Manual measurement systems
Measuring enterprise-wide carbon emissions is currently an extremely manual process in most businesses. They lack the right systems infrastructure to pull data from OT and IT systems while also incorporating manual data inputs, leading to a data gap, both in terms of data quality and data processing.
Rather than a repeatable, scalable process for gathering, normalizing, conditioning, and analyzing carbon emissions data, businesses rely on a small team with a 50-tab Excel document performing the calculus themselves, putting it into a report, and hoping for the best.
A lack of methodological rigor
This is true on both a micro and macro level. For the reasons listed above, different enterprises have developed different methodologies for interpreting and presenting carbon emissions data. That makes comparisons between businesses extremely difficult, and even within businesses there’s an incentive to cherry-pick the methodologies that show the enterprise in a best light rather than compared to an agreed standard.
Data is retrospective and disconnected from decision-making
Businesses often only get figures on carbon emissions six to nine months after those emissions happened. And, even then, because of the manual nature of the collection and analysis there may well be disagreement over the accuracy of the data.
Both factors mean that carbon emissions data is highly retrospective and disconnected from decision making at both an enterprise and operational level. The data simply doesn’t have the fidelity or resolution levels required to make holistic decisions like a price on carbon or a plan to reduce carbon emissions.