Reference-based ranking procedure for environmental decision making: Insights from an ex-post analysis. (January 2018)

• Record Type: Journal Article
• Authors: Ferretti, V.; Liu, J.; Mousseau, V.; Ouerdane, W.
• Is Part Of: Environmental modelling & software. Volume 99(2018)
• Page Start: 11
• Page End: 24
• Publication Date: 2018-01
• Subjects: Landfill location -- Multiple Criteria Decision Analysis -- Preference learning -- Ordinal data; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.09.009
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Preference elicitation is a challenging activity in any decision-making process, yet preferences are fundamental since the recommendations are meaningful and acceptable only if the Decision Maker's values are taken into account. This study proposes an ex-post application of a recent ranking method named Simple Ranking with Multiple Points (S-RMP) to support a participatory decision-making process. The method has been tested on a real-world case study simulating the selection of the most suitable site for locating a new landfill. The purpose of the research is twofold: (i) to explore the applicability and relevance of the S-RMP method to support environmental decision-making processes and (ii) to provide guidelines for the elicitation of preference parameters of the S-RMP ranking method. The results highlight that the proposed method opens a promising line of research in the environmental decision-making domain, thanks to its ability to use heterogeneous information consistently with the increasing amount of qualitative data embedded in real decision-making processes. Highlights: This study proposes an application of a recently developed preference learning method. The proposed approach is the Simple Ranking with Multiple Points. The aim is to test the operability and applicability of the method. The method has been tested on a real-world case study.