The Local Turn in a Global Sea: Identifying Sustainability Trade‐Offs in Regionalized Marine Aquaculture Systems
Bibliographic record
Abstract
ABSTRACT Marine aquaculture, like the broader seafood industry, relies heavily on international trade and global supply chains for both production and sales. Recent global disruptions, including the COVID‐19 pandemic, the Russian invasion of Ukraine, the conflicts in the Middle East, and trade tensions, have exposed the social and economic vulnerabilities inherent in a globalized production system. In response, these events have sparked growing interest in transitioning to localized and regional supply chain models. Calls to “buy national” and support domestic economies highlight this trend toward regionalization. This study explores the sustainability implications of regionalizing marine aquaculture by examining the four key segments of the supply chain. These are (1) upstream inputs and resources (2) aquaculture production (3) downstream added value‐processing and (4) distribution–transportation. Potential benefits of regional production models include increased resilience to disruptions, lower transportation‐related carbon emissions, and support for local economies. However, such models may also introduce trade‐offs, including reduced production efficiency, supply and sales limitations, and implications for social, cultural, and governance structures. Our analysis reveals that the sustainability outcomes of regionalization are complex and context‐dependent. It is influenced by the specific characteristics of existing supply chains and the regional contexts in which they operate. While regionalization may offer advantages in certain contexts, it does not guarantee improved sustainability. Thus, it is crucial to critically assess the assumption that regionalization inherently leads to improved sustainability outcomes. Proactive evaluation of these dynamics is essential to develop strategies that maximize benefits while addressing potential trade‐offs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".