Understanding cooling potential of urban trees in a typical North America neighborhood
Bibliographic record
Abstract
Urban areas are experiencing rising temperatures leading to the exploration of mitigation solutions to cool cities. Common solutions rely on trees. However, trees can both enhance and deteriorate pedestrian thermal comfort in different areas of a neighborhood and at different times of the day. The objective of this paper is to document and quantify the overall contribution of trees to the pedestrian comfort in an entire neighborhood, with a special attention to cooling. Various vegetation scenarios are applied for a typical North American urban neighborhood, inspired by a neighborhood in Montreal, during a heat-wave period. The study focuses on 6 central urban lots including alleys, streets and a large boulevard, forming an area of 250 m x 300 m, surrounded by 14 urban lots. We use all-physics computational modeling to assess the multifaceted effects of trees at urban scale, using a custom CFD-based coupled solver developed by the authors, urbanMicroclimateFoam, based on OpenFOAM. This urban microclimate model sequentially solves turbulent air flow, heat and moisture transport in porous media, and radiative exchanges. The Universal Thermal Climate Index (UTCI) is used to document outdoor thermal comfort. The analysis reveals that trees in ventilation corridors, i.e. streets aligned with the primary wind, reduce average pedestrian comfort in the neighborhood by blocking wind despite providing shade. Conversely, trees in private gardens or in crosswind corridors can enhance both local thermal comfort and overall average pedestrian comfort throughout the neighborhood, thereby improving walkability. The paper highlights the non-local effects of trees over the entire neighborhood.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".