An Intuitionistic fuzzy approach to modeling the drivers to promote Energy-Efficient textile Manufacturing: Implications for sustainable development

1 Introduction

As an intangible yet fundamental resource, energy supports a complex web of interconnected systems and processes. Energy forms the bedrock of a nation's economy, acting as a catalyst for societal and economic advancement while exerting the most significant influence on sustainable development. It is commonly understood that energy plays a critical role in poverty eradication, economic prosperity, long-term infrastructure development, and national security (Kanwal et al., 2022). Energy is fundamental to existence, and its constant and omnipresent supply provides a region with much-needed economic growth and prosperity (Abbas et al., 2018). A rise of 1% in economic growth in developing nations results in a concomitant escalation of energy consumption (Raihan & Bari, 2024). A higher investment-to-gross domestic product (GDP) ratio is significantly associated with an increase in the growth rate of energy use (Raihan & Bari, 2024). Beyond its economic implications, energy also influences other important aspects such as the environment, sustainability, health and well-being, energy security, technological innovation, and geopolitical considerations. With the rapid economic development of emerging economies, the acceleration of industrialization and urbanization, expanding industrial energy consumption, and increasing reliance on energy, there are growing concerns about resource scarcity, supply insufficiency, and disruption (Raihan & Bari, 2024). Energy inefficiency, especially in industries, negatively impacts the achievement of Sustainable Development Goals (SDGs) 7, 8, and 12, which deal with access to affordable and clean energy, decent work and economic growth, and sustainable production and consumption patterns, respectively. As outlined in SDGs 7, 8, and 12, ensuring equitable access to cost-effective, dependable, and environmentally friendly energy sources is critical to relieving poverty, stimulating economic expansion, and promoting sustainable patterns of resource consumption (Alam et al., 2024b).

The textile industry is a traditional pillar industrial sector in emerging economies like Vietnam, India, and Bangladesh, with distinct international competitive advantages (Swazan & Das, 2022). This industry is often critical to economic development in these regions. The industry significantly contributes to boosting the market, absorbing labor, empowering women, hastening urbanization, and supporting harmonious social development (Zhao & Lin, 2019). Energy is an essential resource for textile manufacturing, permeating every process stage and substantially impacting operational efficiency, cost structure, and environmental sustainability (Debnath et al., 2024). The exponential expansion of both population and economy will lead to a significant upsurge in textile manufacturing and consumption (Lin & Bai, 2020). The intricate interplay of complex processes in textile manufacturing necessitates a significant amount of energy to power the complicated machineries involved, allowing the transformation of raw materials into high-quality textiles via processes such as spinning, weaving, and dyeing. Energy-intensive machines are crucial in smoothly converting fibers into fabrics with diverse compositions and textures. Furthermore, the thermal energy supplied from fossil fuels or renewable sources used in the dyeing process assures vivid pigmentation and colorfastness of textiles, making them both visually pleasing and commercially successful. Several finishing processes, such as drying, curing, and heat-setting, also use a substantial amount of energy (Nunes et al., 2018). Additionally, energy is also consumed in the manufacture of apparel, where electricity is required by the cutting, sewing, and other machines to stitch fabrics together and in utility services and supply chain and logistics for the transportation of raw materials and finished good. According to the China Energy Investment Corporation (CEIC) database, the Chinese textile industry's specific energy consumption was 16.67 million tons of oil equivalent (Mtoe) in 1985, rising to 21.14 Mtoe in 2000. Following that, it quickly increased to 51.07 Mtoe in 2016. Energy usage has increased by 67.36% since 1985 (Lin & Bai, 2020).

