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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]

This study contrasts two different approaches to inform European‐scale decision‐making to mitigate the environmental impacts of the end‐of‐life tires (ELT) management system. The first analysis is a traditional life cycle assessment (LCA) that compares the environmental performances of the 12 main available European end‐of‐life (EOL) technologies in ELT processing while restricting the boundaries to the EOL stage. The second analysis has a broader scope, addressing the optimization of the ELT distribution within the 12 considered pathways to minimize the environmental impacts of the total tire use in Europe under present capacity and constraints. The results of the traditional LCA show that, except for landfill, all the tested EOL routes present environmental benefits. Material recovery pathways bring the most environmental credits, whereas civil engineering pathways are the least promising. The LCA results that emerged from the optimization of ELT management technologies yield two optimal technological mixes that maximize the quantity of ELT recycled in molded objects production: such results represent a hypothetical case with no constraints. When considering constraints, that is, limitations on maximum quantities of ELT that can undergo retreading, pyrolysis, or recycling in synthetic turfs, in molded objects and in production, the number of optimal technology mixes increases to five. The type of technologies favored depends on the minimized impact categories (climate change, fossil and nuclear energy use, human health, and ecosystem quality). A comparison between constrained and unconstrained scenarios shows that achieving the best environmental performances is conditional to the accessibility of the EOL technologies as well as their individual environmental impacts. [ABSTRACT FROM AUTHOR]

Aluminum recycling follows a downcycling dynamic where wrought alloys are transformed into cast alloys, accumulating tramp elements at every cycle. With the saturation of stocks of aluminum and the reduction of the demand for cast alloy due to electrification of transport, improvement in the recycling system must be made to avoid a surplus of unused recycled aluminum, reduce the overall environmental impacts of the industry, and move toward a circular economy. We aim to evaluate the potential environmental benefits of improving sorting efforts by combining operations research, prospective material flow analysis, and life cycle assessment. An optimization defines the optimal sorting to minimize climate change impacts according to different sorting efforts, dismantling conditions, and collection rates. Results show how the improvement of sorting can reduce by around 30% the greenhouse gas emissions of the industry, notably by reducing unused scrap generation and increasing the recycled content of the flows that supply the demand of aluminum. The best performance is achievable with four different sorting pathways. Further improvements occur with a better dismantling and an increase of collection rates, but it requires more sorting pathways. Results point to different closed‐loop recycling initiatives that should be promoted on priority in specific sectors, like the building and construction sector and the aluminum cans industry. To implement a better material circularity, the mobilization of different stakeholders is needed. From a wider perspective, the article shows how operations research can be used to project a circular future in a specific industry. This article met the requirements for a Gold–Gold JIE data openness badge described at http://jie.click/badges. [ABSTRACT FROM AUTHOR]

Aluminum is an energy‐intensive material that is typically used as an alloy. The environmental impacts caused by its production can potentially be spread out over multiple uses through repeated recycling loops. However, inter‐alloy contamination can limit the circularity of aluminum, which highlights the importance of analyzing prospective stock dynamics of aluminum at an alloy and alloying element level to inform a more sustainable management of this resource. A dynamic material flow analysis (MFA) of aluminum alloys was developed in line with the shared socioeconomic pathways (SSP) framework to generate consistent scenarios of the evolution of aluminum stocks and flows from 2015 to 2100 covering 11 economic sectors in 5 world regions. A sector‐specific and bottom‐up modeling approach was developed. Results show no saturation of global stock per capita before 2100, reaching a range between 200 and 400 kg per capita according to different socioeconomic scenarios. For the business‐as‐usual scenario, the global annual inflow rises to 100 Mt in 2050 and peaks at 130 Mt in 2090, showing a saturation in total stock. Electricity‐sector demand has the highest relative growth over the century, while building and construction demand saturates and decreases from 2090. No major mismatch between inflows and outflows of aluminum alloy is observed. This means that with appropriate dismantling and sorting, changes in alloy demand would not limit the implementation of a closed‐loop aluminum industry. This study demonstrates the advantages of combining detailed MFAs and SSPs, both for greater consistency in circular economy modeling and for furthering scenario development efforts. 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) 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]

