The real climate and transformative impact of ICT: A critique of estimates, trends, and regulations

By C. Freitag, Mike Berners-Lee, Kelly Widdicks, Bran Knowles, G. Blair, Adrian Friday. Patterns, 2021

560 citations recorded by the source corpus.

Summary In this paper, we critique ICT's current and projected climate impacts. Peer-reviewed studies estimate ICT's current share of global greenhouse gas (GHG) emissions at 1.8%–2.8% of global GHG emissions; adjusting for truncation of supply chain pathways, we find that this share could actually be between 2.1% and 3.9%. For ICT's future emissions, we explore assumptions underlying analysts' projections to understand the reasons for their variability. All analysts agree that ICT emissions will not reduce without major concerted efforts involving broad political and industrial action. We provide three reasons to believe ICT emissions are going to increase barring intervention and find that not all carbon pledges in the ICT sector are ambitious enough to meet climate targets. We explore the underdevelopment of policy mechanisms for enforcing sector-wide compliance, and contend that, without a global carbon constraint, a new regulatory framework is required to keep the ICT sector's footprint aligned with the Paris Agreement.

Subfields: Electrical and Electronic Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/ecf561ef2e263199a5cb069314ba40fe5128997a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Chen Liang
2. David A. Patterson
3. Quoc V. Le
4. David R. So
5. Joseph E. Gonzalez
6. Daniel Rothchild
7. Wenhu Chen

In Media Technology

1. Quoc V. Le
2. Joseph E. Gonzalez

Citations in the corpus

• Six Human-Centered Artificial Intelligence Grand Challenges (2023, unknown): “This is in addition to the emissions that are already projected to increase unless there is meaningful intervention from information communication technologies (Freitag et al., 2021) more broadly.”
• The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink (2022, background): “More than 1000 papers cite this paper as the source for the impact on carbon emissions of ML models, e.g., [1,6,7,17,18,19,20].”