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Life cycle assessment is a complicated concept that is often subject to uncertainty. Assumptions often have to be made behind the scenes, and data of varying quality is processed.
This is why Yook developed the accuracy score to create transparency on the quality of every product's carbon footprint. It indicates limitations due to data quality and availability. On top of the accuracy score, we determine a safety margin to avoid underestimating the carbon footprint.
The accuracy score is the average of three sub-scores: product data quality, background data reliability, and mapping precision.
The accuracy score is derived from a weighted average of three sub-scores, each assessing different data layers for every life cycle stage. This score is calculated for each product part and life cycle stage.
Product data quality
Reflects the availability and quality of product data.
Relevant product data points such as weight, material composition, location, production, and supply chain information are assigned relative importance depending on the product category.
This enables targeted data collection and improvement where it matters most.
Background data reliability
Background data refer to emission factors or other information about a product's environmental impact
This sub-scores measures the quality of background data connected with the product data (i.e., the quality of emission factors).
Depending on whether suppliers' environmental impact information is available, background data can be primary or secondary. We evaluate the reliability and quality of such background data.
mapping precision
This reflects how precisely we could map a data point.
It corresponds to the DQR's main areas of technical representativeness (the degree of similarity) and temporal and geographical representativeness (the degree of precision and similarity).
For every sub score, we have a unique calculation process:
Product Data quality
As this reflects the product data availability and quality, we check whether all relevant product information needed for LCA is available. These include the following:
product weight
material type
material composition/ weights
material origin
material properties
Background data reliability
Background data refer to emission factors or other information about a product's environmental impact. If no primary information (e.g., a supplier specific LCA or EPD) are available, we use secondary data from LCA databases such as Ecoinvent, Idemat, Agribalyse, etc.
For the reliability assessment we evaluate the source with respect to its general reliability, transparency, documentation and plausibility. Additionally we flag controversial materials by adding them to our "material red list".
The secondary background data reliability is an average of
source_reliability: indicates the general trustworthiness of our EF database
activity_reliability: refers to the material being on the redlist of controversial materials
Mapping precision
The mapping precision sub-score is aligned to the PEF DQR. This means, in order to have the DQR for PEF compliant studies, it can easily be extracted.
Three criteria for the mapping were assessed based on the ISO DQR system:
Technological representativeness
Geographical representativeness
Time representativeness
Next, in order to determine the overall accuracy score of the product's carbon footprint, we compute two weighted averages, each founded on a materiality assessment, specifically regarding the relative carbon impact. Hence, we proceed with the following three steps:
item accuracy score: equally weighted average of product data quality, background data reliability and mapping precision. Calculated per life cycle stage. This means, every part of a product gets its own accuracy score for materials, production, transport and packaging.
life cycle stage accuracy score: based on the relative impact of different materials/ production steps to the respective life-cycle stage, we weight the accuracy scores of each element to calculate a weighted average accuracy score per life cycle stage.
overall product accuracy score: as a last step we calculate the final accuracy score based on the relative impact of each life cycle stage to the final PCF.
The weighting of the accuracy score ensures that the materials, production steps or parts of the product with the highest carbon impact count more than small parts that have almost no significance. It also helps with targeted data collection and improvement where it matters most.
The accuracy score is given on a scale of 1 to 100, where 1 is the lowest and 100 is the highest. An accuracy score of up to 90 can be achieved, as no model can ever represent the full reality.
We have developed a system to assign the scores to a qualitative description for accuracy score.
Low
1 - 20
Very inaccurate Spend-based and Average data
No specific product primary data (e.g., material composition, weight) is available
Medium
21 - 40
Poor availability/ quality of primary data,
Used heuristics and proxies or very low secondary data quality
Elevated
41 - 60
Primary data available for some datapoints (e.g., product weight, main materials)
Good background data quality
(This level is usually obtained after the first iteration of PCF calculations)
High
61-80
Detailed primary data availability (e.g., as above + material origins + specifications, production details) Good background data quality (This level is usually obtained with some data collection effort)
Very high
81-90
Very good availability and quality of primary data
Supplier specific information on materials/ components.
Good background data quality Precise mapping of product and background data.
-
91 - 100
Since no model can perfectly reflect reality and climate sciences are always subject to uncertainty, an accuracy score of 100 can never be reached.
The safety margin is a factor determined by the accuracy score by which the calculated product carbon footprint is multiplied in order to control for uncertainties and to avoid underestimating carbon emissions.
1 - 20
1.5
21 - 40
1.4
41 - 60
1.3
61 - 80
1.2
81 - 90
1.1
