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Additional file 1. is a copy of the final version of the questionnaire.

Additional file 2. is a Word file (.docx) and is a table containing the design criteria with median, interquartile range, its interpretation and the number of panellists.

The interest in studying energy systems’ resilience is increasing due to a rising awareness of the importance of having a secure energy supply. This growing trend is a result of a series of recent disruptions, among others also affecting electricity systems. Therefore, it is of crucial importance for policymakers to determine whether their country has a resilient electricity supply. Starting from a set of 12 indicators, this paper uses data envelopment analysis (DEA) to comprehensively evaluate the electricity supply resilience of 140 countries worldwide. Two DEA models are applied: (1) the original ratio-based Charnes, Cooper, and Rhodes (CCR) model and (2) a novel hybrid framework for robust efficiency analysis incorporating linear programming and Monte Carlo simulations. Results show that the CCR model deems 31 countries as efficient and hence lacks the capability to differentiate them. Furthermore, the CCR model considers only the best weight vectors for each country, which are not necessarily representative of the overall performance of the countries. The robustness analysis explores these limitations and identifies South Korea, Singapore and Canada as the most resilient countries. Finally, country analyses are conducted, where Singapore’s and Japan’s performances and improvement potentials are discussed., Energies, 13 (7), ISSN:1996-1073

Several initiatives have been proposed nationally and internationally to collect information on accidents in the energy sector, assuming that a detailed, integral and targeted analysis of them can reveal the weak points in the energy infrastructure. The influence and relevance of the descriptors (e.g., country, energy chain, infrastructure type) of energy accidents on the outcome, such as fatalities, has so far not been performed on a comparative basis. This paper presents the first attempt to explore these relationships. Furthermore, it contributes to the resilience literature by exploring the capacity of an energy accidents dataset in storing and retrieving information on past events to tackle the forthcoming ones with more awareness of the possible impacts. This research employed a knowledge extraction method (i.e., rough set analysis) to analyse data on energy accidents for natural gas from the most authoritative information source for accidents in the energy sector, i.e., the ENergy-related Severe Accident Database (ENSAD). The main goal of this paper is to show that the rough set analysis can have a substantial contribution in understanding (i) the capacity of the structure of ENSAD to distinguish the accidents with respect to objective measures of outcome; (ii) the decision rules that clearly and simply explain the combination of attributes’ values and outcome, in this case fatality ranges; and (iii) how the rules can guide the decision-making process when there is an interest in knowing which class (i.e., low, medium, high) of fatalities an energy accident with a specific set of descriptors could have., Resilience. The 2nd International Workshop on Modelling of Physical, Economic and Social Systems for Resilience Assessment, ISBN:978-92-79-74443-3, ISBN:978-92-79-74444-0, ISBN:978-92-79-74445-7

Sustainability assessments require the management of a wide variety of information types, parameters and uncertainties. Multi criteriadecision analysis(MCDA) has been regarded as a suitable set of methods to perform sustainability evaluations as a result of its flexibility and the possibility of facilitating the dialogue between stakeholders, analysts and scientists. However, it has been reported that researchers do not usually properly define the reasons for choosing a certain MCDA method instead of another. Familiarity and affinity with a certain approach seem to be the drivers for the choice of a certain procedure. This reviewpaperpresents the performance of five MCDA methods (i.e. MAUT, AHP, PROMETHEE, ELECTRE and DRSA) in respect to ten crucial criteria that sustainability assessments tools should satisfy, among which are a life cycle perspective, thresholds and uncertainty management, software support and ease of use. The review shows that MAUT and AHP are fairly simple to understand and have good software support, but they are cognitively demanding for the decision makers, and can only embrace a weak sustainability perspective as trade-offs are the norm. Mixed information and uncertainty can be managed by all the methods, while robust results can only be obtained with MAUT. ELECTRE, PROMETHEE and DRSA are non-compensatory approaches which consent to use a strong sustainability concept, accept a variety of thresholds, but suffer from rank reversal. DRSA is less demanding in terms of preference elicitation, is very easy to understand and provides a straightforward set of decision rules expressed in the form of elementary “if … then …” conditions. Dedicated software is available for all the approaches with a medium to wide range of results capability representation. DRSA emerges as the easiest method, followed by AHP, PROMETHEE and MAUT, while ELECTRE is regarded as fairly difficult. Overall, the analysis has shown that most of the requirements are satisfied by the MCDA methods (although to different extents) with the exclusion of management of mixed data types and adoption of life cycle perspective which are covered by all the considered approaches.

After a short overview of the consistent development of renewable energies in the EU, this paper analyses the two main incentive schemes, FEED-IN and Tradable Green Certificates (TGC) mechanisms, adopted to stimulate the diffusion of "clean energy" in the European Union. In the first chapters, a brief description of these instruments is provided and considerations about positive and negative aspects with also suggestions to improve the systems are presented. The last part is dedicated to the assessment of these policies, by means of an analysis of their effectiveness, economic efficiency and expected revenues and profits for investors. In addition, the reasons why the FEED-IN system is more successful than the TGC one are illustrated, and general characteristics of a credible incentive system are presented.

