The Private Sector as a Partner for SDG 6-Related Issues in Megacities: Opportunities and Challenges in Rio de Janeiro, Brazil
Bibliographic record
Abstract
This article reviews recent studies that address water sustainable management opportunities and challenges in megacities around the world, with an emphasis on the case of Rio de Janeiro Metropolitan Region, one of the two megacities in Brazil. With reference to recent debates on water, megacities, and the climate crisis, as well as UN Water and Global Report Initiative documents, we focused on the implementation of the 2030 Agenda Sustainable Development Goal 6: Clean Water and Sanitation for All. The new Brazilian sanitation legal framework regulates public–private partnerships. In this context, the manuscript discusses the main question concerning water, sanitation, and hygiene that arises in the Brazilian case study: is universality possible in profit-oriented models? Through the current technical and academic literature consulted, the paper compares initiatives involving multiple stakeholder governance models that depend on private resources to implement universal access to drinking water, sanitation, and water-related extreme event controls, pointing out alternatives that can help to achieve the targets of SDG. Validation by key informants supports the synthesis of the reviewed documents, and the findings illustrate that concerted public efforts together with market mechanisms can help to overcome challenges and surpass the profit-oriented logics of private companies to achieve access to healthy and safe water, adequate sanitation, and improved hygiene, especially for vulnerable populations. This finding has transferability to other megacities in emerging countries that are facing public–private partnership debates on the provision of clean water and sanitation for all.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".