Marcello Basili
DEPS, USiena
Federico Crudu
DEPS, USiena e CRENoS
Abstract
This paper assesses the probability of occurrence of tipping points conditional on a given temperature scenario by combining probability intervals from elicited experts opinions using the data of Kriegler et al. (2009). The computation of such conditional probabilities is based on the aggregation of imprecise probability judgments through the Steiner point. In addition, the probability of a tipping point can be updated via the standard Bayes rule to generate tipping point scenarios. Our results suggest that tipping events may happen with relatively large probabilities, in contrast with the view that tipping points are low-probability-high-impact events.
Keywords
Bayesian updating; aggregation; global warming; judgmental forecasting; Steiner point; tipping points.
Jel Codes
Q54; D81; C10
