Exploring Industry 4.0’s Role in Sustainable Supply Chains: Perspectives from a Bibliometric Review

Abstract

Background: Industry 4.0 (I4.0) technologies have transformed supply chain (SC) logistics and production. However, their environmental impact, particularly on CO₂ emissions and carbon footprints, remains underexplored. This study examines the impact of I4.0 tools on SCs sustainability, focusing on reducing carbon footprints. Methods: A bibliometric analysis was conducted in October 2023 to quantitatively evaluate the scientific literature, examining publication characteristics to assess current research and forecast future trends. The Scopus database was utilized with specific filters to identify studies on the impact of I4.0 technologies in SC domains on sustainability, focusing on CO₂ emissions and carbon footprint reduction. VOSviewer software version 1.6.15 was used to analyze selected papers, revealing key keyword clusters and relationships. Results: Five clusters were identified, offering insights for supply chain managers and highlighting links between I4.0 and CO₂ reduction in supply chains: “LCA towards zero carbon”, “Supply chain carbon footprint”, “Risk and decarbonization analysis”, “Industry 4.0 and stochastic models for sustainability”, and “Biodiversity and environmental impact”. Key findings emphasize the strong connection between LCA, carbon footprint analysis, emission control, and the role of I4.0 technologies like blockchain and IoT in reducing emissions. Conclusions: This study highlights the environmental benefits of I4.0 in SC management, supporting global decarbonization goals.

1. Introduction

The emergence of Industry 4.0 (I4.0), conceptualized in 2011 [1], has revolutionized supply chain (SC) design and management through advanced technologies [2]. The potential of I4.0 technologies has enhanced the ability to manage complex and uncertain systems [3], making them well-suited for closed-loop supply chains (CLSCs) [4]. These, defined by reverse flows of end-of-life or end-of-use products returning to manufacturers [5], add new layers of complexity to supply chain planning and management. This scenario has raised concerns among all involved parties about the feasibility and benefits of adopting sustainable practices. I4.0 technologies have been pivotal in dealing with such uncertainties and complexity of SCs’ different aspects. Regarding CLSC reverse logistics, Ferraro et al. [6] discussed Sustainable Logistics 4.0, enabled by I4.0 technologies. On the recovery processes, Butzer et al. [7] remarked that Remanufacturing 4.0 drivers can be enabled by cyber–physical Systems, radio frequency identification, the Internet of Things (IoT), augmented reality, and big data analytics. Managerial aspects have been investigated by Yang et al. [8], who emphasized smart life cycle data, smart factories, and smart services as smart remanufacturing enablers. Also, Kerin et Pham [9] discussed I4.0 smart tools enabling complex recovery operations such as (i) robotic disassembly cells using intelligent sensing and real-time adaption; (ii) big data and artificial intelligence solutions for core identification and tracking of component quality, quantity, and location; (iii) virtual reality for aggregation and visualization of information; (iv) digital twins to link and enable interaction between the real and virtual world; and (v) data-driven simulations for modelling the operations complexities.

The correlation between Industry 4.0 technologies and SC sustainability has garnered increasing attention in the recent literature, leading to significant studies that explore various analytical perspectives. For example, Karmaker et al. [10] examined the performance of sustainable SCs under the influence of I4.0 technologies. Their research emphasized the need for emerging companies to prioritize critical decision-making points when adopting sustainable practices alongside I4.0 tools to enhance supply chain effectiveness. Similarly, Machado et al. [11] explored the barriers, enablers, and reciprocal cause-and-effect relationships involved in integrating I4.0 technologies with sustainability. They proposed a framework to support decision-making in the real-life supply chains of micro, small, and medium enterprises. The study of Li et al. [12] proposed a fuzzy-based model to quantitatively assess the impact of Industry 4.0 technologies on sustainability performance, finding that manufacturing execution systems, industrial IoT, and additive manufacturing are key technologies to be monitored. However, they remarked the need for more empirical research to obtain concrete evidence of the impact of I4.0 technologies on sustainable development performance in emerging supply chains.

It is clear that Industry 4.0 tools have significant potential not only to manage and control the increasing complexity and uncertainties affecting SCs, but also to advance sustainability practices. Berg et al. [13], in their report, discuss the environmental impact of I4.0 technologies, highlighting both their advantages and drawbacks. Direct benefits for sustainability include reducing environmental impact by design, tracking emissions, extending the lifetimes of manufacturing equipment, and avoiding unnecessary activities. Indirect environmental benefits should be the information flows for consumers and recyclers, low footprint of digital goods, and the tracking of natural stocks. However, the negative effects on sustainability involve high energy demands, greenhouse gas emissions, increasing waste streams, digital obsolescence, and lock-in effects. The adoption of I4.0, therefore, implies several barriers including cybersecurity concerns, high implementation costs, limited management commitment, and insufficient training and resources. Moreover, the complexity of integrating diverse technologies and the lack of global standards for data sharing and interoperability among SC partners further hinder the widespread adoption of I4.0. This scenario underscores the need for further exploration and validation of I4.0 s impact on SC sustainability. In particular, the environmental implications of adopting these technologies—specifically regarding CO₂ emissions and carbon footprints—remain insufficiently studied, while life cycle assessment studies could be further refined through those evaluations. Such a gap calls for a critical re-evaluation of I4.0 technologies, especially since the I4.0 “technology for technology” ideal shifted towards the “technology for human” paradigm of Industry 5.0 [14]. This shift emphasizes a more human-centric and sustainability-driven approach, underscoring the importance of incorporating environmental considerations into the analysis of technological advancements. As the focus moves toward aligning technology with societal and ecological needs, there is a growing imperative to examine how these innovations can contribute to sustainable practices and reduce environmental impacts. Accordingly, this study aims at filling the gap through a comprehensive bibliometric analysis focused on how Industry 4.0 technologies impact on supply chains in terms of CO₂ emissions and carbon footprint. Even if I4.0 can improve the sustainability of processes, both internal and external [15], more research is required to evaluate their role in the domain of SC. Thus, the main research question guiding this work is: What is the role of I4.0 tools and technologies in supply chain models, CO₂ emissions reduction, and carbon footprint impact?

