Life cycle emissions and lifetime costs of medium-duty diesel and alternative fuel trucks. A case study for Toronto
Bibliographic record
Abstract
This thesis analyzes a Class 6 diesel truck, compressed natural gas truck, hybrid-electric truck and battery-electric truck, in terms of energy consumption, life cycle greenhouse gas (GHG) emissions and cost of ownership. The energy consumption simulation and life cycle emissions for the trucks are based on payload, temperature and two drive cycles in Toronto. The energy consumption simulations are performed using Autonomie, a simulation software. The simulation data are used in GHGenius to calculate the life cycle GHG emissions. The cost of GHG emissions abatement for each alternative fuel truck is determined based on the differences in cost and GHG emissions between the alternative trucks and the diesel (reference) truck. The results of the analyses indicate that there is no dominant technology that reduces both GHG emissions and ownership cost under all driving conditions, a conclusion that can inform transportation climate change policies.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".