Towards Sustainable Development through the Perspective of Construction 4.0: Systematic Literature Review and Bibliometric Analysis

Abstract

The construction industry utilizes a substantial number of resources, which has negative impacts on both environmental and socioeconomic aspects. Therefore, it is important to reduce these negative impacts and maintain sustainable development (SD). Recent studies suggest that integrating Industry 4.0 (also called Construction 4.0 (C4.0) in the construction industry) and SD may help address these concerns, which is a new and ever-evolving field. In order to fully understand SD in the C4.0 context, this paper offers a verifiable and reproducible systematic literature review and bibliometric analysis of associated topics. Through a review of 229 works, this article presents the publication trend, the most prolific journals, countries, institutions, researchers, and keywords analysis, as well as the content analysis of C4.0 impacts on SD based on triple-bottom-line (TBL) dimensions. The authors also identify and summarize the critical success factors (CSFs) of C4.0 toward SD. Overall, findings reveal the potential benefits of C4.0 on SD and contribute to the evaluation of sustainable C4.0 innovations. The key topics and CSFs identified in this work could potentially serve as the basis for future investigations, encouraging and directing interested researchers, and thus supporting both theoretical and practical progress in this evolving research area.

1. Introduction

At the dawn of the 21st century, the world is experiencing the fourth industrial revolution of several organizations and industries, which is commonly referred to as Industry 4.0 (I4.0), in order to improve their quality and productivity [1]. I4.0 is a concept that links embedded system production technologies with intelligent production processes, and it is drastically reshaping the industry and production value chains and business structures [2]. This industrial revolution is brought about by a movement toward a physical-to-digital-to-physical relationship provided by digital technologies, including cognitive and high-performance computing, autonomous robots, big data analytics, the internet of things (IoT), and visualization technologies (virtual and augmented reality), among other technologies [1,2]. Since the proposal of the term “Industrie 4.0” in 2011, the digital transformation enabled by I4.0 has garnered instant attention from industrialists and researchers worldwide. Consistently, the effects of I4.0 on sustainability and the ways in which it might help sustainable growth are garnering greater attention.

Sustainability is a holistic concept with several definitions. The most frequently recognized definition is provided by Brundtland’s report (Our Common Future), which established the term sustainable development (SD) as “the development that meets the needs of the present without compromising the ability of future generations to meet their own needs” [3]. In the wake of the first two major United Nations conferences on environmental and sustainable development, SD has been interpreted as an effort to strike to balance between environmental, economic, and social performance, i.e., the triple bottom line (TBL) [4]. The notion of SD in the context of I4.0 challenges traditional problem-solving approaches and necessitates more systematic approaches to change management. This implies that the present advancement of SD necessitates a move from homogenous systems of ‘doing things better’ to holistic systems of ‘doing better things’ [5]. Stock and Seliger [6] argued that I4.0 offers considerable promise in realizing sustainable industrial value development in the TBL. The construction industry has also profited from the growth of the fourth industrial revolution, which has been named Construction 4.0 (C4.0) [7,8].

Construction is one of the most important industries. However, it has historically relied heavily on craft-based processes and has been linked with poor performance and quality. The slow rate of innovation is significant due to the industry’s negative economic (e.g., low-profit margins, budget overruns, and significant project delays), social (e.g., high accident rate and poor working conditions), and environmental (e.g., high water, material, and energy consumption and waste generation) impacts [7,9,10]. Previous studies stated that C4.0 could help construction companies increase productivity, reduce project delays and cost overruns, manage project complexity, and enhance safety, quality, and resource efficiency [11]. According to World Economic Forum [12], full digitization in the non-residential building would result in annual worldwide cost reductions of 13 to 21 percent during the construction phase and 10 to 17 percent during the operating phase within ten years. In addition, by conserving water, energy, and natural resources via recycling, reuse, innovative waste design management, and pollution control, SD in the construction industry may assist in satisfying the demands of present and future generations [13,14].

Despite being in its nascent stages, the impacts of C4.0 on SD are anticipated to be significant. To the authors’ knowledge, only a small number of reviews have evaluated SD from a C4.0 standpoint. This suggests that the transitions between the C4.0 and SD paradigms are still in their infancy. Thus, this study aims to extend existing knowledge of C4.0 and SD by clarifying their interrelationship and proposing a framework that addresses the critical success factors (CSFs) (i.e., antecedents) of using C4.0 for SD. This review was conducted with the following objectives in mind to achieve the study aim:

• To identify the global trend of scientific publications on C4.0 related to SD;
• To explore the research methods and technologies adopted in the field of C4.0 and SD;
• To examine the current research efforts of using C4.0 for SD and analyze the impacts of C4.0 on SD;
• To identify the CSFs of using C4.0 for SD.

To achieve the above research objectives, the remaining sections of the paper are structured as follows. Section 2 presents the previous review papers’ contributions and gaps. Section 3 explains the review methods utilized for this investigation. In Section 4, descriptive findings from the bibliometric analysis are presented. Section 5 included a comprehensive assessment of the C4.0 and SD within construction. A framework with CSFs for C4.0 towards SD is proposed in Section 6. Section 7 discusses the research contributions, limitations, and potential for future research, and conclusions are drawn in Section 8.

2. Summary of Relevant Previous Review Papers

There are several review studies on C4.0 and SD; however, some literature reviews solely focus on particular components or technologies of C4.0 impacts on SD. Specifically, Llatas et al. [15] performed a comprehensive review to investigate the potential to combine building information modeling (BIM) and life-cycle sustainability assessment for SD problems during the building design stages. Khan et al. [16] offered an evaluation of the inherent potential of 3D printing technology (also known as additive manufacturing) for its assessment of sustainability in terms of labor cost, material flexibility, design flexibility, and operation agility. In addition, Figueiredo et al. [17] conducted an exhaustive literature analysis on blockchain technology, concentrating on its potential for attaining a sustainable building environment.

Moreover, several researchers explored I4.0 and its impacts with respect to TBL sustainability on a broad scale rather than in a specific industry (e.g., the construction industry), while others focused on the implications of C4.0-related technologies on SD. Limited research has attempted to understand the CSFs of C4.0 to SD [18,19]. For example, Beier et al. [20] presented a general review of I4.0 from a sociotechnical perspective. They identified the key features of the concept I4.0 based on content analysis and analyzed how far the key features of I4.0 reflect sustainability. Birkel and Müller [18] investigated the potential of I4.0 for sustainable supply chain management. They indicated that the economic rationales of businesses must be matched with their economic, ecological, and social potentials. Gomez-Trujillo and Gonzalez-Perez [19] conducted a systematic literature review to synthesize prior findings about the mutual relationship between sustainability and digital transformation at the level of the enterprise. When implementing sustainability and digital transformation, they proposed that companies evaluate their current liabilities necessary to overcome structural shifts and potential firm-specific benefits via their value chain. Also, the digital transformation or the fourth industrial revolution should be seen as a driver and a forerunner of SD. Ali and Phan [21] assessed the state-of-the-art of I4.0 technologies and sustainable warehousing of logistics activities, with the latter being condemned as one of the most significant contributors to greenhouse gas emissions and environmental damage. They developed a framework to demonstrate the mediating influence of TBL between I4.0 technologies and sustainable warehouses.

Meanwhile, some researchers have explored the relationships between I4.0 and SD within the context of construction. For instance, Balasubramanian et al. [22] analyzed and categorized the key C4.0 technologies and their positive and negative impacts on TBL sustainability. They developed a framework to map the C4.0 impact on sustainability from automation, digitalization, and advancement of manufacturing, integration and collaboration, and intelligent environment perspectives, which revealed that the positive impacts of C4.0 on the environment and economy far outweigh its negative effects. Circular economy (CE) is a developing mentality that aims for the sustainable use of natural resources, which requires a shift from the linear paradigm of “take, make, use, and discard” to the circular model of “reduce, reuse, recycle, recover, remanufacture, and redesign” [23]. Elghaish et al. [24] conducted a thematic analysis to explore the emerging interrelationship between I4.0 technologies and CE in the built environment. They identified several barriers and enablers for CE strategies, including time- and labor-intensive nature, high initial investment, legislation, and management problems. Also, a conceptual model integrating IoT and blockchain to track building elements and services such as carbon emissions, energy consumption rates, and heating and cooling systems throughout the operation and maintenance phase was presented. In [14], the authors conducted a bibliometric review and presented an overview of publications of I4.0 technologies to SD and related challenges and opportunities in the built environment. Some key opportunities provided by I4.0 were identified, for example, reduction of natural resource consumption, safe work, real-time monitoring, schedule monitoring, traceability of materials, and decision-making assistance.

Table 1. Comparison of related literature reviews.

Literature	Time Frame	Reviewed Papers	Industry	Keyword Search—Bibliometric Analysis—Content Analysis—Framework	Main Focus on C4.0 and SD
[20]	2013–December 2018	51	Not specific	Yes—No—Yes—No	The key features of the concept of Industry 4.0 and its incorporation with sustainability
[18]	January 2011–December 2019	55	Not specific	Yes—No—Yes—Yes	The TBL in the context of Industry 4.0 within the supply chain management
[19]	2010–2020	75	Not specific	Yes—Yes—Yes—No	The adoption of digital transformation and sustainability at the firm level
[22]	2018–2021	29	Construction	Yes—No—Yes—No	The implementation potentials of Industry 4.0 technologies on sustainability achievement
[23]	2015–2021	115	Construction	Yes—Yes—Yes—Yes	The interrelationships between circular economy and sustainability and the role of emerging technologies in fostering circular economy
[21]	2010–November 2021	46	Not specific	Yes—Yes—Yes—Yes	The impacts of Industry 4.0 technologies on sustainable warehousing
[14]	2017–February 2022	27	Construction	Yes—Yes—Yes—No	Assessing progress toward Industry 4.0 technologies and sustainability
This study	1 January 2014–24 July 2022	229	Construction	Yes—Yes—Yes—Yes	The impacts of C4.0 technologies on TBL sustainability dimensions and the CSFs of C4.0 toward SD

summarizes the recent literature reviews and analyses on the I4.0 and SD.

To conclude, previous studies have investigated the topic of I4.0 (or C4.0) and SD from different perspectives (e.g., key features, firm level, supply chain management, logistics, barriers, and enablers). Despite these studies’ existence, the examination of them indicates that they interact at the intersection of only two or three of the four subjects (research trend, C4.0 and its related technologies, TBL sustainability, and CSFs). As such, this paper is needed to bridge the gaps mentioned in the existing review papers.

3. Research Methods

This study aims to review and synthesize the knowledge areas of the existing research about the impacts of C4.0 on SD. In line with the research aim and objectives, our review method adopts a mixed-review approach: systematic review [25] and bibliometric analysis [26]. The systematic review was conducted in compliance with the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines [25]. According to Massaro et al. [27], PRISMA is a rigorous technique that generates information and adds to identifying research patterns and pathways, as well as a prospective future study. Systematic review can provide a thorough picture of the research terrain [28], in contrast to a narrative literature review, in which conclusions primarily focus on descriptive findings of a single topic of knowledge and may be subject to selection bias. In addition, bibliometrics are a common way of constructing a large picture in a literature review [29]. Bibliometric begins with the formulation of questions to be answered, such as when, where, who, and what; when relates to years or the designated period, where to nations or other geographical places, who to authors, and what to key terms [29]. In this study, a three-stage review process is employed, including planning, conducting, and reporting, which is illustrated below.

3.1. Planning Phase

The planning phase starts with determining the scope of the study and the research topics. This study aims to assess the interrelationships between C4.0 and SD as described in the literature. The research questions raised for this investigation are reciprocal between these two aspects.

Table 2. Relevant research questions.

#	Research Questions	Corresponding Section
RQ1	What is the global trend of scientific publications on C4.0 related to SD?	Descriptive results: annual scientific production (Section 4.1), production channels (Section 4.2), the total number of articles published in each country and collaboration network (Section 4.3), top institutions’ production (Section 4.4), and top authors (Section 4.5)
RQ2	Which key investigative research methods are being adopted in C4.0 and SD research?	Methodologies employed in primary studies (Section 4.6)
RQ3	What technologies have been mostly implemented to address SD issues in the C4.0 context?	C4.0-related technologies linked with SD (Section 4.7)
RQ4	What keywords and topics are mostly investigated in the research of C4.0 and SD?	Keywords and themes under C4.0 and SD (Section 4.8)
RQ5	What are the current research efforts and opportunities for SD within the context of C4.0?	Relationships between C4.0 and SD (Section 5.1) and C4.0-related technologies toward SD (Section 5.2)
RQ6	What are the critical success factors of using C4.0 for SD?	An innovation framework illustrating CSFs of C4.0 towards SD (Section 6)

presents the defined research questions of this study.

3.2. Conducting Phase

The second phase comprised selecting databases, defining search strategy, and applying inclusion and exclusion criteria for article filtration. Based on the objectives of this study, the first step consisted of collecting data from previous studies using C4.0 for SD. Structured searches for primary studies were carried out in the main collection of the Scopus and Web of Science Core Collection (WoSCC) databases, as they are the largest citation and abstract databases of the peer-reviewed literature that provides an overview of the world’s high-quality research output in the fields of engineering, the environment, etc. [7,30].

Then, a comprehensive list of effective and alternative terms was compiled and utilized to obtain bibliometric data. In order to do this, C4.0-related keywords were extracted from prior literature studies [31,32,33], whereas SD-related keywords were extracted from [34].

Table 3. Set of keywords.

Category	Search Keywords
Construction 4.0	(“Construction 4.0” OR “Industry 4.0” OR “fourth industrial revolution” OR “4th Industrial revolution” OR “smart manufacturing” OR “smart production” OR “smart factory” OR “smart operations management” OR “smart supply chain” OR “digital twin” OR “cloud manufacturing” OR “cloud computing” OR “cyber-physical system” OR “artificial intelligence” OR “internet of things” OR “industrial automation” OR “intelligent manufacturing” OR “building information model*” OR “digital technolog*” OR “digital transformation”)
Sustainable	(Sustainab* OR Green OR “Social performance” OR “Environmental performance” OR “Economic performance” OR “triple-bottom-line”)
Construction industry	(Construction OR Building OR “Built environment”)

* represents singular and plural.

contains the pertinent keywords and search string. These keywords were joined using Boolean operators (‘OR’ and ‘AND’) to encompass numerous elements of C4.0 and SD, yielding results including terms related to both concepts within the context of construction.

The third process involved identifying inclusion and exclusion criteria to filter the retrieved studies and retain the relevant ones, as seen in

Table 4. Inclusion and exclusion criteria for literature.

Selection Criteria	Inclusion	Exclusion
Document type	Journal papers (including ones in press variants or ahead-of-print)	The document does not qualify as a journal article (e.g., conference proceedings, editorials, or white papers)
Access type	Full-text publications that are scientifically accessible	Non-accessible
Language	English language literature	Other languages
Period	Manuscripts published after 2014	Date before 2014
Industry	Literature covering the construction sector as defined by the AECO industry: architecture, engineering, construction, and operation of built assets	Manuscripts focusing on areas other than construction or built environment (e.g., tourism, energy, and automotive sectors)
Subject area	Literature that reports on the fourth industrial revolution and sustainable development	Others

. The time frame of this study was set to the period 1 January 2014, to 24 July 2022. 2014 was chosen as the starting year because it was the first year the I4.0 was considered in relation to the construction industry [33].

On 24 July 2022, a total of 6362 articles were collected from the Scopus and WoSCC databases. Then, the authors adopted the PRISMA protocol to evaluate and screen the retrieved studies. PRISMA offers evidence-based results while simultaneously enhancing the reporting quality of the review through a transparent literature selection process [25]. According to the PRISMA protocol, the article extraction process is separated into four steps: (1st) Identification, (2nd) Selection, (3rd) Eligibility, and (4th) Inclusion, as shown in Figure 1.

In the identification phase, a pre-defined search string was used in the database search. At least one term from C4.0, SD, and the construction industry occurred in the titles, keywords, or abstracts of the journal articles that appeared in the search results retrieved. The results were then loaded into the Covidence platform (https://www.covidence.org/), an online tool that expedites the evaluation of documents by streamlining the review process and facilitating investigator collaboration. The authors then applied the predefined exclusion criteria to the screening procedure. In the third step (eligibility), full texts were read, and only those articles whose content was directly connected to the issue and fell within the scope of C4.0, SD, and the construction industry were chosen. Finally, 229 articles were included for further analysis.

3.3. Data Processing and Analysis Phase

A systematic review and bibliometric analysis were performed for the examination and interpretation of developed research on C4.0 and SD. The mixed review approach employs both quantitative and qualitative methods concurrently in a single study. In addition to mixing the creation of ideas with empirical evaluation, Gough [35] states that the mixed review technique permits numerous levels and viewpoints. The present study uses the mixed review technique to gain a comprehensive understanding of the reviewed issue.

The systematic review is intended to provide a complete picture of existing research in order to identify knowledge gaps and forecast future research directions. Several researchers have utilized the systematic review methodology in construction management studies, such as [36,37]. A systematic review was deployed to classify the included articles in this study.

In addition to complementing the systematic review, the bibliometric technique provides a quantitative scientific mapping methodology [26,29]. It focuses on the visualization of dynamic and structural components of scientific study. Visual maps can more naturally portray the environment of study and aid the concentration on essential research subjects. Such visual text mining tools as VOSviewer, VantagePoint, Gephi, CiteSpace, and BibExcel are available. The selection of relevant tools is contingent upon the capabilities and characteristics of each tool and analytical technique. In this study, VOSviewer version 1.6.16 was employed to conduct these analyses because of its emphasis on graphical representations of maps, suitability for viewing bigger networks, and text mining capabilities [38]. Science mapping was carried out in two stages: (i) the building of networks based on the co-occurrence of terms in the selected articles, and (ii) the generation of maps that extract relevant information such as patterns, trends, evolution, and outliers [38].

4. Bibliometric Analysis Results of the Selected Articles

The 229 selected papers were analyzed using the above-mentioned review techniques. The results of the analyses are presented in this section.

4.1. Annual Number of Publications on C4.0 and SD

This analysis was used to find out the annual number of C4.0 and SD-related publications published between 1 January 2014, and 24 July 2022. Figure 2 illustrates a rising trend in publishing, with a notably steep increase from 2018 to 2021. Seventeen publications pertaining to the 229 primary studies were published in 2018, 32 in 2019, 45 in 2020, and 68 in 2021. This expanding trend in papers illustrates the importance of C4.0 and its role in SD, especially during the past four years. This tendency implies that academics and practitioners consider SD as an essential component of the C4.0 revolution and a means of solving industry-related issues in the construction sector.

4.2. Journals for C4.0 and SD Publications

The credibility of a publication plays a vital role in influencing its public impression. Publications in the fields of C4.0 and SD are gathered from several journals and knowledge domains. This mapping review reveals that Sustainability (n = 37, IF = 3.889) and Journal of Cleaner Production (n = 29, IF = 11.072) are the most prevalent venues for publishing among those under review. Other notable periodicals mentioned in the original investigations are Buildings (n = 16, IF = 3.324), Automation in Construction (n = 14, IF = 10.517), Journal of Building Engineering (n = 9, IF = 7.144), Engineering, Construction and Architectural Management (n = 8, IF = 3.850), Sustainable Cities and Society (n = 8, IF = 10.696), and many others, as shown in

Table 5. Top ten journals ranked by the number of publications on C4.0 and SD.

Source Title	Papers	IF	Best Quartile
Sustainability (Switzerland)	37	3.889	Q2
Journal of Cleaner Production	29	11.072	Q1
Buildings	16	3.324	Q2
Automation in Construction	14	10.517	Q1
Journal of Building Engineering	9	7.144	Q1
Engineering, Construction and Architectural Management	8	3.850	Q2
Sustainable Cities and Society	8	10.696	Q1
Architectural Engineering and Design Management	6	2.256	Q3
Building and Environment	6	7.093	Q1
Journal of Management in Engineering	5	6.415	Q1

Note: IF: impact factor (2021 journal citation report^®), Best Quartile: journals in the 25% top journals of a category are Q1.

. Notably, the Impact Factor (IF) of the journals included in this analysis was obtained from the 2021 Journal Citation Reports (JCR). From a research standpoint, the articles published in these periodicals discuss the significance of C4.0 and its SD implications. Moreover, the quantity of papers in these various and well-respected periodicals highlights the interdisciplinary character of C4.0 and SD in general.

4.3. Main Countries of C4.0 and SD Publications

Figure 3 illustrates the primary study locations and highlights the distribution of C4.0 and SD across continents. China has produced the most articles, 34, followed by Australia and the United Kingdom, both with 29. The global reach of the topic demonstrates the significance of digital transformation and sustainability in the construction industry.

In addition,

Table 6. Top ten countries that contribute to the topic of C4.0 and SD.

Country	TP	TC	SCP	MCP	TC/TP
China	34	2917	17	17	85.79
Australia	29	2130	10	19	73.45
United Kingdom	29	1017	15	14	35.07
Malaysia	18	1269	10	8	70.50
United States of America	16	1711	4	12	106.94
South Korea	15	732	8	7	48.80
Italy	11	1015	7	4	92.27
Singapore	11	1408	8	3	128.00
Canada	10	714	5	5	71.40
Portugal	10	244	7	3	24.40

Note: TP: total number of publications, TC: total number of citations, SCP: single-country publications, MCP: multiple country publications.

presents the top 10 nations ranked by the total number of publications, total citations, and average citations per document. These Top 10 nations account for about 80 percent of the retrieved papers on this burgeoning topic. It is worth mentioning that the number of publications involving international cooperation is relatively large (>45%), indicating that the issue is conducive to international collaboration. According to the average number of citations per paper, Singapore, the United States of America, Italy, China, and Australia are the five nations with the most academic impact.

Moreover, Figure 4 illustrates the network of international collaboration using VOSviewer. In the VOSviewer map, the size of the circle and color correspond to the frequency of occurrence and cluster type of each individual keyword, respectively [38]. The distance between keywords is established by their relative co-occurrence; for example, two keywords adjacent to each other co-occur more frequently, whereas a large distance between two keywords indicates that they do not occur simultaneously [38]. For the first inquiry, the kind of analysis performed was bibliographic coupling, and the unit of analysis was nations. This was undertaken in order to identify the major locations where organizations of studies are located. China, with 34 papers and 2917 citations, was the most significant area, followed by Australia and the United Kingdom, with 29 documents and 2130 and 1017 citations, respectively. This type of analysis is used to identify the main areas and determine the partnerships between institutions so that in future studies, entities that have been established and have produced positive results in academia can be identified.

4.4. Main Institutions Contributing to the Publications

In addition, the R package is combined with the institution data file to provide insight into the institution with the highest contribution to C4.0 and SD research.

Table 7. Top 20 contributing institutions.

Affiliations	Country	No. of Published Articles
Hong Kong Polytechnic University	China	14
University of New South Wales	Australia	12
Polytechnic University of Milan	Italy	8
National University of Singapore	Singapore	7
Federal University of Technology Akure	Nigeria	6
University of Hong Kong	China	6
Ariel University	Israel	5
University of Johannesburg	South Africa	5
University of Minho	Portugal	5
Deakin University	Australia	4
University of Seville	Spain	4
Western Sydney University	Australia	4
Cairo University	Egypt	3
Cardiff University	United Kingdom	3
Chongqing University	China	3
Chung-Ang University	South Korea	3
Loughborough University	United Kingdom	3
Tongji University	China	3
University of Alberta	Canada	3
University of Malaya	Malaysia	3

lists the names of the twenty organizations with the highest number of published publications.

The university with the most articles published is Hong Kong Polytechnic University in China (n = 14), followed by the University of New South Wales in Australia (n = 12). The Polytechnic University of Milan is ranked third and has contributed eight publications. The finding reflects that the research efforts of C4.0 and SD have been contributed by various institutions on a global scale.

4.5. Main Authors Contributing to the Publications

The R package was used to obtain the author field from the data file.

Table 8. Top ten contributing authors and the number of papers they have authored.

Authors	No. of Published Articles
Olawumi, T.O.	6
Chan, D.W.M.	5
Sepasgozar, S.M.E.	5
Oke, A.E.	5
Carvalho, J.P.	4
Reychav, I.	4
Haddad, A.	4
Bragança, L.	4
Mateus, R.	4
Jalaei, F.	3

lists the ten most prolific writers (with single and multiple co-authorships). Olawumi T.O has contributed the most papers among the top ten authors with a total of six, followed by Chan D.W.M (n = 5), Sepasgozar S.M.E (n = 5), and Oke, A. E (n = 5).

In addition, the author dominance factor (DF) reveals an author’s dominance in article publication. DF is a ratio that indicates the proportion of publications with multiple authors in which the author appears as the first author [39]. The DF ranking measures an author’s dominance in article publication. The mathematical expression for DF is:

$$DF=\frac{Nmf}{Nmt}$$

(1)

Where Nmf represents the number of collectively authored papers in which the author is the first author, and Nmt represents the number of collectively authored papers authored by the author. The DF of contributing authors is shown in

Table 9. Author dominance factor and number of published articles.

Authors	Dominance Factor	Total Articles	Single-Authored	Multi-Authored	First-Authored	Rank by Articles	Rank by DF
Oke, A.E.	1	4	0	4	4	2	1
Carvalho, J.P.	1	4	0	4	4	5	1
Jalaei, F.	1	3	0	3	3	10	1
Olawumi, T.O.	0.83	6	0	6	5	1	4
Sepasgozar, S.M.E.	0.5	5	1	4	2	2	5
Reychav, I.	0.25	4	0	4	1	5	6
Chan, D.W.M.	0.2	5	0	5	1	2	7
Haddad, A.	0	4	0	4	0	5	8
Bragança, L.	0	4	0	4	0	5	8
Mateus, R.	0	4	0	4	0	5	8

. As the first author of all multi-authored publications, Oke, A.E., Carvalho, J.P., and Jalaei, F. have a DF of 1 and rank first.

4.6. Research Methods of the Previous Studies

The retrieved studies were analyzed to identify employed research techniques on the topic of C4.0 and SD. Approximately 18% of the research is conceptual, 15% is simulation-based, and 14% is survey-based. The remaining research relies on an alternative modeling approach, case studies, mixed methods, experiments, secondary data analysis, and interviews, as indicated in Figure 5. Mixed-method research employs both qualitative and quantitative approaches. The majority of the findings indicate that qualitative research methods, such as secondary data analysis and interviews, are underutilized and warrant more investigation.

