Automatic Data Quality Assessment (DQA) in Earthster
To enable faster and more scalable LCA and EPD development, Earthster provides an automatic Data Quality Assessment (DQA) for every process within your cycles. This feature helps you evaluate how well your background data aligns with your specific modeling choices.
The core objective of the data quality assessment is to determine if a selected data source is "fit for purpose" based on its temporal, geographical, and technological relevance.
The DQA Scoring System
Earthster evaluates three key indices for each process flow. Each index is scored on a scale from 1 to 5 following the Product Environmental Footprint Category rules (PEFCR):
1 - Very Good
2 - Good
3 - Fair
4 - Poor
5 - Very Poor
1. Temporal Correlation
This index measures how representative the added data is relative to the time period you are modeling. Earthster compares the date of your current cycle with the date of the consumed data source (the background process).
By default the date of your cycle is assumed to be the same as the created at date. You can specify another date in the Cycle Settings -> Date.
Date Difference | Score | Rating |
0 – 1 years | 1 | Very Good |
1 – 3 years | 2 | Good |
3 – 6 years | 3 | Fair |
6 – 10 years | 4 | Poor |
> 10 years | 5 | Very Poor |
For background data, the dataset year is determined as follows:
ecoinvent
The upper bound of the data range is used as the dataset year.
Although this does not strictly represent the original reference year of the inventory, it best reflects the effective data vintage, especially since key system datasets (e.g., electricity mixes) are updated regularly in each release.ILCD-based databases (e.g. PEF)
The reference year provided in the dataset is used (typically corresponding to the lower bound of the time range).
2. Geographical Correlation
This index assesses how well the geography of the data source matches the geography of the parent process (bundle, custom process, stage) it was added.
Condition | Score | Rating |
Exact match (e.g., both are "Portugal") | 1 | Very Good |
The process geography is contained within the data geography (e.g., Process is "Portugal", Data is "Europe") | 2 | Good |
Any other situation, or the data source is "Rest of World" (RoW) | 4 | Poor |
3. Technological Correlation
This index evaluates whether the dataset represents a good approximation of the actual technology or activity you intend to model based on the data source.
Note: While background databases are high-quality, your own cycles are considered a better technological match, therefore by default your own cycles get a score of 1 and everything else gets 2.
Condition | Score | Rating |
User-defined cycle: The data comes from your own custom models. | 1 | Very Good |
Background database: The data comes from standard LCI databases (e.g., ecoinvent). | 2 | Good |
Overriding Automatic Scores
While Earthster provides these scores automatically to save you time, you need to check and specify the correct scores for your specific case.
Data quality is defined as fitness for purpose in ISO 14040. The methodology is addressed in ISO 14040 and ISO 14044. And for example a data quality assessment is required for EPDs.
Specifying your own scores (overriding automatic scores)
Open the cycle menu
Click on the Data quality
Click on any of the scores to overwrite it.
Select the score you want to use
Click the Save changes button to apply the score
Overriden values are highlighted.
Checking and restoring the automatic scores
You can always check the calculated values compared to what you have selected by clicking on the current score.
Restoring the automatic scores
In the Data quality section, click the score you want to restore
Click on the Do not override button
Click the Save changes button to apply the change
Exporting your data quality assessment matrix
You can download your data quality assessment matrix by exporting the "LCI data" report.