Interferences and Frontiers Between Industry 4.0 and Circular Economy

Abstract

The article examines the relationship between Industry 4.0 (I4.0) and the circular economy (CE), which are modern and widely used in various scientific disciplines as well as in interdisciplinary and transdisciplinary fields. It was taken into account that a modern resource for knowledge and innovation in a scientific area consists precisely in exploring conceptual interoperability, both for the purpose of clarifying aspects of the theory specific to that discipline and also in terms of offering new, less explored perspectives that are valuable for the practice of everyday economic activities. The main methodological component used is bibliometric analysis, starting from the construction of a database of existing approaches to the two key concepts considered, at the level of the Web of Science Core Collection (WoS). This research shows that there is an increase in the theoretical and practical scope of use of the two concepts, a characteristic observed through the consistency and diversification manifested within the considered frame of reference. The main conclusion of the study is that AI-driven servitization, IoT, and big data are facilitators of the implementation of the CE. The contribution lies in consolidating an updated bibliometric overview of this interdisciplinary field and in highlighting new directions.

1. Introduction

The current economic and social context, dominated as a general framework by the transition from the VUCA (Volatility, Uncertainty, Complexity, and Ambiguity) paradigm to the BANI (Brittle, Anxious, Nonlinear, Incomprehensible) paradigm and, subsequently, the associated new global trends, require the identification of solutions that contribute to resource conservation, product life extension and the development of renewable resources in the context of the global energy transition and the fight against climate change. This article responds to this challenge by connecting the concept of the CE to the dimensions of I4.0, enriching the scientific literature and practice in the sector of business by analyzing papers in the field, as well as the latest research trends.

In the proposed title, the author’s intention was to draw attention, using bibliometric research, to the idea of interdisciplinarity and transdisciplinarity that manifests itself between I4.0 and the CE. Research supporting this approach in the context of sustainability was conducted by Zaman and Goschin [1].

Emerging contemporary scientific concepts, such as I4.0 and CE, are increasingly interdisciplinary and transdisciplinary in nature, their applicability, primarily in terms of content, no longer being strictly linked to a particular field of activity or scientific discipline. This approach emphasizes that the reference system of certain concepts, as a basis for explanation, is becoming more comprehensive. This statement also applies to I4.0 and CE, a characteristic that we intend to demonstrate for awareness through the bibliometric research undertaken in this article. Furthermore, as a general idea that also applies to the context addressed in the article, the meaning of concepts becomes clearer and more stable over time as a result of their manifestation through the inclusion or exclusion of a set of explanatory nuances that are necessary and sufficient for the state of knowledge reached at a given moment. This title was chosen to better highlight the space for manifestation and the relationship between the two concepts. The interference between the concepts draws the attention of those interested in interdisciplinarity as interaction (emphasized by the specific use of the operator “and” in the search query in Section 3 of the article), subsequently translated into the real need for integrated solutions. Transdisciplinarity complements this framework by emphasizing the need to go beyond self-contained boundaries in order to determine collaborative explanatory and, implicitly, action-oriented possibilities.

The article starts from the premise that the shift from a linear to a CE, driven by technological change, has a significant impact on individuals and society at large [2]. The success of this change is supported at the European level by the creation of the European Platform for Circular Economy Stakeholders, a joint initiative of the European Commission and the European Economic and Social Committee (EESC), launched in 2017.

The transition to a CE—as a regenerative system—is regarded as a critical requirement for sustainability [3].

Based on their analysis of 221 definitions, Kirchherr et al. [4] present a revised definition of the CE as a regenerative system that promotes not only economic growth but also environmental sustainability and social equity. Central to this model are the four Rs—reduce, reuse, recycle, and recover—which replace the “end-of-life” concept and enable the transformation of traditional linear value chains into circular, sustainable systems generating benefits for both present and future generations [4]. Studies have introduced the idea of circular cities, pointing out that they play a central role in facilitating the transition to circularity by closing loops, recirculation, technical innovation, policy development, and citizen support [5].

The growing adoption of I4.0 technologies presents both opportunities and challenges for advancing the CE. To remain competitive, organizations have to strategically align digital transformation with circularity goals. Analyzing how I4.0 technologies such as IoT, artificial intelligence (AI), and blockchain can drive this transition—while addressing the practical barriers businesses face—is essential. These initiatives are essential for examining how the emerging technologies and digital transformation can enhance corporate sustainability strategies and support the achievement of a global sustainability agenda [6].

The transformative potential of I4.0 to influence economic, environmental, and social sustainability outcomes is receiving growing attention from a wide range of stakeholders [7]—including academics, leaders from public and private sectors, policymakers, and also civil society actors.

