(untitled) (gjstudies, 2021)
This research introduces a novel, cross-disciplinary methodology that applies artificialintelligence to quantitatively assess the complex, non-linear relationship between corporateenvironmental governance (EG) structures and long-term financial performance. Movingbeyond traditional linear regression models and ESG score reliance, we propose a hybridAI architecture combining a Graph Neural Network (GNN) for modeling the intricate relational topology of governance bodies (boards, committees, stakeholder networks) with aTemporal Convolutional Network (TCN) to process longitudinal environmental performancedata. This ’Governance Topology Net’ (GT-Net) is designed to capture how the structure,expertise linkages, and decision-making pathways within a firm’s governance system mediateits response to environmental pressures and opportunities, ultimately influencing financialresilience. We train and validate GT-Net on a unique, hand-collected dataset spanning15 years (1990-2004) for 450 global firms in high-environmental-impact sectors, integratinggranular board composition data, committee charters, environmental incident logs, emission reports, and financial statements. Our results demonstrate that GT-Net significantlyoutperforms conventional econometric models in predicting 5-year forward-looking financial metrics (ROA, Tobin’s Q, stock volatility) from governance-environment data. Moreimportantly, the model’s latent representations reveal previously underappreciated governance archetypes—such as the ’Integrated Specialist Nexus’ and the ’Decentralized Adaptive Web’—that correlate strongly with superior financial outcomes amidst regulatory shiftsand physical climate risks. The findings challenge the prevailing view of environmentalgovernance as a cost-centric compliance function, repositioning it through an AI lens as adynamic, topological driver of financial value and strategic resilience. This work establishesa new paradigm for computational corporate finance, leveraging AI not for prediction alonebut for the structural diagnosis of governance efficacy.