Issue 2 / 2026 
Inner covers
Article: Process improvement and quality management in the textile industry using Lean Six Sigma methodologies and tools
Authors: DEVI SUBRAMANIAM, BARKAVI GANESAN ELANGOVAN,SANTHI VENKATAKRISHNAN
Pages: 185–195
DOI: 10.202539/IT.077.02.2024183
Published online: April 2026
Abstract
An important part of global manufacturing is the textile industry, which produces a wide range of products. In this
research, 3 primary textile hubs in Tamil Nadu were selected for data collection: Karur, Tiruppur, and Coimbatore. This
study investigates the optimisation of production processes in industry by incorporating the Lean Six Sigma (LSS)
methodology. It also examined 5 key theories that addressed crucial issues such as production, defect rates, process
inefficiencies, and cycle times. This research also used statistical tools such as Microsoft Excel and Minitab to conduct
capacity assessment and process-control defect-rate analysis. This study also used Value Stream Mapping (VSM) and
Measurement System Analysis (MSA) for rectifying the above defects. The Lean Six Sigma (LSS) techniques improved
productivity in the textile industry by reducing cycle time from 62.5 to 53.1 minutes (15% improvement), lowering defect
rates from 19.43% to 12.38% (36.3% improvement), and increasing the sigma level from –3.36 to 0.41 (3.77-unit
improvement). The outcomes showed that, when supported by advanced statistical analysis and process mapping, LSS
can dramatically boost productivity, product quality, and process reliability in the textile sector.
Keywords: Lean Six Sigma, textile industry, DMAIC framework, cycle time, defect rate, process optimisation, quality improvement
Citation: Subramaniam, D., Elangovan, B.G., Venkatakrishnan, S., Process improvement and quality management in the textile industry using Lean Six Sigma methodologies and tools, In: Industria Textila, 2026, 77, 2, 185–195, https://doi.org/10.35530/IT.077.02.2024183
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Article: Impact of cleaning process modifications on the efficiency of improved working parts
Authors: KADAM JUMANIYAZOV, SHERZOD KORABAYEV, FAZLIDDIN EGAMBERDIEV, ABUBAKIR SALOMOV
Pages 196–204
DOI: 10.35530/IT.077.02.202545
Published online: April 2026
Abstract
In the context of increasing demands for cotton fibre quality and energy-efficient processing, this study focuses on
enhancing the effectiveness of cleaning systems by modifying working parts in fibre cleaning machines. The research
investigates the influence of guiding devices and the geometric profile of grates on flow behaviour and pressure
distribution in dual-drum saw-type fibre cleaners. A mathematical model based on the compressible flow of fibrous
material was developed to describe the interaction between the fibre stream and the inclined grate surfaces. The
pressure-density relationship and flow velocity were analysed through nonlinear differential equations, and analytical
solutions were obtained under specific boundary conditions. Simulation results demonstrate that changes in the initial
thickness of the fibre stream and the shape of the guiding device significantly affect the cleaning efficiency. An optimised
profile of the guiding system reduces fibre loss and improves impurity removal by up to 15% compared to conventional
systems. The proposed modifications offer a promising solution for adapting fibre cleaning equipment to handle
machine-harvested, highly contaminated cotton in modern ginning operations.
Keywords: cotton fibre, fibre cleaner, guiding device, flow modelling, pressure distribution, cleaning efficiency
Citation: Jumaniyazov, K., Korabayev, S., Egamberdiev, F., Salomov, A., Impact of cleaning process modifications on the efficiency of improved working parts, In: Industria Textila, 2026, 77, 2, 196–204, https://doi.org/10.35530/IT.077.02.202545
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Article: Modelling of factors of corporate sustainability of textile industry companies
Authors: MARINA JOVANOVIX, SNEZANA UROSEVIC, MILOVAN VUKOVIC, IVANA MLADENOVIC RANISAVLJEVIC
Pages 205–212
DOI: 10.35530/IT.077.02.202547
Published online: April 2026
Abstract
Corporate sustainability significantly affects the commitment of employees in the textile industry, thus providing a basis
for recommendations to companies that wish to implement sustainable practices. Factors such as sustainable
development practices, job satisfaction, motivation, corporate image, leadership and professional development are key
to achieve organizational commitment. The research emphasises the importance of a holistic approach to integrating
sustainability into the operations of the textile industry, with a special focus on the environment and responsible
business. The research results provide a holistic overview of employee attitudes, highlighting key factors for the
successful implementation of sustainability.
