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Organized into a global network of critical infrastructures, the oil & gas industry remains to this day the main energy contributor to the world’s economy. Severe accidents occasionally occur resulting in fatalities and disruption. We build an oil & gas accident graph based on more than a thousand severe accidents for the period 1970–2016 recorded for refineries, tankers, and gas networks in the authoritative ENergy-related Severe Accident Database (ENSAD). We explore the distribution of potential chains-of-events leading to severe accidents by combining graph theory, Markov analysis and catastrophe dynamics. Using centrality measures, we first verify that human error is consistently the main source of accidents and that explosion, fire, toxic release, and element rupture are the principal sinks, but also the main catalysts for accident amplification. Second, we quantify the space of possible chains-of-events using the concept of fundamental matrix and rank them by defining a likelihood-based importance measure γ . We find that chains of up to five events can play a significant role in severe accidents, consisting of feedback loops of the aforementioned events but also of secondary events not directly identifiable from graph topology and yet participating in the most likely chains-of-events.

The accident risk of severe (≥5 fatalities) accidents in fossil energy chains (Coal, Oil and Natural Gas) is analyzed. The full chain risk is assessed for Organization for Economic Co-operation and Development (OECD), 28 Member States of the European Union (EU28) and non-OECD countries. Furthermore, for Coal, Chinese data are analysed separately for three different periods, i.e., 1994–1999, 2000–2008 and 2009–2016, due to different data sources, and highly incomplete data prior to 1994. A Bayesian Model Averaging (BMA) is applied to investigate the risk and associated uncertainties of a comprehensive accident data set from the Paul Scherrer Institute’s ENergy-related Severe Accident Database (ENSAD). By means of BMA, frequency and severity distributions were established, and a final posterior distribution including model uncertainty is constructed by a weighted combination of the different models. The proposed approach, by dealing with lack of data and lack of knowledge, allows for a general reduction of the uncertainty in the calculated risk indicators, which is beneficial for informed decision-making strategies under uncertainty.

High safety standards of operators and regulators for dams in Switzerland require periodic assessments of risk mitigation measures at dams. Therefore, risk assessments need to include the estimation of life loss (LL) due to a potential dam break. This study demonstrated the benefits of applying the HEC-LIFESim software for modelling LL due to the instantaneous break of a hypothetical dam in Switzerland. HEC-LIFESim overcomes limitations of empirical methods by modelling evacuation and warning processes. Furthermore, for credible LL estimates, metamodelling was used to quantify uncertainty in model parameters. Polynomial chaos expansion (PCE) was applied to approximate the LL model of HEC-LIFESim using only 550 runs. Uncertainty in the model inputs was propagated through the metamodel to quantify uncertainty in the LL estimates. Finally, a global sensitivity analysis was performed by calculating Sobol’ and Borgonovo indices. The results demonstrate that the three-parameter population in a locality within all considered localities, fatality rate in the chance zone, and warning issuance delay contributed most to the variability of the LL estimates. The application of the proposed methodology can support risk management by providing detailed and accurate risk measures and helping in prioritizing safety measures to be considered and implemented.

Uncertainties in instantaneous dam-break floods are difficult to assess with standard methods (e.g., Monte Carlo simulation) because of the lack of historical observations and high computational costs of the numerical models. In this study, polynomial chaos expansion (PCE) was applied to a dam-break flood model reflecting the population of large concrete dams in Switzerland. The flood model was approximated with a metamodel and uncertainty in the inputs was propagated to the flow quantities downstream of the dam. The study demonstrates that the application of metamodeling for uncertainty quantification in dam-break studies allows for reduced computational costs compared to standard methods. Finally, Sobol’ sensitivity indices indicate that reservoir volume, length of the valley, and surface roughness contributed most to the variability of the outputs. The proposed methodology, when applied to similar studies in flood risk assessment, allows for more generalized risk quantification than conventional approaches.

