Socio-economic factors, climate, and people’s behaviours determine urban tree health
Bibliographic record
Abstract
Urban trees provide numerous benefits, including improved air quality, reduced urban heat, and enhanced well-being for city dwellers. However, the distribution of these benefits is often uneven, with wealthy urban areas typically receiving greater advantages than poorer urban areas, highlighting a trend of green inequality in urban environments. The health of urban trees, which is crucial for maintaining environmental and social benefits, is likely affected by the interactive effects of socio-economic factors and climatic conditions. Yet, empirical research demonstrating these complex interactions is lacking, leaving a significant gap in our understanding of urban forest dynamics. In this study, we assessed the relationships between socio-economic factors, climate variables and people's behaviours with urban tree health across 11 suburbs along a climate gradient in Sydney, Australia. Additionally, we evaluated people's perceptions towards urban trees. Our analysis revealed that suburbs with high economic resources, precipitation and temperature were associated with healthier urban tree communities. Additionally, we found that suburbs where residents reported that they were actively engaged in tree care practices, such as providing water and fertiliser or mulch, also exhibited healthier trees. Our research uncovered significant differences in public perception and tree care practices across the studied suburbs. These variations appeared to be influenced by both socio-economic factors and local climate conditions, suggesting a complex interplay among socio-economic resources, climatic conditions and human behaviour in shaping and determining urban tree health. Our research underscores the need for targeted urban forestry strategies that address green inequalities and promote equitable distribution of urban forest benefits across diverse city landscapes. • Urban tree health is positively associated with higher economic resources and favourable climate conditions. • Active engagement in tree care practices by residents correlates with healthier urban trees. • Socio-economic factors and local climate influence public perception and tree care practices. • Green inequality exists in urban environments, with wealthier areas having healthier residential street trees. • Strategies are needed to address green inequalities and promote equitable distribution of urban forest benefits.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".