Cost Benefit Analysis of Vehicle Emissions Reduction Policies in Canada: A Case Study of Zero-Emission Vehicles
Bibliographic record
Abstract
Canada has been at the forefront of mitigating climate change by adopting strategies that align with the international objective of limiting global warming. For instance, the Canadian government has intervened in the transport sector by enacting vehicle emission reduction policies such as the ZEVs policy that encourages the adoption of EVs, FCVs, and PHEVs. The policy aligns with the Canadian government’s ambitious target of getting more ZEVs on Canadian roads as a strategy to achieve “100 percent zero-emission vehicles by 2040, with interim goals of 10 percent by 2025 and 30 percent by 2030”. However, although ZEVs offer Canada an opportunity to reduce its GHG emissions in the transport sector, there has been concern about the upfront costs associated with adopting ZEVs, which continue to be a major deterrent despite their operation and maintenance costs being low. The following research paper conducts a CBA on ZEVs compared to CVs in Canada in terms of ownership costs and environmental impact.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".