The rise in popularity of central bank digital currencies. A systematic review
Bibliographic record
Abstract
Central bank digital currencies (CBDCs) have been growing in popularity since 2018, as worldwide countries explore their impact and implementation options. This article analyzes the state of research around central bank digital currencies and the evolving landscape of CBDCs, and explores emerging areas of research and trends by using the PRISMA method and VOSviewer, with the goal of showing the main opportunities and challenges related to them. AMSTAR, DistillerSR, Eppi-Reviewer, ROBIS, and SRDR were the screening and quality evaluation tools employed for study eligibility criteria, design screening and content selection, text analysis data extraction, methodological quality predictors, and reliable and reproducible evidence assessment. A total of 1024 articles on central bank digital currencies were identified in Scopus and the Web of Science, out of which 747 have been included in the review (documents which were not in English language and not categorized as journal articles were excluded). Through an analysis of the relevant literature, the study categorizes CBDC research into positive, negative and neutral research, with a particular focus on sustainability issues, and conducts a keyword co-occurrence analysis using VOSviewer, following a narrowing down of the relevant articles to be included in the study by applying the PRISMA framework. This generates an overall view for experts and researchers who can use the main analyzed features of CBDCs and adapt them accordingly, taking into account relevant macroeconomic characteristics. The study highlights the need to continue interdisciplinary research, by adapting the research and CBDC characteristics to keep up with the latest technologies and with the shift towards green finance, and explores the elaborate relationship between finance, technology and sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".