(untitled) (gjstudies, 2025)
This paper introduces a novel framework for AI-Enabled Environmental Accounting Systems (AI-EAS) designed to facilitate real-time sustainability tracking across organizationaland supply chain boundaries. Traditional environmental accounting methods are often retrospective, fragmented, and lack the temporal granularity required for proactive decisionmaking in dynamic operational environments. Our research addresses this gap by proposinga cross-disciplinary methodology that integrates principles from environmental science, distributed systems theory, and unconventional AI paradigms. The core innovation lies in abio-inspired, neuromorphic data processing architecture that models environmental impactflows—such as carbon emissions, water usage, and waste generation—as a dynamic, weighteddirected graph. Nodes represent processes or entities, while edges encode real-time impacttransfers, updated via a network of IoT sensors and transactional data feeds. We employ ahybrid AI approach combining sparse, event-triggered neural networks for anomaly detection with a quantum-inspired optimization algorithm for resource re-allocation and impactminimization. This system moves beyond simple monitoring to enable predictive ’what-if’scenario modeling and autonomous, context-aware adjustments to operational parameters.A prototype implementation was tested in a multi-node manufacturing supply chain simulation. Results demonstrate the system’s ability to reduce aggregated carbon footprintby 18.7% and water usage by 22.3% over a six-month period compared to baseline quarterly accounting methods, while providing continuous, auditable impact ledgers. The paperconcludes by discussing the original contributions of this work: a reconceptualization ofenvironmental accounting as a real-time, graph-based control system; a novel application ofneuromorphic and quantum-inspired computing to sustainability logistics; and a frameworkthat enables a shift from compliance-driven reporting to strategic, operational sustainabilityintegration. The implications for policy, corporate governance, and sustainable industrialecology are significant.