Pathways to sustainability in Industry 4.0: insights from a systematic literature review

Abstract

Paper aims  In response to the growing digitalization of industry and the demand for sustainable production, this study conducts a Systematic Literature Review (SLR) to examine how Industry 4.0 contributes to sustainability across the economic, environmental, and social dimensions. The paper aims to identify trends, barriers, and impacts related to the adoption of emerging Technologies such as IoT, AI, and Big Data, with particular attention to small and medium-sized enterprises (SMEs).

Originality  This study introduces the IRIS Framework (Industry 4.0 for Integrated Sustainability), an integrated model designed to assess and guide sustainable Industry 4.0 practices across five dimensions: environmental, economic, social, governance, and technological. The framework operates through a four-stage cycle: Diagnose, Design, Execute, and Evaluation (DDEE), ensuring a responsible and sustainable digital transition.

Research method  The inclusion criteria comprised the study, which reviewed 1,123 articles from the Scopus, Web of Science, and Emerald Insight databases. A temporal filter was applied, restricting the analysis to articles published between 2015 and 2024, to capture the most recent developments related to Industry 4.0 and sustainability. After applying the inclusion and exclusion criteria, 56 studies were selected for in-depth analysis, providing a comprehensive, evidence-based synthesis of the literature.

Main findings  The results show that Industry 4.0 can enhance industrial sustainability when supported by inclusive strategies, effective governance, and continuous performance monitoring. However, the findings also reveal a digital paradox: technological advancements simultaneously generate sustainability benefits and new environmental, economic, and social challenges. The IRIS Framework offers practical guidance for companies and policymakers, aligning technological innovation with sustainability goals.

Implications for theory and practice  The study advances theoretical understanding of the intersection between Industry 4.0 and sustainability and provides a practical tool to support sustainable management across diverse industrial contexts.

Keywords:
Industry 4.0; Sustainability; Circular economy; Social technologies; PRISMA

1. Introduction

Industry 4.0 represents one of the most significant paradigmatic transformations in contemporary production systems, promoting deep digitalization, automation, and the integration of cyber-physical technologies into industrial processes. Since its emergence, the concept has been widely explored in academic literature, particularly regarding its implications for operational efficiency and business competitiveness (Ghadge et al., 2022; Muniz Junior et al., 2023). However, as environmental degradation, climate change, and resource scarcity intensify, sustainability has become a central dimension in evaluating industrial transformation. In this context, Industry 4.0 is no longer understood solely as a technological transition, but increasingly as a structural driver of sustainable industrial development aligned with circular economy principles and responsible resource management (Lu et al., 2024; Bai et al., 2020; Quiroz-Flores et al., 2024).

The intersection between Industry 4.0 and sustainability has therefore become a recurring theme in recent research, emphasizing the need to evaluate its effects on the economic, environmental, and social pillars of sustainable development (Sharma et al., 2023a; Braccini & Margherita, 2018; Brozzi et al., 2020). Stricter environmental regulations, stakeholder pressures, and market demands for responsible production systems reinforce the growing relevance of this topic. Previous studies demonstrate that technologies such as the Internet of Things, Artificial Intelligence, Big Data analytics, and additive manufacturing can reduce waste, increase energy efficiency, improve traceability, and enhance supply chain transparency (Liu et al., 2023; Castro et al., 2023; Kersten et al., 2024). At the same time, important implementation challenges persist, particularly for small and medium-sized enterprises, which face financial, structural, and capability-related barriers to adopting advanced digital technologies (Santos & Sant’Anna, 2024; Tascón et al., 2022). Although the literature highlights substantial benefits in operational performance and emissions reduction, important gaps remain regarding the conditions under which these technologies deliver sustainable outcomes and how organizations can systematically overcome adoption barriers (Fatimah et al., 2020; Karmaker et al., 2023; El Baz et al., 2022).

Despite the increasing number of studies addressing this intersection, much of the existing research remains fragmented and predominantly descriptive, focusing on mapping trends, sectors, or technological applications. There is still limited theoretical integration that can explain the relational mechanisms by which Industry 4.0 technologies influence sustainability outcomes across the three pillars. In particular, the literature lacks a structured analysis of internal organizational factors and external environmental conditions that shape digital transformation processes, as well as an integrated framework that connects strategic positioning, sustainability performance dimensions, and systemic industrial dynamics (Balasubramanian et al., 2021; Saleh & AlShafeey, 2025; Helo & Thai, 2024). Furthermore, while several studies identify positive correlations between digital capabilities and sustainable performance, fewer investigations critically examine trade-offs, rebound effects, governance limitations, and uneven social impacts across production sectors (Gholami et al., 2022; Taddei et al., 2024; Ağseren & Şimşek, 2024).

In this context, the central problem motivating this systematic literature review lies in understanding not only whether Industry 4.0 technologies contribute to sustainability, but how and under which structural, technological, and organizational conditions such contributions materialize. Rather than merely cataloging trends, this study seeks to clarify the mechanisms linking digitalization and sustainability performance, identifying enabling capabilities, institutional pressures, implementation barriers, and strategic alignments that influence outcomes across the economic, environmental, and social dimensions. By doing so, the review moves beyond descriptive synthesis to adopt a more integrative and explanatory perspective on sustainable digital transformation (Narula et al., 2021; Uwamahoro et al., 2025).

Therefore, the objective of this systematic literature review is to critically analyze and theoretically integrate existing studies on the relationship between Industry 4.0 and sustainability, identifying patterns, mechanisms, challenges, and opportunities embedded in the literature. The research aims to examine how digital technologies affect production efficiency, natural resource consumption, and organizational economic viability, while also considering social implications and governance dimensions (Gholami et al., 2022; Cricelli et al., 2024; Bottani et al., 2020). In addition, it seeks to identify which industrial sectors have been most extensively studied, which sustainability dimensions have received greater analytical attention, and which conceptual and empirical gaps persist in current research (Taddei et al., 2024; Matos et al., 2024; Pletsch et al., 2025). By structuring the evidence through strategic and analytical lenses that allow the identification of strengths, weaknesses, opportunities, and threats, and by organizing sustainability impacts into integrated dimensions, this study develops a coherent conceptual articulation capable of supporting both theoretical advancement and practical application (Varela et al., 2019; Camargo et al., 2024; Piccarozzi et al., 2024).

The contribution of this study goes beyond the mere aggregation of prior research. By systematically synthesizing fragmented empirical and conceptual evidence, this review identifies consistent patterns, contradictory findings, and underexplored dimensions in the relationship between Industry 4.0 and sustainability. It clarifies where empirical support is robust, where it remains inconclusive, and how different sustainability pillars interact within digitally transformed industrial systems.

This structured and theory-informed synthesis contributes to a more transparent understanding of the mechanisms by which Industry 4.0 can foster sustainable industrial transformation, while also revealing structural constraints and trade-offs that must be addressed to ensure a balanced transition. In addition, this study proposes the IRIS Framework as an integrative and operational model that bridges the gap between sustainability assessment and digital transformation management, explicitly incorporating governance and technological dimensions, as well as the trade-offs associated with Industry 4.0.

Accordingly, this systematic literature review addresses the following research question: How do Industry 4.0 technologies influence the economic, environmental, and social pillars of sustainability, and what structural, technological, and organizational mechanisms condition their effective implementation? By answering this question, the study deepens the conceptual understanding of digitalization-driven sustainability, highlighting not only positive impacts but also limitations, tensions, and implementation challenges reported in the literature. This analytical perspective supports the development of more coherent strategic frameworks and evidence-based decision-making processes to design industrial systems that are digitally advanced, sustainable, resilient, and socially responsible.

2. Methodological procedures applied to theoretical support

The Systematic Literature Review (SLR) was adopted as the methodological strategy to ensure rigor, transparency, and reproducibility in the study's theoretical foundation. The SLR is widely recognized as a structured and reliable approach for identifying, analyzing, and synthesizing scientific evidence on a specific research topic (Christou et al., 2024). Systematically mapping existing academic production enables researchers to consolidate the current state of the art, critically examine accumulated knowledge, and identify theoretical, methodological, and empirical gaps that may guide future investigations (Pereira et al., 2024; Hernández-Contreras et al., 2024).

Beyond summarizing previous findings, the SLR advances knowledge by organizing dispersed evidence, highlighting emerging trends, and supporting the development of new analytical perspectives. In this sense, it prevents duplication of effort. It strengthens the research's scientific basis, ensuring that the proposed study effectively contributes to unexplored or insufficiently addressed dimensions of the theme.

To guarantee methodological robustness, the review followed the PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), as proposed by Liberati et al. (2009). PRISMA is internationally recognized for enhancing the clarity and transparency of systematic reviews by structuring the process into four sequential phases: identification, screening (selection), eligibility, and inclusion. The operationalization of these stages in this research is illustrated in Figure 1, which presents the flow diagram adapted from the PRISMA reference model.

Figure 1
Application to the PRISMA model. Source: Adapted from the reference model of Liberati et al. (2009).

