From Coast to Coast: Understanding Electric Vehicle Adoption Across Canadian Regions
Bibliographic record
Abstract
Battery electric vehicles (BEVs) have experienced rapid growth in market share both in Canada and globally over the past decade. However, adoption rates vary significantly across Canadian regions, raising questions about the sustainability of current growth trajectories. This study (1) identifies the drivers of regional disparities in BEV adoption within Canada, and (2) projects nationwide adoption levels. We exploit a proprietary, nationwide Canadian dataset of vehicle transactions, geocoded at the postal-code level, from 2016 through 2024. Using this dataset, we calibrate and compare three technology‐diffusion models to capture historical uptake and forecast future penetration. Our analysis reveals that regional BEV adoption is predominantly shaped by socio-demographic characteristics while charging station availability and other regional attributes exert comparatively smaller effects. Model comparisons indicate substantial uncertainty in long-term forecasts: adoption trajectories vary markedly depending on model structure and parameterization. These findings underscore the importance of targeted policy interventions and localized strategies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".