How much time do you spend sourcing, updating, and verifying emission factors every year?
For most, it’s at least a few weeks. Following the lengthy process of collecting your business activity data—including the thousands of items that fall into scope 3—each line of data needs to be mapped to a fitting emission factor. To cover them all, you’ll likely need to use multiple data sources, each of which needs to be individually verified for its suitability.
Despite the critical role emission factors play in carbon accounting, finding the right data remains one of the most frustrating and resource-intensive parts of the process. Emission factor datasets quickly become outdated as new research and data are published, leaving sustainability teams constantly updating data manually rather than doing more impactful work. On top of that, these manual processes are prone to errors that can undermine the accuracy and credibility of your reporting.
All that time could be better spent on reducing emissions, improving reporting, or driving sustainability initiatives forward. Here we’ll take a look at what makes sourcing emission factor data so hard, and what you can do about it.
To understand their importance, it’s worth first stepping back to ask what an emission factor is. In simple terms, an emission factor is the data that tells you how much greenhouse gas is released per unit of a given activity. For example, how many kilograms of CO2e are emitted per kilowatt-hour of electricity consumed, or per euro spent on construction materials. Emission factors are the building blocks of carbon accounting. They allow businesses to turn raw activity data (like energy use, distance traveled, or materials purchased) into an estimate of emissions.
The problem is that emission factors aren’t one-size-fits-all. To find the right one, you first need a clear understanding of your own activity data and the fundamentals of carbon accounting. Which region does your data relate to? What lifecycle stage does the emission factor need to cover? Should it be based on spend, or on physical activity data?
Matching activities to the correct emission factor is rarely straightforward. It often requires deep expertise, a significant amount of research, and plenty of time. The challenge isn’t just about knowing what you’re looking for, it’s about navigating the inconsistent landscape of emission factor data. Climatiq’s emission factor matching engine, Autopilot, uses AI to match text input to fitting emission factors, circumventing much of this process.
Emission factors come from a huge amount of sources and they all have different rules attached to them. Emission factor sources often handle a specific industry; for example, OEKOBAUDAT covers construction, Agribalyse is for food and agricultural processes, Greenview is specifically for hotels, and Plastics Europe (perhaps unsurprisingly) contains emission factors for plastics. This just scratches the surface—there are hundreds of emission factor sources with their own areas of specialization. This can make it time consuming to search through sources looking for an emission factor that is compatible with your activity data. It’s not only the activity itself you need to pay attention to, but also the unit, year, and region, adding to the workload of the task.
Unfortunately it’s not as simple as finding a data provider based in your region. While a data source might be based in a specific country, its data can be globally applicable. Conversely, you may need to search through global datasets to find a regionally applicable emission factor to match your data.
Take EXIOBASE, for example, which has global coverage but is split between 44 countries and five rest-of-world regions (and, to make things more complicated, 163 different industries). You may know the emission factor you need is in there somewhere, but finding the one for your activity data amongst thousands of factors is the real challenge. Multiply this by hundreds of activity items across multiple datasets and it becomes a monumental task.
It's also possible that you’re reporting emissions for a region that isn’t often generally in global datasets, and therefore need to source data from niche data providers like CAEP in order to meet your needs.
Even two emission factors that look similar can be hard to compare and understand. They may use different units, different years, or cover different parts of the product lifecycle. As mentioned earlier, there’s no single, unified provider of emission factors or a universal framework to follow, meaning each data provider is free to format and present emission factors however they see fit. When compiling data, this can make it hard to compare emission factors against each other or to discern whether new factors meet your needs, are compatible with your project, or with existing activity and emission factor data. This is why normalization is necessary. Normalization aligns all data to follow one standardized schema and format—for example, changing all weight units to kg, or all gases to CO2e. Check our blog to learn more about the process and its importance for building an emission factor landscape that supports everyone at scale.
Each provider follows a specific methodology to derive emission factors. Methodologies vary between providers, but they generally combine activities such as source testing and mathematical modeling. The methodology used to derive the emission factor can change its value, and therefore affect the estimated emissions. For this reason it’s important to ensure that the methodology used follows rigorous scientific standards and has been calculated using reliable methods.
This information is usually included in a methodology file along with the dataset. The file needs to be separately extracted and interpreted, can be very complex to understand, and is therefore a very time consuming process. Without doing so, however, it’s impossible to ascertain whether emission factors are usable. At Climatiq, the methodology behind all datasets in our database has undergone review by our team of climate scientists to ensure reliability and scientific rigor.
While these challenges may be manageable in small projects, they quickly become overwhelming at scale when different activity types and regions come into play. To work around them, sustainability managers and teams often curate their own emission factor datasets. However, this approach brings its own complications: coverage is typically incomplete, since compiling thousands of emission factors is highly resource-intensive; the data becomes outdated quickly, as most sources release annual updates; and manual updates are slow and create the risk of errors.
That’s where we saw the gap in the market for a single easy way to search and browse datasets to find the emission factors you need on any project. From using spend-based factors to sourcing the detailed, regional emission factors for activity-based reporting, it’s all there in the Climatiq database. That means that you only require one subscription for every emission factor you need. Plus, Climatiq’s database is updated monthly, and will always give you the most recent possible emission factors.
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