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The uptake of Design for Safety (DfS) practices in developing countries like Ghana has been limited. This study aims to provide an in-depth understanding of the barriers across regulatory, organizational, cultural and educational dimensions that restrict DfS assimilation in the Ghanaian construction sector. Identifying the key impediments can inform policy initiatives and industry efforts to facilitate safer construction.

A postpositive philosophy underpinned the quantitative research. Multi-stage research was used. A comprehensive questionnaire survey was designed and given to six industry experts to assess clarity, relevance and effectiveness after a thorough literature review. In all, 164 professionals were reached to take part in the study using purposive sampling and consequently snowballing. “Variables” were ranked using mean score ranking and normalization techniques; exploratory factor analysis was then used to group variables into clusters.

Emergent findings revealed four distinct clusters of challenges; Design Process and Communication Challenges; Regulatory and Expertise Limitations; Planning and Education Constraints; and Attitudinal and Perception Barriers. These findings help identify targeted solutions to overcome barriers including developing robust regulatory frameworks, promoting collaboration among stakeholders and cultivating a positive safety culture.

This study provides new insights into the integration of DfS in the context of the developing construction industry in Ghana. This study expands the knowledge base to drive further research in enhancing construction safety in developing countries. Practical recommendations for overcoming these challenges are proposed.

Recent United Nations Climate Change Conferences recognise extreme climate change of heatwaves, floods and droughts as threatening risks to the resilience and success of public–private partnership (PPP) infrastructure projects. Such conferences together with available project reports and empirical studies recommend project managers and practitioners to adopt smart technologies and develop robust measures to tackle climate risk exposure. Comparatively, artificial intelligence (AI) risk management tools are better to mitigate climate risk, but it has been inadequately explored in the PPP sector. Thus, this study aims to explore the tools and roles of AI in climate risk management of PPP infrastructure projects.

Systematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed.

The results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures. The roles of AI in climate risk management of PPPs include risk detection, analysis, controls and prediction.

For researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain.

This article highlights the AI tools in mitigating climate crisis in PPP infrastructure management.

This article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.