Spherical fuzzy evidential reasoning for vehicle-to-grid enabled electric vehicle adoption: A data-driven analysis of public perception

• SFS-ER was applied to quantify the public perception of V2G adoption technology. • Awareness and interest were identified as key drivers of V2G Enabled EV Adoption. • Respondents were clustered into Supporters, Skeptical, and Resistant groups. • Proposed recommendations to enhance public awareness, incentives, and infrastructure solutions. The successful adoption of Vehicle-to-Grid (V2G) technology depends on public awareness, infrastructure readiness, and policy support. This study presents a Spherical Fuzzy Set-Evidential Reasoning (SFS-ER) framework to assess public perceptions of V2G adoption, leveraging survey data from 729 respondents in Okanagan, British Columbia (BC), Canada. The study quantifies belief structures through three dimensions: True Belief ( m T ), False Belief ( m F ), and Uncertainty ( m U ), and computes utility scores to evaluate adoption likelihood. K-Means clustering with the Elbow Method identifies three distinct respondent groups: Supporters (high belief and adoption potential), Skeptical (moderate false beliefs), and Resistant (high skepticism and reluctance toward adoption). The findings reveal that awareness ( m T = 0.78) and interest ( m T = 0.75) are the strongest predictors of adoption, whereas familiarity alone has a limited impact. Higher-income and university-educated respondents demonstrate greater awareness, while apartment and townhouse residents face structural barriers due to limited charging infrastructure. Moreover, sensitivity analysis confirms that awareness-driven interventions can increase utility scores by 5.4 %. Based on the results, the key policy recommendations include expanding educational initiatives, implementing financial incentives, and integrating V2G-ready infrastructure in multi-unit dwellings.