The impact of artificial intelligence on firms’ energy and resource efficiency: Empirical evidence from China

The Impact of Artificial Intelligence Development on Urban Energy Efficiency—Based on the Perspective of Smart City Policy

“…The control variables selected here are specifically as follows (Table 1): the economic development level (GDP), which is expressed as the GDP of prefecture-level cities (please note that due to the consideration of factors such as city size, the GDP per capita normalization is not used in here); industrial structure (HHI), measured by the ratio of the increase in tertiary industry to GDP; prefectural-level city science and technology expenditure (SCS) is expressed using the ratio of prefectural-level city science and technology expenditure to local government general budget revenues; the advanced industrial structure (ISS), measured as the sum of primary industry value added to GDP, the sum of two times the secondary industry value added to GDP, and three times the tertiary industry value added to GDP; foreign direct investment (FDI), expressed as the ratio of the actual utilization of foreign investment to GDP. And the control variables in this paper are referenced from the literature selection methods of Deng and Xiao and Zhao and Li et al [36][37][38][39][40][41][42][43].…”

“…However, with the increase in the proportion of the tertiary industry and the total urban economic aggregate, the total energy consumption also increases correspondingly, resulting in a decrease in energy efficiency. Therefore, controling the market position of competitors in each industry, stimulating their motivation to improve and innovate, and reducing the negative impacts of the uneven distribution of resources between industries are key concerns in the process of coordinating economic growth and environmental development in China [41]. Considering that the research topic of this paper is related to the Environmental Kuznets Curve (EKC), this paper further explored whether there is a similar U-shaped relationship between energy efficiency and income as measured by the disposable income of urban residents in prefecture-level cities.…”

Robots, firm relocation, and air pollution: unveiling the unintended spatial spillover effects of emerging technology

“…We argue that the introduction of robots could reshape the uneven distribution of production efficiency across different regions (Luan et al 2022). Greater robot penetration may improve local air quality by directly improving industrial structures, increasing energy efficiency, and reducing local pollution abatement costs, which is identified as the first environmental externality Li et al 2023;Wu, 2023). Furthermore, a regional increase in robot usage can also make former high-polluting industries uncompetitive locally, prompting physical or operational relocation to other regions with less emerging technology coverage (Acemoglu and Restrepo, 2020).…”

Artificial Intelligence and Operational Efficiency of Chinese Manufacturing Firms

“…Artificial Intelligence emerges as a promising solution to address the challenges associated with operational efficiency in manufacturing firms. AI technologies, including machine learning algorithms and data analytics, have the potential to revolutionize production processes by enabling predictive maintenance, optimizing inventory management, and automating routine tasks (Li et al, 2023). The implementation of AI-driven solutions according to Bahoo et al (2023) can lead to more agile and responsive manufacturing systems, allowing firms to adapt swiftly to changing market conditions and customer preferences.…”