Examination of Iran's Crude Oil Production Peak and Evaluating the Consequences: A System Dynamics Approach
Bibliographic record
Abstract
Despite considerable efforts to give diversity to world's energy supply portfolio, oil still has a significant share among energy carriers and plays a major role in economy of countries. Regarding dependency of Iran's economy on revenues of crude oil exports, investigations on the dynamics of crude oil production rate (considering the factors such as technological, economic, political, etc.) are of high importance for the country. In this paper, factors influencing the Iran's crude oil production peak are investigated by system dynamics approach. Through results obtained by the model it is shown how different factors, within causal relationships, affect the occurrence time and the volume of produced oil at its peak. The model is also used to evaluate different scenarios on oil price, geological uncertainty, production depletion, and foreign investment level in the country. Moreover, it can be used to simulate behavior of main variables in the industry under different policy options. The model predicts that the peak will occur sometime between 2035 and 2042 with various production volumes in different scenarios. The developed model can help practitioners, especially policy makers, in the oil sector to gain a systemic and comprehensive insight of influencing factors and the relationships which cause occurrence of Iran's crude oil peak. Investments in all exploration and production sectors, which depend on the oil prices, might be the most crucial variable on the future of the industry and its success to help the developing country achieve its goals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".