The rest of this paper is organized as follows: Section 2 provides the methodology for the bibliometric analysis conducted in this study. In Section 2.1, the selected keywords for the bibliometric search are introduced. Section 2.2 and Section 2.3 provide the documents search process from the Scopus database, including the search strategy, filtering criteria, and final dataset, also discussing the applied Scopus filters. Section 2.4 outlines the VOSviewer analysis, explaining the settings used to identify and analyze keyword clusters, as well as the additional bibliometric insights, including publication trends and key journals. Section 3 discusses the results. In Section 3.1, Section 3.2, Section 3.3, Section 3.4 and Section 3.5, the results from each cluster analysis are discussed, including a correlation analysis with other clusters. Section 4 concludes this paper by highlighting practical implications (Section 4.1), theoretical contributions (Section 4.2), and final remarks (Section 4.3). The key findings from the bibliometric analysis are summarized and the implications of Industry 4.0 technologies in enhancing supply chain sustainability are remarked upon.

2. Methods

2.1. Identification and Selection Significant Keywords for Scopus Search

A bibliometric analysis was conducted in October 2023 using the Scopus database, whose trustworthiness for large-scale analyses is well known [16]. PRISMA guidelines for reporting systematic reviews were followed. This study uses the protocol in [17] for a systematic review.

The first step of the analysis was a keyword selection, consistently with the research question stated above. Assuming that abbreviations and acronyms should be always spelled out in the abstract [18], in this study, the keyword searches were conducted using the extended term or expression of interest. Keywords were chosen based on the study’s domain, framework, and scope, which focus on exploring the impact of Industry 4.0 tools and technologies on sustainability within supply chain models. Specifically, we selected the general domain-related keyword, “Supply Chain”, meant as per Mentzer et al. [19]: “a set of three or more entities (organizations or individuals) directly involved in the upstream and downstream flows of products, services, finances, and/or information from a source to a customer”. In order to have the widest set of results, we decided to not limit the SC domain to circular arrangements (such as the CLSC), since both the forward and the reverse flows are of interest in assessing the role of I4.0 tools on environmental concerns. The first search—limited to the domain—provided 152,240 documents. Then, the search was expanded to the technological framework, “Industry 4.0”. The study of Culot et al. [20] presents a wide set of keywords related to I4.0 that are, in practice, its definitions and enabling tools. For the purpose of this study, we decided to keep the most general meaning of I4.0, using the main keyword. The majority of papers on I4.0 concerns implicitly refer to the supply chain domain. The updated keyword search “Supply Chain” OR “Industry 4.0” provided 205,456 results. The context focus of this study is the effect of I4.0 practices and tools on SC sustainability. The scientometric review of Abeydeera et al. [21] highlights that the most frequently co-occurring keywords in carbon emission research are “carbon footprint” and “CO₂ emission”. Consequently, the previous search results were refined using the operator “AND” with the keywords “CO₂ reduction” OR “carbon footprint”. The term “CO₂ reduction” was prioritized to maintain a focused emphasis on reducing CO₂ emissions, as the term “emissions” in this context is redundant.

2.2. Documents Search

The search in Scopus database within the section “Article Title, Abstract, and Keywords” using the selected terms, “Industry 4.0” OR “Supply Chain” AND “CO₂ reduction” OR “carbon footprint”, returned 1596 articles. Then, the results were refined by applying 6 filtering criteria as follows:

• Keyword Priority: Assuming that the documents’ keywords reflect the central theme developed, the search was restricted to keywords only, excluding titles and abstracts. This reduced the number of articles to 1017.
• Publication Year: The systematic science mapping analysis on I4.0 and SC from Nùnez-Merino et al. [2] shows that the trend of publications number becomes exponential from 2020. After all, it can be assumed that outdated and irrelevant articles do not provide significant contributions, as well as that relevant but older studies should have already been referenced in recent studies (in terms of citations and thematic depth). Therefore, our analysis included publications from 2021 to the current date of access to the database (2023). Such filtering reduced the number of articles to 344.
• Subject Area: The third filtering criterion focused on selecting only relevant subject areas, namely:
  • Environmental Science and Energy, focusing on environmental impact analysis, CO₂ emissions, and carbon footprint;
  • Engineering, to include industrial engineering aspects related to CLSC production processes;
  • Business, Management, and Accounting, considering economic evaluations of Industry 4.0 applications in the supply chain, particularly in terms of costs and profits linked to CO₂ emissions;
  • Decision Science, to involve studies on modeling complex decision-making processes in supply chain management.

This filter reduced the number of articles to 298.

4.

Document Type: Assuming that journal papers represent the highest quality of work in terms of form and content compared to other document types, only journal papers were included. This step further reduced the number of articles to 223.

5.

Language: By selecting only articles written in English, the dataset was reduced to 218.

6.

Abstract content analysis. The reading and content analysis of the abstract allowed for the selection of works consistent with the objectives of this study. After applying this last filter, the number of articles was reduced to 97.

Figure 1 summarizes the documents filtering steps following the PRISMA scheme.

2.3. Scopus Filter Analysis

The filters applied in Scopus have been analyzed to assess the current trends in years, country distribution, subject area, source title, and affiliation. This choice enables the authors to quantitatively evaluate current literature. For years, country, and subject area, a graph was reported, showing the distribution of papers. For what concerns source title and affiliation, a Pareto analysis was carried out to categorize the records in A, B, and C blocks, trying to understand if there are any signs of concentration of papers or if their production is widespread. Therefore, both the Pareto graph and a table reporting the most relevant journals and affiliation is reported with the percentage of papers per each of them, the cumulative percentage of them, and the cumulative percentage of the journal/affiliation. This structure enables comparison of the two cumulative percentages, easily evaluating the presence of concentration trends.