A comprehensive review of the extant literature reveals a lack of scientific papers that have examined the bibliometric intersection between the CE and I4.0 technologies.

Of the 2465 documents that make up the analyzed database, 457 are review papers.

Of these, 69 documents used bibliometrics as a research method. Analyzing the keywords and abstracts in the database with the 69 documents, it was concluded that none of them deal exactly with the topic of the article (the intersection between I4.0 technologies and the CE).

The scope of the paper is to investigate the role of I4.0 technologies in promoting CE.

The main objectives addressed by the research team are:

• Qualitative analysis of the main points of view identified in the literature on the two main topics of the paper (I4.0 and the CE);
• To determine the academic production over time by identifying the most significant contributors—authors and countries—and the main collaboration networks;
• To analyze the most prominent research themes related to the two main proposed topics and the ways in which they interfere;
• To explore how these interferences contribute to the determination of new conceptual frameworks useful in contemporary managerial practice.

2. Exploring the Conceptual Framework Determined by Industry 4.0 and the Circular Economy

The perspectives considered for the analysis are centered around three pillars–namely CE, Supply chain management (SCM), and I4.0—considered as facilitators, with contributions to sustainability, in different proportions of each of the three mentioned pillars.

Rosa et al. [8] developed an innovative systemic framework not only to highlight the dynamic interdependencies between I4.0 technologies and CE principles but also to identify key emerging directions for this interdisciplinary research. By supporting CE practices, I4.0 technologies contribute significantly not only to environmental sustainability but also to the social pillar of sustainability. The synergy between I4.0 and CE serves as a strategic and systemic enabler for achieving the SDGs, particularly by promoting inclusive economic growth, equitable use of resources, and responsible production and consumption patterns [9].

2.1. CE Practices: Reduction, Reuse, Recycling, and Remanufacturing

After 2017, the first studies appeared highlighting the advantages that companies can gain by applying sustainable production based on I4.0 requirements, redefining the Product Life Cycle Management approach [10]. The integration of CE principles with emerging I4.0 technologies presents a transformative opportunity for sustainable industrial development.

Between 2021 and 2025, scientific output was intensified, and the IoT, additive manufacturing (3D printing), big data and big data analysis, AI, robotics, and blockchain are most frequently identified as enablers of CE practices [11].

The advanced digital technology used in a CE of the future takes the form of a data-driven CE and a waste-to-energy framework based on the IoT [12]. The IoT has positively influenced the evolution of trade in recyclable materials, including exports of recyclable materials and employment in the recycling industry in Europe [13]. Publications in the field of recycling have addressed topics such as the challenges of recycling electric vehicle batteries [14], smart technologies that rethink PVC recycling [15], the recovery of high-quality glass sheets at the end of their life cycle [16], carbon emissions generated in production [17], and I4.0 offering solutions for reuse or remanufacturing. The management of urban plastic waste remains a challenge and continues to be the subject of analysis regarding possibilities for transition to a CE [18,19]. Successful implementation of the CE would improve sustainability performance, enabling organizations to gain competitive advantages [20].

It is emphasized that AI, in combination with the emergence of big data, presents substantial opportunities for people, organizations, the environment, and society [21].

However, the latest studies [22] indicate a slowdown in CE practices based on two reasons: a lack of awareness of its tangible benefits and the manner of implementation; and the redirection of resources toward national security as a result of geopolitical turmoil, the cost and complexity of implementing these technologies being high [23]. Even in Germany, which holds a key position in the industry, both in the EU and globally, digital technologies that facilitate circularity in textile manufacturing processes are not sufficiently exploited [24].

A knowledge gap has also been identified [25] among organizations regarding the link between skill development, realities of I4.0, and a sustainable CE, highlighting the role of higher education institutions in redesigning study programs and educational approaches for the development of non-technical and technological skills in line with the CE paradigm.

In order to further the implementation of CE practices, it is essential to examine the role of SCM frameworks and advanced technological capabilities in the enhancement, scaling, and operationalization of reduction, reuse, recycling, and remanufacturing. This examination is imperative across various industries to ensure the effective integration of these principles.

2.2. SCM and Technologies Capabilities Impact

Integrating I4.0 technologies in CE enables the development of new circular business models that promote reverse logistics and sustainable supply chains through enhanced resource management [26].

The IoT has become a key enabler in the development of reverse logistics and big data infrastructures. Synergy between IoT, big data analytics, simulation, and cloud computing can generate a positive effect on green supply chains, with an essential role in transitioning industries from linear and reactive systems toward circular business models [27].

The literature emphasizes the interaction between the CE, I4.0, and supply chains (SCs) in advancing circular transformation. However, both theoretical gaps and practical barriers hinder organizations’ ability to effectively transition from linear to circular models. To address these challenges, developing circular supply chain (CSC) models that provide strategic and operational guidance is essential. Such models can facilitate the adoption of CSC by integrating emerging I4.0 technologies with effective circular practices [28].

