A report on the Paris Climate Change Agreement and its implications for tourism: why we will always have Paris
Bibliographic record
Abstract
Sustained international diplomatic efforts culminated in the signing of the Paris Climate Agreement by 196 countries in December 2015. This paper provides an overview of the key provisions of the agreement that are most relevant to the tourism sector: much strengthened and world-wide participation in greenhouse gas emission reduction ambitions, an enduring framework for increased ambitions over time, improved transparency in emissions reporting and a greater emphasis on climate risk management through adaptation. The declared carbon emission reduction ambitions of the tourism sector and international aviation are found to be broadly compatible with those of the Paris Agreement, however, claims of reduced emission intensity in the tourism sector since 2005 and a roadmap by which emission reduction ambitions for 2020 and 2035 might realistically be achieved both remain equivocal. The need for international tourism leadership to improve sectoral scale emission monitoring capacity to meet the increasing requirements for transparency, convene an assessment of risks from climate change and climate policy, foster greater collaboration on destination climate resilience and accelerate technological, policy and social innovation to put tourism firmly on a pathway to the low-carbon economy are all emphasized, as is the need for dialogue between tourism and tourism researchers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".