What are robust strategies in the face of uncertain climate threshold responses?

Abstract

We use an integrated assessment model of climate change to analyze how alternative decision-making criteria affect preferred investments into greenhouse gas mitigation, the distribution of outcomes, the robustness of the strategies, and the economic value of information. We define robustness as trading a small decrease in a strategy's expected performance for a significant increase in a strategy's performance in the worst cases. Specifically, we modify the Dynamic Integrated model of Climate and the Economy (DICE-07) to include a simple representation of a climate threshold response, parametric uncertainty, structural uncertainty, learning, and different decision-making criteria. Economic analyses of climate change strategies typically adopt the expected utility maximization (EUM) framework. We compare EUM with two decision criteria adopted from the finance literature, namely Limited Degree of Confidence (LDC) and Safety First (SF). Both criteria increase the relative weight of the performance under the worst-case scenarios compared to EUM. We show that the LDC and SF criteria provide a computationally feasible foundation for identifying greenhouse gas mitigation strategies that may prove more robust than Electronic supplementary material The online version of this article (548 Climatic Change (2012) 112:547-568those identified by the EUM criterion. More robust strategies show higher near-term investments in emissions abatement. Reducing uncertainty has a higher economic value of information for the LDC and SF decision criteria than for EUM.

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“…Traditional decision-making based on predicted futures has been likened to 'dancing on the tip of a needle ' (McInerney et al 2012). Such plans gamble on the fact that the predictions of the future turn out to be correct.…”

“…Rosenhead et al 1973;Wald 1945;Savage 1951;Simon 1955;Kouvalis and Yu 1997). In the EMA literature, robustness has been defined as the first order derivative of the objective function (McInerney et al 2012); as a reasonable performance over a wide range of plausible futures (Lempert and Collins 2007); relative, based on regret (Lempert et al 2003); and as sacrificing a small amount of optimal performance in order to be less sensitive to violated assumptions (Lempert and Collins 2007). This last definition shares a family resemblance with the local robustness model employed in Info-Gap Decision Theory (Ben Haim 2001).…”

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“…The marginals of the associated objective functions can also generate values suitable for consideration as social cost of carbon estimates. For example, as used by McInerney et al (2012), the measure of the worst outcome for the LDC criterion is conditional value at risk, which is the expected value of the worst q-th quantile of the outcome distribution; i.e., the LDC criterion maximizes…”

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“…For example, the "Limited Degree of Confidence" criterion maximizes a weighted average of the expected utility in all possible cases and the expected utility in the worst 1 % scenarios. The "Safety First" criterion maximizes the expected utility with the constraint that the probability of reaching some lower-bound utility is bounded (see Hall et al 2012;McInerney et al 2012). However, these approaches still require a prior distribution over possible outcomes, and both predict even more mitigation than maximizing expected utility.…”