Seasonal Regulation Mechanisms of Urban Parks on Land Surface Temperature: A Case Study of the Built-up Area in Xi’an City
Bibliographic record
Abstract
As a consequence of the dual challenges of global warming and increasingly frequent extreme heatwaves, the Urban Heat Island (UHI) effect has become a major threat to urban ecological environments and the quality of life in densely built-up areas. Heat risk (HR) poses significant challenges to public health and urban resilience. As nature-based solutions, urban parks play an important role in mitigating HR and enhancing urban adaptability. This study examines 45 urban parks of various types within Xi’an’s built-up area, integrating multi-source remote sensing data and machine learning approaches to evaluate their regulatory effects on land surface temperature (LST), capacity to alleviate summer HR, and the underlying mechanisms driving these effects. The results reveal that: (1) The cooling effects of urban parks in Xi’an exhibit marked seasonal variation, with the greatest cooling intensity and spatial extent of influence (typically within a 100–200 m radius) occurring in summer; (2) Ecological and comprehensive parks, characterized by abundant vegetation and integrated water features, exhibit year-round regulatory capacity, whereas community, recreational, and cultural heritage parks, predominantly composed of deciduous vegetation, demonstrate limited cooling effects during winter; and (3) The Normalized Difference Water Index (NDWI) exerts a greater cooling influence in high-LST areas than the Normalized Difference Vegetation Index (NDVI), highlighting the synergistic role of water and vegetation in enhancing park cooling efficacy. This study highlights the essential function of urban green spaces in sustainable urban development and offers scientific evidence and practical guidance for improving urban planning and optimizing the configuration of green and water elements.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".