Mitigating Tannery Pollution in Sub-Saharan Africa and South Asia
Bibliographic record
Abstract
The global leather market is worth more than $270 billion annually, and provides an important and accessible source of manufacturing exports for countries in the Global South. Leather is the source for a range of apparel items, including handbags, belts, shoes, wallets, gloves, and various other products, such as furniture, car seats, and luggage. Behind all leather goods is the tannery industry, with much of the raw materials processing located in the Global South (Lund-Thomsen, 2009, Journal of Business Ethics, vol. 90, p. 57). Unlike most synthetic fibers, which are derived from plastics and associated with the petrochemical industry, leather has the potential for a comparatively lighter footprint because it is based on natural and renewable materials not associated with the carbon emissions of fossil fuels. However, leather has suffered from various concerns, including animal rights and toxic effluents. It is ranked as the fourth most dangerous global industry to human health, with many tanneries in the Global South lacking basic protection for the workers and leaching toxic chromium into rivers (Green Cross and Pure Earth, 2016, World’s worst pollution problems: The toxics beneath our feet). This article explores the prospects for reducing the environmental footprint of tanneries in the Global South, focusing on the Sustainable Manufacturing and Environmental Pollution (SMEP) program, a series of projects in South Asia (SA) and Sub-Saharan Africa (SSA) that explore ways to reduce manufacturing pollution. The article lays out a series of technical and managerial interventions that would vastly reduce the negative impacts on human health and the natural environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".