Analysis of the potentials of multi criteria decision analysis methods to conduct sustainability assessment

By Marco Cinelli, Stuart R. Coles, Kerry Kirwan. 2014

826 citations recorded by the source corpus.

Sustainability assessments require the management of a wide variety of information types, parameters and uncertainties. Multi criteria decision analysis (MCDA) has been regarded as a suitable set of methods to perform sustainability evaluations as a result of its flexibility and the possibility of facilitating the dialogue between stakeholders, analysts and scientists. However, it has been reported that researchers do not usually properly define the reasons for choosing a certain MCDA method instead of another. Familiarity and affinity with a certain approach seem to be the drivers for the choice of a certain procedure. This review paper presents the performance of five MCDA methods (i.e. MAUT, AHP, PROMETHEE, ELECTRE and DRSA) in respect to ten crucial criteria that sustainability assessments tools should satisfy, among which are a life cycle perspective, thresholds and uncertainty management, software support and ease of use. The review shows that MAUT and AHP are fairly simple to understand and have good software support, but they are cognitively demanding for the decision makers, and can only embrace a weak sustainability perspective as trade-offs are the norm. Mixed information and uncertainty can be managed by all the methods, while robust results can only be obtained with MAUT. ELECTRE, PROMETHEE and DRSA are non-compensatory approaches which consent to use a strong sustainability concept, accept a variety of thresholds, but suffer from rank reversal. DRSA is less demanding in terms of preference elicitation, is very easy to understand and provides a straightforward set of decision rules expressed in the form of elementary “if … then …” conditions. Dedicated software is available for all the approaches with a medium to wide range of results capability representation. DRSA emerges as the easiest method, followed by AHP, PROMETHEE and MAUT, while ELECTRE is regarded as fairly difficult. Overall, the analysis has shown that most of the requirements are satisfied by the MCDA methods (although to different extents) with the exclusion of management of mixed data types and adoption of life cycle perspective which are covered by all the considered approaches.

Subfields: Building and Construction, Management, Monitoring, Policy and Law, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/bc83e06c438702b8e766f281ad8a59024aeb42e4

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Citations in the corpus

Sustainable Decision-Making in Civil Engineering, Construction and Building Technology (2017, methodology): “Mixed information and a wide variety of information types can be managed by applying multi-criteria decision analysis methods [37].”
How to support the application of multiple criteria decision analysis? Let us start with a comprehensive taxonomy (2020, methodology): “These characteristics do not usually have an associated objective level of attainment, which obliged the authors to develop evaluation scales (mostly ordinal) on their own [129,159,135,25,112,148,31,76].”
Comparing the sustainability of local and global food products in Europe (2017, neutral): “…but the “preference ranking organization method for enrichment evaluations” (PROMETHEE) is applied in this study because it takes weights as coefficients of importance, makes it possible to define threshold values and it can integrate qualitative and quantitative data (Cinelli et al., 2014).”
Evaluating the factors that influence blockchain adoption in the freight logistics industry (2020, background): “…MCDM/A, is a performance aggregation-based approach, which requires the identification of utility functions and weights for each attribute that can be assembled in a unique synthesizing criterion, with the additive and multiplicative aggregations being the most widely applied (Cinelli et al, 2014).”
Interpretation of comparative LCAs: external normalization and a method of mutual differences (2017, methodology): “Previously, outranking has been proposed as an alternative method of normalization that can serve as preparation for weighting (Cinelli et al. 2014).”