Real-Time ESG Compliance Scoring Engine Using IoT–AI Synergy for Green Investment Management

Abstract

Environmental, Social and Governance (ESG) compliance has now become a crucial component of sustainable investment decision-making; however, existing mechanisms for scoring ESG adhere to periodic disclosures, manual reporting and retrospective audits, which do not provide real-time transparency and precision in operations. These limitations result in loss of investor confidence and, therefore, delay of responsive green investment actions. To mitigate this data gap, this study introduces the Green Sense Engine (GSE) as a real-time ESG compliance score framework that leverages Internet of Things (IoT) sensor networks, Artificial Intelligence (AI), federated learning, reinforcement learning, and blockchain-enabled transparency. The system is constantly collecting environmental and operational metrics like energy usage, carbon emissions and workplace safety metrics using the distributed IoT sensors, and the edge-based artificial intelligence models are running for anomaly detection and feature extraction in real-time. A federated learning architecture enables privacy-preserving joint model training across multiple enterprises without centralised data sharing, and reinforcement learning dynamically adjusts the weight parameters of ESG in response to changing regulations and operations. Blockchain-based smart contracts help guarantee the unchangeable, auditable recording of compliance events, thereby improving accountability and building stakeholder trust. Experimental simulations show that data reliability has improved, predictive support for investment decisions is stronger, and update latency has been significantly reduced compared with conventional ESG frameworks. The proposed IoT-AI synergy takes ESG evaluation to the next level, providing a proactive, transparent, and continuously adaptive decision-support mechanism for next-generation green investment management.