A Practical Guide to Quantifying Carbon Emissions for Machine Learning researchers and practitioners
Year: 2021
Type: report
Abstract: The goal of this short guide is to help the Machine Learning (ML) community better understand their carbon impact and to take steps to mitigate it. Carbon Tracking At the center of the climate crisis is a commonplace but very important concept: that of carbon dioxide (CO 2), low amounts of which occur naturally in the Earth's atmosphere, but its concentration has been rapidly increasing due to human activity. This increase is dangerous because of CO 2 's effect as a greenhouse gas, which contributes to global warming. It is therefore important to: 1) quantify the carbon impact of our actions; and 2) reduce, or mitigate, that impact in order to help slow down global warming and climate change more broadly.
Source: HAL
Authors: Ligozat, Anne-Laure, Luccioni, Sasha
Language: English
Topic: Green IT and Sustainability
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W4285580186