Bibliographic record
Abstract
Why this book?The emergence of crypto assets, particularly virtual currencies, and their implications for taxation puts a bright spotlight on existing tax measures and, perhaps, the need to develop new tax policies. The decentralized nature of crypto assets has given rise to several overarching issues that need to be addressed at a supranational level. Key issues discussed in this publication include those relating to the regulatory and governance framework, the transparency and reporting framework and the emergence of central bank digital currencies. From a national perspective, many jurisdictions have issued specific guidance or created targeted legislation to deal with certain crypto-asset transactions.However, the way in which these new challenges are addressed varies widely from country to country. The aim of this book is to provide tax authorities, policymakers, courts and practitioners with an overview of tax measures implemented in different jurisdictions. Therefore, the income and capital gains tax implications of crypto-asset origination and extinction events, as well as the implications of using of crypto assets in investment and business transactions, are discussed from both a domestic and international perspective. The treatment of crypto assets in terms of VAT and other taxes, such as inheritance and gift taxes, is also discussed, and each author concludes by offering an outlook on the future of crypto-asset policy in their respective jurisdiction.The book comprises 36 national reports from countries across the globe, as well as three special reports on current developments in the field of crypto assets and is the outcome of the conference “Crypto Assets: Tax Law and Policy” that took place from 29 June 2023 to 1 July 2023 in Rust, Austria. More than 100 experts, including the authors of the national reports, were brought together to discuss recent developments in the field of crypto assets, with a special focus on the current tax challenges in this field. The general report highlights the most important findings of the conference.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".