A Bibliometric and Thematic Review of Metaverse and Reverse Logistics for a Sustainable Circular Economy

A Bibliometric and Thematic Review of Metaverse and Reverse Logistics for a Sustainable Circular Economy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Bibliometric and Thematic Review of Metaverse and Reverse Logistics for a Sustainable Circular Economy Tran Cong Toan, Le Thi Tuyet Thanh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7221291/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Apr, 2026 Read the published version in Discover Sustainability → Version 1 posted 9 You are reading this latest preprint version Abstract Reverse logistics has become one of the vital foundations of the circular economy, through recovery, reuse and recycling of materials and products. At the same time, the current development of technologies related to the metaverse (the so-called digital twins, VR, and blockchain) is unlocking opportunities to streamline the work of reverse logistics systems by making it virtual, tracking in real-time, and decision-making capacity. Furthermore, an inclusive review at the trio of reverse logistics, circular economy, and immersive technologies is yet to have a review into it. To fill in this gap, this paper performs a systematic literature review in addition to bibliometric analysis of 198 publications found in the Scopus database between 2003–2025. Using SPAR-4-SLR protocol and a collection of bibliometric methods, such as performance analysis, keyword co-occurrence analysis, bibliographic coupling, and thematic evolution analysis, the paper obtains the main authors, trend of publications and citations in the field, intellectual structure, and significant thematic clusters within it. Namely, there are six keyword co-occurrence clusters and seven bibliographic coupling clusters identified and such emerging topics as blockchain-enabled circularity, reverse logistics of electric vehicles, remanufacturing optimization, the future of the metaverse are mentioned. This research will enrich the literature with the synthesis of conceptual landscape, feat of mapping and the rise of thematic evolution, and a future research agenda. The triangulation of themes is based on the concept of creating a theoretical framework that will guide the action of scholars and practitioners to exploit metaverse technologies in achieving sustainable reverse logistics in circular economy paradigm. Reverse logistics Circular economy Immersive technologies Metaverse blockchain IoT VR AR Simulation SLR Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Metaverse is one of the most popular emerging digital technologies, described as a three-dimensional space in Web 3.0, in which individuals can communicate with the use of different technologies: virtual reality (VR), augmented reality (AR), artificial intelligence (AI) (Chen et al., 2023 ; Pamucar et al., 2023 ; Ritterbusch & Teichmann, 2023 ). Web 3.0 is a decentralized Internet development based on blockchain technology, which opens great possibilities in the reorganization of the traditional market (Anderson et al., 2023 ; Dwivedi et al., 2022 ). So far, metaverse was studied and implemented in areas like healthcare, education, production and, importantly, in supply chain management (Sadeghi et al., 2025 ; Xu et al., 2023 ). In this field, the presence of the metaverse in the logistics system, especially the reverse logistics (RL), allows all the parties working in the supply chain to observe the whole chain in the interactive virtual space and makes decisions more accurate and efficient (Hajian et al., 2024 ; Messinger et al., 2009 ; Pamucar et al., 2023 ; Queiroz et al., 2023 ). Reverse logistics refers to a group of operations designed to retrieves the products to the consumer to be further processed or venerate its value such as returning the used products to their recovery point often referred to as take-back systems (Bockholt et al., 2020 ; Erol et al., 2010 ; Ghoreishi et al., 2011 ). RL is extremely important in the context of the circular economy (CE) by facilitating the recirculation of products, components, and materials, including via the constructive means of reusing, recycling, and remanufacturing products (Geissdoerfer et al., 2017 ; Singh & Ordoñez, 2016 ). Nevertheless, using RL in practice continues to present many problems mainly because of the deficiency in knowledge information, implementation skills and sufficient support structures (Mallick et al., 2023 ; Vaz et al., 2013 ; Waqas et al., 2018 ). More so, the traditional logistics management systems fall apart in terms of traceability, transparency, and information security, thus contributing to poor results in the entire supply chain giảm (Nwosu et al., 2024 ). At that, the incorporation of blockchain metaverse implementation into the sphere of logistics will provide a range of high-value technological tools like virtualization of the logistic processes, real-time process surveillance, multi-agent environment, and decentralized data control, used in the context of RL optimization in respect to sustainability as one of the main aspects (Chang et al., 2022 ; Xu et al., 2023 ). Although a number of articles have been dedicated to the consideration of metaverse in the context of sustainability (Chang et al., 2022 ; De Giovanni, 2023 ; Johri et al., 2024 ) and reverse logistics in relation to the circular economy (Correa-Vaca et al., 2023 ; de Almeida et al., 2024 ; Yu et al., 2018 ), there is no research that integrates these topics with emerging technologies like the metaverse. There is also the lack of holistic comprehension of the evolution, systemization, and key themes of knowledge, which explain the interrelation of the metaverse, reverse logistics, and the circular economy. A comprehensive analysis that is sufficiently organized and integrative is necessary in helping scholars, authorities, and enterprises to develop sustainable logistics approaches by utilizing technological creativity. On this backdrop, this paper aims at doing a bibliometric and systematic review to investigate the functions and usage pattern of the metaverse in reverse logistics through the prism of the circular economy. The research also seeks to determine the structure of knowledge, science hubs, and prevailing trends in the development of the studied field by examining scholarly data gathered using the Scopus database. Precisely, the paper aims at resolving the following four research questions: RQ1: How are publications and citations trends in metaverse and reverse logistics research in the environments of the circular economy? RQ2: Which sources, scholarly works, and authors in the field of metaverse and reverse logistics in the circular economy are the most influential ones? RQ3: Which are the chief themes that are being discussed concerning the position of the metaverse in reverse logistics in terms of the perspective of the circular economy? RQ4: How can it be determined what future research should be given priorities to further understand the integration between the metaverse and the reverse logistics as the way of advancing the circular economy? Reaching the answers on these questions, the research will use various bibliometric methods, such as performance analysis to determine the most impact researchers and documents; keyword co-occurrence analysis and bibliographic coupling that will introduce a stringent and multidimensional picture of the broad settings of the research streams; thematic evolution to follow the changes of the research content with time (Donthu et al., 2022 ; Kumar et al., 2021 ). Also, the approaches adopted in the science mapping based on the triangulation method correspond to the recommendations of Lim and Zhang ( 2022 ) that imply analytical rigor and validity of reporting in bibliometric studies. 2. Method In management and, more recently, in marketing, systematic literature reviews (SLRs) are a scientifically sound way to do secondary research (Floren et al., 2020 ; Lim et al., 2021 ). Different kinds of SLRs have been made based on different fields of study. These include structured topic-based, theory-based, bibliometric, hybrid, and conceptual reviews (Lim, Rasul, et al., 2022 ). A mixed method that includes both bibliometric analysis (quantitative) and thematic analysis (qualitative) has been used in this study to answer the research questions. Specifically, bibliometric performance analysis has been used to answer RQ1 and RQ2; scientific mapping of major themes has been used to answer RQ3; and a thematic map and content analysis have been used to evaluate the field's growth and suggest future research directions (RQ4). This review is therefore a hybrid systematic literature review that uses both bibliometric analysis and structured thematic analysis. The “systematic” part of this review was based on the SPAR-4-SLR (Scientific Procedures and Rationales for Systematic Literature Reviews) protocol, which is a new and strict way to do systematic literature reviews (Paul et al., 2021 ). The goal of this review is to be thorough and methodologically sound by systematically finding and analysing all relevant studies on Reverse Logistics and the Metaverse in the Circular Economy using a clear and repeatable process. This kind of review is thought to be the most thorough, strict, and trustworthy compared to other types of reviews, like narrative reviews that don't use a systematic approach (Balaid et al., 2016 ; Boell & Cecez-Kecmanovic, 2015 ; Denyer & Tranfield, 2009 ; Fosso Wamba & Mishra, 2017 ; Paul et al., 2021 ; Verma & Yadav, 2021 ). As a result, a combination of a systematic literature review and a bibliometric analysis has been used. The SPAR-4-SLR protocol created by Paul et al. ( 2021 ) is the main framework that has been used. There are three steps in this protocol: putting things together, organising them, and evaluating them. It was chosen because it is new and has a high level of methodological rigour in the social sciences. This makes it a strong alternative to the older PRISMA protocol, which is often used in social science research but started in the natural sciences. Also, a set of bibliometric analysis methods has been used, such as performance analysis and science mapping, which includes content analysis, keyword co-occurrence analysis, and bibliographic coupling, as suggested by (Donthu et al., 2021 ). Because they can handle and analyse a lot of literature in the field, these methods have been used to show the performance (publication and citation trends, key sources, works, and authors) and intellectual structure (core theories, themes, and components) of the field of Reverse Logistics and the Metaverse under the Circular Economy. Figure 1 shows how the systematic literature review and bibliometric analysis work. The next sections will go into more detail about these processes. 2.1 Assembling There are two parts to the assembling stage: identification and acquisition. This study's main goal was to find literature related to Reverse Logistics and the Metaverse within the Circular Economy in order to answer research questions about the field's academic performance (e.g., publication and citation trends, key sources, publications, and authors) and intellectual structure (e.g., underlying theories, themes, and conceptual frameworks). English-language journal articles are the only types of sources that have been used because they are seen as the main way to add to the body of academic knowledge. The review didn't include other types of sources like books and book chapters because they tend to be more descriptive and don't have clear academic contributions (Ledro et al., 2022 ; Paul et al., 2021 ; Phan et al., 2025 ; Radicchi et al., 2004 ). Scopus has been selected as the data source due to its adherence to rigorous indexing criteria (Garousi et al., 2019 ; Materla et al., 2019 ). Scopus is the world's biggest database of abstracts and citations (Fahimnia et al., 2015 ). It has more information than other databases (Paul & Bhukya, 2021 ; Yong-Hak, 2013 ). The search only looked for articles that were published before June 2025. The study found relevant records by looking at their titles, abstracts, and keywords. The search terms were carefully chosen to cover all the main topics, using keywords from past bibliometric studies. oolean operators (e.g., “OR”, “AND”) were employed to construct a dataset that is both broad and precise. The search terms were: (“circular economy” OR “circular supply chain” OR “closed-loop supply chain”) AND (“reverse logistics” OR “product returns” OR “remanufacturing” OR “waste valorisation”) AND (“metaverse” OR “virtual reality” OR “augmented reality” OR “immersive technology” OR “digital twin” OR “blockchain” OR “simulation”). A total of 232 documents were retrieved during the data collection phase. 2.2 Arranging The arranging stage consists of two sub-steps: organization and filtration. For the organization process, coding criteria have been applied to classify the documents based on language and document type. Regarding filtration, only those documents meeting the following conditions have been retained for further analysis: (1) written in English; and (2) categorized as “articles” or “reviews,” as these types of publications typically undergo a full academic peer-review process. As a result, a total of 198 documents have been retained following the arranging stage. 2.3 Assessing The Assessing phase consists of two sub-stages: evaluation and reporting. This study uses an inductive approach to evaluation, which means it draws conclusions from patterns in the data được (Fahimnia et al., 2015 ; Seuring & Müller, 2008 ). It also uses a set of bibliometric techniques, which are divided into performance analysis and science mapping (Baier-Fuentes et al., 2019 ; Donthu et al., 2021 ). Specifically, quantitative descriptive statistics are used to look at publication and citation trends (RQ1) and find the most important sources, articles, and authors in the field (RQ2). At the same time, two methods are used for science mapping: keyword co-occurrence analysis and bibliographic coupling. These methods help us find core concepts and thematic knowledge clusters (RQ3). Finally, a thematic map and content analysis are used to put together the big picture of what we know about Reverse Logistics and the Metaverse in the Circular Economy (RQ4). This includes looking at the different stages of development, the contributions made by scholars, and the possible future research directions Tools such as Microsoft Excel are used for descriptive and content analysis (Lim, Kumar, et al., 2022 ), while Biblioshiny in R (Aria & Cuccurullo, 2017 ) and VOSviewer (Van Eck & Waltman, 2014 ) are employed to construct and visualize networks emerging from keyword co-occurrence and bibliographic coupling—aligning with the bibliometric analysis recommendations by Donthu et al. ( 2021 ). Using both tools together takes advantage of what makes each one unique: VOSviewer is great at showing large networks in a clear and useful way, especially when it comes to showing highly connected keywords and documents. Biblioshiny, on the other hand, has a user-friendly interface and advanced features like thematic mapping, trend charts, and bibliometric indices. This mixed method strikes the best balance between depth of analysis and clarity of visuals, which is important for modern bibliometric research that uses multiple tools. Each cluster in the network stands for a common theme. Nodes show keywords or cited documents that stand for sub-topics, and links between nodes show how they are related to each other in the larger thematic context of the cluster đó (Donthu et al., 2021 ; Radicchi et al., 2004 ; Van Eck & Waltman, 2010 ). The thickness of the links and the size of the nodes show how important the elements they stand for are (Clauset et al., 2004 ; Radicchi et al., 2004 ; Tran & Khoa, 2025 ; Van Eck & Waltman, 2014 ). Regarding result reporting, the study employs a combination of visualizations, tables, and narrative descriptions to present the findings, following reporting conventions recommended by Paul et al. ( 2021 ). There are also annotated tables with different bibliometric indicators that are explained in writing to give a clear and useful picture of both performance and knowledge structure in the field. The review process and methods have been carried out according to well-known rules from the literature on systematic reviews (Paul et al., 2021 ) and bibliometric analysis (Donthu et al., 2021 ). The research team has also tried to be as open as possible in their reporting. 3. Results The results of this review are shown in two primary sections: which are based on the research questions. The bibliometric performance analysis (RQ1, RQ2) looks at the performance of the fields of Reverse Logistics and the Metaverse in the Circular Economy by looking at publication trends over time, citation metrics, and the top articles, authors, and countries. The science mapping part (RQ3) has co-citation analysis and keyword co-occurrence analysis, which help find the main ideas and groups of knowledge that are most important in the field. 3.1 Performance analysis 3.1.1 Publication and citation trend Trends in publishing and citing research on Reverse Logistics and the Metaverse in the context of the Circular Economy The Scopus dataset for the years 2003 to 2025 has 198 publications from 118 different sources (journals and monographs). This collection shows a field that is growing quickly and getting more academic attention, with an average annual growth rate of 15.54%. The dataset isn't very big, but the average of 33.57 citations per article shows that it has had a big impact on research (Table 1 , Section A). Most of the works are fairly new, with an average age of 4.67 years. This shows that the field is very relevant to current events. The fact that there are 181 empirical articles and only 17 review papers shows that there is a strong focus on new research and discovery. The rapid growth can be linked to the rise of new technologies, policies that support the circular economy, and the global need for environmentally friendly logistics solutions. According to Scopus data (Table 1 , Section B), the documents in the dataset have received a total of 6,647 citations, which is an average of 33.57 citations per publication. There have been 10,477 references cited, which shows that the field has a strong theoretical base and a lot of knowledge. The high citation average shows that the papers have a lot of academic influence, but it's important to remember that a few highly cited papers could change the distribution. Table 1 Descriptive Statistics of Dataset on Reverse Logistics and the Metaverse under the Circular Economy Panel A. Publication information Statistic Total publications (TP) 198 Total sources (TS) 118 Number of active years (NAY) 20 Annual growth rate (AGR) 15,54% Document average age (DAA) 4,67 years Panel B. Citation information Total citations (TC) 6004 Average citations per publication (TC/TP) 33,57 Number of references (REF) 10.477 Panel C. Authorship information Number of contributing authors (NCA) 625 Authors of single-authored publications (ASA) 8 Authors of co-authored publications (ACA) 578 Single-authored publications (SA) 10 Co-authored publications (CA) 188 Co-authors per document (CAD) 3,53 International co-authorships (IC) 25,76% Panel D. Document information Articles 181 Reviews 17 Keywords Plus (ID) 1489 Author’s keywords (DE) 593 The dataset has 586 contributing authors, which is a lot of authorship. Eight of them wrote ten articles by themselves, and the other 576 authors worked together on 188 publications with other authors (Table 1 , Section C). The average of 3.53 authors per article shows how collaborative the field is and how important interdisciplinary research teams are. The 25.76% rate of international collaboration shows that there is a moderate amount of cross-border activity, but there is a lot of room for growth in the future. There are 181 empirical research articles (91.41%) and only 17 non-empirical review articles (8.59%). There are 10,477 references and 2,082 keywords in these documents. 1,489 of the keywords are automated (Keywords Plus) and 593 are provided by the authors. These are the keywords that were used to create the science mapping part of this study (Table 1 , Section D). 3.1.2 Top sources for Reverse Logistics and the Metaverse under the Circular Economy Table 2 shows the top 10 places to publish in the field of Reverse Logistics and the Metaverse under the Circular Economy. Sustainability (Switzerland) (Q1) is in first place with 15 publications. These publications make a big difference in the wide range of research on sustainable logistics, remanufacturing, and circular technologies. However, its relatively low total link strength (4) suggests that citations are spread out across the literature. Table 2 Top 10 Publication Sources in Research on Reverse Logistics and the Metaverse under the Circular Economy Source Documents Citations Total Link Strength Sustainability (Switzerland) (Q1) 15 369 4 Journal of Cleaner Production (Q1) 12 714 8 International Journal of Production Research (Q1) 6 355 19 Journal of Remanufacturing (Q2) 6 141 8 Computers and Industrial Engineering (Q1) 5 336 15 European Journal of Operational Research (Q1) 4 336 15 International Journal of Production Economics (Q1) 4 214 15 Business Strategy and the Environment (Q1) 4 499 4 International Journal of Advanced Manufacturing Technology (Q2) 3 149 8 Transportation Research Part E: Logistics and Transportation Review (Q1) 3 112 3 Omega (United Kingdom) (Q1) 2 54 4 International Journal of Advancements in Computing Technology (Q3) 2 8 4 The Journal of Cleaner Production (Q1) comes in second with 12 articles and 714 citations. This clearly shows how important it is to apply circular economy principles to reverse logistics. The International Journal of Production Research (Q1) comes in third with 6 publications and 355 citations. It also has the highest total link strength (19), which shows that it has a big impact on the academic community. The Journal of Remanufacturing (Q2) also has 6 articles and focuses on specialized studies in remanufacturing. It has 141 citations. There are five articles in Computers and Industrial Engineering (Q1), which focuses on optimization models in closed-loop logistics. The European Journal of Operational Research (Q1), the International Journal of Production Economics (Q1), and Business Strategy and the Environment (Q1) all have four publications. The last one stands out with 499 citations. The other three journals in the top ten—International Journal of Advanced Manufacturing Technology (Q2), Transportation Research Part E (Q1), and Omega (United Kingdom) (Q1)—are important for helping to improve technology and operations in the field of reverse logistics. 3.1.3 Top publications for Reverse Logistics and the Metaverse under the Circular Economy Table 3 shows the ten most cited articles in the fields of Reverse Logistics and the Metaverse under the Circular Economy. The article by Centobelli et al. ( 2022 ), which was published in Information & Management, is at the top of the list. It has 517 citations and an average of 129.25 citations per year, which is the most of any entry. The study suggests a framework for digital transformation that can be used to bring technology into closed-loop logistics. Dev et al. ( 2020 ), which has 368 citations, comes in second. It is about circular logistics strategies in resource management and recycling and was published in Resources, Conservation & Recycling. Vlachos et al. ( 2007 ), which has 359 citations, is in third place. It is one of the first studies on hybrid models for production and remanufacturing and was published in Computers & Operations Research. (Kerin & Pham, 2019 ), which was published in the Journal of Cleaner Production and focuses on life cycle assessment in remanufacturing, has since received 307 citations. Georgiadis and Vlachos ( 2004 ) are two classic studies that have each been cited 180 times and have set the stage for optimization in closed-loop supply chains (CLSC). Liu et al. ( 2023 ) has already received 176 citations, which is an average of 58.67 citations per year. This makes Liu the second most cited author after Centobelli et al. ( 2022 ). Lee and Chan ( 2009 ), Khan et al. ( 2021 ), and Kim et al. ( 2018 ) have also done research that adds to our understanding of how to use technology in reverse logistics, how to make operations more data-driven, and how to plan for sustainable development. Table 3 The 10 Most Cited Articles in the Field PAPER Total Citations TC per Year Normalized TC Centobelli et al. ( 2022 ) 517 129.25 11.59 Dev et al. ( 2020 ) 368 61.33 4.4 Vlachos et al. ( 2007 ) 359 18.9 1.52 Kerin and Pham ( 2019 ) 307 43.86 4.2 Georgiadis and Vlachos ( 2004 ) 180 8.19 1 Fleischmann et al. ( 2003 ) 180 7.83 1 Liu et al. ( 2023 ) 176 58.67 10.76 Lee and Chan ( 2009 ) 144 8.47 1.31 Khan et al. ( 2021 ) 133 26.2 2.11 Kim et al. ( 2018 ) 123 15.38 3.57 3.1.4 Top authors for Reverse Logistics and the Metaverse under the Circular Economy Figure 2 shows the 10 most prolific authors in the reverse logistics and circular economy. On top are Zhang S, with 2012 to 2021, with 6 publications largely in the areas of fuzzy control and dynamic management of closed-loop supply chains in uncertainties. The next two excepted ones, Wang T and Zhao X, have five publications each. Wang T (20172023) has been working on models of dynamic systems and the development of blockchain in the logistics. Conversely, Zhao X (20122015) is reported to make use of fuzzy robust control strategies in order to optimise supply chain performance. Georgiadis P (publication period, 2004–2022) has the longest run of publishing the most articles in the group (4), and they concern the foundation of dynamic modeling in remanufacturing. Li Y, Zhang H and Zhang Y published 4 papers, but Li Y is the most recent publication. Li Y investigates the ethical implications of the concept of circular value chains. Li Y, Rashid A, and Zhang H examined the possibility of industrial digital platforms based on the rental-recovery business model. Asif FMA and Cannella S have three publications both. Asif (20172024) is a young scholar who has taken a multi-method simulation in circular economy studies and Cannella (20162021) has managed to make contributions at the research studies in terms of remanufacturing configurations as well as complex supply chains. On the whole, these authors represent a variety of methodologies: modeling, simulation, and control systems, and a reunion in the major issues like circular economy, remanufacturing, and reverse logistics. 