Completed Climate, Earth & Environment Materials & Manufacturing

Back to Baselines in Circular Fashion & Textiles

In plain English

AI plain-English summary

The fashion and textile industry is generating thousands of tonnes of waste that ends up incinerated or landfilled in the UK every year, while consumption continues to rise. This matters because the sector is a major contributor to UK GDP but also a major source of carbon emissions and environmental damage. Without a clear picture of the industry’s full environmental impacts—especially within the UK—efforts to meet Net Zero targets risk being misdirected or ineffective. The research network will establish that baseline: a robust, accurate assessment of the current state of the industry’s sustainability credentials. If successful, the network will identify the specific barriers—technical, cultural, skills-based, and disciplinary—that block a shift to more sustainable practices. It will also pinpoint where targeted interventions could yield the highest environmental gains. By mapping disconnects between design, manufacturing, retail, use, and disposal, the work could reshape how the entire supply chain operates, reducing waste and emissions without sacrificing economic contribution. The result would be a strategy grounded in evidence, not aspiration, for transforming a sector that quietly underpins much of daily life.

View original technical description
The fashion and textile sector is a major contributor to UK GDP, but the global industry is a major contributor to carbon emissions and other negative environmental impacts, including generation of thousands of tonnes of textile waste incinerated/landfilled in the UK every year. With UK fashion consumption continuing to rise, there is a pressing need to develop strategy for sustainable transformation, which means first establishing a clear picture of the current state of the industry's full environmental impacts, particularly as it relates to the UK. This large interdisciplinary Network will determine how to assess, evidence, and monitor the sustainability credentials of current and proposed practices across the fashion and textile industry to ensure congruence with Net Zero targets. The work will identify the sectoral, disciplinary, technical, cultural, and skills-based barriers to transitioning to more sustainable practices, as well as those areas where specific interventions could result in the highest impacts, to decide where best to target future innovation and priorities. The Network will also take account of ongoing disconnects between design, manufacturing, retail, use and end-of-life disposal that contribute to environmental impacts. A robust, accurate and honest picture of the current 'baseline' position of the industry will be presented, from which the best strategy to meet Net Zero and other mandated targets can be based.

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Researchers

Amanda Briggs-Goode (Co-Investigator)Dawn Ellams (Co-Investigator)Effie Kesidou (Co-Investigator)Jane Harris (Co-Investigator)Jane Scott (Co-Investigator)John Barrett (Co-Investigator)Lisa Macintyre (Co-Investigator)Liz Barnes (Co-Investigator)Mark Sumner (Co-Investigator)Parikshit Goswami (Co-Investigator)Phil Purnell (Co-Investigator)Stephen Russell (Principal Investigator)Susan Postlethwaite (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

UK Fashion & Textiles: Data-driven platform, enabling manufacturing supply chain real-time decision-making, effective track & trace & sustainability
Grow your own textiles: Fostering a circular economy through self sufficiency.
Expansion of decentralised make on-demand apparel manufacturing network to reduce manufacturing waste and increase apparel usability and recycling
Greener and smarter clothing to address climate change: an approach to learning from nature and utilising cutting-edge technologies
UKRI Interdisciplinary Circular Economy Centre for Textiles: Circular Bioeconomy for Textile Materials

Original classification

Research Grant

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.