Modeling the fate of chemicals across their life cycle when considering all potential uses can be challenging because of the data gaps arising from issues like confidential business information (data accessibility) and complex processing schemes (involvement in formulations, reactions, and separations) across multiple industries, products, and applications. Thus, assessing chemicals for safety and/or sustainability requires developing an extensive knowledge of chemical releases along the various conditions of use (CoU) to identify potential impacts on human health and the environment. The first step in this process is mapping the flow of a chemical throughout its various downstream uses, which can be time-intensive. Here, a chemical mapping methodology is developed to qualitatively assess the allocation of a chemical of interest from its manufacture through its CoU in consumer, commercial, and industrial products. The chemical flow mapping combines knowledge from searches of publicly available data sources based on the chemical's Chemical Abstracts Service number to determine viable chemical flow paths. Examples of data sources when applying this approach to chemicals in the United States include the Chemical Data Reporting database, the Toxics Release Inventory, the North American Industry Classification System, and the Chemical and Products Database. The methodology is demonstrated using case studies of methylene chloride and triphenyl phosphate. The value of this approach is its ability to be automated and enable rapid determination of the life cycle chemical flow for expedited chemical assessment.

In response to pressing societal challenges, scholars are increasingly focusing on research aimed at fostering sustainable futures. We contribute to that discussion by theorizing the circular economy (CE) as an "ecology of practices." The ecology of practices concept helps to make sense of a developing field that has been heavily practitioner-driven. Through an analysis of the diverse CE practices in analytical and operational contexts, we investigate the roles, disciplinary influences, and visions for the future and categorize their trajectories. Drawing on the sociology of expectations, we consider the articulations of CE in practice, advocating for inclusive dialogue among stakeholders and collective engagement with ontological multiplicity in shaping CE futures. We propose a framework that contributes to broader debates in organization and management studies, emphasizing the significance of everyday practices in shaping sustainable futures beyond the realm of CE. In so doing, we focus on unpicking how sustainable futures are variously enacted as a way of enabling collaboration that might otherwise be hindered by disciplinary obligations.

Plastic additives are as essential as polymers to the production and performance of plastic materials. Additive content can vary in composition and functionality depending on the product, producer, application, and production method. Such variation may be a barrier to achieving high-quality recycling and planning for plastic circular economy futures. Yet, as found in Part I, although there is increasing awareness of the importance of additives in plastics, they are often poorly disclosed or only briefly discussed in life cycle assessments (LCAs). In part II, we focus on the inclusion of additives in plastic processes in the database most used in plastic LCAs to date (Ecoinvent) and find that additives have historically been omitted from plastic granulate data and in production processes in the evaluated database. Thus, many practitioners will need to separately include additives in their models of plastic life cycles. To support practitioners in this endeavor, we then assess the availability of the 13,587 additives identified in the recent UN Chemicals in Plastics Report across the three major LCI databases (CarbonMinds, Ecoinvent, and LCA for Experts [GaBi]). We find that databases currently cover only 1,209 of these additives. Moreover, we assert that transparency regarding additive inclusion in plastics datasets, availability of additive datasets, and additive data completeness are major barriers to additive inclusion in plastic LCAs. Thus, we recommend focusing on the development of additive datasets, and we provide a tool for the identification of additive dataset availability and data gaps to improve the quality of plastic LCAs.

Recent calls have been made for equity tools and frameworks to be integrated throughout the research and design life cycle -from conception to implementation-with an emphasis on reducing inequity in artificial intelligence (AI) and machine learning (ML) applications. Simply stating that equity should be integrated throughout, however, leaves much to be desired as industrial ecology (IE) researchers, practitioners, and decision-makers attempt to employ equitable practices. In this forum piece, we use a critical review approach to explain how socioecological inequities emerge in ML applications across their life cycle stages by leveraging the food system. We exemplify the use of a comprehensive questionnaire to delineate unfair ML bias across data bias, algorithmic bias, and selection and deployment bias categories. Finally, we provide consolidated guidance and tailored strategies to help address AI/ML unfair bias and inequity in IE applications. Specifically, the guidance and tools help to address sensitivity, reliability, and uncertainty challenges. There is also discussion on how bias and inequity in AI/ML affect other IE research and design domains, besides the food system-such as living labs and circularity. We conclude with an explanation of the future directions IE should take to address unfair bias and inequity in AI/ML. Last, we call for systemic equity to be embedded throughout IE applications to fundamentally understand domain-specific socioecological inequities, identify potential unfairness in ML, and select mitigation strategies in a manner that translates across different research domains.

