Risk identification and analysis for the green redevelopment of industrial brownfields: a social network analysis

The green redevelopment of industrial brownfields (GRIB) is an important means to solve the shortage of urban land resources and realize sustainable urban renewal and green development. The identification and analysis of risk factors in GRIB projects are of immense significance for completing construction tasks and ensuring the planned benefits. In this study, work and risk breakdown structures and expert interviews are used to construct the risk network model of a GRIB project, based on three dimensions: process, subject, and system sources. The software package UCINET is used to conduct social network analysis and determine the key risk factors and relationships. The results of this study suggest that the four risk factors with the most brokerage roles and the highest node betweenness centralities are located at the core of the network; the six risk relationships with the highest line betweenness centralities are those with the strongest transmission capacities; the key risk factors are mostly response and stress risks; the main source is the design unit; and the key risk relationships are the influence of the decision-making stage on the design stage and of the design stage on the construction stage. Surpassing the limitations of traditional linear research, this study explains the internal relationship among the risk factors of GRIB projects and identifies the risk factors that play a brokerage role and the risk relationship that plays a conductive role, providing a theoretical basis for introducing social network analysis tools into the risk assessment of such complex construction projects.

Automated summary

The green redevelopment of industrial brownfields is an important means to solve the shortage of urban land resources and achieve sustainable urban renewal and green development. This study constructed a risk network model of a green redevelopment project using work and risk breakdown structures and expert interviews, and used social network analysis to identify key risk factors and relationships.