Natural Hazards

, Volume 95, Issue 1–2, pp 271–287 | Cite as

Reassessment of global climate risk: non-compensatory or compensatory?

  • L. P. Zhang
  • P. ZhouEmail author
Original Paper


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.


Composite 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).


  1. Adenle AA, Ford JD, Morton J, Twomlow S, Alverson K, Cattaneo A, Cervigni R, Kurukulasuriya P, Huq S, Helfgott A, Ebinger JO (2017) Managing climate change risks in Africa—a global perspective. Ecol Econ 141:190–201CrossRefGoogle Scholar
  2. Alfieri L, Feyen L, Dottori F, Bianchi A (2015) Ensemble flood risk assessment in Europe under high end climate scenarios. Glob Environ Change 35:199–212CrossRefGoogle Scholar
  3. 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
  4. Athanassoglou S (2015) Revisiting worst-case DEA for composite indicators. Soc Indic Res 128(3):1259–1272CrossRefGoogle Scholar
  5. Attardi R, Cerreta M, Sannicandro V, Torre CM (2018) Non-compensatory composite indicators for the evaluation of urban planning policy: the Land-Use Policy Efficiency Index (LUPEI). Eur J Oper Res 264:491–507CrossRefGoogle Scholar
  6. Bandura R (2011) Composite indicators and rankings: inventory 2011 (unpublished paper)Google Scholar
  7. Binita KC, Shepherd JM, Gaither CJ (2015) Climate change vulnerability assessment in Georgia. Appl Geogr 62:62–74CrossRefGoogle Scholar
  8. Bloomberg MR, Pavarina D, Pitkethly G, Thimann C, Sim YL (2017) Final report: Recommendations of the task force on climate-related financial disclosuresGoogle Scholar
  9. Böhringer C, Jochem PEP (2007) Measuring the immeasurable—a survey of sustainability indices. Ecol Econ 63:1–8CrossRefGoogle Scholar
  10. Carreño ML, Cardona OD, Barbat AH (2007) A disaster risk management performance index. Nat Hazards 41:1–20CrossRefGoogle Scholar
  11. Carter JG (2018) Urban climate change adaptation: exploring the implications of future land cover scenarios. Cities 77:73–80CrossRefGoogle Scholar
  12. Chen Y, Liu R, Barrett D, Gao L, Zhou M, Renzullo L, Emelyanova I (2015) A spatial assessment framework for evaluating flood risk under extreme climates. Sci Total Environ 538:512–523CrossRefGoogle Scholar
  13. Cherchye L, Kuosmanen T (2004) Benchmarking sustainable development: a synthetic meta-index approach. Research paper, UNU-WIDER, United Nations UniversityGoogle Scholar
  14. Cherchye L, Knox Lovell CA, Moesen W, Van Puyenbroeck T (2007a) One market, one number? A composite indicator assessment of EU internal market dynamics. Eur Econ Rev 51:749–779CrossRefGoogle Scholar
  15. Cherchye L, Moesen W, Rogge N, Puyenbroeck TV (2007b) An introduction to ‘benefit of the doubt’ composite indicators. Soc Indic Res 82:111–145CrossRefGoogle Scholar
  16. Cherchye L, Moesen W, Rogge N, Van Puyenbroeck T, Saisana M, Saltelli A, Liska R, Tarantola S (2008) Creating composite indicators with DEA and robustness analysis: the case of the Technology Achievement Index. J Oper Res Soc 59:239–251CrossRefGoogle Scholar
  17. Decancq K, Lugo MA (2013) Weights in multidimensional indices of wellbeing: an overview. Econ Rev 32:7–34CrossRefGoogle Scholar
  18. Despotis DK (2005a) Measuring human development via data envelopment analysis: the case of Asia and the Pacific. Omega 33:385–390CrossRefGoogle Scholar
  19. Despotis DK (2005b) A reassessment of the human development index via data envelopment analysis. J Oper Res Soc 56:969–980CrossRefGoogle Scholar
  20. Diaz-Balteiro L, Romero C (2004) In search of a natural systems sustainability index. Ecol Econ 49:401–405CrossRefGoogle Scholar