3.2 Science mapping 3.2.1 Co-word analysis Based on the keywords that authors used in their publications, the keyword co-occurrence analysis found six different keyword clusters (themes), each with a total of 34 keywords that describe the network's knowledge structure. Figure 3 and Table 4 show the representative keywords and network metrics for each new theme: average publication year(APY), which indicates the degree of hotness (more recent) or coldness (least recent) of the keyword; average citation(AC), which indicates the average citation received by documents that enlist the keyword; occurrence(OC), which indicates the frequency of keyword appearance in the corpus; degree of centrality(DG), which indicates the number of relational ties associated to the keyword; closeness centrality(CC), which indicates the reciprocal summation of the shortest route between the keyword and its neighboring keywords; betweenness centrality(BC), which indicates the knowledge dissemination potential of the keyword in the cluster; and PageRank(PR), which indicates the importance of the keyword to the cluster based on the quality and number of links directed toward the keyword (Brin & Page, 1998 ) (Donthu, Kumar, Mukherjee, et al., 2021 ; Brin & Page, 1998 ). Cluster 1 (Red): The Circular Economy and Industry 4.0 in Reverse Logistics The first cluster of keywords is about digital technologies (like blockchain and Industry 4.0) and the circular economy (like disassembly, life cycle assessment, and process simulation) that help improve waste valorization. The main word in this group is "circular economy," which has the highest scores on all network metrics: average citations (AC: 33.80), occurrence count (OC: 56), degree centrality (DG: 23), closeness centrality (CC: 0.0189), betweenness centrality (BC: 296), and PageRank (PR: 0.1461). The average publication year (around 2022) shows that research in this cluster, which is about how to combine Industry 4.0 technologies with the circular economy, is both new and still changing. This cluster of studies looks at how digital technologies like blockchain, the Internet of Things (IoT), artificial intelligence (AI), and simulation tools can help promote circular economy practices. Several publications suggest frameworks to help with digital transformation in circular supply chains. For example, they suggest using blockchain to promote industrial symbiosis and find hidden barriers in the circular economy (Shrivastav & Bag, 2024 ; Ventura et al., 2025 ). There are also reviews of new Industry 4.0 technologies that help with reverse logistics (Kerin & Pham, 2019 ; Mohammed et al., 2024 ; Sangari & Mashatan, 2022 ) and case studies on optimizing disassembly and waste reuse using LCA and process simulation. These show how advanced technologies can make the circular economy work better (Arias et al., 2022 ; García-Chirino et al., 2024 ; Rebolledo-Leiva et al., 2023 ; Serrano-Munoz et al., 2023 ). Cluster 2 (Green): Modeling battery recycling and the Internet of Things The second group of keywords is about recycling and reusing things in a circular economy, with a focus on the uncertainty of return flows. The main word in this group is "recycling," which has some interesting numbers: AC: 29.25, OC: 8, DG: 8, CC: 0.0135, BC: 1, and PR: 0.0121. This theme has gotten a lot of academic attention since the late 2010s and is still relevant. The average publication year is between 2018 and 2020. The studies in this group look at how to recycle materials that have already been used, often focusing on uncertainty in return flows. Battery recycling studies are of special interest because they show how important it is to take apart industrial equipment and get as much useful material back as possible (Glöser-Chahoud et al., 2021 ). Strict environmental rules and the lack of important materials (Niri et al., 2024 ) have made reverse logistics for electric vehicle (EV) batteries more important. People often use simulation models to predict changes in recovered volumes. For example, Monte Carlo methods are used to figure out how risky it is to receive scrap (Kabiri et al., 2022 ). Additionally, IoT technologies are recommended for keeping an eye on waste and return flows (Deng et al., 2022 ; Tavana et al., 2024 ), coming up with models for sustainable battery supply chain networks, and using IoT to manage battery returns more effectively. In general, Cluster 2 focuses on the study of material flows, predicting changes in returns, and finding problems in Li-ion reverse logistics. The goal is to improve the processes for recovering and recycling EV batteries. Table 4 Bibliometric information on the keyword co-occurrence of themes Themes and keywords OC Total Link Strength APY AC Cluster circular economy 56 71 2022.46 33.80 1 (Red) blockchain 10 18 2023.20 17.40 industry 4.0 10 18 2022.00 121.50 waste valorization 10 10 2023.70 5.70 life cycle assessment 5 8 2022.00 16.40 disassembly 3 6 2022.67 35.00 process simulation 3 5 2023.67 2.67 recycling 8 11 2019.63 29.25 2 (Green) electric vehicle 4 7 2023.75 10.50 internet of things 4 5 2022.75 90.00 dynamic model 3 5 2011.00 25.67 monte carlo simulation 3 5 2022.33 6.33 uncertainty 3 7 2020.00 7.00 blockchain technology 12 17 2023.17 54.42 3 (Dark blue) sustainability 11 25 2022.82 23.27 supply chain 6 10 2022.00 51.17 inventory management 3 7 2022.33 21.33 literature review 3 2 2022.33 25.33 sustainable supply chain management 3 6 2023.00 15.67 reverse logistics 27 50 2016.81 53.67 4 (Yellow) simulation 16 33 2020.00 27.19 bullwhip effect 11 25 2019.09 43.18 closed-loop supply chains 9 21 2019.33 41.44 supply chain dynamics 4 10 2019.25 52.00 remanufacturing 51 85 2019.76 29.43 5 (Purple) closed-loop supply chain 42 53 2018.26 20.14 game theory 7 11 2021.71 10.29 fuzzy robust control 3 2 2013.00 10.00 stackelberg game 3 2 2023.33 0.00 system dynamics 20 41 2016.65 52.75 6 (Light blue) supply chain management 13 28 2016.46 57.69 closed loop supply chain 10 20 2017.50 21.90 capacity planning 5 10 2014.00 100.20 waste management 5 6 2023.40 15.00 Cluster 3 (Dark Blue): Blockchain Technology and Sustainable Supply Chain Management These keywords are all about sustainable supply chain management (SSCM) in the context of the circular economy. The main word here is "sustainability." The network metrics are as follows: AC: 23.27, OC: 11, DG: 13, CC: 0.0149, BC: 7, and PR: 0.0350. The average year of publication (about 2022) shows how new and important this research area is becoming. The cluster also includes new technologies like "blockchain technology," which is a main keyword (OC: 12, AC: ~54.4), showing that people are very interested in how blockchain can be used. A lot of the articles in this group talk about how blockchain makes circular supply chains and reverse logistics more open and trustworthy. For example, Centobelli et al. ( 2022 ) talk about how blockchain can help make circular supply chains more traceable, open, and trustworthy for all parties involved. Other research looks at how to combine blockchain with the Internet of Things (IoT) in reverse logistics to make it easier to keep track of products and cut down on waste (Hrouga et al., 2022 ; Hu & Sinniah, 2024 ). There are also keywords like "inventory management" and "literature review" that suggest that some studies give theoretical overviews of inventory strategies in circular supply chains or holistic assessments of digital technologies in reverse logistics (Barretti et al., 2023 ; Su et al., 2021 ). To sum up, this group has a strong academic base that combines sustainability goals with blockchain-based technologies to make reverse logistics systems more reliable and efficient. Cluster 4 (Yellow): How the supply chain works in reverse The main ideas in this cluster are reverse supply chains, dynamics, and simulation. Some of the most important terms are "reverse logistics," "whip effect," "supply chain dynamics," and "simulation." Reverse logistics is the most important keyword, with the highest numbers in AC (53.67), OC (27), DG (18), CC (0.0167), BC (70), and PR (0.0720). The average year of publication, on the other hand, is fairly low (around 2015–2017). This suggests that research on closed-loop supply chain dynamics has not received as much recent attention and is based on earlier foundational work in the field. This group shows that people were interested in problems in reverse supply chains early on. When returns are involved, representative works look at how demand changes along the supply chain and use simulation models to measure and lessen these effects (Lin et al., 2022 ). For instance, system dynamics and discrete-event simulations have been used to create data governance processes for the circular economy (Charnley et al., 2019 ) and to look into how hybrid CLSC systems make decisions about product recovery (Yang et al., 2023 ). These studies help us understand better how demand changes spread through return networks and give us ways to lessen the bad effects. Cluster 5 (Purple): Improving closed-loop supply chains and remanufacturing This cluster is all about making closed-loop supply chains work better, with a focus on remanufacturing and making strategic decisions. Some important keywords are "remanufacturing," "closed-loop supply chain," and analytical tools like "game theory" (especially Stackelberg models) and "fuzzy robust control." The core keyword "remanufacturing" has the highest values in AC (29.43), OC (51), DG (20), CC (0.0192), BC (229), and PR (0.1450). The average year of publication (2018–2020) shows that there is still active research going on, building on models that were proposed earlier. Many studies use Stackelberg game models to look into the best ways for manufacturers and collectors to set prices, make things, and get their money back (Saha et al., 2019 ; Yan & Sun, 2012 ; Zhang & Zhang, 2025 ). This group also includes environmentally friendly control methods to make sure that remanufacturing works well even when things are uncertain. For example, there are strong control strategies for dual-channel closed-loop supply chains (Xia & Li, 2023 ; Zhang & Zhao, 2014 ). Cluster 5 is mostly about finding solutions to problems that come up in reverse logistics systems, like managing capacity limits and coordinating inventory and production between new and remanufactured goods. Cluster 6 (Blue): Planning and modeling the reverse supply chain This cluster of keywords has to do with capacity planning and dynamic modeling in closed-loop supply chains. The words "capacity planning," "closed loop supply chain," "system dynamics," and "waste management" suggest that this group focuses on strategic planning and policy simulation in reverse logistics systems. The central keyword system dynamics has the highest values in AC: 52.75, OC: 20, DG: 11, CC: 0.0139, BC: 14, and PR: 0.0596. This theme has roots that go back further than 2016, when the first publications on it appeared. It serves as a base for later research. Several important studies have tried to figure out the best facility sizes for long-term capacity planning for recycling and remanufacturing networks. For example, Fleischmann et al. ( 2003 ) set the stage for looking at the costs of recovery networks, inventory control, and reverse logistics. Vlachos et al. ( 2007 ) were the first to use dynamic simulations to change the capacity of remanufacturing systems over time. Georgiadis and Athanasiou ( 2013 ) then suggested flexible planning for processing capacities. System dynamics modeling has been used to simulate long-term waste management policies (Lehr et al., 2013 ) in addition to optimization. This group as a whole takes a long-term, big-picture view of circular supply chain management. It stresses the need for capacity balancing and policy planning through simulation. 3.2.2 Bibliographic coupling The present study conducts bibliographic coupling as an alternativebibliometric analysis technique to triangulate the findings from the keyword co-occurrence analysis (Goodell et al., 2021 ). In bibliographic coupling, documents with common referencing patternsconverge and form bibliographic couples that reflect a common theme (Kessler, 1963 ). Cluster 1: Operational Strategies and Optimization in Closed-Loop Supply Chains with Product Returns (36 Publications) The first bibliographic coupling cluster had 36 studies that got a total of 705 citations, or an average of 19.58 citations per publication. About 47% of the documents were published in the last three years, and the oldest one was from 2009. This shows that there are both old and new contributions to the management of closed-loop supply chains (CLSCs). The group mostly talked about operational strategies and optimization in CLSCs, with an emphasis on pricing, channel coordination, and policies for taking back and recycling. Several studies looked at the problems of coordinating new and remanufactured products, such as whether to "buy now or later" and how to target strategic customers in remanufactured markets (Liu et al., 2023 ; Wu et al., 2017 ; Yanliang Zhang et al., 2025 ). Other studies looked at planning the supply chain when return flows and demand are uncertain (Kim et al., 2018 ; Liao et al., 2020 ) or came up with strong control strategies for uncertain dual-channel CLSCs (Xia & Li, 2023 ; Zhang et al., 2016 ; Zhang & Zhao, 2014 ; Zhang et al., 2014 ). External factors such as tax policy, trade mechanisms, and government subsidies were also evaluated for their impact on CLSCs (Zhang et al., 2024 ; Yuhao Zhang et al., 2025 ). Overall, Cluster 1 focused on improving CLSC operations while also looking at how new factors like blockchain and environmental policies could affect them. Cluster 2: Reverse Logistics for Electric Vehicle Batteries and IoT Technologies (31 Publications) There were 31 publications in this cluster that had a total of 1,153 citations, or an average of 37.19 citations per article, which was the third-highest number of citations among the clusters. Notably, about 90% of the studies were published from 2021 onward, with the earliest from 2018. The main focus was on reverse logistics for electric vehicle (EV) batteries, which included planning policies for inventory, coordinating product-specific remanufacturing, and additive manufacturing. There was a lot of focus on how to recover lithium-ion batteries and how to use digital technologies. In the context of Industry 4.0 and circular economy (CE) capabilities, social factors that affect costs, like investment in collection systems and the size of the end-user market, were looked at (Dev et al., 2020 ). The growing global EV fleet, which is driven by goals to be carbon neutral, needed circular solutions. The growing global EV fleet—driven by carbon neutrality goals—necessitated circular solutions, and repurposing/remanufacturing strategies were recommended for battery life extension (Castro et al., 2021 ; Deng et al., 2022 ; Huster et al., 2022 ). The group also talked about how IoT, virtual reality (VR), and augmented reality (AR) can be used in remanufacturing and battery waste management to monitor, collect data in real time, analyze it, and simulate it (Glöser-Chahoud et al., 2021 ; Kerin & Pham, 2019 ; Tavana et al., 2024 ). Cluster 2 showed both academic and practical efforts to create reverse supply chains for EV batteries using advanced technologies. Cluster 3: Reverse Logistics and Closed-Loop Supply Chain Foundations (29 Publications). There were 29 publications in Cluster 3, which had a total of 1,413 citations. This means that each article had an average of 48.72 citations, which was the second highest citation rate across all clusters. About 41% of the publications were from the last three years, and the first one came out in 2004. This is based on a group of important and well-known studies in reverse logistics and CLSCs. Some of the main topics were designing reverse logistics networks, the bullwhip effect, optimizing product recovery systems, and how reverse flows affect production and inventory planning. Economic and environmental concerns were identified as the primary drivers of CLSC development. We used dynamic modeling and simulation to look at long-term capacity planning policies based on how profitable the supply chain is (Georgiadis & Vlachos, 2004 ; Vlachos et al., 2007 ). Kumar and Yamaoka ( 2007 ) used system dynamics (SD) models to look at CLSC design in the Japanese automotive industry. Lehr et al. ( 2013 ) improved our understanding of supply chain dynamics and the effects of reverse flows. Because of this, Cluster 3 was seen as the academic base for research on reverse logistics and the circular economy. It provided conceptual and operational frameworks that are still widely used in the literature. Cluster 4: Advanced Recovery Strategies and Simulation in Reverse Logistics (24 Publications) Cluster 4 had 24 publications and 615 total citations (25.62 citations per article). It had a good mix of old and new studies, with 46% of the documents published between 2021 and 2023 and the oldest one dating back to 2006. The group looked into high-tech ways to recover and take apart products. For example, Tozanlı et al. ( 2020 ) came up with a "trade-in-to-upgrade" strategy that used blockchain to make transactions clear. This encouraged customers to trade in old products for newer ones. This example showed how marketing strategies could help reverse logistics by encouraging returns. The group also had decision models for reverse supply chain management that were based on simulations and data. Charnley et al. ( 2019 ) made digital simulation models to help people make decisions based on data in the circular economy. These models let companies test out recovery and recycling scenarios before putting them into action. Cluster 4 demonstrated the intersection of traditional optimization/simulation models with modern business strategies and technologies in closed-loop supply chains. Cluster 5: Blockchain-Enabled Circular Economy (23 Publications) Cluster 5 had the most studies published since 2021, with a total of 1,639 citations—71.26 citations per article, the most of any cluster. The main topic was how to use blockchain in circular supply chains and reverse logistics. This group of studies put blockchain as a game-changing tool that improves trust, traceability, and openness among people working in sustainable supply chains. It was shown to help people keep better track of how waste moves and how products are returned (Centobelli et al., 2022 ). Blockchain was also used in reverse logistics to decentralize, track, and monitor goods after they had been sold (Samadhiya et al., 2023 ). This helped make the supply chain more resilient and keep data safe (Mohammed et al., 2024 ). The pros and cons of blockchain adoption were looked at closely. Govindan ( 2022 ) found major problems with using blockchain for remanufacturing to reach the SDGs (for example, SDG4, SDG8, SDG9, and SDG17) and suggested ways for managers to get around these problems. Cluster 5 gave us both theoretical and practical ideas about how to make digital transformation happen in reverse supply chains, which will improve transparency and efficiency throughout the product lifecycle. Cluster 6: Analytical Foundations for Cost-Efficient Product Recovery Networks (16 Publications) There were 16 publications in this cluster, and they were cited 514 times, or an average of 32.12 times per article. Only 37.5% of the works were published in the last three years, and the oldest one is from 2003. This shows that cost-optimization modeling is a key focus in reverse logistics networks. There have been a number of studies that have helped to shape this field. For example, Fleischmann et al. ( 2003 ) looked at recovery network design, inventory control, and cost management. Some common methods were fuzzy mixed-integer nonlinear programming (Vahdani et al., 2012 ), integrated vehicle routing and scheduling for forward and reverse logistics (Rahbari et al., 2024 ), and coordinated production-distribution planning in CLSC systems (Kabiri et al., 2022 ). Aydin et al. ( 2024 ) extended the analytical scope by integrating environmental impact assessment (via LCA) and system simulation (Monte Carlo, discrete event simulation, agent-based modeling, and SD). Overall, Cluster 6 provided strong quantitative frameworks for reverse logistics that helped with making decisions about how to design and run a recovery network that is cost-effective. Cluster 7: Performance Assessment of CLSCs and Influencing Factors (12 Publications ) There were 12 publications in the seventh cluster, and they got 395 citations, which is an average of 32.92 citations per article. About 58% of the studies were published after 2021, and the first one was in 2006. This group looked at how well closed-loop and reverse logistics systems worked and what factors affected them. We used simulations and real-world methods to find out how different reverse logistics factors, like return lead times, information uncertainty, and return volume variability, affect the efficiency of the supply chain (Cannella et al., 2016 ; Ponte et al., 2020 ). Specific focus was placed on bullwhip effects and the impacts of PULL, DUAL, and Separate PULL policies in CLSC settings (Zanoni et al., 2006 ), as well as demand shocks (de Arquer et al., 2022 ). In summary, Cluster 7 provided insights into how operational and environmental factors affect the stability and performance of closed-loop systems through modeling and experimentation. The science mapping analysis has named the backbones of research in the field of reverse logistics and circular economy, where the opposing key words and thematic groups are strongly interrelated. One of the most noticeable groups revolves around the application of digital technologies in reverse logistics and includes such concepts as blockchain, the Internet of Things (IoT), data analytics, and artificial intelligence (AI). The combination of these technologies is common since the current trend is characterized by implementing Logistics 4.0 principles in order to make closed-loop supply chains more efficient. The second cluster is devoted to circular economy and sustainability where keywords are sustainability, recycling, waste reduction, and closed-loop supply chains. This cluster focuses on the reverse logistics practices on the environment and the 3R framework ( reduce, reuse, recycle).Other clusters are associated with the field of supply chain management and operation performance including such terms as supply chain management, cost efficiency, customer service, and stakeholder collaboration. These are managerial and strategic aspect of the reverse logistics. Nevertheless, such technologies as blockchain and IOT seem to emerge, whereas core Metaverse technologies, including virtual reality (VR), augmented reality (AR), digital twins, and immersive simulation, are still not a popular topic. It is an indication that the Metaverse-reverse logistics integration is in its early phases. Other studies indicate that the reverse logistics and specifically the return operations are one of the least explored areas of research related to Metaverse, even though the concept of the twins and simulation are among the core topics. These results reveal the research gap and show that the immersive and simulation technologies should be more integrated with reverse logistics to realize the potential of connecting the physical and digital world in supply chain management with closed-loop management. These reflections are used as the basis of the further part of detailed accounting of the new role the Metaverse plays in this sphere. 