People’s perceptions of urban trees are more negative after COVID-19 lockdowns
Bibliographic record
Abstract
Abstract The ways people think about urban nature affect how people engage with and support nature-based solutions for climate change adaptation in cities. While geographical and socio-demographic characteristics are known to influence people’s thoughts about urban nature, there is little knowledge on how these perceptions can shift over time, especially in response to major events that disrupt the human-nature relationship (such as hurricanes, wildfires, and pandemics). Considering urban trees are a key nature-based solution in cities, this study explores the shift in people’s perceptions about urban trees before and after the COVID-19 pandemic lockdowns. We also assessed how urban context and socio-demographics influenced this shift. Using Melbourne, Australia, as a case study, we delivered an online panel survey based on validated psychometrics about urban trees in summer 2020 (pre-COVID-19 lockdowns) and again in summer 2023 (post-COVID-19 lockdowns). The survey helped us explore temporal changes related to values and beliefs associated with urban forests and trees. Our results showed a change in two perceptions, with a 2% decrease in the importance of urban trees for nature (p = 0.02, r = 0.04) and a 4.3% increase in negative beliefs about trees (p < 0.01, r = 0.08) in 2023, compared to 2020. These shifts were greatest in outer urban areas. Furthermore, we observed that most socio-demographic groups rated the importance of the natural values lower and rated negative beliefs about urban trees higher in 2023, compared to 2020. While previous studies have found people had a more positive connection to urban nature during COVID-19 lockdowns, our study highlights that perceptions of urban trees may shift over time, which can lead to future changes in community support and engagement with urban forest management.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".