Which plug-in electric vehicle policies are best? A multi-criteria evaluation framework applied to Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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