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The role of industrial intelligence in peaking carbon emissions in China

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  • Wang, Linhui
  • Chen, Qi
  • Dong, Zhiqing
  • Cheng, Lu

Abstract

As industrial intelligence empowers economic advancement and technological progress, its capacity to enable decarbonization during China's low-carbon transition remains uncertain. Existing literature concentrates on the environmental impact of information and communication technologies or uses single-dimension measuring indicators, failing to capture the comprehensiveness of industrial intelligence. Based on the concept of full-cycle management, this study constructs a comprehensive index to evaluate industrial intelligence from intelligent inputs, intelligent production capacity, and intelligent outputs. Using 2006–2020 Chinese provincial panel data and fixed-effects models, we analyze the effects, mechanisms and thresholds of industrial intelligence on carbon emissions. The results show that: (1) The level of industrial intelligence plays a role in reducing carbon emissions after a certain threshold. (2) Industrial intelligence reduces carbon emissions by advancing green technology and optimizing production structure. (3) Industrial intelligence mitigates carbon emissions in eastern and north China but not western China. According to current growth rates of industrial intelligence, nine Chinese provinces may achieve a carbon peak through industry intelligence by 2030. (4) Moreover, industrial intelligence's carbon emission reduction effect is significant in labor- and technology-intensive industries but does not work in capital-intensive industries. These revelations have meaningful implications for orienting industrial intelligence development and decarbonization initiatives.

Suggested Citation

  • Wang, Linhui & Chen, Qi & Dong, Zhiqing & Cheng, Lu, 2024. "The role of industrial intelligence in peaking carbon emissions in China," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    • Handle: RePEc:eee:tefoso:v:199:y:2024:i:c:s004016252300690x
    DOI: 10.1016/j.techfore.2023.123005
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