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JRC Publishes DPP Data Methodology: What Changes for 2026

JRC145830 introduces a 3-tier framework for DPP data: essential, strongly recommended and voluntary. What it means for compliance.

· 7 min read · InfoDPP

JRC Publishes a Framework for DPP Data Prioritisation

On 19 March 2026 the European Commission’s Joint Research Centre (JRC) published JRC145830 — Methodology for defining data requirements for the Digital Product Passport under the ESPR framework. The report presents a structured, repeatable methodology for proposing which data should appear in a Digital Product Passport and why.

It is one of the clearest public research outputs from the Commission side on a question many companies preparing for DPP are asking: how can data points be prioritised as essential, strongly recommended, or voluntary? It is a research methodology intended to inform delegated acts and impact assessments, not a binding legal field list in itself.

The Core Idea: Three Tiers

The JRC methodology classifies DPP data elements into three tiers based on a transparent value–effort and feasibility assessment:

TierWhat it meansPractical implication
EssentialDirectly required by regulation or necessary for the DPP to function legallyNon-negotiable once a delegated act is in force
Strongly recommendedHigh value-to-effort ratio; clearly useful to regulators, consumers or value-chain partnersMay inform later mandatory field lists, but is not automatically mandatory
VoluntaryNiche value; not broadly justified by the assessmentStrategic choice, not a compliance requirement

The framework does not replace the ESPR or sector-specific regulations. It provides a principled way to prioritise data preparation before final field lists are confirmed in delegated acts.

Why This Matters Now

1. It reduces guesswork

Until now, companies had to interpret ESPR recitals, the working plan, and early CIRPASS outputs to decide which data to prepare. The JRC methodology offers a documented, step-by-step approach that translates regulatory intent into concrete data-priority decisions.

2. It validates early preparation

A recurring concern for companies starting DPP work in 2026 is whether their data preparation will survive changes in delegated acts. The three-tier framework makes this risk more manageable: essential data is already visible from the regulation’s core provisions, and strongly recommended data becomes easier to prioritise even before formal field lists are adopted.

3. It applies beyond ESPR

The JRC explicitly notes that the modular methodology can be applied outside the ESPR framework where equivalent context and feasibility analyses are carried out. This is relevant for standalone DPP mandates such as the Toy Safety Regulation (from 1 August 2030) and Regulation (EU) 2026/405 on detergents (from 23 September 2029).

What the Methodology Does Not Do

The report intentionally limits its scope:

  • It does not define technical data formats or encoding standards — that work falls to CEN/CENELEC harmonised standards.
  • It does not publish final field lists for any product category — those will come through delegated acts.
  • It addresses DPP system architecture only to the extent needed to inform data access rights, governance and granularity decisions.

In other words, JRC145830 answers what data should be in a DPP and why, not how it should be technically delivered.

Practical Takeaway for Companies

If you are already mapping product data for DPP purposes, the JRC methodology supports a structured prioritisation workflow. One practical way to apply it is the model described in the DPP data requirements guide: use five core data layers (identity, operator, composition, sustainability, traceability) and apply the three-tier classification as a prioritisation lens within each layer for your product category.

For a detailed walkthrough of the three tiers and sector-by-sector examples, see the full reference article: DPP Data Classification: Essential, Recommended, Voluntary.

Official Sources


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