Carbon Emissions Analysis by AI Techniques

Dear Colleagues,

In the face of escalating global concern over carbon (CO₂) emissions and their impact on climate change, innovative solutions are paramount for societies of all sizes. The advent of artificial intelligence (AI) offers transformative potential to address these challenges, marking a pivotal shift in applied energy research. This Special Issue delves into the cutting-edge intersection of AI-driven technologies and carbon emissions efficiency, showcasing pioneering research and methodologies aimed at a sustainable, low-carbon future.

Despite the promising horizon, the application of AI, machine learning, and related technologies in carbon emissions evaluation and forecasting faces notable research gaps. By bringing together the latest in AI-related technologies—including machine learning, data mining, time series analytics, data-driven prediction and forecasting, the Internet of things (IoT), sensor networks, and cutting-edge computing—this Special Issue aims to chart a course toward actionable interpretable data-driven strategies for energy conservation, optimal clean energy utilization, and significant reductions in carbon emissions. This Special Issue serves as a platform for exchanging high-quality research findings, innovative solutions, and discussions that bridge these gaps. It encourages submissions that leverage data-driven techniques with a focus on enhancing interpretability, efficiency, and effectiveness in carbon emissions analytics, modeling, and forecasting.

This Special Issue highlights a spectrum of topics central to this discourse, including, but not limited to: AI-driven smart energy savings, energy efficiency and management, carbon emissions and energy forecasting, machine learning and big data analytics, smart urban development with clean energy, and energy modeling as well as optimization. Each topic represents a facet of the comprehensive approach required to tackle the multifaceted challenges of the reduction in carbon emissions and energy management in the 21st century.

Best regards,

Dr. Tian Li
Dr. Sicheng Zhan
Guest Editors

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• AI-driven energy analysis
• carbon emissions and energy forecasting
• machine learning and big data analytics
• energy efficiency and management
• smart urban development with clean energy
• energy modeling and optimization