Emerging economies are striving to raise both their economic and living standard in line with the demands of their rapidly growing populations. However, some issues are refraining them from achieving the goal, such as less diversified energy policies and inefficient energy consumption (Mancheri et al., 2019). Against the backdrop of the ongoing conflict between Russia and Ukraine, inflationary pressures, and the post-COVID disruption situation, the need for energy conservation becomes even more pronounced, with complex ramifications for economic stability, geopolitical dynamics, and environmental sustainability (Orhan, 2022). The COVID-19 pandemic has exerted profound disruptions on the global textile industry, manifesting in supply chain disturbances and a significant shift in consumer demand (Debnath et al., 2024). The war has already put nations that depend on Russia or Ukraine at risk for energy, minerals, fertilizers, and agricultural products (Alam et al., 2024b). Energy resources, particularly natural gas, have become critical geopolitical instruments and economic leverage. Large textile establishments in Bangladesh face a delicate situation due to various challenges. Due to the strong dollar exchange rate, the crisis of electricity shortage and rising production costs, including increased expenditure on importing fossil fuels, coals, etc., have put financial pressure on the textile manufacturing sector (Kanwal et al., 2022). Additionally, natural gas use in Bangladesh has been substantially higher in the last 15 years than that of natural gas discovery. The total amount of recoverable natural gas was anticipated to be around 27 trillion cubic feet (TCF) by the end of 2017, with 15.22 TCF previously consumed (Shetol et al., 2019). As a result, the remaining amount of gas is 12 TCF, only enough to cover the country's demand for the next 10–12 years (Shetol et al., 2019). In addition, the scarcity and higher prices of textile raw materials in the international market have further compounded these difficulties. The impact of the Ukraine-Russia war has led to a decline in purchasing power in the main buyer markets, namely the United States and Europe, resulting in a 50% reduction in orders (Liadze et al., 2023). These groups' dependency on imported raw materials necessitates maintaining a six-month stock, which incurs significant financial and insurance costs (Debnath et al., 2024). Additionally, the recent 250% increase in gas prices has affected these groups' production costs, while the availability of gas and consistent power supply remains unreliable, causing disruptions and increased expenses (Milewska & Milewski, 2022). These energy challenges, along with decreased demand in the international market, pose a risk to the overall economic sustainability of the business. Furthermore, the combination of inflationary pressures and the COVID-19 epidemic mandates a strategic emphasis on energy saving.

Due to the severe condition of scarce energy resources and environmental difficulties, achieving energy efficiency has become increasingly critical for textile manufacturing in emerging economies. Accounting for the lack of energy sources in the country and the economic implications of the textile manufacturing sector, improving energy efficiency is an important means for effectively alleviating the disparity between energy demand and energy supply, as well as an effective way of promoting economic and environmental sustainability (Zhao & Lin, 2019).

Hence, it is obvious that energy-efficient strategies should be executed in the textile sector, reducing consumption to move towards a more sustainable business and reducing the country's reliance on energy imports (Costa et al., 2019). Many studies have already been carried out regarding energy efficiency in various aspects. Traditional energy efficiency was measured and decomposed using data envelopment analysis, while the Biennial Malmquiste Luenberger (BML) productivity index (Zhao & Lin, 2019) and dynamic energy performance evaluation were done by non-radial direction distance function (NDDF) (Lin & Bai, 2020). Very few studies are centered on exploring the driving factors and barriers to energy efficiency (Hasan et al., 2019), using only statistical methods. However, the overall factors to promote energy efficiency considering every stage of textile manufacturing in the context of emerging economies to achieve sustainable consumption remain unexplored in the existing literature. The consequences of the Russia-Ukraine war on energy have not yet been investigated much. There has been no investigation into the driving factors that can help overcome the impact of the energy crisis arising from the COVID-19 pandemic and the Russia-Ukraine conflict on energy, particularly for emerging economies like India and Bangladesh. To ensure the smooth functioning of all textile-related industries, achieve sustainability, and ensure the country’s economic security, correctly identifying and evaluating the interactions of these driving factors is pivotal. The principal objective of this study is to fill the above-mentioned research gaps by investigating the following research questions (RQs):

RQ1: What are the driving factors promoting energy-efficient textile manufacturing in the context of an emerging economy like Bangladesh?

RQ2: How are these factors interrelated with each other?

RQ3: How will assessing factors benefit industry managers and policymakers in achieving energy efficiency and sustainability?

To address these RQs, past research has been reviewed to identify the driving factors for energy-efficient textile manufacturing, and interviews have been conducted with several Bangladeshi textile manufacturing companies to refine the identified driving factors. Before having these criteria validated by specialists, preliminary systematic literature research has been conducted to discover the initial driving factors from the literature. The respondents have then finalized the factors. Therefore, this research endeavor will contribute to two sectors of the existing literature, including sustainability and energy-efficient textile manufacturing, by accomplishing the underscored research objectives (ROs):

RO1: Identifying driving factors that encourage energy efficiency in emerging economies' textile industries.

RO2: Proposing an appropriate analytical framework to examine and assess the drivers.

RO3: Looking into how the factors affect each other and helping lawmakers to emphasize and promote environmentally friendly and energy-efficient textile manufacturing practices.

