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Record W2140281966 · doi:10.5267/j.msl.2012.06.006

Assessing the effects of removing of energy subsidies on urban passenger transportation within the city of Tehran based on a system dynamics approach

2012· article· en· W2140281966 on OpenAlexvenueno aff
Saeed Mirzamohammadi, Farid Ghaderi, Mohammad Jadidi Ardakani

Bibliographic record

VenueManagement Science Letters · 2012
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
Fundersnot available
KeywordsSubsidyEnergy (signal processing)Transport engineeringBusinessSystem dynamicsEnvironmental economicsDynamics (music)Passenger transportComputer scienceEconomicsPsychologyEngineeringStatisticsMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Cheap subsidized fuel supplied to transportation section has the most impact on traffic jam and air pollution in the Iranian megacities, especially Tehran. The 5th Five-Year Development Plan of the country aims at elimination of energy subsidies. Accordingly, this study examines the effects of the increase in the price of energy carriers in two different scenarios on megacities’ traffic and the corresponding variables. A system dynamics model is first designed to identify the most effective variables on traffic and urban transportation. The model considers an increase in the price of energy carriers based on the world price trends, until 2014 (the final year of the Subsidy Targeting Program), and evaluates the its impact on the relevant variables. The results reveal a short time reduction in transportation traffic volume. However, due to incremental trends in demand and production of vehicles, it returns to its first state.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.062
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2012
Admission routes1
Has abstractyes

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