Integrating Insights from Social-Ecological Interactions into Sustainable Land Use Change Scenarios for Zanzibar
Bibliographic record
Abstract
Small islands in the Western Indian Ocean face complex social-ecological challenges arising from climate change and anthropogenic pressures. These greatly impact the way in which people interact with their environment to meet their basic needs. Consequently, there is a need to explore social-ecological relationships and their dynamics in response to change. This project used a water-energy-food nexus lens to explore these social-ecological relationships in the two largest islands that comprise the Zanzibar archipelago, Unguja and Pemba. These insights were integrated into alternative scenario narratives to produce contextually relevant and robust models for future resource security. Key findings across the project showed land use and resource competition, deforestation, climate change and insufficient resource infrastructure caused resource insecurity. Areas further inland was found to experience a differentiated set of water-energy-food challenges currently not well represented in wider research in small islands. Spatial characteristics such as remoteness, intensity of land use and amount of natural resource capital impact the scale and strength of resource insecurity. Scenarios modelling indicated that deforestation, saltwater incursion, and a reduction in permanent water bodies was expected by the year 2030 in a Business as Usual Scenario. Three alternative scenario narratives were developed by participants, these included Adaptation, Ecosystem Management and Settlement Planning. However, the effectiveness of actions under the scenario options were predicted to differ across the islands, indicating the importance of understanding the suitability of national policies across scales. Synergies across the scenario narratives also emerged, these included integrated approaches for managing environmental change, community participation in decision-making, effective protection of forests, cultural sensitivity to settlement planning, and poverty alleviation. These synergies could be used to plan strategic action towards effectively strengthening water-energy-food security in Zanzibar.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 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 teacher head, 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".