenges are the size of 

the parameter space and the supports for the probability density 
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functions (pdfs) of values for the various input parameters. A key 

advantage of IGEM is that the latter concern is automatically dealt 

with, at least to a certain extent, as every parameter of its cost, 

expenditure and demand functions has an associated standard 

error. 

Computable general equilibrium models are complex systems of 

numerous equations that make detailed understanding of the 

drivers of results difficult to determine. Both the ADAGE and the 

IGEM model results are presented as scenario results rather than 

as predictions of the future. This is appropriate given the great 

deal of uncertainty of many key parameters embedded in the 

models. It would be valuable for EPA to fund analyses that 

explicitly account for our uncertainties about key parameters. 

Monte Carlo analyses with the ADAGE and IGEM models would 

improve our understanding of the economic impact of 

environmental policy to a great measure.18

The ADAGE model does not attempt to measure the benefits of 

improved environmental quality from policy action. 

Documentation for the IGEM model shows how IGEM can be 

used to model benefits from environmental policy – in particular 

improvements in morbidity and mortality effects. There is no 

discussion of the sensitivity of results to changes in assumptions 

of morbidity and mortality effects in its discussion of the costs and 

benefits of the Clean Air Act (Chapter 3 of IGEM documentation, 

part I). 

3. Usefulness for Evaluating the Effectiveness of Policy 

Instruments 

a) Are the model results reasonable and credible? 

Given our limited understanding of important technological 

changes that climate policy (and other environmental policies) will 

engender and the considerable uncertainty about agricultural and 

land use impacts, the model results are reasonable and credible. 

However, there are a few dimensions of the models which are less 

than totally satisfactory. One is the allowance price. One is 

always suspicious when two different models generate the exact 

same trajectory of prices over a half century for something as 

centrally important as the price of allowances. This is apparently 

because the price of allowances is effectively exogenous, at least 

in the context of HR2454 and S, 1733. What is exogenous is an 

estimate of the supply curve for international offsets (which is fairly 

elastic). If the constraint on international allowances is not 

 

18 Here we have in mind analyses along the lines of Webster et al. (2008). 
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binding, then the price of US allowances is set by the international 

market. This appears to be the case for HR 2454. Our 

recommendation is that this important component of the model 

determining allowance prices be documented more explicitly, 

including data inputs. 

Another issue is the electricity sector. When the models are run in 

conjunction with the IPM (Integrated Planning Model), which is a 

very detailed model of the electric power sector, there are very 

significant differences between forecast fuel use in the sector 

between the CGE models and the IPM.19 Working on merging a 

more detailed technology-based representation of electricity into 

the CGE framework would be useful. 

One area where greater justification for modeling assumptions 

would be valuable – or perhaps greater sensitivity analysis 

presented – is the assumption in IGEM of perfect capital mobility. 

While this assumption might be valid for long-run general 

equilibrium models, it seems inappropriate for models that purport 

to measure impacts in both the short and long run. The 

assumption of perfect capital mobility should drive costs of climate 

policy down. A similarly high responsiveness occurs in labor 

supply with IGEM assuming a high compensated labor supply 

elasticity. Here again some sensitivity analysis and Monte Carlo 

analysis around these key assumptions would be valuable for 

policy assessment. 

b) Does the model produce forecasts that are 

meaningfully differentiated based upon the use of 

different policy instruments? 

For climate policy, a key difference between price-based 

instruments (a carbon fee or tax) and quantity-based instruments 

(allowance system) is their operation under uncertainty (see 

Weitzman, 1974 and subsequent analysis by Newell and Pizer, 

2003 and Karp and Zhang, 2005, among others, for discussion of 

instrument choice under uncertainty). In both the IGEM and 

ADAGE models the two policy approaches are functionally 

equivalent in the absence of uncertainty. 

One way to address this issue would be to undertake Monte Carlo 

analyses to obtain stochastic variability in the marginal cost of 

abatement curves. This would actually be quite useful given the 

focus on cost containment in a number of the Senate and House 

climate policy bills that have been brought forward in the past 

year. We note however that Monte Carlo analyses are not the 

 

19 Compare pages 17 and 26 of the EPA analysis of HR 2454. 
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same as modeling explicitly under the assumption of uncertainty. 

In Monte Carlo, economic agents are not explicitly taking the 

uncertainty over future prices and policies into account when 

making decisions. Monte Carlo should best be thought of as 

sophisticated sensitivity analysis rather than a rigorous inclusion 

of uncertainty in a CGE model. 

c) Does the model have sufficient transparency to 

produce defensible forecast scenarios in the face of 

public criticism? 

