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