A Tale of Tails: Uncertainty and the Social Cost of Carbon Dioxide

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in Juan Carlos Ciscar IPTS, European Commission, SevilleAbstract Recent thinking about the economics of climate change has concerned the uncertainty about the upper bound of both climate sensitivity to greenhouse gases and the damages that might occur at high temperatures. This argument suggests that the appropriate probability distributions for these factors may be fat-tailed. The matter of tail shape has important implications for the calculation of the social cost of carbon dioxide (SCCO2). In this paper a probabilistic integrated assessment model is adapted to allow for the possibility of a thin, intermediate or fat tail for both (i) the climate sensitivity parameter and (ii) the damage function exponent. Results show that depending on the tail shape of the climate sensitivity parameter the mean SCCO2 rises by 29 to 85 percent. Changes in the mean SCCO2 due to the adjustments to the damage function alone range from a reduction of 7 percent to a rise of 12 percent. The combination of both leads to rises of 33 to 115 percent. Greater rises occur for the upper percentiles of the SCCO2 estimates. Given the uncertainties in both the science and the economics of climate change different tail shapes deserve consideration due to their important implications for the range of possible values for the SCCO2.

Creating Agent-Based Energy Transition Management Models That Can Uncover Profitable Pathways to Climate Change Mitigation

“…This, combined with the limited availability of data, leads statisticians like Ikefuji et al to apply Weitzman's "Dismal Theorem" [199] which gives the risk profile a relatively "fat tail" like a Student's distribution [200]. The result is an SCC of hundreds of dollars [201][202][203]. Economists like Nordhaus dispute the applicability of the Dismal Theorem [204] and treat climate change as a continuous cost subject to cost-benefit analysis (e.g., the price of electricity) to arrive at a much lower SCC.…”

Tall tales and fat tails: the science and economics of extreme warming

“…The significance of this result has been debated but the implications for integrated assessment modeling are clear; that the possibility of extreme warming should be accounted for. A number of IAM studies have pursued this issue (Ackerman et al 2010;Dietz 2011;Pycroft et al 2011).…”

“…Weitzman did this very roughly with a toy model, although he considered uncertainty about the whole distribution at once, making it difficult to infer the importance of uncertainty about the tail. Pycroft et al (2011) went further by varying the upper 50 % of the pdf while holding constant the lower half of the distribution. One of the ways we seek to advance the literature is to investigate the sensitivity of the economic value of a mitigation policy to the shape of the tail of the pdf, while fixing everything except the shape of the upper tail.…”

Quantifying catastrophic and climate impacted hazards based on local expert opinions

“…As noted in the introduction, these distributions have been applied to the analysis of extreme events, such as hurricanes (Levi and Partrat, 1991;Braun, 2011), earthquakes (Braun, 2011), extreme rainfall (Esteves, 2013;Papalexiou et al, 2013), floods (Mathew et al, 2012), climate sensitivity (Pycroft et al, 2011) and sea-level rise (Pycroft et al, 2014).…”

“…In particular, we will illustrate the approach using asymmetric and heavy-tailed distributions such as the Lognormal, Weibull and Burr XII distribution. All or some of these distributions have been utilised in studies focused on extreme events and have been applied to hurricanes (Levi and Partrat, 1991;Braun, 2011), earthquakes (Braun, 2011), extreme rainfall (Esteves, 2013;Papalexiou et al, 2013), floods (Mathew et al, 2012), climate sensitivity (Pycroft et al, 2011) and sea-level rise (Pycroft et al 2014). Using the socalled loss distribution approach (LDA) that has gained popularity in the financial sector for modelling insurance claims or losses arising from operational and credit risks within the banking industry (Klugman, et al, 1998; Bank for International Settlements, 2001) we will then illustrate how the derived distributions can be used to quantify existing catastrophic and climate impacted hazards also over a longer time horizon.…”