1. Introduction
Addressing greenwashing practices in Chinese enterprises proves vital for advancing the realization of the United Nations’ Sustainable Development Goals (SDGs). Implementing strengthened regulatory supervision alongside blockchain-enabled traceability systems enables the systematic detection of carbon footprint misrepresentations and ESG data anomalies, thereby augmenting the veracity of sustainability reporting and mitigating methodological discrepancies (SDG 12.6). Imposing regulatory penalties on greenwashing practices stimulates corporate transitions toward renewable energy adoption, reinforces Paris Agreement emission reduction obligations, and coordinates with integrated climate change mitigation initiatives (SDG 13.2). Additionally, promoting harmonization between China’s green financial standards and international frameworks enhances global trust mechanisms, facilitates regulatory alignment in sustainable finance, and strengthens multi-stakeholder collaboration for sustainable development (SDG 17.16). These regulatory innovations not only enhance environmental disclosure transparency but also establish critical instruments to expedite progress toward the 2030 Agenda’s comprehensive sustainability objectives.
This paper is based on a sample of publicly listed companies in China, covering the period from 2012 to 2022. It develops an AI index using text analysis and word frequency statistics to empirically assess the influence of AI on corporate greenwashing. A series of robustness tests and endogeneity treatments are conducted to ensure the reliability of the findings presented in this paper. Moreover, this research investigates the transmission mechanism of AI in reducing corporate greenwashing by introducing green innovation as a mediating variable, examines the potential mediating effects between AI and corporate greenwashing, and proposes new strategies for curtailing such behaviors. Additionally, from the perspective of external legitimacy pressures, the moderating roles of imitation pressure and fiscal pressure on this relationship are empirically examined. This research mainly focuses on the following contributions: Firstly, while existing research predominantly focuses on artificial intelligence (AI)’s impacts on corporate innovation, performance, labor substitution rates, and digital transformation, this study reveals AI’s significant inhibitory effect on corporate greenwashing from a corporate social responsibility (CSR) perspective. This finding extends the research boundaries of AI applications in sustainable development. Secondly, by integrating green innovation into the analytical framework of AI-greenwashing relationships, this study clarifies the mechanistic pathway through which AI reduces greenwashing behaviors by fostering green innovation initiatives. This breakthrough elucidates the underlying causal mechanisms between technological advancement and ethical corporate practices, offering substantial practical implications. Thirdly, on the basis of analyzing the influencing mechanism of artificial intelligence on enterprise greenwashing, this paper further discusses the regulatory role of imitation pressure and financial pressure. From the perspective of external legitimacy pressure, it enriches the theoretical connotation of artificial intelligence in promoting corporate social responsibility and provides empirical support for promoting the high-quality development of enterprises.
5. Conclusions, Implications, and Limitations
5.1. Conclusions
Based on data from Chinese A-share listed companies from 2012 to 2022, this paper empirically analyzes the mechanisms and internal dynamics by which AI impacts corporate greenwashing. The findings demonstrate that the application of AI technology significantly inhibits corporate greenwashing behavior (supporting H1). This conclusion is robust, having been affirmed through various robustness tests including the propensity score matching method, instrumental variable method, Heckman two-stage method, and placebo test and by using samples from different stages. Heterogeneity analysis reveals that the inhibitory effect of AI on corporate greenwashing behavior is more pronounced in non-state-owned firms, large firms, and firms in high-pollution industries. The mechanism analysis indicates that the application of AI technology can enhance the green innovation capabilities of corporations, thereby improving their greenwashing behavior (supporting H2). Moreover, the presence of imitation pressure and financial pressure further enhances the inhibitory effect of AI technology on corporate greenwashing (H3, H4). Overall, this study thoroughly examines how enterprises utilize artificial intelligence technologies to significantly curtail greenwashing behaviors, thereby providing an empirical foundation for the high-quality development of enterprises.
5.2. Implications
Although AI technology shows great regulatory potential, it still needs to pay attention to its application boundary. Based on these findings, this paper offers the following recommendations:
First and foremost, as primary agents of green development, enterprises have a crucial responsibility to transform the concept of environmental protection into tangible actions. Therefore, based on the results of this study, companies can establish a dedicated AI and green development synergy department responsible for breaking down internal barriers, fostering cross-departmental communication and cooperation, and ensuring the consistent implementation of AI technology in environmental management. Additionally, firms should develop a continuous employee training system and regularly invite industry experts to provide professional training to enhance employees’ proficiency in using AI tools for green innovation. This training should extend beyond technical operations to include a thorough understanding and practical application of green development principles. By deeply integrating green concepts and AI practices into their daily operations, companies can address greenwashing behavior at its root. This is essential for companies to ensure that their greening strategies are more than merely superficial statements and represent actual, sustainable actions.
Second, as leaders in fostering corporate green development, governments should enhance their oversight of corporate environmental behaviors by utilizing cutting-edge technological tools. Regulators can employ AI technology to establish an intelligent environmental monitoring system. This system would collect and analyze corporate production and operational data in real time, accurately identifying potential risky behaviors related to greenwashing. On this basis, governments should further strengthen legal requirements for environmental information disclosure. It is essential that enterprises include specific applications of AI technology and the results of their assessments of environmental impact in these disclosures. By performing so, transparency regarding enterprises’ environmental behaviors will increase, providing more accurate information to investors and the public. Additionally, imposing penalties on enterprises that fail to comply with regulations or engage in greenwashing serves as a crucial means to restrain corporate behavior and maintain market order. Through these comprehensive measures, governments can ensure that enterprises pursue economic benefits while also assuming the social responsibility of environmental protection. In this way, the entire market can advance in a greener and more sustainable direction.
Finally, as a bridge between enterprises, industry associations should commit to creating favorable industry ecology. An industry association should take the initiative in organizing a common technology research alliance for AI and green development within an industry. This would involve integrating upstream and downstream enterprises in the industry chain with the scientific research capabilities of universities, focusing on addressing common issues such as the intelligent monitoring of pollution. Moreover, an industry association should establish an open industry knowledge-sharing platform and organize high-end forums, experience-exchange workshops, and other activities both online and offline. It should invite benchmark enterprises to share their practical experiences in using AI technology to overcome the challenge of superficial green initiatives and to achieve substantial green advancements. Through mutual learning and emulation among peers, the efficiency and time cost of knowledge sharing are improved. This approach can accelerate the spread of advanced practice patterns across an industry, enhancing the industry’s overall intelligence level to tackle greenwashing and fostering a healthy industry development pattern.
5.3. Limitations
This paper requires further enhancements and extensions in the future. Currently, many policies on AI are emerging; however, there is a lack of policy implications in this paper. Future research could introduce policies related to AI and analyze them empirically using the difference-in-differences method. Furthermore, this study only examines the two moderating variables of imitate pressure and financial pressure. Future research could investigate other potential moderating variables.
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Xueying Tian www.mdpi.com
