The current state of legal regulation of cryptocurrency in Ukraine. International experience in cryptocurrency market regulation
Bibliographic record
Abstract
The article examines the definition of «cryptocurrency,» its legal status, and prospects for regulation in Ukraine. The authors analyze contemporary approaches to understanding cryptocurrency as a digital asset, considering it either as a new form of money or as an object of civil rights. Particular attention is paid to the provisions of the Law of Ukraine «On Virtual Assets,» adopted on February 17, 2022, and its significance in creating the legal foundation for cryptocurrency market regulation. However, it is emphasized that this law has not yet come into force due to the absence of corresponding amendments to tax legislation, complicating the legalization of cryptocurrency transactions. The distinction between «virtual assets» and «cryptocurrencies» is discussed, highlighting key limitations of existing legislation, particularly the prohibition on using virtual assets as a payment method in Ukraine. The article outlines issues such as the lack of a transparent regulatory environment and a taxation system, which hinder the development of the cryptocurrency market, reduce its investment appeal, and create risks for market participants. The article also analyzes international cryptocurrency regulation experiences, particularly in the United States, Canada, Japan, and the European Union. Special attention is devoted to the European regulation Markets in Crypto Assets (MiCA), which could serve as a foundation for developing a unified regulatory framework in Ukraine. The authors stress the importance of harmonizing Ukrainian legislation with European standards within the framework of European integration. The potential benefits of cryptocurrency legalization are highlighted, including attracting foreign investments, developing financial technologies, reducing the shadow economy, strengthening consumer protection, and creating new markets. Specific recommendations are provided to improve legislation, such as implementing transparent regulatory mechanisms, taxation, investor protection, and ensuring cybersecurity. This article is a significant contribution to the study of the prospects for the development of the cryptocurrency market in Ukraine, outlining the challenges and opportunities for integrating Ukraine’s financial system into the international space through the adoption of MiCA standards.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".