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Spatiotemporal Dynamics of Ecosystem Services and Their Trade-Offs and Synergies in Response to Natural and Social Factors: Evidence from Yibin, Upper Yangtze River

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  • Chaojie Tian

    (Institute of Geography and Resources Science, Sichuan Normal University, Chengdu 610101, China
    Sustainable Development Research Center of Resource and Environment of Western Sichuan, Sichuan Normal University, Chengdu 610101, China)

  • Liheng Pang

    (Institute of Geography and Resources Science, Sichuan Normal University, Chengdu 610101, China
    Sustainable Development Research Center of Resource and Environment of Western Sichuan, Sichuan Normal University, Chengdu 610101, China)

  • Quanzhi Yuan

    (Institute of Geography and Resources Science, Sichuan Normal University, Chengdu 610101, China
    Sustainable Development Research Center of Resource and Environment of Western Sichuan, Sichuan Normal University, Chengdu 610101, China)

  • Wei Deng

    (Institute of Geography and Resources Science, Sichuan Normal University, Chengdu 610101, China
    Sustainable Development Research Center of Resource and Environment of Western Sichuan, Sichuan Normal University, Chengdu 610101, China)

  • Ping Ren

    (Institute of Geography and Resources Science, Sichuan Normal University, Chengdu 610101, China
    Key Lab of Land Resources Evaluation and Monitoring in Southwest, Ministry of Education, Sichuan Normal University, Chengdu 610066, China)

Abstract

During the rapid urbanization phase, the trade-off between ecosystem services is the most severe and also the most effective stage to implement ecological management. Exploring the natural—social driving mechanisms for trade-offs contributes to the coordinated development of the social economy and nature. Taking the typical mountainous city (Yibin) that is currently in the rapid urbanization phase and ecologically fragile as an example, utilizing a combination of difference comparison, trade-off–synergy index (TSI), optimal-parameter-based geographical detector model (OPGD), and multi-scale geographically weighted regression (MGWR), we spatially assess the nature and intensity of ES relationships and explore its social–natural driving mechanisms. Our findings reveal the following: (1) Varied geospatial patterns of four ESs—habitat quality (HQ), carbon storage (CS), soil conservation (SC), and water yield (WY)—with the greatest fluctuations in WY. (2) Significant changes in the nature and intensity of ES relationships over time, showing predominant positive synergies between WY-HQ, WY-SC, and HQ-CS, and negative synergies between HQ and SC, and trade-offs between WY-CS and SC-CS. (3) Distinct, time-varying driving factors for different ES relationships: climate and topography for WY, vegetation and topography for CS, topography and economic factors for HQ, and climate and topography for SC. Rapid urbanization has diminished the role of natural factors. (4) The regression coefficients reveal the local mechanisms of various driving factors, based on which targeted recommendations can be proposed. For instance, the establishment of interconnected small wetlands and green spaces in urban areas contributes to the enhancement of multiple ESs. The purpose of this study is to provide scientific insights into the driving mechanisms and optimizations of the key ecosystem services’ relationships in areas that are currently undergoing rapid urbanization.

Suggested Citation

  • Chaojie Tian & Liheng Pang & Quanzhi Yuan & Wei Deng & Ping Ren, 2024. "Spatiotemporal Dynamics of Ecosystem Services and Their Trade-Offs and Synergies in Response to Natural and Social Factors: Evidence from Yibin, Upper Yangtze River," Land, MDPI, vol. 13(7), pages 1-33, July.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:7:p:1009-:d:1430553
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    References listed on IDEAS

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    1. He, Juan & Shi, Xueyi & Fu, Yangjun & Yuan, Ye, 2020. "Evaluation and simulation of the impact of land use change on ecosystem services trade-offs in ecological restoration areas, China," Land Use Policy, Elsevier, vol. 99(C).
    2. A. Stewart Fotheringham & Wenbai Yang & Wei Kang, 2017. "Multiscale Geographically Weighted Regression (MGWR)," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 107(6), pages 1247-1265, November.
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