Abstract
The increasing intricacy and regulatory scrutiny regarding Environmental, Social, and Governance (ESG) policies within smart infrastructure projects have escalated the need for Artificial Intelligence (AI) technologies to improve monitoring, auditing, and responsibility at all levels. This research aims to examine the specific AI technologies of Natural Language Processing (NLP), machine learning, and real-time anomaly detection to determine their impact on enabling infrastructure development that is compliant with ESG policies and is efficient, scalable, and transparent. This research synthesizes theoretical and empirical literature from 2000 to 2022 to determine the impact of AI on ESG document classification, environmental monitoring, and automation of governance frameworks in large infrastructure projects. Most respondents noted that the application of AI in ESG frameworks significantly aids the detection of ESG risks, decreases compliance cost, and enhances the consistency of reporting throughout the project life cycle. Among the challenges AI faces in implementing ESG policies are data heterogeneity, algorithmic bias, lack of defined ESG and AI regulatory frameworks, and lack of defined governance frameworks. This research were pointed out the importance of setting uniform criteria for ESG data standards, compliance frameworks, human-in-the-loop governance, and capacity-building initiatives for ESG and AI professionals. These findings underscore the critical role of AI in bridging technological innovation with sustainable and ethical infrastructure development.
Keywords
Smart Infrastructure, Capacity Building, AI regulatory, environmental monitoring
Citations
IRE Journals:
Carlos Umoru
"AI-Driven ESG Compliance Monitoring in Smart Infrastructure Project's" Iconic Research And Engineering Journals Volume 6 Issue 11 2023 Page 993-998
IEEE:
Carlos Umoru
"AI-Driven ESG Compliance Monitoring in Smart Infrastructure Project's" Iconic Research And Engineering Journals, 6(11)
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