Energy systems are regularly subject to major disruptions affecting economic activities, operation of infrastructure and the society as a whole. Resilience assessment comprises the pre-event oriented classical risk assessment as a central element, but it goes beyond that because it also includes and evaluates post-event strategies to improve the functioning of the system during its future operation. First, an overview of resilience definitions used across various scientific disciplines is presented, followed by an in-depth analysis of resilience assessment and quantification for energy systems. The relevant literature is classified by approach and according to four key functions of resilience: resist, restabilize, rebuild, and reconfigure. Findings show that irrespective of the research field, a resilient system always operates with an aim to minimize the potential consequences resulting from a disruptive event and to efficiently recover from a potential system performance loss.

The accurate definition of 3-D crustal structures and, in primis, the Moho depth, are the most important requirement for seismological, geophysical and geodynamic modelling in complex tectonic regions. In such areas, like the Mediterranean region, various active and passive seismic experiments are performed, locally reveal information on Moho depth, average and gradient crustal V_p velocity and average V_p/V_s velocity ratios. Until now, the most reliable information on crustal structures stems from controlled-source seismology experiments. In most parts of the Alpine region, a relatively large number of controlled-source seismology information are available though the overall coverage in the central Mediterranean area is still sparse due to high costs of such experiments. Thus, results from other seismic methodologies, such as local earthquake tomography, receiver functions and ambient noise tomography can be used to complement the controlled-source seismology information to increase coverage and thus the quality of 3-D crustal models. In this paper, we introduce a methodology to directly combine controlled-source seismology and receiver functions information relying on the strengths of each method and in relation to quantitative uncertainty estimates for all data to derive a well resolved Moho map for Italy. To obtain a homogeneous elaboration of controlled-source seismology and receiver functions results, we introduce a new classification/weighting scheme based on uncertainty assessment for receiver functions data. In order to tune the receiver functions information quality, we compare local receiver functions Moho depths and uncertainties with a recently derived well-resolved local earthquake tomography-derived Moho map and with controlled-source seismology information. We find an excellent correlation in the Moho information obtained by these three methodologies in Italy. In the final step, we interpolate the controlled-source seismology and receiver functions information to derive the map of Moho topography in Italy and surrounding regions. Our results show high-frequency undulation in the Moho topography of three different Moho interfaces, the European, the Adriatic–Ionian, and the Liguria–Corsica–Sardinia–Tyrrhenia, reflecting the complexity of geodynamical evolution.

A web-based software, called MCDA Index Tool (https://www.mcdaindex.net/), is presented in this paper. It allows developing indices and ranking alternatives, based on multiple combinations of normalization methods and aggregation functions. Given the steadily increasing importance of accounting for multiple preferences of the decision-makers and assessing the robustness of the decision recommendations, this tool is a timely instrument that can be used primarily by non-multiple criteria decision analysis (MCDA) experts to dynamically shape and evaluate their indices. The MCDA Index Tool allows the user to (i) input a dataset directly from spreadsheets with alternatives and indicators performance, (ii) build multiple indices by choosing several normalization methods and aggregation functions, and (iii) visualize and compare the indices’ scores and rankings to assess the robustness of the results. A novel perspective on uncertainty and sensitivity analysis of preference models offers operational solutions to assess the influence of different strategies to develop indices and visualize their results. A case study for the assessment of the energy security and sustainability implications of different global energy scenarios is used to illustrate the application of the MCDA Index Tool. Analysts have now access to an index development tool that supports constructive and dynamic evaluation of the stability of rankings driven by a single score while including multiple decision-makers’ and stakeholders’ preferences.