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Solving Infinite Horizon Growth Models with an Environmental Sector

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Listed author(s):
  • 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.

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File URL: http://www.escholarship.org/uc/item/3hd4c4v3.pdf;origin=repeccitec
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Paper provided by Department of Economics, UC Santa Barbara in its series University of California at Santa Barbara, Economics Working Paper Series with number qt3hd4c4v3.

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Date of creation: 25 Jul 1999
Handle: RePEc:cdl:ucsbec:qt3hd4c4v3
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Keywords: Solving Infinite Horizon Growth Models with an Environmental Sector;

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  1. Kolstad, Charles D., 1996. "Learning and Stock Effects in Environmental Regulation: The Case of Greenhouse Gas Emissions," Journal of Environmental Economics and Management, Elsevier, vol. 31(1), pages 1-18, July.
  2. Hansen, Gary D., 1985. "Indivisible labor and the business cycle," Journal of Monetary Economics, Elsevier, vol. 16(3), pages 309-327, November.
  3. Christiano, Lawrence J, 1987. "Is Consumption Insufficiently Sensitive to Innovations in Income?," American Economic Review, American Economic Association, vol. 77(2), pages 337-341, May.
  4. Stephen C Peck & Thomas J. Teisberg, 1992. "CETA: A Model for Carbon Emissions Trajectory Assessment," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 55-78.
  5. Alan Manne & Richard Richels, 1992. "Buying Greenhouse Insurance: The Economic Costs of CO2 Emission Limits," MIT Press Books, The MIT Press, edition 1, volume 1, number 026213280x, July.
  6. 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.
  7. Manne, Alan & Mendelsohn, Robert & Richels, Richard, 1995. "MERGE : A model for evaluating regional and global effects of GHG reduction policies," Energy Policy, Elsevier, vol. 23(1), pages 17-34, January.
  8. repec:cdl:ucsbec:32-98 is not listed on IDEAS
  9. den Haan, Wouter J & Marcet, Albert, 1990. "Solving the Stochastic Growth Model by Parameterizing Expectations," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 31-34, January.
  10. Hansen, Gary D. & Sargent, Thomas J., 1988. "Straight time and overtime in equilibrium," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 281-308.
  11. Gene M. Grossman & Alan B. Krueger, 1995. "Economic Growth and the Environment," The Quarterly Journal of Economics, Oxford University Press, vol. 110(2), pages 353-377.
  12. Peck, Stephen C. & Teisberg, Thomas J., 1993. "Global warming uncertainties and the value of information: an analysis using CETA," Resource and Energy Economics, Elsevier, vol. 15(1), pages 71-97, March.
  13. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-1370, November.
  14. Christiano, Lawrence J., 1988. "Why does inventory investment fluctuate so much?," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 247-280.
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Cited by:

  1. 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.
  2. 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.
  3. 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.
  4. Leach, Andrew J., 2007. "The climate change learning curve," Journal of Economic Dynamics and Control, Elsevier, vol. 31(5), pages 1728-1752, May.
  5. David García-León, 2016. "Adapting to Climate Change: an Analysis under Uncertainty," Working Papers 2016.10, Fondazione Eni Enrico Mattei.
  6. Lemoine, Derek M. & Traeger, Christian P., 2011. "Tipping points and ambiguity in the economics of climate change," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt9nd591ww, Department of Agricultural & Resource Economics, UC Berkeley.
  7. 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.
  8. 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.
  9. 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.

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