French eco-score for textiles: Why and how to ensure data reliability

Introduction

In the previous module, you explored the regulatory framework behind the French Eco-score and the calculation methodology used to assess the environmental cost of textile products.

This module focuses on its practical implications for your organisation, and more specifically on a critical component: product data.

Let’s start with a simple example.

A 100% cotton pair of jeans weighing 450 g, with spinning in Bangladesh, dyeing in Türkiye and assembly in Morocco, obtains a score of around 4,800 points when all information is accurately documented.

But if the product sheet is incomplete (i.e. with missing details on countries, processes or transport) the calculation defaults to worst-case assumptions.

The result? The very same jeans can score up to 7,500 points.

This gap illustrates a simple truth: incomplete data can artificially inflate your environmental impact.

And the challenge goes far beyond the calculation itself. As environmental labelling becomes mainstream, the quality of your data directly shapes your regulatory credibility, your ability to benchmark products, and ultimately the public perception of your impact.

So beyond calculation accuracy, why has data reliability become a strategic priority for brands, and how can you ensure it remains reliable over time?

Three reasons to ensure data reliability before calculating your products’ environmental cost

1. Your products’ environmental costs will be public and comparable

Within a year, French regulation will allow third parties, including consumers, NGOs, retailers and competitors, to access, compare and publish the environmental costs of textile products.

In other words, you will no longer control the narrative alone.

Imprecise data or missing information will no longer go unnoticed: your scores may be compared with similar products that are better documented.

The risk? A pair of jeans scoring 7,500 points will be perceived as more harmful for the planet, whether the score reflects reality or not.

As transparency becomes a strong purchasing criterion, poor-quality data turns into a reputational risk.

Ensuring data reliability also means protecting your brand and preventing incomplete information from backfiring.

2. Your products’ environmental cost evolves over time

Beyond reputational risks, imprecise data may also put you at regulatory risk. Environmental scores are not static: they reflect your product at a specific moment in time, and must evolve as the product evolves.

Whenever a parameter changes (material, supplier, production country, process or transport) the calculation must be updated to remain accurate (see the dedicated regulatory module).

The decree governing environmental labelling requires these regular updates to maintain accuracy in information communicated to consumers.

This means that data collection and management cannot be a one-off exercise. They must become a continuous process, embedded into product lifecycle management and your sourcing operations.

By structuring and securing your data, you’re building a living system able to adapt to changes in your supply chain and prevent outdated scores from jeopardising your compliance.

3. Your data will feed into other regulatory frameworks

The French eco-score is only the first step in a broader transformation toward product traceability and environmental performance.

The same datasets (composition, weight, origin, manufacturing processes, durability) will soon be required for other regulations:

• The ESPR (Ecodesign for Sustainable Products Regulation), whose delegated acts for textiles are expected in 2027, will likely define the EU-wide methodology for calculating product impacts, based on the PEF (Product Environmental Footprint). A PEF update is planned for 2026 to incorporate microplastic release.
• The Digital Product Passport (DPP), a pillar of the ESPR, which will require the same information defined in the 2027 delegated acts.
• Upcoming eco-modulation schemes, which may adjust financial contributions based on the environmental performance of your products.

In short, the effort you invest today to secure your data won’t only serve for French eco-score calculation: it will form the foundation of your future compliance, and a competitive advantage for your brand.

How to ensure your data is reliable

1. Centralise your scattered data

Most brands already have large amounts of information… but rarely a unified view.

ERP systems for product sheets, Excel sheets for suppliers, emails for transport details, PLM tools for certificates… Data is fragmented, and this fragmentation multiplies inconsistencies.

The first step is therefore to bring all this information together in a single source of truth, linking each piece of data to the relevant product (SKU, reference or PO level).

A centralised base allows you to:

• Cross-check information from multiple systems,
• Trace the origin of each data point (file, supplier, document),
• Identify inconsistencies more easily (weight, processes, countries, certificates),
• Automate part of the quality checks,
• And most importantly, reuse this data across regulatory frameworks (AGEC, ESPR, CSRD…).

By structuring your data today, you gain a competitive and operational advantage tomorrow.

2. Ensure the reliability of your data

Centralisation is not enough. Reliable data requires a combination of controls, rules, validations and evidence, ensuring that published information truly reflects your product.

