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Review

Industry 4.0, Circular Economy and Sustainable Development Goals: Future Research Directions Through Scientometrics and Mini-Review

by
Maximo Baca-Neglia
1,
Carmen Barreto-Pio
1,
Paul Virú-Vásquez
1,*,
Edwin Badillo-Rivera
1,
Mary Flor Césare-Coral
2,
Jhimy Brayam Castro-Pantoja
2,
Alejandrina Sotelo-Méndez
3,
Juan Saldivar-Villarroel
4,
Antonio Arroyo-Paz
5,
Raymunda Veronica Cruz-Martinez
4,
Edgar Norabuena Meza
6 and
Teodosio Celso Quispe-Ojeda
7
1
Faculty of Environmental Engineering and Natural Resources, Universidad Nacional del Callao, Callao 07011, Peru
2
Faculty of Sciences, Universidad Nacional Agraria La Molina, Lima 15024, Peru
3
Facultad de Zootecnia, Universidad Nacional Agraria La Molina, Lima 15024, Peru
4
Facultad de Agronomía, Universidad Nacional de Cañete, Lima 15024, Peru
5
Facultad de Ingeniería, Universidad Tecnológica del Peru, Lima 15024, Peru
6
Facultad de Ingeniería Química y Textil, Universidad Nacional de Ingeniería, Lima 15001, Peru
7
Faculty of Environmental Engineering, Universidad Nacional de Ingeniería, Lima 15001, Peru
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(14), 6468; https://doi.org/10.3390/su17146468
Submission received: 20 June 2025 / Revised: 6 July 2025 / Accepted: 14 July 2025 / Published: 15 July 2025
(This article belongs to the Special Issue Towards SDGs and Sustainable ESG Performance: The Synergistic Influence of Industry 4.0 and Governance Structure)
Browse Figures
Versions Notes

Abstract

The global pursuit of sustainable development has intensified the need to integrate Circular Economy (CE), Sustainable Development Goals (SDGs), and Industry 4.0 (I4.0) as mutually reinforcing frameworks. This study explores the scientific evolution and interconnections among these pillars through a dual approach: (i) a scientometric analysis using CiteSpace, VOSviewer, and Bibliometrix in RStudio (2024.12.1+563), and (ii) a targeted mini-review of high-impact literature. A dataset of 478 Scopus-indexed articles (2016–2024) was analyzed, revealing CE and I4.0 as key technological and strategic enablers of the SDGs—particularly SDG 12 (Responsible Consumption and Production), SDG 9 (Industry, Innovation and Infrastructure), and SDG 13 (Climate Action). Moreover, the results underscore an increasing role of enabling digital technologies—such as IoT, blockchain, and big data—in shaping sustainable production systems. An important insight from this work is the growing relevance of policy frameworks as catalysts for implementing CE and I4.0 strategies, especially within national and international sustainability agendas. However, the low citation frequency of “policy” as a keyword indicates a gap in the literature that merits further exploration. Future research is encouraged to conduct in-depth bibliometric studies focused on sustainability-related policies, including regulations that operationalize CE and I4.0 to support SDG achievement. This study contributes a comprehensive overview of emerging research trends, identifies strategic knowledge gaps, and highlights the need for cohesive governance mechanisms to accelerate the digital–ecological transition.

1. Introduction

The concept of sustainability is derived from the Latin “sustinere” (tenere: to hold; sus: up). This concept, which involves maintaining certain conditions or capabilities over time, gained global attention in 1987 when the World Commission on Environment and Development raised concerns about “our common future” [1]. The United Nations Rio +20 summit in Brazil in 2012 committed governments to create a set of Sustainable Development Goals (SDGs) that would be integrated into the follow-up to the Millennium Development Goals after their 2015 deadline [2].
In 2015, world leaders augmented these efforts by adopting the 2030 Agenda for Sustainable Development, a comprehensive global action plan encompassing 17 SDGs [3] (Figure 1).
The SDGs represent a worldwide call to safeguard the environment, address climate change, eliminate poverty, and guarantee a high standard of living and prosperity for people [6]. In response to SDGs, the incorporation of environmental considerations into industrial organizational processes is becoming increasingly necessary.
The high level of competitiveness and the pursuit of operational excellence in industries have led many of them to make a gradual transition towards the Industry 4.0 (I4.0) model, which encompasses the digitization, computerization, and management of data, along with information exchange, within advanced manufacturing engineering systems [7]. I4.0 has driven a profound transformation in production systems by enabling the interconnection of devices, systems, and actors throughout the entire value chain. I4.0 is also known as The Fourth Industrial Revolution, and it seeks to enhance globalization and enable greater customization in manufacturing processes. It involves the current movement toward increased automation and data integration within manufacturing technologies, including the Internet of Things (IoT), cloud computing, big data, cybersecurity, additive manufacturing, augmented/virtual reality, simulation/digital twin, and autonomous robots [8], with the ultimate goal of creating smart factories [9]. During the transition to I4.0, positive impacts can be generated within one of the dimensions of sustainability [10], as it can reduce the resource and energy consumption of manufacturing activities through detection and data analysis [11]. However, some studies have highlighted possible negative unintended effects of I4.0 technologies on environmental and social sustainability [12,13]. It is important to note that sustainability encompasses different dimensions, and there is limited literature that demonstrates I4.0’s positive impacts across all dimensions.
The circular economy (CE) can be defined as an approach that aims to make better use of resources—a common denominator in many definitions, although what exactly constitutes “better” remains debatable. Despite this ambiguity, it is evident that the global depletion of natural resources and the associated carbon emissions continue to accelerate, while paradoxically, waste and pollution are still accumulating. CE thus seeks to address these issues by designing out waste, keeping products and materials in use, and regenerating natural systems [14]. The CE serves as a tool to drive progress towards achieving the SDGs, supporting various stakeholders in their pursuit of these goals [15]. The CE, a key component of sustainability, is closely linked to the environmental and economic dimensions of sustainability. However, some scholars have noted that there are still gaps in understanding how the CE can simultaneously foster economic growth while also safeguarding the environment and society [16,17].
In recent years, the relationship between the CE and the SDGs has gained increasing attention [18]. Research highlights how the implementation of the CE influences the achievement of the SDGs; for example, for SDG 6—Clean water and sanitation [19,20,21], SDG 7—Affordable and clean energy [22,23], SDG 12—Responsible consumption and production [19,24,25,26], SDG 13—Climate action [19,21,22,27], SDG 14—Life below water [19,21], and SDG 17—Partnerships for the goals [28,29]. There is limited literature that integrates the CE, I4.0, and the SDGs in a comprehensive manner. Therefore, exploring their combined application and identifying future trends or hotspots is an interesting area of research, and it represents an opportunity to understand the behavior of upcoming global research, and conducting a mini-review will help to understand this interrelationship in an integrative way.
In this research, (i) a scientometric analysis was conducted to comprehensively evaluate the intellectual core and landscape of the general body of knowledge on the CE, I4.0, and the SDGs through a bibliometric analysis. For this purpose, a sources analysis and a keywords analysis were performed, along with a (ii) mini-review to examine the trending articles that address the interrelation of the proposed topic.

