Impact of Urban Agglomeration and Physical and Socioeconomic Factors on Surface Urban Heat Islands in the Pearl River Delta Region, China
Bibliographic record
Abstract
An understanding of the driving factors of urban heat islands could improve the urban thermal environment and provide planning strategies for sustainable urban/regional development. We selected Guangdong province in China as a case area, which covers 21 cities and more than 110 million people. We used multitemporal remote sensing data from multiple sources and socioeconomic statistical data to analyze the effects of various drivers of surface urban heat islands (SUHIs). The tested drivers include urban agglomeration, physical indicators (e.g., vegetation and built-up indices), and socioeconomic indicators (e.g., population, gross domestic production, and nightlight intensity). The results show that physical indicators are determinants of daytime SUHIs whereas socioeconomic indicators are determinants of nighttime SUHIs, which indicates that daytime and nighttime SUHIs have different causal mechanisms in this region. Moreover, the results reveal that the influence of urban agglomeration on urban surface temperature is more significant at nighttime than in daytime, which implies that the warming effect of urban agglomeration is stronger at night. Together, the results indicate that joint control of urban size, urbanization level, and socioeconomic activities is crucial to alleviate SUHIs and safeguard sustainable development in this region.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".