Title: Method applied to the background analysis of energy data to be considered for the European Reference Life Cycle Database (ELCD)
Authors: FAZIO SIMONEGARRAIN DanielRECCHIONI MARCODE LA RUA CristinaMATHIEUX FABRICELECHÒN Yolanda
Citation: SPRINGERPLUS vol. 4 p. 150
Publisher: SPRINGER SCIENCE AND BUSINESS MEDIA DEUTSCHLAND GMBH
Publication Year: 2015
JRC N°: JRC91318
ISSN: 2193-1801
URI: http://www.springerplus.com/content/4/1/150
http://publications.jrc.ec.europa.eu/repository/handle/JRC91318
DOI: 10.1186/s40064-015-0914-x
Type: Articles in periodicals and books
Abstract: Under the framework of the European Platform on Life Cycle Assessment, the European Reference Life-Cycle Database (ELCD - developed by DG JRC, European Commission), provides core Life Cycle Inventory (LCI) data from front-running EU-level business associations and other sources. Within the ELCD, several energy-related data (i.e. power generation and fuels) are provided. The present study aims to point out the methods that will be used for the analysis of the quality of energy data for European markets, available in 3rd party life cycle databases and from authoritative sources, that are, or could be used in the context of the ELCD. The methodology was developed and tested on the most relevant energy datasets for the EU context derived from GaBi (the reference DB used to derive datasets for the ELCD), Ecoinvent, E3 and Gemis. The criteria for the database selection were based on the availability of EU-related data, the inclusion of wide datasets on energy products and services, and the broad approval by the scientific community. The proposed approach has been based on the quality indicators developed within the International Reference Life Cycle Data System (ILCD) handbook, that have been further refined to facilitate their use in the analysis of energy systems. The overall Data Quality Rating (DQR) of the energy datasets can be calculated by summing up the achieved quality rating (ranging from 1 to 5, being 1 very good and 5 very poor in quality), for each of the quality criteria indicator, divided by the total number of considered indicators. The quality of each dataset can be estimated for each indicator and then, compared across the different databases/source. The results can be used to point out the weak points of each dataset, and the average quality of the different sources, this can lead to further improvement to enhance the data quality as regards the established criteria. The methodology applied to two of the selected datasets has been disclosed in this paper in order to show the potentials of the methodological approach.
JRC Directorate:Sustainable Resources

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