Carbon emission reduction in the digital age: the impact and applications of artificial intelligence

还原(数学) 心理学 碳纤维 业务 经济 政治学 计算机科学 数学 算法 几何学 复合数
作者
Qili Tian,Jing Zang,Hongyan Dai,Zhenhao Xu
出处
期刊:Chinese Management Studies [Emerald (MCB UP)]
被引量:4
标识
DOI:10.1108/cms-06-2024-0421
摘要

Purpose This study aims to explore the impact of artificial intelligence (AI), an important avenue for sustainable development in the digital age, on carbon emission reduction and its pathways of influence. Design/methodology/approach The study empirically examines the impact of AI development on carbon emission levels by choosing a two-way fixed effects model based on panel data from 30 provinces in China from 2011 to 2019. Findings The results show that AI has a significant inhibitory effect on carbon emissions. Green innovation, energy efficiency and industrial agglomeration are effective transmission mechanisms for AI to suppress carbon emissions. Based on the provincial heterogeneity, it is found that there is a threshold effect of AI level on carbon emissions, and the carbon emission reduction effect of AI development is more significant in regions with high industrial structure level and high human capital level. Research limitations/implications AI has a significant inhibitory effect on carbon emissions. Therefore, China should emphasize investment in AI development, promote the development of intelligent infrastructure and broaden the field of AI applications, thereby realizing the carbon reduction effect of AI. Practical implications This study reveals the key role of AI in addressing climate change and provides effective practical solutions for achieving the "dual carbon" goals. China should fully consider the empowering role of AI in carbon reduction and balance the relationship between economic development and environmental sustainability. Originality/value This study expands the field of research on the economic consequences of AI and the factors affecting carbon emissions, reveals its intrinsic transmission mechanisms and deepens the theoretical understanding of the effects of AI on carbon emission reduction. This paper not only provides a scientific basis for achieving the “dual-carbon” goals but also provides policy recommendations for the joint response to the challenge of climate change.
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