2.3.1. Years

Figure 2 shows that although a significant decrease in publications is observed in 2022 (28% of the selected articles), in 2023, research productivity increased (40%). Recent studies, in fact, confirm the importance of the investigated topic of sustainability (economic, social, and environmental) as a crucial element in supply chain management [22].

2.3.2. Country

Figure 3 shows that approximately 60% of the selected articles come from China (18%), the United Kingdom (15%), the USA (14%), and India (13%), in line with the findings of Zavala-Alcívar et al. [23]. It is not surprising that the world’s leading economies are also those publishing the largest quantity of articles on the subject.

2.3.3. Subject Areas

Figure 4 reports that approximately 80% of the articles fall within the topics: “Engineering” (blue, 28%), “Environmental Science” (green, 27%), and “Energy” (orange, 26%). SC requires practices related to engineering and re-engineering of processes, and sustainability is particularly valued in this context, due to significant impact in terms of carbon footprint and energy consumption and green production. The other topics represent the minority, 15% for “Business, Management and Accounting” (yellow) and 4% for “Decision Sciences” (gray), as an SC requires various management practices and strategies, but they are shaped by engineering and directly impact the environment and energy.

2.3.4. Source

Table 1. Paper distribution per source title.

Source Title	N° Art.	% on Tot	C. Art	C. Journal
Journal Of Cleaner Production	17	16%	16%	6%
Computers And Industrial Engineering	5	5%	21%	11%
Energies	5	5%	26%	17%
Journal Of Industrial Ecology	5	5%	31%	22%
Environmental Research Letters	4	4%	35%	28%
Sustainability Switzerland	4	4%	38%	33%
Energy	3	3%	41%	39%
Energy Conversion And Management X	3	3%	44%	44%
International Journal Of Production Research	3	3%	47%	50%
Renewable And Sustainable Energy Reviews	3	3%	50%	56%
Sustainable Production And Consumption	3	3%	53%	61%
Bioresources	2	2%	55%	67%
Energy Economics	2	2%	57%	72%
Energy Policy	2	2%	59%	78%
Environmental Science And Pollution Research	2	2%	61%	83%
Fuel	2	2%	63%	89%
International Journal Of Production Economics	2	2%	64%	94%
Journal with only 1 article	37	36%	100%	100%

summarizes the analysis of the journals. Notably, the most involved journal is the Journal of Cleaner Production, with 17 articles (16% of the total). The remaining journals considered individually hold an insignificant share, resulting, based on Pareto, in 1 journal in rank A, 16 in rank B (from 2 to 5), and 37 in rank C (1). It is valuable to underline how, at first, 50% of articles are spread across 56% of the journals. There is then a clear trend toward specialization of some journals on the topic of SC sustainability.

2.3.5. Affiliation

In

Table 2. Affiliation paper distribution.

Affiliation	N. Art.	% Tot	C Art.	C. Institution
Kyushu University	4	2%	2%	13%
Ministry of Education of the PRC	3	1%	3%	25%
The University of Sheffield	3	1%	5%	38%
Beijing Normal University	3	1%	6%	50%
Tsinghua University	3	1%	8%	63%
ETH Zürich	3	1%	9%	75%
Institutions with only 2 articles	56	28%	37%	88%
Institutions with only 1 article	126	63%	100%	100%

, it is observed that only Kyushu University has 4 papers (2% of the total articles), while another 5 entities have 3 (5%), 56 entities have 2 (28%), and 126 have just 1 publication (63%). This indicates a very dispersed distribution of publications among different institutions, suggesting significant diversification in the field under study and broad participation without dominance by any single affiliation.

2.4. VOSviewer Analysis

The second type of analysis carried out in this study regards keywords correlations, supplementing the Scopus filter results. VOSviewer software was selected to perform the analysis both for its technical robustness and suitability for exploratory bibliometrics [24]. VOSviewer allowed for grouping keywords in clusters and studying their occurrences and the links within the same cluster and with others. This kind of analysis required tuning specific settings consistently to the purpose of the research.

The following VOSviewer settings were applied:

• Co-occurrence Analysis: The frequency of two or more keywords appearing together in different articles was analyzed, yielding 1240 keywords. The minimum number of times a keyword must appear to be included in the software output was set to 2, resulting in 195 keywords. While this inclusive approach might appear too broad, it is aligned with the exploratory nature of this study.
• Relevant Keywords: Specific keywords were excluded due to limited relevance to the topic or redundancy. These include the following:
  • Duplicates of keywords already present with higher occurrence counts, e.g., “block-chain”, “carbon”, “carbon emissions”, “carbon footprint”, “closed-loop supply chain”, “environmental sustainability”, “life cycle analysis”, “renewable energy source”, “sustainable supply chain management”, and “zero carbon”.
  • Generic terms lacking precise meaning without context, such as “business”, “canning”, “case studies”, “fish”, “green”, “investments”, and “sales”.
  • Niche or specific terms irrelevant to the broader analysis, such as “decomposition analysis”, “lithium-ion batteries”, “structural decomposition analysis”, and “water stress”.
• Lines Parameter (Minimum Strength): This parameter represents the minimum number of links for a line connecting two keywords to be displayed. A value of 3 was chosen as optimal.
• Cluster Analysis: The parameter “Min. Cluster Size” was set to 14, meaning that each cluster needed at least 14 keywords to be considered valid. This choice was based on the following experiments:
  • Setting the parameter to 1 resulted in clusters with as few as three keywords, producing meaningless outcomes.
  • Setting it to 4 identified 8 clusters, but two clusters contained very generic terms.
  • Increasing the value to 11 resulted in 6 clusters, with the smallest containing 13 keywords.
  • Finally, setting the value to 14 identified 5 clusters. The smallest cluster from the previous attempt was merged with others, leading to the hypothesis that 5 clusters is the optimal number for analysis.

