Strategic deployment of urban trees to achieve thermal resilience in a Canadian community
Bibliographic record
Abstract
Abstract Climate change and urban heat islands are intensifying the frequency and severity of heatwaves, emphasizing the need for resilient and sustainable strategies to cool urban outdoor and indoor spaces. Urban trees are identified as an effective solution, yet limited studies address how different tree deployment strategies enhance building thermal resilience against heatwaves. This study examines the impact of strategic urban tree deployment on building thermal resilience across a neighborhood in London, Canada. Two deployment strategies are assessed: a straightforward strategy based on outdoor temperature hotspots, and a more complex strategy based on building indoor heat stress. The analysis incorporates tree growth and its effect on canopy coverage. A coupled microclimate-building performance simulation evaluates outdoor and indoor thermal conditions, with thermal resilience quantified using a novel method integrating microclimate effects, heat stress intensity, and exposure duration. Results indicated that when canopy coverage increases from 6% to the Nature Canada-recommended 30%, both strategies achieve similar maximum reductions in building surrounding outdoor air temperature (4.0 °C) and Standard Effective Temperature (6.9 °C), as well as comparable reductions in indoor thermal stress. However, at lower canopy coverage levels (≤20%), the indoor based strategy achieves a more uniform resilience distribution and enhances thermal resilience for the majority of buildings with poorer baseline conditions. At 30% canopy coverage and above, the differences between the two strategies become less pronounced, making tree deployment based on outdoor temperature hotspots a straightforward yet effective strategy for improving neighborhood thermal resilience.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".