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Record W2142214305 · doi:10.1002/atr.1249

A methodological framework for the economic evaluation of CO<sub>2</sub> emissions from transport

2013· article· en· W2142214305 on OpenAlexvenueno aff
Silvio Nocera, Federico Cavallaro

Bibliographic record

VenueJournal of Advanced Transportation · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsExternalityLiberalizationRealisationEconomicsGlobal warmingEnvironmental economicsClimate changeMicroeconomics

Abstract

fetched live from OpenAlex

SUMMARY As the main cause of the global warming, CO 2 emissions are a relevant externality in the transport sector. However, feasibility assessments do not always take these effects into adequate account, because a number of scientific and economic uncertainties make it difficult to determine a reliable estimate for a unitary CO 2 cost. This paper first analyses the methods generally used to determine the cost of CO 2 emissions, showing that market‐based prices are not always suitable for this aim. Avoidance and damage cost methods are then thoroughly discussed, evaluating their pros and cons, including an extensive review of previous studies of methods for comparing costs. To determine the most reliable values, a method based on both avoidance and damage costs is proposed here. This method is then applied to the case study of the Brenner Base Tunnel, comparing the outcomes of three different scenarios: ‘minimum’ suggests the maintenance of the ‘do‐nothing’ option (no tunnel realisation), whereas ‘trend’ and ‘consensus’ both imply the construction of the tunnel with different political choices, namely, a complete market liberalisation in trend and sustainable interventions in consensus. Results up to 2035 reveal that, in comparison with the do‐nothing option, the enlightened transport policy shown in consensus could bring about a CO 2 economic saving of up to around €331m for the community, whereas a simple liberalisation (trend) increases the costs derived from global warming by about €228m. Copyright © 2013 John Wiley &amp; Sons, Ltd.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.237
GPT teacher head0.367
Teacher spread0.130 · 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 designTheoretical or conceptual
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

Citations41
Published2013
Admission routes1
Has abstractyes

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