Anti-SELL monoclonal antibody, clone NFM-25 Functional Grade

This study contributes to a theoretical and empirical understanding of whether and how administrative environmental innovations (AEIs)-implemented to help track and manage a firm's environmental impacts-are related to environmental disclosure. Drawing on the Belief-Action-Outcome framework, we posit that the motivation of individuals (employees, managers, and the leadership) within the firm to access, use, and act on the environmental information available to them would be enhanced by the firm's implementation of AEIs, resulting in more extensive environmental disclosure by the firm. Additionally, building on the literature on supply chain networks, we posit that the structural position of the firm vis-a-vis its supply network-reflecting information flows, network learning, and status-moderates the AEI implementation-environmental disclosure relationship. To test our hypotheses, we build a multi-industry dataset of 3,106 firm-year observations based on 67,809 dyadic cost-of-goods-sold-based relationships obtained from Bloomberg's supply chain relationships database to construct the supply networks of focal firms. We also draw on Bloomberg's environmental, social, and governance (ESG) data for our AEI implementation and environmental disclosure measures. We find significant evidence to support our hypothesis that AEI implementation is positively associated with the extent of environmental disclosure. However, the implementation of both internal and external forms of AEIs has a more pronounced positive relationship with the extent of environmental disclosure, compared to the implementation of either form alone. With regard to supply network structure, we identify three principal variables-accessibility, control, and interconnectedness-that influence network learning and status of the focal firm and find that they moderate the AEI implementation-environmental disclosure relationship. We provide insights for theory and practice based on our findings.

Trade-offs between privacy and cost are studied for a smart grid consumer, whose electricity consumption is monitored in almost real time by the utility provider (UP) through smart meter (SM) readings. It is assumed that an electrical battery is available to the consumer, which can be utilised both to achieve privacy and to reduce the energy cost by demand shaping. Privacy is measured via the mean squared distance between the SM readings and a target load profile, while time-of-use pricing is considered to compute the cost incurred. The consumer can also sell electricity back to the UP to further improve the privacy-cost trade-off. Two privacy-preserving energy management policies (EMPs) are proposed, which differ in the way the target load profile is characterised. A more practical EMP, which optimises the energy management less frequently, is also considered. Numerical results are presented to compare the privacy-cost trade-off of these EMPs, considering various privacy indicators.