4. The triangulation of majorthemes and Thematic analysis The thematic clusters that come from bibliographic coupling and co-word analysis show a clear convergence and successful triangulation. This proves that the core themes that make up the knowledge structure of research on Reverse Logistics and the Metaverse under the Circular Economy are reliable and valid, as this review shows (Table 5 ). Table 5 Triangulation of Thematic Clusters: Co-occurrence vs. Bibliographic Coupling Common Theme Co-occurrence Cluster Coupling Cluster Thematic Focus Contribution Industry 4.0 and Circular Economy in reverse logistics Cluster 1 (Red) Cluster 4 overlaps Cluster 5 Application of blockchain and Industry 4.0 to enhance transparency, traceability, and collaboration in reverse logistics A digital transformation framework for circular logistics: blockchain enhances industrial symbiosis collaboration, transaction transparency and reliability, data integrity and management; IoT, Virtual Reality (VR), and Augmented Reality (AR) support real-time monitoring and data collection. Battery recycling modeling and IoT Cluster 2 (Green) Cluster 2 Optimization of EV battery and finite resource collection and recycling processes. Emphasizes industrial disassembly and material recovery, forecasting recovery variability, IoT deployment for reverse flow management, and proposing sustainable battery supply chain design models. Sustainable supply chain management and Blockchain Cluster 3 (Dark Blue) Cluster 5 Integrating blockchain and IoT to support inventory management and transparent product flows Explores blockchain applications and potential in reverse logistics and closed-loop supply chains, aligned with SDGs, using case analysis and sustainable theoretical frameworks. Dynamics and simulation in reverse supply chains Cluster 4 (Yellow) Cluster 3 Applies simulation and system dynamics (SD) to quantify the bullwhip effect, optimize recovery processes, and support long-term decision-making Simulation quantifies amplified demand fluctuations along the supply chain. System dynamics and discrete-event simulation model data governance procedures and how demand shocks propagate in recovery networks, with strategies to mitigate negative impacts. Optimization and remanufacturing in CLSC Cluster 5 (Purple) Cluster 1 Covers both foundational and new research in closed-loop supply chain management Focuses on operational and optimization strategies: pricing, distribution channel, take-back/recycling policies, Stackelberg game models, sustainable control approaches, operational problem-solving in reverse supply chains, and capacity management. System modeling and capacity planning in CLSC Cluster 6 Cluster 6 System dynamics, capacity balancing, and policy planning based on system simulation. Cost optimization problems in reverse logistics networks using LCA and simulation methods (Monte Carlo, discrete event simulation, agent-based modeling (ABM), and system dynamics (SD)). Performance of closed-loop/reverse logistics supply chains and influencing factors Cluster 4 overlaps with Cluster 1 Cluster 7 Evaluation of performance and key influencing factors in closed-loop/reverse supply chains. Provides empirical evidence and modeling on how factors such as return lead time, reverse demand information, bullwhip effect, policies, return volume uncertainty, and demand shocks affect CLSC performance. This study uses author keywords as input data to make a strategic diagram that shows how personalized marketing research will develop in the future. The vertical axis shows impact (density) and the horizontal axis shows centrality (Cobo, López-Herrera, et al., 2011 ; Cobo, López-Herrera, et al., 2011 ). Centrality shows how important a theme is and how it interacts with other themes, while density shows how much it has grown on its own. Figure 5 shows that the strategic diagram is split into four quadrants based on Cahlik ( 2000 ) classification model. Motor themes are in the upper-right quadrant. They are both very well-developed and very important. Some examples of these types of keywords are "recycling," "reuse," and "closed-loop supply chain management," which have interesting metrics (for example, recycling – centrality = 1129.69; density = 0.00159). These themes are very important to the knowledge structure of modern reverse logistics and are related to co-word cluster 2 and bibliographic coupling cluster 2. They have to do with digital technologies like the Internet of Things (IoT) in battery recovery and recycling systems, as well as multi-objective optimization and uncertainty modeling. Some of the main ideas in the lower-right quadrant are "circular economy," "remanufacturing," "supply chain management," and "system dynamics." These ideas all have high centrality but low density (for example, remanufacturing has a centrality of 712.91 and a density of 0.00149). These are the main ideas in the field that need to be built on to keep up with new problems that come up in digital transformation and sustainable development. These themes are related to clusters 1 and 5 (co-occurrence) and clusters 1 and 6 (bibliographic coupling). This shows that there are strong connections but not a lot of depth in how the methods and applications are combined. The upper-left quadrant has niche themes like "capacity planning," "product lifecycle," and "interrelated sustainability dimensions." These themes are very dense (which means they are developing strongly on their own), but they are not very central, which means they are not very connected to the main themes. They are good for more in-depth technical study, especially in niche areas like reverse logistics for electric vehicles or electronic equipment, where capacity planning and lifecycle analysis are very important. Finally, the lower-left quadrant has themes that are either new or fading, like "life cycle assessment," "environmental sustainability," and "waste management." These keywords are important for both academics and real life, but they don't seem to be very central or dense, which suggests that they aren't being used very much in mainstream discussions. But with more pressure from ESG and environmental standards, these themes could come back to life through systems modeling, life cycle assessment tools, and AI technology integration. In short, the strategic diagram shows well-known theoretical themes, new areas of research, and areas that haven't been studied enough and need more funding. Recycling, closed-loop logistics, and remanufacturing should continue to be the main ideas, but LCA, capacity planning, and waste management should be more closely linked to make them more useful in real life and in research. In line with the results of the science mapping, the thematic analysis showed that a number of core themes have been identified in the published literature on the "Metaverse in reverse logistics." One of the most noticeable trends is the implementations of digital technologies and Metaverse-related tools in reverse logistics. Early research is primarily on blockchain and IoT in order to maximize traceability, transparency and management of the flow of returns in closed-loop supply chains. They are technologies that integrate data and automate the processes, and they can make a basis of effective reverse logistics systems. Nevertheless, the analysis also demonstrates the trend of moving away from the classical data-focused technologies (e.g., IoT, blockchain) in favor of immersive technologies related to the Metaverse to further streamline reverse logistics. In particular, a virtual reality (VR), augmented reality (AR), and digital twins are becoming prospective solutions. VR and AR have proven to have potentials to enhance disassembly and remanufacturing processes and allow immersive training and remote assistance. The tools can minimize the unnecessary returns by helping the customers solve minor problems, decreasing the costs of processing and improving their experience. Besides technology, reverse logistics also incorporates governance, policy and design. The studies in governance state multi-stakeholder cooperation towards workable product-take-back. Literature with a policy orientation emphasizes extended producer responsibility (EPR), particularly as a motivator to recycling efforts, whereas design-oriented literature focuses on how designs can be made easy to take apart and reuse. Together with immersive technologies, in particular, VR, AR, simulations, and digital twins, these forces expand the frontier of research and confirm the potential of the Metaverse to address the shortcomings of the reverse logistics as applied in traditional supply chains and catalyze the emerging closed-loop supply chains. 5. Conclusion 5.1 Key Findings This systematic review of reverse logistics and Metaverse technologies in the context of the circular economy (see Table 5 ) shows that there are a few important things to note. The study first found 198 articles that were published between 2003 and mid-2025 (RQ1). More than 60% of these were published in the last five years. This trend shows that more and more academics are interested in sustainable supply chains and using Industry 4.0 technologies, especially the Metaverse and blockchain, in reverse logistics. Second, the Journal of Cleaner Production has the most citations, while Sustainability (Switzerland) has the most articles (RQ2). The best journals cover a wide range of fields, including operations management, environmental engineering, and digital innovation. This shows how interdisciplinary this research area is. Third, the most productive authors, like Zhang S, Wang T, and Zhao X, mostly work for schools in Asia and Europe. However, there is a clear lack of practical involvement from industry stakeholders. This shows that academia–industry collaboration needs to be improved in order to speed up the use of the Metaverse in real-world logistics operations. Fourth, keyword and citation analyses show that the main topics right now are blockchain, digital twin, VR/AR, and simulation in making reverse logistics better. But there is still a lot of work to be done on user experience and technology acceptance. Fifth, there aren't many complete theoretical frameworks that connect Metaverse technologies with closed-loop supply chains, even though many simulation-based models have been made. Future research should focus on making these kinds of integrated models to help both theory and practice move forward. 5.2 Theoretical Contributions At the macro level , this review gives a systematic and comprehensive picture of the most important research areas that connect reverse logistics, digital technologies, and the circular economy. The study finds three main theoretical pillars by cross-mapping two knowledge networks: keyword co-occurrence (6 clusters) and bibliographic coupling (7 clusters). Using technologies like blockchain and the Internet of Things (IoT) to make logistics more digital (Co-Cluster 1, BiC-Cluster 1); Using digital twins and system dynamics to model and simulate reverse logistics operations (Co-2, BiC-2, BiC-5); Sustainable methods that try to close the product lifecycle loop (Co-3, Co-5, BiC-3). This study adds a new theoretical lens of "virtualization/immersiveness" to the analytical framework by including Metaverse technologies like VR/AR, immersive simulations, and digital spaces. This is the next step in the evolution from digitization to immersive transformation in closed-loop supply chains. At the micro level , the study suggests an integrated conceptual framework to show how the Metaverse fits into reverse logistics. It focuses on three main parts: Technological enablers, such as digital twins, AR/VR, and simulation (related to BiC-2, Co-2, and Co-4); Mechanisms of influence, like better understanding of the system, skill training, co-creation, and decision optimization (related to Co-4 and BiC-6); Results include better recovery efficiency, lower inventory levels, longer product life cycles, and a positive effect on sustainability (BiC-3, BiC-7, Co-5). This framework not only organizes the Metaverse's role in reverse logistics, but it also gives us a way to look at how smart supply chains have changed over time, from automation to digitization and now to immersive technologies. It makes a big theoretical contribution and sets the stage for future hybrid research models that combine supply chain management, immersive technologies, and sustainable development. 5.3 Managerial Implications This study provides several practical insights for managers aiming to apply Metaverse technologies in reverse logistics toward sustainability goals: First, the results give managers and policymakers a structured overview that helps them understand the state of the research in reverse logistics within a circular economy framework and how far along different research streams are. The keywords in the top-right quadrant of the strategic diagram, like "recycling," "reuse," and "closed-loop supply chain management," show that they are very important and central to the topic. These ideas are very important in models for product recovery and recycling, especially in industries where products have short life cycles or strict traceability requirements, like electronics, EV batteries, and high-tech goods. These ideas can help professionals create effective return networks and set standards for assessing the life cycle of a product. Second, the themes in the bottom-right quadrant, like "remanufacturing," "system dynamics," "supply chain management," and "circular economy," are theoretically important but not very well developed in terms of how they are studied. This opens the door for the use of cutting-edge technologies like blockchain, AI, big data analytics, and system simulation in the management of reverse logistics. The co-word (Clusters 1, 5) and bibliographic coupling clusters (Clusters 1, 6) also show that these themes connect theoretical areas that have to do with network design, multi-objective optimization, and digital platform development for circular supply chain management. Third, niche themes like "capacity planning," "product lifecycle," and "interrelated sustainability dimensions" are well-developed within their own communities, but they are still not very connected to mainstream research networks. These areas, on the other hand, are very useful for making decision-support models for planning remanufacturing capacity, analyzing costs and benefits when there is uncertainty, and improving environmental performance across the closed-loop supply chain. Fourth, the review brings attention to a number of new or underexplored topics, such as "life cycle assessment," "waste management," and "environmental sustainability," that have a lot of potential for the future but aren't yet fully integrated into mainstream conversations. These subjects can be added back into ESG strategies, systems for measuring sustainability, and policy modeling. It is both possible and necessary to strengthen these themes as pillars in reverse logistics design and governance, especially as the demand for supply chain transparency, traceability, and producer responsibility grows. Overall, the results of both the co-occurrence and bibliographic coupling analyses show that reverse logistics practices are moving toward a model of digital-sustainable-systemic integration. Managers shouldn't just look at specific tools or technologies; they should also think about all the factors that can affect a business as a whole, like the volatility of return flow, the readiness to share data, infrastructure investment, and the ability to measure economic and environmental value over time. 5.4 Future Research Based on the outlined gaps in the research, it can be suggested that there are a number of attractive directions to pursue the role of the Metaverse in reverse logistics and the circular economy further: To begin with, future research must examine stakeholder attitudes, behaviors, and preparedness e.g. the warehouse workers, drivers, managers, and customers toward the VR/AR and digital twin technologies in reverse logistics. By using theoretical frameworks such as the Technology Acceptance Model (TAM) or Unified Theory of Acceptance and Use of Technology (UTAUT), researchers have been able to discover the obstacles of adoption (e.g. to virtual reality) and the facilitators of user adoption of post-sale Metaverse platforms. This information would assist companies in developing successful implementation plans which must improve user experience and reduce resistance. The next important criterion is a quantitative determination of the environmental and economic consequences of merging Metaverse. The comparison of carbon footprint of reverse logistic processes with and without Metaverse can be done using a Life Cycle Assessment(LCA) model. Likewise, cost-benefit analysis can tell us whether the investments into VR equipment and creating digital twins are paid off by long-term profitability in logistics efficiency and liquidation value of the assets. The development of an empirical research is also required to assess the improvements of performance at particular stages of reverse logs progress (e.g. sorting, inspection, repair, remanufacturing) under the influence of enhanced VR/AR. The creation of a combined version of digital twins in blockchain networks may help engage into the end-to-end accounting of returned goods. The way to synchronize data between the Metaverse systems and distributed ledgers to be adequately clear and effective in terms of reverse logistics management should be studied in the future. Lastly, the researchers ought to investigate the revolutionary Metaverse-led scenarios in the circular economy. Another new idea is a virtual ecosystem whereby post-sale products are followed up digitally through the whole lifecycle of the product, i.e., the first user, refurbishment, and the handing over to the latter users. These visions need a cross disciplinary thinking and are likely to open up fully new forms of circular business. 5.5 Limitations of the Research First, the analysis only looks at the Scopus database, so it might miss important studies that are listed in Web of Science, IEEE, or domain-specific conferences in digital and industrial technologies. Second, only publications in English were looked at, which could leave out important contributions in other languages. Third, the review doesn't have any real-world examples or field data from companies that are currently using Metaverse technologies in closed-loop logistics systems. This is an exploratory study, so it doesn't test hypotheses or cause-and-effect relationships. Meta-analyses or empirical validations may be used in future research to confirm the proposed links. This study is a good starting point because it brings together a lot of current information and suggests many promising ways for research to move forward in the areas of reverse logistics, the Metaverse, and circular economy principles. Declarations Conflicts of Interest: The authors declare no conflict of interest. Author Contribution Author Contributions: Conceptualization, L.T.T.T.; methodology, T.C.T.; software, T.C.T.; validation, T.C.T. and L.T.T.T.; formal analysis, T.C.T.; investigation, L.T.T.T.; data curation, T.C.T.; writing—original draft preparation, T.C.T. and L.T.T.T.; writing—review and editing, T.C.T. and L.T.T.T.; visualization, T.C.T.; supervision, T.C.T. ; project administration, L.T.T.T. All authors have read and agreed to the published version of the manuscript. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Anderson C, Carvalho A, Kaul M, Merhout JW. Blockchain innovation for consent self-management in health information exchanges. Decis Support Syst. 2023;174:114021. Aria M, Cuccurullo C. bibliometrix: An R-tool for comprehensive science mapping analysis. J informetrics. 2017;11(4):959–75. Arias A, Feijoo G, Moreira MT. Assessing of the most appropriate biotechnological strategy on the recovery of antioxidants from beet wastes by applying the life cycle assessment (LCA) methodology. Food Bioprod Process. 2022;135:178–89. Aydin R, Al-Shihabi ST, Alzarooni SI, Jaber H. Assessing the economic and environmental performance of a closed-loop supply chain for waste tires: an industrial case study. Environ Sci Pollut Res. 2024;31(57):65513–32. Baier-Fuentes H, Merigó JM, Amorós JE, Gaviria-Marín M. International entrepreneurship: a bibliometric overview. Int Entrepreneurship Manage J. 2019;15:385–429. Balaid A, Abd Rozan MZ, Hikmi SN, Memon J. Knowledge maps: A systematic literature review and directions for future research. Int J Inf Manag. 2016;36(3):451–75. Barretti JW, Mergulhão RC, Mendes JV. Evolution of blockchain technology in sustainable supply chain management: a theoretical perspective. World Rev Intermodal Transp Res. 2023;11(4):396–414. Bockholt MT, Kristensen JH, Colli M, Jensen PM, Wæhrens BV. Exploring factors affecting the financial performance of end-of-life take-back program in a discrete manufacturing context. J Clean Prod. 2020;258:120916. Boell SK, Cecez-Kecmanovic D. On being ‘systematic’in literature reviews in IS. J Inform Technol. 2015;30(2):161–73. Brin S, Page L. The anatomy of a large-scale hypertextual web search engine. Comput networks ISDN Syst. 1998;30(1–7):107–17. Cahlik T. (2000). Search for fundamental articles in economics. scientometrics , 49 (3), 389–402. Cannella S, Bruccoleri M, Framinan JM. Closed-loop supply chains: what reverse logistics factors influence performance? Int J Prod Econ. 2016;175:35–49. Castro FD, Cutaia L, Vaccari M. End-of-life automotive lithium-ion batteries (LIBs) in Brazil: Prediction of flows and revenues by 2030. Resour Conserv Recycl. 2021;169:105522. Centobelli P, Cerchione R, Del Vecchio P, Oropallo E, Secundo G. Blockchain technology for bridging trust, traceability and transparency in circular supply chain. Inf Manag. 2022;59(7):103508. Chang A, El-Rayes N, Shi J. Blockchain technology for supply chain management: A comprehensive review. FinTech. 2022;1(2):191–205. Charnley F, Tiwari D, Hutabarat W, Moreno M, Okorie O, Tiwari A. Simulation to enable a data-driven circular economy. Sustainability. 2019;11(12):3379. Chen P-K, Ye Y, Huang X. The metaverse in supply chain knowledge sharing and resilience contexts: an empirical investigation of factors affecting adoption and acceptance. J Innov Knowl. 2023;8(4):100446. Clauset A, Newman ME, Moore C. Finding community structure in very large networks. Phys Rev E—Statistical Nonlinear Soft Matter Phys. 2004;70(6):066111. Cobo MJ, López-Herrera AG, Herrera-Viedma E, Herrera F. An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. J informetrics. 2011;5(1):146–66. Cobo MJ, López-Herrera AG, Herrera‐Viedma E, Herrera F. Science mapping software tools: Review, analysis, and cooperative study among tools. J Am Soc Inform Sci Technol. 2011;62(7):1382–402. Correa-Vaca A, Miralles C, Márquez-Sánchez F, Vergara-Romero A. (2023). Circular economy based on reverse logistics: A systematic literature review. Kurd Stud, 12 (1). de Almeida ITGV, Ribeiro ARB, Ramalho LL, de Sousa Floriano L, de Araújo RSC. Circular economy and reverse logistics: A systematic review. Revista de Gestão Social e Ambiental. 2024;18(3):1–14. de Arquer M, Ponte B, Pino R. Examining the balance between efficiency and resilience in closed-loop supply chains. CEJOR. 2022;30(4):1307–36. De Giovanni P. Sustainability of the Metaverse: A Transition to Industry 5.0. Sustainability. 2023;15(7):6079. https://doi.org/10.3390/su15076079 . Deng S, Kpodzro E, Maani T, Li Z, Huang A, Yih Y, Zhao F, Sutherland JW. Planning a circular economy system for electric vehicles using network simulation. J Manuf Syst. 2022;63:95–106. Denyer D, Tranfield D. (2009). Producing a systematic review. Dev NK, Shankar R, Qaiser FH. Industry 4.0 and circular economy: Operational excellence for sustainable reverse supply chain performance. Resour Conserv Recycl. 2020;153:104583. Donthu N, Gremler DD, Kumar S, Pattnaik D. Mapping of Journal of Service Research themes: A 22-year review. J service Res. 2022;25(2):187–93. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res. 2021;133:285–96. https://doi.org/10.1016/j.jbusres.2021.04.070 . Dwivedi YK, Hughes L, Baabdullah AM, Ribeiro-Navarrete S, Giannakis M, Al-Debei MM, Dennehy D, Metri B, Buhalis D, Cheung CM. Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int J Inf Manag. 2022;66:102542. Erol I, Nurtaniş Velioğlu M, Sivrikaya Şerifoğlu F, Büyüközkan G, Aras N, Çakar D, N., Korugan A. Exploring reverse supply chain management practices in Turkey. Supply Chain Management: Int J. 2010;15(1):43–54. Fahimnia B, Sarkis J, Davarzani H. Green supply chain management: A review and bibliometric analysis. Int J Prod Econ. 2015;162:101–14. Fleischmann M, Van Nunen JA, Gräve B. Integrating closed-loop supply chains and spare-parts management at IBM. Interfaces. 2003;33(6):44–56. Floren J, Rasul T, Gani A. Islamic marketing and consumer behaviour: a systematic literature review. J Islamic Mark. 2020;11(6):1557–78. Fosso Wamba S, Mishra D. Big data integration with business processes: a literature review. Bus process Manage J. 2017;23(3):477–92. García-Chirino J, Van Eygen G, Todd R, Ramírez-Zamora RM, Van der Bruggen B. Waste-treating-waste: Effective heavy metals removal from electroplating wastewater by ladle slag. Chemosphere. 2024;361:142532. Garousi V, Felderer M, Mäntylä MV. Guidelines for including grey literature and conducting multivocal literature reviews in software engineering. ‎Inf Softw Technol. 2019;106:101–21. Geissdoerfer M, Savaget P, Bocken NM, Hultink EJ. The Circular Economy–A new sustainability paradigm? J Clean Prod. 2017;143:757–68. Georgiadis P, Athanasiou E. Flexible long-term capacity planning in closed-loop supply chains with remanufacturing. Eur J Oper Res. 2013;225(1):44–58. Georgiadis P, Vlachos D. The effect of environmental parameters on product recovery. Eur J Oper Res. 2004;157(2):449–64. Ghoreishi N, Jakiela MJ, Nekouzadeh A. A cost model for optimizing the take back phase of used product recovery. J Remanuf. 2011;1(1):1. Glöser-Chahoud S, Huster S, Rosenberg S, Baazouzi S, Kiemel S, Singh S, Schneider C, Weeber M, Miehe R, Schultmann F. Industrial disassembling as a key enabler of circular economy solutions for obsolete electric vehicle battery systems. Resour Conserv Recycl. 2021;174:105735. Goodell JW, Kumar S, Lim WM, Pattnaik D. Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. J Behav Experimental Finance. 2021;32:100577. Govindan K. Tunneling the barriers of blockchain technology in remanufacturing for achieving sustainable development goals: A circular manufacturing perspective. Bus Strategy Environ. 2022;31(8):3769–85. Hajian A, Daneshgar S, Sadeghi K, Ojha D, Katiyar G. From theory to practice: Empirical perspectives on the metaverse's potential. Technol Forecast Soc Chang. 2024;201:123224. Hrouga M, Sbihi A, Chavallard M. The potentials of combining Blockchain technology and Internet of Things for digital reverse supply chain: A case study. J Clean Prod. 2022;337:130609. Hu X, Sinniah S. The Role of Green Risk Management Approaches in Promoting Green and Sustainable Supply Chain Management. Nat Eng Sci. 2024;9(2):33–54. Huster S, Glöser-Chahoud S, Rosenberg S, Schultmann F. A simulation model for assessing the potential of remanufacturing electric vehicle batteries as spare parts. J Clean Prod. 2022;363:132225. Johri A, Joshi P, Kumar S, Joshi G. Metaverse for Sustainable Development in a bibliometric analysis and systematic literature review. J Clean Prod. 2024;435:140610. Kabiri NN, Emami S, Safaei AS. Simulation–optimization approach for the multi-objective production and distribution planning problem in the supply chain: using NSGA-II and Monte Carlo simulation: NN Kabiri et al. Soft Comput. 2022;26(17):8661–87. Kerin M, Pham DT. A review of emerging industry 4.0 technologies in remanufacturing. J Clean Prod. 2019;237:117805. Kessler MM. Bibliographic coupling between scientific papers. Am Doc. 1963;14(1):10–25. Khan SAR, Zia-ul‐haq HM, Umar M, Yu Z. Digital technology and circular economy practices: An strategy to improve organizational performance. Bus Strategy Dev. 2021;4(4):482–90. Kim J, Do Chung B, Kang Y, Jeong B. Robust optimization model for closed-loop supply chain planning under reverse logistics flow and demand uncertainty. J Clean Prod. 2018;196:1314–28. Kumar S, Lim WM, Pandey N, Westland C, J. 20 years of electronic commerce research. Electron Commer Res. 2021;21(1):1–40. Kumar S, Yamaoka T. System dynamics study of the Japanese automotive industry closed loop supply chain. J Manuf Technol Manage. 2007;18(2):115–38. Ledro C, Nosella A, Vinelli A. Artificial intelligence in customer relationship management: literature review and future research directions. J Bus Industrial Mark. 2022;37(13):48–63. Lee CK, Chan T. Development of RFID-based reverse logistics system. Expert Syst Appl. 2009;36(5):9299–307. Lehr CB, Thun J-H, Milling PM. From waste to value–a system dynamics model for strategic decision-making in closed-loop supply chains. Int J Prod Res. 2013;51(13):4105–16. Liao H, Shen N, Wang Y. Design and realisation of an efficient environmental assessment method for 3R systems: a case study on engine remanufacturing. Int J Prod Res. 2020;58(19):5980–6003. Lim JS, Zhang J. Adoption of AI-driven personalization in digital news platforms: An integrative model of technology acceptance and perceived contingency. Technol Soc. 2022;69:101965. Lim WM, Kumar S, Verma S, Chaturvedi R. Alexa, what do we know about conversational commerce? Insights from a systematic literature review. Psychol Mark. 2022;39(6):1129–55. Lim WM, Rasul T, Kumar S, Ala M. Past, present, and future of customer engagement. J Bus Res. 2022;140:439–58. Lim WM, Yap S-F, Makkar M. Home sharing in marketing and tourism at a tipping point: What do we know, how do we know, and where should we be heading? J Bus Res. 2021;122:534–66. Lin J, Zhou L, Spiegler VL, Naim MM, Syntetos A. Push or Pull? The impact of ordering policy choice on the dynamics of a hybrid closed-loop supply chain. Eur J Oper Res. 2022;300(1):282–95. Liu Z, Zhang M, Chen J, Diallo C. Buy now or later? New or remanufactured? How to attract strategic customers. Transp Res E. 2023;172:103068. Mallick PK, Salling KB, Pigosso DC, McAloone TC. Closing the loop: Establishing reverse logistics for a circular economy, a systematic review. J Environ Manage. 