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Analysis of Spatial and Temporal Variations in Ecosystem Service Functions and Drivers in Anxi County Based on the InVEST Model

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  • Wen Li

    (Anxi College of Tea Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China
    Multifunctional Agricultural Application Research Institute, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Jianwei Geng

    (College of Landscape Architecture and Art, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Jingling Bao

    (Multifunctional Agricultural Application Research Institute, Fujian Agriculture and Forestry University, Fuzhou 350002, China
    College of Rural Revitalization, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Wenxiong Lin

    (College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Zeyan Wu

    (College of Life Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

  • Shuisheng Fan

    (Multifunctional Agricultural Application Research Institute, Fujian Agriculture and Forestry University, Fuzhou 350002, China
    College of Rural Revitalization, Fujian Agriculture and Forestry University, Fuzhou 350002, China)

Abstract

Background: Exploring the spatial and temporal evolution patterns of regional ecosystem service functions and their main drivers can provide effective support for formulating regional ecological conservation policies and coordinating sustainable economic–ecological development. Methods: This study quantifies the service functions of the water production, soil conservation, carbon storage, habitat quality, and net primary productivity (NPP) in the study area based on the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model and the Carnegie–Ames–Stanford Approach (CASA) model and constructs a comprehensive index for ecosystem services (CES) based on the analytic hierarchy process (AHP) to reflect the total supply of various ecosystem services spatially and explore the main driving mechanisms of their spatial variation. The main driving mechanisms of the spatial variation were investigated. Results: (1) Water production in the study area from 2010 to 2020 showed a trend of increasing before decreasing, soil retention showed a trend of continuously decreasing, carbon storage and biotope quality showed a trend of decreasing before increasing, and the NPP showed a trend of continuously increasing. (2) The mean CES of the study area from 2010 to 2020 (0.5398, 0.5763, 0.5456) showed a trend of increasing before decreasing. The improvement areas were mainly concentrated in the western, southwestern, and northeastern parts of the study area, and the degraded areas were mainly distributed in the southeast and northwest. (3) The fit of the geographically weighted regression (GWR) was higher than that of the ordinary least squares (OLS) in all the periods, and the main driving factors affecting the spatial variation in the CES were the NDVI and tea plantation area (T-Area). Conclusion: This study constructed the CES model, explored the regional CES spatiotemporal evolution pattern and its main driving mechanism, and provided a reference basis for promoting the high-quality development of specialized tea regions.

Suggested Citation

  • Wen Li & Jianwei Geng & Jingling Bao & Wenxiong Lin & Zeyan Wu & Shuisheng Fan, 2023. "Analysis of Spatial and Temporal Variations in Ecosystem Service Functions and Drivers in Anxi County Based on the InVEST Model," Sustainability, MDPI, vol. 15(13), pages 1-16, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10153-:d:1179987
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    References listed on IDEAS

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    Cited by:

    1. Kangwen Zhu & Jun He & Xiaosong Tian & Peng Hou & Longjiang Wu & Dongjie Guan & Tianyu Wang & Sheng Huang, 2024. "Analysis of Evolving Carbon Stock Trends and Influencing Factors in Chongqing under Future Scenarios," Land, MDPI, vol. 13(4), pages 1-16, March.
    2. Yanfei Kou & Sanming Chen & Kefa Zhou & Ziyun Qiu & Jiaming He & Xian Shi & Xiaozhen Zhou & Qing Zhang, 2024. "Spatiotemporal Patterns and Coupling Coordination Analysis of Multiscale Social–Economic–Ecological Effects in Ecologically Vulnerable Areas Based on Multi-Source Data: A Case Study of the Tuha Region," Land, MDPI, vol. 13(3), pages 1-30, February.

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