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 burdens. 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 physical modes of transport) and identifies cross-country differences in green travelling 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 physical travel exhibiting a much more consistent profile, across countries, in comparison to the use of public transport. Results indicate a positive relationship between living in urban areas and the time proportion of green travel, but estimates by country differ in magnitude and depend on the mode. We also find that some countries are more prone to green travel, and that transport infrastructure is more related to the proportion of time travelled by physical transport than by public transport. Our findings help in understanding who is committed to green mobility, while revealing systematic differences across countries that are worth analyzing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".