Future Lies in Hybrid Models Where High-Level LCAs Are Supplemented by Granular Data

Despite EIM being open-source, technology-agnostic, and increasingly industry-validated, adoption isn’t yet universal. What, according to you, are the key reasons—technical, cultural, or psychological—that many laundries and brands continue to resist implementing such measurement tools?
Begoña García: Resistance stems from technical limitations in older facilities, fear of exposure to poor environmental performance, and in some cases, cultural reluctance to digitise artisanal processes. However, the tools are ready—it's about creating a shared accountability mindset. Adoption is growing, but the shift requires both incentives and education.

Denim finishing has always been a craft. Now, technology is reframing this space with laser precision and environmental accounting. How do you see traditional 'craft' and data-driven 'science' evolving in the coming decade? In what way do you think they will co-exist?
Begoña García: Craft and data are not at odds—they are complementary forces. Technology doesn’t erase creativity; it enables sustainable expression of it. Over the next decade, we’ll see data informing design choices while respecting the artistry of denim. EIM simply makes the invisible impacts visible, allowing better creative decisions.

The report identifies a host of high-impact innovations—ozone treatments, smart foam, synthetic abrasives—that offer dramatic environmental gains. Yet many of these remain on the margins. Which technologies do you believe are the most underleveraged today, and what’s preventing their scale-up?
Begoña García: Technologies like smart foam, ozone treatments, and synthetic abrasives offer significant environmental benefits, but they remain underutilised due to factors like lack of awareness, cost concerns, and resistance to change. In fact, 16% of processes still rely on pumice, highlighting that these alternatives are still on the fringes. What’s needed is stronger industry support, pilot programmes, and cross-brand collaboration to reduce the risks of early adoption.

Your data shows that denim finishing processes still carry a high chemical impact—often due to legacy practices like pumice stones or potassium permanganate. Beyond compliance, how can brands and chemical suppliers drive systemic reform in formulation, selection, and application?
Begoña García: Beyond compliance, the key lies in chemical reformulation and pre-selection at source. Brands must demand ZDHC-compliant, Screened Chemistry-rated products, and suppliers must innovate accordingly. We also need more data transparency in chemical impact to drive smarter formulation choices, and that’s exactly what EIM enables.

The report highlights how fabric selection can significantly influence downstream water, energy, and chemical use. Are we close to developing predictive, perhaps AI-driven, models for fabric behaviour during finishing? Could such tools become standard across the supply chain?
Begoña García: We're not quite there yet, but the foundations exist. Tools like Jeanologia’s Light Sensitive Test already offer predictive insights into how fabrics react to finishing. With the rise of AI, fabric-level predictive modelling is likely to emerge and become standard—streamlining process design and improving sustainability outcomes from the start.

You make a strong case for primary, facility-level data, instead of relying on generalised life cycle assessments. How can the industry balance the need for detailed, granular measurement tools like EIM with the commercial and logistical appeal of broad LCA data?
Begoña García: LCAs are useful for broad narratives but lack the precision and facility-specific insight that EIM provides. The future lies in hybrid models, where high-level LCAs are supplemented by granular EIM data—creating a full-spectrum view of impact that satisfies both operational and regulatory needs.

With mounting regulatory scrutiny on green claims—particularly from the EU—what safeguards does EIM have to ensure that its scores aren’t used out of context or manipulated in sustainability marketing? Is there a certification or usage framework in place to prevent greenwashing?
Begoña García: We take greenwashing seriously. EIM scores come with transparent calculation rules and are supported by external validation and user accreditation. We are developing a usage protocol and certification framework to guide responsible communication of EIM scores in marketing contexts—because credibility is everything.

“If competition got us into this mess, collaboration can get us out”—your report quotes this sentiment powerfully. But what does effective pre-competitive collaboration look like? What kinds of industry alliances or shared data models do you believe are most urgently needed?
Begoña García: Pre-competitive collaboration means shared data models, common scoring systems, and joint technology pilots. Industry-wide adoption of platforms like EIM can standardise impact language, while alliances like the EIM Working Group allow brands and laundries to innovate collectively—not competitively—on sustainability.

Denim finishing happens everywhere—from high-tech facilities in Europe to low-cost operations in South Asia. How adaptable is the EIM framework across such varied production contexts? Does one global benchmark risk ignoring local constraints, or can flexibility be built in without compromising integrity?
Begoña García: EIM is designed to be technology-agnostic and globally scalable—it works whether you're using Jeanologia tech or not. But we do recognise regional constraints. That’s why EIM allows for customisable thresholds based on garment type and usage context, offering both standardisation and local flexibility without compromising data integrity.