The Rise of Lifecycle Analysis and the Fall of Sustainability: Berlin 202030
This is the basis of a virtual presentation delivered to Berlin 202030 on 7/9/2022
https://gcbhr.org/backoffice/resources/the-rise-of-lcas-and-the-fall-of-sustainability.pdf
As a quick preface, I shall be referring to a number of sources in this presentation. You will, of course, want to check them yourselves before using any of it, so I will post this presentation on my website: https://www.veronicabateskassatly.com/ later this week.
I am here to open the second day of this Fall session of Berlin 202030, to talk to you a little bit about the question of inter-stakeholder trust, and the trust of consumers - not to mention law enforcement - when it comes to ‘sustainable’ apparel’s use, misuse, and frankly, complete misunderstanding of data - of what constitutes reliable data and what is just meaningless numbers that don’t accurately represent any reality.
I recently published a white paper in association with Profesor Doro Baumann Pauly of Geneva University’s Center for Business and Human Rights: The Rise of Life Cycle Analysis and the Fall of Sustainability which attempts to condense and clarify the risks of basing sustainability claims on Life Cycle Analysis (LCA). Today I am going to tell you a bit about what the findings of our report mean for the trustworthiness of prevailing sustainability claims.
Before doing that I would quickly like to refresh your memories of what I believe you discussed yesterday - namely that a sustainable world is one that operates within planetary boundaries whilst meeting the needs of all global citizens. It’s a world that lives within the doughnut. And here is a nice screenshot of that doughnut, to remind you.
It’s obvious that for those currently falling short of life’s essentials to be lifted above the doughnut’s social foundation, they must, by definition, consume more. Those in the global north will have to compensate by further reducing their consumption to leave space within the boundaries for those who currently go without.
In apparel - in fashion - this automatically means that the only way to achieve a net reduction in global emissions is for the global north as a whole to buy fewer clothes and to wear each and every item more times. This in turn means that it is obviously not impact at the factory gate that matters. It is impact per wear.
Clothes are not sandwiches. If your jeans have a production impact of 11kg CO2-e and they are worn 10 times, that’s 1.1 kilos of CO2 per wear. If they have an impact of 20kg CO2-e, but they are worn 100 times, that’s only 0.02 kilos of CO2 per wear. Moreover, in the second case, after 100 wears there is only one pair of discarded jeans to process. In the first case, there are 10 pairs.
Manufacturing and marketing clothes in a manner that increases the number of times customers wear each item, is doubly beneficial. It reduces impact per wear and it reduces waste. Contrary to what everyone has been telling you, the most important criteria of whether a brand is or is not sustainable, lies not on the production side but on the sales side. It lies in what a brand sells and how it sells it. By whether it persuades customers that the little extra in price is more than compensated by the staying power of the product. Or whether its sales model is constant drops and discounts, championed by battalions of ‘influencers’ who churn through new outfits daily and convince their followers to do the same.
The other obvious conclusion for apparel sustainability to be drawn from the doughnut, is that much of fashion comes from the global south. It comes from the indigenous Quechua and Ayamara who raise alpaca in Peru. It comes from subsistence smallholders in Benin, and Burkina Faso who rotate their food crops with a cash crop - cotton - in order to pay for everything from their children’s education, to fuel and health care. It comes from garment workers in Bangladesh, Myanmar, and China, who cut and trim so much of the world’s clothing.
The positive message that I would like you all to take away with you today is that this automatically means that fashion is uniquely placed to assist in lifting the world's most deprived into the safe just space that all the major brands claim to be aiming for - since they all claim to adhere to the UN SDGs. These aims can be realized, by such simple measures as paying a living wage, introducing more equitable purchasing contracts, and swapping synthetics for farmed fibers.
Do LCAs measure any of this? The answer is no. LCAs measure environmental impact and environmental impact alone. This means of course, that if all you have is an LCA or some environmental data and you can’t prove that whatever it is that you are referring to, made the farmers/workers involved better off - and self-reported ‘data’ by an implementing initiative is categorically not proof - then you can only refer to whatever it might be, as having improved environmental impact.
Don’t use the word ‘sustainability’ at all, unless you can satisfy the socio-economic condition.
The Rise of Life Cycle Analysis (LCAs) and the Fall of Sustainability
Which brings us neatly on to the topic of today’s presentation: our recent white paper on LCAs and this is a nice screenshot of the cover.
https://gcbhr.org/backoffice/resources/the-rise-of-lcas-and-the-fall-of-sustainability.pdf
So, LCAs don’t measure sustainability. They only measure environmental impact. How well do they do that? And the answer, I’m afraid, is that it rather depends.
