Executives' green experience, green innovation and corporate environmental performance: A machine learning-based news sentiment analysis in China

Drawing on Upper Echelons Theory and the Resource-Based View, this study investigates the impact of executives' green experience on corporate environmental performance (SCEP). Using a sample of Chinese A-share listed firms from 2016 to 2023, we measure SCEP by applying a machine learning-based sentiment analysis to corporate environmental news. Our findings indicate that executives with green experience significantly enhance SCEP, a result that remains robust after addressing endogeneity concerns and conducting a series of sensitivity checks. Mechanism analyses reveal that both substantive and symbolic green innovations serve as parallel yet complementary mediators in this relationship. Heterogeneity tests show that the positive effect is more pronounced in firms facing higher financing constraints and those located in large cities, whereas it is attenuated for heavy polluters and firms under stringent environmental pressure. Furthermore, while both green experience and improved SCEP are found to increase financial distress risk, the executives' green experience can effectively mitigate the adverse effect of SCEP on financial health. This study elucidates the channel through which executive characteristics translate into environmental performance via corporate news sentiment, providing robust empirical support for policy-making and sustainable corporate development.