Work

Improving the Cost Modelling of Environmental, Social and Governance Using Artificial Intelligence: A Systematic Review

Year: 2026

Type: review

Abstract: ABSTRACT 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.

Source: Sustainable Development

DOI: 10.1002/sd.71615

Authors: Ziru Tao, Paul Baguley, Rashid Maqbool, Obuks Ejohwomu, Clara Cheung

Institution: University of Manchester

Language: English

Topic: Life Cycle Costing Analysis

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Climate action

Open Access status: hybrid

Estimated APC paid: $4,250