Integrated Sustainability Assessment Framework of Industry 4.0 from an Energy Systems Thinking Perspective: Bibliometric Analysis and Systematic Literature Review

Abstract

Sustainable energy systems based on efficiency, low-carbon, and smart technologies are essential for the future energy transition. A new integrated sustainability assessment framework (ISAF) is required to evaluate cross-cutting subjects and future research. Sustainability analysis based on conventional dimensions and complementary categories is needed for a digital energy transition. Industry 4.0 created a new platform and technological portfolio to improve the efficiency and automation of cleaner energy systems (lower environmental and social impacts and high performance). To address these aspects, a new methodology based on bibliometric analysis, systematic literature review, and energy systems thinking was developed. From Scopus and Web of Science databases, 1521 and 959 documents were respectively compiled and merged to select 181 articles related to these research subjects between 2017 and 2021. Out of this total, 62 articles from industrial manufacturing were identified as the most representative energy consumption sub-sector. These articles were analysed from the ISAF using conventional dimensions (environmental, economic, and social) and complementary categories of sustainability (technological innovation, governance and life cycle). The main findings reveal that worldwide studies addressing the nexus between Industry 4.0, Energy and Sustainability have increased significantly in recent years, primaly in high-income countries. These studies have centred on the industrial manufacturing subsector, assessing sustainability unevenly by focusing mainly on technological and environmental issues. Research gaps indicate that a comprehensive assessment of social, governance, and life cycle aspects is still required.

1. Introduction

Approximately 55% (~4.2 billion people) of the world’s population currently live in urban areas, and it is expected to increase up to 65% by 2050 [1]. This demographic transition has a strong influence on the development of megacities in developing countries and industries [2,3]. These aspects are important drivers for economic growth, employment creation, and life quality improvement [4]. The increase in urbanisation also generates negative issues regarding air pollution, natural resources depletion, energy consumption, and significant amounts of waste generation, among other sustainability issues [3,5]. All these critical issues require to be addressed in the short to medium time horizons by government authorities, sector regulators, and decision-makers in close collaboration with industry, academia, and other relevant society stakeholders [6,7].

The Fourth Industrial Revolution (also known as Industry 4.0) has brought a new opportunity for industries to optimise their manufacturing processes, improve energy efficiencies, and incorporate automation at a larger scale for the delivery of high-quality products and services [8,9]. The Industry 4.0 model, which was launched by the Federal German Government in 2011, refers to the intelligent networking of machines and processes for industry using new information and communication technologies [10,11]. It involves the integration of new intelligent technologies, such as the Internet of Things (IoT), Cyber-physical systems (CPS), Cloud computing, Big Data Analytics, and Artificial intelligence [11,12,13]. Smart manufacturing industry processes have been proposed to improve competitiveness, efficiency, productivity, integrated quality of services and products, and safer working conditions for humans [14,15]. Industry and energy sectors should, therefore, pursue the aforementioned benefits. Similar to all previous Industrial Revolutions [16,17]: mechanical production and steam power (1.0); mass production, assembly line and electricity (2.0); and automation and computers (3.0), Industry 4.0 is related to energy production and consumption models [18]. Industry 4.0, along with its derived models, may also be associated with the Energy 4.0 concept, which refers to the smart generation management of energy systems through advanced digital technologies [9,15,19]. Intelligent and sustainable energy management mainly focuses on improving energy efficiency [14], cost optimisation [20], secure and affordable energy supply [19], and the use of clean renewable energy sources [21].

However, from a holistic sustainability perspective, the paradigms of Industry 4.0 and Energy 4.0 have rarely been evaluated together with the life cycle assessment (LCA) of products and services [9,21,22]. LCA should comprise the following crucial stages: (i) the extraction of raw materials and transport [23]; (ii) the administration/substitution of scarce resources [24]; (iii) the manufacturing processes; (iv) the operation and transport; and (v) the end of life (recycling, reuse, waste disposal); including the major environmental burdens caused by the energy and material consumptions [23].

The implementation of the Industry 4.0 model and smart energy management might represent an opportunity to meet the objectives of the Sustainable Development Goals (SDG) agenda at local, regional, and global levels in different productive sectors [10,15,25]. A decade after Industry 4.0 onset, numerous studies have been published worldwide aiming at the sustainable implementation of new technologies in which energy is one of the key topics. Although other concepts of Industry 4.0 have emerged, the analysis of sustainability implications has not been widely discussed. Beyond Industry 4.0, the Industry 5.0 model (or Fifth Industrial Revolution) emerged in 2017, derived from the Society 5.0 model presented by Japan at the CeBIT (German acronym for Centrum für Büroautomation, Informationstechnologie und Telekommunikation) fair. Although the Industry 5.0 model was proposed for a sustainable industry focused on a human-centric vision, Industry 5.0 is still in an immaturity phase from the point of view of practitioners and the research community [26].

In this research work, a comprehensive evaluation of the interrelationship between Industry 4.0 and energy systems was carried out from an integrated sustainability assessment framework (ISAF), here referred to as a the nexus of Industry 4.0, Energy and Sustainability. The specific goals of this work were, firstly, to carry out a robust bibliometric analysis and systematic literature review based on an innovative methodology that improves the abductive research approach and to apply an ISAF for energy systems. The bibliometric analysis was used to observe the trends and performance that worldwide publications are following on this nexus to address the following research questions:

• What are the countries that lead the publications on research subjects?
• What are the institutions leading the publications?
• What are the annual publication trends?
• What is the growing trend of research publications addressing the nexus of Industry 4.0, Energy and Sustainability?
• What are the subject areas addressed by international peer-review publications?
• What are the trends regarding peer-review journals?
• What are the sectors or sub-sectors of the energy system that address the nexus between Industry 4.0 and sustainability?
• What are the sustainability dimensions and complementary categories?
• What are the technical keywords used to link the nexus of Industry 4.0, Energy, and Sustainability?

This innovative methodology was developed based on the energy systems thinking (EST) perspective, which classifies citation records from a technical nomenclature recommended by the International Energy Agency (IEA) for energy supply, consumption, and end-use sectors [27,28]. The coupling of EST with a new conceptual ISAF, composed of conventional dimensions (environmental, economic, and social) and complementary categories (technological innovation, governance, and life cycle perspective), was applied for the first time in the present work to enable the identification and analysis of scientific and technological gaps, and the new opportunities for future research on the nexus of Industry 4.0, Energy and Sustainability. With this vision, an innovative and robust methodology for the analysis of future sustainable energy systems was successfully developed and reported in this investigation. Additional data, supplementary references, and a list of acronyms are included in Supplementary Materials.

2. Work Methodology

A schematic diagram showing the work methodology used in this investigation is presented in Figure 1. As a crucial part of this methodology, a robust bibliometric analysis and systematic literature review based on the integrated key topics “Industry 4.0”, “Energy”, and “Sustainability” (or the nexus of Industry 4.0, Energy and Sustainability) was developed: Figure 1a–e.

