Sustainability Assessment of the Municipal Solid Waste Management in Russia Using the Decoupling Index
Bibliographic record
Abstract
The annual growth of the municipal solid waste (MSW) generated and the exhaustion of existing landfills capacity have led to the processes of reforming the waste sector in Russia. But the question remains open: what is the optimal ratio between waste management practices for building a sustainable MSW management system? The purpose of this article is to evaluate the sustainability of the MSW management system in Russia according to various scenarios of its development using the decoupling index. Based on the strategic documents, authors constructed three scenarios for the MSW industry development in Russia until 2024: scenario 1 (basic), scenario 2 (MSW utilization via recycling), scenario 3 (MSW utilization via recycling and MSW incineration at WTE plants). After that, the decoupling index for all scenarios was calculated. In general, calculations of the decoupling index for 2022-2024 showed that for all scenarios (except for scenario 3 in 2022), the industry is moving into the zone of absolute sustainability. The greatest sustainability is achieved in scenario 2 – for this scenario the absolute value of the decoupling index is maximum in 2023 and 2024, thereby confirming the role of recycling in increasing the sustainability of the MSW management system. The results can be used by decision makers when reforming the MSW management system to choose the optimal ratio between the MSW management practices and the appropriate regulatory tools.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".