The United States National Park System: Overview, Challenges and Policy Recommendations for China
Bibliographic record
Abstract
This article conducts a comparative analysis of national park legislation, focusing on insights from the US National Park System to inform the development of China's emerging national park framework. Against the backdrop of a global conservation movement, the US system serves as a valuable model for China, which initiated its own national park system in 2017. On October 12, 2021, China has formally established its inaugural set of national parks, comprising the Three-River-Source National Park, the Giant Panda National Park, the Northeast China Tiger and Leopard National Park, the Hainan Tropical Forests National Park, and the Wuyishan National Park. Encompassing approximately 230,000 square kilometres, these five national parks safeguard nearly 30% of China's crucial terrestrial wildlife species. As China endeavours to enact comprehensive legislation for its national parks, this research aims to contribute to the ongoing efforts by addressing key questions such as the efficacy of the US's "One National Park, One Law" model, the governance dynamics between federal, state, and local entities, and strategies for balancing conservation with diverse land uses. The analysis spans five sections, exploring the historical evolution of the US National Park System, its legal framework, challenges faced by US national parks, and policy recommendations for China. The US experience highlights the importance of establishing clear legal authorities, fostering robust public participation mechanisms, and harmonizing relationships with Indigenous communities. The findings presented in this study aspire to facilitate a nuanced understanding of national park legislation, promoting international collaboration between the US and China for the sustainable management of natural lands and the protection of global biodiversity.
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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 | 0.001 | 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) | 0.000 | 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".