Key OECM policies for Taiwan’s marine conservation
Bibliographic record
Abstract
The Other Effective Area-Based Conservation Measures (OECMs) represent a transformative shift in global conservation by recognizing diverse governance systems and conservation outcomes beyond traditional protected areas (PAs). They are crucial for achieving the 30 by 30 target under the Kunming-Montreal Global Biodiversity Framework, which aims to conserve 30% of the of the world’s land, inland water, and coastal and marine areas by 2030. As such, understanding and implementing effective OECMs is a global priority. This study presents a preliminary assessment of existing policies and key considerations in Taiwan’s OECMs, offering insights for developing more effective environmental policies. Using a mixed-methods approach, we combine analysis of protected area data from the World Database on PAs with a systematic review of relevant literature. Our findings reveal that Taiwan currently has 92 designated PAs and OECMs, with national parks accounting for 99.9% of the total marine protected area. To date, due to the fact that the regulations regarding OECM in the Ocean Conservation Act passed in 2024 were only announced and implemented on 1st of July this year, the Marine Conservation Administration has not yet announced any OECM sites in Taiwan. Therefore, we analyzed and researched external public databases for reference. The qualitative analysis identifies three primary policy themes: marine spatial planning, environmental impact assessment, and marine scientific research. While Taiwan’s recent Marine Conservation Act (2024) demonstrates progress in aligning with global conservation frameworks, our analysis suggests that OECM implementation remains at an early stage. The study highlights the need for more integrated governance approaches and identifies gaps in current conservation strategies. These findings contribute to ongoing discussions about area-based conservation measures in high-density coastal regions and provide a foundation for future research on OECM effectiveness in Taiwan.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".