2023;328:117017. Materla T, Cudney EA, Antony J. The application of Kano model in the healthcare industry: a systematic literature review. Total Qual Manage Bus Excellence. 2019;30(5–6):660–81. Messinger PR, Stroulia E, Lyons K, Bone M, Niu RH, Smirnov K, Perelgut S. Virtual worlds—past, present, and future: New directions in social computing. Decis Support Syst. 2009;47(3):204–28. Mohammed MA, De-Pablos-Heredero C, Montes Botella JL. A Systematic Literature Review on the Revolutionary Impact of Blockchain in Modern Business. Appl Sci. 2024;14(23):11077. Niri AJ, Poelzer GA, Zhang SE, Rosenkranz J, Pettersson M, Ghorbani Y. Sustainability challenges throughout the electric vehicle battery value chain. Renew Sustain Energy Rev. 2024;191:114176. Nwosu A, Goyal S, Rajawat AS, Kemat BB, Mahmood WMABW. Integrating metaverse and blockchain for transparent and secure logistics management. Applied Data Science and Smart Systems. CRC; 2024. pp. 334–42. Pamucar D, Deveci M, Gokasar I, Delen D, Köppen M, Pedrycz W. Evaluation of metaverse integration alternatives of sharing economy in transportation using fuzzy Schweizer-Sklar based ordinal priority approach. Decis Support Syst. 2023;171:113944. Paul J, Bhukya R. Forty-five years of International Journal of Consumer Studies: A bibliometric review and directions for future research. Int J Consumer Stud. 2021;45(5):937–63. Paul J, Lim WM, O’Cass A, Hao AW, Bresciani S. Scientific procedures and rationales for systematic literature reviews (SPAR-4‐SLR). Int J Consumer Stud. 2021;45(4):O1–16. Phan HH, Tran CT, Khoa BT. Science Mapping of the Development and Trends in Green Finance: A Bibliometric Analysis. Global Bus Finance Rev. 2025;30(2):57. Ponte B, Naim MM, Syntetos AA. The effect of returns volume uncertainty on the dynamic performance of closed-loop supply chains. J Remanuf. 2020;10(1):1–14. Queiroz MM, Wamba F, Pereira S, S. C. F., Chiappetta Jabbour CJ. The metaverse as a breakthrough for operations and supply chain management: implications and call for action. Int J Oper Prod Manage. 2023;43(10):1539–53. Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D. (2004). Defining and identifying communities in networks. Proceedings of the national academy of sciences , 101 (9), 2658–2663. Rahbari A, Mahdi Nasiri M, Jolai F, Konur D. Integrated forward and reverse cross-docking with vehicle routing and scheduling under the uncertainty of inspection results: A genetic-mathematic algorithm. Transp Res Rec. 2024;2678(9):169–95. Rebolledo-Leiva R, Sillero L, Moreira MT, González-García S. Identifying the environmental hotspots of dietary fibres extraction from chickpea hull. Food Bioprod Process. 2023;142:59–69. Ritterbusch GD, Teichmann MR. Defining the Metaverse: A Systematic Literature Review. IEEE access. 2023;11:12368–77. https://doi.org/10.1109/access.2023.3241809 . Sadeghi K, Ojha D, Kaur P, Mahto RV, Dhir A. Metaverse technology in sustainable supply chain management: Experimental findings. Decis Support Syst. 2025;191:114423. Saha S, Nielsen IE, Majumder S. Dilemma in two game structures for a closed-loop supply chain under the influence of government incentives. J Industrial Eng Int. 2019;15(Suppl 1):291–308. Samadhiya A, Kumar A, Agrawal R, Kazancoglu Y, Agrawal R. Reinventing reverse logistics through blockchain technology: a comprehensive review and future research propositions. Supply chain forum: An international journal; 2023. Sangari MS, Mashatan A. A data-driven, comparative review of the academic literature and news media on blockchain-enabled supply chain management: Trends, gaps, and research needs. Comput Ind. 2022;143:103769. Serrano-Munoz A, Arana-Arexolaleiba N, Chrysostomou D, Bøgh S. Learning and generalising object extraction skill for contact-rich disassembly tasks: an introductory study. Int J Adv Manuf Technol. 2023;124(9):3171–83. Seuring S, Müller M. From a literature review to a conceptual framework for sustainable supply chain management. J Clean Prod. 2008;16(15):1699–710. Shrivastav SK, Bag S. Blockchain technology in circular economy: Unpacking the potential issues and critical echoes through data triangulation and natural language processing. Bus Strategy Environ. 2024;33(6):5463–84. Singh J, Ordoñez I. Resource recovery from post-consumer waste: important lessons for the upcoming circular economy. J Clean Prod. 2016;134:342–53. Su Z, Zhang M, Wu W. Visualizing sustainable supply chain management: a systematic scientometric review. Sustainability. 2021;13(8):4409. Tavana M, Sohrabi M, Rezaei H, Sorooshian S, Mina H. A sustainable circular supply chain network design model for electric vehicle battery production using internet of things and big data. Expert Syst. 2024;41(7):e13395. Tozanlı Ö, Kongar E, Gupta SM. Trade-in-to-upgrade as a marketing strategy in disassembly-to-order systems at the edge of blockchain technology. Int J Prod Res. 2020;58(23):7183–200. Tran AV, Khoa BT. Global Research Trends in Circular Economy: A Bibliometric Analysis in E-Commerce. Hum Behav Emerg Technol. 2025;2025(1):8645845. Vahdani B, Razmi J, Tavakkoli-Moghaddam R. Fuzzy possibilistic modeling for closed loop recycling collection networks. Environ Model Assess. 2012;17(6):623–37. Van Eck N, Waltman L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. scientometrics , 84 (2), 523–538. Van Eck NJ, Waltman L. Visualizing bibliometric networks. Measuring scholarly impact: Methods and practice. Springer; 2014. pp. 285–320. Vaz CR, Grabot B, Uriona Maldonado M, Selig PM. Some reasons to implement reverse logistics incompanies. Int J Environ Technol Manage. 2013;16(5–6):467–79. Ventura V, La Monica M, Bortolini M, Cutaia L, Mora C. Blockchain and industrial symbiosis: a preliminary two-step framework to green circular supply chains. Int J Environ Sci Technol. 2025;22(1):17–30. Verma S, Yadav N. Past, present, and future of electronic word of mouth (EWOM). J Interact Mark. 2021;53(1):111–28. Vlachos D, Georgiadis P, Iakovou E. A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chains. Comput Oper Res. 2007;34(2):367–94. Waqas M, Dong Q-l, Ahmad N, Zhu Y, Nadeem M. Critical barriers to implementation of reverse logistics in the manufacturing industry: a case study of a developing country. Sustainability. 2018;10(11):4202. Wu Z, Kwong CK, Aydin R, Tang J. A cooperative negotiation embedded NSGA-II for solving an integrated product family and supply chain design problem with remanufacturing consideration. Appl Soft Comput. 2017;57:19–34. Xia Y, Li C. Robust control strategy for an uncertain dual-channel closed-loop supply chain with process innovation for remanufacturing. IEEE Access. 2023;11:97852–65. Xu M, Guo Y, Hu Q, Xiong Z, Yu D, Cheng X. A trustless architecture of blockchain-enabled metaverse. High-Confidence Comput. 2023;3(1):100088. https://doi.org/10.1016/j.hcc.2022.100088 . Yan N-N, Sun B-W. Optimal Stackelberg strategies for closed-loop supply chain with third-party reverse logistics. Asia-Pac J Oper Res. 2012;29(05):1250026. Yang Y, Lin J, Hedenstierna C, Zhou L. The more the better? The impact of the number and location of product recovery options on the system dynamics in a closed-loop supply chain. Transp Res E. 2023;175:103150. Yong-Hak J. (2013). Web of science. Thomson Reuters . Yu Z, Kwilinski A, Trushkina N. Reverse logistics in a system of the circular economy: theoretical aspect. Экономический вестник Донбасса. 2018;4(54):29–37. Zanoni S, Ferretti I, Tang O. Cost performance and bullwhip effect in a hybrid manufacturing and remanufacturing system with different control policies. Int J Prod Res. 2006;44(18–19):3847–62. Zhang H, Zhang R. Remanufacturing Modes Selection in Competitive Closed-Loop Supply Chains. Systems. 2025;13(4):257. Zhang S, Li X, Zhang C. A fuzzy control model for restraint of bullwhip effect in uncertain closed-loop supply chain with hybrid recycling channels. IEEE Trans Fuzzy Syst. 2016;25(2):475–82. Zhang S, Zhao X. Fuzzy robust control for an uncertain switched dual-channel closed-loop supply chain model. IEEE Trans Fuzzy Syst. 2014;23(3):485–500. Zhang S, Zhao X, Zhang J. Dynamic model and fuzzy robust control of uncertain closed-loop supply chain with time-varying delay in remanufacturing. Ind Eng Chem Res. 2014;53(23):9805–11. Zhang X, Zheng H, Zheng X. Impact of government fund policy and blockchain technology on closed-loop supply chains in textile and apparel industry. J Clean Prod. 2024;434:140037. Zhang Y, Zhang H, Cheng Y. The adoption strategy of blockchain technology for new and remanufactured products considering price and consumer preference. J Industrial Prod Eng. 2025;42(2):103–26. Zhang Y, Zhang Q, Hu R, Yang M. Optimal Strategy and Performance for a Closed-Loop Supply Chain with Different Channel Leadership and Cap-and-Trade Regulation. Sustainability. 2025;17(3):1042. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Apr, 2026 Read the published version in Discover Sustainability → Version 1 posted Editorial decision: Revision requested 19 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviews received at journal 27 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers invited by journal 19 Aug, 2025 Editor assigned by journal 14 Aug, 2025 Submission checks completed at journal 12 Aug, 2025 First submitted to journal 12 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7221291","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504824028,"identity":"5ef9a736-2cea-4cc8-a58d-154d212606ed","order_by":0,"name":"Tran Cong Toan","email":"","orcid":"","institution":"Industrial University of Ho Chi Minh City","correspondingAuthor":false,"prefix":"","firstName":"Tran","middleName":"Cong","lastName":"Toan","suffix":""},{"id":504824029,"identity":"689c2f30-fcc7-4933-8960-98741e1f0af2","order_by":1,"name":"Le Thi Tuyet Thanh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYDACCQYGZoYCBgZ+CbhQAn4dPGAtBgYSkjPgqonVYnCDWC320s3PHhcY/Kkzvt38dDPvDxsGfvYcA2beHXhskTlmbjwDaIvZnWNmt3kS0hgke94AtZzB57AEM2kekJYbOWxALYcZDG7kGDDObMOnJf0bWIvxDLCW/wz2hLXkQGwxkABrOcAAZBgwfMSn5UZOGVCLseSMG2lmN+ekJfNInHlWcACfFvYZ6dukeSrk+PlnJD+78cbGTo6/PXnjg0Q8WjCtBREHSNAwCkbBKBgFowALAAAzmEXNEBdVMgAAAABJRU5ErkJggg==","orcid":"","institution":"Ho Chi Minh City University of Technology and Education","correspondingAuthor":true,"prefix":"","firstName":"Le","middleName":"Thi Tuyet","lastName":"Thanh","suffix":""}],"badges":[],"createdAt":"2025-07-26 12:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7221291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7221291/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43621-026-03277-8","type":"published","date":"2026-04-26T15:58:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90045364,"identity":"bc7acfb9-1357-4ec4-8038-75df6b4aacae","added_by":"auto","created_at":"2025-08-27 18:06:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":53873,"visible":true,"origin":"","legend":"\u003cp\u003eReview procedure using the SPAR‐4‐SLR protocol. SPAR‐4‐SLR\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7221291/v1/ca28e625a87455efed20435a.png"},{"id":90044231,"identity":"097777a1-6445-4d9d-bfec-1d0b97453914","added_by":"auto","created_at":"2025-08-27 17:50:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31065,"visible":true,"origin":"","legend":"\u003cp\u003eTop 10 Most Prolific Authors in the Field\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7221291/v1/a8e1ccbb4cc187430f0e2231.png"},{"id":90044234,"identity":"38480e27-9717-4f56-ad31-dfd442c9827f","added_by":"auto","created_at":"2025-08-27 17:50:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":277234,"visible":true,"origin":"","legend":"\u003cp\u003eKeyword co‐occurrence of themes network.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7221291/v1/780465552d4051f3ade5ae95.png"},{"id":90044237,"identity":"d159e654-d403-460d-a29d-0e3fb7220367","added_by":"auto","created_at":"2025-08-27 17:50:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1376878,"visible":true,"origin":"","legend":"\u003cp\u003eThematic Clusters Based on Bibliographic Coupling Analysis\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7221291/v1/2fda7692cc53e23984852b37.png"},{"id":90044798,"identity":"0e911824-1ca1-4b3b-a406-e256ed324c7e","added_by":"auto","created_at":"2025-08-27 17:58:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":157056,"visible":true,"origin":"","legend":"\u003cp\u003eThematic Map of Research on Reverse Logistics and Metaverse from a Circular Economy Perspective\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7221291/v1/da0f1d869d19766877e933b7.png"},{"id":107928420,"identity":"d421a991-5cf6-4066-be30-135ea864ba9e","added_by":"auto","created_at":"2026-04-27 16:10:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2325702,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7221291/v1/34c83d2d-beaf-47bb-b7da-67f57e68799e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Bibliometric and Thematic Review of Metaverse and Reverse Logistics for a Sustainable Circular Economy","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMetaverse is one of the most popular emerging digital technologies, described as a three-dimensional space in Web 3.0, in which individuals can communicate with the use of different technologies: virtual reality (VR), augmented reality (AR), artificial intelligence (AI) (Chen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Pamucar et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ritterbusch \u0026amp; Teichmann, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Web 3.0 is a decentralized Internet development based on blockchain technology, which opens great possibilities in the reorganization of the traditional market (Anderson et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Dwivedi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). So far, metaverse was studied and implemented in areas like healthcare, education, production and, importantly, in supply chain management (Sadeghi et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this field, the presence of the metaverse in the logistics system, especially the reverse logistics (RL), allows all the parties working in the supply chain to observe the whole chain in the interactive virtual space and makes decisions more accurate and efficient (Hajian et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Messinger et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Pamucar et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Queiroz et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eReverse logistics refers to a group of operations designed to retrieves the products to the consumer to be further processed or venerate its value such as returning the used products to their recovery point often referred to as take-back systems (Bockholt et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Erol et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ghoreishi et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). RL is extremely important in the context of the circular economy (CE) by facilitating the recirculation of products, components, and materials, including via the constructive means of reusing, recycling, and remanufacturing products (Geissdoerfer et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Singh \u0026amp; Ordo\u0026ntilde;ez, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Nevertheless, using RL in practice continues to present many problems mainly because of the deficiency in knowledge information, implementation skills and sufficient support structures (Mallick et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Vaz et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Waqas et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMore so, the traditional logistics management systems fall apart in terms of traceability, transparency, and information security, thus contributing to poor results in the entire supply chain giảm (Nwosu et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). At that, the incorporation of blockchain metaverse implementation into the sphere of logistics will provide a range of high-value technological tools like virtualization of the logistic processes, real-time process surveillance, multi-agent environment, and decentralized data control, used in the context of RL optimization in respect to sustainability as one of the main aspects (Chang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough a number of articles have been dedicated to the consideration of metaverse in the context of sustainability (Chang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; De Giovanni, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Johri et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and reverse logistics in relation to the circular economy (Correa-Vaca et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; de Almeida et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yu et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), there is no research that integrates these topics with emerging technologies like the metaverse. There is also the lack of holistic comprehension of the evolution, systemization, and key themes of knowledge, which explain the interrelation of the metaverse, reverse logistics, and the circular economy. A comprehensive analysis that is sufficiently organized and integrative is necessary in helping scholars, authorities, and enterprises to develop sustainable logistics approaches by utilizing technological creativity. On this backdrop, this paper aims at doing a bibliometric and systematic review to investigate the functions and usage pattern of the metaverse in reverse logistics through the prism of the circular economy. The research also seeks to determine the structure of knowledge, science hubs, and prevailing trends in the development of the studied field by examining scholarly data gathered using the Scopus database.\u003c/p\u003e\u003cp\u003ePrecisely, the paper aims at resolving the following four research questions:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eRQ1: How are publications and citations trends in metaverse and reverse logistics research in the environments of the circular economy?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRQ2: Which sources, scholarly works, and authors in the field of metaverse and reverse logistics in the circular economy are the most influential ones?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRQ3: Which are the chief themes that are being discussed concerning the position of the metaverse in reverse logistics in terms of the perspective of the circular economy?\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eRQ4: How can it be determined what future research should be given priorities to further understand the integration between the metaverse and the reverse logistics as the way of advancing the circular economy?\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eReaching the answers on these questions, the research will use various bibliometric methods, such as performance analysis to determine the most impact researchers and documents; keyword co-occurrence analysis and bibliographic coupling that will introduce a stringent and multidimensional picture of the broad settings of the research streams; thematic evolution to follow the changes of the research content with time (Donthu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kumar et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Also, the approaches adopted in the science mapping based on the triangulation method correspond to the recommendations of Lim and Zhang (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) that imply analytical rigor and validity of reporting in bibliometric studies.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cp\u003eIn management and, more recently, in marketing, systematic literature reviews (SLRs) are a scientifically sound way to do secondary research (Floren et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lim et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Different kinds of SLRs have been made based on different fields of study. These include structured topic-based, theory-based, bibliometric, hybrid, and conceptual reviews (Lim, Rasul, et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A mixed method that includes both bibliometric analysis (quantitative) and thematic analysis (qualitative) has been used in this study to answer the research questions. Specifically, bibliometric performance analysis has been used to answer RQ1 and RQ2; scientific mapping of major themes has been used to answer RQ3; and a thematic map and content analysis have been used to evaluate the field's growth and suggest future research directions (RQ4). This review is therefore a hybrid systematic literature review that uses both bibliometric analysis and structured thematic analysis.\u003c/p\u003e\u003cp\u003eThe \u0026ldquo;systematic\u0026rdquo; part of this review was based on the SPAR-4-SLR (Scientific Procedures and Rationales for Systematic Literature Reviews) protocol, which is a new and strict way to do systematic literature reviews (Paul et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The goal of this review is to be thorough and methodologically sound by systematically finding and analysing all relevant studies on Reverse Logistics and the Metaverse in the Circular Economy using a clear and repeatable process. This kind of review is thought to be the most thorough, strict, and trustworthy compared to other types of reviews, like narrative reviews that don't use a systematic approach (Balaid et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Boell \u0026amp; Cecez-Kecmanovic, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Denyer \u0026amp; Tranfield, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Fosso Wamba \u0026amp; Mishra, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Paul et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Verma \u0026amp; Yadav, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAs a result, a combination of a systematic literature review and a bibliometric analysis has been used. The SPAR-4-SLR protocol created by Paul et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) is the main framework that has been used. There are three steps in this protocol: putting things together, organising them, and evaluating them. It was chosen because it is new and has a high level of methodological rigour in the social sciences. This makes it a strong alternative to the older PRISMA protocol, which is often used in social science research but started in the natural sciences.\u003c/p\u003e\u003cp\u003eAlso, a set of bibliometric analysis methods has been used, such as performance analysis and science mapping, which includes content analysis, keyword co-occurrence analysis, and bibliographic coupling, as suggested by (Donthu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Because they can handle and analyse a lot of literature in the field, these methods have been used to show the performance (publication and citation trends, key sources, works, and authors) and intellectual structure (core theories, themes, and components) of the field of Reverse Logistics and the Metaverse under the Circular Economy.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows how the systematic literature review and bibliometric analysis work. The next sections will go into more detail about these processes.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Assembling\u003c/h2\u003e\u003cp\u003eThere are two parts to the assembling stage: identification and acquisition.\u003c/p\u003e\u003cp\u003eThis study's main goal was to find literature related to Reverse Logistics and the Metaverse within the Circular Economy in order to answer research questions about the field's academic performance (e.g., publication and citation trends, key sources, publications, and authors) and intellectual structure (e.g., underlying theories, themes, and conceptual frameworks).\u003c/p\u003e\u003cp\u003eEnglish-language journal articles are the only types of sources that have been used because they are seen as the main way to add to the body of academic knowledge. The review didn't include other types of sources like books and book chapters because they tend to be more descriptive and don't have clear academic contributions (Ledro et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Paul et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Phan et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Radicchi et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eScopus has been selected as the data source due to its adherence to rigorous indexing criteria (Garousi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Materla et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Scopus is the world's biggest database of abstracts and citations (Fahimnia et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It has more information than other databases (Paul \u0026amp; Bhukya, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yong-Hak, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe search only looked for articles that were published before June 2025. The study found relevant records by looking at their titles, abstracts, and keywords. The search terms were carefully chosen to cover all the main topics, using keywords from past bibliometric studies. oolean operators (e.g., \u0026ldquo;OR\u0026rdquo;, \u0026ldquo;AND\u0026rdquo;) were employed to construct a dataset that is both broad and precise. The search terms were: (\u0026ldquo;circular economy\u0026rdquo; OR \u0026ldquo;circular supply chain\u0026rdquo; OR \u0026ldquo;closed-loop supply chain\u0026rdquo;) AND (\u0026ldquo;reverse logistics\u0026rdquo; OR \u0026ldquo;product returns\u0026rdquo; OR \u0026ldquo;remanufacturing\u0026rdquo; OR \u0026ldquo;waste valorisation\u0026rdquo;) AND (\u0026ldquo;metaverse\u0026rdquo; OR \u0026ldquo;virtual reality\u0026rdquo; OR \u0026ldquo;augmented reality\u0026rdquo; OR \u0026ldquo;immersive technology\u0026rdquo; OR \u0026ldquo;digital twin\u0026rdquo; OR \u0026ldquo;blockchain\u0026rdquo; OR \u0026ldquo;simulation\u0026rdquo;).