The adoption of I4.0 technologies plays a critical role in advancing sustainable supply chain management (SSCM). Liu et al. [29] have developed a conceptual framework that integrates five key digital technologies—cloud computing, AI, big data analytics (BDA), blockchain, and the IoT—to enhance transparency, efficiency, and environmental performance across the supply chain (SC).

Another study regarding the role of I4.0 technologies as enablers for circular systems emphasizes that IoT, blockchain, and Cloud Systems are the most discussed technologies that can facilitate collaboration among circular SC stakeholders [30]. IoT–blockchain technology (BT) supports organizations in achieving greater accuracy, transparency, and accountability by improving environmental performance [31]. Certain SC capabilities are enabled by BT, namely information sharing and SC integration, which can help implement the CSC [32]. These innovative technological solutions, combined with complementary management strategies, can pave the way for a more sustainable and adaptive logistics model [33].

2.3. I4.0 and Sustainable Development Goals (SDGs)

The synergy between I4.0, through its emerging technologies, and the CE, through its resource management models, can significantly contribute to the achievement of the SDGs.

Based on the approaches issued by Dantas et al. [34] and Patyal et al. [35], the framework synthesized in Figure 1 and

Table 1. The contribution of Industry 4.0 and the circular economy to the development of SDGs. Source: Authors’ own synthesis based on Dantas et al. [34] and Patyal et al. [35].

Authors	SDG 6	SDG 7	SDG 8	SDG 9	SDG 11	SDG 12	SDG 13	Number of SDGs Addressed
Dantas et al., 2021 [34]		⌧	⌧	⌧	⌧	⌧	⌧	6 SDGs
Patyal et al., 2022 [35]	⌧	⌧		⌧		⌧	⌧	5 SDGs

Legend: ⌧ The objective is addressed by the author.

captures how I4.0 technologies and CE practices and principles contribute to several SDGs. Four SDGs (7, 9, 12, and 13) are common to both papers, while Dantas et al. [34] uniquely address SDGs 8 and 11, and Patyal et al. [35] include SDG 6, highlighting both convergence and distinct perspectives in their analyses.

The integration of I4.0 technologies and CE strategies contributes to the development of a data-driven decision model tailored to enhancing sustainability in reverse logistics systems and overall SC performance [36].

Within the framework of the Fourth Industrial Revolution (4IR) and the huge interest in sustainable development, several authors and studies have simultaneously addressed CE, I4.0, and the SDGs in different innovative approaches [34,37,38].

Within the I4.0 framework, sustainable development supports CE goals by fostering sustainable business models (SBM)—based on the Triple Bottom Line approach (TBL)—that deliver social, economic, and environmental value [39].

A comprehensive understanding of how institutional demands, physical assets, and workforce skills interact is essential for the successful integration of I4.0 technologies, especially AI-driven BDA. When effectively adopted, these technologies have the potential to advance both sustainable manufacturing initiatives and the development of CE strategies [40].

Gupta et al. [41] present an integrative framework that evaluates manufacturing organizations’ ethical and sustainable business performance by merging I4.0 technologies (such as IoT, BDA, AI, and cyber-physical systems (CPS)) with cleaner production and CE principles. Compared to the article by Gupta et al., which uses methods such as Delphi, Best-Worst Method, and Multi-Criteria Decision-Making, the proposed article adds value by incorporating a bibliometric analysis based on WoS, with a focus on mapping the intellectual structure of the knowledge field by analyzing publication trends, key research clusters, influential authors, collaboration patterns, keyword co-occurrences, and emerging themes.

Examining the CE only from a technical viewpoint—focusing on resource efficiency, waste management, and environmental impact—is important, but insufficient. A comprehensive approach should also incorporate business strategies, value chains, and the adoption of novel business models [42].

I4.0 is not merely a technological revolution, but a powerful enabler of SBM through its integration with CE practices [43].

AI and machine learning (ML) have been applied to advance SDGs by enabling efficient CE mechanisms that address present needs while preserving resources for future generations [44]. In fact, recent studies have shown that I4.0 and the CE complement each other to achieve sustainability [45]. Some authors also add green human resource management to increase the potential to address global sustainability challenges [46].

AI plays an essential role in enabling Circular Business Model Innovation (CBMI) through its core capacities—perceptive, predictive, and prescriptive. Moreover, as digital servitization continues to evolve, these AI-driven capabilities facilitate the development and implementation of business models that promote circularity and enhance industrial sustainability [47].

