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Uncertainty in Integrated Assessment Models of Climate Change: Alternative Analytical Approaches

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Abstract

Uncertainty plays a key role in the economics of climate change, and research on this topic has led to a substantial body of literature. However, the discussion on the policy implications of uncertainty is still far from being settled, partly because the uncertainty of climate change comes from a variety of sources and takes diverse forms. To reflect the multifaceted nature of climate change uncertainty better, an increasing number of analytical approaches have been used in the studies of integrated assessment models of climate change. The employed approaches could be seen as complements rather than as substitutes, each of which possesses distinctive strength for addressing a particular type of problems. We review these approaches—specifically, the non-recursive stochastic programming, the real option analysis, and the stochastic dynamic programming—their corresponding literatures and their respective policy implications. We also identify the current research gaps associated with the need for further developments of new analytical approaches.

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Notes

  1. As for the effects of uncertainty on policy instruments, the special issue specifically includes two articles that address the issue of allocation of emission allowances. Allowances allocation under uncertainty needs to seek two competing goals of containing costs of climate policy and of controlling the damage of climate change. Golub and Keohane [23] solve the problem of allocation and size of allowances reserve for a given countrywide emission trading scheme for containing price of carbon allowances at the level not higher than a politically acceptable level with a reasonable probability, Meanwhile, Aubin et al. [7] deal with the issue of how to translate an overall climate mitigation objective into an allocation of emission reduction objectives among polluters. The study proposes a method for dynamically allocating pollutant emissions rights among polluters, given that the emissions growth rates of the various polluters cannot be controlled, or even predicted. The problem is solved with mathematical and algorithmic tools of viability theory. With given maximum growth rates of emissions of each polluter in the worst case, the method of these authors provides the allocation rule for emissions rights and the required initial emissions.

  2. There is a closely related theoretical literature on the quasi-option value in environmental economics [4, 28]; see [5] for the relation to ROA).

  3. The mathematical conditions for which the principle of optimality holds can be found, e.g., in Stokey and Lucas [62].

  4. Although their analysis focuses on energy security (unpredictable energy supply) rather than on the first-best climate policy, Babonneau et al. [10] also attempt robust optimization under uncertainty in the context of climate change, They conduct a type of chance-constrained programming that consider uncertain constraints that are satisfied most of the time but not always.

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Acknowledgments

We thank two anonymous referees and the editors, whose comments helped us improve the paper substantially. An earlier version of the paper was presented at the EAERE 2011 Rome conference, and we appreciate helpful comments by the participants. Daiju Narita was funded by the German Federal Ministry of Education and Research through the funding program Ökonomie des Klimawandels. All remaining errors are ours.

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Authors and Affiliations

  1. American University, 4400 Massachusetts Avenue, NW, Washington, DC, 20016, USA

    Alexander Golub

  2. Kiel Institute for the World Economy, Hindenburgufer 66, 24105, Kiel, Germany

    Daiju Narita

  3. Potsdam Institute for Climate Impact Research, Telegraphenberg A31, 14473, Potsdam, Germany

    Matthias G. W. Schmidt

  4. The Russian Presidential Academy of National Economy and Public Administration, Moscow, Russian Federation

    Alexander Golub

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  1. Alexander Golub
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Correspondence to Daiju Narita.

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Golub, A., Narita, D. & Schmidt, M.G.W. Uncertainty in Integrated Assessment Models of Climate Change: Alternative Analytical Approaches. Environ Model Assess 19, 99–109 (2014). https://doi.org/10.1007/s10666-013-9386-y

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