Material efficiency for transport decarbonization: a case study of carsharing in montreal
Bibliographic record
Abstract
• Carsharing can lower carbon footprint by improving material efficiency. • Largest gains come from low-mileage users shifting to shared vehicles. • High-mileage users yield smaller or no carbon benefits with carsharing. • EV-based carsharing cuts carbon footprint up to 29% at 60% adoption rate. • Results offer a baseline for policy targeting appropriate carsharing users. This study quantifies the climate impacts and benefits of carsharing by focusing on one key mechanism: improved material efficiency through the more intensive use of shared vehicles. We assess this mechanism by evaluating how variations in annual driving distance and resulting lifetime mileage affect carbon footprints from a life cycle perspective. Adoption scenarios reflect a continuum in which users progressively shift to carsharing based on their annual driving distance, while their travel demand remains constant. Results show that most carbon footprint reductions occur between 10 % and 60 % adoption, beyond which marginal benefits decline. These reductions are driven by the collective contribution of users driving less than 15,000 km/year, with per-person impacts vary significantly depending on individual annual driving distance. While the study does not incorporate behavioral change or system dynamics, it offers a conservative, mechanism-based estimate of carsharing’s environmental potential. The findings complement behavior-oriented research on carsharing for transport decarbonization.
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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".