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Most life cycle assessment (LCA) studies use the attributional methodology. This approach attributes a share of global environmental impacts to one or multiple functions provided by a normatively circumscribed system. Multifunctional systems that are not technologically subdivisible between co‐functions are frequently encountered in LCA studies. It then becomes necessary to resort to co‐production modeling techniques, like the substitution approach. The use of substitution modeling in attributional LCA (ALCA) is, however, discouraged amongst practitioners, due to the alleged violation of central requirements of the attributional methodology. The objective of this research is to shed light on common misconceptions about the compatibility of substitution with ALCA. The first misconception is that the use of substitution in ALCA violates the conservation of total environmental impacts. We find that this idea arises from a confusion regarding the attribution of impacts to the secondary product(s). The second misconception stipulates that substitution is not coherent with the state‐descriptive characteristic of ALCA. We conclude that we can describe a given system as resulting from an inferred (substitution) change, rather than as disrupted by this change. Finally, we discuss the choice of the substituted technology, and argue there is a logic to marginal substitution in ALCA. We therefore recommend accepting substitution modeling in ALCA. [ABSTRACT FROM AUTHOR]

Life cycle assessment (LCA) is a method to evaluate the environmental impacts of technologies from cradle to grave. However, LCAs are commonly defined in terms of the consumption of a single unit of a product and thus ignore scaling issues in large‐scale deployment of technologies. Such product‐level LCAs often do not consider capital manufacturing capacity and supply chain bottlenecks that may hinder the rapid, widespread uptake of emerging technologies entering the market; emerging technologies often require the expansion of existing supply chains or the development of entirely new supply chains, such as the manufacturing of novel materials. As a result, such LCA studies are limited in their ability to realistically assess impacts at the macro‐scale and thus to guide large‐scale decisions. In this work, we present ECOPT2, a generalized adaptable model that combines these constraints to the LCA approach using a mathematical programming approach and dynamic stock modeling. ECOPT2 combines LCA factors with transition scenarios from energy systems models to determine the environmentally optimal deployment of new technologies while accounting for material circularity constraints and barriers to uptake. We also introduce the structure of the software tool and demonstrate its features using a stylized vehicle electrification scenario. [ABSTRACT FROM AUTHOR]

Hybrid life cycle assessment (HLCA) strives to combine process‐based life cycle assessment (PLCA) and environmentally extended input–output (EEIO) analysis to bridge gaps of both methodologies. The recent development of HLCA databases constitutes a major step forward in achieving complete system coverage. Nevertheless, current applications of HLCA still suffer from issues related to incompleteness of the inventory and data gaps: (1) hybridization without endogenizing the capital inputs of the EEIO database leads to underestimations, (2) the unreliability of price data hinders the application of streamlined HLCA for processes in some sectors, and (3) the sparse coverage of pollutants in multiregional EEIO databases limits the application of HLCA to a handful of impact categories. This paper aims at offering a methodology for tackling these issues in a streamlined manner and visualizing their effects on impact scores across an entire PLCA database and multiple impact categories. Data reconciliation algorithms are demonstrated on the PLCA database ecoinvent3.5 and the multiregional EEIO database EXIOBASE3. Instead of performing hybridization solely with annual product requirements, this hybridization approach incorporates endogenized capital requirements, demonstrates a novel hybridization methodology to bypass issues of price unavailability, estimates new pollutants to EXIOBASE3 environmental extensions, and thus yields improved inventories characterized in terms of 13 impact categories from the IMPACT World+ methodology. The effect of hybridization on the impact score of each process of ecoinvent3.5 varied from a few percentages to three‐fold increases, depending on the impact category and the process studied, displaying in which cases hybridization should be prioritized. This article met the requirements for a Gold—Gold JIE data openness badge described at http://jie.click/badges. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Summary: While life cycle assessment (LCA) is a tool often used to evaluate the environmental impacts of products and technologies, the amount of data required to perform such studies make the evaluation of emerging technologies using the conventional LCA approach challenging. The development paradox is such that the inputs from a comprehensive environmental assessment has the greatest effect early in the development phase, and yet the data required to perform such an assessment are generally lacking until it is too late. Previous attempts to formalize strategies for performing streamlined or screening LCAs were made in the late 1990s and early 2000s, mostly to rapidly compare the environmental performance of product design candidates. These strategies lack the transparency and consistency required for the environmental screening of large numbers of early‐development candidates, for which data are even sparser. We propose the Lifecycle Screening of Emerging Technologies method (LiSET). LiSET is an adaptable screening‐to‐LCA method that uses the available data to systematically and transparently evaluate the environmental performance of technologies at low readiness levels. Iterations follow technological development and allow a progression to a full LCA if desired. In early iterations, LiSET presents results in a matrix structure combined with a "traffic light" color grading system. This format inherently communicates the high uncertainty of analysis at this stage and presents numerous environmental aspects assessed. LiSET takes advantage of a decomposition analysis and data not traditionally used in LCAs to gain insight to the life cycle impacts and ensure that the most environmentally sustainable technologies are adopted. [ABSTRACT FROM AUTHOR]

