Akomea-Frimpong, I; Dzagli, J R A D; Eluerkeh, K; Bonsu, F B; Opoku-Brafi, S; Gyimah, S; Asuming, N A S; Atibila, D W and Kukah, A S (2025) A systematic review of artificial intelligence in managing climate risks of PPP infrastructure projects. Engineering, Construction and Architectural Management, 32(4), pp. 2430-2454. ISSN 0969-9988
Abstract
Purpose: 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. Design/methodology/approach: Systematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed. Findings: 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. Research limitations/implications: For researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain. Practical implications: This article highlights the AI tools in mitigating climate crisis in PPP infrastructure management. Originality/value: This article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | artificial intelligence; climate change; climate risk management; machine learning; PPP infrastructure projects; robotic automations |
| Index terms: | infrastructure project, methodology, deep learning, project manager, investigation, artificial intelligence, climate change, conference, risk management tool, empirical study, fuzzy logic, United Nations, science, climate risk, practitioner, partnership, machine learning, drought, heatwave, journal, infrastructure management, building information modelling, exposure, automation, systematic literature review |
| Subjects: | practitioner, automation and robotics, research methods, specialized education, public and environmental health, institutional frameworks, climate science, partnership management, information systems, artificial intelligence, data collection methods, environmental hazards, profession, financial risk, research evaluation and metrics, infrastructure engineering, research dissemination and communication, infrastructure and transport systems, data science |
| Topics: | Governance, Sustainability, Digital Applications, Engineering Principles, Stakeholder Management, Health and Safety, Roles and Professions, Research Practice, Education, Cost Management |
| Descriptive scope: | 5 PCTEA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here