Boosting Resilience in South Korea's Seaweed Industry: Status, Policies and Practice
Bibliographic record
Abstract
As the global demand for sustainable food sources rises, aquaculture has emerged as a critical solution to meet that need. It is becoming a cornerstone of global food security and economic opportunity. It provides a sustainable alternative to overexploited marine resources, particularly seaweed production systems, which are increasingly vital in ensuring resilient and sustainable coastal communities. South Korea is one of the major seaweed producing nations globally. This study investigated the status, policies and practices related to seaweed cultivation in South Korea. Between 2013 and 2023, the industry saw significant growth, from 1.13 million tons to 1.74 million tons, with the main cultivation species, including Pyropia spp., Saccharina japonica, and Undaria pinnatifida. These species constitute 97% of the total production and the industry was valued at 855.8 billion KRW ($658 million USD) in 2023. Pyropia spp. alone achieved export revenues of 1 trillion KRW ($770 million USD), underscoring the sector's growing export market globally. The country holds 2,150 seaweed farming licenses covering 90,571 hectares in 2023 and has expansion plans for 2024, adding new licenses to 2,700 hectares for Pyropia spp. aquaculture. Domestic seaweed consumption is also rising, increasing from 17.4 kg/capita in 2013 to 25.6 kg/capita in 2022. Around 16 seaweed farms and six processing companies earned Aquaculture Stewardship Council (ASC) certifications in 2024 and sustainability remains a priority, enhancing global trust in Korea’s products and a commitment to the blue economy agenda. The government's Aquaculture Disaster Insurance (ADI) program, is unique to South Korea and supports the industry by offering protection against natural disasters and diseases, fostering stability, and enabling productivity improvements. Additionally, advanced satellite technology, such as the Geostationary Ocean Colour Imager (GOCI), has also helped the growth of the industry through monitoring for oceanic risks, such as macroalgal blooms of floating brown algae Sargassum horneri, which can impede seaweed production. Further efforts by the seaweed industry to enhance sustainability, includes thereplacement of traditional plastic buoys with eco-friendly alternatives. Research into advancing alternative cultivation practices (e.g., on-shore cultivation) in response to increasing seawater temperatures that may hamper seaweed cultivation in the marine environment in the future, are also on-going thus safeguarding the long-term success of South Korea’s seaweed industry.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".