“Electric vehicles do not work in the winter”: Exploring the role of misperceptions and socio-psychological factors in zero-emission vehicle adoption
Bibliographic record
Abstract
“Electric vehicles do not work in the winter” is one of the misperceptions affecting interests in low-carbon transportation and hindering the electrification of passenger transportation. This study aimed to examine the role of misperceptions of battery electric vehicles and socio-psychological factors in influencing zero-emission vehicle adoption with empirical evidence from Saskatchewan, Canada. This research collected a sample of 590 households via a web-based survey. Using univariate and multivariate analysis of variance, this research found statistically significant differences in various demographic and socio-psychological variables for the preferred vehicle groups. Participants who preferred zero-emission vehicles reported higher levels of positive attitudes, subjective norms, perceived behavioural control, intentions to adopt clean energy technologies, technology lifestyle, environmental concerns and knowledge of zero-emission vehicles, but lower levels of misperceptions, compared to those who preferred conventional vehicles. Drawing on an extended Theory of Planned Behaviour, hierarchical multiple regression analyses showed that attitudes towards zero-emission vehicles, subjective norms and perceived behavioural control were statistically significant in influencing behavioural intentions to purchase zero-emission vehicles. The extended model revealed that intentions to adopt residential solar panels and heat pumps predicted behavioural intentions. This study developed a scale to measure misperceptions of battery electric vehicles and found that misperceptions negatively affected behavioural intentions to purchase battery electric vehicles. These findings were rarely discussed in the existing literature. Policy implications for accelerating the transition to zero-emission vehicles were discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".