E-Wastes: Bridging the Knowledge Gaps in Global Production Budgets, Composition, Recycling and Sustainability Implications
Bibliographic record
Abstract
Rapid urbanization, advancements in science and technology, and the increase in tech-savviness of consumers have led to an exponential production of a variety of electronic equipment. The global annual growth rate of e-waste volume exceeds the growth rate of the human population. Electronic waste has now become a point of concern globally (53.6 million metric tons, 2019). However, merely 17.4% of all global e-waste is properly collected and recycled. China is the largest contributor to the global production of e-waste (~19%), the second being the United States. Indeed, only 14 countries generated over 65% of global e-waste production in 2019. E-wastes contain a wide range of organic, and inorganic compounds including various metals. Emerging contaminants like plastics are amongst the fastest growing constituents of electronic waste. The current challenges include the lack of reliable data, inadequate identification and quantification of new emerging materials, limited effectiveness of current recycling technologies, need for cutting-edge detection and recycling technologies, and the lack of e-waste management policies and international collaboration. In this review, we strive to integrate the existing data on production rates at different spatial scales, composition, as well as health, economical, and environmental challenges, existing recycling technologies; explore tangible solutions; and encourage further sustainable technology and regulatory policies.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".