The bibliometric analysis is a well-known mathematical and statistical method used by the scientific community to provide quantitative analysis of the performance and productivity of publications, which may be applied to identify emerging thematic areas and crucial gaps and opportunities towards future research [29,30,31,32]. On the other hand, the systematic literature review is a method used to identify and critically assess relevant research, including the collection and analysis of data [33]. The aim of a systematic literature review is to identify and collect data or information that address a particular research question or hypothesis [34].

The work methodology developed in the present investigation was complemented by using an improvement of the general abductive research approach proposed by Seuring and Müller [35], Prajapati et al. [36], and Ghobakhloo and Fathi [37]. A comparative analysis of differences among the research methodologies used in previous literature reviews on Industry and Sustainability was conducted. Summarised information about eleven benchmarking criteria used in these reviews is reported in

Table 1. Comparative analysis of previous studies based on significant differences in research methodologies used for conducting systematic literature reviews.

Benchmarking Criteria	Articles Published in Previous Systematic Literature Reviews
	Seuring and Müller [35]	Prajapati et al. [36]	Ghobakhloo and Fathi [37]	Present Work
Study/Scope	Systematic literature review and the proposal of a conceptual sustainability framework.	Systematic literature review.	Content-centric qualitative literature review to identify the energy sustainability functions of Industry 4.0.	Systematic literature review, database merging and statistical analysis, literature mapping review based on the EST, and the proposal of a novel ISAF.
Research subject	Sustainable supply chain management.	Sustainable reverse logistics.	Industry 4.0 and energy sustainability.	Industry 4.0, energy, and integrated sustainability.
Sector	Manufacturing industry.	Diverse industries (focus on manufacturing).	Energy systems (including manufacturing).	Manufacturing industry (applicable to all energy sectors).
Research methodology	Qualitative content analysis, inductive and deductive categories.	Abductive Research Approach (original and adapted proposal).	Content-centric qualitative literature review, interpretive structural modelling, and cross-impact multiplication matrix.	Abductive Research Approach, systematic mapping review, and the EST.
Search strategy	Structured keyword search.	Structured keyword search.	Structured keyword search.	Structured keyword search.
Citation Databases	EBSCO, Scopus, Metapress and Subito	Scopus	Google Scholar and Web of Science	Scopus and Web of Sciences
Citation records/ Publication language	Peer-review articles English	Peer-review articles English	Peer-review articles English	Peer-review articles English
Total number of articles/ Selected articles analysed	Not reported/191	5959/499	318/51	2480 (1521 from Scopus and 959 from WoS)/181 (bibliometric analysis) and 62 (category analysis)
Methodology stages	Material collection Descriptive analysis Category selection Material evaluation	Material collection Material refinement Descriptive study Selection of categories Categorical analysis Results and conclusion	Material collection and refinement, including backward review of citations. Qualitative analysis. Interpretive structural modelling Reachability matrix Structural model of Industry 4.0 functions for energy sustainability Discussion, concluding remarks and conclusions	Material collection and refinement Database merging and statistical descriptive analysis Literature review mapping based on the EST Selection and analysis of conventional sustainability dimensions and complementary categories Key findings and conclusions (see Figure 1)
Bibliometric analysis/ Rankings	Time distribution Scientific journals Research methodologies Dimensions of sustainable development (environmental, social, and sustainable *)	Time distribution Scientific journals Publishing editorials Universities Authors (Most prolific) Geographic distribution	Not reported	Geographical distribution Time evolution Subject areas Scientific journals Keywords analysis
Sustainability approach	Economic, environmental, social, and sustainable *	Environmental, social, and economic.	Social, economic, and environmental.	Environmental, social, economic, technological innovation, governance, and life cycle perspective.

* Combination of environmental and social dimensions.

.

Seuring and Müller [35] reported a systematic literature review based on sustainable supply chain management and a conceptual research framework towards their theorisation. The authors reported a descriptive analysis supported by a compilation of 191 published articles by considering conventional dimensions of sustainability (environmental, economic, and social). Prajapati et al. [36] performed a systematic review through an abductive research approach based on reverse logistic topics, which enabled research gaps to be identified as future research. Ghobakhloo and Fathi [37] carried out a mapping review to identify the energy sustainability functions of Industry 4.0 and their interactions.

Although the work methodologies used by these three previous studies on Industry and Sustainability present some similarities with the present work (e.g., systematic literature review, structured keywords in various bibliographic databases, and the selection of peer-review articles published in the English language), some other key differences among these studies are clearly identified.

To overcome the limitations found in previous studies, the present work methodology was planned to consider the following improvements:

• The use of a robust bibliometric analysis to address the research questions;
• The use of an optimised merging procedure both to analyse the articles reported by Scopus and WoS databases and to generate a representative bibliographic database of peer review articles without duplicated citation records;
• The application of a new method based on the nexus of Industry 4.0, Energy, and Sustainability by using the EST concept, which provides practical implications for sustainable development and technological innovation;
• The use of a literature review mapping to classify the articles according to the IEA and the United Nations (UN) comprehensive perspective of energy supply and consumption sectors. This classification was necessary to facilitate a comparative analysis among the studies reported in productive sectors or sub-sectors to address challenges, risks, opportunities, and indicators;
• The additional integration of complementary categories of sustainability in comparison with the conventional dimensions is frequently addressed. These categories provide a holistic approach to sustainability and the externalities linked to Industry 4.0;
• The use of text mining for analysing the full text of selected articles and to evaluate their scope in the context of the nexus of Industry 4.0, Energy, and Sustainability;
• The capability of the present innovative methodology to be applied in other sectors, systems or knowledge areas that may be influenced by the Industry 4.0 model (e.g., Cities 4.0, Transport 4.0, Finance 4.0, Education 4.0, Health 4.0, and Agriculture 4.0).

To address all these aspects, a new Integrated Sustainability Assessment Framework (ISAF) methodology based on bibliometric analysis, a systematic literature review, and an Energy Systems Thinking (EST) approach was performed. This methodology involved the following stages: (i) Material collection and refinement (based on a multilevel search screening carried out by accessing the Web of Science and Scopus databases); (ii) Database merging and statistical descriptive analysis; (iii) Literature review mapping (for comparative analysis among the compiled articles); and (iv) Selection and analysis of conventional sustainability dimensions and complementary categories based on a text mining process. Five major methodological stages are schematically shown in Figure 1a–e, which are described as follows.

2.1. Stage (i): Material Collection and Refinement

The first stage consists of a bibliographic search of citation records for a wide variety of documents published in English from 2017 to 2021. The search was conducted by using Scopus and Web of Science (WoS). These databases are considered the most reliable bibliographic sources to find the most relevant documents published in any scientific knowledge areas [38]. Sánchez et al. [39] and Echchakoui [40] pointed out that WoS and Scopus are actually considered complementary databases because they are not “mutually exclusive”.

