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Model Uncertainty in Climate Change Economics: A Review and Proposed Framework for Future Research

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Abstract

We review recent models of choices under uncertainty that have been proposed in the economic literature. In particular, we show how different concepts and methods of economic decision theory can be directly useful for problems in environmental economics. The framework we propose is general and can be applied in many different fields of environmental economics. To illustrate, we provide a simple application in the context of an optimal mitigation policy under climate change.

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Notes

  1. See, for example, Pindyck (2007, 2013b), Heal and Millner (2014), Convery and Wagner (2015), Millner et al. (2013); and Berger et al. (2017).

  2. The SCC is the damages caused by emitting carbon. According to Burke et al (2016, p. 292), the SCC estimates the “monetized change in social welfare over all future time from emitting one more tonne of carbon today, conditional on a specific trajectory of future global emissions and economic and demographic growth.”

  3. See Arrow (1951), Hansen (2014), Marinacci (2015); and Hansen and Marinacci (2016) for a discussion, and Aydogan et al. (2018) for empirical evidence.

  4. A model probabilizes uncertainty using “analogies with canonical random mechanisms that serve as benchmark” (Marinacci 2015, p. 1000). So, we can regard random mechanisms as the thermometers of probability.

  5. The notion of “a true model” is a methodologically delicate one that here we take in a pragmatic sense. It permits, inter alia, to abstract from model misspecification issues (a decision-theoretic attempt to model these issues is in Cerreia-Vioglio et al. (2020).

  6. As is usual, we write \(a\succsim b\) if the decision maker prefers action a to action b (i.e., either strictly prefers action a to action b, \(a\succ b\), or is indifferent between the two, \(a\sim b\)).

  7. The state that obtained is possibly not observable. In a dynamic setting, ex post observability becomes a key modelling issue (see Battigalli et al. 2019).

  8. The differences observed in the projection of the climate response to \({\hbox {CO}}_{2}\) emissions are due to different reasons such as different transient climate responses or carbon sensitivities as explained in the Supplementary Information of Matthews et al. (2009).

  9. DICE stands for Dynamic Integrated Climate and Economy (Nordhaus 1993; Nordhaus and Sztorc 2013). The damage function presented in Eq. (4) is the one in the DICE code in Nordhaus and Sztorc (2013). It is a slight variant of the version of the quadratic form presented in the theoretical description of DICE, in which climate damages are bounded to \(100\%\) (i.e., climate change is assumed to only reduce current income, but may not destroy pre-existing assets). At low temperature increases, the two versions are virtually identical.

  10. For an overview of these studies, see Pindyck (2013a), and Heal and Millner (2014).

  11. Models used for projections of future temperature increases are those whose results on transient climate response are reported in the IPCC fifth assessment report. The hypothesis that current nationally determined contributions are projected beyond 2030 is made here for these projections. See Bosetti et al. (2017) for more details.

  12. That is, \(\delta _{\theta }\left( \theta \right) =1\) and \(\delta _{\theta } \left( \theta ^{\prime } \right) =0\) if \(\theta ^{\prime }\ne \theta\).

  13. To ease matters, we restrict our attention to finite state and model spaces. Integrals with respect to probability density functions would arise without such assumption.

  14. Note that these decision criteria have axiomatic behavioral foundations that clarify their nature. We refer interested readers to Gilboa and Marinacci (2013).

  15. The unanimity criterion (13) is based on the general form of Bewley’s (2002) model studied by Gilboa et al. (2010) but it is conceptually different in that here \({\varTheta }\times {\varSigma }\) is posited.

  16. See Klibanoff et al. (2005), who show that \(\lambda\) can be interpreted as the coefficient of absolute of ambiguity aversion.

  17. The gross level of output and the abatement costs are also potentially uncertain. However, because we focus on the type of uncertainty described previously, here we do not consider these additional sources of uncertainty.

  18. In this example, the consequence function is simply the net output computed as \(\rho (a,\varepsilon ,\theta )=\frac{Y_{{\text {gross}}}-C(a)}{ 1+D(a,\varepsilon ,\theta )}\), where \(Y_{{\text {gross}}}\) is the gross output, \(D(a,\varepsilon ,\theta )\) represents the damages associated with climate change, and C(a) is the abatement cost. Both damages and costs depend on the action taken (a represents the level of GHG emissions). The abatement cost function is assumed to be nearly cubic as in Nordhaus and Sztorc (2013). The von Neumann-Morgernstern utility function u used is a power function, with a constant relative risk aversion coefficient of 1.5.

  19. A certainty equivalent is nothing but a monotonic transformation of an expected utility.

  20. In this example, the model uncertainty aversion function v is also a power function, with a constant relative model uncertainty aversion coefficient of 15. The prior distribution remains the uniform one.

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Acknowledgements

We are grateful to Valentina Bosetti, Laurent Drouet, Johannes Emmerling and Phoebe Koundouri for helpful comments and discussions. We acknowledge audiences at the 23rd conference of the European Association of Environmental and Resource Economists and at the 6th World Congress of Environmental and Resource Economists for their helpful comments.

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Authors and Affiliations

  1. CNRS, IESEG School of Management, Univ. Lille, UMR 9221 - LEM, 59000, Lille, France

    Loïc Berger

  2. RFF-CMCC European Institute on Economics and the Environment (EIEE), Centro Euro-Mediterraneo sui Cambiamenti Climatici, 20123, Milan, Italy

    Loïc Berger

  3. Department of Decision Sciences and IGIER, Universita Bocconi, 20136, Milan, Italy

    Massimo Marinacci

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Correspondence to Loïc Berger.

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This work was supported by the AXA Chair in Risk at Bocconi University, the European Research Council (ERC) under the European Union’s [Seventh Framework Programme (FP7-2007-2013)] (Grant Agreement No. 336703) and [Horizon 2020 research and innovation programme] (Grant Agreement No. 670337), and the French Agence Nationale de la Recherche (ANR), under Grants ANR-17-CE03-0008-01.

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Berger, L., Marinacci, M. Model Uncertainty in Climate Change Economics: A Review and Proposed Framework for Future Research. Environ Resource Econ 77, 475–501 (2020). https://doi.org/10.1007/s10640-020-00503-3

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