Untangling Carbon-free Energy Attribution and Carbon Intensity Estimation for Carbon-aware Computing
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866911771188527104 |
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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. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_06680 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Untangling Carbon-free Energy Attribution and Carbon Intensity Estimation for Carbon-aware Computing Maji, Diptyaroop Bashir, Noman Irwin, David Shenoy, Prashant Sitaraman, Ramesh K. Distributed, Parallel, and Cluster Computing 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. |
| title | Untangling Carbon-free Energy Attribution and Carbon Intensity Estimation for Carbon-aware Computing |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2308.06680 |