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Comparative Economic Analysis of Conventional and Plug-in Battery Electric Vehicles in Canada

2022· article· en· W4313562796 on OpenAlexafffundabout
Muhammad M. Rehman, Walid G. Morsi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGasolineMiles per gallon gasoline equivalentTotal cost of ownershipElectricityBattery (electricity)Automotive engineeringInternal combustion engineElectric vehicleEconomic analysisBattery electric vehicleVolatility (finance)Environmental economicsBusinessGreen vehicleEconomicsEngineeringFinanceFuel efficiencyElectrical engineeringAgricultural economicsWaste managementAccountingPower (physics)

Abstract

fetched live from OpenAlex

Conventional vehicles typically use gasoline for their internal combustion engines (ICEs). On the other hand, plug-in battery electric vehicles (PBEVs) use electricity to charge their batteries, and hence they do not need gasoline. With the soaring gasoline prices in Canada and around the world, the interest in electric vehicles from the public and the government has increased. However, given the wide range in prices of PBEVs, the high maintenance cost of conventional vehicles and the volatility in gasoline prices, there is a need for a comparative economic analysis to address the following two main questions: (1) What should be the minimum ownership period of a PBEV to be economical than a conventional vehicle? (2) At what gasoline prices do the PBEVs become more economical than conventional vehicles? The work in this paper addresses these questions to assist customers in making the right decision when they intend to purchase a new vehicle. The results have shown that the longer the ownership period is, the PBEVs become more economical compared to conventional vehicles. The study has shown that the total ownership cost savings may reach up to $88,482 over 15 years.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.639

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.005
GPT teacher head0.185
Teacher spread0.179 · 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 designSimulation or modeling
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

Citations6
Published2022
Admission routes3
Has abstractyes

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