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Intelligent Prediction of Annual CO2 Emissions Under Data Decomposition Mode

Author

Listed:
  • Yelin Wang

    (Guangdong University of Technology)

  • Ping Yang

    (Kunming University of Science and Technology)

  • Zan Song

    (University of Nottingham)

  • Julien Chevallier

    (IPAG Business School (IPAG Lab)
    University of Paris 8 (LED))

  • Qingtai Xiao

    (Kunming University of Science and Technology
    Kunming University of Science and Technology)

Abstract

CO2 emissions have contributed to global warming and belong to high-noise, non-stationary and nonlinear systems. An accurate prediction method for annual CO2 emissions can improve the effectiveness of emission reduction policies. However, the existing prediction methods for small-scaled samples (i.e., hourly or daily time series) are unsuitable for regional policy benchmarks. Hence, a novel hybrid prediction model under data decomposition mode is developed for annual CO2 emissions in this work. For illustration, the five representative CO2 emissions (i.e., China, United States, India, Russian, and Japan) from 1970 to 2019 are collected to verify performance, which are taken from Global Carbon Project. The results show that the average prediction accuracy of the proposed prediction model is up to 97.95%, which whole performance improved by more than 1.61% compared with others. The total of five countries’ annual CO2 emissions in 2020 (18,311.72 metric tons) is approximately equal to that in 2018 (18,353.63 metric tons). The proposed model is a reliable prediction tool for annual CO2 emissions and can assist policymakers in adjusting reduction measures and regulators to access the current effects.

Suggested Citation

  • Yelin Wang & Ping Yang & Zan Song & Julien Chevallier & Qingtai Xiao, 2024. "Intelligent Prediction of Annual CO2 Emissions Under Data Decomposition Mode," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 711-740, February.
    • Handle: RePEc:kap:compec:v:63:y:2024:i:2:d:10.1007_s10614-023-10357-8
    DOI: 10.1007/s10614-023-10357-8
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    References listed on IDEAS

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