Spatial Analysis of Demand for Hybrid Electric Vehicles and Its Potential Impact on Greenhouse Gases in Montreal and Quebec City, Canada
Bibliographic record
Abstract
Personal cars contribute 56% of greenhouse gas (GHG) emissions generated from passenger transportation. As vehicle ownership increases, a promising solution to reduce emissions is to turn to greener technologies, namely electric and hybrid-electric vehicles. The hybrid vehicle fleet in Quebec has increased dramatically from 339 in 2003 to 7,085 in 2008. People are turning to cleaner technologies as a means to reduce fuel costs. They are also motivated by government incentives in the form of tax rebates. This study explores the link between socio-demographics, travel behavior and market penetration of hybrid vehicles in Montreal and Quebec City using a negative binomial regression. Moreover, the impact on GHG of different hybrid market penetration scenarios is evaluated. The regression shows that population density, post-secondary education and trip distances have the most significant influence on hybrid vehicle ownership. This implies that the market is geared towards households with higher incomes, living in dense neighborhoods with high accessibility to transit and service. Although hybrid vehicles have the potential for great GHG reductions, the spatial market distribution indicates that this technology will have a more modest impact than what is expected. From an optimistic perspective where 25% of the vehicle fleet is converted to hybrid vehicles, there would only be a 10% decrease in GHGs in both cities. This is a daily savings of 390.9 tonnes in Montreal and 297.3 tonnes in Quebec City.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".