Sustainable Manufacturing and Environmental Pollution Programme (SMEP): A Circular Economy Experiment in the Global South
Bibliographic record
Abstract
The circular economy (CE) is a topic of growing interest, spurred by climate change and increasing recognition of the considerable costs of energy and materials waste, that reflect increasing stress on global environmental systems. Those costs range from physical landfill expenses to effects on human and natural world health. While there are a growing number of articles about the CE, there remains a great deal of ambiguity around pathways to implement it, and even fewer practical examples. Lieder and Rashid (2016) conclude in their overarching examination of CE research that while it is broad and multidisciplinary it is also fragmented, highly granular, and “rarely touching implementation.” In this article, we review recent efforts to identify models for scaling up circular economy practices in specific sectors of Sub-Saharan Africa and South Asia economies, based on information produced by the Sustainable Manufacturing and Environmental Pollution (SMEP) program. The SMEP program has been established by the United Kingdom’s Foreign, Commonwealth & Development Office (FCDO) and is being implemented in partnership with the United Nations Conference on Trade and Development (UNCTAD). SMEP seeks to reduce pollution in manufacturing in the Global South. After a brief discussion of the CE concept, this article focuses on the innovative features of the SMEP program, its preliminary findings and lessons for the transition to circularity.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".