IDEAS home Printed from https://ideas.repec.org/a/spr/comgts/v9y2012i3p339-362.html
   My bibliography  Save this article

An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty

Author

Listed:
  • Mort Webster
  • Nidhi Santen
  • Panos Parpas

Abstract

Analyses of global climate policy as a sequential decision under uncertainty have been severely restricted by dimensionality and computational burdens. Therefore, they have limited the number of decision stages, discrete actions, or number and type of uncertainties considered. In particular, two common simplifications are the use of two-stage models to approximate a multi-stage problem and exogenous formulations for inherently endogenous or decision-dependent uncertainties (in which the shock at time t+1 depends on the decision made at time t). In this paper, we present a stochastic dynamic programming formulation of the Dynamic Integrated Model of Climate and the Economy (DICE), and the application of approximate dynamic programming techniques to numerically solve for the optimal policy under uncertain and decision-dependent technological change in a multi-stage setting. We compare numerical results using two alternative value function approximation approaches, one parametric and one non-parametric. We show that increasing the variance of a symmetric mean-preserving uncertainty in abatement costs leads to higher optimal first-stage emission controls, but the effect is negligible when the uncertainty is exogenous. In contrast, the impact of decision-dependent cost uncertainty, a crude approximation of technology R&D, on optimal control is much larger, leading to higher control rates (lower emissions). Further, we demonstrate that the magnitude of this effect grows with the number of decision stages represented, suggesting that for decision-dependent phenomena, the conventional two-stage approximation will lead to an underestimate of the effect of uncertainty. Copyright Springer-Verlag 2012

