Evaluating greenhouse gas emissions benefits of emerging green technologies in passenger transportation in the Quebec context
Bibliographic record
Abstract
The transport produces 43.5% of Quebec's greenhouse gas (GHG) emissions; more than half of these emissions come from passenger transportation. In Quebec, transport emissions have grown by 30% from 1990 to 2009. Accordingly, this research evaluates the impact on GHG of alternative fuels and technologies in public transit and personal motor vehicles in the Quebec context using link-level GHG estimation methods. The transit technologies examined were analyzed using a lifecycle approach, mainly focusing on fuel production and vehicle operation phases, with the aid of GHGenius and MOVES. The demand for hybrid vehicles, its determinants as well as some potential market penetration scenarios were also investigated for Quebec City and the Island of Montreal. Different sources of data were combined to generate GHG inventories and estimate motor vehicle travel demand including: GPS, train and vehicle fleet fuel consumption rates, the Canadian Census, origin-destination surveys, and vehicle registration records.The results demonstrate that the use of alternative technologies can lead to significant GHG reductions. Among the bus technologies, it was found that hybrid buses are the best option with savings of 43.3%, followed by compressed natural gas (20.5%) and biodiesel (12.5%). For commuter rail, electric technology can reduce emissions by 98%; however, hydrogen fuel cell trains may be competitive in terms of cost-benefit ratio. Although hybrid personal vehicles have the potential for great GHG reductions, the limited spatial distribution of purchasers indicates that this technology will have a more modest impact than what might be expected. From an optimistic perspective where the vehicle fleet is composed of 25% hybrid vehicles, the impact would only lead to a 10% decrease in GHGs.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".