Health and Climate Incentives for the Deployment of Cleaner On-Road Vehicle Technologies
Bibliographic record
Abstract
Reducing greenhouse gas (GHG) emissions of private passenger vehicles, transit buses, and commercial vehicles with newer technology can improve air quality, and, subsequently, population exposure and public health. For the Greater Toronto and Hamilton Area, we estimated the burden of each vehicle fleet on population health in the units of years of life lost and premature deaths. We then assessed the separate health benefits of electrifying private vehicles, transit buses, and replacing the oldest commercial vehicles with newer trucks. A complete deployment of electric passenger vehicles would lead to health benefits similar to replacing all trucks older than 8 years (i.e., about 300 premature deaths prevented) in the first year of implementation; however, GHG emissions would be mainly reduced with passenger fleet electrification. Transit bus electrification has similar health benefits as electrifying half of the passenger fleet (i.e., about 150 premature deaths prevented); however, the GHG emission reductions reached under the bus electrification scenario are lower by 90%. By accelerating policies to electrify cars and buses and renew older trucks, governments can save hundreds of lives per year and mitigate the impacts of climate change.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".