The model documentation appears sufficiently detailed to allow 

professional economists to assess and defend forecast scenarios 

in the face of public criticism. It is important for EPA to stress the 

limitations of any economic analysis extending out forty or more 

years in the face of considerable technological uncertainty. 

Models have great value for understanding the consequences of 

policy action (or inaction) but they are not forecasts of the future. 

However, the models do remain as black boxes to many. It might 

be useful to both improve the clarity of the documentation of IGEM 

and the detail of the documentation of ADAGE. Furthermore, the 

EPA might consider developing small versions of each of these 

models that are more transparent. 

d) Does the model have enough flexibility to support the 

analysis of multiple types of regulatory regimes and 

input data sources? 

Both the ADAGE and IGEM models are best suited to supporting 

the analysis of market-based policies. This follows from the 

simple fact that both these models are Arrow-Debreu general 

equilibrium models in which price adjustment to clear markets is 

central to the models' operation. Technology mandates and other 

command-and-control type regulation can be modeled by ADAGE 

and IGEM to the extent that reasonable constraints can be 

designed that capture the regulatory regime. But it is unclear that 

all of the nuances of actual legislation can actually be represented 

in either of these models. 

4. Conclusions

We begin this summary by noting that both the ADAGE and IGEM 

models are extraordinarily sophisticated economic models with 

richness of detail in modeling various aspects of the U.S. 

economy. The model developers are to be commended for the 
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excellent work that they have done in recent years to make these 

models useful for the EPA and other government agencies 

interested in modeling the impacts of policies to address 

greenhouse gas emissions and other environmental issues. 

We have made a number of suggestions in the body of this report, 

none of which is vital for the validity of these models. Rather, we 

urge EPA to view the long-term improvement of these models as 

in the EPA’s interest, so that future analyses may be improved. 

Some of these recommendations are listed below, though the list 

is not intended to be comprehensive (refer to the body of the 

report for other suggestions): 

• Improve the Documentation of both IGEM and ADAGE 

• Improve the documentation of the carbon abatement and 

allowance price determination in both models 

• Work to reconcile electricity sector results from either 

model and bottom up electricity supply models such as 

IPM 

• Improve the justification of perfect capital mobility in IGEM 

or consider alternative formulations 

• Provide more documentation and justification for the 

parameterization in ADAGE 

• Provide more publically available sensitivity analysis of 

both models 

• Work to improve the representation of endogenous 

technological change 

On balance, these are two excellent models that provide useful 

information to EPA as it goes about its work of evaluating climate 

policies. While we may have issues with elements of the models, 

in general we find these to be first-rate and state-of-the-art. These 

models are essential tools for the analysis of environmental policy 

in general and climate policy in particular. 
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Attachment 3

Response to Peer Review Panel prepared by Dale

Jorgenson Associates regarding IGEM
Response to Charles D. Kolstad, Gilbert E. Metcalf, Ian Sue Wing, and Roberton C.

Williams III, “Peer Review of Computable General Equilibrium Models for Climate

Change Analysis,” January 28, 2010.

by

Dale W. Jorgenson, Harvard University, Daniel T. Slesnick, University of Texas,

Austin, Peter J. Wilcoxen, Syracuse University, Richard Goettle IV, Northeastern

University, and Mun S. Ho, Resources for the Future, Inc., February 19, 2010.

The conclusions of the Peer Review are presented on pages 31-32: “We begin this

summary by noting that both the ADAGE and IGEM models are extraordinarily

sophisticated economic models with richness of detail in modeling various aspects of the

U.S. economy. The model developers are to be commended for the excellent work that

they have done in recent years to make these models useful for the EPA and other

government agencies interested in modeling the impact of policies to address greenhouse

as emissions and other environmental issues.”

We are very grateful to the reviewers for the care they have taken in preparing the

Peer Review and we agree with their views about research priorities for the future. Here

is a summary of our response, following the recommendations on page 32:

The documentation of IGEM discusses the current version used in policy

evaluations for the Environmental Protection Agency, as well as a new version that will

be implemented during the coming year. We will revise the documentation to focus on

the new version. This will simplify the presentation and make it more user-friendly.

The documentation of carbon abatement and allowance price determination

deserves special attention.

Integration of IPM and IGEM should be undertaken when this becomes a matter

of priority in policy analysis.

The term “perfect capital mobility” used in the Peer Review does not convey the

role in IGEM of external costs of adjustment along the transition path, as outlined below.

We will try to frame this issue better in the documentation for the new version of IGEM.

We will provide standard measures of uncertainty for outcomes of IGEM, as well

as the parameter estimates. Sensitivity analysis, recommended at several places in the

Peer Review, cannot provide such measures. More detail is devoted to this central point

below.