Keywords: agent-based modeling; big data; data analytics; geographic information systems (GIS); industrial ecology; machine learning (ML) EN agent-based modeling big data data analytics geographic information systems (GIS) industrial ecology machine learning (ML) 6 11 6 02/16/22 20220201 NES 220201 IE research is increasingly taking advantage of new strategies to acquire and curate data, enabling our field to quantitatively tackle research questions that could only be approached qualitatively or conceptually until recently....The different articles of this special issue effectively document (1) practical applications of data innovations in real-world IE practice; (2) transformations and new opportunities for IE research; and (3) the development and consolidation of IE datasets. Innovations in data-enabled IE modeling Not only do massive datasets provide high-resolution data on systems, but they enable novel modeling techniques. Big data, data analytics, agent-based modeling, geographic information systems (GIS), industrial ecology, machine learning (ML) DISCUSSION The different articles of this special issue effectively document (1) practical applications of data innovations in real-world IE practice; (2) transformations and new opportunities for IE research; and (3) the development and consolidation of IE datasets. [Extracted from the article]

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]

Recently, Merciai and Heijungs (2014) demonstrated that monetary input–output (IO) analysis can lead to system descriptions that do not conserve mass when the assumption of homogeneous prices is violated. They warn that this violation of basic balance laws can lead to biased estimates of environmental impacts, and they therefore recommend performing IO analysis in a physically accounted framework. We take a broader scope on this issue and present price inhomogeneity as a special case of product mix inhomogeneity. We demonstrate that even a fully physically accounted IO analysis or lifecycle assessment will violate balance laws if it suffers from inhomogeneous aggregation. The core issue is not whether a system is described using monetary or physical units, but rather whether product groups are too aggregated to allow for the concurrent respect of energy, mass, financial and elemental balances. We further analyze the link between the violation of physical balances and the introduction of biases. We find that imbalances are neither a necessary nor a sufficient condition for the presence of systematic errors in environmental pressure estimates. We suggest two ways to leverage the additional explanatory power of multi-unit inventory tables to reduce instances of imbalances and aggregation biases. [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]

Industrial ecology (IE) is a maturing scientific discipline. The field is becoming more data and computation intensive, which requires IE researchers to develop scientific software to tackle novel research questions. We review the current state of software programming and use in our field and find challenges regarding transparency, reproducibility, reusability, and ease of collaboration. Our response to that problem is fourfold: First, we propose how existing general principles for the development of good scientific software could be implemented in IE and related fields. Second, we argue that collaborating on open source software could make IE research more productive and increase its quality, and we present guidelines for the development and distribution of such software. Third, we call for stricter requirements regarding general access to the source code used to produce research results and scientific claims published in the IE literature. Fourth, we describe a set of open source modules for standard IE modeling tasks that represent our first attempt at turning our recommendations into practice. We introduce a Python toolbox for IE that includes the life cycle assessment (LCA) framework Brightway2, the ecospold2matrix module that parses unallocated data in ecospold format, the pySUT and pymrio modules for building and analyzing multiregion input-output models and supply and use tables, and the dynamic_stock_model class for dynamic stock modeling. Widespread use of open access software can, at the same time, increase quality, transparency, and reproducibility of IE research. [ABSTRACT FROM AUTHOR]

A wide spectrum of accounting frameworks and models is available to describe socioeconomic metabolism (SEM). Despite the common system of study, a large variety of terms and representations of that system are used by different models. This makes it difficult for practitioners to compare and choose a model or model combination that is fit for purpose. To facilitate model comparison, we analyze the system structure of material flow analysis (MFA); life cycle assessment (LCA); supply and use tables (SUTs); Leontief, Ghosh, and waste input-output analysis; integrated assessment models; and computable general equilibrium models. We show that the typical system structure of MFA and LCA is a directed graph. For the other models and some MFA and LCA studies, the system structure is a bipartite directed graph. We demonstrate that bipartite directed graphs and SUTs are equivalent representations of SEM. We show that the system structures of the models above are special cases of a general system structure, which models SEM as a bipartite graph. The general system structure includes industries, markets, the final use phase, products, waste, production factors, resources, and emissions. From the general system structure, we derive an accounting framework in the form of a generalized SUT. The general system structure facilitates the development of clear and unambiguous terminology across models. It helps to identify rules for the correct accounting of waste flows and stock changes. It facilitates model comparison and can serve as a blueprint for a model-independent database of SEM. [ABSTRACT FROM AUTHOR]

