Central bank digital currencies: experience of pilot projects and conclusions for the NBU
Bibliographic record
Abstract
An overview of the definitions of central bank digital currency (CBDC), formulated by researchers of the International Monetary Fund (IMF), the Bank for International Settlements (BIS), the Bank of England, is presented, and the essence of the CBDC is revealed. It is stated that the existing electronic money is a digital form of obligations of financial intermediaries, and CBDC is a form of emission and obligations of central banks. The types and forms of CBDC are generalized, namely: retail or wholesale, account-based or token-based ones. The structure and functionality of the register, payment authentication, access to infrastructure, and governance are defined as factors taken into account during CBDC designing. Similar models of launching national CBDC by the Bank of England (economy-wide access or financial institutions access, and financial institutions plus CBDC backed narrow bank access) and BIS (direct, indirect, hybrid) are under consideration. The synthetic CBDCs are marked as a theoretical concept of CBDC. The overview of projects of the People's Bank of China – "e-renminbi", the Central Bank of the Uruguay – "e-peso", the Central Bank of the Bahamas – "sand dollar" and the Eastern Caribbean Central Bank affirm the interest of developing countries in launching national retail CBDCs. It was found that apart from the Riksbank with the successful "e-krona" project, most of the monetary authorities of developed countries (BIS, Bank of Japan, Bank of Canada, Deutsche Bank, FRS) are just planning or starting to experiment with the issuance of digital securities, which demonstrates their concern about the restructuring of the banking system and the changes of global role of traditional currencies. Among the positive consequences of the introduction of CBDC for the domestic banking system are the emergence of an alternative payment instrument, the implementation of effective monetary policy through increased influence on interest rates, and regulation of the legal regime of crypto currencies. At the same time, the introduction of CBDC involves certain changes in financial intermediation (replacement of the deposits of commercial banks with the CBDC, the performance of functions inherent to commercial banks by the central bank or fintech companies), and will require powerful technical capabilities, including those related to protection from cyber risks. The results of the study point to the need for a cautious approach to the implementation of the Ukrainian CBDC only after the NBU assesses the public demand for new forms of money and the impact of the launch of CBDC models on price and financial stability, and compares available payment technologies that can achieve the same goals as the CBDC.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".