Comparative analysis of the contribution of municipal waste management policies to GHG reductions in China
Bibliographic record
Abstract
e with waste disposal accounting for 3.2%. With rapid urbanization trends, municipal solid waste (MSW) has become a global challenge which needs to be addressed. A large fraction of MSW such as food wastes, e-waste among others still ends up with unregulated dumps or openly burned in low-income countries. As a response, China initiated the 'zero-waste' pilot program which has been running since 2019. To investigate the potential contribution of MSW management to GHG reductions, this study selected four 'zero-waste' cities in China, namely Shenzhen, Panjin, Xining and Tongling, as case studies to assess the impacts of different MSW management policies on GHG reductions from 2015 to 2019. Results demonstrated that Shenzhen city achieved progress in reducing GHGs, which decreased by more than 40% between 2015 and 2019. This study provides policy recommendations and waste management approaches and practices to optimize MSW management and reduction of GHGs.
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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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| 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".