Environmental Policy in Managing E-Waste Recycling: Promoting a Clean Environment in Public Policy
Bibliographic record
Abstract
Rapid internet-based technological developments have a tremendous impact on the production of electronic devices.The management of electronic waste or e-waste has become a problem for both developed and developing countries.In global scale, e-waste is the fastest growing waste, around 3-5% per year or about three times faster than normal household waste generation.Accordingly, Indonesian government classified e-waste as hazardous and toxic waste.Yet, Indonesia has not provided an adequate and comprehensive electronic waste management system.It is caused by lack of relevant environmental policy and regulations made specifically to handle e-waste management.This research aims to investigate the environmental regulations and the methods of managing e-waste and the role of the government in managing the recycling of e-waste by considering the intertwined interests to balance a clean environment in one side and economic growth on the other.By using an empirical juridical approach, the results showed that insofar the relevant regulation to cope with the problems is Government Regulation No. 101 of 2014 concerning Management of Toxic and Hazardous Waste.There are some provisions to manage e-waste in Indonesia such as by clinical processing methods, physical processing methods, biological processing methods and hazardous waste disposal method.However, in general electronic waste in Indonesia until now does not have clarity in management, regulation, and economic potential that can be worked on.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".