A bibliometric analysis of carbon neutrality: Research hotspots and future directions

Xu, Xiang; Gou, Xunjie; Zhang, Weike; Zhao, Yuchen; Zhang, Xu

doi:10.1016/j.heliyon.2023.e18763

Cited by 14 publications

(8 citation statements)

References 65 publications

(65 reference statements)

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“…The rest of the affiliations listed in Figure 7 have three articles published. Similar papers have been analyzed, and Xu et al [44] found that the Chinese Academy of Sciences is the most relevant university, with 52 papers published related to climate change and software implementations, such as IoT, in order to reduce global warming and identify energy storage systems. An important aspect of bibliometric analysis is represented by the authors' countries, providing information about the international interest in a domain and where specialists can be found.…”

Section: Authors' Explanationmentioning

confidence: 92%

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Domenteanu,

Cibu,

Delcea

2024

Sustainability

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Over the past years, machine learning and big data analysis have emerged, starting as a scientific and fictional domain, very interesting but difficult to test, and becoming one of the most powerful tools that is part of Industry 5.0 and has a significant impact on sustainable, resilient manufacturing. This has garnered increasing attention within scholarly circles due to its applicability in various domains. The scope of the article is to perform an exhaustive bibliometric analysis of existing papers that belong to machine learning and big data, pointing out the capability from a scientific point of view, explaining the usability of applications, and identifying which is the actual in a continually changing domain. In this context, the present paper aims to discuss the research landscape associated with the use of machine learning and big data analysis in Industry 5.0 in terms of themes, authors, citations, preferred journals, research networks, and collaborations. The initial part of the analysis focuses on the latest trends and how researchers lend a helping hand to change preconceptions about machine learning. The annual growth rate is 123.69%, which is considerable for such a short period, and it requires a comprehensive analysis to check the boom of articles in this domain. Further, the exploration investigates affiliated academic institutions, influential publications, journals, key contributors, and most delineative authors. To accomplish this, a dataset has been created containing researchers’ papers extracted from the ISI Web of Science database using keywords associated with machine learning and big data, starting in 2016 and ending in 2023. The paper incorporates graphs, which describe the most relevant authors, academic institutions, annual publications, country collaborations, and the most used words. The paper ends with a review of the globally most cited documents, describing the importance of machine learning and big data in Industry 5.0.

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Section: Authors' Explanationmentioning

confidence: 92%

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Domenteanu,

Cibu,

Delcea

2024

Sustainability

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“…In order to swiftly get a clear understanding of the intellectual landscape formed by documents in the field, we utilize the bibliometrics methodology to support the entire analysis process. Bibliometrics analysis helps us to process massive bibliometric data systematically and logically (Xu et al, 2023). On the one hand, it is able to investigate the top performers in a specific field from different angles without losing a hold of the whole picture (Qian et al, 2023a).…”

Section: Methodsmentioning

confidence: 99%

The Development and Progress of Engineering Economics: A Retrospect and Prospect Based on Visual Analysis

Qian,

Gou,

2024

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Engineering economics is a cross subject with a wide range of applications, and it has taken on different characteristics with the changing times. The aim of this paper is to depict a sufficiently elaborate and vivid knowledge map of this field, with further discussion and outlook on the hotspots presented therein. Based on the principles and methods of bibliometrics, we use several visualization tools, mainly Vosviewer, to present the characteristics of the published literature within the field of engineering economics from multiple perspectives. Specifically, we collect 624 engineering economics documents published in the Web of Science core collection database between 1915 and 2021, and quantitatively analyze them in the following three aspects: (1) basic data characteristics, including annual publications, annual citations, research directions, and highly cited publications; (2) outstanding performers and cooperations in the four levels of country/region, institution, source and author, including co-authorship, bibliographic coupling, co-citation and co-occurrence analyses; and (3) keyword analyses, including co-occurrence analyses, burst detection analyses, and high-frequency word clouds. In addition, we further explore important topics within the field represented by intelligent and green transformation.

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“…Currently, many tools are available for bibliometric analysis, including VOSviewer, Bibliometrix R-package, CiteSpace, BibExcel, etc. One of the most popular bibliometric analysis tools is VOSviewer (Xu et al, 2023), and its ability to perform cluster analysis is beneficial in identifying similar research interests or study areas (van Eck & Waltman, 2010). The VOSviewer is used in this study to analyze the literature extensively on the application of FST to the CE and to pinpoint its potential future directions (Zhang et al, 2021b).…”

Section: Analysis Tool and Data Acquisitionmentioning

confidence: 99%

Circular Economy and Fuzzy Set Theory: A Bibliometric and Systematic Review Based on Industry 4.0 Technologies Perspective

Gou,

Xu,

et al. 2024

Technological and Economic Development of Economy

Self Cite

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The Circular Economy (CE) is receiving more attention, especially in Industry 4.0 (I4.0). In the face of several ambiguous and uncertain information, fuzzy techniques based on Fuzzy Set Theory (FST) are essential for developing CE strategies. This paper uses bibliometric methods to analyze the characteristics of the authors, nations/regions, institutions of the literature of FST and CE, and the collaborations relations between them, and then summarize the literature on fuzzy techniques in the CE and identify the specific role that FST can play in each stage of CE, its primary effects on the CE’s pre-preparation stage, design and production stage, and recycling and reuse stage. Meanwhile, the paper explores the advantages of I4.0 technologies for CE and analyzes the research on the role of fuzzy techniques based on FST for CE and I4.0 technologies. Last but not least, this paper is concluded by summarizing the knowledge gained from the bibliometric and content analyses of the literature and suggesting further research directions of investigation. This research will draw attention to FST’s contribution and encourage its advancement in CE and I4.0 technologies.

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A bibliometric analysis of carbon neutrality: Research hotspots and future directions

Cited by 14 publications

References 65 publications

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

Mapping the Research Landscape of Industry 5.0 from a Machine Learning and Big Data Analytics Perspective: A Bibliometric Approach

The Development and Progress of Engineering Economics: A Retrospect and Prospect Based on Visual Analysis

Circular Economy and Fuzzy Set Theory: A Bibliometric and Systematic Review Based on Industry 4.0 Technologies Perspective

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