Exploring water-energy-food nexus connections between climate action and regional development in the East African community
Bibliographic record
Abstract
Policy siloes between national adaptation plans (NAPs), nationally determined contributions (NDCs) and sustainable development hinder effective climate action and resource governance in East Africa. Further, rapid population growth and climate change impacts intensify demands for water, energy, and food (WEF), fuelling resource exploitation. This study employs a mixed-qualitative methodology using document analysis, and semi-structured interviews to examine the interlinkages between NAPs, NDC and regional development priorities. Results show implied connections between policy instruments, sustainable development, and climate action form the crux of WEF interlinkages. In practice, incoherence between these instruments create competition and trade-offs that increase WEF resource security. For example, the focus on food security, mostly through extensification, has created tradeoffs with water and energy security, undermining development goals. There are implicit interlinkages in policy and, to a certain extent, in practice. Although insufficient, these are foundations for a bottom-up approach to implementing integrated climate action commitments. Understanding the interconnectedness and interdependencies between sector policies, climate actions, and supranational development plans could catalyse and accelerate sustainable development while building resilience, through a multi-sectoral approach. We posit the need for a transdisciplinary, WEF approach to catalyse cooperation for development and climate action in East Africa. Ultimately, a transdisciplinary approach focused on equity, social justice, sustainability, and a just transition is required to support development agendas.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".