Digital transformation and corporate environmental performance: Evidence from Chinese listed companies

Yuegang Song, Chongmiao Du, Peiliang Du, Rong Liu and Zhou Lu

Technological Forecasting and Social Change, 2024, vol. 201, issue C

Abstract: This paper constructs a theoretical model to explain the impact of digital technology transformation on corporate environmental performance (CEP). Then, data mining technology is used to crawl the annual report data of Shanghai and Shenzhen A-share listed companies from 2008 to 2021. With the help of text mining analysis, this study quantifies the digital transformation of enterprises and empirically analyses the impact of digital transformation on CEP. The results indicate that digital transformation has significantly improved CEP. The evidence remains valid after a series of robustness tests, including replacing core variables, eliminating the impact of external major events, improving sample quality and instrumental variable regression. Mechanism verification shows that Digital transformation (DT) enhances CEP through ‘green technology upgrading’, ‘increasing external media attention’ and ‘enhancing internal management control’ effects. Heterogeneity analysis shows that DT has a more significant positive impact on non-private, industry technology-intensive, high regional environmental regulation, high energy consumption and high emission CEP, Internet business models are less conducive to improving corporate environmental performance than other digital technologies. Further tests show that resource reallocation improves the industry’s environmental performance and is an important channel for DT to play a catalytic role.

Keywords: Corporate environmental performance; Digital transformation; Digital technology heterogeneity; Text-mining analysis; Resource reallocation effect (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:201:y:2024:i:c:s0040162523008442

DOI: 10.1016/j.techfore.2023.123159

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