A combination of the Interval Valued Type 2 Intuitionistic Fuzzy (IVT2IF) set theory and the Decision-making trial and evaluation laboratory (DEMATEL) have been used in this study to evaluate the significant factors (overall prominence) and uncover the causal interrelationships and hierarchy of relevance of the resulting driving factors in pursuit of the defined ROs. Integrating Interval-Valued Type 2 Intuitionistic Fuzzy Numbers (IVT2IFN) with DEMATEL presents a versatile approach to addressing uncertainty and vagueness in decision-making (Zulqarnain et al., 2022). By combining the IVT2IFN theory with DEMATEL, the imperfect and unpredictable features of human perceptions can be effectively managed by integrating the IVT2IFN theory with DEMATEL. This allows one to overcome the limitations of the DEMATEL method and enhances the precision of the interrelationships among various driving factors (Zulqarnain et al., 2022).

This study facilitates the existing literature by empirically exploring the driving factors for production methods that use less energy in the textile manufacturing industry of a developing economy. This expands the body of knowledge since it allows us to identify a wide range of driving factors and their corresponding causal interrelationships. Additionally, this research can aid policymakers and practitioners in better planning by helping them understand the interdependence and reliance among the driving variables for boosting energy efficiency in the textile industry. The study also allows policymakers and practitioners to implement more efficient production processes in the textile industry by identifying the priority factors that should receive the most investment and attention.

2 Related Works, research gaps, and research contributions

The textile sector is widely recognized as being highly energy-intensive. Numerous studies have investigated energy efficiency measures that can be adopted in textile manufacturing. The production processes involved in textile manufacturing, such as spinning, weaving, knitting, dyeing, and finishing, require substantial energy (Hasan et al., 2019). However, incorporating energy-efficient processes in textiles will lead to a sustainable future. Efficient energy management is essential for reducing costs, minimizing environmental impact, and enhancing overall sustainability in the textile sector. Hence, this study conducted a comprehensive review of existing literature spanning from 2018 to 2023. The objective was to assess the relevant works on energy efficiency and identify various factors contributing to adopting energy-saving production processes in the textile sector. Furthermore, the review exposed a research void in this field, prompting the current study. A research approach incorporating databases, temporal resolution, keywords, inclusions, exclusion criteria, etc., was developed during the literature review process, as shown in Table 1. A cross-referencing analysis was executed, and pertinent literature on the efficacy of resource utilization was delved into.

Christensen (2021) proposed a circular economy model for textile recycling. Ahmed et al. (2020) and Hasan et al. (2018) conducted their research in Bangladesh to assess energy consumption and perform energy evaluations. In another study, Islam & Khan (2017) accomplished a review of energy scenarios in Bangladesh. These studies collectively provide valuable insights into the potential for sustainable practices and energy efficiency improvements in the textile and manufacturing sectors. Table A1, Appendix A of supplementary materials presents the summary of the recent literature on energy efficiency.

After studying the previous relevant literature, the following research gaps were revealed:

1. Though unearthing the consequences of energy efficiency approaches has been popular among researchers, very few studies have identified the driving factors for promoting energy efficiency in the textile manufacturing sector.

2. There has been no investigation into the driving factors that can help overcome the impact of the energy crisis arising from the COVID-19 pandemic and the Russia-Ukraine conflict.

3. No study has been found to explore the driving factors in the context of emerging economies.

4. The existing literature falls short of uncovering the mutual relationships among the driving factors to promote energy efficiency and how they contribute to sustainability.

This study aims to address these gaps and offer valuable insights into energy efficiency in textile sectors of emerging economies by considering multiple perspectives. By combining the IVT2IF theory and DEMATEL method, this study intends to evaluate the findings (overall prominence to learn how the resulting causative factors are connected to one another and rank in importance. Furthermore, the IVT2IF DEMATEL technique produces more robust findings when taking decisions in perplexing condtions, deriving both priority and causal linkages concurrently. By implementing the recommended suggestions in this study, the proposed framework will assist managers in making proactive and reactive decisions to enhance energy effectiveness in emerging economies. Such a strategic stand will retrieve emerging economies from energy loss and waste, enabling energy security and establishing sustainable textile manufacturing.

