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Robust Simulation of Global Warming Policies Using the DICE Model

Author

Listed:
  • 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)

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.

Suggested Citation

  • Zhaolin Hu & Jing Cao & L. Jeff Hong, 2012. "Robust Simulation of Global Warming Policies Using the DICE Model," Management Science, INFORMS, vol. 58(12), pages 2190-2206, December.
  • Handle: RePEc:inm:ormnsc:v:58:y:2012:i:12:p:2190-2206
    DOI: 10.1287/mnsc.1120.1547
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    References listed on IDEAS

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    6. Hongyong Fu & Bin Dan & Xiangkai Sun, 2014. "Joint Optimal Pricing and Ordering Decisions for Seasonal Products with Weather-Sensitive Demand," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-8, March.
    7. Yanikoglu, I. & den Hertog, D. & Kleijnen, Jack P.C., 2013. "Adjustable Robust Parameter Design with Unknown Distributions," Other publications TiSEM 47fec228-1ffe-4803-8e97-5, Tilburg University, School of Economics and Management.
    8. Tamaki, Tetsuya & Nozawa, Wataru & Managi, Shunsuke, 2017. "Evaluation of the ocean ecosystem: climate change modelling with backstop technology," MPRA Paper 80549, University Library of Munich, Germany.
    9. Zhaolin Hu & L. Jeff Hong, 2022. "Robust Simulation with Likelihood-Ratio Constrained Input Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 34(4), pages 2350-2367, July.
    10. Wei, Yi-Ming & Mi, Zhi-Fu & Huang, Zhimin, 2015. "Climate policy modeling: An online SCI-E and SSCI based literature review," Omega, Elsevier, vol. 57(PA), pages 70-84.
    11. Tamaki, Tetsuya & Nozawa, Wataru & Managi, Shunsuke, 2017. "Evaluation of the ocean ecosystem: Climate change modelling with backstop technologies," Applied Energy, Elsevier, vol. 205(C), pages 428-439.
    12. Chengjing Wang, 2016. "On how to solve large-scale log-determinant optimization problems," Computational Optimization and Applications, Springer, vol. 64(2), pages 489-511, June.
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    17. Duan, Hongbo & Mo, Jianlei & Fan, Ying & Wang, Shouyang, 2018. "Achieving China's energy and climate policy targets in 2030 under multiple uncertainties," Energy Economics, Elsevier, vol. 70(C), pages 45-60.

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