Intelligent algorithms and methodologies for low‐carbon smart manufacturing: Review on past research, recent developments and future research directions

Abstract

Significant attention has been given to low‐carbon smart manufacturing (SM) as a strategy for promoting sustainability and carbon‐free emissions in the manufacturing industry. The implementation of intelligent algorithms and procedures enables the attainment and enhancement of low‐carbon clever manufacturing processes. These algorithms facilitate real‐time monitoring and predictive maintenance, ensuring efficient and sustainable operations and data‐driven decision‐making, increasing resource utilisation, waste reduction, and energy efficiency. The research examines the utilisation of algorithms in the context of low‐carbon smart manufacturing, encompassing machine learning, optimisation algorithms, and predictive analytics. A comprehensive literature evaluation spanning from 2011 to 2023 is conducted to assess the significance of low‐carbon approaches in the context of smart manufacturing. An integrated approach is used using content analysis, network data analysis, bibliometric analysis, and cluster analysis. Based on the published literature the leading contributors to low‐carbon manufacturing research are India, China, United States, United Kingdom, Singapore, and Italy. The results have shown five main themes—Low‐carbon smart manufacturing and applications of Algorithms; Industry 4.0 technologies for low‐carbon manufacturing; low carbon and green manufacturing; Low‐carbon Manufacturing, and Product design and control; Lean Systems and Smart Manufacturing. The purpose of this study is to provide policymakers and researchers with a guide for the academic development of low‐carbon manufacturing by evaluating research efforts in light of identified research deficits.

INTRODUCTION

The United Nations 2030 Agenda emphasises the significance of incorporating sustainability principles into business strategies. This encompasses decarbonisation of industries, sustainable manufacturing, and low-carbon production [1]. Notwithstanding the criticality of intelligent technologies in attaining sustainable and low-carbon manufacturing, there exists a limited body of exhaustive research in this field [2, 3]. Low-carbon management, which is widely recognised by academic and industrial researchers, incorporates more intelligent manufacturing practises that have demonstrated promise in numerous research domains and disciplines [4]. The increasing attention from various sectors indicates that low-carbon smart manufacturing (L-CSM) is poised for swift progress, despite being in its nascent phase [4]. The development of interdisciplinary research may accelerate industrial applications [5]. L-CSM is the application of contemporary processes and technologies in the manufacturing sector with the aim of reducing emissions and improving efficiency [6]. It integrates the functionalities of intelligent manufacturing, which leverages data analytics and digital technologies to streamline production processes, with the tenets of low-carbon manufacturing, which prioritise the eradication of greenhouse gas emissions [7]. Prominent enablers within the IE 4.0 framework facilitate the establishment of automated and digital production environments [8], which collectively improve production processes [9].

As a result of persistent demands to reconcile environmental concerns, sustainable development, and competitiveness, L-CSM has become an indispensable prerequisite in the contemporary industrial environment [10, 11]. Furthermore, the concurrent rise in apprehensions regarding climate change, resource scarcity, and the ecological ramifications of industrial activities has generated a demand for low-carbon manufacturing [12]. The carbon emissions of the global community are significantly influenced by the manufacturing sector's energy consumption and reliance on conventional sources for industrial processes [13]. Manufacturers are currently adopting state-of-the-art approaches and technologies in order to surmount these obstacles and attain sustainability objectives while maintaining efficiency and competitiveness [14].

Manufacturers can promote economic growth and substantially mitigate carbon emissions by integrating cutting-edge technologies with environmentally sustainable practises [15]. Contemporary manufacturing companies are intrigued by the integration of eco-control systems as part of cooperative endeavours aimed at establishing an ecosystem for low-carbon supply chains [16]. Therefore, a manufacturing approach that is both innovative and integrated incorporates optimised processes, intelligent systems, and digital technologies in an effort to reduce carbon emissions and maximise resource utilisation [17]. Low-carbon SM is predicated primarily on the incorporation of renewable energy sources [18]. Conventional manufacturing practises are highly dependent on conventional resources, which make a substantial contribution to greenhouse gas emissions [19] by implementing healthier production practises and substantially reducing their carbon footprints through the use of renewable energy sources [20, 21]. The implementation of energy-efficient sources is a critical component in the pursuit of low-carbon SM [12]. This entails the implementation of intelligent systems and automation in order to maximise energy efficiency across the entire manufacturing process [22]. By utilising intelligent sensors, real-time monitoring, and data analytics, energy consumption can be precisely regulated and optimised, preserving resources while maximising output [23].

Furthermore, efficient refuse management is critical for low-carbon SM [5, 24]. Through the implementation of waste reduction and recycling strategies, manufacturers have the ability to mitigate the negative consequences [25]. The adoption of circular economy principles, material recycling, and waste reduction not only serve to enhance the environmental friendliness of manufacturing processes but also result in financial gains and improved profitability [9, 26]. Moreover, optimisation of the supply chain is vital to low-carbon SM [27, 28]. Efficient logistics and transportation planning have the potential to significantly reduce energy consumption associated with the transportation of products [29, 30]. The optimisation of carbon-intensive activities, such as route planning, inventory management, and transportation modalities, can be achieved through the utilisation of technologies [31]. Collaboration and the exchange of knowledge among stakeholders in the industry are critical for the successful implementation of low-carbon smart manufacturing [32, 33]. Collaboration among governments, industry associations, research institutions, and manufacturers is imperative in order to institute policies, standards, and incentives that foster the adoption of sustainable manufacturing practises. The imperative to tackle climate change and attain sustainable development underscores the need for intelligent manufacturing with low-carbon emissions [34, 35].

Thus, this study has conducted a comprehensive review on the intelligent algorithms and methodologies for low-carbon smart manufacturing. The study investigates the applications of algorithms, including ML, optimisation algorithms, and predictive data analytics, in low-carbon smart manufacturing using best practices, use-cases, and academic review of technological advancements that are accelerating the adoption of low-carbon approaches across the industry. By extending on previous SLRs, this research work contribute to the existing body of literature currently available on the use of algorithms in low-carbon smart manufacturing. Firstly, it provides a thematically organised, innovative classification of earlier studies in terms of their potential applications, constraints, and suggestions. Secondly, based on the results of the SLR, we suggest a framework for synthesising information to highlight potential concerns that need academic attention to advance the current state of knowledge presently. The present article conduct an assessment on the use of algorithms, such as computational intelligence techniques including algorithmic learning, machine learning, optimisation algorithms, and predictive analytics, in low-carbon smart manufacturing, explores the potential for these advanced technologies to drive sustainability and efficiency in the manufacturing industry while reducing carbon emissions. This investigation aims to evaluate three research questions to understand the research progress in this area, including:

• RQ1. What is the current research landscape regarding the utilisation of intelligent algorithms in low-carbon smart manufacturing?

