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DICER: A tool for analyzing climate policies

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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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    Cited by:

    1. 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.
    2. 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.
    3. Alexander Golub & Jon Anda & Anil Markandya & Michael Brody & Aldin Celovic & Angele Kedaitiene, 2022. "Climate alpha and the global capital market," Working Papers 2022.19, Fondazione Eni Enrico Mattei.
    4. 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.
    5. Laurence J. Kotlikoff & Andrey V. ZUBAREV & Andrey POLBIN, 2021. "Will the Paris accord accelerate climate change [Ускоряет Ли Парижское Соглашение Изменение Климата?]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, vol. 1, pages 8-37, February.
    6. Koji Tokimatsu & Louis Dupuy & Nick Hanley, 2019. "Using Genuine Savings for Climate Policy Evaluation with an Integrated Assessment Model," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 72(1), pages 281-307, January.
    7. 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.
    8. Miftakhova, Alena, 2021. "Global sensitivity analysis for optimal climate policies: Finding what truly matters," Economic Modelling, Elsevier, vol. 105(C).

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    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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