\u003c/p\u003e\u003cp\u003eA total of 232 documents were retrieved during the data collection phase.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Arranging\u003c/h2\u003e\u003cp\u003eThe arranging stage consists of two sub-steps: organization and filtration.\u003c/p\u003e\u003cp\u003eFor the organization process, coding criteria have been applied to classify the documents based on language and document type. Regarding filtration, only those documents meeting the following conditions have been retained for further analysis:\u003c/p\u003e\u003cp\u003e(1) written in English; and (2) categorized as \u0026ldquo;articles\u0026rdquo; or \u0026ldquo;reviews,\u0026rdquo; as these types of publications typically undergo a full academic peer-review process.\u003c/p\u003e\u003cp\u003eAs a result, a total of 198 documents have been retained following the arranging stage.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Assessing\u003c/h2\u003e\u003cp\u003eThe Assessing phase consists of two sub-stages: evaluation and reporting.\u003c/p\u003e\u003cp\u003eThis study uses an inductive approach to evaluation, which means it draws conclusions from patterns in the data được (Fahimnia et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Seuring \u0026amp; M\u0026uuml;ller, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). It also uses a set of bibliometric techniques, which are divided into performance analysis and science mapping (Baier-Fuentes et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Donthu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSpecifically, quantitative descriptive statistics are used to look at publication and citation trends (RQ1) and find the most important sources, articles, and authors in the field (RQ2). At the same time, two methods are used for science mapping: keyword co-occurrence analysis and bibliographic coupling. These methods help us find core concepts and thematic knowledge clusters (RQ3). Finally, a thematic map and content analysis are used to put together the big picture of what we know about Reverse Logistics and the Metaverse in the Circular Economy (RQ4). This includes looking at the different stages of development, the contributions made by scholars, and the possible future research directions\u003c/p\u003e\u003cp\u003eTools such as Microsoft Excel are used for descriptive and content analysis (Lim, Kumar, et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), while Biblioshiny in R (Aria \u0026amp; Cuccurullo, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and VOSviewer (Van Eck \u0026amp; Waltman, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) are employed to construct and visualize networks emerging from keyword co-occurrence and bibliographic coupling\u0026mdash;aligning with the bibliometric analysis recommendations by Donthu et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Using both tools together takes advantage of what makes each one unique: VOSviewer is great at showing large networks in a clear and useful way, especially when it comes to showing highly connected keywords and documents. Biblioshiny, on the other hand, has a user-friendly interface and advanced features like thematic mapping, trend charts, and bibliometric indices. This mixed method strikes the best balance between depth of analysis and clarity of visuals, which is important for modern bibliometric research that uses multiple tools.\u003c/p\u003e\u003cp\u003eEach cluster in the network stands for a common theme. Nodes show keywords or cited documents that stand for sub-topics, and links between nodes show how they are related to each other in the larger thematic context of the cluster đ\u0026oacute; (Donthu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Radicchi et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Van Eck \u0026amp; Waltman, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The thickness of the links and the size of the nodes show how important the elements they stand for are (Clauset et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Radicchi et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Tran \u0026amp; Khoa, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Van Eck \u0026amp; Waltman, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRegarding result reporting, the study employs a combination of visualizations, tables, and narrative descriptions to present the findings, following reporting conventions recommended by Paul et al. (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There are also annotated tables with different bibliometric indicators that are explained in writing to give a clear and useful picture of both performance and knowledge structure in the field.\u003c/p\u003e\u003cp\u003eThe review process and methods have been carried out according to well-known rules from the literature on systematic reviews (Paul et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and bibliometric analysis (Donthu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The research team has also tried to be as open as possible in their reporting.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe results of this review are shown in two primary sections: which are based on the research questions. The bibliometric performance analysis (RQ1, RQ2) looks at the performance of the fields of Reverse Logistics and the Metaverse in the Circular Economy by looking at publication trends over time, citation metrics, and the top articles, authors, and countries. The science mapping part (RQ3) has co-citation analysis and keyword co-occurrence analysis, which help find the main ideas and groups of knowledge that are most important in the field.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Performance analysis\u003c/h2\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 Publication and citation trend\u003c/h2\u003e\u003cp\u003eTrends in publishing and citing research on Reverse Logistics and the Metaverse in the context of the Circular Economy\u003c/p\u003e\u003cp\u003eThe Scopus dataset for the years 2003 to 2025 has 198 publications from 118 different sources (journals and monographs). This collection shows a field that is growing quickly and getting more academic attention, with an average annual growth rate of 15.54%. The dataset isn't very big, but the average of 33.57 citations per article shows that it has had a big impact on research (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Section A). Most of the works are fairly new, with an average age of 4.67 years. This shows that the field is very relevant to current events. The fact that there are 181 empirical articles and only 17 review papers shows that there is a strong focus on new research and discovery. The rapid growth can be linked to the rise of new technologies, policies that support the circular economy, and the global need for environmentally friendly logistics solutions.\u003c/p\u003e\u003cp\u003eAccording to Scopus data (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Section B), the documents in the dataset have received a total of 6,647 citations, which is an average of 33.57 citations per publication. There have been 10,477 references cited, which shows that the field has a strong theoretical base and a lot of knowledge. The high citation average shows that the papers have a lot of academic influence, but it's important to remember that a few highly cited papers could change the distribution.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics of Dataset on Reverse Logistics and the Metaverse under the Circular Economy\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePanel A. Publication information\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStatistic\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal publications (TP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e198\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal sources (TS)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of active years (NAY)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnnual growth rate (AGR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15,54%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDocument average age (DAA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,67 years\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePanel B. Citation information\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal citations (TC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAverage citations per publication (TC/TP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33,57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of references (REF)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.477\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePanel C. Authorship information\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of contributing authors (NCA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e625\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuthors of single-authored publications (ASA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuthors of co-authored publications (ACA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e578\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle-authored publications (SA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCo-authored publications (CA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e188\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCo-authors per document (CAD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternational co-authorships (IC)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25,76%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePanel D. Document information\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArticles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e181\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReviews\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKeywords Plus (ID)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1489\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuthor\u0026rsquo;s keywords (DE)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e593\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe dataset has 586 contributing authors, which is a lot of authorship. Eight of them wrote ten articles by themselves, and the other 576 authors worked together on 188 publications with other authors (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Section C). The average of 3.53 authors per article shows how collaborative the field is and how important interdisciplinary research teams are. The 25.76% rate of international collaboration shows that there is a moderate amount of cross-border activity, but there is a lot of room for growth in the future.\u003c/p\u003e\u003cp\u003eThere are 181 empirical research articles (91.41%) and only 17 non-empirical review articles (8.59%). There are 10,477 references and 2,082 keywords in these documents. 1,489 of the keywords are automated (Keywords Plus) and 593 are provided by the authors. These are the keywords that were used to create the science mapping part of this study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Section D).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 Top sources for Reverse Logistics and the Metaverse under the Circular Economy\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the top 10 places to publish in the field of Reverse Logistics and the Metaverse under the Circular Economy. Sustainability (Switzerland) (Q1) is in first place with 15 publications. These publications make a big difference in the wide range of research on sustainable logistics, remanufacturing, and circular technologies. However, its relatively low total link strength (4) suggests that citations are spread out across the literature.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTop 10 Publication Sources in Research on Reverse Logistics and the Metaverse under the Circular Economy\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDocuments\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCitations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal Link Strength\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSustainability (Switzerland) (Q1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJournal of Cleaner Production (Q1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternational Journal of Production Research (Q1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e355\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJournal of Remanufacturing (Q2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComputers and Industrial Engineering (Q1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEuropean Journal of Operational Research (Q1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternational Journal of Production Economics (Q1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBusiness Strategy and the Environment (Q1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternational Journal of Advanced Manufacturing Technology (Q2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransportation Research Part E: Logistics and Transportation Review (Q1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOmega (United Kingdom) (Q1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInternational Journal of Advancements in Computing Technology (Q3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe Journal of Cleaner Production (Q1) comes in second with 12 articles and 714 citations. This clearly shows how important it is to apply circular economy principles to reverse logistics. The International Journal of Production Research (Q1) comes in third with 6 publications and 355 citations. It also has the highest total link strength (19), which shows that it has a big impact on the academic community.\u003c/p\u003e\u003cp\u003eThe Journal of Remanufacturing (Q2) also has 6 articles and focuses on specialized studies in remanufacturing. It has 141 citations. There are five articles in Computers and Industrial Engineering (Q1), which focuses on optimization models in closed-loop logistics.\u003c/p\u003e\u003cp\u003eThe European Journal of Operational Research (Q1), the International Journal of Production Economics (Q1), and Business Strategy and the Environment (Q1) all have four publications. The last one stands out with 499 citations. The other three journals in the top ten\u0026mdash;International Journal of Advanced Manufacturing Technology (Q2), Transportation Research Part E (Q1), and Omega (United Kingdom) (Q1)\u0026mdash;are important for helping to improve technology and operations in the field of reverse logistics.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.1.3 Top publications for Reverse Logistics and the Metaverse under the Circular Economy\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the ten most cited articles in the fields of Reverse Logistics and the Metaverse under the Circular Economy. The article by Centobelli et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which was published in Information \u0026amp; Management, is at the top of the list. It has 517 citations and an average of 129.25 citations per year, which is the most of any entry. The study suggests a framework for digital transformation that can be used to bring technology into closed-loop logistics. Dev et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which has 368 citations, comes in second. It is about circular logistics strategies in resource management and recycling and was published in Resources, Conservation \u0026amp; Recycling. Vlachos et al. (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), which has 359 citations, is in third place. It is one of the first studies on hybrid models for production and remanufacturing and was published in Computers \u0026amp; Operations Research. (Kerin \u0026amp; Pham, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which was published in the Journal of Cleaner Production and focuses on life cycle assessment in remanufacturing, has since received 307 citations. Georgiadis and Vlachos (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) are two classic studies that have each been cited 180 times and have set the stage for optimization in closed-loop supply chains (CLSC). Liu et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) has already received 176 citations, which is an average of 58.67 citations per year. This makes Liu the second most cited author after Centobelli et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Lee and Chan (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), Khan et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Kim et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) have also done research that adds to our understanding of how to use technology in reverse logistics, how to make operations more data-driven, and how to plan for sustainable development.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe 10 Most Cited Articles in the Field\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAPER\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal Citations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTC per Year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNormalized TC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCentobelli et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e517\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e129.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDev et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVlachos et al. (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e359\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKerin and Pham (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e307\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeorgiadis and Vlachos (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFleischmann et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiu et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLee and Chan (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKhan et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKim et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.1.4 Top authors for Reverse Logistics and the Metaverse under the Circular Economy\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the 10 most prolific authors in the reverse logistics and circular economy. On top are Zhang S, with 2012 to 2021, with 6 publications largely in the areas of fuzzy control and dynamic management of closed-loop supply chains in uncertainties.\u003c/p\u003e\u003cp\u003eThe next two excepted ones, Wang T and Zhao X, have five publications each. Wang T (20172023) has been working on models of dynamic systems and the development of blockchain in the logistics. Conversely, Zhao X (20122015) is reported to make use of fuzzy robust control strategies in order to optimise supply chain performance.\u003c/p\u003e\u003cp\u003eGeorgiadis P (publication period, 2004\u0026ndash;2022) has the longest run of publishing the most articles in the group (4), and they concern the foundation of dynamic modeling in remanufacturing.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eLi Y, Zhang H and Zhang Y published 4 papers, but Li Y is the most recent publication. Li Y investigates the ethical implications of the concept of circular value chains. Li Y, Rashid A, and Zhang H examined the possibility of industrial digital platforms based on the rental-recovery business model. Asif FMA and Cannella S have three publications both. Asif (20172024) is a young scholar who has taken a multi-method simulation in circular economy studies and Cannella (20162021) has managed to make contributions at the research studies in terms of remanufacturing configurations as well as complex supply chains.\u003c/p\u003e\u003cp\u003eOn the whole, these authors represent a variety of methodologies: modeling, simulation, and control systems, and a reunion in the major issues like circular economy, remanufacturing, and reverse logistics.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Science mapping\u003c/h2\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Co-word analysis\u003c/h2\u003e\u003cp\u003eBased on the keywords that authors used in their publications, the keyword co-occurrence analysis found six different keyword clusters (themes), each with a total of 34 keywords that describe the network's knowledge structure. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e show the representative keywords and network metrics for each new theme:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eaverage publication year(APY), which indicates the degree of hotness (more recent) or coldness (least recent) of the keyword;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eaverage citation(AC), which indicates the average citation received by documents that enlist the keyword;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eoccurrence(OC), which indicates the frequency of keyword appearance in the corpus;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003edegree of centrality(DG), which indicates the number of relational ties associated to the keyword;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ecloseness centrality(CC), which indicates the reciprocal summation of the shortest route between the keyword and its neighboring keywords;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ebetweenness centrality(BC), which indicates the knowledge dissemination potential of the keyword in the cluster; and\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003ePageRank(PR), which indicates the importance of the keyword to the cluster based on the quality and number of links directed toward the keyword (Brin \u0026amp; Page, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) (Donthu, Kumar, Mukherjee, et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Brin \u0026amp; Page, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster 1 (Red): The Circular Economy and Industry 4.0 in Reverse Logistics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe first cluster of keywords is about digital technologies (like blockchain and Industry 4.0) and the circular economy (like disassembly, life cycle assessment, and process simulation) that help improve waste valorization.\u003c/p\u003e\u003cp\u003eThe main word in this group is \"circular economy,\" which has the highest scores on all network metrics: average citations (AC: 33.80), occurrence count (OC: 56), degree centrality (DG: 23), closeness centrality (CC: 0.0189), betweenness centrality (BC: 296), and PageRank (PR: 0.1461). The average publication year (around 2022) shows that research in this cluster, which is about how to combine Industry 4.0 technologies with the circular economy, is both new and still changing.\u003c/p\u003e\u003cp\u003eThis cluster of studies looks at how digital technologies like blockchain, the Internet of Things (IoT), artificial intelligence (AI), and simulation tools can help promote circular economy practices. Several publications suggest frameworks to help with digital transformation in circular supply chains. For example, they suggest using blockchain to promote industrial symbiosis and find hidden barriers in the circular economy (Shrivastav \u0026amp; Bag, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ventura et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). There are also reviews of new Industry 4.0 technologies that help with reverse logistics (Kerin \u0026amp; Pham, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mohammed et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sangari \u0026amp; Mashatan, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and case studies on optimizing disassembly and waste reuse using LCA and process simulation. These show how advanced technologies can make the circular economy work better (Arias et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Garc\u0026iacute;a-Chirino et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rebolledo-Leiva et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Serrano-Munoz et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster 2 (Green): Modeling battery recycling and the Internet of Things\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe second group of keywords is about recycling and reusing things in a circular economy, with a focus on the uncertainty of return flows. The main word in this group is \"recycling,\" which has some interesting numbers: AC: 29.25, OC: 8, DG: 8, CC: 0.0135, BC: 1, and PR: 0.0121. This theme has gotten a lot of academic attention since the late 2010s and is still relevant. The average publication year is between 2018 and 2020.\u003c/p\u003e\u003cp\u003eThe studies in this group look at how to recycle materials that have already been used, often focusing on uncertainty in return flows. Battery recycling studies are of special interest because they show how important it is to take apart industrial equipment and get as much useful material back as possible (Gl\u0026ouml;ser-Chahoud et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Strict environmental rules and the lack of important materials (Niri et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) have made reverse logistics for electric vehicle (EV) batteries more important.\u003c/p\u003e\u003cp\u003ePeople often use simulation models to predict changes in recovered volumes. For example, Monte Carlo methods are used to figure out how risky it is to receive scrap (Kabiri et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, IoT technologies are recommended for keeping an eye on waste and return flows (Deng et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tavana et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), coming up with models for sustainable battery supply chain networks, and using IoT to manage battery returns more effectively. In general, Cluster 2 focuses on the study of material flows, predicting changes in returns, and finding problems in Li-ion reverse logistics. The goal is to improve the processes for recovering and recycling EV batteries.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBibliometric information on the keyword co-occurrence of themes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThemes and keywords\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal Link Strength\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAPY\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCluster\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecircular economy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2022.