How Can Enterprises’ Green Innovation Persist? A Study Based on Explainable Machine Learning

Huaping Zhao, Jian Wang () and Yuan Yuan
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Huaping Zhao: School of Management Science and Engineering, Shanxi University of Finance and Economics, Taiyuan 030006, China
Jian Wang: School of Management Science and Engineering, Shanxi University of Finance and Economics, Taiyuan 030006, China
Yuan Yuan: School of Management Science and Engineering, Shanxi University of Finance and Economics, Taiyuan 030006, China

Sustainability, 2025, vol. 17, issue 22, 1-21

Abstract: Based on the strategy tripod framework, this study identifies 27 feature variables that influence the persistence of enterprise green innovation. In addition, utilizing data from Chinese listed enterprises between 2012 and 2022, this study employs machine learning models and the SHAP method to analyze the driving factors and their underlying mechanisms. The findings indicate that the persistence of enterprise green innovation results from multiple factors, among which enterprise size, R&D investment, and technological utilization capability rank as the top three most important determinants. Enterprise size has a positive linear effect on the persistence of green innovation, while market competition has a negative linear effect. R&D investment, technological utilization capability, enterprise green culture, financing capacity, and integration capability all show non-linearly positive effects. The conclusions provide theoretical guidance and micro-level evidence for promoting high-quality enterprise green development in enterprises and supporting governmental policy formulation.

Keywords: strategy tripod framework; persistence of green innovation; machine learning model; SHAP; XGBoost (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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