Post-disaster reconstruction of the built environment represents a key global challenge that looks set to remain for the foreseeable future, but it also offers significant implications for the future sustainability and resilience of the built environment. The purpose of this research is to explore the current applications of advanced digital/Industry 4.0 technologies in the post-disaster reconstruction (PDR) process with a view to improving its effectiveness and efficiency and the sustainability and resilience of the built environment. The extant research literature from the Scopus database on built environment reconstruction is identified and described. In a novel literature review approach, small language models are used for the classification and filtering of technology-related articles. A qualitative content analysis is then carried out to understand the extent to which Industry 4.0 technologies are applied in current reconstruction practice, mapping their applications to specific phases of the PDR process and identifying dominant technologies and key trends in technology deployment. The study reveals a rapidly evolving landscape of technological innovation with transformative potential in enhancing the efficiency, effectiveness, and sustainability of rebuilding efforts, with dominant technologies including GIS, remote sensing, AI, and BIM. Key trends include increasing automation and data-driven decision-making, integration of multiple Industry 4.0/digital technologies, and a growing emphasis on incorporating community needs and local knowledge into reconstruction plans. The study highlights the need for future research to address key challenges, such as developing interoperable platforms, addressing the ethical implications of using AI and big data, and exploring the contribution of Industry 4.0/digital technologies to sustainable reconstruction practices.

The construction sector is central to achieving the objectives of the European Green Deal (EGD). While existing research on transition risks predominantly focuses on project- or firm-level challenges, less is known about the transition risks implied by high-level EU policy documents. This study addresses this gap by systematically analysing 101 EGD-related policy and guidance documents published between 2019 and February 2025. A mixed human–AI content analysis approach was applied, combining human expert manual coding with automated validation using large language models (Kimi K2 and GLM 4.6). The final dataset contains 2752 coded risk references organised into eight main categories and twenty-six subcategories. Results show that transition risks are most frequently associated with environmental, economic, and legislative domains, with Climate Change Impact, Cost of Transition, Pollution, Investment Risks, and Implementation Variability emerging as the most prominent risks across the corpus. Technological and social risks appear less frequently but highlight important systemic and contextual vulnerabilities. Overall, analysis of the EGD policy texts reveals the green transition as being constrained not only by environmental pressures but also by financial feasibility and execution capacity. The study provides a structured, policy-level risk profile of the EGD and demonstrates the value of hybrid human–LLM analysis for large-scale policy content analysis and interpretation. These insights support policymakers and industry stakeholders to anticipate structural uncertainties that may affect the construction sector’s transition toward a low-carbon, circular economy.