For each database, the first search criterion was defined by selecting and combining the following keywords and Boolean operators: {“fourth industrial revolution” OR “industry 4.0” AND “energy”}, which must be included in the article title, abstract and author keywords (including the KeyWords Plus^® defined by WoS). From the first search, two Primary Databases (Scopus and WoS) were created: Figure 1a. To select the articles to be compiled in these Primary Databases, two inclusion filters based on articles published in strict peer-review journals and the keyword of sustainability were used as the second and third search criteria. These filters were recommended by Nugent and Sovacool to include sustainability studies published in reputable journals [41,42]. The resulting filtered sets of citation records were defined as Secondary Sub-databases (Scopus and WoS): Figure 1b. A third search criterion was applied to limit the number of articles published within a strict context of sustainability which was achieved by considering the keywords and Boolean operators: “sustainable” OR “sustainability”. Tertiary Sub-databases for Scopus and WoS searching were respectively obtained: Figure 1b.

2.2. Stage (ii): Database Merging and Statistical Descriptive Analysis

To separate duplicated articles and obtain a Preliminary Merged Sub-Database, a computational merging process between the Scopus and WoS Tertiary Sub-Databases was conducted: Figure 1c. The merging process was carried out using the method proposed by Echchakoui [40], which basically considered: (i) the exportation of the bibliography files from the two Tertiary Sub-databases (Scopus and WoS) to BibTex (*.bib) and plain text (*.txt) formats, respectively, and (ii) the combination of these two text files using an RStudio [43] script proposed by Aria [44], which has been included in the supplementary Table S1. According to the RStudio script output, an Excel file (combinedmci.xlsx) containing the merged information from the two Tertiary Sub-Databases was created. This Excel file was manually examined to eliminate “false positives” (i.e., articles “that have been incorrectly matched” [45]) and discard articles from the WoS Tertiary Sub-Database where the KeyWords Plus^® were not found at least once in the text body. The resulting unified Sub-Database was defined as the Preliminary Merged Sub-Database (PREMDB).

A descriptive statistical analysis of the PREMDB was carried out by including the standardisation of the article’s keywords (i.e., titles, abstracts, and authors) to obtain some statistical indicators about the productivity of scientific articles on the nexus of Industry 4.0, Energy, and Sustainability. The specific objective of this descriptive statistical analysis was to present a current overview of the worldwide interest in the nexus of Industry 4.0, Energy, and Sustainability. This task is schematically represented in Figure 1c.

2.3. Stage (iii): Literature Review Mapping

A literature review mapping, as an innovative section of the present methodology, was also conducted for the comparative analysis among the published articles compiled in the PREMDB: Figure 1d. The goal of the review mapping was to select articles of the PREMDB by considering the classification proposed by the IEA on energy supply and consumption and end-use sectors (Figure 2) [27,28]. To our knowledge, the conceptualisation of the present bibliographic classification was based, for the first time, on the EST concept, which refers to the well-known “Systems Thinking” approach in the context of the energy sector [46,47]. This approach considers the system “as a whole” through a cross-sector integration, which comprises the management and interactions among all types of energy (i.e., production, transformation, storage, management, infrastructure, and consumption) [46,47].

EST has been previously used in other engineering and science applications of energy modelling (e.g., buildings, cities, energy production sector, and manufacturing sub-sectors): Shrubsole et al. [48], Gómez Martín et al. [49], Boodhna et al. [50], Mohan and Katakojwala [51], and Yusaf et al. [52]. All the articles compiled in PREMDB were classified using this approach, which enabled a holistic perspective of the article’s relevance in the energy system to be analysed. The IEA classification encompassed the energy supply of primary and secondary energy sectors, including the energy consumption in the end-use sectors, which were adopted from the International Standard Industrial Classification of All Economic Activities of the UN [27,28,53].

By assuming these IEA sub-sectors, the articles compiled in PREMDB were reclassified using groups and disaggregated blocks (sectors, sub-sectors, and clusters), which are schematically represented in Figure 2, which also includes hydrogen, due to its importance to achieving decarbonisation goals in the future. As a result of this grouping, a final database named ENERGY 4.0_DB was created: Figure 1d.

2.4. Stage (iv): Selection and Analysis of Sustainability Dimensions and Complementary Categories

According to UNESCO, “Sustainability is a paradigm for thinking about a future in which environmental, social and economic considerations are balanced in the pursuit of development and an improved quality of life” [54]. This paradigm “is often thought of as a long-term goal (i.e., a more sustainable world), while sustainable development refers to the many processes and pathways to achieve it” [54]. The concept of sustainable development was established in 1987 by the Brundtland Commission Report as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” [55]. This report indicated that the integration of economic, environmental, and social objectives in the long term to achieve sustainability must be based on public policies [55]. Led by the United Nations, the global community has focused on achieving sustainable development that holistically promotes sustainability in the productive sectors. In particular, for the past decade, the world has been moving towards a technological transformation to develop smarter and cleaner production processes based on the Industry 4.0 model. This model focuses on improving efficiency and managing energy and other resources to foster sustainable performance through the support of new intelligent technologies and innovative strategies. Since the Industry 4.0 model was launched, several studies around the world have explored and discussed the sustainability of its implementation in diverse manufacturing companies. These studies indicate that the implementation of Industry 4.0 might help manufacturing industries to have a more intelligent and sustainable performance. However, according to these documents and studies, there is no overall consensus on the concept of sustainability regarding the implementation of Industry 4.0 and the balance among the sustainability dimensions. Moreover, it is uncertain whether the conventional three dimensions (environmental, economic, and social) are sufficient to incorporate all sustainability-related implications of Industry 4.0 in an evolving global context or if new dimensions/categories are still needed [54,56].

Therefore, to address these aspects, three strategic tasks were conducted for the selection and analysis process, which was composed of conventional dimensions [environmental (1); economic (2); and social (3)] and complementary categories [technological innovation (4); internal (corporate) and external (regulations and public policy) governance (5); and life cycle perspective (6)]: Figure 1e. This selection is based on the proposal of diverse international bodies and authors to consider other categories in their assessment frameworks to address diverse challenges of sustainability that have arisen in the last decades. In this sense, the concept of sustainability has evolved since the Brundtland Report to adapt it to the complex global changes [56]. The process of selecting the sustainability dimensions and categories considered in the present work is described below:

2.4.1. Task 1. Benchmarking of Sustainability Assessment Frameworks

Before carrying out the selection and analysis of conventional sustainability dimensions and complementary categories, a benchmarking process to evaluate sustainability assessment frameworks (SAFs) previously published in the literature was carried out.

Table 2. Sustainability dimensions and supplementary categories considered for Integrated Sustainability Assessment Framework (ISAF).

