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Record W7117539757 · doi:10.1016/j.envsci.2025.104304

A diagnostic framework for integrated flood risk governance: Conceptual foundations and insights from Lagos and Accra

2025· article· en· W7117539757 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEnvironmental Science & Policy · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFlood Risk Assessment and Management
Canadian institutionsRoyal Roads University
FundersDeutscher Akademischer AustauschdienstAlexander von Humboldt-Stiftung
KeywordsFlood mythCorporate governanceRisk governanceFlood risk assessmentStakeholderStakeholder engagementPrivate sector

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.431
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.260
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it