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Record W2067858420 · doi:10.3141/1702-02

Transportation Market Reforms for Sustainability

2000· article· en· W2067858420 on OpenAlexaff
Todd Litman

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicTransportation Planning and Optimization
Canadian institutionsTransport Canada
Fundersnot available
KeywordsEquity (law)SustainabilityBusinessRevenueSustainable transportEconomic efficiencyTraffic congestionEnvironmental economicsEconomicsSocial equalityProductivityPublic economicsFinanceTransport engineeringMicroeconomicsEconomic growthEngineeringMarket economy

Abstract

fetched live from OpenAlex

The potential of using market-based reforms to address economic, social, and environmental problems associated with transportation is explored. Various transport-market distortions are identified and discussed, and strategies to reduce the distortions are described. Several technically feasible strategies are identified that increase consumer choice and encourage more efficient use of transportation resources. These are called “win-win transportation solutions.” Three packages of state-level market reforms are evaluated for their impacts on vehicle travel, emissions, congestion, consumer expenses, tax revenue, and equity. The proposed reforms represent “no regrets” actions that are cost-effective and justified for their direct economic benefits while also achieving social and environmental objectives. This approach is particularly important for sustainable transportation. Win-win solutions are predicted to meet Kyoto emission-reduction targets, increase economic productivity and competitiveness, provide net benefits to consumers, and increase overall equity, while helping solve common transportation problems such as traffic congestion and facility costs.

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)0.011
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.064
GPT teacher head0.410
Teacher spread0.346 · 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

Citations8
Published2000
Admission routes1
Has abstractyes

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