Subsidies, Standards, or Both? Trade-Offs among Policies for 100% Zero-Emissions Vehicle Sales
Bibliographic record
Abstract
Numerous regions are committed to reaching 100% light-duty zero-emissions vehicle (ZEV) sales by 2035 or earlier. For the case of Canada, we explore two policy pathways toward this goal: (i) a stringent ZEV sales standard (or mandate) and (ii) a purchase subsidy-based strategy (of three different durations). The AUtomaker-consumer Model (AUM) is used to compare policy impacts on ZEV sales, GHG mitigation, vehicle markups and prices, and automaker profits from 2023 to 2035. The examined subsidy approach ($15k per ZEV) is ineffective, raising ZEV new market share to 44-69% by 2035, while increasing automaker profits in part due to 13-18% capture of the subsidy value (incidence). In contrast, the strong ZEV sales standard can induce 95-100% ZEV sales by 2035, while inducing more ZEV-supportive strategies by the automakers, including an average 22% reduction in the prices of ZEVs, a 6% increase in the price of conventional vehicles, and a doubling of ZEV-related Research & Development (R&D) investment. The ZEV standard decreases cumulative automaker profits relative to the baseline (2023-2035), though annual 2035 profits are still higher than annual profits in 2023. Finally, the combination of the subsidy and standard can achieve the same positive outcomes while somewhat mitigating profit losses.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".