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15 January 2026

Artificial Intelligence and Sustainability in Industry 4.0 and 5.0: Trends, Networks of Leading Countries and Evolution of the Research Focus

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Faculty of Logistics, University of Maribor, Mariborska Cesta 7, 3000 Celje, Slovenia
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Sustainability2026, 18(2), 877;https://doi.org/10.3390/su18020877
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Abstract

In the context of environmental challenges and digital transformation, artificial intelligence (AI) plays a key role in promoting sustainable development within Industry 4.0 and the emerging paradigm of Industry 5.0. This study systematically reviewed the literature (2015–2025) from Scopus and Web of Science on the connections between AI, circular economy, industrial paradigms, and the Sustainable Development Goals (SDGs), with a particular focus on supply chains and SDG 12—responsible consumption and production. The majority of research emphasizes managerial aspects, the application of machine learning and robotics, as well as waste reduction, resource optimization, and circular economy practices within supply chain and production–consumption systems. Geographical analysis shows that larger economies serve as central research hubs, while some countries that are not among the most populous often achieve the highest average citations per document. Temporal keyword trends indicate a shift in research focus from operational efficiency in traditional supply chains (optimization) toward supply chain digitalization (artificial intelligence) and sustainability (circular economy). Keyword trends reveal four thematic clusters: supply chain digitalization, agritech, smart industry, and sustainability. The study highlights future research directions, including integrating circular economy with managerial and technical approaches, linking Industry 5.0 with SDG 12, and applying advanced AI in sustainable industrial practices. The increasing attention to ethical and social dimensions underscores the need for AI solutions that are both technologically advanced and sustainability oriented.

1. Introduction

1.1. Background

The sustainability challenges of the modern world are becoming increasingly complex and interconnected, making them difficult to manage. Individual sectors, such as energy, agriculture, transportation, and industry, are not, but operate as part of dynamic, interlinked systems [1]. These systems generate enormous volumes of data, whose analysis and interpretation require advanced technologies. Artificial intelligence (AI) can play a key role in managing this complexity [2], enabling the processing of large datasets, optimizing processes, and providing effective decision support [3]. Moreover, AI acts as a powerful enabler of sustainability, offering innovative approaches to optimize resource use, reduce emissions, and support ecosystem management [4].
In industry, the implementation of AI has enabled significant improvements in areas such as quality control, predictive maintenance, production process optimization, and demand forecasting [5]. Industry 4.0 represents the shift from manufacturing driven mainly by machinery, traditional automation, and IoT technologies toward digitally enabled production supported by cloud computing and large-scale data analytics [6]. However, the strong technological focus of Industry 4.0 highlighted the need for a more human-centered approach, leading to the development of the Industry 5.0 paradigm. Industry 5.0 builds on the principles of Industry 4.0 to create a synergistic interaction between humans and smart industrial systems [7]. Its goal is to support a transition toward a more sustainable, resilient, and human-centric industrial environment [8]. The human-centric perspective emphasizes that people should continue to play a central role in guiding industrial processes [9].
Alongside the development of smart industry, the role of the circular economy is becoming increasingly important [10]. The principles go beyond traditional waste management practices [11]. The concept aims to extend the lifespan of materials and promote recycling, maximizing the utility of resources while reducing environmental impacts and overall resource consumption [12].
Both the circular economy and Industry 5.0 face challenges such as technological limitations, inadequate regulations, and fragmented supply chains [13]. Industry 5.0 further requires the complex integration of advanced technologies with human-centric principles [8]. Consequently, AI in the context of Industry 4.0 and 5.0 is emerging as a key enabler for achieving Sustainable Development Goal 12 (SDG 12), which focuses on responsible consumption and production [14]. We selected SDG 12 because it is critical for achieving a sustainable future and closely aligned with circular economy principles. SDG 12 encompasses measurable indicators such as waste reduction, re-source efficiency, and greenhouse gas mitigation—areas where AI and industrial paradigms can make significant contributions. The goal also explicitly addresses the entire supply chain—from production to end-of-life consumption—making supply chains a particularly suitable focus for assessing how AI-enabled industrial innovations contribute to sustainable consumption and production. Therefore, AI applications in other sustainability domains, such as energy systems, smart cities, or healthcare, fall outside the scope of this review. Previous research has shown strong connections between the implementation of circular economy practices and SDG 12, supporting the logical focus on this goal. At the same time, a clear gap exists in the literature: while many studies address AI, Industry 4.0 in 5.0, or circular economy individually, few analyze their combined impact on SDG 12.
Despite extensive literature, few studies comprehensively examine the combined impact of AI, circular economy principles, and industrial paradigms on SDG 12, particularly across geographical and temporal contexts. There is also a lack of studies identifying which countries are most active in AI-related research and how research focus has evolved over time. To address these gaps, this study investigates the intersection of AI, circular economy principles, and emerging industrial paradigms, focusing on their contributions to sustainable consumption and production, while tracking trends and collaborations across countries over the past decade. This analysis sets the stage for the following literature review, which examines the roles of AI, Industry 4.0 and 5.0, and the circular economy in promoting sustainability, while also highlighting unresolved challenges and research gaps.

