Sustainable Strategies and Policy for Plastic Waste Collection and Management in Germany and Canada: Lessons for Lagos State, Nigeria
Bibliographic record
Abstract
Plastic waste is a major problem in many developing countries, which needs urgent attention as the population increases. Plastic is constructed from high-density polyethylene terephthalate (PET) and low-density polyethylene (LDPE). Hence, those types of plastics are associated with the environmental problem of plastic pollution, particularly in Lagos State, Nigeria. Furthermore, as the volume of plastic waste continues to increase in Lagos State, the existing infrastructure for sustainable plastic recycling is inadequate. In this research, Lagos is taken as a case study because of its increasing population, urbanization, and industrialization. They are connected with confounding urban challenges. The research further investigates Germany and some selected provinces in Canada to give European and North American perspectives of plastic waste management systems in the context of EPR schemes. The research also discusses how the two categories of the EPR schemes were applied in Germany and Canada. In addition, the research explains the implementation of the strict ban and enforcement on plastic bags in Rwanda and the regulation of plastic bags through a levy in Ireland. The empirical part of this research follows qualitative data analyses obtained through field observation. Questionnaires are used to gather oral interviews from policymakers and other stakeholders involved. The research identifies significant parameters. It proposes EPR strategies and its associated components needed to be adopted in Lagos state, with the Nigerian knowledge. The research results may help target a high rate of plastic recovery, reuse, recycling. They may also promote the reuse and recycling economy of plastics production in Lagos State, which is a pathway in achieving a circular economy.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".