Robust Simulation of Global Warming Policies Using the DICE Model

Zhaolin Hu (), Jing Cao () and L. Jeff Hong ()
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Zhaolin Hu: School of Economics and Management, Tongji University, 200092 Shanghai, China
Jing Cao: School of Economics and Management, Tsinghua University, 100084 Beijing, China
L. Jeff Hong: Department of Industrial Engineering and Logistics Management, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong

Management Science, 2012, vol. 58, issue 12, 2190-2206

Abstract: Integrated assessment models that combine geophysics and economics features are often used to evaluate and compare global warming policies. Because there are typically profound uncertainties in these models, a simulation approach is often used. This approach requires the distribution of the uncertain parameters clearly specified. However, this is typically impossible because there is often a significant amount of ambiguity (e.g., estimation error) in specifying the distribution. In this paper, we adopt the widely used multivariate normal distribution to model the uncertain parameters. However, we assume that the mean vector and covariance matrix of the distribution are within some ambiguity sets. We then show how to find the worst-case performance of a given policy for all distributions constrained by the ambiguity sets. This worst-case performance provides a robust evaluation of the policy. We test our algorithm on a famous integrated model of climate change, known as the Dynamic Integrated Model of Climate and the Economy (DICE model). We find that the DICE model is sensitive to the means and covariance of the parameters. Furthermore, we find that, based on the DICE model, moderately tight environmental policies robustly outperform the no controls policy and the famous aggressive policies proposed by Stern and Gore. This paper was accepted by Dimitris Bertsimas, optimization.

Keywords: environment; global warming; programming; semidefinite; simulation; applications (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (19)

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