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

Many organizations, including governments, utilities, and businesses, have set ambitious targets to reduce carbon emissions as a part of their sustainability goals. To achieve these targets, these organizations increasingly use power purchase agreements (PPAs) to obtain renewable energy credits, which they use to offset their ``brown'' energy consumption. 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 also separately claimed by organizations that own PPAs. Such ``double counting'' of renewable energy production could lead to organizations with PPAs to understate their carbon emissions and overstate their progress towards their sustainability goals. Further, we show that commonly-used carbon reduction measures, such as load shifting, can have the opposite effect of increasing emissions if such measures were to use inaccurate carbon intensity signals. For instance, users may increase energy consumption because the grid's carbon intensity appears low even though carbon intensity may actually be high when renewable energy attributed to PPAs are excluded. Unfortunately, there is currently no consensus on how to accurately compute the grid's carbon intensity by properly accounting for PPAs. The goal of our work is to shed quantitative light on the renewable energy attribution problem and evaluate its impact of inaccurate accounting on carbon-aware systems