Keywords: sustainable development, textile manufacturing, organisational commitment, employee attitudes, holistic approach
Citation: Jovanović, M., Urošević, S., Milovan, V., Ranisavljević, I.M., Modelling of factors of corporate sustainability of textile industry companies, In: Industria Textila, 2026, 77, 2, 205–212, https://doi.org/10.35530/IT.077.02.202547
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Article: The impact of AI-powered personalisation on consumer purchase decisions in the textile sector
Authors: HORIA MIHALCESCU, RALUCA-GIORGIANA CHIVU (POPA), DAVID-FLORIN CIOCODEICĂ, IONUȚ-CLAUDIU POPA, MARIA-CRISTIANA MUNTHIU
Pages 213–221
DOI: 10.35530/IT.077.02.2025108
Published online: April 2026
Abstract
In the context of accelerated digital transformation and shifting consumer expectations regarding online shopping
experiences, digital marketing in the textile industry faces both significant challenges and emerging opportunities. This
study investigates how artificial intelligence (AI) contributes to shaping consumer preferences by personalising
commercial communication, generating relevant product recommendations, and automating interactions between
brands and users.
By combining a literature review with primary data collected through a survey of 358 Romanian consumers active in
digital environments, the research explores their perceptions of AI-driven online marketing. The results indicate that 61%
of respondents view artificial intelligence as a helpful factor in their decision-making process, while 55% consider
personalised recommendations to be relevant in selecting clothing products. At the same time, a substantial segment of
users expresses concerns related to algorithmic transparency and the level of control offered.
The article proposes a conceptual model for integrating AI into digital marketing strategies, with a focus on personalising
the shopping experience and influencing purchase intention. The findings support the need for data-driven, consumer-
centred approaches that leverage automation to deliver added value in the textile industry through the use of
advanced technologies.
Keywords: consumer decision-making, algorithmic personalisation, digital user experience, data-driven marketing, customer trust in AI, textile retail innovation
Citation: Mihălcescu, H., Chivu (Popa), R.-G., Ciocodeică, D.-F., Popa, I.-C., Munthiu, M.-C., The impact of AI-powered personalisation on consumer purchase decisions in the textile sector, In: Industria Textila, 2026, 77, 2, 213–221, https://doi.org/10.35530/IT.077.02.2025108
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Article: Design of an antibacterial medical face mask with oleuropein additive heterogeneity
Author: DEVRIM SOYASLAN DEMIRAY
Pages 222–229
DOI: 10.35530/IT.077.02.202528
Published online: April 2026
Abstract
This study seeks to develop a protective mask infused with oleuropein to ensure excellent defence against bacteria and
viruses. The mask features a three-layer construction, with the inner and outer layers composed of PP spunbond
nonwoven fabric, while the centre layer consists of PP (polypropylene) meltblown nonwoven fabric. The leaves of olive
trees in the Antalya region are harvested, air-dried, and subsequently pulverised into fine particles. Ground olive
particles are removed utilising a Soxhlet apparatus. The extracted material is treated with a mask cloth using a method
called “soak-hold-dry”, and then it is tested for antibacterial activity according to the AATCC 147 standard.
Staphylococcus aureus bacteria are selected to represent the gram-positive group, while Escherichia coli bacteria
represent the gram-negative group. The zone diameters are measured at 47 mm for S. aureus and 40 mm for E. coli.
