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Solving Infinite Horizon Growth Models with an Environmental Sector

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  • Kelly, David L
  • Kolstad, Charles D

Abstract

This paper concerns computational models in environmental economics and policy, particularly so-called integrated assessment models. For the most part, such models are simply extensions of standard neoclassical growth models, extended by including the environment and pollution generation. We review the structure of integrated assessment models, distinguishing between finite horizon and infinite horizon models, both deterministic and stochastic. We present a new solution algorithm for infinite horizon integrated assessment models, relying on a neural net approximation of the value function within an iterative version of the Bellman equation. Copyright 2001 by Kluwer Academic Publishers

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  • Kelly, David L & Kolstad, Charles D, 2001. "Solving Infinite Horizon Growth Models with an Environmental Sector," Computational Economics, Springer;Society for Computational Economics, vol. 18(2), pages 217-231, October.
    • Handle: RePEc:kap:compec:v:18:y:2001:i:2:p:217-31
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    Cited by:

    1. Lemoine, Derek M. & Traeger, Christian P., "undated". "Tipping points and ambiguity in the economics of climate change," CUDARE Working Papers 120349, University of California, Berkeley, Department of Agricultural and Resource Economics.
    2. 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.
    3. 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.
    4. Aleksandar Arandjelovi'c & Pavel V. Shevchenko & Tomoko Matsui & Daisuke Murakami & Tor A. Myrvoll, 2024. "Solving stochastic climate-economy models: A deep least-squares Monte Carlo approach," Papers 2408.09642, arXiv.org.
    5. Christian Traeger, 2014. "A 4-Stated DICE: Quantitatively Addressing Uncertainty Effects in Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 59(1), pages 1-37, September.
    6. García-León, David, "undated". "Adapting to Climate Change: an Analysis under Uncertainty," EIA: Climate Change: Economic Impacts and Adaptation 232216, Fondazione Eni Enrico Mattei (FEEM).
    7. Leach, Andrew J., 2007. "The climate change learning curve," Journal of Economic Dynamics and Control, Elsevier, vol. 31(5), pages 1728-1752, May.
    8. Jensen, Svenn & Traeger, Christian P., 2014. "Optimal climate change mitigation under long-term growth uncertainty: Stochastic integrated assessment and analytic findings," European Economic Review, Elsevier, vol. 69(C), pages 104-125.
    9. repec:cdl:agrebk:qt9nd591ww is not listed on IDEAS
    10. repec:cdl:agrebk:qt6jx2p7fv is not listed on IDEAS
    11. repec:cdl:agrebk:qt9034k05t is not listed on IDEAS
    12. Luke G. Fitzpatrick & David L. Kelly, 2017. "Probabilistic Stabilization Targets," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 4(2), pages 611-657.
    13. J. Farmer & Cameron Hepburn & Penny Mealy & Alexander Teytelboym, 2015. "A Third Wave in the Economics of Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(2), pages 329-357, October.

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