Beating urban heat: Multimeasure-centric solution sets and a complementary framework for decision-making
Bibliographic record
Abstract
Urban areas are experiencing excessive heating. Addressing the heat is a challenging but essential task where not only engineering and climatic knowledge matters but also a deep understanding of social and economic dimensions. We synthesize the state of the art in heat mitigation technologies and develop an ‘ITE index’ framework that evaluates the investment (I), time for implementation (T), and effectiveness (E) of candidate heat mitigation measures. Using this framework, we assess 247 multimeasure-centric solution sets composed of all possible combinations of 8 individual measures. The multidimensional ITE index is quantified for heat mitigation effectiveness based on different urban scales, investment levels, the impact of local climate zones (LCZs), and professionals' perceptions using the analytical hierarchy process. The top 50 unique solution sets consist of 4–7 individual measures across all LCZs, with the use of thermally efficient buildings and high-efficiency indoor cooling being the two recurrent measures contributing to the best solution sets. While every city varies in terms of its ideal solution sets, we provide a multimeasure-centric framework for decision-making in which different dimensions can be integrated, understood, and quantified.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".