Improving energy efficiency is an important way to achieve low-carbon economic development, a common goal of most nations. Based on the comprehensive survey data of enterprises above a designated size in Guangdong Province, this paper studies the impact of artificial intelligence on the energy efficiency of manufacturing enterprises. The results show that: (1) artificial intelligence, as measured by the use of industrial robots, has significantly improved the energy efficiency of manufacturing enterprises. This conclusion is still robust after introducing data on industrial robots in the United States over the same time period as the instrumental variable for the endogeneity test. (2) The mechanism test shows that artificial intelligence mainly promotes the improvement in energy efficiency by promoting technological progress; the impact of artificial intelligence on the technological efficiency of enterprises is not significant. (3) Heterogeneity analysis shows that the age of the manufacturing enterprises inhibits a promoting effect of artificial intelligence on energy efficiency; manufacturing enterprises’ performance can enhance the promoting effect of artificial intelligence on energy efficiency, but this promoting effect can only be shown when the enterprise performance is positive. The paper clarifies both the impact of artificial intelligence on the energy efficiency of manufacturing enterprises and its mechanism of action; this will help provide a reference for future decision-making designed to improve manufacturing enterprises’ energy efficiency.

Using transnational panel data from 61 nations and regions from 2000 to 2019, this article empirically examines both the influence of artificial intelligence on the Global Value Chain as it pertains to the manufacturing industry and its mechanism of action. According to the report, AI significantly improves the industrial sector’s GVC position; this finding still holds after multiple robustness and endogeneity tests of the model. The findings of the heterogeneity test at the national level demonstrate that, in developing nations as opposed to developed countries, AI has a stronger impact on advancing the GVC position of the manufacturing industry. Heterogeneity tests at the industry level show that AI has a significant role in promoting the GVC of high, medium and low technology manufacturing industries. The mechanism test demonstrates three primary ways by which AI contributes to improving the GVC position of the manufacturing industry: by improving both production efficiency and technological innovation capacity, and by reducing trade costs. This study provides policy implications for the promotion of AI with respect to China’s manufacturing industry GVC position.