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Logarithmic Mean Divisia Index Analysis and Dynamic Back Propagation Neural Network Prediction of Transport Carbon Emissions in Henan Province

Author

Listed:
  • Changjiang Mao

    (School of Automobile and Transportation, Xihua University, Chengdu 610039, China)

  • Jian Luo

    (School of Automobile and Transportation, Xihua University, Chengdu 610039, China)

  • Shengyang Jiao

    (School of Automobile and Transportation, Xihua University, Chengdu 610039, China)

  • Bin Zhao

    (School of Automobile and Transportation, Xihua University, Chengdu 610039, China)

Abstract

Amid escalating global concerns over climate change and sustainable development, carbon emissions have emerged as a critical issue for the international community. The control of carbon dioxide (CO 2 ) emissions is particularly crucial for meeting the objectives of the Paris Agreement. This study applied the LMDI decomposition method and a BP neural network model to thoroughly analyse the factors influencing carbon emissions in Henan Province’s transportation sector and forecast future trends. Our core contribution is the development of an integrated model that quantifies the impact of key factors on carbon emissions and offers policy recommendations. This study concludes that by optimizing the energy structure and enhancing energy efficiency, China can meet its carbon peak and neutrality targets, thereby providing scientific guidance for sustainable regional development.

Suggested Citation

  • Changjiang Mao & Jian Luo & Shengyang Jiao & Bin Zhao, 2025. "Logarithmic Mean Divisia Index Analysis and Dynamic Back Propagation Neural Network Prediction of Transport Carbon Emissions in Henan Province," Energies, MDPI, vol. 18(7), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1630-:d:1619517
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