IDEAS home Printed from https://ideas.repec.org/a/spr/endesu/v25y2023i9d10.1007_s10668-022-02453-w.html
   My bibliography  Save this article

Building a novel multivariate nonlinear MGM(1,m,N|γ) model to forecast carbon emissions

Author

Listed:
  • Pingping Xiong

    (Nanjing University of Information Science and Technology)

  • Xiaojie Wu

    (Nanjing University of Information Science and Technology)

  • Jing Ye

    (Nanjing University of Finance & Economics
    Nanjing University of Aeronautics and Astronautics)

Abstract

With the proposal of the carbon neutrality target, China's attention to carbon emissions has been further enhanced. Effective prediction of future carbon emissions is important for the formulation of carbon neutralization target and action plans in the region. Many factors affecting carbon emissions, cause their development trends may be nonlinear. To forecast the carbon emissions of coal and natural gas in the industrial sector more accurately, a new MGM(1,m,N|γ) model considering nonlinear characteristics is proposed in this paper. The new model introduces power function γ as nonlinear parameter, and the γ value is solved by nonlinear constraint function. We further deduce the simulation and prediction formula and then apply the improved model to the carbon emission forecast. The comparisons show that the nonlinear parameters can modify the trend of sequences and improve the prediction accuracy, which verifies the validity of the model. Finally, according to the influencing factors and forecast results, this paper analyzes the causes of high carbon emissions and puts forward reasonable suggestions for China's carbon governance.

