Analysis of the Synergy Between the Objectives of the National Policy on Solid Waste (PNRS) and the Sustainable Development Goals (SDGs) in the Focus on Construction Waste
Bibliographic record
Abstract
In light of the growing challenges related to sustainability, the construction industry stands out for its economic importance and, at the same time, for its high potential for socio-environmental impact. Despite its contribution to the Brazilian economy, the activity generates significant environmental impacts resulting from the intensive consumption of natural resources, pollutant emissions, landscape alterations, and the substantial generation of Construction and Demolition Waste (CDW), which is often improperly disposed. These factors underscore the need to redirect the sector’s practices by adopting approaches more consistent with the principles of sustainable development. The National Solid Waste Policy (PNRS), established by Federal Law No. 12.305/2010, sets guidelines and responsibilities for solid waste management in the country, including CDW. In this context, the present study has analyzed the alignment between the goals of the PNRS and those of the Sustainable Development Goals (SDGs), identifying the strength of the links between these two instruments as strong, moderate, or weak in the context of CDW. Based on this analysis, a SWOT matrix was used to map strengths, weaknesses, opportunities, and threats from the interface between the PNRS and the SDGs, resulting in the proposal of 20 strategic actions to strengthen this relationship and promote more sustainable management of CDW. Among the recommended actions are: 1. Promoting technological innovation in the construction industry by replacing conventional methods with industrialized processes. 2. Training the construction workforce to reduce CDW generation and enable on-site waste segregation. 3. Strengthening spaces for social participation in decision-making processes related to waste management. 4. Promoting both formal and non-formal environmental education to encourage behavioral changes in consumption and adoption of circular economy principles. 5. Requiring transportation companies to implement tracking systems, including generator registration, optimized routing, use of covered containers, and tools to ensure CDW traceability. These interventions aim to consolidate a more efficient and sustainable management system for construction and demolition waste, contributing to the protection of public health and the environment through waste prevention, reduction, reuse, recycling, and environmentally safe disposal of residues. The study concludes that the PNRS requires specific actions to incorporate the objectives and targets set forth by the SDGs.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".