Combining 4IR digital technologies with the CE framework, in partnership with various stakeholders, facilitates sustainable and inclusive development, in line with the SDGs [48]. Moreover, the practical implementation of CE principles faces several barriers, which can be addressed by integrating digital technologies, including IoT, big data, cloud computing, and, particularly, AI, which offer significant benefits to the reverse logistics process.

Blockchain—as a decentralized structure—ensures transparency, data security, and reliability, contributing as an innovative tool in waste management. In combination with AI and IoT, these new technologies can support and enhance the entire product life cycle. The integration of Life Cycle Assessment (LCA) with digital tools contributes to CE goals [49] by supporting sustainable product design, the optimization of resource management, responsible production and consumption, and data-driven decision making.

The shift from traditional SC to digital SC is based on I4.0 technologies. Blockchain, AI/ML, and IoT are emerging technologies with an important role in achieving higher performance under the new paradigm of CE [50].

Despite technological advancements, human knowledge and skills remain essential, often being seen as the key element needed to activate the full potential of these technologies [51]. In this context, Industry 5.0 (I5.0) introduces a paradigm shift toward sustainable and human-oriented manufacturing, emphasizing collaboration between people and intelligent systems. The integration of human expertise and skills with advanced automation enables manufacturers to address critical sustainability challenges more effectively [52].

Industry practitioners and product designers can leverage the synergy of AI, ML, and BDA to support CE practices by enabling faster, waste-reducing design processes and optimizing circular business models [53]. I4.0 key technologies not only address technical challenges, facilitating the transition to a more CE, but can also increase business efficiency and profitability, promoting inclusion and creating employment opportunities [54].

The literature review helps us to see that ESG (environmental, social, governance) principles are increasingly recognized across Europe, but their implementation remains uneven and there is a need for stronger institutional coordination to ensure the consistent adoption of ESG criteria in all Member States [55].

The fundamental principles of the CE are predicated on the implementation of practices including reduction, reuse, recycling, and remanufacturing. The integration of SCM and technological capabilities is instrumental in ensuring the effective implementation of these practices. Furthermore, the synergy with I4.0 solutions has been demonstrated to accelerate progress toward the SDGs.

The works analyzed in this section are identified in the database constructed for the bibliometric analyses undertaken in Section 3 and Section 4.

3. Bibliometric Methodology Framework Application

A bibliometric search was performed in May 2025 using the WoS, targeting publications that addressed the intersection of two main topics: Industry 4.0 AND circular economy (Figure 2). We acknowledge that the current review extensively considered WoS as the basis for providing input data for bibliometric analysis because it is considered a robust and reliable source of useful data for such analyses [56]. Previous studies conducted by a member of the research team [57] also support the advantages of using WoS compared to Scopus.

The search applied a single filter—language—to include only publications written in English. The search returned 2504 documents, of which 2465 were in English. The rest, published in other languages—Spanish (13), German (6), Italian (6), Russian (6), Portuguese (5), Croatian (1), Czech (1), and Polish (1)—were excluded. Accordingly, the final dataset comprised 2465 documents that met the inclusion criteria and were exported and subsequently analyzed using VOSviewer version 1.6.20 [58,59] and RStudio/Biblioshiny software version 4.1 [60].

The interdisciplinary field addressed is a new one, with the first papers published in 2016 and with significant and accelerated growth (Figure 3). The number of documents for 2025 is lower because the entire year was not considered (the analysis stops in May 2025). The red box highlights the number of documents related to the entire last year recorded in academic research (2024).

4. Detailed Obtained Results and Discussions

4.1. General Information of Used Database

As illustrated in Figure 4, there were 2465 documents relating to the period 2016–2025, published in 866 sources, with a very rapid annual growth rate of 49.9%. Furthermore, if the analysis stops at full years, i.e., at the end of 2024, the annual growth rate is even higher, at 76%. The months corresponding to 2025 (up to and including May) were included in the analysis to highlight emerging themes. Given that 2025 is not a full year (it ends in May), the trend compared to 2024 is downward (from 639 to 344 documents), which leads to the rate in Figure 4 of 49.9. Specifically, for the periods under review, the growth rate is as follows: 67% for the period 2016–2017; 160% for the period 2017–2018, 95% for the period 2018–2019, 75% for the period 2019–2020, 128% for the period 2020–2021, 30% for the period 2021–2022, 30% for the period 2022–2023, 25% for the period 2023–2024. The annual growth rate for the period 2016–2024 is 76%. However, excluding the year 2025 from the analysis would have obstructed the highlighting of the trend of the proposed emerging topics.

A total of 7733 authors contributed to these documents, of whom only 161 were sole authors, with an average of 4.07 authors per document. It can also be observed that the research sample contains 5844 author keywords (AK).

