Digital transformation of enterprises under the pressure of air pollution

ABSTRACT

Although the effects of air pollution on economics have been reflected in recent research, very little literature has explored whether enterprises are actively adopting digital transformation under the pressure of air pollution. Considering the serious air pollution in China and the importance of digital transformation, this study uses the panel data of Chinese A-share listed companies and air quality data, from 2014 to 2020, to investigate the impact of air pollution pressure on digital transformation. The results show that air pollution pressure forces enterprises to engage in digital transformation. The conclusion still holds after endogeneity and robustness tests. Further, a mechanism analysis indicates that air pollution pressure affects digital transformation through green cognition, green innovation, and government subsidies. Meanwhile, regional informatization positively moderates the relationship between air pollution pressure and three mechanisms. Finally, a heterogeneity analysis shows that air pollution significantly affects digital transformation in eastern regions, state-owned, capital-intensive, and technology-intensive enterprises.

KEYWORDS:

• Air pollution pressure
• digital transformation
• executive green cognition
• enterprise green innovation
• government subsidies

JEL CLASSIFICATION:

• M21
• Q53
• O33
• O38

Disclosure statement

The authors have no conflicts of interest to disclose.

Author contributions

All the authors contributed to this manuscript. Qiang Lu and Kangkang Yu: Conceptualization, Funding acquisition, Project administration, Supervision. Yun Shen: Data curation, Software, Formal analysis, Writing – Original draft preparation, Writing – Review and Editing. All authors read and approved the final manuscript.

Consent to publish statement not applicable

This study does not contain any individual person’s data in any form.

Competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

Data and code availability

Data are available on request from the corresponding authors.

Ethical approval

Ethical approval was not required for secondary analysis of anonymous data in this study.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [No. 71902007; 72072174] and The Major Program of the National Social Science Foundation of China [No. 22andZD096]