Greenhouse gas emissions trends and fleet renewal of ride-hailing in Toronto, Canada
Bibliographic record
Abstract
This study examines the evolution of ride-hailing (RH) in Toronto, from January 2020 to December 2023, focusing on greenhouse gas (GHG) emissions, driver long-term operational patterns, and total cost of ownership (TCO) across electric, hybrid, and gasoline vehicles. Using descriptive analysis based on three years of data from 179 million trips, our findings indicate growth in RH trips, distances traveled, and GHG emissions, with deadheading rates remaining at 33-37 % over the past two years. By January 2023, the percentage of electric vehicles (EVs) in Toronto's private transportation company (PTC) fleet reached 2.3 %, slightly higher than Ontario's 1.7 %. Driver operational patterns significantly impact efficiency and emissions. We identified that 42.9 % of drivers operate less than 200 km/week, covering less than 200 km/week with a higher turnover rate and 43.9 % deadheading mileage. In contrast, 40.2 % of drivers exceed 400 km/week, work more than five days per week, and are more likely to remain in the business. This group shows higher efficiency, increased trip cancellations and multi-platform operation. Our TCO analysis focused on active drivers suggests EVs are more cost-competitive than hybrids and gasoline vehicles, with new models offering 7-46 % lower ownership costs.
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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) | 0.000 | 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".