Testing of a Monitoring, Reporting & Verification (MRV) Scheme for the integration of non-CO2 aviation effects into EU ETS
Notice bibliographique
Résumé
In addition to carbon dioxide, aviation affects the climate through other emissions and atmospheric processes, such as contrail formation and the impact of NOx emissions on ozone and methane. No non-CO2 policy instruments have yet been established in aviation, since non-CO2 effects are not yet fully understood and still linked with medium to high uncertainties. But with non-CO2 effects accounting for about 2/3 of the total climate impact of aviation, and with uncertainties not going to disappear in the near future, we have to learn to cope with them. Risk assessment is required to better understand the impact of uncertainties on the calculation of non-CO2 effects and thereby on the potential of setting wrong incentives. \nTo address the lack of incentivizing airlines to internalize their climate costs, this study focuses on the development and testing of a monitoring, reporting and verification (MRV) system, as a first step for the full integration of non-CO2 effects into the EU ETS. For this purpose, non-CO2 effects are integrated according to the principle of equivalent CO2 emissions (CO2e). Since several CO2e calculation methods are in principle available, the selection process involves a trade-off between the level of atmospheric uncertainties, the level of climate mitigation incentives, and the resulting effort of MRV activities. Simple CO2e factors (constant, distance- or latitudedependent) are heavily criticized and proven to be inappropriate since they further increase the focus on CO2 reduction, might create false incentives (incentive to fly higher rather than lower) and “penalize" climate-cost-efficient routings (due to the increased fuel burn). To incentivize mitigation of non-CO2 impacts, more comprehensive CO2e factors (altitude-, location- or weather dependent) are therefore needed, which all require monitoring of flight data. \nWithin this study, we test the operational feasibility of a location-dependent CO2e approach, which seems to be technically possible today. For this purpose, we use flight monitoring data from 400 intra-European flights provided by European Air Transportation Leipzig, a German cargo airline owned by Deutsche Post. To keep the MRV effort as low as possible, most monitoring and reporting (airline perspective) as well as verification steps (authority perspective) are automated via software tools that might be provided for the users (e.g. by the EC). We show some possible steps forward and formulate recommendations for integrating non-CO2 effects into the EU ETS. As next steps, we recommend policymakers to select promising CO2e approaches, to analyze the economic impact and to conduct further pilot projects.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».