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<p>Urbanizing mountainous regions often exhibit strong spatial mismatches between ecosystem service (ES) supply and demand, yet the internal coordination mechanisms among services remain poorly understood. This study uses existing remote sensing and socioeconomic datasets, combined with an ecosystem service supply–demand ratio (ESDR), self-organizing map (SOM) clustering, and network analysis, to examine ES supply–demand patterns and interactions in Chongqing, China, from 2000 to 2020. The results reveal a pronounced spatial polarization of ES supply–demand bundles along the urban–mountain gradient, with urban-deficit bundles concentrated in the central basin and ecological-surplus bundles dominating mountainous regions. ESDR values in urban areas are consistently negative (ESDR &lt; 0), whereas mountainous regions exhibit relatively high values, particularly for regulating services. Network analysis shows that positive correlations (<i>r</i> &gt; 0.3) account for the majority of significant relationships, indicating widespread synergistic interactions among ecosystem services. In contrast, urban bundles display fragmented network structures with fewer significant links, while ecological-surplus bundles exhibit denser and more connected networks. Across bundles, regulating services—particularly carbon storage and soil conservation—consistently exhibit higher centrality (e.g., hub scores), suggesting their important roles in maintaining network connectivity and multifunctionality. These patterns are associated with land use structure and shared ecological drivers, such as vegetation cover and soil conditions. These findings highlight the need for spatially differentiated governance strategies that simultaneously address urban demand pressure, conserve ecological supply zones, and enhance multifunctionality in agricultural systems. It should be noted that the results are subject to uncertainties related to model-based ES estimation, proxy indicators, and correlation-based network analysis, which reflect statistical associations rather than causal relationships.</p>

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From supply–demand imbalance to functional synergy: ecosystem service bundles and network structures in a mountainous megacity, China

  • 期刊论文

  • 发表日期：

    2026年7月14日

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  • Dawen Qian,
  • Hongyi Guo,
  • Limin Tan,
  • Zechang Chen,
  • Yunlong Zhang,
  • Chen Ge

摘要

Urbanizing mountainous regions often exhibit strong spatial mismatches between ecosystem service (ES) supply and demand, yet the internal coordination mechanisms among services remain poorly understood. This study uses existing remote sensing and socioeconomic datasets, combined with an ecosystem service supply–demand ratio (ESDR), self-organizing map (SOM) clustering, and network analysis, to examine ES supply–demand patterns and interactions in Chongqing, China, from 2000 to 2020. The results reveal a pronounced spatial polarization of ES supply–demand bundles along the urban–mountain gradient, with urban-deficit bundles concentrated in the central basin and ecological-surplus bundles dominating mountainous regions. ESDR values in urban areas are consistently negative (ESDR < 0), whereas mountainous regions exhibit relatively high values, particularly for regulating services. Network analysis shows that positive correlations (r > 0.3) account for the majority of significant relationships, indicating widespread synergistic interactions among ecosystem services. In contrast, urban bundles display fragmented network structures with fewer significant links, while ecological-surplus bundles exhibit denser and more connected networks. Across bundles, regulating services—particularly carbon storage and soil conservation—consistently exhibit higher centrality (e.g., hub scores), suggesting their important roles in maintaining network connectivity and multifunctionality. These patterns are associated with land use structure and shared ecological drivers, such as vegetation cover and soil conditions. These findings highlight the need for spatially differentiated governance strategies that simultaneously address urban demand pressure, conserve ecological supply zones, and enhance multifunctionality in agricultural systems. It should be noted that the results are subject to uncertainties related to model-based ES estimation, proxy indicators, and correlation-based network analysis, which reflect statistical associations rather than causal relationships.