Regional Assessment of Current and Future States of GHGs Emissions: Impacts of Transportation Policy Scenarios
Bibliographic record
Abstract
Estimating the future state of greenhouse gases (GHGs) and air quality associated with transportation policies and infrastructure investments is key to the development of meaningful transportation and planning decisions. This study describes the design and application of an integrated transportation emission model for the prediction of GHGS in CO2eq in the Greater Montreal Region as a result of transport policy scenarios and Land use scenarios. For this purpose, a travel demand model linked with models for traffic assignment and emissions, was used to simulate GHG emissions in a base year (2008) and a horizon year (2031) while incorporating population and demographic projections. Various stakeholders were consulted in the development of future scenarios affecting land-use and transportation through a web-based survey and workshop.In the 2031 business as usual scenario, an average decrease of 30% in GHG emissions was estimated compared to the 2008 base case. This decrease is primarily attributed to projected improvements in vehicle technology. The modelling system was used to evaluate the impact of a 20% market penetration in electric vehicles, revealing significant reductions in GHG emissions across the region. Work is currently underway to estimate the emissions of nitrogen oxides (NOx) and NO2 concentrations to assess air quality and individuals’ daily exposure by tracking activity locations and trajectories of the population and observe the level of reduction in daily exposures compared to the base case.This study is funded by a collaborative grant from the Canadian Institutes of Health Research (CIHR) and the Natural Sciences and Engineering Research Council of Canada (NSERC) and Ouranos.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".