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

Changjiang Mao, Jian Luo (), Shengyang Jiao and Bin Zhao
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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

Energies, 2025, vol. 18, issue 7, 1-18

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.

Keywords: carbon emissions forecasting; LMDI decomposition method; BP neural network; policy recommendations (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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