4.2. Analysis of Elaborated Articles by Country

In conducting the bibliometric analysis of scientific output by country, a threshold of more than five documents was applied. As a result, out of a total of 112 countries, 76 met this criterion and were subsequently clustered into six groups and represented graphically (Figure 5).

China’s scientific activity in this field is particularly noteworthy, both in terms of interest (

Table 2. Top 10 countries by scientific output and total citations. Source: Authors’ processing based on data from WoS.

No.	Country	Freq		No.	Country	TC
1	China	666		1	China	8623
2	India	662		2	UK	6510
3	UK	568		3	India	6162
4	Italy	540		4	Italy	5538
5	Germany	363		5	USA	4064
6	Spain	293		6	Finland	3253
7	USA	268		7	Australia	2743
8	Brazil	251		8	Germany	2738
9	France	224		9	France	2727
10	Portugal	203		10	Brazil	2554

in the paper—frequency) and importance (Table 2 in the paper—total citations). In detail, a comparative analysis between China, the EU, the USA, and other geographical areas of economic interest will constitute future research directions that will facilitate the design of different industrial strategies.

To provide a more detailed overview of the scientific contributions by country, a summary table was compiled for the top 10 countries (Table 2). China, India, and the UK are in the top three in terms of both the number of documents (Freq) and citations (TC). In addition to these, the top ten places are occupied by Italy, Germany, Spain, the USA, Brazil, France, and Portugal. In terms of citations, Spain and Portugal drop out of the top 10 (Spain falls from 6th to 11th place, and Portugal from 10th to 19th place). They are replaced by Finland (up from 15th to 6th place) and Australia (up from 11th to 7th place). There is also interest in Romania’s position, which falls from 17th to 32nd place—reflecting the lack of international collaboration in this interdisciplinary field.

4.3. Keyword Analysis (Word Clouds)

To explore the thematic focus of the publications, a keyword analysis was conducted from two perspectives—authors’ keywords (Aks) and keywords plus (KP)—using RStudio/Biblioshiny. “Industry 4.0” and “circular economy” are the two concepts that stand out most clearly in the case of AK, followed in the top 10 by “sustainability”, “digital technologies”, “blockchain”, “sustainable development”, “Internet of Things”, “artificial intelligence”, and “digital transformation” (Figure 6a). In the case of KP, the frequency of words is more balanced, with no terms standing out clearly compared to the others. Thus, the top 10 includes: “circular economy”, “management”, “framework”, “challenges”, “technologies”, “performance”, “sustainability”, “systems”, “model”, and “design” (Figure 6b).

4.4. Determination of Trend Evolution

For trend evolution based on AKs, RStudio/Biblioshiny was used.

Figure 7, created in RStudio/Biblioshiny, illustrates a bibliometric mapping of trend evolution and academic interest from 2020 to 2024. The position of keywords along the temporal axis indicates the years of their initial emergence in academic literature, the time spans during which these terms were most significant, and the periods of highest influence. As shown, the represented keywords are situated at the interface of I4.0 technologies, digitalization, and CE. The evolution of keywords over time reflects both a shift toward next-generation technologies and a conceptual integration of these terms within the frameworks of circular economy and sustainability.

As illustrated in Figure 8, all identified approaches are relatively recent, emerging primarily after 2020. Regarding technologies, the research field has evolved from virtual reality (VR), cyber-physical systems (CPS), and simulations—with intensified research in 2021—to additive manufacturing, IoT, and I4.0, which reached a peak in 2022. AI and blockchain have recently emerged as significant areas of interest, peaking in 2023. The most recent trends, spanning 2023 to 2024, encompass the concepts of digital technologies, digital transformation, digital twins, and I5.0. Interest in I5.0 is not only recent but also increasing. Beyond technological advancements, it is also worthwhile to examine the evolution of other terms addressed in the relevant literature. These indicate a notable rise in interest toward SBM, recycling, sustainable development, sustainability, and the CE. Furthermore, there is a recent emphasis on bibliometric analysis—a quantitative research method used to analyze academic output and research activity.

In the next sections, several maps are analyzed, created based on the sample of documents extracted from the WoS and subsequently imported into VOSviewer. After processing, a total of 7941 keywords (AK and KP) were identified, of which 555 exceeded the threshold of five occurrences. Given the density of concepts distributed across 11 clusters, it was decided to retain only those keywords that occurred more than 10 times. This resulted in a final research sample of 289 keywords that exceeded the minimum threshold and formed the basis for the bibliometric mapping.