• RO1: To investigate and analyse the current research landscape regarding the utilisation of intelligent algorithms in low-carbon smart manufacturing.

• RQ2. What are the primary applications wherein low-carbon smart manufacturing has been applied?

• RO2: To systematically identify and categorise the primary applications and domains where low-carbon smart manufacturing practices have been implemented.

• RQ3. What are the future avenues in low-carbon smart manufacturing that might benefit from the application of algorithms?

• RO3. To investigate and identify future avenues within the domain of low-carbon smart manufacturing that could realise significant benefits from the strategic application of algorithms.

Six sections constitute the remaining part of the article. The literature was discussed in Section 2. In Section 3, the bibliometric analysis is explained. In Section 4, network analysis will be addressed along with the most recent developments in manufacturing companies' digital supply chains. The study is summarised in Section 5 with recommendations for further investigation.

SYSTEMATIC LITERATURE REVIEW (SLR)

SLR is a rigorous process utilised to conduct a thorough examination and analysis of existing and contemporary work undertaken within a specific area or field. It facilitates the evaluation and further investigation of the prevailing patterns in a particular research area. By doing so, it simplifies the identification of limitations and potential avenues for future research [36]. By evaluating and investigating prior research endeavours, the study adopts the principles of SLR to acquire a comprehensive understanding of the previously defined research topic [37]. Figure 1 depicts the research framework.

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Selecting a database

Choosing a database to search through the current and prior work is the first step in beginning a literature review. In the present research, publications by Emerald Publishing, Taylor and Francis, Elsevier, Springer and IEEE those are listed in SCOPUS and Web of Science databases. The timeframe for the study is 2011:2023.

Choosing keywords

Based on the literature, the article examines the adoption methods in the industrial sector. Choosing the right keywords is crucial for article gathering in every area. The search items for secondary data for searching ‘Low carbon’ AND ‘Smart manufacturing’; ‘Data Driven" AND ‘Smart Manufacturing’; ‘Data driven’ AND ‘low-carbon’ AND ‘smart manufacturing’; ‘Life Cycle Assessment (LCA)’ AND ‘Smart Manufacturing’; ‘End-of-Life’ AND ‘Smart Manufacturing’; ‘Energy Management Systems’ AND ‘Smart Manufacturing’. Table 1 demonstrates the search items and systematic literature review done in the present study.

TABLE 1 Search terms and systematic literature review.

	Search Strings	Iteration 1	Iteration 2	Iteration 3	Iteration 4
1	‘Low-carbon’ AND ‘smart manufacturing’	105	94	40	37
2	‘Data driven’ AND ‘smart manufacturing’	142	122	93	84
3	‘Data driven’ AND ‘low-carbon’ AND ‘smart manufacturing’	122	112	92	83
4	‘Life Cycle assessment (LCA)’ AND ‘smart manufacturing’	88	23	22	19
5	‘End-of-life’ AND ‘smart manufacturing’	138	32	29	27
6	‘Energy management systems’ AND ‘smart manufacturing’	112	21	19	17
7	‘Lean manufacturing’ AND ‘smart manufacturing’	118	47	38	34
		720	357	293	264

Acceptance and rejection criteria

The only items provided are the subjects specified (Business, Management, and Accounting) and the calendar year (20011:2023). A grand total of 264 documents were discovered. For future research, the ‘Scopus’ and ‘Web of Science’ databases will be queried using every single search parameter.

Inclusion and exclusion criteria

English-language inclusion criteria include conferences, peer-reviewed journals, and book chapters. The exclusion criteria encompass articles that have been published in conferences, non-refereed journals, and magazines.

BIBLIOMETRIC ANALYSIS

Bibliometric analysis is an evidence-based research methodology that examines trends and correlations within academic publications, with a particular focus on citation and publication data as indicators of the significance and impact of particular research papers [40]. A variety of quantitative applications are utilised in bibliometric analysis to analyse bibliometric data, such as citation studies on published works [41]. A total of 720 articles in the field of low-carbon manufacturing and smart technologies were identified through bibliometric analysis using Scopus and Web of Science. Following multiple rounds of iterations, the authors ultimately assessed 264 articles that were published between 2011 and 2023. A variety of operations and supply chain management domains were investigated in prior research in preparation for the bibliometric analysis. ‘Digital technologies and sustainable manufacturing’ [41]; ‘disruption and risk management in manufacturing operations’ [42]; ‘sustainable manufacturing’ [43] comprise the broad scope of the areas. In light of the advancements in the field of low-carbon manufacturing, bibliometric analysis plays an important role in discerning current trends and assessing prospective research developments. The application of bibliometric techniques permits the methodical evaluation and analysis of scholarly literature pertinent to a given domain. Additionally, it offers significant insights and identifies critical trends. Furthermore, the objective of the analysis is to ascertain the countries of origin, influential contributors, institutions, highly cited articles, and areas that require additional research. A bibliometric analysis was previously performed by Zhang et al. [44] on the commitment of manufacturing firms to achieve carbon neutrality.

Two decades of bibliometric research on low carbon was conducted by Wang et al. [45]. Publication characteristics and academic cooperation relationships as determined by social network analysis were the criterion. Wang et al. [8] analysed articles published from 1990 to 2021 in a bibliometric and SLR study with the objective of identifying the research trajectories associated with the low-carbon economy. Table 2 presents the exhaustive literature review, which is organised according to sub-research areas, methodologies, techniques, and keywords.

TABLE 2 Past Literature reviews.

