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Optimal Pricing of Climate Risk

Author

Listed:
  • Thomas F. Coleman

    (University of Waterloo)

  • Nicole S. Dumont

    (University of Waterloo)

  • Wanqi Li

    (University of Waterloo)

  • Wenbin Liu
  • Alexey Rubtsov

    (Ryerson University)

Abstract

The climate change model of Daniel et al. (Proc Natl Acad Sci USA 116(42):20886-20891, 2019. https://doi.org/10.1073/pnas.1817444116) is an important contribution to the carbon pricing literature. However, the computational methodology proposed in this paper is costly, thus limiting the flexibility and scalability of this basic approach. In this paper we introduce several modern computational techniques, often used in optimization applications, such as vectorization and automatic differentiation for gradient computation, to dramatically improve computational performance: this allows for increased scalability and sensitivity analysis (including the analysis of suboptimal policies). Such studies are reported in this paper yielding a number of new experimental insights. For example, our new code illustrates the cost of postponing climate change mitigation is significant. Specifically, postponing climate change mitigation until 2030 is equivalent to giving up $5.4 trillion (in 2015 US dollars).

Suggested Citation

  • Thomas F. Coleman & Nicole S. Dumont & Wanqi Li & Wenbin Liu & Alexey Rubtsov, 2022. "Optimal Pricing of Climate Risk," Computational Economics, Springer;Society for Computational Economics, vol. 60(3), pages 1101-1134, October.
    • Handle: RePEc:kap:compec:v:60:y:2022:i:3:d:10.1007_s10614-021-10179-6
    DOI: 10.1007/s10614-021-10179-6
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    References listed on IDEAS

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    2. Kim, Seokmin & Koop, Anthony & Fowler, Glenn & Israel, Kimberly & Takeuchi, Yu & Lieurance, Deah, 2023. "Addition of finer scale data and uncertainty analysis increases precision of geospatial suitability model for non-native plants in the US," Ecological Modelling, Elsevier, vol. 484(C).

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    More about this item

    Keywords

    Automatic differentiation; Vectorization; Large-scale optimization; Climate change; Social cost of carbon;
    All these keywords.

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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