Figure 5 reports the visual output, where 5 clusters can be identified:

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Red cluster “LCA towards zero carbon”;

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Green cluster “Supply Chain carbon footprint”;

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Blue cluster “Risk and decarbonization analysis”;

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Yellow cluster “Industry 4.0 and stochastic models for sustainability”;

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Purple cluster “Biodiversity and environmental impact”.

3. Results and Discussion

This section presents and discusses the results from VOSviewer analysis. Specifically, for each identified cluster, the results are discussed, and a correlation analysis with the others is conducted. Throughout the section, numbers in round parentheses are used to indicate the number of occurrences of each keyword.

3.1. Cluster 1 (Red)—LCA Towards Zero Carbon

Cluster 1 reveals that the most important words, with the highest number of occurrences, are “life cycle” (28) and “greenhouse gases” (25), which have a connection strength of 11. This cluster clearly focuses on assessing the impact of a “production process” (3) throughout its “life cycle assessment (LCA)” (6) in terms of “gas emissions” (16), particularly greenhouse gas emissions, which significantly impact “climate change” (14). This demonstrates how CO₂ production can be effectively analyzed using life cycle assessment, enabling an understanding of the emissions generated by a manufacturing process. This is fundamental, as without a methodology to measure and evaluate current carbon emissions, it becomes impossible to assess the improvements achieved by various solutions. In this context, I4.0 technologies can be leveraged to monitor CO₂ production in real time, minimize information asymmetries across the supply chain, and reduce waste along with the associated carbon footprint. Consequently, the importance of a proper “life cycle assessment” (11) is evident, as relates to the entire “supply chain” (12) and strongly influences “decision making” (17), including decisions concerning “risk management” (2), with the goal of achieving “carbon neutrality” (4).

From the analysis of connection strengths, several observations can be made:

• Strength 5 for the connection between “gas emissions” and “life cycle”.
• Strength 7 for the connection between “climate change” and “life cycle”.
• Strength 13 for “greenhouse gases” and “gas emissions”.
• Strength 3 for “decision making” and “life cycle”.

The above considerations apply to various sectors, including “packaging” (2), “timber” (2), “glass” (2), and “fisheries” (3). Finally, this cluster also addresses issues related to “waste management” (5) and “energy utilization” (3) within a supply chain context.

Cluster 1 Correlation Analysis (Table 3)

Focusing on the internal connections, strong links emerge between “life cycle” and “greenhouse emission” (11) and “climate change” (7), as well as the obvious connection with “life cycle assessment” (11). In detail, Cluster 1 results being strongly connected with Cluster 2, where “greenhouse gases”, “life cycle”, and “climate change” are closely related to “supply chains”, “carbon footprint”, and “emission control” (24/25/10, 26/28/12, 12/14/8). In fact, mitigating the impacts of climate change and reducing greenhouse gas emissions is a primary societal goal [25]. Additionally, “decision making” and “life cycle assessment/life cycle assessment (LCA)” are widely linked to “carbon footprint” and “supply chains” (17/14, 11/10), as demonstrated by Melis et al. [26]. This highlights that within the supply chain, reducing emissions and the carbon footprint is an integral part of decision-making processes, as it significantly affects the greenhouse gas production (and overall climate impact) generated by a company. Consequently, these aspects must be considered throughout the entire product and process life cycle through an LCA study. Notably, both the GHG (greenhouse gases) method and the LCA method aim to quantify carbon emissions at operational and corporate levels in line with CSRDs (corporate sustainability reporting directives) compliance and are prerequisites for CO₂ emission reduction [27]. It is remarkable that Cluster 1 is solely connected to Cluster 2, underscoring Cluster 1’s heavy dependence on Cluster 2 (despite Cluster 1’s larger size). Cluster 1 takes a broader approach, addressing greenhouse gases and climate change, while Cluster 2 focuses specifically on CO₂. Given that CO₂ is the most extensively studied gas in the literature, this strong correlation was anticipated—particularly since Cluster 1 also encompasses LCA, specifically designed to evaluate carbon emissions. Lastly, the connection with Cluster 5 stems from the logical relationship between climate change and broader environmental issues.

Table 3. Links to Cluster 1: “LCA towards zero carbon”.

	Cluster 1	Cluster 2	Cluster 3	Cluster 4	Cluster 5
greenhouse gases	gas emissions (13), life cycle (11)	supply chains (24), carbon footprint (25), emission control (10)	-	-	environmental impact (6)
life cycle	life cycle assessment (11), greenhouse gases (11), climate change (7)	supply chains (26), carbon footprint (28), emission control (12)	carbon dioxide (6), supply chain management (6)	sustainable development (11)	environmental impact (8)
life cycle assessment/life cycle assessment (LCA)	life cycle (11/0)	carbon footprint (11/6), supply chains (10/0)	-	-	-
decision making	-	carbon footprint (17), supply chains (14)	-	sustainable development (11)	-
climate change	life cycle (7)	carbon footprint (14), supply chains (12), emission control (8)	-	-	-
supply chain	-	carbon footprint (12), supply chains (8)	-	-	-

Table 3. Links to Cluster 1: “LCA towards zero carbon”.

