Machine Learning Techniques for Environmental Cost Trend Analysis and Forecasting

Abstract

This research introduces a novel, cross-disciplinary framework that applies machine learning (ML) to the emerging and complex problem of environmental cost
trend analysis, a domain traditionally dominated by linear econometric models. We
posit that the non-linear, multi-variate, and temporally dynamic nature of environmental expenditures—encompassing regulatory compliance, pollution abatement,
carbon pricing, and ecosystem service valuations—is inadequately captured by conventional methods. Our originality lies in the formulation of the problem not as a
pure economic forecast, but as a high-dimensional pattern recognition and sequence
modeling task, drawing methodological inspiration from computational biology and
signal processing. We develop and evaluate a hybrid ensemble methodology, the
Temporal Convolutional-Gated Recurrent (TCGR) network, which synergistically
combines dilated causal convolutions for multi-scale feature extraction with recurrent attention mechanisms for capturing long-term dependencies and regime shifts
in cost drivers. This architecture is uniquely tailored to handle the sparse, heterogeneous, and often non-stationary data typical of environmental economics, including
policy announcement shocks and technological disruption events. We further introduce a novel data fusion layer that integrates traditional economic indicators with
non-traditional data streams, such as satellite-derived environmental indices and
textual sentiment from regulatory documents, processed via a lightweight transformer module. Our empirical investigation utilizes a purpose-built, multi-source
dataset spanning 1990-2004, focusing on manufacturing and energy sectors across
three jurisdictions. Results demonstrate that the TCGR ensemble significantly outperforms both standard autoregressive integrated moving average (ARIMA) models
and standalone recurrent neural networks (RNNs) in multi-step forecasting accuracy, particularly in predicting inflection points following policy interventions. A
key unique finding is the model’s emergent capability to identify latent ’cost transition pathways’—clusters of similar temporal trajectories that cut across industrial
classifications, suggesting common underlying technological or strategic responses
to environmental pressures. The study concludes that ML-driven trend analysis
offers a paradigm shift, moving from explanatory modeling of historical averages to
predictive analytics of complex cost trajectories, with substantial implications for
corporate strategy, risk management, and the design of more efficient environmental
policy instruments. This work establishes a new research avenue at the intersection
of machine learning, environmental science, and industrial economics.