A Chinese model for 30 × 30: ecological redlines as other effective area-based conservation measures
Bibliographic record
Abstract
Achieving the "30 × 30" target is a core commitment of the Kunming–Montreal Global Biodiversity Framework. However, the key challenge lies in effectively managing large-scale protected areas, particularly for emerging economies facing significant development pressures. China has implemented the Ecological Protection Redline (EPRL) system, which scientifically delineates 32% of its terrestrial territory as ecological space, thereby establishing a systematic conservation network that covers 90% of terrestrial ecosystem types and 85% of key species. This approach has significantly curbed ecological degradation and promoted species recovery. This study proposes that designating approximately 12% of the EPRL area—which has high conservation value but remains outside the formal protected area system—as "other effective area-based conservation measures" (OECMs) could swiftly confer strict protection under the existing governance framework. This pathway would bridge the conservation gap with lower costs and institutional resistance. The EPRL to OECM strategy not only offers a feasible solution for China to achieve its 30 × 30 goal but also provides a replicable model of innovating governance for other countries facing similar challenges, emphasizing functional conservation outcomes over mere spatial coverage.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".