Application of the TOPSIS and intuitionistic fuzzy set approaches for ranking the life cycle sustainability performance of alternative vehicle technologies

Abstract This research involves two novel elements to advance the body of knowledge in existing sustainability assessment frameworks for alternative vehicle technologies. First, we developed an input–output based hybrid life cycle sustainability assessment model using several macro-level social, economic, and environmental indicators, taking into consideration the manufacturing of vehicles and batteries, operation, and end-of-life phases. Second, the results of a hybrid life cycle sustainability assessment for different conventional and alternative vehicles technologies (internal combustion electric vehicles, hybrid electric vehicles, plug-in-hybrid electric vehicles, and battery electric vehicles) are incorporated into the Technique for Order-Preference by Similarity to Ideal Solution and Intuitionistic Fuzzy Sets. Two policy scenarios are considered in this analysis, with Scenario 1 being based on existing electric power infrastructure in the U.S. with no additional infrastructure requirements, while Scenario 2 is an extreme scenario in which the electricity to power electric vehicles is generated exclusively via solar charging stations. The Intuitionistic Fuzzy Multi-Criteria Decision Making and Technique for Order Preference by Similarity to Ideal Solution methods are then utilized to rank the life cycle sustainability performance of alternative passenger vehicles. Furthermore, since expert judgments play an important role in determining the relative performance of alternative vehicle technologies, a sustainability triangle analysis is also presented to show how the weighting applied to each dimension affects the selection of different alternatives. The results indicate that hybrid and plug-in hybrid electric vehicles are the best alternatives for both Scenarios 1 and 2 when all of the indicators are considered. On the other hand, the ranking of vehicles changes significantly when each of the environmental, economic, and social indicators are evaluated individually. This proposed method can be a useful decision making platform for decision-makers to develop more effective policies and guide the offering of incentives to the right domains for sustainable transportation.

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