Modeling Uncertainty in Integrated Assessment of Climate Change: A Multimodel Comparison

By Kenneth T. Gillingham, William Nordhaus, David Anthoff, G. Blanford, Valentina Bosetti, P. Christensen, H. Mcjeon, J. Reilly. Journal of the Association of Environmental and Resource Economists, 2018

112 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Environmental Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/3a37b198c7771f07fa558ee3cd104b9c2705921d

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Citations in the corpus

The Cost of Reducing Greenhouse Gas Emissions (2018, background): “The estimate of $46/ton is in the range of the academic literature, although some estimates are much higher (as one example, see Gillingham et al. 2018).”
Evolution of modeling of the economics of global warming: changes in the DICE model, 1992–2017 (2018, methodology): “Several studies have estimated the uncertainties associated with IAMs, such as Gillingham et al. (2018) as an example.”