Nonlinear relationship between digital and intelligent transformation and energy conservation and emission reduction in China
Abstract The manufacturing industry’s continuous digital intelligence transformation has triggered an extensive discussion on the environmental impact. In order to further explore this relationship, the panel data of 30 provinces in China from 2011 to 2021 is used in this analysis, the index system...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-15821-z |
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| Summary: | Abstract The manufacturing industry’s continuous digital intelligence transformation has triggered an extensive discussion on the environmental impact. In order to further explore this relationship, the panel data of 30 provinces in China from 2011 to 2021 is used in this analysis, the index system of digital intelligent transformation of manufacturing industry is constructed from two aspects of digitalization and intelligence of manufacturing industry, and the principal component analysis method is used to calculate. The two-way fixed effect model is used to analyze its impact on energy consumption and carbon emissions. The results show that the level of digital intelligence transformation of manufacturing industry in the eastern region is relatively high. In addition, the digital intelligence transformation of the manufacturing industry has a U-shaped impact on the scale and intensity of energy consumption and carbon emissions, and there is a positive linear relationship between the digital intelligence transformation of the manufacturing industry and the energy consumption structure. Further research has found that the impact of different regions and digital transformation indicators on energy consumption and carbon emissions is heterogeneous. This study establishes a foundation for quantitative analysis of the digital intelligence transformation of manufacturing industry, and makes up for the deficiency of existing research that pays more attention to linear relationship. It provides both theoretical basis and policy recommendations for energy conservation and emission reduction in manufacturing. |
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| ISSN: | 2045-2322 |