Determinants of CO2 emissions generated by air travel vary across reasons for the trip
Bibliographic record
Abstract
Abstract This study estimates factors of importance for the carbon dioxide equivalent (CO2e) emissions generated by travellers flying for different reasons based on representative Austrian micro data for the period 2014–2016. The annual average number of flights taken by adults vary between 0.1 (visiting friends) and 0.8 (going on holiday), and the amount of CO2e emissions generated by each return flight is approximately 1100 kg. This leads to a total of 6 million tonnes CO2e emissions per year. Results of the Pseudo Poisson Maximum Likelihood estimations reveal that the amount of CO2e emissions created is related to socio-demographic, locational and seasonal factors, although mainly for the largest group of travellers: the holiday makers. In this group, individuals with university degrees, young persons (16–24 years) and capital city residents generate the largest amounts of emissions, as opposed to persons with children and large households. Residents of the capital region each quarter cause 64 kg more CO2e emissions than inhabitants of rural areas, persons with university degrees create 74 kg larger emissions than those without degrees and young adults instigate 90 kg more emissions than middle-aged persons. CO2e emissions of holiday flights are highest in the first quarter of the year. The importance of education is also pronounced for CO2e emissions related to business travel, as is gender.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".