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Continuous-Time Methods for Integrated Assessment Models

Author

Listed:
  • Yongyang Cai
  • Kenneth L. Judd
  • Thomas S. Lontzek

Abstract

Continuous time is a superior representation of both the economic and climate systems that Integrated Assessment Models (IAM) aim to study. Moreover, continuous-time representations are simple to express. Continuous-time models are usually solved by discretizing time, but the quality of a solution is significantly affected by the details of the discretization. The numerical analysis literature offers many reliable methods, and should be used because alternatives derived from "intuition" may be significantly inferior. We take the well-known DICE model as an example. DICE uses 10-year time steps. We first identify the underlying continuous-time model of DICE. Second, we present mathematical and computational methods for transforming continuous-time deterministic perfect foresight models into systems of finite difference equations. While some transformations create finite difference systems that look like a discrete-time dynamical system, the only proper way to view the finite difference system is as an approximation of the continuous-time problem. DICE is an example where the usage of finite difference methods from numerical analysis produces far superior approximations than do simple discrete-time systems.

Suggested Citation

  • Yongyang Cai & Kenneth L. Judd & Thomas S. Lontzek, 2012. "Continuous-Time Methods for Integrated Assessment Models," NBER Working Papers 18365, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:18365
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    Cited by:

    1. Mariia Belaia & Michael Funke & Nicole Glanemann, 2017. "Global Warming and a Potential Tipping Point in the Atlantic Thermohaline Circulation: The Role of Risk Aversion," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 67(1), pages 93-125, May.
    2. Laurence Kotlikoff & Felix Kubler & Andrey Polbin & Jeffrey Sachs & Simon Scheidegger, 2021. "Making Carbon Taxation A Generational Win Win," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 62(1), pages 3-46, February.
    3. Brock, W. A. & Xepapadeas, A., 2015. "Modeling Coupled Climate, Ecosystems, and Economic Systems," Climate Change and Sustainable Development 206837, Fondazione Eni Enrico Mattei (FEEM).
    4. Christoph Hambel & Holger Kraft & Eduardo Schwartz, 2015. "Optimal Carbon Abatement in a Stochastic Equilibrium Model with Climate Change," NBER Working Papers 21044, National Bureau of Economic Research, Inc.
    5. Hambel, Christoph & Kraft, Holger & Schwartz, Eduardo S., 2019. "Optimal carbon abatement in a stochastic equilibrium model with climate change," SAFE Working Paper Series 92, Leibniz Institute for Financial Research SAFE, revised 2019.
    6. 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.
    7. Annicchiarico, Barbara & Diluiso, Francesca, 2019. "International transmission of the business cycle and environmental policy," Resource and Energy Economics, Elsevier, vol. 58(C).
    8. Brock, William A. & Engström, Gustav & Grass, Dieter & Xepapadeas, Anastasios, 2013. "Energy balance climate models and general equilibrium optimal mitigation policies," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2371-2396.
    9. van der Ploeg, Frederick & Rezai, Armon, 2021. "Optimal carbon pricing in general equilibrium: Temperature caps and stranded assets in an extended annual DSGE model," Journal of Environmental Economics and Management, Elsevier, vol. 110(C).
    10. 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.
    11. 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.
    12. Raphael Calel & David Stainforth & Simon Dietz, 2015. "Tall tales and fat tails: the science and economics of extreme warming," Climatic Change, Springer, vol. 132(1), pages 127-141, September.
    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. Yongyang Cai & Thomas S. Lontzek, 2019. "The Social Cost of Carbon with Economic and Climate Risks," Journal of Political Economy, University of Chicago Press, vol. 127(6), pages 2684-2734.

    More about this item

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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