Building on its expertise in data science, management research, and sustainability, MagIC proposes a multi-pronged approach to address the challenges outlined in the call and contribute to the following expected outcomes:

1. Systemic Solutions for Textile Sorting with AI-powered Disintegration:

• Leverage Data Science, particularly AI and Machine Learning, to develop automated systems for identifying and separating non-textile components like buttons, zippers and trims.
• Collaborate with partners to design a digital information system using Blockchain technology to track the composition and disassembly needs of textile products throughout the value chain.

2. Improved Textile Recycling Technologies:

• Apply Data Science to optimize existing mechanical recycling processes to improve the yield and quality of recycled textile fibers.
• Advocate for policies that incentivize the use of recycled content in textiles utilizing Public Policy research and communication.

3. Sustainable Life Cycle Management:

• Utilize Life Cycle Assessment (LCA) methodologies to quantify the environmental benefits of MagIC's proposed solutions compared to traditional textile sorting and recycling practices.
• Develop innovative solutions for the treatment and reuse of side streams generated during the recycling process.
• Employ Public Policy research to develop recommendations for policies that promote sustainable value chain approaches

4. Knowledge Sharing and Collaboration:

• Utilize Information Systems research to develop a platform for knowledge sharing and collaboration among stakeholders in the circular textile economy.
• Conduct research on successful circular business models in other sectors and adapt those learnings to the textile sector using Management research methods.
• Develop training materials for workers in the circular textile economy using MagIC's expertise in data-driven skills gap analysis.

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