Unlocking the “Code” of Green Innovation Based on Machine Learning: Evidence from Manufacturing Enterprises in China

Enhancing green innovation performance is crucial for manufacturing enterprises to achieve sustainable development. This paper employs the strategic tripod framework (organization, industry, institution) using the K-means clustering algorithm to identify types of manufacturing performed by listed companies in China’s Shanghai and Shenzhen markets and adopts the CART decision tree algorithm to analyze influencing factors of green innovation performance across different enterprise types. The study finds that manufacturing enterprises can be divided into three types, with significant differences in influencing factors of green innovation performance. From the perspective of internal drivers, the improvement in green innovation performance mainly relies on organizational resource endowments, among which R&D ability is particularly key. From the perspective of the external institutional environment, the driving logic of mimetic pressure shows differentiated characteristics between different enterprise groups and differentiated response strategies need to be formulated accordingly. In addition, when the overall impact of external factors is weak, the level of industrial structure still has a prominent promoting effect on green innovation performance. Based on the data-driven perspective, this paper identifies the influencing factors of green innovation performance of different types of manufacturing enterprises, which is helpful to improve the green innovation performance of manufacturing enterprises.