Nonlinear relationship between digital and intelligent transformation and energy conservation and emission reduction in China
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Summary
This summary is machine-generated.Digital intelligence transformation in manufacturing shows a U-shaped impact on energy consumption and carbon emissions, with regional variations. This study offers insights for sustainable manufacturing practices and emission reduction strategies.
Area Of Science
- Industrial Ecology
- Environmental Economics
- Manufacturing Engineering
Background
- The manufacturing sector's digital transformation raises concerns about environmental consequences.
- Existing research often overlooks non-linear relationships between digital transformation and environmental impact.
Purpose Of The Study
- To analyze the impact of manufacturing's digital intelligence transformation on energy consumption and carbon emissions in China.
- To investigate the heterogeneous effects across different regions and digital transformation indicators.
Main Methods
- Construction of a digital intelligence transformation index system for manufacturing (digitalization and intelligence).
- Application of Principal Component Analysis (PCA) for index calculation.
- Utilization of a two-way fixed-effect model for impact analysis on energy consumption and carbon emissions.
Main Results
- The eastern region exhibits a higher level of digital intelligence transformation in manufacturing.
- A U-shaped relationship exists between digital intelligence transformation and the scale/intensity of energy consumption and carbon emissions.
- A positive linear relationship is observed between digital intelligence transformation and the energy consumption structure.
Conclusions
- Digital intelligence transformation in manufacturing has a complex, non-linear impact on environmental factors.
- Regional and indicator-specific heterogeneity is crucial for understanding energy and emission trends.
- Findings provide a quantitative basis and policy recommendations for energy conservation and emission reduction in manufacturing.
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