2. Materials and Methods

This research article has employed scientometric analysis, as scientometric methods offer a structured, systematic, and repeatable approach for assessing research areas [30]. Furthermore, scientometric methods provide a clear, systematic, and reproducible framework for evaluating research areas [30]. Early discussion using bibliometrics started in the 1950s [31], which suggests that the bibliometric methodology is not new [32]. Figure 2 shows a structured methodology for conducting a bibliometric analysis, divided into the following four main phases: (i) data collection, (ii) data exportation and visualization, (iii) bibliometric analysis, and (iv) mini-review.
Each phase followed a systematic approach to ensure the accuracy and relevance of the research findings. (i) Data collection was based on retrieving relevant scientific literature from the Scopus database, since Scopus includes more journals than Web of Science (WoS) [33], and it was validated in this research that there were twice as many documents indexed in Scopus compared to WoS. An advanced search strategy was applied, specifying document types such as journal articles, conference papers, and reviews, all in the English language. The search equation incorporated keywords related to “Industry 4.0”, “Circular Economy”, “Sustainable Development Goals”, and “Environmental, Social, and Governance”.
To enhance the focus of the analysis, specific document types such as book chapters, editorials, and trade publications were excluded. For the research, only articles published between 2016 and 2024 were included, and articles published in 2025 were excluded from the analysis. The research search was conducted on 21 March 2025. Initially, 550 articles were retrieved, but after applying filters, the dataset was reduced to 478 articles (Table S1).
The second phase, (ii) data exportation and visualization, involved preparing the extracted data for analysis. The selected articles were exported in BibTeX format and then transformed using CiteSpace 6.4.R2. Additionally, the dataset was converted into RIS format to be processed in VOSviewer 1.6.20, a widely used tool for bibliometric analysis [34]. Additionally, the Bibliometrix package in R-Studio 2024.12.1+563 [35] was used in (iii) bibliometric analysis, to apply structured methods to extract meaningful insights from the dataset. The analysis included a source analysis, which identified the major journals and sources contributing to the field, and a keyword analysis, which highlights the most frequently used terms to determine dominant research themes. The key outputs of this phase included the annual publication trends, which provided an overview of the evolution of research over time; sources information, which identified influential journals and conference proceedings; and research trends and hotspots, which reveal emerging areas of study and future research directions. In this step it was separated into three periods to better understand the research trends and hotspots.
For the final phase, (iv) a mini-review was conducted to identify and analyze articles in the database that explore the interconnections among the CE, I4.0, and the SDGs, based on researching specific articles.

3. Results

3.1. Annual Publications

Figure 3 shows the bibliometric analysis of the scientific output on a specific topic. In panel (a), the distribution of document types is shown, with journal articles accounting for the largest share (301), followed by conference papers (106) and review articles (71). Panel (b) illustrates the evolution of the number of publications from 2016 to 2024, highlighting an exponential increase up to 2023, followed by a slight decline in 2024. The fitted linear trend (R2 = 0.9162) indicates a strong positive correlation between time and the number of publications, suggesting a growing interest in the topic under study.

3.2. Sources

Table 1 shows the local source impact. It displays key bibliometric indicators commonly used to assess the scientific performance of academic journals in terms of productivity and impact. Among these indicators, the h-index, g-index, and m-index stand out, all of which are widely recognized and applied in research evaluation studies. The h-index, proposed by [36], is a metric that combines productivity and citation impact into a single value. Specifically, a journal has an h-index of h if it has published h articles that have each received at least h citations. This metric has been widely adopted due to its simplicity and its ability to minimize distortion caused by a small number of highly cited papers. However, its main limitation lies in its inability to capture the influence of publications that receive citations far exceeding the h threshold, potentially underestimating its true scientific impact.
The g-index, introduced by [37], is a proposed improvement over the h-index that gives greater weight to highly cited publications. According to this metric, a journal has a g-index of g if its g most cited articles collectively have at least g2 citations. Unlike the h-index, the g-index places more emphasis on the cumulative impact of the most influential articles, making it useful for capturing not only consistency but also peaks of academic impact. Its main disadvantage is that it may overvalue cases in which a few articles account for a disproportionately high number of citations.
The m-index is a variant of the h-index that adjusts the score based on the time elapsed since the first publication. It is calculated as the h-index divided by the number of active publishing years (m = h/years). This metric enables a fairer comparison between journals or authors with careers of different lengths. For example, a journal with a low h-index but a high m-index may indicate increasing productivity and citation impact over a short period of time. This index has been advocated as a fairer way to evaluate early career researchers or emerging publications. Upon analyzing Table 1 with these indicators, it is observed that the Journal of Cleaner Production has the highest value across all metrics (h_index = 34, g_index = 46, m_index = 4.857), indicating a high scientific output, many influential articles, and a sustained impact over time. Sustainability (Switzerland) also demonstrates strong performance, particularly in the g_index (g = 43), suggesting the presence of several highly cited articles. In contrast, journals such as the International Journal of Production Economics and the Journal of Enterprise Information Management, both with an h-index and g-index of 6, reflect lower impact—possibly due to their recent entry into the publication landscape (starting in 2021, Start Period “SP”). Table 1 also shows the Total Citations (TC) and number of publications (NP) and Impact Factor (IF).

3.3. Keywords Analysis

Figure 4 is a keyword co-occurrence map in the scientific literature that illustrates the evolution of major research trends from 2016 to 2024, based on key terms grouped into distinct clusters. This analysis helps identify emerging patterns in the interconnectivity of concepts. The CiteSpace software uses a spectral clustering algorithm to automatically group topics and provides three different methods for identifying representative keywords for each cluster [38].
The graph is organized by research clusters, where each color represents a community of terms that have been interrelated during specific time periods. A strong presence of terms such as CE and I4.0 is observed, indicating a growing focus on the integration of digitalization with CE models. Starting in 2020, topics such as blockchain, automation, supply chain management, and data analysis emerge, suggesting an increased exploration of enabling technologies for CE and sustainability. Each cluster in the map is color-coded and labeled numerically. These clusters represent groups of closely related keywords that frequently appear together in academic publications.
For instance, Cluster #0, titled circular manufacturing, represents a mature research area with high centrality. It includes terms such as conceptual framework business, digital transformation, and business, suggesting a strong focus on integrating digital strategies into manufacturing processes. Cluster #1 centers on the circular economy, and includes terms such as big data, Internet of Things (IoT), and blockchain and environmental technology. This indicates a thematic focus on how smart technologies support circular practices. Cluster #2 is also focused on the CE, but from a sustainable development perspective. It includes keywords like trial economics, big data, Industry 4.0 and sustainable development. Cluster #3 highlights barriers to the adoption of circular practices, referencing terms such as environmental economics, sustainability, cleaner production, literature review, and industrial technology. Cluster #4 (green)appears to focus on quantitative empirical methods, as seen through terms like data analytics, industrial research, artificial intelligence, information management, and philosophical aspects. Cluster #5 is related to waste management, supply chain management, decision making, and supply chains. Cluster #6 is related to systematic literature review and food supply chain. Cluster #7 (dark blue) deals with digital technology. Finally, Cluster #8 (pink) relates to circular business models, suggesting an emerging field focused on redesigning value propositions and delivery systems under circular logic. Overall, this visualization illustrates a clear evolution of themes—from foundational and technological discussions (2016–2019) toward more applied, managerial, and empirical approaches (2020–2024). This progression reflects the academic community’s shift from conceptual exploration to practical implementation and evaluation, particularly in the face of global sustainability challenges and digital transformation.
Figure 5 shows the 30 keywords with the strongest citation bursts in academic publications from 2016 to 2024.
The “Strength” column indicates the intensity of the citation burst—that is, how much the citation frequency of a term increased compared to others. “Begin” marks the year when the significant rise in citations started, while “End” indicates when the trend began to stabilize or decline. The bars use two colors: red represents the exact period during which the keyword experienced its citation burst, while light blue displays the full timeline of the study (2016–2024). For example, Green Manufacturing exhibited a strong citation burst (3.92) between 2016 and 2020, while more recent terms like Decision Making have emerged prominently during 2023–2024. This reflects research trends aligned with contemporary topics such as I4.0, CE, and the SDGs, indicating a growing interest in digitalization, sustainable manufacturing, and technological innovation in industrial systems and supply chains.

3.4. Research Trends and Hotspots Through Time

The map of the occurrence keywords for the three periods are shown in Figure 6, considering the CE, SDG and I4.0.
For Period I (2016–2018), several clusters are displayed, each represented by a different color. A cluster is a group of keywords, authors, or articles that are closely related through their frequency of co-occurrence or thematic connections. During period I, emerging topics such as Fourth Industrial Revolution, Circular business models, big data, and digital technologies stand out. The clusters in this period appear clearly differentiated, with distinct groups of keywords. This suggests that, as an initial stage, the topics were relatively new and not yet strongly interconnected.
Cluster 1 includes topics like behavioral research, decision making, information use, and environmental protection. Behavioral research explores the psychological, cultural, and social factors that shape individuals’ and organizations’ attitudes toward circular practices. This directly informs decision making, which in the context of the CE involves choosing strategies. Information use is a critical enabler in this process, as decisions grounded in accurate, timely, and transparent data are more likely to align with environmental objectives. For example, when companies or policymakers have access to lifecycle data or environmental performance indicators, they can make more sustainable choices. Ultimately, all these elements contribute to environmental protection, which is both a goal and a result of informed, responsible, and behaviorally conscious decision making processes. Together, these keywords reflect a research focus on the human and cognitive dimensions of the CE, emphasizing that technical solutions alone are not enough—behavior, values, and information flows are also essential for effective environmental action in the context of revolution of I4.0.
For period II (2019–2021), in cluster 1, it revolves around I4.0, focusing on digitalization, remanufacturing, recycling, stakeholders, and conceptual frameworks. The main research theme here addresses how advanced technologies support the transition to CE practices, particularly through the redesign of industrial systems to enable reuse, resource efficiency, and product lifecycle extension. Stakeholder engagement is also highlighted as essential for implementing these transformations, while conceptual frameworks help formalize emerging models for circularity enabled by I4.0 tools like IoT and automation.
Cluster 2 is centered on sustainable development, encompassing economic and social effects, planning, decision making, and case studies. It represents the systemic and policy-oriented aspects of sustainability, where the focus is on understanding how I4.0 affects society, the labor market, and sustainable governance. Industrial research supports empirical insights, while case studies demonstrate real-world implementations of sustainability strategies. This cluster shows how technology is embedded in broader social, ethical, and economic considerations, emphasizing inclusive and planned transitions.
Here, researchers explore how big data analytics and environmental tech enable real-time monitoring, predictive planning, and carbon footprint reduction. This cluster connects technological infrastructure with sustainable logistics, reinforcing the importance of smart supply chain systems (Cluster 3) as a foundation for both I4.0 and CE practices. Cluster 4 includes digital technologies, business models, disassembly, and simulation, focusing on virtual tools and design strategies that support sustainability. Disassembly and simulation are essential in designing products for reuse, remanufacturing, or material recovery, which are key principles of the CE. Business models explored in this context often involve servitization, leasing, or sharing approaches enabled by digital platforms. This cluster illustrates how technological modeling and digital innovation shape new value creation methods in I4.0.
For period III (2022–2024), unlike the first and second periods, a greater number of research topics can be observed, and the keywords appear more diverse and interconnected. Cluster 1 emphasizes the integration of sustainability and strategic management. Topics such as sustainable development goals, stakeholder and technology adoption illustrate how organizations embed CE principles into strategic planning frameworks under I4.0. Cluster 2 focuses on 3D printing, MCDM (Multi-Criteria Decision Making), and % reductions which suggest a focus on optimizing industrial processes through the adoption of smart technologies and decision-support tools. Cluster 3 relates to blockchain, industrial research, food supply chain, bibliometric analysis, and digital transformation. It emphasizes the convergence of transparency-oriented technologies and academic research in the pursuit of more resilient and sustainable supply chains. The presence of blockchain signals a growing interest in enhancing traceability, data security, and trust across supply chain networks—particularly in sensitive sectors such as the food supply chain, where real-time verification of product origin, handling, and compliance is critical for food safety and sustainability. Digital transformation acts as an enabler, allowing for the integration of blockchain into broader smart supply chain architectures. Through the automation of transactions and decentralized data management, blockchain supports real-time monitoring, reduces administrative inefficiencies, and combats fraud—thus aligning closely with CE goals such as transparency, accountability, and extended product responsibility. Simultaneously, industrial research and bibliometric analysis indicate a growing body of academic work that maps the evolution and diffusion of these technologies within industry. Bibliometric tools provide insights into how blockchain and digitalization are being adopted, what gaps remain in the literature, and which sectors (like agri-food) are leading in implementation. This analytical lens supports the formulation of data-driven policies and research agendas that guide sustainable digital transformation. Cluster 4 centers on sustainable performance, project management, product-design, SMES, and environmental impact. Effective project management plays a crucial role in operationalizing these sustainability efforts, ensuring that environmental considerations are integrated from the planning phase to execution. Tools like life cycle assessment (LCA), sustainability indicators, and agile project methodologies help teams track and optimize sustainable performance throughout a product’s development cycle. Cluster 5 involves cyber-physical systems, automation, and operational resilience, particularly in the context of uncertainties such as COVID-19. These systems enable real-time monitoring and adaptive control of production processes, supporting circular performance metrics.

3.5. An Overview of CE and I4.0

CE is a strategic concept for businesses today, situated within an industrial context that encompasses multiple components: sustainability (environmental, economic, and social); guiding principles (systemic perspective and R-framework); cycles (biological and technical); key areas (production, consumption, waste management, secondary raw materials, innovation, and investment); and implementation strategies (both top-down and bottom-up) [39]. The complexity of the industrial sector has increased since the Industrial Revolution, driven by technological innovations that enhanced efficiency and productivity. This has required new methods to manage the growing interactions and dependencies in increasingly competitive environments [40]; consequently, companies have increasingly adopted I4.0 technologies as a strategic means to enhance their competitiveness and adapt to the complexities of modern industrial environments. In this context, companies have integrated CE principles and I4.0 technologies as part of their competitiveness and strategic management.
The intersection between I4.0 and the CE has emerged as a popular research topic in recent years [41]. I4.0 is considered part of digital technologies because it represents the application of advanced digital tools within industrial environments to enhance efficiency, automation, and decision making. Within the field of digital technologies are the Internet of Things (IoT), Artificial Intelligence, Big Data Analytics [42], Radio Frequency Identification (RFID) [43], and blockchain [44].
IoT for CE practices aims primarily to improve efficiency, sustainability, and traceability in waste management through real-time data collection, process automation, and data-driven decision making. This enables the optimization of collection routes, reduction in operational costs, minimization of environmental impact, and promotion of citizen engagement through alerts or digital interfaces. For example, the application of IoT in circular practices such as the sharing economy [45], waste management [46,47], circular business models [48], and recycling [49].
In today’s information-driven age, advances in technologies such as the Internet and cloud computing have generated an immense and rapidly growing stream of diverse data. This vast, complex, and fast-moving resource is commonly referred to as big data [50]. Big data plays a key role in the CE by enabling industries to optimize resources, reduce waste, and make more sustainable decisions based on large-scale data analysis. For instance, in the fashion industry, big data supports the redesign of business models toward circularity by analyzing consumption patterns and material flows [51].
RFID is used to create Material Passports for recycled concrete aggregates. RFID is linked to the CE because it allows for the tracking, classification, and verification of the quality of these materials throughout their lifecycle, promoting their efficient and sustainable reuse in new construction projects [43,52]. In the case of blockchain, which is a virtual distributed ledger where data can be permanently stored—usually without the need for a central database or authority [53]—it has been increasingly applied across various industrial sectors such as manufacturing [54], energy, and automotive [44], although blockchain was originally developed for other purposes.
On the other hand, a prominent topic currently being researched is the use of nanotechnology [55] as a driver of the CE within the context of I4.0, where the shift from a traditional linear economy—based on extraction, production, and disposal—toward a CE is accelerating. While nanotechnology offers various possibilities to support this transition, notable challenges remain due to a limited understanding of the (eco)toxicological risks and the occurrence of nanomaterials within waste streams [56].

