G-22 (Buildings) / G-23 (Products) Data quality

Aspect G-22 (Buildings) / G-23 (Products) Data quality
Description Generally speaking, the data quality in LCA refers to the relevance of generic or specific LCA data in accordance with the goal and scope of the study. Depending on the background data sources (literature, industry data), and the boundaries and context of the study (e.g. for which purposes or countries are the data being used?), the data quality can be very different within one study. Although data may be of good quality for context Y, this will not necessarily be the case for context Z, as the requirements (e.g. geographical representativeness) may be different. The practitioner should keep in mind that data quality is always a relative concept. In order to point out this aspect as transparently as possible, the quality of the data has to be justified in the context of its use.How should the quality of the LCA data be described, and can the practitioner ensure that data of a specific quality are needed and produced?

related study objective

stand-alone LCA comparative assertion

related study phase

goal and scope definition inventory analysis (LCI) impact assessment (LCIA) interpretation reporting

relevant for

new buildings existing buildings building products screening LCA simplified LCA complete LCA
Provisions The data quality of LCA studies should at least meet the requirements of EN 15978 and En 15804, and the documentation, nomenclature, method and review should be implemented accordingly.
Rules from
EN 15978
10.3 Data quality

Different criteria are given in EN 15978, e.g. technological, geographical and temporal coverage (the data should be not older than 10 years). The data should be based on a one-year average (if relevant); check the plausibility and the significance of the data in the final result.

EN 15804
6.3.7 Data quality requirements


6.8 Representativeness and appropriateness of LCI data provisions
6.8.2 Technological representativeness
Provisions 6.8.3 Geographical representativeness
Provisions 6.8.4 Time-related representativeness

12 Annex A: Data quality concept and approach

12.1 Introduction and overview
In the ILCD Handbook, two approaches are defined – 1) data quality in a stricter sense; and 2) the data quality documentation and review:

1) ILCD overall data quality indicators include: technological, geographical, time-related representativeness, completeness, precision/uncertainty, methodological appropriateness and consistency.

2) Data quality documentation aspects cover: documentation, review, nomenclature (e.g. same list of elementary flows)

12.2 Data quality aspects
The ILCD Handbook defines ‘accuracy’ as a term covering all the related data quality aspects, such as:

“Representativeness (technological, geographical, time-related)
Methodological appropriateness and consistency”

The ILCD Handbook also provides rules for not mixing the data quality aspects (falling under the term ‘accuracy’) with the other concept of ‘precision’ (or ‘uncertainty’). A graphical illustration (see Figure 11) illustrates this point.

Figure 11: Concept of precision

ISO 14044 Data quality requirements
In current practice it is usually not possible to choose datasets for quality reasons, but for availability in the construction sector. The practitioner should keep in mind that data quality is crucial for the LCA study. The most appropriate data should be chosen (in terms of representativeness and consistency) as far as possible. Documentation of the used datasets and the quality level is needed as far as possible.The ILCD Handbook provides some recommendations on data quality, depending on the relative significance of the data in the final results. A graphical illustration (see Figure 12) illustrates this point.Figure 12: Focusing efforts on key data

For product LCA, the user of commercial software can also find information on data quality in the documentation for the LCI data used for the process tree of his LCA study.

For building LCA, in dedicated LCA software, information on data quality may not be currently available. The practitioner should, however, be cautious when using LCA data (check whether a methodological report is available, check whether  a critical review or verification was done, assess whether the data are relevant for the context of the study etc.).

In practice, the data quality assessment should be conducted according to the goal and scope of the LCA study. For instance, an LCA study of a building within a European project may require the use of generic European datasets from available LCA databases (ecoinvent, ELCD, ESUCO etc.). By contrast, a screening LCA study in a national context may require the use (if available) of generic national LCA data on building materials, and on components sold on the national market (i.e. representing the ‘consumption mix’ according to ILCD).

For building LCA, the data quality assessment can be conducted at two levels:

– a data quality check for the entry data (depending on the goal and scope of the study) by means of data quality indicators, e.g. A, B, C, D that may be based on the Pedigree matrix [Weidema 1996];

– a data quality check for the LCA results for buildings (depending on the contribution analysis). In this case, if a building material accounts for 1% of, say, the acidification potential indicator, and the data are not very precise or accurate (e.g. generic data, randomly selected), it does not matter. Conversely, if a key building material (e.g. reinforced concrete) accounts for 45% of, say, the GWP indicator, and the data are not of high quality, the practitioner should possibly refine the LCA data, as it is sensitive to the results. These examples are also valid for LCA data on energy and water processes (e.g. fuel, electric mix, water treatment processes), as they may also drive most of the impacts of a building.

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