Mega infrastructure projects (MIPs) have become increasingly important to the realization of sustainable development in China. Sustainable development is a process of dynamic balance, and coordinating the triple bottom line (the environmental, social, and economic dimensions) will enable more sustainable development of MIPs. However, previous studies have lacked consideration of coordination when applying sustainable development principles to the systematic identification of risks to MIPs. The goals of this study were to clarify the definition and dimensions of the sustainable development of MIPs and to identify the key risks of MIPs. A literature review was performed to extend the definition of sustainable development of MIPs by combining the triple bottom line with a fourth coordination dimension. A conceptual model of MIP risk identification was then proposed from an extended sustainable development perspective, 22 sustainability elements and 75 risk factors were identified, and the key risk factors were determined based on the interview responses and fuzzy set theory. The results show that economic risks have a high probability, social risks have a high loss, environmental risks have an intermediate probability and loss, and coordination risks have the greatest impact. In addition, the three most important key risk factors were found to be construction and installation cost overruns, land acquisition and resettling cost overruns, and information sharing with the public. Identifying key risk factors can provide information to help stakeholders understand the risk factors associated with MIPs and formulate reasonable risk response strategies.

Construction engineering projects are costly and require large amounts of labor, physical, and financial resources. The failure of a construction engineering project typically brings huge losses. Previous studies have focused on the identification of risks, but insufficient attention has been given to strategic resource allocation for risk management after risk identification. Statistics show that most construction engineering project failures are caused by common risks. Common risks are called gray rhino risks. This metaphor illustrates that many risks are obvious but dangerous. This study was motivated by the challenge of efficiently managing gray rhino risks with limited inputs. The literature suggests that gray rhino risks are abundant in construction engineering projects and that there are mutual eliciting relationships between them, which make it difficult for the manager to devote enough resources to the prevention of key risks. Considerable resources are wasted on unimportant risks, resulting in key risk occurrence and failure of construction engineering projects. Therefore, this study describes an innovative multi-criteria decision making (MCDM) technique for ranking risks based on the strength of the eliciting relationships between them. This study used the fuzzy technique and created an interference fuzzy analytical network process (IF-ANP) method. By employing the IF-ANP alongside a decision-making trial and evaluation laboratory (DEMATEL) approach, the subjectivity can be effectively reduced and the accuracy improved during expert risk evaluation for construction engineering projects. IF-ANP was used to quantify eliciting relationships between risks and DEMATEL was used to rank risks based on the IF-ANP result. An empirical study was done to meticulously rank five risks that were selected from the gray rhino risks in the Chengdu–Chongqing Middle Line High-speed Railway construction engineering project. They are capital chain rupture, decision failure, policy and legal risk, economic downturn, and stakeholder conflict. The results showed that the policy and legal risk was the source of other risks, and that these other risks were symptoms rather than the disease.

Mega infrastructure projects (MIPs) are exposed to numerous interdependent risks of various natures which pose difficulties in risk management. Thus far, the research on the risk interactions of MIPs has been focused on developing static risk networks within a single category of risks, at certain stages of the project. It is essential to understand the risk interactions at various stages of MIPs to identify the key risks and key risk relationships that jeopardise their success. This is especially relevant nowadays, as MIPs are expected to be delivered sustainably. Therefore, to analyse the dynamic risk interaction of MIPs, initially, through literature analysis and expert interviews, combined with the four dimensions of sustainable development and the four stages of MIPs, 98 risk factors of MIPs were identified. Subsequently, semi-structured interviews were conducted to determine risk relationships and weights. Risk networks were developed for each stage of MIPs, and improved social network analysis was applied to these risk networks. Finally, the key risks and key risk relationships in each stage of MIPs were identified by analysing the changes of multi-level network indicators. This aided in determining risk control strategies. The results demonstrate that the key risks and key risk relationships are different for each stage of MIPs. Furthermore, the risks of different dimensions of sustainable development have different relationships at different stages. This research is the first to identify the risk relationships involved in MIPs by taking into consideration the whole project life cycle and its sustainable development. This research provides theoretical support for the risk management of MIPs, and strategic suggestions for controlling the risks at each stage of the project.

This study explores the relationships among the variables: willingness to quarantine, perception of the epidemic, willingness to do outdoor activities, government credibility, and public morality. To understand these relationships, it integrates theories including epidemic prevention and control, government credibility, public morality, and social network systems. Structural equation modeling (SEM) with bootstrapping estimation was conducted using data calculated from 368 healthy citizens who are quarantining at home due to COVID‐19 in Chinese cities that have launched a first‐level primary public safety incident response. The results show that residents’ perception of the epidemic has a positive effect on their willingness to quarantine, and willingness to do outdoor activities has a negative effect. Moreover, government credibility and public morality have a mediating effect on the relationship between the perception of epidemic and willingness to quarantine. What is more, government credibility has a moderating effect on the relationship between willingness to do outdoor activities and willingness to quarantine. We discuss the implications of these results for beating future epidemics that may break out.

Contemporary Chinese artist villages (CCAVs) have been developing rapidly and have become an important locus for developing cultural and creative industries, providing a strong pathway for China to achieve high-quality development and promote new urbanization. However, in practice, there are still many CCAVs that struggle with sustainable development. At present, the academic research on CCAVs mainly focuses on demonstrating the role of artist villages in promoting development in economic, social, artistic, and other fields without forming a systematic, sustainable development model or targeted policy recommendations. Therefore, the CCAV must be taken as a research object, and the influencing factors and mechanisms of its development must be analyzed through systematic quantitative research to provide a theoretical basis for the sustainable development (SD) of CCAVs. This paper identifies the factors influencing the sustainable development of CCAVs through grounded theory and expert interviews and creates a list of 21 influencing factors. Based on internal and external considerations, these influencing factors can be divided into the following four categories: artists, government, company/capital, and outside environment. This paper constructs a mechanism model for the SD of CCAVs with the help of a structural equation model, which is a quantitative analysis tool based on empirical research. The key mechanisms influencing the SD of CCAVs are obtained through research. Using quantitative research, the paper determines four categories of influencing factors and the mechanisms influencing the SD of CCAVs. This study provides an important resource for governments at all levels to promote the sustainable development of artist villages in the region.