The first problem, as our white paper demonstrates, is that LCAs are not absolute. They are not a sausage machine, or a cookie cutter. From any given set of raw data, there is no single, unique value that will automatically be generated for greenhouse gas emissions, water consumption, eutrophication, etc. and vastly different purported impacts can be obtained from exactly the same data, by using different models, methodologies, and/or boundaries.
There is no established LCA model that everyone in the apparel sector uses. The International Organization for Standardization (ISO) has guidelines, and many commercial LCAs in particular, will tell you that they were produced to ISO standards, but an array of different methodologies and boundaries are ISO compliant, whilst a standard for data collection - and data is the most crucial variable in every LCA model - does not appear to exist.
Imagine then, that you are a brand, manufacturer, or initiative and that you decide to commission an LCA. You are faced with an array of choices:
The first, is will your LCA be proprietary, or will you make the detailed analysis open source and freely available? ISO 14021:2016 in fact states that consumer facing claims cannot be made if the verification depends on confidential business information. But nobody adheres to this. From the Higg MSI, to brand and manufacturer websites, claims are made based on LCAs that are not available to the general public. And as we shall see, this is highly problematic.
The next choice is which type of LCA will you use? Will it be attributional, where you measure the impact of the average producer, or consequential where you measure the impacts of the producers who are most likely to increase or reduce production respectively, in the face of a change in market conditions?
Then, what boundaries will you select to determine which up or downstream impacts are going to be included? Will you include the upstream impacts of manure for example - primarily the methane emitted by the cows in producing the manure - or will you exclude them?
What method of allocation will you use in the case of by or co products, whether these are inputs or outputs? Economic allocation, where impacts are allocated to each co-product in proportion to the contribution that they make to the lifetime value of the whole? Or bio-physical allocation, e.g. by protein, where impacts are allocated between meat and wool, for example, based on their relative protein content. Or will your LCA allocate between co-products by system expansion – typically used in consequential LCAs – which looks at what the co-product could or does replace, and deducts the impact of one from the other?
As a data based model of course, the most critical variable is the quality of the underlying data. No matter how rigorous your LCA, how inclusive of every possible up or downstream impact, if the base data is bad ie. if it is out of date, unrepresentative data that was collected without adequate scientific understanding, then the impact values generated will be meaningless nonsense or GIGO - garbage in, garbage out. So, which cohort will you obtain your data from? Who will collect the data? How large and representative will your sample be?
All of these variables will significantly impact the outcomes. Since you get to choose, you will probably select between the options based on opportune interests. For farmed fibers for instance, there will be a temptation to choose years and locations where conditions were particularly propitious in terms of rainfall and pest incidence. For industrial production, you would likely be unwilling to allow evaluation of any units that are not best in class. Data collection is a science. Sample cohorts must be both representative, and large enough. The data must be independently collected. Brands, manufacturers, or initiatives reporting their own data does not constitute a sound basis for evaluation.
An excellent example of the importance of independent collection in obtaining valid data rather than GIGO, is provided by two studies - an LCA, and a social and environmental impact assessment or SEIA - that were both commissioned by Cofra Industries’ Laudes (formerly C&A) Foundation, a long time promoter and supporter of organic cotton production. The aim was to compare outcomes for 3 types of farmer: conventional cotton, BCI (Better Cotton Initiative) cotton, and organic cotton farmers. For both studies, the data was collected in the Khargone area of Madhya Pradesh, India, in 2017-2018. But it was collected from different sample sizes and in different ways.
The data for the SEIA was collected from 3,600 farmers (1,200 of each type), whilst the LCA data was collected from only 300 farmers (100 of each type).
In the case of the SEIA, data collection was undertaken by a third party. For the LCA, it appears to have been collected by the initiatives concerned “with the help of C&A (Laudes) Foundation.”
From a statistical point of view then, the SEIA, given its larger sample size and its independent data gathering method, is considerably more reliable. SEIAs and LCAs collect very similar data, but in different forms. An LCA for example, looks at the volume of irrigation used in tonnes per hectare. An SEIA will look at how much the farmer spent on irrigation. This chart, which comes from our report, summarizes these findings neatly.
https://gcbhr.org/backoffice/resources/the-rise-of-lcas-and-the-fall-of-sustainability.pdf
When we compare the two studies, we see that the LCA claims outcomes for organic cotton are far more favorable to the organic production system than those identified by the SEIA. Concretely, the LCA claims to have found that organic farmers used 60% less irrigation than their conventional neighbors.