The systematic literature review developed in this research aimed to identify, select, and analyze scientific studies addressing the impacts of Industry 4.0 on business sustainability, considering its three fundamental pillars: economic, environmental, and social. The search was conducted in March 2025 in the Scopus, Web of Science, and Emerald Insight databases, considering a predefined temporal scope covering publications from 2015 to 2024, accessed through the Capes Journal Portal.

Clear inclusion and exclusion criteria were established to ensure methodological rigor, prioritizing peer-reviewed scientific articles, published in English, with full-text access and direct alignment with the research scope. After retrieving records from the selected databases, a duplicate-removal procedure was conducted using Mendeley Reference Manager prior to the screening stage. All records were imported into the software, where duplicate entries were automatically and manually identified and removed to ensure dataset consistency. The remaining records were then used for the subsequent screening and eligibility phases.

The definition of the 2015–2024 time horizon is theoretically and contextually grounded. The year 2015 represents a critical inflection point in the global sustainability agenda, marked by the adoption of the United Nations Sustainable Development Goals (SDGs) and the consolidation of sustainability as a central element in industrial and policy discussions. This period also aligns with the growing academic and practical relevance of sustainable development within supply chains and industrial systems (Narula et al., 2021; Govindan, 2023; Liu et al., 2023; Kazancoglu et al., 2023; Karmaker et al., 2023). Simultaneously, this timeframe coincides with the acceleration of Industry 4.0 adoption, driven by advancements in digital technologies such as the Internet of Things, artificial intelligence, and big data analytics (Ghadge et al., 2022; Castro et al., 2023; Helo & Thai, 2024; Bai et al., 2020; Beltrami et al., 2021).

Extending the analysis until 2024 ensures the inclusion of the most recent empirical and conceptual developments, capturing the maturation of the field and the transition from exploratory studies to more integrative and application-oriented research (Lopes et al., 2023; Santos & Sant’Anna, 2024; Saleh & AlShafeey, 2025; Piccarozzi et al., 2024; Cricelli et al., 2024). Therefore, this temporal delimitation allows for a coherent and up-to-date synthesis of the intersection between Industry 4.0 and sustainability.

Identification

In the identification stage, structured search strings were developed using Boolean operators to maximize retrieval accuracy and thematic adherence. The search expression combined the descriptors: “Industry 4.0” AND “Sustainability” AND “Economic Sustainability” AND “Environmental Sustainability” AND “Social Sustainability.”

As illustrated in Figure 1, this procedure resulted in 1,123 records: 496 from Scopus, 330 from Web of Science, and 297 from Emerald Insight. A temporal filter was then applied to select publications within the defined research period, excluding 283 studies and reducing the sample to 840 documents.

Selection

In the screening phase, additional refinement criteria were applied. First, documents were filtered by subject area and document type, excluding materials that were not classified as scientific articles or were not aligned with the relevant research fields. This step resulted in the exclusion of 294 records.

Next, filters related to database access and language were applied, removing 7 publications that did not meet the established requirements (English language and full-text availability). After these filters, the remaining articles underwent title screening. The title reading stage led to the exclusion of 392 studies that did not demonstrate clear thematic alignment with the relationship between Industry 4.0 and the three sustainability pillars.

Eligibility

Following the title screening, abstracts of the remaining studies were carefully analyzed to verify conceptual depth and adherence to the research objectives. In this stage, 39 articles were excluded for presenting only peripheral references to sustainability or for focusing exclusively on technological aspects without integrating economic, environmental, and social dimensions.

The studies that remained after abstract screening were read in full to confirm their theoretical and empirical contributions. During this full-text assessment, 23 additional articles were excluded for not fully meeting the analytical criteria defined in the protocol. To ensure methodological rigor and transparency in selecting studies, a structured quality assessment protocol was applied to all articles evaluated during the eligibility phase. The criteria, dimensions, and minimum inclusion thresholds are presented in Table 1.

Table 1
Quality assessment criteria for inclusion of studies.

Dimension	Evaluation Criteria	Description
Relevance	Alignment with the research objective	The degree to which the study directly addresses Industry 4.0 and sustainability (economic, environmental, and social)
Methodological rigor	Clarity of research design	Explicit description of methods, data collection, and analysis procedures
Theoretical contribution	Conceptual depth	Presence of frameworks, models, or theoretical integration
Empirical evidence	Data support	Use of empirical data, case studies, or validated models
Sustainability coverage	Triple Bottom Line Integration	Coverage of economic, environmental, and social dimensions
Transparency	Clarity and reproducibility	Availability of sufficient detail for replication

• Source: Authors (2025).

The quality assessment process was based on a structured scoring system, in which each study was evaluated across six dimensions: relevance, methodological rigor, theoretical contribution, empirical evidence, sustainability coverage, and transparency. For each of these dimensions, a score ranging from 0 to 2 was assigned based on the level of compliance with the established criteria.

A score of 0 was assigned when the study did not meet the evaluated criteria or presented a significant lack of information. A score of 1 indicated partial compliance, characterized by methodological limitations, low conceptual depth, or incomplete coverage of the analyzed dimensions. A score of 2 was assigned to studies that fully met the criterion, demonstrating methodological robustness, strong theoretical grounding, consistent empirical support, and high transparency in the description of procedures.

The sum of the scores across all dimensions resulted in a total score ranging from 0 to 12 points for each study. As an inclusion criterion, a minimum threshold of 7 points was established to ensure that only studies with adequate methodological quality and substantial alignment with the research objectives were considered in the final analysis. Studies scoring below this threshold were excluded during the full-text assessment stage. This procedure standardized the selection process, reduced subjective bias, and ensured greater rigor, transparency, and reproducibility in the systematic review.

Inclusion

At the conclusion of the process, 56 articles met all inclusion criteria and composed the final corpus of analysis, as presented in Figure 1. These studies provided consistent and methodologically sound evidence regarding the interconnections between Industry 4.0 technologies and business sustainability across its three pillars.

The analytical stage went beyond descriptive mapping. Given that the objective of this research is to construct integrative analytical frameworks, a structured qualitative content analysis was conducted with the 56 selected articles to derive the elements of the SWOT analysis, the dimensions of the IRIS structure, and the stages of the DDEE cycle. The content analysis followed three sequential coding phases: open coding, axial coding, and selective coding.

In the open coding phase, all selected articles were read in full and examined line by line to identify recurring themes, conceptual constructs, empirical findings, enabling factors, barriers, performance indicators, governance mechanisms, and sustainability outcomes. Codes were generated inductively to capture both explicit and implicit references to technological capabilities, organizational conditions, environmental impacts, economic performance, social implications, risks, and strategic opportunities. This procedure was particularly informed by empirical and conceptual contributions that explore the relationship between Industry 4.0 and sustainable supply chains (Ghadge et al., 2022; Sharma et al., 2021), as well as studies emphasizing digital capabilities as drivers of circular and sustainable systems (Liu et al., 2023; Lu et al., 2024; Matos et al., 2024).

In the axial coding phase, the initial codes were grouped into higher-order categories based on conceptual proximity and relational logic. This stage enabled the identification of patterns linking Industry 4.0 technologies to sustainability dimensions and the differentiation between internal organizational attributes and external environmental dynamics. Categories such as digital integration, operational efficiency, emissions reduction, stakeholder pressure, regulatory influence, supply chain collaboration, technological readiness, and investment barriers emerged during this analytical step. These dimensions are strongly reflected in studies addressing risk management and circular transition frameworks (Kazancoglu et al., 2023; Taddei et al., 2024), as well as in analyses of digitalization as a structural transformation mechanism toward smart circular economy models (Govindan, 2023; Matos et al., 2024).

In the selective coding phase, these analytical categories were synthesized into integrative constructs aligned with the study's conceptual architecture. Internally controllable factors were classified within the SWOT framework as strengths or weaknesses, while externally conditioned dynamics were categorized as opportunities or threats. Simultaneously, sustainability impacts were organized within the IRIS structure, integrating technological, operational, and performance dimensions across the economic, environmental, and social pillars. This integrative perspective is consistent with qualitative and systematic assessments that evaluate the ecological and social potential of Industry 4.0 (Stock et al., 2018; Lopes et al., 2023) and its implications for sustainable business models and logistics systems (Strandhagen et al., 2017), as well as studies focusing on SMEs and organizational-level sustainability transitions (Santos & Sant’Anna, 2024; Piccarozzi et al., 2024; Helo & Thai, 2024).

The DDEE cycle was derived from recurrent temporal and strategic patterns identified across the literature, reflecting iterative processes of digitalization, operational transformation, sustainability impact generation, and adaptive governance. This cyclical logic mirrors the evolutionary view of digital transformation described in works that connect Industry 4.0 technologies to circular economy implementation and sustainable supply chain management (Fatimah et al., 2020; Lu et al., 2024).

To enhance analytical rigor, the coding process was iterative, involving multiple rounds of review to refine category definitions and ensure internal coherence. A coding protocol was developed specifying classification criteria and decision rules for allocating evidence to SWOT quadrants and IRIS dimensions. Recoding procedures were conducted after a set interval to verify consistency and minimize subjective bias, thereby reinforcing reliability and methodological transparency.

