IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i14p2169-d1432856.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/14/2169/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/14/2169/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhu, Bangzhu & Ye, Shunxin & Jiang, Minxing & Wang, Ping & Wu, Zhanchi & Xie, Rui & Chevallier, Julien & Wei, Yi-Ming, 2019. "Achieving the carbon intensity target of China: A least squares support vector machine with mixture kernel function approach," Applied Energy, Elsevier, vol. 233, pages 196-207.
    2. Ang, B. W., 2005. "The LMDI approach to decomposition analysis: a practical guide," Energy Policy, Elsevier, vol. 33(7), pages 867-871, May.
    3. 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.
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Huadun & Du, Qianxi & Huo, Tengfei & Liu, Peiran & Cai, Weiguang & Liu, Bingsheng, 2023. "Spatiotemporal patterns and driving mechanism of carbon emissions in China's urban residential building sector," Energy, Elsevier, vol. 263(PE).
    2. Zhang, Shulin & Su, Xiaoling & Singh, Vijay P & Ayantobo, Olusola Olaitan & Xie, Juan, 2018. "Logarithmic Mean Divisia Index (LMDI) decomposition analysis of changes in agricultural water use: a case study of the middle reaches of the Heihe River basin, China," Agricultural Water Management, Elsevier, vol. 208(C), pages 422-430.
    3. Lu, I.J. & Lin, Sue J. & Lewis, Charles, 2007. "Decomposition and decoupling effects of carbon dioxide emission from highway transportation in Taiwan, Germany, Japan and South Korea," Energy Policy, Elsevier, vol. 35(6), pages 3226-3235, June.
    4. Ronald E. Miller & Umed Temurshoev, 2017. "Output Upstreamness and Input Downstreamness of Industries/Countries in World Production," International Regional Science Review, , vol. 40(5), pages 443-475, September.
    5. Jia Peng & Xianli Hu & Xinyue Fan & Kai Wang & Hao Gong, 2023. "The Impact of the Green Economy on Carbon Emission Intensity: Comparisons, Challenges, and Mitigating Strategies," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    6. Trotta, Gianluca, 2020. "Assessing energy efficiency improvements and related energy security and climate benefits in Finland: An ex post multi-sectoral decomposition analysis," Energy Economics, Elsevier, vol. 86(C).
    7. GUPTA Monika, 2019. "Decomposing The Role Of Different Factors In Co2 Emissions Increase In South Asia," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 14(1), pages 72-86, April.
    8. de Freitas, Luciano Charlita & Kaneko, Shinji, 2011. "Decomposition of CO2 emissions change from energy consumption in Brazil: Challenges and policy implications," Energy Policy, Elsevier, vol. 39(3), pages 1495-1504, March.
    9. Linghua Qiu & Junhao He & Chao Yue & Philippe Ciais & Chunmiao Zheng, 2024. "Substantial terrestrial carbon emissions from global expansion of impervious surface area," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    10. Wang, Miao & Feng, Chao, 2017. "Analysis of energy-related CO2 emissions in China’s mining industry: Evidence and policy implications," Resources Policy, Elsevier, vol. 53(C), pages 77-87.
    11. Zhen, Wei & Qin, Quande & Wei, Yi-Ming, 2017. "Spatio-temporal patterns of energy consumption-related GHG emissions in China's crop production systems," Energy Policy, Elsevier, vol. 104(C), pages 274-284.
    12. Román-Collado, Rocío & Colinet, María José, 2018. "Are labour productivity and residential living standards drivers of the energy consumption changes?," Energy Economics, Elsevier, vol. 74(C), pages 746-756.
    13. Mohlin, Kristina & Camuzeaux, Jonathan R. & Muller, Adrian & Schneider, Marius & Wagner, Gernot, 2018. "Factoring in the forgotten role of renewables in CO2 emission trends using decomposition analysis," Energy Policy, Elsevier, vol. 116(C), pages 290-296.
    14. Hwang, In Chang, 2013. "Stochastic Kaya model and its applications," MPRA Paper 55099, University Library of Munich, Germany.
    15. Zheng, Jiali & Mi, Zhifu & Coffman, D'Maris & Milcheva, Stanimira & Shan, Yuli & Guan, Dabo & Wang, Shouyang, 2019. "Regional development and carbon emissions in China," Energy Economics, Elsevier, vol. 81(C), pages 25-36.
    16. Congxin Li & Xu Zhang, 2022. "The Influencing Mechanisms on Global Industrial Value Chains Embedded in Trade Implied Carbon Emissions from a Higher-Order Networks Perspective," Sustainability, MDPI, vol. 14(22), pages 1-38, November.
    17. Ouyang, Xiaoling & Lin, Boqiang, 2015. "An analysis of the driving forces of energy-related carbon dioxide emissions in China’s industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 838-849.
    18. Ang, B.W. & Goh, Tian, 2019. "Index decomposition analysis for comparing emission scenarios: Applications and challenges," Energy Economics, Elsevier, vol. 83(C), pages 74-87.
    19. Ang, B.W. & Liu, Na, 2007. "Energy decomposition analysis: IEA model versus other methods," Energy Policy, Elsevier, vol. 35(3), pages 1426-1432, March.
    20. Kamel Louhichi & Aymeric Ricome & Sergio Gomez y Paloma, 2022. "Impacts of agricultural taxation in Sub‐Saharan Africa: Insights from agricultural produce cess in Tanzania," Agricultural Economics, International Association of Agricultural Economists, vol. 53(5), pages 671-686, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:14:p:2169-:d:1432856. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.