Abstract
As an emerging technology, industrial intelligence focus on the integration of artificial intelligence and production, which creates a new access to achieve the goal of carbon emissions reduction. Using data on provincial panel data from 2006 to 2019 in China, we empirically analyze the impact and spatial effects of industrial intelligence on industrial carbon intensity from multiple dimensions. Results show an inverse proportionality between industrial intelligence and industrial carbon intensity, and the mechanism is to promote green technology innovation. Our results remain robust after accounting for endogenous issues. Viewed from spatial effect, industrial intelligence can inhibit not only the industrial carbon intensity of the region but also the surrounding areas. More strikingly, the impact of industrial intelligence in the eastern region is more obvious than that in the central and western regions. This paper effectively complements the research on the influencing factors of industrial carbon intensity and provides a reliable empirical basis for industrial intelligence to reduce industrial carbon intensity, as well as a policy reference for the green development of the industrial sector.
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Data Availability
The data will be made available on request from the corresponding author.
Notes
“Made in China 2025” was officially issued by the State Council of China on May 19, 2015. The theme of the policy is to promote the innovative development of China’s manufacturing industry and is focused on accelerating the depth of integration between the new generation of information technology and the manufacturing sector.
We used the consumption of eight fossil fuels such as coal, natural gas and coke in China’s provinces to calculate industrial CO2 emissions, based on the method in the IPCC National Greenhouse Gas Emission Inventory Guidelines 2006.
The Super-SBM model is used to measure China’s industrial carbon emission efficiency. The input indicators selected in this paper include labour force (number of people employed in industrial sectors by region), total capital (net value of industrial fixed assets above scale by region) and total energy (total energy consumption by region). The expected output indicator is selected from the main business income of industrial enterprises above the size of the sub-region. The undesired output is the sub-regional industrial co2 emissions.
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Funding
This work received financial support from research on stability of industry-university research coupling symbiosis network of oil and gas re-source-based cities (no.19JYB139), and Research on the Evaluation and Realization Path of the Coupled and Coordinated Development of “Water-Energy-Grain” Linkage System in Heilongjiang Province (no. 21JYB139)Data availability The datasets generated and/or analyzed during the current study are property of the National Bureau of Statistics; they are available from the corresponding author who will inform the National Bureau of Statistics that the data will be released on reasonable request.
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Sijia Tao conceived and designed the research question and wrotethe paper. Yanqiu Wang and Yingnan Zhai reviewed and edited the manuscript. All authors read and approved the manuscript.
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Tao, S., Wang, Y. & Zhai, Y. Can the application of artificial intelligence in industry cut China’s industrial carbon intensity?. Environ Sci Pollut Res 30, 79571–79586 (2023). https://doi.org/10.1007/s11356-023-27964-5
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DOI: https://doi.org/10.1007/s11356-023-27964-5