Policy Considerations for Zero-Emission Vehicle Infrastructure Incentives: Case Study in 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
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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