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e33.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003e1 (Red)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eblockchain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eindustry 4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2022.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e121.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewaste valorization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003elife cycle assessment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2022.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e16.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edisassembly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2022.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eprocess simulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erecycling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2019.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003e2 (Green)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eelectric vehicle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003einternet of things\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2022.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e90.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edynamic model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2011.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emonte carlo simulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2022.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003euncertainty\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2020.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eblockchain technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e54.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003e3 (Dark blue)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esustainability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2022.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esupply chain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2022.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e51.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003einventory management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2022.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eliterature review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2022.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esustainable supply chain management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ereverse logistics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2016.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e53.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003e4 (Yellow)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esimulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2020.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ebullwhip effect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2019.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eclosed-loop supply chains\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2019.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e41.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esupply chain dynamics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2019.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eremanufacturing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2019.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003e5 (Purple)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eclosed-loop supply chain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2018.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e20.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egame theory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2021.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003efuzzy robust control\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2013.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003estackelberg game\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esystem dynamics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2016.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003e6 (Light blue)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003esupply chain management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2016.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e57.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eclosed loop supply chain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2017.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecapacity planning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2014.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100.20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ewaste management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2023.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster 3 (Dark Blue): Blockchain Technology and Sustainable Supply Chain Management\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThese keywords are all about sustainable supply chain management (SSCM) in the context of the circular economy. The main word here is \"sustainability.\" The network metrics are as follows: AC: 23.27, OC: 11, DG: 13, CC: 0.0149, BC: 7, and PR: 0.0350. The average year of publication (about 2022) shows how new and important this research area is becoming.\u003c/p\u003e\u003cp\u003eThe cluster also includes new technologies like \"blockchain technology,\" which is a main keyword (OC: 12, AC: ~54.4), showing that people are very interested in how blockchain can be used.\u003c/p\u003e\u003cp\u003eA lot of the articles in this group talk about how blockchain makes circular supply chains and reverse logistics more open and trustworthy. For example, Centobelli et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) talk about how blockchain can help make circular supply chains more traceable, open, and trustworthy for all parties involved. Other research looks at how to combine blockchain with the Internet of Things (IoT) in reverse logistics to make it easier to keep track of products and cut down on waste (Hrouga et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hu \u0026amp; Sinniah, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThere are also keywords like \"inventory management\" and \"literature review\" that suggest that some studies give theoretical overviews of inventory strategies in circular supply chains or holistic assessments of digital technologies in reverse logistics (Barretti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo sum up, this group has a strong academic base that combines sustainability goals with blockchain-based technologies to make reverse logistics systems more reliable and efficient.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster 4 (Yellow): How the supply chain works in reverse\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe main ideas in this cluster are reverse supply chains, dynamics, and simulation. Some of the most important terms are \"reverse logistics,\" \"whip effect,\" \"supply chain dynamics,\" and \"simulation.\" Reverse logistics is the most important keyword, with the highest numbers in AC (53.67), OC (27), DG (18), CC (0.0167), BC (70), and PR (0.0720). The average year of publication, on the other hand, is fairly low (around 2015\u0026ndash;2017). This suggests that research on closed-loop supply chain dynamics has not received as much recent attention and is based on earlier foundational work in the field. This group shows that people were interested in problems in reverse supply chains early on. When returns are involved, representative works look at how demand changes along the supply chain and use simulation models to measure and lessen these effects (Lin et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, system dynamics and discrete-event simulations have been used to create data governance processes for the circular economy (Charnley et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and to look into how hybrid CLSC systems make decisions about product recovery (Yang et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These studies help us understand better how demand changes spread through return networks and give us ways to lessen the bad effects.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster 5 (Purple): Improving closed-loop supply chains and remanufacturing\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis cluster is all about making closed-loop supply chains work better, with a focus on remanufacturing and making strategic decisions. Some important keywords are \"remanufacturing,\" \"closed-loop supply chain,\" and analytical tools like \"game theory\" (especially Stackelberg models) and \"fuzzy robust control.\" The core keyword \"remanufacturing\" has the highest values in AC (29.43), OC (51), DG (20), CC (0.0192), BC (229), and PR (0.1450). The average year of publication (2018\u0026ndash;2020) shows that there is still active research going on, building on models that were proposed earlier. Many studies use Stackelberg game models to look into the best ways for manufacturers and collectors to set prices, make things, and get their money back (Saha et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yan \u0026amp; Sun, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhang \u0026amp; Zhang, \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This group also includes environmentally friendly control methods to make sure that remanufacturing works well even when things are uncertain. For example, there are strong control strategies for dual-channel closed-loop supply chains (Xia \u0026amp; Li, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang \u0026amp; Zhao, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Cluster 5 is mostly about finding solutions to problems that come up in reverse logistics systems, like managing capacity limits and coordinating inventory and production between new and remanufactured goods.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster 6 (Blue): Planning and modeling the reverse supply chain\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis cluster of keywords has to do with capacity planning and dynamic modeling in closed-loop supply chains. The words \"capacity planning,\" \"closed loop supply chain,\" \"system dynamics,\" and \"waste management\" suggest that this group focuses on strategic planning and policy simulation in reverse logistics systems. The central keyword system dynamics has the highest values in AC: 52.75, OC: 20, DG: 11, CC: 0.0139, BC: 14, and PR: 0.0596. This theme has roots that go back further than 2016, when the first publications on it appeared. It serves as a base for later research. Several important studies have tried to figure out the best facility sizes for long-term capacity planning for recycling and remanufacturing networks. For example, Fleischmann et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) set the stage for looking at the costs of recovery networks, inventory control, and reverse logistics. Vlachos et al. (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) were the first to use dynamic simulations to change the capacity of remanufacturing systems over time. Georgiadis and Athanasiou (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) then suggested flexible planning for processing capacities. System dynamics modeling has been used to simulate long-term waste management policies (Lehr et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) in addition to optimization. This group as a whole takes a long-term, big-picture view of circular supply chain management. It stresses the need for capacity balancing and policy planning through simulation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Bibliographic coupling\u003c/h2\u003e\u003cp\u003eThe present study conducts bibliographic coupling as an alternativebibliometric analysis technique to triangulate the findings from the keyword co-occurrence analysis (Goodell et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In bibliographic coupling, documents with common referencing patternsconverge and form bibliographic couples that reflect a common theme (Kessler, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1963\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster 1: Operational Strategies and Optimization in Closed-Loop Supply Chains with Product Returns (36 Publications)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe first bibliographic coupling cluster had 36 studies that got a total of 705 citations, or an average of 19.58 citations per publication. About 47% of the documents were published in the last three years, and the oldest one was from 2009. This shows that there are both old and new contributions to the management of closed-loop supply chains (CLSCs). The group mostly talked about operational strategies and optimization in CLSCs, with an emphasis on pricing, channel coordination, and policies for taking back and recycling. Several studies looked at the problems of coordinating new and remanufactured products, such as whether to \"buy now or later\" and how to target strategic customers in remanufactured markets (Liu et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yanliang Zhang et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Other studies looked at planning the supply chain when return flows and demand are uncertain (Kim et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liao et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) or came up with strong control strategies for uncertain dual-channel CLSCs (Xia \u0026amp; Li, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Zhang \u0026amp; Zhao, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). External factors such as tax policy, trade mechanisms, and government subsidies were also evaluated for their impact on CLSCs (Zhang et al., \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yuhao Zhang et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Overall, Cluster 1 focused on improving CLSC operations while also looking at how new factors like blockchain and environmental policies could affect them.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster 2: Reverse Logistics for Electric Vehicle Batteries and IoT Technologies (31 Publications)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThere were 31 publications in this cluster that had a total of 1,153 citations, or an average of 37.19 citations per article, which was the third-highest number of citations among the clusters. Notably, about 90% of the studies were published from 2021 onward, with the earliest from 2018. The main focus was on reverse logistics for electric vehicle (EV) batteries, which included planning policies for inventory, coordinating product-specific remanufacturing, and additive manufacturing. There was a lot of focus on how to recover lithium-ion batteries and how to use digital technologies. In the context of Industry 4.0 and circular economy (CE) capabilities, social factors that affect costs, like investment in collection systems and the size of the end-user market, were looked at (Dev et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The growing global EV fleet, which is driven by goals to be carbon neutral, needed circular solutions. The growing global EV fleet\u0026mdash;driven by carbon neutrality goals\u0026mdash;necessitated circular solutions, and repurposing/remanufacturing strategies were recommended for battery life extension (Castro et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Deng et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Huster et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The group also talked about how IoT, virtual reality (VR), and augmented reality (AR) can be used in remanufacturing and battery waste management to monitor, collect data in real time, analyze it, and simulate it (Gl\u0026ouml;ser-Chahoud et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kerin \u0026amp; Pham, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tavana et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Cluster 2 showed both academic and practical efforts to create reverse supply chains for EV batteries using advanced technologies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster 3: Reverse Logistics and Closed-Loop Supply Chain Foundations (29 Publications).\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThere were 29 publications in Cluster 3, which had a total of 1,413 citations. This means that each article had an average of 48.72 citations, which was the second highest citation rate across all clusters. About 41% of the publications were from the last three years, and the first one came out in 2004. This is based on a group of important and well-known studies in reverse logistics and CLSCs. Some of the main topics were designing reverse logistics networks, the bullwhip effect, optimizing product recovery systems, and how reverse flows affect production and inventory planning. Economic and environmental concerns were identified as the primary drivers of CLSC development. We used dynamic modeling and simulation to look at long-term capacity planning policies based on how profitable the supply chain is (Georgiadis \u0026amp; Vlachos, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Vlachos et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Kumar and Yamaoka (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) used system dynamics (SD) models to look at CLSC design in the Japanese automotive industry. Lehr et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) improved our understanding of supply chain dynamics and the effects of reverse flows. Because of this, Cluster 3 was seen as the academic base for research on reverse logistics and the circular economy. It provided conceptual and operational frameworks that are still widely used in the literature.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster 4: Advanced Recovery Strategies and Simulation in Reverse Logistics (24 Publications)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCluster 4 had 24 publications and 615 total citations (25.62 citations per article). It had a good mix of old and new studies, with 46% of the documents published between 2021 and 2023 and the oldest one dating back to 2006. The group looked into high-tech ways to recover and take apart products. For example, Tozanlı et al. (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) came up with a \"trade-in-to-upgrade\" strategy that used blockchain to make transactions clear. This encouraged customers to trade in old products for newer ones. This example showed how marketing strategies could help reverse logistics by encouraging returns. The group also had decision models for reverse supply chain management that were based on simulations and data. Charnley et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) made digital simulation models to help people make decisions based on data in the circular economy. These models let companies test out recovery and recycling scenarios before putting them into action. Cluster 4 demonstrated the intersection of traditional optimization/simulation models with modern business strategies and technologies in closed-loop supply chains.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster 5: Blockchain-Enabled Circular Economy (23 Publications)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eCluster 5 had the most studies published since 2021, with a total of 1,639 citations\u0026mdash;71.26 citations per article, the most of any cluster. The main topic was how to use blockchain in circular supply chains and reverse logistics. This group of studies put blockchain as a game-changing tool that improves trust, traceability, and openness among people working in sustainable supply chains. It was shown to help people keep better track of how waste moves and how products are returned (Centobelli et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Blockchain was also used in reverse logistics to decentralize, track, and monitor goods after they had been sold (Samadhiya et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This helped make the supply chain more resilient and keep data safe (Mohammed et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The pros and cons of blockchain adoption were looked at closely. Govindan (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found major problems with using blockchain for remanufacturing to reach the SDGs (for example, SDG4, SDG8, SDG9, and SDG17) and suggested ways for managers to get around these problems. Cluster 5 gave us both theoretical and practical ideas about how to make digital transformation happen in reverse supply chains, which will improve transparency and efficiency throughout the product lifecycle.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster 6: Analytical Foundations for Cost-Efficient Product Recovery Networks (16 Publications)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThere were 16 publications in this cluster, and they were cited 514 times, or an average of 32.12 times per article. Only 37.5% of the works were published in the last three years, and the oldest one is from 2003. This shows that cost-optimization modeling is a key focus in reverse logistics networks. There have been a number of studies that have helped to shape this field. For example, Fleischmann et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) looked at recovery network design, inventory control, and cost management. Some common methods were fuzzy mixed-integer nonlinear programming (Vahdani et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), integrated vehicle routing and scheduling for forward and reverse logistics (Rahbari et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and coordinated production-distribution planning in CLSC systems (Kabiri et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Aydin et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) extended the analytical scope by integrating environmental impact assessment (via LCA) and system simulation (Monte Carlo, discrete event simulation, agent-based modeling, and SD). Overall, Cluster 6 provided strong quantitative frameworks for reverse logistics that helped with making decisions about how to design and run a recovery network that is cost-effective.\u003c/p\u003e\u003cp\u003e\u003cb\u003eCluster 7: Performance Assessment of CLSCs and Influencing Factors (12 Publications\u003c/b\u003e)\u003c/p\u003e\u003cp\u003eThere were 12 publications in the seventh cluster, and they got 395 citations, which is an average of 32.92 citations per article. About 58% of the studies were published after 2021, and the first one was in 2006. This group looked at how well closed-loop and reverse logistics systems worked and what factors affected them. We used simulations and real-world methods to find out how different reverse logistics factors, like return lead times, information uncertainty, and return volume variability, affect the efficiency of the supply chain (Cannella et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ponte et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Specific focus was placed on bullwhip effects and the impacts of PULL, DUAL, and Separate PULL policies in CLSC settings (Zanoni et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), as well as demand shocks (de Arquer et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In summary, Cluster 7 provided insights into how operational and environmental factors affect the stability and performance of closed-loop systems through modeling and experimentation.\u003c/p\u003e\u003cp\u003eThe science mapping analysis has named the backbones of research in the field of reverse logistics and circular economy, where the opposing key words and thematic groups are strongly interrelated. One of the most noticeable groups revolves around the application of digital technologies in reverse logistics and includes such concepts as blockchain, the Internet of Things (IoT), data analytics, and artificial intelligence (AI). The combination of these technologies is common since the current trend is characterized by implementing Logistics 4.0 principles in order to make closed-loop supply chains more efficient. The second cluster is devoted to circular economy and sustainability where keywords are sustainability, recycling, waste reduction, and closed-loop supply chains. This cluster focuses on the reverse logistics practices on the environment and the 3R framework ( reduce, reuse, recycle).Other clusters are associated with the field of supply chain management and operation performance including such terms as supply chain management, cost efficiency, customer service, and stakeholder collaboration. These are managerial and strategic aspect of the reverse logistics.\u003c/p\u003e\u003cp\u003eNevertheless, such technologies as blockchain and IOT seem to emerge, whereas core Metaverse technologies, including virtual reality (VR), augmented reality (AR), digital twins, and immersive simulation, are still not a popular topic. It is an indication that the Metaverse-reverse logistics integration is in its early phases. Other studies indicate that the reverse logistics and specifically the return operations are one of the least explored areas of research related to Metaverse, even though the concept of the twins and simulation are among the core topics.\u003c/p\u003e\u003cp\u003eThese results reveal the research gap and show that the immersive and simulation technologies should be more integrated with reverse logistics to realize the potential of connecting the physical and digital world in supply chain management with closed-loop management. These reflections are used as the basis of the further part of detailed accounting of the new role the Metaverse plays in this sphere.