A Continental‐Scale Hydroeconomic Model for Integrating Water‐Energy‐Land Nexus Solutions
Bibliographic record
Abstract
Abstract This study presents the development of a new bottom‐up large‐scale hydroeconomic model, Extended Continental‐scale Hydroeconomic Optimization (ECHO), that works at a subbasin scale over a continent. The strength of ECHO stems from the integration of a detailed representation of local hydrological and technological constraints with regional and global policies, while accounting for the feedbacks between water, energy, and agricultural sectors. In this study, ECHO has been applied over Africa as a case study with the aim of demonstrating the benefits of this integrated hydroeconomic modeling framework. Results of this framework are overall consistent with previous findings evaluating the cost of water supply and adaptation to global changes in Africa. Moreover, results provide critical assessments of future investment needs in both supply‐ and demand‐side water management options, economic implications of contrasting future socioeconomic and climate change scenarios, and the potential trade‐offs among economic and environmental objectives. Overall, this study demonstrates the capacity of ECHO to address challenging research questions examining the sustainability of water supply and the impacts of water management on energy and food sectors and vice versa. As such, we propose ECHO as useful tool for water‐related scenario analysis and management options evaluation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".