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Research on industrial carbon emission prediction method based on CNN–LSTM under dual carbon goals

Author

Listed:
  • Xuwei Xia
  • Dongge Zhu
  • Jiangbo Sha
  • Rui Ma
  • Wenni Kang

Abstract

In order to achieve the dual carbon goal, a prediction method of industrial carbon emissions based on CNN–LSTM was studied. The extended Kaya identity is used to measure the emissions, and the LMDI decomposition method is used to determine the influencing factors. The model inputs historical emission data, extracts spatial features through CNN, and then makes time series prediction by LSTM, and finally outputs the prediction results. Experiments show that this method can effectively predict carbon emissions in different scenarios and provide support for the goal of double carbon.

Suggested Citation

  • Xuwei Xia & Dongge Zhu & Jiangbo Sha & Rui Ma & Wenni Kang, 2025. "Research on industrial carbon emission prediction method based on CNN–LSTM under dual carbon goals," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 580-589.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:580-589.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf012
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