But the SEIA found that organic farmers devoted 25% more labor days, and 11% more expenditure, to irrigation, than their conventional counterparts. Since these farmers were all in the same place at the same time, presumably, they all paid the same per unit of irrigation water. Which means they cannot have spent 11% more on 60% less. In other words, the SEIA found that organic farmers were using at least as much, and probably 10% more irrigation than the conventional farmers. Not less – let alone 60% less, as the LCA claims. Which means that if brands or consumers followed the outcomes of the LCA, and purchased organic cotton in the belief that they were reducing global water consumption, they in reality, increased it.
Yet you will repeatedly see brands, initiatives and even proposed legislation basing claims on LCAs without providing any insight whatsoever, into where, when, and how the data was collected, let alone the boundaries or methodologies employed.
As to the importance of methodologies, as already pointed out, anyone commissioning an LCA has an array of ISO compliant methodologies to choose from. The chart that you see now only covers a single environmental impact variable - Greenhouse Gas or GHG emissions, and we note that the most commonly used ‘sustainability’ index in the apparel sector, the Higg MSI, has 5 impact variables, whilst the proposed EU Product Environmental Footprint, or PEF, has 16. We can presumably expect these kinds of variations to apply to all 5 or 16 variables.
https://gcbhr.org/backoffice/resources/the-rise-of-lcas-and-the-fall-of-sustainability.pdf
Chart 1 is adapted from an open access, blind peer reviewed, wool LCA published in The International Journal of Life Cycle Assessment, that compared purported Greenhouse Gas Emissions (GHGs) using the same data from four different sheep farms, and then applying seven different methods of allocation between wool and meat. What product was being studied is not, however, what we are interested in. The chart is here purely to illustrate the huge differences in impact that can be calculated by any given LCA from any given set of data. If we just look at the red bar (which represents the GHG impact of one farm - Farm 1), GHG emissions per kilogram of greasy wool vary from minus 27 to plus 39 Kg CO2e – a difference of 66 Kg CO2e, per kilo of wool – purely as a function of the method of allocation selected.
Entities commissioning LCAs will, of course, tend to choose the most favorable allocation method for their fiber. To interpret LCA results, then, it is vital to understand if vested interests were involved in commissioning the LCA in the first place, as this could bias results.
Second, there must be transparency over the allocation method used, as only LCAs using exactly the same method of allocation are potentially comparable. Precisely the same caveats apply to interpreting comparisons between brands and manufacturers. Based on the chart above, if told that the gray producer (Farm 4) had a GHG impact of only 11 kg CO2e/kilo of wool, and that the red one (Farm 1) had an impact of almost 40 kg CO2e/kilo of wool, how many would consider asking what method of allocation was used? Who would then realize that even “protein allocation” produces radically different results depending on whether the direct protein to meat or protein utilization is considered, and that when the same method is applied to both producers, both end up having very similar GHG impacts?
The Higg MSI, for example, uses allocation by protein for wool. But it uses economic allocation for both hides and silk. How many people are aware of this, or realize the difference that this makes? If allocation by protein were also used for silk, purported impacts for that fiber would drop by 60%.
In short, when presented with ostensibly massively different impacts ‘based on LCAs’, it is perfectly possible that the impacts are not radically different at all, and that with different boundaries and methodologies applied, the relative rankings could easily be reversed.
It should be clear by now that when someone tells you that LCAs have demonstrated that this product is less environmentally harmful than that one, you should take it with a considerable pinch of salt.
Closer examination will generally reveal that data quality is poor and the purported impact values, unsubstantiated and misleading. Moreover, since LCA outcomes cannot be compared unless the methodologies and boundaries are identical, and since there is no suite of LCAs for global fibers all produced using identical boundaries and methodologies, let alone robust and representative data, the numbers currently bandied around, from the Sustainable Apparel Coalition’s (SAC) Higg Materials Sustainability Index (Higg MSI) – the most widely used index in the global apparel and leather sector – to the individual product claims on many brand and manufacturer websites, are in fact, at best meaningless, at worst pernicious.
What does this mean if you are a brand? In a nutshell, if someone tells you that they have an index or an app that you can apply to your offering to tell consumers how much water or CO2 they saved by buying your tops or trainers rather than anyone else's, and by just how much you will enhance your sales if you do this, you might want to think twice, and consider your potential liability - both legal and reputational.
The Norwegian Consumer Authority just ruled that Norrona’s use of the Higg MSI for organic cotton to make consumer facing claims was misleading, and such claims are no longer permitted. A class action lawsuit against H&M, for making misleading comparative sustainability claims on its website, has just been filed in New York. As one leading authority in fashion law, Alan Behr, recently put it:
“The key learning is: unless you and you alone really can be sure that what you are doing is better for the environment... it is far too early in all this to start boasting about it in your marketing materials. Since no one can be entirely sure about the environmental impact of much of fast fashion at this time, making a point of it until science has done more groundwork could well lead to more troubles like this."
I suggest that you listen to him.