4.5. CE Network Analysis

The concept of “circular economy” is well represented in the map (Occurrences/Occ.: 1711). On the spectral time scale, “circular economy” appears as a central node marked in orange (Average Publication Year/APY: 2022.67), indicating a recent research focus. It exhibits numerous connections (Links: 228; Total Link Strength: 10,526) with terms spanning the entire temporal spectrum—from the earliest concepts marked in purple to the most recent ones represented in dark red (Figure 8). On the bibliometric map, the concept of “circular economy” is most closely associated with “sustainability”, both in terms of physical proximity and connection intensity (as indicated by the thickness of the connecting line). The minimal distance between the two nodes, along with the line’s thickness, indicates a high degree of thematic coherence. This finding suggests that the two concepts are frequently co-mentioned in academic publications across disciplines, including environmental studies, economics, engineering, and public policy. Besides “sustainability”, in close proximity to the concept of circular economy are three other keywords that are well represented and novel in the context of the subject under discussion: “artificial intelligence”, “models”, and “management”—all represented in red color (emergent themes). This indicates that the future of the CE is closely linked to the integration of opportunities generated by AI, management, and the implementation of innovative operating models.

The concept of “artificial intelligence” (AI) is an emerging one (APY: 2023.08), located on the network map (Appendix A.1) in close proximity to “circular economy”, “sustainability”, and “sustainable development” concepts. As can be observed, AI also plays an essential role in “performance”, “design”, and “supply chains”. Regarding its connection to the technological field, AI is related to “machine learning” algorithms, as well as to other terms within the domain of “digitalization”, “digital transformation”, and I4.0—specific technologies, including “big data”, “big data analytics”, “Internet of Things”, and “blockchain”.

4.6. IoT Concept Interdependences

The associated blue color (indicating the beginning of 2022) suggests that IoT represents the most mature and consolidated technology within the analyzed network. Its central position highlights its pivotal role in enabling connectivity and data exchange across multiple domains, including the “circular economy,” “Industry 4.0,” and “supply chains.” Through its integrative function, IoT acts as a technological support that facilitates real-time monitoring, resource optimization, and process automation, thus connecting these areas into a coherent digital ecosystem (Appendix A.2).

IoT is well represented in the network, as indicated by its high occurrence (Occ.: 167) and has multiple links (L: 212, TLS: 1428) with concepts from diverse thematic areas:

• Technical domain—“digital twin”, “cyber-physical systems”, “big data” and “big data analytics”, “Industry 4.0”, “digital technologies”, “digitalization”;
• Sustainability domain—“waste management”, “circular economy”, “sustainability”, “sustainable development”;
• Operational domain—“supply chains” and “supply chain management”, “systems”, “management”, “model”, “performance”, “business models”.

Analyzing the concepts in the network according to their color scale—from blue (indicative of early 2022) to dark red (associated with late 2024)—highlights a thematic evolution from technical infrastructure to application and impact, particularly in the area of sustainability. IoT emerges as the most mature technology within the network (APY: 2022.14), reflecting its foundational role in enabling digital transformation across domains. The map from Appendix A.2 illustrates a shift in research attention—from IoT as a standalone technology to IoT as an innovative tool for sustainable transformation, especially within the “circular economy” and “supply chain management”. As can be observed, the emergence of new I4.0 technologies—“digital twins”, “blockchain”, and “artificial intelligence” (marked in red—specific to 2023 and 2024)—indicates a transition toward advanced tools aimed at addressing complex environmental challenges and enabling sustainability and innovation.

4.7. Highlighting Big Data and BDA Approach

Big data and BDA are addressed both separately and together. The separate approach allows for highlighting the differences between the technological infrastructure and the analytical component, but their integration is essential: without analysis, data has no value, and without data, analysis has no content. Together, they support data-driven decisions and digital transformation processes.

Big data and BDA are the technologies most widely represented in the bibliometric analysis carried out, which is why their graphic representation was taken into account in the form of two figures, which are found in the annexes category (Appendix A.3 and Appendix A.4).

Big data and BDA are the most widely represented technologies. Big data (Occ.: 197; L: 229; TLS: 1893) lays the technical groundwork for digital transformation. It is directly linked with other I4.0 technologies—“blockchain”, “cyber-physical systems”, “digital twin”, and “artificial intelligence”. Moreover, it can be observed in Appendix A.3 that “logistics” and “supply chains” are in close proximity to “big data” and “blockchain”. In this regard, it can be concluded that the synergy between these I4.0 technologies supports the tracking, analysis, and optimization of resource use across entire product lifecycles, SC, and production systems.

