Coping with heat : community perceptions and experiences of urban forests in Metro Vancouver, Canada
Bibliographic record
Abstract
During the 2021 Western North America heat wave, British Columbians experienced unprecedented temperatures and heat-related illnesses and deaths. As more extreme temperatures are anticipated in the future, it is vital to find ways to alleviate urban heat. Urban forests can reduce temperatures via shading and evapotranspiration, thereby contributing to climate resilience and mitigating heat-related health impacts. However, it is not well understood how residents’ preferences and social, economic, and environmental factors impact their use of urban forests to cope with heat. This limits the capacity of municipalities to design urban forests that meet the heat resilience needs of diverse populations. My research contributes to this field of inquiry by (1) presenting a literature review of the emerging intersections between urban forestry, environmental justice, public health, and heat resilience, and (2) exploring how heat vulnerability status and certain environments/activities (i.e., being at home versus participating in recreation outdoors) influence thermal comfort, cooling method use, and perceptions of street tree protection from heat in Metro Vancouver, Canada. In addition, I situate the diverse perceptions and experiences uncovered by my analyses to inform how urban forestry policy can help mitigate, not exacerbate, existing health and environmental inequities. Findings from the narrative literature review highlight the need for cross-disciplinary collaboration between urban forestry and public health policy makers for heat resilience, with a focus on integrating community perspectives into future planning efforts. My findings suggest that trees are an efficient, potentially equitable way to provide heat relief. However, trees cannot solve everything; they are part of a context-dependent, integrated city heat resilience plan that prioritizes environmental and health equity for heat-vulnerable people and neighborhoods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".