The findings indicated that fabrics infused with olive leaf extract, which contains oleuropein, exhibit significant
antibacterial efficacy. The average bacterial filtering effectiveness of masks without an oleuropein additive is 96.7%, but
masks with an oleuropein ingredient have an efficiency of 98.5%. The mean breathability rose from 3.7 mm H2O to 4.8
mm H2O. This data indicates that the breathability performance diminished with the addition of oleuropein.
Keywords: olive leaf, antibacterial activity, face mask, oleuropein
Citation: Demiray, D.S., Design of an antibacterial medical face mask with oleuropein additive, In: Industria Textila, 2026, 77, 2, 222–229, https://doi.org/10.35530/IT.077.02.202528
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Article: Research on the virtual simulation of the drape of cotton-linen blended fabrics of high bending rigidity
Authors: ANDREJA RUDOLF, ALEXANDRA DE RAEVE, SIMONA VASILE
Pages 230–243
DOI: 10.35530/IT.077.02.202556
Published online: April 2026
Abstract
A major challenge in virtual 3D garment prototyping is still the lack of precise and reliable simulation of the drape of
textile materials. Accurate simulation of the fabric drape depends on the CAD 3D programme used, the physical and
low-stress mechanical properties of the fabrics and the possibility of varying the simulation parameters, like the density
of the 3D fabric polygon mesh, which is particularly important for high-bending-rigidity fabrics. Hence, in this study, the
effect of the polygon mesh density on the virtual drape of heavy cotton-linen fabrics with high rigidity, suitable for spring
outwear garment, was investigated. Four fabrics were developed with identical yarns and fabric count and differentiated
by fabric weave. The virtual drape simulations of the fabrics were performed using the OptiTex software with variable
polygon mesh density (0.3–0.9) and were compared with the real fabric drape assessed by the Cusick Drape Tester. In
addition, the draping of two virtual 3D garments was analysed to identify differences due to variable polygon mesh
density. Largely different physical and low-stress mechanical properties of the fabrics, depending on weave, resulted in
different drape behaviour, with plain and satin fabrics displaying the highest (0.91) and lowest (0.86) drape coefficient,
respectively. Analysis of the drape coefficients of both real and virtually simulated fabrics suggests that more reliable
and realistic simulations of high rigidity fabrics can be achieved with low polygon mesh density (0.7 and 0.9). The density
of the 3D fabric polygon mesh slightly influences garment drape simulation and appearance. Specifically, for the skirt,
orthogonal projections reveal fold displacement and variations in fold shape and depth correlated with polygon mesh
density. These results can help garment developers to carry out realistic simulations of high-rigidity fabrics.
Keywords: cotton-linen blended fabrics, fabric weave, fabric real drape, virtual drape simulation, polygon mesh density
Citation: Rudolf, A., De Raeve, A., Vasile, S., Research on the virtual simulation of the drape of cotton-linen blended fabrics of high bending rigidity, In: Industria Textila, 2026, 77, 2, 230–243, https://doi.org/10.35530/IT.077.02.202556
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Article: Digital lifeboat: Can Fintech development prevent shipwrecks in the textile industry?
Authors: QIONGWEN ZHANG, HUIFANG LIU, HONGJUN ZENG, HAN YAN, SHENGLIN MA
Pages 244–258
DOI: 10.35530/IT.077.02.202551
Published online: April 2026
Abstract
The textile industry’s lengthy supply chains, narrow profit margins, and substantial working capital pressures render
financial risk management a critical determinant of sustainable industry development. Digital finance, through
technological innovation, offers textile firms novel risk management tools. Drawing on information asymmetry theory and
financing constraint theory, this study examines how digital finance affects textile firms’ financial risk and the underlying
mechanisms. Using 3,443 firm-year observations from Chinese A-share textile industry listed companies spanning
2014–2023, we employ two-way fixed effects models to identify these relationships. Results show that digital finance
development significantly reduces textile firms’ financial risk levels. Mechanism tests reveal that digital finance operates
through two pathways, namely alleviating information asymmetry and easing financing constraints, with these pathways
exhibiting complementary effects. Heterogeneity analysis demonstrates that digital finance’s risk-mitigating effects are
more pronounced in central and western regions, firms with greater media coverage, non-state-owned enterprises, and
large firms. These findings provide theoretical foundations and practical guidance for textile firms to leverage digital
finance tools to optimise financing strategies and enhance risk management capabilities, while also offering policy
insights for promoting digital transformation and high-quality development in the textile industry.
