Circular Economy a Footstep toward Net Zero Manufacturing: Critical Success Factors Analysis with Case Illustration

Abstract

Increasing populations and the extravagant consumption of virgin resources are key issues in developing economies these days. The Paris Climate Accords of 2015 have also highlighted the importance of resource conservation and sustainable consumption. Developing economies, which rely on linear practices and traditional resources, require urgent attention regarding such issues. Practices of the circular economy (CE) provide an edge to achieving self-sustainability in materials and energy and lead a guiding path towards net-zero manufacturing. Net-zero manufacturing practices can significantly reduce environmental impact, conserve resources, and contribute to a more sustainable economy. In the current research paper, the authors have studied the critical success factors (CSFs) for implementing the circular economy in Indian small and medium enterprises (SMEs). The authors identified the CSFs through a literature review and expert opinions. To categorize and establish a structural model among the identified CSFs, the authors used Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) techniques. The model’s robustness and expert bias were checked through predisposition analysis. To verify and validate the findings of the analysis, the authors conducted a case study of an Indian SME using the Strengths, Weaknesses, Opportunities, and Threats (SWOT) methodology. The authors of the current study observed that government policies and regulations on CE, consumer awareness and demand for CE products, economic incentives available for CE products, and new global business opportunities due to CE are the leading cause groups of CSFs. A reduction in energy and waste-related costs, the availability of infrastructure for CE practices, and an improvement in information-sharing transparency among supply chain members are the leading effects of CSFs. The influence graph shows that ‘Govt. policy and regulation on CE’, ‘Consumer awareness and demand for CE products’, ‘Economic incentives available for CE products’, and ‘New global business opportunity due to CE’ have a higher out-degree influence on other factors. From the case study, the authors observed that the strategic implementation of CE practices, green policy, reverse logistics, staff training, and new technology adoption have improved the use of repaired and refurbished materials in the case organization from 1–2% in 2019 to 9–10% in 2023. The findings of the current study imply that management commitment and strategies for building a ‘Green image’, coordination with suppliers, technological upgrades, reverse supply chain practices, workforce skills and training, and customer awareness and demand for CE products are crucial factors in successfully adopting CE and transitioning towards net-zero manufacturing.

1. Introduction

The United Nations Environment Programme (UNEP) report on emission gaps, 2019, highlighted the differences between the targets and realities of carbon emission reduction [1]. More than 130 countries have signed the Paris Agreement and proposed emission reduction roadmaps to achieve carbon neutrality. China has pledged to reach carbon emission peak by 2030 and to achieve carbon neutrality by 2060 [2]. The circular economy is a closed loop system, designed to work on the philosophy of reducing, reusing, repairing, or recycling when the life of a product is over. CE practices are imperative for sustainable development and net-zero economies through sustainable production and consumption [3]. Traditionally, the practices of a linear economy are endured in developing economies. In a linear economy, the products are made from fresh materials and are thrown away in nature after use. These practices create pressure on natural resources and have environmental, economic, and social impacts. On other hand, CE aims to minimize waste, keep resources in use for as long as possible, and help in reducing carbon footprints [4]. This, in turn, reduces the need for energy-intensive extraction and production processes, thereby contributing to the goal of achieving net zero manufacturing.

CE has been recognized as a unifying framework capable of solving societal problems linked to environmental pollution and resource depletion. Its adoption is rapidly reforming manufacturing, production, consumption, and recycling across various segments of the economy [3]. CE creates a balance of industrial development and economic growth with the environment and human health through carbon-footprint reduction [5]. The framing of policies, regulatory measures, and administrative guidance on CE are required at the different levels of provinces, cities, industrial sectors, and industries [6]. The material flow analysis and life cycle assessment approaches have high potential for assessing the long-term environmental implications of CE policy measures [7]. A regulatory framework and economic tools are required to promote the CE and less carbon intensive practices [8].

Transparency, monitoring, feedback, and the recognition of progress with rewards and penalties are enablers that can change the behavior of producers and consumers towards a CE. A CE is an ideal approach to achieve sustainability globally with zero waste and decarbonization [9]. CE practices help in achieving the goals of sustainable development (SDGs). CE mitigates the environmental concerns of SDG through the optimum utilization of energy, materials, and waste reduction [10]. A scarcity of resources is a major issue for developing economies and finding a potential solution in CE practices [11]. SMEs are a major contributor in India’s GDP and exports, and CE practices have importance in Indian SMEs [12]. Developed economies have the privilege of finances and technologies in adopting CE practices in comparison with developing economies [13].

Considering the importance of CE [3,5,6,7,8,9,10] and the need for a holistic study on CE in relation to Indian SMEs [11,12,13,14], the objectives of the current study are excogitated in the following research questions:

RQ1.

What are the CSFs of CE in Indian SMEs?

RQ2.

What is the relationship between the CSFs of CE?

RQ3.

How can CE practices be implemented for achieving net zero manufacturing in Indian SMEs?

To observe the venerability of the CE concept and to find the answer to the above-raised research questions, the authors of current study have identified, analyzed (by using Fuzzy Decision-making trial and evaluation laboratory techniques), and verified (using a case study using Strength, Weakness, Opportunity, and Threat analysis) the CSFs of CE implementation in Indian SMEs. Fuzzy DEMATEL helps in finding the influential factors and developing the interrelationship between them [15].

This study begins with the introduction, which includes the research’s background. A literature review is in Section 2. The methodology is in Section 3. Section 4 explains the results obtained from the analysis of the Fuzzy DEMATEL, with a predisposition analysis and a case study. In Section 5, the findings of the study are discussed. Section 6 has a summary of the study with conclusions, limitations, and future research directions.

2. Literature Review

In modern times, pressure on virgin resources highlights the importance of resource conservation, recycling, and carbon-footprint reduction [2]. Major economies have set their targets of achieving carbon neutrality by 2050, China by 2060, and India by 2070 [3]. By 2020, more than 100 countries have made commitments towards carbon neutrality [4]. The limitations of a linear economy and the awareness of resource conservation has brought the attention of the government, non-government organizations, and societies towards CE practices. CEs focus on creating an ecosystem of expanding a product’s useful life by adopting practices of reducing, reusing, recycling, recovering, redesigning, and remanufacturing [9]. The demand of remanufactured products with consumer response are the major success factors of CE adoption in SMEs [13]. Various issues like population growth, change in human consumption patterns, and the very recent pandemic disruptions have significantly grabbed the attention of both developed and developing economies with regards to a CE [14].

