Synergetic Water Demand and Sustainable Supply Strategies in GCC Countries: Data-Driven Recommendations
Bibliographic record
Abstract
Abstract The Gulf Cooperation Council (GCC) countries, characterized with poor water resources and high oil/gas income, rely heavily on seawater desalination and non-renewable groundwater abstraction. The need to shift to demand-side solutions and sustainable supply alternatives has been long advocated; yet this study is the first to “quantify” its impact on the water management system of Qatar – considered a study case of GCC countries. In this research, a scenario-based approach was utilized to predict the impact of water demand control and wastewater reuse (and the resulting synergies) on desalinated water consumption, groundwater resources and wastewater infrastructure. To this effect, country-specific models for Qatar were developed to forecast yearly household water demand, wastewater generation and residential construction growth, up to year 2050. The outcomes showed that tariff reforms and regulated greywater reuse would reduce the household demand for desalinated water by up to 27% and 7%, respectively. Also, intensive reuse of Treated Sewage Effluent (TSE) would reduce 40–80% of total groundwater abstraction for irrigation. Finally, adopting an integrated water strategy, with combined demand and supply management targets, creates synergies that would: (1) limit the exploitation of groundwater resources to a range close to their safe yield; and (2) delay the need for expansion of the water and wastewater infrastructure by more than a decade. Data-driven recommendations were provided accordingly.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 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".