Bibliographic record
Abstract
The first climate action stocktake cycle under the Paris Agreement was completed in 2023 to assess global progress on mitigation, adaptation, and climate finance goals. State-driven action needs to be complemented by sectoral efforts, and this paper builds on the first Tourism Stocktake undertaken in 2023 that examined climate action in the tourism sector. First, an expert elicitation survey was conducted to validate and deepen the findings from the stocktaking activity. Second, an analysis of the Stocktake’s 40 metrics was undertaken against six criteria of indicator quality. The expert survey revealed a sector still in the early stages of implementing its climate response, with a consensus that 2030 tourism emission reduction targets would not be achieved. Tourism policy and investment were deemed inconsistent with climate ambitions, and current adaptation is insufficient for projected climate change, so that future impacts will restrict tourism development in some destinations. Analysis of the metrics exposed significant data gaps and a core set of 13 robust metrics to measure change over the next stocktaking cycle is identified. The paper concludes with recommendations to advance sector capabilities and collaboration to monitor progress on climate action for an updated Tourism Stocktake in 2026.
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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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".