Reassessment of global climate risk: non-compensatory or compensatory?
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Evidence shows the global climate will continue to change over this century and beyond. A clear understanding of the climate change risk is suggested to be the foundation of the human adaptation. The plausible climate risk index reported by Germanwatch may be criticized as the fully compensatory assumption among underlying indicators, and the risk performance of each country in absolute terms cannot be assessed as the information on indicator level lost. We formulate an enhanced non-compensatory assessment scheme to reassess country’s risk performance under climate change by means of penalizing underlying indicators that fail to satisfy certain criteria. Based on the new scheme, we can genuinely restrict the compensability among underlying indicators and provide informative decision aiding. A case study is performed to illustrate the effectiveness of our analysis by constructing a new climate risk index for 119 countries in terms of death toll, deaths per 100,000 inhabitants, absolute losses in PPP and losses per GDP unit.
KeywordsComposite indicator Climate risk Normalization Non-compensatory
This work was supported by the National Natural Science Foundation of China (Nos. 71573119 and 71625005), the China Scholarship Council (No. 201703780115), China Postdoctoral Science Foundation (2017M611811) and the Funding of Jiangsu Innovation Program for Graduate Education (No. KYZZ16_0159).
- ASC (2016) UK climate change risk assessment 2017 synthesis report: priorities for the next five years. Adaptation Sub-Committee of the Committee on Climate Change, LondonGoogle Scholar
- Bandura R (2011) Composite indicators and rankings: inventory 2011 (unpublished paper)Google Scholar
- Bloomberg MR, Pavarina D, Pitkethly G, Thimann C, Sim YL (2017) Final report: Recommendations of the task force on climate-related financial disclosuresGoogle Scholar
- Cherchye L, Kuosmanen T (2004) Benchmarking sustainable development: a synthetic meta-index approach. Research paper, UNU-WIDER, United Nations UniversityGoogle Scholar
- Eckstein D, Künzel V, Schäfer L (2017) Global Climate Risk Index 2018: who suffers most from extreme weather events? Weather-related loss events in 2016 and 1997 to 2016. Germanwatch e.V, BonnGoogle Scholar
- Ewert F, Rötter RP, Bindi M, Webber H, Trnka M, Kersebaum KC, Olesen JE, van Ittersum MK, Janssen S, Rivington M, Semenov MA, Wallach D, Porter JR, Stewart D, Verhagen J, Gaiser T, Palosuo T, Tao F, Nendel C, Roggero PP, Bartošová L, Asseng S (2015) Crop modelling for integrated assessment of risk to food production from climate change. Environ Model Softw 72:287–303CrossRefGoogle Scholar
- Fung CF, Lopez A, New M (2011) Modelling the impact of climate change on water resources. Wiley, HobokenGoogle Scholar
- IPCC (2014) Climate change 2014: synthesis report. In: Core Writing Team, Pachauri RK, Meyer LA (eds) Contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. IPCC, GenevaGoogle Scholar
- New M, Liverman D, Anderson K (2009) Mind the gap. Nature reports climate change 143Google Scholar
- OECD/EU/JRC (2008) handbook on constructing composite indicators: methodology and user guide. OECD Publishing, ParisGoogle Scholar
- OECD (2019) Composite leading indicator (CLI). https://doi.org/10.1787/4a174487-en
- UNDP (2017) Human development report 2016: human development for everyone. United Nations, New YorkGoogle Scholar
- USGCRP (2017) In: Wuebbles DJ, Fahey DW, Hibbard KA, Dokken DJ, Stewart BC, Maycock TK (eds) Climate science special report: fourth national climate assessment, vol I. U.S. Global Change Research Program, Washington, DCGoogle Scholar