Differences in Canadian consumers’ awareness and preferences for zero-emissions vehicles from 2013 to 2023
Bibliographic record
Abstract
Goals to sell only zero-emissions vehicles (ZEVs) by the 2030s require substantial changes in consumers’ vehicle purchases over the next decade. Anticipating this transition, we investigate differences in consumer ZEV awareness and preferences over the last decade using cross-sectional samples of Canadian new-vehicle buyers from 2013 (n = 1,754), 2017 (n = 2,123), and 2023 (n = 2,555). Some measures of awareness significantly increased from 2013 to 2023, including familiarity and experience with ZEV technology, understanding of how to “fuel” battery electric vehicles, and awareness of public chargers. However, higher rates of confusion about hybrids and plug-in hybrids persist. Consumer preferences for ZEV drivetrains appear mostly unchanged over the study period, while valuations of home and fast charging have significantly increased in 2023. In short, consumers’ ZEV valuations are slow to change even with increased awareness and experience; preference change does not explain past ZEV sales increases, nor should it be relied upon to drive future sales increases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".