S.No	Authors	Sub-research areas	Methodology	Techniques	Keywords covered
1	Xu et al. [42]	Low carbon transition in heavy industries	Multi-variance analysis	Content analysis	CO2 emissions; the heavy industry; nonparametric additive regression models
2	Yip et al. [43]	Trend analysis on sustainable manufacturing	Bibliometric analysis	Themetic analysis	Sustainable manufacturing; thematic analysis; bibliometric analysis; Evolution; Text mining
3	Zhang et al. [44]	Evaluate impact of digital economy on green and low-carbon manufacturing	Multi-variance analysis	Empirical research	Digital economy; manufacturing; low carbon; green transformation
4	Wang et al. [45]	Investigation on low carbon transformation	Bibliometric analysis	Review	Low carbon; bibliometric method; Classical R/S method; Mann–Kendall test; social network analysis
5	Wang et al. [8]	Review articles and identify the research trajectories of the energy storage systems for low-carbon economy.	Bibliometric analysis	Review	Energy storage systems; low carbon; manufacturing
6	Wang et al. [46]	Review articles on smart technology solutions for low carbon future	Systematic literature review (SLR)	Review	Smart city; smart industrial park; low-carbon society; demand side management
7	Bueno et al. [39]	SLR on low carbon smart production planning and control in industry 4.0	Multi-variance analysis	Empirical research	Smart capabilities; industry 4.0; internet of things; cyber-physical systems; big data and analytics; systematic review
8	Tian et al. [47]	Authors reviewed the challenges and opportunities for energy transition under COVID-19.	Text mining and content analysis	Review	COVID-19; energy transition; green recovery scheme; green finance; low-carbon transition
9	Chapman et al. [48]	Investigation on energy transition to low carbon energy society	Multi-variance analysis	Empirical research	Low-carbon Energy; policy Transition
10	Guo et al. [49]	Investigate the smart knowledge method for conceptual design of low-carbon products	Multi-variance analysis	Experimental design	Smart knowledge deployment; conceptual design; low-carbon products; interactive genetic algorithm
11	Moshood et al. [50]	Green and low carbon manufacturing	Systematic literature review (SLR)	Review	Sustainability practices; low carbon emissions; green supply chain management
12	Zhou, and Shan [51]	Low carbon development	Multi-variance analysis	Empirical research	Intelligent manufacturing; carbon efficiency; industrial structure; upgrade low-carbon transition
13	Wang et al. [52]	Digital twin technologies in smart manufacturing	Systematic literature review (SLR)	Review	Digital twin smart energy application low carbon city smart grid electrified transportation energy storage system
14	Kim and Jeong [53]	Low-carbon prediction for smart manufacturing	Text mining and content analysis	Review	IoT; EMS; low carbon; smart factory
15	Tao et al. [54]	Digital twins and low carbon manufacturing	Systematic literature review (SLR)	Review	Data models; industries; computational modelling; smart manufacturing
16	Tao et al. [55]	Digital twins and Cyber–Physical systems for smart manufacturing	Systematic literature review (SLR)	Review	Cyber–physical systems (CPS); digital twin (DT); smart manufacturing
17	Davis et al. [56]	Smart manufacturing and low carbon manufacturing	Text mining and content analysis	Review	Advanced manufacturing, low carbon, control and platforms, digital manufacturing, smart manufacturing
18	Kusiak [57]	Smart manufacturing and big data	Text mining and content analysis	Review	Smart manufacturing; big data
19	Kusiak [58]	Service manfacturing and low carbon	Systematic literature review (SLR)	Review	Service manufacturing; low carbon
20	Kusiak [59]	Smart manufacturing and low carbon manufacturing	Text mining and content analysis	Review	Industry 4.0; smart manufacturing
21	Mittal et al. [60]	Smart manufacturing and small and medium enterprises	Text mining and content analysis	Review	Smart manufacturing industry 4.0; industry 4.0; smart factory; small and medium enterprises (SMEs)
22	Mittal et al. [61]	To evaluate smart manufacturing, technologies and critical factors	Systematic literature review (SLR)	Review	Industry 4.0; low carbon; cyber-physical production systems, smart factory, intelligent manufacturing and advanced manufacturing
23	Mittal et al. [62]	Proposed smart manufacturing adoption framework for SMEs based on the literature review	Text mining and content analysis	Review	Smart manufacturing industry 4.0; industry 4.0; smart factory; small and medium enterprises (SMEs)
24	Shukla et al. [63]	Assessment of technology—organistion- environment framework (TOE) for smart manufacturing among SMEs	Systematic literature review (SLR) and multi-criteria decision analysis	Review + quaitiative research	Technology –organistion- environment framework (TOE); small and medium enteprises (SMEs); smart manufacturing
25	Shukla et al. [64]	Assessment of smart manufacturing preparedness among SMEs	Systematic literature review (SLR) and multi-criteria decision analysis	Review + quaitiative research	Smart manufacturing; readiness assessment; SMEs

The study delineates a number of noteworthy regions within the discipline, encompassing decarbonisation processes, carbon-free trading policies, food footprints, and the advancement of energy-efficient systems. Insights regarding the function and consequences of low-carbon economic policies on prospective energy scenarios are offered by the research results. In light of existing research deficiencies and the pressing need to perform a bibliometric analysis on the application of intelligent algorithms and methodologies in low-carbon manufacturing, this study undertook a bibliometric analysis to stimulate discourse and guide decision-making and innovation in the direction of a more environmentally sustainable and green manufacturing sector. The purpose of this study is to provide policymakers and researchers with a guide for the academic development of low-carbon manufacturing by evaluating research efforts in light of identified research deficits. Furthermore, the utilisation of bibliometric analysis can enhance the circulation of knowledge by drawing attention to groundbreaking research concerns and providing guidance for future research endeavours.

This enables researchers to ascertain the most widely used algorithms, the application domains within low-carbon smart manufacturing, and the most productive products. The objective of this study is to assess the document layouts presently available pertaining to the specified subject. Using bibliometric analysis, a researcher can conduct a straightforward, reversible, and intentional writing survey. In contrast to prior approaches, it has yielded more exhaustive and unbiased analyses. In order to evaluate the present importance of a specific subject matter, it is reasonable to employ a range of indicators that are frequently cited in published articles. In addition, it permits the examination of government and academic research collaborations [46]. The study's data overview is presented in Table 3.

TABLE 3 Data overview.

Description	Results
Key information
Research time frame	2011:2023 (till 15 June 2023)
Published sources	144
Research documents	261
Per annum growth rate in percentage	23.6%
Authors	681
Authors of single authored documents	17
International Co-authors	25%
Co-authors per document	3.95
Authors' keywords	767
Average published documents	4.3
Average citation published per document	15.35
Total references	9940

Overview of selected Scopus and web of science data used for analysis

The literature documents utilised in this study were obtained from the ‘Scopus’ and ‘Web of Science’ databases. The presented data pertains to the research area and spans the years 2011–2023 (as of 15 June 2023). In Table 3, the sources, authors, co-authors, and document information are also detailed. The authors uncovered 24 documents pertaining to this subject in 2023; all published articles were included. A total of 264 articles were obtained from 147 sources till 2023. In total, 767 author keywords are utilised. A total of 684 researchers whose works were selected from the Scopus and Web of Science databases were related; however, 29 of those papers were authored by a single individual.

The annual total number of publications is illustrated in Table 4. According to statistical data, the aforementioned matter exhibits a regional trend shift. The volume of work increased from 2018 to 2022 (27, 16, 24, 32, respectively), and as of 15 June 2023, 24 articles had been published. The emergence of the notions of Industry 4.0 and smart manufacturing in the literature began in 2014, as indicated in Table 5. Table 5 shows the annual average number of citations.

