MétaCan
Menu
Back to cohort
Record W200512521 · doi:10.14512/gaia.18.1.11

Aviation and Climate Protection Flugverkehr und Klimaschutz – Ein Überblick über die Erfassung und Regulierung der Klimawirkun gen des Flugverkehrs

2009· article· en· W200512521 on OpenAlexaff
Andreas M. Fischer, R. Sausen, Dominik Brunner, J. Staehelin, U. Schumann

Bibliographic record

VenueGAIA - Ecological Perspectives for Science and Society · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsInternational Civil Aviation Organization
Fundersnot available
KeywordsAviationRadiative forcingAir traffic controlCirrusCivil aviationGreenhouse gasEnvironmental scienceMeteorologyEmissions tradingClimate changeClimatologyGeographyEngineering

Abstract

fetched live from OpenAlex

International air traffic is not yet embedded in international frameworks to reduce anthropogenic greenhouse gas emissions, apart from the EU emission trading system. This paper reviews the state of knowledge on the climate effects due to aviation. Recent findings reveal a much smaller radiative forcing of line-shaped contrails than previously estimated. The greatest uncertainties regarding aviation's climate effects revolve around changes in cirrus cloud properties. The paper addresses issues on how to include aviation in international frameworks on climate protection. Today's proposed methods to cover non-CO 2 effects from air traffic (Radiative Forcing Index and Emission Weighting Factor) are shown to include significant shortcomings that prohibit their application in policy relevant measures. More elaborate techniques are under development. Then, different approaches are presented to exemplify how emissions from international aviation can be allocated to national territories. For Switzerland an estimate based on the residence approach is explained in more detail. Finally, the paper elucidates the measures taken by the European Union to include the aviation sector into its emission trading scheme.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.284
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueGAIA - Ecological Perspectives for Science and SocietySame topicAviation Industry Analysis and TrendsFrench-language works237,207