Impact of Climate Change on Tourism Sector in Nepal
Bibliographic record
Abstract
Climatic variations might have a negative impact on the activities and services provided by the tourism industry. However, this idea has not yet been investigated in the context of Nepal. This study examines the monetary impact that climate change has had and will continue to have on Nepal's tourism industry and makes projections on such impacts. The economic impact assessment of climate change on Nepal's tourism sector is the first study of its sort to be conducted anywhere in the nation. This research is predicated on a conceptual model that was established on the theoretical foundation, a mathematical model that generated the tourism demand function, and an economic effect analysis that was performed on the tourism sector using secondary data. The findings of the study's analysis have been broken down into three distinct sections: a trend analysis of tourism and its contribution; regression results based on the models that have been established; and a projection of GDP for the years 2020-2030 and 2100. It is abundantly clear from the trend analysis, the analysis of loss and damage from climate-induced hazards, the econometric modeling for tourism function analysis and correlation on tourism GDP, and other research that the tourism sector is extremely vulnerable to the effects of climate change and that it will have significant repercussions. The projection of tourism's contribution to the national GDP shows that this sector has the potential to make a larger contribution, provided that the conditions that could prevent this from happening do not change. On the other hand, the rising trend of climatic factors and climate-induced dangers could potentially lead to a greater overall loss and damage in the tourism industry. As a result, it will have an immediate bearing on the contribution made to the national economy.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".