The AVOID programme’s new simulations of the global benefits of stringent climate change mitigation View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2013-09

AUTHORS

R. Warren, J. A. Lowe, N. W. Arnell, C. Hope, P. Berry, S. Brown, A. Gambhir, S. N. Gosling, R. J. Nicholls, J. O’Hanley, T. J. Osborn, T. Osborne, J. Price, S. C. B. Raper, G. Rose, J. Vanderwal

ABSTRACT

Quantitative simulations of the global-scale benefits of climate change mitigation are presented, using a harmonised, self-consistent approach based on a single set of climate change scenarios. The approach draws on a synthesis of output from both physically-based and economics-based models, and incorporates uncertainty analyses. Previous studies have projected global and regional climate change and its impacts over the 21st century but have generally focused on analysis of business-as-usual scenarios, with no explicit mitigation policy included. This study finds that both the economics-based and physically-based models indicate that early, stringent mitigation would avoid a large proportion of the impacts of climate change projected for the 2080s. However, it also shows that not all the impacts can now be avoided, so that adaptation would also therefore be needed to avoid some of the potential damage. Delay in mitigation substantially reduces the percentage of impacts that can be avoided, providing strong new quantitative evidence for the need for stringent and prompt global mitigation action on greenhouse gas emissions, combined with effective adaptation, if large, widespread climate change impacts are to be avoided. Energy technology models suggest that such stringent and prompt mitigation action is technologically feasible, although the estimated costs vary depending on the specific modelling approach and assumptions. More... »

PAGES

55-70

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10584-013-0814-4

DOI

http://dx.doi.org/10.1007/s10584-013-0814-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1027572298


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