Pages 2-8 provide general comments and the reviewers’ responses to general

questions posed by EPA. The first conclusion, presented in page 3 is: “Both the ADAGE

and IGEM models are sophisticated computable general equilibrium models in which
great care has been taken to model aspects of the economy particularly important for

analyzing environmental policy in general and climate policy in particular.” Our response

begins with the treatment of four major issues discussed in the Peer Review. The first is

the representation of technology, the second is modeling household behavior, the third is

the treatment of uncertainty, and the fourth is the solution of the model.

Production Modeling.

The approach to modeling the production sector in IGEM is based on recent

research on induced technical change by Jin and Jorgenson (2010). This has been

published in a refereed journal, the JOURNAL OF ECONOMETRICS. The principal

achievement of this research is to integrate the estimation of the price elasticities that

describe substitution with the rates and biases that describe technical change. The results

are thoroughly discussed in the IGEM documentation.

The Jin-Jorgenson model of induced technical change provides an endogenous

treatment of technical change. In fact, “induced” is a synonym for “endogenous”.

Furthermore, this model makes it possible to quantify the importance of policy-induced

technical change in the evaluation of changes in policy. As in all the published literature

on environmental and climate policy analysis, the role of policy-induced technical change

in evaluation of policy outcomes is empirically very minor.

The representation of endogenous technical change in the Jin-Jorgenson model of

is based on econometric estimates, fitted to historical data for each of 35 industries, five

representing the energy sector. The vast bulk of economic activity relevant to

environmental and climate policy evaluation is represented in the historical data.

Econometric methods are essential for summarizing these data and introducing them into

the simulations. An approach that ignores the historical data makes it impossible to

evaluate the results using standard measures of uncertainty discussed below.

All econometric estimation involves imposing structure to achieve identification.

IGEM imposes the structure required to use the economic theory of production that

underlies concepts like price elasticities and technical change. On page 14 the reviewers

make the “philosophical” point that indicators of innovation such as R&D expenditures

should be integrated into econometric models. The data required to implement this

approach for a model like IGEM are not available. However, these data are under

construction by the Bureau of Economic Analysis and we will incorporate them into

IGEM when they are released.

IGEM’s estimation techniques impose concavity restrictions at all historical data

points, following a technique first discussed in the econometric literature by Gallant and

Golub (1984) and cited in the IGEM documentation. This is superior to “global”

concavity restrictions that require a sacrifice of flexibility in representing substitution

patterns. Although this point is well known to econometricians, it has been slow to

penetrate the modeling literature. IGEM uses nonlinear econometric estimation

techniques for both household and production models.
IGEM is unique among models of energy and environmental policy in using

econometric methods to calibrate the unknown parameters. This is essential in providing

the measures of uncertainty described below. However, the econometric approach leaves

open the question of how to introduce technologies that are not represented in the

historical data. Our solution to this problem is standard in the literature, namely, we use

engineering representations in the form of marginal abatement cost (MAC) curves. This

technique has been employed in previous versions of IGEM and in other models for

evaluating energy and environmental policies.

On page 27 the reviewers assert that new technologies such as carbon capture and

storage (CCS) “simply cannot be incorporated into the analysis.” Actually, CCS and all

of the technologies (except electric vehicles) discussed in the Peer Review are

represented by the marginal abatement cost (MAC) curves in IGEM. These are

introduced as endogenous responses to energy and environmental policies. Later on page

27 the reviewers point out that new technologies are introduced endogenously in response

to price changes in IGEM and continue: “In general, this is an appropriate grafting of new

technologies and policy options into a model that has been developed from

econometrically derived relations.”

On pages 14 and 15, the reviewers properly credit ADAGE for employing the

“state-of-the art technique” developed by Hyman, et al., (MIT – JCSPGC – Report No.

94, December 2002) for modeling non-CO2 marginal abatement costs. The non-CO2

MAC curves are supplied by the Environmental Protection Agency and are widely shared

among the modeling community. In ADAGE these curves are used to calibrate the

parameters of a constant elasticity of substitution (CES) composite production function,

illustrated in Figure 1 below. Abatement depends on the price of allowances relative to

the composite price of other inputs -- labor, capital and intermediate goods and services.

The reviewers go on to say, “Given IGEM’s representation of the aggregate

economy and the availability of EPA’s detailed GHG emissions accounts for the US, it
isn’t apparent why the authors could not distribute emissions amongst sectors and subtiers of the production and consumption hierarchies, and directly model abatement

possibilities using the aforementioned state-of-the-art technique, instead of the now dated

method of using MAC curves directly as a side-constraint on the general equilibrium

problem.”

