Theorizing stakeholders of sustainability in the digital age

Stakeholder theory, originally introduced in 1984 by philosopher Edward Freeman, is among the most influential theories today addressing the complex interplay of societal actors. It underwent several transformations and expansions, but the original Freeman model as well as the latest approaches places the corporation at the center positioning the theory as management driven. In this article—from a sustainability science perspective—we argue that sustainability could also be considered as the center, around which societal actors are grouped, because everyone, individuals as well as stakeholders, have a stake in a ‘common future’ that is built on the transformative concept of sustainability. Next to this shift of perspective from corporation to sustainability at the center, we advance the concept of sustainability stakeholders with the new paradigm of the digital age we (are about to) live in: the proposed sustainability-centered stakeholder theory is developed to incorporate novel parameters as brought about by digitalization (such as big data, real-time transparency, algorithmic correlations, predictive analytics, or changing privacy standards). Hence, we classify the stakeholders of sustainability according to their roles as “big data stakeholders:” collectors, generators, and utilizers of big data. This digital sustainability stakeholder model operationalizes the complex interplay between stakeholders focused on their ‘stake’ in sustainability and a common future and illustrates their roles in the digital age. Thus, it offers a normative framework to analyze stakeholders’ responsibility to contribute to, advance, promote, and achieve sustainability.     

Prenatal DNA diagnosis of Noonan syndrome in a fetus with massive hygroma colli,pleural effusion and ascites

Prenatal molecular genetic diagnosis for Noonan syndrome I is reported. Noonan syndrome was suspected because of large cystic hygroma colli, massive pleural effusion and ascites at 23 weeks of gestation and normal karyotype (46,XX). DNA was prepared from amnion cells and screened for mutations in the PTPN11 gene. In exon 8, a missense mutation (S285F) was found. Delivery was induced at 33 weeks of gestation because of silent cardiotocography (CTG). Despite immediate drainage of the hydrothorax, mechanical ventilation was insufficient and the child died 9 h after birth due to severe pulmonary hypoplasia. Pleural punctate was enriched for small lymphocytes and thus was characterized as chylus. Prenatal ultrasound findings in Noonan syndrome usually are unspecific and rarely lead to a diagnosis. However, with the combination of cystic hygroma, pleural effusion, ascites and normal karyotype Noonan syndrome should be considered and DNA testing for PTPN11 mutations may be appropriate. Malformations of lymphatic vessels and/or chylothorax in Noonan syndrome seem to be more frequent than usually anticipated. Copyright © 2005 John Wiley & Sons, Ltd.     

Testing of a GIS model ofEucalyptus Iargiflorens health on a semiarid,saline floodplain

Irrigated agriculture has resulted in substantial changes in water flows to the lower reaches of the River Murray. These changes have led to large-scale occurrences of dieback inEucalyptus largiflorens (black box) woodlands as well as increased inputs of salt to the river. Management options to address problems of this scale call for the use of spatial data sets via geographic information systems (GIS). A GIS exists for one floodplain of the River Murray at Chowilla, and a simple model predicted six health classes ofEucalyptus largiflorens based on groundwater salinity, flooding frequency, and groundwater depth.To determine the usefulness of the model for vegetation management, the quality of both the model and the GIS data sets were tested. Success of the testing procedure was judged by the degree of spatial matching between the model's predictions of health and that assessed from aerial photographs and by field truthing. Analyses at 80 sites showed that tree health was significantly greater where groundwater salinity was less than 40 dS/m or flooding occurred more frequently than 1 in 10 years or depth to groundwater exceeded 4 m. Testing of the GIS data sets found that vegetation was misclassified at 15% of sites. Association was shown between GIS-predicted values and field-truthed values of groundwater salinity but not groundwater depth. The GIS model of health is a useful starting point for future vegetation management and can be further improved by increasing the quality of the data coverages and further refining of the model to optimize parameters and thresholds.