| Title |
Can the application of artificial intelligence in industry cut China's industrial carbon intensity? |
| ID_Doc |
32083 |
| Authors |
Tao, SJ; Wang, YQ; Zhai, YN |
| Title |
Can the application of artificial intelligence in industry cut China's industrial carbon intensity? |
| Year |
2023 |
| Published |
Environmental Science And Pollution Research, 30, 33 |
| DOI |
10.1007/s11356-023-27964-5 |
| 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. |
| Author Keywords |
China's industrial sector; Artificial intelligence; Carbon intensity; Influence mechanism; Spatial effects; Regional balanced |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:001011289600003 |
| WoS Category |
Environmental Sciences |
| Research Area |
Environmental Sciences & Ecology |
| PDF |
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