Textile Exchange's New Cotton LCA Model Comparison

Blog Authors: Juanita Barrera, Senior Sustainability Analyst and Tess Konnovitch, Scientific Marketing Manager

Imagine handing the exact same dataset to six different teams of LCA experts and asking them all to calculate the environmental impacts of cotton cultivation. You might expect the results to be fairly consistent. After all, the inputs are identical.

What you might not expect is that the results for some impact categories could vary by more than 200%.

That is precisely what a recent study published by Textile Exchange found — and EarthShift Global was one of the participating organizations. The report, Life Cycle Assessment for Cotton: Model Comparison (March 2026), is one of the most transparent and methodologically honest exercises we have seen in the LCA space, and we are glad to have been part of it.

What the Study Set Out to Do

The goal was straightforward: understand how methodological choices and assumptions influence LCA results, using cotton cultivation as a case study. Textile Exchange provided all participating model providers with the same standardized inventory dataset representing a hypothetical but realistic cotton production system in Texas. Two scenarios were defined — one based on mineral conventional fertilizer application, one based on organic fertilizer — and each organization ran the data through their own models using their standard methodologies.

The participating organizations included Blonk, Cascale and Worldly, EarthShift Global, Field to Market, Quantis, and Sphera. Each brought a different modeling framework, background database, and set of methodological assumptions to the exercise.

What the Results Showed

For climate change — the most widely used and compared impact category in LCA — results showed moderate to medium variation across models, with minimum and maximum values differing by up to -14% and +30% from the average. That range alone is significant: many farming interventions studied in the literature aim to achieve emissions reductions in the range of 30%, meaning that variation between models can be as large as the effect being measured.

For other impact categories, the variation was considerably larger. Eutrophication results showed very large variation, with minimum and maximum values differing by up to -93% and +225% from the average in scenario 1. Water use, ecotoxicity, and acidification also showed large to very large ranges depending on the scenario.

The key drivers of this variation were not errors or carelessness — they were legitimate methodological differences. These included the choice of emission factors for nitrous oxide (N?O), the treatment of nitrate leaching, the selection of background datasets for fertilizer production, and the characterization factors applied for water use. Each of these choices is defensible. And yet together they produce results that can look very different from one another.

Why This Matters

The findings reinforce something the LCA community has long understood but that practitioners, brands, and policymakers don't always fully appreciate: LCA results from different studies or databases should not be compared directly.

This is not a flaw in LCA as a methodology. It is a reflection of the complexity of environmental systems and the many legitimate ways to model them. But it does have real implications for how LCA data is used in decision-making — particularly in industries like fashion and apparel, where brands are increasingly relying on databases and benchmarks to make procurement decisions and environmental claims.

As the report puts it, variation arising solely from the use of different agricultural LCA models can exceed the differences that studies typically aim to evaluate. That is a finding worth taking seriously.

EarthShift Global's Role and Perspective

EarthShift Global participated in this study through the contributions of Juanita Barrera and Lise Laurin, and our involvement builds on work we have done in the cotton LCA space more broadly. Earlier this year, we authored From Data to Impact: How to Get Cotton LCAs Right in collaboration with Better Cotton Initiative, Cotton Incorporated, Cotton Australia, and the U.S. Cotton Trust Protocol — a position paper that addresses many of the same methodological integrity questions this comparison exercise raises.

Our perspective has been consistent: rigorous, transparent, and reproducible LCA requires not just good data, but clear documentation of the assumptions, emission factors, methodologies, and background datasets behind every result. This study is a valuable demonstration of why that documentation matters — and what happens when it is absent or inconsistent.

What This Means for Your Organization

If your organization is using LCA data to make sourcing decisions, set targets, or communicate environmental performance, this study is a useful reminder to ask the right questions: Are the results you are comparing based on the same methodology, background databases, and impact assessment methods? If not, the comparison may not be meaningful — regardless of how credible the underlying studies appear.

EarthShift Global can help you navigate these questions. Whether you are commissioning an LCA, interpreting existing data, or building a supplier engagement program grounded in credible environmental metrics, we bring the methodological rigor and transparency that this kind of work demands.

If your organization is navigating LCA data for materials sourcing, supplier engagement, or environmental claims, EarthShift Global can help you cut through the methodological complexity. Book a free 30-minute consultation to talk through your sustainability goals and find out how rigorous, transparent LCA can work for your business.

About the Authors

Juanita Barrera is a Senior Sustainability Advisor and Technical Manager at EarthShift Global, based in Bogotá, Colombia. She holds an M.Sc. in Industrial Ecology from the Universities of Leiden and Delft and brings deep technical expertise in life cycle assessment across a range of industries and impact categories. Her work includes projects on biogenic carbon, land use change, and industrial process optimization. Juanita served as one of EarthShift Global's technical representatives in the Textile Exchange cotton LCA model comparison exercise, contributing directly to the modeling and review process at the heart of this study.

Tess Konnovitch is the Scientific Marketing Manager at EarthShift Global, where she leads the communication of life cycle assessment insights across global audiences. She holds a B.S. in Environmental Science and Biology and an M.S. in Computational Biology, and brings a background that bridges ecology, quantitative analysis, and science communication. She is a course instructor for Data Visualization in Life Cycle Assessment and has spoken on LCA communication and data visualization at ACLCA, ISSST, and the LCA Institute.

Interested in learning more? Read the full Textile Exchange report here and our cotton LCA position paper here.