Electronic Waste Recycling Business: Solution, Choice, Survival
Bibliographic record
Abstract
This study aimed to investigate factors impacting the electronic waste management in Thailand and recommend guidelines to drive the electronic waste recycling business in the country. The study used the Balanced Scorecard (BSC) as a conceptual framework. The in-depth interview was carried out using the semi-structured interview with the target agencies including government agencies, local administrative organizations, establishments related to electronic waste management (Factories in categories 105 and 106) as well as community junk shops in Chatuchak District, Bangkok. The study findings revealed that in considering the volume of electronic waste generated in Thailand and the share of important basic metals and plastics as components in electrical and electronic equipment to be used as secondary raw materials, the potential value from electronic waste recycling (household electrical appliances) could reach over 9,000,000,000 baht (9,165,701,106 baht) with the increasing trend following the increased volume of electronic waste. The market of the electronic waste recycling business in Thailand had the potential to grow. Upgrading of the electronic waste management system in Thailand was required for more efficiency, in particular, the process of collection, buy-back of product waste, reuse, and increased technological potential. Advanced technology needed to be developed to extract metals from electronic waste in order to obtain more varied metals.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".