Exposure to green and blue spaces during travel does not have immediate effect on subjective happiness and stress: evidence from a GPS survey in England
Bibliographic record
Abstract
• GEMA evaluated effects of travel environments on post-trip happiness and stress. • Exposure to green/blue spaces while traveling has no immediate effect on happiness or stress. • Active travel modes immediately boost happiness and reduce stress. • Urban design should prioritize active travel to enhance daily happiness and reduce stress. • Active travel shows no clear link to the availability of green or blue spaces. How wellbeing can be improved in cities, has attracted increasing attention. This paper studies urban stress and happiness in relation to daily travel behaviour through a large app-based geographic ecological momentary assessment study conducted in three English cities: Birmingham, Leeds, and Brighton and Hove. The key questions are whether, and to what extent, environmental factors—specifically, green and blue spaces, and weather conditions—affect urban travellers’ happiness and stress levels immediately following travel. GPS data from 606 participants were collected and combined with survey questions asking participants to score their current happiness and stress levels at the end of trips. Environmental data were linked to the GPS location data. The results indicate that exposure to green and blue spaces during trips had no immediate effect on happiness or stress levels. However, active transportation modes, such as walking and biking, were associated with higher happiness and lower stress compared to car use. These findings suggest that while exposure to green and blue spaces may provide long-term environmental values within an urban context; promoting active travel modes could yield more immediate benefits for urban wellbeing.
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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 | 0.001 | 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".