Yao Song, Li Yonghua, Quan Xiangyang, Huang Guoping, Xu Jiren
Department of Regional and Urban Planning, Zhejiang University, Hangzhou, 310058, China; Center for Balance Architecture, Zhejiang University, Hangzhou, 310028, China; School of Social and Environmental Sustainability, University of Glasgow, Dumfries, DG1 4ZL, UK.
Department of Regional and Urban Planning, Zhejiang University, Hangzhou, 310058, China; The Architectural Design and Research Institute of Zhejiang University Co., Ltd., Hangzhou, 310028, China.
J Environ Manage. 2025 Aug 25;393:127028. doi: 10.1016/j.jenvman.2025.127028.
Understanding the interactions among ecosystem services (ESs) is essential for effective ecological management. However, comprehensive investigations into the scale effects, socio-ecological drivers, and bundles of these interactions remain limited, thereby constraining our ability to apply trade-offs and synergies (TOSs) knowledge in ecological management. This study introduced a framework for exploring TOSs among ESs and tested it in the Yangtze River Delta Urban Agglomeration (YRDUA). First, the Integrated Valuation of Ecosystem Services model was employed to map four typical ESs: carbon sequestration (CS), water yield (WY), soil conservation (SC), and habitat quality (HQ). Second, the Pearson correlation coefficient and the geographically weighted regression model were utilized to analyze the relationships between ESs. The optimal parameters-based geographical detector model was then employed to identify socio-ecological drivers, and the self-organizing map was used to delineate the TOS bundles. Finally, ecological management strategies were formulated at both county and city scales. The results showed that: (1) The four ESs demonstrated spatial clustering at both county and city scales, with stable spatial patterns, although their quantities varied over time. From 2000 to 2020, the total values of CS and HQ declined by 1.19 % and 5.14 %, respectively, while WY and SC increased by 23.97 % and 41.58 %, respectively. (2) All ES pairs presented synergies, though their spatial patterns varied according to changes in spatial scale. The synergistic strengths of CS-WY, WY-SC, and WY-HQ decreased from 2000 to 2020, while those of CS-SC, CS-HQ, and SC-HQ increased. (3) Natural drivers exhibited stronger explanatory power than land use and socio-economic drivers, with precipitation being the most influential driver. Precipitation accounted for 38.89 % of the dominant drivers for ES interactions at the county scale and 44.44 % at the city scale. (4) Four TOS bundles were identified at the county scale and three at the city scale. (5) Knowledge of TOSs aids in ecological management decisions, particularly in zoning and strategic planning. These findings can optimize ecological management to minimize trade-offs and maximize the synergies among ESs in the YRDUA and other similar regions worldwide.
了解生态系统服务（ESs）之间的相互作用对于有效的生态管理至关重要。然而，对这些相互作用的规模效应、社会生态驱动因素和组合的全面调查仍然有限，从而限制了我们在生态管理中应用权衡与协同（TOSs）知识的能力。本研究引入了一个探索生态系统服务之间权衡与协同的框架，并在长江三角洲城市群（YRDUA）进行了测试。首先，采用生态系统服务综合价值评估模型绘制了四种典型的生态系统服务：碳固存（CS）、产水量（WY）、土壤保持（SC）和栖息地质量（HQ）。其次，利用皮尔逊相关系数和地理加权回归模型分析生态系统服务之间的关系。然后采用基于最优参数的地理探测器模型识别社会生态驱动因素，并使用自组织映射来划分权衡与协同组合。最后，在县和市尺度上制定了生态管理策略。结果表明：（1）四种生态系统服务在县和市尺度上均呈现空间集聚，空间格局稳定，但其数量随时间变化。2000年至2020年，碳固存和栖息地质量的总值分别下降了1.19%和5.14%，而产水量和土壤保持分别增加了23.97%和41.58%。（2）所有生态系统服务对均呈现协同效应，但其空间格局随空间尺度的变化而变化。2000年至2020年，碳固存-产水量、产水量-土壤保持和产水量-栖息地质量的协同强度下降，而碳固存-土壤保持、碳固存-栖息地质量和土壤保持-栖息地质量的协同强度增加。（3）自然驱动因素的解释力强于土地利用和社会经济驱动因素，降水是最具影响力的驱动因素。降水在县尺度上占生态系统服务相互作用主导驱动因素的38.89%，在市尺度上占44.44%。（4）在县尺度上识别出四个权衡与协同组合，在市尺度上识别出三个。（5）权衡与协同知识有助于生态管理决策，特别是在分区和战略规划方面。这些发现可以优化生态管理，以尽量减少权衡，并最大限度地提高长江三角洲城市群及全球其他类似地区生态系统服务之间的协同效应。
