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This study aims to clarify the psychological mechanism of international contractors' fraud by linking performance pressure to fraudulent intention through the displacement of responsibility and addressing the moderating role of moral intensity.

Based on moral disengagement theory, performance pressure was hypothesized to be positively associated with fraudulent intention by mediating the displacement of responsibility. Drawing on the issue-contingent theory, moral intensity was hypothesized to inhibit the relationship between performance pressure and displacement of responsibility in three aspects: magnitude of consequences (MC), probability of effect (PE) and social consensus (SC). The scenario-based questionnaire was conducted to collect information from contractors spread across 50 countries. The partial least squares structural equation modeling was employed to assess the proposed model.

The results demonstrated that performance pressure was positively associated with the fraudulent intention, and displacement of responsibility exerted a positive partial mediating impact between performance pressure and fraudulent intention. Regarding moral intensity in the moderating analysis, the negative moderating role of MC and PE was significant, while that of SC was insignificant.

This study provides international construction practitioners with a deep understanding of the formation mechanism of fraud at the psychological level.

It clarifies the psychological mechanism from performance pressure to fraudulent intention by integrating a mediation impact from the displacement of responsibility and a moderation effect from MC and PE. It contributes to the sparse research on how situational factors shape individuals' fraudulent intentions in the international context. It provides a fresh perspective on fraud by constructing a formation model from moral psychological theories.

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.