Environmental Cost Forecasting Using Machine Learning and Accounting Information (gjstudies, 2026)
This paper introduces a novel, cross-disciplinary methodology for forecasting environmental costs by integrating machine learning techniques with traditional accounting information systems. Traditional approaches to environmental cost prediction have largely relied onlinear regression models or expert judgment, often failing to capture the complex, non-linearrelationships between operational activities, regulatory changes, and ecological impacts. Ourresearch addresses this gap by proposing a hybrid framework that synergizes the predictivepower of ensemble machine learning models—specifically, a custom-built model combiningaspects of gradient boosting and evolutionary algorithms—with the structured, auditabledata from managerial accounting systems. We formulate the forecasting problem not merelyas a prediction task but as a multi-objective optimization challenge that balances predictive accuracy, interpretability for stakeholders, and alignment with sustainability reportingstandards such as the Global Reporting Initiative (GRI). The methodology was validatedusing a unique, longitudinal dataset spanning 15 years from a multinational manufacturing consortium, incorporating variables rarely combined in prior studies, including granularactivity-based costing data, real-time sensor data on resource consumption, and qualitativeassessments of regulatory stringency. Our results demonstrate that the proposed hybridmodel achieves a mean absolute percentage error (MAPE) of 8.7% in forecasting annual environmental remediation and compliance costs, a significant improvement over benchmarkmodels including ARIMA (MAPE: 22.3%) and standard regression trees (MAPE: 18.1%).More importantly, the analysis reveals previously underappreciated non-linear thresholds inresource use that disproportionately drive costs, providing actionable insights for proactiveenvironmental management. The study’s primary contribution is threefold: it presents anovel methodological fusion of accounting and machine learning, offers empirical evidenceon the predictive superiority of such hybrid models in a critically underexplored application area, and provides a new analytical framework for conceptualizing environmental costsas dynamic, learnable functions of organizational activity rather than static overhead allocations. This research opens a new avenue for sustainable business informatics, whereaccounting systems evolve from historical record-keepers into core components of predictiveecological intelligence.