3.6. Interrelation Between CE, SDG and I4.0

In 2000, leaders from 149 countries endorsed the Millennium Development Goals (MDGs), establishing a unified framework for global development that introduced common terminology and monitoring mechanisms [57]. In 2015, the MDGs were expanded into the SDGs which adopt a more comprehensive approach by addressing economic, social, and environmental dimensions [58]. The SDGs comprise 17 goals and 169 targets to guide global efforts toward sustainable development [59].
Achieving these ambitious global goals require not only policy efforts but also technological innovation [60] and industrial transformation. Sustainable performance is widely recognized as being dependent on continuous innovation, as noted by various scholars. Technological advancements and innovation can generate transformative effects at multiple levels, from individuals to entire supply chains and communities. In this context, emerging technologies associated with I4.0 are seen as key enablers for advancing progress toward the SDGs, particularly within the industrial sector [61].
This reflects a growing research interest in how CE practices and I4.0 technologies act as enablers of the SDGs. In recent years, several studies have specifically explored the links between I4.0, CE, and SDGs [19,22,24,28]. Although this trend is expected to continue, there is a need for more detailed classification across different industrial sectors. It is especially valuable to understand how, over time, CE practices and I4.0 have increasingly aligned with the SDG framework, acting as key drivers in advancing sustainability goals.
Figure 7 presents an integrative framework highlighting the role of I4.0 as a key enabler in the transition toward sustainability by facilitating the implementation of both the CE and the SDGs. In part (a), the figure illustrates the conceptual interaction among these three pillars. It suggests a bidirectional relationship, where digital technologies from I4.0 support the operationalization of circular practices and the achievement of the SDGs, while both CE and SDG frameworks provide strategic direction for aligning I4.0 applications with sustainability objectives. Part (b) reinforces this idea through a visual metaphor: I4.0 is depicted as the engine of a truck carrying the CE and SDG frameworks toward a common goal—sustainability. The vehicle’s progress is supported by international agreements and policy measures, represented as the “fuel” needed to advance. Overall, this representation emphasizes that the synergy between emerging technologies, circular models, and global development agendas is essential to accelerate the transition toward more resilient and sustainable industrial systems.
CE policies have been progressively incorporated into international frameworks through various agreements and initiatives. The first formal international agreement on CE cooperation was established between the EU and China in 2018, though its impact has been limited by political tensions and conflicting narratives that hinder deep collaboration [62]. In the EU, soft-law mechanisms such as circular agreements have emerged as innovative governance tools that engage stakeholders in setting shared circularity objectives where binding regulation is lacking [63]. To support global harmonization, the International Organization for Standardization (ISO) has launched the ISO/TC 323 (ISO—Technical Committees (TC) for Circular Economy) initiative to establish standardized metrics for CE implementation [64]. Similarly, in Eurasia, World Trade Organization accession processes and trade liberalization efforts are opening new pathways for circular transition in the region [65].

