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Research on Factors Influencing Global Carbon Emissions and Forecasting Models

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  • 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
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    References listed on IDEAS

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    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.
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    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.

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