IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v26y2024i7d10.1007_s10668-023-03325-7.html
   My bibliography  Save this article

Estimating China’s CO2 emissions under the influence of COVID-19 epidemic using a novel fractional multivariate nonlinear grey model

Author

Listed:
  • Wen Lei

    (Wuhan University of Technology)

  • Shuhua Mao

    (Wuhan University of Technology)

  • Yonghong Zhang

    (Wuhan University of Technology)

Abstract

The COVID-19 pandemic has posed a severe challenge to the global economic recovery and China’s economic growth. Although China has achieved stage victory against the epidemic, the impact and influence of the epidemic on China’s economy will linger. On the positive side, the epidemic has led to a dramatic reduction in air pollution levels and a sharp slowdown in greenhouse gas emissions such as CO2. Forecasting China’s CO2 emissions under the impact of the COVID-19 epidemic is crucial for formulating policies that contribute to smooth economic development and rational energy consumption. To this end, this paper improves on the traditional grey model, GM(1, N), by developing a novel fractional multivariate nonlinear grey model, FMNGM(q, N). These improvements include two key points. First, different power exponents are assigned to each relevant factor variable to explain their nonlinear, uncertain, and complicated relationships with the system characteristic variables. Second, the integer accumulation is changed to fractional accumulation to preprocess the data, and the Caputo-type fractional derivative is used to characterize the endogenous relationships among the system characteristic data. In addition, the quantum particle swarm optimization (QPSO) algorithm is used to optimize the above parameters with the goal of minimizing MAPE. The collected data on China’s CO2 emission from 2001 to 2018 were divided into two parts according to different stages of economic development: 2001–2009 and 2010–2018. Both sets of data were modelled and analysed separately and compared with other models. Results showed that the proposed model had better prediction accuracy than other models. Finally, the model built using the 2010–2018 data was used to forecast China’s CO2 emissions. Results show that China’s CO2 emissions in 2020 under the impact of COVID-19 will decrease by approximately 3.15% from 2019 to 9893 million tons. The results bear important policy implications for planners in investment in clean energy and infrastructure to achieving the goal of a low-carbon transition.

