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A Data-Driven Bayesian Belief Network Influence Diagram Approach for Socio-Environmental Risk Assessment and Mitigation in Major Ecosystem- and Landscape-Modifier Projects

Author

Listed:
  • Salim Ullah Khan

    (School of Economics & Management, Beihang University, Beijing 100191, China)

  • Qiuhong Zhao

    (School of Economics & Management, Beihang University, Beijing 100191, China)

  • Muhammad Wisal

    (School of Electronics & Information Engineering, Beihang University, Beijing 100191, China)

  • Kamran Ali Shah

    (Goldwind Science & Technology Co., Ltd., Beijing 100176, China)

  • Syed Shahid Shah

    (School of Electronics & Information Engineering, Beihang University, Beijing 100191, China)

Abstract

Infrastructure projects that transform ecosystems and landscapes, such as hydropower developments, are essential for economic growth but pose significant socio-environmental challenges. Addressing these complexities requires advanced, dynamic management strategies. This study presents the Bayesian integrated risk mitigation model (BIRMM), a novel probabilistic framework designed to augment traditional environmental impact assessments. BIRMM enables comprehensive risk evaluation, scenario-based analysis, and mitigation planning, empowering stakeholders to make informed decisions throughout project lifecycles. BIRMM integrates socio-environmental and economic risks using a three-dimensional risk assessment approach grounded in a Bayesian belief network influence diagram. It provides a holistic view of risk interactions by capturing interdependencies across spatial, temporal, and magnitude dimensions. Through simulation of risk dynamics and adaptive evaluation of mitigation strategies, BIRMM offers actionable insights for resource allocation, enhancing project resilience, and minimizing socio-environmental disruptions. The framework was validated using the Balakot Hydropower Project in Pakistan. BIRMM successfully simulated proposed risks and assessed mitigation strategies under varying scenarios, demonstrating its reliability in navigating complex socio-environmental challenges. The case study highlighted its potential to support adaptive decision-making across all project phases. With its versatility and practical ease, BIRMM is particularly suited for large-scale energy, transportation, and urban development projects. By bridging gaps in traditional methodologies, BIRMM advances sustainable development practices, promotes equitable stakeholder outcomes, and establishes itself as an indispensable decision-support tool for modern infrastructure projects.

Suggested Citation

  • Salim Ullah Khan & Qiuhong Zhao & Muhammad Wisal & Kamran Ali Shah & Syed Shahid Shah, 2025. "A Data-Driven Bayesian Belief Network Influence Diagram Approach for Socio-Environmental Risk Assessment and Mitigation in Major Ecosystem- and Landscape-Modifier Projects," Sustainability, MDPI, vol. 17(8), pages 1-32, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:8:p:3537-:d:1635109
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    References listed on IDEAS

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    1. Lenio Prado & Marcelo Fonseca & José V. Bernardes & Mateus G. Santos & Edson C. Bortoni & Guilherme S. Bastos, 2023. "Forecast of Operational Downtime of the Generating Units for Sediment Cleaning in the Water Intakes: A Case of the Jirau Hydropower Plant," Energies, MDPI, vol. 16(17), pages 1-20, September.
    2. Al-Rashdan, Dina & Al-Kloub, Bashar & Dean, Angela & Al-Shemmeri, Tarik, 1999. "Environmental impact assessment and ranking the environmental projects in Jordan," European Journal of Operational Research, Elsevier, vol. 118(1), pages 30-45, October.
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