Destination net-zero: what does the international energy agency roadmap mean for tourism?
Bibliographic record
Abstract
The tourism sector has recommitted itself to be ‘climate neutral’ by 2050 through its 2021 Glasgow Declaration: A Commitment to a Decade of Tourism Climate Action. The declared ambition is consistent with the Paris Climate Agreement and net-zero emission targets; however, lacks specific actions by which such a transition might be achieved. The highly influential International Energy Agency (IEA) has produced the most detailed global roadmap to a 2050 net-zero future. This paper examines its implications for the tourism sector. Getting to net-zero is imperative to ensure the societal disruption of a + 3 °C or warmer world are avoided, but the IEA net-zero scenario would nonetheless be as transformative for tourism as the internet was. International air travel and tourism growth projections from the tourism sector are not compatible with the IEA net-zero scenario. The geography of transition risk will influence tourism patterns unevenly. The incoherence of tourism and climate policy represents an increasing vulnerability for tourism development. While any business and destination in tourism can act immediately to reduce emissions, the findings compel a critical new research agenda to determine how the assumptions of the IEA, or any net-zero scenario, could be achieved and how this will affect tourism development.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".