Which plug-in electric vehicle policies are best? A multi-criteria evaluation framework applied to Canada
Bibliographic record
Abstract
Policy is an important driver for the deployment of plug-in electric vehicles (PEVs). Most PEV policy research focuses on effectiveness in the short-term, even though policymakers i) typically consider a wider range of evaluation criteria and ii) are setting PEV sales goals in the longer-term (e.g., 2030 or 2040). This study develops a more comprehensive evaluation framework, considering five criteria: (i) effectiveness at increasing PEV adoption in the long-term (2040), (ii) government spending, (iii) public support, (iv) policy simplicity and (v) “transformational signal”, the latter being a measure of a policy's ability to stimulate confidence and investment in a PEV transition. We apply this framework to Canada by assessing eight policy types implemented across the country, as well as stronger versions of each policy. We also illustrate trade-offs by constructing three policy packages with similar effectiveness (i.e., PEVs making up 40% of light-duty vehicle sales by 2040). These packages include strong financial incentives ($6,000 CAD per PEV for 20 years), a Zero-Emissions Vehicle (ZEV) sales mandate (requiring 40% PEV sales by 2040), or strengthened light-duty vehicle emissions standards (decreasing to 71 g CO2e per km by 2040). These packages differ in terms of government expenditure, policy simplicity, public support and transformative signal. Our framework provides an accessible tool for policymakers to assess such tradeoffs with PEV-supportive policies and to identify approaches that best suit their jurisdiction.
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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 | 0.034 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".