Evaluating Change and Innovation of Marine Governance Arrangements under the European Green Deal
Bibliographic record
Abstract
Marine ecosystems are facing substantial stress due to global environmental challenges that have escalated in magnitude and impact. The European Green Deal (EGD) is acting as a driver to change and innovation of marine governance through the development of ambitious sustainability objectives around among others marine energy, plastics, transport and biodiversity. For example, offshore wind energy is an expanding sector, decarbonization of the maritime transport has become a priority, more and more plastic pollution sources are being regulated, and marine conservation and protection will expand with more marine protected areas. Realizing such ambitious sustainability objectives requires orchestrating change and innovation within the marine governance domains. Change and innovation takes place under multiple enabling and constraining conditions that shape how multiple governance levels and economic sectors on national and transboundary spatial dimensions are coordinated.Under the PERMAGOV project[1], we developed a Multi-layered Collaborative Marine Governance (MLCMG) Model to evaluate change and innovation processes of marine governance arrangements (comprising actors/coalitions that work together to develop and implement regulation and policies) under the EGD. The EGD has led to the development and updating of legislation for e.g. marine energy, plastics, transport and biodiversity. Through this legislation, both existing and new actors are affected and required to change their daily, economic practices. The MLCMG model enables the evaluation of how actors in marine governance arrangements navigate these new regulatory developments, what the institutional barriers are that impede achieving marine sustainability objectives within the EGD, focusing specifically on the governance capabilities of various actors, collaboration between actors on multiple governance levels, and the role of e-governance to process information and make decision upon.The analysis by using the model is twofold. First, the model is used to identify how marine governance arrangements under the EGD are constrained by existing, institutional barriers and how actors lack (e-)governance capabilities to collaborate and overcome these institutional barriers. Second, the model helps to identify how the EGD is changing marine governance arrangements and thus enables governance capabilities, collaboration and use of e-governance tools. The model thus helps to reflect on how implementation of the EGD is affected by the interplay between enabling and constraining governance dynamics. This reflection offers a starting point for developing governance interventions that consider the institutional complexity as well as capability of actors and their means available to navigate this institutional complexity. [1] See https://www.permagov.eu/project. PermaGov is funded through the EU's Horizon Europe programme under grant 101086297, and through UK Research and Innovation under grants 10045993, 10062097, 101086297.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".