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Climate change is a critical concern for HPC systems, but GHG protocol carbon-emission accounting methodologies are difficult for a single system, and effectively infeasible for a collection of systems. As a result, there is no HPC-wide carbon reporting, and even the largest HPC sites do not do GHG protocol reporting. We assess the carbon footprint of HPC, focusing on the Top 500 systems. The key challenge lies in modeling the carbon footprint with limited data availability. With the disclosed Top500.org data, and using a new tool, EasyC, we were able to model the operational carbon of 391 HPC systems and the embodied carbon of 283 HPC systems. We further show how this coverage can be enhanced by exploiting additional public information. With improved coverage, then interpolation is used to produce the first carbon footprint estimates of the Top 500 HPC systems. They are 1.4 million MT CO2e operational carbon (1 Year) and 1.9 million MT CO2e embodied carbon. We also project how the Top 500’s carbon footprint will increase through 2030. A key enabler is the EasyC tool which models carbon footprint with only a few data metrics. We explore availability of data and enhancement, showing that coverage can be increased to 98% of Top 500 systems for operational and 80.8% of the systems for embodied emissions.
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Predicting embodied carbon emissions from purchased electricity for United States counties
Predicting the embodied scope 3 carbon dioxide equivalent (CO2e) emissions from purchased electricity for end users in the United States is challenging due to electricity transmission within interconnected power grids. Existing methods only focus on large aggregation areas, thereby ignoring potentially significant emission factor (EF) variations, so this study proposes a novel method to translate the CO2e emissions from the balancing authority (BA)-level to the county-level by utilizing explicit finite-difference theory for electricity flow predictions, and then employing economic input–output theory to evaluate the scope 3 embodied lifecycle CO2e emissions. Results show that the generation-based EFs at the BA-level range from 0.007 to 0.905 MT-CO2e/MWh with a mean value of 0.400 MT-CO2e/MWh and a standard deviation of 0.229 MT-CO2e/MWh. The consumption-based EFs at the BA-level range from 0.008 to 0.836 MT-CO2e/MWh with a mean value of 0.378 MT-CO2e/MWh and a standard deviation of 0.019 MT-CO2e/MWh. Results also show that sixteen BA consumption-based EFs deviate by more than 20% compared to their generation-based EFs, which indicates the significance of accounting for electricity interchanges in emissions quantification processes. A larger range of possible consumption-based EFs is revealed at the county-level: 0.007 to 0.902 MT-CO2e/MWh, with a mean value of 0.452 MT-CO2e/MWh and a standard deviation of 0.123 MT-CO2e/MWh. Results also indicate significant variations in EFs of counties within each BA: 20 BAs have county-level EFs range greater than 0.1 MT-CO2e/MWh, 13 BAs have county-level EFs range greater than 0.2 MT-CO2e/MWh and 6 BAs have county-level EFs range beyond 0.3 MT-CO2e/MWh.
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- Award ID(s):
- 1738782
- PAR ID:
- 10287004
- Date Published:
- Journal Name:
- Applied energy
- Volume:
- 292
- ISSN:
- 0306-2619
- Page Range / eLocation ID:
- https://doi.org/10.1016/j.apenergy.2021.116898
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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