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Record W2510784684 · doi:10.1504/ijatm.2016.076445

Smart transportation and the economic effects of the Quebec-California caps and trade market

2016· article· en· W2510784684 on OpenAlexaffabout
Anastassios Gentzoglanis, Philippe Dumont Lefrançois

Bibliographic record

VenueInternational Journal of Automotive Technology and Management · 2016
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsConsumption (sociology)Public transportGovernment (linguistics)Baseline (sea)BusinessEconomic impact analysisEconomicsProduct (mathematics)Economic interventionismAgricultural economicsTransport engineeringEngineeringMicroeconomics

Abstract

fetched live from OpenAlex

There is an increasing trend in the demand for electric vehicles (EV) but consumer choice is very limited compared to the gamut of conventional cars. The demand for the latter is still growing despite their negative impact on the environment and government policies to incentivise the reduction of their use. This paper examines the consumption patterns of the Canadian commuters who travel by car. It estimates the demand for car transportation services for the province of Quebec and makes simulations to predict the evolution of this demand till the year 2040. The estimations of the 'baseline scenario' are made using some key variables such as car price, annual kilometres driven, and the price of substitutes such as public transit. In the first simulation, there is no government intervention to modify the consumption patterns of drivers. A second model is used to investigate the impact of the pollution permits on the demand for vehicles. The new Quebec-California caps and trade (C&T) market is analysed and its impact on the demand for cars and the environment is empirically estimated.

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 score0.894
Threshold uncertainty score0.193

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 studies0.0000.001
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 head0.002
GPT teacher head0.189
Teacher spread0.187 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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