Suggested Citation

  • Mort Webster & Nidhi Santen & Panos Parpas, 2012. "An approximate dynamic programming framework for modeling global climate policy under decision-dependent uncertainty," Computational Management Science, Springer, vol. 9(3), pages 339-362, August.
  • Handle: RePEc:spr:comgts:v:9:y:2012:i:3:p:339-362
    DOI: 10.1007/s10287-012-0147-1
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10287-012-0147-1
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10287-012-0147-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. Scott, Michael J. & Sands, Ronald D. & Edmonds, Jae & Liebetrau, Albert M. & Engel, David W., 1999. "Uncertainty in integrated assessment models: modeling with MiniCAM 1.0," Energy Policy, Elsevier, vol. 27(14), pages 855-879, December.
    3. Lemoine, Derek M. & Traeger, Christian P., 2010. "Tipping Points and Ambiguity in the Economics of Climate Change," CUDARE Working Papers 98127, University of California, Berkeley, Department of Agricultural and Resource Economics.
    4. Mort Webster, 2008. "Incorporating Path Dependency into Decision-Analytic Methods: An Application to Global Climate-Change Policy," Decision Analysis, INFORMS, vol. 5(2), pages 60-75, June.
    5. Popp, David & Newell, Richard G. & Jaffe, Adam B., 2010. "Energy, the Environment, and Technological Change," Handbook of the Economics of Innovation, in: Bronwyn H. Hall & Nathan Rosenberg (ed.), Handbook of the Economics of Innovation, edition 1, volume 2, chapter 0, pages 873-937, Elsevier.
    6. Gerst, Michael D. & Howarth, Richard B. & Borsuk, Mark E., 2010. "Accounting for the risk of extreme outcomes in an integrated assessment of climate change," Energy Policy, Elsevier, vol. 38(8), pages 4540-4548, August.
    7. Alan S. Manne & Richard G. Richels, 1994. "The Costs of Stabilizing Global CO2 Emissions: A Probabilistic Analysis Based on Expert Judgments," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 31-56.
    8. William D. Nordhaus & David Popp, 1997. "What is the Value of Scientific Knowledge? An Application to Global Warming Using the PRICE Model," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 1-45.
    9. Donald L. Keefer & Samuel E. Bodily, 1983. "Three-Point Approximations for Continuous Random Variables," Management Science, INFORMS, vol. 29(5), pages 595-609, May.
    10. K. J. Arrow, 1971. "The Economic Implications of Learning by Doing," Palgrave Macmillan Books, in: F. H. Hahn (ed.), Readings in the Theory of Growth, chapter 11, pages 131-149, Palgrave Macmillan.
    11. Mort Webster, 2002. "The Curious Role of "Learning" in Climate Policy: Should We Wait for More Data?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 97-119.
    12. 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.
    13. Leach, Andrew J., 2007. "The climate change learning curve," Journal of Economic Dynamics and Control, Elsevier, vol. 31(5), pages 1728-1752, May.
    14. Crost, Benjamin & Traeger, Christian P., 2010. "Risk and Aversion in the Integrated Assessment of Climate Change," CUDARE Working Papers 90935, University of California, Berkeley, Department of Agricultural and Resource Economics.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Guo, Jian-Xin & Zhu, Lei & Fan, Ying, 2016. "Emission path planning based on dynamic abatement cost curve," European Journal of Operational Research, Elsevier, vol. 255(3), pages 996-1013.
    2. Chang, Charles W., 2014. "DICESC: Optimal Policy in a Stochastic Control Framework," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170831, Agricultural and Applied Economics Association.
    3. Fertig, Emily, 2018. "Rare breakthroughs vs. incremental development in R&D strategy for an early-stage energy technology," Energy Policy, Elsevier, vol. 123(C), pages 711-721.
    4. Olaleye, Olaitan & Baker, Erin, 2015. "Large scale scenario analysis of future low carbon energy options," Energy Economics, Elsevier, vol. 49(C), pages 203-216.
    5. Maier, Sebastian & Pflug, Georg C. & Polak, John W., 2020. "Valuing portfolios of interdependent real options under exogenous and endogenous uncertainties," European Journal of Operational Research, Elsevier, vol. 285(1), pages 133-147.
    6. Mort Webster & Karen Fisher-Vanden & David Popp & Nidhi Santen, 2017. "Should We Give Up after Solyndra? Optimal Technology R&D Portfolios under Uncertainty," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 4(S1), pages 123-151.
    7. Santen, Nidhi R. & Anadon, Laura Diaz, 2016. "Balancing solar PV deployment and RD&D: A comprehensive framework for managing innovation uncertainty in electricity technology investment planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 560-569.
    8. Delavane B. Diaz, 2015. "Integrated Assessment of Climate Catastrophes with Endogenous Uncertainty: Does the Risk of Ice Sheet Collapse Justify Precautionary Mitigation?," Working Papers 2015.64, Fondazione Eni Enrico Mattei.
    9. Giacomo Marangoni & Gauthier De Maere & Valentina Bosetti, 2017. "Optimal Clean Energy R&D Investments Under Uncertainty," MITP: Mitigation, Innovation and Transformation Pathways 256056, Fondazione Eni Enrico Mattei (FEEM).