Globally, economies are changing their focus from use and throw practices to CE practices. This has evolved as a leading approach to support the goals of sustainability. CE is an approach that provides multiple value creation mechanisms, which are decoupled from the consumption of finite resources [16]. The transition from linear to CE needs fundamental changes in the value chain. Alone in Europe, the business benefits linked with CE practices are estimated to be 1.8 trillion Euro/year till 2030 [16]. CE implementation has special importance in manufacturing SMEs due to their contribution to economies all over the globe [17]. Despite CEs’ interests and benefits, manufacturing SMEs face challenges in implementing CE. It has been observed that CEs are insignificantly implemented globally [18]. Only 2–5% of organizations have successfully implemented CE practices [19]. A lack of knowledge on CEs leads to failure during implementation [20].

The availability of finance, favorable regulation, infrastructure, and the ease of circular practices can help in the success of CE and its practices in manufacturing industries [21,22]. Affordable waste management techniques and the affordable cost of CE practices and products help in making consumers’ perception favorable towards CE practices [23]. Contriving policies by consulting all stacks holders and supply chain members helps in implementing CE practices [24]. Knowledge, the skills of managerial staff in CE, an investment in technology, and training are key parameters of the transformation from a linear to a CE [25,26]. Market demand and the returns on investment of circular products are key CSFs of CE [27]. Technologies of Industry 4.0 like the internet of things (IoT), the industry internet of things (IIoT), cloud computing, cyber physical systems (CPS), and automation, can help in CE data-integrated practices [28,29].

Coordination, collaboration, and information sharing among supply chain members can enable technological changes, especially in SMEs [15]. SMEs hesitate in sharing information due to competition and the fear of losing business [30]. A lack of technologies and the standardization of recycled materials are big challenges in adopting CE [31]. The transformation from linear to circular also depends on consumer behavioral, social, and cultural backgrounds [32,33]. Government regulation and legislations on circular economy practices can help organizations adopt CE practices. On the other hand, frequently changing and non-favorable regulations can also create hindrances in CE implementation [34]. Converting one organization’s waste raw materials to another is highly dependent on government regulations, which further help in managing price fluctuation and volatility [35]. Uncertainty and geographical dispersion associated with the reverse supply chain make its management more difficult and more costly, hence the products of the CE become more costly [36]. The study of the barriers and accelerators of CE transformation help in its implementation [37]. The organizations of developed economies have the privilege of finances and technologies while adopting practices of the CE [38].

SMEs have an awareness of the advantages of CE and resource reuse. The lack of financial resources and technical skills are two challenges in the transformation to CE [39]. A lack of awareness from customers about CE practices makes the demand of its products uncertain [40]. Support from supply chain members and customers motivates SMEs towards CE adoption [41]. The low bargaining power of SMEs in the supply chain push them towards isolation on policy and regulation issues. Government rules/regulation, organization policies, information flow among supply chain members, and the technical knowledge of workforces are key factors in CE adoption in SMEs [42,43,44,45]. The smart and digital technologies of Industry 4.0 assist the practitioners in the adoption of smart techniques like CE and net zero [46]. “Top management commitment”, “Legislation on CE practices”, “Bionomic scarcity of resources”, “Knowledge of CE practices”, “Financial support on research and development on CE”, and “Motivation from stake holders for CE products” are key CSFs of CE adoption [47].

Reductions in waste, used products handling, and asset analysis are key factors for CE implementation in manufacturing organizations. Industry 4.0 technologies enable both strategic and technology implementation decisions [48]. “Market Competition”, “Cross-functional teams formation”, “Focus on global standards”, “Skilled workforce”, “Consumer awareness on CE”, and “Dedicated fund for CE” are key CSFs of CE for eco-friendly, innovative, flexible, and e-waste management systems [49]. In the construction industry, “Data-driven digital tools and circularity plan”, “Capacity building and pre-demolition auditing”, “Systemic circularity guidelines and commitment”, and “Circular metric and secondary market development” are key CSFs of CE implementation [50]. “Improve secondary material quality and value”, “Incentives for secondary materials utilization”, “Incentives for waste-recovery”, “Standards for secondary materials”, “Advanced sourcing and processing technologies”, “Information platforms and markets for C&D waste”, “Site waste management”, and the “Use of durable materials” are eight CSFs for implementing CE practices in the construction industry [51].

CE implementation can be enabled in the manufacturing sector by enhancing “repair, refurbishment, reuse, remanufacturing, and recycling activities”, “Improving service system of products”, “reducing the costs associated with raw material, energy”, and waste management [52]. CE-supportive regulations, a change in the demand and consumption of products and synergies, and cooperation between stakeholders across the value chain are key enablers of CE implementation in the plastic industry [53]. Product lifecycle, service-based models, circular design, reverse flows, an awareness of consumers and users, and collaborations between SC members are key factors of CE adoption [54]. Sustainable production is developing with the changes actuated by the rise of circular economy practices and Industry 4.0 [55]. Digital technologies can enable sustainable management and CE implementation. IoT helps in data collection, AI in predictive maintenance, big data analytics in demand forecasting, and blockchain technology in tracking product origins. The level or degree of digitalization vary by size and type of company [56]. High investment, a lack of financial backing, a lack of legislative support, and a lack of top management support are key barriers of CE implementation [57]. On the basis of the above literature review, the authors observed that there is a scarcity of holistic research on CE implementation in Indian SMEs. A few of the observed research gaps are as follows:

-

The majority of the research is focused on the analysis of the enablers and barriers of CE in developed economies. There is a need for study on the implementation of CE in developing economies.

-

Large-scale organizations are preferred for research on CEs even in developed economies. There is a need for holistic research on CE implementation in the SMEs of developing economies.

-

For the successful implementation of CE and its practices in SMEs, there is a need to find and analyze the CSFs of CE.

To answer the above-mentioned research gaps, the authors of the current study have performed an analysis of CSFs and a case illustration in the forthcoming sections. The sixteen CSFs identified from the above literature review are summarized in

Table 1. CSFs of CE implementation in Indian SMEs.

