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:
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’s 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 ).
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.
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 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.
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 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.
With great computing power comes great responsibility
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.
We have published this short paper to the Climate Change AI workshop at NeurIPS 2019.
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.
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 intensity 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.