Dangerous Climate Change: The Role for Risk Research

The notion of "dangerous climate change" constitutes an important development of the 1992 United Nations Framework Convention on Climate Change. It persists, however, as an ambiguous expression, sustained by multiple definitions of danger. It also implicitly contains the question of how to respond to the complex and multi-disciplinary risk issues that climate change poses. The invaluable role of the climate science community, which relies on risk assessments to characterize system uncertainties and to identify limits beyond which changes may become dangerous, is acknowledged. But this alone will not suffice to develop long-term policy. Decisions need to include other considerations, such as value judgments about potential risks, and societal and individual perceptions of "danger," which are often contested. This article explores links and cross-overs between the climate science and risk communication and perception approaches to defining danger. Drawing upon nine articles in this Special Issue of Risk Analysis, we examine a set of themes: limits of current scientific understanding; differentiated public perceptions of danger from climate change; social and cultural processes amplifying and attenuating perceptions of, and responses to, climate change; risk communication design; and new approaches to climate change decision making. The article reflects upon some of the difficulties inherent in responding to the issue in a coherent, interdisciplinary fashion, concluding nevertheless that action should be taken, while acknowledging the context-specificity of "danger." The need for new policy tools is emphasised, while research on nested solutions should be aimed at overcoming the disjunctures apparent in interpretations of climate change risks.     

Ausstiegsstrategien in EigenverantwortungSelf-Responsibility for Exit-Strategies

The German social security code (SGB II) explicitly demands self-responsibility of people receiving long term unemployment benefits (ALG II) to overcome the need for help. However, it is not sufficiently defined how this responsibility is to be practiced and what kind of assistance those people can expect. Especially families whose earned income is supplemented by ALG II not only have to meet the demands of employment but also the requirements of an activating labour market policy. At the same time, they have to coordinate duties of care and education with a prospectively changed labour market participation.     

Population Dynamics in Italy in the Middle Ages: New Insights from Archaeological Findings

Studies of settlements in Italy indicate that during the Early Middle Ages (6th–9th centuries) the Italian population was stagnant, whereas a slow but persistent growth followed during the High Middle Ages (10th–13th). However, the components of the dynamics of the Italian population in the long period between the Justinian Plague (around 565 in Italy) and the Black Death (1348) are largely unknown. In this article, data from anthropometric analysis of the skeletons in 154 Italian cemeteries that date between the 1st century BCE and the 13th century are used to gain new insights on mortality of adults and nutrition. Adult mortality was higher during the Early Middle Ages than during the Roman Empire and the High Middle Ages, suggesting that the stagnation of population was determined by higher mortality. During the Early Middle Ages, however, nutritional levels were higher, as suggested by taller statures, more widespread meat consumption, and longer periods of breastfeeding. We discuss some possible research lines to resolve this apparent contradiction.     

Conditional failure occurrence rates for semi-Markov chains*

ABSTRACT

In the present paper, we aim at providing plug-in-type empirical estimators that enable us to quantify the contribution of each operational or/and non-functioning state to the failures of a system described by a semi-Markov model. In the discrete-time and finite state space semi-Markov framework, we study different conditional versions of an important reliability measure for random repairable systems, the failure occurrence rate, which is based on counting processes. The identification of potential failure contributors through the conditional counterparts of the failure occurrence rate is of paramount importance since it could lead to corrective actions that minimize the occurrence of the more important failure modes and therefore improve the reliability of the system. The aforementioned estimators are characterized by appealing asymptotic properties such as strong consistency and asymptotic normality. We further obtain detailed analytical expressions for the covariance matrices of the random vectors describing the conditional failure occurrence rates. As particular cases we present the failure occurrence rates for hidden (semi-) Markov models. We illustrate our results by means of a simulated study. Different applications are presented based on wind, earthquake and vibration data.