From Data to Impact – How to Get Cotton LCAs Right

From Data to Impact – How to Get Cotton LCAs Right

Speakers:

Eleanor Turner, Climate Reporting & Project Development Manager at Better Cotton Initiative

Mariana Ortega Ramirez, Senior Sustainability Analyst at EarthShift Global

Date and Time: Thursday, June 25th, 2026 at 1PM EST

About the Webinar:

Life Cycle Assessment is a powerful tool for identifying environmental hotspots in cotton production and guiding long-term sustainability improvements. But when LCAs are used in isolation—to justify sourcing shifts, make comparative claims, or minimize farmer engagement—they often produce the opposite of intended impact.

This brown bag webinar dives into how LCA data should and shouldn't be used, drawing on a collaborative position paper developed by Better Cotton, Cotton Australia, Cotton Incorporated, and the U.S. Cotton Trust Protocol. We'll explore why attributional LCAs can mislead when applied to sourcing decisions, and introduce practical frameworks for responsible data use—whether you're reporting Scope 3 emissions, making marketing claims, or planning supply chain investments.

About the Speakers:

Eleanor Turner, Climate Reporting & Project Development Manager

Eleanor is a sustainability professional with over 7 years' experience working across the environment and carbon landscape. Her expertise spans carbon accounting, life cycle assessments, environmental impact methodology, voluntary carbon markets and end-to-end carbon project development. Eleanor currently works on carbon and sustainability programmes at BCI where she focuses on farmer level GHG quantifications, carbon project design and market-facing climate solutions.

Mariana Ortega Ramirez, Senior Sustainability Analyst

Mariana leads lifecycle assessments for agriculture, textiles, and energy sectors. She recently contributed to From Data to Impact: How to Get Cotton LCAs Right—a position paper challenging how the fashion industry uses LCAs, emphasizing that data alone doesn't drive impact. Mariana holds a Master's in Industrial Ecology and specialized LCA training from the Institute of Environmental Sciences in Leiden.

Edited Transcript:

[0:08] Introduction

Hi everyone, my name is Tess Konnovich and I'm the Scientific Marketing Manager here at EarthShift Global. And it's my absolute pleasure today to introduce Eleanor Turner and Mariana Ortega Ramirez as they present "From Data to Impact: How to Get Cotton LCAs Right."

Eleanor is a sustainability professional with over seven years of experience working across the environment and carbon landscape. Her expertise spans carbon accounting, life cycle assessments, environmental impact methodology, voluntary carbon markets, and end-to-end carbon project development. Eleanor currently works on carbon and sustainability programs at BCI, where she focuses on farmer-level GHG quantifications, carbon project design, and market-facing climate solutions.

Mariana leads life cycle assessments here at EarthShift Global for agriculture, textiles, energy sectors, and many other things. She recently contributed to "From Data to Impact: How to Get Cotton LCAs Right," a position paper challenging how the fashion industry uses LCAs, emphasizing that data alone doesn't drive impact. Mariana holds a master's in Industrial Ecology and specialized LCA training from the Institute of Environmental Sciences in Leiden.

And today I will be moderating our webinar. So first, Eleanor and Mariana will be presenting their slides, and at the end we'll have a Q&A section. I encourage you, during this webinar, at the bottom of your screen you'll see a Q&A feature — you can pop all of your questions into that space, and at the end I'll be reading them aloud. You can always ask questions during the Q&A portion as well. So without further ado, I'm going to hand it over to Eleanor.

[1:52] Eleanor: Agenda and Introduction to BCI

Thank you so much for that introduction, Tess. So great to be here with you all today. So we're really excited to be here with you today to speak about LCAs in more detail.

A quick agenda run-through: we're going to start with an introduction to BCI, the Better Cotton Initiative. We're going to speak to you in more detail about the position paper that we've collaborated on together, and why we've decided to run this initiative. Then I'm going to hand over to Mariana, who's going to go into a deep dive on our position paper and provide you with a little bit more information about LCAs — what you can and cannot do with them. I'll then be speaking to you about how to integrate LCAs into sustainability strategies, our call to action on the back of today's session, and then we will hand back over to Tess for the Q&A session.

