Embracing sustainable development: ESG enhancement and corporate systematic risk

Traditional ESG ratings are inadequate for capturing dynamic market sentiment, and existing research has not comprehensively examined how ESG influences corporate systemic risk in China’s A-share market, thereby leaving a critical research gap. Utilizing data from Chinese A-share listed firms spanning 2007–2021, this study constructs a sentiment-based ESG score through natural language processing of corporate news sourced from the Datago database and investigates the impact of ESG on systemic risk employing a high-dimensional fixed-effects model. Endogeneity concerns are addressed through instrumental variables, the Heckman two-step approach, and propensity score matching, while machine learning techniques are applied to validate robustness. The results reveal that enhanced ESG performance significantly reduces stock systemic risk, with the effect being particularly pronounced in the environmental dimension. The risk mitigation mechanism operates through attracting institutional and long-term investors and reducing financial leverage. Heterogeneity analysis indicates that non-state-owned enterprises, firms with elevated stock price crash risk, and capital-intensive firms exhibit greater sensitivity to governance performance. This study provides comprehensive empirical evidence and policy implications for corporations seeking to manage systemic risks through ESG practices and contributes to the sustainable development of emerging capital markets.