A diagnostic framework for integrated flood risk governance: Conceptual foundations and insights from Lagos and Accra
Bibliographic record
Abstract
Amid escalating urban flood risks driven by climate change and poorly managed urban growth, there is growing recognition of the need to strengthen and integrate flood risk governance systems. However, existing governance arrangements in many cities remain fragmented, siloed, and inadequately inclusive. This paper addresses a critical gap in the literature by proposing and applying an Integrated Flood Risk Governance framework that systematically assesses governance integration through three interrelated dimensions: institutional interaction, actor relationships, and policy mixes. Drawing on policy document analysis and in-depth interviews, the study explores the applicability of the Integrated Flood Risk Governance framework in two high-risk urban settings: Lagos, Nigeria, and Accra, Ghana. The findings reveal that although integration is emphasized in formal policies, practical implementation is hampered by highly centralized governance structures, limited stakeholder participation, and weak coordination mechanisms. In both cities, the private sector remains marginally involved, and policy coherence is often undermined by poor enforcement and funding constraints. This study demonstrates the utility of the Integrated Flood Risk Governance framework in diagnosing governance fragmentation and highlights the need for more inclusive, adaptive, and participatory approaches to flood risk governance. • Introduces the IFRG framework to assess integration in flood risk governance across key governance dimensions. • Applies IFRG to Lagos and Accra to evaluate governance performance in flood-prone urban settings. • Identifies gaps in coordination, stakeholder inclusion, and policy coherence in urban flood governance. • Shows limited community engagement and private sector involvement in both cities. • Offers a transferable tool to improve urban flood governance in climate-vulnerable contexts.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".