Factor No.	CSFs	References
1	Stakeholders’ awareness and pressure for CE	[58,59,60,61]
2	Environmental concerns of Organization/Green image	[62,63,64,65]
3	New Global business opportunity due to CE	[11,21,60,66]
4	Top management motivation and strategies on CE	[39,40,67]
5	Availability of new material, R&D, and technical facilities	[29,33,68]
6	Knowledge and skills of manpower on CE	[15,41,69]
7	Availability of funds/resources/FDI on CE	[22,28,36]
8	Consumer awareness and demand for CE products	[28,42,70,71]
9	Govt. Policy and regulation on CE	[43,44,58]
10	Coordination and collaboration among SC members	[15,30,33]
11	Availability of infrastructure for CE practices	[13,34,37,44]
12	Reduction in Energy and waste related costs	[26,43]
13	Economic incentives available for CE products	[24,26,42]
14	Improvement of Competitiveness	[24,41,45]
15	Technical tools enabling CE innovations	[29,32,46,72]
16	Improvement of transparency of information across supply chain	[24,73,74,75,76]

.

3. Methodology

In the current research paper, authors have identified the CSFs of implementing CE in Indian SMEs and have prioritized them using the Fuzzy Decision-making trial and evaluation laboratory (DEMATEL) techniques. This technique develops the interrelationship between the CSFs and helps in differentiating the influential CSFs [77]. This technique has been found to be useful in finding interrelationships between factors in any system [37]. Further, the equivocalness of expert opinion can be conciliated using the fuzzy notations of this technique. This technique has been favored in comparison with other multi-criteria decision making (MCDM) techniques due to its characteristics of being a structural modeling technique with the capability of differentiating factors in two groups of cause and effect, by exploring their relationship and severity. This technique also gives experts a wide range of replies (0, 1, 2, 3, and 4) (Table 3) [15,38,77]. SMEs can form strategies based on quantitative and visual kinship among CSFs through an impact relation diagram [77].

The initial procedure of this technique started with collecting experts’ opinions from industry and academia through a survey questionnaire. Ten years of experience at the managerial level or in research with a professional qualification was the minimum criterion for selecting experts from industry and academia, respectively. From various Indian states and using various modes of communication (email, telephonic conversation, and personally), the experts were reached for survey. The purpose of the research was well explained in the survey questionnaire. In total, sixty experts (thirty from industry and academia each) were contacted for the survey. Forty experts (fourteen from industry and sixteen from academia) participated in the survey. From studying the literature, the authors observed that the majority of research applied a sample size of 10–40 in DEMATEL technique [15,29,37,38,77,78]. In the current study, the authors received and used thirty-six complete responses. Detailed steps of the methodology are shown graphically in Figure 1.

3.1. Steps of Fuzzy DEMATEL Techniques

The complete procedure for fuzzy DEMATEL techniques comprise of eight steps, as discussed in following paragraph:

• Step 1. Effectuate Initial Direct-Relation Matrix (DRM)

The first step of the fuzzy DEMATEL technique is to set up the initial direct relation matrix (

Table 2. Initial Direct Relation Matrix for CSFs of CE.

CSFs	01	02	03	04	05	06	07	08	09	10	11	12	13	14	15	16
01	NI	VHI	VLI	VHI	VHI	VHI	VHI	VLI	NI	HI	VHI	LI	NI	VLI	HI	LI
02	LI	NI	HI	LI	VHI	VHI	VHI	HI	VLI	LI	HI	HI	VLI	HI	HI	VLI
03	VHI	VHI	NI	VHI	VHI	VHI	VHI	VLI	NI	HI	VHI	LI	VLI	HI	HI	HI
04	VLI	VHI	LI	NI	VHI	VHI	VHI	VLI	NI	HI	VHI	LI	VLI	HI	LI	VHI
05	VLI	VHI	HI	LI	NI	HI	LI	VLI	NI	HI	VLI	VHI	VLI	VHI	VHI	VHI
06	LI	HI	HI	LI	HI	NI	VLI	LI	NI	HI	LI	HI	VLI	HI	HI	HI
07	LI	HI	VLI	HI	VHI	HI	NI	VLI	NI	HI	VHI	VLI	VLI	LI	HI	HI
08	VHI	VHI	VLI	VHI	VHI	VHI	HI	NI	VLI	HI	HI	HI	VLI	LI	HI	HI
09	VHI	HI	HI	VHI	VHI	VHI	LI	VLI	NI	HI	VHI	HI	VLI	HI	HI	LI
10	LI	LI	HI	VLI	HI	VHI	VHI	LI	NI	NI	VHI	HI	VLI	HI	HI	VHI
11	NI	VLI	VLI	VLI	LI	VLI	NI	NI	NI	NI	NI	VLI	NI	VLI	VLI	VLI
12	NI	VHI	HI	LI	LI	LI	VLI	VLI	NI	LI	NI	NI	VLI	LI	LI	VLI
13	HI	HI	LI	HI	HI	LI	LI	VLI	VLI	LI	VLI	LI	NI	LI	VLI	VLI
14	HI	HI	LI	LI	VLI	LI	VLI	VLI	NI	LI	NI	HI	VLI	NI	LI	LI
15	LI	LI	LI	LI	VHI	LI	VLI	VLI	NI	LI	NI	VHI	VLI	HI	NI	LI
16	LI	VLI	VLI	VLI	VLI	VLI	VLI	VLI	NI	VHI	VLI	LI	VLI	HI	VLI	NI

) utilizing the linguistic variables shown in

Table 3. Linguistic terms and Triangular Fuzzy Numbers.

Linguistic Terms	Influence Score	Triangular Fuzzy Numbers (TFNs)
No Influence (NI)	0	(0, 0, 0.25)
Very Low Influence (VLI)	1	(0, 0.25, 0.50)
Low Influence (LI)	2	(0.25, 0.50, 0.75)
High Influence (HI)	3	(0.50, 0.75, 1)
Very High Influence (VHI)	4	(0.75, 1, 1)

.

• Step 2. Transform linguistic variables into corresponding Triangular Fuzzy Numbers (TFNs)

The second step of the fuzzy DEMATEL technique transforms linguistic variables into corresponding triangular fuzzy numbers (TFNs) as per the design for linguistic variables (Table 3)

• Step 3. Transmute TFNs in the DRM F

The third step of fuzzy DEMATEL technique is to apply the convert fuzzy data into crisp scores (CFCS) technique for computing triangular fuzzy numbers (TFNs). After applying the sequential steps of CFCS, the direct-relation matrix F is formulated (

Table 4. Direct Relation Matrix for CSFs of CE.

