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Record W2811341895 · doi:10.1155/2018/6205714

Passenger Aviation and High Speed Rail: A Comparison of Emissions Profiles on Selected European Routes

2018· article· en· W2811341895 on OpenAlexvenueno aff
Matteo Prussi, Lonza Laura

Bibliographic record

VenueJournal of Advanced Transportation · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicAviation Industry Analysis and Trends
Canadian institutionsnot available
Fundersnot available
KeywordsAviationTrainComputer scienceAlgorithmEnvironmental scienceAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Air transport has been constantly growing and forecasts seem to confirm the trend; the resulting environmental impact is relevant, both at local and at global scale. In this paper, data from various datasets have been integrated to assess the environmental impact of modal substitution with high speed rail. Six intra-EU28 routes and a domestic route have been defined for comparison. The airports have been chosen considering the share of the total number of passengers on flights to/from other EU Member States. Three scenarios have been proposed in the time period 2017–2025; aircraft types, distance bands, and occupancy rate are investigated on each scenario. The comparison with HSR service has been carried out only on passenger service and not for freight. The energy consumption and the consequent emissions for the aircraft have been estimated on the base of the available data for the mix of aircraft types, performing the routes. The results indicate the advantage of the high speed trains, in terms of direct <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant="normal">C</mml:mi><mml:mi mathvariant="normal">O</mml:mi></mml:mrow><mml:mrow><mml:mn mathvariant="normal">2</mml:mn><mml:mi mathvariant="normal">e</mml:mi><mml:mi mathvariant="normal">q</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> emissions per passenger km. Compared to a neutral scenario, with an annual passenger increment of 3.5%, the HSR substitution of the 5% and the 25% of this increment allow a GHG saving of 4% and 20%, respectively. Some of the analysed routes (e.g., Frankfurt Main–Paris CDG) have interesting GHG savings but the duration of the trip today is limiting for a real substitution. Moreover, there is general agreement that the extreme weather events induced by climate change will affect the functioning of the European transport system. In this sense, transportation by the rail mode is expected to play a significant role in strengthening the EU transport system, its resilience, and its reliability, as it is less immediately subject to the impacts of severe weather conditions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.026
GPT teacher head0.262
Teacher spread0.236 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations46
Published2018
Admission routes1
Has abstractyes

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