4.7. Key Technologies of C4.0

The associated research question seeks to categorize the main technologies of C4.0 that support SD. In Figure 6, twelve technologies and general C4.0 concepts and theories were identified in these articles. The majority of the primary studies investigated BIM (n = 122), either as an individual technology or integrated with other technologies, to address sustainability issues. Integration of BIM as a core technology with other C4.0-related technologies is the most prevalent kind of SD research. This makes sense, as BIM is key and completely integrated into all phases of construction projects, and it is essential that all information be digitally represented [40,41,42].

The remaining studies highlight that C4.0 concepts (n = 26), 3D printing (n = 18), machine learning (n = 14), digital twin (n = 12), and IoT (n = 12) were mostly adopted within the context of SD. Some studies centered on the C4.0 concept, and exhibited the technologies to promote SD, which clearly illustrate the theoretical and practical consequences of C4.0 for practitioners and researchers in the SD field. Also, 3D printing, digital twin, machine learning, and IoT have been frequently implemented to address SD-related problems.

4.8. Keywords Co-Occurrence Analysis

This section analyzes the major keywords covered by the 229 articles under consideration. According to [43], keyword analysis is a technique that enables researchers to study large amounts of text without losing their ability to focus in-depth on small portions of the material. By evaluating the co-occurrence connections of the keywords, it is easy to comprehend the internal composition relationship and structure of a given academic domain and to identify the research frontiers of the field.

Keywords are categorized and evaluated in this study using social network analysis by VOSviewer. The keywords with similar meanings are merged, along with those with identical meanings but different spellings. For example, “sustainability” and “sustainable development” are merged into “sustainability,” while “BIM”, “building information modeling”, and “building information modelling” are renamed “building in-formation modelling.” In the end, a total of 417 keywords appeared in all the collected 229 articles on the topic of C4.0 and SD. In order to comprehend the link between the articles, a selection criterion was established in VOSviewer, which allows the software to detect terms that appear more than five times. The software successfully found a total of 107 qualifying keywords as a consequence of the screening. Figure 7 depicts the 2486-link keyword co-occurrence network. Each node represents a keyword, and its size corresponds to the number of papers in which that keyword appears. In addition, the thickness of a line between two terms indicates the frequency with which they appear together.

Table 10. Keyword occurrences and total link strength.

No.	Keywords	Occurrences	Total Link Strength
1	Building information modeling	112	805
2	Sustainability	110	932
3	Construction	107	996
4	Design	85	787
5	Life-cycle assessment	35	349
6	Energy analysis	35	295
7	Environmental impact	34	361
8	Material	33	300
9	Building	32	312
10	Theory	31	361
11	Green building	30	200

lists the terms having a minimum of 30 occurrences.

Regarding the technologies of C4.0, the keyword “building information modeling” (112 occurrences) is the leading technology adopted in SD research. This indicates that SD solutions related to building information modeling draw greater attention from researchers. Figure 8 demonstrates that “building information modeling” is linked with different areas of SD (e.g., energy analysis, resource management, carbon emission, safety, material, and recycling). Other C4.0-related technologies such as “big data” (26 occurrences), “3D printing” (23 occurrences), “internet of things” (21 occurrences), “machine learning” (21 occurrences), “cloud computing” (19 occurrences), “virtual reality” (18 occurrences), “digital twin” (18 occurrences), and “blockchain” (18 occurrences) are mostly mentioned in the retrieved articles’ keywords. The findings are consistent with the previous section. Also, the keyword “integration” has occurred 24 times, indicating that some studies have addressed SD issues via integrated tools rather than a single technology.

Within the context of C4.0, the SD activities frequently appeared in the network are “life cycle assessment” (35 occurrences), “energy analysis” (35 occurrences), “material” (33 occurrences), “carbon emission” (26 occurrences), “circular economy” (24 occurrences), “cost analysis” (20 occurrences), “risk assessment” (17 occurrences), and “safety” (15 occurrences). Besides, the stages of “construction” (107 occurrences) and “design” (85 occurrences) of construction projects have been mostly investigated, followed by “maintenance” (21 occurrences) and “procurement” (10 occurrences).

5. Construction 4.0 and Sustainable Development

The bibliometric analysis provides a high-level grouping of C4.0 and SD research for researchers; nevertheless, a thorough review should be done to identify specific study topics and gaps in C4.0 and SD studies. The 229 papers included for the bibliometric analysis are therefore submitted to a systematic review in order to give a comprehensive summary of C4.0 and SD research.

5.1. The Interaction between C4.0 and SD

C4.0 and SD are both developing research fields within the construction industry. The notion of C4.0 is derived from a convergence of trends and technologies that can potentially transform how built environment assets are conceived, created, and operated. C4.0’s primary characteristics include automation, integration, cooperation, innovation, optimization, decentralization, and sustainability [44]. Theoretically, C4.0 incorporates several cutting-edge technologies and tackles numerous theoretical concepts connected to the ongoing fourth industrial revolution in building. Its primary objective is to innovate the physical environment, boost productivity and collaboration, and optimize SD at the corporate and social levels.

In this study, C4.0 is investigated primarily from the viewpoints of its implementation in order to realize TBL sustainability advantages; for instance, efficient use of resources, defect reduction, and other environmental enhancements make operations more sustainable. Many countries and organizations are adopting innovative technology, sustainable materials, clean and efficient processes, environmental protection, and renewables in order to reduce environmental impacts, utilize renewable energy, reuse new materials, and meet the present and future demands of society, as stated in [45]. Figure 9 depicts the Venn diagram of the TBL sustainability of gathered materials, based on the systematic review. 52 articles, or 22.7%, address all three pillars, as shown by the findings. The plurality of the research, 84 papers, or 36.7%, focused solely on the environmental pillar, as this is the one that is most addressed owing to legal requirements and market preference. In order to improve comprehension of C4.0 and its technological practices on TBL performance, the following section provides an overview of the technological impacts of C4.0 on SD.

5.2. C4.0-Related Technologies and Sustainable Performance

This section illustrated the possible implications of C4.0-related technologies on TBL dimensions. Table 11 provides an overview of C4.0 and SD performance association studies. The sustainability performance categories for both social and economic impacts are based on previous studies [22,46]. These social performance categories include ethics and privacy consideration, long-term value to society, stakeholder engagement and satisfaction, health and safety, and wages and working conditions. The economic performance categories are supply chain collaboration, financial and economic feasibility, cost, quality and schedule management, operational and maintenance costs, and innovation and productivity. According to the global reporting initiative (GRI) guidelines [47], the areas of environmental impact include raw materials used, recycled materials used, energy consumption, renewable energy utilized, effluents and waste, emissions, and environmental impact assessment.

5.2.1. Social Impacts

As for social performance, many studies highlight the potential use of C4.0 technologies for its improvement. The C4.0 technologies could help stakeholders to sustain business and life quality for the long term so as to create long-term value for society. Reviewed studies confirm, for instance, that BIM is expected to encourage stakeholders to agree with common value objectives during the whole project life cycle [48], which can guarantee the business performance of the project. Construction projects produce a large amount of data that could be processed by technologies to acquire more valuable information to ensure social value. Using a variety of sensors and actuators, for instance, the virtual world connects with the real world. IoT can facilitate the development of several applications that make use of vast quantities and types of data [49] and help assure societal benefits following the completion of a project [50]. Also, by using big data tools, stakeholders may better access and evaluate data collected from a range of sources. Wey and Peng [51] utilized big data to construct a dynamic model to simulate changes in the built environment and identify management strategies, thus improving social equity and livability.

In the operation and maintenance stages, user-generated data could be gathered and analyzed to monitor situations specific to a region, such as community necessities [52]. Some researchers have developed digital twin frameworks, enabling integrated data analytics solutions to improve the ability of volunteer respondents, residents, community members, and other stakeholders to cope with the events in the built environment [52,53,54]. Based on project data, Olukan et al. [55] used machine learning to classify laterites-compressed earth bricks, which is a viable alternative to traditional construction materials. Additionally, some studies [50,56,57] pointed out that 3D printing could be used for sustainable low-income housing delivery, supporting sustainable human settlement, especially for the urban poor.

In addition, C4.0 technologies can help improve the engagement and satisfaction of stakeholders. To support stakeholders in making decisions and predicting the risk of Engineering Procurement and Construction (EPC) projects, Choi et al. [58] established the Engineering Machine-learning Automation Platform (EMAP), a cloud-based comprehensive analytical tool based on the combination of big data, artificial intelligence (AI), and machine learning technologies. EMAP was further validated through case studies and proved to be effective for bid analysis, cost estimation, error checking, forecasting, and predictive maintenance. Also, BIM can be used as a powerful method for organizing various, multidisciplinary, and competing components in the life cycle of a project, allowing stakeholders to collaborate and share information, thus enhancing project control and management [59,60].