National energy system models play a crucial role in climate policy. However, they often overlook environmental impacts beyond territorial greenhouse gas emissions. Here we evaluate a territorial net-zero carbon dioxide emissions energy scenario for Switzerland coupled with life cycle assessment to quantify non-domestic environmental burdens. We highlight the limitations of considering only territorial emissions. Indeed, even if domestic greenhouse gas emissions are reduced to net zero by 2050, 2 to 5 megatonnes of carbon dioxide equivalent per year persist abroad due to imports and energy-related infrastructure. These extra-territorial emissions are influenced by global climate policies. Additionally, broadening the scope of environmental indicators is crucial as more countries pursue net-zero goals. Our findings highlight trade-offs, showing how environmental impacts other than those on climate change (ecosystem impacts, air pollution, natural resource use) could increase and shift beyond Switzerland as the country electrifies its economy. Even if domestic greenhouse gas emissions were reduced to net zero in Switzerland by 2050, 2 to 5 megatonnes of carbon dioxide equivalent per year could persist abroad due to imports and energy-related infrastructure needs, according to a life cycle assessment of model simulations.

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.

The impacts of energy accidents are of primary interest for risk and resilience analysts, decision makers, and the general public. They can cause human health and environmental impacts, economic and societal losses, which justifies the interest in developing models to mitigate these adverse outcomes. We present a classification model for sorting energy accidents in the natural gas sector into hazard classes, according to their potential fatalities. The model is built on decision rules, which are knowledge blocks in the form of “if (condition), then (classification to hazard class x)”. They were extracted by the rough sets method using natural gas accident data from 1970–2016 of the Energy-related Severe Accident Database (ENSAD) of the Paul Scherrer Institut (PSI), the most authoritative information source for accidents in the energy sector. This was the first attempt to explore the relationships between the descriptors of energy accidents and the consequence (fatalities). The model was applied to a set of hypothetical accidents to show how the decision-making process could be supported when there is an interest in knowing which class (i.e., low, medium, high) of fatalities an energy accident could cause. The successful use of this approach in the natural gas sector proves that it can be also adapted for other energy chains, such as oil and coal.

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.

Natural gas covers more than 20% of Europe’s primary energy demand. A potential disruption could lead to supply shortages with severe consequences for the European economy and society. History shows that such a vast and complex network system is prone to exogenous and endogenous disruptions. A dedicated large-scale dataset of the European natural gas network from publicly available information sources is assembled first. The spatial coverage, completeness and resolution allows analyzing the behavior of this geospatial infrastructure network (including consumption) and its components under likely disruptive events, such as earthquakes, and/or technical failures. Using the developed system state simulation engine, the disruption impact is mapped. The results show that storage facilities cannot in all cases compensate for a pipeline disruption. Moreover, critical pipelines, such as the Transitgas pipeline crossing the Alps and the Trans-Mediterranean pipeline bringing natural gas from Northern Africa, are identified. To analyze the pipelines with high impact on the system performance, a detailed scenario analysis using a Monte Carlo simulation resulting in supply grade mapping is conducted and presented for the case of Italy. Overall, it can be concluded that locations with a dead-end, sole supply, and without storage facility nearby, are remarkably exposed to natural gas supply losses.

The January 2013 attack on the In Amenas natural gas facility drew international attention. However this attack is part of a portrait of energy infrastructure targeting by non-state actors that spans the globe. Data drawn from the Energy Infrastructure Attack Database (EIAD) shows that in the last decade there were, on average, nearly 400 annual attacks carried out by armed non-state actors on energy infrastructure worldwide, a figure that was well under 200 prior to 1999. This data reveals a global picture whereby violent non-state actors target energy infrastructures to air grievances, communicate to governments, impact state economic interests, or capture revenue in the form of hijacking, kidnapping ransoms, theft. And, for politically motivated groups, such as those engaged in insurgencies, attacking industry assets garners media coverage serving as a facilitator for international attention. This research note will introduce EIAD and position its utility within various research areas where the targeting of energy infrastructure, or more broadly energy infrastructure vulnerability, has been addressed, either directly or indirectly. We also provide a snapshot of the initial analysis of the data between 1980-2011, noting specific temporal and spatial trends, and then conclude with a brief discussion on the contribution of EIAD, highlighting future research trajectories.