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

Abstract

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. © 2019 KIIE.