The Dynamic Interplay between International Crude Imports and Exports and Domestic Production of Indonesia
Bibliographic record
Abstract
Abstract This study investigates the factors influencing oil refinery input in Indonesia between 2013 and 2023, with a focus on crude oil imports, domestic oil production, and exports. Using secondary data from the government, a multiple linear regression analysis was employed to assess the impact of these variables on refinery input. The findings indicate that crude oil imports and domestic production have a significant positive effect on refinery input, while exports exhibit a slight negative relationship. The results highlight the importance of a balanced approach between import policies and domestic production to ensure stable refinery operations. A key finding from the regression analysis shows that for every unit increase in imports, oil refinery input increases by 0.44 units, and for each unit increase in domestic production, input rises by 0.36 units. Conversely, a unit increase in exports reduces refinery input by 0.10 units. Goodness-of-fit statistics, such as an R-squared value of 0.797 and an adjusted R-squared value of 0.709, demonstrate the model’s robustness. These findings provide valuable insights for policymakers and industry stakeholders aiming to optimize refinery operations and ensure energy security in Indonesia’s evolving energy landscape.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".