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.

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By utilizing systems methodology and thinking logic, this paper derives a general theorem to characterize when and how a market signals for additional competition from market players. Then, it establishes conditions for when government policies actually work in real life, where firms' performances are effectively promoted no matter what offer the firms produce. Among others, the established conditions include improving managerial and resource efficiencies, promoting information and knowledge sharing, joining in organizational networks, forming manufacturing agglomerations, and localizing economic policies. By using the difference-in-difference method, a real-life case analysis with data from China is used to confirm the six formal propositions established systemically in this paper. In the conclusion section, recommendations for policy makers, such as government officers, are provided regarding when and how adopted policies will potentially produce anticipated results, while directions and unsettled questions are also posted for the forthcoming academic endeavors.