[3:04] So for anybody who is not familiar, I just wanted to provide a quick intro to BCI, the Better Cotton Initiative. We are the world's largest cotton sustainability organization, supporting farming communities globally. We work across a multi-stakeholder network, from farm-level organizations to brands and government bodies, to promote sustainably produced cotton. Over the past 16 years, we've been able to channel €200 million to cotton-growing communities globally — that's over a million farmers worldwide.

[3:40] So at BCI, we are known for our farm-level standard, known as the Principles and Criteria — a sustainability standard for the cotton production sector that defines social, environmental, and economic requirements that cotton producers are expected to meet to be certified to sell their cotton as BCI cotton. Our P&C spans crop protection, climate change mitigation and adaptation (which is what my role predominantly focuses on), sustainable livelihoods, decent work, fiber quality, natural resources, gender equality, and management. All of these combined is what makes a farmer eligible to produce BCI-certified cotton, and we work with a variety of retailers and brands worldwide to get that cotton from farmers to retailers on a global basis.

[4:40] Our vision at BCI is to create a world where all cotton produced is sustainable, while helping the communities that produce this cotton survive and thrive, and also protecting our environment. When producing cotton, there are so many different variables that go into it, from irrigation to pesticide use, so we want to support these farming communities to really understand how they can best grow cotton using regenerative agricultural practices.

[5:19] So that's a little bit of an introduction to BCI, and I urge you to do some more research on the organization if you're not too familiar with what we do, because we've just launched our Regenerative Standard and we're really looking to expand the work we do with farmers globally.

[5:41] Eleanor: The Position Paper — Setting the Scene

So the reason that we're all here today is to learn a little bit more about LCAs. To kick things off, I'm just going to set the scene about the projects we've been working on. Over the past few years, LCAs have definitely become a go-to reference point within the textile and apparel industry. Brands are increasingly using them to make sourcing decisions, to contribute to sustainability reports, and citing them increasingly due to regulations. This is really encouraging companies to back up their environmental claims with data, so often we find that they turn to LCA data to support that.

[6:19] Cotton, as one of the most widely grown and traded fibers, sits right in the middle of that. So what we started to notice was that LCA results for cotton were being used really inconsistently — sometimes information is cherry-picked, or it's cited without the correct context needed to interpret it properly. The gap that we wanted to flag is exactly what this position paper focuses on.

[6:56] So we came together and produced something jointly. This paper is a collaboration between EarthShift Global, who's hosting this webinar today — they were really able to provide that methodological credibility, rather than it just being a position paper put out by the different programs you can see listed on the slide deck here. So this is a jointly endorsed position paper. This isn't just BCI's view of how we should use LCAs, but a shared set of recommendations that all of these organizations stand behind, and I think that's relatively rare in this space. It definitely signals something about our sector and the importance of where our sector needs to go — it's great that we have so much cross-collaboration and opportunity to really drive transparency, particularly when it comes to data reporting going forward.

So I'm going to hand you over now to Mariana, who is going to speak to you through LCAs in more detail.

[8:08] Mariana: What LCA Can and Cannot Do

Thank you, Eleanor. So we're going to do a little deep dive into the position paper. Basically, what we want to cover today is the problem statement — I think Eleanor already presented it very well — and we want to go into what LCAs can and cannot do. The position paper also covers a little bit on attributional versus consequential LCAs and when to use one or the other, and a little bit on the conclusions and recommendations from the paper that came out of this collaboration.

[8:46] So the problem, as Eleanor said, is sometimes the misapplication of LCA data. We wrote this paper based on the methodology of doing a stakeholder consultation process, and all of these organizations participated — experts from these organizations who are living through the challenges of how LCA is being used, or has been used. The challenges identified were that some stakeholders have sometimes incorrectly used LCA results.

[9:27] And what do we mean by incorrectly? There have been comparisons across geographies and fiber types without a proper comparative study being done. There are databases of life cycle assessment of generic products, and one can see their values for different geographies and different technologies of how to produce something — but to just take those and compare them is not a proper use of the databases. You have to have a study made with a comparative purpose, and there are standards for that. So doing that is problematic. And then making sourcing decisions based on those generic numbers is also not good, because you would be disregarding other sustainability dimensions.

[10:35] So that's the first big point: misapplication. The other point is that farm data — and with other sectors it's similar — we want primary data as much as possible in our studies, and getting that takes resources. So it's desired, but in the case of farmers, sometimes there is no corresponding benefit being offered to them for the extra hard work involved in providing that data.