Similarly, on the basis of a decentralized peer-to-peer (P2P) infrastructure, blockchain provides uniform standards and protocols for information exchange with greater transparency and security. Jiang et al. [61] presented a blockchain-enabled platform to address the difficulties of information exchange among project participants in the Modular Integrated Construction (MiC) project. Based on the findings, the framework not only simplifies construction progress traceability and real-time KPI measurement, but also enhances information dependability, immutability, and transparency. Augmented reality (AR) generates the real-time display of a 3D building prototype with associated design information in a real-world context, which can aid stakeholders in creating and exchanging massive volumes of heterogeneous data [62]. In practice, to counteract the influence of the COVID-19 epidemic on social distance, Tea et al. [63] developed a multiuser immersive VR application that would enable stakeholders to communicate and participate in the same virtual space to facilitate real-time design review.

The construction industry is reported to be the fourth most hazardous industry in terms of deaths. The application of C4.0 technologies can bring positive impacts on health and safety (H&S) in the working environment. For instance, BuHamdan et al. [64] utilized BIM and discrete event simulation (DES) to evaluate and visualize health risks associated with cross-laminated wood construction processes, which may cause hand–arm vibration syndrome (HAVS) risks. To simulate the fire evacuation scenarios and minimize safety risks, Hosseini and Maghrebi [65] utilized the BIM and social force model (SFM) to model the scenarios and simulate the evacuation of complex construction sites. This strategy gives reliable safety decision-making support to project managers. In the study of [66], the effectiveness of IoT for accident prevention on construction sites was examined based on Heinrich’s domino theory of accident causation. The results quantified the potential of IoT to prevent construction accidents. In addition, Alibrandi [67] integrated digital twin and machine learning technologies to develop a risk-aware digital twin in order to conduct full consideration and model the related risks and uncertainties throughout the product’s lifecycle. Safety education is also an important strategy to promote a safe and healthy working environment in construction. To provide construction practitioners with real practical and safe experiences, Le et al. [68] suggested a framework for an online social VR system that enables role-playing, dialogic learning, and social interaction for construction and health education.

Some researchers demonstrated that C4.0 technologies could improve the working environment. For example, Zahid et al. [69] developed an optimization approach named ‘DynamicPMV’ to achieve the indoor thermal comfort for workers. By combining BIM and IoT, this approach takes advantage of the geometric and parametric depth of BIM models and the real-time streaming of environmental data (such as humidity, temperature, and carbon emissions) gathered by IoT. Additionally, a VR-enabled experiment was undertaken in the study [70] to attenuate the temperature-color interaction effects that influence thermal perception in terms of thermal acceptability, thermal comfort, and temperature estimation. The findings suggest that glazing technologies with saturated colors (e.g., transparent photovoltaics) can be used during the design phase, therefore enhancing the indoor comfort of employees.

On the other hand, C4.0 technologies may also bring negative effects on social performance in terms of ethics and privacy. The issues of ethics, data security, and privacy protection are major challenges to big data in construction [71,72]. Weak data security may quickly arouse individual or organizational opposition and can cause severe reputational harm to a corporation. Also, Abioye et al. [73] stated that the ethics and governance of AI technologies is a critical issue that is of great importance to society at large. The competencies of AI technologies could be dangerous if not properly regulated and governed.

5.2.2. Economic Development

Some studies examine the possible implications of C4.0 technologies on the economic and business performance of construction projects. In terms of supply chain collaboration and management, Liu and Lin [74] used the Delphi method and data envelopment analysis to analyze the performance of the application of artificial intelligence to supply chain management. They indicated that AI could enhance the performance of supply chain management from six categories, namely, understanding customers’ true value appeal, supply chain whole process visualization, building modularized supply chain operation structure, real-time supply chain planning and execution of connection system, supply chain early warning, and building operation-sensitized supply chains. In the study conducted by Teisserenc and Sepasgozar [75], they identified the benefits and drivers of sustainable a blockchain-based digital twin (BCTD) for the supply chain management. For instance, BCTDs can enable smart contracts to trace the provenance and usage of commodities and services, thus improving the transparency of regulation of the supply chain. The potential of using cloud computing and IoT for supply chain collaboration is also investigated by researchers. In this case, Ko et al. [76] presented a tracking system based on a cloud-computing service integrated with IoT technologies for automated tracking with ubiquitous access in the supply network (e.g., fabrication, delivery, and installation processes). The cloud-computing platform offers an efficient option for overcoming faults and delays in information flow and enhancing the collaboration of firms in the building supply chain, particularly for small and medium-sized businesses (SMEs).

With regard to a company’s financial and economic feasibility, some researchers argue that investment in C4.0 technologies can bring long-term economic profits to the company. Reizgevičius et al. [77] discussed and calculated the return on investments (ROI) in BIM based on interviews with stakeholders engaged in BIM-based design. The results show a strong relationship between ROI and the gross wage, estimating BIM may provide 20% ROI for the first year. In addition, Kim et al. [78] developed a survey questionnaire to investigate the anticipated impact of utilizing blockchain in construction. They indicated that the smart contract based on a blockchain platform could substantially enhance efficiencies during the contract negotiation and formation process, improving the financial feasibility of the company.

Moreover, the cost–quality–schedule trade-off within the project scope has become a significant criterion to judge whether the project is successful, which is a great concern of researchers and all parties in construction projects. Among the C4.0 technologies, BIM is regarded as an important approach to boosting construction project effectiveness. Vite and Morbiducci [79] stated that BIM could generate multiple benefits for the whole project lifecycle regarding quality and cost and optimize the project schedule. For infrastructure projects, Han et al. [80] linked BIM, IoT, and intelligent compaction to create a foundational platform for monitoring and managing project quality. The practicality and validity of the platform’s prototype system were assessed on an actual construction site. The case demonstrated that the BIM-IoT and intelligent compaction system provide quality real-time monitoring and construction schedule management for road construction. In the study of [81], a BIM-enabled VR platform was developed to track the cost and footprint of building projects during the design stage.

In addition, the design of composite material for practical applications is time-consuming and costly because of multiple parameters in experimental tests. To promote efficacy and efficiency in the development of composite material for cost and time savings, Mahjoubi et al. [82] proposed three machine learning models to predict the life-cycle cost, mechanical properties, and carbon footprint of strain-hardening cementitious composite (SHCC). The application of robotics in construction is also practical for cost reduction and quality improvement. Han et al. [83] conducted experiments to analyze the performance of robotics in steel pipe pile head-cutting works in terms of quality, cost, productivity, and safety. The results found that robotics can improve productivity (45.78%) and annual cost reduction (38.07%) in steel pipe pile head-cutting works.

Moreover, the importance of the operation and maintenance of buildings has been largely underestimated as it has been regarded as unproductive. According to Ihsan and Alshibani [84], building operation and maintenance costs account for more than fifty percent of the construction industry’s total input and more than sixty percent of the contracts awarded. Some researchers argue that the technologies of C4.0 may optimize the maintenance costs of existing buildings. For instance, Kaewunruen and Xu [53] investigated BIM adoption for railway station buildings for the purpose of maintenance and reconstruction. They proved that BIM could optimize maintenance operations and logistics, as well as cut costs and carbon impact. Kim et al. [78] provided a framework for the optimum maintenance a BIM-enabled decision-making model that takes into account eco-friendly materials decreasing building maintenance costs. The economic analysis demonstrated that BIM might save the net greatest amount of energy expenses during the building’s lifetime and incur the lowest renovation costs. In the study of [85], BIM was used in repairing and maintenance of a hospital building. They stated that BIM could provide the initial identification and judgment along with providing the required information on the existing building structure, which is one of the most time-consuming and costly issues of maintenance work. Additionally, Heiskanen [86] demonstrated that IoT evaluates the performance of buildings and equipment in order to forecast problems and respond more effectively through preventative maintenance in order to minimize costs.

Innovation and productivity have been identified as critical for achieving significant improvements in the construction industry [87]. Even though the construction business is recognized as one of the most important economic contributors, it has been criticized for its lack of innovation in comparison to areas such as manufacturing, entertainment, and retail [11]. To cope with trends of digital transformation and solve SD issues, researchers have contributed to exploring innovative approaches and technologies for production improvement and economic growth. For instance, IoT enables the exchange of real-time data within and between organizations and projects to continually monitor physical processes that may have a significant influence on productivity [50,88]. Similarly, additional technologies like BIM, 3D printing, and robots might be adopted to boost productivity by enhancing human–robot cooperation [89,90,91].

Also, researchers found that the application of machine learning and AI in predicting the structural parameters (e.g., compressive strength, flexural strength, and splitting tensile strength) of concrete has been effective [92,93,94,95], improving accuracy and reducing testing time and rework. Additionally, prefabrication technology is a manufacturing process for productivity improvement, which is regarded as the first level of industrialization in construction. C4.0 technologies (e.g., BIM, IoT, digital twin, robotic, and blockchain) can serve as eligible contributors for prefabrication construction improvement since they can mitigate the problems of the prefabrication process; for example, addressing the absence of process continuity, poor interoperability among heterogeneous stakeholders, inadequate visibility and traceability of real-time data [88].

