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.

A thorough understanding of residents’ demands plays an important role in realizing the rational distribution of urban retail (UR) and promoting the habitability of cities. Unfortunately, these demands for UR are currently under-researched. To solve this problem, this study aims to quantify the capitalization effect of UR on housing prices and explores the impact of heterogeneity in housing structure characteristics, price quantile, and space on the residents’ demands for UR according to the hedonic price model, quantile regression, and geographically weighted regression in Chengdu. The results of these models show the following: (1) good property management and building sound insulation can reduce the negative influence of UR on residents’ lives; (2) only the owners of low-price houses are willing to pay a premium for UR; and (3) residents’ demands for UR increase from the central area to the peripheral area of Chengdu, and an inverted U-shaped relationship was found between housing prices and the UR level. A comprehensive analysis of the heterogeneity of residents’ demands for UR can provide a reference for planning departments, real-estate developers, and UR owners and promote the sustainable development of UR.