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Spatiotemporal Patterns and Drivers of Trade-Offs and Synergy in the Beijing–Tianjin Sand Source Control Project: A Bayesian Belief Network-Based Analysis

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  • Jiahao Li

    (Institute of Ecological Protection and Restoration, Chinese Academy of Forestry, Beijing 100091, China)

  • Ming Cui

    (Institute of Ecological Protection and Restoration, Chinese Academy of Forestry, Beijing 100091, China)

  • Qi Cai

    (Institute of Ecological Protection and Restoration, Chinese Academy of Forestry, Beijing 100091, China)

  • Yuguo Liu

    (Institute of Ecological Protection and Restoration, Chinese Academy of Forestry, Beijing 100091, China)

  • Wenjing Bo

    (Institute of Ecological Protection and Restoration, Chinese Academy of Forestry, Beijing 100091, China)

Abstract

Understanding the interactions between ecosystem services is the foundation for optimizing ecosystem management and improving human well-being. However, studies on the driving mechanism of ecosystem service relationship formation in arid and semiarid climates are scarce. The Beijing–Tianjin Sand Source Control Project (BTSSCP) has been underway for more than 20 years (2001–2022), and a comprehensive scientific assessment of the effects of its implementation is important for managing ecosystems more efficiently. Taking the BTSSCP region as a study area, four ecosystem services (water conservation (WC), soil conservation (SC), wind erosion control (WEC), and net primary productivity (NPP)) were quantified and mapped in 2000, 2010, and 2020. In this study, a Bayesian belief network (BBN) model was used to analyze ecological processes and determine the relationship between the potential influencing factors and ecosystem services. A sensitivity analysis identified the key factors affecting ecosystem service supply on the basis of a Bayesian belief network simulation. The results showed an increasing trend for four ecosystem services over the past 20 years. Regarding spatial distribution, WC, SC, and NPP exhibited an overall “high in the east and low in the west” pattern, while the spatial distribution of WEC was more dispersed. The intensity of the trade-offs among WC, SC, and NPP has increased, while that of the trade-offs between the rest of the variables has decreased in the BTSSCP over the past 20 years. The results of the Bayesian network modeling indicated that precipitation, NDVI, land use, and temperature were the major variables influencing the strength of ecosystem service trade-offs. The conditional probabilities of the key variables in different states showed that the Sunit Left Banner, Sunit Right Banner, and other areas of control of the desertification of arid grassland had a high probability of trade-offs in WC_SC and SC_NPP. However, the probability of a trade-off between WEC and NPP was higher in the southeastern part of the Yanshan Hills Mountain Water Source Reserve than in the other regions; thus, it should be prioritized as an area for ecological restoration in future planning. This paper provides a scientific reference for the effective protection of ecosystems and the formulation of sustainable policies.

Suggested Citation

  • Jiahao Li & Ming Cui & Qi Cai & Yuguo Liu & Wenjing Bo, 2024. "Spatiotemporal Patterns and Drivers of Trade-Offs and Synergy in the Beijing–Tianjin Sand Source Control Project: A Bayesian Belief Network-Based Analysis," Sustainability, MDPI, vol. 16(4), pages 1-19, February.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:4:p:1617-:d:1339292
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    1. Costanza, Robert & de Groot, Rudolf & Braat, Leon & Kubiszewski, Ida & Fioramonti, Lorenzo & Sutton, Paul & Farber, Steve & Grasso, Monica, 2017. "Twenty years of ecosystem services: How far have we come and how far do we still need to go?," Ecosystem Services, Elsevier, vol. 28(PA), pages 1-16.
    2. Costanza, Robert & d'Arge, Ralph & de Groot, Rudolf & Farber, Stephen & Grasso, Monica & Hannon, Bruce & Limburg, Karin & Naeem, Shahid & O'Neill, Robert V. & Paruelo, Jose, 1998. "The value of ecosystem services: putting the issues in perspective," Ecological Economics, Elsevier, vol. 25(1), pages 67-72, April.
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