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How we calculate a product carbon footprint

What exactly a product carbon footprint (PCF) is, we have already explained in this blogpost using the example of a cup of black coffee. This article is about how we calculate the PCF. How do we come to the result that a package of Goldeimer toilet paper has a carbon footprint of 2.7 kg CO2eq and the footprint of a pair of sports leggings made from recycled polyamide is 1.7 kg CO2eq?

Carbon emissions as a unit are difficult to grasp, and it is thus important for us to create transparency in a way that everyone understands how we arrive at our results. However, it is important to consider that this is work in progress: We are constantly evolving, learning and improving our methodology, based on scientific and regulatory developments that are constantly evolving, too.

So here's what you'll learn in this article:

  • What 4 main components make up the PCF

  • What data we use and how we deal with data gaps

  • How we ensure transparency and what our accuracy score is

Our Methodology

We calculate a carbon footprint on the cradle-to-gate basis, which means that we do not include the use and end-of-life phase of a product. This is mainly because the extent of these phases varies greatly depending on whether, for example, a laptop is charged with green electricity, or how often a T-shirt is worn and washed. Though, we are working on a solution to include the use and end-of-life phase in our calculations in the long term. We also focus on calculating CO2eq emissions, which is why we calculate the global warming potential over 100 years (GWP 100) and include other greenhouse gases in addition to CO2. Other environmentally relevant factors, such as water, plastics or biodiversity, are not covered (yet).

4 main components of the product carbon footprint

1. Material carbon footprint

The CO2equ emissions caused by the material are captured in the first PCF component. Our algorithm extracts the main materials from the product data and combines them with the respective emission factors from our database.

Our database is composed of different sources: public, licensed and private libraries, as well as continuously updated data based on LCAs (Life Cycle Assessments), EPDs (Environmental Product Declarations), other PEFs (Product Environmental Footprints) and data published in scientific literature is collected and harmonized. We strive to create comparability of data, as not all data is collected based on the same framework. Our emission factor database is continuously expanded and updated.

2. Manufacturing carbon footprint

The second part of the total PCF represents those emissions that occur during production. Here we include industrial standards of production processes based on product categories. An important factor here is, for example, the energy mix of the country of production.

3. Transport carbon footprint

In the third part, we add up the emissions from transportation and product packaging. Here we proceed in two steps: The transport from the production site to Germany is considered, followed by the transport from the (air)port to the department store. Depending on data availability, we calculate with average values or exact values for distances and means of transport. The emission factors for the various means of transport, for example ship, airplane and truck, are calculated using average values and given in kg CO2eq per kg product. Transport during the upstream steps (material extraction and production) is already taken into account in the respective parts.

4. Last-mile delivery carbon footprint

As the fourth step, carbon emissions from product shipping are mapped. This is often referred to as last mile delivery. We also include shipping packaging and return rates in this component.

Again, the more data the better. However, if data availability is limited, we calculate with standard and average values. For example, we assume an average value of 400 km distance from the warehouse to the delivery destination, which is covered by a delivery vehicle. We also calculate average values for packaging using the standard material mix per product category if the exact data is not available. The return rate is also interesting: especially in the fashion industry many ordered products are returned, so we also calculate a multiplier here based on the product category and store data.

To sum up, our algorithm works in 5 steps:

  1. We receive the available product data from the company via digital interfaces.

  2. Our algorithm extracts and sorts them and fills data gaps using intelligent, continuously updated industry information and assumptions.

  3. This data is then linked to our databases and combined with emission factors.

  4. Our software automatically calculates the cradle-to-gate carbon footprint of a wide range of products.

  5. Our Accuracy Score is used to apply correction factors to results, controlling for uncertainties and avoiding underestimation of the PCF.

All steps are continuously improved and updated. In general, the more data we have, the fewer assumptions need to be made and the higher the accuracy of the PCF.

Of course we know that not all data can always be provided directly. Therefore, we work with assumptions and inferred values calculated by our intelligent algorithm. In fact, a combination of primary data and industry averages is even seen as the most accurate, as even primary data is subject to uncertainties. To avoid underestimation of the PCF, when in doubt about correctness of the data or estimates, we have implemented the Accuracy Score and a correlating correction factors.

Accuracy Score by Yook

Transparency is our top priority. Therefore, in addition to each PCF calculation, we provide the Accuracy Score, which reflects the data quality and availability on a point scale from 0 to 100. Based on a weighted assessment of the availability and quality of data and emission factors, such as product weight, main materials, origin of materials and production location, the PCF components are adjusted. Depending on the scenario, we adjust the PCF by up to 50%. Thus, we ensure that the PCF becomes more accurate with better data availability and quality and that PCF underestimation is avoided.

Many brands also show their own sustainability commitment and publish the PCF of their products themselves or offset emissions by supporting climate protection projects. Of course, we also include this in our calculations and correct the PCF of more sustainable products.


Our PCF method has two goals: To create transparency and to make it as simple as possible. Since the scientific and legislative debate is constantly evolving, we are also continuously advancing our algorithm. Our mission of maximum transparency thus includes not only disclosing our methodology, but also being transparent about the challenges we face. Here you go:

How do we deal with missing data?

In the best case, we receive all necessary data, for example the product weight, origin of materials, production location and means of transport, directly from a company. However, since this data is often not available and we want to make things as simple as possible, our algorithm finds good alternative data in the case of data gaps. For example, data scraping and other methods are used to supplement the existing data. If no good alternative data can be found, the algorithm calculates a placeholder, for example based on the average value of similar products. For example, if the weight of a t-shirt of a certain brand is missing, the average weight of a similar t-shirt is used.

What standards does our methodology meet?

Our PCF calculation is as accurate as necessary to identify hotspots, identify reduction potential, and provide specific carbon offsets. At the same time, we place great importance to being fast and effective - after all, there is no time to lose when it comes to climate protection! We incorporate guidelines of the GHG Protocol, ISO 14067 as well as the PEF Guidelines of the EU where they fit, but since these often leave a lot of room for interpretation, we disclose our methodology and create transparency. Additionally, our self-developed Accuracy Score is always displayed next to the PCF, indicating the accuracy of the calculation.

Ready to get started?

You see - even with little data we can get started and create carbon transparency. This is an important starting point which is urgently needed, since the climate clock is ticking. We want to enable brands and e-commerce stores to calculate their product emissions and make data-based sustainability promises - and all this without expensive and time-consuming LCAs, which only very few can afford.

Do you have questions, comments or suggestions for improvement? We are always happy to receive your feedback!

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