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To make credible environmental impact claims, fashion brands need access to credible data. DENBY ROYAL says the data is available – you just need to find it

LONDON – In the recent debate around the use of the Higg MSI (material sustainability index) tool in fashion – or not as the case may be in Norway – one of the most common issues raised is data. Is data being used to underpin Higg labels of sufficient quality? Is it up to date and credible?

We all hear about environmental impact data and know it’s a heated topic. It’s like the kitchen is on fire, but no one knows what an oven is. We are all talking about data, but not many know exactly what it is or how to measure it. This brings us to the broader issue of the availability of environmental impact data in the fashion industry. Even if the Higg MSI was no longer around, fashion brands would still need an evidence-based solution to make claims about the impacts of their supply chains. Fashion brands are also under growing regulatory and investment pressure to measure carbon emission reduction targets, initially scope 1 but now steadily fanning out to scopes 2 and 3.

Regardless of the travails of the Higg MSI, the misrepresentation of data needs to be addressed. So here I will set out some of the key concerns around the Higg MSI before looking at issues and challenges around properly communicating impact data generally.

Global vs. Regional Specific impact data

The biggest issue surrounding the miscommunication of data with Higg MSI by the Norwegian Consumer Authority was that the investigated brands were basing their metrics on global averages instead of region-specific ones. There are a couple of problems with this methodology. One is that global averages miss many regional-specific links in the supply chain, like transportation, energy grids, water usage, and all the other parts that make up a product’s unique journey. For example, if a brand sells jeans made in China and another sells jeans made in India the Higg MSI can’t differentiate between the two. So as a consumer you can’t compare the two alternatives and be part of the transition.

Product differentiation and comparison

The Higg-MSI tool compares the impact of raw material usage only (i.e. organic cotton to conventional cotton) instead of taking the extra step to compare the usage of a productto compare products to products. For example, a polyester t-shirt should be compared to all t-shirts in its category (conventional cotton, organic cotton, etc.), assessing each product’s individual Life Cycle Assessment (LCA) and generating unique metrics for that specific product. LCA used by Higg to get the impact data from the cotton starts at the actual cotton seed, but omits crucial stages in raw material extraction that limits CO2 in synthetic fibres, like polyester and nylon, and makes an unfair comparison to a natural material like cotton. If all factors were taken into account, it would drastically add to the emissions footprint from synthetics.


A broader issue is that we need a standardised way to calculate environmental impact. The EU is pushing for this standardisation with its Green Deal, which states: “Companies making ‘green claims’ should substantiate these against a standard methodology to assess their impact on the environment.” And lest we forget the Competition & Market Authority’ As Green Deal, which has recently put pressure on ASOS and Boohoo. 

This brings us to the EU’s Product Environmental Footprint methodology (PEF). The PEF assesses all the environmental consequences that happen during the life cycle of a product –from emissions to water, air, and soil. It also includes resource use and the implications of land and water usage. The PEF methodology has stricter standards than a standard LCA and contains product-category-specific regulations, all standardised and decided upon by the European Commission.

The method is still under review, but some evidence suggests PEF evaluations are more reliable than single LCAs.

Take the pie charts below. The chart on the left is based on Arbor’s framework for calculations for cotton based on standard LCA methodology compared with PEF on the right. According to the PEF framework, we must include dyeing, finishing, transportation, packaging, and accessories, to calculate the number for the production phase.

​​This example shows how important it is to choose the right system boundary and scope for a calculation. The impact caused by the dyeing, finishing, transportation, and packaging processes, which are missing from an LCA study with a limited system boundary, is a considerable portion of the textile-making process.

The future of measuring environmental impact

The world of impact measurement is new and growing, and LCAs alone are just the starting point. We can create a complete data supply chain that tracks everything from cradle-to-cradle and across all scopes of supply chain emissions.

At Arbor, our way of looking at environmental impact data is entirely based on transparency and trust, ensuring that a company’s environmental claims match its unique environmental impact. We have met with the Norwegian Consumer Authority and walked them through our methodology and approach. We will continue to engage with them and other regulatory bodies to ensure our methodology is up to their rigorous standards, streamline action, and that our tool is in full regulatory compliance.

In particular, we use regional data instead of global data, and we look at the impact of products and their composition, not just the materials in general. We are also open and transparent about collecting and communicating environmental data to the businesses we work with.

So what do we do, and how do we get our data?

The data for our assessments comes from a few different places. The ideal case is when we have valid data from the company itself, but as that is sometimes not the case, we have to use data from other sources, like EcoInvent. If the data we need is not in EcoInvent, we call it a data gap and find the data elsewhere, such as in newly peer-reviewed and published studies. We also have an internal Data auditing system to root out misleading or outdated data. In the upcoming update, each calculation of a product’s unique journey will accompany a confidence score for both businesses and consumers to adapt end-to-end transparency whenever making product footprint claims.

Data tells a story, and there are many characters that come into play. While we can’t give away exactly what we are using, we can say that while our calculations depend on LCAs, we also combine scientific studies, the PEF methodology, and NGO and governmental data to come up with just one calculation for a single product. And just like a good story, the characters are always developing. We are working diligently to improve our country-specific database as well as the implementation of waste and water impact. 

There is much talk in the industry about whether the data exists to measure fashion’s environmental impact. In my experience, the data is readily available; it is just tough to sift through and comprehend. We offer a solution that’s easy to use and tailored to fit a business’s specific needs. We can get our hands on a vast amount of data through our proprietary AI and machine learning. An added benefit of machine learning, other than speeding up the process from months to minutes, is that it reduces the risk of human error. 

The automation of our product is what makes the cost approachable. So framing it as being too expensive isn’t necessarily true. To do impact assessments manually takes time and resources which can be a barrier to entry for many brands to track. 

If nothing else, the debate around the Higg MSI (as well as discussions around greenwashing) has brought to the fore the importance of using validated and heterogeneous data sources to underpin credible marketing claims.

It is too early to state where this debate will go in the coming months and years, but a few obvious conclusions can be drawn. One is that fashion’s marketing teams will need to think twice before making environmental impact claims about their business and its products without accurate data to support it. 

The second development we may see is more brands turning to third parties to help them better understand where and how claims can be made, including how they can interpret data, whether from their supply chains or third parties.

The final development may be that fashion brands continue to try and tackle these issues individually rather than collectively. But the impact data ecosystem is no longer the Wild West. As Henry Ford said: ​​“If I had asked people what they wanted, they would have said faster horses.” It is time for cohesive solutions to collect and measure data with transparent guidelines and techniques that consider every unique step of a product’s journey. This accountability gives brands greater control over their processes and operation and provides the necessary insight to make critical decisions for their consumers and the planet.  

Denby Royal is the partnerships lead at Arbor


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