Can tourism deliver its “aspirational” greenhouse gas emission reduction targets?
Bibliographic record
Abstract
This review paper examines the greenhouse gas (GHG) emission reduction targets postulated by a range of organizations seeking to reduce the consequences of global climate change and how, or if, the global tourism sector can achieve its share of those targets. It takes both existing estimates of current tourism GHG emissions and emissions projected in a business-as-usual scenario through to 2035 and contrasts them with the “aspirational” emission reduction targets proclaimed by the sector. Analysis reveals that with current high-growth emission trends in tourism, the sector could become a major global source of GHGs in the future if other economic sectors achieve significant emission reductions. Success in achieving emission reductions in tourism is found to be largely dependent on major policy and practice changes in air travel, and stated tourism emission reduction targets do not appear feasible without volumetric changes considering the limited technical emission reduction potential currently projected for the aviation sector. The opportunities and challenges associated with a shift towards a low-carbon global economy are anticipated to transform tourism globally and in all respects. Much greater consideration and dissemination of these issues is required to inform future tourism development and travel decisions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".