Sustainability Dimensions and Supplementary Categories	Representative Literature References that Propose the Concept of Sustainability Assessment Frameworks (SAFs)
Environmental *	PESTEL [57,58,59,60]; Triple Bottom Line [61]; Four pillars [62]; Conducting Sustainability Assessments [63]; decision-support framework for three sustainability dimensions [64]; OECD Environment and Energy Indicators [65,66]; adaptation from “ecology” of a holistic evaluation [67]; and five dimensions [68].
Economic *	PESTEL [57,58,59,60]; Triple Bottom Line [61]; Four pillars [62]; Conducting Sustainability Assessments [63]; decision-support framework for three sustainability dimensions [64]; OECD Economy and Finance Indicators [65]; holistic evaluation [67]; and five dimensions [68].
Social *	PESTEL [57,58,59,60]; Triple Bottom Line [61]; Four pillars [62]; Conducting Sustainability [63]; decision-support framework for three sustainability dimensions [64]; OECD Health, Jobs, Education and Society Indicators [65]; adaptation from “society and politics” of a holistic evaluation [67]; and five dimensions [68].
Technological innovation	PESTEL [57,58,59,60]; technical aspect [69]; OECD Innovation and Technology Indicators [65]; holistic evaluation [67]; and five dimensions [68].
Governance (internal and external)	PESTEL overlapping “Political” and “Legislative” into “Governance” PESTEL [57,58,59,60]; Four pillars (institutional) [62]; Conducting Sustainability Assessments [63]; OECD Government Indicators [65]; adaptation from “society and politics” of a holistic evaluation [67]; and five dimensions [68].
Life cycle perspective	Decision-support framework for three sustainability dimensions [64]; OECD Indicators of circular economy [65,66]; and holistic evaluation [67].

* Environmental, economic, and social categories are commonly referred to as the sustainability dimensions.

presents a compilation of selected publications reported on these SAFs.

These publications, along with other SAFs, are reviewed below:

• Aguilar [57] proposed the preliminary use of the PESTEL framework (Political, Economic, Societal, Technological, Environmental, and Legal) for analysing external factors that may influence the sustainable performance of companies, projects, or sectors. This framework has also been referred to either as PESTEL or PEST [58,59,60,70,71];
• The Organisation for Economic Co-operation and Development (OECD) suggested the use of a Pressure-State-Response (PSR) framework for evaluating the cause-effect of environmental impacts and their implications [72];
• Elkington [61] proposed the use of the Triple Bottom Line (TBL) framework for evaluating the results of a sustainable performance using the three major pillars of sustainability (environmental, social, and economic) with similar weighting factors;
• Santoyo-Castelazo and Azapagic [64] established a decision-support framework for an ISAF of energy systems by considering three sustainability dimensions and a life cycle perspective. This framework has been referenced in numerous sustainability studies, and it is recognised in some bibliometric reviews as the most cited framework regarding the assessment of energy systems toward energy sustainability [73];
• Sahabuddin and Khan [74] proposed an innovative method based on multi-criteria decision analysis (MCDA) to consider and prioritise economic, environmental, and social dimensions for the sustainability assessment of the energy sector;
• Weigel et al. [67] proposed a holistic evaluation framework for the German electricity system using sustainability assessments and Industry 4.0. This framework uses MCDA and LCA tools, together with expert interviews for the sustainability assessment, including diverse criteria structured in four main categories: (i) technology; (ii) ecology; (iii) economy; and (iv) society and politics;
• Kabeyi and Olanrewaju [68] determined the sustainability of energy transition (electricity generation and supply) by incorporating and analysing five dimensions: (i) environmental, (ii) economic, (iii) social, (iv) technical, and (v) institutional/political.

Regardless of the diversity of frameworks currently available to assess sustainability, there is not a unique SAF to evaluate, specifically the Industry 4.0 linked to the energy systems. In addition, there is no consensus on what other categories should be considered in addition to the triple bottom line. Therefore, the present study proposes a selection of additional categories that should be considered in an ISAF focused on energy regarding the implementation of the Industry 4.0 model.

2.4.2. Task 2. Selection of Sustainability Complementary Categories

Although the broad diversity of the SAFs reported in the literature, there is not currently a standardised framework to integrally assess the sustainability of energy systems in the context of Industry 4.0, which has been defined as one of the main goals of this study. Previous SAFs have considered some other categories beyond the three conventional dimensions of sustainability (environmental, economic, and social) [67,68]. To address most of the externalities of sustainability, complementary categories are required to integrate into a more realistic sustainability framework. Such complementary instruments are needed for measuring variables that are complex or difficult to be quantified with accuracy within the conventional dimensions of sustainability. For example, the technology dynamics cannot only be evaluated by the economic or environmental dimensions. It should be better evaluated in a specific category, such as technological innovation, because it actually responds to the accelerated changes of technology, where these efforts may be allocated to measure these changes, e.g., the technology readiness level (TRL) metrics [75].

With respect to politics, institutional and legislation, these issues should be better grouped into a more specific category of governance, which may be divided into internal and external. For example, the actualisation of laws regarding their rapid implementation in public policies (to regulate and promote the implementation of Industry 4.0) and the governance of sector regulators [76] cannot also be addressed by economic or social dimensions. New and improved environmental requirements are currently demanded for products and services (e.g., the environmental product declaration schemes proposed by the European Commission [77]). In this context, the productive sectors also require including a whole life cycle perspective when assessing the impacts derived from the entire supply chain (i.e., to involve the end-use or recycling stages). The life cycle perspective is therefore crucial to evaluate accurately the positive and negative impacts and the ecological footprint of products and services. The proposal of these complementary categories of sustainability may cover these aspects, which enable the new generation of sustainable energy systems linked with Industry 4.0 to be better monitored.

2.4.3. Task 3. Comprehensive Analysis of the ENERGY 4.0_DB

Each article compiled in the ENERGY 4.0_DB was analysed using a text mining procedure to identify the most relevant articles’ keywords used for addressing a holistic sustainability perspective (conventional dimensions and complementary categories). The objective of this text mining was to extract unstructured information and text data collection by analysing the full text of each article. To perform the text mining, a Python script was codified for collecting the frequency of keywords according to the dimensions and complementary categories of sustainability. The text mining was conducted through the following steps:

• To perform a clean analysis of the text body of each article in a plain text format by extracting references, headers, footers, hyphens, and other non-relevant information. This text body extraction has been recommended by Saha et al. [78] and Zhu and Cole [79];
• To create listings of the main keywords (most written as morphemes) used by all the selected articles regarding sustainability concepts. The listings of these keywords are included in the Table S2 of the supplementary material;
• To analyse each article, a simple Python script was developed for this study. The text mining procedure consisted of a searching algorithm for keywords in a predetermined sample of published articles (Figure 3). To carry out this analysis, three processing files were used as input and output data: (1) the first input data file containing the listing of keywords; (2) the second input data file containing the article to be analysed; and (3) the output data file which enabled the text mining results to be saved.

