Breaking Down the Barriers: Understanding Consumer Resistance to Zero-Emission Vehicle Purchases
Bibliographic record
Abstract
Canada has established ambitious goals to reduce transportation emissions, targeting all new vehicle sales to be zero-emission vehicles (ZEVs) by 2040. In this study, ZEVs refer to battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), and fuel cell electric vehicles (FCEVs), consistent with Canadian policy definitions. Despite increasing policy support, a large segment of the population remains resistant to adopting ZEVs. While prior research has primarily focused on factors driving adoption, the reasons underlying non-adoption remain less explored. This study investigates ZEV resistance using data from the British Columbia ZEV Market Research Survey, comprising 3,850 respondents, of which 3,253 individuals who have never owned a ZEV are analyzed. A random parameter multinomial logit model is employed to classify resistance into five categories: cost, charging and range limitations, vehicle unavailability, environmental concerns, and other reasons, while accounting for unobserved heterogeneity. The results reveal that resistance is highly segmented across population groups. Younger and lower-income individuals are primarily constrained by cost, whereas higher-income individuals are more likely to resist due to limited availability of preferred vehicle models. Older adults and residents of multi-unit dwellings are significantly more affected by charging and range-related barriers. These findings demonstrate that ZEV resistance is not uniform but driven by distinct constraints across population segments. The results provide targeted policy insights to support more effective and equitable strategies for accelerating ZEV adoption in Canada.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".