Heat, smoke, and urban health: cooling and cleaner air centres as a tool for adaptation in a Canadian urban region
Bibliographic record
Abstract
Urban climate change impacts, particularly heatwaves and wildfire smoke, are becoming increasingly severe, prompting cities like those in the Greater Vancouver area of Western Canada to develop adaptation measures that address rising temperatures and deteriorating air quality. One such intervention is the establishment of cooling and cleaner air centres, which offer temporary refuge during extreme weather events. However, their implementation to date has often been reactive, fragmented, and lacking in sustained coordination. To better understand how these centres are planned and deployed, we conducted a systematic review of academic and grey literature and conducted interviews with 16 public sector and civil society professionals involved in their implementation in the Greater Vancouver area. The study reveals that while these centres are increasingly seen as vital infrastructure, their effectiveness is limited by governance challenges, under-resourcing, and inconsistent coordination across sectors. Our findings underscore the need for stronger institutional coordination, proactive planning, and equity-oriented design. Motivated by the priorities of regional government partners, this research represents a transdisciplinary effort to generate actionable insights that can inform more inclusive and strategic approaches to urban climate adaptation.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".