To ensure methodological rigor and transparency in the qualitative content analysis, the coding process was conducted by two researchers. Initially, the primary researcher performed the full coding procedure in accordance with the established protocol. Subsequently, a second independent researcher coded a randomly selected subset corresponding to 30% of the articles, using the same coding guidelines.

Intercoder reliability was assessed using Cohen’s Kappa coefficient, yielding 0.78, indicating substantial agreement according to established benchmarks. Discrepancies between coders were systematically discussed and resolved through consensus, leading to refinement in category definitions and coding rules. This procedure strengthens the reliability, consistency, and reproducibility of qualitative analysis, reducing subjectivity and ensuring greater robustness of the derived analytical constructions.

Thus, the methodological design combines a PRISMA-based systematic review, summarized in Figure 1, with a structured qualitative content analysis protocol. This integrated approach ensures transparency, reproducibility, and theoretical robustness in deriving the SWOT elements, IRIS dimensions, and DDEE cycle from the selected body of literature.

To provide greater analytical depth, Table 2 presents the 56 studies that comprise the final corpus of the systematic literature review. All studies included in the table met the eligibility criteria and achieved a minimum quality assessment score of 7 points, according to the evaluation protocol presented in Table 1. The “Eligibility” column indicates the final score assigned to each study based on the six evaluation dimensions: relevance, methodological rigor, theoretical contribution, empirical evidence, sustainability coverage, and transparency.

Table 2
Selected works on Industry 4.0, Logistics 4.0, Circular Economy, and Business Sustainability.