[11:13] So the challenges/problems, and the risks associated with that — well, when things like this happen and we're making claims based on reports not specifically made for making comparisons, we can damage our credibility as firms, as sustainability managers. If we make sourcing decisions based on those, those can be poor sourcing decisions, and we would be undermining consumer trust and penalizing transparency. Most importantly, at the core, we might be blurring priority hotspots. What I want to say is the numbers that we show with LCAs — those indicators are important and are a tool for us — but at the core, we want to do good with these numbers. We want to understand how they are, what they are, and what we can do to change the processes behind them and do better for the world.

[12:39] Mariana: A Hypothetical Example

So that's what I'll try to describe in more detail, considering a hypothetical case example. Imagine I am a sustainability manager, and I see this database with all this data on cotton from different regions — in this case, Region A and Region B. I have the choice: do I use Region A or Region B, considering one is 5 kilograms of CO2 per kilogram of product and the other is double? Should I work with one region or the other? Maybe I'm already working with Region B — do I shift to Region A?

[13:27] The good answer is, I cannot choose. I cannot make that choice with only this data, because I have to look at more things. If I follow the motto of the SDGs — leaving no one behind — then I have to look at more than only the environmental indicator on climate change.

[13:55] So as a sustainability manager, I might be sitting here with internal and external pressures present, and the need to demonstrate that my company has a commitment to sustainability. I also have to do my reporting of different indicators periodically, and there are several company material issues, and regulatory forces that are constantly changing, and increasing opportunity from stakeholders in the sector — including my customers.

[14:37] So one pathway I could take is doing a responsible data interpretation of those data sets. What does being responsible mean? It's maybe engaging with my suppliers, identifying what their material issues are, and incorporating that into my company as part of the supply chain I'm working with. Resources of all kinds may be needed to start planning for the long term — what can we do together to measure the baseline of where we are right now, and where we want to be in the future, and measure that change.

[15:30] Usually, the experience is that when we implement mitigation aspects in the field, we get a reduction in emissions, but there are also co-benefits involved that may bring even more important aspects of well-being for farming communities. By getting involved with my suppliers in that way, I'm making sure I can make claims that can be verified, and I can have certainty that what I intended to happen is actually happening — that there's a process of continuous improvement, checking, and improving where there's a need to improve.

[16:28] So it's more likely that if I go this route, I have a positive social return on investment that I can actually measure. This is an idea of how it might happen, and it can go different ways, in a messy way — a lot of things go into this. That's why I sometimes say being a sustainability manager is a lot of co-creation, conversations, and what they do is sometimes like art.

[17:04] But there's another route — one that may be shorter to walk — and that is maybe I can do a selective use of the data, and go with the 5-kilogram-per-kilogram impact instead of the higher one, and claim some anticipated footprint improvement because of that, since I have some data to show that might be happening, and do a sourcing shift based on that. But this may come with unintended outcomes, both for me and for the farming communities — because maybe in one region water stress increases, and in the other it stays the same, since farmers tend to use all the water that's available anyway, and other things to be considered as well. So there would be uncertainty in terms of: am I really doing good, or could be, but I'm not sure — and what cost in the medium-to-long term can that have? It is risky. It's more risky for my organization.

[18:25] Mariana: What LCA Can and Cannot Do

So the position paper, which I invite you to download and read if you haven't already, goes into more detail of what I'm trying to communicate in these few minutes. It's full of quotes from experts who are in the field, getting that experience. I really like this one — because shifting just from our desks, from 10 to 5, to less impact, is not really making a real change. Real change will happen when, in the field, I'm helping the suppliers I'm working with to be better.

[19:13] Going back to the technical side — specifically LCA — what can we do, and what can't we do, with LCA? LCA is very good for identifying hotspots and supply chain risks, and for supporting regulatory compliance when we have to provide data. But it's not good for accurately comparing cotton types or cotton regions without an aligned methodology — a specific report made for doing that comparison.

[19:43] With LCA, yes, we can support Scope 3 emissions reporting and science-based target setting, but we don't capture the social and economic parts, and we also don't cover impacts that are really important for the sector, like biodiversity and the impacts of microfibers, because we just don't have the science or the methods ready yet.

