Why do consumers reject zero-emissions vehicles? Applying a comprehensive framework of resistance to Canadian car buyers
Bibliographic record
Abstract
Most of the consumer research on zero-emissions vehicles (ZEVs) has focused on what motivates adoption. This study explores the concept of “ZEV resistance”: why consumers might not want to adopt ZEVs in the near- to long-term. Such resistance challenges governments seeking 100% ZEV sales. We develop a comprehensive conceptual framework that assesses functional, environmental, social influence, and status quo aspects of resistance, implemented via 43 questions in a survey of Canadian new vehicle buyers ( n = 2555). Our analyses identify eight subcategories of ZEV perceptions. Regression and latent class choice analyses find that at least one aspect of all four categories is associated with ZEV resistance. First, functional aspects of resistance are primarily driven by perceived lack of benefits from ZEV usage (rather than purchase concerns). The second association is negative perceptions of pro-societal aspects, which include environmental impacts and moral norms towards ZEVs. Third is the perception that ZEVs do not convey social approval (whereas perceptions of ZEVs' commonality and inspirational qualities are less associated with resistance). The last association is satisfaction with the status quo of conventional vehicles (rather than opposition to change more generally). ZEV resistance is complex and linked to many varied dimensions of consumer perceptions, and if left unaddressed could be challenging for a smooth transition to 100% ZEVs.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".