by Lotte Schmidt and Maike Reichert

The collection of scope 3 emissions data poses major challenges for companies. There are several crucial considerations: the data type, the data quality, the data collection method, and the possibility of closing data gaps, thereby creating transparency. Before beginning scope 3 data collection, it is vital to have clear objectives to be able to determine these points.  

 

Why is scope 3 emissions data important for calculating carbon footprints?  

Scope 3 includes all greenhouse gas emissions that occur up and down a company's value chain. These emissions result from a company’s activities and therefore are not directly controlled by the company. On average, three quarters of a company's total carbon footprint falls within scope 3.    

Accounting for and reducing scope 3 emissions is therefore an important task. However, many manufacturers face difficulties in collecting the required data from their suppliers. In a study by the Task Force on Climate-Related Financial Disclosures (TCFD), 80% of companies preparing a TCFD report indicated that disclosing scope 3 emissions data is difficult or quite difficult.   

By engaging with suppliers in the data collection process, companies can improve the quality of data obtained and evaluate the role of primary data in measuring their own carbon footprint. 

 

Different types of data: What are primary and secondary data?   

In the context of scope 3 emissions, primary data refers to emissions data based on a supplier’s energy and material flows. On the other hand, secondary data refers model-based data derived from material-based emission factors (e.g. GEMIS, ecoinvent, etc.).   

The type of data chosen depends on the goal of the data evaluation. For example, if the aim is to understand the relative extent of different Scope 3 activities and identify emission hotspots, secondary data can often suffice. Secondary data can also serve to close data gaps, for example with reference to expert studies or examples from another company. But if the aim is to develop specific decarbonisation targets for each area of an organisation, secondary data may not be detailed enough, and primary data will be needed.  

 

What needs to be considered when collecting scope 3 emissions data? 

The process of data collection can also lead to challenges. Data collection should be as transparent as possible, meaning that it is cleanly documented and disclosed, and the process needs to be repeatable. Consistent data is needed for purposes like baseline comparisons, auditing, and the further calculation of emissions. However, this leads to a high effort in data collection and assessment. To get a first overview of the relevant emission hotspots, secondary data from databases may be an adequate or even a preferred option. This is especially true if the quality of available primary data is unsatisfactory or insufficient.  

For a more in-depth assessment that goes beyond a hotspot analysis and when dealing with large amounts of data, an 80/20 split between primary and secondary data is recommended, especially in the first years of data collection. This means: 

• focusing on the most important hotspots in scope 3 
• collecting ∼80 % primary data 
• using databases for the remaining ∼20 %.   

With this approach, companies get an initial overview and a starting point for scope 3 calculations in the following years. From this basis, system boundaries can be adjusted over time and data quality can be gradually improved by involving suppliers or introducing a data management system.  

When companies review their emission figures, it is important to understand the relationship between the primary and secondary data used for the calculation. Depending on how advanced a company is in climate action, this ratio can vary. Before determining the appropriate type of data and the ratio between primary and secondary data, the objective of the emission calculation  should always be known. 

Data quality is an important criterion in the calculation of carbon emissions 

For the calculation of carbon footprints, the origin and quality of the data is important. All types of data should undergo a quality assessment before being used in emission calculations. The criteria for this include:   

• the timeliness of the data (recommendation: <3 years)   
• its regionality (recommendation: country-specific)   
• whether it is sector-specific (recommendation: yes)   
• the data soure (recommendation: official study, recognised database, or own calculation).   

Particularly in the case of external auditing, data quality is paramount. Thorough documentation regarding the methodology, data, and emission factors used is crucial. Data should always be as transparent, plausible, and realistic as possible, especially if it is used as model data. 

 

What is the impact of improved data quality? 

Companies are advised to select the best type of data in terms of timeliness, geography, and technology, and always use the most representative, reliable, and complete data available.   

Companies can either choose a hybrid or a supplier-specific approach. If both primary and secondary data are used, this is called a hybrid approach. Most companies start by multiplying consumption data for purchased material quantities (primary) by a relevant emission factor obtained from a database (secondary). A supplier-specific approach would still include the primary consumption data for the purchased material quantities, but instead of a database value, it would now use an emission factor provided by the supplier to determine the emissions.   

When a company's data management moves from a hybrid approach to a supplier-specific approach, meaning the company begins to use only primary data, it requires obtaining more specific data about suppliers’ processes, company, and/or materials. With this information, data from different suppliers can be better evaluated and compared. In addition, a company's own system boundaries can be separated from those of its suppliers more clearly. 

Improved transparency achieved through more accurate data collection in turn has an impact on the derived reduction strategies. More precise primary data enables more targeted reduction measures and helps to determine a decarbonisation roadmap. 

High-quality data can alter emission figures in the supply chain   

Higher quality data better reflects reality, which can result in a change in the calculated emissions. For example, if a raw material supplier is not able to specify the energy source for its production at the time of calculation, the national average might be used instead. If the energy source is subsequently identified as 100% green electricity, the introduction of primary data would lead to a reduction in emissions. This is because the actual energy source for production – the use of green electricity – causes fewer emissions than originally assumed.   

The flipside is that higher data quality can also lead to an increase in emission figures. An example is if a supplier assumes a fuel mix for medium-duty trucks for its transport emissions, but it later turns out that the majority of its transport is carried out with heavy-duty diesel trucks. In this case, recalculating the emissions based on the primary data would increase the sum of the emissions. Such increases can be surprising when deriving reduction measures but are nonetheless important to identify.   

When emissions are accounted for in the supply chain and data quality is improved by a higher proportion of primary data, fluctuations in the amount of calculated emissions may occur. It is therefore important to understand the real-time emission drivers and to address the right hotspots. Transparency about emissions data is crucial for the successful implementation of climate action measures.   

Advantages and disadvantages of primary data versus secondary data at a glance 

 

Start the data journey and keep improving quality   

The goal of data collection is to obtain up-to-date primary data for all parts of a product or service, in order to measure a company's carbon footprint more accurately. Until then, it is a long road where every small step towards transparency counts. The best approach to data collection depends on the processes, structures, and relationships between a company and its suppliers. Getting to the bottom of emission data in the supply chain is no easy task. External help is often needed.    

 

ClimatePartner's software solution for decarbonising the supply chain   

ClimatePartner’s Network Platform enables companies to drive decarbonisation within their value chains. This data-driven software enables manufacturers to collect and analyse primary data from their suppliers. In return, suppliers get access to training and education. This not only facilitates decarbonisation, but also increases transparency about emissions within a company's value chain. The platform also enables larger retailers and manufacturers, among others, to achieve validation of their supplier engagement targets through the Science Based Targets initiative (SBTi) and become a climate action leader.  

Want to learn more about scope 3 emissions data collection opportunities for your value chain? Contact us to kick off your climate action today! 

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