Benefits of Cross-Border Cooperation for Achieving Water-Energy-Land Sustainable Development Goals in the Indus Basin
Bibliographic record
Abstract
The Indus Basin, a densely irrigated area home to about 300-million people, has expected growing demands for water, energy and food in the coming decades. With no abundant surface water left in the basin and accelerating use of groundwater, long-term strategic and integrated management of water and its interlinked sectors (water-energy-land) is fundamental for the sustainable development of the region. Cooperation among riparian countries is an alternative to current situation that could help achieving water-energy-land related Sustainable Development Goals, maximizing socio-environmental benefits and minimizing costs. We show a scenario-based analysis using numerical models (The Nexus Solution Tool) where we link local issues and policies to the Sustainable Development Goals, showing magnitude and geographical location of required investments to meet SDG and the associated impacts. Finally, we discuss the barriers to cross-border cooperation and explore cases of partial cooperation, which confirms significant environmental and economic benefits.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".