A DEA model for using qualitative data to rank options for adapting to climate change

This paper is motivated by observations on the decision process for prioritizing adaptation options using Multi-criteria decision analysis (MCDA) in Korea. To overcome the drawbacks of MCDA such as weighting and handling uncertainty, this paper formulates the problem of prioritizing climate change adaptation options as a DEA-like model. The decisions on how to assess such adaptation options involve qualitative evaluations that rely on subjective judgements, which results in data uncertainty. We propose a Monte-Carlo simulation-based method to provide robust analysis against the data uncertainty. The proposed method is applied to a case study in which 11 adaptation options in water sector are evaluated. Whereas a conventional deterministic approach that ignores data uncertainty may mislead decision-makers, a stochastic approach provides more reliable information because of its statistical evidence.