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Loaded DICE: Refining the Meta-analysis Approach to Calibrating Climate Damage Functions

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  • Howard, Peter
  • Sterner, Thomas

Abstract

Climate change is one of the preeminent policy issues of our day, and the social cost of carbon (SCC) is one of the foremost tools for determining the socially optimal policy response. The SCC is estimated using Integrated Assessment Models (IAMs), of which Nordhaus’ DICE is the oldest and one of the best respected. These numerical models capture the various steps in the climate and economic processes that translate a marginal unit of CO2 emissions into economic damage. While accuracy at each of these steps is necessary to precisely estimate the SCC, correct calibrating the climate damage function, which translates a temperature change into a percentage change in GDP, is critical. Calibration of the damage function determines which climate damages are included and excluded from the cost of carbon. Traditionally, Nordhaus calibrated the DICE damage function using a global damage estimate calculated by aggregating a series of region-sector specific damage estimates (Nordhaus and Boyer, 2000; Nordhaus, 2008). However, in DICE-2013, Nordhaus moved to calibrating the DICE damage function using a meta-analysis at the global scale (Nordhaus and Sztorc, 2013). This paper critiques this meta-analysis approach as it is currently applied and re-estimates the DICE-2013 damage function using up-to-date meta-analysis techniques to more accurately reflect climate damages and the uncertainty underlying them. This paper finds that DICE-2013 damage function significantly under-estimates climate damages by a factor of two to three.

Suggested Citation

  • Howard, Peter & Sterner, Thomas, 2014. "Loaded DICE: Refining the Meta-analysis Approach to Calibrating Climate Damage Functions," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169952, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea14:169952
    DOI: 10.22004/ag.econ.169952
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    Keywords

    Environmental Economics and Policy; Research Methods/ Statistical Methods; Risk and Uncertainty;

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