Towards an Efficient E-Waste Management Regime in Nigeria
Bibliographic record
Abstract
Electronic waste (e-waste) management has emerged as a critical environmental and public health challenge in Nigeria, driven by increasing digitalisation, high importation of used electronics and inadequate disposal practices. Despite existing regulatory efforts, the country continues to struggle with informal recycling, unsafe dismantling methods and limited enforcement of e-waste policies. This article explores the deficiencies in Nigeria’s current e-waste management framework and proposes a regulatory blueprint for a more sustainable approach. Drawing on global best practices, the study advocates for a comprehensive regulatory regime that includes Extended Producer Responsibility (EPR), stricter import controls, improved formal recycling infrastructure, and strengthened enforcement mechanisms. It also highlights the importance of public awareness campaigns, private-sector collaboration, and policy incentives to encourage responsible e-waste disposal. By transitioning to a structured and legally enforceable e-waste management system, Nigeria can mitigate environmental risks, safeguard public health, and unlock economic opportunities through resource recovery and job creation. Through doctrinal and critical analysis, this paper underscores the urgent need for a multi-stakeholder approach that aligns regulatory frameworks with sustainability principles, ensuring Nigeria’s readiness to tackle the growing e-waste crisis effectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".