Sustainable risk and disaster management in the built environment has become a critical research focus amid escalating environmental challenges. Building Information Modeling (BIM) is recognized as a key digital tool for enhancing disaster resilience through simulation, data integration, and collaborative management. This study systematically reviews BIM applications in sustainable risk and disaster management from 2014 to 2024, employing the PRISMA framework, literature coding, and network analysis. Five primary research clusters are identified: (a) sustainable construction and life cycle assessment, (b) performance evaluation and implementation, (c) technology integration and digital innovation, (d) Historic Building Modeling (HBIM) and post-disaster reconstruction, and (e) project management and technology adoption. Despite increasing scholarly attention, the field remains dominated by conceptual studies, with limited empirical exploration of emerging technologies such as artificial intelligence (AI). Four key challenges are highlighted: weak foundational integration with structural risk research, technological bottlenecks in AI and digital applications, limited practical implementation, and insufficient linkage between sustainability and risk management. Future trends are expected to focus on achieving Industry 4.0 interoperability, advancing AI-driven intelligent disaster response, and adopting multi-objective optimization strategies balancing resilience, sustainability, and cost-effectiveness. This study provides a comprehensive overview of the field’s evolution and offers insights into strategic directions for future research and practical innovation.

A crucial measure to accelerate the low-carbon transformation of enterprises in the industrial sector involves stringent control over carbon emissions attributed to logistics and transportation activities. In this study, a multi-objective workshop layout optimization model is developed, aiming to minimize logistics cost per unit area and carbon emissions, and maximize the non-logistics relationship. The objective is to mitigate avoidable transportation-related carbon emissions during enterprise operations, while facilitating the co-development of the enterprise’s economy and environment. The model is solved utilizing an enhanced NSGA-II algorithm, with the initial solution set optimized through a combination of system layout design method, dynamic adaptive crossover, and variation strategies. Additionally, the distribution function is introduced to enhance the elite retention strategy and boost the algorithm’s search rate. By using an actual case study, the usefulness of the enhanced algorithm is demonstrated, and the plant’s initial low-carbon layout is realized in order to advance the enterprises’ sustainable growth.