Cycling and GHG Emissions: How Infrastructure Makes All the Difference
Bibliographic record
Abstract
One practical approach to reduce GHG emissions is to shift from driving to modes with lower emissions, such as cycling. One key component of supporting cycling is the quality and quantity of cycling infrastructure. This study analyzes the relationship between the quality (or comfort) and quantity of bicycle infrastructure, the likelihood of cycling, and the emissions. The first objective of this study is to analyze the influence of various variables on cycling choice using an interpretable ensemble learning approach. Second, a scenario-based analysis is applied to examine the influence of various policy scenarios (related to cycling infrastructure) on the transportation life cycle GHG emissions. Using origin–destination survey data from Montreal and Laval, Canada, policy modelling results suggest that without current cycling infrastructure, cycling mode share would be 5.3% less, driving mode share would be 4% higher, and GHG emissions would be 10.2% higher among all trips of a reasonable cycling distance starting from home. Then, policy scenarios modelling for this subset of trips suggests that improving the quality of bikeways, increasing their quantity, and reducing the trip distances by 25% can reduce the GHG emissions by 3.9%, 6.6%, and 29.3%, and increase the number of cycling trips by 8.1%, 14%, and 24.4%, respectively.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".