This study suggests a framework to prioritize the plans in strategic environmental assessment (SEA) with incomplete information. The Monte Carlo method for the data gaps in SEA and the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, which is a multicriteria decision analysis (MCDA) method, are used. The VIKOR method is used to prioritize the plans based on a number of decision criteria because its decision philosophies, considering both the utility and regret of performance measures in MCDA, support the main objective of SEA, which is to minimize potential negative impacts and maximize potential positive impacts of plans. In this study, the proposed framework is applied to the SEA that is part of the long-term plan for dam construction in Korea. This study quantifies the environmental feasibility scores of ten alternative dam construction sites based on multiple criteria, including landscape and geology, ecological value, water quality, and environmental toxicity, and generates sets of random numbers to fill the gaps resulting from the incomplete data. By varying the importance between the regret and utility of performance measures, the rankings of feasible sites are quantified with the uncertainty bounds from the randomly generated numbers. We find that the resulting ranks among the sites can vary significantly according to the decision philosophy of stakeholders. Our results imply that the proposed framework can be utilized to provide quantitative information for decision making in SEA, considering various decision criteria pertaining to environmental aspects, uncertainty of incomplete data, and decision flexibility according to decision-makers' tendency.
Bibliographical noteFunding Information:
This research was supported by grants from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government (14AWMP-B082564-01) and from Climate Change Correspondence R&D Program funded by Ministry of Environment of Korean government (2013001310002).
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence