Robust sustainable development assessment with composite indices aggregating interacting dimensions: The hierarchical-SMAA-Choquet integral approach

• Main Authors: Angilella, S. (Author), Catalfo, P. (Author), Corrente, S. (Author), Giarlotta, A. (Author), Greco, S. (Author), Rizzo, M. (Author)
• Format: Article
• Language: English
• Published: Elsevier B.V. 2018
• Subjects: Choquet integral preference model; Composite index; Composite indices; Integral equations; Necessary and possible preference; Planning; Preference modeling; Regional planning; Robust ordinal regression; Robust ordinal regressions; Rural development; Stochastic models; Stochastic multicriteria acceptability analysis; Stochastic multicriteria acceptability Analysis; Stochastic systems; Sustainable development
• Online Access: View Fulltext in Publisher

 The evaluation of sustainable development –and, in particular, rural development– through composite indices requires taking into account a plurality of indicators, which are related to economic, social, and environmental aspects. The points of view evaluated by these indices are naturally interacting: thus, a bonus has to be recognized to units performing well on synergic criteria, whereas a penalisation has to be assigned on redundant criteria. An additional difficulty of the modelization is the elicitation of the parameters for the composite indices, since they are typically affected by some imprecision. In most approaches, all these critical points are usually neglected, which in turn yields an unpleasant degree of approximation in the computation of indices. In this paper we propose a methodology that allows one to simultaneously handle these delicate issues. Specifically, to take into account synergy and redundancy between criteria, we suitably aggregate indicators by means of the Choquet integral. Further, to obtain recommendations that take into account the space of fluctuation related to imprecision in non-additive weights (capacity of the Choquet integral), we adopt the Robust Ordinal Regression (ROR) and the Stochastic Multicriteria Acceptability Analysis (SMAA). Finally, to study sustainability not only at a comprehensive level (taking into account all criteria) but also at a local level (separately taking into account economic, social, and environmental aspects), we apply the Multiple Criteria Hierarchy Process (MCHP). We illustrate the advantages of our approach in a concrete example, in which we measure the rural sustainability of 51 municipalities in the province of Catania, the largest city of the East Coast of Sicily (Italy). © 2018