Contribution of Protected Area Networks to Achieving Global Biodiversity Framework Targets and Sustainable Development Goals: Evidence From Coastal Shandong, China
Bibliographic record
Abstract
The protected area network (PAN) is a vital strategy for enhancing ecological integrity and connectivity. However, its contributions to the Kunming-Montreal Global Biodiversity Framework (GBF) 2030 Targets and Sustainable Development Goals (SDGs) remain understudied due to limited indicator assessments and regional case comparisons. This study applied circuit theory to construct and optimize a PAN in China's coastal region. First, resistance surfaces were mapped, connectivity corridors identified, and network key points determined. Second, the network structure was analyzed to classify source areas and corridors by importance and to identify potential conservation zones. Finally, the PAN's contributions to GBF Targets 2 and 3, SDG 15 indicators, and related synergistic benefits were quantified using coverage-based performance metrics and spatial analysis. The results show that the PAN raises protected-area coverage to 33.3%, meeting the GBF 30 × 30 target, boosts SDG 15 terrestrial protection by 50% above the regional average, and supports other SDGs. Key connectivity points align along the coastal belt, with eight regions exhibiting both pinch points and barriers, underscoring the complexity of coastal-terrestrial ecotones. This study highlights the unique role of coastal PANs in global conservation and recommends refined assessment frameworks and collaborative planning to enhance their international applicability.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".