Incorporating 'catastrophic' climate change into policy analysis
Elizabeth Kopits,
Alex Marten and
Ann Wolverton
Climate Policy, 2014, vol. 14, issue 5, 637-664
Abstract:
Although existing economic research is informative with regard to the importance of including potential 'catastrophic' climate change impacts in the analysis of GHG mitigation benefits, the generic and abstract form of the 'catastrophe' implemented has led to a lack of specific policy implications. This article provides an important starting point for a discussion of how to improve economic modelling of potential large-scale impacts of climate change. It considers how the term 'abrupt climate change' has been used in the scientific literature to describe changes in the climate system and carefully reviews the characteristics of the events that have been discussed in this context. The findings are compared to the way in which the economic literature has modelled potential economic and human welfare impacts of these 'catastrophic' events. In general, the economics literature is found to have modelled such impacts in a uniform way that fails to account for differences in relevant end points and timescales. The result is policy recommendations based on events that do not resemble those of concern. Better treatment of these events in integrated assessment modelling would help ensure that future research efforts can serve as meaningful policy input. Policy relevance It has often been stated that current studies aimed at understanding the magnitude of optimal climate policy fail to adequately capture the potential for 'catastrophic' impacts of climate change. Existing economic modelling has provided evidence that, in general, potential climate catastrophes might significantly influence the optimal path of abatement. However, there is a need to move beyond experiments that are detached from important details of the climate problem to substantively inform the policy debate and improve analyses of GHG mitigation benefits (e.g. social cost of carbon estimates). This article identifies areas where modelling could be improved even within current frameworks and others where additional work is needed.
Date: 2014
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DOI: 10.1080/14693062.2014.864947
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