Climatic Change

, Volume 121, Issue 2, pp 301–315 | Cite as

Assessing climate change vulnerability with group multi-criteria decision making approaches

  • Yeonjoo Kim
  • Eun-Sung Chung


This study developed an approach to assess the vulnerability to climate change and variability using various group multi-criteria decision-making (MCDM) methods and identified the sources of uncertainty in assessments. MCDM methods include the weighted sum method, one of the most common MCDM methods, the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy-based TOPSIS, TOPSIS in a group-decision environment, and TOPSIS combined with the voting methods (Borda count and Copeland’s methods). The approach was applied to a water-resource system in South Korea, and the assessment was performed at the province level by categorizing water resources into water supply and conservation, flood control and water-quality sectors according to their management objectives. Key indicators for each category were profiled with the Delphi surveys, a series of questionnaires interspersed with controlled opinion feedback. The sectoral vulnerability scores were further aggregated into one composite score for water-resource vulnerability. Rankings among different MCDM methods varied in different degrees, but noticeable differences in the rankings from the fuzzy- and non-fuzzy-based methods suggested that the uncertainty with crisp data, rather widely used, should be acknowledged in vulnerability assessment. Also rankings from the voting-based methods did not differ much from those from non-voting-based (i.e., average-based) methods. Vulnerability rankings varied significantly among the different sectors of the water-resource systems, highlighting the need to assess the vulnerability of water-resource systems according to objectives, even though one composite index is often used for simplicity.


Vulnerability Assessment Triangular Fuzzy Number Negative Ideal Solution Borda Count Positive Ideal Solution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study was supported by grants from the Climate Change Correspondence R&D Program funded by Korea Ministry of Environment (RE201206045) and the Advanced Water Management Research Program funded by Korea Ministry of Land, Infrastructure and Transport (12-TI-C01)

Supplementary material

10584_2013_879_MOESM1_ESM.pdf (968 kb)
ESM (PDF 968 kb)


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  1. 1.Korea Environment InstituteSeoulSouth Korea
  2. 2.Department of Civil EngineeringSeoul National University of Science and TechnologySeoulSouth Korea

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