Keywords: textile value chain firms, digital finance, financial risk, information asymmetry, financing constraints
Citation: Zhang, Q., Liu, H., Zeng, H., Yan, H., Ma, S., Digital lifeboat: Can Fintech development prevent shipwrecks in the textile industry?, In: Industria Textila, 2026, 77, 2, 244–258, https://doi.org/10.35530/IT.077.02.202551
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Article: Research on colour matching recommendation for clothing users based on the DBSCAN clustering algorithm
Authors: REN XIANGFANG, FAN RU, SHEN LEI
Pages 259–267
DOI: 10.35530/IT.077.02.202510
Published online: April 2026
Abstract
Colour plays an important role in promoting the sales relationship between clothing brands and users. This article is
based on publicly available e-commerce image data from Tmall and the brand’s official website. Taking the Taiping Bird
brand as an example, a large amount of image data is preprocessed using the Structural Similarity SSIM index, which
is used to evaluate image similarity. The image data is vectorised using a non-linear conversion formula from RGB
format to HSV format, and K-means clustering analysis is performed to obtain the main colours of the image. Finally, the
BDSCAN clustering algorithm is used to cluster the large amount of colour data, continuously modifying the
neighbourhood threshold eps and minimum sample threshold minPts parameters until a sufficient number of clusters
are obtained and a relatively low noise rate is achieved. Finally, for each category, a greedy algorithm is used to solve
for the minimum subset of samples that can represent each cluster. The results are presented in the form of web pages,
divided into two-page entrances for users and brand owners. It provides competitive brand colour recommendations and
real-time sales to brand owners, and personalised colour preference recommendations and direct links to recommended
products to users.
Keywords: BDSCAN clustering algorithm, greedy algorithm, computer vision, clothing, colour matching recommendation
Citation: Xiangfang, R., Ru, F., Lei, S., Research on colour matching recommendation for clothing users based on the DBSCAN clustering algorithm, In: Industria Textila, 2026, 77, 2, 259–267, https://doi.org/10.35530/IT.077.02.202510
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Article: Credit risk-inclusive reverse factoring model for textile supply chains
Authors: PENG DU, XIAOHAN WANG, YUNPENG LU, XIN ZHANG, SHENGYING ZHAO
Pages 268–278
DOI: 10.35530/IT.077.02.202527
Published online: April 2026
Abstract
This paper investigates a two-tier supply chain comprising a core retailer and a capital-constrained supplier within the
textile industry. Utilising the Stackelberg game model, the study examines three distinct scenarios for implementing
reverse factoring: unsecured, third-party external guarantees, and platform-mediated factoring. The research
incorporates the reputation loss risk faced by retailers in the textile sector and analyses the optimal strategies for reverse
factoring financing under conditions of random market demand. The findings indicate that external guarantees do not
significantly mitigate the adverse effects of reputation loss risk when core retailers proactively engage in reverse
factoring financing. This suggests that the introduction of external guarantees in reverse factoring offers limited utility.
Conversely, platform-mediated reverse factoring financing proves to be an effective mechanism for reducing the impact
of reputation loss risk, with its efficacy increasing as the factoring company’s credit line decreases. Furthermore, the
study concludes that when the platform’s service fee rate is low, textile retailers should opt for platform-mediated reverse
factoring financing to optimise their financial operations.
