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Deep-sea mining and its risks for social-ecological systems: Insights from simulation-based analyses

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  • Lubna Alam
  • Kumara Perumal Pradhoshini
  • Raphaelle A Flint
  • U Rashid Sumaila

Abstract

The pros and cons of deep-sea mining (DSM) is currently hotly debated. Here, we assess the environmental, economic, and social risks of DSM by comparing scenarios with and without DSM involvement. The “Without” scenario relies solely on land-based mining and circular economy solutions, while the “With” scenario incorporates DSM alongside circular strategies, highlighting the dangers of heavy DSM dependence. Through literature review and expert interviews, our study identifies key risk indicators across environmental, economic, and social dimensions, forming a comprehensive assessment framework. Through the application of qualitative data and fuzzy cognitive mapping, the analysis reveals that environmental factors are the most influential (centrality: 1.46), followed by social (1.32) and economic (1.0) factors. In the “With DSM” scenario, all indicators show increased risks, with environmental factors, particularly “coastal state vulnerability,” experiencing a 13% rise. Social risks, including “violation of law,” “participatory rights,” “lack of effective control,” and “degraded reputation,” increase by 8–11%, while economic risks, such as “contractual violations,” “lack of special provision,” “knowledge gap on economic assistance fund” and disputes among “multiple stakeholders,” see an 11% uptick. Our results suggest that the risks DSM poses to deep-sea marine ecosystems are likely too significant to justify its pursuit and advocates for circular economy solutions as viable alternatives to mitigate environmental, social, and economic risks. We recommend that policies should promote circular practices through resource recovery incentives.

Suggested Citation

  • Lubna Alam & Kumara Perumal Pradhoshini & Raphaelle A Flint & U Rashid Sumaila, 2025. "Deep-sea mining and its risks for social-ecological systems: Insights from simulation-based analyses," PLOS ONE, Public Library of Science, vol. 20(3), pages 1-23, March.
  • Handle: RePEc:plo:pone00:0320888
    DOI: 10.1371/journal.pone.0320888
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