Status of Production, Consumption, and End-of-Life Waste Management of Plastic and Plastic Products in Nigeria: Prospects for Circular Plastics Economy
Bibliographic record
Abstract
This study is motivated by the need to understand and proffer sustainable circular economy solutions to the persistent challenges associated with plastic waste management in Nigeria. Despite the emerging awareness and increased number of studies conducted on plastic and plastic products in Nigeria, the challenges and opportunities associated with their production, consumption, and post-consumption management are still poorly understood. Besides the large quantity of plastic and plastic products produced locally in Nigeria, a substantial quantity of these products in various forms and polymer types also comes into the country through importation. This results in a high consumption rate and, by extension, a huge volume of plastic waste generated daily exceeding the capacity and ability of the authorities to manage. In this work, we reviewed the available literature to analyze the status of the production, importation, consumption, and post-consumption management of plastic waste in Nigeria. It is estimated that out of 27.3 million tonnes of municipal solid waste currently generated in Nigeria’s urban cities per annum about 11.2 million tonnes is collected, of which about 1.1 million tonnes is plastics. It is further projected that by 2040, about 40.5 million tonnes of municipal solid waste will be generated, and only about 1.6 million tonnes of the plastic component will be collected for disposal. Based on the outcome of the analysis, the current study further suggested how adopting circular economy principles can help mitigate the impact of plastic waste on Nigerian society.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".