Strengthening climate resilient tourism sector in Nepal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tourism plays a crucial role in Nepal's gross domestic product (GDP) and employment generation. However, Nepal’s tourism industry is highly dependent on seasonality and environmental conditions, which means deviations in these factors can significantly disrupt tourism activities and services. These disruptions have both direct and indirect effects on economic activities and the livelihoods of communities reliant on tourism. Additionally, the increasing frequency and intensity of climate variables and extreme events adversely impact the health and safety of tourists and those involved in tourism, threatening the sector's sustainability. Current tourism models are also linked to carbon-intensive and polluting activities contributing to ecosystem degradation and exacerbating the climate crisis.This study employs a mixed-methods approach to gather and analyse field-based data and stakeholder opinions, providing recommendations for policy interventions aimed at enhancing climate resilience in Nepal’s tourism sector. Field visits revealed significant climate trends and the impact of disasters on livelihoods, economies, and tourism. National stakeholder consultations and interactions highlighted the multi-level effects of climate vulnerability on local tourism, including infrastructural damage, economic setbacks, and safety concerns. This underscores the urgent need for robust adaptation measures.Engaging intensively the businesses, private, academia, non-government, and government bodies is essential to fostering a climate-resilient tourism sector. Such collaboration can promote local participation and drive sustainable tourism growth in Nepal.
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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 | 0.004 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it