Economic valuation of benefits and costs associated with the coordinated development and management of the Zambezi river basin
Bibliographic record
Abstract
The Zambezi river basin is of utmost importance to its riparian countries in terms of energy, food production and natural resources. Even though there is no legal agreement on the sharing of Zambezi waters, an assessment of basin-wide economically efficient allocation policies will provide valuable information at a time where water managers and policy makers in the region are negotiating the establishment of a unified river basin institution, called the Zambezi Watercourse Commission (ZAMCOM). That institution would be responsible for, amongst other things, the design of allocation rules. In this study, basin-wide allocation policies are derived from a hydro-economic model that considers the largest existing and planned hydraulic infrastructure and irrigation schemes in the basin. Our results illustrate that the economic value of water varies spatially, driven primarily by large changes in elevation and on the locations of existing or proposed dams. This observation may have implications for future decisions about the siting of expansions in irrigated agriculture. For example, some of the planned irrigation schemes in upstream countries are not economically sound if the power stations that are in an advanced planning phase are implemented. This study also reveals that the economic value of the three largest storage infrastructure (Kariba, Itezhitezhi, Cahora Bassa) is around US$443 million/year.
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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 | 0.005 |
| 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 | 0.002 | 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".