Evolution, reconfiguration and low-carbon performance of green space pattern under diverse urban development scenarios: A machine learning-based simulation approach

Abstract

Ecological restoration, green space morphology, and carbon emissions are intricately interconnected. Previous research utilizing case studies and econometric modeling has demonstrated that effective ecological restoration and well-structured green space configurations can significantly enhance carbon sequestration, reduce carbon emissions, and mitigate the adverse effects of climate change during urbanization. However, as urban land-use patterns become increasingly complex and development trajectories more diverse, the relationship between green space morphology and carbon dynamics (emissions and sequestration) reveals notable heterogeneity and non-linear characteristics. Understanding these complex, non-linear relationships and the underlying mechanisms is both theoretically and technically innovative, with profound implications for urban planning, policy formulation, climate change mitigation, and ecological conservation. In this study, we applied machine learning algorithms to model the non-linear relationships and threshold effects between green space evolution and carbon emissions/sequestration at different stages of ecological restoration in the Yangtze River Basin, China. Furthermore, we simulated the low-carbon performance of green spaces under various urban development scenarios. The key findings include: (1) Wetlands, particularly those composed of shallow water bodies and land–water interfaces such as marshes and mangroves, are critical determinants of the low-carbon performance of green spaces, with their effects on carbon emissions and sequestration exhibiting significant temporal dynamics throughout different stages of ecological restoration. (2) The fragmentation and shape complexity of green spaces substantially influence carbon efficiency. Preserving the connectivity and integrity of green spaces, particularly in large-scale forests and wetlands, while minimizing fragmentation and reducing shape complexity, can enhance carbon sequestration capacity. (3) Carbon performance varies markedly across different urban development trajectories. In cities prioritizing ecological restoration, future carbon emissions tend to stabilize, whereas in rapidly growing megacities, despite recent declines in carbon emissions, further optimization of green space configurations is required to effectively manage emissions and bolster sequestration. In summary, this study provides robust scientific evidence for ecological restoration and green space planning in the Yangtze River Basin, offering valuable insights for carbon management and ecological restoration in similar regions globally. These findings contribute to global environmental governance efforts and the ongoing fight against climate change.

Keywords

• Green space pattern
• Low-carbon performance
• Urban development scenarios