Summary: The divide between attributional and consequential research perspectives partly overlaps with the long‐standing methodological discussions in the life cycle assessment (LCA) and input‐output analysis (IO) research communities on the choice of techniques and models for dealing with situations of coproduction. The recent harmonization of LCA allocations and IO constructs revealed a more diverse set of coproduction models than had previously been understood. This increased flexibility and transparency in inventory modeling warrants a re‐evaluation of the treatment of coproduction in analyses with attributional and consequential perspectives. In the present article, the main types of coproductions situations and of coproduction models are reviewed, along with key desirable characteristics of attributional and consequential studies. A concordance analysis leads to clear recommendations, which call for important refinements to current guidelines for both LCA/IO practitioners and database developers. We notably challenge the simple association between, on the one hand, attributional LCA and partition allocation, and on the one hand, consequential LCA and substitution modeling. [ABSTRACT FROM AUTHOR]

Summary: Many countries see biofuels as a replacement to fossil fuels to mitigate climate change. Nevertheless, some concerns remain about the overall benefits of biofuels policies. More comprehensive tools seem required to evaluate indirect effects of biofuel policies. This article proposes a method to evaluate large‐scale biofuel policies that is based on life cycle assessment (LCA), environmental extensions of input‐output (I‐O) tables, and a general equilibrium model. The method enables the assessment of indirect environmental effects of biofuels policies, including land‐use changes (LUCs), in the context of economic and demographic growth. The method is illustrated with a case study involving two scenarios. The first one describes the evolution of the world economy from 2006 to 2020 under business as usual (BAU) conditions (including demographic and dietary preferences changes), and the second integrates biofuel policies in the United States and the European Union (EU). Results show that the biofuel scenario, originally designed to mitigate climate change, results in more greenhouse gas emissions when compared to the BAU scenario. This is mainly due to emissions associated with global LUCs. The case study shows that the method enables a broader consideration for environmental effects of biofuel policies than usual LCA: Global economic variations calculated by a general equilibrium economic model and LUC emissions can be evaluated. More work is needed, however, to include new biofuel production technologies and reduce the uncertainty of the method. [ABSTRACT FROM AUTHOR]

The complexity of data and methods in industrial ecology (IE) keeps growing, and the demand for comprehensive and interdisciplinary assessments increases. To keep up with this development, the field needs a data infrastructure that allows researchers to annotate, store, retrieve, combine, and exchange data at low cost, without loss of information, and across disciplines and model frameworks. A consensus-building debate about how to describe the common object of study, socioeconomic metabolism (SEM), is necessary for the development of practical data structures and databases. We review the definitions of basic concepts to describe SEM in IE and related fields such as integrated assessment modeling. We find that many definitions are not compatible, are implicit, and are sometimes lacking. To resolve the conflicts and inconsistencies within the current definitions, we propose a hierarchical system of terms and definitions, a practical ontology, for describing objects, their properties, and events in SEM. We propose a typology of object properties and use sets to group objects into a hierarchical, mutually exclusive, and collectively exhaustive (H-MECE) classification. This grouping leads to a general definition of stocks. We show that a MECE representation of events necessarily requires two complementary concepts: processes and flows, for which we propose general definitions based on sets. Using these definitions, we show that the system structure of any interdisciplinary model of SEM can be formulated as a directed graph. We propose guidelines for semantic data annotation and database design, which can help to turn the vision of a powerful data infrastructure for SEM research into reality. [ABSTRACT FROM AUTHOR]

Conservation of mass and energy are essential to physical accounting, just as price and market balances are essential to economic accounting. These principles guide data collection and inventory compilation in industrial ecology. The resulting balanced surveys, however, can rarely be used directly for life cycle assessment (LCA) or environmentally extended input-output (EEIO) analysis; some modeling is necessary to recast coproductions by multifunctional activities as monofunctional unit processes (a.k.a. Leontief production functions or technical 'recipes'). This modeling is done with allocations in LCA and constructs in input-output. In this article, we ask how these models respect or perturb the balances of the original inventory. Which allocations or constructs, applied to what type of data set, have the potential to simultaneously respect its multiple physical, financial, and market balances? Our analysis builds upon the recent harmonization of allocations and constructs and the ongoing development of multilayered supply and use inventory tables. We derive the necessary and sufficient conditions for balanced models, investigate the role of data aggregation, and clarify these models' relation to system expansion. We find that none of the modeling families in LCA and EEIO are balanced in general, but special data characteristics can allow for the respect of multiple balances. An analysis of these special cases allows for clear guidance for data compilation and methods integration. [ABSTRACT FROM AUTHOR]