Electric vehicles and traffic related pollution reduction: a simulation model for Hamilton, Ontario, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<div> <p>This paper analyzes the potential contribution of electric vehicles in greenhouse gas (GHG) emissions reduction over the next decade following a simulation procedure. &nbsp;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<a href="#_ftn1" name="_ftnref1" title="">[1]</a> 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.</p> </div> <div><br clear="all" /> <hr align="left" size="1" width="33%" /> <div id="ftn1"> <p><a href="#_ftnref1" name="_ftn1" title="">[1]</a> 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.</p> </div> </div> <p>&nbsp;</p>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it