Toward Identifying and Analyzing Key Risk Factors and Their Interrelationships in Post-Disaster Reconstruction: A Comprehensive Study of Project Challenges and Case Analysis

Byiringiro David, Jie Liu (), Yanhua Wang and Irankunda Georges
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Byiringiro David: School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China
Jie Liu: School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan 430070, China
Yanhua Wang: Central & Southem China Municipal Engineering Design and Research Institute Co., Ltd., Wuhan 430010, China
Irankunda Georges: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Sustainability, 2025, vol. 17, issue 8, 1-28

Abstract: Post-disaster reconstruction projects are critical for restoring communities and infrastructure, yet they are fraught with complex risks and interdependent challenges. This study aims to identify and analyze key risk factors in post-disaster reconstruction, focusing on their interrelationships and impacts on project outcomes. The research integrates a comprehensive literature review and experts’ perceptions to identify and validate primary risk factors, with the aim of designing a hypothetical interrelationship. This study employs a mixed-methods approach, including an empirical survey to collect data from key experienced stakeholders, factor analysis (EFA), structural equation modeling (SEM), and seven critical risk factors, including resource-, environmental-, financial-, management-, socioeconomic-, technical-, and organizational-related risk factors, which are extracted, and their interrelationship model is further examined and validated using SPSS AMOS V24. A case study analysis was conducted to examine how these factors interact in real-world settings. After consulting case study recovery participants, the results indicate significant influence from identified critical risk factors in the context of the case project. While the methods offer strong insights, this study is limited by case-specific factors. Advanced statistical modeling like SEM provides detail but may not be fully generalizable due to local variations in conditions, stakeholder dynamics, and reconstruction processes. Nevertheless, by providing actionable insights and tools, this research serves as a guide to policymakers, project managers, and community leaders, helping them predict and model risks to develop appropriate strategies for improving the resilience and efficiency of future reconstruction efforts.

Keywords: post-disaster reconstruction projects; reconstruction risk factors; factor analysis; structural equation modeling; case study (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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