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

被引:0
作者
Khan, Salim Ullah [1 ]
Zhao, Qiuhong [1 ]
Wisal, Muhammad [2 ]
Shah, Kamran Ali [3 ]
Shah, Syed Shahid [2 ]
机构
[1] Beihang Univ, Sch Econ & Management, Beijing 100191, Peoples R China
[2] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China
[3] Goldwind Sci & Technol Co Ltd, Beijing 100176, Peoples R China
基金
中国国家自然科学基金;
关键词
risk modeling; bayesian networks; data-driven decision-making; socio-environmental risk assessment; scenario analysis; ECOLOGICAL RISK; IMPACT; CONSTRUCTION; CLIMATE; DAM;
D O I
10.3390/su17083537
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:32
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