The COVID-19 pandemic, characterized by high uncertainty and difficulty in prevention and control, has caused significant disasters in human society. In this situation, emergency management of pandemic prevention and control is essential to reduce the pandemic’s devastation and rapidly restore economic and social stability. Few studies have focused on a scenario analysis of the entire emergency response process. To fill this research gap, this paper applies a cross impact analysis (CIA) and interpretive structural modeling (ISM) approach to analyze emergency scenarios and evaluate the effectiveness of emergency management during the COVID-19 crisis for outbreak prevention and control. First, the model extracts the critical events for COVID-19 epidemic prevention and control, including source, process, and resultant events. Subsequently, we generated different emergency management scenarios according to different impact levels and conducted scenario deduction and analysis. A CIA-ISM based scenario modeling approach is applied to COVID-19 emergency management in Nanjing city, China, and the results of the scenario projection are compared with actual situations to prove the validity of the approach. The results show that CIA-ISM based scenario modeling can realize critical event identification, scenario generation, and evolutionary scenario deduction in epidemic prevention and control. This method effectively handles the complexity and uncertainty of epidemic prevention and control and provides insights that can be utilized by emergency managers to achieve effective epidemic prevention and control.

Since China declared that the post-epidemic era would begin in April 2020, the prevention and control of epidemics have become routine. The capacity of cities to respond to future public health emergencies will be enhanced if the resilience of cities is accurately measured and an emphasis is placed on improving resilience levels. Under the 4R framework, this study quantifies and analyzes the level of resilience of the cities in Jiangsu Province from both subjective and objective perspectives. By selecting explanatory variables and developing a GWR model, the spatial distribution characteristics of the quantified scores of resilience and the spatial characteristics of the influencing factors are analyzed. The results indicate that cities in southern Jiangsu should invest more in economic development and medical resources in the post-epidemic period. Northern Jiangsu should prioritize boosting the health and social work sector’s gross domestic product. Coastal cities must enhance their capacity for innocuous waste treatment.

Fostering the physical resilience of building systems and ensuring their functionality during disasters is paramount. To unlock the physical resilience capacities of building systems, this study applied an enhanced network-based approach comprising four steps: (1) classifying the building systems and developing a comprehensive building system network can be achieved by treating building components as nodes and their functional, resource, information, and physical interdependence as connections; (2) characterizing the physical resilience capacities of building systems through robustness and redundancy; (3) setting up both deliberate and random attack scenarios and revealing the robustness of building systems by using the relative size of maximum connected subgraphs and global network efficiency; and (4) proposing a novel redundancy index to reveal the capacities to maintain function in the face of disruptions. A real-world case study was conducted to analyze the physical resilience of a building system under disruptions, illustrating the feasibility of the proposed approach. The case study identified several key systems and operation strategies for enhancing the resilience of building systems. In summary, the proposed approach contributes to a comprehensive understanding of the building system and quantitatively reveals its robustness and redundancy capacities. Practically, these findings offer valuable insights and practical strategies for facility managers to foster resilient and sustainable operations during building operations.

Metro construction in ecologically sensitive areas faces a major construction management challenge, as high energy use and environmental impacts can compromise the construction legitimacy required under strict regulations. Existing studies mainly focus on isolated technical solutions or retrospective evaluations, providing limited guidance on their systematic integration into project-level management. This study develops a five-stage meta-network framework grounded in Resource Dependence Theory (RDT) to assess and enhance energy-saving capability by translating external legitimacy pressures into manageable internal resource relationships. The framework systematically evaluates energy-saving capability by analyzing coordination among agents, tasks, energy types, and knowledge. Using Wuxi Metro Line 5 as a case study, clear stage-specific variations are identified, with task–energy matching reaching its lowest level during earth excavation and the recurrence of task–energy resource relationships reaching its highest level during main structure construction. Results show that energy-saving capability can be strengthened through coordinated resource integration, while energy-structure transformation contributes to managing external dependencies and maintaining construction legitimacy. The framework supports proactive construction management by diagnosing coordination deficiencies and guiding resource optimization, while extending RDT to sustainable metro construction management.

The global aging population is growing rapidly, and due to its large population, China is expected to become a ‘super-aged society’ within the next decade. Therefore, ensuring that older people can ‘age in place’ has become a major national priority. Community-care facilities (CCFs) play a key role in this process, but their service performance often faces challenges, with high input costs and limited output benefits. This paper aims to evaluate and improve the social and economic performance of CCFs by proposing a comprehensive framework based on the 4E (Economy, Efficiency, Effectiveness, Equity) theory. Specifically, based on the 4E theory, a literature review and correlation analysis are conducted to select performance indicators. The input–output relationships are then established according to the four dimensions of the 4E theory. These indicators and input–output relationships form the foundation for applying the δ-SBM DEA model, which is used to assess the performance of 75 CCFs in Nanjing. The study finds that the economic performance of CCFs generally lags behind their social performance, with many facilities showing inefficiency in economic indicators, characterized by high operational costs and limited profitability. Projection analysis reveals major issues in low-performing CCFs, including uneven regional distribution of facility performance, poor profitability, a shortage of skilled older people care professionals, and insufficient older people’s participation. Based on these findings, the paper provides specific policy recommendations for government authorities, industry associations, and CCFs, focusing on improving cost-effectiveness, increasing the number of professionals, and enhancing older people’s participation.