The cost of noncooperation in international river basins
Why this work is in the frame
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Bibliographic record
Abstract
In recent years there has been a renewed interest for water supply enhancement strategies in order to deal with the exploding demand for water in some regions, particularly in Asia and Africa. Within such strategies, reservoirs, especially multipurpose ones, are expected to play a key role in enhancing water security. This renewed impetus for the traditional supply‐side approach to water management may indeed contribute to socioeconomic development and poverty reduction if the planning process considers the lessons learned from the past, which led to the recommendations by the World Commission on Dams and other relevant policy initiatives. More specifically, the issues dealing with benefit sharing within an efficient and equitable utilization of water resources are key elements toward the successful development of those river basins. Hence, there is a need for improved coordination and cooperation among water users, sectors, and riparian countries. However, few studies have explicitly tried to quantify, in monetary terms, the economic costs of noncooperation, which we believe to be important information for water managers and policy makers, especially at a time when major developments are planned. In this paper we propose a methodology to assess the economic costs of noncooperation when managing large‐scale water resources systems involving multiple reservoirs, and where the dominant uses are hydropower generation and irrigated agriculture. An analysis of the Zambezi River basin, one of the largest river basins in Africa that is likely to see major developments in the coming decades, is carried out. This valuation exercise reveals that the yearly average cost of noncooperation would reach 350 million US$/a, which is 10% of the annual benefits derived from the system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it