Do environmental courts drive corporate green innovation? A study based on double machine learning

Abstract

To investigate whether environmental court (EC) can drive corporate green innovation (GI) and the specific driving mechanisms, this study utilizes panel data from A-share listed companies from 2003 to 2019. Employing a dual-machine learning model, it explores the specific impact of EC on GI, the influencing pathways, and their heterogeneous effects on GI with different motivations, modes, and targets. The findings revealed that (1) EC significantly promote GI, a conclusion that remains valid after a series of robustness tests. (2) EC primarily drive GI through two pathways: deterrence and alert mechanisms. (3) Heterogeneity analysis reveals that EC exert differentiated impacts based on the varying motivations, modes, and targets of GI. In terms of motivation, compared to strategic GI, EC have a more significant promotional effect on substantive GI. As for the mode, EC can clearly enhance the level of utilization GI, but their promotional effect on exploration GI is not yet apparent. Regarding targeting, EC contribute more marginnally to GI in source control than in end-of-pipe. These empirical findings deepen our understanding of how EC promote GI. Furthermore, this study reveals the potential role of dual-machine learning in solving environmental governance issues.