Cooling hot cities: a systematic and critical review of the numerical modelling literature
Bibliographic record
Abstract
Abstract Infrastructure-based heat reduction strategies can help cities adapt to high temperatures, but simulations of their cooling potential yield widely varying predictions. We systematically review 146 studies from 1987 to 2017 that conduct physically based numerical modelling of urban air temperature reduction resulting from green-blue infrastructure and reflective materials. Studies are grouped into two modelling scales: neighbourhood scale, building-resolving (i.e. microscale); and city scale, neighbourhood-resolving (i.e. mesoscale). Street tree cooling has primarily been assessed at the microscale, whereas mesoscale modelling has favoured reflective roof treatments, which are attributed to model physics limitations at each scale. We develop 25 criteria to assess contextualization and reliability of each study based on metadata reporting and methodological quality, respectively. Studies have shortcomings with respect to neighbourhood characterization, reporting areal coverages of heat mitigation implementations, evaluation of base case simulations, and evaluation of modelled physical processes relevant to heat reduction. To aid comparison among studies, we introduce two metrics: the albedo cooling effectiveness (ACE), and the vegetation cooling effectiveness (VCE). A sub-sample of 47 higher quality studies suggests that high reflectivity coatings or materials offer ≈0.2 °C–0.6 °C cooling per 0.10 neighbourhood albedo increase, and that trees yield ≈0.3 °C cooling per 0.10 canopy cover increase, for afternoon clear-sky summer conditions. VCE of low vegetation and green roofs varies more strongly between studies. Both ACE and VCE exhibit a striking dependence on model choice and model scale, particularly for albedo and roof-level implementations, suggesting that much of the variation of cooling magnitudes between studies may be attributed to model physics representation. We conclude that evaluation of the base case simulation is not a sufficient prerequisite for accurate simulation of heat mitigation strategy cooling. We identify a three-phase framework for assessment of the suitability of a numerical model for a heat mitigation experiment, which emphasizes assessment of urban canopy layer mixing and of the physical processes associated with the heat reduction implementation. Based on our findings, we include recommendations for optimal design and communication of urban heat mitigation simulation studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.031 | 0.022 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".