Life Cycle Assessment and Material Flow Analysis: Two Under-Utilized Tools for Informing E-Waste Management
Bibliographic record
Abstract
The unprecedented technological development and economic growth over the past two decades has resulted in streams of rapidly growing electronic waste (e-waste) around the world. As the potential source of secondary raw materials including precious and critical materials, e-waste has recently gained significant attention across the board, ranging from governments and industry, to academia and civil society organizations. This paper aims to provide a comprehensive review of the last decade of e-waste literature followed by an in-depth analysis of the application of material flow analysis (MFA) and life cycle assessment (LCA), i.e., two less commonly used strategic tools to guide the relevant stakeholders in efficient management of e-waste. Through a keyword search on two main online search databases, Scopus and Web of Science, 1835 peer-reviewed publications were selected and subjected to a bibliographic network analysis to identify and visualize major research themes across the selected literature. The selected 1835 studies were classified into ten different categories based on research area, such as environmental and human health impacts, recycling and recovery technologies, associated social aspects, etc. With this selected literature in mind, the review process revealed the two least explored research areas over the past decade: MFA and LCA with 33 and 31 studies, respectively. A further in-depth analysis was conducted for these two areas regarding their application to various systems with numerous scopes and different stages of e-waste life cycle. The study provides a detailed discussion regarding their applicability, and highlights challenges and opportunities for further research.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.029 | 0.028 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".