1.2. Literature Review

In recent years, scientific literature on digital transformation, sustainability, and emerging industrial paradigms has grown significantly. Concepts such as AI, Industry 4.0, and the more recent Industry 5.0 have become key drivers of change in manufacturing, business models, and environmental and social practices [15]. Alongside digitalization, companies are increasingly adopting principles of sustainable consumption and production as well as the circular economy practices, to reduce environmental impacts and improve resource efficiency [16].
AI has rapidly evolved and accelerated development in this period, evolving from a futuristic concept into a technological reality that is deeply integrated into almost all sectors of modern life [17]. It is one of the most extensively studied technologies in the context of sustainable development and production. The main branches of AI include machine learning, neural networks, expert systems, robotics, fuzzy logic, and natural language processing (NLP) [18].
Machine learning is a branch of AI focused on developing methods and algorithms that enable computer systems to learn autonomously from data and experience [19]. A neural network is a computational model inspired by biological neurons, composed of interconnected nodes organized in multiple layers that can recognize complex patterns in input data [20]. An expert system is defined as a computer system containing a well-organized knowledge base, mimicking expert problem-solving skills within a specific domain of expertise [21]. Robotics is a key AI area, as robots perceive the environment, make decisions, and physically interact with the world. Due to their physical presence, they are increasingly integrated into everyday environments, from homes to industries, transforming how we work and live [22]. Fuzzy logic provides a framework for handling uncertainty and imprecision, making it particularly useful in NLP applications [23]. Natural language processing (NLP) is an interdisciplinary field combining AI, computer science, and linguistics enabling computer systems to understand, interpret, and generate human language [24].
AI has proven to be a promising enabler for accelerating the transition to a circular economy [25]. The circular economy aims to extend the lifespan of products and materials and prevent waste generation by promoting reuse, repair, recycling, and other forms of resource circulation [26]. Nonetheless, the circular economy concept has also faced notable criticism. In particular, circular economy narratives often lack emphasis on socially beneficial outcomes [27], whereas sustainable development is commonly framed around economic, environmental, and social aspects [28]. Consequently, it remains uncertain whether the circular economy can genuinely improve social outcomes, including greater individual well-being [29] or enhanced social equity. Even if the circular economy proves socially beneficial, it is still unclear how these positive effects can be ensured or realized.
The circular economy serves as a linking element between Industry 4.0 and Industry 5.0. With the rise of Industry 4.0, cleaner production techniques have also been developed, allowing industries to support sustainable development by reducing natural resource consumption [6]. Despite the numerous advantages offered by Industry 4.0, some sustainability challenges persist, as this paradigm still demands significant use of resources, raw materials, data, and energy, potentially harming the environment [30].
While Industry 4.0 primarily focuses on technological digitalization, Industry 5.0 emphasizes the role of humans and is based on three key pillars: resilience, sustainability, and human-centricity [31]. Although Industry 5.0 holds significant potential to enhance circular economy practices and promote sustainable consumption and production (SDG 12), it remains a relatively new field with limited empirical data. Consequently, this study primarily focuses on Industry 4.0 practices, as the current data still predominantly reflect this paradigm. Further theoretical and empirical research is needed to comprehensively assess the contributions of Industry 5.0 across different industrial sectors. Increasing awareness of environmental risks and growing public pressure have fueled the rise in sustainability-oriented industrial approaches. In this context, Industry 5.0 is emerging, emphasizing the integration of humans into production processes. It relies on close human–machine collaboration to achieve higher quality, greater efficiency, and more sustainable outcomes. The synergy between human and AI is crucial, enabling more flexible, humane, and environmentally friendly solutions [32]. Industry 5.0 thus represents a significant advancement, supporting the development of systems that utilize renewable energy sources, reduce waste, and expand the role of AI across various economic sectors [33].
The application of AI, the circular economy, and the development of Industry 4.0 and 5.0 paradigms together form the foundation for achieving a common goal: sustainable consumption and production. In 2015, the United Nations adopted 17 SDGs, providing an operational framework to guide sustainable development by 2030 [34]. SDG 12 is particularly important, as it aims to increase resource efficiency, reduce waste generation, and promote sustainable practices across entire supply chains [35]. Achieving this requires a systemic approach and collaboration among all actors in the supply chain [36].
SDG 12 encompasses multiple sub-targets that, in addition to technical indicators such as emissions, energy efficiency, and resource use, also include broader behavioral, organizational, and policy dimensions. These include, for example, responsible corporate practices (12.6), sustainable public procurement (12.7), and consumer awareness (12.8) [34]. In this context, AI has significant potential, as it can support both technical indicators and the broader dimensions of SDG 12.
AI algorithms for optimizing energy, material, and water consumption can contribute to sustainable resource use, while machine learning for demand forecasting and inventory management can help reduce food loss. AI systems for automated monitoring, analysis, and visualization of sustainability data enable companies to improve sustainability reporting. At the same time, these systems indicate potential support for promoting responsible corporate practices and raising consumer awareness. Such an approach allows AI to contribute comprehensively to both the technical and broader dimensions of SDG 12.
In recent years, research has clearly shown that AI, as part of the technological capabilities of Industry 4.0 and Industry 5.0 paradigms, strongly supports the circular economy, particularly by optimizing resource use, extending product lifecycles, and reducing waste [37]. As AI is increasingly applied across various sectors, new opportunities for sustainable development emerge, alongside several challenges [3]. Among the positive impacts are the reduction in greenhouse gas emissions, support for sustainable practices [38], and the facilitation of smart energy grids, which enhance system stability and the integration of renewable energy sources [39].
Despite its many advantages, AI also presents significant environmental and social challenges. Environmental impacts include high energy consumption during operation, water use for cooling, depletion of rare resources, and the generation of electronic waste at the end of the lifecycle [40]. Social challenges also arise, such as data privacy concerns, algorithmic bias, and ethical issues [41]. Therefore, a regulatory framework is needed to ensure the safe, transparent, and ethical use of AI, while promoting the transition to renewable resources to reduce environmental impacts [42] and support a more inclusive and sustainable future [43].

