The Challenges of Environmental Law Enforcement to Implement SDGs in Indonesia
Bibliographic record
Abstract
The 1945 Constitution of the Republic of Indonesia mandates that a good and healthy environment is a human right and constitutional right for every Indonesian citizen. Therefore, the state, government, and all stakeholders must protect and manage the environment to implement sustainable development. The Indonesian environment can remain a source and support for the Indonesian people; this is in line with implementing the SDGs. The study aims to analyze environmental law enforcement efforts in Indonesia towards SDGs implementation. The research method used a normative approach, with statutory and a conceptual process. The data collect the use of secondary data with literature and statue approach. The study results showed that environmental law enforcement in Indonesia (Number 32/2009) concerning Environmental Protection and Management is preventive and repressive. Three legal instruments in environmental law enforcement are recognized administrative, civil, and criminal law. Environmental law enforcement and the implementation of SDGs in Indonesia are connected. The government implements preventive and repressive law enforcement as regulated in Law by granting expansive powers to local governments to provide protection and environmental management in their respective regions so that the environment remains sustainable. The regulation is in line with the Goals of 6, 7, 12, 13, 14, and 15 of the SDGs directly related to environmental sustainability.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| 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".