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

Adham Alsharkawi (), Emran Al-Sherqawi, Kamal Khandakji and Musa Al-Yaman
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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

Sustainability, 2025, vol. 17, issue 15, 1-19

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).

Keywords: Sustainable Development Goals (SDGs); Artificial Intelligence (AI); CO 2 emissions forecasting; time-series analysis; environmental sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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