[20:08] LCA is good for tracking long-term change, but it cannot reflect short-term changes, especially in farming practices. So it doesn't serve as a standalone sustainability metric. It can highlight trade-offs, but in terms of — yes, if I change, for example, my way of practicing agriculture and I'm using more fertilizer because that's driving my yields higher, and I was able to decrease my carbon footprint by capturing more — but maybe there's a trade-off because you increased emissions elsewhere. Those kinds of trade-offs we can see with an LCA study, but it doesn't allow us to justify prescriptive sourcing decisions in isolation, because those environmental trade-offs aren't the only important thing — there are other sustainability metrics to be considered as well.

[21:39] So LCA can enhance transparency about what's going on in the system we're studying, when contextualized. But it is not good for supporting overgeneralized, headline claims, and we have to be very careful with how we make our claims, and work together as sustainability managers and LCA experts with our marketing teams.

[22:09] Mariana: Attributional vs. Consequential LCA

That being said, there are two types of LCAs we can use: attributional and consequential. Which one is more common, and when do you use one or the other?

Attributional LCA is the most common, widely used, and provides numbers that represent the modeling of a production process or service — a photograph, a picture in time, with its limits. In this case, we're looking at the impact of cotton lint, and together with the lint, seed is produced, and there are other byproducts, but we do our allocation, set our system boundaries, and focus only on that.

[23:03] With consequential LCA, we look more at the consequences of shifts in the market. For example, if I shift my sourcing of lint from one region to the other, then less lint is produced — less cotton plant is grown — and not only is lint produced from that, but also cottonseed oil. So changing the amount of lint I have in the market inherently changes the availability of cottonseed oil, and cottonseed oil in the market doesn't function on its own — it's connected to other things. So that change in the market might shift how demand in another system is behaving — for example, palm oil.

[24:12] So with consequential LCA, you've got to know a little bit more — it involves more things, more knowledge. But it's better to use this one if you're going to make a significant change in the market by making a shift in sourcing. So that's the recommendation I want to leave you with.

[24:42] Mariana: Conclusions and Recommendations

So, conclusions and recommendations we have from the position paper, in general: a complete sustainability toolkit, yes, includes LCA, but includes much more — the social and economic aspects, and the focus on farmers, is very important. Along that line, there's a need for addressing the problem of funding for capacity building and farm-level decision-making, for having more data measurement and sharing in the sector, for improving — or creating, where there are gaps — standards, and how to enforce that; for how to do LCA, but also how to do the claims and the communication around those numbers; and LCA narrative and literacy training on this topic for all of us who are involved in using these numbers, because the number is important, but what's behind the number is key to understand in order to use the number correctly.

[26:06] Another quote — I'll stay silent for a minute so you can read it — about cotton programs and their responsibility and concern to do good by the farmers and everyone involved in the supply chain.

[26:25] So, we provide recommendations per stakeholder. For brands and manufacturers: use available resources targeted at ensuring you are adequately using LCA data — use it for hotspot identification and continuous improvement, and co-develop solutions and support continuous improvement with your cotton supplier, rather than shifting suppliers based solely on LCA numbers. Consider supporting cotton programs and farming communities to collect and verify data, including fair compensation for the work that implies, and work with cotton programs to agree on consistent indicators and alignment with established frameworks.

[27:17] For cotton programs: lead the development and harmonization of consistent indicators and data quality frameworks. Work toward standardizing and streamlining data collection. Ensure transparency in data use, modeling assumptions, and methods so results are trusted and meaningful, and secure fair compensation and benefit-sharing models, with incentives linked to data contribution and sustainability improvements at the farms.

[28:02] And for policymakers: don't use LCAs alone to inform policy. Set enforceable standards for environmental claims, including guidelines for labeling and marketing that prevent misinformation and reflect the full spectrum of environmental and social impacts. Promote harmonization of methodologies and integration of social, economic, and biodiversity indicators across jurisdictions in agriculture industries, and support ethical data governance frameworks that balance transparency with farmer privacy and data sovereignty.

[28:45] So this is a lot, and the position paper covers even more. I invite you to check it out if you haven't — I think there are learnings there that go beyond the cotton industry and are applicable to other sectors as well.

