How do carbon footprints from LCA and EEIOA databases compare? A comparison of ecoinvent and EXIOBASE. (6th May 2022)

• Record Type: Journal Article
• Authors: Steubing, Bernhard; de Koning, Arjan; Merciai, Stefano; Tukker, Arnold
• Is Part Of: Journal of industrial ecology. Volume 26:Number 4(2022)
• Page Start: 1406
• Page End: 1422
• Publication Date: 2022-05-06
• Subjects: carbon footprint -- ecoinvent -- environmentally extended input output analysis (EEIOA) -- EXIOBASE -- industrial ecology -- life cycle assessment (LCA); Industrial ecology -- Periodicals; Product life cycle -- Environmental aspects -- Periodicals; Industrial management -- Environmental aspects -- Periodicals; Écologie industrielle -- Périodiques; 658.56
• DOI: 10.1111/jiec.13271
• Languages: English
• ISSNs: 1088-1980

Abstract

Abstract: Life cycle assessment (LCA) and environmentally extended input output analysis (EEIOA) are two widely used approaches to assess the environmental impacts of products and services with the aim of providing decision support. Here, we compare carbon footprint (CF) results for products and services in the ecoinvent 3.4 cut‐off and the hybrid version of EXIOBASE. While we find that there is good agreement for certain sectors, more than half of the matched products differ by more than a factor 2. Best fits are observed in the energy, manufacturing, and agricultural sectors, although deviations are substantial for renewable energy. Poorer fits are observed for waste treatment and mining sectors. Both databases have a limited differentiation in the service sector. Differences can, to some degree, be explained by methodological differences, such as system boundaries and approaches used to resolve multi‐functionality, and data differences. The common finding that, due to incomplete economic coverage (truncation error), LCA‐based CFs should be lower than EEIOA‐based CFs, could not be confirmed. The comparison of CFs from LCA and EEIOA databases can provide additional insights into the uncertainties of CF results, which is important knowledge when guiding decision makers. An approach that uses the coefficient of variation to identify strategic database improvement potentials is also presented and highlights several product groups that could deserve additional attention in both databases. Further strategic database improvements are crucial to reduce uncertainties and increase the robustness of decision support that the industrial ecology community can provide for the economic transformations ahead of us. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges .