Challenges and Opportunities in the Operationalization of the Water-Environment-Energy-Food (WE<sup>2</sup>F) Nexus: Case Study of the Upper Niger Basin and Inner Niger Delta, West Africa
Bibliographic record
Abstract
The ever-increasing demand for water, food, and energy is putting unsustainable pressure on natural resources worldwide, often leading to environmental degradation that, in turn, affect water, food, and energy security. The recognition of the complex interlinkages between multiple sectors has led to the creation of various holistic approaches to environmental decision making such as Integrated Natural Resources Management (INRM), Integrated Water Resources Management (IWRM), Virtual Water (VW), Water Footprint (WF) and lately the Food-EnergyEnvironment-Water nexus (WE2F). All these approaches aim to increase resource use efficiency and promote sustainability by increasing the cooperation between traditionally disjoint sectors, and mainly differ by the number and relative weights of the sectors included in their framework. They also suffer from the same face and the same barriers for implementation, some of which may never be fully overcome. The paper discusses the benefits of adopting a WE2F nexus approach in the Upper Niger Basin (UNB) and the Inner Niger Delta (IND), but also the multiple difficulties associated with its practical implementation. IWRM/WE2F initiatives in the UNB/IND such as the BAMGIRE project piloted by Wetlands International and funded by the Dutch Embassy in Mali to secure livelihoods and biodiversity in a changing environment, is taken as an example of partial success in the use of a nexus approach to watershed management. It was shown there are multiple barriers to the operational implementation of the WE2F. However, while a full understanding of all interlinkage between sectors may never be possible, data collection, scientific research and model development can improve our ability to understand the complex system in which we live, and hence take better decisions
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".