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Record W4410769777 · doi:10.1080/13563467.2025.2504390

The geoeconomics of Central Banks Digital Currencies (CBDCs): the case of the European Central Bank (ECB)

2025· article· en· W4410769777 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNew Political Economy · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of CanadaUniversité du Luxembourg
KeywordsCentral bankFinancial systemEconomicsEconomyBusinessInternational economicsInternational tradeMonetary policyKeynesian economics

Abstract

fetched live from OpenAlex

Digital transformations and the expansion of digital finance have elicited intense debates on what ought to be the role of central banks as issuers of digital currencies (CBDCs). We ask why the European Central Bank (ECB), the European Union (EU) central bank, has taken a starring role in the introduction of a retail CBDC: the digital euro. We offer an explanation rooted in geoeconomics: the ECB has decided to be a ‘paladin’ of the digital euro to safeguard the ‘monetary sovereignty’ of the euro area and protect the ‘strategic autonomy’ of its retail payment system. As the rivalry of great powers intensifies, the ECB worries about the issuing of private digital currencies by (mostly, non-EU) private actors as well as the dominance of non-EU companies in retail payments. Currencies and payments are important public goods that can be weaponised. This explanation contributes to the emerging literature on central banks as geoeconomic actors and teases out the implications of this development for the ‘traditional’ mandate of central banks and their core tasks.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.534
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.220
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it