Decision making under catastrophic risk and learning: the case of the possible collapse of the West Antarctic Ice Sheet

Abstract

A collapse of the West-Antarctic Ice Sheet (WAIS) would cause a sea level rise of 5-6 m, perhaps even within 100 years, with catastrophic consequences. The probability of such a collapse is small but increasing with the rise of the atmospheric concentrations of greenhouse gas and the resulting climate change. This paper investigates how the potential collapse of the WAIS affects the optimal rate of greenhouse gas emission control. We design a decision and learning tree in which decision are made about emission reduction at regular intervals: the decision makers (who act as social planners) have to decide whether to implement the environmental or not (keeping then the flexibility to act later). By investing in the environmental policy, they determine optimally the date of the optimal emission reduction. At the same time, they receive new information on the probability of a WAIS collapse and (2008) 91: [193][194][195][196][197][198][199][200][201][202][203][204][205][206][207][208][209] the severity of its impacts. The probability of a WAIS collapse is endogenous and contingent on greenhouse gas concentrations. We solve this optimisation problem by backward induction. We find that a potential WAIS collapse substantially bring the date of the optimal emission reduction forward and increases its amount if the probability is high enough (a probability of 1% per year for the worst case), if the impacts are high enough (a worst case damage of 10% of GDP for a 3• C warming) or if the decision maker is risk averse enough (for example a social damage due to pollution equal to 1% GDP for an atmospheric temperature of 3• C). We also find that, as soon as a WAIS collapse is a foregone fact, emission reduction falls to free up resource to prepare for adapting to the inevitable. By contrast, adaptation (such as building dikes along the coast) postpones policy intervention because that strategy reduces the risk of catastrophic damages.

Productive Base Sustainability Under Climate Change: Theoretical Results and Empirical Evidence

“…Regarding the parameter values, the initial stock of pollution from CO 2 emissions P 0 was 785:3 billion tons of CO 2 obtained from Guillerminet and Tol (2005) and the initial level of global total emissions ( ‡ow) Z 0 was 6:15 billion tons of CO 2 and was obtained from Guillerminet and Tol (2005). The exponential pollution decay of emissions m; was taken at a value of 0:0083 from Reillly and Richards (1993).…”

“…Based on these results it seems that our theoretical framework might be capable of providing an assessment regarding the productive base sustainability conditions of economies, along with some policy suggestion for attaining or maintaining productive base sustainability under conditions of climate change. 6 A non-optimizing economy is an economy where government, whether by design or by incompetence, does not choose policies that maximize intergenerational welfare (Arrow et al, 2003). 7 The 23 countries used in our analysis are the following: Canada, U.S.A, Austria, Belgium, Denmark, Finland, France, Greece, Italy, Portugal, Spain, Sweden, Switzerland, U.K., Japan, Iceland, Ireland, Netherlands, Norway, Australia, Mexico, Turkey, Luxembourg.…”

Prediction of Galloping Accidents in Power Transmission Line Using Logistic Regression Analysis

“…For example, Tehrany et al have used decision tree method to predict flood susceptible areas and have resulted in up to 90% accuracy in validation [10]. M.-L. Guillerminet have used decision tree to predict the case of the possible collapse of the West Antarctic Ice Sheet [11]. Dieu et al employed spatial prediction models to predict shallow landslide hazards with support vector machine (SVM), logistic regression, and various machine learning methods.…”

Catastrophic Risk, Precautionary Abatement, and Adaptation Transfers

“…Adapting to possible catastrophic events can be very costly, while they can be prevented more effectively by directly addressing the source of the problem, GHG emissions (Pearce et al, 1996;Posner, 2004;Guillerminet and Tol, 2008;Lenton et al,. 2008).…”