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A DPSIR-Bayesian Network Approach for Tourism Ecological Security Early Warning: A Case Study of Sichuan Province, China

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Listed:
  • Xin Huang

    (Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610213, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Ting Li

    (Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610213, China)

  • Li Li

    (School of Ecological Engineering, Guizhou University of Engineering Science, Bijie 551700, China)

  • Qiurong Liu

    (Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610213, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Qing Liu

    (Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610213, China)

Abstract

As a subset of the human–environment system, the tourism ecosystem focuses on the complex dynamics and interactions between tourism activities and the natural environment. Among these, tourism ecological security (TES) is one of the core issues in the study of tourism ecosystems, aiming to balance economic development and ecological environment protection. Currently, the risk early warning of TES has not received widespread attention, and there is an urgent need for a tourism ecological safety risk early warning system to achieve TES monitoring, risk assessment, and decision support. Therefore, this study established a comprehensive TES evaluation system, systematically analyzed the evolution of TES in Sichuan Province from 2010 to 2022, and used the geographical detector to reveal the influencing factors and driving mechanisms of TES. Based on these achievements, an early risk warning system for TES was established based on the Bayesian network model, simulating the response of TES under single-variable and multi-variable scenarios. The research results reveal that TES changes with environmental changes, resource utilization and consumption, and the development of the tourism industry, and there are differences in the driving factors of TES under different conditions. There is a synergistic effect between the influencing factors of TES, and there is a threshold effect in the regulation of tourism ecological safety, revealing the efficiency and limitations of different regulatory strategies. The early risk warning model for TES based on the Bayesian network has high prediction accuracy and can provide effective support for the management and regulatory policies of TES.

Suggested Citation

  • Xin Huang & Ting Li & Li Li & Qiurong Liu & Qing Liu, 2025. "A DPSIR-Bayesian Network Approach for Tourism Ecological Security Early Warning: A Case Study of Sichuan Province, China," Sustainability, MDPI, vol. 17(4), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:4:p:1555-:d:1590614
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    References listed on IDEAS

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    1. Xia Xie & Hui Sun & Jun Gao & Feifei Chen & Chunshan Zhou, 2021. "Spatiotemporal Differentiation of Coupling and Coordination Relationship of Tourism–Urbanization–Ecological Environment System in China’s Major Tourist Cities," Sustainability, MDPI, vol. 13(11), pages 1-17, May.
    2. Dario Bertocchi & Nicola Camatti & Silvio Giove & Jan van der Borg, 2020. "Venice and Overtourism: Simulating Sustainable Development Scenarios through a Tourism Carrying Capacity Model," Sustainability, MDPI, vol. 12(2), pages 1-15, January.
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