期刊
SUSTAINABILITY
卷 15, 期 16, 页码 -出版社
MDPI
DOI: 10.3390/su151612437
关键词
carbon emissions; artificial intelligence; monopolistic competition model; two-way fixed-effect model; regional heterogeneity
This paper collected balanced panel data for 30 Chinese provinces during 2006-2019 and studied the impact of AI development on local carbon emissions. The results show that AI has significantly lowered carbon emissions by improving energy structure and technological innovation. Furthermore, the regional heterogeneity test detected that the emission reduction effect of AI is best in the East, followed by the West, and not significant in the Central region. Therefore, the paper suggests accelerating intelligent transformation, formulating differentiated AI development strategies, promoting the green transformation of energy usage, and strengthening local human capital accumulation to fully exploit the positive effects of AI on carbon emissions.
While artificial intelligence (AI) has had a great impact on the global economy, it has also brought new hope and opportunities for environmental protection. In this context, the authors of this paper collected balanced panel data for 30 Chinese provinces during 2006-2019 and studied the impact of AI development on local carbon emissions by using a two-way fixed-effect model. The results show that AI has significantly lowered carbon emissions. Using a series of robustness tests and instrumental variable (IV) analysis, it was found that the results are still reliable. Furthermore, mechanism analysis revealed that AI mainly reduces carbon emissions by improving energy structure and technological innovation. The lower the dependence on fossil energy, the higher technological innovation becomes, and the better the carbon reduction effect of AI. In addition, the regional heterogeneity test detected that the emission reduction effect of AI is best in the East, followed by the West, and not significant in the Central region. Therefore, to fully exploit the positive effects of AI on carbon emissions, this paper suggests accelerating intelligent transformation, formulating differentiated AI development strategies, promoting the green transformation of energy usage, and strengthening local human capital accumulation.
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