Assessing the Legal Implications of Alleged Fuel Blending on Indonesia's Compliance with the Paris Agreement
Bibliographic record
Abstract
This study examines the impact of PT Pertamina Niaga's alleged fuel mixing case on Indonesia's commitment to the Paris Agreement. The research analyzes how mixing higher octane fuels (RON 92) with lower octane fuels affects carbon emissions and contributes to environmental degradation. Through case study methodology comparing Indonesia's policies with successful implementations in Canada and the Philippines, the research reveals significant challenges in Indonesia's emission reduction efforts. The findings demonstrate that Indonesia faces obstacles in meeting its Nationally Determined Contribution targets due to heavy reliance on fossil fuels, inadequate investment in renewable energy infrastructure, and weak regulatory enforcement. The study highlights the ineffectiveness of the Paris Agreement's enforcement mechanisms and proposes solutions including expanding green finance policies, transitioning to clean energy, strengthening compliance mechanisms, and implementing carbon pricing strategies similar to the Carbon Border Adjustment Mechanism. This research contributes to understanding the intersection between corporate practices, environmental regulations, and international climate commitments in developing nations.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".