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Driver Analysis and Integrated Prediction of Carbon Emissions in China Using Machine Learning Models and Empirical Mode Decomposition

Author

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  • Ruixia Suo

    (College of Management, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Qi Wang

    (College of Management, Xi’an University of Science and Technology, Xi’an 710054, China)

  • Qiutong Han

    (College of Management, Xi’an University of Science and Technology, Xi’an 710054, China)

Abstract

Accurately predicting the trajectory of carbon emissions is vital for achieving a sustainable shift toward a green and low-carbon future. Hence, this paper created a novel model to examine the driver analysis and integrated prediction for Chinese carbon emission, a large carbon-emitting country. The logarithmic mean divisia index (LMDI) approach initially served to decompose the drivers of carbon emissions, analyzing the annual and staged contributions of these factors. Given the non-stationarity and non-linear characteristics in the data sequence of carbon emissions, a decomposition–integration prediction model was proposed. The model employed the empirical mode decomposition (EMD) model to decompose each set of data into a series of components. The various carbon emission components were anticipated using the long short-term memory (LSTM) model based on the deconstructed impacting factors. The aggregate of these predicted components constituted the overall forecast for carbon emissions. The result indicates that the EMD-LSTM model greatly decreased prediction errors over the other comparable models. This paper makes up for the gap in existing research by providing further analysis based on the LMDI method. Additionally, it innovatively incorporates the EMD method into the carbon emission study, and the proposed EMD-LSTM prediction model effectively addresses the volatility characteristics of carbon emissions and demonstrates excellent predictive performance in carbon emission prediction.

Suggested Citation

  • Ruixia Suo & Qi Wang & Qiutong Han, 2024. "Driver Analysis and Integrated Prediction of Carbon Emissions in China Using Machine Learning Models and Empirical Mode Decomposition," Mathematics, MDPI, vol. 12(14), pages 1-16, July.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:14:p:2169-:d:1432856
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    References listed on IDEAS

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    2. Feipeng Guo & Linji Zhang & Zifan Wang & Shaobo Ji, 2022. "Research on Determining the Critical Influencing Factors of Carbon Emission Integrating GRA with an Improved STIRPAT Model: Taking the Yangtze River Delta as an Example," IJERPH, MDPI, vol. 19(14), pages 1-20, July.
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    4. Du, Zhili & Xu, Jie & Lin, Boqiang, 2024. "What does the digital economy bring to household carbon emissions? – From the perspective of energy intensity," Applied Energy, Elsevier, vol. 370(C).
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