A turizmus 1995 és 2021 közötti karbonhatékonyságának vizsgálata hazánkban az egyes üvegházhatású gázok szerinti bontásban = Evaluation of the carbon-efficiency in tourism forthe different greenhouse gasesbetween 1995 and 2021 in Hungary
Bibliographic record
Abstract
Jelen kutatásban elemeztük az I nemzetgazdasági ág (Szálláshely-szolgáltatás, vendéglátás) karbonhatékonyságának magyarországi alakulását, levetítve azt az egyes üvegházhatású gázokra, amelyeket a Kiotói Jegyzőkönyv nevesít. A karbonhatékonyság számításához az Eurostat Database-adatait használtuk. Az egységnyi bruttó hozzáadott értékre fordítandó kibocsátás mennyisége (karbonhatékonyság) jelentősen csökkent a vizsgált időszakban (1995–2021) a szén-dioxid esetében, szignifikáns javulás figyelhető meg a metánkibocsátásra vonatkozóan is. A nitrózus gázok, a perfluorkarbonok, a kén-hexafluorid és a nitrogén-trifluorid nem mutattak szignifikáns változást, míg a fluorozott szénhidrogének kibocsátását növekvő tendencia jellemezte. Ezek a gázok mint hűtőközegek váltották fel a korábbi, ózonkárosító anyagokat a Montreali Egyezményt követően. | This research analysed the tendencies of carbon efficiency of the section I (Accommodation and food service activities) in Hungary, broken down to the individual greenhouse gases listed in the Kyoto Protocol. Eurostat Database data have been used to calculate the carbon efficiency. The emitted quantity of greenhouse gas per unit of gross value added (carbon efficiency) has decreased significantly over the period (1995–2021) for carbon dioxide, with a significant improvement for methane emissions. Nitrous gases, perfluorocarbons and sulphur hexafluoride, nitrogen trifluoride showed no significant change, while emissions of hydrofluorocarbons showed an increasing trend. These gases replaced the former ozone depleting substances as refrigerants after the Montreal Convention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".