Machine Learning Techniques for Environmental Liability Recognition and Measurement

Abstract

This paper introduces a novel, hybrid machine learning framework for the recognition
and measurement of environmental liabilities, a critical yet persistently challenging area in
accounting and environmental management. Traditional methods, reliant on expert judgment and deterministic models, often fail to capture the complex, non-linear, and uncertain
nature of environmental remediation costs and future obligations. Our research diverges
from conventional approaches by formulating the problem not as a point estimation task,
but as a probabilistic, multi-faceted prediction challenge integrating temporal, spatial, and
regulatory 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, temporal
sequence modeling, and probabilistic deep learning. We validate ELNE on a novel, curated
dataset of 450 historical environmental remediation cases across North America, featuring
multi-modal data including geological surveys, regulatory filings, and cost histories. Results demonstrate that ELNE significantly outperforms standard regression techniques and
expert panel assessments, reducing mean absolute percentage error by 38% and providing
calibrated uncertainty quantification that improves financial provisioning accuracy. Furthermore, the model’s latent representations reveal previously unrecognized clusters of liability
risk profiles, offering strategic insights for environmental managers. This work represents
a fundamental shift from descriptive, backward-looking accounting to a predictive, datadriven science for environmental liability, with profound implications for corporate financial
reporting, risk management, and sustainable investment