Does artificial intelligence suppress firms' greenwashing behavior? Evidence from robot adoption in China

Caiquan Bai, Di Yao and Qihang Xue

Energy Economics, 2025, vol. 142, issue C

Abstract: Determining approaches to effectively suppress firms' greenwashing practices has become a popular topic in academic and professional circles. Given that artificial intelligence (AI) applications in production are mainly achieved using industrial robots, we investigate the impact of AI applications on firms' greenwashing behavior using industrial robot data from the International Federation of Robotics and data from Chinese listed firms from 2011 to 2019. The results demonstrate that AI applications can significantly suppress firms' greenwashing practices. We also identify three key mechanisms through which AI achieves this effect by reducing costs and increasing profits, improving productivity, and alleviating information asymmetry. Furthermore, the inhibitory effect of AI applications on firms' greenwashing behavior is more significant for firms with insufficient cash flow, firms without bank relationships, and those located in regions with good institutional environments and high human capital levels. This study provides a new perspective for suppressing firms' greenwashing practices by promoting AI applications, filling gaps in the existing literature.

Keywords: Artificial intelligence; Greenwashing behavior; Robots replacing people; Production efficiency; Information asymmetry (search for similar items in EconPapers)
JEL-codes: O33 Q51 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0140988324008776
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:142:y:2025:i:c:s0140988324008776

DOI: 10.1016/j.eneco.2024.108168

Access Statistics for this article

Energy Economics is currently edited by R. S. J. Tol, Beng Ang, Lance Bachmeier, Perry Sadorsky, Ugur Soytas and J. P. Weyant

More articles in Energy Economics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().