This work presents the PULPO (ython-based ser-defined ifecycle roduct ptimization) framework, developed to efficiently integrate life cycle inventory (LCI) models into life cycle product optimization. Life cycle optimization (LCO), which has found interest in both the process systems engineering and life cycle assessment (LCA) communities, leverages LCA data to go beyond simple assessments of a limited number of alternatives and identify the best possible product systems configuration subject to a manifold of choices, constraints, and objectives. However, typically, aggregated inventories are used to build the optimization problems. Contrary to existing frameworks, PULPO integrates whole LCI databases and user inventories as a backbone for the optimization problem, considering economy-wide feedback loops between fore- and background systems that would otherwise be omitted. The open-source implementation combines functions from Brightway2 for the manipulation of inventory data and pyomo for the formulation and solution of the optimization problem. The advantages of this approach are demonstrated in a case study focusing on the design of optimal future global green methanol production systems from captured CO and electrolytic H. It is shown that the approach can be used to assess sector-coupling with multi-functional processes and prospective background databases that would otherwise be impractical to approach from a standalone LCA perspective. The use of PULPO is particularly appealing when evaluating large-scale decisions that have a strong impact on socioeconomic systems, resulting in changes in the technosphere on which the background system is based and which is often assumed constant in standard LCO approaches regardless of the decisions taken. This article met the requirements for a gold-gold data openness badge described at http://jie.click/badges.

Material stocks of infrastructure, buildings, and machinery are the biophysical basis of production and consumption. They are a crucial lever for resource efficiency and a sustainable circular economy. While material stock research has proliferated over the last years, most studies investigated specific materials or end-uses, usually not embedded into an economy-wide perspective. Herein, we present a novel version of the economy-wide, dynamic, inflow-driven model of material inputs, stocks, and outputs (), and present a global, country-level application. Currently, MISO2 covers 14 supply chain processes from raw material extraction to processing, trade, recycling, and waste management, as well as 13 end-uses of stocks. The derived database covers 23 raw materials and 20 stock-building materials, across 177 countries from 1900 to 2016. We find that total material stocks amount to 1093 Gt in 2016, of which the majority are residential (290 Gt) and non-residential buildings (234 Gt), as well as civil engineering (243 Gt), and roads (313 Gt). The other nine end-uses covering stationary and mobile machinery, as well as short-lived products, amount to 13 Gt. Material stocks per capita are highly unequally distributed around the world, with one order of magnitude difference between low- and high-income countries. Results agree well with similar global country-level studies. Low data quality for some domains, especially for lower-income countries and for sand and gravel aggregates, warrant further attention. In conclusion, the MISO2 model and the derived database provide stock-flow consistent perspectives of the socio-economic metabolism around the world, enabling multiple novel and policy relevant research opportunities. This article met the requirements for a silver-gold data openness badge described at http://jie.click/badges.

Management of building materials' stocks and flows is a major opportunity for circularity and de-carbonization. We examine the relationship between material consumption and associated greenhouse gas (GHG) emissions under different scenarios in Israel, a developed country with an already high population density that expects tremendous growth in its housing stock by 2050. We created scenarios of varying housing unit sizes and additional material efficiency practices: fabrication yield, lifetime extension, material substitution, recycling, and their combination, resulting in 18 scenarios. In each scenario, the material flows and stocks needed to supply the housing demand and the resulting life-cycle GHG emissions are quantified. No single material efficiency practice achieves a reduction in all indicators, suggesting a potential conflict between circular economy and decarbonization policies: The material substitution scenario allows for the biggest reduction in material consumption (12%-40% concrete reduction and 15%-51% steel reduction in 2050 compared with the baseline), while the recycling scenario achieves the biggest reduction in GHG emissions (22%-43% reduction in 2050 compared with the baseline). In the long-term, the life-extension scenario reduces most demolition waste. These findings can help policymakers and stakeholders consider the impacts of raw materials consumption and implement this knowledge in light of their priorities in policy packages. The results suggest a narrow window of opportunity within the next decade to influence material consumption and emissions to 2050. The findings could also shed light on the sustainability trajectories of other countries with similarly rapidly developing building stock, which have received little attention in this field.

