Economic impacts of large dams on downstream brickmaking in developing countries
Bibliographic record
Abstract
Large dams have positive and negative impacts, including disrupting brickmaking on the floodplains downstream due to flow regulation and sediment reduction, affecting the supply of essential construction material, notably in developing countries. In this study, we introduce an analytical framework to assess the economywide effects of large dams on downstream brickmaking, focusing on Traditional Fired Clay Brick (TFCB). The framework includes three steps: characterizing the impacts on river flow and sediment load using river system modeling and secondary data, understanding the role of TFCB production in the economy based on survey and economic data, and quantifying the economywide impacts of changes in TFCB production using dynamic computable general equilibrium modeling. We demonstrate the functionality of the approach by conducting a case study of the impacts of the Grand Ethiopian Renaissance Dam (GERD) on the Sudanese economy due to changes in TFCB production by comparing two scenarios: “with GERD” and “no GERD.” Results show that Sudan’s accumulated (2023–2050) discounted (at 0.5% annually) Gross Domestic Product (GDP) at factor cost would decline by US$ 6 billion (−0.38%) due to a reduction in TFCB production. Consumer flexibility regarding brick types and the ability of alternative brick sources to fill the demand gap are key determinants of the impacts.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".