The emissions accounting in IGEM is precisely that recommended by the

reviewers. The MAC curves are introduced endogenously in determining the allowance

prices that clear markets under a cap-and-trade regime and do not serve as a “side

constraint on the general equilibrium problem”. While the mechanics differ,

determination of allowance prices, abatement costs, and emissions reductions is identical

between the IGEM and ADAGE. Moreover, the IGEM methodology recognizes the

possibilities of irregularities, regions of “no regrets” and threshold pricing, which is

impossible using the approach outlined in Figure 1. By way of example, the IGEM

approach permits MAC curves like the “composite” shapes depicted below rather than

relying on the CES approximation indicated by “sigma”:
On page 5 the reviewers propose “...remedying the lack of non-marginal (GHGsaving) technological change by incorporating discrete alternative energy demand and

supply technologies, which come on line endogenously in response to price.” This is the

purpose of the marginal abatement cost (MAC) curves used in IGEM. Given the

exhaustive analysis of historical data on production by Jin and Jorgenson, the most

important opportunity for improvement of models of technology is for EPA to obtain and

utilize additional engineering studies. This is, of course, very resource-intensive.

We must re-iterate that the time path of allowance prices is determined

endogenously. This time path clears the market for a given time path of the cap on

emissions. The abatement opportunities represented by econometric models of production

in IGEM must compete with external abatement opportunities represented by MAC

curves. These include capped non-CO2 sources, domestic offsets such as agricultural

sequestration, international offsets, and new energy demand and supply technologies.

The time path of allowance prices also recognizes provisions for banking and borrowing

and the rules governing the use of domestic and international offsets.

The mix of abatement from IGEM and the external MAC schedules is also

endogenous. We introduce the endogenous abatement costs from the external sources into

IGEM, so that emissions accounting in IGEM and ADAGE is fully comparable. Pollution

abatement results from output reductions, input and output substitution, and end-of-pipe

treatments of pollutants. Price-induced substitution and technical change are based on

observed behavior over the last half century. .

In conducting policy simulations for the EPA, a variety of scenarios involving the

domestic and international offsets are considered. For example, in the EMF 22 exercise,

the U.S. simulations were conducted with no international permit trading and,

alternatively, no domestic offsets. In these simulations, the time paths of allowance

prices between IGEM, ADAGE and the MIT-EPPA model are remarkably similar for the

least restrictive caps on cumulative greenhouse gas emissions (287 and 203 GtCO2-e).

As the cumulative emissions constraint tightens to 167 GtCO2-e, the price trajectories

diverge with MIT-EPPA showing the lowest long-run allowance prices, ADAGE the

highest and IGEM in between (the 203 and 167 GtCO2-e EMF 22 results are reproduced

below).
We conclude with a list of additional issues raised in the Peer Review about

modeling production. IGEM has the capability to treat both real and nominal values of

government purchases as fixed. The reviewers provide a very sophisticated discussion of

modeling the impact of the government revenue structure on page 22. The IGEM

modeling team will make use of this in its future work. Fuel switching is modeled in

IGEM at the industry level. Fuel switching at the plant level is modeled in IPM; the

results can be integrated with those in IGEM, if required for policy analysis. We agree

that merging IPM and IGEM would be valuable for some purposes and should be

undertaken when this has high priority and resources are available. The amount of capital

available to the economy is the result of past investment. This limits production patterns

in every period to those that are consistent with the initial stock of capital. We believe

that imposing “ex post” fixed coefficients does not describe technologies actually

available to the economy with any kind of accuracy.
Modeling Household Behavior.

We turn next to the modeling of household behavior in IGEM. It is not correct to

say on page 5 of the Peer Review that “Incidence is also poorly represented in the models

(IGEM and ADAGE) – how are different income classes and regions differentially

affected by policies?” This is specifically addressed in the paper by Jorgenson, et al.,

“The Distributional Impact of Climate Policy,” presented at the Energy Policy

Symposium held on January 20-21, 2010, in Washington, DC. It is understandable that

the reviewers did not discuss this paper, since the Peer Review was completed only a few

days later. However, this is a critical omission.

The reviewers recommend to “... move away from the representative agent

assumption and include more heterogeneous households. ...” IGEM has never used a

representative agent model. The model documentation demonstrates this and a peerreviewed article published in the JOURNAL OF ECONOMETRICS by Jorgenson and

Slesnick (2008), long before the Peer Review was initiated, presents the latest version of

the household model. While previous versions of the model, discussed in Jorgenson’s

(1998) book, are based on heterogeneous households, the new version extends this

treatment to labor supply.