4. Discussion

Over time, in recent years, increasingly specialized research in bibliometrics has been conducted on topics such as CE [66,67,68], I4.0 [69,70], and the SDGs [71,72,73]. Conducting bibliometric analysis helps us to understand the relationships between different research topics and to explore their potential interconnections or synergies. For this reason, performing a period-based analysis is particularly useful [30,74] as it enables the segmentation of research into specific timeframes, allowing for a clearer understanding of the evolution of research hotspots and emerging trends.
In Period I, clearly differentiated research themes can be observed. However, what stands out the most is the emphasis on user-informed decision making. For example, Lin (2018) [75] highlighted, within the glass-recycling industry, the importance of aligning product design with user experience to address market uncertainty and overcome design barriers.
In Period II, there is a strong emphasis on the supply chain context. Table 2 highlights several articles that explore the interrelationship between CE and I4.0; however, none of these studies explicitly focus on the achievement of the Sustainable Development Goals (SDGs).
In Period III, it represents a much greater point of interconnection between the SDGs, I4.0, and the CE. Table 3 illustrates how CE and I4.0 strategies contribute to specific SDGs across various sectors. It is important to consider that the SDGs are grouped into three overarching dimensions, with each dimension aligning with specific sets of SDGs. These three categories are: Economy (SDG 8, SDG9, SDG10, SDG12); Society (SDG1, SDG2, SDG3, SDG4, SDG5, SDG7, SDG11 and SDG16); and Biosphere (SDG6, SDG13, SDG14, SDG15).
A clear pattern emerges around SDG 12 (Responsible Consumption and Production), which is addressed in nearly every case that involves circular strategies like eco-design, recycling, reverse logistics, and recovery among others, highlighting the centrality of this goal in circular initiatives. Notably, eco-design [19] stands out as a key driver not only for SDG 12, but also for SDG 9 (Industry, Innovation and Infrastructure), SDG 13 (Climate Action), and SDG 11 (Sustainable Cities and Communities), suggesting that upstream design decisions are fundamental to long-term sustainability in waste-intensive sectors like manufacturing and construction.
Adding to this discussion, ref. [79] demonstrated how eco-design in the ceramic industry—leveraging IoT and life cycle analysis—can create sustainable value across supply chains, directly supporting SDGs 9, 12, and 13. These technologies transform CE from a static concept into a dynamic process, scaling its benefits across environmental and economic dimensions. Together, these findings emphasize the synergy between digital transformation and circular thinking as a foundation for sustainable development, particularly through innovation in design, supply chains, and waste valorization—positioning them as core strategies to achieving the SDGs.
It is observed that there is a lack of explicit studies addressing the connection between the CE, I4.0, and certain social-oriented SDGs, specifically SDG 1 (No Poverty), SDG 3 (Good Health and Well-being), and SDG 16 (Peace, Justice, and Strong Institutions). Although the CE primarily focuses on the environmental and economic aspects [80], recent research has begun to explore its relevance to certain socially driven goals. For instance, emerging studies have linked CE to SDG 2 (Zero Hunger) [22], SDG 4 (Quality Education) [22,81], SDG 5 (Gender Equality) [81,82], SDG 7 (Affordable and Clean Energy) [22,23], and SDG 11 (Sustainable Cities and Communities) [79], indicating a gradual but growing recognition of its broader societal impact.
Table 3. Mapping the Contribution of CE and I4.0 to Sector-Specific SDG Implementation.
Table 3. Mapping the Contribution of CE and I4.0 to Sector-Specific SDG Implementation.
ReferenceCEI4.0SectorSDG1SDG2SDG3SDG4SDG5SDG6SDG7SDG8SDG9SDG10SDG11SDG12SDG13SDG14SDG15SDG16SDG17
[22]Reduce of waste, Energy RecoveryIoT, RFID, Blockchain, AIWarehouse Management X X X X XX
[28]Reverse LogisticsDEMATEL and fuzzy logicTextile industry supply chain X X X
[24]Waste-To-Energy ConversionDT and Big DataRenewable energy consumption X
[19]Eco-friendly DesignData-DrivenWaste biomass X XXX
[25]Circular Business Model-Manufacturing sector X
[23]Renewable Energies Output-oriented Data Envelopment AnalysisRenewable energy consumption X
[82]Recover- Waste Management X X
[20]Rainwater Harvesting-Water Management X
[26]CMUR and Eco-investmentAIAI and Eco-investment X
[83]Recycling, reuse and recovery-Textiles Industry X
[21]Biodegradability and valorization-Algal-based Bioplastics X XXXX
[29]Recovery -Apparel manufacturing industry X
[84]Reusing animal wasteAutomatization Packaging films X
[85]Composting, Recycling, PyrolisisIoTWaste management systems X X X
[18]Value Retention StagesBig DataDifferent sectors XX X X XX X
[79]Circular Business Model and Eco-DesignIoTCeramic Industry XX XXX X
Note. SDG1: No poverty, SDG2: Zero hunger, SDG3: Good health and well-being, SDG4: Quality education, SDG5: Gender equality, SDG6:Clean water and sanitation, SDG7: Affordable and clean energy, SDG8: Decent work and economic growth, SDG9: Sanitation and infrastructure, SDG10: Reduce inequalities, SDG11: Sustainable cities and communities, SDG12: Responsible consumption and production, SDG13: Climate action, SDG14: Life below water, SDG15: Life on land, SDG16: Peace, justice and strong institutions, SDG17: Partnerships for the goals; IoT: Internet of things; CBM: Circular Business models; SM Smart manufacturing; AI: Artificial intelligence; DEMATEL: Decision-Making Trial and Evaluation Laboratory; DT: Digital Technology; CMUR: Circular Material Use Rate.
Without the support of coherent policy frameworks, achieving long-term sustainability remains unattainable. Although the bibliometric analysis (Figure 6) does not reveal a high frequency of the keyword “policy”, some targeted studies do underscore its critical role. As highlighted in Table 4, the presence of such policy-oriented research, though limited, is expected to grow, given that policies act as the essential driving force—“the fuel”—for accelerating the transition toward sustainable development.
In line with this, recent studies—although still relatively few—have begun to emphasize the necessity of establishing strong policy frameworks to enable the implementation of CE strategies. For instance, ref. [86] explores how I4.0 technologies—such as AI, IoT, big data, and smart devices—act as critical enablers for circular economy (CE) practices among European SMEs. Using the ReSOLVE framework and firm-level data, the study finds a strong positive relationship between digital transformation and the adoption of circular strategies like energy efficiency, resource optimization, and sustainable product development. However, it also highlights that not all CE actions, such as sharing or reusing materials, are equally impacted by I4.0 tools. The findings underscore the need for policy support to overcome barriers like limited technical skills and funding, suggesting that fostering the digital–ecological transition in SMEs is essential to achieving the EU’s Green Deal goals. Reference [87], focused on Luxembourg and Sweden, highlights the role of national CE policies in specific sectors such as construction.
These countries implement strategies that promote the use of non-virgin materials and encourage eco-design aimed at disassembly, supported by digital tools such as design software and material passports. However, they face challenges including institutional fragmentation, weak cross-sector collaboration, and a lack of clear guidance from policymakers. This illustrates that even in advanced economies, the circular transition demands more cohesive governance and more effective coordination mechanisms. In contrast, reference [88] proposes a broader and more structural approach. It advocates for global harmonization of policies, regulation of cross-border waste flows, and systematic classification of anthropogenic resources through the UNFC framework. This approach acknowledges the vast disparities between countries in terms of technical and regulatory capacities and highlights how illegal flows and the recycling of toxic materials undermine global sustainability. Although ambitious, this proposal offers a comprehensive roadmap for aligning the CE with SDGs—provided there is effective international cooperation.
These frameworks are vital for adapting circular principles to diverse national and regional realities. Since each country operates under unique legal and institutional structures, understanding the specific context is key to effective policy design. It is therefore anticipated that research in this area will continue to expand, especially in conjunction with the evolution of Industry 5.0 [89], which places greater emphasis on human centered and socially inclusive innovation.
On the other hand, through the comparative analysis of the studies by [90,91,92], a growing interest in incorporating the social dimension into the circular economy (CE) framework becomes evident. These works highlight issues such as equity, social justice, governance, and quality of life, demonstrating that CE can serve as a catalyst for social transformation. However, the integration with structural frameworks such as the Sustainable Development Goals (SDGs) and Industry 4.0 (I4.0) remains incipient. Although some studies acknowledge indirect links with SDGs such as Goal 10 (Reduced Inequalities) and Goal 11 (Sustainable Cities and Communities), their treatment is limited or implicit. Similarly, the role of enabling technologies from I4.0—such as IoT, big data, or automation—is either superficially mentioned or not addressed at all. This reflects a conceptual fragmentation in the literature, where the social dimension is approached in parallel to technological and institutional aspects. Therefore, the challenge remains to develop integrated frameworks that connect human capabilities, digital advancements, and global sustainability commitments within a truly systemic vision of CE.