2. Materials and Methods

In this study, we conducted a comprehensive systematic review of the available literature on AI, responsible consumption and production, and the role of Industry 4.0, Industry 5.0, and the circular economy.
The search approach was intentionally limited to the application of AI within supply chains. This focused scope allows for a detailed analysis of implementation practices, impacts on process optimization, and sustainability benefits within supply chain management, while ensuring clarity and relevance of the findings. This approach allows for a manageable and high-quality set of studies, including research that is directly comparable and relevant to the supply chain context. It is acknowledged that there is a broader literature on sustainable and human-centric approaches in industry that is not directly related to supply chains. Nevertheless, the review addresses sustainability impacts and contributions to responsible production and consumption in line with SDG 12.
For the identification of scientific publications, we used the Scopus and Web of Science databases, as they are among the most frequently used and differ in coverage [44]. The search was conducted in March 2025, and only publications from the period 2015–2025, published in English, were included in the analysis. The search strategy was based on the following query: supply AND chain AND artificial AND intelligence AND sustainable AND production OR consumption.
The literature search was conducted across article titles, abstracts, and author keywords. The initial search yielded 332 records from Scopus and 218 records from Web of Science. After removing irrelevant records and restricting the search to English-language publications, 175 records remained from Scopus and 157 records from Web of Science. Duplicate records in both databases were identified using DOI matching and title comparison. After removing duplicates, a total of 235 unique articles remained and were retained for bibliometric analysis. Of these, 78 articles were indexed exclusively in Scopus, 60 exclusively in Web of Science, and 97 articles were indexed in both databases. The literature identification and selection process is presented in the PRISMA flow diagram (Figure 1).
Figure 1. PRISMA flow diagram of the bibliometric data collection process.
The database of reviewed literature, comprising 235 references [45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269], was analyzed in three phases.
During the first phase, a bibliometric analysis was conducted, covering basic parameters of the collected studies, such as the year of publication. In the second phase, a thematic classification was performed, in which articles were categorized based on selected variables of research focus (Managerial and Technical focus); industrial concept (Industry 4.0 and Industry 5.0); sector (sub-variables Primary, Secondary, Tertiary and Quaternary), type of AI (machine learning, neural networks, expert systems, robotics, natural language processing, and fuzzy logic) and sustainability aspect (variables: energy usage and efficiency, waste reduction, greenhouse gas emissions, and carbon footprint reduction) and their sub-variables. Classification was based on the following thematic categories: research focus (managerial or technical perspective), industrial concepts (Industry 4.0 and 5.0), inclusion of the circular economy, sector of application, role of AI, and the presence of sustainability aspects, particularly in relation to sustainable production and consumption.
In the third phase, scientific mapping was performed using VOSviewer version 1.6.20 [270]. This phase included: (1) an analysis of the geographical distribution of research to identify countries with the highest contributions to the field as well as those with lower impact as well as to identify if there are some smaller countries that focuses on AI research intensively as expected regarding the size of country’s economy; (2) an assessment of the average number of citations per document according to author affiliations; (3) a co-occurrence analysis of keywords to identify central research themes; and (4) an analysis of the average citations of documents in which specific keywords appear. Additionally, the temporal dynamics of keywords were analyzed to determine which topics were prominent in the past and which have gained importance in recent years.
Based on the analyzed literature and identification of evolving research focus over the last decade, key areas with significant potential for further research were identified. Among these, the circular economy, Industry 5.0, sustainable development, and AI stand out, as their interconnections provide numerous opportunities for interdisciplinary research.

3. Results

3.1. Relations to Journals of Publication

The study considered 235 published articles from international journals covering the period between 2015 and 2025. For 2025, only studies published up to early March were included.
No studies on the topic were published in 2015. By 2021, only 17 publications had appeared. From 2021 onwards, a marked increase in research output can be observed, with 31 publications in that year alone, exceeding the total number of publications from 2015 to 2020 combined. The peak was observed in 2024, as data for 2025 are incomplete (up to March 2025).
Among the total of 235 publications, 13 of which were conference proceedings, the most productive sources of scientific articles were the following journals: Sustainability (25 articles), Journal of Cleaner Production (10 articles), IEEE Access (5 articles), and International Journal of Production Research (5 articles). These results indicate that journals focusing on sustainability, cleaner production, and the management of production systems have the greatest influence in the studied research field, confirming the importance of these topics in the literature. At the same time, the presence of IEEE Access highlights researchers’ interest in the technical and technological aspects of the investigated issues. This suggests that the research is not limited to theoretical discussions but also emphasizes practical, application-oriented solutions.

3.2. Categorization of Published Studies by Variables and Sub-Variables

Subsequently, the published studies were classified into thematic categories, including research focus (managerial or technical perspective), industrial concepts (Industry 4.0 and 5.0), the circular economy, application sectors, AI, and sustainable production and consumption.
The published studies were analyzed across several domains, namely:
  • Sector, where economic activities are classified into: primary (agriculture, forestry, fisheries), secondary (industry, mining, construction, energy), tertiary (trade, tourism, transportation), and quaternary (healthcare, education, culture) [271];
  • Types of AI, divided into: (1) machine learning, (2) neural networks, (3) expert systems, (4) robotics, (5) natural language processing, and (6) fuzzy logic [18];
  • Sustainability aspects, focusing on SDG 12, which addresses responsible consumption and production. On this basis, we included the variables energy usage and efficiency, waste reduction, greenhouse gas emissions, and carbon footprint reduction [272]. In addition, in accordance with the document of the European Banking Authority, we also included the variables air pollutants and water usage and recycling [273].
Table 1 presents the distribution of articles by variable and sub-variable. The findings clearly show that research primarily focuses on managerial aspects (71.06%), while technical approaches are less represented (28.94%). Within the industrial domain, studies related to Industry 4.0 dominate (46.38%), whereas contributions addressing Industry 5.0 are considerably fewer (9.79%). The circular economy appears in approximately one-quarter of all articles (38.30%), reflecting its growing yet still complementary role.
Table 1. Overview of Published Studies by Research Variable.
The analysis further reveals that research is most frequently focused on the secondary sector (44.68%), confirming the central role of manufacturing and industrial activities as a central area of study. In terms of AI applications, studies on machine learning (69.36%) and robotics (53.19%) dominate, whereas expert systems and natural language processing are considerably less represented. A similar pattern is observed for sustainability aspects: most articles address waste reduction and energy efficiency, while topics such as water usage, air pollution, and carbon footprint are less explored.

