Assessing the Impact of Bus Technology on GHG Emissions Along a Major Corridor: Comparing an Instantaneous Speed Emission Model with an Average-Speed Model
Bibliographic record
Abstract
Recently, a large number of local transit agencies in North America have been considering new strategies to reduce fossil fuel consumption and greenhouse (GHG) emissions. These strategies often include a combination of operational improvements and alternative technologies. This is the case for the Montreal transit system, run by the Societe de transport de Montreal (STM), which manages and operates a fleet of 1,696 buses with the majority running on diesel. This study focused on a busy bus transit corridor. The objectives include 1) evaluating the impact of alternative bus transit technologies including compressed natural gas (CNG), biodiesel, and diesel-electric hybrid on GHG emissions using a lifecycle analysis (LCA) approach, and 2) comparing the operational emissions of buses running on these different technologies using an instantaneous speed and an average speed emission model. Local geographic and driving conditions along the corridor are incorporated in the estimation of operational emissions. The results indicate that operational emissions make-up the largest portion of lifecycle emissions (more than 80%). This implies that detailed LCA are not necessary to assess the environmental impact of alternative bus technologies since the analysis of operational emissions would have been sufficient. The bus technologies are ranked in increasing order of lifecycle GHG emissions generated: 1) hybrid, 2) CNG, 3) biodiesel, and 4) diesel. GHG savings range from 8.4-29.0 kg of carbon dioxide (CO2)-equivalent (12.5-43.3%) for a single bus operating on a single route during the morning and afternoon peak periods when converting the current diesel technology to one of the alternative fuels tested. When comparing instantaneous vs. average speed emissions the authors observe that both methods produce consistent results for diesel emissions. However, the average speed method underestimates biodiesel emissions by 21% and overestimates CNG emissions by 16%.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".