5. Conclusions

The analysis of keyword co-occurrence and temporal segmentation (2016–2018, 2019–2021, and 2022–2024) reveals a progressive integration of CE, I4.0, and the SDGs. The first period is characterized by emerging conceptual frameworks focused on digitalization and circular business models; the second period marks a shift toward supply chain applications and empirical validation; while the third period reflects a higher degree of integration among sustainability, digital innovation, and organizational performance.
The findings confirm that CE and I4.0 serve as strategic enablers of several SDGs, particularly SDG 12 (Responsible Consumption and Production), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action). These synergies are driven by digital technologies such as the Internet of Things (IoT), blockchain, big data, and automation, which support more resilient, circular, and sustainable production systems.
Although the role of public policy is recognized as essential to enable structural transformation, its low visibility in the keyword analysis suggests a clear gap in the current literature. Future bibliometric studies should focus on sustainability-related policies, regulatory frameworks, international cooperation mechanisms (e.g., ISO/TC 323), and national strategies that accelerate the digital–ecological transition toward SDG achievement.
The mini-review highlights that each country operates within unique institutional, technical, and regulatory environments. These national idiosyncrasies influence the feasibility and effectiveness of CE and I4.0 implementation. Therefore, adaptive and context-specific policy design is required to align technological and circular strategies with local governance structures, socioeconomic conditions, and cultural dimensions. On the other hand, while the mini-review is intentionally descriptive to align with the study’s bibliometric focus, future research should pursue deeper analytical syntheses to enhance the interpretive value of key contributions.
The study anticipates a growing interest in topics related to governance, the evaluation of CE–I4.0 policy impacts on SDG progress, and the development of digital monitoring tools. These areas present promising opportunities for future scientometric analyses and for designing performance indicators to track the integration of CE, I4.0, and sustainability frameworks in both industrial practice and policy agendas.
However, this study is not without limitations. The exclusive use of the Scopus database may have led to the exclusion of relevant studies indexed in other platforms such as Web of Science, which differ in coverage and citation metrics. Scopus tends to offer broader coverage in engineering and applied sciences but may underrepresent certain high-impact journals in policy, law, or regional studies. Future research should consider cross-database comparisons to enhance comprehensiveness and reduce database-driven biases. Finally, this study underscores the need for integrative frameworks that bridge the social dimension of the circular economy with Industry 4.0 technologies and the Sustainable Development Goals, advancing toward a truly systemic and inclusive sustainability agenda.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su17146468/s1, Table S1: Scopus data for scientometric analysis.

