Search Results (162)

This paper presents an Industrial–Ecological Symbiosis Framework that enables industrial operations to achieve quantifiable ecological gains without compromising operational efficiency. The model integrates Mixed-Integer Linear Programming (MILP) with AI-optimised forecasting to allow real-time adjustments to production and resource use. It was tested across the apparel manufacturing, metalworking, and mining sectors using publicly available benchmark datasets. The framework delivered consistent improvements: fabric waste was reduced by 10.8%, energy efficiency increased by 15%, and carbon emissions decreased by 14%. These gains were statistically validated and quantified using ecological equivalence metrics, including forest carbon sequestration rates and wetland restoration values. Outputs align with national carbon accounting systems, SDG reporting, and policy frameworks—specifically contributing to SDGs 6, 9, and 11–13. By linking industrial decisions directly to verified environmental outcomes, this study demonstrates how adaptive optimisation can support climate goals while maintaining productivity. The framework offers a reproducible, cross-sectoral solution for sustainable industrial development. Full article

Infant clothing represents a critical yet overlooked exposure pathway for heavy metals, with significant implications for child health and sustainable consumption. This study investigates cadmium (Cd) and chromium (Cr) contamination in 33 textile samples, integrating in vitro bioaccessibility assays, cytotoxicity analysis, and risk assessment models to evaluate dermal exposure risks. Results reveal that 80% of samples exceeded OEKO-TEX Class I limits for As (mean 1.01 mg/kg), Cd (max 0.25 mg/kg), and Cr (max 4.32 mg/kg), with infant clothing showing unacceptable hazard indices (HI = 1.13) due to Cd (HQ = 1.12). Artificial sweat extraction demonstrated high bioaccessibility for Cr (37.8%) and Ni (28.5%), while keratinocyte exposure triggered oxidative stress (131% ROS increase) and dose-dependent cytotoxicity (22–59% viability reduction). Dark-colored synthetic fabrics exhibited elevated metal loads, linking industrial dye practices to health hazards. These findings underscore systemic gaps in textile safety regulations, particularly for low- and middle-income countries reliant on cost-effective apparel. We propose three policy levers: (1) tightening infant textile standards for Cd/Cr, (2) incentivizing non-toxic dye technologies, and (3) harmonizing global labeling requirements. By bridging toxicological evidence with circular economy principles, this work advances strategies to mitigate heavy metal exposure while supporting Sustainable Development Goals (SDGs) 3 (health), 12 (responsible consumption), and 12.4 (chemical safety). Full article

This study extends our prior blockchain-based traceability framework, WEave, for application to a furniture supply chain scenario, while using the original multi-tier apparel supply chain as an anchoring use case. We integrate circular economy principles such as product reuse, recycling traceability, and full lifecycle transparency to bolster sustainability and resilience in supply chains by enabling data-driven accountability and tracking for closed-loop resource flows. The enhanced approach can track post-consumer returns, use of recycled materials, and second-life goods, all represented using a closed-loop supply chain topology. We describe the extended network architecture and smart contract logic needed to capture circular lifecycle events, while proposing new metrics for evaluating lifecycle traceability and reuse auditability. To validate the extended framework, we outline simulation experiments that incorporate circular flows and cross-industry scenarios. Results from these simulations indicate improved transparency on recycled content, audit trails for returned products, and acceptable performance overhead when scaling to different product domains. Finally, we offer conclusions and recommendations for implementing WEave functionality into real-world settings consistent with the goals of digital, resilient, and sustainable supply chains. Full article

This paper presents research in the field of computer-aided 3D clothing design, focusing on an investigation of three methods for determining the mechanical properties of woven fabrics and their impact on 3D clothing simulations in the context of sustainable apparel development. Five mechanical parameters were analyzed: tensile elongation in the warp and weft directions, shear stiffness, bending stiffness, specific weight, and fabric thickness. These parameters were integrated into the CLO3D CAD software v.2025.0.408, using data obtained via the KES-FB system, the Fabric Kit protocol, and the AI-based tool, SEDDI Textura 2024. Simulations of women’s blouse and trousers were evaluated using dynamic tests and validated by real prototypes measured with the ARAMIS optical 3D system. Results show average differences between digital and real prototype deformation data up to 6% with an 8% standard deviation, confirming the high accuracy of 3D simulations based on the determined mechanical parameters of the real fabric sample. Notably, the AI-based method demonstrated excellent simulation results compared with real garments, highlighting its potential for accessible, sustainable, and scalable fabric digitization. Presented research is entirely in line with the current trends of digitization and sustainability in the textile industry. It contributes to the advancement of efficient digital prototyping workflows and emphasizes the importance of reliable mechanical characterization for predictive garment modeling. Full article

