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April 15, 2026Land0 citationsOpen Access

Improving the Urban Thermal Environment in Chengdu: A Multi-Objective Land-Use Optimization Framework Integrating Remote Sensing, Numerical Simulation, and NSGA-II

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JRJinqiao RenYCYanxin CaiMPMingshuo Pan

Key Points

  • The aim is to understand how Chengdu's urban structure impacts its thermal environment and to devise planning strategies for improvement.
  • Tracked spatial structure changes using Google Earth Engine across key districts in 2017, 2021, and 2025.
  • Employed Local Climate Zone scheme to analyze the relationship between urban forms and climate.
  • Combined Weather Research and Forecasting model with NSGA-II algorithm for optimization scenarios.
  • Confirmed a strong, non-linear relationship between built form and local climate in urban areas.
  • Optimized scenarios could reduce peak temperatures by 1.72–2.75 °C in the urban core.
  • Recommendations include limiting compact high-risk types and promoting natural surfaces to achieve thermal equity.

Abstract

This study examines how the city’s evolving spatial structure shapes its thermal environment. Using Google Earth Engine (GEE) and the Local Climate Zone (LCZ) scheme, we tracked structural changes across Chengdu and its central districts (Jinjiang and Wuhou) in 2017, 2021, and 2025. We then combined the Weather Research and Forecasting (WRF) model with the NSGA-II algorithm. This allowed us to explore links between LCZ patterns and the Universal Thermal Climate Index (UTCI) in the urban center. Results confirm a strong but non-linear relationship between built form and the local climate. Optimized scenarios, respecting practical planning constraints, show that rebalancing LCZ proportions can reduce peak temperatures in the core area by 1.72–2.75 °C. Future plans for Chengdu should therefore limit high-risk compact types (LCZ 1, 3, 8), expand mid-rise and open arrangements (LCZ 4, 5), and preserve or restore natural surfaces (LCZ A–C) to achieve a more thermally equitable urban landscape.

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Cite This Study

Ren et al. (2026) studied this question.

synapsesocial.com/papers/69df2bcae4eeef8a2a6b0c48https://doi.org/10.3390/land15040630
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