Keywords: reverse factoring, risk of reputation loss, guarantee, platform
Citation: Du, P., Wang, X., Lu, Y., Zhang, X., Zhao, S., Credit risk-inclusive reverse factoring model for textile supply chains, In: Industria Textila, 2026, 77, 2, 268–278, https://doi.org/10.35530/IT.077.02.202527
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Article: Economic growth, industrial concentration, and carbon emissions in the textile industry
Authors: FENG LIU, FEI ZOU
Pages 279–296
DOI: 10.35530/IT.077.02.202566
Published online: April 2026
Abstract
Abstract: Against the backdrop of global “dual-carbon” goals and China’s economic transformation, the textile industry, as a key
high-emission sector, has attracted much attention regarding the dynamic correlations between its carbon emissions,
economic growth, and industrial concentration. Based on China’s textile industry data from 2001 to 2020, this paper
constructs a Vector Autoregression (VAR) model and systematically explores the interactive relationships among the
three through methods such as the Granger causality test, the cointegration test, impulse response, and variance
decomposition. The findings are as follows: the Granger causality test shows that only the growth rate of carbon
emissions in the textile industry (D_CO2) is a significant Granger cause of changes in industrial concentration (D_IC),
while there is no significant causal relationship between other variables, indicating that changes in carbon emissions
have a one-way driving effect on the adjustment of industrial concentration; the unrestricted cointegration test indicates
that there is one long-term cointegration relationship among the three; in the long-term equilibrium, D_IC has a
significant impact on D_CO2, reflecting that the improvement of industrial concentration can effectively curb carbon
emissions; impulse response analysis shows that the response of D_CO2 to its own shock is significant in the short term,
and the impact of D_IC shock on it is transient; D_IC shows a positive response to D_CO2 shock, while the impact of
its own shock is weak; the response of D_ISV (growth rate of economic growth) to D_CO2 shock lasts longer, and the
dynamic interaction among variables is centered on D_CO2; the variance decomposition results show that the long-term
explanatory power of D_CO2 to D_IC reaches 48.85%, but the explanatory power of D_IC to D_CO2 is only 11.75%; the
impact of D_IC on D_ISV (11.63%) is stronger than that of D_CO2 (7.32%); in the long run, the forecast error variances
of the three variables are mainly dominated by their own shocks (about 80% for D_CO2 and D_ISV, and about 50% for
D_IC), and the system eventually tends to equilibrium. The study reveals the key role of carbon emissions in the textile
industry in adjusting industrial concentration, providing empirical evidence for coordinating industrial concentration and
carbon emission governance through policy guidance.
Keywords: economic growth, industrial concentration, carbon emissions in the textile industry, VAR model
Citation: Liu, F., Zou, F., Economic growth, industrial concentration, and carbon emissions in the textile industry, In: Industria Textila, 2026, 77, 2, 279–296, https://doi.org/10.35530/IT.077.02.202566
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Article: Cashmere, silk and wool blended woven fabrics: an investigation of physical and handle properties
Authors: OZNUR ÇETIN, PELIN GURKAN UNAL
Pages 297–305
DOI: 10.35530/IT.077.02.202578
Published online: April 2026
Abstract
This study investigates the physical, mechanical, and handle properties of woven fabrics produced using various luxury
animal fibres, including 100% cashmere, superfine wool, wool/silk (70/30), and cashmere/silk (70/30) blends. All fabrics
were woven under identical construction conditions, with only the weft yarn composition varying. Comprehensive testing,
covering breaking strength, tear resistance, seam slippage, dimensional stability, elongation, air permeability, and
bending rigidity, was conducted before and after finishing processes. Results showed that 100% cashmere fabrics
exhibited the highest breaking strength, while wool/silk blends offered comparable performance with significant cost
advantages. Coarser wool yarns (21.5 μm) provided superior tear strength, whereas silk blends enhanced elongation
and resilience. Wool/silk fabrics also demonstrated the best seam slippage resistance and the highest air permeability.
Cashmere and silk-containing fabrics, though softer and more drapable, showed greater dimensional shrinkage after
finishing. Statistical analysis revealed that fabric properties were significantly influenced by weft yarn composition
(p<0.05), with finishing treatments affecting elongation, permeability, and rigidity. Notably, wool/silk (70/30) fabrics
emerged as the most balanced option, combining mechanical performance, tactile comfort, and economic feasibility.