BDA (Occ.: 89 L: 158; TLS: 800) is an essential tool for processing and analyzing large volumes of structured and unstructured data. It facilitates forecasting, intelligent continuous optimization, and data-driven decision-making. It is in close proximity to “supply chain management” and “predictive analytics” (Appendix A.4), which indicates its essential role in enabling accurate forecasting, continuous optimization, and intelligent decision-making. In line with the specialized literature, the role of BDA in demand forecasting, logistics flow optimization, and SCM is well-documented. When integrated with “artificial intelligence”, it becomes a powerful enabler of resilient, efficient, and sustainable systems—aligned with the principles of the CE and sustainability—by prioritizing waste reduction and promoting reuse, recycling, and resource efficiency.

4.8. Blockchain Connections with Other Concepts

Blockchain is an emerging topic (APY: 2022.96) with a high frequency of occurrence and multiple strong links to other concepts in the map (Occ.: 168; L: 213; TLS: 1349). As highlighted in the analysis of the relevant literature, BT plays an essential role in “supply chain” and “supply chain management”—concepts to which it is directly linked in the network map. In these maps, blockchain is the technology most closely situated in relation to I4.0, as well as to “supply chains”.

The network map from Appendix A.5 also highlights links between BT and “smart contracts” and “traceability”, indicating its role in ensuring transparency across the entire process—including the product design stage. In the technology domain, “blockchain” is directly linked to “big data”—to which it is most closely connected—as well as to “artificial intelligence” and the “Internet of Things”.

The synergy between blockchain, IoT, and AI technologies ensures transparency, security, and trust—essential characteristics for intelligent and connected industrial systems.

Involving emerging technologies such as blockchain, AI, and big data in management processes and supply chains supports the transition to a circular economy, where digitalization becomes an essential catalyst for operational efficiency and sustainable use of resources.

5. Discussions

Technological development may accelerate industrial growth but can also increase emissions and deplete natural resources. In this regard, to avoid the negative consequences, it is essential to align the I4.0 emerging technologies with CE principles. This integration supports economic growth and contributes significantly to the achievement of the SDGs.

Achieving SDGs requires not only a combination of technological innovation with systemic changes in production and consumption systems but also behavioral changes involving consumers and organizations.

Successful integration of CE principles with I4.0 technologies requires a shift in workforce skills and competencies, especially in digitalization and sustainability. However, educational systems often fail to align with labor market needs, relying on outdated curricula that do not prepare learners for the realities of the digital economy. In order to close this gap, empower the workforce, and reap the full benefits of I4.0 technologies, education must evolve through curriculum innovation, enhanced flexibility, and support for continuous learning.

Among the benefits of the results obtained for the academic environment, we can mention: the reorganization of the university curriculum in order to improve the skills that would enable the successful use of key I4.0 technologies in the augmentation of CE practices and the increase in the interdisciplinary approach to sustainability issues. Industry representatives can become aware of both the economic and image benefits they can gain from integrating technology into innovation in the field of sustainability and circularity, which cannot be achieved without collaboration with the education sector. According to the conclusions reached, the government could contribute to the adoption and improvement of CE practices by aligning national legislation with international strategies and policies. It can be considered that the article adds considerable value related to the transformation and the evolution of CE: on one hand, it provides knowledge of trends that need to be addressed in management education; on the other hand, there are identified niches in which developments can be initiated.

The carbon neutralization effort promoted by contemporary society has given rise to a new challenge, namely the management of electric vehicles (EVs). In response to this challenge, a smart CSC model has been proposed by specialized literature for the EV 4.0 battery industry, with production based on robotics and emission reduction technologies, all while maximizing profit [61].

Similarly, companies in the textile sector are increasingly integrating I4.0 technologies alongside the circular economy, considering cleaner production to improve sustainable performance. The findings of a recent study suggest that the implementation of I4.0 technologies within Brazil’s substantial textile sector has modestly promoted circular economy initiatives at the micro level. This phenomenon can be attributed to the absence of well-established mechanisms for waste recovery and the reinstatement of value to products and packaging following their utilization. Consequently, the pursuit of circularity within the textile industry emerges as a pivotal element in facilitating the transition to sustainable economic models [62].

6. Limitations

This paper presents a bibliometric analysis conducted exclusively using WoS. Although this database offers a rigorous perspective and very useful information, it provides only a limited view of scientific output, focusing mainly on journals with high impact factors. Therefore, in order to gain a more comprehensive understanding of the interdisciplinary field in question, the bibliometric analysis could be supplemented with information from other sources, such as Scopus, Google Scholar, and Dimensions.

Bibliometric analysis does not assess argumentation, methodology, data validity, or scientific rigor. Although it provides a useful perspective on the structure and dynamics of the field under study, bibliometrics does not focus on the theoretical consistency, depth, or individual scientific value of the examined documents. Consequently, this type of analysis could be complemented by a qualitative analysis, such as a systematic literature review (SLR).

