Carbon accounting is an essential tool for companies to measure and manage their greenhouse gas (GHG) emissions and to align their strategies with climate goals. However, carbon accounting is not an exact science. It involves many assumptions, estimations, and uncertainties that can affect the accuracy and reliability of the results. How can sustainability consultants deal with these uncertainties and communicate them effectively to their clients? How can they use software to simplify and streamline the uncertainty analysis process?
In this blogpost, we will recap the main points from our webinar (april 2024) where we discussed the topic of uncertainty quantification in carbon accounting. We will explain why uncertainty assessment is important, what the Greenhouse Gas Protocol (GHGP) recommends, and how we have implemented uncertainty quantification in our software, Carbon+Alt+Delete.
Why uncertainty assessment is important
Carbon accounting is based on the calculation of GHG emissions from various sources and activities, using activity data (such as energy consumption, fuel use, or travel distance) and emission factors (such as kgCO₂e per kWh, per liter, or per km). Both activity data and emission factors are subject to uncertainty, which can arise from various sources, such as measurement errors, data gaps, estimation methods, or temporal, spatial, or technological variability. For example, the certainty on activity data for business travel can depend on the accuracy of invoices, on the method of data collecting and on the interpretation of travel logs. The emission factor value for the use of electricity on the other hand, can vary depending on the time and the location of application, and on the accuracy of emissions data going into the calculation. The activity data for business travel can depend on the accuracy of invoices, receipts, or mileage logs.
Uncertainty assessment is crucial for properly interpreting the results of carbon accounting and for making informed decisions based on them. Uncertainty assessment can help to:
- Comply with the GHGP reporting requirements (see next section).
- Check the robustness and validity of the carbon footprint results and avoid over- or underestimation of emissions.
- Identify the sources of uncertainty and the data quality issues, and prioritize the improvement actions.
What the Greenhouse Gas Protocol recommends
The GHGP is the most widely used and accepted standard for corporate and product carbon accounting. The GHGP provides guidance and tooling for uncertainty assessment, both qualitatively and quantitatively. Qualitative uncertainty assessment involves describing the uncertainty sources and their impacts using words, such as high, medium, or low, or using a grading matrix. Quantitative uncertainty assessment involves expressing the uncertainty using numerical values, such as ranges, confidence intervals, or probability distributions.
The GHGP requires uncertainty assessment for product carbon accounting, and recommends it for scope 3 carbon accounting. The GHGP suggests that uncertainty assessment should be done at least qualitatively, but preferably quantitatively, as this provides more objective and consistent information. The GHGP also provides a methodology and a tool for quantifying and propagating uncertainty through the GHG inventory, using a Monte Carlo simulation approach. However, this approach can be challenging to implement in practice, as it requires a lot of data, time, and mathematical skills.
How we have implemented uncertainty quantification in our software
At Carbon+Alt+Delete, we have developed a software solution that simplifies and streamlines the carbon accounting process for sustainability consultants. Our software allows you to calculate the carbon footprint of your clients, using the analytical GHGP methodology, and to generate reports and insights that help them to reduce their emissions and achieve their climate goals. Our software also includes a feature that enables you to quantify and visualize the uncertainty of your carbon footprint results, using a simplified and user-friendly approach that is based on the GHGP principles.
Our approach consists of the following steps:
- Step 1: Our user decides the uncertainty on the activity data provided. For this they choose a single parameter uncertainty for each activity data point, using a predefined list of options that reflect the data quality and representativeness. For example, you can choose between very good, good, fair, or poor, depending on the source and accuracy of your data. Each option corresponds to a lognormal distribution with a geometric standard deviation (GSD) that defines the 95% confidence interval for that parameter. For example, a good certainty assesment means that the activity data value can vary between -10% and +10% with 95% certainty.
- Step 2: A matching emission factor is identified, and our platform automatically assigns an uncertainty quantification of that emission factor, based on information from the source. This information can either be a direct numerical uncertainty interval, or it can be in the form of a parameter uncertainty assessment on the individual emission factor, using a similar framework as described in step 1. For example a fair certainty assessment means that the emission factor value can vary between -35% and +60% with 95% certainty.
- Step 3: We propagate the parameter uncertainty of both the activity data and the emission factor through the GHG inventory, using a formula that aggregates the uncertainty of each parameter according to its sensitivity (i.e., its relative influence on the inventory subtotal or total). The formula calculates the GSD of the inventory subtotal or total, which can be converted into a 95% confidence interval. For example, if the inventory subtotal is 570 tCO₂e, and the GSD² is 1.17, the confidence interval is between 487 tCO₂e and 667 tCO₂e.
- Step 4: We visualize the uncertainty of the GHG inventory, using charts and tables that show the uncertainty ranges and the uncertainty contributions of each parameter. You can also compare the uncertainty of different scenarios, such as baseline and target, or different years, to see how the uncertainty affects the emission reduction performance.
Conclusion
Uncertainty quantification is an important aspect of carbon accounting, as it helps to improve the data quality, the result interpretation, and the decision making. The GHGP provides a standard methodology and a tool for uncertainty quantification, but they can be difficult to apply in practice.
Our software, Carbon+Alt+Delete, offers a simplified and user-friendly solution for uncertainty quantification, based on the GHGP principles, that allows you to quantify and visualize the uncertainty of your carbon footprint results with ease and confidence.
If you want to learn more about our software and our uncertainty quantification feature, you can visit our website, our support pages, or our community forum, where you can find more information and resources on carbon accounting topics.
For further discussions or queries, feel free to reach out at info@carbonaltdelete.eu.