Do cryptocurrencies have the potential to mitigate the impact of sanctions
Bibliographic record
Abstract
Purpose- Sanctions, as an alternative to the use of military force, are used as a means of diplomatic coercion for the target country to abandon some of its decisions or avoid some possible practices. While half a century ago, sanctions included issues such as trade and travel restrictions and arms embargo, today the form and content of sanctions have changed significantly. In the last decade, most of the sanctions imposed on some states, especially to Iran and the Russian Federation, are financial sanctions. These financial sanctions, it is aimed to cut the target country's ties with the global financial markets, to disrupt the cash/capital inflow and outflow to the target country, to prevent trade by removing them from global payment systems such as SWIFT and to restrict some activities of central banks. However, the issue of whether cryptocurrencies, which we have heard frequently since 2009, can be used as a means of mitigating or overcoming the financial sanctions is frequently on the agenda. In this context, we analyze whether cryptocurrencies can be used to mitigate or overcome the financial sanctions imposed on the target country. Methodology- The size of the financial sanctions applied by the USA, Canada, Australia, Japan and EU countries against the Russian Federation, after the war started in Ukraine on 24 February 2022 and the foreign money inflow/outflow volume needed by the Russian Federation as a result of its removal from the SWIFT system are compared with the volume of cryptocurrencies owned by the Russian Federation. Findings- The blockchain database system, which underpins cryptocurrencies, still struggles with a number of challenges. Especially the ability to increase the capacity (scalability) of the blockchain network is one of these problems. The scalability problem hinders the effective use of cryptocurrencies by the Russian Federation. In addition, when the financial transaction capacity and the size of the sanctions applied to the Russian Federation are compared with the crypto market size of the Russian Federation, it is seen that there is a significant difference in sizes. Conclusion- In today's conditions, cryptocurrencies stay away from the capacity to mitigate or overcome the financial sanctions applied to countries with large trading capacities such as the Russian Federation and to be used as a means of payment. However, in the coming years, in the case of developments in cryptocurrency technologies, cryptocurrencies have the capacity to be used to circumvent financial sanctions. Keywords: Sanctions, financial sanctions, payment instrument, crypto assets, blockchain. JEL Codes: F51, G20, B17
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".