Tourism-generated energy use characteristics and sustainability transitions
Bibliographic record
Abstract
Mountain tourism destinations are characterized as significantly impacted by their remoteness, seasonal climatic variations, and fragile ecosystems. These factors greatly influence the development, distribution, and consumption of energy sources in the tourism sector. With the increasing popularity of mountain parks and protected areas as tourism destinations, it is critical to understand the interplay between tourism development (e.g. expansion of tourism facilities), patterns of energy sources (diversity and consumption levels) in the tourism sector, and overall sustainability of resources use at the destination level. Current literature reveals a dearth of research on energy issues in mountain protected areas, which is somewhat surprising as energy and resource consumption issues are becoming more important from a climate change perspective. This paper examines contemporary energy use patterns in the Sagarmatha (Mount Everest) National Park of Nepal. A mixed methods is applied to analyze development trends and transitions in energy use. Data were collected based on questionnaire surveys of tourism facilities, complemented by semi-structured interviews. The findings indicate that energy sources used in the facilities have gone through a significant change, from a firewood-dependent tourism center to increasing use of alternative sources including liquified petroleum gas (LPG) and hydroelectricity. About one-third of the facilities did not use firewood. The heterogeneous geographic distribution of the facilities affects the spatial use of energy sources. Furthermore, we argue that the coordination efforts between the national park administration and local communities, the growth of tourism, the construction of hydropower plants, and the advancement of transportation are the leading causes of the changes in energy use patterns. Knowledge of the energy use characteristics and drivers influencing the energy transitions can inform future policies that promote a low-carbon economy in mountain destinations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".