Rise of the Central Bank Digital Currencies: Drivers, Approaches and Technologies
Bibliographic record
Abstract
Central bank digital currencies (CBDCs) are receiving more attention than ever before. Yet the motivations for issuance vary across countries, as do the policy approaches and technical designs. We investigate the economic and institutional drivers of CBDC development and take stock of design efforts. We set out a comprehensive database of technical approaches and policy stances on issuance, relying on central bank speeches and technical reports. Most projects are found in digitised economies with a high capacity for innovation. Work on retail CBDCs is more advanced where the informal economy is larger. We next take stock of the technical design options. More and more central banks are considering retail CBDC architectures in which the CBDC is a direct cash-like claim on the central bank, but where the private sector handles all customer-facing activity. We conclude with an in-depth description of three distinct CBDC approaches by the central banks of China, Sweden and Canada.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".