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DICER: A tool for analyzing climate policies

Author

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  • Ortiz, Ramon Arigoni
  • Golub, Alexander
  • Lugovoy, Oleg
  • Markandya, Anil
  • Wang, James

Abstract

Modeling the economy and the planet's climate involves a great number of variables and parameters, some of them very uncertain given the current stage of knowledge regarding technology and the science of climate. The DICER model (or DICE-Regional) is a recently constructed Integrated Assessment Model (IAM), based on the structure of the DICE family of models, which was developed as an instrument for the analysis of uncertainties in climate policy. This paper aims to describe the basic version of DICER on which future developments addressing uncertainty in climate policy analysis will be based. Our results suggest a few interesting conclusions when compared to other IAMs: (i) under a plausible set of assumptions and parameters DICER indicates that an optimal global climate policy would imply higher costs of climate change in the short run but a faster (and more expensive) decarbonization process in all regions, resulting in a faster stabilization of the climate; (ii) lower peak temperatures that occur earlier in time; (iii) considerable sensitivity of results to key parameters such as climate sensitivity, but lower than expected sensitivity to the social discount rate.

Suggested Citation

  • Ortiz, Ramon Arigoni & Golub, Alexander & Lugovoy, Oleg & Markandya, Anil & Wang, James, 2011. "DICER: A tool for analyzing climate policies," Energy Economics, Elsevier, vol. 33(S1), pages 41-49.
  • Handle: RePEc:eee:eneeco:v:33:y:2011:i:s1:p:s41-s49
    DOI: 10.1016/j.eneco.2011.07.025
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    References listed on IDEAS

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    1. Nordhaus, William D & Yang, Zili, 1996. "A Regional Dynamic General-Equilibrium Model of Alternative Climate-Change Strategies," American Economic Review, American Economic Association, vol. 86(4), pages 741-765, September.
    2. Tol, Richard S. J., 2005. "The marginal damage costs of carbon dioxide emissions: an assessment of the uncertainties," Energy Policy, Elsevier, vol. 33(16), pages 2064-2074, November.
    3. Manne, Alan & Mendelsohn, Robert & Richels, Richard, 1995. "MERGE : A model for evaluating regional and global effects of GHG reduction policies," Energy Policy, Elsevier, vol. 23(1), pages 17-34, January.
    4. Richard Tol, 2002. "Estimates of the Damage Costs of Climate Change. Part 1: Benchmark Estimates," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 21(1), pages 47-73, January.
    5. Ackerman, Frank & Stanton, Elizabeth A. & Bueno, Ramón, 2010. "Fat tails, exponents, extreme uncertainty: Simulating catastrophe in DICE," Ecological Economics, Elsevier, vol. 69(8), pages 1657-1665, June.
    6. Roughgarden, Tim & Schneider, Stephen H., 1999. "Climate change policy: quantifying uncertainties for damages and optimal carbon taxes," Energy Policy, Elsevier, vol. 27(7), pages 415-429, July.
    7. Dowlatabadi, Hadi, 1998. "Sensitivity of climate change mitigation estimates to assumptions about technical change," Energy Economics, Elsevier, vol. 20(5-6), pages 473-493, December.
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    Cited by:

    1. Alexander Golub & Oleg Lugovoy & Anil Markandya & Ramon Arigoni Ortiz & James Wang, 2013. "Regional IAM: analysis of risk-adjusted costs and benefits of climate policies," Working Papers 2013-06, BC3.
    2. Lugovoy, O. & Polbin, A., 2016. "On Intergenerational Distribution of the Burden of Greenhouse Gas Emissions," Journal of the New Economic Association, New Economic Association, vol. 31(3), pages 12-39.
    3. DeCarolis, Joseph F. & Hunter, Kevin & Sreepathi, Sarat, 2012. "The case for repeatable analysis with energy economy optimization models," Energy Economics, Elsevier, vol. 34(6), pages 1845-1853.
    4. Laurence J. Kotlikoff & Andrey Polbin & Andrey Zubarev, 2016. "Will the Paris Accord Accelerate Climate Change?," NBER Working Papers 22731, National Bureau of Economic Research, Inc.

    More about this item

    Keywords

    Climate change; Integrated Impact Assessment Model;

    JEL classification:

    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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