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Modeling Uncertainty in Climate Change: A Multi-Model Comparison

Kenneth Gillingham (), William Nordhaus (), David Anthoff (), Valentina Bosetti (), Haewon McJeon, Geoffrey Blanford, Peter Christensen, John Reilly and Paul Sztorc
Additional contact information
Haewon McJeon: PNNL, College Park, Maryland, USA
Geoffrey Blanford: EPRI, USA
Paul Sztorc: Yale University, New Haven, USA

No 2016.13, Working Papers from Fondazione Eni Enrico Mattei

Abstract: 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.

Keywords: Climate Change; Integrated Assessment Models (search for similar items in EconPapers)
JEL-codes: Q54 (search for similar items in EconPapers)
Date: 2016-03
New Economics Papers: this item is included in nep-agr, nep-ene and nep-env
References: View references in EconPapers View complete reference list from CitEc
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Related works:
Working Paper: Modeling Uncertainty in Climate Change: A Multi-Model Comparison (2016) Downloads
Working Paper: Modeling Uncertainty in Climate Change: A Multi-Model Comparison (2015) Downloads
Working Paper: Modeling Uncertainty in Climate Change: A Multi-Model Comparison (2015) Downloads
Working Paper: Modeling Uncertainty in Climate Change: A Multi-Model Comparison (2015) Downloads
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