	Authors	Titles	Approach	Relation	Eligibility
1	Varela et al. (2019)	Evaluation of the relation between lean manufacturing, Industry 4.0, and sustainability	Conceptual and empirical analysis	Examine operational efficiency and the integration of sustainability, reinforcing the pathway between digitalization and performance improvement.	12
2	Bai et al. (2020).	Industry 4.0 technologies assessment: A sustainability perspective	Multi-criteria sustainability assessment of Industry 4.0 technologies	Provides a structured evaluation of Industry 4.0 technologies through a sustainability lens, directly supporting the identification of technological pathways to improve economic, environmental, and social performance.	12
3	Sharma et al. (2021)	Industry 4.0 adoption for sustainability in the multi-tier manufacturing supply chain in emerging economies	Combines AHP and DEMATEL to identify drivers and barriers to Industry 4.0 adoption in multi-tier supply chains, with sustainability considerations.	Analyzes the challenges and opportunities of adopting Industry 4.0 for sustainability in the supply chain.	12
4	Fatimah et al. (2020)	Industry 4.0-based Sustainable Circular Economy Approach for Smart Waste Management System to Achieve Sustainable Development Goals: A Case Study of Indonesia	Literature review and case study on a waste management model with IoT and ICT.	Analyzes how Industry 4.0 technologies contribute to sustainability in waste management.	12
5	Fatorachian & Kazemi (2021)	Impact of Industry 4.0 on supply chain performance	Empirical study (quantitative analysis)	Investigates how Industry 4.0 technologies enhance supply chain performance, highlighting improvements in integration, responsiveness, and operational efficiency.	12
6	Stock et al. (2018)	Industry 4.0 as an enabler for sustainable development: A qualitative assessment of its ecological and social potential	Qualitative approach based on literature review and interviews to assess the potential of Industry 4.0 for sustainability.	Evaluates the impact of Industry 4.0 on ecological and social aspects of business sustainability.	12
7	Liu et al. (2023)	Leveraging digital capabilities toward a circular economy: Reinforcing sustainable supply chain management with Industry 4.0 technologies	Bibliometric analysis and systematic review proposing the CAB2IN framework to optimize sustainable decisions through AI, big data, blockchain, and IoT.	Examines how Industry 4.0 digital capabilities drive sustainable supply chain management.	12
8	Braccini & Margherita (2018)	Exploring organizational sustainability of Industry 4.0 under the triple bottom line	Case study applying the Triple Bottom Line framework	Links Industry 4.0 adoption to organizational sustainability across economic, environmental, and social pillars, reinforcing the integrative sustainability perspective.	12
9	Beltrami et al. (2021)	Industry 4.0 and sustainability: Towards conceptualization and theory	Conceptual/theoretical paper	Develops a theoretical framework linking Industry 4.0 and sustainability, clarifying key constructs and proposing pathways for sustainable value creation.	12
10	Karmaker et al. (2023)	Impact of Industry 4.0 technologies on sustainable supply chain performance: The mediating role of green supply chain management practices and circular economy	Empirical study (quantitative, mediation analysis)	Examines how Industry 4.0 technologies improve sustainable supply chain performance, highlighting the mediating effects of green supply chain practices and circular economy principles.	12
11	Lu et al. (2024)	Integrating circular economy and Industry 4.0 for sustainable supply chain management: a dynamic capability view	Systematic literature review proposing a conceptual framework for integrating Industry 4.0 and circular economy.	Explores how Industry 4.0 can strengthen circular economy and sustainability in supply chain management.	12
12	Brozzi et al. (2020)	The advantages of Industry 4.0 applications for sustainability	Empirical survey with manufacturing firms	Demonstrates practical sustainability gains from Industry 4.0 implementation, supporting the identification of strengths in the SWOT framework.	12
13	Govindan (2023)	How digitalization transforms the traditional circular economy into a smart circular economy for achieving SDGs and net zero	Literature review and application of Grey DEMATEL to analyze digitalization in the circular economy.	Discusses how digitalization drives the smart circular economy and sustainability.	11
14	Zekhnini et al. (2021)	A model integrating lean and green practices for viable, sustainable, and digital supply chain performance	Empirical study (quantitative, structural modeling)	Proposes and validates a model integrating lean and green practices with digitalization to enhance sustainable and viable supply chain performance.	11
15	Sharma et al. (2023)	Green logistics driven circular practices adoption in Industry 4.0 era: A moderating effect of institutional pressure and supply chain flexibility	Empirical study (quantitative, moderation analysis)	Investigates the adoption of circular practices in green logistics within Industry 4.0, emphasizing the moderating role of institutional pressure and supply chain flexibility on sustainable outcomes.	11
16	Balasubramanian et al. (2021)	Construction Industry 4.0 and sustainability: An enabling framework	Conceptual framework / qualitative analysis	Proposes an enabling framework for integrating Industry 4.0 technologies into the construction sector to support sustainability goals, emphasizing digital transformation as a driver of environmental and operational improvements.	11
17	Ghadge et al. (2022)	Link between Industry 4.0 and green supply chain management: Evidence from the automotive industry	Empirical study using Interpretive Structural Modeling (ISM) and Structural Equation Modeling (SEM) to analyze the relationship between Industry 4.0 and green supply chains in the automotive sector.	Evaluates the impact of Industry 4.0 technologies on adopting sustainable practices in the supply chain.	11
18	Kazancoglu et al. (2023)	Framework for a sustainable supply chain to overcome risks in transition to a circular economy through Industry 4.0	Uses MCDM methods (Fuzzy AHP and TODIM) to analyze risks in the transition from linear supply chains to circular economy with Industry 4.0.	Investigates how Industry 4.0 can reduce risks and make the transition to circular economy more efficient.	11
19	Garrido-Hidalgo et al. (2018)	IoT heterogeneous mesh network deployment for human-in-the-loop challenges towards a social and sustainable Industry 4.0	Technical / experimental study (IoT implementation)	Explores IoT-based network architectures enabling human-in-the-loop systems, supporting social and sustainable dimensions of Industry 4.0 through enhanced connectivity and real-time data integration.	11
20	El Baz et al. (2022)	A framework of sustainability drivers and externalities for Industry 4.0 technologies	Best-Worst Method (BWM) to identify drivers and externalities	Identifies internal and external sustainability drivers, directly informing the classification of opportunities and threats in the analytical model.	11
21	Taddei et al. (2024)	Circular supply chains: Theoretical gaps and practical barriers: A model to support approaching firms in the era of Industry 4.0	Literature review and expert validation to propose a model for transitioning to circular supply chains.	Identifies barriers and presents a model for adopting Industry 4.0 in circular supply chains.	11
22	Strandhagen et al. (2022)	Sustainability challenges and how Industry 4.0 technologies can address them: A case study of a shipbuilding supply chain	Case study (qualitative, empirical)	Analyzes how Industry 4.0 technologies address sustainability challenges in a shipbuilding supply chain, demonstrating practical applications for improving environmental and operational performance.	11
23	Narula et al. (2021)	Applicability of Industry 4.0 technologies in the adoption of GRI standards	Empirical-analytical study linking Industry 4.0 and sustainability reporting	Connects digital technologies to sustainability governance and reporting standards, supporting the governance dimension of the DDEE cycle.	10
24	García-Muiña et al. (2021)	Industry 4.0-based dynamic social organizational life cycle assessment to target the social circular economy in manufacturing	Conceptual framework / methodological study	Proposes a dynamic social life cycle assessment approach enabled by Industry 4.0 technologies to support the transition toward a social circular economy in manufacturing systems.	10
25	Agarwal & Ojha (2024)	Prioritizing implications of Industry 4.0 on the sustainable development goals: A perspective from the analytic hierarchy process in manufacturing operations	Multi-criteria decision-making (AHP) / quantitative approach	Prioritizes the impacts of Industry 4.0 technologies on Sustainable Development Goals, highlighting their relative importance for advancing sustainability in manufacturing operations.	10
26	Govindan (2024)	Unlocking the potential of quality as a core marketing strategy in remanufactured circular products: A machine learning enabled multi-theoretical perspective	Quantitative / machine learning-based analysis with multi-theoretical framework	Explores how quality perception in remanufactured circular products can be leveraged as a marketing strategy, using machine learning to support decision-making within circular economy and Industry 4.0 contexts.	10
27	Afum et al. (2023)	Lean production systems, social sustainability performance and green competitiveness: The mediating roles of green technology adoption and green product innovation	Empirical study (quantitative, mediation analysis)	Examines how lean production systems influence social sustainability and green competitiveness, highlighting the mediating roles of green technology adoption and green product innovation.	10
28	Becerra et al. (2023)	Optimising location, inventory and transportation in a sustainable closed-loop supply chain	Quantitative modeling / optimization approach	Develops an optimization model for location, inventory, and transportation decisions in a sustainable closed-loop supply chain, supporting efficiency and circular economy integration.	9
29	Sharma et al. (2024)	An empirical investigation of the influence of Industry 4.0 technology capabilities on agriculture supply chain integration and sustainable performance	Empirical study (quantitative, structural modeling)	Examines how Industry 4.0 technological capabilities enhance supply chain integration and sustainable performance in the agricultural sector, emphasizing the role of digitalization in improving coordination and sustainability outcomes.	9
30	Santos & Sant’Anna (2024)	Industry 4.0 technologies for sustainability within small and medium enterprises: A systematic literature review and future directions	Systematic review of 42 studies on the impact of Industry 4.0 on sustainability in SMEs.	Highlights the benefits and challenges of Industry 4.0 for sustainability in small and medium enterprises.	9
31	Gholami et al. (2022)	Scrutinizing state-of-the-art Industry 4.0 technologies toward sustainable products development under fuzzy environment	Multi-criteria decision-making (fuzzy methods) / quantitative approach	Evaluates and prioritizes Industry 4.0 technologies for sustainable product development under uncertainty, highlighting the role of fuzzy decision-making in supporting sustainability-oriented innovation.	9
32	Quiroz-Flores et al. (2024)	Industry 4.0, circular economy and sustainability in the food industry	Systematic literature review	Integrates Industry 4.0 and circular economy perspectives, strengthening the systemic sustainability pathway.	9
33	Lopes et al. (2023)	Industry 4.0, multinationals, and sustainable development: A bibliometric analysis	Bibliometric analysis	Maps the intellectual structure and research trends linking Industry 4.0, multinational enterprises, and sustainable development, identifying key themes, gaps, and future research directions.	9
34	Sharma et al. (2024)	Role of Industry 4.0 technologies and human-machine interaction for de-carbonization of food supply chains	Conceptual / analytical study	Examines how Industry 4.0 technologies combined with human–machine interaction contribute to the decarbonization of food supply chains, emphasizing sustainability and emission reduction strategies.	9
35	Cricelli et al. (2024)	Assessing the impact of Industry 4.0 technologies on the social sustainability of agrifood companies	Empirical quantitative assessment	Expands the debate beyond environmental and economic aspects, emphasizing social sustainability impacts.	9
36	Piccarozzi et al. (2024)	Industry 4.0 technologies as a lever for sustainability in the communication of large companies to stakeholders	Empirical study (qualitative/quantitative analysis)	Investigates how Industry 4.0 technologies support sustainability communication strategies in large companies, highlighting their role in stakeholder engagement and transparency.	9
37	Muniz Junior et al. (2023)	Industry 4.0 at Brazilian modular consortium: Work, process and knowledge in engine supply chain	Case study (qualitative)	Examines the implementation of Industry 4.0 in a Brazilian modular consortium, highlighting impacts on work organization, processes, and knowledge management within the engine supply chain.	9
38	Saleh & AlShafeey (2025)	Examining the synergies between Industry 4.0 and sustainability dimensions	Text mining and sentiment analysis	Identifies emerging thematic patterns and synergies, supporting the derivation of integrative constructs such as IRIS and DDEE.	9
39	Siagian et al. (2024)	Circular economy approaches in the palm oil industry: Enhancing profitability through waste reduction and product diversification	Empirical / case-based analysis	Explores how circular economy practices improve profitability and sustainability in the palm oil industry, emphasizing waste reduction and product diversification strategies.	9
40	Ferreira et al. (2020)	Towards a contribution to sustainable management of a dairy supply chain	Case study / applied research	Analyzes sustainable management practices in a dairy supply chain, highlighting operational improvements and sustainability integration in agri-food systems.	8
41	Bottani et al. (2020)	Economic and environmental sustainability dimensions of a fashion supply chain: A quantitative model	Quantitative modeling	Develops a quantitative model to evaluate economic and environmental sustainability in a fashion supply chain, supporting decision-making for sustainable operations.	8
42	Pletsch et al. (2025)	The potential of Industry 4.0 technologies in transforming agricultural and livestock practices	Systematic review	Demonstrates sectoral transformation pathways, reinforcing the evolutionary and adaptive nature of sustainable digital transformation.	8
43	Kersten et al. (2024)	Traceability in the agri-food supply chain: A new perspective under the circular economy approach	Conceptual / analytical study	Explores traceability in agri-food supply chains from a circular economy perspective, emphasizing transparency, resource efficiency, and sustainability improvements.	8
44	Matos et al. (2024)	Life cycle sustainability assessment of the agri-food chain: empirical review and bibliometrics	Systematic/empirical literature review with bibliometric analysis	Analyzes life cycle sustainability assessment in agri-food supply chains, mapping research trends, methodologies, and gaps related to environmental, social, and economic dimensions.	8
45	Becerra et al. (2024)	Simulation optimisation of a sustainable copper mining closed-loop supply chain	Simulation-based optimization / quantitative modeling	Develops a simulation-optimization model for a sustainable closed-loop supply chain in copper mining, supporting decision-making for resource efficiency, cost reduction, and circular economy implementation.	8
46	Uwamahoro et al. (2025)	Assessing the influence of supply chain collaboration on the performance of manufacturing SMEs in Rwanda	Empirical study (quantitative analysis)	Examines how supply chain collaboration influences performance in manufacturing SMEs, highlighting the role of coordination and integration in improving operational and organizational outcomes.	8
47	Ağseren & Şimşek (2024)	Touch sensors used in Industry 4.0 machines in the manufacturing industry on occupational health and safety	Technical / applied study	Investigates the use of touch sensor technologies in Industry 4.0 environments, emphasizing their impact on occupational health and safety in manufacturing systems.	8
48	Shen et al. (2023)	The role of Industry 4.0 and circular economy for sustainable operations: The case of bike industry	Case study (qualitative/empirical)	Examines how Industry 4.0 and circular economy practices jointly support sustainable operations, highlighting their application in the bicycle industry to improve efficiency and environmental performance.	8
49	Castro et al. (2023)	Data science for Industry 4.0 and sustainability	Systematic survey and data-driven analysis	Highlights the role of data science and analytics as enablers of sustainable digital transformation.	8
50	Tascón et al. (2022)	Flexibility of operations in developing countries with Industry 4.0: A systematic review of literature	Systematic literature review	Reviews how Industry 4.0 technologies influence operational flexibility in developing countries, identifying key drivers, challenges, and research gaps.	7
51	Camargo et al. (2024)	A novel methodology to obtain optimal economic indicators based on the Argentinean production chain under uncertainty	Quantitative modeling / methodological study under uncertainty	Proposes a novel methodology to derive optimal economic indicators in a production chain under uncertainty, supporting decision-making and performance evaluation in complex supply chain environments.	7
52	Bertoli Gonçalves (2024)	Indústria 4.0 no Brasil: Análise da contribuição do Centro de Excelência em Tecnologia 4.0	Qualitative study / case-based analysis	Examines the development of Industry 4.0 in Brazil, highlighting the role of institutional initiatives and innovation centers in fostering technological adoption and industrial transformation.	7
53	Sharma et al. (2023)	Decisive drivers contributing towards modern last mile delivery operations: A qualitative analysis using ISM	Qualitative study (ISM)	Identifies and structures key drivers influencing last-mile delivery operations, emphasizing logistical performance and decision-making factors in modern supply chains.	7
54	Capioto et al. (2019)	Suppliers’ network analysis under the perspective of structural, relational and cognitive embeddedness: an exploratory study	Exploratory qualitative study (network analysis)	Analyzes supplier networks through structural, relational, and cognitive embeddedness perspectives, highlighting how inter-organizational ties influence collaboration and supply chain behavior.	7
55	Helo & Thai (2024)	Logistics 4.0 – Digital transformation with smart connected tracking and tracing devices	Exploratory multiple-case study	Examine how tracking and tracing technologies enable Logistics 4.0, enhancing operational efficiency, visibility, transparency, and value creation in supply chains.	7
56	Strandhagen et al. (2017)	Logistics 4.0 and emerging sustainable business models	Conceptual analysis and case-based discussion	Explores how Logistics 4.0 enables the development of sustainable business models through digitalization, integration, and efficiency in supply chains.	7

• Source: Authors (2025).

