Highlights

• This study uses machine learning to measure RETI through patent data, offering a novel way to quantify corporate environmental innovation.
• RETI significantly reduces ESG greenwashing, especially when companies have diverse boards and high media attention.
• The study provides a practical framework for reducing ESG greenwashing in emerging markets, with an emphasis on authentic corporate governance and transparency.

Summary

This study examines how renewable energy technology innovation (RETI) impacts corporate environmental, social, and governance (ESG) greenwashing, particularly in Chinese listed companies. Using machine learning to analyze patent data, the study finds that firms engaging in RETI tend to exhibit lower levels of ESG greenwashing, indicating more authentic sustainability efforts.

The research further shows that board diversity and media attention enhance the negative impact of RETI on greenwashing, suggesting that diverse board experiences and public scrutiny strengthen genuine environmental practices.

The study contributes by providing a framework for evaluating RETI’s role in curbing greenwashing and highlights the need for reliable governance and transparent reporting practices. This work is particularly relevant for firms in emerging economies aiming to enhance sustainability and reduce deceptive ESG practices.

Y. Huang, N. Xiong, and C. Liu, “Renewable energy technology innovation and ESG greenwashing: Evidence from supervised machine learning methods using patent text,” Journal of Environmental Management, vol. 370, p. 122833, Nov. 2024, doi: 10.1016/j.jenvman.2024.122833.