• Energy Research

Not only has artificial intelligence changed the production methods of traditional industries; it has also presented a great opportunity for future industrial development to decouple from environmental degradation and the promotion of green economic growth. The article studies the influence of artificial intelligence on green economic growth and its mechanism. The research shows that (1) artificial intelligence can promote green economic growth in China. After accounting for spatial factors, it was found that artificial intelligence could promote local green economic growth, but had a siphon effect on neighboring green economic growth. From the perspective of dynamic effects, in the short term, artificial intelligence will not significantly dampen green economic growth in neighboring regions. In the long run, artificial intelligence will have a stronger role in promoting green economic growth, and the siphon effect on neighboring cities will be more significant. (2) As the level of human capital increases, the negative spillover effect of artificial intelligence will be significantly weakened. The promotion effect of artificial intelligence on green economic growth is relatively weak in resource-based cities. (3) Artificial intelligence has obvious attenuation characteristics on the spatial spillover effect of green economic growth, but significant influence is limited to within 200 km. (4) Artificial intelligence has the greatest impact on productivity, accounting for 30.59% in promoting green economic growth. The green innovation effect was 0.0181, accounting for 5.64%. The resource allocation effect is 0.0011, accounting for 3.44%. This paper provides policy enlightenment for promoting industrial intelligence and green economic growth.

This paper analyzes the impact of technology innovation on capacity utilization of enterprises located in the Yangtze River Economic Belt through logic reasoning and empirical modeling. Our analysis shows that the mechanism of how technology innovation affects capacity utilization is that the former promotes the latter through meeting market demand, improving production and management efficiency, and optimizing industrial structures. Our empirical results indicate that the influence of technology innovation on the capacity utilization of enterprises in the Yangtze River Economic Belt evidently possesses positive “U” characteristics. Compared with the upstream and downstream regions of the river, the technology innovation of enterprises in the middle reaches can break the U-shaped inflection point earlier. Compared with light industrial enterprises, heavy industrial enterprises can also break the U-shaped inflection point earlier. Compared with non-overcapacity enterprises, those with overcapacity can break the U-shaped inflection point earlier. The technology innovation of non-state-owned enterprises has obvious positive “U” characteristics in the impact of capacity utilization, while the technology innovation of state-owned enterprises has no significant impact on capacity utilization.

Abstract Artificial Intelligence (AI) is becoming the engine of a new round of technological revolution and industrial transformation; as such, it has attracted much attention of scholars in recent years. Surprisingly, scarce studies have shed lights on the effects of AI on the environment, especially with respect to carbon intensity. Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, we use Chinese industrial sector data from 2005 to 2016 to investigate how AI affects carbon intensity. The empirical results show that AI, as measured separately by the adoption of robotics by industry and the number of academic AI-related papers, significantly reduces carbon intensity. The results remain robust after addressing endogenous issues. We find that there are both stages and industrial heterogeneity in the effects of AI on carbon intensity. AI had a more decrease effect on carbon intensity during the 12th Five-Year Plan than the 11th. Compared with capital-intensive industries, AI tends to have a more decrease effect on carbon intensity in the labor-intensive and tech-intensive industries. To enlarge the effects of AI on reducing carbon intensity, the government should promote the development and application of AI and implement differentiated policies in line with the industry characteristics.

This paper analyses the impact of digital technological innovation on the carbon emission intensity of enterprises and conducts an empirical test based on the data of listed enterprises in China from 2009 to 2021. The study finds that (1) digital technological innovation can significantly reduce carbon emission intensity. (2) Enterprises’ digital attention and investment can significantly increase their operating income but not reduce carbon emissions. Digital technology patents can significantly reduce carbon emissions in the short term. In the long run, even new digital technologies will have a carbon rebound effect once they are deployed on a large scale. Therefore, digital technology innovation is still challenging in the long run to realize the synergy effect of “increasing production and reducing carbon.” (3) Mechanism tests show that digital technology innovation can reduce carbon intensity by improving operational efficiency, promoting cleaner production, and improving human capital. (4) If the government pays moderate attention to digital development, digital technological innovation by enterprises can significantly reduce carbon intensity. Meanwhile, this effect is more significant in regions with higher levels of intellectual property protection. Digital technology innovation can significantly reduce carbon intensity for mature, high-tech, and technology-intensive enterprises.

Urban governance is an important cornerstone in the modernization of a national governance system. The establishment of smart cities driven by digitalization will be a vital way to promote economic green and sustainable growth. By using the data of 274 prefecture-level cities in China from 2004 to 2017, we study the impact of smart city policy on economic green growth and the underlying mechanism of the impact. It is shown that the establishment of smart cities has significantly promoted the green growth of China's economy. This conclusion is further confirmed by using exogenous geographic data as instrumental variables and robustness tests, such as the quasi-experimental method of Difference in Difference with Propensity Score Matching (PAM-DID). The mechanism test shows that promoting economic growth, reducing per unit GDP energy consumption, and lowering waste emissions represent three ways for smart cities to promote green economic growth. The heterogeneity test shows that smart city policy has an obvious promotional effect on the economic green growth of both large cities and non-resource-based cities. This paper is expected to provide a reference for the urban development and economic transformation of emerging economies.