Environmental data: How can we turn regulatory requirements into a driver of performance?

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Climate pressure is no longer just a matter of corporate social responsibility. It is becoming a global strategic issue for fashion and apparel brands, and environmental data is key to addressing it.

Collecting data just to fill out a report. Tracking metrics just to check a regulatory box. Measuring performance simply because you’re told to. This is still the reality for many brands today.

Yet the brands that go the extra mile have grasped a fundamental truth: well-structured environmental data is not a cost—it is an asset. It opens the door to financing, helps win bids, reduces supplier risks, and keeps teams on track—teams that, without it, would be flying blind.

This is precisely the topic that WARO and Trace for Good explored during a conference held as part of the Fashion Act trade show: "Turning environmental data into a driver of differentiation and performance." This article summarizes the key takeaways.

The initial problem: data that is expensive to obtain but yields little return

What the teams experience on a daily basis

Before we talk about management or differentiation, let’s take a look at the reality on the ground. What CSR, Procurement, and Product teams experience on a daily basis often looks like this:

• A process that is still largely manual, relying on Excel file exchanges and repetitive follow-ups with suppliers: a time-consuming, unrewarding process that ties up time in administrative tasks rather than decision-making.
• Data that has been collected but is underutilized: without a system to organize it chronologically, without comparison to historical data, and without a clear purpose. Every use case (reporting, impact assessment, DPP) starts from scratch.
• Data that is too imprecise to act on: a carbon footprint based on generic sector-wide data either underestimates or overestimates actual emissions. The identified levers are poorly targeted and often unattainable. And this lack of precision has another adverse effect: brands hesitate to communicate about their initiatives, for fear of not being able to defend them. This is known as “greenhushing ”: companies remain silent out of caution, and miss an opportunity to showcase their efforts.
• A lack of visibility into the supply chain: without mapping beyond tier 1, product decisions (choice of materials, suppliers, and modes of transport) are made without insight into their environmental impacts or the associated supplier risks.
• Leadership that doesn't follow through: without a demonstrable ROI, management won't approve the budgets. And without a budget, it's impossible to establish a serious initiative. It's a vicious cycle that's hard to break.

Why is this issue becoming critical now?

This isn’t just a matter of regulatory compliance (ESPR, CSRD, DPP, etc.). It’s a source of real business pressure: supplier risk management, brand reputation, access to financing, and the ability to meet the requirements of major retailers. Brands that haven’t structured their data yet are falling behind on issues that are becoming key criteria for business partnerships.

What brands that go the extra mile get out of it: documented ROI

Before discussing methodology, let’s lay out the facts. Brands that have organized their environmental data—combining traceability and impact measurement—are seeing tangible results across several areas:

• Financing: Access to sustainability-linked loans at preferential rates thanks to a credible and verifiable track record
• Sales: Tenders won thanks to data accuracy and supply chain transparency
• Operational efficiency: Up to a 50% reduction in time spent on traceability, allowing for more time to be dedicated to high-value-added management and analysis
• Greenwashing risk: Exposure to penalties of up to 10% of revenue (UK Green Claims Act)
• Resilience: 6 to 18 months ahead of the competition in DPP compliance
• Path: Low-carbon path developed over two months and approved by the Executive Committee

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Part 1 - Structuring Data: Three Mistakes to Avoid

Common mistakes that hinder ROI

Many brands still treat data as a one-off project. The most advanced ones treat it as a strategic asset. The difference often comes down to the initial decisions.

Mistake #1 — Collecting data without knowing what to do with it

Implementing a system "just because it's a good idea," without a clear objective or an explanation of the process to suppliers, drives up costs and increases team burnout. Without a clear objective, the ROI cannot be demonstrated internally, so leadership does not support it.

Mistake #2 — Trying to collect everything right from the start

Asking a supplier for energy consumption data when you’ve never discussed these topics with them before is the surest way to never get a response and to burden the supplier with unusable data. A relationship is built gradually. You explain why before you ask what.

Mistake #3 — Outsourcing without maintaining control

Entrusting your data to a "black box" that delivers results weeks later, with no transparency regarding the methods used, almost always leads to disappointment: data that is difficult to act on, low internal buy-in, and an inability to track progress over time or reuse the data for other purposes.

Data as a management infrastructure

The most forward-thinking companies no longer view environmental data as a CSR issue or a one-off deliverable. Instead, they treat it as a core component of their business management infrastructure.

Well-structured data is data that:

• Can be used for multiple purposes without having to start from scratch (regulatory reporting, impact assessment, DPP, communication)
• Provides visibility into the supply chain beyond Tier 1, enabling the anticipation of supplier risks before they escalate into crises
• Is precise and detailed, enabling reliable impact assessments and defensible claims

The 3-step method

Step 1 — Assessment of the current situation

The first question isn’t “What data should we collect?” but “How will this data be integrated into the organization and used by teams?” Identify gaps, assess what can be reused, and prioritize based on the next strategic step (roadmap, DPP, RFP). Don’t start from scratch.

Step 2 — Start with the regulations

Regulatory requirements are often the best place to start—they force you to establish a basic database. But brands that stop there remain focused solely on compliance. The challenge is to use this constraint to build a competitive advantage.

