Explaining Water Pricing through a Water Security Lens
Bibliographic record
Abstract
Can water security serve as a platform for developing a long-term solution to ongoing water crises? Many regions around the world are experiencing severe water problems, including water scarcity, water-borne diseases, water-related natural hazards, and water conflicts. These issues are expected to increase and intensify in the future. Both developed and developing economies face a water supply and demand imbalance that will potentially influence their water pricing structures. Institutions and policies that govern the pricing of this natural capital remain crucial for driving food production and providing services. The complex and multifaceted issues of sustainable water management call for a standard set of tools that can capture and create desired water security scenarios. Water pricing is an important contributing factor for achieving these scenarios. In this paper, we analyze how water pricing can be used as a tool to enact the water security agenda. This paper addresses these issues from three facets: (1) Economic aspects—the multiple processes through which water is conceptualized and priced; (2) analysis of water pricing considering its effect in water consumption; and (3) arguments for assessing the potential of water pricing as a tool to appraise water security.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".