New regulations and accounting standards have laid out that businesses will report emissions for years to come. The announcements reveal an integrated approach to emissions reporting, with financial and sustainability data put into a single reporting framework. This sets the stage for reliable and comparable emissions data from businesses, including emissions from supply chains.

But today, I would sum up the quality of emissions data in one word: abysmal. Emissions data is stale, incomplete, defined in different ways and not well matched to how it is used.

We’re far from a set of emissions data that enables apples-to-apples comparisons across time and reporting entities. No one would run their business on data of this quality—but with the fast-moving regulations and standards for emissions reporting, this is exactly what businesses are being asked to do.

The Main Sources Of Emissions Data

At the heart of every bit of emissions reported by businesses is the simple equation:

Emissions = Activity x Emissions Factor

In accounting, activities are easily and rigorously measured. Activities include miles traveled, energy purchased (such as kWh, therms, etc.) or the number of units purchased. Activity data are produced by financial and accounting systems by well-established methods, and as a result, have that apples-to-apples quality. But where do emissions factors come from and how reliable are they?

There are three main sources of emissions data:


Sensors can be placed on equipment or vehicles to directly measure the emissions released. Credible data requires sensor calibration and testing, documented processes for data handling, and a tested methodology to ensure completeness so that all emissions from the source are captured. None of this happens today.


Every country has national income accounts, a report that shows inflows and outflows to the national economy by sector. These accounts can be used to trace the use of energy across industries, and the emissions from energy use can be derived sector by sector. The data format is known as an input-output table, essentially a structured spreadsheet of input industries (rows) and output industries (columns).

But the data is coarse: In the U.S., the input-output table covers 435 industries. In France, the national economy is broken up into 19 sectors. Algeria’s economy is shown as five sectors. Also, the latest input-output table for emissions data in the U.S. reflects economic data from 2017.

Because input-output tables are a single, unified view of a nation, the cross-industry flows are noted by the amount spent, not units shipped. If the auto industry spent $1.2 trillion on steel in a year, which itself contains $400 million of spend on energy, all the data must be transformed into units of activity to derive the kilograms of CO2 per ton of steel. Despite best efforts, this is intrinsically a tough task with a lot of error and approximation.


While a consulting industry exists to help businesses calculate emissions, consultants have no new emissions data. They use the bill of materials (a list of the product’s ingredients) to mix and match sensor data and emissions data from governments. No new data is created, and existing data is just repackaged.

Once a business reports its emissions, a second industry refines and scrubs that data for use by investors and capital market analysts. Again, no new data is introduced; existing data is just repackaged.

As a result, our existing emissions data system is constantly recycling industry-average data from government reports with a sprinkle of sensor data. Businesses can’t show emissions reductions as they lack the necessary accurate emissions data. Numerous studies have shown that the current system produces confusing results with little consistency over time or across industries.

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