Bibliographic record
Abstract
Sustainable Development Goal 6 aims to achieve universal access to water and sanitation services by 2030; this is expected to cost an estimated US$150 billion per year. Where will this funding come from? One possibility is private finance in the form of direct equity investment from private water companies and lending from commercial banks. Evidence suggests, however, that private investments in water and sanitation have not materialised as planned due to the sector's risk - return profile. Water and sanitation are considered ‘too risky’ by private investors and returns insufficiently rewarding. One alternative that may help to fill the water supply and sanitation (WSS) funding gap is an as yet untapped source of public finance: public banks. There are over 900 public banks in the world, with US$49 trillion in assets; they have, however, been largely underestimated as an important source of water and sanitation funding and have also been neglected by academic research and by mainstream policy organisations such as the World Bank. There is a need to better understand how public banks can be mobilised as effective funders of public water. In this article we provide a brief history of public banking practices in the water sector, review their pros and cons, and discuss the significance of the emergence of a new type of public water operator and the potential these entities offer for financing in this sector.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.002 |
| Insufficient payload (model declined to judge) | 0.357 | 0.156 |
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".