	Cluster 1	Cluster 2	Cluster 3	Cluster 4	Cluster 5
greenhouse gases	gas emissions (13), life cycle (11)	supply chains (24), carbon footprint (25), emission control (10)	-	-	environmental impact (6)
life cycle	life cycle assessment (11), greenhouse gases (11), climate change (7)	supply chains (26), carbon footprint (28), emission control (12)	carbon dioxide (6), supply chain management (6)	sustainable development (11)	environmental impact (8)
life cycle assessment/life cycle assessment (LCA)	life cycle (11/0)	carbon footprint (11/6), supply chains (10/0)	-	-	-
decision making	-	carbon footprint (17), supply chains (14)	-	sustainable development (11)	-
climate change	life cycle (7)	carbon footprint (14), supply chains (12), emission control (8)	-	-	-
supply chain	-	carbon footprint (12), supply chains (8)	-	-	-

3.2. Cluster 2 (Green)—Supply Chain Carbon Footprint

In this cluster, the keywords “carbon footprint” (95) and “supply chains” (73) emerge as the most prominent. These terms are closely connected, as understanding the CO₂ emissions across the entire supply chain and its “environmental footprint” (6) is becoming increasingly crucial, with the ultimate goal of achieving a “green supply chain” (5).

Specifically, the connection strengths reveal:

• Strength 73 for the link between “carbon footprint” and “supply chains”;
• Strength 5 for the link between “carbon footprint” and “green supply chain”;
• Strength 4 for the link between “supply chains” and “green supply chain”.

Additionally, this cluster highlights the main sectors with a significant carbon footprint, such as transportation—including “ships” (2) and “electric vehicles” (2)—and “agricultural products” (2). Notably, there is a connection strength of 2 between “electric vehicles” and “carbon footprint”, as well as between “agricultural products” and “carbon footprint”, as remarked by Arena et al. [28].

It is therefore important to conduct “economic analysis” (5) and “profitability” studies (5), while also considering “socio-economics” (2) aspects and “economic and social effects” (8). These analyses should relate to the use of biological resources such as “bio-energy” (2), “biofuels” (3), and “biomass” (3), as well as “renewable energies” (3), while ensuring “energy efficiency” (4).

The above considerations are also essential for demonstrating high “corporate environmental responsibility” (2), which brings benefits to the entire “commerce” (5). Notably, there is a connection strength of 2 between “green supply chain” and “corporate environmental responsibility”. To enhance corporate sustainability and achieve green SCs, reducing the carbon footprint is essential. Industry 4.0 can add significant value in this regard by enabling real-time monitoring across the entire supply chain and providing predictive insights into the environmental impact of decisions.

Cluster 2 Correlation Analysis (Table 4)

Cluster 2 is the most interconnected with other clusters, emphasizing the centrality of the investigated topic. Despite focusing in detail on only three keywords, these are the most central terms in the entire bibliometric analysis. The term “supply chains” is strongly connected with Cluster 1 both for the production of “greenhouse gases” (24) and the entire “life cycle” (28). Additionally, Cluster 2 shows a strong connection with the words within its own cluster, particularly “carbon footprint” (73) and “emission control” (32). This demonstrates the critical importance of emissions control when analyzed in the context of the supply chain, which is the focus of this research. Specifically, supply chain activities, including production and transportation, significantly contribute to emissions. However, the most alarming finding is that more than a third of supply chain managers are unaware of their chain’s emissions [29]. This finding aligns with the connections in Cluster 3, highlighting the importance of “supply chain management” (9), associated “costs” (8), and “sensitivity analysis” (8) to evaluate “decarbonization” (6) goals. This keyword is also linked to “sustainable development” (26) and the “circular economy” (6), showing how applying the latter to the entire supply chain can foster sustainable, low “environmental-impact” (15) development, as evident in connections with Cluster 5.

Table 4. Links to Cluster 2: “Supply Chain carbon footprint”.

	Cluster 1	Cluster 2	Cluster 3	Cluster 4	Cluster 5
supply chains	greenhouse gases (24), life cycle (26), gas emissions (15), decision making (14), climate change (12), life cycle assessment (10), supply chain (8), global warming (7)	carbon footprint (73), emission control (32), economic and social effects (6), environmental technology (6)	carbon dioxide (13), supply chain management (9), costs (8), sensitivity analysis (8), decarbonization (6)	sustainable development (26), circular economy (6)	environmental impact (15), sustainability (7), food supply (6)
carbon footprint	greenhouse gases (25), life cycle (28), decision making (17), gas emissions (16), climate change (14), supply chain (12), life cycle assessment (11), global warming (8), life cycle assessment (lca) (6)	emission control (38), supply chains (73), economic and social effects (8), environmental technology (6), environmental footprint (6)	supply chain management (24), carbon dioxide (17), sensitivity analysis (9), costs (8), decarbonization (7), environmental economics (6)	sustainable development (30), circular economy (7), recycling (7)	environmental impact (16), sustainability (11), input/output analysis (7), food supply (6), China (6)
emission control	life cycle (12), greenhouse gases (10), gas emissions (9), climate change (8)	carbon footprint (38), supply chains (32)	supply chain management (8), carbon dioxide (6)	sustainable development (6)	-

Table 4. Links to Cluster 2: “Supply Chain carbon footprint”.

	Cluster 1	Cluster 2	Cluster 3	Cluster 4	Cluster 5
supply chains	greenhouse gases (24), life cycle (26), gas emissions (15), decision making (14), climate change (12), life cycle assessment (10), supply chain (8), global warming (7)	carbon footprint (73), emission control (32), economic and social effects (6), environmental technology (6)	carbon dioxide (13), supply chain management (9), costs (8), sensitivity analysis (8), decarbonization (6)	sustainable development (26), circular economy (6)	environmental impact (15), sustainability (7), food supply (6)
carbon footprint	greenhouse gases (25), life cycle (28), decision making (17), gas emissions (16), climate change (14), supply chain (12), life cycle assessment (11), global warming (8), life cycle assessment (lca) (6)	emission control (38), supply chains (73), economic and social effects (8), environmental technology (6), environmental footprint (6)	supply chain management (24), carbon dioxide (17), sensitivity analysis (9), costs (8), decarbonization (7), environmental economics (6)	sustainable development (30), circular economy (7), recycling (7)	environmental impact (16), sustainability (11), input/output analysis (7), food supply (6), China (6)
emission control	life cycle (12), greenhouse gases (10), gas emissions (9), climate change (8)	carbon footprint (38), supply chains (32)	supply chain management (8), carbon dioxide (6)	sustainable development (6)	-

Regarding “emission control” it reconnects with Cluster 1 in terms of reducing “greenhouse gases” (10) and “climate change” (8) and with “supply chains” (32) in Cluster 2, underscoring how emission control along the supply chain is a crucial topic. Currently, global energy consumption and greenhouse gas emissions are reaching unprecedented levels [30].

