Thermal environment control units for multi-objective urban optimization in territorial spatial planning
Bibliographic record
Abstract
Urban heat island phenomenon significantly impacts urban thermal conditions, human well-being, and energy consumption, necessitating innovative urban thermal environment management approaches. This study introduces thermal environment control units (TECUs) to optimize urban thermal environments in territorial spatial planning, addressing the gap between current assessment tools and actionable, data-driven planning solutions. By leveraging the urban weather generator (UWG) model, we developed an optimization framework, dividing the central urban area of Guangzhou into 234 TECUs. The methodology involves UWG-based simulations and multi-objective parametric optimization. Analysis revealed significant spatial heterogeneity in thermal performance indicators: urban heat island intensity (0.87 °C-2.42 °C), universal thermal climate index (17.1 °C-27.0 °C), and cooling energy demand (5.32–28.76 W/m²). A case study optimization of a representative TECU demonstrated simultaneous improvements in all indicators through parameter tuning, including building height, density, and green coverage. The TECU approach bridges micro-scale thermal phenomena and macro-scale urban planning, providing a quantitative basis for incorporating thermal performance into the planning system. This methodology offers a pathway toward climate-resilient and thermally comfortable urban environments, addressing the challenges of rapid urbanization and climate change. The findings have significant implications for urban policy, design practices, and future research in creating sustainable and livable cities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".