Towards Smart Active Buildings: Reducing Carbon Footprint and Achieving Sustainable Development through Artificial Intelligence
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Towards Smart Active Buildings: Reducing Carbon Footprint and Achieving Sustainable Development through Artificial Intelligence

Submissions Open: 1 September 2025  •  Submission Deadline: 31 March 2026

Special-Issue Editors

Cosimo Magazzino

Department of Political Science, Roma Tre University

cosimo.magazzino@uniroma3.it

Environmental economics • Energy economics • Circular economy • Applied Economics • Climate change

Special-Issue Information

Greenhouse gas (GHG) emissions have increased significantly due to rising energy use in homes and businesses. Despite the initiatives taken in different parts of the world, an increasing trend in energy usage is being experienced throughout the globe. Many analysts think this rising trend is because of improved living standards. Energy efficiency in building development is strictly regulated in several countries. European buildings consume 40% of overall energy (Pérez-Andreu et al., 2018). This represents about 39% of the total energy in the United States and about 27.5% in China. 40% of global direct and indirect GHG emissions and 33% of global energy use are attributed to building energy (Dominković et al., 2018). To decrease reliance on the power grid and reduce GHG emissions, Artificial Intelligence (AI)-based ‘Smart Active Buildings’ (SABs) and ‘Net-Zero Energy Buildings’ (NZEBs) try to maintain indoor thermal comfort while using as little energy as possible (Castagneto Gissey et al., 2021; Motlagh et al., 2020). Smart active building modelling has significant advantages for improving building energy efficiency, storage, and the development of sustainable energy systems (Bourdeau et al., 2019). To achieve the United Nations’ Sustainable Development Goals (SDGs), SABs have become an attractive option for addressing global imperatives and strict environmental standards connected to energy and sustainability. Moreover, several AI tools have been used in recent years to analyze energy efficiency and the comfortable indoor living environment (Mehmood et al., 2019) or the interrelationships among energy, environment, and economic development (Magazzino et al., 2022).

SMAs, boosted by AI, stand at the forefront of sustainable development, playing a crucial role in mitigating carbon footprints. These innovative structures leverage advanced technologies to optimize energy consumption, enhance efficiency, and promote environmental sustainability. AI algorithms analyze data from sensors embedded within the building’s infrastructure, dynamically adjusting heating, cooling, and lighting systems based on real-time occupancy and external conditions. This intelligent automation ensures optimal comfort for occupants and significantly reduces energy wastage. Furthermore, SMAs contribute to the broader goal of environmental sustainability by integrating renewable energy sources such as solar panels and wind turbines. AI algorithms manage the seamless integration of these renewables into the building’s energy grid, maximizing the utilization of clean energy (Morelli et al., 2022). These systems can forecast energy demand through predictive analytics, allowing for proactive adjustments and reducing reliance on traditional, carbon-intensive power sources during peak periods. In essence, the fusion of AI with SMAs offers a transformative solution for minimizing environmental impact and advancing sustainable development, providing a blueprint for a future where our structures actively contribute to a healthier planet.

Objective & Scope

The objectives of this Special Issue include the effect of smart active buildings on the environment and national carbon footprint. This Special Issue aims to bridge the knowledge gap among smart buildings, environmental sustainability, and AI innovation in the 21st-century digital age by collecting high-quality research papers from a global perspective.

Researchers are encouraged to submit both original research and review articles for this Special Issue on AI applications for the realization of SMAs and NZEBs. The potential topics of this Special Issue include, but are not limited to:

  • Possibility of converting existing buildings into SMAs.
  • AI-based sustainable energy technologies for SMAs.
  • AI-based energy management and control in SMAs.
  • AI-based energy performance assessment and optimization of SMAs.
  • AI-based energy tools for reducing energy wastage.
  • Role of AI in providing energy flexibility and demand-response for SMAs.
  • Inclusive approach covering economic, social, and environmental dimensions of SMAs.
  • Recent progress on NZEBs.
  • New technologies for NZEBs.
  • NZEBs and SMAs’ contribution to GHG emission reduction.

Keywords

Smart Active Buildings (SABs) • Artificial Intelligence (AI) • Net-Zero Energy Buildings (NZEBs) • Energy Efficiency • Carbon Footprint Reduction • Sustainable Development • AI-based Energy Management • Greenhouse Gas (GHG) Emissions • Renewable Energy Integration • Predictive Energy Analytics

Articles

No articles in this Special Issue yet.

References

Bourdeau, M., Zhai, X., Nefzaoui, E., Guo, X., & Chatellier, P. (2019). Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society, 48. https://doi.org/10.1016/J.SCS.2019.101533

Castagneto Gissey, G., Zakeri, B., Dodds, P. E., & Subkhankulova, D. (2021). Evaluating consumer investments in distributed energy technologies. Energy Policy, 149, 112008. https://doi.org/10.1016/J.ENPOL.2020.112008

Dominković, D. F., Dobravec, V., Jiang, Y., Nielsen, P. S., & Krajačić, G. (2018). Modelling smart energy systems in tropical regions. Energy, 155, 592–609. https://doi.org/10.1016/J.ENERGY.2018.05.007

Magazzino, C., Mele, M., Schneider, N., & Shahzad, U. (2022). Does Export Product Diversification Spur Energy Demand in the APEC region? Application of a New Neural Networks Experiment and a Decision Tree Model. Energy and Buildings, 258, 111820, https://doi.org/10.1016/j.enbuild.2021.111820

Mehmood, M. U., Chun, D., Zeeshan, Han, H., Jeon, G., & Chen, K. (2019). A review of the applications of artificial intelligence and big data to buildings for energy-efficiency and a comfortable indoor living environment. Energy and Buildings, 202, 109383. https://doi.org/10.1016/j.enbuild.2019.109383

Morelli, G., Magazzino, C., Gurrieri, A.R., Pozzi, C., & Mele, M. (2022). Designing Smart Energy Systems in an Industry 4.0 Paradigm towards Sustainable Environment. Sustainability, 14, 3315. https://doi.org/10.3390/su14063315

Motlagh, N. H., Khatibi, A., & Aslani, A. (2020). Toward Sustainable Energy-Independent Buildings Using Internet of Things. Energies, 13, 5954. https://doi.org/10.3390/EN13225954

Pérez-Andreu, V., Aparicio-Fernández, C., Martínez-Ibernón, A., & Vivancos, J. L. (2018). Impact of climate change on heating and cooling energy demand in a residential building in a Mediterranean climate. Energy, 165, 63–74. https://doi.org/10.1016/J.ENERGY.2018.09.015

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