The Impact of High Environmental Standards in Trade Clauses on Bilateral Aquatic Product Value Chain Linkages
Bibliographic record
Abstract
Aquatic product value-added trade constitutes a vital component of agricultural food security. Particularly in developing coastal nations, aquatic products serve as the backbone of the agricultural sector. However, illegal, unreported, and unregulated (IUU) fishing activities not only disrupt the global marine aquatic products value chain but also accelerate the degradation of marine ecosystems and the depletion of marine resources, posing severe challenges to sustainable fisheries and environmental governance. In 2022, the World Trade Organization reached a consensus on fisheries subsidy negotiations, while regional agreements such as the United States–Mexico–Canada Agreement (USMCA) and the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) incorporated fisheries into relevant provisions under environmental rules. This indicates that high-standard environmental trade agreements are emerging as crucial tools for cross-border fisheries governance. This study employs open economy theory and a deep text protocol database to conduct an empirical analysis of the impact of high-standard bilateral environmental provisions on the interconnections within the aquatic products value chain. Findings reveal that environmental provisions significantly strengthen these linkages by lowering market access barriers, promoting technology spillovers, and reinforcing horizontal and vertical labor division. Heterogeneity analysis further shows that the extent of these effects varies with trade provisions, political distance, and network position. These insights offer new perspectives for seafood exports and upstream–downstream coordination in aquatic products, providing policy implications for regions seeking to enhance their value chain advantages.
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.
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 | 0.015 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".