Robust Simulation of Global Warming Policies Using the DICE Model

• Published in: Management science Vol. 58; no. 12; pp. 2190 - 2206
• Main Authors: Hu, Zhaolin, Cao, Jing, Hong, L. Jeff
• Format: Journal Article
• Language: English
• Published: Hanover, MD INFORMS 01.12.2012; Institute for Operations Research and the Management Sciences
• Subjects: Algorithms; Ambiguity; Analysis; applications; Applied sciences; Climate change; Climate change policy; Climate models; Climatic changes; Covariance matrices; Economic aspects; Economic models; Economic policy; Economics; environment; Environmental economics; Environmental policy; Exact sciences and technology; General aspects; Global warming; Modeling; Multivariate analysis; Pollution control; programming; semidefinite; Simulation; Simulations; Studies; Hong Kong; China; Performance evaluation; Error estimation; Environmental policy; Worst case method; simulation; Gaussian distribution; Economic sciences; Modeling; Uncertain system; Economy; Covariance; Greenhouse gas; Robustness; programming; Estimation error; Geophysics; Multivariate distribution; Climate models; Covariance matrix; semidefinite; Dynamical climatology; Climate change; environment; global warming; Ambiguity; applications
• ISSN: 0025-1909; 1526-5501
• DOI: 10.1287/mnsc.1120.1547

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.