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Systematic review reveals critical limitations in traditional ESG cost modelling across asset management, suggesting multi-tiered artificial intelligence enables dynamic risk monetisation.
The integration of Environmental, Social and Governance (ESG) criteria into sustainable development exposes critical limitations in traditional cost modelling for Asset Lifecycle Management. Through a systematic review of 64 studies (2016–2025), this research identifies gaps including the compartmentalisation of ESG factors, methodological fragmentation, and validation asymmetries. To bridge these gaps, we develop a novel conceptual framework integrating Artificial Intelligence (AI) across descriptive, predictive, and prescriptive tiers. This framework employs Natural Language Processing to structure unstructured ESG data and Explainable AI (XAI) to monetise dynamic risks. Grounded in the Resource‐Based View, the model embeds Human‐AI Synergy and Institutional Governance Mechanisms, offering a structured basis for transparent and adaptive ESG‐cost integration, advancing the practice of cost engineering and sustainable project delivery.
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Tao et al. (2026) studied this question.
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