THE IMPACT OF ESG-LINKED EXECUTIVE COMPENSATION ON GREEN INVESTMENT IN CHINESE LISTED COMPANIES

Against the backdrop of a deepening global concept of sustainable development, environmental, social and governance (ESG) elements have gradually risen to become key components of corporate strategic choices. Within the framework of China's ‘dual-carbon’ strategic goal, green investment, as a key path to promote sustainable development, has attracted widespread attention for its intrinsic driving mechanism. This study selects a sample of Chinese A-share listed companies between 2019 and 2023, and systematically analyses the impact of ESG-linked executive compensation on corporate green investment and its mechanism of action. By establishing a multiple regression model and applying the mediation effect and moderating effect analysis method, the following empirical results are obtained: First, ESG-linked executive compensation has a significant positive impact on green investment; Second, Environmental awareness plays a partially mediating role in the relationship between ESG-linked executive compensation and green investment, suggesting that ESG-linked executive compensation not only acts directly on investment decisions, but also indirectly promotes green investment by enhancing corporate environmental awareness; Third, the nature of enterprise ownership and industry characteristics have a significant moderating effect on this effect, compared with private enterprises, ESG-linked executive compensation promotes green investment in state-owned enterprises more obviously; at the same time, the sensitivity of high-pollution industries to ESG-linked executive compensation is higher than that of low-pollution industries, which suggests that ESG-linked executive compensation has a stronger ‘corrective’ effect in industries facing higher environmental risks. To ensure the robustness of the findings, the study was validated by replacing the dependent variable (EPI for environmental investment), replacing the empirical model (Tobit model), and dealing with endogeneity issues (lag analysis and propensity score matching method), all of which yielded results consistent with the underlying research hypotheses.