AIMES Member Featured Research: Carlo Giupponi

Integrated modelling of social-ecological systems for climate change adaptation

Carlo Giupponi1*, Anne-Gaelle Ausseil2, Stefano Balbi3,4, Fabio Cian1, Alexander Fekete5, Animesh K. Gain6,1, Arthur Hrast Essenfelder1,8, Javier Martínez-López3,7, Vahid Mojtahed9,1, Celia Norf5, Hélder Relvas10, Ferdinando Villa3,4

1 Department of Economics, Ca’ Foscari University of Venice, Italy

2 Manaaki Whenua Landcare Research, Wellington, New Zealand

3 BC3 – Basque Centre for Climate Change, Bilbao, Spain

4 IKERBASQUE, Basque Foundation for Science, Bilbao, Spain

5 Institute of Rescue Engineering and Civil Protection, TH Köln – University of Applied Sciences, Cologne, Germany

6 Environmental Policy and Planning (EPP) Group, Department of Urban Studies and Planning (DUSP), Massachusetts Institute of Technology (MIT), Cambridge, MA, USA

7 Soil Erosion and Conservation Research Group, CEBAS-CSIC, Spanish Research Council, Campus de Espinardo, Murcia, Spain

8 CMCC Foundation – Euro-Mediterranean Center on Climate Change and Ca’ Foscari University of Venice, Venice, Italy

9 Institute for Global Food Security, School of Biological Sciences, Queen’s University, Belfast

10 Department of Environment and Planning & CESAM, University of Aveiro, Aveiro, Portugal

Analysis of climate change risks in support of policymakers to set effective adaptation policies requires an innovative yet rigorous approach towards integrated modelling (IM) of social-ecological systems (SES). Despite continuous advances, IM still faces various challenges that span through both unresolved methodological issues as well as data requirements. On the methodological side, significant improvements have been made for better understanding the dynamics of complex social and ecological systems, but still, the literature and proposed solutions are fragmented. This paper explores available modelling approaches suitable for long-term analysis of SES for supporting climate change adaptation (CCA). It proposes their classification into seven groups, identifies their main strengths and limitations, and lists current data sources of greatest interest. Upon that synthesis, the paper identifies directions for orienting the development of innovative IM, for improved analysis and management of socio-economic systems, thus providing better foundations for effective CCA.

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