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Information can be provided by studying and evaluating past emergencies and the response in connection to them. This information would then be useful in efforts directed at preventing, mitigating and/or preparing for future emergencies. However, the analysis and evaluation of emergency response operations is not an easy task, especially when the operation involves several cooperating actors (e.g. the fire and rescue services, the police, the emergency medical services, etc.). Here, we identify and discuss four aspects of this challenge: (1) issues related to the values governing the evaluation, (2) issues related to the complexity of the systems involved, (3) issues related to the validity of the information on which the analysis and evaluation is based and (4) issues related to the limiting conditions under which the emergency response system operated. An outline of a framework for such an analysis and evaluation, influenced by systems theory, accident investigation theories and programme evaluation theories dealing with the above aspects, is introduced, discussed and exemplified using empirical results from a case study. We conclude that the proposed framework may provide a better understanding of how an emergency response system functioned during a specific operation, and help to identify the potential events and/or circumstances that could significantly affect the performance of the emergency response system, either negatively or positively. The insights gained from using the framework may allow the actors involved in the response operation to gain a better understanding of how the emergency response system functioned as a whole, as well as how the actors performed as individual components of the system. Furthermore, the information can also be useful for actors preparing for future emergencies.

Critical infrastructure systems must be both robust and resilient in order to ensure the functioning of society. To improve the performance of such systems, we often use risk and vulnerability analysis to find and address system weaknesses. A critical component of such analyses is the ability to accurately determine the negative consequences of various types of failures in the system. Numerous mathematical and simulation models exist that can be used to this end. However, there are relatively few studies comparing the implications of using different modeling approaches in the context of comprehensive risk analysis of critical infrastructures. In this article, we suggest a classification of these models, which span from simple topologically-oriented models to advanced physical-flow-based models. Here, we focus on electric power systems and present a study aimed at understanding the tradeoffs between simplicity and fidelity in models used in the context of risk analysis. Specifically, the purpose of this article is to compare performance estimates achieved with a spectrum of approaches typically used for risk and vulnerability analysis of electric power systems and evaluate if more simplified topological measures can be combined using statistical methods to be used as a surrogate for physical flow models. The results of our work provide guidance as to appropriate models or combinations of models to use when analyzing large-scale critical infrastructure systems, where simulation times quickly become insurmountable when using more advanced models, severely limiting the extent of analyses that can be performed.

A new method for identifying and ranking critical components and sets of components in technical infrastructures is presented. The criticality of a component or a set of components is defined as the vulnerability of the system to failure in a specific component, or set of components. The identification of critical components is increasingly difficult when considering multiple simultaneous failures. This is especially difficult when dealing with failures of multiple components with synergistic consequences, i.e. consequences that cannot be calculated by adding the consequences of the individual failures. The proposed method addresses this problem. In exemplifying the method, an analysis of an electric power distribution system in a Swedish municipality is presented. It is concluded that the proposed method facilitates the identification of critical sets of components for large-scale technical infrastructures.