Intelligent Manufacturing and Carbon Emissions Reduction: Evidence from the Use of Industrial Robots in China

Hao Lv, Beibei Shi (), Nan Li and Rong Kang
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Hao Lv: School of Economics & Management, Northwest University, Xi’an 710127, China
Beibei Shi: School of Economics & Management, Northwest University, Xi’an 710127, China
Nan Li: School of Economics & Management, Northwest University, Xi’an 710127, China
Rong Kang: School of Economics & Management, Northwest University, Xi’an 710127, China

IJERPH, 2022, vol. 19, issue 23, 1-20

Abstract: Driven by the information technology revolution, using artificial intelligence to promote intelligent manufacturing while achieving carbon emissions reduction is increasingly the focus of international attention. Given this, based on the fact that China’s industrial manufacturing is more intelligent, this paper uses industrial sector data and robot data from 2000 to 2017 to examine the impact of intelligent manufacturing on industrial carbon dioxide emissions and to discuss its internal mechanism. The research found that intelligent manufacturing significantly inhibits carbon dioxide emissions in the industrial sectors. The emission reduction effect is more obvious in industries with higher carbon emissions and intelligence. The mechanism test shows that intelligent manufacturing mainly achieves industrial emission reduction by reducing fossil energy consumption in the production process and improving energy use efficiency. The research findings of this paper provide favorable evidence for using new technologies, such as artificial intelligence, to achieve carbon emissions reduction, and validate the importance of intelligent manufacturing in tackling climate change in the future. It provides an essential reference for developing countries to use artificial intelligence for their carbon emissions reduction goals.

Keywords: intelligent manufacturing; climate governance; energy consumption; carbon emissions (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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