This study focuses on the shift to a circular economy (CE) in the textile, apparel, and fashion (TAF) sectors, which generate tons of waste annually. Thus, embracing CE practices is essential for contributing to UN Sustainable Development Goals. This study employs a mixed-methods approach, integrating PRISMA for systematic literature selection, BERTopic modeling and AI-driven SDG mapping, and case study analysis to explore emerging CE themes, implemented circular practices, and systemic barriers. Machine-learning-based SDG mapping reveals strong linkages to SDG 9 and SDG 12, emphasizing technological advancements, industrial collaborations, and circular business models. Moderately explored SDGs, namely, SDG 8, SDG 6, and SDG 7, highlight research on labor conditions, water conservation, and clean energy integration. Reviewing 655 peer-reviewed papers, the BERTopic modeling extracted six key themes, including sustainable recycling, circular business models, and consumer engagement, whereas case studies highlighted regulatory frameworks, stakeholder collaboration, and financial incentives as critical enablers. The findings advance institutional theory by demonstrating how certifications, material standards, and regulations drive CE adoption, reinforcing SDG 12 and SDG 16. The natural resource-based view is extended by showing that technological resources alone are insufficiently aligned with SDG 9. Using the Antecedents–Decisions–Outcomes framework, this study presents a structured, AI-driven roadmap for scaling CE in the TAF industry, addressing systemic barriers, and supporting global sustainability goals, highlighting how multistakeholder collaboration, digital traceability, and inclusive governance can improve the impact of CE. The results recommend that CE strategies should be aligned with net-zero targets, carbon credit systems, and block-chain-based supply chains. Full article

The clothing industry greatly impacts the global economy by producing billions of pieces of clothing and employing millions. However, it negatively impacts the environment, as it is one of the most polluting sectors in the world. This bibliometric review aims to identify influential authors and affiliations, journals, productive and cited countries, emerging and recent themes, and future research directions focusing on the dynamics of clothing brands’ sustainability practices. A comprehensive dataset from Scopus and the Web of Science contains 612 articles, and Biblioshiny and VOSviewer were used to analyze the data. Findings reveal that sustainability is not just a concern for developed countries but is also gaining attention in emerging economies like India. This bibliometric analysis presents its relationship with sustainable development goals (SDGs), combines performance analysis and science mapping of clothing brands’ sustainability practices, and evaluates thematic clusters to highlight future research scopes to fill the literature gap for further concentration on behavioral aspects, advanced supply chains, effective communication, and promoting the usage of sustainable technologies, which can help to align with business models for sustainability and resilience. Therefore, clothing brands’ sustainability practices should focus on smart and functional clothing through eco-friendly manufacturing and designing long-lasting clothes to enrich clothing performance. They should adopt innovative technologies for resource utilization, recycling, waste management, supply chain, and also emphasize communication with the consumers to encourage them to purchase eco-friendly and long-lasting clothes. Full article

The widespread phase-out of long-chain per- and poly-fluoroalkyl substances (PFASs) has created an urgent need for durable, fluorine-free water-repellent finishes that match the performance of legacy chemistries while minimising environmental impact. Here, the performance of an eco-friendly hybrid organic–inorganic treatment obtained by the in situ hydrolysis–condensation of triethoxy(octyl)silane (OS) in an amino-terminated polydimethylsiloxane (APT-PDMS) aqueous dispersion was investigated. The sol was applied to plain-weave cotton and polyester by a pad-dry-cure process and benchmarked against a commercial fluorinated finish. Morphology and chemistry were characterised by SEM–EDS, ATR-FTIR, and Raman spectroscopy; wettability was assessed by static contact angle, ISO 4920 spray ratings, and AATCC 193 water/alcohol repellence; and durability, handle, and breathability were evaluated through repeated laundering, bending stiffness, and water-vapour transmission rate measurements. The silica/PDMS coating formed a uniform, strongly adherent nanostructured layer conferring static contact angles of 130° on cotton and 145° on polyester. After five ISO 105-C10 wash cycles, the treated fabrics still displayed a spray rating of 5/5 and AATCC 193 grade 7, outperforming or equalling the fluorinated control, while causing ≤5% loss of water-vapour permeability and only a marginal increase in bending stiffness. These results demonstrate that the proposed one-step, water-borne sol–gel process affords a sustainable, industrially scalable route to high-performance, durable, water-repellent finishes for both natural and synthetic textiles, offering a viable alternative to PFAS-based chemistry for outdoor apparel and technical applications. Full article

