Putting Transportation Emission Reduction Strategies in Perspective: Why Incremental Improvements Will Not Do
Bibliographic record
Abstract
Governments at all levels have recently been setting new aggressive targets for reduced GHG emissions, but despite improvements in vehicle fuel efficiency and pollutant emission rates, the trend in urban areas is towards increased fossil fuel use (and thus, GHG emissions) per capita for transportation. Municipalities across the country have outlined various strategies for reducing GHG emissions from transportation, but to date, few have linked the relative impacts of these strategies with stated targets. Using the Greater Toronto and Hamilton metropolitan region as an example, this paper quantifies the GHG impacts of several different levels of transit service ranging from business as usual to a very high level of transit investment with supporting TDM measures and technological advancements. Urban transportation emissions in the region are assessed using transportation demand models and Transport Canada's Urban Transportation Emissions Calculator. Results show that the highest level of transit service increases will reduce GHG per capita emissions by approximately 30 percent, which only just off-sets the impacts of population growth. These results indicate that municipalities across Canada cannot rely on transit improvements alone to address sustainability objectives and aggressive GHG reduction targets.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".