How central banks are shaping the future of digital currencies
Bibliographic record
Abstract
This paper examines the accelerating global momentum behind central bank digital currencies (CBDCs), exploring how central banks are responding to the twin pressures of financial digitalization and geopolitical competition. It provides a comprehensive analysis of the motivations driving CBDC development from enhancing payment system efficiency and financial inclusion to preserving monetary sovereignty in the face of private digital currencies. Through a comparative lens, the paper analyzes divergent CBDC models - retail, wholesale and hybrid - adopted across advanced and emerging economies, with particular attention to design features, operational challenges and strategic objectives. It also evaluates multilateral initiatives such as Project mBridge and Project Dunbar, highlighting their potential to transform cross-border payments and recalibrate global financial power. The paper concludes by assessing the key risks of CBDC implementation, including privacy, cybersecurity and financial disintermediation and explores the future trajectory of CBDCs as programmable policy tools within an evolving digital monetary order. The findings underscore that while the path to global CBDC adoption is complex and fragmented, the direction is clear: digital sovereign money is likely to play a central role in shaping the next era of international finance.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".