Achievements of Canada’s municipal solid waste management: how a policy and technology synergy can inform a new municipal solid waste management framework for Cameroon
Bibliographic record
Abstract
Cameroon’s sustained economic growth following the successful completion of its second Structural Adjustment Programme with the International Monetary Fund (IMF) has resulted in increased wealth generation accompanied by a corresponding rise in municipal solid waste. To address the growing challenges of waste management, there is a critical need for comprehensive policy reform. Drawing inspiration from international best practices, this paper examines Canada’s experience as a model for potential adaptation in Cameroon. Canada’s waste management system, guided primarily by the Canadian Environmental Protection Act (CEPA, 1991), has evolved through a combination of federal legislation, provincial initiatives, and municipal implementation. Key strategies such as the Zero Waste initiative, Extended Producer Responsibility (EPR), the adoption of Circular Economy principles and the deployment of colour-coded bin systems have collectively contributed to significant improvements in waste diversion and reduction of landfill dependency. Between 2002 and 2018, Canada’s total diverted waste increased from 6.6 million tonnes to a national diversion rate of 48%. A report by the Fraser Institute indicates that several provinces including Quebec, Ontario, Saskatchewan, British Columbia and Manitoba, achieved reductions in per capita waste generation during this period. Notably, by 2020, Prince Edward Island, British Columbia and Nova Scotia reported diversion rates of 51%, 39% and 43%, respectively. Despite continued increases in absolute volumes of landfilled waste, Canada’s efforts demonstrate a decoupling of waste generation from economic output. This paper draws on Canada’s achievements and enabling policy environment to propose a contextualized waste management framework for Cameroon, with the goal of promoting sustainable development and environmental resilience.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".