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<p>Marine ecosystems face increasing threats, yet legal protections often fail due to fragmented enforcement and a lack of monitoring tools. While qualitative assessments exist, systematic comparisons of policy effectiveness remain elusive. Here, we present an AI-powered legal observatory quantifying marine environmental law enforcement across 15 French-speaking African coastal states. Analyzing seven key prohibitions, including plastic bags, bottom trawling, and oil discharges, reveals stark disparities. Although 92% of states ban plastic bags, only 38% impose penalties. Coastal sand extraction is restricted in just 40% of these countries, with significant gaps in operational sanctions and monitoring. By integrating empirical legal indicators with machine learning, we demonstrate how AI automates cross-country comparisons, identifies enforcement weaknesses, and tracks policy evolution over time. This approach provides a replicable framework for dynamic, data-driven marine governance, essential for tackling transboundary challenges like climate change and biodiversity erosion.</p>

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Leveraging AI to objectively analyze legal frameworks protecting the marine environment: a focus on enforced bans

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月29日

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  • Marie Bonnin,
  • Youssef Al Mouatamid,
  • Margot Perdereau,
  • Chaymae Kaouri,
  • Youness Taissir,
  • Sebastien Hervé,
  • Adrien Comte,
  • Sophie Lanco,
  • Jihad Zahir

摘要

Marine ecosystems face increasing threats, yet legal protections often fail due to fragmented enforcement and a lack of monitoring tools. While qualitative assessments exist, systematic comparisons of policy effectiveness remain elusive. Here, we present an AI-powered legal observatory quantifying marine environmental law enforcement across 15 French-speaking African coastal states. Analyzing seven key prohibitions, including plastic bags, bottom trawling, and oil discharges, reveals stark disparities. Although 92% of states ban plastic bags, only 38% impose penalties. Coastal sand extraction is restricted in just 40% of these countries, with significant gaps in operational sanctions and monitoring. By integrating empirical legal indicators with machine learning, we demonstrate how AI automates cross-country comparisons, identifies enforcement weaknesses, and tracks policy evolution over time. This approach provides a replicable framework for dynamic, data-driven marine governance, essential for tackling transboundary challenges like climate change and biodiversity erosion.