CSFs	01	02	03	04	05	06	07	08	09	10	11	12	13	14	15	16
01	0	0.832246	0.105952	0.832246	0.966667	0.832246	0.832246	0.211905	0	0.697826	0.832246	0.445455	0.105952	0.211905	0.348913	0.445455
02	0.57164	0	0.832246	0.706061	0.706061	0.832246	0.706061	0.348913	0.10119	0.222727	0.57164	0.832246	0.207143	0.697826	0.348913	0.211905
03	0.832246	0.832246	0	0.966667	0.832246	0.832246	0.832246	0.32868	0.10119	0.697826	0.706061	0.57164	0.450104	0.697826	0.697826	0.697826
04	0.454865	0.966667	0.57164	0	0.832246	0.832246	0.832246	0.454865	0.10119	0.832246	0.966667	0.706061	0.323918	0.697826	0.57164	0.966667
05	0.211905	0.832246	0.454865	0.32868	0	0.697826	0.32868	0.32868	0.10119	0.697826	0.454865	0.832246	0.323918	0.832246	0.706061	0.706061
06	0.445455	0.57164	0.454865	0.32868	0.454865	0	0.211905	0.32868	0.10119	0.697826	0.32868	0.697826	0.207143	0.57164	0.454865	0.697826
07	0.445455	0.697826	0.32868	0.697826	0.966667	0.697826	0	0.32868	0.10119	0.697826	0.966667	0.454865	0.323918	0.445455	0.57164	0.57164
08	0.832246	0.966667	0.32868	0.832246	0.832246	0.832246	0.697826	0	0.202381	0.57164	0.832246	0.57164	0.323918	0.445455	0.697826	0.697826
09	0.966667	0.697826	0.57164	0.832246	0.832246	0.832246	0.57164	0.211905	0	0.697826	0.832246	0.697826	0.323918	0.697826	0.57164	0.445455
10	0.445455	0.57164	0.454865	0.454865	0.697826	0.832246	0.832246	0.445455	0.10119	0	0.832246	0.697826	0.207143	0.697826	0.697826	0.832246
11	0.222727	0.32868	0.211905	0.32868	0.445455	0.454865	0.105952	0.105952	0	0.222727	0	0.454865	0	0.32868	0.32868	0.32868
12	0	0.966667	0.697826	0.445455	0.445455	0.445455	0.211905	0.211905	0	0.445455	0	0	0.207143	0.445455	0.445455	0.211905
13	0.697826	0.697826	0.445455	0.697826	0.697826	0.445455	0.445455	0.211905	0.202381	0.445455	0.211905	0.445455	0	0.445455	0.211905	0.211905
14	0.697826	0.697826	0.445455	0.445455	0.211905	0.445455	0.211905	0.211905	0	0.445455	0	0.697826	0.207143	0	0.445455	0.445455
15	0.445455	0.445455	0.445455	0.445455	0.966667	0.445455	0.211905	0.211905	0	0.445455	0	0.966667	0.207143	0.697826	0	0.445455
16	0.445455	0.211905	0.211905	0.211905	0.211905	0.211905	0.211905	0.211905	0	0.966667	0.211905	0.445455	0.207143	0.697826	0.211905	0

). The CFCS defuzzification method can offer a better crisp value for fuzzy numbers in comparison with other existing methods and consists of five steps, as discussed below: (Table 4,

Table 5. Total Relation Matrix for CSFs of CE.

CSFs	01	02	03	04	05	06	07	08	09	10	11	12	13	14	15	16
01	0.111533	0.243507	0.12413	0.206373	0.248032	0.23474	0.193365	0.094896	0.0179	0.208604	0.202582	0.203186	0.070022	0.166583	0.155837	0.177849
02	0.173559	0.17778	0.194741	0.204764	0.23139	0.240944	0.187211	0.109426	0.02797	0.172177	0.180793	0.243137	0.083434	0.21456	0.161411	0.16096
03	0.226989	0.294774	0.14575	0.26073	0.284668	0.281138	0.229742	0.126959	0.032467	0.252732	0.225667	0.262073	0.120332	0.253015	0.222953	0.239767
04	0.189862	0.30024	0.196881	0.167238	0.277635	0.275691	0.224238	0.136142	0.031906	0.259901	0.243733	0.269822	0.106968	0.250109	0.2086	0.259155
05	0.136242	0.242256	0.157006	0.161809	0.155138	0.218545	0.145448	0.103832	0.026769	0.206175	0.158057	0.237647	0.090375	0.22208	0.186082	0.196667
06	0.1405	0.195702	0.139139	0.143596	0.176516	0.132511	0.120471	0.093523	0.024262	0.187263	0.132678	0.201363	0.071007	0.177258	0.146375	0.178139
07	0.162998	0.241326	0.150706	0.202919	0.25679	0.230975	0.122518	0.109076	0.028301	0.216798	0.218596	0.214079	0.093809	0.196694	0.182986	0.196282
08	0.220152	0.296923	0.171273	0.241304	0.276102	0.272754	0.21065	0.091038	0.040967	0.232636	0.230737	0.253436	0.104865	0.222118	0.215937	0.231389
09	0.236069	0.27854	0.196242	0.245686	0.279865	0.277476	0.202933	0.113358	0.0217	0.247671	0.233879	0.268527	0.106577	0.248098	0.208417	0.212236
10	0.169166	0.237505	0.166551	0.18773	0.239305	0.248344	0.202852	0.122423	0.028614	0.159679	0.209566	0.242155	0.08618	0.225028	0.199509	0.224754
11	0.078118	0.115761	0.078018	0.096144	0.120307	0.120889	0.068065	0.047433	0.008501	0.094165	0.058531	0.124602	0.030603	0.105022	0.09269	0.098809
12	0.086872	0.210919	0.15005	0.138952	0.157042	0.156536	0.10737	0.074587	0.013451	0.144236	0.088193	0.117838	0.064857	0.149106	0.130932	0.117332
13	0.168779	0.21673	0.143196	0.184862	0.208288	0.184204	0.149588	0.086446	0.035409	0.170409	0.132564	0.184723	0.053954	0.170812	0.129658	0.14031
14	0.152079	0.192409	0.127998	0.143143	0.14154	0.160705	0.111326	0.075984	0.013111	0.150417	0.092306	0.185584	0.065285	0.10846	0.132938	0.141674
15	0.136945	0.185722	0.138482	0.151844	0.220051	0.172787	0.118023	0.082239	0.014585	0.163324	0.098895	0.22481	0.071291	0.187721	0.102973	0.15401
16	0.113635	0.124378	0.089482	0.101955	0.117605	0.117593	0.094242	0.065999	0.010688	0.177702	0.095327	0.140509	0.056473	0.153747	0.096353	0.083091

and

Table 6. (D+R), (D-R) values and their ranking for CSFs.

