Renewable energy technology innovation and ESG greenwashing: Evidence from supervised machine learning methods using patent text

As global environmental pollution worsens, environmental governance has become a critical aspect of corporate development. In environmental, social, and governance (ESG) risk management, how firms address the threat of greenwashing has emerged as a central focus in achieving sustainable green development. This study explores an under-researched factor contributing to ESG greenwashing: renewable energy technology innovation (RETI). Using supervised machine learning and text analysis methods, the study constructs a proxy variable for RETI and applies it to a sample of Chinese listed companies. The findings reveal that RETI reduces corporate ESG greenwashing, and this effect remains consistent after a series of endogeneity and robustness tests. The inhibitory impact of RETI on ESG greenwashing is more significant when board experiential diversity and media attention are higher. This study contributes to the theoretical basis and demonstration for the research on RETI, greenwashing, managerial experience, and corporate governance.