Systemic Risk Centre

Abstract. This paper examines the development over historical time of the meaning and uses of the term resilience. The objective is to deepen our understanding of how the term came to be adopted in disaster risk reduction and resolve some of the conflicts and controversies that have arisen when it has been used. The paper traces the development of resilience through the sciences, humanities, and legal and political spheres. It considers how mechanics passed the word to ecology and psychology, and how from there it was adopted by social research and sustainability science. As other authors have noted, as a concept, resilience involves some potentially serious conflicts or contradictions, for example between stability and dynamism, or between dynamic equilibrium (homeostasis) and evolution. Moreover, although the resilience concept works quite well within the confines of general systems theory, in situations in which a systems formulation inhibits rather than fosters explanation, a different interpretation of the term is warranted. This may be the case for disaster risk reduction, which involves transformation rather than preservation of the "state of the system". The article concludes that the modern conception of resilience derives benefit from a rich history of meanings and applications, but that it is dangerous – or at least potentially disappointing – to read to much into the term as a model and a paradigm.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is an evolving infectious disease that dramatically spread all over the world in the early part of 2020. No studies have yet summarized the potential severity and mortality risks caused by COVID-19 in patients with chronic obstructive pulmonary disease (COPD), and we update information in smokers. METHODS: We systematically searched electronic databases from inception to March 24, 2020. Data were extracted by two independent authors in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Study quality was assessed using a modified version of the Newcastle-Ottawa Scale. We synthesized a narrative from eligible studies and conducted a meta-analysis using a random-effects model to calculate pooled prevalence rates and 95% confidence intervals (95%CI). RESULTS: In total, 123 abstracts were screened and 61 full-text manuscripts were reviewed. A total of 15 studies met the inclusion criteria, which included a total of 2473 confirmed COVID-19 patients. All studies were included in the meta-analysis. The crude case fatality rate of COVID-19 was 7.4%. The pooled prevalence rates of COPD patients and smokers in COVID-19 cases were 2% (95% CI, 1%-3%) and 9% (95% CI, 4%-14%) respectively. COPD patients were at a higher risk of more severe disease (risk of severity = 63%, (22/35) compared to patients without COPD 33.4% (409/1224) [calculated RR, 1.88 (95% CI, 1.4-2.4)]. This was associated with higher mortality (60%). Our results showed that 22% (31/139) of current smokers and 46% (13/28) of ex-smokers had severe complications. The calculated RR showed that current smokers were 1.45 times more likely [95% CI: 1.03-2.04] to have severe complications compared to former and never smokers. Current smokers also had a higher mortality rate of 38.5%. CONCLUSION: Although COPD prevalence in COVID-19 cases was low in current reports, COVID-19 infection was associated with substantial severity and mortality rates in COPD. Compared to former and never smokers, current smokers were at greater risk of severe complications and higher mortality rate. Effective preventive measures are required to reduce COVID-19 risk in COPD patients and current smokers.

Abstract Groundwater resources are vital to ecosystems and livelihoods. Excessive groundwater withdrawals can cause groundwater levels to decline 1–10 , resulting in seawater intrusion 11 , land subsidence 12,13 , streamflow depletion 14–16 and wells running dry 17 . However, the global pace and prevalence of local groundwater declines are poorly constrained, because in situ groundwater levels have not been synthesized at the global scale. Here we analyse in situ groundwater-level trends for 170,000 monitoring wells and 1,693 aquifer systems in countries that encompass approximately 75% of global groundwater withdrawals 18 . We show that rapid groundwater-level declines (>0.5 m year −1 ) are widespread in the twenty-first century, especially in dry regions with extensive croplands. Critically, we also show that groundwater-level declines have accelerated over the past four decades in 30% of the world’s regional aquifers. This widespread acceleration in groundwater-level deepening highlights an urgent need for more effective measures to address groundwater depletion. Our analysis also reveals specific cases in which depletion trends have reversed following policy changes, managed aquifer recharge and surface-water diversions, demonstrating the potential for depleted aquifer systems to recover.

