Urban mobility and carbon emissions: Decoding the influence of sociodemographic factors, trip-level built environment, and travel behaviour of workers in three UK cities
Bibliographic record
Abstract
Quantifying how individual-level travel patterns intersect with built environment features and sociodemographic characteristics is essential for addressing transportation-related CO₂ emissions. Existing evidence is predominantly based on aggregated individual and/or area-based data, neglecting important drivers of emissions at the trip level and variations by individual characteristics, including employment types. GPS-tracked mobility patterns from a random sample of 587 workers in three UK cities (Brighton and Hove, Leeds, and Birmingham) are analysed to estimate CO 2 emissions at the trip-level and person-level, accounting for multi-modal travel. Walkability and public transport availability at trip origins is associated with reduced emissions at the trip level but not overall individual emissions. Employing latent class analysis, participants were grouped based on multiple sociodemographic characteristics. Women with no access to a car were identified as low emitters, while individuals with access to a car as high emitters, regardless of gender and education level. These findings reinforce the need for policy frameworks that extend beyond traditional single-location strategies. Enhancing walkability and public transit connectivity in key activity hubs such as commercial, leisure, and other high-traffic areas, alongside incentives for behaviour change, offers significant potential to reduce transportation CO₂ emissions. Moreover, as dynamic population shifts and evolving travel patterns weaken the effectiveness of monocentric urban structures, our findings suggest that transitioning toward a polycentric model can be a more effective strategy for lowering transport-related CO₂ emissions. By emphasizing this broader spatial reach, urban planners and policymakers can better tailor interventions to distinct population segments, effectively supporting low-carbon travel and advancing sustainable urban mobility goals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".