The treatment of coproducts is one of the most persistent methodological challenges for both input-output (IO) analysis and life cycle assessment (LCA). The two fields have developed distinct modeling traditions to artificially extract independent Leontief production functions (technological 'recipes') for products of multioutput activities; whereas IO operates in terms of system-wide models named constructs, LCA practitioners usually use allocations or system expansion on a process-by-process basis. Recently, there have been renewed efforts to connect these two modeling traditions on the basis of their underlying assumptions. A formal description of a unified framework for the treatment of coproducts is still lacking, however. The present article strives to fill this gap. From a single generalized allocation equation, we derive all practical LCA allocations and IO constructs. This approach extends previous studies by arranging the different models in a formal 'taxonomic tree,' clarifying the relation between the different LCA allocation and IO construct models. This framework also clarifies the relation of certain models to classical system expansion. We then analyze the properties of these models when combined with different types of inventories and make recommendations for best practice in inventory compilation. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have no tailpipe emissions, but the production of their batteries leads to environmental burdens. In order to avoid problem shifting, a life cycle perspective should be applied in the environmental assessment of traction batteries. The aim of this study was to provide a transparent inventory for a lithium-ion nickel-cobalt-manganese traction battery based on primary data and to report its cradle-to-gate impacts. The study was carried out as a process-based attributional life cycle assessment. The environmental impacts were analyzed using midpoint indicators. The global warming potential of the 26.6 kilowatt-hour (kWh), 253-kilogram battery pack was found to be 4.6 tonnes of carbon dioxide equivalents. Regardless of impact category, the production impacts of the battery were caused mainly by the production chains of battery cell manufacture, positive electrode paste, and negative current collector. The robustness of the study was tested through sensitivity analysis, and results were compared with preceding studies. Sensitivity analysis indicated that the most effective approach to reducing climate change emissions would be to produce the battery cells with electricity from a cleaner energy mix. On a per-kWh basis, cradle-to-gate greenhouse gas emissions of the battery were within the range of those reported in preceding studies. Contribution and structural path analysis allowed for identification of the most impact-intensive processes and value chains. This article provides an inventory based mainly on primary data, which can easily be adapted to subsequent EV studies, and offers an improved understanding of environmental burdens pertaining to lithium-ion traction batteries. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) coupled with low-carbon electricity sources offer the potential for reducing greenhouse gas emissions and exposure to tailpipe emissions from personal transportation. In considering these benefits, it is important to address concerns of problem-shifting. In addition, while many studies have focused on the use phase in comparing transportation options, vehicle production is also significant when comparing conventional and EVs. We develop and provide a transparent life cycle inventory of conventional and electric vehicles and apply our inventory to assess conventional and EVs over a range of impact categories. We find that EVs powered by the present European electricity mix offer a 10% to 24% decrease in global warming potential (GWP) relative to conventional diesel or gasoline vehicles assuming lifetimes of 150,000 km. However, EVs exhibit the potential for significant increases in human toxicity, freshwater eco-toxicity, freshwater eutrophication, and metal depletion impacts, largely emanating from the vehicle supply chain. Results are sensitive to assumptions regarding electricity source, use phase energy consumption, vehicle lifetime, and battery replacement schedules. Because production impacts are more significant for EVs than conventional vehicles, assuming a vehicle lifetime of 200,000 km exaggerates the GWP benefits of EVs to 27% to 29% relative to gasoline vehicles or 17% to 20% relative to diesel. An assumption of 100,000 km decreases the benefit of EVs to 9% to 14% with respect to gasoline vehicles and results in impacts indistinguishable from those of a diesel vehicle. Improving the environmental profile of EVs requires engagement around reducing vehicle production supply chain impacts and promoting clean electricity sources in decision making regarding electricity infrastructure. [ABSTRACT FROM AUTHOR]