3 Methodology

3.1

3.1 Survey Design and Data Collection

A detailed and acute survey is performed for an in-depth investigation of the literature to identify potential driving factors and examine their interrelationships that promote energy-efficient manufacturing processes in the textile sector of an emerging economy to attain sustainable development. The survey has been accomplished in several distinct stages. However, as depicted in Fig. 1, the study's framework starts with defining the research goal of systematically analyzing the drivers to promote energy-efficient textile manufacturing in emerging economies. Firstly, the snowballing technique, a randomly selected sequential sampling method, was used to identify initial driving factors to increase energy efficiency in textile manufacturing (Debnath et al., 2024). As outlined in Table 1, the research protocol was utilized to initially skim through more than 120 papers chosen from a rigorous literature assessment using the keywords with a timeline from 2018 to 2023. After careful full-text screening and appropriate application of the study protocol, the 55 most pertinent publications, projects, and documents were finally reexamined in order to determine the contributing elements and respond to the particular RQs.

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Fig. 1

Study design and the methodological framework.

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Utilizing the application of the afore-mentioned elaborate research technique, 18 potential driving factors promoting energy efficiency in textile manufacturing in a rising economy were unveiled. Table A1, located in Appendix A associated with the supplementary materials, provides a preliminary list of the factors examined in the literature study. The list was then sent to appropriate field decision experts (DEs) for confirmation. A total of 12 responses were collected from the DEs during the validation stage to finalize the list of factors for further evaluation. The validation process resulted in the finalized list of 16 factors, where the experts deducted four factors and added two more relevant factors. The two added factors are “Adoption of green emerging technologies” and “Employing motion and occupancy sensors and timer-based power control system”, while the subtracted four factors are “Embracing biotechnological advancement”, “Procurement of energy-efficient materials, resources, and technologies”, “Developing an energy efficiency network (EEN)”, and “Installation of variable frequency drive (VFD)”. Selection of the research decision experts (DEs) was carried out using the purposive sampling method (Alam et al., 2024a; Bhattacharya et al., 2024; Shakur et al., 2024), a method that is useful for choosing decision experts in order for particular assessment towards achieving the research goal (Bari et al., 2024; Alam et al., 2024b). Fig. 1 shows the whole framework followed in this research study.

In the second stage, after finalizing the list of driving factors, a survey questionnaire was designed to examine these factors further. A linguistic scale corresponding to IVT2IFN was applied to collect the feedback of the experts regarding their interrelationships. This process begins with gathering the DEs' feedback via semi-structured interviews and an organized questionnaire. Next, the IVT2IF-DEMATEL approach is applied to ascertain the factors ranking, grouping, and causal relationships. Subsection 2.4 provides a full description of the IVT2IF-DEMATEL method's process. The final step involved the DEs validating the research results. The research findings and their many ramifications were finally discussed following the validation.

All the respondents were professional energy and textile experts with at least eight years of experience in the relevant field. A face-to-face physical interview was conducted to gather expert feedback from DEs. About 16 DEs from the textile-energy sector were approached, and 12 of them (75% response rate) consented to take part in the survey work, both in validating factors as well as analysis. The 12 participating decision experts' general profiles are shown in Table 3.

Table 3 Overview of the Experts’ Profile.

Decision Experts	Background	Rank	Institution Category	Experience (In Years)
1	Ph.D. in Textile Engineering	Associate Professor	University	13
2	M.Sc. in Industrial Engineering	Textile General Manager	Private Textile Company	11
3	B.Sc. in Electrical Engineering	Regulatory Commission Member	Government Power Division	11
4	M.Sc. in Mechanical Engineering	Maintenance Manager	Private Textile Company	10
5	B.Sc. in Electrical Engineering	Audit Manager	Government Power Division	12
6	Ph.D. in Textile Engineering	Assistant Professor	University	9
7	M.Sc. in Environmental Science & Engineering	Sustainability and Compliance Manager	Multinational Textile Company	13
8	B.Sc. in Electrical Engineering	Power Manager	Government Power Division	9
9	M.Sc. in Textile Engineering	Assistant Maintenance Manager	Private Textile Company	8
10	Ph.D. in Mechanical Engineering	Professor	University	18
11	M.Sc. in Industrial Engineering	Associate Professor	University	10
12	B.Sc. in Textile Engineering	Textile manufacturing Manager	Private Textile Company	8

4 Results

By using the IVT2IF-DEMATEL technique, the prominence order of the driving factors encouraging energy-efficient textile manufacturing in emerging economies has been uncovered, as shown in the following Table 5. The ranking order of the driving factors is ascertained based on their level of influence (D+R) as F4 > F6 > F2 > F15 > F12 > F13 > F7 > F1 > F14 > F12 > F9 > F3 > F8 > F10 > F5 > F16.