The rapid advancement of artificial intelligence (AI) in the traditional-apparel-manufacturing sector is accelerating innovation and transformation, as cutting-edge AI applications have been increasingly integrated into the industry in recent years. While China has made outstanding achievements in applying AI in the apparel-manufacturing sector, the adoption of AI by traditional apparel manufacturers has progressed slowly. This study aims to develop a sustainable triple-layer framework of an AI-enabled innovation ecosystem from grounded required AI capabilities and barriers to AI adoption, thereby generating the conceptual propositions for micro, small, and medium-sized Chinese apparel manufacturing. Through semi-structured interviews conducted with 20 organizations, this study qualitatively analyzes interviews with representatives from enterprises, universities, and apparel associations to determine the required AI capabilities and barriers to adopting AI. It proposes 13 propositions within a theoretical framework that addresses barriers and aligns multi-actor collaborations, ultimately forming a sustainable AI-enabled Triple-Layer Innovation Ecosystem Framework. This novel framework reflects the dynamic interplay between external knowledge absorption capacity and a firm’s internal innovation capacity, providing a theoretical foundation for understanding and advancing AI-driven innovation in the apparel-manufacturing sector. Full article

This research aims to investigate, using the C-A-B theory, the buying decision-making processes of Gen Z consumers in the United States when exposed to fast fashion brand advertising messages including greenwashing elements. Responses of 345 valid participants from the Amazon Mturk platform were analyzed through Mplus 8.11 and SPSS 29. Two-step, structural equation modeling was implemented to test the hypothesis. Additionally, 5000 bootstrapping iterations were used to examine the indirect effects. Study findings indicated that Gen Z consumers responded positively and negatively to fast fashion brands’ product promotional messages. Despite feeling skeptical and betrayed over the greenwashing assertion, they intend to purchase the goods. A contributing factor to this unforeseen purchasing intention may be their indifference towards environmental concerns. Moreover, when greenwashing assertions are infused with product advantages through strategic ingenuity and aligned with the specific demands of certain generations, the perception of positive emotional reaction supersedes the negative, hence facilitating the purchase of the green product. Furthermore, there is evidence of optimism biases, a cognitive bias where they exaggerate their capacity to identify instances of greenwashing, prioritize more on their certain needs, and underestimate the associated environmental risk for others. This clarifies the paradoxical buying patterns of Gen Z consumers. Although Gen Z is the youngest demographic, their tastes for fast fashion apparel may alter as they develop and their lifestyles adapt, influenced by both positive and negative emotional reactions to fast fashion brands. Consequently, the fast fashion business must retain this customer by utilizing sustainability messaging instead of misleading greenwashing assertions in the future. Full article

A significant proportion of waste generated by the fashion industry is either landfilled or incinerated, primarily due to the high cost and complexity of collecting and separating mixed textile materials. While research in textile recycling often emphasizes post-consumer waste, less attention is given to pre-consumer waste, particularly cutting surpluses generated during apparel manufacturing—a labour-intensive sector with low automation and operational inefficiencies. This study addresses this gap by presenting a case study on the implementation of an automated system for collecting, transporting, sorting, and storing textile surpluses in an apparel manufacturing environment. The research aims to identify the barriers, benefits, and sustainability impact of such automation. Using both qualitative and quantitative data, the system is evaluated through key performance indicators including time reduction, ergonomic improvement, and process reliability. Results suggest that automation enhances intralogistics, reduces non-value-added labour, and enables better utilization of human resources. This case study offers a practical framework for apparel manufacturers to assess the potential of automating textile-waste handling, helping to justify such investments based on labour use, process variability, and environmental benefits. The study contributes to the broader discourse on sustainable manufacturing and supports the apparel industry’s shift toward digital transformation and circular economy practices. Full article

The textile and apparel industry faces significant sustainability challenges due to the high volume of waste it generates and the limitations of current recycling systems. Automation in textile waste management has emerged as a promising solution to enhance material recovery through accurate and efficient sorting. This systematic literature review, conducted using the PRISMA-guided PSALSAR methodology, examines recent advancements in computer-based sorting technologies applied in textile recycling. This study identifies and evaluates major technological methods often integrated with machine learning, deep learning, or computer vision models. The strengths and limitations of these approaches are discussed, highlighting their impact on classification accuracy, reliability, and scalability. This review emphasizes the need for further research on blended fiber detection, data availability, and hybrid models to advance automated textile waste management and support a sustainable circular economy. Full article

