Beyond ambition: a review of tourism climate change declaration outcomes and prospects from Baku
Bibliographic record
Abstract
As the international community gathered in Baku, Azerbaijan at the 29th United Nations climate conference (COP-29) tourism holds a new place of prominence, being included in the host Presidency strategic agenda for the first time. The series of initiatives and high-level meetings culminated in the Baku Declaration on Enhanced Climate Action in Tourism. This is the fifth such declaration by the tourism sector over the past two decades. This paper examines to what extent the tourism sector has delivered outcomes related to 38 actionable climate pledges identified in previous declarations. A visible gap exists between pledges and performance, with limited to no demonstrable progress found on 25 climate action pledges. Time is the enemy, and the tourism sector must move beyond ambition to produce results at scale to stabilize and reduce emissions and build climate resilience. The Baku Declaration, endorsed by 69 countries and 9 non-state actors, does not provide a robust programme of action to accelerate progress on past climate action pledges that have gone unfulfilled. The launch of a new UN led global partnership mechanism to catalyze climate action holds more prospect but will need to overcome barriers that led to inertia following past Declarations.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".