TABLE 4 Annual scientific productions.

Year	Articles
2023	24
2022	52
2021	32
2020	24
2019	16
2018	27
2017	21
2016	14
2015	18
2014	10
2013	9
2012	9
2011	5

TABLE 5 Average citations per year.

Year	Mean TCperArt	N	Mean TCperYear	Citable years
2011	12	5.00	0.92	13
2012	16.89	9.00	1.41	12
2013	13.89	9.00	1.26	11
2014	18.4	10.00	1.84	10
2015	28.39	18.00	3.15	9
2016	35.86	14.00	4.48	8
2017	20.76	21.00	2.97	7
2018	32.07	27.00	5.34	6
2019	19.56	16.00	3.91	5
2020	15.12	24.00	3.78	4
2021	7.69	32.00	2.56	3
2022	5.19	52.00	2.60	2
2023	0.46	24.00	0.46	1

The leading contributors to low-carbon manufacturing research are India, China, the United States, the United Kingdom, Singapore, and Italy, as shown in Figure 2. These nations conducted their studies under the overarching theme of smart manufacturing for low-carbon generation. Green manufacturing, carbon footprint, low-carbon manufacturing, lean manufacturing, intelligent manufacturing, and carbon emission were the keywords broadly utilised.

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According to the Table 6, Journal of Cleaner Production (JCLP) holds the top spot with 20 published documents, followed by IFIP Advances with 12 research documents.

TABLE 6 Most relevant sources.

Sources	Articles
Journal of cleaner production	20
IFIP advances in information and communication technology	12
International journal of advanced manufacturing technology	9
Jisuanji Jicheng Zhizao Xitong/Computer integrated manufacturing systems, CIMS	9
IOP conference series: Earth and environmental science	7
International journal of computer integrated manufacturing	6
Proceedings of the institution of mechanical engineers, Part B: Journal of engineering manufacture	5
Processes	5
Sustainability (Switzerland)	5
Energy Procedia	4

Institutional analysis

The 10 institutions that have produced the most publications in the field of intelligent algorithms and methodologies for low-carbon smart manufacturing are enumerated in Table 7 of the study. As shown in Table 7, Chongqing University is the academic institution with the greatest number of articles in the Scopus database, at 18. With 15 articles published, the Indian Institute of Technology (ISM) and Xi'an Jiaotong University rank second, respectively. Third place is held by the Wuhan University of Science and Technology, which has published 11 works. Most pertinent affiliations are shown in Table 7.

TABLE 7 Most relevant affiliations.

Affiliation	Articles
Chongqing University	18
Indian Institute of technology (ISM)	15
Xi'an Jiaotong University	15
Wuhan University of Science and Technology	11
Xi'an Jiaontong University	10
IK Gujral Pubjab Technical University	9
Huazhong University of Science and Technology	8
Tongji University	8
Loughborough University	7
Qingdao University of Technology	7

Author's statistics

The top authors in Table 8 are grouped by the number of publications on intelligent algorithms and methodologies for low-carbon smart manufacturing. The order of authors is based on how much research they have also accomplished in this field and the number of times it has been published in reputable journals. The entire publication is mentioned in Table 8. This indicates that, Li.C., who has 18 publications, is at the top list, followed by Zhang. C with 10 publications.

TABLE 8 Author's production over time.

Authors	Articles
Li, C.	18
Zhang, C.	10
Liu, Z.	9
Sharma, S.	9
Zhou, G.	9
Tripathi. V.	8
Zhang, H.	8
Cao, H.	7
Chattopadhyaya, S.	7

Country analysis

This segment elucidated the country analysis pertaining to intelligent algorithms and methodologies for low-carbon smart manufacturing. It offers an all-encompassing scrutinise recent advancements, previous research trends, and prospective research trajectories in this domain. The analysis specifically focuses on the most productive and influential nations as determined by bibliometric indicators. The systematic literature review illuminates the progress that various nations have achieved in the implementation of intelligent algorithms and methodologies for the purpose of establishing low-carbon smart manufacturing. The analysis comprises an extensive array of subjects, such as automation techniques, machine learning, data analytics, and optimisation algorithms. The document delineates significant developments, obstacles, and prospects pertaining to the integration of intelligent algorithms into low-carbon smart manufacturing. In addition, the country analysis illuminates the divergences in research emphasis and methodologies among various nations, thereby furnishing significant perspectives on the worldwide terrain of this swiftly progressing discipline. The results of this study will provide valuable insights for industry initiatives, policy development, and future research endeavours that seek to promote energy-efficient and sustainable manufacturing practises on a global scale.

The information presented in Table 9 includes the primary contributors' country of origin, their individual contributions, the quantity of single country publications (S.C.P.) involving authors from the same country, which signifies intra-country collaboration, and the number of multiple country publications (M.C.P.) that involve authors from different countries. China is distinguished by its exceptionally high publication count. China possesses the most documents, totaling 122, which signifies a substantial influence within the realm of research. The relatively high tendency for self-citation in Chinese-authored papers is indicated by the SCP of 95. Furthermore, a total of 27 collaborations involving multiple countries are present, demonstrating China's active participation in international research endeavours. A frequency score of 0.462 indicates that the rate of publication is moderate. The MCP ratio of 0.221 suggests that although China contributes to publications involving multiple countries, it does not hold a preponderant position in those collaborative efforts. In contrast, the United Kingdom's collection of documents is comparatively limited to 17 publications. The 11th SCP indicates a moderate degree of self-citation in papers authored in the United Kingdom. Six collaborations involving multiple countries are present, suggesting the presence of international research participation. The country in question exhibits a comparatively reduced publication rate in light of its frequency score of 0.064. On the contrary, based on the MCP ratio of 0.353, it can be inferred that substantial contributions from the United Kingdom are more probable in multi-country publications. The country analysis of the corresponding author is illustrated in Table 9.

TABLE 9 Corresponding author's country analysis.

Country	Documents	SCP	MCP	Freq	MCP_Ratio
China	122	95	27	0.462	0.221
United Kingdom	17	11	6	0.064	0.353
India	14	7	7	0.053	0.500
USA	12	11	1	0.045	0.083
Spain	5	5	0	0.019	0.000
Italy	4	3	1	0.015	0.250
Greece	3	3	0	0.011	0.000
Ethiopia	2	0	2	0.008	1.000
Finland	2	1	1	0.008	0.500
Iran	2	2	0	0.008	0.000

India turned out be the third contributing country, followed by USA and Spain. Also, China and USA are the two most significant and vital countries in the region, as shown in Figure 3.

