Untangling Carbon-free Energy Attribution and Carbon Intensity Estimation for Carbon-aware Computing
E-Energy ’24, June 04–07, 2024, Singapore, Singapore
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
ACM|The 15th ACM International Conference on Future and Sustainable Energy Systems
2024
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| Online Access: | https://hdl.handle.net/1721.1/155784 |
| _version_ | 1811084563442565120 |
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| author | Maji, Diptyaroop Bashir, Noman Irwin, David Shenoy, Prashant Sitaraman, Ramesh K. |
| author_facet | Maji, Diptyaroop Bashir, Noman Irwin, David Shenoy, Prashant Sitaraman, Ramesh K. |
| author_sort | Maji, Diptyaroop |
| collection | MIT |
| description | E-Energy ’24, June 04–07, 2024, Singapore, Singapore |
| first_indexed | 2024-09-23T12:52:46Z |
| format | Article |
| id | mit-1721.1/155784 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T12:52:46Z |
| publishDate | 2024 |
| publisher | ACM|The 15th ACM International Conference on Future and Sustainable Energy Systems |
| record_format | dspace |
| spelling | mit-1721.1/1557842024-09-09T04:45:33Z Untangling Carbon-free Energy Attribution and Carbon Intensity Estimation for Carbon-aware Computing Maji, Diptyaroop Bashir, Noman Irwin, David Shenoy, Prashant Sitaraman, Ramesh K. E-Energy ’24, June 04–07, 2024, Singapore, Singapore Many organizations, including governments, utilities, and businesses, have set ambitious targets to reduce carbon emissions for their Environmental, Social, and Governance (ESG) goals. To achieve these targets, these organizations increasingly use power purchase agreements (PPAs) to obtain renewable energy credits, which they use to compensate for the “brown” energy consumed from the grid. However, the details of these PPAs are often private and not shared with important stakeholders, such as grid operators and carbon information services, who monitor and report the grid’s carbon emissions. This often results in incorrect carbon accounting, where the same renewable energy production could be factored into grid carbon emission reports and separately claimed by organizations that own PPAs. Such “double counting” of renewable energy production could lead organizations with PPAs to understate their carbon emissions and overstate their progress toward sustainability goals, and also provide significant challenges to consumers using common carbon reduction measures to decrease their carbon footprint. Unfortunately, there is no consensus on accurately computing the grid’s carbon intensity by properly accounting for PPAs. The goal of our work is to shed quantitative and qualitative light on the renewable energy attribution and the incorrect carbon intensity estimation problems. 2024-07-24T18:34:20Z 2024-07-24T18:34:20Z 2024-05-31 2024-06-01T07:55:59Z Article http://purl.org/eprint/type/ConferencePaper 979-8-4007-0480-2 https://hdl.handle.net/1721.1/155784 Maji, Diptyaroop, Bashir, Noman, Irwin, David, Shenoy, Prashant and Sitaraman, Ramesh K. 2024. "Untangling Carbon-free Energy Attribution and Carbon Intensity Estimation for Carbon-aware Computing." PUBLISHER_CC en 10.1145/3632775.3662164 Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ The author(s) application/pdf ACM|The 15th ACM International Conference on Future and Sustainable Energy Systems Association for Computing Machinery |
| spellingShingle | Maji, Diptyaroop Bashir, Noman Irwin, David Shenoy, Prashant Sitaraman, Ramesh K. Untangling Carbon-free Energy Attribution and Carbon Intensity Estimation for Carbon-aware Computing |
| title | Untangling Carbon-free Energy Attribution and Carbon Intensity Estimation for Carbon-aware Computing |
| title_full | Untangling Carbon-free Energy Attribution and Carbon Intensity Estimation for Carbon-aware Computing |
| title_fullStr | Untangling Carbon-free Energy Attribution and Carbon Intensity Estimation for Carbon-aware Computing |
| title_full_unstemmed | Untangling Carbon-free Energy Attribution and Carbon Intensity Estimation for Carbon-aware Computing |
| title_short | Untangling Carbon-free Energy Attribution and Carbon Intensity Estimation for Carbon-aware Computing |
| title_sort | untangling carbon free energy attribution and carbon intensity estimation for carbon aware computing |
| url | https://hdl.handle.net/1721.1/155784 |
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