The low potency of many man-made estrogenic chemicals, so-called xenoestrogens, has been used to suggest that risks arising from exposure to individual chemicals are negligible. Another argument used to dismiss concerns of health effects is that endogenous steroidal estrogens are too potent for xenoestrogens to contribute significantly to estrogenic effects. Using a yeast reporter gene assay with the human estrogen receptoralpha, we tested these ideas experimentally by assessing the ability of a combination of 11 xenoestrogens to affect the actions of 17ss-estradiol. Significantly, each xenoestrogen was present at a level well below its no-observed-effect concentration (NOEC). To derive accurate descriptions of low effects, we recorded concentration-response relationships for each xenoestrogen and for 17ss-estradiol. We used these data to predict entire concentration-response curves of mixtures of xenoestrogens with 17ss-estradiol, assuming additive combination effects. Over a large range of concentrations, the experimentally observed responses decisively confirmed the model predictions. The combined additive effect of the 11 xenoestrogens led to a dramatic enhancement of the hormone's action, even when each single agent was present below its NOEC. Our results show that not even sub-NOEC levels of xenoestrogens can be considered to be without effect on potent steroidal estrogens when they act in concert with a large number of similarly acting chemicals. It remains to be seen to what degree these effects can be neutralized by environmental chemicals with antiestrogenic activity. Nevertheless, potential human and wildlife responses induced by additive combination effects of xenoestrogens deserve serious consideration.

Public opposition to genetically modified (GM) food and crops is widely interpreted as the result of the public's misperception of the risks. With scientific assessment pointing to no unique risks from GM crops and foods, a strategy of accurate risk communication from trusted sources has been advocated. This is based on the assumption that the benefits of GM crops and foods are self-evident. Informed by the interpretation of some qualitative interviews with lay people, we use data from the Eurobarometer survey on biotechnology to explore the hypothesis that it is not so much the perception of risks as the absence of benefits that is the basis of the widespread rejection of GM foods and crops by the European public. Some respondents perceive both risks and benefits, and may be trading off these attributes along the lines of a rational choice model. However, for others, one attribute-benefit-appears to dominate their judgments: the lexicographic heuristic. For these respondents, their perception of risk is of limited importance in the formation of attitudes toward GM food and crops. The implication is that the absence of perceived benefits from GM foods and crops calls into question the relevance of risk communication strategies for bringing about change in public opinion.

In recent years, there has been a gradual increase in research literature on the challenges of interconnected, compound, interacting, and cascading risks. These concepts are becoming ever more central to the resilience debate. They aggregate elements of climate change adaptation, critical infrastructure protection, and societal resilience in the face of complex, high-impact events. However, despite the potential of these concepts to link together diverse disciplines, scholars and practitioners need to avoid treating them in a superficial or ambiguous manner. Overlapping uses and definitions could generate confusion and lead to the duplication of research effort. This article gives an overview of the state of the art regarding compound, interconnected, interacting, and cascading risks. It is intended to help build a coherent basis for the implementation of the Sendai Framework for Disaster Risk Reduction (SFDRR). The main objective is to propose a holistic framework that highlights the complementarities of the four kinds of complex risk in a manner that is designed to support the work of researchers and policymakers. This article suggests how compound, interconnected, interacting, and cascading risks could be used, with little or no redundancy, as inputs to new analyses and decisional tools designed to support the implementation of the SFDRR. The findings can be used to improve policy recommendations and support tools for emergency and crisis management, such as scenario building and impact trees, thus contributing to the achievement of a system-wide approach to resilience.

Cascading effects and cascading disasters are emerging fields of scientific research. The widespread diffusion of functional networks increases the complexity of interdependent systems and their vulnerability to large-scale disruptions. Although in recent years studies of interconnections and chain effects have improved significantly, cascading phenomena are often associated with the “toppling domino metaphor”, or with high-impact, low-probability events. This paper aimed to support a paradigm shift in the state of the art by proposing a new theoretical approach to cascading events in terms of their root causes and lack of predictability. By means of interdisciplinary theory building, we demonstrate how cascades reflect the ways in which panarchies collapse. We suggest that the vulnerability of critical infrastructure may orientate the progress of events in relation to society’s feedback loops, rather than merely being an effect of natural triggers. Our conclusions point to a paradigm shift in the preparedness phase that could include escalation points and social nodes, but that also reveals a brand new field of research for disaster scholars.

Existing environmental risk assessment procedures are limited in their ability to evaluate the combined effects of chemical mixtures. We investigated the implications of this by analyzing the combined effects of a multicomponent mixture of five estrogenic chemicals using vitellogenin induction in male fathead minnows as an end point. The mixture consisted of estradiol, ethynylestradiol, nonylphenol, octylphenol, and bisphenol A. We determined concentration-response curves for each of the chemicals individually. The chemicals were then combined at equipotent concentrations and the mixture tested using fixed-ratio design. The effects of the mixture were compared with those predicted by the model of concentration addition using biomathematical methods, which revealed that there was no deviation between the observed and predicted effects of the mixture. These findings demonstrate that estrogenic chemicals have the capacity to act together in an additive manner and that their combined effects can be accurately predicted by concentration addition. We also explored the potential for mixture effects at low concentrations by exposing the fish to each chemical at one-fifth of its median effective concentration (EC50). Individually, the chemicals did not induce a significant response, although their combined effects were consistent with the predictions of concentration addition. This demonstrates the potential for estrogenic chemicals to act additively at environmentally relevant concentrations. These findings highlight the potential for existing environmental risk assessment procedures to underestimate the hazard posed by mixtures of chemicals that act via a similar mode of action, thereby leading to erroneous conclusions of absence of risk.

