A review of assessments of the greenhouse gas footprint and abatement potential of information and communication technology

The results show that the ICT sector causes between 1.5% and 4% of global GHG emissions, a major share of which is due to the production of ICT end-user devices.

ICT affects greenhouse gas (GHG) emissions in two ways: direct effects (also called the GHG footprint) refer to the GHG emissions caused by producing, using and disposing of ICT hardware, indirect effects refer to changes in GHG emissions due to ICT applica tions

While direct effects increase GHG emissions by definition, indirect effects can either lead to a decrease of GHG emissions (called GHG abatement) through optimization and substitution effects or to an increase in emissions through rebound or induction effects.

The aim of the work presented in this article is to provide an overview on the current state of knowledge on two central GHG impacts of ICT: the GHG footprint of the ICT sector and the GHG abatement potential of ICT.

Studies of the GHG footprint of the ICT sector usually define the ICT sector as the sum of ICT end-user devices (e.g., smartphones, laptop computers), data centers, as well as fixed and mobile telecommunication networks.

Studies of the GHG abatement potential of ICT usually either focus on one specific ICT use case which is investigated in detail or roughly estimate the GHG abatement potential of several ICT use cases across different sectors such as buildings, energy, transport or industrial production (Bieser and Hilty, 2018b).

Table description: Overview of direct and indirect effects based on Hilty and Aebischer (2015) and Horner et al. (2016).

For ICT end-user devices, production impacts dominate life cycle GHG emissions and are often larger than 50% (Bieser et al., 2021; Cl ́ement et al., 2020; Jattke et al., 2020). The relevance of the production phase is expected to rise in the future, due to increasing energy efficiency during the use phase.

For data centers, most of the life cycle-related GHG impacts are caused during the use phase (Andrae and Edler, 2015; Belkhir and Elmeligi, 2018; Malmodin and Lund ́en, 2018). Roughly 60% of the GHG emissions from the data centers are caused by ICT components such as servers, storage systems and networks; 40% are caused by in frastructures for cooling, air conditioning, secure power supply and other related services (Avgerinou et al., 2017; Lawrence,2019; Masanet et al., 2020).

Also for telecommunication networks, it is assumed that electricity consumption for operating infrastructure dominates life cycle-related GHG emissions (Andrae and Edler, 2015; Malmodin and Lund ́en, 2018).

The relative GHG abatement potentials (the share of the GHG abatement potential of the total emissions estimated in the respective year and region) allow to compare identified GHG abatement potentials regardless of the region and year under consideration. This value lies between 1.3% and 20% consideringall scenarios and between 8.9% and 20%, if only the optimistic scenarios are taken into account.

Academic literature emphasizes the importance of considering rebound and induction effects in environ mental assessment of ICT use (Bieser, 2020; Coroam ̆ a and Mattern, 2019). Nevertheless, most of the analyzed studies do not take such ef fects into account.

The study by Hilty and Bieser (2017) considers direct and/or indirect rebound effects for all use cases based on a review of other studies. The assumed sizes of the rebound effects lie between 4% for energy savings through building automation and energy feedback systems due to re-spending and 37% for e-commerce due to an increasein logistic services required for goods delivery.

Studies show that the GHG footprint of the ICT sector today accounts for 1.5% to 4% of global GHG emissions.

The production of end-user devices causes the most GHG emissions and may increase in future, in particular due to the GHG-intensive production and the growing number of devices.

A major source of uncertainty are rebound effects, and in particular economy-wide rebound effects, which were not considered by any of the studies. Estimating rebound effects is challenging as they depend on complex elasticities of supply and demand, which are difficult to predict.