Evaluation of Tourism Water Capacity in Agricultural Heritage Sites
Bibliographic record
Abstract
Agricultural heritage sites have been gaining popularity as tourism destinations. The arrival of large numbers of tourists, however, has created serious challenges to these vulnerable ecosystems. In particular, water resources are facing tremendous pressure. Thus, an assessment of tourism water footprint is suggested before promoting sustainable tourism. This paper uses the bottom-up approach to construct a framework on the tourism water footprint of agricultural heritage sites. The tourism water footprint consists of four components, namely accommodation water footprint, diet water footprint, transportation water footprint and sewage dilution water footprint. Yuanyang County, a representative of the Honghe Hani rice terraces, was selected as the study area. Field surveys including questionnaires, interviews and participant observation approaches were undertaken to study the tourism water footprint and water capacity of the heritage site. Based on the results, measures to improve the tourism water capacity have been put forward, which should provide references for making policies that aim to maintain a sustainable water system and promote tourism development without hampering the sustainability of the heritage system. The sewage dilution water footprint and the diet water footprint were top contributors to the tourism water footprint of the subject area, taking up 38.33% and 36.15% of the tourism water footprint, respectively, followed by the transportation water footprint (21.47%). The accommodation water footprint had the smallest proportion (4.05%). The tourism water capacity of the heritage site was 14,500 tourists per day. The water pressure index was 97%, indicating that the water footprint was still within the water capacity, but there is a danger that the water footprint may soon exceed the water capacity. As a consequence, we suggest that macro and micro approaches, including appropriate technologies, awareness enhancement and diversified tourism product development throughout the whole year that can alleviate the water pressure at critical times, could be taken to optimize the water management of the heritage sites.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".