Three levers strengthen data quality:

1. Standardising your data

Before even analysing your dataset, you must standardise how data is named, formatted and structured. One material can appear as “cotton”, “Ctn”, “CO”, “100% cotton”, and one supplier may appear under multiple spellings.

Without standardisation, this creates invisible duplicates, inconsistent entries and unnecessary back-and-forth with suppliers.

Harmonising units, controlled vocabularies and formats allows you to:

• Eliminate duplicates upon import,
• Secure the integrity of your data model,
• Compare products more easily,
• Avoid repeated supplier requests,
• Ensure each data point correctly feeds the environmental scores of your products.

Standardisation is a non-negotiable prerequisite for usable and trustworthy data.

2. Applying coherence rules

A weight consistent with the type of product, a composition that adds up to 100%, compatibility between countries and processes, realistic transport distances… Together, these rules act as an automatic control system capable of detecting inconsistencies instantly.

They allow you to identify anomalies before they affect the environmental cost calculation and ensure that every data point used is relevant, coherent and reliable.

3. Collecting supporting evidence

Material certificates, technical datasheets, invoices, due-diligence statements: each piece of information provided by a supplier can be supported by documentation. These proofs not only validate the accuracy of the data but also ensure full traceability. By systematically linking them to the information you collect, you strengthen the credibility of your approach and are able to demonstrate the reliability of the environmental costs you publish.

Ultimately, ensuring reliability means guaranteeing that every data point is accurate, complete, coherent and fully justifiable.

3. Engage your suppliers in the process

Suppliers hold a significant share of the essential data: exact composition, processes used, resource consumption, transport distances... Without their contribution, it is impossible to obtain a reliable score.

However, when they are asked to complete multiple Excel files or respond to repetitive requests, supplier fatigue quickly sets in, leading to delays, partial information or inconsistencies.

The solution is to simplify, harmonise and streamline data collection:

• Provide a single space where suppliers can submit information once
• Standardise formats to reduce manual data entry
• Filter and prioritise requests to avoid unnecessary solicitation
• Offer clear guidance and support to secure the quality of responses

A supplier who understands what is being asked, and why, delivers data that is more accurate and more complete.

4. Structure your internal processes to ensure long-term reliability

Ensuring data reliability does not rely on technology alone. It requires a robust internal organisation capable of maintaining data quality over time. This is often where brands encounter the limits of siloed ways of working.

To keep data reliable, each team must understand its role and responsibilities.

• CSR and compliance teams oversee regulatory requirements and manage the relevant indicators
• Production and quality teams ensure the technical coherence of the information provided (weights, processes, materials)
• Sourcing and supply chain teams play a key role in coordinating supplier relationships and collecting supporting evidence
• IT and data teams ensure system interoperability, smooth workflows and proper data flow across tools

This shared governance must be supported by clear rules. Which checks need to be carried out? At what stage? By whom? Which data points must be validated before a score can be generated? Who approves the final version that will be published?

Structuring your internal processes means ensuring that data quality does not depend on one-off efforts, but on an organisation designed to maintain reliability over time, aligned with the pace of your collections and the growing expectations of regulatory frameworks.

Conclusion

Ensuring the reliability of product data is not a technical formality: it is the foundation for calculating an environmental score that is both accurate and representative of your practices.

As we saw, data quality does more than influence calculation accuracy. It directly impacts:

• Your reputation, in a context where scores will be publicly comparable;
• Your compliance, as environmental costs must evolve with your products;
• Your ability to anticipate upcoming regulatory requirements, from ESPR to future eco-modulations.

By centralising your information, securing its quality, engaging your suppliers and structuring your internal processes, you build a system capable of supporting both real-time updates and future compliance needs.

In short: you build a strong, durable data foundation your brand can rely on.

And yet, ensuring data reliability is only the first step.

Once your products’ environmental costs are calculated, a new question emerges: how should you interpret, present and communicate these scores to your consumers?

Calculation your French eco-scores is not just a regulatory requirement: it is a driver for transparency, differentiation and trust.

In the next module, we’ll explore how to transform a technical score into a clear, controlled and compelling message, without falling into greenwashing.