IGSM

Estimates of the mitigation costs associated with varying levels of emission reductions are generated through use of a response surface derived from runs of the Integrated Global System Model (IGSM). These runs of IGSM were published in a 2007 study undertaken as part of the U.S. Climate Change Science Program.

Overview

Model name Integrated Global System Model (IGSM)

Brief description
IGSM combines a sophisticated earth system model with the Emissions Prediction and Policy Analysis (EPPA) model, a geographically disaggregated and sectorally complex integrated assessment model (IAM). The data on GDP under different emissions scenarios used in the response surfaces was generated by EPPA, so the remainder of this write-up focuses primarily on that part of IGSM.

Model developer(s)
EPPA developers include Z. Yang, Richard S. Eckaus,A. Denny Ellerman, Henry D. Jacoby, Mustafa H. Babiker, John M. Reilly, Monika Mayer, Ian Sue Wing, Robert C. Hyman, Sergey Paltsev, James McFarland, Marcus Sarofim, and Malcolm Asadoorian

Institutional affiliation of developer(s)
MIT Joint Program on the Science and Policy of Global Climate Change

Date created 1996

Date of most recent revision 2005

Model accessibility
EPPA is run in the lab, with results published in acadmic papers and reports issued by the Joint Center.

Documentation

• Sergey Paltsev, John M. Reilly, Henry D. Jacoby, Richard S. Eckaus, James McFarland, Marcus Sarofim, Malcolm Asadoorian and Mustafa Babiker. 2005. The MIT Emissions Prediction and Policy Analysis(EPPA) Model: Version 4. MIT Joint Program on the Science and Policy of Global Change Report No. 125

• Mustafa H. Babiker, John M. Reilly, Monika Mayer, Richard S. Eckaus, Ian Sue Wing and Robert C. Hyman. 2001. The MIT Emissions Prediction and Policy Analysis (EPPA) Model: Revisions, Sensitivities, and Comparisons of Results. MIT Joint Program on the Science and Policy of Global Change Report No. 71.

• Andrei P. Sokolov, C. Adam Schlosser, Stephanie Dutkiewicz, Sergey Paltsev, David W. Kicklighter, Henry D. Jacoby, Ronald G. Prinn, Chris E. Forest, John Reilly, Chien Wang, Benjamin Felzer, Marcus C. Sarofim, Jeff Scott, Peter H. Stone, Jerry M. Melillo and Jason Cohen. 2005. MIT Integrated Global System Model (IGSM) Version 2: Model Description and Baseline Evaluation. MIT Joint Program on the Science and Policy of Global Change Report No. 124

Key publications
Leon E. Clarke, James A. Edmonds, Henry D. Jacoby, Hugh M. Pitcher, John M. Reilly, Richard G. Richels. 2007. Scenarios of Greenhouse Gas Emissions and Atmospheric Concentrations. U.S. Climate Change Science Program (CCSP)
Synthesis and Assessment Product 2.1a.

Click here for "information that was collected from the modeling teams to support the development" of this CCSP report.

Click herefor a complete list of publications issued in connection with this CCSP report, including public review comments and meeting minutes.

Model attributes

Model type
EPPA is an integrated assessment model. The earth systems model to which is it linked in IGSM is a general circulation model (GCM).

Geographic scope Global

Geographic resolution
16 regions (with greater detail possible for analysis of policies in Europe)

Start date 2000

End date 2100

Time step 5 years

Data sources
Detailed information on the data sources used in EPPA can be found in Paltsev et al. 2005 and Babiker et al. 2001.

Approach for addressing risk/uncertainty
The develelopers of the EPPA model address uncertainty by running the model with differing values for key variables and assessing the degree of variation in the model runs that result.

Key modules and linkages between them
Detailed information on all of EPPA's modules and the linkages between them can be found in Paltsev et al. 2005 and Babiker et al. 2001.

The module used in the Climate Collaboratorium is a response surface that shows the relationship between atmospheric concentration of CO2 and reduction in global gross domestic product in the emission stabilization scenarios when they are compared against the reference scenario. This response surface was based on runs of IGSM published in Clark et al. 2007|http://www.climatescience.gov/Library/sap/sap2-1/finalreport/sap2-1a-final-all.pdf]. The steps undertaken in creating this response surface are outlined below in the section entitled "Variables and key assumptions."

