National Forest Parks in China: Origin, Evolution, and Sustainable Development
Bibliographic record
Abstract
The concept of National Forest Park (NFP) is mainly used in mainland China. Originating in 1982, NFP embodies a “top-down” concept and associated program launched by the Chinese government. It is aimed at promoting forest-based tourism and economic development under the premise of protecting forest resources. After 30 years of development, NFPs have made great achievements in protecting specific forest resources, promoting forest-based tourists, promoting regional economic development, and they have gained popularity worldwide. However, due to the fast pace of NFP expansion, lack of predictable planning and innovative thinking, and ineffective governance, some problems like overexploitation, scenic pollution, monotonous development patterns, and ecological degradation associated with NFP constrain its sustainable development. In order to solve these problems effectively, a holistic review of the status of NFPs in China is needed. To help meet this need, the origin, evolution, and current status of NFPs in China were analyzed. The presented research also included retrospective analyses of challenges and opportunities for NFPs sustainable development in China. Results show that from 1982 to 2015, the number of NFPs grew dramatically, and this development occurred in four phases. In addition, NFP development has been unbalanced in regional distribution. When analyzing the evolution of NFPs, the main issues to date have included failure to implement Master Plans in practice, unclear supervisory responsibilities, ambiguous classification, unbalanced distribution, destruction of natural resource and ecosystems, insufficient cultural protection, weak awareness of nature education, lack of resource statistics, monotonous planning, and weak marketing. Study findings can contribute to promoting the sustainable future development of NFPs and support the forest-based tourism industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".