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.
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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 | 0.001 | 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".