(untitled) (gjstudies, 2022)
This research introduces a novel, cross-disciplinary framework that applies machine learning (ML) to the emerging and complex problem of environmental costtrend analysis, a domain traditionally dominated by linear econometric models. Weposit 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 apure economic forecast, but as a high-dimensional pattern recognition and sequencemodeling task, drawing methodological inspiration from computational biology andsignal processing. We develop and evaluate a hybrid ensemble methodology, theTemporal Convolutional-Gated Recurrent (TCGR) network, which synergisticallycombines dilated causal convolutions for multi-scale feature extraction with recurrent attention mechanisms for capturing long-term dependencies and regime shiftsin cost drivers. This architecture is uniquely tailored to handle the sparse, heterogeneous, and often non-stationary data typical of environmental economics, includingpolicy announcement shocks and technological disruption events. We further introduce a novel data fusion layer that integrates traditional economic indicators withnon-traditional data streams, such as satellite-derived environmental indices andtextual sentiment from regulatory documents, processed via a lightweight transformer module. Our empirical investigation utilizes a purpose-built, multi-sourcedataset spanning 1990-2004, focusing on manufacturing and energy sectors acrossthree jurisdictions. Results demonstrate that the TCGR ensemble significantly outperforms both standard autoregressive integrated moving average (ARIMA) modelsand standalone recurrent neural networks (RNNs) in multi-step forecasting accuracy, particularly in predicting inflection points following policy interventions. Akey unique finding is the model’s emergent capability to identify latent ’cost transition pathways’—clusters of similar temporal trajectories that cut across industrialclassifications, suggesting common underlying technological or strategic responsesto environmental pressures. The study concludes that ML-driven trend analysisoffers a paradigm shift, moving from explanatory modeling of historical averages topredictive analytics of complex cost trajectories, with substantial implications forcorporate strategy, risk management, and the design of more efficient environmentalpolicy instruments. This work establishes a new research avenue at the intersectionof machine learning, environmental science, and industrial economics.