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Record W2135501413 · doi:10.5539/mas.v9n11p101

The Economic Role of Petrochemical Industry in Iran

2015· article· en· W2135501413 on OpenAlexvenueno aff
Mansoor Maitah, Bassam Abdoljabbar

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
Fundersnot available
KeywordsPetrochemicalRevenueInflation (cosmology)Gross domestic productExchange rateProduct (mathematics)Petroleum industryEconomicsBusinessMonetary economicsEngineeringWaste managementEconomic growthFinanceMathematics

Abstract

fetched live from OpenAlex

<p>Iran’s economy is characterized by over dependence on the oil sector. Iran has been gradually growing into a centre for production of petrochemicals in the world. Petrochemical industry is one of the significant components of oil industry and is one of the principal industries in Iran which has an influential role in Iran’s economy. Although it is widely acknowledged that exports, particularly through manufactured components, play an important role as a potential source of economic growth. Hence, the aim of this research is to analysis the impact of petrochemical products export revenue on economic growth. Therefore the main objective of this research is the study of export-led growth hypothesis (ELG hypothesis) of Iran’s economy in the petrochemical industry by taking a time series data for the period of 1990-2010. It applies ordinary least square (OLS) method to investigate the relationship between gross domestic product, exports of petrochemical products, real exchange rate and inflation. The results of the study show that there is a positive relationship between export of petrochemical products and economic growth which validate export-led growth hypothesis in petrochemical industry while negative impact of inflation and real exchnage rate is observed.</p>

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.040
GPT teacher head0.222
Teacher spread0.181 · 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 designTheoretical or conceptual
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

Citations4
Published2015
Admission routes1
Has abstractyes

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