Despite the many benefits of greenhouses, it is challenging to meet their heating demand, as greenhouses belong to the most energy-intensive production systems in the agriculture sector. Industrial symbiosis can bring an effective solution by utilizing waste heat from other industries to meet the greenhouse heat demand. This study proposes an optimization framework by which optimum symbiotic relationships can be identified. For this aim, the spatial analysis is integrated into an optimization model, in which geographical, technical, and economic parameters are considered simultaneously to identify the optimal location for developing new agricultural greenhouses. The objective function is to minimize the heating costs, that is, the investment cost of piping and electricity cost for pumping heat-carrying fluid from supplier to demand. The model is applied to the case study of Switzerland, and currently existing municipal solid waste incinerators, cement production plants, and biogas plants are considered potential waste heat sources. Results show that the import of tomato, cucumber, and lettuce to Switzerland can theoretically be replaced by vegetable production in new waste-heat supplied greenhouses (zero import scenarios). Accounting for the economy of scale for pipeline investment costs leads to selecting large-scale greenhouses with a cost reduction of 37%. The optimization results suggest that 10% of the greenhouses needed to satisfy the total domestic demand for lettuce, tomato, and cucumber could be placed on a suitable land plot in the direct vicinity of a waste heat source, with low costs of waste heat supply.

The emerging field of prospective life cycle assessment (pLCA) offers opportunities for evaluating the environmental impacts of possible future consumption shifts. One such shift involves a transition from meat-based to plant-forward diets, acknowledged to mitigate environmental impacts of the food system under present day conditions. Current diets are often meat intensive ("meat-based"), whilst "plant-forward" diets include mainly plant-based foods, encompassing flexitarian, vegetarian, and vegan diets. Here we illustrate the application of pLCA in a case study of meal options, implementing shared socio-economic pathway scenarios in the LCA background system to represent future production conditions. We assess the climate footprints and land-based biodiversity footprints of a typical meat-based meal in Germany and Indonesia compared to a plant-forward meal in both countries (i.e., four meals), now and in 2050. Our findings show that the plant-forward alternative maintains a lower impact per serving in all future scenarios. At the same time, the reduction in impact for the meat-based meals is more pronounced in future scenarios due to shifts in the agricultural system. Our findings highlight the importance of supply-side measures to produce lower-impact ingredients, complementing demand-side interventions to reshape food consumption. Results are further evaluated in cultural and nutritional contexts, highlighting the practical decision-making constraints faced by consumers. We find potential "leakage" effects in calories and nutrition when choosing a lower-impact, plant-forward meal. These leakage effects should be considered in future studies seeking to evaluate the environmental implications of meal substitutions in the context of broader dietary requirements.

Material flow analysis (MFA) is used to quantify and understand the life cycles of materials from production to end of use, which enables environmental, social, and economic impacts and interventions. MFA is challenging as available data are often limited and uncertain, leading to an under-determined system with an infinite number of possible stocks and flows values. Bayesian statistics is an effective way to address these challenges by principally incorporating domain knowledge, quantifying uncertainty in the data, and providing probabilities associated with model solutions. This paper presents a novel MFA methodology under the Bayesian framework. By relaxing the mass balance constraints, we improve the computational scalability and reliability of the posterior samples compared to existing Bayesian MFA methods. We propose a mass-based, child and parent process framework to model systems with disaggregated processes and flows. We show posterior predictive checks can be used to identify inconsistencies in the data and aid noise and hyperparameter selection. The proposed approach is demonstrated in case studies, including a global aluminum cycle with significant disaggregation, under weakly informative priors and significant data gaps to investigate the feasibility of Bayesian MFA. We illustrate that just a weakly informative prior can greatly improve the performance of Bayesian methods, for both estimation accuracy and uncertainty quantification.

