Having a tree in front of one’s home is associated with GREATER subjective wellbeing in adult residents in Melbourne, Australia, and Toronto, Canada
Bibliographic record
Abstract
While urban trees can be important determinants of human health and wellbeing in world cities, the specific influence of nearby urban trees upon human wellbeing has not been adequately explored. While many studies have associated urban greenery abundance with wellbeing scores, many measures of urban greenery do not specify the type of vegetation or the impact of co-location. Here we fill this gap by associating self-reported measures of the presence of nearby trees (tree in front of one's home) with validated subjective wellbeing (SWB) scores. We also tested for the mediating role of what people thought about trees and nature, with a focus on the values people associate with urban trees and nature relatedness (NR). We used electronic panel survey data based on a demographic and geographical representative sample of more than 3400 residents living in Toronto, Canada, and Melbourne, Australia. We analysed these data using regression-based mediation and path analyses. We found that having a tree in front of one's home was strongly and positively associated with SWB scores in both cities with similar results (Melbourne, β = 0.17, p < 0.05; Toronto, β = 0.18, p < 0.05), while accounting for NR, values associated with urban trees, and demographics (e.g., age, education, home ownership). The mediating role of NR and values was small. The specific pathways of association between tree in front of one's home, SWB, NR, and values, varied by city, when accounting for demographics. We discuss how increasing the abundance of nearby urban trees in cities may also increase human wellbeing.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".