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The assessment conducted by Bornmann et al. (2015) examined the nations that receive the highest number of citations. As per Figure 3, China is the most referenced nation globally, followed by United Kingdom, and India, according to statistics on citations for all countries (Table 10) on the basis of country-associated data for the original authors. By fostering technological innovation for a greener and more efficient manufacturing sector and promoting sustainable practises, low-carbon smart manufacturing is having a substantial impact on industries in China, the United Kingdom, and India [39, 47]. The implementation of low-carbon smart manufacturing is also influencing manufacturing companies in Spain, Italy, and the United States of America through the promotion of sustainable practises, the mitigation of environmental impact, and the development of technologically advanced manufacturing processes that are more efficient and environmentally benign [48]. By driving the transition towards sustainable and environmentally responsible practises, optimising energy efficiency, reducing carbon emissions, and promoting the integration of advanced technologies for a greener and more resilient global industrial ecosystem, low-carbon smart manufacturing is thus revolutionising global manufacturing and supply chains. The authors have compiled a list of highly cited countries, total citations, and average article citations in Table 10.

TABLE 10 Highly cited countries.

Country	T.C.	Average article citations
China	2171	17.80
Norway	535	267.50
United Kingdom	375	22.10
USA	153	12.80
India	112	8.00
Spain	75	15.00
Italy	40	10.00
Finland	27	13.50
Korea	19	9.50
Sweden	18	18.00

Due to the scarcity of empirical data concerning low-carbon manufacturing, it is imperative that a comprehensive literature review be conducted in order to address this research void and enhance our comprehension of sustainable manufacturing methodologies.

Document analysis

Table 11 presents the 10 journal publications in the region that have had the greatest impact on papers. Systematic literature research is undertaken utilising this information to identify contemporary practises within the field.

TABLE 11 Highest cited documents.

Paper	DOI	Total citations	TC per year	Normalised TC
Buer SV, 2018, Int J Prod Res		446	74.33	13.91
Chen W, 2018, J Clean Prod		208	34.67	6.48
Luo Z, 2016, Eur J Oper Res		156	19.50	4.35
Li C, 2015, J Intell Manuf		116	12.89	4.09
Tang L, 2022, Chin J Mech Eng Engl Ed		107	53.50	20.61
Yi Q, 2015, J Clean Prod		92	10.22	3.24
Buer SV, 2021, Int J Prod Res		89	29.67	11.58
Lv J, 2014, J Clean Prod		88	8.80	4.78
Glampieri A, 2020, Appl Energy		82	20.50	5.42
Cao H, 2012, J Clean Prod		82	6.83	4.86

Keyword analysis

Table 12 and Figure 4 display the keyword data that was obtained for the purpose of analysing the most frequently used terms related to the research subject of green supply chain and carbon emissions in India. It indicates that during the inquiry, the terms ‘manufacturing’ and ‘low-carbon manufacturing’ appeared 98 and 96 times, respectively.

TABLE 12 Most Frequent words.

Words	Occurrences
Manufacture	98
Low-carbon manufacturing	96
Carbon	75
Energy utilisation	50
Carbon emissions	40
Life cycle	33
Industrial research	32
Energy efficiency	31
Smart manufacturing	31
Sustainable development	29

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NETWORK ANALYSIS

VOSviewer, an extensively recognised and utilised software program, is employed for biblometric analysis. Maps of authors, articles, or countries may be generated. VOSviewer can create keyword maps using co-occurrence and co-citation data. By emphasising its distinctive characteristics, VOSviewer is capable of displaying maps in an assortment of methods. Researchers may conduct examinations of a multitude of writer bibliometric networks, periodicals, publications, countries, or institutions. To evaluate keywords, VOSviewer devises a text mining approach that places emphasis on the content of titles, abstracts, and keywords. Subsequently, an additional cluster of objects is identified and categorised using the identical cluster hues.

Authors' collaboration

An analysis was conducted on the 264 selected publications, comprising a total of 684 authors, in order to ascertain the manner in which authors worked together to generate research contributions in the chosen field of study. The authors have established a minimum of two authors and a maximum of 25 per document. By this criterion, 216 items satisfy the requirement. The constructed network ultimately comprises 13 clusters, each containing 152 objects. In the end, five clusters comprising 40 keywords were formed, each with 46 connections and a total link strength of 878. The crimson cluster labelled Cluster 1, consisting of 22 items, is the largest, as illustrated in Figure 5.

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Keyword network

A total of 801 keywords were extracted from the articles that were chosen to comprise the keyword network. In order to reduce the quantity of keywords to 40, all terms that appeared at least four times are grouped together.

An analysis of the keyword network [Figure 6] identifies Clusters 1, 2, 3, 4, and 5. Each Cluster comprised the following number of keywords: 56, 49, 31, 25, and 22. The fifth cluster, which contains 22 keywords and is the smallest cluster, is shown in Table 13.

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TABLE 13 Country collaboration.

Group 1 (6 items) [Yellow]	Australia; Austria; Germany; Greece; South Korea; Spain
Group 2 (6 items) [Green]	China; India; Ireland; Morocco; Singapore; Taiwan
Group 3 (5 items) [Blue]	Canada; Denmark; France; Hong Kong; United Kingdom
Group 4 (4 items) [Red]	Italy; Mexico; Norway; Sweden
Group 5 (4 items) [Violet]	Finland; Romania; Slovakia; United States

Country collaboration

Country analysis is comprised of 264 publications, of which 25 are from different nations. The country collaborations across the five identified clusters are depicted in Figure 7.

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Table 13 detailed the collaboration between countries. Countries comprising group 1 are South Korea, Spain, Austria, Germany, Greece, and Austria. China, India, and Ireland comprise the Morocco, Singapore, and Taiwan comprise group 2. Canada, Denmark, France, Hong Kong, and the United Kingdom comprise group 3. Four nations Sweden, Italy, Mexico, and Norway make up Group 4. In contrast, the United States, Finland, Romania, and Slovakia comprise group 5.

CLUSTER ANALYSIS

Cluster analysis is a method of multidimensional analysis that groups attributes or characteristics according to a set of attributes or characteristics selected by the user. Information retrieval, data processing, deep learning, and pattern recognition are all domains that rely on this critical and indispensable stage of data mining, which is also a widely employed technique for data analysis. Cluster analysis illustrates the diverse array of duties performed within a specific domain. A bibliographic coupling analysis was conducted in order to identify distinct keyword clusters. It assists the researcher in locating various author collectives that have contributed to similar research domains. A comprehensive set of five clusters containing 183 keywords was established, as shown in Table 14.

TABLE 14 Clusters and keywords.