The third keyword, “carbon footprint”, is naturally connected to Cluster 1 for the reasons mentioned earlier (i.e., reducing CO₂ emissions across the entire supply chain through LCA systems) and closely tied to other words in its own cluster. Notably, it is also linked to “economic and social effects” (8) and the need for “environmental technology” (6) to reduce the “environmental footprint” (6). This keyword also connects with other clusters. In Cluster 3, it is associated with “supply chain management” (24), “sensitivity analysis” (9), “costs” (8), and “decarbonization” (7), again highlighting the importance of the carbon footprint in supply chain management and its costs, aiming for complete decarbonization so that the overall economy respects the environment. For instance, decarbonizing transportation is a critical factor in achieving long-term goals, such as mitigating climate change [31]. These concepts are further reinforced by Cluster 4 connections, particularly with “sustainable development” (30), “circular economy” (7), and “recycling” (7). Regarding Cluster 5, connections emerge with “environmental impact” (16) and general “sustainability” (11). Finally, a significant connection with “China” is noted. The evident overlap of certain concepts in Cluster 2 arises from the shared theme of supply chains and CO₂. Specifically, the redundant topics include “carbon footprint” and “carbon dioxide” as well as “decarbonization” from Cluster 3; “supply chains” and “supply chain” from Cluster 1; and “supply chain management” from Cluster 3.

3.3. Cluster 3 (Blue)—Risk and Decarbonization Analysis

Cluster 3 highlights the keywords “supply chain management” (24) and “carbon dioxide” (17), emphasizing that the primary focus of this study must account for CO₂ emissions with the aim of achieving “decarbonization” (7). The connection strength between “supply chain management” and “carbon dioxide” is 5. Specifically, through “sensitivity analysis” (9), “scenario analysis” (3), and a broad “uncertainty analysis” (4), it is essential to conduct “socioeconomic impact” (2) and “environmental-economic” (6) analyses. These results clearly highlight the need for I4.0 capabilities. With technologies such as digital twins and big data, it becomes possible to conduct precise simulations and scenario analyses, effectively addressing uncertainty and evaluating CO₂ production with greater accuracy. These should identify “costs” (8), “risks assessment” (2), and “risk perception” (2). By achieving a more accurate evaluation of business risks—both operational and financial—companies can make more informed decisions. This improved understanding also provides greater clarity about the potential environmental impacts of the company’s activities. This approach can enhance “international trade” (5) from an environmental perspective by promoting a “global supply chain” (3) that is low-impact, leading to “economic growth” (2).

This cluster identifies the two sectors most responsible for CO₂ emissions. The first is the “fashion industry” (2), due to its extensive and globalized logistics chains and high “water consumption” (2) and “water footprint” (2). The second is the transportation sector, particularly “maritime transportation” (2), where the use of “hydrogen” (2) as fuel is increasingly being studied.

Cluster 3 Correlation Analysis (Table 5)

The absence of connections between keywords (with a strength of at least six) is evident. This suggests that, despite the homogeneity of the keywords, there are still few studies sufficiently linking supply chain management with various types of analysis, decarbonization, and associated costs.

Table 5. Links to Cluster 3: “Risk and decarbonization analysis”.

	Cluster 1	Cluster 2	Cluster 4
supply chain management	life cycle (6)	carbon footprint (24), supply chains (9), emission control (8)	sustainable development (7)
carbon dioxide	life cycle (6)	carbon footprint (17), supply chains (13), emission control (6)	-
Sensitivity analysis	-	carbon footprint (9), supply chains (8)	-
costs	-	carbon footprint (8), supply chains (8)	-
decarbonization	-	carbon footprint (7), supply chains (6)	-

Table 5. Links to Cluster 3: “Risk and decarbonization analysis”.

	Cluster 1	Cluster 2	Cluster 4
supply chain management	life cycle (6)	carbon footprint (24), supply chains (9), emission control (8)	sustainable development (7)
carbon dioxide	life cycle (6)	carbon footprint (17), supply chains (13), emission control (6)	-
Sensitivity analysis	-	carbon footprint (9), supply chains (8)	-
costs	-	carbon footprint (8), supply chains (8)	-
decarbonization	-	carbon footprint (7), supply chains (6)	-

Focusing instead on relationships with other clusters, “supply chain management” is linked to “life cycle” (6) in Cluster 1, highlighting its importance in managing a generic supply chain. Strong connections with Cluster 2 are also evident, particularly with “carbon footprint” (24), “supply chains” (9), and “emission control” (8). The carbon footprint is increasingly becoming a key parameter adopted by companies because it helps identify energy-intensive processes, enabling optimization to reduce energy consumption [32].

Lastly, other key concepts within the cluster, namely, “sensitivity analysis”, “costs”, and “decarbonization” are only associated with Cluster 2, particularly with “carbon footprint” (9/8/7) and “supply chains” (8/8/6).

