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Many organizations, including governments, utilities, and businesses, haveset ambitious targets to reduce carbon emissions as a part of theirsustainability goals. To achieve these targets, these organizationsincreasingly use power purchase agreements (PPAs) to obtain renewable energycredits, which they use to offset their ``brown'' energy consumption. However,the details of these PPAs are often private and not shared with importantstakeholders, such as grid operators and carbon information services, whomonitor and report the grid's carbon emissions. This often results in incorrectcarbon accounting where the same renewable energy production could be factoredinto grid carbon emission reports and also separately claimed by organizationsthat own PPAs. Such ``double counting'' of renewable energy production couldlead to organizations with PPAs to understate their carbon emissions andoverstate their progress towards their sustainability goals. Further, we showthat commonly-used carbon reduction measures, such as load shifting, can havethe opposite effect of increasing emissions if such measures were to useinaccurate carbon intensity signals. For instance, users may increase energyconsumption because the grid's carbon intensity appears low even though carbonintensity may actually be high when renewable energy attributed to PPAs areexcluded. Unfortunately, there is currently no consensus on how to accuratelycompute the grid's carbon intensity by properly accounting for PPAs. The goalof our work is to shed quantitative light on the renewable energy attributionproblem and evaluate its impact of inaccurate accounting on carbon-awaresystems.

Untangling Carbon-free Energy Attribution and Carbon Intensity Estimation for Carbon-aware Computing