Cluster 1 (56 items) Low carbon smart manufacturing and applications of Algorithms	Artificial Intelligence; Artificial Neural Network; Classificiation of Information; Cloud Computing; Clustering Algorithms; Conditional monitoring; convolution; convolution neural network; cutting tools; data fusion; data-driven approach; data-driven fault diagnosis; decision trees; deep learning; deep neural networks; defects; e-learning; failure analysis; fault detection; fault diagnosis; feature extraction; forecasting; health; learning algorithms; learning systems; legacy systems; long short-term memory; machine learning; machine learning techniques; machine tools; mechine-learning; machinery; machining; miling (machining); multilayer neural network; neural networks; predictive maintenance; predictive modelling; predictive models; prognostics and health informatics; quality assurance; random forests; recurrent neural network; regression analysis; remaining useful lives; semantics; support vector machine; surface roughness; systems engineering; time series; tool wear; tool wear prediction; transfer learning; wear of materials; wear prediction.
Cluster 2 (49 items) Industry 4.0 technologies for low carbon manufacturing	Advanced analytics; Automation; Automotive industry; big data; big data analytics; Costs; cyber physical system; cyber physical systems; data analytics; data driven decision; data handling; data integration; embedded systems; energy efficiency; energy management; energy management system; engineering education; flow control; information and communication; information management; information systems; information use; intelligent manufacturing; internet; internet of things; life Cycle; manufacturing; manufacturing company; manufacturing domains; manufacturing environment; manufacturing industries; manufacturing IS; manufacturing paradigm; metadata; network architecture; predictive analysis; sustainability; Traditional manufacturing; Uncertainity analysis
Cluster 3 (31 items) Low carbon and green manufacturing	Assembly; anomoly detection; benchmarking; computer control system; controllers; data driven; data acquisition; data analysis; data-driven model; decision making; dynamics; industry internet of things; intelligent systems; manufacturing operations; product design; production control; production scheduling; real time systems; scheduling; stochastic systems; supply chains; sustainable manufacturing; throughput
Cluster 4 (25 items) Low carbon manufacturing, and product design and control	Agile manufacturing system; cyber physicals; cyber-physical production; digital lean manufacturing; digital manufacturing; digitalisation; ecosystems; floors; industrial research; industry 4.0; IoT; lean manufacturing; lean production; network security; production environment; production system; productivity; quality control; shopfloors; smart manufacturing; sustainable development
Cluster 5 (22 items) Lean systems and smart manufacturing	Competition; Complex networks; data driven approach; decision support system; digital transformation; digital twin; e-learning; efficiency; engineering education; integration; knowledge management; manufacturing enterprises; manufacturing process; optimisation; production efficiency; reinforcement learning; robotics; semiconductor device; semiconductor manufacturing; smart manufacturing system; virtual reality

Cluster 1 is a rich tapestry of 56 objects that are located at the junction of low-carbon smart manufacturing and the novel applications of algorithms. This dynamic cluster is responsible for bringing to the forefront a wide variety of cutting-edge technologies and approaches, which is a reflection of the varied nature of the subject. Artificial Intelligence, which is heavily highlighted, acts as the cornerstone, with Artificial Neural Networks, Convolutional Neural Networks, and multilayer neural networks standing out as among the most important contributors. The incorporation of technologically advanced computing paradigms is highlighted by the inclusion of words such as cloud computing and clustering algorithms. This cluster contains the breadth of intelligent algorithm applications, from the complexities of machining operations, as illustrated by cutting tools and milling, to the complicated web of data-driven fault diagnostics and predictive maintenance. This approach emphasises the convergence of technological expertise with considerations of human-centric aspects and semantic comprehension.

Cluster 2 is comprised of a complete assortment of 49 items that are centred around the technologies of Industry 4.0 for low-carbon manufacturing. The purpose of this cluster is to operate as a focal point for the investigation of the revolutionary possibilities of advanced analytics, automation, and Big Data in the manufacturing sector. The seamless connection of the digital and physical realms is brought to light by the inclusion of words such as cyber-physical systems and embedded systems. One of the most important domains is the automobile industry, which highlights the significant impact that technologies related to Industry 4.0 have on the production of vehicles and their environmental impact. The concept of sustainability, which is central to this cluster, places an emphasis on a forward-looking approach that aims to bring commercial practises and environmental concerns into harmony with one another. This cluster lays the framework for a nuanced investigation of how the technologies of Industry 4.0 may be utilised to propel low-carbon manufacturing into a future that is both technologically sophisticated and environmentally sustainable. The focus of this cluster is on predictive analysis and uncertainty analysis.

Cluster 3 dives into the various components of industrial operations and places an emphasis on the crucial role that intelligent systems play in supporting sustainability. A holistic approach to improving manufacturing efficiency is reflected in the cluster, which encompasses everything from the fundamental aspects of assembly and benchmarking to the more advanced domains of anomaly detection and computer control systems. The importance of utilising information to drive decision-making processes is highlighted by the fact that data-driven models and data analytics emerge as crucial components. The manufacturing environment requires a high level of adaptability and reactivity, which is reflected in the focus placed on real-time systems and dynamic scheduling. Considerations of product design, production control, and throughput are all included in the scope of sustainable manufacturing, which is the primary focus of this discussion.

This cluster acts as a focal point for the investigation. The utilisation of cutting-edge technology in the process of changing production environments is highlighted by the inclusion of phrases such as ‘industry 4.0,’ ‘Internet of Things,’ and ‘smart manufacturing.’ There is a convergence of digital technology and lean principles, which emphasises efficiency and waste reduction. Concepts such as cyber-physicals and digital lean manufacturing illustrate this junction. Indicating a holistic perspective that encompasses everything from shop floors to network security issues, ecosystems, both physical and digital, play an important part in the process. Providing a complete view of how sophisticated technologies are harnessed to generate innovation, sustainability, and efficiency in the field of low-carbon manufacturing and product design, this cluster places a significant emphasis on quality control, production systems, and productivity.

Lean Systems and Smart Manufacturing are the two overarching concepts that are represented by the 22 elements are included in Cluster 5. It is important to note that the incorporation of words such as digital transformation and digital twin highlights the paradigm shift towards the adoption of cutting-edge technology in order to improve organisational efficiency and competitiveness. The scope of this cluster goes beyond the work floor, with a particular emphasis on engineering education, knowledge management, and integration. Learning through reinforcement and robots are two technologies that play a significant part in the optimisation of industrial processes, with an emphasis on adaptability and automation. Semiconductor device and manufacturing phrases point to a concentration on high-tech sectors, which are characterised by the integration of virtual reality and intelligent manufacturing systems to reshape the environments in which they operate. In this cluster, the emphasis placed on production efficiency, optimisation, and the integration of knowledge and technologies paints a rich picture of how lean manufacturing principles and smart manufacturing principles converge to propel industries towards a future that is characterised by innovation, competitiveness, and technological excellence.

