ASSESSMENT OF TRANSPORTATION GHG MITGATING SOLUTIONS
Bibliographic record
Abstract
The passenger transportation sector is notoriously difficult to decarbonize. In this thesis, two distinct and novel methodologies to estimate the environmental impact of alternative and conventional transportation technologies are developed. In Chapter 2, a provincial fleet policy-driven linear programming model is developed to minimize the cost of three passenger vehicle electrification policies in Ontario under a 30% GHG reduction target by 2030. Provincial life-cycle emissions and total-cost-of-ownership associated with policy allocation is estimated. The results highlight that electrification of on-road passenger transportation will not be sufficient to meet the 30% reduction target despite Ontario's low-carbon electricity grid. Instead, reductions of between 24% to 26% are forecasted at an annual cost (for ten years) of between CAD 0.29 to 0.3 billion annually indicating that additional policies are necessary to realize a 30% reduction target. In Chapter 3, a trip-level vehicle framework is developed to determine under what operating conditions transit buses and passenger cars will be environmentally beneficial across the dimensions of technology, service mode, and power source pathway. The well-to-wheel energy consumption and GHG emissions are simulated for over 450 operating scenarios. Emissions are then normalized through passenger-trip emission thresholds to facilitate equivalent comparison across all dimensions. The results indicate that the most beneficial solution are fuel-cell electric car-share, battery electric car-share, and battery electric bus all powered by low-carbon intensity power sources at average occupancy (7.9-19.7 gCO2e passenger-service-mode-trip-km-travelled-1). Furthermore, transit bus technologies have the potential to reduce up to 2.3 times more GHG per passenger-trip than comparable ride-share passenger cars at average occupancies. The results of Chapter 2 and 3 highlight that technology alone may not be sufficient to achieve significant GHG reductions; policy which leverage local operating data and target GHG reduction associated with passenger-trips are critical to informing under what conditions a mobility solution is environmentally beneficial.
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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 | 0.002 | 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".