skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Modeling Uncertainty in Integrated Assessment of Climate Change: A Multi-Model Comparison

Abstract

The economics of climate change involves a vast array of uncertainties, complicating our understanding of climate change. This study explores uncertainty in baseline trajectories using multiple integrated assessment models commonly used in climate policy development. The study examines model and parametric uncertainties for population, total factor productivity, and climate sensitivity. It estimates the probability distributions of key output variables, including CO2 concentrations, temperature, damages, and social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting distributions provide a useful input into climate policy discussions

Authors:
 [1];  [1];  [2];  [3];  [4];  [5];  [6];  [7]
  1. Yale University
  2. University of California, Berkeley
  3. OFFICE OF FELLOWSHIP PROGRAMS
  4. Fondazione Eni Enrico Mattei
  5. University of Illinois at Urbana-Champaign
  6. BATTELLE (PACIFIC NW LAB)
  7. Massachusetts Institute of Technology
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1567732
Report Number(s):
PNNL-ACT-SA-10291
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Journal of the Association of Environmental and Resource Economists
Additional Journal Information:
Journal Volume: 5; Journal Issue: 4
Country of Publication:
United States
Language:
English
Subject:
climate policy, integrated assessment models, Uncertainity

Citation Formats

Gillingham, Kenneth, Nordhaus, William, Anthoff, David, Blanford, Geoffrey J., Bosetti, Valentina, Christensen, Peter, McJeon, Haewon C., and Reilly, J M. Modeling Uncertainty in Integrated Assessment of Climate Change: A Multi-Model Comparison. United States: N. p., 2018. Web. doi:10.1086/698910.
Gillingham, Kenneth, Nordhaus, William, Anthoff, David, Blanford, Geoffrey J., Bosetti, Valentina, Christensen, Peter, McJeon, Haewon C., & Reilly, J M. Modeling Uncertainty in Integrated Assessment of Climate Change: A Multi-Model Comparison. United States. doi:10.1086/698910.
Gillingham, Kenneth, Nordhaus, William, Anthoff, David, Blanford, Geoffrey J., Bosetti, Valentina, Christensen, Peter, McJeon, Haewon C., and Reilly, J M. Mon . "Modeling Uncertainty in Integrated Assessment of Climate Change: A Multi-Model Comparison". United States. doi:10.1086/698910.
@article{osti_1567732,
title = {Modeling Uncertainty in Integrated Assessment of Climate Change: A Multi-Model Comparison},
author = {Gillingham, Kenneth and Nordhaus, William and Anthoff, David and Blanford, Geoffrey J. and Bosetti, Valentina and Christensen, Peter and McJeon, Haewon C. and Reilly, J M.},
abstractNote = {The economics of climate change involves a vast array of uncertainties, complicating our understanding of climate change. This study explores uncertainty in baseline trajectories using multiple integrated assessment models commonly used in climate policy development. The study examines model and parametric uncertainties for population, total factor productivity, and climate sensitivity. It estimates the probability distributions of key output variables, including CO2 concentrations, temperature, damages, and social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting distributions provide a useful input into climate policy discussions},
doi = {10.1086/698910},
journal = {Journal of the Association of Environmental and Resource Economists},
number = 4,
volume = 5,
place = {United States},
year = {2018},
month = {10}
}