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Record W1502701661

A cost-benefit analysis of tunnel investment and tolling alternatives in Antwerp

2005· article· en· W1502701661 on OpenAlexafffund
Stef Proost, Saskia van der Loo, André de Palma

Bibliographic record

VenueLirias (KU Leuven) · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of CanadaEuropean Commission
KeywordsTollExternalityRevenueOrder (exchange)BusinessRing roadTransport engineeringEconomicsFinanceEngineeringMicroeconomics
DOInot available

Abstract

fetched live from OpenAlex

A proposal has been made to build a new tunnel under the Scheldt river near the centre of Antwerp in
\norder to relieve traffic congestion on the ring road and in an existing tunnel. The new tunnel is expected
\nto cost more than €1 billion, and tolls have been suggested to help finance construction and to manage
\ndemand. This paper conducts a preliminary cost-benefit analysis of a new tunnel and three alternative
\ntolling schemes, and compares them with a do-nothing scenario and an option to toll the existing tunnel
\nwithout building a new one. The two tunnels are treated as imperfect substitutes, and a multi-year
\naccounting framework is adopted that accounts for emissions, accidents and noise externalities, road
\ndamage, revenues accruing to the national and regional governments from existing transport user charges,
\nand the salvage value of the new tunnel. With the base-case parameter values it is found that building the
\ntunnel is worthwhile with all three tolling regimes and yields a higher benefit than not building the tunnel
\nand tolling the old one. Nevertheless, the net benefit from building the tunnel differs appreciably between
\ntolling regimes, and it is sensitive to the value assumed for the marginal cost of public funds.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.069
GPT teacher head0.263
Teacher spread0.194 · 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

Citations8
Published2005
Admission routes2
Has abstractyes

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