Machine Learning Tools for Assessing Environmental Risk in Investment Decisions (gjstudies, 2025)
This paper introduces a novel methodological framework that integrates machine learningwith traditional financial risk assessment to quantify and integrate environmental risk intoinvestment decision-making. While environmental, social, and governance (ESG) factors areincreasingly recognized, their integration remains largely qualitative and subjective, creatinga significant gap in quantitative financial modeling. Our research addresses this by formulating environmental risk not as a peripheral concern but as a core, quantifiable financialvariable with direct impact on asset volatility and long-term returns. We propose a hybridarchitecture combining a Graph Convolutional Network (GCN) to model the complex, interconnected web of environmental dependencies and regulatory pressures, with a TemporalConvolutional Network (TCN) to process time-series data on environmental metrics andasset performance. This dual-network approach, which we term the Environmental RiskIntegration Network (ERIN), is trained on a unique, multi-modal dataset we constructed.This dataset synthesizes corporate financial data, granular environmental violation recordsfrom regulatory bodies, satellite-derived geospatial data on operational sites, and climatemodel projections, creating a temporal panel for over 2,000 publicly traded firms from 1995to 2004. A key innovation is our ’risk translation layer,’ which learns a mapping functionbetween the latent environmental risk representations generated by the GCN-TCN modeland observed financial outcomes, such as stock price volatility, credit rating changes, andincident-related cost spikes. Our results, validated against a held-out test set, demonstratethat the ERIN model significantly outperforms standard ESG score-based models and traditional financial models in predicting downside risk events linked to environmental factors.The model identifies non-linear, threshold-based relationships between cumulative environmental pressures and financial repercussions that are missed by linear regression techniques.We conclude that this machine learning-driven approach provides a more robust, dynamic,and actionable tool for investors, enabling the pricing of environmental risk into capitalallocation with a precision previously unattainable, thereby bridging a critical gap betweensustainable finance theory and practical investment analytics