LIFE CYCLE ASSESSMENT

Our Life Cycle Assessment (LCA) studies

Textile Exchange is leading a series of seven Life Cycle Assessment (LCA) studies designed to improve the quality and robustness of environmental impact data for raw material production across the fashion, textile, and apparel industry.

By addressing critical data gaps and methodology variability for priority fibers, the studies provide credible, fully documented data that supports more accurate modeling and tracking of greenhouse gas emissions.

LCA STUDIES

The raw materials and fibers covered

All Textile Exchange LCA studies cover the production stages of raw materials and fibers from cradle to gate.

The first LCA on cotton was published in March 2026. Throughout 2026 and 2027, we will publish further LCA studies on polyester, cashmere, nylon, leather hide, wool produced to Textile Exchange’s Responsible Wool Standard, and mohair produced to Textile Exchange’s Responsible Mohair Standard.

These priority raw materials and fibers were selected based on:

  • Total industry volume
  • The current availability of impact data
  • The quality of existing datasets

This was to ensure that the research focuses on where it can deliver the greatest value and industry-wide impact.

cotton lca
SCIENTIFICALLY ROBUST

Transparent methodologies and multistakeholder engagement

Each study followed a strict framework to reinforce the scientific robustness and credibility of the results. They all include fully documented assumptions, methodological decisions, limitations, and underlying datasets, ensuring transparency and enabling consistent use.

Independent leadership

Each study is led by a third-party consultant with expertise in both LCA and the specific raw material or fiber production system.

Peer review

Every assessment has been critically reviewed by an independent expert panel.

ISO conformity

All studies are conducted in accordance with relevant ISO standards, for example 14040:2006—Life Cycle Assessment and 14044:2006 Environmental management—Life Cycle Assessment—Requirements and Guidelines

Multistakeholder engagement

A broad range of industry stakeholders were consulted to ensure the integrity of the research.

PUBLISHED

Studies and guidance published so far

Life Cycle Assessment for Cotton

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Ensuring Integrity in the Use of Life Cycle Assessment Data

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GLOBAL DATABASES

Integrating the data into global databases

Once published, all Textile Exchange LCA data will be submitted to global industry databases to support more accurate modeling and progress tracking of greenhouse gas emissions and other impacts.

The data is primarily intended to serve as proxy data, in cases where source-specific LCA data is not available. It is designed to provide a credible, consistent foundation for understanding the impacts of cotton production and to inform data-driven decision-making.

The database platforms include:

regenerative agricultural for textiles

Read our guidance on responsible use of LCA data

As with all LCA data, the findings from Textile Exchange’s LCA studies should be used carefully and in the appropriate context. Results from LCA studies are sensitive to methodological choices, assumptions, and boundaries. The same dataset can also produce different results when applied within different methods. For these reasons, comparisons should not be made between studies or between datasets within individual studies, such as production systems or regions.

Ensuring Integrity in the Use of Life Cycle Assessment Data

A comprehensive position paper outlining best practice for the use of LCA data. It provides guidance on how data should be used, and—equally importantly—how it should not be used. It is intended for anyone looking to develop a greater understanding of the use of LCA studies and LCA data in the textile industry—particularly for brands using LCA data directly or those who rely on it for their impact modeling or progress tracking.

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Textile Exchange’s LCA Model Comparison—Cotton Case Study

An analysis of how different methods can produce varying results from the same dataset.

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