CONTENT ANALYSIS

The systematic literature review pertaining to intelligent algorithms and methodologies for low-carbon smart manufacturing offers a comprehensive examination of previous investigations, contemporary advancements, and prospective avenues of research within this domain. In this instance, the concepts and assertions within a given set of qualitative data (i.e. text) are identified through content analysis. The cluster analysis facilitated the process of keyword identification and categorisation, culminating in the formation of five distinct clusters. The content analysis uncovers a number of significant research themes pertaining to intelligent algorithms and methodologies for low-carbon smart manufacturing, as indicated by the keywords. The following themes are encompassed:

Theme 1: Low-carbon smart manufacturing and applications of algorithms

It emphasises fundamental machine learning and artificial intelligence concepts. It consists of artificial neural networks, convolutional neural networks, deep neural networks, and recurrent neural networks, among others. Additionally, it comprises interconnected methodologies such as support vector machines and transfer learning. Additionally, the cluster exemplifies methodologies for data analysis and modelling. Classification of data, decision trees, clustering algorithms, feature extraction, learning algorithms, predictive modelling, random forests, regression analysis, semantics, and time series analysis are among the subjects covered. Using a data-driven methodology, the primary emphasis is on extracting meaningful information and patterns from data. Furthermore, this cluster places significant emphasis on the maintenance, monitoring, and infrastructure components of predictive maintenance and artificial intelligence. Systems engineering, prognostics and health informatics, cloud computing, conditional monitoring, fault detection, fault diagnosis, and predictive maintenance are among the subjects covered. Ensuring the dependability, effectiveness, and efficiency of systems and processes is the primary objective. Furthermore, the cluster is concerned with domain-specific elements of predictive maintenance, specifically as they relate to manufacturing and machining. Health monitoring, cutting tools, defects, failure analysis, surface roughness, tool wear, tool wear prediction, wear of materials, and surface roughness are among the subjects covered. Additionally, legacy systems and residual useful lives are addressed. The primary emphasis is on the analysis and prediction of tool and material deterioration and performance with the ultimate goal of optimising maintenance strategies and enhancing overall efficiency.

$(P1)$

Proposition

The effective implementation of intelligent algorithms and methodologies in the operations of low-carbon manufacturing firms can significantly enhance supply chain efficiency and promote sustainable practices.

Theme 2: Industry 4.0 technologies for low-carbon manufacturing

The primary emphasis is on the application of sophisticated analytics methods and big data analytics within the context of low-carbon manufacturing. The text comprises a compilation of keywords that are associated with various domains such as information management, data analytics, data-driven decision making, data integration, metadata, predictive analysis, and uncertainty analysis. Utilising data and analytics to optimise processes, enhance decision-making, and confront uncertainties within the manufacturing environment is of paramount importance. Furthermore, the keywords also allude to the thematic integration of cyber-physical systems and automation focused on industry 4.0 within the realm of low-carbon manufacturing. The compilation comprises essential terms associated with network architecture, automation, cyber-physical systems, embedded systems, intelligent manufacturing, and the internet of things (IoT). Integration of digital technologies with physical systems is the primary objective in order to facilitate intelligent and efficient control systems, energy management, and manufacturing processes. Furthermore, this theme concerns the precise implementation of Industry 4.0 technologies within the automotive sector as it relates to the production of low-carbon goods. The text comprises essential terms associated with sustainability, energy management, energy efficiency, and energy management systems. The primary objective is to leverage technological advancements in order to enhance sustainability practises, decrease carbon emissions, and optimise energy efficiency in the automotive manufacturing industry. Furthermore, the cluster comprises keywords that pertain to the manufacturing sector as a whole and its evolution in the context of industry 4.0 in order to achieve low-carbon manufacturing. In general, the cluster emphasises the significance of the internet and information and communication technologies (ICT) in the context of industry 4.0's low-carbon manufacturing. It contains keywords associated with the internet, information and communication, and low-carbon manufacturing. Utilising ICT infrastructure and connectivity to facilitate efficient information exchange, collaboration, and real-time monitoring in order to attain low-carbon manufacturing objectives is the primary emphasis.

$(P2)$

Proposition

To identify the industry 4.0 applications for low-carbon manufacturing, to generate sustainable competitive advantage among smart manufacturing firms.

Theme 3: Low carbon and green manufacturing

It emphasises the application of intelligent systems and data-driven methodologies to low-carbon and environmentally sustainable manufacturing. The compilation comprises keywords that are pertinent to intelligent systems, data acquisition, data analysis, data-driven modelling, and decision making. The primary focus is on utilising data to enhance the efficiency of manufacturing operations and inform sustainable decision-making. The cluster then describes the assembly procedure and the application of computer control systems and controllers in environmentally friendly and low-carbon manufacturing. The compilation comprises essential terms that are associated with manufacturing operations, assembly, computer control systems, controllers, and real-time systems. The primary emphasis is on integrating real-time control systems and optimising assembly processes in order to enhance operational effectiveness and mitigate carbon emissions. Furthermore, in the context of low carbon and green manufacturing, this theme encompasses quality control, benchmarking, and the optimisation of production control and scheduling. The compilation comprises essential terms associated with throughput, anomaly detection, benchmarking, production control, and scheduling stochastic systems. The primary objectives are the detection of irregularities, the enhancement of production control methodologies, and the coordination of workflows in order to optimise output while reducing energy usage and ecological repercussions. In a general sense, the cluster comprises terms associated with the incorporation of the internet of things (IoT) into environmentally friendly and low-carbon production processes, such as sustainable manufacturing, product design, and supply chains. Utilising Internet of Things (IoT) technologies to optimise product design, improve supply chain efficiency, and advance sustainable practises across the entire manufacturing lifecycle is the primary objective.

$(P3)$

Propositions

Utilising data-driven decision-making methods to develop low-carbon manufacturing for sustainable production.

