How to integrate wetlands in urban planning to achieve greater resilience? The case of Douala IV urban municipality (Cameroon)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Integrating wetlands into urban planning is a critical challenge for sustainable and resilient cities worldwide. This study examines the Douala IV urban municipality in Cameroon, where wetlands face intense pressures from population growth, industrial expansion, and unregulated urbanization. Using a mixed-methods approach combining GIS-based spatial analysis, field observations, and 27 semi-structured interviews with key stakeholders, we quantified the extent and rate of wetland loss. Our results indicate that mangrove areas decreased from 1591 ha in 1990 to 541 ha in 2024, corresponding to an annual loss rate of 2.40 % between 1990 and 2012 and 2.32 % between 2012 and 2024, reflecting persistent degradation despite the adoption of a municipal land-use plan in 2012. This rapid decline amplifies the socio-environmental vulnerability of local populations, compromising natural flood mitigation, water purification and groundwater recharge. Stakeholder interviews reveal that governance inefficiencies, overlapping institutional roles, and weak enforcement of environmental regulations contribute significantly to wetland encroachment. Our findings highlight the urgent need for targeted interventions, including zoning revisions, and participatory planning, to integrate wetlands into urban planning. By linking wetland degradation with urban vulnerability, this study provides evidence-based insights for policymakers and urban managers seeking to strengthen socio-ecological resilience and reduce urban population vulnerability.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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