Addressing conflict of laws and facilitating Digital Product Passports for critical raw materials value chains: From centralisation to mutual recognition
Bibliographic record
Abstract
Abstract The value chains for critical raw materials (CRM) used in electric vehicle (EV) batteries often involve mining in the Global South, Australia and Canada, production in Asia, and consumption in the Global North. Starting in 2027, EU law will require a ‘digital product passport’ (DPP) for market entry. These passports will provide EU consumers, investors, regulators and others with products and sustainability data throughout the entire value chain. The EU DPP aims to improve ESG (Environmental, Social and Governance) compliance by ensuring high transparency and verifiable data from miners, producers and recyclers. However, legal, geopolitical, commercial and technological factors suggest that major economies in the up‐and mid‐stream of the value chains, such as Australia, China and Japan, may maintain or develop their own traceability laws, which might only partially overlap with the EU's system. These laws could potentially be linked through mutual recognition agreements with the EU. Our paper explores how such a system could function, with varying degrees of decentralisation, inspired partly by private international law mechanisms that have evolved to handle cross‐border traceability of documents. Examples include systems for recognising marriage and other personal or commercial certificates, arbitral awards and foreign judgments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.075 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.044 |
| Scholarly communication | 0.025 | 0.029 |
| Open science | 0.006 | 0.032 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".