Applying Multi-Criteria Decision Analysis to the Life-Cycle Assessment of vehicles. (16th November 2015)

• Record Type: Journal Article
• Authors: Domingues, Ana Rita; Marques, Pedro; Garcia, Rita; Freire, Fausto; Dias, Luís C.
• Is Part Of: Journal of cleaner production. Volume 107(2015:Nov. 15)
• Page Start: 749
• Page End: 759
• Publication Date: 2015-11-16
• Subjects: Environmental indicators -- Life-Cycle Impact Assessment (LCIA) -- Normalization -- Multi-Criteria Decision Analysis (MCDA) -- ELECTRE TRI -- Light-duty vehicles; AC Acidification -- AD Abiotic Depletion -- BEV Battery Electric Vehicle -- EUT Eutrophication -- FC Fuel Consumption (primary energy) -- FU Functional Unit (FU) -- GW Global Warming -- HEV Hybrid Electric Vehicle -- LCA Life-Cycle Assessment -- LCIA Life-Cycle Impact Assessment -- MCDA Multi-Criteria Decision Analysis -- OLD Ozone Layer Depletion -- PHEV Plug-in Hybrid Electric Vehicles -- PM Particulate Matter -- PO Photochemical Oxidation; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2015.05.086
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: This article presents a methodology to classify light-duty vehicles according to their environmental impacts. The classification is based on Life-Cycle Impact Assessment indicators and vehicle operation indicators, which are aggregated using a Multi-Criteria Decision Analysis (MCDA) method. In contrast with most literature combining Life-Cycle Assessment (LCA) and MCDA, vehicles are not compared directly; they are compared to pre-established profiles defining a set of classes. These profiles are established relatively to the impacts of the country's light-duty fleet. The ELECTRE TRI method is chosen for MCDA classification, thus avoiding complete substitutability among criteria and allowing for imprecision in the data. MCDA typically incorporates the subjective values of decision makers, namely through criteria weighting. To obtain conclusions that are not contingent on a given weight vector, we consider a space of weight vectors defined by constraints with a clear rationale and obtain all the possible results compatible with those constraints. The methodology is applied to classify six vehicles available in Portugal with different powertrains: Gasoline and Diesel Internal Combustion Engine Vehicles, Plug-in Hybrid Electric Vehicles (10 and 40-mile battery range) and Battery Electric Vehicle. The discussion suggests how this methodology might be useful for a decision-making entity that wishes to classify vehicles according to their environmental impacts. Highlights: LCA and MCDA were combined for sorting vehicle alternatives in classes. The normalization was made using the 2011 Portuguese light-duty fleet average impacts. The classification was obtained without value trade-offs or precise criteria weights. BEV and PHEV are the only vehicles that can achieve the top class. Veto thresholds and criteria weight constraints can be used for model tuning.