Modeling Uncertainty in Climate Change: A Multi-Model Comparison
The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity. It estimates the pdfs of key output variables, including CO2 concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insights on tail events.
The authors are grateful to the Department of Energy and the National Science Foundation for primary support of the project. Reilly and McJeon acknowledge support by the U.S. Department of Energy, Office of Science. Reilly also acknowledges the other sponsors the MIT Joint Program on the Science and Policy of Global Change listed at http://globalchange.mit.edu/sponsors/all. The Stanford Energy Modeling Forum has provided support through its Snowmass summer workshops. Kenneth Gillingham currently works as a Senior Economist for the Council of Economic Advisers (CEA). The CEA disclaims responsibility for any of the views expressed herein, and these views do not necessarily represent the views of the CEA or the United States government. None of the authors has a conflict of interest to disclose. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.