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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

    as
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