3.3. Bibliometric Analysis of Reviewed Literature Network Connectivity

Using VOSviewer, we conducted the following bibliometric analyses: the geographical distribution of research and the co-occurrence analysis of all keywords. These analyses were selected to gain a better understanding of the main actors in the research field and to identify the most prominent topics in the existing scientific literature.

3.3.1. Geographical Distribution Analysis of Countries

This analysis provides insights into the structure of research collaboration by revealing connections among individual authors and institutions at the international level. It allows for the identification of key actors and the recognition of countries with the highest scientific contribution, thereby contributing to an understanding of the geographical distribution of research activity and the global collaboration network.
Our analysis included only countries that had published a minimum of five scientific papers. This threshold was set to ensure greater clarity in the visualization and to highlight countries with a more significant contribution to the development of the field. Four research clusters were formed. Among the 69 identified countries, 20 met this criterion. The network visualization (Figure 2), based on co-authorship of publications, illustrates four clearly distinguishable clusters formed according to the intensity of collaboration. Node size represents the number of publications, while link thickness indicates the strength of collaboration, expressed as total link strength.
Figure 2. Geographical Distribution of Source Countries.
The red cluster includes India, the United Kingdom, Iran, Vietnam, Malaysia, South Korea, and Taiwan. India represents the largest node in the network and stands out as part of the most active group of countries due to its extensive research system, large number of scientists, and strategic partnerships, particularly with the United Kingdom. This indicates both strategic collaboration and high research productivity. The other countries within the cluster are interconnected, with the strongest links to India, placing it at the central position in the network.
The green cluster comprises France, Italy, Turkey, the Russian Federation, and Spain. France and Italy are the central countries in this cluster due to long-standing collaborative practices with European and Eurasian partners. The connections are strong but somewhat less extensive than in the red cluster, indicating more targeted collaborations.
The blue cluster includes China, Pakistan, Saudi Arabia, and South Africa. China is the leading country in this group, connecting both regional partners and countries from other clusters, particularly India and Italy. Its links with India and Italy also reflect its influence beyond regional boundaries. The strength of connections is relatively evenly distributed, suggesting a balanced collaboration network.
The yellow cluster consists of the United States, Canada, and Australia. This is a small but tightly connected group, linked by cultural and linguistic affinity as well as shared research values. Strong connections with European partners (e.g., France) and Asian partners (United Kingdom) confirm their global integration.

