You are viewing an old version of this page. View the current version.

Compare with Current View Page History

« Previous Version 2 Next »

Model name C-LEARN

Brief description C-LEARN is a simplified, Web-accessible version of the Climate Rapid Overview and Decision Support (CROADS) Simulator, which is designed for use by policy makers to enable real time assessment of proposals under consideration as a part of the United Nations Framework Convention on Climate Change (UNFCCC) process and other international negotiations over emission reduction and land use targets.

Model developer(s) Tom Fiddaman, Lori S. Siegel, Elizabeth Sawin, Andrew P. Jones, John Sterman

Institutional affiliation of developer(s) Sustainability Institute, Ventana Systems, and System Dynamics Group at the MIT Sloan School of Management

Date created 2008

Date of most recent revision 2009

Model accessibility C-LEARN is available on the Web at http://forio.com/simulation/climate-development/index.htm. C-ROADS can be run on a personal computer using VenSim, a simulation application developed by Ventana Systems. C-ROADS is currently used in workshops and events moderated by members of the Climate Interactive team.

Documentation Tom Fiddaman, Lori S. Siegel, Elizabeth Sawin, Andrew P. Jones, and John Sterman. C-ROADS Simulator Reference Guide. January, 2009.

Key publications Robert Watson, Eric Beinhocker, Bert de Vries, Klaus Hasselmann, David Lane, Jorgen Randers, Stephen Schneider. Summary Statement from the C‐ROADS Scientific Review Panel. February 2009 (see here for a brief description of the scientific review process and its findings).
Elizabeth R. Sawin, Andrew P. Jones, Tom Fiddaman, Lori S. Siegel, Diana Wright, Travis Franck, Andreas Barkman, Tom Cummings, Felicitas von Peter, Jacqueline McGlade, Robert W. Corell, and John Sterman. Using C-ROADS—A Simple Computer Simulation of Climate Change—To Support Long-Term Climate Policy. Climate Change—Global Risks, Challenges, and Decisions Conference, University of Copenhagen. March 2009.



Return to C-LEARN

  • No labels