Energy Infrastructure and Geohazard Risk in China's Urban Expansion: A Hybrid <scp>MCDA</scp> Framework Integrating Bayesian Networks for Sustainable Resource Management and Climate Adaptation

ABSTRACT This study proposes a comprehensive multi‐criteria decision analysis (MCDA) framework designed to prioritise sustainability criteria for urban development in China, addressing key environmental, economic, social, governance, technological and resilience‐related challenges. The approach combines expert knowledge with advanced analytical techniques, including the Delphi method to refine criteria, probability distribution modelling to quantify expert judgements, hierarchical Bayesian networks to capture interdependencies, and the best‐worst method (BWM) to determine the relative importance of criteria and sub‐criteria. The results indicate that environmental sustainability and governance are the most critical dimensions, with carbon emissions reduction, renewable energy integration, and long‐term planning identified as the most influential sub‐criteria. The probabilistic analysis revealed strong consensus among experts regarding environmental priorities, whereas opinions on technological integration were more varied. Furthermore, the Bayesian network analysis highlighted significant interconnections among criteria, particularly emphasising the central role of governance in facilitating sustainability outcomes. These findings are consistent with China's national objectives, including its commitment to carbon neutrality by 2060, and contribute to the broader global discourse on sustainable urbanisation. The study offers practical insights for policymakers and urban planners by providing a robust, adaptable, and replicable framework that can be applied to other developing contexts. By integrating expert‐driven approaches with probabilistic and decision‐making models, the research advances understanding of urban sustainability and supports the development of informed, balanced and strategic policy decisions.