The ranking of the causes and effects in order of importance is presented in Table 6. 8 driving factors are found as causal factors (when D-R >0) and 8 driving factors as effect factors (when D-R < 0). F2 > F12 > F13 > F15 > F7 > F6 > F4 > F11 are the driving factors in the cause group, while F3 > F14 > F16 > F5 > F1 > F10 > F9 > F8 are the driving factors in the effect group.

Tables 5 and 6 are used to generate a cause-and-effect diagram, as shown in Fig. 2. The diagram is illustrated in cartesian coordinates, with D+R alongside the X-axis and D-R alongside the Y-axis. The driving factors are divided into four clusters: driving factors (C1), independent factors (C2), critical factors (C3), and impact factors (C4). The cause group incorporates the driving factors (C1) and critical factors (C3), whilst independent factors (C2) and impact factors (C4) are in the effect group. The critical factors exhibit the highest influencing factor, retaining its location in the high influence and causal group region. Policymakers need to focus more on these driving factors to promote energy-efficient textile manufacturing in emerging economies. Contrariwise, impact factors (C4) appear to be the most influential driving factor cluster located in the effect group.

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Fig. 2

Illustration of the cause-and-effect group of the driving factors.

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The causal relationship between the driving factors is illustrated in Fig. 3. A total of sixteen × sixteen (16×16) relationships are possible. Since it is difficult to illustrate every connection, the threshold value (θ), which is equal to the total relation matrix's (T) mean (μ) plus standard deviation value (τ), was set to produce the digraph. Consequently, the study's threshold value, θ = 0.246 + 0.046 = 0.292, is determined.

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Fig. 3

Co-relationship among the driving factors.

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5 Discussion

From the prominence ranking shown in Table 6, “Implementing effective in-house energy management measures (F4)” appears to be the most significant driving factor in promoting energy-efficient textile manufacturing in emerging economies among all the driving factors. Energy management is vital since it continuously minimizes companies' energy use and associated energy expenditures (Lawrence et al., 2019). Putting a strong emphasis on in-house energy management measures is not only crucial for improving internal cost-effective operations but also for adopting energy-efficient practices, reducing environmental impact, conserving resources, encouraging technological innovation, and thus, improving the energy efficiency by 3–4% (Hasan et al., 2019). Furthermore, as emerging economies’ textile industries are anxious to prevent the energy sector unpredictabilities caused by obvious disruptions in energy due to the Russia-Ukraine war, it is evident that implementing energy management strategies is the most significant driving factor that emerging economies must prioritize to boost resiliency during the crisis time.

“Adopting advanced machinery & total productive maintenance (F6)” has been found to be the second most prominent driving factor in promoting energy efficiency in textile manufacturing. In textile firms, the usage of obsolete machines raises power consumption, while old-fashioned and outdated production techniques require additional processing; thus, embracing modern and efficient technology-driven machineries can significantly reduce machine power consumption, waste, and rework in the production process, as well as eliminate superfluous motion (Chen & Lin, 2020). Furthermore, total productive maintenance not only upholds an orderly operational environment but also nurtures a collective commitment towards equipment upkeep, early error identification, and preemptive malfunction mitigation, thereby fostering heightened inter-departmental synergy. Its cascading effect simplifies machinery efficiency, safety, and dependability while imbuing them with energy frugality (Achamu et al., 2018).

The third most influential driving factor is “Adopting renewable energy (F2)”, indicating that manufacturers must focus more on renewable energy usage rather than burning fossil fuels (Amin et al., 2022). Renewable energy adoption in the energy-intensive textile industry offers a substantial and beneficial contribution to sustainable development by abating the depletion of natural resources, replenishing expeditiously than the rate of usage, dwindling dependence on fossil fuels, and optimizing the use of agricultural waste and algae biofuel for power generation in the textile sector, thereby mitigating environmental harm and enhancing overall efficiency (Alam et al., 2024b). For example, textile manufacturers can use solar energy which can reduce reliance on natural gas and fuel in various aspects of textile manufacturing, including lighting, cooling, office equipment, and temperature control systems (Irfan et al., 2019).

“Adoption of green emerging technologies (F15)” is found as the fourth most significant driving factor in fostering energy efficiency. For instance, according to experts, by creating an environmentally conscious architectural design that maximizes natural daylight, promotes efficient air circulation, and integrates personalized lighting circuits and temperature controls, energy consumption can be minimized through diminished reliance on artificial lighting and mechanical ventilation which, in turn, increases energy efficiency (Alagbe et al., 2019). However, government support, including financial incentives, attractive loans, subsidies for audits, and tax exemptions, is crucial to motivating textile owners to adopt energy-efficient production methods (Hasan et al., 2018). This makes “Strengthening government support (F12)” the fifth most significant driving factor.

