Mapping flows of blue economy finance: Ambitious narratives, opaque actions, and social equity risks
Bibliographic record
Abstract
The blue economy provides a sustainability framework for ocean governance, but it is unclear whether narratives are matched by binding financial commitments and disbursements. Amid attention being paid to "funding gaps" in the Sustainable Development Goals, a lack of transparency in financial flows means that the blue economy concept risks being co-opted to facilitate further exploitation of ocean spaces and resources without contributing to environmental sustainability or social equity. Here, we analyze blue-economy-labeled money flows disbursed between 2017 and 2021 to identify sources and recipients and potential social equity impacts on the ground. Financing is predominantly disbursed to Europe and Central Asia and skewed toward business development and renewable energy. Our analysis reveals widespread occurrence of "red flags" for social equity outcomes. Although constrained to money flows that actively employ blue economy language, our findings show disconnects between finance and narratives of equity, inclusion, and sustainability. We offer a baseline for critical examination of blue finance flows in delivering equity and environmental sustainability.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".