Does Intelligent Manufacturing Contribute to the Enhancement of Carbon Emission Performance? Evidence from Total Factor Carbon Emission Performance

The deep integration of intelligent technology and the manufacturing industry is a crucial driving force for promoting green and low-carbon development, which is a key strategy for achieving sustainable development. Using panel data from 30 provinces in mainland China from 2010 to 2022, this study measures the level of intelligent development and the total factor carbon emission performance (TFCEP). Additionally, a mediating effect model is constructed to explore the impact of intelligent manufacturing (IM) on carbon emission performance (CEP) and its underlying mechanisms. The findings reveal that (1) the intellectualization of the manufacturing industry significantly enhances CEP, a conclusion that remains robust under various tests; (2) the impact of IM on CEP varies by regional geographical locations, the degree of economic agglomeration (EA), and whether the province is a low-carbon pilot area; and (3) the mechanism analysis indicates that IM improves CEP by promoting EA. Given that China is the world’s largest manufacturing country and the largest carbon emitter, analyzing the impact of its IM on CEP provides valuable theoretical insights and practical experiences for China and other manufacturing countries aiming to achieve a win–win situation of sustainable economic development and environmental improvement.

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Publication Details

Journal
Sustainability
Published
2024-09-27
DOI
https://doi.org/10.3390/su16198443
Primary Topic
Energy, Environment, Economic Growth
Type
article
Field-Weighted Citation Impact
0.00

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article

Does Intelligent Manufacturing Contribute to the Enhancement of Carbon Emission Performance? Evidence from Total Factor Carbon Emission Performance

Yao Xu, Y.‐J. Kim, Yi Yan, Yuqi Zhang et al.
Sustainability
Energy, Environment, Economic Growth
article

Does Intelligent Manufacturing Contribute to the Enhancement of Carbon Emission Performance? Evidence from Total Factor Carbon Emission Performance

Yao Xu, Y.‐J. Kim, Yi Yan, Yuqi Zhang, Yi Zhang, Weibo Jin
article en

Abstract

The deep integration of intelligent technology and the manufacturing industry is a crucial driving force for promoting green and low-carbon development, which is a key strategy for achieving sustainable development. Using panel data from 30 provinces in mainland China from 2010 to 2022, this study measures the level of intelligent development and the total factor carbon emission performance (TFCEP). Additionally, a mediating effect model is constructed to explore the impact of intelligent manufacturing (IM) on carbon emission performance (CEP) and its underlying mechanisms. The findings reveal that (1) the intellectualization of the manufacturing industry significantly enhances CEP, a conclusion that remains robust under various tests; (2) the impact of IM on CEP varies by regional geographical locations, the degree of economic agglomeration (EA), and whether the province is a low-carbon pilot area; and (3) the mechanism analysis indicates that IM improves CEP by promoting EA. Given that China is the world’s largest manufacturing country and the largest carbon emitter, analyzing the impact of its IM on CEP provides valuable theoretical insights and practical experiences for China and other manufacturing countries aiming to achieve a win–win situation of sustainable economic development and environmental improvement.

SustainabilityVol. 16(19)
Nanjing Normal University (CN), Qilu University of Technology (CN), Qingdao University of Technology (CN), Ocean University of China (CN)
National Natural Science Foundation of China, Natural Science Foundation of Shandong Province
Industry, innovation and infrastructure
Openalex Percentile: Top 79%
Energy, Environment, Economic Growth
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Does Intelligent Manufacturing Contribute to the Enhancement of Carbon Emission Performance? Evidence from Total Factor Carbon Emission Performance — Yao Xu, Y.‐J. Kim, et al. · Sustainability (2024) | TGRS Research Map | TGRS