Consumers continue to be confused about electric vehicles: comparing awareness among Canadian new car buyers in 2013 and 2017
Bibliographic record
Abstract
Abstract Despite policy support and technological progress, consumer adoption of electric vehicles remains limited globally. One important barrier to electric vehicle adoption may be limited consumer awareness. We investigate trends in consumer awareness, familiarity, and experience with electric vehicles by comparing cross-sectional survey responses from two representative samples of Canadian new vehicle-buyers collected in 2013 ( n = 2922) and in 2017 ( n = 1808). While a significantly higher proportion of 2017 respondents have ‘heard of’ key electric vehicle models, stated familiarity and experience are low for both samples. Further, about three-quarters of respondents in both samples are confused about the basic notion of how to refuel (or recharge) electric vehicles—and how these vehicles differ from hybrids. Conversely, over half of 2017 respondents report having seen at least one electric vehicle charger in public, which is more than double the proportion reported in the 2013 sample. These trends hold in analyses of three Canadian provinces, including two that have engaged in significant consumer outreach activities over this time frame. Overall, in contrast to expectations, our results suggest that consumer awareness remains low and stagnant, which may hinder market growth and inhibit the climate mitigation potential of electric vehicles.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".