IGSM response surface
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.
The response surface methodology used is only intended to approximate the results from the original IGSM model. In some cases the assumptions used in the original models differ from those in C-LEARN runs that generate inputs for the response surface, and this may lead to further inaccuracies. We believe, however, that the results of these models are are still accurate enough to be useful in providing a general sense of the likely impacts.
Model name Integrated Global System Model (IGSM)
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.
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
- 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 here for a complete list of publications issued in connection with this CCSP report, including public review comments and meeting minutes.
Model attributes #
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
16 regions (with greater detail possible for analysis of policies in Europe)
Start date 2000
End date 2100
Time step 5 years
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.
The module used in the Climate CoLab 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 2007 Clark et al. 2007. The steps undertaken in creating this response surface are outlined below in the section entitled "Variables and key assumptions."
Variables and key assumptions #
Global atmospheric concentration of carbon dioxide (CO2) at ten-year intervals between 2000 and 2100. (e.g. 2000, 2010, 2020, etc.)
- The CCSP study (Clark et al. 2007)
- 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
- Reference scenario, which assumed continuation of current reliance on fossil fuels
- The IGSM model was run for each of these five scenarios, and key data outputs at 10 year intervals were published 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 CoLab 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.
|Atmos. Conc. CO2||Reference||750 ppm||650 ppm||550 ppm||450 ppm|
|% Reduct. Global GDP||Reference||750 ppm||650 ppm||550 ppm||450 ppm|
- For each year in which data was reported, the CoLab 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 in the attached spreadsheet, entitled "IGSMresponsesurface.xls."
- After deriving these equestions, the 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 proposals. 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 CoLab's "Impacts" 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.
- The response surface derived from runs of IGSM provides CoLab users with a quickly calculable estimate of the projected economic impact of emission reduction pathways. These estimates are approximations, since the C-LEARN runs that generate inputs for the response surface do not correspond in every detail with the runs of IGSM that served as the basis for the response surface. For example, IGSM assumes that lowest cost emission reduction options occur first. But in C-LEARN runs, the regional emission reduction targets may depart from the most economically efficient approach. C-LEARN also incorporates land use policy levers, which are not included in IGSM.
- In relying on these response surfaces, the CoLab community 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 CoLab community 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.).