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

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  • Mort Webster

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  • Nidhi Santen

    ()

  • Panos Parpas

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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
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    References listed on IDEAS

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    Citations

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    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. Giacomo Marangoni & Gauthier De Maere & Valentina Bosetti, 2017. "Optimal Clean Energy R&D Investments Under Uncertainty," Working Papers 2017.16, Fondazione Eni Enrico Mattei.
    3. 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.
    4. repec:wsi:ccexxx:v:08:y:2017:i:04:n:s2010007817500142 is not listed on IDEAS
    5. Olaleye, Olaitan & Baker, Erin, 2015. "Large scale scenario analysis of future low carbon energy options," Energy Economics, Elsevier, vol. 49(C), pages 203-216.
    6. 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.
    7. Erin Baker & Olaitan Olaleye & Lara Aleluia Reis, 2015. "Decision Frameworks and the Investment in R&D," Working Papers 2015.42, Fondazione Eni Enrico Mattei.
    8. 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.
    9. Mort Webster & Karen Fisher-Vanden & David Popp & Nidhi Santen, 2015. "Should We Give Up After Solyndra? Optimal Technology R&D Portfolios under Uncertainty," NBER Working Papers 21396, National Bureau of Economic Research, Inc.
    10. Mort D. Webster & Karen Fisher-Vanden & David Popp & Nidhi R. Santen, 2015. "Should We Give Up After Solyndra? Optimal Technology R&D Portfolios under Uncertainty," CESifo Working Paper Series 5448, CESifo Group Munich.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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, pages 329-357.

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