ML CO2 Impact

Machine Learning has a carbon footprint.

We've made a tool to help you estimate yours:


Compute your GPU's carbon emissions


Push for more transparency in our field by including the results in your publication (research paper, blog post etc.)


Install codecarbon to integrate carbon estimations in your Python workfow.

Compute your ML carbon impact
Loading Data


We believe that an important step towards reducing carbon emissions is the generalization of emissions reporting in papers, blog posts and publications in general.

To that end, here is a LateX template you can use in your publication to report the emissions you have calculated with the Calculator:

Generated Latex Template


\subsection{CO2 Emission Related to Experiments}

Experiments were conducted using {cloud provider} in region {region of compute}, which has a carbon efficiency of  kgCO$_2$eq/kWh. A cumulative of {hours used} hours of computation was performed on hardware of type {hardware type} (TDP of W).

Total emissions are estimated to be {emission} kgCO$_2$eq of which {percentage offset} percents were directly offset by the cloud provider.
%Uncomment if you bought additional offsets:
%XX kg CO2eq were manually offset through \href{link}{Offset Provider}.

Estimations were conducted using the \href{}{MachineLearning Impact calculator} presented in \cite{lacoste2019quantifying}.

  title={Quantifying the Carbon Emissions of Machine Learning},
  author={Lacoste, Alexandre and Luccioni, Alexandra and Schmidt, Victor and Dandres, Thomas},
  journal={arXiv preprint arXiv:1910.09700},

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This section will present some of the important concepts to better understand the carbon footprint of your ML research, including what providers do to help you offset it


At the center of the climate crisis is a commonplace but very important concept: that of carbon dioxide (CO2), 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 CO2'effect as a greenhouse gas, meaning that it absorbs and emits infrared radiation in the wavelength range emitted by the Earth, which contributes to the global warming of the planet ( IPCC Glossary ).

CO2 eq.

Since other gases such as methane, nitrous oxide or even water vapor also have this warming effect, a standardized measure for describing how much warming a given amount of gas will have is often provided in CO2-equivalents (CO2eq), for simplification purposes. For instance, the carbon intensity of transportation is measured in grams of CO2-equivalent per person-km, whereas the carbon intensity of energy is measured in grams of CO2-equivalent per kilowatt hour.

Renewable vs. Nonrenewable Energy

A key distinction to be made is that between renewable and nonrenewable energy sources. Renewable energy is collected from sources which are naturally replenished on a human timescale: this includes wind, sunlight, tides, geothermal heat, etc. In contrast, nonrenewable energy sources such as coal and petrol are not naturally replenished after their usage, and can take millions of years to be formed again. Finally, not all renewable energy sources are carbon-free, since the burning of biomass (such as plants or algae) still produces CO2-equivalents.

Carbon Offsetting

Carbon offsetting is a reduction in emissions of CO2-equivalents that is made in order to compensate for the emissions made by another actor. The money paid via carbon offsetting is invested towards different types of projects, including renewable energy or energy efficiency, carbon sequestration, methane abatement, among others. These projects are run by private companies, more often than not in a different place than where the offsetting is carried out. A large part of carbon offsetting goes towards funding renewable energy projects such as building wind or solar farms, hydroelectric dams, and extracting biofuel, in the hopes of reducing the cost of renewable production.

Renewable Energy Credits

An indirect form of offsetting that is often used by large companies is the purchasing of Renewable Energy Credits (RECs), which involves directly purchasing quantities of energy produced by renewable sources. This is considered to be an indirect of offsetting because in order to convert RECs into offsets, the energy purchased is translated into carbon reductions under the assumption that the clean energy is displacing an equivalent amount of electricity produced by non-renewable methods. Other commonly used forms of carbon offsetting include reforestation, sequestration, replacement of legacy equipment such as coal-burning stoves by more modern ones, etc.

Carbon Neutral

Carbon neutral is a term used to indicate a net zero carbon footprint of an individual or organization. While a small part of carbon-neutral entities rely entirely on renewable energy that is zero-carbon, the large majority of entities balance their emissions of CO2-equivalents, using approaches such as carbon offsetting or the purchase of RECs. Many major technology companies such as Google and Microsoft are carbon-neutral, matching 100% of their electricity use with renewable energy purposes (Google, 2018; Microsoft, 2018). This does not mean, however, that their operations do not produce CO2-equivalents, since this is dependent on the energy sources of the locations in which their infrastructure is located.

Here are a few maps from Our World In Data which illustrate the complexity of carbon emissions mitigations. For a complete analysis, visit their webstie

greenhouse gas scenarios

Carbon Efficiency

Carbon efficiency: the amount of CO2 emitted per unit energy (grams of CO2 emitted per kilowatt-hour). This is largely related to a country’s energy mix. An economy powered by coal-fired energy will produce higher CO2 emissions per unit of energy versus an energy system with a high percentage of renewable energy. As economies increase their share of renewable capacity, efficiency improves and the amount of CO2 emitted per unit energy falls.

