Keywords:-
Article Content:-
Abstract
This study develops a smart AI-based framework to predict and understand global progress toward clean and sustainable energy. The proposed Intelligent Green Transition Index (IGTI) combines multiple factors such as carbon emissions, renewable energy share, energy efficiency, economic growth, and green technology innovation into one meaningful measure of sustainability. Using advanced machine learning and deep learning models, the system can forecast how countries’ energy systems will evolve and identify the most influential drivers of change. The model also includes a self-improving mechanism that updates its forecasts as new data and policies emerge, ensuring more accurate and timely insights. A built-in scenario simulation tool allows researchers to explore how actions like renewable investments, carbon pricing, or adoption of green hydrogen could shape the global energy landscape by 2040. Results show that renewable investments, technological innovation, and strong policy frameworks are the main forces behind sustainable transitions. Overall, this research offers a meaningful, data-driven, and adaptive AI approach to guide governments and industries in achieving carbon-neutral and energy-secure futures.
References:-
References
X. Peng, X. Guan, Y. Zeng, and J. Zhang, “Artificial intelligence-driven multi-energy optimization: Promoting green transition of rural energy planning and sustainable energy economy,” Sustainability, vol. 16, no. 10, Art. 4111, 2024.
H. Elmousalami, A. A. Alnaser, and F. K. P. Hui, “Sustainable AI-driven wind energy forecasting (SAI-WEFS): Advancing zero-carbon cities and environmental computation,” Artificial Intelligence Review, vol. 58, Art. 191, 2025.
C. J. Ejiyi et al., “Comprehensive review of artificial intelligence applications in renewable energy systems: Current implementations and emerging trends,” Journal of Big Data, vol. 12, Art. 169, 2025.
C. Huang and B. Lin, “How digital economy index selection and model uncertainty will affect energy green transition,” Energy Economics, vol. 136, 2024.
C. J. Ejiyi et al., “Comprehensive review of artificial intelligence applications in renewable energy systems: Current implementations and emerging trends,” Journal of Big Data, 2025.
S. K. Sharma and A. A. Alnaser, “Artificial intelligence in renewable energy systems: Applications and security challenges,” Energies, vol. 18, no. 8, 1931, 2025.
B. Yu, Y. Zhang, and P. Li, “A review of enhancing wind power with AI: Applications, economic implications, and green innovations,” Digital Economy and Sustainable Development, 2025.
A. R. Raj et al., “Artificial intelligence in energy economics research: A bibliometric review,” Energies, vol. 18, no. 2, 434, 2025.
M. Khan, S. Ali, and R. Joseph, “Nexus between artificial intelligence, renewable energy, and economic development: A multi-method approach,” Economics, vol. 12, 2024.
Y. Sun and T. R. Mahmud, “Machine learning in renewable energy,” Energies, vol. 16, no. 5, 2260, 2023.
A. Patel, R. Chen, and L. Wang, “LTPNet: Integration of deep learning and environmental decision support systems for renewable energy demand forecasting,” arXiv preprint arXiv: 2410.15286, 2024.