5.2.3. Environmental Sustainability

Environmental sustainability comprises empirical research observing changes in resource consumption (i.e., the use of raw and recycled materials as well as non-renewable and renewable energy), waste creation, water use, emissions and air pollution, and environmental impact assessment. Environmental impact assessments are intended to identify the potential impact issues of a construction project, such as energy, water, waste, material, lighting, ventilation, and noise, as well as any alternatives or mitigation measures [96]. In general, most environmental assessment methods are developed based on LCA [97], which is an integrated ‘cradle to grave’ approach used to assess the environmental performance of products and services. In order to suit the environments and conditions of different countries, some green building certification systems of environmental assessment have been developed [98], such as Building Research Establishment Environmental Assessment Methodology (BREEAM) in the UK, Leadership in Energy and Environmental Design (LEED) in the US, Council Alliance for a Sustainable Built Environment (CASBE) in Japan and Building Environmental Performance Analysis System (BEPAS) in China. The systems have used LCA criteria for buildings as part of their plans to reduce the environmental impact of buildings [98].

Most studies included BIM and LCA technologies at the conceptual stage to assist designers in selecting components and materials that are relatively more environmentally friendly [99,100]. For example, Jalaei et al. [96] investigated the workability and usefulness of the BIM-LCA interface to estimate and calculate environmental impacts. The results show that the integration of BIM-LCA is efficient for environmental impact estimation, which can reduce waste and emissions at the early stage. In practice, Alhumayani et al. [101] applied LCA to evaluate the environmental effect of 3D printing with that of conventional construction processes employing two distinct types of building material (i.e., cob and concrete). The results indicate that the environmental impact of 3D-printed concrete is mostly dependent on the proportions of the mix’s components; thus, adjusted mixes can lessen the environmental impact of 3D-printed concrete. In addition, Yoffe et al. [102] highlighted that the use of big data could improve environmental impact assessment rating systems by providing spatial–temporal quantitative evaluation methodologies. Nevertheless, obstacles such as inconsistent data coverage, standardization of data formats, and unequal access to data by users may now hinder the adoption of big data in environmental impact assessment.

Also, several studies emphasize the C4.0 prospects for resource consumption enhancement through optimization, real-time monitoring, and management. Uddin et al. [103] investigated the BIM implementation for green buildings in terms of construction materials consumption by considering the availability of local construction materials. By utilizing BIM, the project team may optimize the different material combinations early in the process and arrive at the optimal option. Huang et al. [104] introduced a method that efficiently integrates BIM and IoT into a geographic information system in order to accomplish adequate soil recycling. The integrated system provides a fundamental digital building model for excavated soil recycling, irrespective of whether it is activated by current application software, or a program tailored to the needs of a particular company or its stakeholders.

Some researchers have explored the feasibility of using 3D printing to optimize the mechanical properties of rapid-hardening and environmentally friendly building materials. In the study [105], 3D printing was used to create an eco-friendly cementitious material by combining magnesium potassium phosphate cement (MKPC) with varied ratios of fly ash replacement. Bong et al. [106] developed an optimal 3D printable geopolymer mixture by optimizing the geopolymer mixture parameters (e.g., type of hydroxide solutions (HS), type of silicate solutions (SS), and SS/HS mass ratio) across multiple performance criteria, such as workability, shape retention ability, extrudability, and compressive strength. In addition, Xiao et al. [107] examined the hardening qualities and studied the feasibility of replacing natural fine aggregates with 100%-recycled fine aggregates via 3D printing. Results reveal that 3D-printed mortar combined with 100%-recycled fine aggregates has superior mechanical characteristics and deformation compared to mortar without fibers and natural fine aggregates.

Moreover, machine learning and AI technologies were utilized to optimize the mechanical characteristics of recycled aggregate concrete. In this instance, Nunez et al. [108] suggested machine learning models forecast the recycled aggregate concrete compressive strength and optimize its mixture design in order to reduce the carbon footprint and avoid the disposal of enormous quantities of building demolition debris. For various compressive strength classes, the machine learning model generated recycled aggregate concrete mixture designs with a decreased environmental footprint. In addition, Duan et al. [109] examined the 28-day compressive strength of recycled aggregate concrete using an AI-based meta-heuristic search of the socio-political algorithm and XGBoost model. They indicated that the proposed technique is effective for predicting the sufficient mechanical performance of recycled aggregate concrete and permitting its safe and environmentally friendly application for construction purposes.

According to the global status report in 2021 [110], the building and construction sectors are responsible for 37% of energy-related carbon dioxide emissions and 36% of final energy consumption. Therefore, it is necessary to increase the efficiency of energy resource utilization. Some researchers indicated that BIM is useful for energy efficiency since it allows for a variety of simulations and analyses of the produced model, such as lighting and energy analyses and natural ventilation studies [111]. Najjar et al. [112] developed a unique framework to combine BIM and life cycle assessment in order to improve the energy efficiency of running buildings. Using the framework, the yearly energy usage intensity may be lowered by around 45%, the life cycle energy consumption and cost can be reduced by more than 50%, and environmental consequences such as acidification and global warming potential can be reduced by more than 30%. Singh and Sadhu [113] presented a comprehensive energy study to optimize the design parameters and energy linkages between the buildings. Variables such as orientation, wall and roof materials, window-to-wall ratios, heating, ventilation and air-conditioning (HVAC) systems, and the Inter-Building Effect are examined and mimicked to study the energy performance.

In addition, a simpler and user-centric BIM-based strategy for improving energy efficiency in buildings was developed and tested in two classrooms of an educational facility [114]. The results showed that the approach based on daylight contribution reduced energy consumption by approximately 8% for one of the rooms and approximately 12% for the other, while the approach that utilized natural ventilation reduced energy consumption by approximately 7% for one of the rooms and approximately 9% for the other. Petri et al. [111] claimed that BIM for energy efficiency is an ongoing process that requires professional training to strengthen the skills and competences of employees and prepare the way for a fundamental shift in providing systematic, measurable, and effective energy-efficient buildings.

Moreover, other technologies like IoT, machine learning, AI, and digital twin may be utilized for energy-efficient matching optimization and to change system parameters routinely in order to better regulate energy usage. In order to lower the energy consumption of a building during the usage phase, IoT-collected data may give pertinent user recommendations. Wu et al. [115] studied distributed solar energy devices and the collaborative design of solar energy devices and buildings based on IoT in order to improve solar energy device monitoring. In addition, a number of studies assert that C4.0 technologies can facilitate the incorporation of renewable energy sources. Nutkiewicz et al. [116] proposed a unique data-driven energy modeling framework that incorporates machine learning approaches to capture the inter-building energy dynamics and effects of the urban setting. Toosi et al. [117] suggested a life cycle sustainability assessment-based optimization methodology to expedite the data processing and optimization of short-term energy storage devices. Moreover, Tariq et al. [118] enhanced the performance of a sun-facing wall-attached vertically oriented traditional solar chimney integrated with a building based on multicriteria for different climatic zones using a digital twin model.

As a result of its contribution to environmental degradation, construction and demolition (C&D) waste have become formidable obstacles for the SD. C4.0 technologies have garnered significant interest as vital solutions for C&D research. During the design phase, Gupta et al. [119] examined the BIM-based C&D waste management strategies and tools that can assist limit C&D waste. Based on the results, BIM may decrease material waste caused by rework, improper design, and design revisions. Su et al. [120] developed an estimating and evaluation method for building demolition waste by combining BIM, GIS, and life-cycle assessment. During the design phase, the tool is able to measure the quantity of waste and evaluate its effects, while automating waste estimation and impact evaluation. In addition, Kang et al. [121] applied BIM to maximize the reuse and recycling of demolition debris through three primary steps: scanning and surveying, building destruction, and garbage transportation. In this situation, demolition planning will be less arduous and not restricted by the expertise and experience of project managers.

In addition, Muthukrishnan et al. [122] explored the potential of incorporating supplementary cementing material of rice husk (i.e., rice husk ash) into 3D printable concrete. Results indicate that 20% of cement by weight can be substituted with rice husk ash, which significantly improves the rheology of mortar at the needed rate for large-scale 3D printing in construction. At the construction stage, prediction models and algorithms are useful for designing concrete mixtures to mitigate their environmental impacts, such as material waste. For instance, Naseri et al. [123] investigated the optimal mixture proportion of concrete based on machine learning. Yang et al. [124] utilized AI techniques to estimate the ultrasonic pulse velocity of concrete containing waste marble dust. In addition, Voorter and Koolen [125] employed blockchain to trace the C&D waste, in order to mitigate the waste from downstream waste-processing activities. Three key pillars in reducing waste in construction were identified based on VR in the study of [126], namely, education documentation and visualization. In this study, VR was utilized to visualize the strategies of building design and the bill of quantities and material stock included in the project.

