Textile Exchange has released the first in a series of seven
Life Cycle Assessment (LCA) studies aimed at improving environmental impact
data across the fashion, textile and apparel industry. The initial study
focuses on cotton and addresses major data gaps and inconsistencies in existing
methodologies.
The cotton LCA brings together new, high-quality data from
key producing countries. It covers organic, regenerative, recycled and
conventional cotton systems. The goal is to provide a clearer and more reliable
view of environmental impacts at the raw material stage.
The study follows a broader “LCA+” approach. It goes beyond
traditional LCA metrics to include additional indicators such as biodiversity,
soil health, and social impacts where relevant. This gives a more complete
understanding of sustainability performance.
Further studies will be released through 2026 and 2027.
These will cover polyester, nylon, cashmere, leather, and certified wool and
mohair. The materials were selected based on industry volume and gaps in
current data.
To ensure credibility, Textile Exchange involved a wide
group of stakeholders. Each study is led by an LCA expert and reviewed by
independent panels. All assessments follow ISO standards, strengthening
scientific rigor and comparability.
Beth Jensen, Chief Impact Officer, said, “This work improves
the quality and transparency of LCA data at the raw material level. It helps
close critical data gaps and supports better tracking of emissions and other
impacts. Through the LCA+ approach, we are also expanding the lens to include
soil health and biodiversity. Better data enables better decisions.”
The cotton study tracks impacts from farming to the point
where lint leaves the gin. It includes data from nine countries: Bangladesh,
Brazil, China, India, Pakistan, Peru, Tanzania, Türkiye and the United States.
A key finding is that field emissions are the main impact
hotspot. These are largely driven by nitrogen imbalance linked to synthetic
fertilizer use. This supports a shift towards organic and regenerative systems,
which aim to reduce or eliminate synthetic inputs. Similar concerns apply to
pesticide use and related ecotoxicity.
The LCA+ analysis indicates that organic and regenerative
practices can improve soil health and reduce pressure on ecosystems. However,
more localised data is needed to fully measure these benefits.
The study also shows that cotton’s environmental impact
varies widely by region. Climate, soil, water use and farming practices all
influence outcomes. No single factor determines impact. Instead, it depends on
how the entire system is managed.
Debra Guo, Lead for Cotton and Crops, said, “Cotton’s impact
depends on where and how it is grown. This study helps identify the most
important practices in different regions and highlights the need for better
origin-linked data. It enables brands to make more informed sourcing
decisions.”
The data will be submitted to major industry databases such
as Higg MSI, Ecoinvent and WALDB during 2026. It is intended as proxy data
where primary, source-specific data is not available. This will support more
accurate emissions modelling and progress tracking.
Textile Exchange also stressed responsible use of LCA data.
Results depend on methodology, assumptions and system boundaries. Comparisons
across studies or regions should be made with caution.
As a global non-profit, Textile Exchange works to drive
climate and nature-positive outcomes in the industry. Its goal is to help
reduce emissions from raw material production by 45% by 2030, while improving
soil health, water systems and biodiversity.
The study follows a broader “LCA+” approach. It goes beyond traditional LCA metrics to include additional indicators such as biodiversity, soil health, and social impacts where relevant. This gives a more complete understanding of sustainability performance. Further studies will be released through 2026 and 2027. These will cover polyester, nylon, cashmere, leather, and certified wool and mohair. The materials were selected based on industry volume and gaps in current data.
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