Within the context of the Italian RETURN (Multi-risk science for resilient communities under a changing climate) project, the objective of WP 7.2 is the definition of national guidelines for the evaluation of the effectiveness of alternatives of intervention in natural risks management, by considering in detail Multi Criteria Analysis (MCA) tools. The focus is on (i) multi-hazard contexts, for which state-of-the-art and knowledge is limited, (ii) the different phases of the risk management chain, and (iii) the variety of structural and non-structural measures that can be adopted. The present contribution describes results reached so far in this direction. First, First, we propose a flowchart that illustrates the process leading to the ranking of alternative strategies through MCA. The objective of the flowchart is to highlight the operative steps required for its implementation, including: (i) the identification of intervention alternatives and their characterization in terms of spatial and temporal scale of effectiveness, potential risk reduction, and secondary impacts on interested communities, (ii) recognition of stakeholder’s objectives and their respective dimensions, (iii) definition of attributes and indicators according to which alternatives are evaluated, (iv) selection of the most appropriate MCA tool and definition of related parameters, and (v) performance of sensitivity analysis. The development of the flowchart emphasized that establishing guidelines for applying MCA to multi-hazard risk management requires two ongoing fundamental steps (i) an in-depth, generalized investigation of the types of elements exposed to the different natural hazards as well as the identification of potential direct and indirect impacts on them in case of an event; (ii) the definition of an abacus of alternatives which identifies the most promising measures that can be implemented in a given context, and characterizes them in terms of potential risk reduction or increase (with respect to different hazards), and temporal and spatial scale of effectiveness.
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Molinari et al. (2024) studied this question.
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