Reaching 30% plug-in vehicle sales by 2030: Modeling incentive and sales mandate strategies in Canada
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".