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

  • Salim Ullah Khan
  • , Qiuhong Zhao*
  • , Muhammad Wisal
  • , Kamran Ali Shah
  • , Syed Shahid Shah
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number3537
JournalSustainability (Switzerland)
Volume17
Issue number8
DOIs
StatePublished - Apr 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy
  2. SDG 8 - Decent Work and Economic Growth
    SDG 8 Decent Work and Economic Growth
  3. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • bayesian networks
  • data-driven decision-making
  • risk modeling
  • scenario analysis
  • socio-environmental risk assessment

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