3.3.2. Co-Occurrence Analysis of All Keywords

For this analysis, only keywords that occurred at least five times were included. This threshold allows for the exclusion of rare or non-representative terms and contributes to the formation of clearer thematic clusters. The keyword analysis thus provides insights into the main research topics, their interconnections, and potential development trends.
Using VOSviewer, we identified 1735 keywords that appeared at least five times. The most frequent keywords were artificial intelligence (97 occurrences), sustainable development (50), sustainability (44), supply chain management (41), and supply chain (40). The keyword network analysis included 25 terms that met this criterion. In the network, each node corresponds to a keyword, and its size indicates the frequency of occurrence. The links between nodes indicate the co-occurrence of keywords within individual publications.
The analysis revealed that research in the examined field clusters into four thematic groups, identified by VOSviewer based on co-occurrence patterns (Figure 3). Colors represent the individual thematic clusters, node size indicates the frequency of keyword occurrences, and the width of the links represents the intensity of their co-occurrence.
Figure 3. Co-occurrence of all keywords.
The red cluster, encompassing terms related to supply chain digitalization, such as artificial intelligence, supply chains, and sustainable development, represents research focused on the application of AI in supply chain management, process optimization, and the integration of sustainability principles in decision-making. This is the largest cluster, reflecting the practical use of AI in business and managerial contexts. Its connections with other clusters indicate that AI serves as a key integrative element across the research field.
The green cluster, covering agritech-related terms such as machine learning, agriculture, and food supply, brings together studies linking machine learning with applications in agriculture, food supply, and product life cycle analysis. Here, sustainability content intersects with advanced data-driven approaches to enhance efficiency and reduce environmental impacts.
The blue cluster, associated with smart industry terms such as Industry 4.0, blockchain, and Internet of Things, includes research focusing on the digital technologies of the fourth industrial revolution. This group represents studies on digital infrastructure, which forms the foundation for integrating AI into sustainable practices.
The yellow cluster, encompassing sustainability terms like circular economy, waste management, and environmental impact, focuses on sustainable development, circular economy, and waste management, where digital technologies and AI play a pivotal role in promoting resource reuse and minimizing waste. Conceptually, this cluster is closest to the green cluster, as both address environmental dimensions.
The next analysis focused on the mean citation count of scientific publications in which each keyword appears. Keywords such as decision support systems, Industry 4.0, big data, Internet of Things, and life cycle appear in publications cited above average (70–80 citations), highlighting their central role in current research discourse, particularly in the context of digital transformation and decision support in sustainability-oriented environments.
In contrast, keywords such as supply chain, waste management, climate change, manufacturing, machine learning, and circular economy are less frequently cited (20–40 citations), which may reflect their more specialized focus or narrower reach within the interdisciplinary research landscape.
Keywords such as artificial intelligence, sustainable development, optimization, environmental impact, and decision making occupy an intermediate position (40–60 citations), indicating a consistent presence in the scientific literature. This distribution clearly demonstrates that research topics at the intersection of artificial intelligence, digital technologies, and sustainable supply chain management generate the highest scientific impact.
Finally, we examined which keywords were prominent in the past and which have gained importance in recent years. For this chronological analysis, the keywords were divided into three periods: 2015–2021, 2022–2024, and from 2025 onwards. The results reveal a gradual shift in research focus from technically oriented concepts toward increasing integration of sustainability and interdisciplinary themes (Table 2).
Table 2. Chronological overview of keywords for tracking research focus.
In the earlier period, the most prominent keywords were supply chain, supply chain management, manufacturing, optimization, life cycle, and decision support systems. These studies primarily focused on operational efficiency, reliability, and decision support within traditional supply chains. In recent years, however, emerging research topics related to digitalization and sustainability have gained prominence. This is reflected in the increasing occurrence of keywords such as artificial intelligence, machine learning, Internet of Things, and blockchain, confirming trends in digitalization and automation. Equally important are terms such as sustainability, sustainable development, circular economy, climate change, and food supply, indicating a shift toward comprehensive research that links technological advancement with long-term sustainability goals. This trend is further reinforced by studies published in 2025, which emphasize the integration of digital technologies with sustainable and interdisciplinary approaches.

