A review of the IPCC Sixth Assessment and implications for tourism development and sectoral climate action
Bibliographic record
Abstract
The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change represents the state of knowledge of anthropogenic disruption to the climate system, its diverse ecosystem and societal impacts, and the imperative for and challenges of mitigation and adaptation responses. It is foundational for global climate policymaking. This paper examines the place of tourism in AR6 and reviews its key findings for tourism’s future. Overall, tourism related content declined relative to previous assessments. While notable improvements in content occurred for Africa, visible knowledge gaps remain in the tourism growth regions of South America, Middle East, and South Asia. There remains limited discussion of many impacts, and very limited understanding of integrated impacts and the effectiveness of adaptation strategies at the destination scale. The contribution of tourism to global emissions was omitted, however tourism was discussed in the context of luxury emissions and just transitions. Tourism is repeatedly identified in solution space discussions, particularly for ecosystem protection, but without consideration of the future of tourism in a rapidly decarbonizing and climate disrupted economy. With only 21% of published climate change and tourism literature in the AR6 review period cited, tourism academics should elevate tourism content and engagement in future assessments.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".