	D	R	D+R	D-R	RANKING (D+R)	RANKING (D-R)
01	2.65914	2.503499	5.162639	0.155641	9	7
02	2.764257	3.554473	6.318731	−0.79022	1	11
03	3.459756	2.369646	5.829402	1.090111	5	3
04	3.398118	2.83905	6.237168	0.559068	2	5
05	2.644128	3.390274	6.034401	−0.74615	3	10
06	2.260302	3.325832	5.586134	−1.06553	6	13
07	2.824854	2.488043	5.312897	0.336811	7	6
08	3.312281	1.533361	4.845642	1.77892	11	2
09	3.377273	0.3766	3.753873	3.000673	15	1
10	2.949362	3.043889	5.99325	−0.09453	4	8
11	1.337658	2.602105	3.939762	−1.26445	14	15
12	1.908273	3.373489	5.281762	−1.46522	8	16
13	2.359933	1.276031	3.635964	1.083902	16	4
14	1.99496	3.050411	5.045371	−1.05545	10	12
15	2.223702	2.573651	4.797353	−0.34995	12	9
16	1.63878	2.812425	4.451205	−1.17364	13	14

).

Step 3.1. Standardization:

$$\begin{gathered} x{a}_{1ij}^k=\raisebox{1ex}{$\left(a_{1ij}^k-min a_{1ij}^k\right)$}\!\left/ \!\raisebox{-1ex}{${\Delta }_{min}^{max}$}\right. \\ x{a}_{2ij}^k=\raisebox{1ex}{$\left(a_{2ij}^k-min a_{2ij}^k\right)$}\!\left/ \!\raisebox{-1ex}{${\Delta }_{min}^{max}$}\right. \\ x{a}_{3ij}^k=\raisebox{1ex}{$\left(a_{3ij}^k-min a_{3ij}^k\right)$}\!\left/ \!\raisebox{-1ex}{${\Delta }_{min}^{max}$}\right. \end{gathered}$$

where, ${\Delta }_{min}^{max}=max r_{ij}^n-min l_{ij}^n$

Step 3.2. Calculate standardized right(rs) and left(ls) values:

$$\begin{gathered} xl{s}_{ij}^k=\raisebox{1ex}{$x{a}_{2ij}^k$}\!\left/ \!\raisebox{-1ex}{$\left(1+x{a}_{2ij}^k-x{a}_{1ij}^k\right)$}\right. \\ xl{s}_{ij}^k=\raisebox{1ex}{$x{a}_{3ij}^k$}\!\left/ \!\raisebox{-1ex}{$\left(1+x{a}_{3ij}^k-x{a}_{2ij}^k\right)$}\right. \end{gathered}$$

Step 3.3. Calculate total standardized crisp values:

$$\raisebox{1ex}{$x_{ij}^k=\left[xl{s}_{ij}^k\left(1-xl{s}_{ij}^k\right)+xr{s}_{ij}^k\times xr{s}_{ij}^k\right]$}\!\left/ \!\raisebox{-1ex}{$\left(1-xl{s}_{ij}^k+xr{s}_{ij}^k\right)$}\right.$$

Step 3.4. Calculate crisp values:

$${\omega }_{ij}^k=min{a}_{ij}^n+x_{ij}^n\times {\Delta }_{min}^{max}$$

Step 3.5. Desegregate values from above step and obtain DRM:

$${\omega }_{ij}^k=1/k\times \left({\omega }_{ij}^1+{\omega }_{ij}^2+\dots +{\omega }_{ij}^k\right)$$

The steps from Step 3.1 to Step 3.4 are repeated for all the thirty-six responses to obtain DRM by applying the formula of Step 3.5 (Table 4).

• Step 4. Setup the standardized DRM

The fourth step of fuzzy DEMATEL technique obtains a normalized direct-relation matrix S by applying the below equation:

Obtain a standardized DRM ‘S’ by using equation below:

$$S=P\times F; P=\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$max{ }_{1\le i\le n}{{\displaystyle \sum }}_j^n=a^{ij}$}\right.$$

• Step 5. Calculate Total Relation Matrix (TRM) ‘M’ as follows:

The fifth step of fuzzy DEMATEL technique is to obtain the total relation matrix M by using the below equation, shown in Table 5.

$$M=S\times {\left(I-S\right)}^{-1}$$

Here, ‘I’ stands for ‘Identity Matrix’ having similarity with ‘S’ on no. of rows and columns.

• Step 6. Calculate D and R

The sixth step of fuzzy DEMATEL technique is to obtain the sum of rows and columns of the total relation matrix (TRM) as D and R, respectively (add the rows of TRM for D and columns for R):

$$D={\left[{{\displaystyle \sum }}_{i=1}^n{t}_{ij}\right]}_{n\times 1}$$

$$R={\left[{{\displaystyle \sum }}_{j=1}^n{t}_{ij}\right]}_{1\times n}$$

• Step 7. Obtain D+R and D-R

The seventh step of fuzzy DEMATEL technique is to calculate the values of D+R and D-R and obtain the causing and effect group of factors (Table 6).

• Step 8. Draw the causal plot

The eighth step of fuzzy DEMATEL technique is to draw the causal plot by plotting the (D+R) values on X-axis and (D-R) on Y-axis (Figure 2a,b).

3.2. Predisposition Analysis

The fuzzy DEMATEL technique collects experts’ opinions and analyses it for driving the various results. For checking the robustness of Fuzzy DEMATEL model and experts’ biases, authors have used predisposition/sensitivity analysis techniques. There are two methods for predisposition analysis: firstly, by changing the weight assigned to every CSF and secondly, by changing the weight assigned to every expert [37]. The current study uses the expert weight variation approach [79]. For final analysis, four cases were used (In case one, first expert having weightage of (40%) and others having (20%) weightage each; similarly in second, third, and fourth cases, the second, third, and fourth expert was given weightage of (40%) and 20% weightage to others). Finally, for all four cases, the cause-and-effect diagrams were plotted.

