Our tool uses data from some of the world’s largest environmental and PEF compliant databases e.g. EcoInvent and EF. These databases contain emission levels on most materials and processes used in industrial production. We calculate data according to the EU PEF method.

In some cases where the finished footprints are not available in the databases, we model the climate footprint data from base raw materials and energy consumption. Our modelling follows inventories from scientifically recognized and peer reviewed litterature.

The tool calculations reflect the geographical location of the origin and production processes of all materials and our results are based on conservative estimates for energy consumption. The more data the user puts in, the more accurate the calculations will be.

Most LCA’s calculate transport solely based on the weight of the goods. This is a source of inaccuracy for many assembled products as their densities usually are so low that weight is not the limiting factor for filling a truck. In these cases, our tool automatically calculates a volume based transport measurement.

Environmental sciences are still in their infancy and therefore data are constantly corrected and improved. Furthermore, the green transitions (in mostly the energy and waste sector) is gradually leading to lower climate emissions. This development is mirrored in our data which the tool automatically update once screened.

DQR is a framework or methodology used to establish criteria for assessing and ensuring the quality of data.

Data quality is essential in various fields, such as data analysis, research, decision-making, and system development. DQR helps define the characteristics and properties that data must possess to meet specific quality standards. These requirements typically cover aspects like accuracy, completeness, consistency, timeliness, validity, reliability, and relevance. DQR works to identify weaknesses in datasets, and thus where data needs to be improved. An LCA’s ability to reflect a real-world scenario is linked to its DQR.

Målbar ensures that all the material and process LCAs, that our tool builds its calculations on, fulfill their DQR. We work with a data quality rating system that measures the average data quality and relevance of all the datasets that are used in a given product screening.  It is ranging from 1-5 where 1 is the datasets being of excellent overall quality and 5 is the datasets being of poor overall quality.

At Målbar, each applied dataset is evaluated on four criteria, defined by PEF:

• Precision. This aspect represents the creation of the dataset, ranging from ‘measured and verified’ to ‘rough estimate with known deficits.’
• Geographical data quality represents the geographical relevance of the dataset for its application, ranging from ‘fully representative’ to ‘not representative.’
• Time data quality represents the time representativeness of the dataset for its application, ranging from ‘application data is within time validity’ to ‘application data is more than 6 years beyond expiry date’.
• Technological data quality represents the technological relevance of the dataset ranging from ‘exact modelling with no significant need for improvement’ to ‘substantial improvement is necessary.’

By utilizing DQR, we can ensure that the applied data is fit for purpose, reliable, and of sufficient quality to create trustworthy calculations on your products.

We continuously evaluate data against the defined requirements and rules.

The data that the tool uses includes emissions of multiple Greenhouse gasses (GHGs). These are gasses like carbon dioxide (CO₂), methane (CH4), nitrous oxide (N₂O) and other effects. The impact of these gasses and effects are measured using a single unit known as carbon dioxide equivalents (CO₂eq).

This takes into account the relative impact on the atmosphere over a 100-year period of each climate gas and sums them up as if they had the same effect as CO₂. Therefore they are called CO₂-equivalents – in short CO₂eq.

For the interested reader who wants to know more about how to calculate climate footprint, we can recommend the book LCA – A practical guide for students, designers and business managers by Joost G. Vogtländer.

For danish readers who are not LCA experts we can recommend Concitos report on how to make the ideal calculation of a carbon footprint (in Danish). Here, Concito also describes the need for product group specific carbon footprint tools. Such a tool which we now have developed.