Developing policy instruments to tackle abandoned, lost, or discarded fishing gear problem in Thailand
Bibliographic record
Abstract
Due to global concern over marine debris and plastic pollution, Thailand has become more conscious of abandoned, lost, or otherwise discarded fishing gear (ALDFG) issues. However, ALDFG management is not fully addressed in existing policies and roadmaps. Consequently, this research aims to examine ALDFG status, impacts, and existing international and national regulations. A systematic review (SR) was used to gather information, and a semi-structured interview with 23 key informants took place to investigate the root cause of ALDFG and explore suitable solutions to ALDFG problems in Thailand. Multi-criteria decision-making (MCDM) method was adopted to prioritize and assess proposed policy options regarding the effectiveness and impacts affecting stakeholders, including the challenges and limitations. The key finding shows that several international and regional seas conventions exist to handle the marine pollution problem. Comprehensive practice overcoming ALDFG mainly falls on non-binding bodies and agreements deterring IUU fishing explicitly prohibiting intentional discharge from fishing gear and banning the use of gear inappropriately marked. At the implementation level, developed countries such as Canada, Norway, The USA, Australia and the EU, have tackled ALDFG by relying on their national laws and having financial support from their states. The mixed combination of policy instruments used by these countries proved that no one solution fits all. In Thailand, many laws govern marine pollution from ships. However, ADLDF is not directly addressed in the existing laws and regulations, leading to ambiguity regarding the responsible agencies and a lack of ALDFG data collection. In terms of the root cause of ALDFG generated by Thai fishery, poor maintenance, severe weather, gear conflict, loss of flag buoy, sunlight and salinity of the sea, and low-quality net texture cause net damage and put a higher chance to become ALDFG. Fishermen who use dynamic gears have less chance of gear-losing events than those who use passive gears. The capacity of gear retrieval procedures by commercial fishery is better than artisanal fishery due to expertise. When fishing gear (FG) is no longer in use, it is taken to be sold at second-hand shops or reused as a henhouse. Unfortunately, some of them were burned. Based on the MCDM method, the prioritized solutions to tackle the ALDFG problem in Thailand are a voluntary program of ALDFG removal scheme and a government-subsidized program of Port Reception Facilities (PRF) for FG, and the Extended Producer Responsibility (EPR) model of FG. As a result, this study calls for relevant authorities to discuss and share responsibilities for dealing with ALDFG issues, starting with the use of FG through the period of fishing gear being ghost gear.
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".