Beyond UHI – how to build and use relevant indicators for heat mitigation studies
Bibliographic record
Abstract
Five years ago the short article Is the Urban Heat Island intensity relevant for heat mitigation studies? (Martilli et al, Urban Climate, 2020) was published, detailing the limitations and shortcomings of using the Urban Heat Island Intensity (UHII) as an indicator of overheating in urban areas. This is because the rural reference, used to estimate the UHII, changes in space and time, and furthermore, it does not represent thermally comfortable conditions. It was also stressed in that paper that urban areas generate unique local climate signatures, not simply perturbations added on top of rural surface climates. Consequently, the UHII is not even a measure of the maximum impact that a heat mitigation/adaptation strategy can provide. On this basis, the aims of this contribution are to: 1) critically analyze the impact of the previously mentioned article on the field, based on the more than 200 citations it has received so far, 2) define features that relevant indexes for heat mitigation strategies should include, and 3) show how they can be used to evaluate the impacts of adaptation/mitigation strategies on negative aspects of urban overheating. To illustrate the last two points, examples from modelling studies over cities in different contexts are discussed.
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 | 0.059 | 0.160 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.019 | 0.030 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".