A Bibliometric Review of the Carbon Emissions and Machine Learning Research in the Post-COVID-19 Era

Peiyi Liao () and Dan Chong
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Peiyi Liao: Shanghai University
Dan Chong: Shanghai University

A chapter in Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate, 2023, pp 1278-1290 from Springer

Abstract: Abstract The COVID-19 pandemic has dramatically changed people’s lives and had a major impact on the field of research. In this study, bibliometrics was used to analyze 783 articles in the field of the post-COVID-19 era, among which 55 articles were the most popular management directions. Based on this background, carbon emissions and machine learning are selected for further analysis, which are the current hot research directions. There were only 22 and 12 related articles. This shows that management direction is a hot research topic in the post-COVID-19 era, while carbon emissions and machine learning are relatively few. This study sorted out and analyzed the specific research content of these 34 literatures. It is found that there are few literatures that apply machine learning to the study of carbon emission. But it also found that machine learning methods can be applied to carbon emissions even in the post-COVID-19 era. This not only provides scholars with new ideas and directions for their research, but also provides a theoretical basis for enterprises and governments to formulate corresponding carbon emission management policies.

Keywords: Post-COVID-19 era; Carbon emissions; Machine learning; Bibliometric (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-981-99-3626-7_99

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