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REVIEW ARTICLE

Explainable and interpretable artificial intelligence for embodied carbon assessment and decarbonization of building materials: A PRISMA-based systematic and bibliometric review

Funmilayo C. Jayeola1* ,  Masengo Ilunga1
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1 Department of Civil and Environmental Engineering and Building Science, College of Science, Engineering, and Technology, University of South Africa, Science Campus (Florida), Roodepoort, Gauteng , South Africa
AJWEP, 026310212 https://doi.org/10.36922/AJWEP026310212
Received: 30 July 2026 | Revised: 19 August 2026 | Accepted: 19 August 2026 | Published online: 16 October 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Embodied carbon (EC) in building materials poses a significant challenge to decarbonizing the construction industry, necessitating data-driven methods for transparent and informed decisions. This study systematically reviews the use of artificial intelligence (AI) and explainable AI (XAI) in assessing EC and promoting low-carbon building materials. A PRISMA 2020–based review and bibliometric analysis of Scopus publications from 2015 to June 2026 explored trends, geographic distribution, research themes, AI/XAI methods, and their applications in EC assessment and material decarbonization. Research has notably increased in 2023, highlighting AI’s growing role in life cycle assessments and material decarbonization. Machine learning methods such as neural networks, random forests, support vector machines, gradient boosting, and deep learning are increasingly used for EC prediction, material performance evaluation, and optimization. However, XAI applications are limited, with more focus on prediction accuracy than model transferability. Key XAI techniques include Shapley Additive exPlanations, partial dependence plots, sensitivity analysis, and feature importance analysis. The review identifies limitations such as regional data gaps, limited model explainability, limited database integration, and limited empirical validation. A conceptual framework is proposed combining localized life cycle data, AI prediction, XAI interpretation, optimization, and decision support. The review concludes that XAI can enhance transparency and aid low-carbon material decisions, though further validation across laboratories, manufacturing, and regions is needed for practical implementation.

Keywords
Embodied carbon
Building materials
Explainable artificial intelligence
Artificial intelligence
Decarbonization
Machine learning
Life cycle assessment
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
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Asian Journal of Water, Environment and Pollution, Electronic ISSN: 1875-8568 Print ISSN: 0972-9860, Published by AccScience Publishing
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