Unexpected growth of an illegal water market
Bibliographic record
Abstract
Abstract Scarce and unreliable urban water supply in many countries has caused municipal users to rely on transfers from rural wells via unregulated markets. Assessments of this pervasive water re-allocation institution and its impacts on aquifers, consumer equity and affordability are lacking. We present a rigorous coupled human–natural system analysis of rural-to-urban tanker water market supply and demand in Jordan, a quintessential example of a nation relying heavily on such markets, fed by predominantly illegal water abstractions. Employing a shadow-economic approach validated using multiple data types, we estimate that unregulated water sales exceed government licences 10.7-fold, equalling 27% of the groundwater abstracted above sustainable yields. These markets supply 15% of all drinking water at high prices, account for 52% of all urban water revenue and constrain the public supply system’s ability to recover costs. We project that household reliance on tanker water will grow 2.6-fold by 2050 under population growth and climate change. Our analysis suggests that improving the efficiency and equity of public water supply is needed to ensure water security while avoiding uncontrolled groundwater depletion by growing tanker markets.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".