As proposed on page 2 of the Peer Review, IGEM measures the effect of

proposed legislation on both household consumption and aggregate consumption. This is

possible within the exact aggregation framework introduced by Jorgenson, Lau, and

Stoker (1980). This approach has generated a vast literature and is the subject of two

review articles in the prestigious JOURNAL OF ECONOMIC LITERATURE, cited in

the IGEM documentation. IGEM measures the impact on households for four different

regions of the country and for subgroups of the population classified by urban vs. rural

location, male versus female heads of household, white versus nonwhite heads of

household, number of children, and number of adults – 244 groups in all. Within each

group IGEM measures the impacts for different levels of living.

IGEM uses an econometric approach to modeling household behavior involving

detailed individual data for 150,000 household units. The estimation process in IGEM is

presented in the peer-reviewed article by Jorgenson and Slesnick (2008). IGEM bears no

resemblance to the calibrated models discussed on page 15 of the Peer Review, which do

not use data on individual households and ignore heterogeneity. The calibrated models

described by the reviewers are based on a memo by Russek (1996) that has never been

published, let alone refereed. The two approaches could not be more different.

The elasticities of labor supply used in IGEM differ by household and level of

living. These elasticities are estimated econometrically. The econometric model

combines commodity demand with labor supply, a first for empirical models of

household behavior. One of the important features of the model is the incorporation of

human capital or labor quality in the representation of labor supply. This is also a novel

feature of the model.
Why does IGEM include human capital, when this is not the standard practice in

much of the literature reviewed by Russek (1996)? This is because the demand for labor

also takes human capital/labor quality into account and supply-demand balance in the

labor market requires consistency. We do not agree with the reviewers that “... IGEM

will tend to overstate the costs of climate change policy.” IGEM provides the first model

of household behavior that meets the needs of general equilibrium modeling for

consistency in integrating demand and supply. Finally, the intertemporal elasticity of

substitution estimated by Jorgenson and Slesnick (2008) is presented in the IGEM

documentation.

Representation of Uncertainty.

We next consider a central issue introduced on pages 3 and 4 of the Peer Review,

namely, the representation of uncertainty. IGEM is unique among models of

environmental and climate policy in providing standard errors for the parameter estimates

used in the household and production models. The current research objective for IGEM is

to introduce a new version of the model. The next step will be to provide confidence

intervals (and regions) for outcome variables, including impacts on households, regions,

and industries, as well as measures of economic welfare. This requires estimated

parameters as a point of departure, so that the econometric approach used for determining

parameters in IGEM is essential.

It is important to distinguish between uncertainty and sensitivity analysis. In

sensitivity analysis the parameter values are varied and the outcome variables

corresponding to each value are calculated. This approach is valuable in providing

measures of the change in outcomes with respect to the change in parameter values.

However, sensitivity analysis does not provide measures of uncertainty in the form of

confidence intervals with a stated level of confidence. An analysis of uncertainty involves

transforming standard errors for the parameters into standard errors for the outcomes.

Without standard errors for the parameters as a starting point, this is impossible.

Measures of uncertainty require standard errors. Sensitivity analysis produces

measures of change in the outcomes with respect to changes in the parameter values, no

standard errors. This simple point is usually overlooked in the literature on computable

general equilibrium models because of the reliance of most models on calibration rather

than estimation in determining parameter values. Calibrated models can produce

measures of sensitivity (derivatives), but not measures of uncertainty (standard errors).

Monte Carlo analysis is not the best approach for dealing with uncertainty, as

suggested on page 29 of the Peer Review, due to dimensionality of the parameter space in

IGEM. Furthermore, models that capture the features of the economy relevant to climate

policy will not be small for reasons outlined above. Standard statistical techniques based

on transformations of random variables are appropriate and feasible for dealing with

uncertainty. Unlike Monte Carlo methods, these techniques can deal with a large number

of parameters. EPA has already developed methods “... that explicitly account for our
uncertainties about key parameters.” These methods should be extended to the outcome

variables generated by IGEM.

Model Solution Algorithms.

Finally, we arrive at the solution of the model, an important issue in any

application of computable general equilibrium modeling. The Fair-Taylor algorithm used

in IGEM is a standard solution technique widely used for the solution of general

equilibrium models. Determination of an equilibrium corresponding to a particular

energy or environmental policy requires a matter of seconds in IGEM, using a standard

desk-top computer running Windows XP. This is despite the large number of variables

and equations in the model. The Path algorithm discussed on page 5, footnote 8, of the

Peer Review is also a valid approach.

How can solutions to IGEM be carried out in a matter of seconds? This is due to

the careful optimization of the computations described in detail in Appendix I of the

IGEM documentation. Since IGEM is highly nonlinear, the calculation of the equilibrium

involves an iterative process. Optimization of the computations involves a carefully

designed sequence of calculations for each step in the iterative solution. The

computational design is of great importance in reducing the time required for the

calculations. Many of the standard algorithms recommended in the literature could be

used, but Fair-Taylor is particularly economical in the use of information.