4. Discussion

The analysis of the classification of published studies reveals that the majority of research focuses on managerial aspects (71.06%), highlighting the critical role of strategic leadership and organizational adaptability in implementing advanced industrial technologies [274]. Most studies address Industry 4.0 (46.38%), which is expected given its earlier stage of development, whereas Industry 5.0 contributions remain fewer, reflecting its emerging focus on social sustainability aspects [31]. Future research is expected to increasingly explore Industry 5.0, with greater attention to social and sustainability dimensions.
Among the sectors, secondary activities were the most represented (44.68%), confirming the key role of the manufacturing sector in economic development and technological innovation [275]. This is expected, as manufacturing often leads technological adoption and represents a substantial sector of the economy.
In the field of AI, machine learning (69.36%) and robotics (53.19%) are dominant, reflecting their widespread application to enhance efficiency and competitiveness in industrial processes [276]. From a sustainability perspective, the greatest emphasis is on waste reduction (50.21%), underscoring the importance of resource optimization and material reuse as central components of sustainable operations. Within this context, the circular economy aims to minimize waste and pollution while conserving resources across the entire production and consumption lifecycle [277]. Future research is expected to focus on developing new AI approaches that more effectively support sustainable practices, particularly in resource optimization and environmental impact reduction.
Through bibliometric analysis, we examined the geographical distribution of research, the average citations per document, keyword co-occurrence, and the temporal evolution of these keywords. The geographical analysis revealed that India, the United Kingdom, China, France, and the United States play a central role in the research network. These countries serve as key hubs connecting different research groups, facilitating knowledge flow and enhancing the global impact of scientific studies.
The analysis of average citations per document by country shows that scientific influence is not necessarily dependent on a country’s size or number of publications. Some countries that are not among the most populous, such as Vietnam, Taiwan, Malaysia, and Canada, achieve higher average citations per document. In contrast, larger countries, such as India and China, despite extensive research systems and a high number of publications, attain a lower average impact, confirming their relatively lower citation per document. These findings indicate that simply tracking publication counts is insufficient to assess scientific performance since numerous scientific publications might be of lower value for the international academic sphere and consequently not as cited as others. Therefore quality- and impact-related indicators must also be considered.
The co-occurrence analysis of keywords reveals a strong interdisciplinary nature, with significant connections among supply chain digitalization (artificial intelligence, supply chains, sustainable development), agritech (machine learning, agriculture, food supply), smart industry (Industry 4.0, blockchain, and Internet of Things), and sustainability (circular economy, waste management, environmental impact). The central node, artificial intelligence, serves as a hub connecting multiple thematic streams, underscoring its key role in contemporary research that integrates technology and sustainability. The analysis indicates that the research field already forms a rich interdisciplinary network. However, future efforts are needed to enhance synergy between clusters, particularly at the intersection of technological advancements and sustainability practices.
An examination of the average number of citations per keyword shows that terms such as decision support systems, Industry 4.0, Big data, Internet of Things, and Life cycle are highly cited and play a central role in research on digital transformation and sustainability. Keywords such as supply chain, waste management, climate change, manufacturing, machine learning, and circular economy are less cited, reflecting their more specialized focus and maybe lack of focus on interconnecting these terms in the past. Terms like artificial intelligence, sustainable development, optimization, environmental impact, and decision-making show stable presence, highlighting the importance of interdisciplinary research at the intersection of AI, digital technologies, and sustainable supply chain management.
The temporal dynamics of keywords reveal a clear shift in research focus from operational efficiency and decision support in traditional supply chains (e.g., supply chain, optimization, decision support systems) toward supply chain digitalization (e.g., artificial intelligence, machine learning, Industry 4.0, blockchain) and sustainability (e.g., sustainability, circular economy, climate change). This indicates a transition toward a more integrated research approach that links technological advancement with long-term sustainability objectives.
Table 3 below identifies areas with high potential for future research, such as circular economy, Industry 5.0, sustainable development, and AI. The interconnections among these topics offer opportunities for interdisciplinary studies, including:
