Greenhouse Gas Emission Reduction Based on Social Recycling: A Case Study with Waste Picker Cooperatives in Brasília, Brazil
Bibliographic record
Abstract
Solid waste is a major contributor to climate change due to the release of greenhouse gases (GHGs) during the decomposition of waste. As a consequence, waste should be avoided, and an appropriate destination should be given to all materials that are discarded. While not the only strategy, recycling is a fundamental process in addressing this problem. In 2013, a study carried out with one waste picker cooperative in São Paulo has paved the way to assessing the impact of recycling on GHG emission reduction, by using the methodological tools of the Clean Development Mechanism of the United Nations Convention on Climate Change. The objective of our study is to evaluate the applicability of this methodology to different work environments, measuring greenhouse gas emission reductions and energy saving as a consequence of recycling. Our study involves three waste picker organizations located in the city of Brasília, Brazil. The three cooperatives have made secondary data for 2019 on their material input and output available. The following variables were considered: type and amount of solid waste collected, type of machines used, energy sources and transport routes. The data analysis verified that waste picker organizations significantly contribute to the reduction of greenhouse gas emissions and energy savings. We conclude that this methodology can be applied successfully to calculate emission reductions and energy savings from material recycling in different recycling contexts. Ultimately, this research recognizes the positive environmental and climate impact of the work of waste pickers, which needs to be recognized and remunerated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".