OBJECTIVES: Benzene has long been recognised as a carcinogen and recent concern has centred on the effects of continuous exposure to low concentrations of benzene both occupationally and environmentally. This paper presents an overview of the current knowledge about human exposure to benzene in the United Kingdom population based on recently published data, summarises the known human health effects, and uses this information to provide a risk evaluation for sections of the general United Kingdom population. METHOD: Given the minor contribution that non-inhalation sources make to the overall daily intake of benzene to humans, only exposure from inhalation has been considered when estimating the daily exposure of the general population to benzene. Exposure of adults, children, and infants to benzene has been estimated for different exposure scenarios with time-activity patterns and inhalation and absorption rates in conjunction with measured benzene concentrations for a range of relevant microenvironments. Exposures during refuelling and driving, as well as the contribution of active and passive tobacco smoke, have been considered as part of the characterisation of risk of the general population. RESULTS: Infants (<1 years old), the average child (11 years old), and non-occupationally exposed adults, receive average daily doses in the range of 15-26, 29-50, and 75-522 microg of benzene, respectively, which correspond to average ranges to benzene in air of 3.40-5.76 microg/m(3), 3.37-5.67 microg/m(3), and 3.7-41 microg/m(3) for infants, children, and adults, respectively. Infants and children exposed to environmental tobacco smoke have concentrations of exposure to benzene comparable with those of an adult passive smoker. This is a significant source of exposure as a 1995 United Kingdom survey has shown that 47% of children aged 2-15 years live in households where at least one person smokes. The consequence of exposure to benzene in infants is more significant than for children or adults owing to their lower body weight, resulting in a higher daily intake for infants compared with children or non-smoking adults. A worst case scenario for exposure to benzene in the general population is that of an urban smoker who works adjacent to a busy road for 8 hours/day-for example, a maintenance worker-who can receive a mean daily exposure of about 820 microg (equal to an estimated exposure of 41 microg/m(3)). The major health risk associated with low concentrations of exposure to benzene has been shown to be leukaemia, in particular acute non-lymphocytic leukaemia. The lowest concentration of exposure at which an increased incidence of acute non-lymphocytic leukaemia among occupationally exposed workers has been reliably detected, has been estimated to be in the range of 32-80 mg/m(3). Although some studies have suggested that effects may occur at lower concentrations, clear estimates of risk have not been determined, partly because of the inadequacy of exposure data and the few cases. CONCLUSIONS: Overall the evidence from human studies suggests that any risk of leukaemia at concentrations of exposure in the general population of 3.7-42 microg/m(3)-that is at concentrations three orders of magnitude less than the occupational lowest observed effect level-is likely to be exceedingly small and probably not detectable with current methods. This is also likely to be true for infants and children who may be exposed continuously to concentrations of 3.4-5.7 microg/m(3). As yet there is no evidence to suggest that continuous exposures to these environmental concentrations of benzene manifest as any other adverse health effect.

We examined the long-term impact of coauthorship with established, highly-cited scientists on the careers of junior researchers in four scientific disciplines. Here, using matched pair analysis, we find that junior researchers who coauthor work with top scientists enjoy a persistent competitive advantage throughout the rest of their careers, compared to peers with similar early career profiles but without top coauthors. Such early coauthorship predicts a higher probability of repeatedly coauthoring work with top-cited scientists, and, ultimately, a higher probability of becoming one. Junior researchers affiliated with less prestigious institutions show the most benefits from coauthorship with a top scientist. As a consequence, we argue that such institutions may hold vast amounts of untapped potential, which may be realised by improving access to top scientists.

