The Economic Role of Petrochemical Industry in Iran
Bibliographic record
Abstract
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.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".