[29:09] Before passing the floor back to Eleanor, I want to reiterate that this position paper was a co-creation of a group of people who have direct knowledge of what's happening in the field. As sustainability and LCA analysts or managers, it's sometimes easy to miss that bigger picture and be very focused on the number we're getting and the reporting we have to do. I'm very thankful to Better Cotton and the other programs that we got to do this together with, and that we're talking about what's going on beyond the number we estimate. So, back to you, Eleanor.

[30:14] Eleanor: Integrating LCA into Sustainability Strategy

Thank you so much, Mariana. Really great to hear more about the position paper and where we stand, and BCI are absolutely aligned with EarthShift Global that LCAs are an incredible tool when used correctly. Unfortunately, they're used incorrectly fairly often, which is why we've come together to create this position paper — to remind brands, retailers, and anybody else in the audience how you can get the most value out of an LCA.

[30:44] So to continue this conversation, I wanted to pick up on what this means in practice for organizations like BCI, and for brands and retailers and anybody else in the audience interested in how they can integrate LCAs into sustainability strategies. As Mariana has covered, LCAs are genuinely valuable, and they really help us understand where in a supply chain environmental impact is concentrated — whether that's at the farm level, in ginning, in textile processing, or elsewhere in the supply chain. That's the beauty of an LCA — it really helps pinpoint where that intensity is taking place.

[31:31] But, the key point of all of this is that LCAs aren't the whole picture, sadly, and they must sit alongside verified field-level data — because especially in the context of cotton, it is so complex, and it really is a multi-dimensional story.

[31:59] One of the things this position paper identifies, and something we feel very strongly about at BCI, is what we call "LCA tunnel vision." This is something that happens consistently across the sustainability industry when carbon becomes the dominant metric. This is very understandably done — it's driven by initiatives like Net Zero commitments, SBTi, Scope 3 reporting, etc. So there's a natural tendency to reach for LCA data that can quantify your carbon and use that as a proxy for overall sustainability performance.

[32:48] But cotton farming also impacts soil health, biodiversity, and livelihoods — over 100 million people worldwide work within cotton farming communities, which is something Mariana touched on earlier. So if you optimize purely for carbon and ignore these other dimensions, you're doing yourself a disservice, because you're not capturing the wide range of co-benefits that come along with programs like we work on at BCI.

[33:26] And finally, there's a regional question — a global average LCA isn't always representative. The environmental profile of cotton grown in India, for example, on a rainfed smallholder farm, is completely different from irrigated cotton coming from the USA. So using a single-number metric doesn't always reflect this.

[33:56] So in order to use LCAs responsibly, we recommend that you use the right data first and foremost, and really get to grips with and understand what data has gone into this LCA, and what you can extract from that. Global averages, particularly in the context of LCAs, exist because they're convenient — but that doesn't necessarily mean they're always fit for purpose, especially when you're trying to understand the impact of cotton from a specific region or farming system. So where possible, we always recommend using regional data where it's available, and being transparent about the methodology backing up the LCA and the data you want to communicate about.

[34:53] LCAs are highly sensitive, and if you're citing LCA results and the methodology behind it isn't visible, that's just going to cause even more confusion. So always ensure that methodology is visible and auditable — that's really going to support you in making credible claims when it comes to the data collection behind any LCA work.

[35:21] And thirdly, don't use LCAs to make claims that the methodology wasn't designed to support. As Mariana mentioned, comparative claims between programs, or even fibers, can be really risky — and it's important to remember that most LCAs aren't designed to be comparable. They're really designed to be a tool to help you interrogate and interpret data, and for you to figure out what your focus should be when it comes to your supply chain.

[36:01] Eleanor: Call to Action

So, the call to action — a lot of what I'm going to communicate here is what Mariana has touched on throughout this webinar. So to close, I wanted to touch on what we're asking of anybody joining us here today, following the release of this position paper.

When you receive an LCA, first and foremost, don't be afraid to ask about the methodology. Don't be afraid to ask about system boundaries. Don't be afraid to ask whether the data is regional or global. These are really important questions to know the answers to, to make sure you can interpret that data correctly. The quality of LCA outputs is only as good as the data that gets fed into the LCA.