To complete the proposed methodology, a comprehensive analysis of Energy 4.0_DB based on the ISAF was carried out to obtain the key findings and research gaps of the present study: Figure 1e.

3. Results and Discussion

This section presents the results obtained after applying the methodology developed for this study:

3.1. Stage (i): Material Collection and Refinement

As a first search criterion, Primary Databases (Scopus and WoS) were created with 1521 and 959 citation records (from documents written in English), respectively, which covered a publication period from 2017 to 2021: Figure 1a. By applying the refinement criteria described in Section 2.1, the following filtering results were obtained:

• Peer-review journal articles. By applying the strict filtering criterion related to the publication of articles in peer-review journals, Secondary Sub-Databases of Scopus and WoS were obtained with 523 (34.39%) and 508 (52.97%) articles, respectively. According to this criterion, 998 (65.61%) and 451 (47.03%) articles were separated, respectively: Figure 1b;
• Sustainability scope. By using the keywords and Boolean operator: “sustainable” OR “sustainability”, Tertiary Sub-Databases of Scopus and WoS were obtained with 142 (9.34%) and 152 (15.85%) articles, respectively. These results implied that a total number of 1379 articles (90.66%) and 807 articles (84.15%) from their Primary Databases were separated, respectively: Figure 1b.

3.2. Stage (ii): Database Merging and Statistical Descriptive Analysis

Database merging and a manual examination were applied to eliminate eight “false positive” documents, which were identified early in the Excel file. From this rejection, 181 articles were collected in the Preliminary Merged Sub-Database (PREMDB), from which 105 articles (58%) were found in both databases (Scopus and WoS), whereas 37 (20%) and 39 (22%) articles were obtained as an initial distribution: Figure 4a.

To elucidate the origin of these search inconsistencies, manual screening of these missing articles was conducted. As a result of this validation, 33 out of 39 articles reported by WoS were found only in Scopus; and 15 out of 37 reported by Scopus were only detected in WoS. These discrepancies have been reported as pitfalls or search errors in studies addressing the simultaneous use of these two databases [80]. After this screening, the final distribution of the PREMDB (181 articles) included a higher number of articles indexed in both databases (153 articles overlapped: 85%) and a small number of discrepancies, which were separately indexed by Scopus (22 articles: 12%) and WoS (6 articles: 3%): Figure 4b. From the resulting merged PREMDB, the descriptive statistical analysis of 181 articles was carried out through the following bibliometric indicators and metrics:

3.2.1. Statistics and Geographical Distribution per Country

A worldwide map showing the geographical statistical distribution of articles per country is shown in Figure 5a. As can be observed, the articles were mostly published in 61 countries; out of these nations, 71% were related to high-income economies: Figure 5b.

The top countries leading the publication of articles were led by Italy, with 25 publications (9.1%), China (7.3%), and Spain (6.9%). A total number of 317 institutions participated in the development of investigation projects that led to the publication of these 181 articles was found. Among these institutions (academic, corporate, and government), the University of Johannesburg (South Africa), the Università degli Studi di Modena e Reggio Emilia (Italy), and the Centre National de la Recherche Scientifique (France) were the most productive institutions with an individual contribution of four articles.

3.2.2. Time Evolution of Published Articles

The time evolution of published articles per year is shown in Figure 6a,b. The first statistical plot in Figure 6a shows the number of articles published per year, whereas the cumulative number of publications is represented by the statistical cumulative frequency plot and its exponential curve function (red dotted line): Figure 6b.

An increasing trend of published articles can be clearly observed in Figure 6a,b, with 2021 being the year with the highest number of publications. As can be observed in these plots, the first six articles based on the nexus of Industry 4.0, Energy, and Sustainability appeared in 2017.

From these publications, and after calculating the compound annual growth rate (CAGR) as a suitable metric to evaluate trends in bibliometric analyses, a value of ~134% was estimated from 2017 to 2021. This parameter was calculated by using the equation suggested by Chakravarty and Sharma [81] and Abdeljaoued et al. [82]:

$$\mathrm{C}\mathrm{A}\mathrm{G}\mathrm{R}={\left(\frac{{\mathrm{A}}_{\mathrm{Y}\mathrm{f}}}{{\mathrm{A}}_{\mathrm{Y}\mathrm{o}}}\right)}^{\left(\frac{1}{{\mathrm{Y}}_{\mathrm{f}}-{\mathrm{Y}}_{\mathrm{o}}}\right)}-1$$

(1)

where A_Yo and A_Yf are the number of articles in the initial and final year, respectively, whereas Y_o and Y_f are the initial and final years, respectively.

A mean value of ~36 articles published yearly was estimated by considering an interval from 1 to 181 articles for five years. After analysing the time evolution parameters, it was inferred that quantitative evidence of the growing interest of authors and journals to address and publish articles related to the nexus of Industry 4.0, Energy, and Sustainability.

3.2.3. Statistical Distribution of Published Articles per Subject Areas

By considering the 181 articles compiled in the PREMDB, 17 out of 250 subject areas were selectively identified from the WoS database: Figure 7a.

The quantitative distribution of articles per subject area is also shown in Figure 7a, from where one article may cover more than one subject area. The most relevant subject areas (provided by the WoS scheme, which comprises approximately 250 subject areas in diverse disciplines) addressed in the 181 articles were: (i) “Engineering” (87/181); (ii) “Energy” (69/181); and (iii) “Environmental Science” (63/181). A full description of the 17 subject areas is included in Table S3 of the supplementary material. This analysis allowed the identification of three key aspects: (1) the role and importance of the nexus of Industry 4.0, Energy and Sustainability among the main stakeholders worldwide; (2) the multidisciplinary, interdisciplinary, and transdisciplinary relationships across various areas of knowledge; and (3) the research collaboration networking among different disciplines.

3.2.4. Statistical Distribution of Published Articles per Journal

From the PREMDB, it was found that the 181 articles were roughly published in 107 peer-review journals: Figure 7b. According to the merging analysis, the top three peer-review journals leading the publication of articles were led by “Sustainability”, “Journal of Cleaner Production”, and “Energies”. A schematic diagram showing the top 10 publishing journals is displayed in Figure 7b. As can be observed, ~34% of the articles were published in four journals; ~10% of articles were published in six journals; and the remaining percentage, ~56% of articles, were published in the other 97 peer-review journals.

3.2.5. Bibliometric Analysis of Articles’ Keywords

A comprehensive analysis of the articles’ keywords was also carried out in all 181 articles compiled in the PREMDB. An overview of technical and sustainability concepts addressed by these articles was obtained. The most relevant keywords included in the title, abstract, and author keywords sections and their interactions (frequency, co-occurrence, closeness centrality, and betweenness centrality) were identified.