Following the financial crisis of 2007-2008, a deep analogy between the origins of instability in financial systems and complex ecosystems has been pointed out: in both cases, topological features of network structures influence how easily distress can spread within the system. However, in financial network models, the details of how financial institutions interact typically play a decisive role, and a general understanding of precisely how network topology creates instability remains lacking. Here we show how processes that are widely believed to stabilize the financial system, that is, market integration and diversification, can actually drive it towards instability, as they contribute to create cyclical structures which tend to amplify financial distress, thereby undermining systemic stability and making large crises more likely. This result holds irrespective of the details of how institutions interact, showing that policy-relevant analysis of the factors affecting financial stability can be carried out while abstracting away from such details.

In this article, we discuss the increasing interdependence of societies, focusing specifically on issues of systemic instability and fragility generated by the new and unprecedented level of connectedness and complexity resulting from globalization. We define the global system as a set of tightly coupled interactions that allow for the continued flow of information, capital, goods, services, and people. Using the general concepts of globality, complexity, networks, and the nature of risk, we analyze case studies of trade, finance, infrastructure, climate change, and public health to develop empirical support for the concept of global systemic risk. We seek to identify and describe the sources and nature of such risks and methods of thinking about risks that may inform future academic research and policy-making decisions.

Abstract. This paper examines the development over historical time of the meaning and uses of the term resilience. The objective is to deepen our understanding of how the term came to be adopted in disaster risk reduction and resolve some of the conflicts and controversies that have arisen when it has been used. The paper traces the development of resilience through the sciences, humanities, and legal and political spheres. It considers how mechanics passed the word to ecology and psychology, and how from there it was adopted by social research and sustainability science. As other authors have noted, as a concept, resilience involves some potentially serious conflicts or contradictions, for example between stability and dynamism, or between dynamic equilibrium (homeostasis) and evolution. Moreover, although the resilience concept works quite well within the confines of General Systems Theory, in situations in which a systems formulation inhibits rather than fosters explanation, a different interpretation of the term is warranted. This may be the case for disaster risk reduction, which involves transformation rather than preservation of the ''state of the system''. The article concludes that the modern conception of resilience derives benefit from a rich history of meanings and applications, but that it is dangerous – or at least potentially disappointing – to read to much into the term as a model and a paradigm. Sagitta in lapidem numquam figitur, interdum resiliens percutit dirigentem. ("An arrow never lodges in a stone: often it recoils upon its sender.") St. John Chrysostom (c. 347–407), Archbishop of Constantinople.

We propose a network-filtering method, the Triangulated Maximally Filtered Graph (TMFG), that provides an approximate solution to the Weighted Maximal Planar Graph problem. The underlying idea of TMFG consists in building a triangulation that maximizes a score function associated with the amount of information retained by the network. TMFG uses as weights any arbitrary similarity measure to arrange data into a meaningful network structure that can be used for clustering, community detection and modeling. The method is fast, adaptable and scalable to very large datasets, it allows online updating and learning as new data can be inserted and deleted with combinations of local and non-local moves. TMFG permits readjustments of the network in consequence of changes in the strength of the similarity measure. The method is based on local topological moves and can therefore take advantage of parallel and GPUs computing. We discuss how this network-filtering method can be used intuitively and efficiently for big data studies and its significance from an information-theoretic perspective.

We study the effects of stock market volatility on risk-taking and financial crises by constructing a cross-country database spanning up to 211 years and across 60 countries. Prolonged periods of low volatility have strong in-sample and out-of-sample predictive power over the incidence of banking crises and can be used as a reliable crisis indicator, whereas volatility itself does not predict crises. Low volatility leads to excessive credit buildups and balance sheet leverage in the financial system, indicating that agents take more risk in periods of low risk, supporting the dictum that “stability is destabilizing.” Received October 28, 2016; editorial decision February 7, 2017 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Abstract Background Coronavirus disease 2019 (COVID-19) is an evolving infectious disease that dramatically spread all over the world in the early part of 2020. No studies have yet summarised the potential severity and mortality risks caused by COVID-19 in patients with chronic obstructive pulmonary disease (COPD), and we update information in smokers. Methods We systematically searched electronic databases from inception to March 24, 2020. Data were extracted by two independent authors in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Study quality was assessed using a modified version of the Newcastle-Ottawa Scale. We synthesised a narrative from eligible studies and conducted a meta-analysis using a random-effects model to calculate pooled prevalence rates and 95% confidence intervals (95%CI). Results In total, 123 abstracts were screened and 61 full-text manuscripts were reviewed. A total of 15 studies met the inclusion criteria, which included a total of 2473 confirmed COVID-19 patients. All studies were included in the meta-analysis. The crude case fatality rate of COVID-19 was 6.4%. The pooled prevalence rates of COPD patients and smokers in COVID-19 cases were 2% (95% CI, 1%–3%) and 9% (95% CI, 4%–14%) respectively. COPD patients were at a higher risk of more severe disease (risk of severity = 63%, (22/35) compared to patients without COPD 33.4% (409/1224) [calculated RR, 1.88 (95% CI, 1.4– 2.4)]. This was associated with higher mortality (60%). Our results showed that 22% (31/139) of current smokers and 46% (13/28) of ex-smokers had severe complications. The calculated RR showed that current smokers were 1.45 times more likely [95% CI: 1.03–2.04] to have severe complications compared to former and never smokers. Current smokers also had a higher mortality rate of 38.5%. Conclusion Although COPD prevalence in COVID-19 cases was low in current reports, COVID-19 infection was associated with substantial severity and mortality rates in COPD. Compared to former and never smokers, current smokers were at greater risk of severe complications and higher mortality rate. Effective preventive measures are required to reduce COVID-19 risk in COPD patients and current smokers.

