The decarbonisation impasse: global tourism leaders’ views on climate change mitigation
Bibliographic record
Abstract
The Paris Climate Agreement is based on pledges from 195 countries to substantially reduce emissions of greenhouse gases (GHG) to prevent dangerous climate change. The tourism sector has likewise pledged to reduce its GHG emissions (−70% by 2050); however, current emission trends would result in a tripling in the same timeframe. In order to understand how the sector understands the decarbonisation challenge, 17 senior tourism leaders were interviewed with regard to their perspectives on the risks and opportunities associated with climate change impacts and action. Respondents affirmed that the climate is already changing, fuelled by human activities, including tourism, and that its impacts on society and tourism will be largely negative and devastating in some regions. Opinion was divided regarding mitigation timelines, the compatibility of continued tourism growth with Paris Climate Agreement decarbonisation goals, and the role of technology and governance in reducing emissions. The paper examines leaders’ perspectives in terms of “belief systems” that interpret information in decision-making, as well as forms of agnogenesis; this is, the fabrication of uncertainty to justify non-action. Belief systems and agnogenesis are thought to represent important barriers to progress on the decarbonisation of tourism, as they are for the global low-carbon transition.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".