The environmental impact of ICT in the era of data and artificial intelligence

TL;DR

This paper examines the environmental impact of AI and ICT, highlighting the increase in emissions from data centers, and proposes a framework for assessing and mitigating AI's net environmental effects.

Contribution

It introduces a comprehensive framework for quantifying both direct and indirect environmental impacts of AI and ICT, addressing current methodological uncertainties.

Findings

AI-related data center emissions are rapidly increasing.

A framework for impact assessment of AI and ICT is proposed.

Recommendations for reducing AI's environmental footprint are provided.

Abstract

The technology industry promotes artificial intelligence (AI) as a key enabler to solve a vast number of problems, including the environmental crisis. However, when looking at the emissions of datacenters from worldwide service providers, we observe a rapid increase aligned with the advent of AI. Some actors justify it by claiming that the increase of emissions for digital infrastructures is acceptable as it could help the decarbonization of other sectors, e.g., videoconference tools instead of taking the plane for a meeting abroad, or using AI to optimize and reduce energy consumption. With such conflicting claims and ambitions, it is unclear how the net environmental impact of AI could be quantified. The answer is prone to uncertainty for different reasons, among others: lack of transparency, interference with market expectations, lack of standardized methodology for quantifying direct and indirect impact, and the quick evolutions of models and their requirements. This report provides answers and clarifications to these different elements. Firstly, we consider the direct environmental impact of AI from a top-down approach, starting from general information and communication technologies (ICT) and then zooming in on data centers and the different phases of AI development and deployment. Secondly, a framework is introduced on how to assess both the direct and indirect impact of AI. Finally, we finish with good practices and what we can do to reduce AI impact.