Although all 56 articles were included in the final corpus, the studies presented in Table 2 were also treated as the core analytical references for developing the SWOT analysis, the IRIS Framework, and the DDEE cycle. The inclusion of these studies was justified by their direct alignment with the research objectives and substantial theoretical and empirical contributions to understanding the relationships among Industry 4.0, Logistics 4.0, the Circular Economy, and Business Sustainability.

Table 2 systematizes the selected works by identifying their authors, titles, methodological approaches, and their direct relationship to the research problem. Table 2 demonstrates a strong predominance of studies focused on sustainable supply chain management, digital transformation, and circular economy transitions, particularly in the context of Industry 4.0 adoption (Ghadge et al., 2022; Lu et al., 2024; Quiroz-Flores et al., 2024). Methodologically, the literature reveals considerable diversity, including empirical investigations using Structural Equation Modeling (SEM) and Interpretive Structural Modeling (ISM) (Ghadge et al., 2022), Multi-Criteria Decision-Making (MCDM) methods such as Fuzzy AHP and DEMATEL (Sharma et al., 2021; Kazancoglu et al., 2023), bibliometric analyses (Liu et al., 2023), systematic literature reviews (Santos & Sant’Anna, 2024; Pletsch et al., 2025), qualitative assessments (Stock et al., 2018; Lopes et al., 2023), and case studies (Fatimah et al., 2020).

This methodological plurality reinforces the robustness of the evidence base. Empirical and modeling studies contribute measurable insights into drivers, barriers, and performance impacts of Industry 4.0 adoption (Bai et al., 2020; Brozzi et al., 2020), while systematic reviews and conceptual frameworks offer integrative perspectives that connect technological capabilities to sustainability outcomes (Lu et al., 2024; Taddei et al., 2024). Notably, several studies emphasize digital capabilities, such as artificial intelligence, big data analytics, blockchain, and IoT, as critical enablers of circular and sustainable supply chain practices (Liu et al., 2023; Castro et al., 2023). Others highlight structural barriers, including high investment costs, technological complexity, organizational resistance, and risk management challenges during the transition from linear to circular models (Sharma et al., 2021; Kazancoglu et al., 2023; Santos & Sant’Anna, 2024).

Another relevant aspect evidenced in Table 2 is the evolution of the debate over time. Earlier works tend to explore the enabling potential of Industry 4.0 for sustainability in conceptual and exploratory terms (Strandhagen et al., 2017; Stock et al., 2018), whereas more recent studies adopt integrative and strategic perspectives, focusing on dynamic capabilities, smart circular economy models, risk mitigation frameworks, and governance mechanisms aligned with sustainable development goals (Govindan, 2023; Lu et al., 2024; Narula et al., 2021). This progression indicates a maturation of the field, moving from exploratory analyses toward structured and operationalizable models capable of guiding implementation strategies.

Furthermore, the table reveals that sustainability is not treated as a unidimensional concept. Instead, the selected studies consistently address the economic, environmental, and social dimensions in an interconnected manner, reinforcing the systemic perspective adopted in this dissertation (Braccini & Margherita, 2018; Cricelli et al., 2024). This integrative orientation directly supports the development of the IRIS structure and the DDEE cycle, as well as the classification logic employed in the SWOT framework, particularly in relation to identifying internal capabilities and external drivers of sustainable digital transformation (El Baz et al., 2022; Varela et al., 2019).

Therefore, Table 2 not only synthesizes the 56 studies included in the final review corpus, but also evidences the conceptual density, methodological rigor, and thematic convergence underpinning this research. The quality scores reinforce the consistency of the selection process and demonstrate that the proposed analytical models are grounded in a consolidated and multidimensional body of knowledge.

3. Results

The industrial evolution has transformed manufacturing and sustainability over the centuries. From the First Industrial Revolution, with steam mechanization, to the Second Revolution, driven by electrification and mass production, efficiency increased, but with a high environmental impact. The Third Revolution brought automation and greater concern for sustainability, while Industry 4.0, with technologies such as IoT, AI, and Big Data, enabled more efficient and sustainable production. By integrating circular economy and clean production, this new era redefines manufacturing processes, balancing technological innovation, economic growth, and environmental responsibility (Bertoli Gonçalves, 2024).

Figure 2 presents the key milestones of Industry 4.0 and its relationship with sustainability over time, highlighting historical events, technological advancements, and challenges faced by global supply chains. These milestones reflect the evolution of manufacturing towards more efficient, digital, and sustainable processes, driven by innovations such as the Internet of Things (IoT), Big Data, Artificial Intelligence (AI), additive manufacturing, and blockchain, along with the pressure from environmental and social regulations (Beltrami et al., 2021; Lopes et al., 2023).

Figure 2
Timeline: Industry 4.0 and Sustainability. Source: Authors (2025).

The first major milestone occurred with the introduction of the concept of Industry 4.0 at the Hannover Fair in 2011, where the importance of the digitalization and automation of production processes for industrial competitiveness was highlighted (Stock et al., 2018; Garrido-Hidalgo et al., 2018; Bertoli Gonçalves, 2024). This initial moment marked the transition to smart factories, capable of autonomously integrating cyber-physical devices and systems, increasing efficiency, reducing waste, and enabling more sustainable management (Gholami et al., 2022; Fatorachian & Kazemi, 2021).

The adoption of disruptive technologies by industries was intensified over the years, with strategies aimed at integrating sustainable practices into digital processes, driving the creation of new business models and more resilient supply chains (Sharma et al., 2023b; Sharma et al., 2024a). Logistics also underwent a profound transformation, with Logistics 4.0 enabling more sustainable and intelligent operations, along with the need for more flexible networks to accommodate the demand for circular economy practices (Sharma et al., 2023a; Strandhagen et al., 2017).

Another crucial moment was the growing global concern with sustainability, especially after the signing of the Paris Agreement in 2015 and the adoption of the United Nations Sustainable Development Goals (SDGs). These events motivated industries to rethink their production chains, seeking to reduce carbon emissions, minimize waste, and adopt circular economy practices (Govindan, 2023; Fatimah et al., 2020; Taddei et al., 2024). In this context, Industry 4.0 began to play a key role, allowing for greater control, traceability, and optimization of processes with less environmental impact (Liu et al., 2023; García-Muiña et al., 2021).

Various industrial sectors began to integrate sustainability concepts with digital capabilities. Circular economy was reinforced through the digitalization of supply chains, promoting a transformation into the traditional production model (Kazancoglu et al., 2023; Shen et al., 2023). Studies show that practices such as additive manufacturing, the use of Big Data, and the Internet of Things help maximize circularity and reduce environmental impacts throughout the product life cycle (Fatimah et al., 2020; Afum et al., 2023; Bertoli Gonçalves, 2024).

Although many companies still do not maintain close relationships with their supply chain partners due to distrust in sharing managerial and technological knowledge (Capioto et al., 2019), in recent years, global crises like the COVID-19 pandemic have further challenged supply chains, accelerating the adoption of digital technologies to ensure the continuity of operations and strengthening the need for resilience and sustainability (Becerra et al., 2023; Sharma et al., 2024b; Becerra et al., 2024). Technologies such as simulation, inventory optimization, and 3D printing have been widely explored to mitigate risks and enhance production processes (Ghadge et al., 2022; Karmaker et al., 2023).

At the same time, new environmental regulations and pressures from increasingly conscious consumers have required companies to adopt production models aligned with sustainable practices (Zekhnini et al., 2021; Govindan, 2024). The adoption of approaches based on machine learning, blockchain, and Artificial Intelligence has been decisive in accelerating green digitalization and enabling sustainable traceability (Liu et al., 2023; Govindan, 2024).

Studies demonstrate that the application of emerging technologies not only improves economic efficiency but also enables the construction of sustainable, viable, and resilient supply chains (Lu et al., 2024; Garrido-Hidalgo et al., 2018). The integration of lean and green practices, along with digitalization, is increasingly necessary to address the complexity of global markets (Zekhnini et al., 2021). In the field of waste management and bioeconomy, Industry 4.0-based initiatives have enabled significant advancements, such as the implementation of smart waste management systems (Fatimah et al., 2020) and the transformation of waste into value-added products (Siagian et al., 2024).

Furthermore, the prioritization of social aspects in sustainability is also gaining strength, with methodologies such as Social Life Cycle Assessment (SLCA) adapted for digital and collaborative environments (García-Muiña et al., 2021). Finally, the continuous evolution of Industry 4.0 highlights that sustainability is an essential factor for competitiveness in the global scenario. Advancements such as the use of AI to optimize energy consumption, sustainable traceability through blockchain, and the development of biodegradable materials for production chains are clear examples of how technology can align innovation, competitiveness, and environmental preservation (Siagian et al., 2024; Zekhnini et al., 2021; Balasubramanian et al., 2021). Thus, the milestones presented in the table demonstrate that the convergence between Industry 4.0 and sustainability is not just a trend but a necessity for the future of global manufacturing (Agarwal & Ojha, 2024).

