Tourism Economic Value of Bukit Pangonan Urban Forest, Lampung, Indonesia
Bibliographic record
Abstract
The quite high community activities in urban areas have caused the Bukit Pangonan Urban Forest to become an alternative tourism spot easily accessible by urban communities. There is, however, the need for natural tourism development in regional planning and conservation towards increasing the economic value of these urban forests. Therefore, this study was conducted to analyze the economic value of nature tourism in Bukit Pangonan Urban Forest and its role in environmental conservation. This involved the selection of 100 respondents as samples through incidental sampling techniques. Data was collected using interviews and analyzed quantitatively with the travel cost method and descriptively. The results showed the economic value of the Bukit Pangonan Urban Forest is Rp 4,557,944,880 per year, with visitors having a surplus of Rp 119,433 per visitor per visit and spending Rp 96,630/person/visit with the highest on transportation. The role of urban forests in environmental conservation is quite significant in preventing environmental damage, and it is possible to enhance this through the addition and diversification of tree species, protection of biodiversity, selection of appropriate tree species, development of environmentally friendly tourism facilities, providing conservation education to visitors, and policy support from the government. Moreover, it is also possible for the government to use Bukit Pangonan Urban Forest as a model to develop urban forest tourism in other areas.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".