[37:08] So more broadly, we just ask that anybody joining us here today, and anybody wanting to better understand how to interpret LCA data — our number one point is to resist the temptation to compete on metrics. This really is just the starting point of the conversation, and we really hope it opens up a broader conversation within the sustainability industry, so we can stop focusing on single metrics and comparisons, and instead focus on our own data, our own supply chains, and look to make continuous improvements.

[37:41] So, that being said, we really hope you will join us in shaping how this sector uses evidence to drive real-world impact and change the way we interpret LCA data. That is all I wanted to touch on for the time being. Hopefully we have a few questions we can touch on and answer, and provide any more clarity. So I'll hand back over to Tess, who's going to lead our Q&A section.

[38:16] Q&A

Tess: Yes, thank you both, that was excellent — I learned a lot, and I'm sure our audience did as well. We have quite a few questions to get through, and plenty of time, so I'll start with the first one. Eleanor, Mariana, you can take turns — agree amongst yourselves who's better positioned to answer.

First: were all of the organizations aligned when they agreed to support this initiative, or did you have to negotiate to get to consensus? If you had to negotiate, how did you get to that agreement?

Mariana: I can take that one. No, I don't think there was — that's why the cotton programs wanted to have this position paper, because there was some alignment in terms of: there is a problem here. Methodologically, we did a series of interviews — first as a group, then individually with each cotton program and participant, and then again as a group, to say, "this is what we're finding from all of you." There were some details we had to have more conversations on, but I wouldn't say there was misalignment, or that we had to come to agreement in terms of how it was being presented. The level of detail may have shifted, and there are stronger opinions about specific topics among the different groups, but I think the spirit was one of collaboration and getting the important message across, explained — and for those important aspects, there was complete alignment.

Tess: Now I'm going to skip ahead to a question I think is aligned with this one, but more focused on the farmers: were the farmers aligned? Were there any negotiations there? Do the farmers benefit from this type of data collection? Can either of you speak to the farmers' perspective on this initiative?

Eleanor: Yeah, I can definitely pick up on that, because at BCI we're working with multiple — not only large farms globally, but also smallholder farmers. We've found increasingly that farmers are really interested in this level of data collection, and that it can really benefit them. At BCI we firmly believe that any data we collect has to create value at the farm level — it cannot just be for our own benefit. What we've found is that when farmers are able to see their own footprint and benchmark that, it can become a really practical tool. It really supports them in showing where inputs like water usage, irrigation, and fertilizer use are not only driving costs up — because the cost of fertilizer globally is ever-increasing — but also where it's driving their emissions up, and where they might be able to take a step back and focus on the inputs to the cotton they're producing.

It definitely takes deliberate design — we've had to do quite a lot of training and upskilling, supporting farmers to interpret the data we're collecting from them, and understand why we're collecting it and how it could benefit them in the long run, especially when it comes to things like fertilizer use. If we're finding that this is a really high-emissions input, and it's costly for the farmers, we're working with them to incorporate regenerative practices — which not only drives down their footprint, but also drives down the cost of fertilizer use. So the purpose of this is to ensure that not only do brands benefit from farmer data collection, but we can also support farmers on the ground to interpret their own data and, as a result, take action.

Tess: Thank you. I'm going to combine the next two questions — tell me if you'd prefer I didn't. The first is: if you could change one thing about how brands currently use LCA data, what would it be? And the second: if a brand wanted to implement recommendations from this paper tomorrow, what would be the first actions you'd prioritize? They kind of go hand in hand to me.

Eleanor: Yeah, I can kick things off, because I do feel quite strongly about the first question. Especially working within different types of sustainability organizations, I've seen this happen firsthand. My recommendation would be: don't see this as a marketing tool, but rather see it as a decision-making tool. Too often, LCA data can be selectively cited to support an individual claim, rather than to genuinely interrogate your supply chain and make improvements. Time after time I've seen brands taking LCA data and wanting to compare it to a competitor, for example. Rather than doing that, I'd really urge you to look at that LCA data and ask: what is our highest-impact area, and how do we address this? — rather than trying to use it as a marketing tool, or to see how you compare to competitors, because it simply isn't the full picture.

Mariana: Yeah, I totally support that. I want to add — my favorite part of projects with clients is when I have the impression that a shift occurs. They come to us because, in many cases, they need to comply with providing a number for a client of their own, or because of some regulation. As part of the process, then, something shifts — we start having working sessions where we're looking at the model together, and they start making sense of why things are happening, and we start co-developing ideas. So yes, not just using the number, but going beyond that, in terms of where it came from and what can happen, considering a more holistic picture.

