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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62388
Volume:
10 |
5 |
May 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of CS & IT
Jain University
Department of CS & IT
Jain University
Department of Commerce
Jain University
Department of Commerce
Jain University
Department of CS & IT
Jain University
Department of CS & IT
Jain University
The fashion industry's current "take-make-waste" system is one of the biggest contributors to climate change. Even though some brands talk about being sustainable, they still make too much clothing, which leads to a lot of greenhouse gas emissions and tons of textile waste. This study looks at how new technologies like AI, blockchain, and digital fashion tools could help create a more circular fashion system. To understand this better, I did both qualitative research by looking at current media and quantitative research through a survey of 160 people to see what consumers think. The survey showed that even though people know buying from bad environmental brands isn't good, they face challenges like high prices and not trusting corporate sustainability claims. Changing consumer behavior is important, but I also created a model for a hyperspectral sorting system that can help AI identify different fiber blends, which can help reduce contaminants in recycling and improve the quality of recycled materials. I believe technology is a key part of the solution, but without proper policies and brand accountability, real change won't happen.
KEYWORDS
This study underscores the considerable potential of emerging technologies to enhance sustainability within the fashion industry. Innovations such as artificial intelligence, blockchain systems, and digital design tools present viable pathways for addressing issues like waste, inefficiency, and lack of transparency. However, their adoption in practical settings remains uneven and limited. A key finding of this research is the disconnect between sustainability-focused discourse and technology-driven development. Addressing this divide will require coordinated efforts across multiple domains, including academic research, industry practice, and policy frameworks. The conceptual model of AI-based textile sorting presented in this study illustrates how targeted technological solutions can contribute directly to improved waste management and the advancement of circular fashion systems. Future research should prioritize real-world testing through pilot implementations, along with detailed assessments of economic feasibility and scalability. Such efforts are essential to determine whether these technologies can be effectively integrated into existing fashion supply chains and deliver meaningful environmental benefits.
1. K. Dhiwar and M. Bedarkar, “Circular economy for fashion waste in the Indian fashion industry,” Fashion Practice, 2024. 2. S. Jung and B. Jin, “A theoretical investigation of slow fashion,” Journal of Fashion Marketing, 2014. 3. R. Kaur, “Impact of circular economy on sustainable fashion,” ResearchGate, 2023. 4. S. H. Kim and E. J. Damhorst, “Environmentalism and materialism in sustainable fashion,” Clothing and Textiles Research Journal, 2013. 5. C. Nam, H. Dong, and Y. A. Lee, “Factors influencing consumers’ purchase intention of green sportswear,” Journal of Fashion Marketing and Management, 2017. 6. S. Ray and L. Nayak, “Marketing sustainable fashion: Trends and future directions,” Sustainability, 2023. 7. A. Sadachar, A. M. Fiore, and L. Niehm, “Predicting environ-mentally responsible apparel consumption behaviour,” Fashion and Sustainability Journal, 2016. 8. “TextileNet: A material taxonomy-based fashion textile dataset,” 2023. 9. “Supervised textile classification via hyperspectral imaging and deep learning,” 2025. 10. “Advancing circular economy practices using AI-powered textile classification,” 2025.
Publication Date
5/9/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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