PRODUCT · DATA QUALITY

    The data quality score your auditor is going to ask about.

    Every PEFCR report ClimatePoint produces is pedigree-scored against the four EF 3.1 Data Quality Rating criteria: technological, geographical, and temporal representativeness, plus precision. Scored at the dataset level. Surfaced at every lifecycle stage. Checked against the PEFCR thresholds your auditor is going to apply.

    02 · PROOF · DQ Overview

    The exact panel your
    auditor will read.

    Sample study across 5 components of a ceramic tile. Same panel structure that ships with every PEFCR ClimatePoint produces.

    Overall Study DQR

    2.13 Good

    Mass-weighted average across 5 components · EF 3.1 / PEFCR data quality rating

    By dimension

    Data Needs Matrix targets

    ≤ 1.6

    Company-specific, most-relevant: DQR ≤ 1.6 with per-dimension limits (P ≤ 3, TeR / TiR / GR ≤ 2).

    ≤ 3.0

    Default secondary datasets: DQR ≤ 3.0 for aggregated EF-compliant datasets.

    ≤ 4.0

    Lower-relevance processes: DQR ≤ 4.0 per the Data Needs Matrix tier.

    Targets apply per process by its DNM situation, not to the study average. The overall DQR above is an indicative mass-weighted figure; a conformant study DQR is weighted by each most-relevant process's environmental contribution.

    03 · THE FRAMEWORK · Four dimensions

    What the four DQR dimensions actually measure.

    TeRTechnological Representativeness
    How well the underlying LCI dataset reflects your actual production technology. A modern low-energy kiln scored against a sector-average kiln dataset drops TeR; matching your process lifts it.
    GeRGeographical Representativeness
    Whether the dataset was built for the geography where the activity actually happens. Pakistan cotton scored against an India cotton dataset lowers GeR; an EF 3.1 Pakistan-specific dataset lifts it.
    TiRTemporal Representativeness
    How current the data is relative to your reference year. A 2015 grid mix used for 2024 production lowers TiR; an annually updated EF 3.1 reference keeps it high.
    PPrecision / Data Source Quality
    Whether the number came from primary supplier data, verified secondary data, or a proxy. Primary beats verified beats proxy, and every DQR increment maps to a traceable source.

    04 · WHY IT MATTERS

    Three things a DQ score has to do
    before an auditor trusts it.

    Threshold compliance, built in.

    Every dataset is scored against its PEF Data Needs Matrix tier: DQR ≤ 1.6 for company-specific data on most-relevant processes (with sub-limits P ≤ 3, TeR / TiR / GR ≤ 2), ≤ 3.0 for default secondary datasets, ≤ 4.0 for lower-relevance processes. Each process is checked against the tier its DNM situation requires, so you can see where the data quality stands before your auditor does.

    Every lifecycle stage, visible.

    The DQR surfaces separately for Raw Materials, Manufacturing, Transport, Use, and End of Life. If a product passes overall but the manufacturing stage is weak, you see it before you publish, not after a third-party review finds it.

    Distribution, not just average.

    An overall DQR of 2.13 can hide four poor components under one excellent one. The panel breaks every report into an Excellent / Acceptable / Poor distribution so the weak spots are not averaged out of sight.

    The next step

    Bring a product that will need a PEFCR next year. We will run the bill of materials through the engine, show you the DQR on your actual data, and give you an honest picture of where you will need primary supplier data to clear the ≤ 1.6 threshold on your most-relevant processes.