4. Statistical analysis

Table 3 displays the data from the review process, specifying the number of articles identified in the selected databases after applying the inclusion and exclusion criteria. The initial search resulted in 1,123 studies, which were reduced to 147 after applying the defined filters. Subsequently, 98 articles were selected for abstract analysis, leading to the retention of 68 studies. Finally, after a full-text reading, 56 articles met the quality and relevance criteria and were included in the systematic literature review.

Table 3
Search analysis performed in the Scopus, Web of Science, and Emerald Insight databases.

Database	Date	# of articles with the string: "industry 4.0" and "sustainability" or "economic sustainability" and "environmental sustainability" and "social sustainability"	S1: applied filters: (1) publication period: 2015 to 2024; (2) only journal articles; (3) engineering field	S2	S3	S4
Web of Science	17/03/2025	330	39	25	11	10
Scopus	17/03/2025	496	47	32	23	19
Emerald Insight	17/03/2025	297	61	41	34	27
Total:		1123	147	98	68	56

• Source: Authors (2025).

Table 3 illustrates the rigorous filtering process of the articles, ensuring that only the most relevant studies on Industry 4.0 and sustainability were included in the systematic literature review. Scopus presented the highest number of articles in the initial search (496), while Emerald Insight contributed the largest number of studies after the application of filters (61). In the final selection stage, Emerald Insight also stood out with the highest number of included articles (27), followed by Scopus (19) and Web of Science (10), demonstrating that all databases made significant contributions to the review.

Figure 3 presents a keyword cloud related to Industry 4.0, generated by the VOSviewer software. This visualization allows for the identification of key themes addressed in scientific literature on the subject, highlighting the frequency and interconnection of the most relevant concepts. At the center of the image, the term "Industry 4.0" stands out as the core concept, surrounded by words of different sizes and colors representing subtopics and areas of interest. The distribution of words suggests distinct thematic clusters, with approaches focusing on efficiency and performance, while others explore challenges, barriers, and sustainable aspects of digital transformation in industry.

Figure 3
Keyword Cloud of Articles Identified in the Database Search. Source: Authors (2025).

Figure 3 highlights how the key concepts are interrelated, revealing trends and challenges in the implementation of Industry 4.0. The presence of terms such as "digitalization," "Internet of Things," "automation," and "blockchain" emphasizes the relevance of emerging technologies in industrial modernization. Words like "performance," "sustainability," and "logistics" indicate the need to balance technological innovation with operational efficiency and environmental responsibility.

Additionally, terms like "barriers," "challenges," and "future" suggest technical, economic, and structural obstacles that must be overcome for the full adoption of Industry 4.0. The connection between words such as "framework," "optimization," and "design" points to the search for methodologies and models that facilitate this transition, while terms like "big data analytics," "cloud," and "artificial intelligence" reinforce the importance of data analysis and advanced computing in this process. Thus, the word cloud provides a visual representation of the main thematic axes that guide research and discussions on Industry 4.0.

As illustrated in Figure 4, the evolution of scientific production between 2015 and 2024 demonstrates a marked upward trajectory in the number of publications addressing Industry 4.0 and sustainability-related themes. After an initial period of low and fluctuating output, the field experienced gradual growth from 2020 onward, followed by a sharp acceleration beginning in 2023 and reaching a peak in 2024. Although 2025 presents a lower number of publications, this figure likely reflects partial annual data rather than a structural decline in research interest.

Figure 4
Number of Publications Carried Out per Year. Source: Authors (2025).

The first phase (2017–2019) can be characterized as exploratory, with limited but emerging academic attention. During this period, publications remained sporadic, indicating that the integration between Industry 4.0 technologies and sustainability frameworks was still in its conceptual infancy. The modest output suggests that research efforts were primarily focused on identifying potential linkages, theoretical foundations, and preliminary applications. The subsequent increase in 2020, followed by stability in 2021 and 2022, marks a consolidation stage in which the field began to gain clearer conceptual boundaries and methodological consistency.

The most significant transformation occurs between 2023 and 2024, when publication volume rises sharply, reaching its highest level in 2024. This acceleration indicates not only growing academic interest but also the maturation of the research domain. The surge may be associated with intensified global discussions on digital transformation, circular economic implementation, ESG pressures, decarbonization strategies, and supply chain resilience. The convergence of technological capability and sustainability imperatives appears to have positioned the topic as strategically relevant within operations and supply chain research.

As shown in Figure 5, the distribution of scientific publications by country demonstrates a heterogeneous but regionally concentrated pattern of academic production. The data indicate that research output is not evenly distributed worldwide, with a limited group of countries accounting for a significant share of the total publications. This mapping enables the identification of leading nations, emerging contributors, and regions where the topic has gained greater institutional and scientific relevance.

Figure 5
Number of Publications by Country. Source: Authors (2025).

India and Italy jointly occupy the leading position, each with eight publications, representing the highest level of academic engagement among the analyzed countries. This leadership suggests that the topic has achieved strategic importance within both national research agendas. In the case of India, the prominence may be associated with expanding investments in digital transformation and sustainable industrial development. Italy’s strong participation may reflect its established research tradition in industrial engineering, manufacturing systems, and sustainability-oriented innovation. Brazil appears in the subsequent position, with six publications, indicating a significant but slightly lower level of engagement. China follows closely with five publications, reinforcing its consistent presence in global scientific production and its alignment with technological modernization and sustainability strategies.

The United Kingdom and Spain contribute three publications each, while the Netherlands, Portugal, and the United States present two publications per country, demonstrating moderate yet relevant participation. In contrast, a broader group of countries, including Malaysia, Germany, Norway, Bangladesh, Ghana, Canada, Australia, Switzerland, Indonesia, Argentina, Morocco, Peru, Egypt, Colombia, Rwanda, Denmark, and Turkey, each account for a single publication. This dispersion suggests emerging or exploration engagement rather than consolidated research streams. Overall, the results indicate that scientific production is concentrated primarily in Asian and European nations, with comparatively limited output from North America and Africa. Such concentration may reflect differences in research funding, industrial priorities, technological readiness, and national sustainability strategies, reinforcing the idea that the topic is particularly aligned with the innovative agendas of specific regions.

As illustrated in Figure 6, the distribution of publications by journal reveals the main scientific outlets responsible for disseminating research on Industry 4.0 and sustainability. The data shows a clear concentration of articles in a limited number of journals, while a broader set of outlets presents only occasional contributions. This configuration allows the identification of the most influential editorial platforms in the field and guides future submissions and academic positioning.

Figure 6
Distribution of Publications by Journal. Source: Authors (2025).

The Journal of Cleaner Production clearly leads the ranking, with 11 publications, significantly surpassing all other journals. This expressive difference indicates that the journal functions as the primary vehicle for knowledge dissemination in the area. Its editorial scope, focused on sustainability, cleaner technologies, environmental management, and industrial innovation, is strongly aligned with the intersection between digital transformation and sustainable development. The prominence of this outlet suggests that research in the field is predominantly framed within environmental performance, resource efficiency, and sustainable production systems.

In the second tier, Production (6 publications) occupies a relevant position, followed by Computers & Industrial Engineering, Sustainability, and Production Planning & Control, each with three publications. Additionally, the International Journal of Industrial Engineering, International Journal of Production Research, IEEE Transactions on Engineering Management, and the International Journal of Production Economics each account for two publications. These journals are traditionally associated with industrial engineering, operations management, production systems, and strategic decision-making. Their presence reinforces the understanding that the topic is deeply connected to production efficiency, digital technologies, supply chain optimization, and managerial innovation within industrial contexts.

A considerable number of journals, including Process Safety and Environmental Protection, Advances in Manufacturing, MethodsX, Business Strategy and the Environment, Machines, and others, appear with only one publication each. This dispersion indicates that the subject has been explored in multiple scientific domains, ranging from environmental protection and manufacturing systems to innovation management and applied sciences. However, the relatively low recurrence in these outlets suggests that the field is still undergoing consolidation, with research streams not yet fully stabilized across all journals.

Taken together, the analyses of temporal evolution, geographical distribution, and journal concentration demonstrate that research on Industry 4.0 and sustainability has experienced significant growth, particularly from 2023 onward. The prominence of India and Italy in publication volume, alongside China and Brazil, highlights the strategic importance of the topic in emerging and industrialized economies. Simultaneously, the central role of the Journal of Cleaner Production and other production-oriented journals indicates that sustainability-driven industrial transformation constitutes the dominant research perspective.

Despite the evident expansion, the dispersion across numerous journals and the limited recurrence in several countries suggest that the field remains in a consolidation phase. This scenario presents opportunities for broader international collaboration, interdisciplinary integration, and theoretical advancement. As industrial systems face increasing pressure to reconcile digital transformation with environmental and social responsibility, future studies are expected to deepen analyses of economic feasibility, social impacts, governance mechanisms, and long-term sustainability performance. Therefore, this chapter not only maps the current scientific landscape but also highlights the dynamic and expanding nature of the field, pointing to promising avenues for continued academic and practical development.

