Electrifying Ride-Hailing in the United States, Europe, and Canada: How to Enable Ride-Hailing Drivers to Switch to Electric Vehicles
Bibliographic record
Abstract
Efforts to reduce transportation emissions through electrification can accelerate their impact by focusing on intensively used vehicles. Vehicles driven on ride-hailing platforms such as Uber and Lyft are intensively used, and their distinct charging patterns can support the development of essential electric vehicle (EV) charging infrastructure. However, vehicles used for ride-hailing are often missed by actions to electrify other intensively used vehicles, and an array of disparately available financial incentives, EV models, and charging options produce a complicated landscape where it is often unclear whether an EV costs more or less than an internal combustion engine (ICE) vehicle or is suitable for ride-hailing. As a result, in U.S., European, and Canadian cities, the share of EVs among vehicles used for ride-hailing is often lower than or similar to the share of EVs in the overall vehicle stock. This paper identifies the largest barriers that prevent ride-hailing drivers from accessing EVs and analyzes ways that governments, industry and other stakeholders can tackle those barriers. It includes city scorecards that evaluate 10 U.S., European and Canadian cities on their progress towards dismantling these barriers, using an original methodology and data from Uber.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".