Barriers and solutions for adopting shared electric mobility: An integrated fuzzy-MCDM approach

This paper presents an integrated fuzzy-MCDM (Multi-Criteria Decision-Making) framework to identify and prioritise barriers to the adoption of Electric Vehicle Sharing Services (EVSS), and to rank the most effective policy solutions. The fuzzy Best-Worst Method (BWM) is employed to determine the relative weights of barriers, while the fuzzy Combined Compromise Solution (CoCoSo), enhanced with Bonferroni mean operators, is used to rank policy solutions. A case study from the United Kingdom is used to validate the proposed framework. Findings highlight behavioural barriers (e.g., habits, perceived cost) as most critical, followed by policy and infrastructural barriers. Key policy solutions include strategic financial investments, optimal station locations, improved infrastructure, and public awareness campaigns. By simultaneously addressing multiple barriers and solutions trade-offs, the study underscores need for a coherent national framework and supportive policies, offering critical insights to promote shared electric mobility and sustainable urban transport. The main contribution of this study lies in offering a robust, adaptable decision-support framework for policymakers to systematically address EVSS adoption challenges. The study facilitates prioritising interventions and optimising resource allocation, thereby supporting the transition towards sustainable urban mobility.