The Dutch Perspective on the Enforcement of the EU Sanctions Against Russia: Legal Challenges, Case Law, and Institutional Practice
Bibliographic record
Abstract
This article examines the evolving legal and institutional framework for implementing and enforcing EU sanctions against Russia in the Netherlands. It highlights key developments, including the Dutch courts’ expanding interpretation of sanctions law, the landmark Dieseko settlement involving the Crimean Bridge, and reforms to the 1977 Sanctions Act. Drawing on recent case law, interviews with legal practitioners, and analysis of enforcement mechanisms, the paper shows how Dutch authorities are balancing regulatory compliance, due process, and financial sector duties. It also addresses institutional fragmentation and the government’s proposal to establish a Central Reporting Office. Through case studies, including trade-based sanctions evasion, real estate linked to sanctioned individuals, forced buyouts of sanctioned minority shareholders, and banking sector disputes; the paper argues that Dutch courts are shaping a nuanced national model of sanctions enforcement. This model emphasizes low thresholds for criminal intent, transparency, and proportionality. The Cicerone case illustrates how courts adapt sanctions enforcement under geopolitical uncertainty, combining EU sanctions law with Ukrainian anticorruption efforts. It reflects a willingness to diverge from EU guidance to protect public interest and legal clarity. Meanwhile, the ABN AMRO case demonstrates a dual expectation of financial institutions: rigorous sanctions compliance and fair treatment of clients. Here, the duty of care doctrine counters excessive risk aversion. Together, this paper offers critical insights for regulators, compliance professionals, and scholars into how EU sanctions are interpreted and enforced at the national level under complex, highrisk conditions. It not only analyzes key court cases, but also contextualizes them within broader legal reforms, institutional dynamics, and evolving enforcement strategies in the Netherlands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".