  21. Dilley M, Chen RS, Deichmann U, Lerner-Lam AL, Arnold M, Agwe J, Buys P, Kjevstad O, Lyon B, Yetman G (2005) Natural disaster hotspots: a global risk analysis (No. 34423). The World Bank, WashingtonCrossRefGoogle Scholar
  22. 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
  23. El-Zein A, Tonmoy FN (2015) Assessment of vulnerability to climate change using a multi-criteria outranking approach with application to heat stress in Sydney. Ecol Ind 48:207–217CrossRefGoogle Scholar
  24. 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
  25. Färe R, Grosskopf S, Pasurka Carl A (2010) Toxic releases: an environmental performance index for coal-fired power plants. Energy Econ 32:158–165CrossRefGoogle Scholar
  26. Ford JD, Keskitalo ECH, Smith T, Pearce T, Berrang-Ford L, Duerden F, Smit B (2010) Case study and analogue methodologies in climate change vulnerability research. Wiley Interdiscip Rev Clim Change 1:374–392CrossRefGoogle Scholar
  27. Fung CF, Lopez A, New M (2011) Modelling the impact of climate change on water resources. Wiley, HobokenGoogle Scholar
  28. Fusco E (2015) Enhancing non-compensatory composite indicators: a directional proposal. Eur J Oper Res 242:620–630CrossRefGoogle Scholar
  29. Gallina V, Torresan S, Critto A, Sperotto A, Glade T, Marcomini A (2016) A review of multi-risk methodologies for natural hazards: consequences and challenges for a climate change impact assessment. J Environ Manag 168:123–132CrossRefGoogle Scholar
  30. Greiving S, Zebisch M, Schneiderbauer S, Fleischhauer M, Lindner C, Lückenkötter J, Buth M, Kahlenborn W, Schauser I (2015) A consensus based vulnerability assessment to climate change in Germany. Int J Climate Change Strat Manag 7:306–326CrossRefGoogle Scholar
  31. Hahn MB, Riederer AM, Foster SO (2009) The Livelihood Vulnerability Index: a pragmatic approach to assessing risks from climate variability and change—a case study in Mozambique. Glob Environ Change 19:74–88CrossRefGoogle Scholar
  32. 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
  33. Johnson K, Depietri Y, Breil M (2016) Multi-hazard risk assessment of two Hong Kong districts. Int J Disaster Risk Reduct 19:311–323CrossRefGoogle Scholar
  34. Jun K-S, Chung E-S, Kim Y-G, Kim Y (2013) A fuzzy multi-criteria approach to flood risk vulnerability in South Korea by considering climate change impacts. Expert Syst Appl 40:1003–1013CrossRefGoogle Scholar
  35. Keeney RL, Raiffa H (1993) Decisions with multiple objectives: preferences and value trade-offs. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  36. Kunreuther H, Heal G, Allen M, Edenhofer O, Field CB, Yohe G (2013) Risk management and climate change. Nat Clim Change 3:447–450CrossRefGoogle Scholar
  37. Lantada N, Pujades LG, Barbat AH (2008) Vulnerability index and capacity spectrum based methods for urban seismic risk evaluation. A comparison. Nat Hazards 51:501CrossRefGoogle Scholar
  38. Munda G, Nardo M (2009) Noncompensatory/nonlinear composite indicators for ranking countries: a defensible setting. Appl Econ 41:1513–1523CrossRefGoogle Scholar
  39. New M, Liverman D, Anderson K (2009) Mind the gap. Nature reports climate change 143Google Scholar
  40. Novelo-Casanova DA, Suarez G (2015) Estimation of the Risk Management Index (RMI) using statistical analysis. Nat Hazards 77:1501–1514CrossRefGoogle Scholar
  41. OECD/EU/JRC (2008) handbook on constructing composite indicators: methodology and user guide. OECD Publishing, ParisGoogle Scholar
  42. OECD (2019) Composite leading indicator (CLI).