Furthermore, with the increasing climate change, C4.0 technologies have been explored to quantify and reduce the carbon footprint. In this case, Yang et al. [127] provided a BIM-enabled LCA technique to support low-carbon design in this instance. Lu et al. [128] developed an analytical framework for carbon emissions based on BIM and LCA. The main steps involved in this framework include defining the boundary of carbon emissions in the life cycle, establishing a carbon emissions coefficients database, calculating carbon emissions, and reporting the results of carbon emissions calculation. Kamari et al. [81] combined BIM and VR to measure carbon footprint and cost, resulting in reduced emissions and expenses and improved design strategy decision making. In addition, predictive models based on machine learning may be linked with optimization approaches to decrease environmental consequences, such as carbon emissions, through design optimization. In the study [129], machine learning technology was used to reduce the embodied carbon content of high-strength concrete mixtures without compromising their mechanical qualities. Moreover, Mahjoubi et al. [82] created a system for predicting the carbon footprint, characteristics, and life-cycle cost of stain-hardening cementitious composites.

Table 11. Summary of literature on C4.0 technologies and SD performance.

Construction 4.0 Technologies	Social Performance
	Ethics and Privacy Considerations		Long-Term Value to Society	Stakeholder Engagement and Satisfaction	Health and Safety		Wages and Working Conditions
Building information modeling			[48]	[59,60]	[64,65]		[69,114]
3D printing			[50,56,57]
Internet of things			[49,50]		[66]		[69]
Big data	[72,130]		[51]	[58]
Digital twin			[52,53,54]		[52,67]
Machine learning			[55]	[58]	[67]
Artificial intelligence	[73,131]			[58]
Augmented reality				[62]
Virtual reality				[63]	[68]		[70]
Cloud computing
Blockchain				[61]
Robotics					[83]
Construction 4.0 technologies	Economic performance
	Supply chain collaboration		Financial and economic feasibility	Cost, quality and schedule management	Operational and maintenance costs		Innovation and productivity
Building information modeling	[77]		[77]	[79,80,81]	[53,85]		[90,126]
3D printing							[91,132,133]
Machine learning	[134]			[82]			[93,94,95]
Digital twin	[75]				[53]		[135]
Internet of things	[76]			[80]	[86]		[50,88]
Artificial intelligence	[74]						[92]
Virtual reality	[63]			[81]			[126]
Cloud computing	[76,136]		[76]
Robotics			[83]	[83]			[89,137]
Blockchain	[75,138]		[78,139]	[78]	[78,86]		[88,139]
Construction 4.0 technologies	Environmental performance
	Raw materials used	Recycled materials used	Energy consumption	Renewable energy used	Effluents and waste	Emissions	Environmental impact assessment
Building information modeling	[103]	[104]	[111,112,113,114]		[96,119,120,121,126]	[81,127,128]	[96,99,100]
3D printing	[105,106]	[107,140]			[122,140]		[101]
Machine learning	[55]	[109]	[116,117]		[123]	[82,129]
Digital twin				[118]
Internet of things		[104]		[115]	[121]
Artificial intelligence		[109]		[118]	[124]
Big data							[103]
Virtual reality					[126]	[81]
Cloud computing
Blockchain					[125]

Table 11. Summary of literature on C4.0 technologies and SD performance.

Construction 4.0 Technologies	Social Performance
	Ethics and Privacy Considerations		Long-Term Value to Society	Stakeholder Engagement and Satisfaction	Health and Safety		Wages and Working Conditions
Building information modeling			[48]	[59,60]	[64,65]		[69,114]
3D printing			[50,56,57]
Internet of things			[49,50]		[66]		[69]
Big data	[72,130]		[51]	[58]
Digital twin			[52,53,54]		[52,67]
Machine learning			[55]	[58]	[67]
Artificial intelligence	[73,131]			[58]
Augmented reality				[62]
Virtual reality				[63]	[68]		[70]
Cloud computing
Blockchain				[61]
Robotics					[83]
Construction 4.0 technologies	Economic performance
	Supply chain collaboration		Financial and economic feasibility	Cost, quality and schedule management	Operational and maintenance costs		Innovation and productivity
Building information modeling	[77]		[77]	[79,80,81]	[53,85]		[90,126]
3D printing							[91,132,133]
Machine learning	[134]			[82]			[93,94,95]
Digital twin	[75]				[53]		[135]
Internet of things	[76]			[80]	[86]		[50,88]
Artificial intelligence	[74]						[92]
Virtual reality	[63]			[81]			[126]
Cloud computing	[76,136]		[76]
Robotics			[83]	[83]			[89,137]
Blockchain	[75,138]		[78,139]	[78]	[78,86]		[88,139]
Construction 4.0 technologies	Environmental performance
	Raw materials used	Recycled materials used	Energy consumption	Renewable energy used	Effluents and waste	Emissions	Environmental impact assessment
Building information modeling	[103]	[104]	[111,112,113,114]		[96,119,120,121,126]	[81,127,128]	[96,99,100]
3D printing	[105,106]	[107,140]			[122,140]		[101]
Machine learning	[55]	[109]	[116,117]		[123]	[82,129]
Digital twin				[118]
Internet of things		[104]		[115]	[121]
Artificial intelligence		[109]		[118]	[124]
Big data							[103]
Virtual reality					[126]	[81]
Cloud computing
Blockchain					[125]

6. A Framework for Construction 4.0 towards Sustainable Development

Determining factors for project or innovation success or failure has been of keen interest to academicians and industrial professionals. Most articles focus on how C4.0 supports SD, but less effort has been paid to understanding the CSFs of this revolution. Although C4.0 and its related technologies have provided many opportunities for SD, the implementation process of C4.0 to SD also involves challenges.

Based on the qualitative data from collected articles, a systematic model was used to structure the CSFs of using C4.0 for SD. By adopting an innovation framework suggested by Ozorhon [141], this study also develops a framework to investigate CSFs of using C4.0 for SD. The innovation framework suggested by Ozorhon [141] explores the innovation process by adopting a systematic perspective in which inputs and outputs are outlined in great detail to facilitate a greater understanding of the entire process. It seeks to comprehend the innovation process within the construction industry by answering the why (drivers), what (inputs, innovation process, and outputs), and how questions (enablers and barriers). The innovation framework was adapted for this particular study, as shown in Figure 10, which served as the basis for the final framework developed in the next step.

From the collected studies, a total of 38 CSFs of the C4.0 for SD were derived. In order to decrease redundancy and duplication, the factors were reorganized by comparing and merging those that were highly connected and identical (e.g., investment in technology and the initial cost of technology). This re-analysis reduced the number of variables from 38 to 21, and

Table 12. Critical success factors of using C4.0 for SD.

CSFs	Description	Supporting References
Drivers	The reasons for adopting C4.0 and its related technologies for SD in construction projects	[141]
Environmental responsibility	Government offers sustainable development policies for the construction industry, which will aid in enhancing organizations’ sustainable environmental impact	[6,13,19,142,143]
Client’s acceptance of adopting new technology	In the era of C4.0, the readiness of clients to accept new technologies or their degree of satisfaction based on past/current experiences	[14,144]
Rapid industrial and corporate growth	In order to improve companies’ competitiveness, the concepts of C4.0 and SD are included in their strategic plan	[10,142]
Inputs	The kinds of resources used to promote C4.0 and SD during the whole process	[141]
Training programs for technologies	The use of training and seminars to educate employees with valuable information and skills to support the implementation of technology	[145]
Financial support from the government	Existence of government-sponsored incentives to encourage technology adoption	[144,146]
Competent technical support team within the company	Existence of necessary technology infrastructure and staff to support technology implementation	[13,144]
Continuous investment in upgrading the system	Organization strategic policies that favor allocation of sufficient budget toward emerging technology adoption	[10,144]
Barriers	The main challenges that are encountered during the whole process	[141]
Poor long-term strategy	The company should consider the possible long-term outcomes and risk assessments. Company leaders worry about the implications of investment mistakes	[10,11,14,147]
Lack of awareness of C4.0	Many organizations are not aware of the benefits of implementing C4.0 and SD concepts; hence they lack motivation for the change	[8,10,11,14,42]
Lack of knowledge and qualified personnel	Adopting C4.0 technology may have an effect on how production or operations are handled, which may result in a disruption of the workplace culture and an increase in the need for qualified employees	[11,14,142,146]
Lack of willingness of staff to learn new technology	The uncertainty of new technologies led to conservatism, which makes staff reluctant to accept new technologies	[141,146,147]
Technology implementation cost	Implementing C4.0 technology incurs considerable expenditures for possessing and operating technology	[11,146,147]
Poor integration of new technological competencies	Incompatible technologies may impede trade interfaces, harmonization procedures, or data transmission	[8,11,14,148]
The change in organizational structure and workflow	Changes within organizational processes (horizontal, vertical and end-to-end) would somehow distort common execution processes	[8,10,144,145,146,147]
Enablers	The factors that can help overcome those challenges	[141]
Support of the senior management	Existence in the C4.0 environment of an organizational atmosphere receptive to learning and supportive of new technologies and sustainability practices	[6,14,145,149]
Better client services	Using technologies (e.g., AR, VR) can improve the interaction with clients and clearly visualize the project plan	[9,147]
Data security and data handling	Using technologies and other approaches to better ensure the protection of data used for enhancing organization performance	[6,17,146,150]
Technology implementation standards for the industry	Existence of good practices and standards to serve as a guide to the implementation of technologies in projects	[11,14,142,145,146]
Productivity improvement of project	Technology enables acceleration of production processes at company and project levels	[142,147]
Cost saving	The technologies would reduce costs such as labor and material cost	[9,22,24,146]
Enhancements of flexibility	Users may now engage with partners at any time and from any location, enhancing the company’s ability to respond swiftly and effectively to inquiries	[9,147]

lists the remaining 21 CSFs, along with a brief explanation and supporting references. Accordingly, a final framework of using C4.0 for SD is developed by incorporating the identified CSFs and their impacts on SD, as shown in Figure 11.