By using the articles’ keywords: “Titles”, “Abstracts”, and “Author’s keywords”, the Biblioshiny tool, WordCloud, and Co-occurrence Network Maps were created [83]. It is important to note that 7 out of 181 articles did not report the author´s keywords, and only two articles did not publish their abstracts. Some keywords with either different inflexions and derivations (bound morphemes) or linked to a number, slash, hyphen, or apostrophe were independently counted by Biblioshiny. Prior to this analysis, a standardisation of articles’ keywords was necessary to obtain a representative analysis of the nexus of Industry 4.0, Energy, and Sustainability. A full description of the standardisation procedure is reported in Table S4. After the standardisation of keywords, the Biblioshiny results obtained from the WordCloud assessment were schematically shown in Figure 8a–c.

These figures display the main keywords, where the most frequent were highlighted using larger font sizes. The top-ranked keywords most commonly used in the 181 articles were led by “Industry”, “Energy”, and “Sustainable”. From the WordCloud analysis, it was consistently found that the most relevant article keywords were industry, energy, and sustainability. This interrelation is in good agreement with the goals and results reported in the present bibliometric analysis and systematic literature review.

With the aim of validating this interrelation, Co-occurrence Network Maps were drawn in Figure 9a–c. This new assessment was used to identify the connections among all the article’s keywords (or nodes).

The co-occurrence mapping showed the association between two connected nodes, as well as the simultaneous frequency analysis of these nodes per article [83,84]. The node size is directly proportional to the co-occurrence frequency, whereas the thicker link lines represent a stronger connection between two nodes [84]. As a part of this co-occurrence mapping, the nodes were also grouped into clusters which were identified by different colours depending on common shared keywords.

These maps were created using the Spinglass clustering algorithm, and the results were displayed according to the following Biblioshiny indexes (Closeness centrality, Betweenness centrality and Node PageRank): According to these indexes, the co-occurrence maps were represented in Figure 9a–c, where their main clusters, nodes and links were displayed. As can be observed, “Industry 4.0”, “Sustainable (or Sustainability)”, and “Energy” keywords were by far the largest nodes identified in the three co-occurrence maps. These keywords or nodes were systematically surrounded by other neighbouring nodes, which have stronger connections among them, as it is exhibited by the thicker link lines. It is also important to remark that, out of the sustainability (dimensions and complementary categories), only “Technology”, “Environmental”, “Economy”, and “Social” appeared in the co-occurrence maps. This analysis identifies the sustainability and energy topics as key factors for Industry 4.0.

3.3. Stage (iii): Literature Review Mapping

According to the literature review mapping, the 181 published articles were classified based on the EST, which was previously represented in Figure 2. As a result of this classification, three major groups and four disaggregated blocks (represented by subgroups, sectors, sub-sectors, and clusters) were identified: Figure 10a.

Detailed information on this classification is also reported in Table S5 of the supplementary material. As a relevant classification result in Figure 10a, 57.5% of the published articles were reported for the “Industry” end-use sector (104 articles). Most of these 104 articles were specifically classified in the “Manufacturing” sub-sector. The obtained results are in good agreement with the review reported by Papadopoulos et al. [85]. These authors reported a debate between Industry 4.0 and sustainability by discussing an innovative Industry 4.0 framework aligned with the SDG. The implementation of advanced and emerging technologies for the next generation of manufacturing was also explored with implications on production planning and operations management.

It is also important to remark that the cluster of manufacturing industry has been recently highlighted in some previous sustainability assessments performed at different levels and system boundaries (e.g., manufacturing processes and other industrial sector levels) [86].

By considering the “Manufacturing” sub-sector, two major clusters were identified and disaggregated as “Single-process, technology or device” and “Integrated processes”, which reported productivity of 42 and 62 published articles, respectively. According to these results, the “Integrated processes” cluster was selected as the most representative sub-database of the “Manufacturing” sub-sector. A comprehensive assessment of the industry’s operational performance from an integrated perspective of corporate sustainability and their interrelated challenges across the supply chain is therefore needed. This representative sub-database was defined as the Energy 4.0_DB, which was finally considered as the main input data for evaluating the applicability of the ISAF: Figure 10b.

3.4. Stage (iv): Selection and Analysis of Conventional Sustainability Dimensions and Complementary Categories

To evaluate the applicability of the ISAF, a benchmarking process of previous SAFs was carried out (Table 2). Based on this process, the following conventional dimensions and complementary categories were proposed. From this ISAF, the cluster of “Integrated processes” containing 62 published articles was analysed by a text-mining procedure using a Python script (Figure 3). The summarised results of this text mining were reported in

Table 3. Summarised results of the text mining analysis using 62 selected articles (ENERGY 4.0_DB).

Dimension and Category	Technological Innovation	Environmental	Economic	Social	Internal Governance	External Governance	Life Cycle Perspective
Total number of keywords	50.3%	19.9%	10.5%	7.2%	5.9%	3.4%	2.8%
	27,383	10,833	5690	3908	3226	1848	1505
Top keywords	industr *	energ *	econom *	social	compan *	polic *	circular economy (CE)
	4693	2389	1751	847	1444	757	612
	product *	environment *	business	human	supply chain *	government	circular *
	3315	2263	1229	410	551	346	375
	technolog *	wast *	cost *	employee *	customer *	standard *	life cycle
	2649	902	601	312	268	295	310
	manufactur *	reduc *	market *	knowledge	stakeholder *	Sustainable Development Goals (SDG)	Life Cycle Assessment (LCA)
	1977	864	419	281	214	251	191
	process *	material *	finance *	manager *	corporat *	regulation *	cradle-to-grave
	1929	789	305	259	172	80	7
	system *	raw	investment *	skill *	collaborat *	law *	cradle-to-cradle
	1750	329	296	231	158	77	6
	management	recycle *	competitive *	health	cooperate *	ISO	cradle-to-gate
	1111	309	228	167	95	26	3
	efficient *	green	profit	people	transparen *	certification *	gate-to-gate
	832	292	158	149	95	16	1
	digital *	emission *	capital	safety	leadership
	824	273	139	148	55
	smart	water	tax	education *	enterprise *
	698	262	116	142	48

* It refers to the different inflections and derivations of the main morpheme analyzed.

and graphically represented in Figure 11a–c. Detailed information on these results is also reported in Table S6 of the supplementary material, which additionally reports the frequency of keywords per sustainability dimension and complementary category. The listing of the 62 articles of the ENERGY 4.0_DB was included in the references section.

