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A Recursive Method for Solving a Climate–Economy Model: Value Function Iterations with Logarithmic Approximations

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  • In Chang Hwang

    (Korea Environment Institute)

Abstract

A recursive method for solving an integrated assessment model of climate and the economy is developed in this paper. The method approximates a value function with a logarithmic basis function and searches for solutions on a set satisfying optimality conditions. These features make the method suitable for a nonlinear model with many state variables and various constraints. The method produces exact solutions to a simple economic growth model and is useful for solving more demanding models such as the well-known DICE model (dynamic integrated model of climate and the economy).

Suggested Citation

  • In Chang Hwang, 2017. "A Recursive Method for Solving a Climate–Economy Model: Value Function Iterations with Logarithmic Approximations," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 95-110, June.
  • Handle: RePEc:kap:compec:v:50:y:2017:i:1:d:10.1007_s10614-016-9583-2
    DOI: 10.1007/s10614-016-9583-2
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    References listed on IDEAS

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    1. Hennlock, Magnus, 2009. "Robust Control in Global Warming Management: An Analytical Dynamic Integrated Assessment," RFF Working Paper Series dp-09-19, Resources for the Future.
    2. Robert M. Solow, 1956. "A Contribution to the Theory of Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 70(1), pages 65-94.
    3. Kenneth L. Judd & Lilia Maliar & Serguei Maliar, 2011. "Numerically stable and accurate stochastic simulation approaches for solving dynamic economic models," Quantitative Economics, Econometric Society, vol. 2(2), pages 173-210, July.
    4. Derek Lemoine & Christian Traeger, 2014. "Watch Your Step: Optimal Policy in a Tipping Climate," American Economic Journal: Economic Policy, American Economic Association, vol. 6(1), pages 137-166, February.
    5. Richard S. J. Tol & In Chang Hwang & Frédéric Reynès, 2012. "The Effect of Learning on Climate Policy under Fat-tailed Uncertainty," Working Paper Series 5312, Department of Economics, University of Sussex Business School.
    6. Kelly, David L. & Tan, Zhuo, 2015. "Learning and climate feedbacks: Optimal climate insurance and fat tails," Journal of Environmental Economics and Management, Elsevier, vol. 72(C), pages 98-122.
    7. H. M. Amman & D. A. Kendrick & J. Rust (ed.), 1996. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 1, number 1.
    8. Leach, Andrew J., 2007. "The climate change learning curve," Journal of Economic Dynamics and Control, Elsevier, vol. 31(5), pages 1728-1752, May.
    9. Hennlock, Magnus, 2009. "Robust Control in Global Warming Management: An Analytical Dynamic Integrated Assessment," Working Papers in Economics 354, University of Gothenburg, Department of Economics.
    10. Kenneth L. Judd, 1998. "Numerical Methods in Economics," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262100711, April.
    11. Mario J. Miranda & Paul L. Fackler, 2004. "Applied Computational Economics and Finance," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262633094, April.
    12. Rust, John, 1996. "Numerical dynamic programming in economics," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 14, pages 619-729, Elsevier.
    13. Kelly, David L. & Kolstad, Charles D., 1999. "Bayesian learning, growth, and pollution," Journal of Economic Dynamics and Control, Elsevier, vol. 23(4), pages 491-518, February.
    14. In Chang Hwang, 2016. "Active learning and optimal climate policy," EcoMod2016 9611, EcoMod.
    15. Hwang, In Chang & Reynès, Frédéric & Tol, Richard S.J., 2017. "The effect of learning on climate policy under fat-tailed risk," Resource and Energy Economics, Elsevier, vol. 48(C), pages 1-18.
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    Cited by:

    1. In Chang Hwang & Richard S. J. Tol & Marjan W. Hofkes, 2019. "Active Learning and Optimal Climate Policy," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 73(4), pages 1237-1264, August.
    2. In Chang Hwang, 2016. "Active learning and optimal climate policy," EcoMod2016 9611, EcoMod.

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