3.3. Case Study

To verify and validate the finding of the analysis, the authors have conducted a case study of Indian SMEs using strength, weakness, opportunities, and threat (SWOT) methodology. Strengths and weaknesses focus on internal factors and opportunities, and threats stress external factors. The SWOT analysis presents a holistic study of a case organization [80]. Authors have conducted a case study in a manufacturing SME, manufacturing different entrance security products in India. Its product range includes security products such as roadblocks, boom barriers, turnstiles, automatic entrance gates, traffic lights, and flap barriers.

4. Results

The practices of CE provide an edge in self-sustainability in materials, energy, and a footstep towards net-zero manufacturing. Net-zero manufacturing practices can significantly reduce environmental impact, conserve resources, and contribute to a more sustainable economy. As mentioned in Section 3, the authors of the current study have analyzed the identified CSFs using Fuzzy DEMATEL techniques and tried to validate these through a case study. The results of the analysis are mentioned in Section 4.1 (Table 2, Table 3, Table 4, Table 5 and Table 6 and Figure 2a,b). The details of the predisposition analysis are shown in Section 4.2 (Table 7 and Table 8 and Figure 3). The results of the case study are shown in Section 4.3 (Table 9 and Table 10).

4.1. Results of Fuzzy DEMATEL Analysis

The details of the procedural steps of Fuzzy DEMATEL analysis (as discussed in Section 3.1) and their results are shown in Table 2, Table 3, Table 4, Table 5 and Table 6. The results of the analysis help in categorizing the CSFs in the ‘Cause’ and ‘Effect’ group (D-R values). Further analysis also helps in finding the influence of factors (D+R). From the results (Table 6), the top five influential factors involve ‘Environmental concerns of organization/Green image’, which has the highest value of (D+R) (6.31). This classification of factors signifies that these factors have a strong relationship with other factors and are crucial for implementing CE practices. The second most influential factor is ‘Top management motivation and strategies on CE’ with a (D+R) score of (6.24). The authors have further observed that the other three influential CSFs impacting the implementation of CE practices in SMEs are ‘Availability of new material, R&D and technical facilities’ (6.03), ‘Coordination and collaboration among Supply Chain members’ (5.99), and ‘New Global business opportunity due to CE’ (5.83).

A further classification of CSFs on the basis of (D-R) helps in categorizing them in ‘cause’ and ‘effect’ groups. Furthermore, the authors have observed the classification of CSFs based on “D-R” values. Positive ‘D-R’ values of CSFs categorize them in the cause group, while negative values categorize them in the effect group (Table 6). ‘Govt. Policy and regulation on CE’ has the highest D-R value (3.000). This signifies the effect of this factor on other factors and emphasizes the need for the attention of policymakers. ‘Consumer awareness and demand for CE products’ (1.77), ‘New Global business opportunity due to CE’ (1.09), ‘Economic incentives available for CE products’ (1.08), and ‘Top management motivation and strategies on CE’ are the top four causes in CSFs.

Furthermore, when observing D-R values, the authors found that ‘Reduction in energy and waste related cost (−1.465)’, ‘Availability of infrastructure for CE practices (−1.264)’, ‘Improvement of transparency of information across supply chain (−1.173)’, ‘Knowledge and skill of manpower on CE (−1.06)’, and ‘Importance of competitiveness (−1.055)’ are the top five in the effect group of CSFs (Table 6). This implies that other CSFs highly influence these CSFs. This also signifies that a reduction in energy- and waste-related costs is impossible to achieve without combined efforts to tackle all other CSFs that affect it. The results for CSFs have also been represented graphically in Figure 2a,b, which can help the SMEs in planning the strategies for the effective implementation of CE to achieve net zero manufacturing.

The diagraph (Figure 4) depicts the influences of CSFs on each other. From the digraph (Figure 4), it is shown that the factors (9, 8, 13, and 3) (Table 1) that have a higher out-degree influence most of the other factors and the factors (11, 12, 16, and 6) that have a higher in-degree receive influence from most of the other factors. Also, the factors (4, 2, 5, 3) from the above digraph represent their importance among all the other factors. These observations help SMEs in planning strategies on CE implementation by identifying the critical factors.

4.2. Results of Predisposition Analysis

For the predisposition analysis, the authors of the current study used the experts weight variation approach. In expert weight variation techniques, the weightage of experts is varied in a systematic manner. The authors formed four cases. In case one, the first expert was applied a weightage of 40% and the other three were applied a weightage of 20% each. Similarly, in the second case, the second expert was applied a weightage of 40% and the other three experts a weightage of 20% each. Similarly, in the third and fourth case, the third and fourth experts were applied a weightage of (40%) and a 20% weightage was applied to the other experts (Table 7). A graphical representation of the predisposition analysis of all four cases is shown in Figure 3. The authors found the results of the predisposition analysis similar to the results of the Fuzzy DEMATEL analysis (Section 4.1). This proves the robustness of the Fuzzy DEMATEL model (Table 6) and also helps in the validation of the results with no expert bias (Table 8).

Table 7. Weight assignment of experts during predisposition tests of CSFs.

	Expert-1	Expert-2	Expert-3	Expert-4
Test-1	0.4	0.2	0.2	0.2
Test-2	0.2	0.4	0.2	0.2
Test-3	0.2	0.2	0.4	0.2
Test-4	0.2	0.2	0.2	0.4

Table 7. Weight assignment of experts during predisposition tests of CSFs.

	Expert-1	Expert-2	Expert-3	Expert-4
Test-1	0.4	0.2	0.2	0.2
Test-2	0.2	0.4	0.2	0.2
Test-3	0.2	0.2	0.4	0.2
Test-4	0.2	0.2	0.2	0.4

Table 8. Ranking of CSFs based on predisposition tests (D-R).

	Original	Test-1	Test-2	Test-3	Test-4
01	7	7	7	8	7
02	11	10	10	10	10
03	3	4	3	3	4
04	5	6	5	5	5
05	10	11	11	11	11
06	13	12	12	14	14
07	6	5	6	6	6
08	2	2	2	2	2
09	1	1	1	1	1
10	8	8	8	7	8
11	15	15	13	15	13
12	16	16	16	16	15
13	4	3	4	4	3
14	12	13	14	12	12
15	9	9	9	9	9
16	14	14	15	13	16

Table 8. Ranking of CSFs based on predisposition tests (D-R).

