Moving toward a Greener China: Is China’s National Park Pilot Program a Solution?
Bibliographic record
Abstract
National parks have been adopted for over a century to enhance the protection of valued natural landscapes in countries worldwide. For decades, China has emphasized the importance of economic growth over ecological health to the detriment of its protected areas. After decades of environmental degradation, dramatic loss of biodiversity, and increasing pressure from the public to improve and protect natural landscapes, China’s central government recently proposed the establishment of a pilot national park system to address these issues. This study provides an overview of the development of selected conventional protected areas (CPAs) and the ten newly established pilot national parks (PNPs). A literature review was conducted to synthesize the significant findings from previous studies, and group workshops were conducted to integrate expert knowledge. A qualitative analysis was performed to evaluate the effectiveness of the pilot national park system. The results of this study reveal that the PNP system could be a potential solution to the two outstanding issues facing CPAs, namely the economic prioritization over social and ecological considerations that causes massive ecological degradation, and the conflicting, overlapping, and inconsistent administrative and institutional structures that result in serious inefficiencies and conflicts.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".