  In recent years, there has been an increased emphasis on reducing the carbon
emissions from electricity consumption. Many organizations have set ambitious
targets to reduce the carbon footprint of their operations as a part of their
sustainability goals. The carbon footprint of any consumer of electricity is
computed as the product of the total energy consumption and the carbon
intensity of electricity. Third-party carbon information services provide
information on carbon intensity across regions that consumers can leverage to
modulate their energy consumption patterns to reduce their overall carbon
footprint. In addition, to accelerate their decarbonization process, large
electricity consumers increasingly acquire power purchase agreements (PPAs)
from renewable power plants to obtain renewable energy credits that offset
their "brown" energy consumption. There are primarily two methods for
attributing carbon-free energy, or renewable energy credits, to electricity
consumers: location-based and market-based. These two methods yield
significantly different carbon intensity values for various consumers. As there
is a lack of consensus which method to use for carbon-free attribution, a
concurrent application of both approaches is observed in practice. In this
paper, we show that such concurrent applications can cause discrepancies in the
carbon savings reported by carbon optimization techniques. Our analysis across
three state-of-the-art carbon optimization techniques shows possible
overestimation of up to 55.1% in the carbon reductions reported by the
consumers and even increased emissions for consumers in some cases. We also
find that carbon optimization techniques make different decisions under the
market-based method and location-based method, and the market-based method can
yield up to 28.2% less carbon savings than those claimed by the location-based
method for consumers without PPAs.