Residential and non-residential buildings are a major contributor to human well-being. At the same time, buildings cause 30% of final energy use, 18% of greenhouse gas emissions (GHGE), and about 65% of material accumulation globally. With electrification and higher energy efficiency of buildings, material-related emissions gain relevance. The circular economy (CE) strategies, , together with wooden buildings, can reduce material-related emissions. We provide a comprehensive set of building stock transformation scenarios for 10 world regions until 2060, using the resource efficiency climate change model of the stock-flow-service nexus and including the full CE spectrum plus wood-intensive buildings. The 2020-2050 global cumulative new construction ranges from 150 to 280 billion m for residential and 70-120 billion m for non-residential buildings. Ambitious CE reduces cumulative 2020-2050 primary material demand from 80 to 30 gigatons (Gt) for cement and from 35 to 15 Gt for steel. Lowering floor space demand by 1 m per capita leads to global savings of 800-2500 megatons (Mt) of cement, 300-1000 Mt of steel, and 3-10 Gt CO-eq, depending on industry decarbonization and CE roll-out. Each additional Mt of structural timber leads to savings of 0.4-0.55 Mt of cement, 0.6-0.85 Mt of steel, and 0.8-1.8 Mt CO-eq of system-wide GHGE. CE reduces 2020-2050 cumulative GHGE by up to 44%, where the highest contribution comes from the CE strategies, that is, lower floorspace and lightweight buildings. Very low carbon emission trajectories are possible only when combining supply- and demand-side strategies. This article met the requirements for a gold-gold data openness badge described at http://jie.click/badges.

Extending multi-regional input-output (MRIO) models with spatially explicit life cycle impact assessment (LCIA) models allows practitioners to quantify biodiversity impacts at every step of global supply chains. Inconsistencies may be introduced, however, when high-resolution characterization factors (CFs) are aggregated so as to match the low spatial granularity of MRIO models. These aggregation errors are greater when CFs are aggregated via proxies, such as ecoregion land shares, instead of based on spatially explicit elementary stressor flows. Here, we describe our approach to tailoring application-specific CFs for use in MRIO studies. We apply a global agricultural production model, Spatial Production Allocation Model (MapSPAM), with the LCIA database, LC-IMPACT, to create crop-specific national CFs. We investigated i) if the differing aggregation approaches and the increased spatial explicitness of the constructed CFs deviate substantially from those in LC-IMPACT, and ii) what the resulting consequences for national production and consumption-based biodiversity footprints are when combining the tailor-made CFs with the EXIOBASE MRIO model. For the year 2020, we observe an increase in global production-based biodiversity impacts of 23.5% for land use when employing crop-specific CFs.

The rollout of electric vehicles and photovoltaic panels is essential to mitigate climate change. However, they depend on technology-critical elements (TCEs), which can be harmful to human health and whose use is rapidly expanding, while recycling is lacking. While mining has received substantial attention, in-use dissipation in urban areas has so far not been assessed, for example, corrosion and abrasion of vehicle components and weather-related effects affecting thin-film photovoltaic panels. Therefore, the question arises to which extent TCEs dissipate during use and which potential non-occupational human health impacts could occur. We assessed the available information on urban in-use dissipation and human health concerns and conducted exploratory modeling of in-use technology stocks, in- and outflows, and in-use dissipation of neodymium, dysprosium, lanthanum, praseodymium, cerium, gallium, germanium, and tellurium contained in 21 vehicle and renewable energy technologies, for Vienna, Austria. In prospective scenarios, TCE dynamics in a trend-continuation vis à vis official city policy plans and a more ambitious transition scenario were then assessed. We find that electrifying the vehicle fleet without demand-reduction is the main driver of TCE consumption, effectively doubling cumulative end-of-life outflows to 3,073 [2,452-3,966] t and cumulative in-use dissipation to 9.3 [5.2-15.7] t by the year 2060. Sufficiency-based measures could reduce demand and in-use dissipation well below levels with continued trends, thus highlighting the need to combine decarbonization with demand-reducing measures. These results help assess potential future in-use dissipation dynamics and inform discussions about potential public health hazards associated with exposure to TCEs accumulating in the urban environment.

