Toward integrated dam assessment: evaluating multi-dimensional impacts of the Grand Ethiopian Renaissance Dam on Sudan
Bibliographic record
Abstract
Abstract The Grand Ethiopian Renaissance Dam (GERD) on the Nile is expected to influence many ecosystem services, such as flood regulation, hydro-electricity production, food supply, and habitat provision, among others. Understanding these impacts (positive and negative) requires a comprehensive evaluation framework. This study develops and applies an integrated simulation framework for assessing the impacts of the GERD on Sudan, focusing on the simultaneous economywide effects of riverine flood hazards, irrigation water supply, hydropower generation, and floodplain-dependent industries, namely traditional fired clay brick production. The simulation framework incorporates three models: a river infrastructure system model, a flood model, and a Computable General Equilibrium Model. Results indicate positive impacts for hydropower generation and flood control, marginal benefits for water supply to existing irrigation, and negative consequences for brick production and the construction sector. Assuming that the GERD starts its long-term operation in 2025, we find an overall positive economic impact on Sudan’s Gross Domestic Product in 2025, with an increase of up to just over 0.1%, subject to river flow conditions. Recognizing the differences in impacts across sectors and income groups, the study emphasizes the need for interventions that ameliorate negative effects. While the study captures several impacts, other effects on the environment, recession agriculture, and soil fertility require further investigation. Still, our findings underscore the importance of adopting an integrated simulation approach to dam evaluation, acknowledging the interconnected nature of water and related sectors in national economies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".