Electric vehicles and traffic related pollution reduction: a simulation model for Hamilton, Ontario, Canada
Bibliographic record
Abstract
This paper analyzes the potential contribution of electric vehicles in greenhouse gas (GHG) emissions reduction over the next decade following a simulation procedure. Emissions were assessed through a stepwise methodological approach at the transportation link level in the Hamilton Census Metropolitan Area (CMA). Firstly, different EV market penetration scenarios were introduced and compared to the base case scenario. Following these, the spatial distribution patterns of EVs were predicted using vehicle registration data for the Hamilton CMA as well as socioeconomic data obtained from census records. Properly modified matrices were used as input into our traffic simulation model in order to assign traffic on the network and estimate volumes for each of the links. To this end MOBILE 6.2C[1] was customized so as to compute the emission factors. The hourly emissions of each link were mapped in a GIS environment. We conclude that different utilization patterns result to varying spatial distributions of traffic related emissions in the links and even a modest adoption of EV technology may lead to their significant reduction. [1] MOBILE 6.2C is a version of MOBILE 6 originally developed by U.S Environmental Protection Agency to reflect the vehicle fleet and it was then modified by Environment Canada to embrace Canadian conditions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".