Rethinking Allocation in High-Baseload Systems: A Demand-Proportional Network Electricity Intensity Metric

As companies and and entire economic sectors begin to respond to the urgent need to reduce carbon emissions and support global decarbonisation, robust assessments of environmental impact are essential to guide their actions. Network operators are increasingly aware and take action to decarbonise their infrastructure, for example by procuring renewable electric. However, the organisations providing the application services that run over the networks, and for which the networks are built and operated, do not currently know how they can support the reduction of the environmental impact. This lack of alignment in decision making is inefficient and undermines the sector’s decarbonisation goals. For high-throughput digital services, such as downloading video games or streaming videos, the current electricity intensity metrics for assessing the energy consumption from use of internet networks allocate a share of energy consumption per average data volume over a duration of typically one year. These metrics thus make no distinction when a service is using the network. However, absolute energy consumption of network devices is strongly dependent on their maximum bandwidth. Increases of peak-time throughput thus drive future expansion of the network capacity, electricity consumption and hardware replacements. By ignoring the variability of demand, electricity intensity metrics disregard the influence that peak-time use of the network has on future environmental impact and overestimate the environmental effects of off-peak use. In this text we investigate the design of an alternative energy intensity metric that redistributes burden of baseline power consumption proportional to data throughput. Such metrics can incentivise demand-shifting of data traffic and thus reduce the pressure on network expansion, which can contribute to a reduction of carbon emissions long-term. We illustrate this approach with an example and consider how it can be combined with carbon intensity metrics for carbon footprinting.