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. The triangulation of majorthemes and Thematic analysis","content":"\u003cp\u003eThe thematic clusters that come from bibliographic coupling and co-word analysis show a clear convergence and successful triangulation. This proves that the core themes that make up the knowledge structure of research on Reverse Logistics and the Metaverse under the Circular Economy are reliable and valid, as this review shows (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTriangulation of Thematic Clusters: Co-occurrence vs. Bibliographic Coupling\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCommon Theme\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCo-occurrence Cluster\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoupling Cluster\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThematic Focus\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eContribution\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry 4.0 and Circular Economy in reverse logistics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCluster 1 (Red)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCluster 4 overlaps Cluster 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eApplication of blockchain and Industry 4.0 to enhance transparency, traceability, and collaboration in reverse logistics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eA digital transformation framework for circular logistics: blockchain enhances industrial symbiosis collaboration, transaction transparency and reliability, data integrity and management; IoT, Virtual Reality (VR), and Augmented Reality (AR) support real-time monitoring and data collection.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBattery recycling modeling and IoT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCluster 2 (Green)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCluster 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOptimization of EV battery and finite resource collection and recycling processes.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEmphasizes industrial disassembly and material recovery, forecasting recovery variability, IoT deployment for reverse flow management, and proposing sustainable battery supply chain design models.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSustainable supply chain management and Blockchain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCluster 3 (Dark Blue)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCluster 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIntegrating blockchain and IoT to support inventory management and transparent product flows\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eExplores blockchain applications and potential in reverse logistics and closed-loop supply chains, aligned with SDGs, using case analysis and sustainable theoretical frameworks.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDynamics and simulation in reverse supply chains\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCluster 4 (Yellow)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCluster 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eApplies simulation and system dynamics (SD) to quantify the bullwhip effect, optimize recovery processes, and support long-term decision-making\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSimulation quantifies amplified demand fluctuations along the supply chain. System dynamics and discrete-event simulation model data governance procedures and how demand shocks propagate in recovery networks, with strategies to mitigate negative impacts.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOptimization and remanufacturing in CLSC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCluster 5 (Purple)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCluster 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCovers both foundational and new research in closed-loop supply chain management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFocuses on operational and optimization strategies: pricing, distribution channel, take-back/recycling policies, Stackelberg game models, sustainable control approaches, operational problem-solving in reverse supply chains, and capacity management.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSystem modeling and capacity planning in CLSC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCluster 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCluster 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSystem dynamics, capacity balancing, and policy planning based on system simulation.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCost optimization problems in reverse logistics networks using LCA and simulation methods (Monte Carlo, discrete event simulation, agent-based modeling (ABM), and system dynamics (SD)).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerformance of closed-loop/reverse logistics supply chains and influencing factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCluster 4 overlaps with Cluster 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCluster 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEvaluation of performance and key influencing factors in closed-loop/reverse supply chains.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProvides empirical evidence and modeling on how factors such as return lead time, reverse demand information, bullwhip effect, policies, return volume uncertainty, and demand shocks affect CLSC performance.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis study uses author keywords as input data to make a strategic diagram that shows how personalized marketing research will develop in the future. The vertical axis shows impact (density) and the horizontal axis shows centrality (Cobo, L\u0026oacute;pez-Herrera, et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Cobo, L\u0026oacute;pez-Herrera, et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Centrality shows how important a theme is and how it interacts with other themes, while density shows how much it has grown on its own. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the strategic diagram is split into four quadrants based on Cahlik (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) classification model.\u003c/p\u003e\u003cp\u003eMotor themes are in the upper-right quadrant. They are both very well-developed and very important. Some examples of these types of keywords are \"recycling,\" \"reuse,\" and \"closed-loop supply chain management,\" which have interesting metrics (for example, recycling \u0026ndash; centrality\u0026thinsp;=\u0026thinsp;1129.69; density\u0026thinsp;=\u0026thinsp;0.00159). These themes are very important to the knowledge structure of modern reverse logistics and are related to co-word cluster 2 and bibliographic coupling cluster 2. They have to do with digital technologies like the Internet of Things (IoT) in battery recovery and recycling systems, as well as multi-objective optimization and uncertainty modeling.\u003c/p\u003e\u003cp\u003eSome of the main ideas in the lower-right quadrant are \"circular economy,\" \"remanufacturing,\" \"supply chain management,\" and \"system dynamics.\" These ideas all have high centrality but low density (for example, remanufacturing has a centrality of 712.91 and a density of 0.00149). These are the main ideas in the field that need to be built on to keep up with new problems that come up in digital transformation and sustainable development. These themes are related to clusters 1 and 5 (co-occurrence) and clusters 1 and 6 (bibliographic coupling). This shows that there are strong connections but not a lot of depth in how the methods and applications are combined.\u003c/p\u003e\u003cp\u003eThe upper-left quadrant has niche themes like \"capacity planning,\" \"product lifecycle,\" and \"interrelated sustainability dimensions.\" These themes are very dense (which means they are developing strongly on their own), but they are not very central, which means they are not very connected to the main themes. They are good for more in-depth technical study, especially in niche areas like reverse logistics for electric vehicles or electronic equipment, where capacity planning and lifecycle analysis are very important.\u003c/p\u003e\u003cp\u003eFinally, the lower-left quadrant has themes that are either new or fading, like \"life cycle assessment,\" \"environmental sustainability,\" and \"waste management.\" These keywords are important for both academics and real life, but they don't seem to be very central or dense, which suggests that they aren't being used very much in mainstream discussions. But with more pressure from ESG and environmental standards, these themes could come back to life through systems modeling, life cycle assessment tools, and AI technology integration.\u003c/p\u003e\u003cp\u003eIn short, the strategic diagram shows well-known theoretical themes, new areas of research, and areas that haven't been studied enough and need more funding. Recycling, closed-loop logistics, and remanufacturing should continue to be the main ideas, but LCA, capacity planning, and waste management should be more closely linked to make them more useful in real life and in research.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn line with the results of the science mapping, the thematic analysis showed that a number of core themes have been identified in the published literature on the \"Metaverse in reverse logistics.\" One of the most noticeable trends is the implementations of digital technologies and Metaverse-related tools in reverse logistics. Early research is primarily on blockchain and IoT in order to maximize traceability, transparency and management of the flow of returns in closed-loop supply chains. They are technologies that integrate data and automate the processes, and they can make a basis of effective reverse logistics systems.\u003c/p\u003e\u003cp\u003eNevertheless, the analysis also demonstrates the trend of moving away from the classical data-focused technologies (e.g., IoT, blockchain) in favor of immersive technologies related to the Metaverse to further streamline reverse logistics. In particular, a virtual reality (VR), augmented reality (AR), and digital twins are becoming prospective solutions. VR and AR have proven to have potentials to enhance disassembly and remanufacturing processes and allow immersive training and remote assistance. The tools can minimize the unnecessary returns by helping the customers solve minor problems, decreasing the costs of processing and improving their experience.\u003c/p\u003e\u003cp\u003eBesides technology, reverse logistics also incorporates governance, policy and design. The studies in governance state multi-stakeholder cooperation towards workable product-take-back. Literature with a policy orientation emphasizes extended producer responsibility (EPR), particularly as a motivator to recycling efforts, whereas design-oriented literature focuses on how designs can be made easy to take apart and reuse. Together with immersive technologies, in particular, VR, AR, simulations, and digital twins, these forces expand the frontier of research and confirm the potential of the Metaverse to address the shortcomings of the reverse logistics as applied in traditional supply chains and catalyze the emerging closed-loop supply chains.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Key Findings\u003c/h2\u003e\u003cp\u003eThis systematic review of reverse logistics and Metaverse technologies in the context of the circular economy (see Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) shows that there are a few important things to note.\u003c/p\u003e\u003cp\u003eThe study first found 198 articles that were published between 2003 and mid-2025 (RQ1). More than 60% of these were published in the last five years. This trend shows that more and more academics are interested in sustainable supply chains and using Industry 4.0 technologies, especially the Metaverse and blockchain, in reverse logistics.\u003c/p\u003e\u003cp\u003eSecond, the Journal of Cleaner Production has the most citations, while Sustainability (Switzerland) has the most articles (RQ2). The best journals cover a wide range of fields, including operations management, environmental engineering, and digital innovation. This shows how interdisciplinary this research area is.\u003c/p\u003e\u003cp\u003eThird, the most productive authors, like Zhang S, Wang T, and Zhao X, mostly work for schools in Asia and Europe. However, there is a clear lack of practical involvement from industry stakeholders. This shows that academia\u0026ndash;industry collaboration needs to be improved in order to speed up the use of the Metaverse in real-world logistics operations.\u003c/p\u003e\u003cp\u003eFourth, keyword and citation analyses show that the main topics right now are blockchain, digital twin, VR/AR, and simulation in making reverse logistics better. But there is still a lot of work to be done on user experience and technology acceptance.\u003c/p\u003e\u003cp\u003eFifth, there aren't many complete theoretical frameworks that connect Metaverse technologies with closed-loop supply chains, even though many simulation-based models have been made. Future research should focus on making these kinds of integrated models to help both theory and practice move forward.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Theoretical Contributions\u003c/h2\u003e\u003cp\u003e\u003cem\u003eAt the macro level\u003c/em\u003e, this review gives a systematic and comprehensive picture of the most important research areas that connect reverse logistics, digital technologies, and the circular economy. The study finds three main theoretical pillars by cross-mapping two knowledge networks: keyword co-occurrence (6 clusters) and bibliographic coupling (7 clusters).\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eUsing technologies like blockchain and the Internet of Things (IoT) to make logistics more digital (Co-Cluster 1, BiC-Cluster 1);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eUsing digital twins and system dynamics to model and simulate reverse logistics operations (Co-2, BiC-2, BiC-5);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSustainable methods that try to close the product lifecycle loop (Co-3, Co-5, BiC-3).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThis study adds a new theoretical lens of \"virtualization/immersiveness\" to the analytical framework by including Metaverse technologies like VR/AR, immersive simulations, and digital spaces. This is the next step in the evolution from digitization to immersive transformation in closed-loop supply chains.\u003c/p\u003e\u003cp\u003e\u003cem\u003eAt the micro level\u003c/em\u003e, the study suggests an integrated conceptual framework to show how the Metaverse fits into reverse logistics. It focuses on three main parts:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eTechnological enablers, such as digital twins, AR/VR, and simulation (related to BiC-2, Co-2, and Co-4);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eMechanisms of influence, like better understanding of the system, skill training, co-creation, and decision optimization (related to Co-4 and BiC-6);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eResults include better recovery efficiency, lower inventory levels, longer product life cycles, and a positive effect on sustainability (BiC-3, BiC-7, Co-5).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThis framework not only organizes the Metaverse's role in reverse logistics, but it also gives us a way to look at how smart supply chains have changed over time, from automation to digitization and now to immersive technologies. It makes a big theoretical contribution and sets the stage for future hybrid research models that combine supply chain management, immersive technologies, and sustainable development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e5.3 Managerial Implications\u003c/h2\u003e\u003cp\u003eThis study provides several practical insights for managers aiming to apply Metaverse technologies in reverse logistics toward sustainability goals:\u003c/p\u003e\u003cp\u003eFirst, the results give managers and policymakers a structured overview that helps them understand the state of the research in reverse logistics within a circular economy framework and how far along different research streams are. The keywords in the top-right quadrant of the strategic diagram, like \"recycling,\" \"reuse,\" and \"closed-loop supply chain management,\" show that they are very important and central to the topic. These ideas are very important in models for product recovery and recycling, especially in industries where products have short life cycles or strict traceability requirements, like electronics, EV batteries, and high-tech goods. These ideas can help professionals create effective return networks and set standards for assessing the life cycle of a product.\u003c/p\u003e\u003cp\u003eSecond, the themes in the bottom-right quadrant, like \"remanufacturing,\" \"system dynamics,\" \"supply chain management,\" and \"circular economy,\" are theoretically important but not very well developed in terms of how they are studied. This opens the door for the use of cutting-edge technologies like blockchain, AI, big data analytics, and system simulation in the management of reverse logistics. The co-word (Clusters 1, 5) and bibliographic coupling clusters (Clusters 1, 6) also show that these themes connect theoretical areas that have to do with network design, multi-objective optimization, and digital platform development for circular supply chain management.\u003c/p\u003e\u003cp\u003eThird, niche themes like \"capacity planning,\" \"product lifecycle,\" and \"interrelated sustainability dimensions\" are well-developed within their own communities, but they are still not very connected to mainstream research networks. These areas, on the other hand, are very useful for making decision-support models for planning remanufacturing capacity, analyzing costs and benefits when there is uncertainty, and improving environmental performance across the closed-loop supply chain.\u003c/p\u003e\u003cp\u003eFourth, the review brings attention to a number of new or underexplored topics, such as \"life cycle assessment,\" \"waste management,\" and \"environmental sustainability,\" that have a lot of potential for the future but aren't yet fully integrated into mainstream conversations. These subjects can be added back into ESG strategies, systems for measuring sustainability, and policy modeling. It is both possible and necessary to strengthen these themes as pillars in reverse logistics design and governance, especially as the demand for supply chain transparency, traceability, and producer responsibility grows.\u003c/p\u003e\u003cp\u003eOverall, the results of both the co-occurrence and bibliographic coupling analyses show that reverse logistics practices are moving toward a model of digital-sustainable-systemic integration. Managers shouldn't just look at specific tools or technologies; they should also think about all the factors that can affect a business as a whole, like the volatility of return flow, the readiness to share data, infrastructure investment, and the ability to measure economic and environmental value over time.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e5.4 Future Research\u003c/h2\u003e\u003cp\u003eBased on the outlined gaps in the research, it can be suggested that there are a number of attractive directions to pursue the role of the Metaverse in reverse logistics and the circular economy further:\u003c/p\u003e\u003cp\u003eTo begin with, future research must examine stakeholder attitudes, behaviors, and preparedness e.g. the warehouse workers, drivers, managers, and customers toward the VR/AR and digital twin technologies in reverse logistics. By using theoretical frameworks such as the Technology Acceptance Model (TAM) or Unified Theory of Acceptance and Use of Technology (UTAUT), researchers have been able to discover the obstacles of adoption (e.g. to virtual reality) and the facilitators of user adoption of post-sale Metaverse platforms. This information would assist companies in developing successful implementation plans which must improve user experience and reduce resistance.\u003c/p\u003e\u003cp\u003eThe next important criterion is a quantitative determination of the environmental and economic consequences of merging Metaverse. The comparison of carbon footprint of reverse logistic processes with and without Metaverse can be done using a Life Cycle Assessment(LCA) model. Likewise, cost-benefit analysis can tell us whether the investments into VR equipment and creating digital twins are paid off by long-term profitability in logistics efficiency and liquidation value of the assets.\u003c/p\u003e\u003cp\u003eThe development of an empirical research is also required to assess the improvements of performance at particular stages of reverse logs progress (e.g. sorting, inspection, repair, remanufacturing) under the influence of enhanced VR/AR. The creation of a combined version of digital twins in blockchain networks may help engage into the end-to-end accounting of returned goods. The way to synchronize data between the Metaverse systems and distributed ledgers to be adequately clear and effective in terms of reverse logistics management should be studied in the future.\u003c/p\u003e\u003cp\u003eLastly, the researchers ought to investigate the revolutionary Metaverse-led scenarios in the circular economy. Another new idea is a virtual ecosystem whereby post-sale products are followed up digitally through the whole lifecycle of the product, i.e., the first user, refurbishment, and the handing over to the latter users. These visions need a cross disciplinary thinking and are likely to open up fully new forms of circular business.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e5.5 Limitations of the Research\u003c/h2\u003e\u003cp\u003eFirst, the analysis only looks at the Scopus database, so it might miss important studies that are listed in Web of Science, IEEE, or domain-specific conferences in digital and industrial technologies. Second, only publications in English were looked at, which could leave out important contributions in other languages. Third, the review doesn't have any real-world examples or field data from companies that are currently using Metaverse technologies in closed-loop logistics systems. This is an exploratory study, so it doesn't test hypotheses or cause-and-effect relationships. Meta-analyses or empirical validations may be used in future research to confirm the proposed links. This study is a good starting point because it brings together a lot of current information and suggests many promising ways for research to move forward in the areas of reverse logistics, the Metaverse, and circular economy principles.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflicts of Interest:\u003c/h2\u003e\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions: Conceptualization, L.T.T.T.; methodology, T.C.T.; software, T.C.T.; validation, T.C.T. and L.T.T.T.; formal analysis, T.C.T.; investigation, L.T.T.T.; data curation, T.C.T.; writing\u0026mdash;original draft preparation, T.C.T. and L.T.T.T.; writing\u0026mdash;review and editing, T.C.T. and L.T.T.T.; visualization, T.C.T.; supervision, T.C.T. ; project administration, L.T.T.T. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnderson C, Carvalho A, Kaul M, Merhout JW. Blockchain innovation for consent self-management in health information exchanges. Decis Support Syst. 2023;174:114021.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAria M, Cuccurullo C. bibliometrix: An R-tool for comprehensive science mapping analysis. J informetrics. 2017;11(4):959\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArias A, Feijoo G, Moreira MT. Assessing of the most appropriate biotechnological strategy on the recovery of antioxidants from beet wastes by applying the life cycle assessment (LCA) methodology. Food Bioprod Process. 2022;135:178\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAydin R, Al-Shihabi ST, Alzarooni SI, Jaber H. Assessing the economic and environmental performance of a closed-loop supply chain for waste tires: an industrial case study. Environ Sci Pollut Res. 2024;31(57):65513\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBaier-Fuentes H, Merig\u0026oacute; JM, Amor\u0026oacute;s JE, Gaviria-Mar\u0026iacute;n M. International entrepreneurship: a bibliometric overview. Int Entrepreneurship Manage J. 2019;15:385\u0026ndash;429.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBalaid A, Abd Rozan MZ, Hikmi SN, Memon J. Knowledge maps: A systematic literature review and directions for future research. Int J Inf Manag. 2016;36(3):451\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBarretti JW, Mergulh\u0026atilde;o RC, Mendes JV. Evolution of blockchain technology in sustainable supply chain management: a theoretical perspective. World Rev Intermodal Transp Res. 2023;11(4):396\u0026ndash;414.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBockholt MT, Kristensen JH, Colli M, Jensen PM, W\u0026aelig;hrens BV. Exploring factors affecting the financial performance of end-of-life take-back program in a discrete manufacturing context. J Clean Prod. 2020;258:120916.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBoell SK, Cecez-Kecmanovic D. On being \u0026lsquo;systematic\u0026rsquo;in literature reviews in IS. J Inform Technol. 2015;30(2):161\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrin S, Page L. The anatomy of a large-scale hypertextual web search engine. Comput networks ISDN Syst. 1998;30(1\u0026ndash;7):107\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCahlik T. (2000). Search for fundamental articles in economics. \u003cem\u003escientometrics\u003c/em\u003e, \u003cem\u003e49\u003c/em\u003e(3), 389\u0026ndash;402.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCannella S, Bruccoleri M, Framinan JM. Closed-loop supply chains: what reverse logistics factors influence performance? Int J Prod Econ. 2016;175:35\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCastro FD, Cutaia L, Vaccari M. End-of-life automotive lithium-ion batteries (LIBs) in Brazil: Prediction of flows and revenues by 2030. Resour Conserv Recycl. 2021;169:105522.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCentobelli P, Cerchione R, Del Vecchio P, Oropallo E, Secundo G. Blockchain technology for bridging trust, traceability and transparency in circular supply chain. Inf Manag. 2022;59(7):103508.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChang A, El-Rayes N, Shi J. Blockchain technology for supply chain management: A comprehensive review. FinTech. 2022;1(2):191\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCharnley F, Tiwari D, Hutabarat W, Moreno M, Okorie O, Tiwari A. Simulation to enable a data-driven circular economy. Sustainability. 2019;11(12):3379.