Causal allocation of fixed impacts in product systems: Assessing the effect of data demand on network energy consumption

Environmental assessments of digital services currently apply an accounting perspective, and for telecommunication networks (TN) allocate electrical energy consumption in proportion to data traffic. Yet, the power draw by wired TN infrastructure is almost independent of the volume of data traffic flowing through it. Previous assessments of the effect of data traffic on energy consumption thus tended to over-estimate the short-term impact on energy consumption.

However, the growth of peak data traffic rates is a main driver of increasing TN bandwidth capacity and has an indirect impact on electrical energy consumption. This nuanced causal relationship has not been consistently represented in allocation approaches used for attributional carbon footprints.

In this text, we apply a form of consequential system expansion by considering the long-term response to peak-traffic growth. This allows us to model long-run marginal changes to product system attributes that are fixed in the short-term. The outcome illustrates a causally consistent allocation approach that avoids contradicting the short-term behavior of the engineered system.

Based on a causal inference graph of the drivers for the fixed baseload power draw by TN, we distinguish between the effects of different types of data as they contribute to traffic peaks. From this, we develop transform functions that re-allocate environmental burden to peak traffic. We present such functions for the specific case of periodically diurnal traffic in TN (including video-on-demand) and discuss the case of sporadic high-throughput events (including video streaming of life sport events and games downloads).

The allocation model incentivizes a reduction of peak demand through avoidance or demand-shifting, to decelerate the long-term expansion of TN infrastructure.