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Computational Methods in Environmental and Resource Economics

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  • Yongyang Cai

Abstract

Computational methods are required to solve problems without closed-form solutions in environmental and resource economics. Efficiency, stability, and accuracy are key elements for computational methods. This review discusses state-of-the-art computational methods applied in environmental and resource economics, including optimal control methods for deterministic models, advances in value function iteration and time iteration for general dynamic stochastic problems, nonlinear certainty equivalent approximation, robust decision making, real option analysis, bilevel optimization, solution methods for continuous time problems, and so on. This review also clarifies the so-called curse of dimensionality, and discusses some computational techniques such as approximation methods without the curse of dimensionality and time-dependent approximation domains. Many existing economic models use simplifying and/or unrealistic assumptions with an excuse of computational feasibility, but these assumptions might be able to be relaxed if we choose an efficient computational method discussed in this review.

Suggested Citation

  • Yongyang Cai, 2019. "Computational Methods in Environmental and Resource Economics," Annual Review of Resource Economics, Annual Reviews, vol. 11(1), pages 59-82, October.
  • Handle: RePEc:anr:reseco:v:11:y:2019:p:59-82
    DOI: 10.1146/annurev-resource-100518-093841
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    Cited by:

    1. Campiglio, Emanuele & Dietz, Simon & Venmans, Frank, 2022. "Optimal climate policy as if the transition matters," LSE Research Online Documents on Economics 117610, London School of Economics and Political Science, LSE Library.
    2. Yongyang Cai & Kenneth L. Judd & Rong Xu, 2013. "Numerical Solution of Dynamic Portfolio Optimization with Transaction Costs," NBER Working Papers 18709, National Bureau of Economic Research, Inc.
    3. Yongyang Cai & Kenneth L. Judd, 2023. "A simple but powerful simulated certainty equivalent approximation method for dynamic stochastic problems," Quantitative Economics, Econometric Society, vol. 14(2), pages 651-687, May.
    4. Villamizar, Rodrigo & Villamizar-Villegas, Mauricio & Arango, Lucia & Castelblanco, Geraldine, 2021. "Sustainability as a Policy Tool," Working papers 82, Red Investigadores de Economía.
    5. Veruska Muccione & Thomas Lontzek & Christian Huggel & Philipp Ott & Nadine Salzmann, 2023. "An application of dynamic programming to local adaptation decision-making," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 119(1), pages 523-544, October.
    6. Lorenzo Reus & Frank J. Fabozzi, 2021. "Robust Solutions to the Life-Cycle Consumption Problem," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 481-499, February.
    7. Peter Schober & Julian Valentin & Dirk Pflüger, 2022. "Solving High-Dimensional Dynamic Portfolio Choice Models with Hierarchical B-Splines on Sparse Grids," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 185-224, January.
    8. Yongyang Cai, 2020. "The Role of Uncertainty in Controlling Climate Change," Papers 2003.01615, arXiv.org, revised Oct 2020.

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