Abstract:    As a key area of China′s environmental governance, the improvement of green innovation performance of heavy polluting enterprises plays an important role in accelerating industrial green transformation and development. In this paper, an analytical framework of the factors influencing green innovation performance of heavy polluting enterprises was first constructed based on the institutional theory and the natural resourcebased view. The machine learning XGBoost (eXtreme Gradient Boosting) model was also applied to predict corporate green innovation performance. On this basis, the additive explanatory model Shapley Additive exPlanations (SHAP) based on the game theory was further used to identify the degree of influence and the direction of the role of each characteristic variable on corporate green innovation performance. The empirical analysis based on the listed firms in the heavily polluting industries from 2011 to 2021 demonstrated that external environment, corporate characteristics, and corporate governance significantly influence the green innovation performance of heavily polluting enterprises. Among them, market-oriented environmental policies and firm size are the most critical factors driving performance improvement, reflecting the key role of external institutional environment and scale effect in innovation. Meanwhile, the effects of secondary factors such as institutional shareholdings, shareholding ratio of the top 10 shareholders, firm age, market value, public concern, command-and-control environmental regulation, and government subsidy further emphasized the significant impact of shareholding structure, market performance, and external incentives on green innovation. It is worth noting that command-and-control environmental regulation exhibits an inhibitory effect on green innovation, suggesting that the efficiency and flexibility of mandatory policy tools in stimulating innovation needs to be improved. Based on the above findings, this paper further proposed governance countermeasures and recommendations to accelerate the promotion of green innovation performance of heavy polluting enterprises in China.

Key words: green innovation performance, heavy polluting enterprise, XGBoost model, interpretable machine learning