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Forecasting and gap analysis of renewable energy integration in zero energy-carbon buildings: a comprehensive bibliometric and machine learning approach

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Abstract

This paper investigates biomass and solar energy's present and future perspectives in low/zero energy and carbon emissions. Its data source is published articles indexed in the Scopus database. By analyzing the articles extracted in Vos viewer software, four main areas of research are found: sustainable development, economic and managerial issues, methods, algorithms, modeling technologies, and renewable energy and its sources and types. In all four sections, research gaps were observed in the field of the third generation of photovoltaics (semi-transparent solar cells )organic)) and algae. As part of the study, advanced bibliometric analysis was carried out by VOS viewer software, and 34129 articles were examined from Scopus, alongside a patent analysis using Google patents, in addition to the bibliometric analysis. It has been shown by machine learning that about 9% of future articles in all energy fields will consist of building articles, and a quarter of these articles will be in the field of renewable energy. While residential and commercial sectors are the dominant areas of renewable energy utilization and commercialization research, the potential of new generations of renewable energy technologies will create significant opportunities to achieve low/zero energy-carbon emission buildings. The paper concludes by predicting the increasing rate of renewable energy and building articles compared to energy articles by 2030, emphasizing the critical role of research in advancing sustainable energy solutions. This data mining analysis helps to identify the current gaps and opportunities. Therefore, great potential will be created to develop and commercialize a new generation of technologies in this industry.

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Datasets analyzed during the current study are available and can be given following a reasonable request from the corresponding author.

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The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Amirhossein Moshari and Rahim Zahedi. The first draft of the manuscript was written by Zahra Zolfaghari and Mohammadreza Malekli and all authors commented on previous versions of the manuscript. Alireza Aslani supervised the manuscript. All authors read and approved the final manuscript.

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Correspondence to Alireza Aslani.

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The authors herewith do confirm that this manuscript has not been published elsewhere and is not also under consideration by the other journals. The authors approve the presented manuscript and do agree with the submission under your management as the editor in chief of Environmental Science and Pollution Research. The current study was carried out under the University of Tehran, Department of Energy Systems Engineering, Tehran, Iran.

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Responsible Editor: Philippe Garrigues

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Moshari, ., Aslani, A., Zolfaghari, Z. et al. Forecasting and gap analysis of renewable energy integration in zero energy-carbon buildings: a comprehensive bibliometric and machine learning approach. Environ Sci Pollut Res 30, 91729–91745 (2023). https://doi.org/10.1007/s11356-023-28669-5

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  • DOI: https://doi.org/10.1007/s11356-023-28669-5

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