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  • 标题:Adaptation to Climate Change: A Comparative Analysis of Modeling Methods for Heat-Related Mortality
  • 本地全文:下载
  • 作者:Simon N. Gosling ; David M. Hondula ; Aditi Bunker
  • 期刊名称:Environmental Health Perspectives
  • 印刷版ISSN:0091-6765
  • 电子版ISSN:1552-9924
  • 出版年度:2017
  • 卷号:125
  • 期号:8
  • 页码:087008
  • DOI:10.1289/EHP634
  • 语种:English
  • 出版社:OCR Subscription Services Inc
  • 摘要:Background: Multiple methods are employed for modeling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known about the relative sensitivity of impacts to “adaptation uncertainty” (i.e., the inclusion/exclusion of adaptation modeling) relative to using multiple climate models and emissions scenarios. Objectives: This study had three aims: a ) Compare the range in projected impacts that arises from using different adaptation modeling methods; b ) compare the range in impacts that arises from adaptation uncertainty with ranges from using multiple climate models and emissions scenarios; c ) recommend modeling method(s) to use in future impact assessments. Methods: We estimated impacts for 2070–2099 for 14 European cities, applying six different methods for modeling adaptation; we also estimated impacts with five climate models run under two emissions scenarios to explore the relative effects of climate modeling and emissions uncertainty. Results: The range of the difference (percent) in impacts between including and excluding adaptation, irrespective of climate modeling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities, the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modeling and emissions uncertainty. Conclusions: Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modeling. We recommend absolute threshold shifts and reductions in slope. https://doi.org/10.1289/EHP634
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