Recently, there has been a 250% spike in gas prices worldwide, which has impacted gas-importing countries, including Bangladesh (Milewska & Milewski, 2022). Hence, the government can do strategic planning by withdrawing energy subsidies that force textile manufacturers to seek cost-effective and energy-efficient alternatives, which entails implementing energy-efficient technologies, processes, and practices, ultimately promoting sustainable and resource-efficient production methods (Abbas et al., 2018). Therefore, “Rising energy prices (F13)” can be justified as the sixth significant driving factor. “Establishing an energy audit system (F7)” is the seventh most important driving factor to promote energy efficiency. It is because implementing continuous sub-metering for energy end-use enables precise allocation of power to equipment, product lines, and processes while also holding departments accountable for energy costs, thus enabling textile companies to assess energy performance, develop solutions, and project investment requirements for energy efficiency enhancements based on the obtained data (Johansson & Thollander, 2018). The eighth significant driving factor emerges to be “Incorporating circular economy in the production process (F1)”. The circular economy concept emphasizes the recycling (both open and closed) of textile waste and the reuse of water (Manickam & Duraisamy, 2019). Incorporating circular economy principles will improve energy efficiency in optimized operations, recover heat energy, minimize redundant stages, and reduce overprocessing (Christensen, 2021). “Raising consumer awareness (F14)” and “Appropriate production planning (F11)” are the ninth and tenth significant driving factors, respectively. Elevating consumer awareness about energy-efficient textile manufacturing can substantially influence demand for sustainable products and push businesses to adopt energy-efficient practices and properly coordinated production processes (Soepardi & Thollander, 2018). Through appropriate production planning, a 25% reduction in SEC value is attainable. Furthermore, manufacturing planning significantly affects the overall energy usage when the dyeing machines are operating at maximum load capacity, which can save a remarkable 23% on energy. (Özer & Güven, 2021).

In this study, the interrelations between driving factors depend on whether the driving factor is associated with the cause or effect group. In the case of the cause group, “Adopting renewable energy (F2)” and “Strengthening government support (F12)” constitute the most significant and critical driving factors, wielding the greatest influence over the other driving factors. Emerging economies' textile sectors are primarily reliant on fossil fuels (Debnath et al.,2024). Due to the disruption and crisis of fossil fuel supply, the adoption of renewable energy has become the top-most importance (Davis et al., 2020). Renewable energy cannot only power modern dyeing machines that require less water, but it also can act as an enticing marketing element, increasing customer awareness and ensuring consistent power for motion sensors (Kumar et al., 2021). In addition, the government needs to step up and express its intention to address the impending energy problem. As an illustration, government programs and regulations can serve to promote the application of IoT devices and smart technologies in energy management (Borowski, 2021). In addition, the government can direct consumers toward energy-conscious choices and provide incentives for businesses to adopt circular economy practices in the textile industry (Patwa et al., 2021). “Rising energy prices (F13)” is the third most important and critical factor in the cause group, as increased energy prices will result in the implementation of other subsequent driving factors to energy efficiency. Escalating energy prices drives textile manufacturers to adopt energy-saving measures such as automated systems, insulation, and compressed air leak reduction to reduce energy consumption and costs in operations. “Adoption of green emerging technologies (F15)” is the fourth most important and critical factor in the cause group, as green architectural design maximizes the use of renewable energy sources like sunlight and air, dwindling dependency on artificial lighting and ventilation that uses electricity produced from burning fossil fuels. Also, the green textile facility is more likely to follow circular economy ideas, such as recycling and material reuse, and it shows the company's dedication to sustainability that can be utilized to inform customers about the value of energy efficiency, raising their awareness (Christensen, 2021). “Establishing an energy audit system (F7)” is the fifth most impactful causal as well as critical factor as it results in accountability for energy cost across the departments, enabling the company to assess energy performance. Energy audits can find compressed air leakage, inefficient or excessive gas combustion and steam generation, and heat loss in machinery, boilers, and transmission systems (Kaya et al., 2021). “Adopting advanced machinery & total productive maintenance (F6)” is the sixth most impactful causal as well as critical factor. Advanced machinery, equipped with built-in sensors, automation capabilities, and IoT integration can enhance efficiency across various processes (Sathyan, 2020). Finally, “Implementing effective in-house energy management measures (F4)” and “Appropriate production planning (F11)” are the two causal factors that correspond to the causal ranking, respectively.

