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The impact of urban morphology on multiple ecological effects: Coupling relationships and collaborative optimization strategies

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Abstract

Urban morphology significantly affects the ecological effects of urban heat islands, ventilation, and atmospheric pollution. Here, we reveal the mechanisms linking the ecological effects of urban morphology to develop a planning approach for the collaborative optimization of multiple ecological effects. Considering Shenyang, a cold city in northern China, as the study area, a multiple regression model of morphological parameters and ecological effects was established, and the impact of morphological parameters on ecological effects was explored. The results show that the aspect ratio of the streets, building density, and vegetation coverage are sensitive to multiple ecological effects. The inflection point of the ecological effect function curve occurs when the aspect ratio of the building and building density are 0.2 and 0.3, respectively. In addition, for optimal design applications in typical areas of the city, to obtain a Pareto-optimal urban morphology, Grasshopper is used to establish a parametric platform, wherein a genetic algorithm solves the multiple regression equation set. Ultimately, five ecological effect indicators are optimized and show 8.4%, 5.0%, 31.6%, 33.1%, and 12.5% improvement. The study effectively constructs a collaborative optimization planning and design method for multiple ecological effects.

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Acknowledgements

This project was financially supported by the General Program of National Natural Science Foundation of China (No. 51978421). The authors express their sincere gratitude to all members of the research team for their invaluable contributions.

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Correspondence to Tiemao Shi or Sui Li.

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The impact of urban morphology on multiple ecological effects: Coupling relationships and collaborative optimization strategies

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Zhou, S., Shi, T., Li, S. et al. The impact of urban morphology on multiple ecological effects: Coupling relationships and collaborative optimization strategies. Build. Simul. 16, 1539–1557 (2023). https://doi.org/10.1007/s12273-023-1057-6

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