Disasters can have devastating impacts on communities and economies, underscoring the urgent need for effective strategic disaster risk management (DRM). Although Artificial Intelligence (AI) holds the potential to enhance DRM through improved decision-making processes, its inherent complexity and "black box" nature have led to a growing demand for Explainable AI (XAI) techniques. These techniques facilitate the interpretation and understanding of decisions made by AI models, promoting transparency and trust. However, the current state of XAI applications in DRM, their achievements, and the challenges they face remain underexplored. In this systematic literature review, we delve into the burgeoning domain of XAI-DRM, extracting 195 publications from the Scopus and ISI Web of Knowledge databases, and selecting 68 for detailed analysis based on predefined exclusion criteria. Our study addresses pertinent research questions, identifies various hazard and disaster types, risk components, and AI and XAI methods, uncovers the inherent challenges and limitations of these approaches, and provides synthesized insights to enhance their explainability and effectiveness in disaster decision-making. Notably, we observed a significant increase in the use of XAI techniques for DRM in 2022 and 2023, emphasizing the growing need for transparency and interpretability. Through a rigorous methodology, we offer key research directions that can serve as a guide for future studies. Our recommendations highlight the importance of multi-hazard risk analysis, the integration of XAI in early warning systems and digital twins, and the incorporation of causal inference methods to enhance DRM strategy planning and effectiveness. This study serves as a beacon for researchers and practitioners alike, illuminating the intricate interplay between XAI and DRM, and revealing the profound potential of AI solutions in revolutionizing disaster risk management.

The global financial system can be represented as a large complex network in which banks, hedge funds and other financial institutions are interconnected to each other through visible and invisible financial linkages. Recently, a lot of attention has been paid to the understanding of the mechanisms that can lead to a breakdown of this network. This can happen when the existing financial links turn from being a means of risk diversification to channels for the propagation of risk across financial institutions. In this review article, we summarize recent developments in the modeling of financial systemic risk. We focus, in particular, on network approaches, such as models of default cascades due to bilateral exposures or to overlapping portfolios, and we also report on recent findings on the empirical structure of interbank networks. The current review provides a landscape of the newly arising interdisciplinary field lying at the intersection of several disciplines, such as network science, physics, engineering, economics, and ecology.

The 28th December 1908 Messina earthquake (Mw 7.1), Italy, caused >80,000 deaths and transformed earthquake science by triggering the study of earthquake environmental effects worldwide, yet its source is still a matter of debate. To constrain the geometry and kinematics of the earthquake we use elastic half-space modelling on non-planar faults, constrained by the geology and geomorphology of the Messina Strait, to replicate levelling data from 1907-1909. The novelty of our approach is that we (a) recognise the similarity between the pattern of vertical motions and that of other normal faulting earthquakes, and (b) for the first time model the levelling data using the location and geometry of a well-known offshore capable fault. Our results indicate slip on the capable fault with a dip to the east of 70° and 5 m dip-slip at depth, with slip propagating to the surface on the sea bed. Our work emphasises that geological and geomorphological observations supporting maps of capable non-planar faults should not be ignored when attempting to identify the sources of major earthquakes.

Artificial neural networks (ANNs) have been extensively used for classification problems in many areas such as gene, text and image recognition. Although ANNs are popular also to estimate the probability of default in credit risk, they have drawbacks; a major one is their tendency to overfit the data. Here we propose an improved Bayesian regularization approach to train ANNs and compare it to the classical regularization that relies on the back-propagation algorithm for training feed-forward networks. We investigate different network architectures and test the classification accuracy on three data sets. Profitability, leverage and liquidity emerge as important financial default driver categories.