Purchase subsidies for 100% zero-emissions vehicle sales goals: Effectiveness, government cost, and supplier capture
Bibliographic record
Abstract
• Simulate zero emission vehicles (ZEVs) sales under purchase subsidies. • Subsidies need to be CAD $40,000 to achieve 100% ZEV sales by 2035. • Free-ridership rates are 50–75%. • Subsidy pass-through to consumers is 77 to 85%, with the rest retained by industry. • Pass-through decreases with increased subsidy duration and value. Globally, purchase subsidies are among the most common policies used to support the deployment of zero-emissions vehicles (ZEVs). However, it is unclear if subsidies alone can effectively and efficiently achieve ambitious long-term ZEV sales goals, such as the 100% by 2035 target adopted by numerous developed countries. To shed insight on subsidy impacts under consumer-supplier dynamics, we use a technology adoption model (AUM) that endogenously represents consumer preferences for (and purchases of) light-duty passenger ZEVs, and automaker decision-making about ZEV pricing, innovation activities, and charger deployment. We use AUM to simulate the impacts of different levels and durations of ZEV purchase subsidies in the 2023–2035 time frame in the case region of Canada. Results indicate that a subsidy-dominated policy mix needs to increase subsidy values to at least $40,000 per ZEV by 2035 to achieve the 100% goal in Canada. In that scenario, average government expenditure on subsidies is 450–820 $/tonne CO 2 e abated, and up to $180 billion in total direct government expenditure. Across subsidy-dominated scenarios, automakers capture 15–23% of subsidy value and increase their overall profit; both trends increase with higher subsidy duration and value. In short, a subsidy-dominated approach to inducing ZEV sales is likely to prove costly; other policies should be considered to lead a policy mix, such as regulation, taxation, or a feebate program.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".