Industrial Robot Applications and Corporate Green Innovation Efficiency: Evidence From China
ABSTRACT With the deepening integration of new‐generation information technologies and the real economy, industrial robot applications (IRAs) have gradually become a new engine for enhancing the green innovation (GI) performance of manufacturing firms. This makes it important to investigate the role of IRAs in jointly advancing economic performance and environmental outcomes. Using a panel of Chinese A‐share listed manufacturing firms from 2011 to 2019, this paper employs two‐way fixed‐effects models to empirically examine the impact of IRAs on corporate GI efficiency and to explore the underlying mechanisms. The results show that IRAs significantly improve GI efficiency in manufacturing firms, and this finding remains robust after a range of endogeneity treatments and robustness checks. This empowerment effect is more prominent in state‐owned enterprises and technology‐intensive enterprises. Mechanism analysis indicates that IRAs enhance GI efficiency by narrowing the pay gap and strengthening the specialized division of labor within firms. Further evidence suggests that environmental information disclosure and the attention of digital transformation positively moderate the relationship between IRAs and GI efficiency in manufacturing firms. This study provides theoretical and practical insights into leveraging artificial intelligence to unlock GI potential and improve GI efficiency.
Authors
- Yufeng Zhang (ORCID: https://orcid.org/0000-0003-3531-8193)
- Hanwen Wang (ORCID: https://orcid.org/0000-0003-2212-6205)
- Yuhan Chen (ORCID: https://orcid.org/0009-0003-0126-2483)
- Xiaoping Tang (ORCID: https://orcid.org/0009-0008-9198-2092)
Institutions
- Nanjing University of Finance and Economics (CN)
- Guizhou University (CN)
- University of Delaware (US)
Publication Details
- Journal
- Managerial and Decision Economics
- Published
- 2026-04-22
- DOI
- https://doi.org/10.1002/mde.70106
- Primary Topic
- Energy, Environment, Economic Growth
- Type
- article
- Field-Weighted Citation Impact
- 0.00