Methodology for PCF Calculations of Lubricants, Greases and other Specialties
If the information required to rate data quality is missing for supplier-provided raw material PCFs, or emission factors used to model upstream processes, the lubricant manufacturer shall assign level 3 (default) for each indicator.
Table 2: Description of data quality indicators, adapted from GHG PP, 2011. Indicator
Description
Technological representative- ness
Temporal representative- ness
Geographical representative- ness
Chemicals can be manufactured through various production technologies utilizing different reaction pathways and plant designs. Hence, relevant data from actual production plants should be utilized for the studied product.
The degree to which the data reflects the actual time (e.g., year) or age of the activity.
Production chains of chemicals vary across regions. Therefore, datasets that accurately reflect the relevant population in terms of geography should be utilized.
Completeness
Completeness includes the percentage of locations and processes for which data is available and used out of the total number that relate to a specific activity. Completeness also addresses seasonal and other normal fluctuations in data.
Reliability
More reliable results are achieved when data is obtained from measurements of production data specific to the site and detailed modeling. On the other hand, simplified process calculations and assumptions result in less reliable data sets.
Relation to data quality
Lubricant manufacturers should select data that are technologically specific.
Lubricant manufacturers should select data that are temporally specific.
Lubricant manufacturers select data that are geographically specific.
Lubricant manufacturers should select data that are complete.
Lubricant manufacturers should select data that are reliable.
Table 3 shows the definitions for each indicator and data quality level.
Version 1.2, 15.10.2025 © ATIEL and UEIL
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