From the application of the text mining to the 62 articles, a total number of 57,496 keywords were quantified. Out of these 57,496 keywords, 3103 corresponded to the morpheme “sustainab*”, which included the “sustainability” or “sustainable” keywords. These morpheme keywords cannot be classified in any dimensions or complementary categories of the ISAF because they are actually involved with the highest hierarchical concept of “sustainability”. The rest of the 54,393 keywords were classified in the corresponding dimensions and complementary categories of the ISAF: Figure 11a. These keywords were therefore distributed as technological innovation (50.3%), environmental (19.9%), economic (10.5%), social (7.2%), internal (5.9%), and external (3.4%) governance, and life cycle perspective (2.8%): see Table 3. In spite of these 62 articles referring to sustainability, the distribution of keywords revealed that the conventional dimensions and complementary categories of sustainability were unequally addressed and mainly focused on technological innovation and environmental aspects: Figure 11a. These results are in good agreement with the WordCloud and Co-occurrence Network Maps shown in Figure 8 and Figure 9.

The imbalance among the ISAF dimensions and categories can also be observed in the ranking plot that shows the top 10 keywords in Figure 11b, where the top three keywords correspond to the technological innovation category. By considering only the conventional pillars of sustainability (environmental, economic, and social dimensions), most of the discussion in the articles was mainly focused on environmental aspects (53%), whereas the economic and social pillars were minimised with only 28% and 19%, respectively: (Figure 11c). These results were in good agreement with those findings reported for sustainable manufacturing practices [86], where the environmental dimension was mainly evaluated in comparison with the economic and social dimensions.

4. Key Findings

This section summarises the outcomes obtained from the application of the new ISAF developed. Among the complementary categories proposed for this ISAF, technological innovation was the most addressed category for the manufacturing industry sub-sector. Technological innovation appears as a central technical topic of the Industry 4.0 model, followed by the next three conventional dimensions of sustainability (environmental, economic, and social). Key findings, research gaps, and new opportunity areas for future investigations on Industry 4.0, Energy, and Sustainability were identified through the text mining process and are summarised as follows:

• The technological innovation category is strongly linked to Industry 4.0, which involves an intelligent technological transformation [9,18]. According to the text mining (Table S6), the 62 articles compiled in the ENERGY 4.0_DB addressed the implementation of new technologies of the Industry 4.0 model (e.g., cleaner production; improvement of management, maintenance, communication, monitoring, planning, inventorying, scheduling, and traceability; application of digitisation to prototyping, customisation, reduction of defects and damaged products; and cyber security concerns, among others). The issues addressed in this category involve the study of intelligent technological transformation based on the implementation of new technologies of the Industry 4.0 model, which has provided innovative and smart solutions for manufacturing industries. New technologies include the Internet of Things (IoT), cloud computing, cyber-physical systems, big data, control systems, smart sensors and machines, robots, automation, machine-to-machine communication, simulation, digital and 3D printing, digital twins, additive manufacturing, and big data analytics. The integration of these technologies is crucial for the successful development of a company in the modern world, not only to increase productivity but also to support sustainable production. Regarding this, the full analysis of the articles compiled in this work has indicated that smart solutions are mainly reflected in optimised and automated sustainable processes, especially in the management of energy and digital resources. Among the issues that require more attention are (i) the affordability and reliability of low-carbon electricity supply (e.g., renewable energy sources); (ii) the acquisition and analysis of reliable, high-quality data to support decision-making (for instance, big data analytics, used to obtain Key Performance Indicators); (iii) the lack of access to technological innovations, updating and replacement components (mainly in middle and low-economies, and small and micro companies); (vi) the difficulty for industries to keep the speed along with the accelerating dynamics of technological change; (v) the cyber security; and (vi) the level of readiness to implement the Industry 4.0 model;
• The environmental dimension of sustainability refers to the rational use of resources and the mitigation of negative externalities by reducing emissions and protecting ecosystems [87,88]. The environmental dimension was the second most relevant sustainability aspect addressed in the 62 articles, with an emphasis on energy, which was the most consumed resource. The articles addressed the environmental benefits of implementing the Industry 4.0 model by mitigating negative impacts generated by manufacturing. These aspects include GHG emissions, pollution reduction, waste management, and depletion of resources (e.g., energy, water, land, renewable, non-renewable, recycled, or raw materials). Among the environmental improvements linked to Industry 4.0, some actions highlight (i) the optimal use of energy, water, and material resources; (ii) the prevention of material losses and product failures; (iii) the waste management, recycling, and reuse; and (iv) the reduction of direct emissions and the global environmental footprint. With the deployment of low-carbon energy technologies (renewable energy) and their use in the manufacturing sub-sector, it is still necessary to assess the negative environmental effects caused by these technologies, including the clean extraction of raw materials for producing machinery and devices. In addition, it is also necessary to understand the actual environmental meaning used in the literature for referring to the manufacturing terms “green”, “eco-friendly”, or “environmentally friendly”;
• The economic dimension refers to the long-term, cost-effective saving practices of production [87,88]. The 62 articles addressed diverse benefits associated with the implementation of the Industry 4.0 model to improve economic growth (i.e., optimizing business performance, productivity, and profitability) through competitiveness increase, new business model creation, and market expansion. These benefits are related to the reduction of manufacturing costs (variable, fixed operating, and administrative). Cost reduction coupled with better management of operational processes and administrative procedures were some aspects addressed for the improvement of the profits. (e.g., savings of time, materials, energy, and other resources). In addition, other aspects of investment, financing, and taxes are also discussed. It is important to indicate that the articles mainly focused on the economic performance of the companies, whereas economic impacts at local, national, regional, or global levels are barely analysed. Although the Industry 4.0 model was created to provide economic benefits for the manufacturing industry, it is still necessary to consider some other economic externalities (positive and negative) that the industries may generate in local, regional, national, or international communities. For example, the economic impacts due to the local labour-force market or the compensation and remediation costs for environmental damage and depletion of natural resources. In spite of the economic benefits detected in this dimension, new business opportunities for material trading among different companies and sectors should be explored. The implementation of new technologies also requires the consideration of key economic challenges, such as the cost-benefit studies to calculate the internal rate of return (e.g., to avoid unnecessary and expensive investments of assets); carbon taxes and economic penalties for pollution; development and access to financing instruments; and the investment in research and development and training;
• The social dimension refers to human development, including human rights and fostering diversity and inclusion [87,88]. The Industry 4.0 model might be associated with diverse benefits, such as quality of life, wages, social security, safety and health, education, and creation of jobs might be some benefits of [9], which were concepts addressed by the articles. With a lower frequency, the authors also mentioned negative social impacts within the company workforce, including gender inequity, layoffs, and stress. Although the social dimension is essential in the sustainability context of digitalisation, it was only mentioned in 7% of the total number of keywords analysed, mainly at the company level. This dimension was identified as the weakest dimension of sustainability, which evidently demands suitable measures, weighting factors on decision variables, and better-integrated sustainability assessments at local, regional, national, and global levels. The social dimension also requires the inclusion of geographic, political, and cultural differences regarding the human capital role in industrial companies [89]. In addition to these aspects, it is still important to incorporate some other issues, such as safe working conditions, fair wages, social security and healthcare, education, employment, inclusion, and gender equality. Regarding the global concern of gender equality, the United Nations states that gender inclusiveness is also essential for prosperity, especially in developing countries [90];
• Governance category. This category refers to regulatory measures to control, facilitate or hinder the implementation of the Industry 4.0 model and is divided into (i) internal governance (at the corporate level), which refers to the organisational structure of the company [20]. The keywords of this division represented approx. 6% of the total number of keywords recorded in the Governance category. These terms were mostly addressed in the studies in comparison with those related to external governance (~3%); (ii) external governance, which comprises national public policies and laws, and international standards [91]. In this division, the keywords represented a lower percentage of the total in this category because new technologies emerge at a faster rate than the advances in legislation.