Based on the analysis provided in Fig. 2 and Table 6, the effect driving factors discovered in this study (F3 > F14 > F16 > F5 > F1 > F10 > F9 > F8) pose to have direct influence by the causal driving factors. “Adopting alternative waterless dyeing technology (F3)”, “Raising consumer awareness about energy efficiency (F14)”, and “Employing motion and occupancy sensors and a timer-based power control (F16)” have been less affected in the causal ranking order, but due to their proximity to the cause group, they remain quite significant. Still, they are also impacted by the causative group factors. As depicted in Fig. 3, “Adopting alternative waterless dyeing technology (F3)”, “Raising consumer awareness about energy efficiency (F14)”, and “Incorporating circular economy in the production process (F1)” are influenced by the “Adopting renewable energy (F2)” and “Strengthening government support (F12)” factors. The waterless dyeing method for polyester, utilizing CO2 or air, which is renewable instead of water, is a sustainable alternative to traditional dyeing procedures (Hossain et al., 2018). Modern dyeing machines that use less water can be powered by renewable energy, which can also be an enticing marketing element for increasing customer awareness (Kumar et al., 2021). Moreover, the government can encourage consumers toward more energy-efficient options and give incentives for enterprises to use circular economy techniques in the textile industry (Patwa et al., 2021). Factors like “Implementing effective in-house energy management measures (F4)” and “Adopting advanced machinery & total productive maintenance (F6)” impact factors such as “Reduction and control of compressed air leaks (F8)”, “Installing IoT devices to manage gas combustion and minimize excess steam generation (F9)” and “Insulation of machinery, boilers & transmission system (F10)”. Advanced machines with improved insulation, IoT integration facilities, and prompt air leak detection features can contribute to energy savings. Besides, a dedicated full-time energy manager should be integrated into the management structure, given the authority to implement energy efficiency measures like air leak checkups, necessary device installation, and machine insulation (Lawrence et al., 2019). “Rising energy prices (F13)” influence factors such as “Raising consumer awareness about energy efficiency (F14)” and “Incorporating circular economy in the production process (F1)”. For instance, consumers may become more conscious of energy-efficient products and practices as energy prices increase. It can also drive textile manufacturers to adopt a circular economy approach that can help reduce energy consumption through recycling and reusing (Christensen, 2021). Finally, “Employing nano-filtration ETP (F5)” is the effect group factor, which is also influenced by a cause group factor.

Fig. 3 also depicts that “Adopting renewable energy (F2)” holds the most significant causal relationship with other driving factors, while the causal driving factors “Strengthening government support (F12)” and “Rising energy prices (F13)” are also heavily linked to other driving factors, respectively. Thus, in order to support energy-efficient textile manufacturing, legislators, decision-makers, and regulatory committees should accentuate the three quintessential factors.

The outcomes of this study differ notably from prior comparable research studies conducted on various industries and nations. For instance, Chang et al. (2022) used big data analytics to create a framework for optimizing production facilities that were capable of saving more than 10% of energy consumption. Hasan et al. (2018) used a case study approach and identified important drivers and barriers to implementing energy efficiency practices. They highlighted cost savings from reduced energy use as the key reason behind energy efficiency among the surveyed steel plants. Several other important elements were also identified as significant contributions to the industry's pursuit of energy efficiency. These included long-term strategic considerations, fears about prospective energy cost rises, and the impact of foreign competitiveness, but they did not rank them by their importance or investigate their interrelationships. Costa et al. (2019) used ecological footprint methodology to analyze energy and resource consumption in dying, printing, and finishing processes in the textile industry and evaluated enhanced technology and cleaner production alternatives that will help to minimize water consumption and effluent volume. Hasan et al. (2019) performed a case study strategy to study the phenomenon of energy efficiency and found that the most important driver for energy efficiency among the textile mills surveyed was the “energy management scheme”, followed by “risk of future high energy prices” and “assistance from energy professionals” but falls short of uncovering the mutual relationships among the driving factors to promote energy efficiency. However, the causal interrelationships and hierarchy of importance of the resulting driving factors can be discovered by employing an Interval-valued Type 2 Intuitionistic Fuzzy (IVT2IF) technique and a decision-making trial and evaluation laboratory (DEMATEL) to assess the findings (overall prominence). Our study found that adopting renewable energy, strengthening government support, and implementing effective in-house energy management measures are the most significant driving factors in promoting energy-efficient textile manufacturing for sustainable energy consumption in the emerging economy context. It also shows which factors are more important than others. This knowledge can help managers and policymakers plan better and invest their resources wisely, making the textile sector more energy-efficient and productive. Nonetheless, no previous study has focused on the cause-and-effect relationship of driving factors promoting energy efficiency, specifically to support the textile industry of an emerging economy. As a result, the findings of this study stand out from those of other related studies.