Theme 4: Low-carbon manufacturing, and product design and control

The compilation comprises terms and concepts, such as intelligent manufacturing, cyber-physicals, agile manufacturing systems, digital lean manufacturing, digital manufacturing, digitalisation, industry 4.0, IoT, and smart manufacturing. The primary emphasis is on harnessing cutting-edge technologies and connectivity in order to optimise processes, enhance production systems, and ultimately increase overall sustainability and efficiency. Additionally, the cluster is relevant to the wider milieu of sustainable development and low-carbon product design and production within ecosystems. Sustainable development, ecosystems, industrial research, lean manufacturing, and lean production are among the keywords included. Throughout the entire value chain, the emphasis is on implementing lean principles, minimising waste, and advocating for sustainable practises in order to attain the objectives of low-carbon manufacturing. Additionally, in low-carbon manufacturing and product design, the cluster places significant emphasis on network security, quality control, and the digitalisation of production systems. The text comprises essential terms associated with shopfloors, digitalisation, network security, the production environment, and the production system. The primary emphasis is on utilising digital technologies to optimise manufacturing environments, guarantee network security, and bolster quality control protocols in order to accomplish the goals of low-carbon manufacturing. The cluster's overarching objective is to enhance productivity and shop floor efficiency in the domains of low-carbon manufacturing and product design. The text comprises essential terms associated with flooring and efficiency. The primary objective is to enhance productivity levels, floor layouts, and operational efficiency in order to attain low-carbon manufacturing targets through the integration of sustainable practises.

$(P4)$

Proposition

Implementing low-carbon manufacturing through product design and control potentially enhance supply chain efficiency and foster sustainable practices.

Theme 5: Lean systems and smart manufacturing

The integration of intelligent manufacturing systems and the digital transformation of manufacturing processes are the focal points of this cluster. This emphasises the significance of manufacturing enterprises employing cutting-edge technologies and digitising their operations in order to improve their competitiveness and efficiency. The cluster also prioritises the implementation of decision support systems and data-driven methodologies in intelligent manufacturing. The statement acknowledges the importance of gathering and evaluating data in order to enhance production efficiency, optimise manufacturing procedures, and make well-informed decisions. The cluster then deliberated on the significance of automation and robotics in lean systems and intelligent manufacturing. It emphasises the utilisation of robotics in manufacturing processes to reduce manual labour and increase production efficiency. Furthermore, the cluster places significant emphasis on the criticality of engineering education and knowledge management within the framework of lean systems and smart manufacturing. It recognises the imperative of ongoing education, proficiency enhancement, and the exchange of knowledge in order to effectively respond to technological progressions within the manufacturing sector. Additionally, the cluster emphasises the application of virtual reality and digital twin technology in intelligent manufacturing. The approach prioritises the development of virtual models and simulations with the aim of enhancing collaboration, optimising manufacturing processes, and improving product design. The aforementioned theme emphasises the criticality of efficiency enhancement and optimisation methods in the context of lean systems and smart manufacturing. The organisation acknowledges the importance of reducing waste, optimising resource utilisation, and streamlining processes in order to attain lean and efficient manufacturing operations. Additionally, intelligent manufacturing and lean systems are highlighted in this cluster. The organisation acknowledges the potential of online learning platforms and reinforcement learning methods in order to improve manufacturing performance by training personnel and optimising processes.

$(P6)$

Proposition

Implementation of lean processes and smart technologies for manufacturing strive to reduce carbon emissions and promote environmentally friendly practices.

Based on the cluster, Figure 8 represents emerging research themes developed based on literature review and Bibliometric analysis.

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CONCLUSION, LIMITATIONS AND FUTURE SCOPE

The advent of digitalisation is causing a profound transformation in industrial value chains as it integrates Internet of Things (IoT) technologies, data exchange, and predictive analytics. The application of algorithms in low-carbon smart manufacturing offers immense potential for improving sustainability, reducing carbon emissions, and optimising operational efficiency. By leveraging algorithms to optimise processes, enable predictive analytics, and support decision-making, manufacturers can achieve significant environmental and economic benefits. However, careful consideration of challenges such as data quality, algorithm complexity, and ethical considerations is essential. In order to evaluate the significance of low-carbon techniques within the context of smart manufacturing, a comprehensive literature review is being carried out that spans the years 2011–2023. The content analysis, network data analysis, bibliometric analysis, and cluster analysis are utilised. To achieve RO1, this study exhibits the present state of research concerning the application of intelligent algorithms in low-carbon smart manufacturing has revealed a field that is ever-changing and dynamic. A literature review of published articles are reviewed and discussed the increasing potential of intelligent algorithms to promote the sustainability objectives of smart manufacturing processes. Considerable progress has been achieved by scholars in comprehending the theoretical underpinnings, formulating innovative algorithms, and implementing sophisticated methodologies to optimise manufacturing operations in terms of both efficiency and environmental sustainability. We have successfully identified recurring patterns, significant trends, and pivotal themes pertaining to the implementation of intelligent algorithms in low-carbon smart manufacturing. A wide range of methodologies, including machine learning applications and optimisation algorithms, have been documented in the literature in an effort to reduce carbon footprints, resource consumption, and environmental impact as a whole. There is a growing recognition that the incorporation of intelligent algorithms into the manufacturing process is a critical element in attaining environmental sustainability and economic competitiveness.

This extensive examination establishes a strong basis for investigating RO2, as it establishes the structure for discerning and classifying the principal domains and applications in which low-carbon smart manufacturing practises have been applied. The knowledge acquired from this examination will enhance the depth of comprehension regarding the pragmatic implications of intelligent algorithms across various manufacturing environments.

In anticipation of RO4, the foundation established in this investigation enables us to discern and hypothesise on prospective pathways in the field of low-carbon smart manufacturing that could greatly advantage from the strategic implementation of algorithms. With the continuous advancement of technology and the emergence of fresh challenges, the integration of sustainable manufacturing practises with intelligent algorithms will undeniably pave the way for novel avenues of innovation.

According to the findings of the bibliometric analysis, India, China, the United States of America, the United Kingdom, Singapore, and Italy are the primary contributors to low-carbon manufacturing research. Also with the cluster, content analysis five major themes are identified: low-carbon smart manufacturing and applications of algorithms; low carbon and green manufacturing; low-carbon manufacturing, and product design and control; lean systems and smart manufacturing; and smart manufacturing and lean systems.

AUTHOR CONTRIBUTIONS

Sudhanshu Joshi: Investigation; methodology; project administration; software; validation; visualization; writing—original draft; writing—review and editing. Manu Sharma: Conceptualization; formal analysis; resources; writing—original draft.

ACKNOWLEDGEMENTS

Open access publishing facilitated by University of Technology Sydney, as part of the Wiley - University of Technology Sydney agreement via the Council of Australian University Librarians.

CONFLICT OF INTEREST STATEMENT

None.

DATA AVAILABILITY STATEMENT

Intelligent algorithms and methodologies for low-carbon smart manufacturing: Review on past research, recent developments and future research directions.

References

Dion, H., Evans, M.: Strategic frameworks for sustainability and corporate governance in healthcare facilities; approaches to energy‐efficient hospital management. Benchmark Int. J. (2023). ahead‐of‐print No. ahead‐of‐print. [DOI: https://dx.doi.org/10.1108/BIJ-04-2022-0219]