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Does Digital-Intelligence Contribute to Carbon Emission Reduction? New Insights from China

Author

Listed:
  • Jian Hou
  • Wei Kang
  • Yao Li
  • Shuming Liang
  • Shuaishuai Geng

Abstract

Digital-intelligence plays an important role in transforming the energy consumption structure and promoting the emission reduction process. To explore the relationship between digital-intelligence and regional carbon emissions, we assume that there is a differential impact in carbon emission reduction effects, given the obvious differences in regional green technology innovation during the development of digital-intelligence in China. Thus, we systematically construct a new digital-intelligence system definition and include the heterogeneous threshold of green technology innovation in the relationship of the impact of digital-intelligence on carbon emissions by using a nonlinear dynamic threshold model. The results show that the development of regional digital-intelligence in China exhibits a relatively stable upward trend, and there is heterogeneity among regions. Interestingly, there is a significant heterogeneous threshold effect from green technology innovation between digital-intelligence and carbon emissions. A lower level of green technology innovation will increase the carbon emission effect of digital-intelligence to a certain extent, but when green technology innovation increases and exceeds a threshold, digital-intelligence is able to dramatically inhibit regional carbon emissions. Our research answers the question of how to achieve digital-intelligence transformation through green technology innovation and reduce total regional carbon emissions and provides new insights for developing countries to curb carbon emissions.

Suggested Citation

  • Jian Hou & Wei Kang & Yao Li & Shuming Liang & Shuaishuai Geng, 2024. "Does Digital-Intelligence Contribute to Carbon Emission Reduction? New Insights from China," SAGE Open, , vol. 14(4), pages 21582440241, December.
    • Handle: RePEc:sae:sagope:v:14:y:2024:i:4:p:21582440241304462
    DOI: 10.1177/21582440241304462
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