SummaryIn this study, we use an improved, more accurate model to analyze the energy footprint of content downloaded from a major online newspaper by means of various combinations of user devices and access networks. Our results indicate that previous analyses based on average figures for laptops or desktop personal computers predict national and global energy consumption values that are unrealistically high. Additionally, we identify the components that contribute most of the total energy consumption during the use stage of the life cycle of digital services. We find that, depending on the type of user device and access network employed, the data center where the news content originates consumes between 4% and 48% of the total energy consumption when news articles are read and between 2% and 11% when video content is viewed. Similarly, we find that user devices consume between 7% and 90% and 0.7% and 78% for articles and video content, respectively, depending on the type of user device and access network that is employed. Though increasing awareness of the energy consumption by data centers is justified, an analysis of our results shows that for individual users of the online newspaper we studied, energy use by user devices and the third-generation (3G) mobile network are usually bigger contributors to the service footprint than the datacenters. Analysis of our results also shows that data transfer of video content has a significant energy use on the 3G mobile network, but less so elsewhere. Hence, a strategy of reducing the resolution of video would reduce the energy footprint for individual users who are using mobile devices to access content by the 3G network.

Interactive devices and the services they support are reliant on the cloud and the digital infrastructure supporting it. The environmental impacts of this infrastructure are substantial-and for particular services the infrastructure can account for up to 85% of the total impact. In this paper, we apply the principles of Sustainable Interaction Design to cloud services use of the digital infrastructure. We perform a critical analysis of current design practice with regard to interactive services, which we identify as the cornucopian paradigm. We show how user-centered design principles induce environmental impacts in different ways, and combine with technical and business drivers to drive growth of the infrastructure through a reinforcing feedback cycle. We then create a design rubric, substantially extending that of Blevis [6], to cover impacts of the digital infrastructure. In doing so, we engage in design criticism, identifying examples (both actual and potential) of good and bad practice. We then extend this rubric beyond an ecoefficiency paradigm to consider deeper and more radical perspectives on sustainability, and finish with future directions for exploration.

Environmental assessments of digital services seeking to take into account the Internet's energy footprint typically require models of the energy intensity of the Internet. Existing models have arrived at conflicting results. This has lead to increased uncertainty and reduced comparability of assessment results. We present a bottom-up model for the energy intensity of the Internet that draws from the current state of knowledge in the field and is specifically directed towards assessments of digital services. We present the numeric results and explain the application of the model in practice. Complementing the previous chapter that presented a generic approach and results for access networks and customer premise equipment, we present a model to assess the energy intensity of the core networks, yielding the result of 0.052kWh/GB. Abstract. Environmental assessments of digital services seeking to take into account the Internet's energy footprint typically require models of the energy intensity of the Internet. Existing models have arrived at conflicting results. This has lead to increased uncertainty and reduced comparability of assessment results. We present a bottom-up model for the energy intensity of the Internet that draws from the current state of knowledge in the field and is specifically directed towards assessments of digital services. We present the numeric results and explain the application of the model in practice. Complementing the previous chapter that presented a generic approach and results for access networks and customer premise equipment, we present a model to assess the energy intensity of the core networks, yielding the result of 0.052kWh/GB.

Estimates of the energy intensity of the Internet diverge by several orders of magnitude. We present existing assessments and identify diverging definitions of the system boundary as the main reason for this large spread. The decision of whether or not to include end devices influences the result by 1-2 orders of magnitude. If end devices are excluded, customer premises equipment (CPE) and access networks have a dominant influence. Of less influence is the consideration of cooling equipment and other overhead, redundancy equipment, and the amplifiers in the optical fibers. We argue against the inclusion of end devices when assessing the energy intensity of the Internet, but in favor of including CPE, access networks, redundancy equipment, cooling and other overhead as well as optical fibers. We further show that the intensities of the metro and core network are best modeled as energy per data, while the intensity of CPE and access networks are best modeled as energy per time (i.e., power), making overall assessments challenging. The chapter concludes with a formula for the energy intensity of CPE and access networks. The formula is presented both in generic form as well as with concrete estimates of the average case to be used in quick assessments by practitioners. The following chapter develops a similar formula for the core and edge networks. Taken together, the two chapters provide an assessment method of the Internet's energy intensity that takes into account dierent modeling paradigms for dierent parts of the network. Abstract. Estimates of the energy intensity of the Internet diverge by several orders of magnitude. We present existing assessments and identify diverging definitions of the system boundary as the main reason for this large spread. The decision of whether or not to include end devices influences the result by 1-2 orders of magnitude. If end devices are excluded, customer premises equipment (CPE) and access networks have a dominant influence. Of less influence is the consideration of cooling equipment and other overhead, redundancy equipment, and the amplifiers in the optical fibers. We argue against the inclusion of end devices when assessing the energy intensity of the Internet, but in favor of including CPE, access networks, redundancy equipment, cooling and other overhead as well as optical fibers. We further show that the intensities of the metro and core network are best modeled as energy per data, while the intensity of CPE and access networks are best modeled as energy per time (i.e., power), making overall assessments challenging. The chapter concludes with a formula for the energy intensity of CPE and access networks. The formula is presented both in generic form as well as with concrete estimates of the average case to be used in quick assessments by practitioners. The following chapter develops a similar formula for the core and edge networks. Taken together, the two chapters provide an assessment method of the Internet's energy intensity that takes into account different ...

PostprintThis is the accepted version of a chapter published in ICT Innovations for Sustainability. Citation for the original published chapter:Hischier, R., Coroama, V., Schien, D., Ahmadi Achachlouei, M. (2015) Grey Energy and Environmental Impacts of ICT Hardware. Abstract. Direct energy consumption of ICT hardware is only "half the story." In order to get the "whole story," energy consumption during the entire life cycle has to be taken into account. This chapter is a first step toward a more comprehensive picture, showing the "grey energy" (i.e., the overall energy requirements) as well as the releases (into air, water, and soil) during the entire life cycle of exemplary ICT hardware devices by applying the life cycle assessment method. The examples calculated show that a focus on direct energy consumption alone fails to take account of relevant parts of the total energy consumption of ICT hardware as well as the relevance of the production phase. As a general tendency, the production phase is more and more important the smaller (and the more energy-efficient) the devices are. When in use, a tablet computer is much more energy-efficient than a desktop computer system with its various components, so its production phase has a much greater relative importance. Accordingly, the impacts due to data transfer when using Internet services are also increasingly relevant the smaller the end-user device is, reaching up to more than 90% of the overall impact when using a tablet computer.