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The heavy reliance on information and communication technology (ICT) has prompted the importance of cyber security more than ever in history. Although there are various studies that explored the importance of cybersecurity solutions, the impact of production and usage of these solutions on the natural environment and their economic impact has been rarely researched. The objective of this research is to investigate the environmental and economic impact of cybersecurity solutions. An extensive Life Cycle Assessment (LCA) has been performed on three cybersecurity solutions to assess and compare their environmental impact. Based on global warming potential, the embodied carbon footprint was determined. Carbon footprint and operational costs were used to calculate Life Cycle Cost (LCC) analysis and these data were then used to compare the eco-efficiency of the three cybersecurity solutions. This research compares three cybersecurity solutions: Next Generation DataGuard (NGD), Current State of Art Solution (CSAS), and a Generic Cybersecurity Solution (GCS), to identify an economically viable and environmentally friendly cybersecurity solution. The result of the analysis indicated that NGD was approximately 58% and 88% more energy-efficient than CSAS and GCS, respectively. Also, the LCC of NGD was approximately 6%- 12% lower than CSAS and 22%- 40% lower than GCS in selected provinces in Canada. The eco-efficiency of CSAS was approximately 60% less than NGD. Next Generation DataGuard is found to be a successful solution in this research among the three solutions. This study will be useful to researchers, cybersecurity industries, and policymakers to develop informed decisions on cybersecurity solutions. [Display omitted] • The life cycle environmental and economic performance of three cybersecurity solutions were compared. • Next Generation DataGuard (NGD) was approximately 58% and 88% more energy-efficient than current state of art solution and generic cybersecurity solution, respectively. • The NGD displayed superior environmental and economic performance for all Canadian cities. [ABSTRACT FROM AUTHOR]

Life cycle inventory (LCI) data gaps present a crucial challenge in research and development using life cycle assessment (LCA). The present study addresses this challenge by using optimized Artificial Neural Networks (ANNs). ANN's adjustable parameters (i.e. topology and hyperparameters) significantly affect prediction capability. Thus, the optimality of those parameters is of paramount importance. However, performing an optimization on the entire ANN's adjustable parameters is computationally demanding, which may result in an intractable problem. To tackle this challenge, the present study proposes a hybrid approach using heuristics and Genetic Algorithm to systematically design optimal ANNs in a tractable fashion. The proposed approach is then assessed through the prediction of data gaps in the Canadian fuel LCI database, GHGenius (an open-access tool for modeling Canadian fuel pathways). This is achieved using an automation workflow that extracts LCI data from GHGenius at the fuel/province/unit process level. It is found that the resulting optimal ANNs not only are accurate in predicting data to fill CO 2 -eq emissions data gaps for unit processes present in the Canadian fuel life cycles but also possess shallower hidden topologies. These salient results are ascribed to the impacts of the input layer on the optimal network. In particular, input layers comprised of categorical and numerical features lead to enhancement of network prediction and/or shallower hidden topology for optimal ANNs. Taken altogether, the present study proposes, implements, and validates a systematic framework to tractably design optimal ANNs for predicting data to fill data gaps in LCI databases. In addition, this study quantifies average contributions of each unit process present in the Canadian fuel life cycles, including fossil-based and renewable pathways, to total CO 2 -eq emissions, which is of particular interest in cut-off analysis. [Display omitted] • Contributions of unit processes of fuel pathways to net emissions are determined. • A tractable approach is proposed to optimally design ANNs to fill data gaps. • The combination of categorical and numerical attributes improves ANNs' optimality. • Emission data gaps are estimated using optimal Artificial Neural Networks (ANNs). [ABSTRACT FROM AUTHOR]