Suggested Citation

  • Pingping Xiong & Xiaojie Wu & Jing Ye, 2023. "Building a novel multivariate nonlinear MGM(1,m,N|γ) model to forecast carbon emissions," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 9647-9671, September.
    • Handle: RePEc:spr:endesu:v:25:y:2023:i:9:d:10.1007_s10668-022-02453-w
    DOI: 10.1007/s10668-022-02453-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10668-022-02453-w
    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/s10668-022-02453-w?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. Xiaoqing Zhu & Tiancheng Zhang & Weijun Gao & Danying Mei, 2020. "Analysis on Spatial Pattern and Driving Factors of Carbon Emission in Urban–Rural Fringe Mixed-Use Communities: Cases Study in East Asia," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
    2. Wang, Qiang & Song, Xiaoxin, 2019. "Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM," Energy, Elsevier, vol. 183(C), pages 160-171.
    3. Chen, Xing & Lin, Boqiang, 2021. "Towards carbon neutrality by implementing carbon emissions trading scheme: Policy evaluation in China," Energy Policy, Elsevier, vol. 157(C).
    4. Yi-Chung Hu & Peng Jiang & Jung-Fa Tsai & Ching-Ying Yu, 2021. "An Optimized Fractional Grey Prediction Model for Carbon Dioxide Emissions Forecasting," IJERPH, MDPI, vol. 18(2), pages 1-12, January.
    5. Shumin Jiang & Chen Yang & Jingtao Guo & Zhanwen Ding & Lixin Tian & Jianmei Zhang, 2017. "Uncovering the Driving Factors of Carbon Emissions in an Investment Allocation Model of China’s High-Carbon and Low-Carbon Energy," Sustainability, MDPI, vol. 9(6), pages 1-15, June.
    6. Shaikh, Faheemullah & Ji, Qiang & Shaikh, Pervez Hameed & Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam, 2017. "Forecasting China’s natural gas demand based on optimised nonlinear grey models," Energy, Elsevier, vol. 140(P1), pages 941-951.
    7. 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.
    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. Wang, Zheng-Xin & Wang, Zhi-Wei & Li, Qin, 2020. "Forecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors," Energy, Elsevier, vol. 200(C).
    2. Pan, Xunzhang & Wang, Lining & Dai, Jiaquan & Zhang, Qi & Peng, Tianduo & Chen, Wenying, 2020. "Analysis of China’s oil and gas consumption under different scenarios toward 2050: An integrated modeling," Energy, Elsevier, vol. 195(C).
    3. Huiping Wang & Zhun Zhang, 2022. "Forecasting CO 2 Emissions Using A Novel Grey Bernoulli Model: A Case of Shaanxi Province in China," IJERPH, MDPI, vol. 19(9), pages 1-22, April.
    4. Fei Yang & Chunchen Wang, 2023. "Clean energy, emission trading policy, and CO2 emissions: Evidence from China," Energy & Environment, , vol. 34(5), pages 1657-1673, August.
    5. Jinhan Yu & Licheng Sun, 2022. "Supply Chain Emission Reduction Decisions, Considering Overconfidence under Conditions of Carbon Trading Price Volatility," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    6. Victor Fernández-Guzmán & Edgardo R. Bravo, 2018. "Understanding Continuance Usage of Natural Gas: A Theoretical Model and Empirical Evaluation," Energies, MDPI, vol. 11(8), pages 1-17, August.
    7. Chenrui Lu & Bing Wang & Tinggui Chen & Jianjun Yang, 2022. "A Document Analysis of Peak Carbon Emissions and Carbon Neutrality Policies Based on a PMC Index Model in China," IJERPH, MDPI, vol. 19(15), pages 1-16, July.
    8. Kuang, Yunming & Lin, Boqiang, 2021. "Performance of tiered pricing policy for residential natural gas in China: Does the income effect matter?," Applied Energy, Elsevier, vol. 304(C).
    9. Wang, Qiang & Li, Shuyu & Li, Rongrong & Ma, Minglu, 2018. "Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model," Energy, Elsevier, vol. 160(C), pages 378-387.
    10. Li, Wei & Lu, Can, 2019. "The multiple effectiveness of state natural gas consumption constraint policies for achieving sustainable development targets in China," Applied Energy, Elsevier, vol. 235(C), pages 685-698.
    11. Ewa Chodakowska & Joanicjusz Nazarko & Łukasz Nazarko, 2021. "ARIMA Models in Electrical Load Forecasting and Their Robustness to Noise," Energies, MDPI, vol. 14(23), pages 1-22, November.
    12. Renjie Zhang & Hsingwei Tai & Kuotai Cheng & Huizhong Dong & Wenhui Liu & Junjie Hou, 2022. "Carbon Emission Efficiency Network: Evolutionary Game and Sensitivity Analysis between Differentiated Efficiency Groups and Local Governments," Sustainability, MDPI, vol. 14(4), pages 1-19, February.
    13. 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.
    14. Xiaoqian Guo & Qiang Yan & Anjian Wang, 2017. "Assessment of Methods for Forecasting Shale Gas Supply in China Based on Economic Considerations," Energies, MDPI, vol. 10(11), pages 1-14, October.
    15. Oleksandr Castello & Marina Resta, 2023. "A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling," Energies, MDPI, vol. 16(12), pages 1-22, June.
    16. Gao, Ming, 2023. "The impacts of carbon trading policy on China's low-carbon economy based on county-level perspectives," Energy Policy, Elsevier, vol. 175(C).
    17. Teng, Yuqiang & Lin, Boqiang, 2024. "The energy-saving effect of industrial chain synergistic division: Evidence from China's industrial chain," Energy Policy, Elsevier, vol. 185(C).
    18. Qiu, Xin & Jin, Jianjun & He, Rui & Mao, Jiansu, 2022. "The deviation between the willingness and behavior of farmers to adopt electricity-saving tricycles and its influencing factors in Dazu District of China," Energy Policy, Elsevier, vol. 167(C).
    19. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    20. Fuquan Zhao & Fanlong Bai & Xinglong Liu & Zongwei Liu, 2022. "A Review on Renewable Energy Transition under China’s Carbon Neutrality Target," Sustainability, MDPI, vol. 14(22), pages 1-27, November.

    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:25:y:2023:i:9:d:10.1007_s10668-022-02453-w. 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.