On page 6 the reviewers argue that “costs of adjustment” must be modeled in

order to capture transitional costs of climate policies. Although internal costs of

adjustment are often used by macro-econometric modelers focusing on economy-wide

aggregates, it has proved to be impossible to obtain a robust set of parameter estimates

appropriate for intertemporal equilibrium modeling involving a large number of

industries. In IGEM external costs of adjustment are captured in modeling the transition

to long-run equilibrium. This avoids misleading simplifications often used by CGE

modelers, including climate modelers, such as limiting policy comparisons to alternative

steady states or even to alternative static equilibrium states.

IGEM models the transition from the initial configuration of outputs and inputs

for the 35 sectors and initial consumption patterns of the thousands of different types of

households along a transition path involving several decades. Relative prices change

along the time path, resulting in costs of adjustment that are external to the producing

units and the households. On page 11 the reviewers repeat their point about adjustment

costs, ignoring the external adjustment costs employed in IGEM’s modeling of transition

paths. These paths are not reversible, since the initial conditions faced by the economy

change as capital is accumulated and relative prices change.

Policy makers may be interested in features of the transition to a new policy

regime over a much shorter period of time, such as the period of 12-18 months used in

macro-econometric forecasting. As the reviewers point out IGEM (and ADAGE) are

inappropriate for this purpose and could be replaced by a dynamic, stochastic, general
equilibrium (DSGE) model like the one under development by the Federal Reserve

Board. Integration of DSGE models with long-run intertemporal models like IGEM is

beyond the scientific frontier at the moment, but is a worthwhile research objective.

However, we would assign much lower priority to this than other research objectives

described above.

Summary and Conclusion.

To conclude: The Peer Review covers the four major points in EPA’s original

request to the panel and provides a well-informed overview of the issues to be considered

in the selection among different approaches to climate policy modeling. We have tried to

clarify points that arise in the review’s characterization of our approach to household

modeling, the treatment of technological change through the introduction of MAC

curves, and the important distinction between confidence intervals and sensitivity

analysis. We will concentrate attention on providing confidence intervals and regions for

outcome measures during the transition to a new version of IGEM. The econometric

estimation approach employed in IGEM is essential for this purpose.

When the new version of IGEM has been successfully introduced, we will

simplify the documentation by focusing attention on the new version of the model. This

should help to dispel some of the ambiguity that is inevitable in the documentation for a

complex modeling project like IGEM. However, we should emphasize in closing that we

are in basic agreement with the reviewers on the central points of modeling production,

analyzing consumer behavior, characterizing uncertainty, and choosing an appropriate

solution algorithm. The Peer Review will be of great value in setting priorities for future

research. This will add to the usefulness of IGEM in evaluating alternative environmental

and energy policies, including the policies for mitigating climate change described on

EPA’s Climate Economics website.
Attachment 4

Response to Peer Review Panel prepared by

Resource Triangle Institute regarding ADAGE
1

RTI ADAGE Model

Peer Review of Computable General Equilibrium Models for Climate Change Analysis

Response to Comments

We are grateful to the reviewers for the thoughtful Peer Review they have put together,

and we expect their recommendations will be very useful as we continue researching ways to

improve the ADAGE model. We are pleased they have concluded the review by noting that

“both the ADAGE and IGEM models are extraordinarily sophisticated economic models with

richness of detail in modeling various aspects of the U.S. economy. The model developers are to

be commended for the excellent work that they have done in recent years to make these models

useful for the EPA and other government agencies interested in modeling the impact of policies

to address greenhouse as emissions and other environmental issues.”

The reviews raise a number of important points for consideration. Our comments below

discuss options for addressing these points, whether through additional documentation of

features already in the ADAGE model that may require clarification or through changes to the

existing model structure. We are currently engaged in the process of revising and improving the

model, and we expect the reviewers’ comments to be particularly helpful in guiding this effort.

Specific points from the peer review along these lines are highlighted in the following

discussion. We will expand the model documentation to cover new features in ADAGE, in

addition to clarifying any ambiguous descriptions of features in the current documentation.

On page 7, the review notes that “ADAGE’s documentation is simple, clear, and well

organized... However, it could be improved by adding more detail throughout, particularly on the

details of how particular aspects of the model are implemented.” Specific suggestions for the

documentation include:

 Identifying key uncertainties (pages 3-4): We can expand the discussion of the model

structure to contrast it with other computable general equilibrium (CGE) models. One

similar model is the CRA MRN-NEEM model, although the publicly-available

documentation does not provide the opportunity for a detailed comparison of important
2

parameters and assumptions. However, since ADAGE is a “calibrated” model that uses

nested constant-elasticity-of-substitution (CES) functions, generally based on the MIT

EPPA model, it is hard to characterize “disagreement” in the literature over specific

parameters, beyond a comparison of various models. As noted in the review, this makes

sensitivity analyses important when evaluating these types of models. Another option

may be to present information on the general-equilibrium elasticities of energy demand

that are implied by particular choices of CES equations in the model.