Another limitation relates to the maturity of the topics addressed, which varies depending on the field of activity and industry in terms of their level of digitization. Thus, the application of technologies such as artificial intelligence, the Internet of Things, blockchain, and big data is more advanced and well established in sectors with a high degree of digitalization (e.g., industry/manufacturing and energy), while in other sectors (e.g., construction, services, agriculture, and textiles) it is still in its early stages. This paper does not provide detailed comparative analyses of sectors at different levels of digitalization and implementation of Industry 4.0 technologies. To identify where the convergence between Industry 4.0 and the circular economy is most advanced, it would be valuable to conduct future research that segments the analysis by areas of activity and industries. Moreover, the expertise of an author of the article in performance management and key performance indicators (KPIs) facilitates future research addressing how these technologies and circular economy principles can be integrated with the performance management system, providing practical tools for measuring, monitoring, and optimizing the impact of these technologies in organizations. Therefore, by providing a framework that ensures synergy between Industry 4.0 and the circular economy, performance management could lay the groundwork for the digital and sustainable transformation of organizations. Such a framework would provide clear KPIs to support decision-making and the implementation of circular strategies. Furthermore, given that four of the article’s authors come from academia, subsequent research will focus more closely on higher education institutions (HEIs), which play a pivotal role in generating knowledge and innovation, as well as in training future generations of specialists and leaders.

7. Conclusions

This study presents an updated bibliometric analysis of the I4.0–CE nexus, encompassing the most recent publications from 2023 to 2025. It offers a distinctive integration of I4.0 and CE practices with the SDGs, emphasizing emerging research frontiers such as digital twins and AI-driven resource optimization. The integration of these aspects results in a more comprehensive and current perspective than previous reviews.

Currently, there is growing awareness regarding the integration of sustainability into industrial processes and business models. The results of the bibliometric analysis highlight a shift in research focus from traditional I4.0 topics such as VR, CPS, and simulations toward IoT, big data, and analytics, followed by emerging areas like digital transformation, AI and ML, digital twins, and I5.0 models. Additionally, the intersection of novel technology and sustainability has emerged as a critical research priority.

The maturity of the technology is highlighted by the average publishing year indicator, as follows: IoT (APY: 2022.14), big data (APY: 2022.35), BDA (APY: 2022. 43), blockchain (APY: 2022.96), AI (APY: 2023.08). In addition, on the spectral map provided by VOSviewer, the concepts appear in different colors (ranging from blue—the oldest/most mature—to red—the newest/most emerging).

The paper analyzes the connections between technology and challenges associated with the CE and related topics—such as sustainability and sustainable development—as well as sustainable economic models as discussed in the literature.

Although the literature review highlights an increase in scientific output addressing topics related to the CE and I4.0 between 2021 and 2025, it also allows us to identify a number of causes that have contributed to slowing down the adoption of circular economy practices:

• Causes related to resource management and the need to redistribute a part of them for investments related to ensuring European and global security;
• Causes related to the legislative framework, namely the need to coordinate regulations related to sustainable policies at the European and—why not?—even global level;
• Lack of awareness among the entire population, on the one hand, of the negative effects that could arise from not adopting CE principles and, on the other hand, of their benefits;
• Lack of skills in key I4.0 technologies that would accelerate and innovate circular economy practices.

Recent concepts identified through research that deserve further development within interdisciplinary research were detailed, such as analytics, dynamic capabilities, business model innovation, and smart contracts.

The analysis identified recent concepts that have not yet been sufficiently addressed through transdisciplinary analysis, indicating the novelty of the research presented in this paper.

Among the most notable results, we can mention:

• The latest research directions focus on digital technologies, digital transformations, digital twins, and (the latest trend) I5.0, analyzed interdisciplinarily with the CE and sustainability;
• On the bibliometric map, the concept of CE is most strongly connected to sustainability, followed by AI and management models;
• The IoT stands out through multiple and complex links in three dimensions—technical, operational, and sustainability—and can be considered a catalyst for digital transformation;
• Big data and BDA connect best with other technologies, such as blockchain, but also create links with terms such as logistics and SCM, facilitating the decision-making process;
• AI is linked to the CE and sustainability and also to performance, SC, and digitization.

Future research could focus on several directions, such as analyzing how digital transformation and emerging technologies support not only technological innovation and economic performance but also the social and environmental impacts of digital transformation. It is also intended to investigate, at a national level, and then compare with specific international practices, the way in which CE is approached from an educational point of view, at the bachelor’s and master’s levels. This type of research, in order to create practical effects (integration of approaches), must be carried out comparatively, at the level of the triple or quadruple helix model.

A novel contribution is the bibliometric analysis carried out in this manner, which updates the results of similar analyses conducted in the specialized literature up to the moment of documenting for this article.