Published November 30, 2024 | Version v3
Preprint Open

Participatory downscaling of global SSP-RCP scenarios to local fisheries social-ecological systems

  • 1. MARBEC, Univ. Montpellier, CNRS, Ifremer, IRD, Montpellier, France
  • 2. CY Cergy Paris Université, Cerema, MATRiS, Nantes, France / Laboratoire CHROME, Université de Nîmes, 30021 Nîmes, France / Université Paris-Saclay, CNRS, GEOPS, 91405 Orsay, France
  • 3. Puget Sound Institute, University of Washington, Tacoma, Washington, USA

Description

Policy makers and managers need projections of the trajectories of change in human societies and natural ecosystems to face the consequences of global change. While there are many projections at the global scale, these have seldom been downscaled to the local scale. We aimed to provide policy-relevant projections of the consequences of climate change and socio-economic orientations for the social-ecological systems of the fisheries of the French North and Mediterranean Seas. Our approach consisted of a downscaling of global scenarios of IPCC Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) for the 2100 horizon. We engaged with fisheries managers, policy makers, scientific experts, and NGOs, and co-constructed four contrasting scenarios through interdisciplinary participatory workshops. Our approach ensured strong anchoring in regional, national, and local contexts and provided references to fisheries scenarios. We then conducted a novel comparative analysis of SSP-RCP scenario narratives from different local marine social-ecological systems in the same country. Across all scenarios, 83% of the themes highlighted by the stakeholders were common between the two social-ecological systems. However, at the level of narrative elements within all themes, only 30% of the narrative elements were mentioned in both systems, reflecting the specificity and richness of these downscaled scenarios and their relevance to local management. We also integrated disruptive changes and emotional aspects by conducting a novel standardized emotion analysis using an artificial intelligence (AI) language model. Overall, these downscaled scenarios identified pathways to avoid and potential actions for transformative change, mitigation, and adaptation to global change at the local scale. Our protocol allowed us to address some criticisms of SSP-RCP scenarios by helping to bridge science-policy gaps and to focus more on the human-nature relationships.

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Appendices 5 a-d - Comparison Analysis.pdf

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Additional details

Dates

Submitted
2024-11-30