Monetary Sovereignty and Central Bank Digital Currencies: Competing Models for Future Cross‐Border Payment Platforms
Bibliographic record
Abstract
ABSTRACT As central banks move to adopt digital currencies (CBDCs), two issues arise: the implications for monetary sovereignty and the potential efficiencies from cross‐border interoperability. The former is particularly a concern for emerging market central banks, while the latter affects all states. Emerging markets have used capital flow management (CFM) tools to control capital flows that may overheat and destabilize the macroeconomy. The effectiveness of CFM implementation depends on how CBDCs carry out cross‐border payments. This article discusses how CFM tools would function under different models of interoperability. While there are three broad models of cross‐border CBDC payments, the key debate centers on two alternatives: the hub‐and‐spoke model and the common‐platform model. Three ongoing projects, in which the Bank of International Settlement and central banks are participating, test these two models. This article compares the differences in these projects on a delegation to private intermediaries and notes the common platform model's demonstrated capacity for implementing jurisdiction‐specific capital flow measures. It concludes with a case analysis of the Chinese e‐CNY and capital flow tools. States should consider the interests of emerging markets in joining cross‐border platforms that allow them to interact with their trading and investment partners while avoiding destabilizing cross‐border flows.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".