Implementing hybrid LCA routines in an input–output virtual laboratory

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 semiautomated manner; and we anticipate that further database integration should lead to higher levels of correction in the future.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybridization of complete PLCA and MRIO databases for a comprehensive product system coverage

Agez

Wood

Margni

et al. 2020

“…As IO‐based analysis are increasingly used to inform policy and demand‐side approaches to climate change mitigation, these differences are highly relevant in terms of prioritization. These variations are relevant especially for hot‐spot analyses in the context of corporate responsibility efforts (Kjaer, Høst‐Madsen, Schmidt, & McAloone, ; Martinez, Delgado, Martinez Marin, & Alvarez, , ; Martinez, Marchamalo, & Alvarez, ), where EE‐MIOTs, also in hybrid LCA‐IOA frameworks (Suh et al., ; Yu & Wiedmann, ), are increasingly applied for calculating footprint accounts of organizations, companies, and products.…”

Section: Discussionmentioning

confidence: 99%

Supply versus use designs of environmental extensions in input–output analysis: Conceptual and empirical implications for the case of energy

Wieland

Giljum

Eisenmenger

et al. 2019

Input-output analysis is one of the central methodological pillars of industrial ecology. However, the literature that discusses different structures of environmental extensions (EEs), that is, the scope of physical flows and their attribution to sectors in the monetary input-output table (MIOT), remains fragmented. This article investigates the conceptual and empirical implications of applying two different but frequently used designs of EEs, using the case of energy accounting, where one represents energy supply while the other energy use in the economy. We derive both extensions from an official energy supply-use dataset and apply them to the same single-region inputoutput (SRIO) model of Austria, thereby isolating the effect that stems from the decision for the extension design. We also crosscheck the SRIO results with energy footprints from the global multi-regional input-output (GMRIO) dataset EXIOBASE. Our results show that the ranking of footprints of final demand categories (e.g., household and export) is sensitive to the extension design and that product-level results can vary by several orders of magnitude. The GMRIO-based comparison further reveals that for a few countries the supply-extension result can be twice the size of the use-extension footprint (e.g., Australia and Norway). We propose a graph approach to provide a generalized framework to disclosing the design of EEs. We discuss the conceptual differences between the two extension designs by applying analogies to hybrid life-cycle assessment and conclude that our findings are relevant for monitoring of energy efficiency and emission reduction targets and corporate footprint accounting. K E Y W O R D Senergy consumption, energy efficiency, energy flow analysis, energy footprint, environmental input-output analysis, industrial ecology

“…The determination of the inputs of C u is not defined in an equation to the best of the authors’ knowledge. This determination was extensively described in the literature however (Strømman & Solli, ; Strømman et al., ; Yu & Wiedmann, ) and can accordingly be translated into the following equation (where

c^{u n c o r r e c t e d}

are coefficients of C u prior to correcting for double‐counting):

c_{M k}^{u n c o r r e c t e d} = a_{M K}^{i o} \cdot h_{K k} \cdot π_{k}

…”

Section: Hybrid Lcamentioning

confidence: 99%

Lifting the veil on the correction of double counting incidents in hybrid life cycle assessment

Agez

Majeau‐Bettez

Margni

et al. 2019

Life cycle assessment (LCA) and environmentally extended input-output analyses (EEIOA) are two techniques commonly used to assess environmental impacts of an activity/product. Their strengths and weaknesses are complementary, and they are thus regularly combined to obtain hybrid LCAs. A number of approaches in hybrid LCA exist, which leads to different results. One of the differences is the method used to ensure that mixed LCA and EEIOA data do not overlap, which is referred to as correction for double counting. This aspect of hybrid LCA is often ignored in reports of hybrid assessments and no comprehensive study has been carried out on it. This article strives to list, compare, and analyze the different existing methods for the correction of double counting. We first harmonize the definitions of the existing correction methods and express them in a common notation, before introducing a streamlined variant. We then compare their respective assumptions and limitations. We discuss the loss of specific information regarding the studied activity/product and the loss of coherent financial representation caused by some of the correction methods. This analysis clarifies which techniques are most applicable to different tasks, from hybridizing individual LCA processes to integrating complete databases. We finally conclude by giving recommendations for future hybrid analyses. K E Y W O R D Sdata quality, environmental input-output analysis, hybrid life-cycle assessment, industrial ecology, life cycle assessment (LCA), life cycle inventory (LCI)