Circular Economy: Barriers and Strategy to Reduce and Manage Solid Waste in the Rural Area at Jepara District, Indonesia
Bibliographic record
Abstract
The term circular economy (CE) refers to a new idea that transforms the old idea of the linear economy, which created the take-make-distribution-disposal-disposal model to take-makedistribution-consume back.When a product reaches its final stage, it will not be discarded; instead, it will be sent back in a form or quality different from the original.The 6R principles: reduce, reuse, recycle, recover, redesign, and remanufacture, are considered during the return process.Several challenges must be overcome for a circular economy to be implemented.However, no one has yet identified and analyzed the difficulties that hinder the implementation of a circular economy in Indonesia, especially in Jepara Regency.This study aims to identify and analyze the difficulties that hinder the implementation of a circular economy in waste management and make plans to overcome them.Five respondents from various stakeholder groups participated in this research, including local government, academics, and nongovernmental organizations.The questionnaire was processed using the Interpretive Structural Modeling (ISM) technique to identify the main obstacles.The results show that the critical factor inhibiting the implementation of a circular economy is the price for building a waste collection, storage, processing, and disposal (B8) system and government initiatives to decide on implementation (B2).Furthermore, based on respondents' assessments, the cost of collecting waste (B16) and the practice of disposing of hazardous waste (B1) waste that is not regulated is at the second level.Waste management requires a model that follows the circular economy principle to answer these inhibiting factors.The waste bank model is the most appropriate to apply to ensure the circular economy concept is running.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".