Digital Transformation and Green Innovation Efficiency in Heavy-Polluting Enterprises: A Focus on Knowledge and Slack Resources

Rui Zhao () and Jingbo Fan ()
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Rui Zhao: School of Government, University of International Business and Economics, Beijing, China.
Jingbo Fan: School of Government, University of International Business and Economics, Beijing, China.

Journal for Economic Forecasting, 2025, issue 1, 101-121

Abstract: This study aims to assess the impact of digital transformation (DT) on the green innovation efficiency of heavy-polluting enterprises (HEGIE), employing the Super-SBM model as the core methodology. Using panel data spanning from 2012 to 2022, this study investigates the relationship between DT and HEGIE and its underlying mechanisms. The findings reveal a positive relationship between DT and HEGIE, highlighting DT as a catalyst for enhancing environmental sustainability within these enterprises. Heterogeneity analysis implies that firms in the growth and maturity stages and firms located in the East and Central regions are more likely to benefit from DT in terms of green innovation efficiency. Mechanism analysis reveals that DT boosts HEGIE by facilitating the acquisition of knowledge resources, increasing high discretion slack resources while reducing low discretion slack resources. This study provides empirical evidence for understanding how DT contributes to green innovation capabilities from the perspectives of knowledge capital optimization and resource allocation, which inspires heavy-polluting enterprises to focus on maximizing the benefits from DT measures, thereby empowering their goal toward achieving green transformation.

Keywords: Digital Transformation; Green Innovation Efficiency; Super-SBM Model; Heavy-polluting Enterprises; Knowledge Resources; Slack Resources (search for similar items in EconPapers)
JEL-codes: M15 O30 Q55 (search for similar items in EconPapers)
Date: 2025
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