Table 3. Future Research Directions.
  • Examining the circular economy in the context of both managerial and technical research focus provides insights into how strategic and operational practices support the effective implementation of circular models to achieve sustainability goals. The managerial perspective defines sustainability objectives, policies, and decision-making frameworks, while the technical perspective, particularly through the application of AI, enables their operational realization by optimizing resources, reducing waste, and enhancing circular processes in supply chain management. Integrating both perspectives with AI is essential for the effective implementation of circular economy practices and for their measurable contribution to SDG 12.
  • Research connecting the development of Industry 5.0 with sustainable development, particularly SDG 12, is essential, as Industry 5.0 emphasizes human-centered approaches and environmental care, which can significantly contribute to more responsible consumption and production. This includes the development of measurable KPIs to monitor the impact of Industry 5.0 on sustainability practices. Integrating Industry 5.0 practices with circular economy principles can enhance sustainable outcomes and provide practical guidance for both research and industry.
  • Integrating AI with sustainable development, particularly SDG 12, enables practical applications across industrial sectors. Machine learning and neural networks can optimize energy consumption in manufacturing, allowing real-time adjustments that reduce greenhouse gas emissions. Predictive maintenance using expert systems helps prevent equipment failures and minimize defective products, contributing to waste reduction and efficient resource use. Robotics combined with computer vision can automate waste sorting, enhancing circularity and resource recovery, while natural language processing can analyze supplier reports to monitor sustainability compliance. Together, these AI applications link operational efficiency with sustainability objectives and provide a foundation for methodological frameworks to systematically assess contributions to circular economy practices and SDG 12.
  • Examining the connections between AI and Industry 4.0 and 5.0 supports the development of solutions that enhance operational efficiency and drive sustainable industrial transformation. For example, in Industry 4.0, machine learning and neural networks can optimize energy consumption in automated production lines, while in Industry 5.0, AI combined with human-centric robotics can assist workers in adaptive assembly processes, improving efficiency and reducing waste. Future research will focus on developing methodological frameworks that link AI techniques with both industrial paradigms, enabling the assessment of their contribution to circularity and SDG 12 through measurable sustainability indicators.
The findings indicate that sustainability will continue to represent a central and integrative research theme. Future studies are expected to place increasing emphasis on responsible consumption and production. This is particularly important in relation to greenhouse gas emissions reduction and the optimization of water use. Future research is also expected to focus on the deeper integration of emerging industrial paradigms, such as Industry 5.0. Emphasis will likely be placed on sustainable practices in the tertiary and quaternary sectors, alongside the development of advanced AI technologies. While the bibliometric and thematic analysis highlights the positive contributions of AI to sustainable supply chains, several critical sustainability considerations must be acknowledged. Environmental rebound effects may offset some of the resource savings achieved through AI-driven optimization. For example, AI-based production scheduling can reduce material waste, but by increasing overall throughput, total energy consumption may rise, partially negating environmental benefits. Similarly, AI-enabled logistics optimization can reduce fuel use per delivery, yet higher delivery frequency may increase overall emissions.
The energy and water consumption associated with AI operations, as well as the dependence on rare earth materials, further contribute to environmental impacts and potential supply chain vulnerabilities. Additionally, AI applications may create systemic trade-offs, such as increased irrigation demand in smart agriculture or overproduction upstream in retail, illustrating the complex balance between efficiency gains and broader sustainability outcomes.
Addressing these challenges in future research is essential to ensure that AI contributes positively to environmental, social, and economic sustainability goals. Integrating quantitative assessments of energy use, water consumption, rebound effects, and material footprints with AI-driven efficiency measures will enable a more holistic evaluation of AI’s sustainability contributions.
Simultaneously, the research focus is expected to increasingly address the ethical and social implications of AI, particularly its impact on the labor market [278]. As AI becomes more integrated into critical systems and everyday decision-making, the emphasis of research is anticipated to gradually shift from technology development toward the design of trustworthy and sustainable solutions. This transition will be crucial for shaping the research agenda beyond 2025 [279].

