Policy Considerations for Zero-Emission Vehicle Infrastructure Incentives: Case Study in Canada
Bibliographic record
Abstract
Transportation accounts for more than 20% of the total Greenouse Gas (GHG) emissions in Canada. Switching from fossil fuels to more environmentally friendly energy sources and to Zero-Emission Vehicles (ZEVs) is a promising option for future transportation but well to wheel emission and charging/refuelling patterns must also be considered. This paper investigates the barriers to and opportunities for electric charging and hydrogen refueling infrastructure incentives in Ontario, Canada and estimates the number of Internal Combustion Engine Vehicles (ICEVs) that would be offset by infrastructure incentives. The paper also assesses the potential of electric and hybrid-electric powertrains to enable GHG reductions, explores the impact of the electricity supply mix for supporting zero-emission vehicles in different scenarios and studies the effect of the utility factor for PHEVs in Ontario. The authors compare the use of electric vehicle charging infrastructures and hydrogen refueling stations regarding overall GHG emission reductions for an infrastructure incentive funded by a 20-million-dollar government grant. The results suggest that this incentive can provide infrastructure that can offset around 9000 ICEVs vehicles using electricity charging infrastructure and 4000–8700 when using hydrogen refuelling stations. Having appropriate limitations and policy considerations for the potential 1.7 million electric-based vehicles that may be in use by 2024 in Ontario would result in 5–7 million tonne GHG avoidances in different scenarios, equivalent to the removal of 1–1.5 million ICEVs from the road.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".