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Study on the influence of industrial intelligence on carbon emission efficiency–empirical analysis of China's Yangtze River Economic Belt.

Authors :
Huang, Qiu
Chen, Qiaoqi
Qin, Xiaochun
Zhang, Xinlei
Source :
Environmental Science & Pollution Research; Jul2023, Vol. 30 Issue 34, p82248-82263, 16p
Publication Year :
2023

Abstract

How to achieve the goal of "carbon peak and carbon neutrality" and explore the compatibility of industrial and ecological civilization is a major challenge for China today. This study analyzes the impact of industrial intelligence on industrial carbon emissions efficiency in 11 provinces of China's Yangtze River Economic Belt, measuring the efficiency of industrial carbon emissions through the non-expected output slacks-based measure (SBM) model, selecting industrial robot penetration to measure the level of industrial intelligence, establishing a two-way fixed model to verify the impact of industrial intelligence on carbon emission efficiency, and testing for intermediary effects and regional heterogeneity. The results show that: (1) the industrial carbon emission efficiency of the 11 provinces shows year-over-year improvement, with significant differences between upstream, midstream, and downstream, where downstream is the highest and upstream is the lowest. (2) The development of industrial intelligence is highly uneven, with the upstream level being the weakest. (3) Industrial intelligence can improve the efficiency of industrial carbon emissions by enhancing green technological innovation and energy use efficiency. (4) The effect of industrial intelligence on industrial carbon emission efficiency also shows regional heterogeneity. Finally, we present policy recommendations. This research provides mathematical and scientific support for achieving carbon reduction targets at an early stage and helps accelerate the construction of a modern, low-carbon China. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
09441344
Volume :
30
Issue :
34
Database :
Complementary Index
Journal :
Environmental Science & Pollution Research
Publication Type :
Academic Journal
Accession number :
164944929
Full Text :
https://doi.org/10.1007/s11356-023-28160-1