From the analysis of Energy 4.0_DB, it was also found that sustainable development and social welfare strongly depend on corporate governance dynamics [92,93]. Environmental, social and governance (ESG) projects currently face a great variety of challenges and issues in companies, which require decision-making actions on better technological innovation, networks, and organisational changes. This complementary category is proposed for the successful development of a manufacturing company, which demands internal policies and assessment methods both to improve the internal sustainability performance and to share information with external stakeholders. However, appropriate government and business policies, standards, and national/international commitments are also proposed as essential external governance indicators to regulate the Industry 4.0 implementation and expansion. Future investigations on the application of these indicators will also be required.

Life cycle perspective category. This category envisages the evaluation of sustainability impacts generated by a product or service [9]. It showed the lowest percentage of keywords in the text mining process (<3%), and the life cycle concept was narrowly addressed to the circular economy aspects, which presently intends to optimise resources [94] and maximise the recycling, reuse, or repair of waste materials or products [89]. Out of the 62 articles, the top seven keywords were found in 50 articles, whereas only 12 articles did not mention any keywords in this category. Although these concepts are included in the articles, most of the studies actually did not report in detail such life cycle approaches. In particular, the LCA is widely recognised as an international standardised methodology (ISO 14040 [95] and 14044 [96]), which was proposed to quantify the environmental impacts of products (goods and services) from a life cycle perspective [22]. A few studies have briefly addressed the use of LCA studies for the environmental impact evaluation of companies (~16%). These LCA studies are mainly focused on exploring how the Industry 4.0 model may contribute both to reducing environmental impacts and improving industrial productivity (i.e., for the evaluation of the performance of services and products). Other investigations linking the technology commercialisation and digitisation with LCA studies have also been identified to complete an integrated sustainability assessment from a holistic perspective [97]. Within this context, the full incorporation into the Industry 4.0 model and sustainability assessments, the creation of life cycle data inventories, the standardisation of the evaluation methods based on ISO Standards 14040–44, and specialised training in LCA are highly required.

As can be observed in this investigation, some aspects of the nexus of Industry 4.0, Energy, and Sustainability may overlap in more than one dimension or complementary category due to multivariate implications of parameters and a wide variety of stakeholders. For example, (i) the use of low-carbon technologies (e.g., renewable energy sources) may be grouped in the technological innovation category and the environmental dimension; (ii) digitisation might help to foster a low or zero-carbon economy as well as incorporate the circular economy model, which are aspects that may also be classified in economic and environmental dimensions; (iii) the difficulty in training workers or recruiting skilled labour to use the new technologies may be classified in economic, social, or technological innovation aspects; and (iv) The cyber-security of systems, data, and sensitive information (e.g., confidential and personal), which may be addressed in technological innovation (management of internet-connected systems), internal governance (policies and protocols to avoid information vulnerability); or external governance (for the development of laws that protect privacy and sanction data theft, information disclosure, and cyber-attacks). Data protection and cyber-attacks are the subjects of significant concern for all stakeholders; therefore, it is required to guarantee the security and reliability of communication networks and data management [98,99].

5. Conclusions

A new Integrated Sustainability Assessment Framework (ISAF) methodology based on bibliometric analysis, a systematic literature review and an Energy Systems Thinking (EST) approach was performed. By using searching criteria on suitable bibliographic keywords and Boolean operators and a merging process, Scopus and WoS databases were used for the selective compilation of 181 articles regarding the nexus of Industry 4.0, Energy, and Sustainability. The resulting bibliographic database was additionally evaluated by using the well-known EST from which the “Manufacturing” industry was identified as the most representative end-use sub-sector of energy systems. A debugged database (Energy 4.0_DB) was generated with 62 representative articles, which were also analysed through a text mining process to identify the Industry 4.0, Energy and Sustainability nexus patterns and trends addressed in the “Manufacturing” industry sub-sector studies. This industry sub-sector was evaluated, for the first time, through the ISAF considering the conventional sustainability dimensions (environmental, economic, and social) and complementary categories (technological innovation, governance, and life cycle perspective). By applying a text mining tool to the full text of the 62 articles, keywords frequency for each sustainability dimension and the complementary category were accounted for. From this analysis, it was found that worldwide studies in the research area outlined in this work have increased significantly in recent years, mainly in high-income countries. These studies have focused mostly on the industrial sector and have assessed sustainability in an unbalanced manner. The results revealed that technological innovation and environmental were the most relevant sustainability aspects addressed in these articles. Text mining indicated that a considerable number of keywords from the analysed studies indicated that the concept of “management” (of resources, processes, and services) of manufacturing production was the most prominent activity linked to the Industry 4.0, Energy, and Sustainability nexus. Additionally, in the technological category, this nexus is largely associated with terminology relating to digitisation, efficiency, and smart technologies. The environmental dimension of the analysed articles mainly highlighted energy, materials, and waste management; meanwhile, the economic dimension focused on the economic growth of companies, businesses, and cost reduction. Regarding the social dimension, it is expected that the Industry 4.0 model might help to improve the quality of life and human development; thus, the issue of knowledge is also discussed, and attention is focused on employees and managers. However, in both the economic and social dimensions, the benefits are centred within the company, but not on the benefits to society or the country. In the internal governance category, the main topics referred to customers, the supply chain, and stakeholder involvement, while in external governance, the studies mainly highlighted public policies and national and international standards. The least covered category is the life cycle perspective, where the studies primarily mention circular economy and circularity. In this context, the areas of opportunity and the research gaps identified for future studies include the evaluation of the positive and negative externalities in all aspects of sustainability, especially addressing the social, governance, and life cycle issues, which are associated with concerns that the modern world demands in this productive sector. In this sense, sustainable development depends heavily on innovative strategies and governance; therefore, it is essential to develop, implement, and evaluate plans and policies associated with the synergies of digitisation and sustainability, as the success of the Industry 4.0 model also depends on the development of laws, standards, agreements, and collaborations among stakeholders. These policies must be oriented towards an intelligent technological transition that fosters economic growth, environmental welfare, and human quality of life during the entire life cycle of products and services.

Finally, based on a broader scope, the methodology applied in this study may enable future studies on the comparison of articles from other sectors by considering a more holistic energy system thinking perspective.