Strategies for Transforming Coastal Governance: Addressing Barriers and Evolving Dependencies
Bibliographic record
Abstract
Coastal areas are places where land and sea meet. They offer many opportunities but also face profound social and environmental challenges, such as rising sea levels, habitat degradation, pollution, and coastal erosion, which are difficult to address due to limitations in current governance systems. Often, land and sea governance are managed separately, leading to a lack of coordination, inconsistent policies, and ineffective responses to rapidly changing coastal environments. Barriers such as unclear mandates and roles, fragmented knowledge, power dynamics, and limited stakeholder involvement are common in coastal governance. Transforming coastal governance is essential to enhance the effectiveness and legitimacy of governance systems. Yet, current practices and past experiences have shown that bringing about necessary changes for sustainable ocean and coastal governance, adaptive decision-making, or improved coordination is anything but easy.Our study explores strategies for transforming coastal governance, drawing on a novel perspective from Evolutionary Governance Theory. It highlights the role of evolving configurations of actors and institutions in shaping governance pathways in coastal areas, emphasizing the dependencies such as path, inter-, goal, and material dependencies that influence both governance outcomes and opportunities for transformation.Through examples from various coastal practices in European case studies, we illustrate how these dependencies manifest in real-world contexts and how strategies can be developed to navigate them. For example, the shift in the Western Scheldt estuary/region (the Netherlands and Belgium), from traditional flood management approaches to more integrated, nature-based solutions reflects governance transformation shaped by historical legacies, technological advances, and climate change impacts. Similarly, the Isle of Wight Biosphere (UK) illustrates the governance challenges of balancing environmental protection with social and economic priorities, especially in the wake of political changes like Brexit. Another example, from the Oslofjord (Norway), highlights the complex relationship between governance strategies and ecosystem health, revealing the need for more holistic, adaptive approaches. Lastly, in Valencia, (Spain) the case of coastal development and erosion offers insights into the barriers posed by institutional misalignment, due to overlapping or conflicting policies and regulations that fail to adapt to environmental change, while also illustrating enablers such as improved scientific data use and stakeholder engagement.Ultimately, these examples underscore the importance of adaptive, inclusive governance strategies that can evolve in response to changing environmental, social, and political dynamics. In line with Evolutionary Governance Theory, we elaborate on how strategies for transforming coastal governance can be developed through continuous reflection and adaptation, which are essential for enhancing their likelihood of success. By fostering collaboration and aligning governance systems with the complex, evolving nature of coastal challenges, we can enhance resilience and ensure more sustainable management of coastal and marine ecosystems in the face of ongoing global changes.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".