Innovative Approaches for Carbon Reduction in the Built Environment: Advancing Energy Efficiency and Sustainability with AI and Beyond
Topic Information
Dear Colleagues,
Buildings account for roughly 37–39% of global carbon emissions, making the built environment a crucial target for climate change mitigation. Recent research trends leverage artificial intelligence (AI) and machine learning (ML) to transform how building carbon emissions are analyzed and reduced. AI techniques (e.g., neural networks, deep learning, genetic algorithms) are increasingly used to optimize building energy efficiency, predict buildings’ carbon footprints, and enhance decision-making in sustainable design. Studies show that AI and big data analytics can significantly improve the accuracy of building energy use and emission predictions, enabling smarter operational controls to achieve emission reduction goals. For example, AI-driven systems can intelligently manage HVAC and lighting based on occupancy and weather, curtailing energy waste and reducing real-time carbon output. By integrating data-driven insights across a building’s life cycle, AI empowers more efficient, low-carbon buildings and supports global net-zero carbon objectives in the built environment.
Prof. Dr. Ali Bahadori-Jahromi
Prof. Dr. Johnson Zhang
Topic Editors
Keywords
- artificial intelligence (AI)
- machine learning (ML)
- building carbon emissions
- carbon footprint
- built environment
- sustainable buildings
- climate change mitigation
- energy efficiency
- low-carbon design
- life cycle assessment (LCA)
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