Environmental Cost Forecasting Using Machine Learning and Accounting Information

Abstract

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 on
linear regression models or expert judgment, often failing to capture the complex, non-linear
relationships between operational activities, regulatory changes, and ecological impacts. Our
research addresses this gap by proposing a hybrid framework that synergizes the predictive
power of ensemble machine learning models—specifically, a custom-built model combining
aspects of gradient boosting and evolutionary algorithms—with the structured, auditable
data from managerial accounting systems. We formulate the forecasting problem not merely
as a prediction task but as a multi-objective optimization challenge that balances predictive accuracy, interpretability for stakeholders, and alignment with sustainability reporting
standards such as the Global Reporting Initiative (GRI). The methodology was validated
using a unique, longitudinal dataset spanning 15 years from a multinational manufacturing consortium, incorporating variables rarely combined in prior studies, including granular
activity-based costing data, real-time sensor data on resource consumption, and qualitative
assessments of regulatory stringency. Our results demonstrate that the proposed hybrid
model achieves a mean absolute percentage error (MAPE) of 8.7% in forecasting annual environmental remediation and compliance costs, a significant improvement over benchmark
models including ARIMA (MAPE: 22.3%) and standard regression trees (MAPE: 18.1%).
More importantly, the analysis reveals previously underappreciated non-linear thresholds in
resource use that disproportionately drive costs, providing actionable insights for proactive
environmental management. The study’s primary contribution is threefold: it presents a
novel methodological fusion of accounting and machine learning, offers empirical evidence
on the predictive superiority of such hybrid models in a critically underexplored application area, and provides a new analytical framework for conceptualizing environmental costs
as dynamic, learnable functions of organizational activity rather than static overhead allocations. This research opens a new avenue for sustainable business informatics, where
accounting systems evolve from historical record-keepers into core components of predictive
ecological intelligence.