5. Conclusions

This study highlights the growing role of AI in sustainability research within the contexts of Industry 4.0 and Industry 5.0. The bibliometric analysis demonstrates an increasing emphasis on human-centric approaches, ethical considerations, and sustainability-oriented practices, while Industry 4.0 continues to dominate the literature. Thematic clusters include supply chain digitalization, agritech applications, smart industry technologies, and broader sustainability topics, with AI serving as a connecting element. Complementary empirical and theoretical findings emphasize the relevance of circular economy principles and the integration of sustainability into industrial processes.
The results indicate that studies frequently integrate managerial and technical perspectives, with machine learning and robotics being the most frequently explored AI applications. Regarding sustainability aspects, waste reduction and energy efficiency are the primary focus, whereas water usage, air pollutants, and carbon footprint remain less explored.
Based on these findings, future research should focus on deeper integration of circular economy principles with technical and managerial approaches, development of Industry 5.0 applications that balance social responsibility with technological innovation, and systematic consideration of ethical and social dimensions in AI deployment.
Despite the extensive findings, this study has certain limitations that also provide opportunities for future research. The analysis focuses on studies related to supply chains and SDG 12, while the application of AI in other sustainability domains, such as energy systems, smart cities, or healthcare, is not covered. Furthermore, it is based on a bibliometric analysis of the literature, which does not allow for a direct assessment of real-world industrial outcomes and cannot replace empirical studies. These limitations clearly define areas where further research is needed and highlight the importance of investigating practical, empirically supported AI applications for sustainable industrial transformation.
Overall, the study confirms the potential of AI and digital technologies to support sustainable industrial transformation and emphasizes the importance of integrating technological, managerial, and ethical approaches to achieve responsible and sustainable implementation in industry.

Author Contributions

Conceptualization M.L. and M.O.; Formal analysis M.L.; Funding acquisition M.L. and M.O.; Methodology M.L. and M.O.; Visualization M.L.; Writing—original draft M.L.; Writing—review and editing M.O. 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.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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