AI-ENABLED ESG RISK MONITORING FOR RESILIENT REGIONAL INDUSTRIAL SUPPLY CHAINS
Abstract
This article develops a methodological framework for applying artificial intelligence to Environmental, Social and Governance (ESG) risk monitoring in regional industrial supply chains. The study argues that ESG management becomes more effective when environmental, social and governance data are transformed into early-warning signals rather than used only for retrospective reporting. Using the IMRAD structure, the research combines comparative analysis, process mapping, indicator grouping, a five-point risk assessment and conceptual modelling. A Regional ESG Resilience Index is proposed to evaluate energy and climate exposure, supplier responsibility, workforce safety, data reliability and governance transparency. The results show that AI-supported monitoring can identify abnormal resource consumption, emerging supplier risks, unsafe operational patterns and reporting gaps before they develop into major disruptions. However, the effectiveness of the system depends on data quality, human oversight, cybersecurity and clearly assigned managerial responsibility. The proposed framework includes three tables and three analytical diagrams and may be adapted by industrial enterprises, regional authorities and investors seeking to improve sustainability, resilience and investment attractiveness.
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