Policy Mixes for Accelerating Zero-emission Vehicle Transition: Experiences in Quebec, British Columbia and Ontario
Bibliographic record
Abstract
Many jurisdictions have adopted different policies to accelerate Zero-Emission Vehicle (ZEV) transition. This study identifies, categorizes, and analyzes, through document analysis and expert interviews, various policies influencing the light-duty ZEV transition in the three-leading provinces in Canada, i.e., Quebec, British Columbia, and Ontario. To this end, two analytical frameworks are employed. The first one is used to categorize identified policies into four main categories, namely demand-side, infrastructure, supply-side, and institutional. The second analytical framework is applied to emphasize the ZEV transition from the creative destruction approach to discussing how provincial policies might influence socio-technical elements around the incumbent regime and the emergent niche. Findings show that the three provinces have collectively employed similar policy mixes. However, closer inspection of their specific policy instruments, policy strength, policy continuity, ambitions to phase out internal combustion engine vehicles, and transition to electric mobility along with their socio-political conditions show differences across the provinces.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".