(untitled) (gjstudies, 2024)
This research introduces a novel, hybrid machine learning framework designed to revolutionize environmental cost accounting and sustainability performance measurement. Traditional approaches, often reliant on static models and manual data aggregation, fail tocapture the complex, non-linear interdependencies between operational activities, resourceflows, and environmental impacts. Our methodology diverges significantly by integrating anensemble of unsupervised and supervised learning techniques—specifically, a modified SelfOrganizing Map (SOM) for pattern discovery in resource consumption data, coupled witha Gradient Boosting Machine (GBM) for predictive impact costing. This hybrid model isuniquely applied to a continuous, multi-source data stream encompassing energy logs, supplychain material transfers, and real-time emissions monitoring, a data integration challengeseldom addressed in accounting literature. The core innovation lies in the framework’s ability to perform dynamic attribution of environmental costs to specific processes or productswithout predefined allocation keys, learning cost drivers directly from the data topology. Wevalidate the framework using a three-year operational dataset from a multi-plant manufacturing consortium. The results demonstrate a 42% improvement in the accuracy of predictedversus actual environmental compliance costs compared to standard activity-based costingmodels. Furthermore, the SOM component identified previously unrecognized patterns ofsynergistic waste generation between disparate production lines, leading to a proposed process redesign estimated to reduce aggregate environmental costs by 18%. The model alsogenerated a novel sustainability performance index, weighted by learned material criticality,which showed a stronger correlation with long-term financial performance than traditionaleco-efficiency metrics. This work provides a foundational shift from descriptive, lagging indicator accounting to a prescriptive, learning-based system capable of adaptive sustainabilitymanagement, offering a new paradigm for integrating artificial intelligence into corporateenvironmental governance.