Research on the factors influencing sustainable fashion consumption, particularly green apparel buying behavior (GABB), has grown significantly in the last decade. Understanding how to promote GABB while reducing fast-fashion consumption is of critical importance to researchers, marketers, and policymakers. However, deriving actionable insights requires robust methodologies. Therefore, the goal of this systematic narrative review was to analyze existing literature on GABB, to identify key drivers, and to critically examine the methodological approaches, applied theoretical backgrounds, and utilized geographical scope. Following a structured multi-stage review process—including a database search, screening, and synthesis—n = 15 empirical studies focusing on GABB were included. The identified drivers are categorized into five factors: sociodemographic, personal, behavioral, social influences, and product attributes. Additionally, the review identified methodological shortcomings, including a predominant reliance on self-reported data, a lack of experimental designs and longitudinal studies, and a limited sampling scope across studies. Addressing these limitations in future research is essential to develop practical interventions that encourage sustainable fashion consumption and guide effective marketing and policy strategies. Full article

Greenwashing, the phenomenon of companies misleading consumers about their sustainability practices, is prevalent in the fashion industry. This study explores consumer opinions on greenwashing through analysis of social media discourse. Cognitive dissonance theory served as the theoretical framework, explaining how consumers reconcile conflicting information about brands’ sustainability claims. In Study 1, 446 comments on 12 Reddit posts were collected using the search term “fashion greenwashing”. Using the Latent Dirichlet Allocation (LDA) algorithm and manual review, we identified three major themes: the phenomenon of fashion greenwashing, consumer empowerment in sustainable fashion, and skepticism towards fast fashion brands’ marketing strategies. In Study 2, using the search term, “#fashiongreenwashing”, two researchers collected and analyzed 76 Instagram posts with 370 comments. A manual review was employed to extract major themes, and network graphs of caption tags within the same theme were constructed. Three major themes emerged: strategies to combat fashion greenwashing, examples of fashion greenwashing, and advocacy and regulation in sustainable fashion. Findings from Studies 1 and 2 revealed that consumers are increasingly aware of brands’ deceptive practices and advocacy for sustainable practices to resolve this dissonance when they see greenwashing information. This study underscored the need for fashion brands to provide transparent and authentic information. Full article

Within the global wave of manufacturing intelligence, AI technologies are revolutionizing industrial frameworks through deep integration. As a resource-intensive sector, fashion has become a pivotal arena for assessing AI’s role in sustainable development. China, the world’s largest apparel producer, faces unique AI integration challenges, highlighting the intersection of innovation and sustainability. To further explore the impact of AI-powered try-on technology on the impulsive buying intentions of young Chinese consumers, this research utilizes a modified version of the stimulus–organism–response (SOR) model. From the lens of online shopping, the research investigates how key features of AI-powered try-on technology, such as visual vividness, interactive control, personalized configuration, and ease of use, affect impulsive buying intentions. Additionally, the study examines the mediating roles of perceived utilitarian value, perceived hedonic value, and perceived immersion, alongside the moderating role of brand trust. A structured online survey was conducted with 366 participants, and the data were analyzed using the partial least squares (PLS) method. The findings reveal that the four core attributes of AI-powered try-on technology have a positive effect on impulsive buying intentions. Furthermore, the mediating roles of perceived utilitarian value, perceived hedonic value, and perceived immersion, along with the moderating influence of brand trust, were substantiated. In the realm of online apparel shopping, AI-powered try-on technology effectively stimulates impulsive buying behavior and drives online purchases. These results offer valuable theoretical insights for enhancing AI-powered try-on applications, while also providing strategic guidance for fashion brands and e-commerce platforms in developing AI-driven sustainable marketing approaches. Full article

Smart clothing integrates advanced technologies such as textile materials, flexible electronics, and data communication systems, playing a pivotal role in driving productivity innovation within the textile and apparel industry. However, this emerging field faces substantial challenges, including high resource consumption, high disposal rates, and difficulties in material recycling and management. This paper presents an integrative review, analyzing 263 studies to examine the ecodesign transformation framework for smart clothing. The findings highlight multiple sustainability challenges associated with the linear lifecycle of traditional smart clothing. By assessing ecodesign strategies across various stages of the lifecycle, the study emphasizes the need for a shift from a product-focused approach to system-level innovation in the ecodesign of smart clothing. Building on this, we propose a systematic, coupling ecodesign framework to facilitate the sustainable transformation of smart clothing. This framework is grounded in the principles of circular economy and Social–Ecological–Technological Systems (SETSs). Our work not only aims to contribute to the achievement of the United Nations 2030 Agenda for Sustainable Development Goals but also aligns with the core objectives of the European Green Deal, focusing on resource efficiency and low environmental impact. We seek to provide a feasible theoretical framework to guide the sustainable transformation of smart clothing. Full article