Suggested Citation

  • Wen Lei & Shuhua Mao & Yonghong Zhang, 2024. "Estimating China’s CO2 emissions under the influence of COVID-19 epidemic using a novel fractional multivariate nonlinear grey model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 17037-17068, July.
    • Handle: RePEc:spr:endesu:v:26:y:2024:i:7:d:10.1007_s10668-023-03325-7
    DOI: 10.1007/s10668-023-03325-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-023-03325-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10668-023-03325-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lu, I.J. & Lewis, Charles & Lin, Sue J., 2009. "The forecast of motor vehicle, energy demand and CO2 emission from Taiwan's road transportation sector," Energy Policy, Elsevier, vol. 37(8), pages 2952-2961, August.
    2. Xu, Ning & Ding, Song & Gong, Yande & Bai, Ju, 2019. "Forecasting Chinese greenhouse gas emissions from energy consumption using a novel grey rolling model," Energy, Elsevier, vol. 175(C), pages 218-227.
    3. Meng, Ming & Niu, Dongxiao, 2011. "Modeling CO2 emissions from fossil fuel combustion using the logistic equation," Energy, Elsevier, vol. 36(5), pages 3355-3359.
    4. Xu, Bin & Lin, Boqiang, 2015. "Carbon dioxide emissions reduction in China's transport sector: A dynamic VAR (vector autoregression) approach," Energy, Elsevier, vol. 83(C), pages 486-495.
    5. Kang, Yuxiao & Mao, Shuhua & Zhang, Yonghong, 2022. "Fractional time-varying grey traffic flow model based on viscoelastic fluid and its application," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 149-174.
    6. Meng, Ming & Niu, Dongxiao & Shang, Wei, 2014. "A small-sample hybrid model for forecasting energy-related CO2 emissions," Energy, Elsevier, vol. 64(C), pages 673-677.
    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. Ofosu-Adarkwa, Jeffrey & Xie, Naiming & Javed, Saad Ahmed, 2020. "Forecasting CO2 emissions of China's cement industry using a hybrid Verhulst-GM(1,N) model and emissions' technical conversion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    2. Ma, Xuejiao & Jiang, Ping & Jiang, Qichuan, 2020. "Research and application of association rule algorithm and an optimized grey model in carbon emissions forecasting," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    3. Meng, Ming & Niu, Dongxiao & Shang, Wei, 2014. "A small-sample hybrid model for forecasting energy-related CO2 emissions," Energy, Elsevier, vol. 64(C), pages 673-677.
    4. Wu, Lifeng & Liu, Sifeng & Liu, Dinglin & Fang, Zhigeng & Xu, Haiyan, 2015. "Modelling and forecasting CO2 emissions in the BRICS (Brazil, Russia, India, China, and South Africa) countries using a novel multi-variable grey model," Energy, Elsevier, vol. 79(C), pages 489-495.
    5. Ming Meng & Wei Shang & Xinfang Wang & Tingting Pang, 2020. "When will China fulfill its carbon‐related intended nationally determined contributions? An in‐depth environmental Kuznets curve analysis," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 10(5), pages 1039-1049, October.
    6. Jiang, Jingjing & Ye, Bin & Liu, Junguo, 2019. "Research on the peak of CO2 emissions in the developing world: Current progress and future prospect," Applied Energy, Elsevier, vol. 235(C), pages 186-203.
    7. Jiefang Dong & Chun Deng & Rongrong Li & Jieyu Huang, 2016. "Moving Low-Carbon Transportation in Xinjiang: Evidence from STIRPAT and Rigid Regression Models," Sustainability, MDPI, vol. 9(1), pages 1-15, December.
    8. Ding, Qi & Xiao, Xinping & Kong, Dekai, 2023. "Estimating energy-related CO2 emissions using a novel multivariable fuzzy grey model with time-delay and interaction effect characteristics," Energy, Elsevier, vol. 263(PE).
    9. Ma, Xuejiao & Wang, Yong & Wang, Chen, 2017. "Low-carbon development of China's thermal power industry based on an international comparison: Review, analysis and forecast," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 942-970.
    10. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    11. Qodri Febrilian Erahman & Nadhilah Reyseliani & Widodo Wahyu Purwanto & Mahmud Sudibandriyo, 2019. "Modeling Future Energy Demand and CO 2 Emissions of Passenger Cars in Indonesia at the Provincial Level," Energies, MDPI, vol. 12(16), pages 1-25, August.
    12. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    13. Reham Alhindawi & Yousef Abu Nahleh & Arun Kumar & Nirajan Shiwakoti, 2020. "Projection of Greenhouse Gas Emissions for the Road Transport Sector Based on Multivariate Regression and the Double Exponential Smoothing Model," Sustainability, MDPI, vol. 12(21), pages 1-18, November.
    14. Atanu Ghoshray & Yurena Mendoza & Mercedes Monfort & Javier Ordoñez, 2018. "Re-assessing causality between energy consumption and economic growth," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-15, November.
    15. Gholami, M. & Barbaresi, A. & Torreggiani, D. & Tassinari, P., 2020. "Upscaling of spatial energy planning, phases, methods, and techniques: A systematic review through meta-analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    16. Bosupeng, Mpho, 2015. "Drivers of Global Carbon Dioxide Emissions: International Evidence," MPRA Paper 77925, University Library of Munich, Germany, revised 2015.
    17. Hong Chang & Wei Sun & Xingsheng Gu, 2013. "Forecasting Energy CO 2 Emissions Using a Quantum Harmony Search Algorithm-Based DMSFE Combination Model," Energies, MDPI, vol. 6(3), pages 1-22, March.
    18. Xinyu Han & Rongrong Li, 2019. "Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model," Energies, MDPI, vol. 12(17), pages 1-24, August.
    19. Chao-Qun Ma & Jiang-Long Liu & Yi-Shuai Ren & Yong Jiang, 2019. "The Impact of Economic Growth, FDI and Energy Intensity on China’s Manufacturing Industry’s CO 2 Emissions: An Empirical Study Based on the Fixed-Effect Panel Quantile Regression Model," Energies, MDPI, vol. 12(24), pages 1-16, December.
    20. Ming Meng & Chenge Song, 2020. "Daily Photovoltaic Power Generation Forecasting Model Based on Random Forest Algorithm for North China in Winter," Sustainability, MDPI, vol. 12(6), pages 1-17, March.

    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:spr:endesu:v:26:y:2024:i:7:d:10.1007_s10668-023-03325-7. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.