Mengkriminalisasi Greenwashing: Menjawab Tantangan Perlindungan Konsumen di Era Keberlanjutan
Bibliographic record
Abstract
Greenwashing is a phenomenon where companies claim their products or policies are environmentally friendly when, in reality, they are not. This phenomenon has grown alongside increasing public awareness of the importance of environmental sustainability. Greenwashing not only harms consumers who are misled by these false claims but also poses a threat to the environment by obscuring corporate responsibility toward sustainability. In the context of criminal law, greenwashing can be categorized as a serious form of environmental fraud; however, many countries still lack clear regulations to address it. As sustainability becomes a central focus across various sectors, the need for stricter regulation and legal enforcement against unethical practices like greenwashing is crucial. This research uses a normative legal method with statutory, conceptual, and comparative approaches. The findings reveal that Indonesia's criminal law is insufficient in addressing greenwashing, which harms both consumers and the environment. Comprehensive legal reform is necessary. Indonesia currently lacks specific regulations that criminalize greenwashing, making it essential to revise laws such as the Criminal Code, the Consumer Protection Law, or Environmental Law. These reforms should include clear definitions, transparent evidence standards, and strict sanctions, including criminal penalties for companies found guilty. Several countries, such as France, Germany, Canada, and Australia, have taken proactive steps by tightening regulations and law enforcement. Indonesia can learn from these countries to develop a more responsive legal system. Adopting international standards and harmonizing regulations across countries is also important to address global challenges that allow multinational companies to evade responsibility....
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".