Bibliographic record
Abstract
Received 28.07.2020. The article examines issues related to the introduction of central bank digital currencies (CBDC) for retail payments and wholesale settlements. The study defines and classifies central bank digital currencies, researches the main models of CBDC systems. The article also analyzes the features of various national projects for issuing Central bank digital currencies. The paper uses methods of economic-statistical and functional-structural analysis. The study concludes that CBDC are a new form of central bank money. Digital currencies can be issued in various issuing systems for the purpose of retail payments or wholesale settlements. Among the models of CBDC systems for retail payments (R-CBDC) the direct system model is the most attractive for its simplicity. This model eliminates the dependence of the Central bank on any financial and payment intermediaries. Models of synthetic and hybrid R-CBDC systems are characterized by reliability and speed in processing multiple transactions which makes them the most promising for implementation. Among the models of CBDC systems for wholesale payments (W-CBDC) the model of the system with a universal digital currency (U-W-CBDC) may be the most suitable for eliminating the main disadvantages of modern cross-border payment systems. However, a large number of technological and financial changes as well as the high operating costs of the U-W-CBDC can make such systems difficult to implement for non-developed financial market infrastructure countries. National financial regulators have different motivations for issuing digital currencies. The main advantages of digital currencies for retail payments may consist in providing users with highly liquid, low-risk, universally available means of payment. The main advantages of wholesale digital currencies are that they offer faster, safer, cheaper cross-border payments. The most advanced projects for issuing R-CBDC can be considered DCEP (People’s Bank of China) E-krona (Central Bank of Sweden). The most successful pilot projects for issuing W-CBDC are the projects Jasper (Central Bank of Canada) and Ubin (Monetary Authority of Singapore), which were able to achieve interoperability in conducting cross-border payments. Currently most CBDC are retail based on the use of distributed ledger technology and implemented in the form of DLT-tokens. Countries that develop digital currency systems can be divided into three groups. The first group is countries where the introduction of CBDC can be designed to support the national demand for central bank money (Sweden, Norway, Singapore, etc.). The second group – countries for which the adoption of digital currencies can afford to keep the place of national currencies in international settlements (USA and EU) or expanding the use of national currencies at the international level (China). The third group represents countries for which the introduction of digital currencies may be associated with the control of national monetary circulation and de-dollarization of the financial system (Uruguay, South Africa, Cambodia, etc.).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".