Tess: Thank you, that was excellent. I'll ask the second question again — you both touched on points that answer it, but why don't each of you give one action? If a global apparel brand wanted to implement the recommendations from this paper tomorrow, what would be one or two actions you would prioritize?

Eleanor: Yeah, certainly. I'd definitely recommend starting with auditing data inputs — understand where you're relying on primary data versus secondary data, and identify which processes need a little more work on that data collection process. Particularly in textiles, and going back to cotton — origin matters enormously, and whether you're using primary or secondary data can really make a difference to the outcome of any LCA. So I'd definitely do an internal audit. And I'd definitely recommend engaging with certification partners — for example, if you're a brand working with the likes of BCI, come and talk to us, and ask about that field-level data. Really understand what farmers are doing on the ground, and what we're doing to support them — so when you're making claims about your supply chain, you can really back that up with real, verifiable data that goes beyond just that single LCA metric, but is actually reflective of what's happening on the ground and the changes you're seeking to make.

Mariana: I would engage with my suppliers, to know what's going on with them. And another thing, on the research and development side: I would invest in developing more methods for those impact categories that are currently more difficult to measure, and yet are so important — like the impact of microplastics, and biodiversity in the fields, both local and larger-scale impacts.

Tess: Thank you both. We have one more question: how should sustainability teams communicate uncertainty in LCA results to executives who are looking for simple, comparable metrics?

Eleanor: Yeah, thank you, Tess, I can kick this off. I'd recommend that you don't hide the uncertainty that comes with LCA data, but don't lead with it either. You can definitely communicate the outcomes of the LCA and what you've been able to interpret as a result of those findings, but don't overstate what the LCA's purpose is, and that it's not actually providing that holistic overview of the system you're trying to better understand. I think working with a range, rather than sticking to an exact decimal and being really solid in that result — this is the result we have, and this is the additional data we can use to support that. Be really clear on the claims you can and can't make with an LCA. I know, particularly when communicating with executives, they just want to know: okay, what can we say, what can we claim off the back of this? I'd just be really clear about what the number can and can't be used for — comparisons across fiber types, for example, would require the same methodology and system boundary. If those conditions aren't met, just say so, so these things don't get published or interpreted incorrectly. So it's all about communicating with the executive so they really understand: there are so many benefits to an LCA study, and it can provide such valuable data, but you don't necessarily need to take that data and make claims about it that can't be supported.

Mariana: And a tip for managers — good comparative LCA reports must include uncertainty. The uncertainty results have to tell you, for which impact categories that you analyzed, you can make claims where the products you're comparing are indistinguishable, and where they are not. For those you can distinguish based on the uncertainty analysis, make claims on those, contextualized. In fact, in the position paper — we didn't mention this, but there's an annex with BCI's proposal of how to make responsible claims. There's a table we prepared there about the LCA or sustainability analyst's statement, and then the marketing statement. So yes, as Eleanor said, I guess there's a lot of communication needed to get from one point to the other — extra detail may be confusing, but also no detail isn't possible, because it cannot go without its needed context.

Tess: Thank you both. We actually had one more question just added: what is a good way to assess social impacts?

Mariana: Not with LCA, obviously — but there are standards also for measuring social change. At EarthShift we use the methodology of Sustainability Return on Investment, but more and more, the Social Value standard is something we're also looking at. So that would be a recommendation. And as I'm mentioning this, I also want to say — I'm talking today, but the team from EarthShift Global that participated with the ideas and writing of the position paper includes many more: Amos, Lise, Nathan, Tom, Karen, Tess. So I just want to acknowledge that.

[54:19] Closing

Tess: Well, thank you both. I just want to take this time to first thank Eleanor and Mariana for such a great talk, and for answering all of our questions with five minutes to spare. I learned a lot, and I'm sure our audience did as well. I encourage our audience again — thank you so much for attending, for engaging, for participating — and I encourage you all to sign up for next month's Brown Bag webinar. We will be announcing that topic soon, via our newsletter every month — if you're not subscribed, you can do so on our website. Thank you all for joining, and I hope you all have a great day.

Thank you so much.