Challenges of tourism in a low‐carbon economy
Bibliographic record
Abstract
This article reviews the interrelationships of tourism and climate change from a mitigation perspective. Tourism is an increasingly important part of the global economy that is dependent on the annual movement of billions of travelers, often over large distances. The current contribution of the tourism sector to global climate change is reliably established at approximately 5% of CO2 emissions, though national tourism economies can be considerably more carbon‐intense. Great uncertainty remains regarding tourism's future emission trajectories. However, in all scenarios, tourism is anticipated to grow substantially and to account for an increasingly large share of global greenhouse gas emissions, particularly if other sectors manage to achieve absolute emission reductions. The emission reduction challenges facing tourism in a low‐carbon economy are analyzed and current industry, government, and consumer responses critically examined. The article ends with a discussion of the implications of business‐as‐usual emissions trajectories versus the +2°C climate policy target for future tourism development. WIREs Clim Change 2013, 4:525–538. doi: 10.1002/wcc.243 This article is categorized under: The Carbon Economy and Climate Mitigation > Decarbonizing Energy and/or Reducing Demand Climate and Development > Decoupling Emissions from Development
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".