 Additional details (page 7): Since parts of ADAGE are based on the MIT EPPA model, a

discussion of how the EPPA model is parameterized can be included in the ADAGE

documentation, to the extent that the EPPA documentation provides additional details not

already covered. Differences between ADAGE and EPPA can be highlighted. One

specific review question asks if the intertemporal elasticity of substitution in ADAGE

comes from the EPPA model – it does, which can be highlighted in the documentation.

 Policy results and sensitivity analyses (page 8): The ADAGE documentation does not

currently include information on policy results and sensitivity analyses. Given how

dependent specific policy results are to a wide range of assumptions about policy

implementation, the most practical option is to refer readers to the detailed policy

analyses conducted for EPA, which are publicly available. These results contain

additional information on input assumptions, model results, and several types of

sensitivity analyses regarding technology and offset assumptions. In addition, we can

refer readers to ADAGE modeling of a domestic climate policy for the Pew Center on

Global Climate Change, which includes sensitivity analyses on the most important

parameter assumptions regarding energy consumption and efficiency improvements.

 Information on future energy consumption in baseline forecasts (page 13): As noted in

the review, ADAGE uses autonomous energy-efficiency improvements (AEEI) to match

baseline energy consumption forecasts from the U.S. Energy Information Administration

(EIA), similar to other CGE models. We can expand the model documentation to clarify

how baseline forecasts are established after the EIA forecasts end in 2035. The current

documentation provides tables of energy consumption through 2050, but a graphical

presentation may also be helpful.
3

 Government actions (pages 20-21): As noted in the review, in accordance with standard

modeling practice, ADAGE holds the government budget deficit fixed during policy

analyses to avoid confusing policy results with changes in government spending. The

documentation can be expanded to clarify how and why this is done. Unlike what is

postulated in the review, ADAGE holds the deficit fixed at baseline levels (determined by

historical information on tax collections and government spending, along with forecasts

of economic activity), rather than forcing the deficit to always be zero. We can also

consider the implications of maintaining government expenditures (in real terms), rather

than the current approach of maintaining a specific pattern of government purchases

(again in real terms). ADAGE adopts the usual approach to this by adjusting government

revenues in a non-distortionary, lump-sum way. It is possible in ADAGE to adjust

specific tax rates instead to maintain the government expenditures (see work by Goulder

for implications of this, as discussed in the ADAGE documentation), however, this

complicates significantly the interpretation of model results and is usually not part of

proposed climate policies.

 Tax rates (pages 21 and 23): The documentation can be expanded to clarify that ADAGE

considers both average and marginal tax rates for labor and capital taxes, although the

focus is on marginal rates as these are what distort economic decisions in a CGE model.

 Financial flows (page 24): The ADAGE documentation can be expanded to describe how

international and interregional financial flows are handled. As part of this effort, we are

also working on describing more formally the equations used in ADAGE, especially

those related to dynamics and investment decisions.

Along with these points that are largely related to clarifying and expanding the current

documentation, the reviewers have several important suggestions for model improvements:

 Transportation modeling (pages 12-13): The reviewers note that, although ADAGE

models some details regarding the provision of public and personal transportation (again

based on the MIT EPPA model - see Figure 4 in their documentation), there are

additional options for modeling advanced transportation technologies. Because changes

in transportation are an important part of the response to climate policies, we hope to
4

expand the modeling of various alternatives in the next version of ADAGE. As noted by

the reviewers, the techniques for modeling new transportation options are relatively

straight-forward. To handle advanced electricity generation, ADAGE already employs

the suggested technique of including dormant technologies at a premium over existing

technologies, which can then enter endogenously depending on changes in fuel prices and

costs of conventional technologies. One of the reasons such transportation options have

not already been included in ADAGE are the difficulties noted in the review associated

with parameterizing the technology functions; i.e., determining what assumptions to use

about technology costs and availability.

 Electricity modeling (pages 4, 12 and 30): We feel that modeling of electricity

technologies is a particular strength of ADAGE, compared with many other CGE models,

although any CGE model by necessity will have far less detail than detailed electricity

dispatch models such as EPA’s Integrated Planning Model (IPM). ADAGE explicitly

models specific advanced generation options such as IGCC with carbon capture and

storage (CCS). The modeling techniques used are similar to those suggested in the

review for transportation modeling, and are the same techniques as those used in the MIT

EPPA model. Currently, assumptions about costs and efficiency of these advanced

electricity options, and any learning-by-doing as new capacity expands, are

conservatively based on the reference case from EIA’s Annual Energy Outlook. The

reviewers point out that there are differences between results for the electricity sector

from ADAGE and IPM. To some extent, this is a function of their being very different

types of models, however, additional steps are being taken to coordinate their responses

for consistency. Beginning with EPA’s January 2010 supplement to the H.R. 2454

analysis, ADAGE is using additional information from the IPM model regarding feasible

nuclear and CCS construction to help calibrate responses between the two models.

