Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/324416 
Year of Publication: 
2025
Series/Report no.: 
Cardiff Economics Working Papers No. E2025/9
Publisher: 
Cardiff University, Cardiff Business School, Cardiff
Abstract: 
Artificial intelligence (AI) is crucial in achieving the carbon peak and neutrality goals and mitigating climate change. Although previous studies have explored cross-sectional differences in corporate carbon emissions, temporal heterogeneities in firm lifecycles have been overlooked. Therefore, this study investigates the effect of AI adoption on carbon emission intensity over firm lifecycles and the micro-level mechanisms of this effect. This study examines panel data from Chinese listed companies (2010-2021) using a two-way fixedeffects model and the difference-in-differences method. The empirical results demonstrate that AI significantly reduces enterprises' carbon emission intensity. However, this effect is mainly observed in growth-stage enterprises and not in decline-stage enterprises. The mechanism analysis reveals that AI primarily reduces enterprises' carbon emission intensity by improving productivity and promoting innovation. The effect on productivity is particularly evident in growth-stage enterprises, whereas the effect on innovation is dominant in decline-stage enterprises. Heterogeneity tests indicate that the effect on state-owned enterprises, mediumsized enterprises, the manufacturing sector, heavily polluting industries, non-high-tech industries, and capital-intensive industries is more pronounced than that on other enterprises. These findings suggest that enterprises should actively adopt AI, and differentiated AI adoption strategies should be formulated based on the needs of enterprises at different lifecycle stages.
Subjects: 
artificial intelligence
carbon emission intensity
firm lifecycle
productivity
JEL: 
O31
O32
O33
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.