3.4. Cluster 4 (Yellow)—Industry 4.0 and Stochastic Models for Sustainability

Cluster 4 clearly references the concepts of “sustainable development” (30) achieved through the use of “Industry 4.0” technologies (4), including “blockchain” (3) and the “internet of things” (2). It is therefore unsurprising that the connection strength is 3 between “sustainable development” and “blockchain”, and 2 between “sustainable development” and “internet of things”. Industry 4.0 enables accurate analyses in “stochastic models” (2) and “stochastic systems” (3), even when “uncertainty” (2) is present. These technologies also promote significant “economic and environmental performance” (2) improvements. Notably, there is a connection strength of 4 between “industry 4.0” and “carbon footprint” from Cluster 2. As a result, significant “% reductions” (3) can be achieved, leading to favorable “cost-effectiveness” (4), even when considering the “carbon tax” (2). It is evident the great impact that I4.0 can have in the controlling CO₂ emissions. For instance, IoT can perform a real-time monitoring of CO₂ emissions, enabling a better understanding of the current status and an evidence-based comparison of the benefits earned. Also, blockchain can help improving integrity, transparency, traceability, durability, and security across SCs, enabling a better knowledge on them and reducing information asymmetries. A concrete example is that blockchain can enable the traceability of products to their origins, ensuring ethical sourcing practices and verifying compliance with labor laws and fair-trade standards.

Furthermore, this cluster remarks that through the reduction in non-renewable energy sources like “fossil fuels” (4) and “natural gas” (2) and the adoption of “alternative fuels” (2), combined with “recycling” (7), supported by 4.0 technologies, it is possible to enhance “social responsibilities” (2) and “environmental sustainability” (2). Connection strengths provide the following:

• Strength of 2 between “sustainable development” and “recycling”;
• Strength of 2 between “sustainable development” and “fossil fuels”.

Cluster 4 Correlation Analysis (Table 6)

In this cluster, focused on Industry 4.0 technologies for sustainability, the only keyword that connects with other clusters is “sustainable development”. It is particularly connected to “life cycle” (11) and “decision making” (11) in Cluster 1, emphasizing the importance of sustainable development in decision-making processes and the consideration of life cycles to achieve it. Furthermore, it is strongly linked to Cluster 2, particularly with “carbon footprint” (30), “supply chains” (26), and “emission control” (6), further emphasizing the critical importance of emissions (and specifically CO₂ emissions) along the entire supply chain for sustainable development. These correlations are also evident with Cluster 3 connections with “supply chain management” (7), remarking that sustainable development must be integrated into supply chain management. The expected connection to Cluster 5 is revealed by the terms “environmental impact” (10) and “sustainability” (8).

Table 6. Links to cluster 4: “Industry 4.0 and stochastic models for sustainability”.

	Cluster 1	Cluster 2	Cluster 3	Cluster 5
sustainable development	life cycle (11), decision making (11)	carbon footprint (30), supply chains (26), emission control (6)	supply chain management (7)	environmental impact (10), sustainability (8)

Table 6. Links to cluster 4: “Industry 4.0 and stochastic models for sustainability”.

	Cluster 1	Cluster 2	Cluster 3	Cluster 5
sustainable development	life cycle (11), decision making (11)	carbon footprint (30), supply chains (26), emission control (6)	supply chain management (7)	environmental impact (10), sustainability (8)

Another noteworthy aspect of this cluster is the absence of connections with a strength of at least six between Industry 4.0 technologies and other keywords. This clearly indicates that research on the topic of 4.0 sustainability is still underdeveloped. Sustaining this, the literature review provided by Brinken et al. [33] argues that Industry 4.0 has a positive impact on the environment, although it is not yet clear how to quantify such impact.

3.5. Cluster 5 (Purple)—Biodiversity and Environmental Impact

Cluster 5 reveals that the most significant keywords, with the highest number of occurrences, are “sustainability” (11) and “environmental impact” (16), indicating that this cluster is particularly focused on environmental aspects, both in terms of sustainability and reducing the impact of emissions. Specifically, it emphasizes the concept of “biodiversity” (2). Notably, there is a connection strength of 2 between “biodiversity” and “environmental impact”. Unsurprisingly, this cluster includes several countries characterized by vast green spaces and forests, such as “Canada” (2), “China” (6), and the “United States” (2). Connection strength analysis shows a strength of 2 for the link between “United States” and “sustainability”, as well as between “Canada” and “sustainability”. It is important to note that I4.0 can provide great benefits in reducing the overall impact on the environment, not only with carbon emission decrease.

Finally, it is observed the emergence of sectors highly responsible for emissions, such as mining “extraction” (4)“coal industry” (2), as well as “food supply” (6) and “land use” (4). In fact, a connection strength of 2 is recorded between “food supply” and “environmental impact”.

Cluster 5 Correlation Analysis (Table 7)

In Cluster 5, two prevalent keywords emerge: “environmental impact” and “sustainability.” The former is connected to “life cycle” (8) and “greenhouse gases” (6) from Cluster 1, while the latter is connected to “carbon footprint” (16) and “supply chains” (15) from Cluster 2, again emphasizing the importance of environmental impact within the supply chain. Lastly, it links to “sustainable development” (10), since minimizing environmental impact is a key step toward achieving sustainable development.

Table 7. Links to cluster 5: “Biodiversity and environmental impact”.

	Cluster 1	Cluster 2	Cluster 4
environmental impact	life cycle (8), greenhouse gases (6)	carbon footprint (16), supply chains (15)	sustainable development (10)
sustainability	-	carbon footprint (11), supply chains (7)	sustainable development (8)

Table 7. Links to cluster 5: “Biodiversity and environmental impact”.

	Cluster 1	Cluster 2	Cluster 4
environmental impact	life cycle (8), greenhouse gases (6)	carbon footprint (16), supply chains (15)	sustainable development (10)
sustainability	-	carbon footprint (11), supply chains (7)	sustainable development (8)

Despite various measures and strategies implemented to limit greenhouse gas emissions, actual progress has been minimal. In this sense, the commitments made remain insufficient to achieve the goals set, such as those outlined in the Paris Agreement [34]. Connections to Cluster 2 are revealed by “carbon footprint” (11) and “supply chains” (7). Connection to Cluster 4 is found in “sustainable development”.