Longer term, we hope to pursue additional methods for linking the two models, although

calibration of responses between a CGE model and a model such as IPM that uses a

linear-programming structure can be challenging.

 Households (page 5 and 26): Regarding income classes and the incidence of policies – in

the next version of ADAGE, we hope to include multiple household classes in each
5

region of the model, similar to the recent MIT US-REP modeling. The IMPLAN

economic data used in ADAGE for the U.S. distinguish nine types of households based

on annual income. While this is not a first-best solution (such as an overlapping

generations model, perhaps), it would improve the model’s ability to examine incidence.

 Agriculture (pages 9-10): The reviewers note that domestic and international emissions

offsets from agriculture play an important role in climate policy analyses and suggest

disaggregating the agricultural industry in ADAGE. Although most dynamic CGE

models used in climate analyses have a single, aggregated agriculture sector, we hope to

expand this sector in the revised version of ADAGE to distinguish crops, livestock, and

forestry so that sources of offsets could be modeled more explicitly. Whether this proves

to be feasible depends on computational limitations of dynamically-optimizing CGE

models and competing needs for additional sectoral detail in other areas of the economy.

 Interstate trade in goods (page 23-24): The reviewers suggest considering alternatives to

the current model structure that has explicit modeling of state-to-state trade flows, given a

lack of good data on which to base this formulation. We are considering adopting a more

aggregate, or national, supply pool for goods, which would be easier to represent than the

current formulation. It is not entirely clear ex ante how this would alter estimated policy

results, if at all. An aggregate supply pool is currently used for crude oil (without the

Armington specification) to represent it as a homogeneous good.

 Environmental benefits (page 29): The reviewers note that the ADAGE model does not

attempt to measure the benefits of improved environmental quality from policy action.

Preliminary work with ADAGE has examined how to include benefits associated with

preventing climate change (see Copenhagen presentation summary), however, these

estimates are not a part of the standard analyses for EPA.

Finally, we would like to clarify how the ADAGE model addresses a few specific points

raised by the reviewers:

 CO2 abatement costs (page 9): The reviewers mention that there is little discussion of the

source of abatement costs estimates in ADAGE. This is because estimated CO2
6

abatement costs realized in policy analyses are a result of the production equations shown

in the documentation. No external estimates of these costs are imposed on ADAGE.

 Non-CO2 abatement costs and emissions (pages 14-15): The reviewers also mention that

it is much more difficult to model non-CO2 abatement costs. ADAGE employs the

solution proposed in Hyman et al. (2003), which allows calibration of model responses to

bottom-up marginal abatement cost (MAC) curves from EPA (constructed from

engineering estimates). We agree that this approach provides advantages over the use of

exogenous MAC curves in the modeling. We have been forced to estimate U.S. regional

baselines for non-CO2 emissions and would be pleased to update our information if better

data are available, but we are not aware of any potential sources for such information.

The exact distribution of interstate emissions will not likely have a great influence on

overall U.S. climate policy results based on national cap-and-trade policies since we are

using the most recent EPA GHG emissions inventory data for total U.S. GHG emissions.

 ADAGE benchmark, or baseline, data (page 9): The reviewers suggest conducting a

program of testing to compare the ADAGE benchmark with available economic data in

order to establish differences between the model base year and reality, and subsequently

adjusting calibration procedures reduce the most serious errors. While these experiments

sound valuable, it is unclear how to proceed with them due to the way a baseline is

established. In ADAGE, assumed economic conditions in the base year of 2010 are

imposed upon the model (based on the most recently available economic data), rather

than allowing the model to make any specific predictions about reality. Similarly,

economic growth and energy consumption into the future are imposed upon ADAGE

based on external forecasts from EIA, rather than allowing the model to predict how the

future will unfold. Some policy analyses conducted for EPA have taken the next-best

step of altering these future forecasts and examining the changes in policy predictions.

 Labor supply elasticities (page 16): As the reviews note, ADAGE uses labor supply

elasticities that are consistent with the empirical literature and those used in other CGE

models. Among the literature considered was the survey by Fuchs et al. (1998),

suggested by the reviewers. Unfortunately, all of the empirical literature tends to give a

relatively wide range of estimates.