Uncertainty in Integrated Assessment Modelling: Can Global Sensitivity Analysis Be of Help?

Barry Anderson, Emanuele Borgonovo, Marzio Galeotti and Roberto Roson

No 52, IEFE Working Papers from IEFE, Center for Research on Energy and Environmental Economics and Policy, Universita' Bocconi, Milano, Italy

Abstract: The complexity of integrated assessment models (IAMs) prevents the direct appreciation of the impact of uncertainty on the model predictions. However, for a full understanding and corroboration of model results, analysts might be willing, and ought to identify the model inputs that influence the model results the most (key drivers), the direction of change associated with the variation of a given input and the overall model structure (interaction analysis). We show that such information is already contained in the data set produced by Monte Carlo simulations commonly used in IAM studies and that can be extracted from it without additional calculations. Our discussion is guided by an application of the proposed methodologies to the well-known DICE model of William Nordhaus (2008). A comparison of the proposed methodology to approaches previously applied on the same model shows that robust insights concerning the dependence of future atmospheric temperature, global emissions and current carbon costs and taxes on the model's exogenous inputs can be obtained. The method avoids the fallacy of a priori deeming the important factors based on the sole intuition

Keywords: Uncertainty; integrated assessment model; climate change; global sensitivity analysis (search for similar items in EconPapers)
JEL-codes: H21 H23 H87 O13 O44 Q5 Q54 (search for similar items in EconPapers)
Pages: 33 pages
Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (1)

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