    10. Soheil Shayegh & Valerie Thomas, 2015. "Adaptive stochastic integrated assessment modeling of optimal greenhouse gas emission reductions," Climatic Change, Springer, vol. 128(1), pages 1-15, January.
    11. 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.
    12. Miftakhova, Alena & Judd, Kenneth L. & Lontzek, Thomas S. & Schmedders, Karl, 2020. "Statistical approximation of high-dimensional climate models," Journal of Econometrics, Elsevier, vol. 214(1), pages 67-80.
    13. Yongyang Cai & Kenneth L. Judd & Thomas S. Lontzek, 2013. "The Social Cost of Stochastic and Irreversible Climate Change," NBER Working Papers 18704, National Bureau of Economic Research, Inc.
    14. John Bistline & John Weyant, 2013. "Electric sector investments under technological and policy-related uncertainties: a stochastic programming approach," Climatic Change, Springer, vol. 121(2), pages 143-160, November.
    15. Baker, Erin & Olaleye, Olaitan & Aleluia Reis, Lara, 2015. "Decision frameworks and the investment in R&D," Energy Policy, Elsevier, vol. 80(C), pages 275-285.
    16. Wonjun Chang & Thomas F. Rutherford, 2017. "Catastrophic Thresholds, Bayesian Learning And The Robustness Of Climate Policy Recommendations," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 1-23, November.
    17. Erin Baker & Olaitan Olaleye & Lara Aleluia Reis, 2015. "Decision Frameworks and the Investment in R&D," Working Papers 2015.42, Fondazione Eni Enrico Mattei.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anderson, Evan W. & Brock, William & Sanstad, Alan H., 2016. "Robust Consumption and Energy Decisions," 2017 Allied Social Sciences Association (ASSA) Annual Meeting, January 6-8, 2017, Chicago, Illinois 250117, Agricultural and Applied Economics Association.
    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 Business School.
    3. Mark Kagan, 2012. "Climate Change Skepticism in the Face of Catastrophe," Tinbergen Institute Discussion Papers 12-112/VIII, Tinbergen Institute, revised 29 Sep 2014.
    4. 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.
    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. 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.
    7. In Chang Hwang, 2016. "Active learning and optimal climate policy," EcoMod2016 9611, EcoMod.
    8. Baker, Erin, 2005. "Uncertainty and learning in a strategic environment: global climate change," Resource and Energy Economics, Elsevier, vol. 27(1), pages 19-40, January.
    9. Lemoine, Derek M. & Traeger, Christian P., 2010. "Tipping Points and Ambiguity in the Economics of Climate Change," CUDARE Working Papers 98127, University of California, Berkeley, Department of Agricultural and Resource Economics.
    10. Olaleye, Olaitan & Baker, Erin, 2015. "Large scale scenario analysis of future low carbon energy options," Energy Economics, Elsevier, vol. 49(C), pages 203-216.
    11. Mort Webster, 2008. "Incorporating Path Dependency into Decision-Analytic Methods: An Application to Global Climate-Change Policy," Decision Analysis, INFORMS, vol. 5(2), pages 60-75, June.
    12. Hwang, In Chang, 2014. "Fat-tailed uncertainty and the learning-effect," MPRA Paper 53671, University Library of Munich, Germany.
    13. Peterson, Sonja, 2006. "Uncertainty and economic analysis of climate change: a survey of approaches and findings," Open Access Publications from Kiel Institute for the World Economy 3778, Kiel Institute for the World Economy (IfW Kiel).
    14. Peterson, Sonja, 2004. "The contribution of economics to the analysis of climate change and uncertainty: a survey of approaches and findings," Kiel Working Papers 1212, Kiel Institute for the World Economy (IfW Kiel).
    15. Wonjun Chang & Thomas F. Rutherford, 2017. "Catastrophic Thresholds, Bayesian Learning And The Robustness Of Climate Policy Recommendations," Climate Change Economics (CCE), World Scientific Publishing Co. Pte. Ltd., vol. 8(04), pages 1-23, November.
    16. Iverson, Terrence, 2012. "Communicating Trade-offs amid Controversial Science: Decision Support for Climate Policy," Ecological Economics, Elsevier, vol. 77(C), pages 74-90.
    17. Seung-Rae Kim, 2005. "Uncertainty, Learning, and Optimal Technological Portfolios: A Dynamic General Equilibrium Approach to Climate Change," Computing in Economics and Finance 2005 54, Society for Computational Economics.
    18. In Chang Hwang & Richard S.J. Tol & Marjan W. Hofkes, 2013. "Active Learning about Climate Change," Working Paper Series 6513, Department of Economics, University of Sussex Business School.
    19. Yongyang Cai & Kenneth L. Judd & Thomas S. Lontzek, 2013. "The Social Cost of Stochastic and Irreversible Climate Change," NBER Working Papers 18704, National Bureau of Economic Research, Inc.
    20. 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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:comgts:v:9:y:2012:i:3:p:339-362. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.