There are things you can do

With great computing power comes great responsibility

Choose your cloud provider wisely

They don't all buy offsets, RECs or invest equally in clean energy sources. Read their sustainability commitments and make an informed choice.
Choose your region

Different regions are powered by different combinations of renewable and non-renewable energy. If regulations and legal aspects allow, select a more sustainable region.
Buy Carbon Offsets

Many platforms provide easy ways to offset your emissions. You can have a big impact by proposing offsetting within your organization. It is also worth doing in your personal life too, for instance by offsetting flights.
Don't Do Grid Search

Hyperparameter search is a great source of ML-related carbon emissions. If there is no literature to help you establish good values and you need to look for relevant ones, at least do it randomly, not via grid search.
Choose Clean Energy

Your organization may have a choice in their power supply. You may too. Choosing a cleaner and more sustainable source of energy will go far in decreasing your carbon impact
Push for more transparency

Include a dedicated section about the carbon emissions of your procedure whenever publishing work (be it a peer-reviewed paper or a blog post)

Fill Out Our Survey

We'd like to understand your general AI usage and your position on ML's emissions. Guaranteed < 1min :)


About ML CO2 Impact

Progress in Machine Learning (ML) in recent years has been meteorical, with major breakthroughs happening in domains such as machine translation, image recognition and generation. While these advances are having widespread applications in many domains, detecting cancers on X-rays and improving the prediction of supply and demand,the carbon impact of ML training has not been a central part of the conversation until recently.

Therefore, as ML practitioners who are also aware of the overall state of the environment, we find that it is important to develop this conversion further and work towards building the tools we need to assess the carbon emissions generated by the models we train, as well as ways to reduce those emissions.

Read our paper

We have published this short paper to the Climate Change AI workshop at NeurIPS 2019.

Look at the Data

The data is hosted on Github to allow for external contributions

The sources are available in the csv file above. Use issues and pull-requests to suggest modifications/additions. The data used for the providers' offset ratio (how much of their emissions they offset) comes from these sources: Google, Microsoft, Amazon. For the latter we assumed non-carbon neutral regions were not offset at all.

We have looked for data about the main cloud providers. If you'd like to contribute and add more providers, please follow these guidelines.

Ongoing work: how to help

We're currently working towards enabling you to compute your emissions when using a private infrastructure. This would require either mapping cities to carbon emissions of local grids or expecting our users (you) to input such a value. The mapping does not (?) exist for now (and for free), and we feel like most users would not know the carbon efficiency of their electricity grid. This is why this is not yet available.

Any help with this issue is welcome, please get in touch.

If you wish to add a cloud provider to our list, here's how.


Victor Schmidt

Victor is a PhD candidate at Mila with Prof. Yoshua Bengio. He focuses on different areas where AI can help mitigate Climate Change. Amongst others, his main focus is on creating visualizations of Climate Change's impact to help the public better understand it. Prior to his PhD, Victor worked as a Public Interest Entrepreneur in France. He obtained his engineering degree from Ecole polytechnique and an MSc. in Machine Learning from UCL.

Alexandra (Sasha) Luccioni

Sasha is a Postdoctoral Researcher working on AI for Humanity initiatives at Mila - Quebec AI Institute, under the supervision of Yoshua Bengio. She obtained her PhD in Cognitive Computing from UQAM in 2018 and spent two years working in applied ML, specifically in applying deep learning and NLP to different industrial applications. She is highly involved in community initiatives, serving on the Research and Policy Committee of Women in Machine Learning (WiML) and on the Advisory board of Kids Code Jeunesse.

Alexandre Lacoste

Alexandre is a research scientist at Element AI. His research interests revolve around multi-task transfer learning, probabilistic machine learning and causal inference. Prior to Element AI, he worked at Google for 3 years on large scale question answering systems using machine learning. He obtained his PhD in theoretical machine learning where he developed bridges between PAC-Bayes and Bayes theories.

alexandre lacoste

Thomas Dandres

After working a few years in the field of energy and climate change (GRAME), Thomas Dandres completed a Ph.D. in CIRAIG (with Prof. Rejean Samson, Polytechnique Montreal) in life cycle assessment (specialization: energy policy analysis) and conducted postdoctoral researches in Synchromedia (with Prof. Mohamed Cheriet, Ecole de technologie superieure) on sustainable ICT (green cloud computing and smart buildings). From 2015 to 2019, he has been a research officer with CIRAIG/Synchromedia and worked on projects related to energy, industry 4.0, ICT and machine learning (energy predictive models). Since 2019, he has started working for Hydro-Quebec (Direction environnement) as an external environmental specialist and CIRRELT as the scientific coordinator of the Chair of transport transformation (Prof. Bernard Gendron, University of Montreal).

thomas dandres