Estimation of PanDA job carbon footprint

1. Carbon emissions and global warming background

Carbon is a chemical element found in various forms on Earth. One of the most common forms is carbon dioxide (CO2), a greenhouse gas naturally present in the atmosphere.

Global warming refers to the long-term increase in Earth’s average surface temperature. It is primarily driven by the greenhouse effect, where certain gases, including CO2, trap heat from the sun in the atmosphere, preventing it from escaping into space. This results in a gradual rise in global temperatures.

Human activities, such as burning fossil fuels (coal, oil, and natural gas) and deforestation, have significantly increased the concentration of CO2 in the atmosphere. This enhanced greenhouse effect has led to accelerated global warming, resulting in various environmental impacts, including rising sea levels, more frequent and severe weather events, and disruptions to ecosystems.

Efforts to address global warming include reducing carbon emissions, transitioning to cleaner energy sources, and implementing international agreements like the Paris Agreement to limit temperature increases and mitigate its consequences.

The ATLAS experiment must continue its scientific program to unlock fundamental insights into particle physics. However, it is also conscientious about estimating and minimizing CO2 emissions and adopting sustainable practices to reduce its carbon footprint and contribute to a more environmentally responsible research endeavor. PanDA, as ATLAS’ workflow management system, wants to help by raising awareness on the emissions of computing workloads.

More information on Global warming:

For comparisons to the emissions from other sources, see:

2. Emission intensities of the power grids

PanDA periodically gathers data on the regional emission intensities (measured in gCO2/kWh) of the power grids in various regions where ATLAS Grid computing centers are operational. These regions can range from individual countries (e.g. Europe) to states in larger countries (e.g. the USA). Emission intensities within a region can vary throughout the day, depending on the energy sources available, such as solar, wind, oil, coal, or nuclear power.

Our primary source for regional emission intensity data is:

PanDA also combines these regional intensities to calculate a global emission intensity, which considers the contribution of each region to the overall ATLAS Grid computing capacity.

3. Estimation of a job’s carbon footprint

In the next step, PanDA calculates the carbon footprint for each job using the following formula:


The core_power_consumption is currently a fixed estimate of the energy consumption of the hardware or computing center. There is an option for sites to update this value in the CRIC information system, and over time, we aim to have more accurate information.

The emission intensity is integrated over the job’s runtime. We compute the carbon footprint of a job by considering both the regional and global emission intensities.

4. Presentation of the carbon footprint information

We have the capability to aggregate carbon footprint data at various levels, including tasks, users, sites, and the entire Grid. We are gradually incorporating this information into monitoring, accounting, and task summary emails.

As a general practice, users and task submitters will view estimates using the global emission intensity rather than regional data. This choice is made for three primary reasons:

  • First, our data is not yet sufficiently detailed to understand which sites are greener than others; hardware lifetimes and lifecycles, the use of renewable energy, how data centers are constructed, and how waste heat is used are among the many considerations when comparing total carbon footprints of sites.

  • Second, pledged CPU does not sit idle — if a user moves their job to another site, a production job will trade places with it, and the total worldwide carbon footprint will be conserved.

  • Third, many users forcing jobs to a limited number of sites will generate a backlog of jobs at that site, as well as additional pressure on network and disks, causing operational difficulties, delays for users and potentially an increased total carbon footprint.