Influences of Climate Change on Tourism Development in Small Pacific Island States
Bibliographic record
Abstract
Tourism-related carbon dioxide (CO2) emissions are a central driver of anthropogenic climate change. At the same time, climate change has both direct and indirect impacts on tourism, varying from damages of tourist assets due to extreme weather events, to losses of biodiversity. Small island developing states (SIDS) heavily depend on international tourism as a source of revenue and income. Therefore, much could be gained by assessing the vulnerability of the SIDS tourism sector and by identifying measures that may assist these islands in their sustainable adaptation efforts. Against this background, this interdisciplinary paper provides a review of tourism development and the implications of its emissions on the global climate, linked with observed and projected influences of climate change in the Pacific region, to explain the growing vulnerability of the overall sector, with a particular focus on SIDS tourism. A description of the effects of COVID-19 on international tourism and its consequences for SIDS complement the analysis. Case studies of two Pacific islands present some evidence of current climate impacts, underscoring the multiple risks small island nations and their tourism sectors face. The paper concludes by stating that some measures may be prioritized by decision-makers, so as to increase the resilience of a transforming tourism sector in SIDS.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".