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen P-K, Ye Y, Huang X. The metaverse in supply chain knowledge sharing and resilience contexts: an empirical investigation of factors affecting adoption and acceptance. J Innov Knowl. 2023;8(4):100446.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eClauset A, Newman ME, Moore C. Finding community structure in very large networks. Phys Rev E\u0026mdash;Statistical Nonlinear Soft Matter Phys. 2004;70(6):066111.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCobo MJ, L\u0026oacute;pez-Herrera AG, Herrera-Viedma E, Herrera F. An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. J informetrics. 2011;5(1):146\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCobo MJ, L\u0026oacute;pez-Herrera AG, Herrera‐Viedma E, Herrera F. Science mapping software tools: Review, analysis, and cooperative study among tools. J Am Soc Inform Sci Technol. 2011;62(7):1382\u0026ndash;402.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCorrea-Vaca A, Miralles C, M\u0026aacute;rquez-S\u0026aacute;nchez F, Vergara-Romero A. (2023). Circular economy based on reverse logistics: A systematic literature review. Kurd Stud, \u003cem\u003e12\u003c/em\u003e(1).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Almeida ITGV, Ribeiro ARB, Ramalho LL, de Sousa Floriano L, de Ara\u0026uacute;jo RSC. Circular economy and reverse logistics: A systematic review. Revista de Gest\u0026atilde;o Social e Ambiental. 2024;18(3):1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ede Arquer M, Ponte B, Pino R. Examining the balance between efficiency and resilience in closed-loop supply chains. CEJOR. 2022;30(4):1307\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDe Giovanni P. Sustainability of the Metaverse: A Transition to Industry 5.0. Sustainability. 2023;15(7):6079. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/su15076079\u003c/span\u003e\u003cspan address=\"10.3390/su15076079\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeng S, Kpodzro E, Maani T, Li Z, Huang A, Yih Y, Zhao F, Sutherland JW. Planning a circular economy system for electric vehicles using network simulation. J Manuf Syst. 2022;63:95\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDenyer D, Tranfield D. (2009). Producing a systematic review.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDev NK, Shankar R, Qaiser FH. Industry 4.0 and circular economy: Operational excellence for sustainable reverse supply chain performance. Resour Conserv Recycl. 2020;153:104583.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDonthu N, Gremler DD, Kumar S, Pattnaik D. Mapping of Journal of Service Research themes: A 22-year review. J service Res. 2022;25(2):187\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDonthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: An overview and guidelines. J Bus Res. 2021;133:285\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jbusres.2021.04.070\u003c/span\u003e\u003cspan address=\"10.1016/j.jbusres.2021.04.070\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDwivedi YK, Hughes L, Baabdullah AM, Ribeiro-Navarrete S, Giannakis M, Al-Debei MM, Dennehy D, Metri B, Buhalis D, Cheung CM. Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int J Inf Manag. 2022;66:102542.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eErol I, Nurtaniş Velioğlu M, Sivrikaya Şerifoğlu F, B\u0026uuml;y\u0026uuml;k\u0026ouml;zkan G, Aras N, \u0026Ccedil;akar D, N., Korugan A. Exploring reverse supply chain management practices in Turkey. Supply Chain Management: Int J. 2010;15(1):43\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFahimnia B, Sarkis J, Davarzani H. Green supply chain management: A review and bibliometric analysis. Int J Prod Econ. 2015;162:101\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFleischmann M, Van Nunen JA, Gr\u0026auml;ve B. Integrating closed-loop supply chains and spare-parts management at IBM. Interfaces. 2003;33(6):44\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFloren J, Rasul T, Gani A. Islamic marketing and consumer behaviour: a systematic literature review. J Islamic Mark. 2020;11(6):1557\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFosso Wamba S, Mishra D. Big data integration with business processes: a literature review. Bus process Manage J. 2017;23(3):477\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Chirino J, Van Eygen G, Todd R, Ram\u0026iacute;rez-Zamora RM, Van der Bruggen B. Waste-treating-waste: Effective heavy metals removal from electroplating wastewater by ladle slag. Chemosphere. 2024;361:142532.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarousi V, Felderer M, M\u0026auml;ntyl\u0026auml; MV. Guidelines for including grey literature and conducting multivocal literature reviews in software engineering. \u0026lrm;Inf Softw Technol. 2019;106:101\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeissdoerfer M, Savaget P, Bocken NM, Hultink EJ. The Circular Economy\u0026ndash;A new sustainability paradigm? J Clean Prod. 2017;143:757\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeorgiadis P, Athanasiou E. Flexible long-term capacity planning in closed-loop supply chains with remanufacturing. Eur J Oper Res. 2013;225(1):44\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeorgiadis P, Vlachos D. The effect of environmental parameters on product recovery. Eur J Oper Res. 2004;157(2):449\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGhoreishi N, Jakiela MJ, Nekouzadeh A. A cost model for optimizing the take back phase of used product recovery. J Remanuf. 2011;1(1):1.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGl\u0026ouml;ser-Chahoud S, Huster S, Rosenberg S, Baazouzi S, Kiemel S, Singh S, Schneider C, Weeber M, Miehe R, Schultmann F. Industrial disassembling as a key enabler of circular economy solutions for obsolete electric vehicle battery systems. Resour Conserv Recycl. 2021;174:105735.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoodell JW, Kumar S, Lim WM, Pattnaik D. Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. J Behav Experimental Finance. 2021;32:100577.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGovindan K. Tunneling the barriers of blockchain technology in remanufacturing for achieving sustainable development goals: A circular manufacturing perspective. Bus Strategy Environ. 2022;31(8):3769\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHajian A, Daneshgar S, Sadeghi K, Ojha D, Katiyar G. From theory to practice: Empirical perspectives on the metaverse's potential. Technol Forecast Soc Chang. 2024;201:123224.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHrouga M, Sbihi A, Chavallard M. The potentials of combining Blockchain technology and Internet of Things for digital reverse supply chain: A case study. J Clean Prod. 2022;337:130609.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHu X, Sinniah S. The Role of Green Risk Management Approaches in Promoting Green and Sustainable Supply Chain Management. Nat Eng Sci. 2024;9(2):33\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuster S, Gl\u0026ouml;ser-Chahoud S, Rosenberg S, Schultmann F. A simulation model for assessing the potential of remanufacturing electric vehicle batteries as spare parts. J Clean Prod. 2022;363:132225.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohri A, Joshi P, Kumar S, Joshi G. Metaverse for Sustainable Development in a bibliometric analysis and systematic literature review. J Clean Prod. 2024;435:140610.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKabiri NN, Emami S, Safaei AS. Simulation\u0026ndash;optimization approach for the multi-objective production and distribution planning problem in the supply chain: using NSGA-II and Monte Carlo simulation: NN Kabiri et al. Soft Comput. 2022;26(17):8661\u0026ndash;87.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKerin M, Pham DT. A review of emerging industry 4.0 technologies in remanufacturing. J Clean Prod. 2019;237:117805.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKessler MM. Bibliographic coupling between scientific papers. Am Doc. 1963;14(1):10\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKhan SAR, Zia-ul‐haq HM, Umar M, Yu Z. Digital technology and circular economy practices: An strategy to improve organizational performance. Bus Strategy Dev. 2021;4(4):482\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim J, Do Chung B, Kang Y, Jeong B. Robust optimization model for closed-loop supply chain planning under reverse logistics flow and demand uncertainty. J Clean Prod. 2018;196:1314\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar S, Lim WM, Pandey N, Westland C, J. 20 years of electronic commerce research. Electron Commer Res. 2021;21(1):1\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKumar S, Yamaoka T. System dynamics study of the Japanese automotive industry closed loop supply chain. J Manuf Technol Manage. 2007;18(2):115\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLedro C, Nosella A, Vinelli A. Artificial intelligence in customer relationship management: literature review and future research directions. J Bus Industrial Mark. 2022;37(13):48\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLee CK, Chan T. Development of RFID-based reverse logistics system. Expert Syst Appl. 2009;36(5):9299\u0026ndash;307.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLehr CB, Thun J-H, Milling PM. From waste to value\u0026ndash;a system dynamics model for strategic decision-making in closed-loop supply chains. Int J Prod Res. 2013;51(13):4105\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiao H, Shen N, Wang Y. Design and realisation of an efficient environmental assessment method for 3R systems: a case study on engine remanufacturing. Int J Prod Res. 2020;58(19):5980\u0026ndash;6003.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLim JS, Zhang J. Adoption of AI-driven personalization in digital news platforms: An integrative model of technology acceptance and perceived contingency. Technol Soc. 2022;69:101965.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLim WM, Kumar S, Verma S, Chaturvedi R. Alexa, what do we know about conversational commerce? Insights from a systematic literature review. Psychol Mark. 2022;39(6):1129\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLim WM, Rasul T, Kumar S, Ala M. Past, present, and future of customer engagement. J Bus Res. 2022;140:439\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLim WM, Yap S-F, Makkar M. Home sharing in marketing and tourism at a tipping point: What do we know, how do we know, and where should we be heading? J Bus Res. 2021;122:534\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin J, Zhou L, Spiegler VL, Naim MM, Syntetos A. Push or Pull? The impact of ordering policy choice on the dynamics of a hybrid closed-loop supply chain. Eur J Oper Res. 2022;300(1):282\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu Z, Zhang M, Chen J, Diallo C. Buy now or later? New or remanufactured? How to attract strategic customers. Transp Res E. 2023;172:103068.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMallick PK, Salling KB, Pigosso DC, McAloone TC. Closing the loop: Establishing reverse logistics for a circular economy, a systematic review. J Environ Manage. 2023;328:117017.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMaterla T, Cudney EA, Antony J. The application of Kano model in the healthcare industry: a systematic literature review. Total Qual Manage Bus Excellence. 2019;30(5\u0026ndash;6):660\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMessinger PR, Stroulia E, Lyons K, Bone M, Niu RH, Smirnov K, Perelgut S. Virtual worlds\u0026mdash;past, present, and future: New directions in social computing. Decis Support Syst. 2009;47(3):204\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMohammed MA, De-Pablos-Heredero C, Montes Botella JL. A Systematic Literature Review on the Revolutionary Impact of Blockchain in Modern Business. Appl Sci. 2024;14(23):11077.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNiri AJ, Poelzer GA, Zhang SE, Rosenkranz J, Pettersson M, Ghorbani Y. Sustainability challenges throughout the electric vehicle battery value chain. Renew Sustain Energy Rev. 2024;191:114176.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNwosu A, Goyal S, Rajawat AS, Kemat BB, Mahmood WMABW. Integrating metaverse and blockchain for transparent and secure logistics management. Applied Data Science and Smart Systems. CRC; 2024. pp. 334\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePamucar D, Deveci M, Gokasar I, Delen D, K\u0026ouml;ppen M, Pedrycz W. Evaluation of metaverse integration alternatives of sharing economy in transportation using fuzzy Schweizer-Sklar based ordinal priority approach. Decis Support Syst. 2023;171:113944.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaul J, Bhukya R. Forty-five years of International Journal of Consumer Studies: A bibliometric review and directions for future research. Int J Consumer Stud. 2021;45(5):937\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePaul J, Lim WM, O\u0026rsquo;Cass A, Hao AW, Bresciani S. Scientific procedures and rationales for systematic literature reviews (SPAR-4‐SLR). Int J Consumer Stud. 2021;45(4):O1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePhan HH, Tran CT, Khoa BT. Science Mapping of the Development and Trends in Green Finance: A Bibliometric Analysis. Global Bus Finance Rev. 2025;30(2):57.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePonte B, Naim MM, Syntetos AA. The effect of returns volume uncertainty on the dynamic performance of closed-loop supply chains. J Remanuf. 2020;10(1):1\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQueiroz MM, Wamba F, Pereira S, S. C. F., Chiappetta Jabbour CJ. The metaverse as a breakthrough for operations and supply chain management: implications and call for action. Int J Oper Prod Manage. 2023;43(10):1539\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRadicchi F, Castellano C, Cecconi F, Loreto V, Parisi D. (2004). Defining and identifying communities in networks. \u003cem\u003eProceedings of the national academy of sciences\u003c/em\u003e, \u003cem\u003e101\u003c/em\u003e(9), 2658\u0026ndash;2663.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahbari A, Mahdi Nasiri M, Jolai F, Konur D. Integrated forward and reverse cross-docking with vehicle routing and scheduling under the uncertainty of inspection results: A genetic-mathematic algorithm. Transp Res Rec. 2024;2678(9):169\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRebolledo-Leiva R, Sillero L, Moreira MT, Gonz\u0026aacute;lez-Garc\u0026iacute;a S. Identifying the environmental hotspots of dietary fibres extraction from chickpea hull. Food Bioprod Process. 2023;142:59\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRitterbusch GD, Teichmann MR. Defining the Metaverse: A Systematic Literature Review. IEEE access. 2023;11:12368\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1109/access.2023.3241809\u003c/span\u003e\u003cspan address=\"10.1109/access.2023.3241809\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSadeghi K, Ojha D, Kaur P, Mahto RV, Dhir A. Metaverse technology in sustainable supply chain management: Experimental findings. Decis Support Syst. 2025;191:114423.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSaha S, Nielsen IE, Majumder S. Dilemma in two game structures for a closed-loop supply chain under the influence of government incentives. J Industrial Eng Int. 2019;15(Suppl 1):291\u0026ndash;308.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSamadhiya A, Kumar A, Agrawal R, Kazancoglu Y, Agrawal R. Reinventing reverse logistics through blockchain technology: a comprehensive review and future research propositions. Supply chain forum: An international journal; 2023.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSangari MS, Mashatan A. A data-driven, comparative review of the academic literature and news media on blockchain-enabled supply chain management: Trends, gaps, and research needs. Comput Ind. 2022;143:103769.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSerrano-Munoz A, Arana-Arexolaleiba N, Chrysostomou D, B\u0026oslash;gh S. Learning and generalising object extraction skill for contact-rich disassembly tasks: an introductory study. Int J Adv Manuf Technol. 2023;124(9):3171\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSeuring S, M\u0026uuml;ller M. From a literature review to a conceptual framework for sustainable supply chain management. J Clean Prod. 2008;16(15):1699\u0026ndash;710.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShrivastav SK, Bag S. Blockchain technology in circular economy: Unpacking the potential issues and critical echoes through data triangulation and natural language processing. Bus Strategy Environ. 2024;33(6):5463\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSingh J, Ordo\u0026ntilde;ez I. Resource recovery from post-consumer waste: important lessons for the upcoming circular economy. J Clean Prod. 2016;134:342\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSu Z, Zhang M, Wu W. Visualizing sustainable supply chain management: a systematic scientometric review. Sustainability. 2021;13(8):4409.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTavana M, Sohrabi M, Rezaei H, Sorooshian S, Mina H. A sustainable circular supply chain network design model for electric vehicle battery production using internet of things and big data. Expert Syst. 2024;41(7):e13395.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTozanlı \u0026Ouml;, Kongar E, Gupta SM. Trade-in-to-upgrade as a marketing strategy in disassembly-to-order systems at the edge of blockchain technology. Int J Prod Res. 2020;58(23):7183\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTran AV, Khoa BT. Global Research Trends in Circular Economy: A Bibliometric Analysis in E-Commerce. Hum Behav Emerg Technol. 2025;2025(1):8645845.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVahdani B, Razmi J, Tavakkoli-Moghaddam R. Fuzzy possibilistic modeling for closed loop recycling collection networks. Environ Model Assess. 2012;17(6):623\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Eck N, Waltman L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. \u003cem\u003escientometrics\u003c/em\u003e, \u003cem\u003e84\u003c/em\u003e(2), 523\u0026ndash;538.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Eck NJ, Waltman L. Visualizing bibliometric networks. Measuring scholarly impact: Methods and practice. Springer; 2014. pp. 285\u0026ndash;320.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVaz CR, Grabot B, Uriona Maldonado M, Selig PM. Some reasons to implement reverse logistics incompanies. Int J Environ Technol Manage. 2013;16(5\u0026ndash;6):467\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVentura V, La Monica M, Bortolini M, Cutaia L, Mora C. Blockchain and industrial symbiosis: a preliminary two-step framework to green circular supply chains. Int J Environ Sci Technol. 2025;22(1):17\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVerma S, Yadav N. Past, present, and future of electronic word of mouth (EWOM). J Interact Mark. 2021;53(1):111\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVlachos D, Georgiadis P, Iakovou E. A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chains. Comput Oper Res. 2007;34(2):367\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWaqas M, Dong Q-l, Ahmad N, Zhu Y, Nadeem M. Critical barriers to implementation of reverse logistics in the manufacturing industry: a case study of a developing country. Sustainability. 2018;10(11):4202.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu Z, Kwong CK, Aydin R, Tang J. A cooperative negotiation embedded NSGA-II for solving an integrated product family and supply chain design problem with remanufacturing consideration. Appl Soft Comput. 2017;57:19\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXia Y, Li C. Robust control strategy for an uncertain dual-channel closed-loop supply chain with process innovation for remanufacturing. IEEE Access. 2023;11:97852\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXu M, Guo Y, Hu Q, Xiong Z, Yu D, Cheng X. A trustless architecture of blockchain-enabled metaverse. High-Confidence Comput. 2023;3(1):100088. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.hcc.2022.100088\u003c/span\u003e\u003cspan address=\"10.1016/j.hcc.2022.100088\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYan N-N, Sun B-W. Optimal Stackelberg strategies for closed-loop supply chain with third-party reverse logistics. Asia-Pac J Oper Res. 2012;29(05):1250026.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang Y, Lin J, Hedenstierna C, Zhou L. The more the better? The impact of the number and location of product recovery options on the system dynamics in a closed-loop supply chain. Transp Res E. 2023;175:103150.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYong-Hak J. (2013). Web of science. \u003cem\u003eThomson Reuters\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu Z, Kwilinski A, Trushkina N. Reverse logistics in a system of the circular economy: theoretical aspect. Экономический вестник Донбасса. 2018;4(54):29\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZanoni S, Ferretti I, Tang O. Cost performance and bullwhip effect in a hybrid manufacturing and remanufacturing system with different control policies. Int J Prod Res. 2006;44(18\u0026ndash;19):3847\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang H, Zhang R. Remanufacturing Modes Selection in Competitive Closed-Loop Supply Chains. Systems. 2025;13(4):257.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang S, Li X, Zhang C. A fuzzy control model for restraint of bullwhip effect in uncertain closed-loop supply chain with hybrid recycling channels. IEEE Trans Fuzzy Syst. 2016;25(2):475\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang S, Zhao X. Fuzzy robust control for an uncertain switched dual-channel closed-loop supply chain model. IEEE Trans Fuzzy Syst. 2014;23(3):485\u0026ndash;500.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang S, Zhao X, Zhang J. Dynamic model and fuzzy robust control of uncertain closed-loop supply chain with time-varying delay in remanufacturing. Ind Eng Chem Res. 2014;53(23):9805\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang X, Zheng H, Zheng X. Impact of government fund policy and blockchain technology on closed-loop supply chains in textile and apparel industry. J Clean Prod. 2024;434:140037.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Zhang H, Cheng Y. The adoption strategy of blockchain technology for new and remanufactured products considering price and consumer preference. J Industrial Prod Eng. 2025;42(2):103\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Zhang Q, Hu R, Yang M. Optimal Strategy and Performance for a Closed-Loop Supply Chain with Different Channel Leadership and Cap-and-Trade Regulation. Sustainability. 2025;17(3):1042.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Reverse logistics, Circular economy, Immersive technologies, Metaverse, blockchain, IoT, VR, AR, Simulation, SLR","lastPublishedDoi":"10.21203/rs.3.rs-7221291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7221291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReverse logistics has become one of the vital foundations of the circular economy, through recovery, reuse and recycling of materials and products. At the same time, the current development of technologies related to the metaverse (the so-called digital twins, VR, and blockchain) is unlocking opportunities to streamline the work of reverse logistics systems by making it virtual, tracking in real-time, and decision-making capacity. Furthermore, an inclusive review at the trio of reverse logistics, circular economy, and immersive technologies is yet to have a review into it. To fill in this gap, this paper performs a systematic literature review in addition to bibliometric analysis of 198 publications found in the Scopus database between 2003\u0026ndash;2025. Using SPAR-4-SLR protocol and a collection of bibliometric methods, such as performance analysis, keyword co-occurrence analysis, bibliographic coupling, and thematic evolution analysis, the paper obtains the main authors, trend of publications and citations in the field, intellectual structure, and significant thematic clusters within it. Namely, there are six keyword co-occurrence clusters and seven bibliographic coupling clusters identified and such emerging topics as blockchain-enabled circularity, reverse logistics of electric vehicles, remanufacturing optimization, the future of the metaverse are mentioned. This research will enrich the literature with the synthesis of conceptual landscape, feat of mapping and the rise of thematic evolution, and a future research agenda. The triangulation of themes is based on the concept of creating a theoretical framework that will guide the action of scholars and practitioners to exploit metaverse technologies in achieving sustainable reverse logistics in circular economy paradigm.\u003c/p\u003e","manuscriptTitle":"A Bibliometric and Thematic Review of Metaverse and Reverse Logistics for a Sustainable Circular Economy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 17:50:14","doi":"10.21203/rs.3.rs-7221291/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-19T07:23:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-01T06:01:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-27T07:10:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159533400247035766771342980569878931714","date":"2025-08-21T15:23:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331148596653004945491399597174012379847","date":"2025-08-19T19:35:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-19T12:43:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-14T16:50:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-12T10:20:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-08-12T10:16:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"790d720a-9b25-4421-bdd7-424c33072b87","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-27T16:08:38+00:00","versionOfRecord":{"articleIdentity":"rs-7221291","link":"https://doi.org/10.1007/s43621-026-03277-8","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2026-04-26 15:58:10","publishedOnDateReadable":"April 26th, 2026"},"versionCreatedAt":"2025-08-27 17:50:14","video":"","vorDoi":"10.1007/s43621-026-03277-8","vorDoiUrl":"https://doi.org/10.1007/s43621-026-03277-8","workflowStages":[]},"version":"v1","identity":"rs-7221291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7221291","identity":"rs-7221291","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}