5. Discussion

The connection between Industry 4.0, sustainability, and its three pillars has become one of the central themes in academic literature and contemporary business debates (Beltrami et al., 2021; Bai et al., 2020). The growing digitalization of production processes, driven by technologies such as IoT, AI, and Big Data, has been associated with significant gains in operational efficiency and decision-making accuracy (Ghadge et al., 2022; Fatorachian & Kazemi, 2021). However, as these innovations are integrated into industrial systems, questions arise regarding their real environmental, economic, and social impacts (Stock et al., 2018; Brozzi et al., 2020; Balasubramanian et al., 2021). While the literature highlights substantial benefits, it also acknowledges structural challenges and contradictions that must be critically examined (El Baz et al., 2022; García-Muiña et al., 2021).

To enrich the discussion on the impacts of Industry 4.0 on sustainability and its three pillars, environmental, economic, and social SWOT analysis was conducted. The strategic relevance of structured assessment tools in evaluating Industry 4.0 technologies from a sustainability perspective is supported by Bai et al. (2020) and El Baz et al. (2022), who emphasize the need to systematically evaluate drivers, externalities, and trade-offs. This approach enables a deeper understanding of the implications of digitalization in production processes and the structural constraints that still limit a fully sustainable transition (Kazancoglu et al., 2023; Taddei et al., 2024).

The SWOT results (Table 4) indicate that Industry 4.0 presents significant strengths in promoting environmental performance. Real-time monitoring, waste reduction, and energy optimization through IoT and AI are widely recognized in the literature (Fatimah et al., 2020; Liu et al., 2023). Traceability of emissions and materials, particularly in circular supply chains, has also been emphasized as a key enabler of sustainable operations (Kersten et al., 2024; Lu et al., 2024). From an economic perspective, automation and digital integration increase production efficiency and reduce operational costs (Fatorachian & Kazemi, 2021; Sharma et al., 2021). Improved supply chain visibility and coordination further enhance competitiveness and resilience (Strandhagen et al., 2022; Govindan, 2023). Socially, intelligent systems may improve workplace safety and create new high-skilled jobs (Ağseren & Şimşek, 2024; Cricelli et al., 2024), while fostering new knowledge-intensive roles (Varela et al., 2019).

Table 4
SWOT Analysis of Industry 4.0 Applied to Sustainability.

	Strengths	Weaknesses
Internal factors	Environmental:	Environmental:
	Reduction of waste through real-time monitoring (Fatimah et al., 2020; Liu et al., 2023; Strandhagen et al., 2022);	High energy consumption of data centers (Stock et al., 2018; Brozzi et al., 2020);
	Energy optimization with IoT and AI (Bai et al., 2020; Gholami et al., 2022; Sharma et al., 2024b);	Technological obsolescence and generation of electronic waste (Gholami et al., 2022; Beltrami et al., 2021);
	Traceability of emissions and raw materials (Kersten et al., 2024); Lu et al., 2024; García-Muiña et al., 2021).	Lack of environmental standardization in digital systems (El Baz et al., 2022; Bai et al., 2020).
	Economics:	Economics:
	Increased production efficiency Fatorachian & Kazemi, 2021; Ghadge et al., 2022; Sharma et al., 2021);	High initial implementation cost (Santos & Sant’Anna, 2024; Sharma et al., 2024a);
	Reduction of operational costs through automation (Brozzi et al., 2020; Strandhagen et al., 2017);	Dependence on qualified human capital (Sharma et al., 2021; Varela et al., 2019);
	Better control of the supply chain (Kazancoglu et al., 2023; Govindan, 2023; Becerra et al., 2023).	Barriers to entry for small and medium-sized enterprises (SMEs) (Santos & Sant’Anna, 2024; Muniz Junior et al., 2023).
	Social:	Social:
	Creation of new jobs in technological fields (Varela et al., 2019; Cricelli et al., 2024);	Structural unemployment due to automation (Sharma et al., 2021; Strandhagen et al., 2017);
	Improved workplace safety with intelligent systems (Ağseren & Şimşek, 2024; Stock et al., 2018);	Inequality in access to technology between countries (Lopes et al., 2023; Narula et al., 2021);
	Potential for digital inclusion in production environments (Narula et al., 2021; Pletsch et al., 2025).	Privacy risks and ethical issues with data usage (Piccarozzi et al., 2024; Garrido-Hidalgo et al., 2018).
External factors	Opportunities	Threats
	Environmental:	Environmental:
	Government incentives for green practices (Agarwal & Ojha, 2024; El Baz et al., 2022);	Greenwashing in digital practices (Beltrami et al., 2021);
	Integration with the circular economy (Govindan, 2023; Quiroz-Flores et al., 2024; Lu et al., 2024);	Lack of global regulations (El Baz et al., 2022; Bai et al., 2020);
	Consumer pressure for sustainable products (Govindan, 2024; Karmaker et al., 2023).	Misuse of rare natural resources for technology (Stock et al., 2018; Gholami et al., 2022).
	Economics:	Economics:
	Expansion of green markets and ESG (Agarwal & Ojha, 2024; Narula et al., 2021);	Vulnerability to cyberattacks and system failures (Garrido-Hidalgo et al., 2018);
	Digitalization as a competitive advantage (Fatorachian & Kazemi, 2021; Ghadge et al., 2022);	Concentration of the technology market in a few countries (Lopes et al., 2023);
	Growth of data-driven business models (Govindan, 2024; Castro et al., 2023).	Global economic instability affecting investments (Strandhagen et al., 2022).
	Social:	Social:
	Professional retraining programs (Pletsch et al., 2025; Narula et al., 2021);	Digital and technological exclusion (Narula et al., 2021; Lopes et al., 2023);
	International cooperation for technology transfer (Lopes et al., 2023; Uwamahoro et al., 2025);	Cultural resistance to automation (Muniz Junior et al., 2023);
	Strengthening of inclusive public policies (Beltrami et al., 2021; El Baz et al., 2022).	Labor conflicts due to the reduction of traditional jobs (Sharma et al., 2021; Strandhagen et al., 2017).

• Source: Authors (2025).

Meanwhile, the weaknesses and threats mapped in the analysis indicate that social, environmental, and economic risks are still substantial. Digital exclusion and structural unemployment are relevant side effects of intensive automation, especially in developing countries. Moreover, the high energy consumption of computational systems and the increasing generation of electronic waste raise doubts about the real environmental sustainability of these solutions. These challenges require not only technological innovation but also robust public policies, effective environmental regulations, and inclusion strategies to ensure that the benefits of Industry 4.0 are widely distributed.

From an environmental standpoint, Industry 4.0 has the potential to drastically reduce material waste and optimize energy consumption through smarter and interconnected processes. The use of real-time sensors and predictive analytics allows companies to monitor and automatically adjust their production patterns, minimizing environmental impacts. However, this same digitalization creates an increasing dependence on robust computational infrastructure, which demands high levels of energy consumption. The growing need for data centers for data processing and storage has intensified, raising questions about the true ecological balance of Industry 4.0.

In the economic aspect, there is evidence that Industry 4.0 improves productivity and reduces operational costs by automating processes that were previously labor-intensive. Companies adopting emerging technologies gain greater predictability in their supply chains, minimizing failures and optimizing resource distribution. However, this same efficiency can deepen economic inequalities, especially for small and medium-sized enterprises (SMEs), which face financial barriers to investing in digitalization. The high implementation costs and the need for highly qualified professionals create a gap between large corporations and smaller companies, making an equitable transition to a more advanced industrial model difficult.

The social impact of Industry 4.0 is also a complex discussion point. On the one hand, new job opportunities arise due to the demand for technological specialization; on the other hand, traditional jobs are being eliminated at an accelerated pace. The replacement of workers by automated systems and robots has a direct impact on structural unemployment, requiring society to rethink education systems and professional retraining. Furthermore, the implementation of these technologies raises ethical questions about privacy and data security, especially in sectors dealing with sensitive information.

Another relevant aspect is the technological and geopolitical dependence associated with Industry 4.0. More developed countries have led this technological revolution, consolidating their supremacy in strategic sectors and hindering the autonomy of developing nations. The lack of access to cutting-edge technologies perpetuates a global imbalance, making Industry 4.0 a factor that intensifies inequalities between emerging economies and industrialized nations. For this revolution to be truly sustainable, it is crucial that public policies aimed at democratizing access to technology and developing inclusive professional training are implemented.

Given this scenario, a critical and balanced approach to the real impacts of Industry 4.0 on sustainability becomes essential. While the promises of efficiency and reduced environmental impacts are appealing, it is crucial to analyze the negative externalities that may emerge from this process. A successful transition to a sustainable industrial model requires a combination of investments in technology, adequate legislation, encouragement of innovation, and a commitment to social justice. Thus, Industry 4.0 should not be seen only as a means of technological enhancement but also as a tool to build a more equitable, resilient, and environmentally responsible society.

