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Record W655692539

THE ROAD FROM KYOTO: HOW MUCH FROM TRANSPORTATION? TRANSPORT POLICIES OF SIX IEA COUNTRIES

2001· article· en· W655692539 on OpenAlexaboutno aff
Lee Schipper

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
Fundersnot available
KeywordsCarbon dioxideRoad transportEnvironmental scienceGreenhouse gasNatural resource economicsFuel efficiencyTransport engineeringEnvironmental engineeringEngineeringEconomicsAutomotive engineering
DOInot available

Abstract

fetched live from OpenAlex

Transportation is a source of carbon dioxide emissions. The underlying factors affecting carbon dioxide emissions from travel and freight are analysed. These include technical efficiency, vehicle characteristics and capacity utilisation. The relationships between vehicle use, travel and emissions are discussed in relation to the situations in European countries, the USA, Japan, Canada and Australia. Changes in the amount people travel have been the major cause of rising emissions. Falling energy intensities of vehicles have reduced emissions. Rising incomes and fuel prices are important factors driving travel activity and subsequent emissions. Measures for reducing carbon dioxide emissions in the future are discussed with reference to technology (intensity, fuel mix) and behaviour (activity and structure). The factors hindering changes in the transport system that would reduce or restrain carbon dioxide emissions are considered. For the covering abstract see ITRD E120563.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.054
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.221
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2001
Admission routes1
Has abstractyes

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