Who uses green mobility? Exploring profiles in developed countries
Bibliographic record
Abstract
Mobility gives individuals access to different daily activities, facilities, and places, but at the cost of imposing environmental externalities. The sustainable growth of society is linked to green mobility (e.g., public transport, walking, cycling) as a way to alleviate individual carbon footprints. This study explores the socio-demographic profile of individuals performing green travel (public and active modes of transport) and identifies cross-country differences in green travel behavior. We rely on information from the Multinational Time Use Study, MTUS, for Bulgaria, Canada, Spain, France, Hungary, Italy, the Netherlands, the United Kingdom, and the United States, from 2000 to 2019. We estimate Ordinary Least Squares regressions modelling individual decisions regarding green mobility. Our results indicate that the socio-demographic and family profile of travelers is not homogenous across green modes of transport, with walking as a mode of travel exhibiting a much more consistent profile, across countries, in comparison to the use of public transport and cycling. Results indicate that some countries are more prone to green travel, and that transport infrastructure is a factor in the proportion of time spent on both public and active transport. Our findings help in understanding who is committed to green mobility, while revealing interesting systematic differences across countries.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".