Reaching 30% plug-in vehicle sales by 2030: Modeling incentive and sales mandate strategies in Canada
Why this work is in the frame
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Bibliographic record
Abstract
Plug-in electric vehicles (PEVs) could play a strong role in decarbonizing the transportation sector, leading some governments to set the goal of PEVs accounting for 30% of new sales by 2030 (e.g., the “[email protected]” campaign). To explore the feasibility of this goal, we use a behaviourally-realistic vehicle adoption model (REPAC) to simulate the impacts of incentives and vehicle mandates on PEV sales over this time frame, using the case study of Canada. We consider a range of technology assumptions, including optimistic and pessimistic battery cost scenarios ($CDN 85/kWh and $CDN 125/kWh, respectively, by 2030). We find that the country’s present policies can only induce PEVs to reach 5–11% new market share by 2030. Without changes in PEV supply, we find that purchase incentives can boost PEV new market share, where a $CDN 6000/vehicle subsidy is needed for 13 years to reach the 2030 goal (in the median technology assumption scenario). We also model ZEV mandate scenarios where automakers must reach 30% or 40% PEV sales by 2030, finding that compliance with both is achievable even in pessimistic technology scenarios, through a combination of increased PEV model availability and intra-firm cross-price subsidies. While incentive-based or mandate-based strategies (or some combination thereof) can achieve 2030 goals, results demonstrate the high government expenditure involved in an incentive-based strategy -- $CDN 15–48 billion undiscounted ($10–28 billion discounted), or around $9000–10,000 per added PEV sale. Policymakers ought to consider these tradeoffs, among others, when designing PEV-supportive policies to achieve long-term climate goals.
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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 it