Institutional Investors and Green Innovation Under Double Externalities: A Machine Learning Optimization Perspective

Abstract

This paper investigates how institutional investors address the double externalities of green innovation (knowledge spillovers and environmental benefits) in China’s transition economy. Methodologically, we integrate fixed-effects econometric models with a double machine learning framework, employing random forest, gradient boosting, Lasso, and Ridge to optimize causal inference under high-dimensional controls. The results consistently show that institutional ownership significantly enhances both the scale and quality of green innovation, particularly when formal institutions inadequately internalize externalities. Mechanism analysis further reveals that corporate transparency acts as a compensatory governance tool, strengthening the role of institutional investors in mitigating market failures. We also document heterogeneous effects across informal institutional environments, where weaker Confucian culture and stronger market sentiment amplify investor influence. By combining econometric identification with machine learning optimization, this study advances methodological approaches to sustainable finance and offers policy insights into leveraging institutional investors as catalysts for environmental governance.