Counting Carbon: A Survey of Factors Influencing the Emissions of Machine Learning

TL;DR

This survey analyzes the carbon emissions of 95 machine learning models across various tasks, examining their energy sources, emissions over time, and relation to performance, highlighting environmental impacts in ML.

Contribution

It provides a comprehensive overview of ML model emissions, covering diverse models and tasks, and advocates for a centralized emissions reporting repository.

Findings

Emissions vary significantly across models and tasks.

Energy source impacts the carbon footprint.

Emissions have evolved over time with model complexity.

Abstract

Machine learning (ML) requires using energy to carry out computations during the model training process. The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the energy source. Existing research on the environmental impacts of ML has been limited to analyses covering a small number of models and does not adequately represent the diversity of ML models and tasks. In the current study, we present a survey of the carbon emissions of 95 ML models across time and different tasks in natural language processing and computer vision. We analyze them in terms of the energy sources used, the amount of CO2 emissions produced, how these emissions evolve across time and how they relate to model performance. We conclude with a discussion regarding the carbon footprint of our field and propose the creation of a centralized repository for reporting and tracking these emissions.