Carbon Emissions Measurements & Calculations Methodologies in Shipping: Which is the Most Accurate?
When taking steps toward reducing carbon emissions, there is a necessary first step of measurement and calculation to find where the organization currently stands. This step establishes the baseline and is used for comparison when making changes and examining the results. This baseline must be as accurate as possible.
However, with the massive task of measuring Scope 3 carbon emissions, it can be a lot like trying to estimate the number of windows in New York City. There is a definite answer in both cases, but finding the most accurate number will take careful planning and methodology. In the window analogy, the options range from counting windows manually to using estimates for each building and car to reach a total. For CO2 emissions, the parallel is either totaling exact measurements or using estimates for each ship, aircraft, or truck, for starters.
By examining the methodology, we can find the one that produces the most accurate results on which to base decisions for reducing CO2 emissions.
Types of Carbon Emission Data Approaches
Unfortunately, measuring an organization’s total Scope 3 carbon emissions is not simple, mainly because there are many moving parts. In fact, there are 15 categories of Scope 3 emissions, which include sources like capital goods, investments, processing of sold products, and transportation and distribution. Within transportation and distribution, each ship, aircraft, truck, or train has various influencing factors, such as the engine type, cargo capacity, speed, and route, even within the same carrier. All these factors, plus a large number of shipments, make it challenging to gather all the needed data and draw the correct conclusions, due to the risk of mistakes and omissions. This is why the approach matters. The options are to use primary data, default data, or modeled data.
Primary data is gathered directly from the source. In theory, this makes it the most precise data possible since it does not involve estimation, only measurements. Examples of primary data may be the fuel consumption of a particular vessel for a specific journey or the distance traveled. What would not be considered primary data is taking the distance between two points if the actual route traveled by the vessel is unknown–this would only be an estimate, not an exact measurement.
Default data is generally accepted, standard data that can be used in place of primary data. It is indirectly derived from primary data to get values that are as accurate as possible; however, it is by nature less precise than primary data. The GLEC framework, the only one in its field used globally, provides default data values. The Smart Freight Centre (SFC) created the GLEC framework in 2016 to bring consistency to calculations and initiatives for carbon emissions and CO2e, carbon dioxide equivalent. Having a standard way to measure and report emissions allows for better data and comparison within global logistics. The SFC is also releasing a new ISO standard (ISO 14083) that builds on the GLEC framework, creating a new go-to guideline.
Within these standards, organizations can find default values, or averages, that assist with their calculations. Default data is basically predetermined values that stand in for primary data, which leads to less accuracy. When measuring NYC to Busan, averages from default data will result in a calculated carbon dioxide equivalent of 1.27 t/TEU. However, using actual measurements—the actual distance, actual vessel IMO, and actual speed—the calculation can range from 0.736 to 1.518 CO2e t/TEU, leaving a significant opportunity for inaccuracy.
Modeled data is an alternative with a high degree of accuracy compared to default data. It uses primary data in addition to data derived from other sources, such as IMO numbers for ships, airline and aircraft codes, carriers, services, equipment types, vessel speeds, and more, to ensure the model is specific to the exact shipment details. Each of these factors can also have sub-categories to research for more detail. With more specificity, it requires more work, but the result is better accuracy for the resulting data set. Modeled data essentially relies on other sources, in addition to primary data when available, to inform and ensure specific data is used in the correct context.
Which Data is More Accurate, and Why?
Between the options of primary, default, and modeled data for calculating baseline freight emissions, it might seem that primary data is the preferred method. To some degree, this is true—after all, exact measurements should have the most accuracy. However, in practice, primary data runs the risk of not showing the full scenario, even leading organizations to incorrect conclusions.
For further clarity, it’s important to understand the difference between two terms often used interchangeably—precision and accuracy.
Precision is how likely a result can be reproduced. This speaks nothing to whether the result or conclusion is correct. Accuracy is the correctness or how close the result is to the true or accepted value.
A dartboard analogy helps visualize this. Precision is a person throwing a series of darts that land clustered together. If the cluster is not at the bullseye, they have precision but not accuracy. If they have accuracy but not precision, the darts land somewhat spread out, but the average of all the darts’ positions equates to the bullseye.
Remember, the goal of the CO2 baseline calculation is accuracy, first and foremost, as close as possible to the actual value for the shipment. In theory, primary data serves this purpose best. However, in reality, there are availability constraints for primary data, and this obstacle makes it difficult to use in many situations. If it’s measuring the CO2 output of a well-connected truck fleet, it might be easy in regions where RFID technology is a standard. In disconnected areas, the primary data approach doesn’t work due to missing tracking devices. Further, primary data doesn’t give any information about alternative routes or influencing factors that can be mitigated, such as changing the mode of transport, avoiding congested areas, or optimizing the route. Primary data can gloss over different factors that require research—and a careful model—to understand when the data is and isn’t accurate.
In Searoutes’ research of methodologies and experience measuring carbon emissions, modeled data is the most accurate. With modeled data, organizations can better move toward an optimal or reduced CO2 output, given the foundation established by the baseline data.
In an ideal world, primary data would be available for every detailed scenario, taking into consideration all factors like vessel type, exact distance, exact speed, etc. Since this is not currently possible, the result is often inaccurate and unreliable due to using default data compared to well-researched modeled data.
The modeled data methodology can be tailored to the needed factors, using data from various sources to achieve the best accuracy. Moreover, it can use historical data and future predictions to clarify reduction potentials and the foundation for actionable sustainability strategies that are achievable in practice, not just on paper.
Let’s take an example illustrating that methodology matters. A company has used averages—like average distance and average speed—to determine their baseline CO2 shipping emissions; from this, they develop a plan to reduce it. If their method of measurement and calculation is not accurate and precise enough to detect the changes made, they won’t be able to prove they reduced their emissions. The data, using averages, will still show the same average distance and speed, but the route may have gotten shorter, the ship may be larger, and the speed may be slower. All this should point to reduced carbon emissions, but without accurate reporting, the data won’t show this result. Only with modeled data can the company get this level of accuracy and precision.
Get Accurate CO2 Data with the Right Solution
Between accuracy and precision, there is no denying that accuracy should be the top priority for data measurement; however, with modeled data, organizations can get data that is both accurate and precise. Without this, organizations would come to the wrong conclusions, preventing them from taking real action, or they would be unable to evaluate the results of their efforts toward reducing carbon emissions.To further evaluate and use data effectively, organizations must have a system for making the data easy to understand, such as a dashboard to provide information at a glance and detail for deeper analysis. Searoutes offers APIs that make this valuable CO2 data accessible and integrated with an existing system. Our APIs provide quality reporting backed by accurate methodology and enable future plans to be built out, like itineraries for different shipments. As the only provider of freight shipping emissions data that is also an API-first company, Searoutes focuses on the technology and methodology to make it easy for any company to use carbon emission data. To learn more, reach out to book a Searoutes demo.