Exploring Natural Tourism in Indonesia’s New National Capital, Nusantara: Opportunities and Challenges for Sustainable Ecotourism Development
Bibliographic record
Abstract
Indonesia's new national capital city, named Nusantara, is being developed as a sustainable forest city supported by a regional landscape with natural resources.This study aims to explore the potential of natural tourism sites in Nusantara and assess their feasibility for sustainable ecotourism development, with a focus on ecological integrity, spatial planning, environmental education, and community empowerment.Primary data were collected through direct observations at several natural tourism sites in the Nusantara region.Secondary data were gathered through a literature review of relevant documents.The research data underwent content analysis of policy documents and field observations, followed by an in-depth exploration of the potential and challenges associated with sustainable ecotourism development in Nusantara.The study identified 26 tourism site in Nusantara, which were classified into three categories: coastal, terrestrial, and mangrove ecosystems.The analysis reveals that not all sites are equally viable for ecotourism; development feasibility is shaped by factors such as natural attraction quality, accessibility, visitor demand, economic potential, demographic composition, safety, and technological readiness.Challenges in transforming natural assets into sustainable ecotourism include environmental carrying capacity, inadequate infrastructure, and limited innovation in service delivery.Realizing the vision of Nusantara as a sustainable forest city requires integrated planning, inclusive governance, and regulatory innovation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 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".