4. Conclusions

This study explored the intersection between Industry 4.0 technologies and supply chain sustainability through a bibliometric analysis that identified five distinct clusters of research. Such clusters illustrate the diverse and evolving approaches to integrating advanced technologies with sustainability goals across global supply chains. Relevant practical implications and theoretical contributions are highlighted in this section, supporting supply chain managers and practitioners aiming to reduce CO₂ emissions in supply chain systems. Also, research limitations and future development are discussed in final remarks.

4.1. Practical Implications

This study provides insights into sustainable-friendly managerial strategies highlighting, among other things, the critical sectors that require greater effort. The main findings can help refine existing decarbonization models through tailored approaches. For example, the coal industry and the food supply sectors were identified as critical. Therefore, greater commitment is required both in the design phase and in the dynamic monitoring of the supply, also based on the practical implications derived from the five analyzed clusters.

Cluster 1 highlights the necessity of conducting life cycle assessment (LCA) studies during the design phase of the supply chain. Managers should allocate resources and time specifically for these studies. Cluster 2 reveals the focus areas for environmental assessments, such as the magnitude of greenhouse gas emissions, critical sectors for carbon footprint, and the need to complement LCA with economic and social evaluations of environmental impacts. Additionally, this cluster emphasizes the importance of transitioning to biological and renewable energy resources, as well as that energy-saving technology opportunities [35] emerge as key objectives for sustainability. The synergy between the first two clusters underscores the need for comprehensive, life cycle-wide evaluations to effectively manage emissions and adopt sustainable practices at every stage of the supply chain. Cluster 3 shifts the focus to the need for managerial evaluations. It suggests that managers should conduct scenario comparisons, sensitivity analyses, socioeconomic studies, and risk assessment and perception evaluations. These actions aim to ensure sustainable supply chains achieve economic growth. This idea of supply chain development that is both environmentally sustainable and economically viable is further supported by Cluster 4, which emphasizes key concepts from the Industry 4.0 paradigm, such as the adoption of blockchain, IoT, and stochastic models for sustainability. Cluster 5 provides valuable strategic insights, particularly emphasizing the importance of biodiversity. It identifies specific sectors with the highest contributions to CO₂ emissions—such as the coal industry, food supply, and land use—warning stakeholders in these sectors to pay close attention to sustainability best practices. By following these recommendations, managers can align their supply chain strategies with sustainability goals while fostering economic and environmental growth.

4.2. Theoretical Contributions

The results of this study emphasize the central role of Industry 4.0 technologies in advancing sustainable practices in supply chains. However, the research also reveals key gaps and theoretical weaknesses, which themselves represent a significant contribution of this study. Therefore, the need for further research in these areas serves as an important reminder for practitioners. Technologies such as IoT, blockchain, and advanced stochastic models offer significant potential for improving the efficiency and precision of sustainability efforts, particularly in managing carbon emissions, resource usage, and waste generation. The IoT enables real-time monitoring of CO₂ emissions, providing a clearer understanding of the current status and facilitating evidence-based comparisons of the improvements achieved by implemented solutions. Blockchain technology enhances integrity, transparency, traceability, durability, and security across supply chains, promoting better insights and reducing information. However, while these technologies hold great promise, a major research effort is needed to better quantify their environmental impact and explore their scalability across different industries and regions, particularly in developing countries. Thus, more robust methodologies—e.g., scenario simulation-based approaches—need to be developed for evaluating the full environmental impact of I4.0 technologies, including their long-term effects on carbon reduction and resource conservation. This is particularly important as it aligns with current policies promoting sustainable development and decarbonization, such as the European Circular Economy Action Plan (CEAP) and the UN’s Sustainable Development Goals (SDGs). Supply chains’ role in such policies is significant and evident in several key areas. For instance, from Cluster 1 and 2, it is deduced that SCs influence product design, material sourcing, manufacturing processes, and distribution methods. The adoption of sustainable practices minimizing waste and environmental impact directly contribute to SDG nr. 12 (“Responsible consumption and production”). Another example is that SCs’ sustainable management strategies—suggested from Cluster 3—involving fair labor practices, safe working conditions, and equitable wages, would contribute to SDG 8, promoting inclusive and sustainable economic growth.

Efforts should be made to investigate how I4.0 technologies can be optimized for diverse supply chain contexts, particularly in regions where technological adoption is limited. It is also important to underline how artificial intelligence (AI), despite its great potential in forecasting and prediction, is missing among the I4.0 technologies, showing how more studies in this direction are required to exploit AI for sustainability purposes. Such challenges call for a supply chain management paradigm shift towards sustainability-driven decisions fueled by sustainability-oriented managers’ soft skills [36] and production strategies [37].

4.3. Final Remarks

The type of study conducted has naturally identified specific Industry 4.0 technologies that can significantly influence SC sustainability. However, it is also revealed that such impacts are not absolute but are instead relative to specific SCs’ sub-domains. Consequently, the effect of particular technologies varies depending on the context. This key finding, derived from this study’s broad exploration of the topic, serves as a foundation for future research focused on specific supply chain sectors.

A first limitation of this study is its exclusive focus on CO₂ emissions as a metric for environmental impact assessment. To provide a more holistic perspective on sustainability, future research should incorporate additional metrics, such as resource efficiency, waste stream mapping, and other pollutants impacting on the environment.

Analysis of the Scopus results indicates that the existing literature is predominantly centered on developed countries, where Industry 4.0 technologies have been adopted earlier and more extensively. This finding is significant, as it sheds light on the progress made in such countries. However, it also underscores the importance of conducting comparative analyses to assess the adoption and impact of these technologies in developing countries. Such studies would offer two main critical insights: first, the current state of Industry 4.0 adoption in supply chains within developing nations, and second, an evaluation of the technologies’ effects on supply chain sustainability.

Additionally, a longitudinal analysis is required to examine how the impact of Industry 4.0 tools on supply chains evolves over time. This would provide valuable insights into the origins and development of the identified gaps, enabling the formulation of strategies and solutions to address these disparities effectively.