Author Contributions

Conceptualization, C.B.-P., M.B.-N. and P.V.-V.; methodology, M.B.-N. and P.V.-V.; software, M.F.C.-C. and P.V.-V.; investigation, M.B.-N., A.S.-M., J.S.-V. and A.A.-P.; resources, E.B.-R. and E.N.M.; writing—original draft preparation, P.V.-V. and J.B.C.-P.; writing—review and editing, M.B.-N. and T.C.Q.-O.; visualization, R.V.C.-M., T.C.Q.-O., E.N.M. and P.V.-V.; supervision, M.B.-N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Categorization of the SDGs [4]. Adapted from [5].
Figure 1. Categorization of the SDGs [4]. Adapted from [5].
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Figure 2. Flow chart of scientometric and mini-review analysis.
Figure 2. Flow chart of scientometric and mini-review analysis.
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Figure 3. Annual Scientific Production.
Figure 3. Annual Scientific Production.
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Figure 4. Keyword co-occurrence map and thematic evolution. Generated using CiteSpace 6.4.R2.
Figure 4. Keyword co-occurrence map and thematic evolution. Generated using CiteSpace 6.4.R2.
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Figure 5. Top 30 Keywords with the Strongest Citation Bursts. Generated using CiteSpace 6.4.R2.
Figure 5. Top 30 Keywords with the Strongest Citation Bursts. Generated using CiteSpace 6.4.R2.
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Figure 6. Research trends and hotspots in different periods. Generated using VOSviewer 1.6.20.
Figure 6. Research trends and hotspots in different periods. Generated using VOSviewer 1.6.20.
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Figure 7. Synergism between I4.0, CE and SDG.
Figure 7. Synergism between I4.0, CE and SDG.
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Table 1. Sources’ Local Impact.
Table 1. Sources’ Local Impact.
Sourceh-Indexg-Indexm-IndexTCNPSPIF
Journal of Cleaner Production34464.857559146201910.0
Sustainability (Switzerland)25433.57119054620193.3
Business Strategy and the Environment13242.6157624202113.3
Computers and Industrial Engineering10141.255201420186.5
Production Planning and Control10113.3334171120236.1
Technological Forecasting and Social Change10102126210202113.3
Resources, Conservation and Recycling991.28617519201910.9
Procedia CIRP81612851620180.5
International Journal of Production Economics661.26106202110
Journal of Enterprise Information Management661.2259620217.6
Information obtained from Bibliometrix package in R-Studio 2024.12.1+563 [35].
Table 2. Mapping the contribution of CE and I4.0 in the supply chain context.
Table 2. Mapping the contribution of CE and I4.0 in the supply chain context.
Sector CE Technique I4.0Main FindingsReference
Agri-foodValorizationBig Data, Cyber-Physical Systems (CPS), IoTA five-step approach was developed to guide sustainable process design using big data for environmental assessment. Among four rice straw pretreatment processes, RSP2 showed the lowest environmental impact; big data enabled robust decision support.[76]
Reverse supply chainIntegration of the ReSOLVE framework (Regenerate, Share, Optimize, Loop, Virtualize, Exchange)IoT, Cyber-Physical Systems (CPS), Additive Manufacturing (AM), cloud-based ERP, RFID, simulationThe study proposes a model that combines I4.0 and circular economy principles to enhance sustainability in reverse logistics. It uses real-time information systems to coordinate production, remanufacturing, and transportation activities. Simulation and Taguchi design methods identify optimal information-sharing and scheduling policies that reduce CO2 emissions and operational costs.[76]
Automotive supply chainCircular economy practices like 6R (Reduce, Reuse, Recycle, Refuse, Rethink, Repair), reverse logisticsSmart factories, IoT, machine learning, digitizationA framework was developed using BWM and ELECTRE to prioritize challenges and solutions for SSCM. Adoption of 6R and digital tools improves sustainability in auto supply chains.[77]
Supply chain management Recycle and repair, reducing emissionsIoT, smart logistics transportation, smart business models. Blockchain, integrated with I4.0 technologies, holds strong potential to enhance sustainability in supply chains, but requires careful design to avoid unintended negative impacts such as increased energy consumption.[78]
Table 4. Overview of Global Policy Frameworks and Technological Challenges in CE Implementation.
Table 4. Overview of Global Policy Frameworks and Technological Challenges in CE Implementation.
ReferenceCountry/RegionPolicy FrameworkProposed/ObservedTechnology FocusBarriers/Challenges
[86]European UnionEuropean Green Deal, Circular Economy Action Plans and ReSOLVE FrameworkPromote twin transition (digital + green), spread Industry 4.0 among SMEs, stimulate CE through tech adoptionBig Data, IoT, AI, 3D printing, Cloud, Blockchain, RFIDHigh transition costs, logistics complexity, lack of SME support, informational gaps
[87]Luxembourg and SwedenEU CE Roadmaps (2011, 2015, 2020); local policy interaction with firms in building sectorIncentivize material reuse, use of non-virgin inputs, design for disassembly, support via digital transparency toolsDigital tools (modeling software, material passports), transparency systemsFragmentation, weak inter-actor collaboration, insufficient guidance from policymakers
[88]Global (focus on USA, EU, Japan, China)Basel Convention, national waste regulations, UNFC classification, regional circular economy plansAdvocate for global policy harmonization, regulate transboundary waste flows, classify anthropogenic resources systematicallyRecycling technologies, urban mining, remanufacturing, waste classification, life cycle assessment (LCA), application of UNFC frameworkIllegal waste flows, lack of toxic material control, unequal technical capacities, regulatory mismatches, downgraded quality of recycled materials
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Baca-Neglia, M.; Barreto-Pio, C.; Virú-Vásquez, P.; Badillo-Rivera, E.; Césare-Coral, M.F.; Castro-Pantoja, J.B.; Sotelo-Méndez, A.; Saldivar-Villarroel, J.; Arroyo-Paz, A.; Cruz-Martinez, R.V.; et al. Industry 4.0, Circular Economy and Sustainable Development Goals: Future Research Directions Through Scientometrics and Mini-Review. Sustainability 2025, 17, 6468. https://doi.org/10.3390/su17146468

AMA Style

Baca-Neglia M, Barreto-Pio C, Virú-Vásquez P, Badillo-Rivera E, Césare-Coral MF, Castro-Pantoja JB, Sotelo-Méndez A, Saldivar-Villarroel J, Arroyo-Paz A, Cruz-Martinez RV, et al. Industry 4.0, Circular Economy and Sustainable Development Goals: Future Research Directions Through Scientometrics and Mini-Review. Sustainability. 2025; 17(14):6468. https://doi.org/10.3390/su17146468

Chicago/Turabian Style

Baca-Neglia, Maximo, Carmen Barreto-Pio, Paul Virú-Vásquez, Edwin Badillo-Rivera, Mary Flor Césare-Coral, Jhimy Brayam Castro-Pantoja, Alejandrina Sotelo-Méndez, Juan Saldivar-Villarroel, Antonio Arroyo-Paz, Raymunda Veronica Cruz-Martinez, and et al. 2025. "Industry 4.0, Circular Economy and Sustainable Development Goals: Future Research Directions Through Scientometrics and Mini-Review" Sustainability 17, no. 14: 6468. https://doi.org/10.3390/su17146468

APA Style

Baca-Neglia, M., Barreto-Pio, C., Virú-Vásquez, P., Badillo-Rivera, E., Césare-Coral, M. F., Castro-Pantoja, J. B., Sotelo-Méndez, A., Saldivar-Villarroel, J., Arroyo-Paz, A., Cruz-Martinez, R. V., Norabuena Meza, E., & Quispe-Ojeda, T. C. (2025). Industry 4.0, Circular Economy and Sustainable Development Goals: Future Research Directions Through Scientometrics and Mini-Review. Sustainability, 17(14), 6468. https://doi.org/10.3390/su17146468

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