Decision-Support Systems Based Multi-Criteria Decision Analysis for Assessing Electric Vehicle Adoption Policies

This paper assesses the challenges and policy responses for the adoption of electric vehicles (EVs) in Africa. We applied a decision support system framework comprising a new integration of the RANking COMparison Method (RANCOM) and Root Assessment Method (RAM) for the first time in the literature to address the multi-criteria decision analysis (MCDA) problems based on expert opinions. Six experts evaluated five criteria along with ten policy responses. While the weights of criteria are computed via the RANCOM method, the RAM approach ranks the policy responses. Moreover, the Compromise Fuzzy Ranking (CFR) method defines the consensus rankings via both positional ranks and preference scores. Furthermore, a three-stage comparative analysis is carried out for criteria weighting, policy responses ranking, and alternative consensus ranking. A sensitivity analysis is carried out including the consideration of experts’ significance according to their experience and their omission. The findings indicated the most critical challenges were the scarcity in charging infrastructure and the affordability and accessibility issues. The resilient charging infrastructure is the most appropriate policy response. The findings direct planners and EVs policymakers across the continent toward a policy that will ensure a clean and sustainable transportation system.