Environmental governance in the selective collection of urban solid waste in the municipality of Fortaleza-CE
Bibliographic record
Abstract
Excessive generation and environmentally inadequate final disposal of solid waste constitute two of the main environmental and public health challenges of modern society. In Brazil, to address these issues, in 2010, the National Solid Waste Policy (PNRS) was published, through Law No. 12.305/2010. The City of Fortaleza, Ceará, to meet PNRS requirements, implemented equipment aimed at selective collection that did not achieve the expected success. To shed light on the issue, this study used the concept of environmental governance to investigate the relationships between the social actors involved in selective collection in Fortaleza and identify the factors that may have affected its performance. From this perspective, the main objective of this study was to understand how environmental governance or lack thereof affects the efficiency of selective collection of urban solid waste in the context of the municipality of Fortaleza-CE. The research, qualitative in nature, is characterized as a case study whose analysis of environmental governance was carried out from the perspective of social actors. Data were collected through interviews with relevant social actors, divided into two groups: Official Interest Group (represented by the Public Power) and Collective Interest Group (represented by civil society); who evaluated the central elements of good governance, classified according to the World Bank, as: accountability, participation, decentralization and transparency. The results showed that the main factors that affected the environmental governance of selective collection were: (i) insufficient financial resources; (ii) market conditions for recyclable materials; (iii) the level of knowledge and environmental awareness of the population regarding selective collection; (iv) the degree of population participation; (v) charging for the waste collection service; and (vi) the absence of an information campaign for the population. The social actors pointed out actions that the City of Fortaleza needs to carry out to improve the environmental governance of selective collection in Fortaleza, such as designing mechanisms to increase social participation in selective collection programs, greater dissemination of the results obtained in the program and greater integration and coordination between City Hall bodies in order to strengthen environmental governance. Furthermore, it is important that City Hall promotes clear dissemination of information that allows citizens and society in general to compare and evaluate government actions in terms of fulfilling the commitments made to the population.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 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".