Using socioeconomic system analysis to define scientific needs: a reverse engineering method applied to the conversion of a coal-fired to a wood biomass power plant
Hendrik Davi, INRAE, URFM, Avignon
Laetitia Tuffery, Univ Montpellier, CNRS, INRAE, Institut Agro, CEE-M, Montpellier
Emmanuel Garbolino, Climpact Data Science
Bernard Prévosto, INRAE, Aix Marseille Univ., RECOVER, le Tholonet
Bruno Fady, INRAE, URFM, Avignon
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One of the greatest challenges when addressing issues in complex social-ecological systems (SES), is the need for an efficient interdisciplinary framework when large-magnitude social and ecological disturbances occur. Teams comprising of scientists from different backgrounds and disciplines are frequently called upon to propose research methods and results that can be useful for policy and decision makers. However, most of the outcomes from these pluri-disciplinary teams appear extremely difficult to implement within a bigger picture because concepts, hypotheses, methods, and results are specific to each discipline. Here, we propose a reverse-engineering (RE) method to define the scientific needs that could help policy makers and citizens to assess the impacts of socioeconomic “disruptors” on social-ecological systems. We present this method using the example of an ongoing wood biomass energy plant (Gardanne) in the French Mediterranean region. In the Mediterranean region, species diversity is high, the forest cover is ample, but difficult access and low forest productivity make any biomass policy an ecological and social disruption. Our method is based on three complementary approaches to (1) describe the social-ecosystems, (2) draw up a map of interactions between actors and the impacts on the ecosystem, and (3) identify relevant questions needed for a global analysis of the impacts and potentialities of adaptation of actors and the ecosystems to the perturbation and the connections needed between the different disciplines. Our analysis showed that knowledge gaps have to be filled to assess forest resource vulnerability and better estimate how the different resource used (solid wood, biomass, landscape) competed together. Finally, we discuss how this method could be integrated into a broader transdisciplinary work allowing a coproduction of knowledge and solutions on a SES.
forest; interdisciplinary; model; reverse-engineering; wood energy
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