Arboreal Urban Cooling Is Driven by Leaf Area Index, Leaf Boundary Layer Resistance, and Dry Leaf Mass per Leaf Area: Evidence from a System Dynamics Model
Bibliographic record
Abstract
Heat waves are becoming more frequent due to climate change. Summer heat waves can be particularly deadly in cities, where temperatures are already inflated by abundant impervious, dark surfaces (i.e., the heat island effect). Urban heat waves might be ameliorated by planting and maintaining urban forests. Previous observational research has suggested that conifers may be particularly effective in cooling cities. However, the observational nature of these studies has prevented the identification of the direct and indirect mechanisms that drive this differential cooling. Here, we develop a systems dynamics representation of urban forests to model the effects of the percentage cover of either conifers or broadleaf trees on temperature. Our model includes physiological and morphological differences between conifers and broadleaf trees, and physical feedback among temperature and energy fluxes. We apply the model to a case study of Vancouver, BC, Canada. Our model suggests that in temperate rainforest cities, conifers may by 1.0 °C cooler than broadleaf trees; this differential increases to 1.2 °C when percentage tree cover increases from 17% to 22% and to 1.7 °C at 30% cover. Our model suggests that these differences are due to three key tree traits: leaf area index, leaf boundary layer resistance, and dry mass per leaf area. Creating urban forests that optimize these three variables may not only sequester CO2 to mitigate global climate change but also be most effective at locally minimizing deadly urban heat waves.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".