Model structure

Variables and key assumptions

Input variables
Global atmospheric concentration of carbon dioxide (CO2) at ten-year intervals between 2000 and 2100. (e.g. 2000, 2010, 2020, etc.)

Key assumptions

• The CCSP study (Clark et al. 2007|http://www.climatescience.gov/Library/sap/sap2-1/finalreport/sap2-1a-final-all.pdf]) published results of 5 emission scenarios generated by runs of IGSM:
  • Reference scenario, which assumed continuation of current reliance on fossil fuels
  • 750 ppm stabilization scenario
  • 650 ppm stabilization scenario
  • 550 ppm stabilization scenario
  • 450 ppm stabilization scenario

• The IGSM model was run for each of these five scenarios, and key data outputs were published at 10 year intervals in the final report and in the accompanying data tables These outputs included emissions, globally and by key region, of CO2 and other greenhouse gases (GHGs); carbon absorption by the ocean and land; atmospheric concentrations of CO2 and other GHGs; radiative forcing caused by each GHG; marginal costs of emission abatement; detailed information about energy production, prices, and amount of carbon sequestration by region and sector; and GDP for the world as a whole and for each key region.

• The Collaboratorium team created a spreadsheet based on two outputs from the IGSM model (data in tables below):
  • atmospheric concentration of CO2
  • percentage reduction in GDP vs. reference scenario.

• For each year in which data was reported, the Collaboratorium team plotted five points, with each point corresponding to one of the emissions scenarios (e.g. Reference, 750 ppm, 650 ppm, etc.) Atmospheric concentrations of CO2 were plotted on the x-axis and reduction in global GDP on the y axis. The team then derived an equations that described a curve that fit these points, with one equation for each year for which data was reported. The equation took the form of y = ax^4 + bx^3 + cx^2 + dx + e, where x was the atmospheric concentration of CO2 in the focal year and y the reduction in global GDP against the baseline scenario. The resulting equations and R^2 values for the fit of the curves are provided below. The values in the tables are the coefficients of the x^n terms of the function:

• After deriving these equestions, he team created a response surface based on them. As noted above, the input variable was the atmospheric concentrations of CO2. The atmospheric concentration input was taken from runs of the C-LEARN model done by users in the process of creating plans. The equations then derived the output, reduction in global GDP when compared against the reference scenario, tied to that atmospheric concentration of CO2 for each year for which data was reported in the CCSP report (2010, 2020, 2030, etc.) These outputs are plotted, with lines connecting the points, in the "Mitigation costs" section of the Collaboratorium's "Outcomes" tab.

• The low and high values from the IGMS model are the lower and upper bounds for the response surface. If the input value of atmospheric concentration of CO2 is below the lower bound or above the upper bound, the response surface will not calculate a value for reduction in global GDP. In such a case, that portion of the curve in the "Mitigation costs" plot would be left blank.

• Using this method, the Collaboratorium team is able to provide users with a quickly calculable estimate of the projected economic impact of multiple emission reduction pathways. These estimates are approximations, scine the C-LEARN runs that generate the inputs do not correspend in every detail with the runs of IGSM that served as the basis for the response surface. For example, IGSM assumes that emissions reductions occur in an economically rational way, with lowest cost emissions occurring first. But in C-LEARN runs, the regional emission reductions targets may result in emissions that depart from the most economically efficient approach. C-LEARN also incorporates land use policy levers, which are not included in IGSM.

• In creating this response surface, the Collaboratorium team seeks to provide users with a quick-running estimate of the relative environmental and economic tradeoffs involved in various proposals to address climate change. To address computational constraints, these response surfaces necessarily sacrifice precision. The Collaboratorium development team hopes to undertake senstivity analysis in the near future to assess how great a loss of precision results from the use of response surfaces like this one.

Output variables
Reduction in global gross domestic product (GDP) at the stated level of global atmospheric concentration of CO2 at ten-year intervals between 2000 and 2100 (e.g. 2000, 2010, 2020, etc.).

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