From a theoretical perspective, existing approaches to sustainability and digital transformation remain fragmented. Traditional models such as the Triple Bottom Line primarily assess environmental, economic, and social outcomes, while digital maturity frameworks focus predominantly on technological adoption and operational performance. These approaches, however, often fail to capture the systemic interactions between technological capabilities, governance structures, and sustainability performance.

In this context, the IRIS Framework advances the literature by integrating environmental, economic, social, governance, and technological dimensions into a unified analytical structure. Unlike static evaluation models, IRIS incorporates a dynamic and iterative cycle (DDEE), enabling the operationalization of sustainable digital transformation. Furthermore, the framework explicitly addresses the digital paradox, recognizing that Industry 4.0 simultaneously generates efficiency gains and new environmental, economic, and social challenges.

Considering these complexities, the IRIS Framework (Industry 4.0 for Integrated Sustainability) is proposed as a structured approach aimed at guiding the responsible adoption of emerging technologies while aligning them with the principles of the three pillars of sustainability: environmental, economic, and social. The framework is organized into five strategic action dimensions, each associated with specific strategic focuses and key performance indicators (KPIs), as presented in Table 5.

Table 5
Proposed IRIS Framework: Industry 4.0 for Integrated Sustainability.

Structure of the iris framework
Dimension	Strategic Focus	Key Indicator	Author (Best Reference)
Environmental	Energy efficiency and process circularity.	Carbon footprint	Lu et al. (2024); Liu et al. (2023)
		% of recycled waste	Quiroz-Flores et al. (2024); Kersten et al. (2024)
Economic	Increased productivity with accessible technologies for SMEs.	Digital ROI	Fatorachian & Kazemi (2021); Ghadge et al. (2022)
		Productivity per employee	Ghadge et al. (2022); Strandhagen et al. (2022)
Social	Digital inclusion, workplace safety, and professional retraining.	Digital inclusion index	Lopes et al. (2023); Narula et al. (2021)
		Employment rate	Sharma et al. (2021); Strandhagen et al. (2017)
Governance	Public policies focused on sustainable innovation and ESG incentives.	Access to green financing	Agarwal & Ojha (2024); Karmaker et al. (2023)
Technological	Interoperability, data ethics, and cybersecurity.	Compliance with LGPD/ISO	El Baz et al. (2022); Bai et al. (2020)
		Reported incidents	Garrido-Hidalgo et al. (2018); Lopes et al. (2023)

• Source: Authors (2025).

Additionally, the IRIS operates through a four-step practical cycle composed of Integrated Sustainable Diagnosis, Responsible Design, Technological Execution with Inclusion, and Evaluation and Continuous Improvement, known as the DDEE cycle. As illustrated in Figure 7, this structure organizes the digital transition into a continuous and iterative process, ensuring that Industry 4.0 initiatives are aligned with ESG principles and long-term sustainability objectives rather than implemented in isolation.

Figure 7
Cycle of application of the DDEE framework. Source: Authors (2025).

In the Diagnose phase, organizations conduct an integrated assessment of their digital maturity and its impacts across environmental, economic, social, governance, and technological dimensions. This stage involves mapping existing processes, identifying ESG risks and opportunities, evaluating carbon footprint and resource efficiency, assessing workforce readiness, and verifying regulatory compliance. Instruments such as maturity models, stakeholder mapping, ESG indicators, and SWOT analysis support evidence-based insights. The objective is to establish a clear baseline that guides strategic decision-making and prevents fragmented or purely technology-driven investments.

The Design phase translates diagnostic findings into structured and responsible planning. Organizations define strategic objectives, key performance indicators, risk mitigation strategies, and governance mechanisms. This includes setting measurable sustainability targets such as emissions reduction, digital inclusion rates, and productivity gains, as well as establishing cybersecurity and data ethics protocols. Workforce reskilling programs and alignment with ESG standards and public policies are also incorporated. Responsible design ensures that technological adoption is economically viable, socially inclusive, and environmentally sound.

During the Execute phase, digital technologies such as IoT, artificial intelligence, big data analytics, and automation are implemented in a controlled and adaptive manner. Execution emphasizes accessibility, workforce training, and system interoperability. Pilot projects and phased implementation reduce operational risks and allow real-time adjustments. Inclusive practices are essential at this stage. Small and medium-sized enterprises should receive technical support, employees must undergo professional retraining, and cybersecurity safeguards need to be activated. The aim is to operate innovation while maintaining transparency, ethical standards, and social responsibility.

The Evaluate phase ensures monitoring, accountability, and continuous improvement. Organizations measure performance against predefined indicators such as carbon footprint, digital return on investment, productivity per employee, employment rate, and compliance metrics. Deviations are analyzed and corrective actions are implemented. Lessons learned are incorporated into the next diagnostic cycle, reinforcing adaptive learning. This iterative logic transforms IRIS into a dynamic governance mechanism rather than a static framework, strengthening resilience and long-term sustainability. This positioning differentiates IRIS from existing frameworks by combining analytical integration with a practical and operational implementation logic for sustainable digital transformation.

Thus, the IRIS Framework complements the SWOT analysis by moving from strategic diagnosis to structured implementation. While SWOT identifies strengths, weaknesses, opportunities, and threats, IRIS operationalizes responses through a cyclical governance model. Its architecture enables companies, researchers, and policymakers to mitigate risks, amplify positive impacts, and systematically align Industry 4.0 with sustainable development goals, positioning digital transformation as a catalyst for equitable and sustainable progress.

6. Conclusion

Industry 4.0 represents a profound transformation in the productive sector, promoting the digitalization, automation, and integration of technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Big Data. While these innovations enhance operational efficiency and create new opportunities for value generation, their contribution to sustainability is neither automatic nor uniformly positive. Instead, it depends on the deliberate alignment of technological adoption with environmental, economic, and social strategies.

The analysis of scientific publications indicates a growing adoption of Industry 4.0 technologies, driven by both innovation demands and global sustainability agendas. However, this expansion is accompanied by significant structural asymmetries, including the concentration of technological capabilities in specific countries and regions, as well as the predominance of certain academic outlets, such as the Journal of Cleaner Production, in shaping the discourse.

In response to these complexities, this study proposed the IRIS Industry 4.0 Framework for Integrated Sustainability, structured around five strategic dimensions (environmental, economic, social, governance, and technological) and operationalized through a continuous DDEE cycle (Diagnose, Design, Execute, and Evaluate). The framework advances existing approaches by offering an integrative, systemic, and operational model capable of translating sustainability principles into actionable strategies across different organizational contexts.

Importantly, the findings highlight the existence of a digital paradox, in which Industry 4.0 simultaneously generates sustainability gains and systemic risks. On one hand, technologies enable resource efficiency, transparency, and innovation. On the other hand, they may intensify challenges such as technological unemployment, electronic waste generation, increased energy consumption, and social inequality. These contradictions reinforce the need for governance mechanisms, ethical considerations, and inclusive policies to mitigate unintended consequences.

Thus, Industry 4.0 should not be interpreted solely as a driver of sustainable development, but as a transformative force whose outcomes depend on how it is implemented and regulated. In this context, the IRIS framework contributes by explicitly incorporating these trade-offs, supporting decision-makers in balancing efficiency gains with social and environmental responsibility.

Future research should prioritize empirical validation of the IRIS framework across different sectors and geographical contexts, as well as the development of quantitative metrics capable of capturing both the benefits and risks of digital transformation. Although intercoder reliability procedures were applied, the qualitative nature of content analysis inherently involves interpretative judgment. Expanding the number of coders and integrating complementary quantitative techniques may further enhance analytical robustness. Ultimately, advancing toward a truly sustainable digital transition requires not only technological innovation, but also critical awareness, institutional coordination, and a commitment to equity and long-term resilience.

Data availability

Research data is available in the body of the article.

• How to cite this article:
  Garcia, M. V., Barbosa, D. H., Cotrim, S. L., & Ponce-Ortega, J. M. (2026). Pathways to sustainability in Industry 4.0: insights from a systematic literature review. Production, 36, e20250098. https://doi.org/10.14488/1980-5411.20250098.
• Financial Support
  None
• Ethical Statement
  This study is a literature-based investigation and did not involve experiments, surveys, interviews, or any direct interaction with human participants or animals. Since the research relied exclusively on previously published studies, approval by a Research Ethics Committee and informed consent were not required.

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• Zekhnini, K., Cherrafi, A., Bouhaddou, I., Chaouni Benabdellah, A., & Bag, S. (2021). A model integrating lean and green practices for viable, sustainable, and digital supply chain performance. International Journal of Production Research, 60(21), 6529-6555. https://doi.org/10.1080/00207543.2021.1994164
  » https://doi.org/10.1080/00207543.2021.1994164

Edited by

• Editor(s)
  Adriana Leiras
  Rodrigo Caiado

Publication Dates

• Publication in this collection
  10 Aug 2026
• Date of issue
  2026

History

• Received
  11 Nov 2025
• Accepted
  27 June 2026