Step 3 — Take it a step further depending on your goal

Depending on what you want to do with the data, the collection process will vary:

• Environmental impact objective → material-specific data, certifications, suppliers’ energy consumption
• DPP Objective → Accurate product information, component traceability
• Supplier Risk Objective → Supply chain mapping beyond Tier 1, subcontracting, social audits

Key takeaway: Start with the basic data (regulatory, audit data, and primary data essential for impact calculations), build the supplier relationship gradually—and only request specific data once the supplier understands why it is being requested.

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The key elements for making it work

1. Flexibility: The collection solution must be adaptable to objectives that will inevitably change (impact, DPP, supplier risk, etc.)
2. Gradual supplier engagement: treat suppliers as strategic partners. Without them, there is no reliable data.
3. Data quality and validation: striking the right balance between strictness and practicality. Automating consistency checks.

Example — Descours & Cabaud: Implementation of a "Data Quality Report" to verify the consistency of critical data in LCA calculations.

Example — Risk Identification: Using the supplier risk module, the team identified a high-risk supplier—red flags regarding the quality of the information provided, and incomplete AGEC information three weeks before launch. Result: The decision was made to terminate the relationship with the supplier. Another reputational risk averted.

Example — Grain de Malice: Integrating specific data from 19 suppliers doubled the calculable reduction potential, unlocking access to green loans. [INTERNAL LINK: sustainable financing in the textile industry]

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Part 2 — Leveraging Data: Measuring Impact to Inform Business Decisions

Once the foundation is in place, the focus shifts from data collection to taking action. And that’s where many brands run into real challenges.

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Two mistakes to avoid

Mistake #1 — Goals that are too far removed from the realities on the ground

Generic data with insufficient granularity produces results that teams cannot take ownership of. The impact remains an CSR issue, not an operational lever.

Mistake #2 — Reacting instead of anticipating

CSR cycles are often out of sync with product development cycles. Product decisions (such as the choice of materials, suppliers, and modes of transport) are made without real-time visibility into their environmental impacts.

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What granular control enables

Truly effective impact management is the ability to:

• See the impact of its collections at the product level, by department, and by material—in real time, not six months later
• Simulate scenarios before committing: change materials, suppliers, or modes of transport, and assess the impact before making a decision
• Present a credible, cost-based plan to management, investors, and distribution partners

Grain de Malice: Thanks to granular data and the integration of LCA, the brand has increased the accuracy of its Scope 3.1 carbon footprint by 50%—enabling it to better highlight its efforts and identify the right levers for developing its reduction strategy.

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Three pillars for better impact management

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1. Set goals that reflect the reality on the ground

There are two stages of maturity:

Stage 1: CSR-led management. The objectives are set by CSR. This is Level 1: we test different approaches, determine what is feasible (given constraints on margins, budgets, and sourcing), and develop trajectory scenarios. But without team-level implementation and regular check-ins, the approach remains fragile.

Phase 2: Involving the business teams. The Purchasing, Product, and Style teams each have their own KPIs. The CSR strategy is broken down into objectives tailored to each team and integrated into their existing tools and processes.

At Celio, theSBTi framework has enabled the company to transition from a qualitative roadmap (a few organic cotton products and scattered initiatives) to quantitative targets, broken down by phase, department, and year. Buyers are now held accountable for their CSR targets in the same way they are for their margin targets. That is what has driven buy-in.

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2. Incorporate eco-design into the product development cycle

There is no one-size-fits-all approach: every brand operates differently. Our recommendation: immerse yourself in the business to understand how teams work, what challenges they face, and what tools they use, before proposing an integration plan.

The three key moments when reviewing environmental data really makes a difference:

1. Product Brief — Establish Design Intent and Guidelines (Priority Materials, Components to Avoid)
2. Material and component selection — balancing environmental impact, cost, and style
3. Pre-production launch — verification of compliance with objectives prior to implementation

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3. Ensure effective monitoring and sustainable adoption

• Data stability: a data collection process that remains consistent over time (traceability, PLM, ERP)
• Increasing precision: gradually incorporating increasingly specific data to achieve the level of granularity needed to measure and take action at the product level
• Customizing metrics: tailoring dashboards to different business teams

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Conclusion: Choose a system, not a tool

Most brands still approach these issues as a matter of choosing tools. In reality, it’s a matter of choosing a system.

Specifically, this system should enable the following:

1. Structure supplier and product data over time—using formats tailored to partners’ maturity levels, standardization that enables integration with other systems, and robust governance (consistency checks, version history, auditability)
2. Manage traceability as a dynamic process—track the progress of data collection, identify bottlenecks, and foster supplier relationships
3. Accurately calculate the impact —based on product LCA, not just generic sector-level data
4. Simulate reduction scenarios —by department, by collection, by category—before committing
5. Making data accessible to business teams — a tool that only the CSR department knows how to use won’t change decisions. Adoption by buyers and designers is key
6. Ensure the method is auditable —to present the timeline to funders, auditors, or distribution partners
7. Linking traceability and impact —traceability fuels the analysis, and the analysis gives meaning to traceability. It is this link that enables us to move from a compliance-driven approach to one focused on management and value creation

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