Global pandemics, such as the Coronavirus Disease 2019 (COVID-19), have serious harmful effects on people′s physical health and mental well-being. It is imperative therefore that we seek to understand community resilience and identify ways to enhance this, especially within our cities and communities. Therefore, great emphasis is now placed on how cities prepare for and recover from such disasters, and community resilience has emerged as a key consideration. Drawing upon research on the theory of resilience, this study seeks to identify the factors that influence community resilience and to analyze their causation toward helping to manage the risks associated with the COVID-19 pandemic. Seventeen factors from the five dimensions of social capital, economic capital, physical environment, demographic characteristics, and institutional factors are used to construct an index system. This is used to establish the structural level and importance of each factor. Data were collected using a questionnaire survey involving 12,000 members of key community groups in the city of Wuhan. An interpretative structural model (ISM) combining the analytic hierarchy process (AHP) method was then used to obtain the multi-level hierarchical structure composed of direct factors, indirect factors, and fundamental factors. The results show that the income level, vulnerability of the population, and the built environment are the main factors that affect the resilience of communities affected by COVID-19. These findings provide useful guidance toward the effective planning and design of urban construction and infrastructure. The results are expected to be useful to inform future decision-making and toward the long term, sustainable management of the risks posed by COVID-19.

Flooding has become the natural disaster that causes the greatest losses, with urban flooding restricting the healthy development of cities. The ability to assess a city’s resilience to flooding is very important and would contribute to improving resilience and also help to inform planning and development. The aim of this study was to determine the key urban flood resilience indicators for three different Chinese cities (Wuhan, Nanjing, and Hefei) and to prioritize these for each city. A combined interpretive structure and network analysis method (ISM-ANP) model was used to evaluate and analyze the selected evaluation indicators. A four-level urban flood resilience evaluation network model was constructed to determine the interdependence between indicators and to calculate the priorities of the flood resilience indicators for the three cities. Overall, rescue capacity was found to be extremely important and was defined as the most important index. For Wuhan, indicators related to the distribution of waters were found to be more important, while for Nanjing, spatial planning and spatial structure of land use were found to be key priorities. In Hefei, the level of investment in infrastructure and the level of public resources occupy a more important position. The framework presented in this study contributes to the understanding of urban flood resilience and has the potential to be extended to other natural hazards.

A supply driven dynamic input-output inoperability model (SDIIM), which extends the classical dynamic inoperability input-output model (DIIM), is described in this paper. The classical DIIM was developed to help understand the infrastructure interdependencies of deliberate external attacks or unfortunate natural disasters, such as terrorist attacks or earthquakes and flooding. However, classical DIIM is a demand-driven model. More supply driven sectors exist than demand-driven sectors in interdependent infrastructure systems, but value-added perturbation is the predominant feature in supply driven sectors for an interrupt event. Moreover, compared to classical DIIM, the value added by the proposed SDIIM can be controlled more easily than the final demand of a disruptive event because the final demand perturbation may be affected by the psychological effect on consumers. A supply driven input-output inoperability model (IIM) has also been proposed to complement the classical IIM. However, the supply driven...

Frequent natural hazards such as flooding and the devastating consequences of severe events make the humanitarian supply chain particularly important in alleviating the suffering of those communities impacted by such events. However, the ambiguity of information and the different goals of stakeholders demand that the humanitarian supply chain must be resilient. This research adopts the use of literature review and expert opinions to identify the indicators that affect the resilience of the humanitarian supply chain using the flood event in Hechuan District, China in 2020 as an example. Based on the combination of fuzzy Decision-making Trial and Evaluation Laboratory and Analytic Network Process (fuzzy-DEMATEL-ANP), the interrelationships between the indicators and the weights of each indicator are calculated. The research results indicate that decision-makers in the humanitarian supply chain should vigorously coordinate the cooperation among stakeholders, ensure the effective transmission of information, and formulate forward-looking strategic plans. At the same time, these key decision makers should also be aware of the need to adjust their strategies at different stages of the flooding event in order to achieve a flexible humanitarian supply chain that responds to the varying demands over the course of a flooding event. The results of this study will help professionals involved in humanitarian supply chains to develop strategies and plans to become more resilient thus helping to reduce losses from natural hazards such as floods.

Due to population growth and human activities, water shortages have become an increasingly serious concern in recent years. The agri-food industry is the largest water-consuming industry among all industries, and various efforts have been made to reduce the loss of water in the agricultural supply chain. Improving the resilience of water resource management is a key measure to reduce the risks in the agricultural supply chain. This study aims to identify the factors affecting the resilience of water management in the agricultural supply chain and to help manage the risks related to water resources use. A total of 14 factors are selected from five dimensions of society, economy, environment, institution, and crop characteristics, and an index institution is constructed. This was used to determine the level and importance of each factor. Data were collected through a questionnaire survey involving 28 experts from the agricultural industry in Northwest China, as well as a thorough literature analysis and interactions with experts. Using a combination of an interpretative structural model (ISM) and an analytical network process (ANP), a hierarchical structure model was developed, composed of direct factors, indirect factors, and basic factors. The results show that the selection of agricultural products, the establishment of a water audit control institution, the recycling of wastewater, and the investment in water-saving technologies are the main factors affecting the resilience of water resources management in the agricultural supply chain. These findings provide useful guidance for practitioners involved in the management of water resources in agricultural supply chains. These results are expected to contribute to the sustainable management and strategic deployment of water by agricultural supply chain stakeholders.

As one of the most frequently occurring natural hazards, flooding can seriously threaten global security and the sustainable development of our communities. Therefore, enhancing the resilience of cities and improving their ability to adapt to flooding have become issues of great significance. This study developed a new comprehensive evaluation model of flood resilience that includes an evaluation index system from the basis of four key dimensions of social resilience, economic resilience, ecological environment resilience and infrastructure resilience. Firstly, interpretative structural modelling (ISM) was applied to analyze the structural issues affecting urban flood resilience. Secondly, the analytic network process (ANP) was then used to calculate the importance of these indicators. Finally, taking three cities (Zhengzhou, Xi’an, and Jinan) in the Yellow River Basin of China as examples, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was used to evaluate their current levels of flood resilience using the findings from the earlier stages. The results show that the levels of rainfall and vulnerability of groups were the fundamental factors affecting urban flood resilience. Indicators such as average annual rainfall, fixed-asset investments, and emergency rescue capabilities were also found to have a greater impact on urban flood resilience. In the study area, Xi’an was found to have a higher level of resilience due to having strong ecological environmental resilience. These findings are expected to provide a useful reference for policymakers and stakeholders involved in the management of flooding events.