Measuring Algorithm Carbon Footprint

The carbon footprint of an algorithm depends on two factors: the energy needed to run it and the pollutants emitted when producing such energy. The former depends on the computing resources used (e.g., number of cores, running time, and data center efficiency) while the later, called carbon intensity, depends on the location and production methods used (e.g., nuclear, gas, or coal).
There are several competing definitions of “carbon footprint,” and in this project, the extended definition from Wright et al.^[76] was used. The climate impact of an event is presented in terms of carbon dioxide equivalent (CO₂e) and summarizes the global warming effect of the GHG emitted in the determined timeframe, here running a set of computations. The GHGs considered were carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O);^[77] these are the three most common GHGs of the “Kyoto basket” defined in the Kyoto Protocol^[78] and represent 97.9% of global GHG emissions.^[79] The conversion into CO₂e was done using Global Warming Potential (GWP) factors from the Intergovernmental Panel on Climate Change (IPCC)^[77, ^80] based on a 100‐year horizon (GWP100).
When estimating these parameters, accuracy and feasibility must be balanced. This study focused on a methodology that could be easily and broadly adopted by the community and therefore, restricts the scope of the environmental impact considered to GHGs emitted to power computing facilities for a specific task. Moreover, the framework presented requires no extra computation, nor involves invasive monitoring tools.