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Hybridization of complete PLCA and MRIO databases for a comprehensive product system coverage
Author(s) -
Agez Maxime,
Wood Richard,
Margni Manuele,
Strømman Anders H.,
Samson Réjean,
MajeauBettez Guillaume
Publication year - 2020
Publication title -
journal of industrial ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.377
H-Index - 102
eISSN - 1530-9290
pISSN - 1088-1980
DOI - 10.1111/jiec.12979
Subject(s) - database , computer science , truncation (statistics) , product (mathematics) , process (computing) , completeness (order theory) , data mining , mathematics , mathematical analysis , geometry , machine learning , operating system
Process‐based Life Cycle Assessments (PLCA) rely on detailed descriptions of extensive value chains and their associated exchanges with the environment, but major data gaps limit the completeness of these system descriptions and lead to truncations in inventories and underestimations of impacts. Hybrid Life Cycle Assessments (HLCA) aim to combine the strength of PLCA and Environmentally Extended Input Output (EEIO) analysis to obtain more specific and complete system descriptions. Currently, however, most HLCAs only remediate truncation of processes that are specific to each case study (foreground processes), and these processes are then linked to (truncated) generic background processes from a non‐hybridized PLCA database. A hybrid PLCA‐EEIO database is therefore required to completely solve the truncation problems of PLCA and thus obtain a comprehensive product system coverage. This paper describes the construction of such a database using pyLCAIO, a novel framework and open‐source software enabling the streamlined hybridization of entire PLCA and EEIO databases. We applied this framework to the PLCA database Ecoinvent3.5 and the multiregional EEIO database EXIOBASE 3. Thanks to the correction for truncation in this new hybrid database, the median and average life cycle global warming potential (GWP) of its processes increased by 7% and 14%, respectively. These corrections only reflect the truncations that could be readily identified and estimated in a semi‐automated manner; and we anticipate that further database integration should lead to higher levels of correction in the future.