Risk entanglement and the social relationality of risk

Relational accounts of risk explain variation in risk perception through situated cognitions defining risk as a relationship between “risk objects” and “objects at risk”. We extend this approach to include not only the relational constitution of cognitive risk objects, but also of the different actors assessing risk. Risk in this perspective is relational because it establishes a link between two different cognitive objects and between two (or more) actors. We argue that this is the case when at least two actors refer to a common risk object while retaining distinct objects at risk. We call this a constellation of risk entanglement across actors. We illustrate our theoretical arguments using data from 68 qualitative interviews and ethnographic fieldwork in the German finance-state nexus. Our analyses indicate how risk entanglement affects and transforms the fundamental logics according to which both of these fields operate

Vulnerability and resilience of living marine resources to the Deepwater Horizon oil spill : an overview

Funding for the project was primarily provided by the Gulf of Mexico Research Initiative through several of its research centers.The 2010 Deepwater Horizon (DWH) oil well blowout in the Gulf of Mexico (GoM) was the largest and perhaps most consequential accidental marine oil spill in global history. This paper provides an overview of a Research Topic consisting of four additional papers that: (1) assemble time series data for ecosystem components in regions impacted by the spill, and (2) interpret temporal changes related to the vulnerability of species and ecosystems to DWH and the ensuing resilience to perturbation. Time series abundance data for many taxa pre-date DWH, often by decades, thus allowing an assessment of population- and community-level impacts. We divided the north central GoM into four interconnected “eco-types”: the coastal/nearshore, continental shelf, open-ocean pelagic and deep benthic. Key taxa in each eco-type were evaluated for their vulnerability to the circumstances of the DWH spill based on population overlap with oil, susceptibility to oil contamination, and other factors, as well their imputed resilience to population-level impacts, based on life history metrics, ecology and post-spill trajectories. Each taxon was scored as low, medium, or high for 13 vulnerability attributes and 11 resilience attributes to produce overall vulnerability and resilience scores, which themselves were also categorical (i.e., low, medium, or high). The resulting taxon-specific V-R scores provide important guidance on key species to consider and monitor in the event of future spills similar to DWH. Similar analyses may also guide resource allocation to collect baseline data on highly vulnerable taxa or those with low resilience potential in other ecosystems. For some species, even a decade of observation has been insufficient to document recovery given chronic, long-term exposure to DWH oil remaining in all eco-types and because of impacts to the reproductive output of long-lived species. Due to the ongoing threats of deep-water blowouts, continued surveillance of populations affected by DWH is warranted to document long-term recovery or change in system state. The level of population monitoring in the open-ocean and deep benthic eco-types has historically been low and is inconsistent with the continued migration of the oil industry to the ultra-deep (≥1,500 m) where the majority of leasing, exploration, and production now occurs.Publisher PDFPeer reviewe

Frameworks and tools for risk assessment of manufactured nanomaterials

Commercialization of nanotechnologies entails a regulatory requirement for understanding their environmental, health and safety (EHS) risks. Today we face challenges to assess these risks, which emerge from uncertainties around the interactions of manufactured nanomaterials (MNs) with humans and the environment. In order to reduce these uncertainties, it is necessary to generate sound scientific data on hazard and exposure by means of relevant frameworks and tools. The development of such approaches to facilitate the risk assessment (RA) of MNs has become a dynamic area of research. The aim of this paper was to review and critically analyse these approaches against a set of relevant criteria. The analysis concluded that none of the reviewed frameworks were able to fulfill all evaluation criteria. Many of the existing modelling tools are designed to provide screening-level assessments rather than to support regulatory RA and risk management. Nevertheless, there is a tendency towards developing more quantitative, higher-tier models, capable of incorporating uncertainty into their analyses. There is also a trend towards developing validated experimental protocols for material identification and hazard testing, reproducible across laboratories. These tools could enable a shift from a costly case-by-case RA of MNs towards a targeted, flexible and efficient process, based on grouping and read-across strategies and compliant with the 3R (Replacement, Reduction, Refinement) principles. In order to facilitate this process, it is important to transform the current efforts on developing databases and computational models into creating an integrated data and tools infrastructure to support the risk assessment and management of MNs.Commercialization of nanotechnologies entails a regulatory requirement for understanding their environmental, health and safety (EHS) risks. Today we face challenges to assess these risks, which emerge from uncertainties around the interactions of manufactured nanomaterials (MNs) with humans and the environment. In order to reduce these uncertainties, it is necessary to generate sound scientific data on hazard and exposure by means of relevant frameworks and tools. The development of such approaches to facilitate the risk assessment (RA) of MNs has become a dynamic area of research. The aim of this paper was to review and critically analyse these approaches against a set of relevant criteria. The analysis concluded that none of the reviewed frameworks were able to fulfill all evaluation criteria. Many of the existing modelling tools are designed to provide screening level assessments rather than to support regulatory RA and risk management Nevertheless, there is a tendency towards developing more quantitative, higher-tier models, capable of incorporating uncertainty into their analyses. There is also a trend towards developing validated experimental protocols for material identification and hazard testing, reproducible across laboratories. These tools could enable a shift from a costly case-by-case RA of MNs towards a targeted, flexible and efficient process, based on grouping and read-across strategies and compliant with the 3R (Replacement, Reduction, Refinement) principles. In order to facilitate this process, it is important to transform the current efforts on developing databases and computational models into creating an integrated data and tools infrastructure to support the risk assessment and management of MNs. (C) 2016 Elsevier Ltd. All rights reserved

Risk management in enterprise resource planning projects

In recent years Enterprise Resource Planning (ERP) systems have received much attention. ERP are extremely complex information systems, whose implementation is often a complex adventure for business enterprises. The organizational relevance and risk of ERP projects make it important for organizations to focus on ways to make ERP implementation successful. However, dealing with risk management in ERP project introduction is an ambitious task. Numerous risk factors have to be taken into account which include technological and managerial aspects, both psychological and sociological; moreover they can be deeply interconnected and have indirect e ects on the project. Therefore, the risk management process is highly difficult and uncertain. The general purpose of this study is to develop an innovative risk management methodology supporting the formulation of risk treatment strategies and actions during ERP introduction projects in order to nally improve the success rate. In this thesis, the research context, framework and methodology are presented; then main phases are introduced and results discussed