Strengthening climate resilient tourism sector in Nepal
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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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".