IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i23p10782-d1539749.html
   My bibliography  Save this article

Research on Factors Influencing Global Carbon Emissions and Forecasting Models

Author

Listed:
  • Ruizhi Ji

    (Department of Financial Technology, School of Economics and Finance, Xi’an Jiaotong University, Xi’an 710049, China)

Abstract

Investigating the determinants of global carbon emissions and developing carbon emission models are essential to meet the 2050 carbon neutrality goal. This paper initially examines the primary factors shaping global carbon emissions over the past two decades, employing case studies and panel data analysis. Subsequently, a CNN-LSTM carbon emissions prediction model is established using data from Hebei Province, China, spanning from 2005 to 2022. This study reveals that global carbon emissions are predominantly affected by elements such as population, economic growth, industrial activities, energy consumption, environmental conditions, and technological advancements. By incorporating these variables, the CNN-LSTM model proposed in this research significantly enhances the average relative accuracy of carbon emission forecasts, thereby contributing substantially to global efforts in energy conservation and emission reduction.

Suggested Citation

  • Ruizhi Ji, 2024. "Research on Factors Influencing Global Carbon Emissions and Forecasting Models," Sustainability, MDPI, vol. 16(23), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10782-:d:1539749
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/23/10782/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/23/10782/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Agga, Ali & Abbou, Ahmed & Labbadi, Moussa & El Houm, Yassine, 2021. "Short-term self consumption PV plant power production forecasts based on hybrid CNN-LSTM, ConvLSTM models," Renewable Energy, Elsevier, vol. 177(C), pages 101-112.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ruixin Xu & Yongwen Yang & Liting Zhang & Qifen Li & Fanyue Qian & Lifei Song & Bangpeng Xie, 2025. "Life Cycle Carbon Emissions Accounting of China’s Physical Publishing Industry," Sustainability, MDPI, vol. 17(4), pages 1-17, February.

    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. Liu, Xingdou & Zhang, Li & Wang, Jiangong & Zhou, Yue & Gan, Wei, 2023. "A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data," Renewable Energy, Elsevier, vol. 211(C), pages 948-963.
    2. Liao, Zhouyi & Coimbra, Carlos F.M., 2024. "Hybrid solar irradiance nowcasting and forecasting with the SCOPE method and convolutional neural networks," Renewable Energy, Elsevier, vol. 232(C).
    3. Neethu Elizabeth Michael & Manohar Mishra & Shazia Hasan & Ahmed Al-Durra, 2022. "Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique," Energies, MDPI, vol. 15(6), pages 1-20, March.
    4. Zheng, Lingwei & Su, Ran & Sun, Xinyu & Guo, Siqi, 2023. "Historical PV-output characteristic extraction based weather-type classification strategy and its forecasting method for the day-ahead prediction of PV output," Energy, Elsevier, vol. 271(C).
    5. Negri, Simone & Giani, Federico & Blasuttigh, Nicola & Massi Pavan, Alessandro & Mellit, Adel & Tironi, Enrico, 2022. "Combined model predictive control and ANN-based forecasters for jointly acting renewable self-consumers: An environmental and economical evaluation," Renewable Energy, Elsevier, vol. 198(C), pages 440-454.
    6. Xue-Bo Jin & Wen-Tao Gong & Jian-Lei Kong & Yu-Ting Bai & Ting-Li Su, 2022. "PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data," Mathematics, MDPI, vol. 10(4), pages 1-17, February.
    7. Li, Xuan & Zhang, Wei, 2022. "Physics-informed deep learning model in wind turbine response prediction," Renewable Energy, Elsevier, vol. 185(C), pages 932-944.
    8. Belqasem Aljafari & Siva Rama Krishna Madeti & Priya Ranjan Satpathy & Sudhakar Babu Thanikanti & Bamidele Victor Ayodele, 2022. "Automatic Monitoring System for Online Module-Level Fault Detection in Grid-Tied Photovoltaic Plants," Energies, MDPI, vol. 15(20), pages 1-28, October.
    9. Mirza, Adeel Feroz & Mansoor, Majad & Usman, Muhammad & Ling, Qiang, 2023. "A comprehensive approach for PV wind forecasting by using a hyperparameter tuned GCVCNN-MRNN deep learning model," Energy, Elsevier, vol. 283(C).
    10. Robert Basmadjian & Amirhossein Shaafieyoun, 2023. "Assessing ARIMA-Based Forecasts for the Percentage of Renewables in Germany: Insights and Lessons for the Future," Energies, MDPI, vol. 16(16), pages 1-19, August.
    11. Dušan P. Nikezić & Uzahir R. Ramadani & Dušan S. Radivojević & Ivan M. Lazović & Nikola S. Mirkov, 2022. "Deep Learning Model for Global Spatio-Temporal Image Prediction," Mathematics, MDPI, vol. 10(18), pages 1-15, September.
    12. Huang, Songtao & Zhou, Qingguo & Shen, Jun & Zhou, Heng & Yong, Binbin, 2024. "Multistage spatio-temporal attention network based on NODE for short-term PV power forecasting," Energy, Elsevier, vol. 290(C).
    13. Ding, Jiaqi & Zhao, Pu & Liu, Changjun & Wang, Xiaofang & Xie, Rong & Liu, Haitao, 2024. "From irregular to continuous: The deep Koopman model for time series forecasting of energy equipment," Applied Energy, Elsevier, vol. 364(C).
    14. Hu, Yusha & Man, Yi, 2023. "Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    15. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2022. "Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    16. Zhang, Weiyi & Zhou, Haiyang & Bao, Xiaohua & Cui, Hongzhi, 2023. "Outlet water temperature prediction of energy pile based on spatial-temporal feature extraction through CNN–LSTM hybrid model," Energy, Elsevier, vol. 264(C).
    17. Parenti, Mattia & Fossa, Marco & Delucchi, Lorenzo, 2024. "A model for energy predictions and diagnostics of large-scale photovoltaic systems based on electric data and thermal imaging of the PV fields," Renewable and Sustainable Energy Reviews, Elsevier, vol. 206(C).
    18. Nikola Mišnić & Bojan Pejović & Jelena Jovović & Sunčica Rogić & Vladimir Đurišić, 2022. "The Economic Viability of PV Power Plant Based on a Neural Network Model of Electricity Prices Forecast: A Case of a Developing Market," Energies, MDPI, vol. 15(17), pages 1-14, August.
    19. Xu, Shaozhen & Liu, Jun & Huang, Xiaoqiao & Li, Chengli & Chen, Zaiqing & Tai, Yonghang, 2024. "Minutely multi-step irradiance forecasting based on all-sky images using LSTM-InformerStack hybrid model with dual feature enhancement," Renewable Energy, Elsevier, vol. 224(C).
    20. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).

    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:jsusta:v:16:y:2024:i:23:p:10782-:d:1539749. 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.