The following propositions are formulated based on the framework above and the results of the systematic review, which can be examined empirically in future studies.

P1. C4.0 technologies have a direct impact (mostly positive) on the SD performance from TBL perspectives.

P2. The implementation of drivers, inputs, and enablers of C4.0 have positive impacts on C4.0 towards SD.

P3. The implementation of barriers of C4.0 have negative impacts on C4.0 towards SD.

7. Implications, Limitations, and Future Works

7.1. Theoretical and Practical Implications of the Study

In the construction industry, the fields of C4.0 and SD are relatively underexplored. The implications of this study are both theoretical and practical. Different contributions have been made to the theoretical development of research on C4.0 and SD. In the first place, it supplements the existing literature by emphasizing the key C4.0 benefits for SD. The research trend and other important constituents are then summarized using a mix-review analysis. Also, the authors analyze the literature regarding the use of C4.0 for SD based on TBL dimensions. By reviewing the literature in each of the three dimensions, this study not only provides conceptual clarity on the topic, but also recognizes research advancements in the field. Moreover, our proposed conceptual framework (see Figure 11) offers the opportunity to test the relationships between constructs, thereby enhancing the potential of the research. In addition, the current review indicates BIM is a fundamental technology of C4.0, which serves as a platform to integrate other digital technologies for construction activities, thus improving project performance and reducing the negative impacts on SD. Future studies could explore efficient ways and build a roadmap for C4.0 based on emerging technologies such as BIM. Finally, a systematic review is always important for further inspiration. Thus, it is expected that this study could be used as the basis for facilitating more promising future research in this area.

From a practical aspect, this study collects a number of previous research findings and contributes to a better understanding of C4.0 and its related technologies, as well as how these technologies are employed in construction projects to address SD issues. This, in turn, would assist in transforming conventional construction project management practices into more innovative ones. In addition, this study analyzes the impacts of C4.0 on SD and proposes a framework of using C4.0 for SD, thus serving as guidance for implementing C4.0 for SD. Specifically, it is recommended that construction stakeholders embark on the following: (i) raising awareness of C4.0 in all company departments; (ii) propose and adopt guidelines for the utilization of C4.0 for SD; (iii) establish reward mechanisms for construction companies that use C4.0 to achieve the purposes of SD; (iv) invest new resources in professional skills training and education for implementing C4.0; and (v) strengthen and encourage greater collaboration between practitioners and researchers, thereby facilitating innovative solutions to address SD issues by adopting C4.0.

7.2. Limitations of the Study

This study provides insights for construction practitioners and academics; however, it is confined to exploratory analysis of studies of C4.0 and SD; therefore, it has certain limits. Firstly, this work is based on a review of the relevant literature utilizing peer-reviewed journal articles from the Scopus and Web of Science databases. Despite being the largest collection of peer-reviewed literature in the world, this selection may have omitted some essential sources for recording C4.0 and SD research, including organizational archives and websites, and conference proceedings. To ensure the quality of the evaluated content, we limited our emphasis to peer-reviewed journal articles. To empirically evaluate and extend the findings of this study, future studies can use more resources from additional scholarly databases (such as IEEE Xplore, ScienceDirect, and Springer). Secondly, our selection of keywords was based on a review of previous literature. It is possible that other relevant keywords will emerge in the future.

7.3. Avenues for Future Research and Recommendations

This study demonstrates how C4.0 adds value to sustainable development and increases awareness of its role in generating sustainable value through digital technology. Based on an extensive literature review of 229 papers, this study’s literature synthesis indicated that the topic of C4.0 and SD was under-explored. To obtain a deeper understanding of the relationship between C4.0 and SD, the following research directions are proposed:

• There is a limited amount of interview-based research that examine the relationships between C4.0 and SD from a methodological standpoint. Interview is considered a qualitative approach due to its in-depth and comprehensive examination of a unique phenomenon. This methodology might be used in the future to obtain a more realistic view of the C4.0 phenomena and to collect industry experts’ opinions on how to implement C4.0 for SD effectively.
• A framework of CSFs for employing C4.0 for SD is proposed, along with three assumptions. This framework is conceptual in nature. Validation and further refinement of the framework could be empirically studied.
• The findings underline the advantages of C4.0 for SD, although C4.0 has negative impacts on SD as well. Cybersecurity is one of the most obvious negative consequences. Thus, the authors recommend more studies to examine the solutions required to solve cybersecurity threats from various perspectives (e.g., managerial, technical, and ethical).
• Compared to other C4.0 technologies, research on the adoption of BIM for SD has expanded significantly. To fully explore the potential of C4.0, the authors recommend that researchers could investigate more on other C4.0 technologies and the integration of multiple technologies to address SD issues.

8. Conclusions

This paper summarizes the current state of the art in the area of C4.0 and SD. Despite the fact that it has only been a few years since the construction industry began to take the I4.0 paradigm seriously, many academics have focused on C4.0 and SD due to the growing need for a technology-driven smart and sustainable built environment. With a rigorous and targeted literature assessment of 229 journal articles, this study could potentially help provide the groundwork for research in this area. In this review, six research questions are proposed at the beginning of this study, and the following findings are concluded as a result of the review:

RQ1. The concept of C4.0 was first proposed in 2014. Since then, the annual publication of C4.0 and SD has increased, especially during the period of 2018 to 2021. The journal of Sustainability has dominated the publications of C4.0 and SD with 37 papers, followed by the Journal of Cleaner Production, Buildings, and Automation in Construction. For individual researchers, Olawumi, T.O. has published the highest quantity of papers on this topic, followed by Chan, D.W.M, Sepasgozar, S.M.E, and Oke, A.E. In addition, Hong Kong Polytechnic University has the most papers on this topic, and China has led the contributions of C4.0 and SD, followed by Australia, the United Kingdom, and Malaysia.

RQ2. In terms of the research instruments used, most studies are conceptual; others such as simulation, survey, modelling, case study, and mixed method have been adopted mostly. However, the qualitative methods of secondary data analysis and interview are less employed in the current study.

RQ3. Many technologies of C4.0 have been utilized to explore their potential for SD. Among them, BIM is discussed mostly, followed by 3D printing, machine learning, digital twin, IoT, VR, and blockchain.

RQ4. As for the keyword’s occurrence in the collected articles, the occurrence of C4.0 technologies is consistent with the findings in Q3. In addition to technology, some keywords such as life cycle assessment, energy analysis, material, carbon emission, circular economy, cost analysis, risk assessment, and safety are mostly indexed.

RQ5. Based on TBL dimensions, the findings indicate that 36.7% of studies focused on the environmental pillar, and 22.7% of articles addressed all three pillars. The ratios of economic and social pillars were taken at 18.8% and 5.7%, respectively. Also, the potential impacts of C4.0 technologies on TBL dimensions are reviewed and summarized. The findings demonstrate that most technologies have positive influences on SD, while they might raise concerns related to ethics and privacy.

RQ6. Based on the collected material, twenty-one CSFs of C4.0 in achieving SD were identified and summarized in this study, which is categorized into drivers, inputs, enablers, and barriers. A final framework of using C4.0 for SD has been developed accordingly.

This study explores the specific opportunities and challenges posed to SD within the construction industry, recognizing that C4.0-related technologies are in the early stage and need to be further studied, examined, and better understood. The authors argue that it is essential for C4.0 pioneers to incorporate measures to mitigate the negative environmental and socioeconomic impacts brought by the construction industry and expand the scientific basis to encourage the exploration of C4.0 technologies. As technological innovation is evolving rapidly, construction stakeholders should improve their awareness of the benefits of C4.0, to ensure that they are not left behind in the development of digital technologies. Similarly, construction companies should ensure their ability and resources to leverage the potential of digital technologies for SD. As more progress is made in addressing the limitations (e.g., high price, low accuracy, incompatibility) of digital technologies, C4.0 will become widely accepted in tackling SD issues in a fast and cheaper way.