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A systematic review of artificial intelligence in managing climate risks of PPP infrastructure projects

Isaac Akomea-Frimpong (School of Engineering, Design and Built Environment, Western Sydney University, Sydney, Australia)
Jacinta Rejoice Ama Delali Dzagli (Department of Construction Technology and Management, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana)
Kenneth Eluerkeh (Department of Construction Technology and Management, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana)
Franklina Boakyewaa Bonsu (Department of Construction Technology and Management, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana)
Sabastina Opoku-Brafi (Department of Construction Technology and Management, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana)
Samuel Gyimah (Department of Construction Technology and Management, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana)
Nana Ama Sika Asuming (Department of Construction Technology and Management, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana)
David Wireko Atibila (Department of Construction Technology and Management, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana)
Augustine Senanu Kukah (School of Engineering, Design and Built Environment, Western Sydney University, Sydney, Australia)

Engineering, Construction and Architectural Management

ISSN: 0969-9988

Article publication date: 25 December 2023

578

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.

Keywords

Acknowledgements

Authors are immensely grateful to anonymous authors and editors for significant contributions to make the paper publishable. The authors are thankful to Western Sydney University, Australia and Kwame Nkrumah University of Science and Technology, Ghana for their support in our academic pursuits.

Citation

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. (2023), "A systematic review of artificial intelligence in managing climate risks of PPP infrastructure projects", Engineering, Construction and Architectural Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ECAM-01-2023-0016

Publisher

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Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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