A Life Cycle Assessment of Diesel-Electric Hybrid Trucks and Conventional Diesel Trucks for Curbside Deliveries
Bibliographic record
Abstract
Purolator is a Canadian courier company that has recently introduced hybrid electric vehicles into their fleets. By using hybrids, Purolator has advertised reductions of 1,900 tonnes of greenhouse gases and 645,000 L of diesel fuel over a distance of 5,200,000 km. This paper independently estimates the fuel savings and greenhouse gas reductions for Purolator’s trucks over their entire life cycles. To accomplish this objective, a life cycle assessment was executed using GHGenius, a Government of Canada model, which can be used to conduct well-to-wheel analysis and vehicle life cycle analysis for conventional and hybrid diesel trucks. Overall, it was found that Purolator’s hybrid diesel trucks reduce GHG emissions by 23% and 8% for city and highway driving, respectively. The results confirm that switching to an HEV fleet could reduce CO2 emissions during vehicle operations by 25% as the vehicle manufacturer (Azure Dynamics) has advertised. Moreover, the modelled emission reductions (1,668 tonnes) and fuel savings (609,000 L) over a distance of 5,200,000 km are similar to Purolator’s advertised values (1,900 tonnes, 645,000 L). The emission reductions on a per kilometer basis are relatively insensitive to changes in the vehicles’ service life, the average payload and the extra weight of the hybrid. At present, the life cycle costs of hybrid delivery trucks do not make them financially favorable alternatives to conventional diesel trucks, though their financial competitiveness is sensitive to the vehicle service life, the price of diesel fuel, the assumed discount rate, and the incremental hybrid cost.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".