Greenhouse Gas Emissions and Potential for Electrifying Transportation Network Companies in Toronto
Bibliographic record
Abstract
This study investigates the spatial and temporal patterns of greenhouse gas (GHG) emissions from transportation network companies (TNCs) in Toronto, Canada. TNC services primarily consist of short-distance trips in the central business district. Deadheading—driving without passengers—contributes 40% of TNC GHG emissions, and part-time drivers had a higher deadheading proportion than full-time drivers. Pooled TNC trips account for 10% of total trips, and just 27% of pooled trips resulted in multiple passengers sharing a vehicle on the same route. The GHG implications of pooled trips were compared with other TNC services and with a consumer driving their own private vehicle, through the estimation of median trip emission intensities of each TNC service in grams of CO 2eq per passenger-km, while accounting for deadheading. Non-pooled internal combustion engine (ICE) ride-hailing trips have a median emission intensity 61% higher than that of a single-occupancy private vehicle. The median emission intensity for a pooled ICE ride-hailing trip is 20% higher than that of driving one’s own vehicle. Vehicle electrification provides a 91% daily GHG emission reduction for this fleet. This reduction is mainly achieved through full-time drivers, who, on average, achieve three times as much GHG savings per electrified vehicle compared with part-time drivers.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".