Addressing the groundwater impacts of informal water markets – coupled human-natural systems modeling of policy options for Jordan
Bibliographic record
Abstract
Unreliable and unequal public water supply already affects around one billion urban residents around the world. In many cities, informal water markets have emerged to fill public supply gaps by delivering water via tanker trucks, depleting scarce rural groundwater sources. A quintessential example of this can be found in the highly water-scarce country of Jordan. In Jordan, intermittent public water supply and rapid urban growth have led to a surge of uncontrolled groundwater abstractions by pervasive illegal tanker water markets.Here, we use a rigorous coupled human-natural systems model to assess a range of policy options for mitigating the groundwater impacts of informal water markets in Jordan with regards to their effectiveness and impacts on household water access. The model represents spatially distributed feedbacks between Jordan’s water sector and groundwater resources in country-wide scenario simulations until 2050. We find that investments in supply augmentation have limited impact on tanker water demand, unless they are combined with a more equitable and efficient distribution of public water supply. Jordan’s current policy of closing illegal tanker wells is found to impede the access of water-stressed households to tanker deliveries. Approaches for the legalization of tanker water markets provide more efficient policy options. Policy design is shown to be decisive for safeguarding household water access. Our findings show that understanding the role of informal water markets in urban water supply can be critical for reconciling sustainable groundwater management and household water security.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".