These findings highlight the potential of superfine wool and silk blends as viable alternatives to cashmere in premium
textile applications.
Keywords: cashmere, wool, silk, woven fabrics, fabric performance, handle, yarn blend
Citation: Çetin, Ö., Ünal, P.G., Cashmere, silk and wool blended woven fabrics: an investigation of physical and handle properties, In: Industria Textila, 2026, 77, 2, 297–305, https://doi.org/10.35530/IT.077.02.202578
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Article: Deep learning-based recognition of Miao ethnic costumes via YOLOv5s: A step toward digital cultural preservation
Authors: RUI GUO, TING CHEN, YAN HONG, XIANYI ZENG
Pages 306–316
DOI: 10.35530/IT.077.02.2025156
Published online: April 2026
Abstract
Miao ethnic costumes, celebrated for rich diversity, intricate craftsmanship, and distinctive patterns, represent an
important aspect of China’s cultural heritage and the broader realm of intangible cultural heritage. In response to the
growing need for digital preservation, this study proposes a deep learning-based approach to recognise and document
Miao costumes effectively. While traditional costume recognition methods face challenges such as high computational
costs and limited analytical capacity, the YOLOv5s framework offers automatic feature extraction and improved
scalability. However, its standard form struggles to adequately focus on critical visual features, reducing recognition
performance accuracy. To overcome this, we introduce the YOLOv5s-SED model, which incorporates a Squeeze-and-
Excitation (SE) attention mechanism and Deformable convolution (DCNv2) into YOLOv5s to enhance feature
representation and improve the recognition of fine details. A dedicated dataset of 4,468 annotated images was compiled,
and the model was refined through hyperparameter tuning and comparative experiments. The results demonstrate
notable performance gains, with precision increasing from 97.1% to 97.6%, recall from 99.3% to 99.8%, and mean
Average Precision (mAP) from 70.7% to 71.5%. These outcomes highlight the model’s strong generalisation ability in
complex environments and its potential to support the digital preservation and promotion of Miao ethnic costumes.
Keywords: Miao ethnic costumes, cultural heritage preservation, deep learning, YOLOv5s-SED, image recognition
Citation: Guo, R., Chen, T., Hong, Y., Zeng, X., Deep learning-based recognition of Miao ethnic costumes via YOLOv5s: A step toward digital cultural preservation, In: Industria Textila, 2026, 77, 2, 306–316, https://doi.org/10.35530/IT.077.02.2025156
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Article: How does supply chain concentration influence the digital transformation-collaborative innovation nexus in the Chinese textile industry?
Author: AN YU-XIA
Pages 317–330
DOI: 10.35530/IT.077.02.202549
Published online: April 2026
Abstract
Supply chain concentration, defined as the reliance on a few customers and suppliers, presents both opportunities and
risks for firms, particularly when seeking to drive innovation through digital transformation. In the Chinese textile industry,
this supply chain concentration could either facilitate or hinder the effectiveness of digital strategies designed to promote
collaborative innovation. Realising the economic significance of the Chinese textile industry, this study explains how
supply chain concentration influences the relationship between digital transformation and collaborative innovation in the
Chinese textile sector. Using data of 942 A-share listed textile firms in China, this study reveals a robust positive
relationship between digital transformation and collaborative innovation, demonstrating that digital strategies have the
potential to foster innovation in the sector. To address potential endogeneity concerns, we further validate this finding
using the two-stage least squares (2SLS) regression model. However, the study uncovers that textile companies
operating within highly concentrated supply chains face constraints that prevent them from fully leveraging digital
transformation for collaborative innovation. Furthermore, heterogeneity analysis shows that industry-specific factors,
such as pollution levels, and firm characteristics, including firm size (FS), significantly moderate this relationship. These
findings suggest that policymakers should focus on improving digital infrastructure, promoting financial inclusion, and
diversifying supply chains to enable textile firms to more effectively utilise digital transformation for driving collaborative
innovation.