Nanotechnology is currently emerging as the next industrial revolution. It enables the production of goods (i.e. nanoproducts, NPs) with enhanced functionalities, which have nonetheless caused mounting concerns about the potential implications they can have on the environment, economy and society. This thesis employs Multiple Criteria Decision Aiding (MCDA), one form of decision support, to aid the sustainable development of nanotechnology. The first original contribution of this doctoral research is the development of a framework of sustainability assessment criteria for NPs, through a three-phase procedure based on the MCDA process, including a literature review, a pilot and a main survey. It lead to a comprehensive framework of 68 criteria, ranked according to their relative importance, allocated to six main domain areas: (i) economic performance; (ii) environmental impacts; (iii) environmental risk assessment; (iv) human health risk assessment; (v) social implications; and (vi) technical performance. All the criteria are reliable and can be used in real case studies to increase the knowledge about the sustainability of NPs. The second original contribution presented in this thesis is a robust model (DRSA-based model) based on green chemistry principles implementation for the classification of synthesis processes of nanomaterials in preference-ordered classes. This tool was developed through knowledge elicitation techniques based on coconstructive MCDA with the collaboration of two experts (the decision makers) in synthesis of nanomaterials. The robustness of the ensuing model was assessed (and confirmed) by means of another model developed ad hoc (ELECTRE-based model), structured on an MCDA method implementing a stochastic multiple criteria classification strategy. The results confirm that MCDA is an effective decision support approach to foster sustainable development of nanotechnology, providing that the analysts who apply it take these considerations into account. They must ensure that (1) multidisciplinary teams are created to perform comprehensive and credible sustainability evaluations; (2) problem structuring and model construction are as important as (if not more important) than the results (i.e. decision recommendations) themselves; (3) identification of the appropriate MCDA method depends on the problem at hand and not vice-versa; and (4) the credibility of the decision recommendations is subject to the preferences of the decision-makers. If these considerations are accounted for, the possibility of advancing nanotechnology on a sustainable path is very concrete and realistic.

This paper proposes a robustness analysis based on Multiple Criteria Decision Aiding (MCDA). The ensuing model was used to assess the implementation of green chemistry principles in the synthesis of silver nanoparticles. Its recommendations were also compared to an earlier developed model for the same purpose to investigate concordance between the models and potential decision support synergies. A three-phase procedure was adopted to achieve the research objectives. Firstly, an ordinal ranking of the evaluation criteria used to characterize the implementation of green chemistry principles was identified through relative ranking analysis. Secondly, a structured selection process for an MCDA classification method was conducted, which ensued in the identification of Stochastic Multi-Criteria Acceptability Analysis (SMAA). Lastly, the agreement of the classifications by the two MCDA models and the resulting synergistic role of decision recommendations were studied. This comparison showed that the results of the two models agree between 76% and 93% of the simulation set-ups and it confirmed that different MCDA models provide a more inclusive and transparent set of recommendations. This integrative research confirmed the beneficial complementary use of MCDA methods to aid responsible development of nanosynthesis, by accounting for multiple objectives and helping communication of complex information in a comprehensive and traceable format, suitable for stakeholders and/or decision-makers with diverse backgrounds., Journal of Cleaner Production, 162, ISSN:0959-6526

To date (April 2021), medical device (MD) design approaches have failed to consider the contexts where MDs can be operationalised. Although most of the global population lives and is treated in Low- and Middle-Income Countries (LMCIs), over 80% of the MD market share is in high-resource settings, which set de facto standards that cannot be taken for granted in lower resource settings. Using a MD designed for high-resource settings in LMICs may hinder its safe and efficient operationalisation. In the literature, many criteria for frameworks to support resilient MD design were presented. However, since the available criteria (as of 2021) are far from being consensual and comprehensive, the aim of this study is to raise awareness about such challenges and to scope experts’ consensus regarding the essentiality of MD design criteria.

GrailQuest (Gamma-ray Astronomy International Laboratory for Quantum Exploration of Space-Time) is an ambitious astrophysical mission concept that uses a fleet of small satellites whose main objective is to search for a dispersion law for light propagation in vacuo. Within Quantum Gravity theories, different models for space-time quantization predict relative discrepancies of the speed of photons w.r.t. the speed of light that depend on the ratio of the photon energy to the Planck energy. This ratio is as small as 10-23 for photons in the γ- ray band (100 keV). Therefore, to detect this effect, light must propagate over enormous distances and the experiment must have extraordinary sensitivity. Gamma-Ray Bursts, occurring at cosmological distances, could be used to detect this tiny signature of space-time granularity. This can be obtained by coherently combine a huge number of small instruments distributed in space to act as a single detector of unprecedented effective area. This is the first example of high-energy distributed astronomy: a new concept of modular observatory of huge overall collecting area consisting in a fleet of small satellites in low orbits, with sub-microsecond time resolution and wide energy band (keV-MeV). The enormous number of collected photons will allow to effectively search these energy dependent delays. Moreover, GrailQuest will allow to perform temporal triangulation of impulsive events with arc-second positional accuracies: an extraordinary sensitive X-ray/Gamma all-sky monitor crucial for hunting the elusive electromagnetic counterparts of Gravitational Waves, that will play a paramount role in the future of Multi-messenger Astronomy. A pathfinder of GrailQuest is already under development through the HERMES (High Energy Rapid Modular Ensemble of Satellites) project: a fleet of six 3U cube-sats to be launched by the end of 2022.