Machine Learning Tools for Assessing Environmental Risk in Investment Decisions

Abstract

This paper introduces a novel methodological framework that integrates machine learning
with traditional financial risk assessment to quantify and integrate environmental risk into
investment decision-making. While environmental, social, and governance (ESG) factors are
increasingly recognized, their integration remains largely qualitative and subjective, creating
a significant gap in quantitative financial modeling. Our research addresses this by formulating environmental risk not as a peripheral concern but as a core, quantifiable financial
variable with direct impact on asset volatility and long-term returns. We propose a hybrid
architecture combining a Graph Convolutional Network (GCN) to model the complex, interconnected web of environmental dependencies and regulatory pressures, with a Temporal
Convolutional Network (TCN) to process time-series data on environmental metrics and
asset performance. This dual-network approach, which we term the Environmental Risk
Integration Network (ERIN), is trained on a unique, multi-modal dataset we constructed.
This dataset synthesizes corporate financial data, granular environmental violation records
from regulatory bodies, satellite-derived geospatial data on operational sites, and climate
model projections, creating a temporal panel for over 2,000 publicly traded firms from 1995
to 2004. A key innovation is our ’risk translation layer,’ which learns a mapping function
between the latent environmental risk representations generated by the GCN-TCN model
and observed financial outcomes, such as stock price volatility, credit rating changes, and
incident-related cost spikes. Our results, validated against a held-out test set, demonstrate
that 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 capital
allocation with a precision previously unattainable, thereby bridging a critical gap between
sustainable finance theory and practical investment analytics