  43. Ojoyi M, Mutanga O, Mwenge Kahinda J, Odindi J, Abdel-Rahman EM (2017) Scenario-based approach in dealing with climate change impacts in Central Tanzania. Futures 85:30–41CrossRefGoogle Scholar
  44. Rogge N (2018) Composite indicators as generalized benefit-of-the-doubt weighted averages. Eur J Oper Res 267:381–392CrossRefGoogle Scholar
  45. Ronco P, Zennaro F, Torresan S, Critto A, Santini M, Trabucco A, Zollo AL, Galluccio G, Marcomini A (2017) A risk assessment framework for irrigated agriculture under climate change. Adv Water Resour 110:562–578CrossRefGoogle Scholar
  46. Roy B, Figueira JR, Almeida-Dias J (2014) Discriminating thresholds as a tool to cope with imperfect knowledge in multiple criteria decision aiding: theoretical results and practical issues. Omega 43:9–20CrossRefGoogle Scholar
  47. Sperotto A, Molina J-L, Torresan S, Critto A, Marcomini A (2017) Reviewing Bayesian Networks potentials for climate change impacts assessment and management: a multi-risk perspective. J Environ Manag 202:320–331CrossRefGoogle Scholar
  48. Tofallis C (2013) An automatic-democratic approach to weight setting for the new human development index. J Popul Econ 26:1325–1345CrossRefGoogle Scholar
  49. Tonmoy FN, El-Zein A (2018) Vulnerability to sea level rise: a novel local-scale indicator-based assessment methodology and application to eight beaches in Shoalhaven, Australia. Ecol Ind 85:295–307CrossRefGoogle Scholar
  50. Tonmoy FN, Wainwright D, Verdon-Kidd DC, Rissik D (2018) An investigation of coastal climate change risk assessment practice in Australia. Environ Sci Policy 80:9–20CrossRefGoogle Scholar
  51. Torresan S, Critto A, Rizzi J, Zabeo A, Furlan E, Marcomini A (2016) DESYCO: a decision support system for the regional risk assessment of climate change impacts in coastal zones. Ocean Coast Manag 120:49–63CrossRefGoogle Scholar
  52. UNDP (2017) Human development report 2016: human development for everyone. United Nations, New YorkGoogle Scholar
  53. 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
  54. Verbunt P, Rogge N (2018) Geometric composite indicators with compromise benefit-of-the-doubt weights. Eur J Oper Res 264:388–401CrossRefGoogle Scholar
  55. Yuan XC, Wei YM, Wang B, Mi Z (2017) Risk management of extreme events under climate change. J Clean Prod 166:1169–1174CrossRefGoogle Scholar
  56. Zanella A, Camanho AS, Dias TG (2015) Undesirable outputs and weighting schemes in composite indicators based on data envelopment analysis. Eur J Oper Res 245:517–530CrossRefGoogle Scholar
  57. Zhang LP, Zhou P (2018) A non-compensatory composite indicator approach to assessing low-carbon performance. Eur J Oper Res 270:352–361CrossRefGoogle Scholar
  58. Zhou P, Ang BW, Poh KL (2007) A mathematical programming approach to constructing composite indicators. Ecol Econ 62:291–297CrossRefGoogle Scholar
  59. Zhou P, Ang BW, Zhou DQ (2010) Weighting and aggregation in composite indicator construction: a multiplicative optimization approach. Soc Indic Res 96:169–181CrossRefGoogle Scholar
  60. Zhou P, Ang BW, Zhou DQ (2012) Measuring economy-wide energy efficiency performance: a parametric frontier approach. Appl Energy 90:196–200CrossRefGoogle Scholar
  61. Zhou P, Delmas MA, Kohli A (2017) Constructing meaningful environmental indices: a nonparametric frontier approach. J Environ Econ Manag 85:21–34CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.College of Economics and ManagementNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.School of Economics and ManagementChina University of PetroleumQingdaoChina

Personalised recommendations