Influencing Factors of Shellfish Consumption at the National Level
Bibliographic record
Abstract
Shellfish is among the most widely consumed seafood products, providing essential nutritional benefits and playing a potential role in mitigating climate change. This study analyzes trends and factors influencing per capita shellfish consumption in the ten largest shellfish-producing countries from 1976 to 2022, using apparent consumption data and a fixed-effects panel model. The analysis explores the relationship between shellfish consumption and key economic and demographic factors, including per capita GDP, Engel’s coefficient, and the availability of substitute seafood products. Additionally, trade openness, population aging, and the greenhouse effect are incorporated as control variables. The findings reveal the following: (1) Global shellfish consumption has experienced moderate growth, primarily driven by China, Chile, and South Korea, whereas demand has declined in Japan, the U.S., and Spain. Consumption remained stable in New Zealand and Canada but fluctuated significantly in Peru. (2) The regression results indicate that per capita GDP and greenhouse gas emissions have a positive impact on per capita shellfish consumption, while Engel’s coefficient, the shellfish substitute index, fisheries trade openness, and an aging population are negatively correlated, with all relationships significant at the 1% level. (3) Robustness and heterogeneity results indicate that European and North American markets face saturation and stabilization challenges, while the greatest growth potential lies in developing countries, particularly in the Asia-Pacific region, including China. To promote shellfish consumption and address regional supply imbalances, the study recommends enhancing international trade, fostering economic development, and capitalizing on the low-carbon advantages of shellfish aquaculture. Furthermore, investments in product innovation and cold chain logistics are essential for improving global resource allocation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".