Keywords: digital transformation, collaborative innovation, supply chain concentration
Citation: Yu-Xia, A., How does supply chain concentration influence the digital transformation-collaborative innovation nexus in the Chinese textile industry?, In: Industria Textila, 2026, 77, 2, 317–330, https://doi.org/10.35530/IT.077.02.202549
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Article: Body shape morphology representation and prototype pattern optimisation by polar diameter on female students’ “waist-to-thigh” zone
Authors: TAO LI, QIANYUN ZHANG, QIAN ZHANG, XIAOJUN DING, YUQING YE
Pages 331–337
DOI: 10.35530/IT.077.02.202573
Published online: April 2026
Abstract
Traditional prototype patterns are typically generated using proportional formulas based on size specifications. This will
only receive identical prototype patterns under the same size specification, directly leading to significant fit issues due
to morphology differences. A 3D point cloud of 353 female students was scanned, and the cross-section layers of the
‘waist-to-thigh’ zone were determined. A polar radial was extracted to represent the surface morphological difference.
Subsequently, the pattern based on the surface flattening was optimised. Additionally, the fitness was evaluated by the
subjective assessment. The results showed that the polar radial could represent the body morphological difference even
under the same size specification. Based on the morphological difference, the 160/68A could be divided into three
categories: 160/68A-Type-I, Uniform (46.79%), 160/68A-Type-II, Flat subelliptic (37.01%) and 160/68A-Type-III, Convex
subcircular (16.2%). The corresponding optimised pattern could effectively improve the garment’s fitness by integrating
the body morphology while maintaining original size specifications. Particularly in the waist and side seam region, the
dressing pressure had been effectively solved because the pattern is more in line with the shape morphology
characteristics. These findings will contribute to the fitness improvement of the same size specification.
Keywords: body morphology, pattern generation, surface flattening, garment fitness, pattern optimisation
Citation: Li, T., Zhang, Q., Zhang, Q., Ding, X., Ye, Y., Body shape morphology representation and prototype pattern optimisation by polar diameter on female students’ “waist-to-thigh” zone, In: Industria Textila, 2026, 77, 2, 331–337, https://doi.org/10.35530/IT.077.02.202573
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Article: A literature review of textile industry carbon emissions research: research hotspots, themes and emerging trends
Authors: XU CHEN, XUFENG WU, PEIHUA HAN, DI WU
Pages 338–351
DOI: 10.35530/IT.077.02.202596
Published online: April 2026
Abstract
As global climate change issues became increasingly severe, the textile industry, characterised as a high-energy
consumption and high-emission sector, attracted widespread attention regarding its carbon emission challenges.
Exploring the current status of textile industry carbon emissions and identifying emission-reduction pathways has
emerged as a prominent focus in environmental science research. To understand the research status of textile industry
carbon emissions and clarify the research trajectories and future directions of textile carbon reduction, this study
employed bibliometric methods, utilising literature on textile industry carbon emissions from the Web of Science
database (2005–2025) as the analytical subject. CiteSpace visualisation software was used to analyse the research
domains and core content of textile industry carbon emissions over 20 years. Through examining citation frequencies,
the study assessed research hotspots in textile industry carbon emissions and predicted development trends in textile
carbon reduction research. The research findings indicated that since 2012, the volume of research on textile industry
carbon emissions has demonstrated exponential growth. Research outcomes were predominantly published in
environmental science and sustainability journals, whilst traditional textile journals exhibited lower publication volumes,
suggesting that interdisciplinary and multidisciplinary research in textile industry carbon emissions remained relatively
weak, with research quality requiring improvement. The development of clean production technologies and circular
economy models influenced and promoted the green transformation of the textile industry, leading the direction towards
a low-carbon textile era.
Keywords: textile industry, carbon emissions, carbon footprint, visualisation analysis, low-carbon textile era
Citation: Chen, X., Wu, X., Han, P., Wu, D., A literature review of textile industry carbon emissions research: research hotspots, themes and emerging trends, In: Industria Textila, 2026, 77, 2, 338–351, https://doi.org/10.35530/IT.077.02.202596
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