	Original	Test-1	Test-2	Test-3	Test-4
01	7	7	7	8	7
02	11	10	10	10	10
03	3	4	3	3	4
04	5	6	5	5	5
05	10	11	11	11	11
06	13	12	12	14	14
07	6	5	6	6	6
08	2	2	2	2	2
09	1	1	1	1	1
10	8	8	8	7	8
11	15	15	13	15	13
12	16	16	16	16	15
13	4	3	4	4	3
14	12	13	14	12	12
15	9	9	9	9	9
16	14	14	15	13	16

Figure 3. Cause–Effect Diagram of CSFs predisposition analysis.

Figure 3. Cause–Effect Diagram of CSFs predisposition analysis.

Figure 4. Influence Digraph of CSFs.

Figure 4. Influence Digraph of CSFs.

4.3. Case Study

The case organization was a pioneer manufacturer of entrance security products in India, having initiated its manufacturing facilities in 1993. Over the past 30 years, the organization has continuously developed its technical and service capabilities in accordance with the latest technologies at its plant in Delhi NCR. Its product range includes security products such as roadblocks, boom barriers, turnstiles, automatic entrance gates, traffic lights, and flap barriers. The case organization set up various in-house facilities for the fabrication of metal sheets, CNC machines for sheet metal working, the assembly of electronic devices, and repair services. To adopt CE practices, the top management of the case organization initiated a separate refurbishing line for their entire product range. Special training was provided to staff members periodically to enhance their expertise in repair and items reuse. In this case study, the authors employed SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis techniques to assess the level of awareness, technological capabilities, and the skills of the workforce in CE practices, as well as to study their effects on the transition toward net-zero manufacturing (Table 9). The authors interacted with the top management, the head of the supply chain, and technical personnel, and documented all relevant details for the case study. The customer base of the case organization spans across sectors including defense, hotels, oil and petroleum, power, and banking. The combination of its facilities and product range makes the case organization an ideal case for implementing CE practices. The authors observed that all the security products manufactured by the case organization involve three to four manufacturing steps, including sheet metal work, fabrication, and the fitting of preassembled electronic components to make them function automatically. All sheet cutting, bending, fabrication, and electronic assembly processes take place in-house. In the post-COVID scenario, the demand for automatic security and other equipment increased. The disruption of local and global supplies due to the pandemic has significantly affected production within the organization. The details of the case study are summarized in Table 9 and the findings are in Table 10.

Table 9. The SWOT analysis of Case organization.

Strengths	Weaknesses
-Technical facilities with CNC machines to work on different grades of steels and other materials. -Facilities of high-grade paints and powder coating. -Fabrication facilities of high grade. -Lab facility to assemble and repair electronic devices required for security devices. -Policy to reuse the sheet metal/materials and refurbish electronic items. -Design for assembly and repair, supportive for repair and reuse of parts.	-Reverse supply chain as new concept and additional cost. -Special process planning and machinery are required for handling reused material and parts. -Training and skill upgradation of technical staff required -Fear of more job changes from technical staff. -Resistance/Inertia of staff -Changes in the processes and increase in the range of products creating managing issues with current workforce.
Opportunity	Threats
-Latest technology tools help in achieving high quality and timely delivery with good market reputation. -All in-house facilities give upper-edge in market competition. -Reuse of the sheet metal materials and refurbish electronic parts helps in lowering the manufacturing cost and improves the net profit of the case company. -Increasing demand for repaired and refurbished products due to awareness and reduced price. -A range of refurbished products have created a new market and customer base. -Company ‘Green Image’ improves by adopting CE practices.	-Fear of market share loss due to shifting company focus from traditional products (virgin) to recycled/reused products. -Competitors strategy on new and fresh (virgin) products. -Cost of reverse supply chain is additional. -Additional resources cost on CE practices.

Table 9. The SWOT analysis of Case organization.

Strengths	Weaknesses
-Technical facilities with CNC machines to work on different grades of steels and other materials. -Facilities of high-grade paints and powder coating. -Fabrication facilities of high grade. -Lab facility to assemble and repair electronic devices required for security devices. -Policy to reuse the sheet metal/materials and refurbish electronic items. -Design for assembly and repair, supportive for repair and reuse of parts.	-Reverse supply chain as new concept and additional cost. -Special process planning and machinery are required for handling reused material and parts. -Training and skill upgradation of technical staff required -Fear of more job changes from technical staff. -Resistance/Inertia of staff -Changes in the processes and increase in the range of products creating managing issues with current workforce.
Opportunity	Threats
-Latest technology tools help in achieving high quality and timely delivery with good market reputation. -All in-house facilities give upper-edge in market competition. -Reuse of the sheet metal materials and refurbish electronic parts helps in lowering the manufacturing cost and improves the net profit of the case company. -Increasing demand for repaired and refurbished products due to awareness and reduced price. -A range of refurbished products have created a new market and customer base. -Company ‘Green Image’ improves by adopting CE practices.	-Fear of market share loss due to shifting company focus from traditional products (virgin) to recycled/reused products. -Competitors strategy on new and fresh (virgin) products. -Cost of reverse supply chain is additional. -Additional resources cost on CE practices.

Table 10. Observations from study of case organization.

C.E. Practices	Year 2019	Year 2021	Year 2022	Year 2023 (Ist Quarter)
Reuse of material/recycled material/refurbished items	1–2% of total sheet metal materials	5–7% of total raw materials and electronic items	7–8% of total raw materials/scrap/damaged and electronic items	9–10% of total raw material and electronic items
Waste reduction and recycling	Process improvement	Special focus on automation and training of staff	Special line setup for refurbishing/repair of materials and items	Focused Special line setup for refurbishing/repair
Reverse logistic practices	Warranty case only	Adopted in all cases	Offered to all customers and suppliers	Offered to all customers and suppliers
Coordination and collaboration with local suppliers	As per profit on purchase	Local suppliers were preferred	Local suppliers were preferred with cost competitiveness	Local suppliers were preferred
Focus on Green image of Organization	Not aware	Advertisement strategy changed as per ‘Green image’	Advertisement strategy changed and product badging as per ‘Green image’	Advertisement strategy and ‘Green image’

Table 10. Observations from study of case organization.

