Urban Transportation Greenhouse Gas Emissions and Their Link with Urban Form, Transit Accessibility, and Emerging Green Technologies
Bibliographic record
Abstract
A greenhouse gas (GHG) emissions inventory is estimated at the household level from disaggregated trip data considering all emitting modes. Trip-level GHG emissions are estimated by combining data sources (e.g., origin–destination surveys, vehicle fleet characteristics, transit rider ship data) and by using modeling tools (traffic assignment and GHG models) developed for Montreal, Quebec, Canada. A simultaneous equation model framework is implemented to investigate links between urban form, transit supply, sociodemographics, and travel GHGs, taking into account the issue of residential self-selection. The potential impacts of land use and transit supply strategies with emerging green technology scenarios are then compared with each other. Findings are consistent with the literature; built environment attributes are statistically significant (10% increase in density, transit accessibility, and land use mix results in 3.5%, 5.8%, and 2.5% GHG reductions, respectively), and the number of workers and retirees make important contributions to GHG emissions at the household level (102% increase from adding one worker and 51% decrease from adding one retiree). Also, if the current transit fleet were replaced with electric trains and hybrid buses, transit GHGs would decrease by 32%. If current trends persist in the private motor vehicle fleet, continued improvements in car fuel economy are estimated to reduce car GHGs 7% by 2020. The two most effective strategies for reducing regional and household GHGs appear to be to improve the fuel efficiency of the private motor vehicle fleet and to increase transit accessibility.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".