On Modeling and Interpreting the Economics of Catastrophic Climate Change

By Martin Weitzman. Review of Economics and Statistics, 2009

1,810 citations recorded by the source corpus.

With climate change as prototype example, this paper analyzes the implications of structural uncertainty for the economics of low-probability, high-impact catastrophes. Even when updated by Bayesian learning, uncertain structural parameters induce a critical tail fattening of posterior-predictive distributions. Such fattened tails have strong implications for situations, like climate change, where a catastrophe is theoretically possible because prior knowledge cannot place sufficiently narrow bounds on overall damages. This paper shows that the economic consequences of fat-tailed structural uncertainty (along with unsureness about high-temperature damages) can readily outweigh the effects of discounting in climate-change policy analysis.

Subfields: Economics and Econometrics, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/9906a97c7d2431ea447eef3ec769ef625de2c338

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Citations in the corpus

Challenges in framing the economics of ecosystems and biodiversity: the TEEB initiative (2010, background): “In the context of catastrophic climate change, Weitzmann [63] has questioned the use of cost–benefit estimates, especially where large-scale impacts with low probabilities are concerned.”
The problem of the commons: still unsettled after 100 years. (2010, neutral): “Hence, it is important to consider the risk of extreme outcomes, that is, catastrophic consequences with small but non-negligible probabilities, in addition to — and some would argue, instead of — conventionally-defined expected values of abatement costs and benefits (Weitzman 2009).”
Climate Change and the Financial System (2020, background): “Climate risk differs from other sources of risk because it is forward-looking, global (yet with differentiated impacts) and characterized by deep uncertainty (Weitzman, 2009), non-linearity (Steffen et al., 2018) and endogeneity (Battiston et al., 2017).”
Carbon Pricing: Effectiveness and Equity (2018, background): “…to calculate expected utility, is deeply problematic in the presence of catastrophic risks whose probability and magnitude are both unknown, as is the treatment of risk aversion and ambiguity aversion (Ingham and Ulph, 2005; Weitzman, 2007, 2009, 2011; Millnar et al., 2013; Ackerman, 2017).”
On the observational determination of climate sensitivity and its implications (2010, background): “Economists who treat 293 climate sensitivity as a probability distribution function [Weitzman, 2009; Stern, 2008; Sokolov et 294 al., 2009] are mistakenly confusing model uncertainty concerning this particular number with the 295 existence of a real range of possibility.”
The equilibrium sensitivity of the Earth's temperature to radiation changes (2008, background): “For example, if the damage function is assumed to increase exponentially with temperature and the tail of climate sensitivity is fat (that is, the damage with temperature increases faster than the probability of such an event), then the expected damage could be infinite, entirely dominated by the ti”
Interacting regime shifts in ecosystems: implication for early warnings (2010, background): “Such fat-tailed probability distributions may be common for 82 important environmental parameters and such distributions have important implications for 83 decisions and management (Weitzman 2009).”
Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry (2012, neutral): “In particular, see Stern ð2006Þ, Nordhaus ð2007Þ, Weitzman ð2007, 2009Þ, Dasgupta ð2008Þ, Mendelsohn et al. ð2008Þ, von Below and Persson ð2008Þ, and Yohe, Tol, and Anthoff ð2009Þ.”
Faraway, So Close: Coupled Climate and Economic Dynamics in an Agent-based Integrated Assessment Model (2018, neutral): “Unfortunately, climate change will significantly impact on our societies and economies even if such ambitious objectives are achieved (Weitzman, 2009; IPCC, 2014; Schleussner et al., 2016) and, in case of failure, the effects will be catastrophic.”
Watch Your Step: Optimal Policy in a Tipping Climate (2014, background): “Gjerde, Grepperud, and Kverndokk (1999) simulate catastrophes that drop utility either to zero or to its level in 1990. They compare exogenous and endogenous hazards but do not consider a case with learning about the hazard. Keller, Bolker, and Bradford (2004) model a tipping point that alters ocean”
Risk Management and Climate Change (2013, neutral): “30), given by the expression C2k = Cpreindustrial exp(ln(2)Tmax/ECS).”