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Geospatial Explainable AI Uncovers Eco-Environmental Effects and Its Driving Mechanisms—Evidence from the Poyang Lake Region, China

Author

Listed:
  • Mingfei Li

    (Architecture and Design College, Nanchang University, Nanchang 330031, China)

  • Zehong Zhu

    (Architecture and Design College, Nanchang University, Nanchang 330031, China)

  • Junye Deng

    (Architecture and Design College, Nanchang University, Nanchang 330031, China)

  • Jiaxin Zhang

    (Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering, Osaka University, 2-1 Yamadaoka, Suita 565-0871, Osaka, Japan)

  • Yunqin Li

    (Architecture and Design College, Nanchang University, Nanchang 330031, China)

Abstract

Intensified human activities and changes in land-use patterns have led to numerous eco-environmental challenges. A comprehensive understanding of the eco-environmental effects of land-use transitions and their driving mechanisms is essential for developing scientifically sound and sustainable environmental management strategies. However, existing studies often lack a comprehensive analysis of these mechanisms due to methodological limitations. This study investigates the eco-environmental effects of land-use transitions in the Poyang Lake Region over the past 30 years from the perspective of the production-living-ecological space (PLES) framework. Additionally, a geographically explainable artificial intelligence (GeoXAI) framework is introduced to further explore the mechanisms underlying these eco-environmental effects. The GeoXAI framework effectively addresses the challenges of integrating nonlinear relationships and spatial effects, which are often not adequately captured by traditional models. The results indicate that (1) the conversion of agricultural space to forest and lake spaces is the primary factor contributing to eco-environmental improvement. Conversely, the occupation of forest and lake spaces by agricultural and residential uses constitutes the main driver of eco-environmental degradation. (2) The GeoXAI demonstrated excellent performance by incorporating geographic variables to address the absence of spatial causality in traditional machine learning. (3) High-altitude and protected water areas are more sensitive to human activities. In contrast, geographic factors have a greater impact on densely populated urban areas. The results and methodology presented here can serve as a reference for eco-environmental assessment and decision-making in other areas facing similar land-use transformation challenges.

Suggested Citation

  • Mingfei Li & Zehong Zhu & Junye Deng & Jiaxin Zhang & Yunqin Li, 2025. "Geospatial Explainable AI Uncovers Eco-Environmental Effects and Its Driving Mechanisms—Evidence from the Poyang Lake Region, China," Land, MDPI, vol. 14(7), pages 1-24, June.
    • Handle: RePEc:gam:jlands:v:14:y:2025:i:7:p:1361-:d:1689198
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    References listed on IDEAS

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