(untitled) (gjstudies, 2021)
This paper introduces a novel, hybrid machine learning framework for the recognitionand measurement of environmental liabilities, a critical yet persistently challenging area inaccounting and environmental management. Traditional methods, reliant on expert judgment and deterministic models, often fail to capture the complex, non-linear, and uncertainnature of environmental remediation costs and future obligations. Our research divergesfrom conventional approaches by formulating the problem not as a point estimation task,but as a probabilistic, multi-faceted prediction challenge integrating temporal, spatial, andregulatory dimensions. We propose the Environmental Liability Neural Estimator (ELNE),a unique architecture that synergistically combines a Graph Convolutional Network (GCN)to model contaminant plume dispersion and site topology, a Temporal Convolutional Network (TCN) to process historical remediation cost data and regulatory change timelines,and a Bayesian neural network head to output full probability distributions for liability estimates. This cross-disciplinary methodology draws from computational hydrology, temporalsequence modeling, and probabilistic deep learning. We validate ELNE on a novel, curateddataset of 450 historical environmental remediation cases across North America, featuringmulti-modal data including geological surveys, regulatory filings, and cost histories. Results demonstrate that ELNE significantly outperforms standard regression techniques andexpert panel assessments, reducing mean absolute percentage error by 38% and providingcalibrated uncertainty quantification that improves financial provisioning accuracy. Furthermore, the model’s latent representations reveal previously unrecognized clusters of liabilityrisk profiles, offering strategic insights for environmental managers. This work representsa fundamental shift from descriptive, backward-looking accounting to a predictive, datadriven science for environmental liability, with profound implications for corporate financialreporting, risk management, and sustainable investment