The impact of artificial intelligence technology on emissions caused by non-renewable energy
Cheng-Xiao Jin et al.
Abstract
Purpose The purpose of this research is to explore how the diffusion of AI technology through patent citations influences firms’ awareness of climate change risks and their subsequent emissions, particularly in the context of non-renewable energy usage in China. Design/methodology/approach This study employs an empirical analysis of publicly listed firms in China, examining the correlation between AI technology patent citations and emissions reductions. We utilize regression models to assess the impact of citing AI versus non-AI patents on firms’ emissions, focusing on the context of climate change and non-renewable energy reliance. Findings Our findings indicate that citing AI technology patents enhances firms’ awareness of climate change risks, resulting in reduced emissions from non-renewable energy sources. Conversely, citations of non-AI patents correlate with increased emissions, supporting the knowledge spillover effect theory. Research limitations/implications This study is limited to publicly listed firms in China, which may not represent the entire industry landscape. Future research could expand to different geographical contexts and include private firms for broader applicability. Practical implications Firms can leverage AI technology to fill resource and capability gaps, thereby enhancing innovation and reducing environmental costs associated with non-renewable energy reliance. This suggests a strategic shift toward integrating AI in sustainability efforts. Social implications The findings underscore the importance of AI in combating climate change, encouraging firms to adopt innovative technologies that contribute to a more sustainable future and promote environmental responsibility. Originality/value This research contributes to the literature by linking AI patent citations to emission reductions, highlighting the role of technology diffusion in environmental sustainability and offering insights into competitive dynamics shaped by AI advancements. Highlights
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.