Plausible response of urban encroachment on ecological land to tourism growth and implications for sustainable management, a case study of Zhangjiajie, China
Bibliographic record
Abstract
Rapid urban expansion in emerging tourism-oriented cities often leads to substantial encroachment on ecological lands and tremendous environmental pressure. Tourism growth and urban encroachment control are indispensable for sustainable development, but their interaction has rarely been reported. Here, 23-years' built-up areas time series with corresponding indicators of Proportion of Land used for Tourism (PLT) are integrated, to explore this interaction and construct Urban Encroachment Probability Curve (UEPC), and to reveals their causation and cycle by Granger analysis. The results show that the probability of encroachment in the next year increases rapidly with the increase of PLT at the urban boundary. When the PLT is between 30 and 35, the probability of encroachment reaches 80%. However, the right shift of UEPC from 1995 to 2018 shows that urban encroachment has become increasingly difficult. An urban encroachment cycle (spatial planning – urban encroachment - tourism growth - population increase - land demand increase - spatial planning) takes at least four years. The monitoring of PLT on the boundary and PLT outside the built-up area has a great role in predicting the place and size of future encroachment. This objective prediction is the reliable basis for sustainable development.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".