Spherical fuzzy evidential reasoning for vehicle-to-grid enabled electric vehicle adoption: A data-driven analysis of public perception
Bibliographic record
Abstract
• 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".