6 Implications.

7 Conclusion

The textile industry, a traditional pillar industry in emerging economies like India and Bangladesh, requires substantial energy for manufacturing. Enhancing energy efficiency is crucial to account for the lack of energy sources in emerging economy countries. It helps to bridge the gap among energy demand and supply and serves as an enforceable strategy for boosting both economic and environmental sustainability. So, in response to the significant amount of energy necessity, scarce energy resources, increased expenditure of fossil fuel, inflationary pressures, and increased production cost, executing energy-efficient strategies is obvious in the textile sector to move towards a more sustainable business and reduce reliance on energy imports. Promoting energy efficiency in emerging economies requires finding the efficiency driving factors contributing to sustainability, considering the impact of COVID-19 and Russia-Ukraine conflict situations on the textile sector that no existing literature has admitted. This research addresses the gap in existing studies by utilizing the IVT2IF-DEMATEL framework to identify the driving factors and evaluate their interrelationships in order to derive long-term conclusions.

The study conducted an initial analysis to identify the factors driving energy efficiency in the textile manufacturing sector. This analysis was based on a complete examination of existing literature. The IVT2IF technique engaged twelve experts to ascertain and authenticate the driving factors of utmost significance. This process culminated in the identification of sixteen factors that were deemed suitable for subsequent examination. The study employed the DEMATEL to analyze the causal relationships among the factors. As a result, eight factors were identified as causes, and eight factors were identified as effects in relation to promoting energy efficiency in the textile industry of developing countries. “Implementing effective in-house energy management measures (F4)” stood out as the most influencing factor. “Adopting renewable energy (F2)”, “Strengthening government support (F12)”, and “Rising energy prices (F13)” turned out to be the most important causal factors. These factors can have a major impact on the other effect group factors listed, hence boosting energy efficiency in the textile industry. The top three influenced factors in the effect group were “Adopting alternative waterless dyeing technology (F3)”, “Raising consumer awareness about energy efficiency (F14)”, and “Employing motion and occupancy sensors and a timer-based power control (F16)”. If the causal elements within the system are adequately treated, the driving forces of the effect group may be automatically addressed as well. This study’s Implications for theoretical development, managerial procedures, and policy-making in the textile industry are highly significant, which have been discussed in detail in section 5. The study outcomes is expected to aid industrial managers and policymakers in promoting energy efficiency in the textile manufacturing sector and thus contribute to attaining various significant sustainable development goals, such as SDG 7, 8, 9, 12, and 13.

This study, like others, has limitations that necessitate additional research. Further work is required to enhance the study's cause-and-effect link by statistical analysis using a larger sample size because the cause-and-effect relationship lacks statistical validity. Other new hybrid approaches, such as Fermatean fuzzy theory, can be applied. Regression-based methods, such as spline regression modeling or fuzzy regression analysis, can also be utilized to predict, handle, and optimize the identified driving factor-implemented regulatory system in a more flexible and interactive manner. Using machine learning (ML) and artificial intelligence (AI) in research can also strengthen future studies and improve the accuracy of previous decisions.

Furthermore, the findings of this research, particularly the identified factors driving energy efficiency, can be applied beyond textiles. For example, industries such as manufacturing, automotive, or chemicals that are also energy intensive could benefit from understanding and implementing similar strategies to improve their energy efficiency as well as sustainability. Finally, the study identifies 16 driving factors, which may not be all-inclusive. In subsequent efforts, more pertinent factors could potentially be identified and evaluated utilizing the same or alternative frameworks.

Funding

The authors received no funds, grants, or other financial support for this research project.

CRediT authorship contribution statement

Saifur Rahman Tushar: Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Md. Shamsul Arefin Imtiazh: Writing – original draft, Visualization, Investigation, Formal analysis, Data curation. Rafsun Bin Noor: Writing – original draft, Formal analysis, Data curation. Md. Fahim Bin Alam: Writing – original draft, Visualization, Formal analysis, Data curation. A.B.M. Mainul Bari: Writing – review & editing, Visualization, Validation, Supervision, Project administration, Investigation. Abu Reza Md. Towfiqul Islam: Writing – review & editing, Validation. Mohammad Mahbub Kabir: Writing – review & editing, Visualization.