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Spatial-Temporal Variation and Tradeoffs/Synergies Analysis on Multiple Ecosystem Services: A Case Study in Fujian

Author

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  • Min Li

    (College of Environment & Safety Engineering, Fuzhou University, Fuzhou 350108, China)

  • Peng Zheng

    (College of Environment & Safety Engineering, Fuzhou University, Fuzhou 350108, China)

  • Wenbin Pan

    (College of Environment & Safety Engineering, Fuzhou University, Fuzhou 350108, China)

Abstract

The rapid urbanization process has severely affected the ecological security and ecosystem services (ESs) in China’s southern province of Fujian, and threated the sustainable development of the local economy and society in the last two decades. This study mapped the spatial-temporal variation of four types of ESs and evaluated the tradeoffs/synergies among the ESs in Fujian Province from 2000 to 2020 on the three scales of the province, city and county. The results showed that: (1) From 2000 to 2020, the three ESs functions of soil conservation service, carbon storage service, and habitat quality decreased, while the function of food supply service increased. (2) At the provincial scale, soil conservation services, carbon storage services, and habitat quality were in a synergistic relationship, and food supply service was always in tradeoffs relationships with soil conservation services, carbon storage services, and habitat quality. At the prefecture-level scale, the tradeoffs/synergies among ESs vary among prefecture-level cities. At the county scale, the Moran’s I index of bivariate spatial autocorrelation was consistent with the correlation coefficient, and the tradeoffs/synergies between ESs showed significant spatial heterogeneity. (3) Based on the correlation analysis method, the Ecosystem Service Tradeoff-synergy Degree (ESTD) model and bivariate spatial autocorrelation analysis can clearly show the relationship between various ESs, and the research results are relatively consistent. The spatial distribution of four ecosystem services in Fujian province showed a specific aggregation pattern. High—high concentrations and low—low concentrations are strong. Low—high agglomeration and high—low agglomeration are weak.

Suggested Citation

  • Min Li & Peng Zheng & Wenbin Pan, 2022. "Spatial-Temporal Variation and Tradeoffs/Synergies Analysis on Multiple Ecosystem Services: A Case Study in Fujian," Sustainability, MDPI, vol. 14(5), pages 1-25, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:3086-:d:765787
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    References listed on IDEAS

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    Cited by:

    1. Suiping Zeng & Xinyao Liu & Jian Tian & Jian Zeng, 2024. "Spatial–Temporal Pattern Analysis and Development Forecasting of Carbon Stock Based on Land Use Change Simulation: A Case Study of the Xiamen–Zhangzhou–Quanzhou Urban Agglomeration, China," Land, MDPI, vol. 13(4), pages 1-26, April.
    2. Li Ming & Jiang Chang & Cheng Li & Yedong Chen & Cankun Li, 2022. "Spatial-Temporal Patterns of Ecosystem Services Supply-Demand and Influencing Factors: A Case Study of Resource-Based Cities in the Yellow River Basin, China," IJERPH, MDPI, vol. 19(23), pages 1-22, December.
    3. Peng Zheng & Lanting Jin & Yuxiao Huang & Wenbin Pan, 2024. "Spatial and Temporal Dynamic Evolution and Correlation of Ecological Quality and Ecosystem Service Value in Fujian Province," Sustainability, MDPI, vol. 16(12), pages 1-19, June.

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