C.E. Practices	Year 2019	Year 2021	Year 2022	Year 2023 (Ist Quarter)
Reuse of material/recycled material/refurbished items	1–2% of total sheet metal materials	5–7% of total raw materials and electronic items	7–8% of total raw materials/scrap/damaged and electronic items	9–10% of total raw material and electronic items
Waste reduction and recycling	Process improvement	Special focus on automation and training of staff	Special line setup for refurbishing/repair of materials and items	Focused Special line setup for refurbishing/repair
Reverse logistic practices	Warranty case only	Adopted in all cases	Offered to all customers and suppliers	Offered to all customers and suppliers
Coordination and collaboration with local suppliers	As per profit on purchase	Local suppliers were preferred	Local suppliers were preferred with cost competitiveness	Local suppliers were preferred
Focus on Green image of Organization	Not aware	Advertisement strategy changed as per ‘Green image’	Advertisement strategy changed and product badging as per ‘Green image’	Advertisement strategy and ‘Green image’

5. Discussion

The practices of CE provide an edge towards self-sustainability in materials and energy and helps in achieving sustainability goals. When observing the contribution of Indian SMEs to gross domestic product (GDP) and employment generation, there is a high need for a focus shift in these firms from linear practices to circular. The results of the current study (Section 4) depict that SMEs’ implementation of CE practices is highly dependent on strategies of top management and the availability of technical facilities. The collaboration and coordination between supply chain members broadens the horizon for SMEs in different areas, especially in the adoption of new techniques like CE.

Concerns of environmental issues and green image are highly dependent on an awareness of environmental issues and the availability of infrastructure in SMEs. The adoption of CE and its practices is a result of the availability of finance, favorable regulation, infrastructure, and the ease of circular practices. The availability of finance, favorable regulation, infrastructure, and the ease of circular practices can help in the implementation of CE and its practices in manufacturing industries [21,22]. Frequently changing and non-favorable regulations can create hindrances in the implementation of CE [34]. Consumer awareness, a demand for CE products, new global business opportunities, and economic incentives for CE products are factors affecting CE adoption in developing countries.

The knowledge and manpower CE, as well as the infrastructure and skill upgradation, offer sustainable CE and low carbon practices an edge in implementation [37]. The observations of the implementation case study also depict that the shift of focus on recycled materials, waste recycling, refurbishing, repairing electronic, a preference for local suppliers, and the training of staff members helped the case organization during the pandemic era. Adopting CE practices by case organization has improved the reuse of material from 1–2% (2019) to 9–10% (2023) (Table 10). CE implementation can be enabled in the manufacturing sector by promoting “repair, refurbishment, reuse, remanufacturing, and recycling activities”; “Improving service system of products”; and by “reducing the costs of raw material and energy” [52]. CE-supportive regulations, a change in demand and the consumption of products and synergies, and cooperation between stakeholders across the value chain are key enablers for CE implementation in plastic industries [53,54].

In the current study, the findings of the analysis and the case study state that the disruptions of the pandemic have shifted the focus towards CE practices and enlightened the path of net-zero manufacturing. Management commitment and strategies for building a ‘Green image’, coordination with suppliers, technological upgrades, reverse supply chain practices, workforce skills and training, and, most importantly, customer awareness and demand for CE products are crucial factors in successfully adopting CE and transitioning towards net-zero manufacturing.

6. Conclusions

Issues of population growth, increasing human consumption, and the scarcity of virgin resources have become critical concerns for developing economies. The adoption of circular practices could help to manage resources in an economical manner and guide us toward achieving the goal of net-zero manufacturing. In the current study, the authors have addressed three research questions (RQ) in the literature review, discussion, and the case study sections, respectively. The authors of the current study have analyzed sixteen CSFs for implementing CE in Indian SMEs. The authors observed that ‘Environmental concerns of the organization/Green image’ is the most significant CSF for SMEs in implementing CE practices. This not only impacts the market but also has the potential to improve brand image and profits. The results of the current study also indicate that ‘Government Policy and regulation on CE’ is the most significant factor influencing all other CSFs. In Indian SMEs, policies and decisions are highly influenced by government regulations [30]. Therefore, SME management must recognize the importance of this CSF and strategically work towards its implementation. Furthermore, the authors observed that a ‘Reduction in energy and waste-related costs’ is the most significant effect group factor. This suggests that CE practices offer benefits and contribute to achieving net-zero manufacturing goals when successfully implemented. To fully leverage circular practices, their successful implementation should be a priority. The influence diagraph showed that ‘Govt. policy and regulation on CE’, ’Consumer awareness and demand for CE products’, ‘Economic incentives available for CE products’, and ‘New global business opportunity due to CE’ have higher out-degree influence on other factors. From the case study, the authors observed that the strategic implementation of CE practices, green policy, reverse logistics, the training of staff, and new technology adoption improved the use of repaired/refurbished materials in the case organization from 1–2% in 2019 to 9–10% in 2023. The findings of the case study also support the selection and classification of CSFs. From the case study, the authors observed that strategic coordination between management and suppliers helped in technological upgrades, reskilling, and the training of manpower. The awareness of customers and the demand for CE products are essential factors in CE adoption and the transition toward net-zero manufacturing.

Implications, Limitations, and Future Research Prospects

In comparison with linear practices, CE practices are more environmentally friendly and sustainable. Various factors influence the implementation of CE practices, such as stakeholder pressure, legislation, and regulatory requirements, among others. The findings of the current study imply that management commitment and strategies for building a ‘Green image’, coordination with suppliers, technological upgrades, reverse supply chain practices, workforce skills and training, and customer awareness and demand for CE products are crucial factors in successfully adopting CE and transitioning towards net-zero manufacturing. The authors observed that holistic research on the implementation of CE practices in Indian SMEs is the need of the hour. This study focused on the identification and analysis of CSFs for CE in Indian SMEs and verified them through a case study.

The findings of the current study, both from the analysis of CSFs and the case study, could help policy makers and managers of SMEs with several solutions. Firstly, this study sorts out the key CSFs of CE implementation from other common factors. SME managers find it very time-consuming to sort the key factors of CE implementation, so this study efficiently align these factors. Secondly, the findings of the case study on CE assist managers in its implementation in SMEs. The findings of the current study motivate the Indian SMEs to transform themselves towards circular practices and enter the era of net-zero manufacturing through planning strategies on CE implementation. The theoretical implication of the current study will help researchers to further explore SDGs and CE to achieve net zero practices in manufacturing and other sectors in developing economies.

However, this research has a few limitations, including constraints related to sector selection and expert opinions. The authors recommend exploring this study in different sectors and economies, with an analysis of enabler, barrier, and implementation case studies.