Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
July 29, 2026Ecological IndicatorsOpen Access

An interpretable–causal learning framework for identifying the drivers of surface urban Heat Island intensity: evidence from 527 urban clusters in China

View Full Paper
Ask AI
Bookmark
Share

Authors

YLYuning LiuJWJun WuSZShengbei Zhou

Discussion

Loading...

Member takes

Overview

Randomized trial identifies drivers of surface urban heat island intensity in urban clusters, suggesting urban planning improvements.

Key Points

  • This study aims to identify the drivers of surface urban heat island intensity (SUHII) and their spatial variability in urban areas.
  • Utilized Google Earth Engine platform for data collection across 527 urban clusters in China.
  • Integrated LightGBM-SHAP with Double Machine Learning for causal inference and correlation analysis.
  • Assessed the effects of various urban morphology factors on SUHII under different climatic conditions.
  • Higher summer daytime SUHII observed in eastern and southern China compared to western and northern regions.
  • Impervious surface fraction (ATE = 0.374) and largest patch index (ATE = 0.253) were identified as strong predictors of SUHII.
  • Water body proportion exhibited a negative effect on SUHII (ATE = -0.240), while other factors showed context-dependent effects.

Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/6a69a2aec8da07d9defa661ehttps://doi.org/10.1016/j.ecolind.2026.115232
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Spatial Patterns and Drivers of Surface Urban Heat Islands in East China: A County‐Level Study Based on the RH‐ SHAP Model2026
  2. 2Spatial and Data-Driven Approaches for Mitigating Urban Heat in Coastal Cities2025
  3. 3Unveiling spatiotemporal dynamics and nonlinear response patterns of the urban heat island: insights from interpretable machine learning2026
  4. 4Study of urban heat island regulation as a configuration problem2026
  5. 5Disentangling Climatic and Surface-Physical Drivers of the Urban Heat Island Using Explainable AI Across U.S. Cities2026