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Advanced Global CO 2 Emissions Forecasting: Enhancing Accuracy and Stability Across Diverse Regions

Author

Listed:
  • Adham Alsharkawi

    (Department of Mechatronics Engineering, The University of Jordan, Amman 11942, Jordan)

  • Emran Al-Sherqawi

    (Member of the IUCN Climate Crisis Commission, 1196 Gland, Switzerland)

  • Kamal Khandakji

    (Department of Electrical Power and Mechatronics Engineering, Tafila Technical University, Tafila 66110, Jordan)

  • Musa Al-Yaman

    (Department of Mechatronics Engineering, The University of Jordan, Amman 11942, Jordan)

Abstract

This study introduces a robust global time-series forecasting model developed to estimate CO 2 emissions across diverse regions worldwide. The model employs a deep learning architecture with multiple hidden layers, ensuring both high predictive accuracy and temporal stability. Our methodology integrates innovative training strategies and advanced optimization techniques to effectively handle heterogeneous time-series data. Emphasis is placed on the critical role of accurate and stable forecasts in supporting evidence-based policy-making and promoting environmental sustainability. This work contributes to global efforts to monitor and mitigate climate change, in alignment with the United Nations Sustainable Development Goals (SDGs).

Suggested Citation

  • Adham Alsharkawi & Emran Al-Sherqawi & Kamal Khandakji & Musa Al-Yaman, 2025. "Advanced Global CO 2 Emissions Forecasting: Enhancing Accuracy and Stability Across Diverse Regions," Sustainability, MDPI, vol. 17(15), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:15:p:6893-:d:1712667
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    References listed on IDEAS

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    1. An, Yimeng & Dang, Yaoguo & Wang, Junjie & Zhou, Huimin & Mai, Son T., 2024. "Mixed-frequency data Sampling Grey system Model: Forecasting annual CO2 emissions in China with quarterly and monthly economic-energy indicators," Applied Energy, Elsevier, vol. 370(C).
    2. Akan, Taner, 2025. "Forecasting the future of carbon emissions by business confidence," Applied Energy, Elsevier, vol. 382(C).
    3. Yuan, Hong & Ma, Xin & Ma, Minda & Ma, Juan, 2024. "Hybrid framework combining grey system model with Gaussian process and STL for CO2 emissions forecasting in developed countries," Applied Energy, Elsevier, vol. 360(C).
    4. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    5. Peng Zhu & Han Zhang & Yunsheng Shi & Wanli Xie & Mingyong Pang & Yuhui Shi, 2025. "A novel discrete conformable fractional grey system model for forecasting carbon dioxide emissions," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(6), pages 13581-13609, June.
    6. Ding, Song & Zhang, Huahan, 2023. "Forecasting Chinese provincial CO2 emissions: A universal and robust new-information-based grey model," Energy Economics, Elsevier, vol. 121(C).
    7. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    8. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    9. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    10. Zhong, Weiyi & Zhai, Dengshuai & Xu, Wenran & Gong, Wenwen & Yan, Chao & Zhang, Yang & Qi, Lianyong, 2024. "Accurate and efficient daily carbon emission forecasting based on improved ARIMA," Applied Energy, Elsevier, vol. 376(PA).
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