Towards a Small Sustainable Tourism Destination Through Zero Waste: Evidence and Development Strategy of Udjo Ecoland, Indonesia
Bibliographic record
Abstract
Recent studies are focusing on integrated sustainable tourism through zero waste concept in response to climate change and unsustainable tourism.Therefore, this study aimed to examine the application of zero waste concept at Udjo Ecoland, a small tourism destination, and propose strategic methods for sustainable tourism development.Using a qualitative case study and indepth representative data of SWOT-TOWS Matrix-AHP analysis, the result showed that further improvement in waste management was needed despite the initiation of zero waste program by Udjo Ecoland.SWOT-TOWS Matrix-AHP analysis results suggest the following development strategy priorities: (1) promoting staff participation and training, (2) developing environmental education and zero waste awareness, (3) developing internal policies and regulations, (4) developing broader collaboration and partnership, (5) market expansion and increasing sales, (6) further study on inorganic waste treatment.These strategies, specifically designed and assessed for Udjo Ecoland, are relevant and have potential benefits for other small tourism destinations with similar concepts, providing a valuable reference point for broader industry implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".