To fight plastic pollution and reach net-zero ambitions, policy and industry set goals to increase the recycling of plastics and the recycled content in products. While this ideally reduces demand for virgin material, it also increases pressure on recyclers to find suitable endmarkets for the recyclate. This may lead to two effects: a multiplication of recycled content in applications already made of plastic and a substitution of non-plastic materials with cheap, low-quality recyclate. Both areas of application may be sources of microplastic (MP) pollution. Combined with the inherent degradation of recyclate during its lifecycle, but also during recycling, we expect the increase in recycled content will subsequently lead to an increase in MP pollution. We propose a framework to investigate the risk of MP generation through plastic applications throughout their subsequent lifecycle of production, use phase, and end of life. We apply the framework to two prominent examples of recyclate endmarkets, that is, textiles and wood-plastic, and point out where the degradation effects can cause higher release. To conclude, we outline a research agenda to support policymakers in their decision making on specifying targets for recycling and recycled content.

As the European Union transitions to the circular use of plastics, robust life cycle assessments are crucial in understanding and preparing for this new economy. Additives are essential to the production of all plastics but were reported as missing from life cycle assessments (LCAs) of plastic materials a decade ago. This study expands upon previous research by investigating if plastic additive impacts are now included in LCAs of recycled plastic materials or if they are still absent. In part I, we conduct a systematic literature review of 93 LCAs on plastics, including mechanical recycling pathways, and distinguish if plastic additive impacts are considered in (i) in-text discussions and (ii) the life cycle inventories (LCIs) of each study. We then compare the types of additive inclusion within the corpus to ascertain whether author knowledge or data availability dictates additive inclusion in plastic LCAs. We find that data disclosure and detailed discussions of specific additive impacts are missing across the corpus due to poor transparency in LCI disclosure or overly generic disclosures of additives. The lack of detailed and transparent discussions and disclosure indicates that additive impacts are missing from LCAs of recycled plastic materials, leading to incomplete analyses of their impacts. Until addressed, such a gap may lead to inaccurate or incomplete circular plastic material LCAs. In part II, we assess the quality of generic disclosures and explore how database quality and transparency have contributed to additive omissions in LCAs.

There is growing environmental concern regarding the increasing quantity of packages in retail eCommerce. This study investigated the environmental impact of two returnable packaging formats, performing life cycle assessment (LCA) case studies based on the Canadian apparel eCommerce market. In case study 1, the brand owner sold and shipped its products to final consumers using an expendable mailer and a returnable mailer that was managed and supplied via the centralized model. In case study 2, the brand owner rented its products to final consumers and shipped them using an expendable corrugated paperboard box and a returnable box that was managed and supplied via the decentralized model. Comparative, contribution, and sensitivity analyses were conducted to analyze and compare the environmental performance of these packaging options. For case study 1, the LCA revealed that the returnable mailer had greater impact than the expandable mailer in 9 of the 10 environmental impact categories, even if the returnable mailer was reused for 40 cycles and the final consumer was in the same city as the brand owner; this was primarily due to the length of transportation. For case study 2, the returnable box had smaller environmental impact than the expendable corrugated paperboard box in 6 of the 10 environmental impact categories, even though the brand owner shipped packages to final consumers a cumulative distance of 9000 km from its starting location. The overall results imply that the environmental burden of returnable packaging is primarily affected by total trip distance and the number of reuses.

To keep global heating and other negative consequences of socioeconomic activities within manageable boundaries, industrialized countries must undergo substantial decarbonization, requiring the exploitation of synergies with other environmental endeavors. Improving resource efficiency-that is, reducing the resources required to generate a unit of economic output-is a prominent goal pursued across levels of scale. How does resource efficiency relate to decarbonization? Do economies decrease their emissions as they become more efficient? We examine this relationship for Austria from 2000 to 2015 by conducting an index decomposition analysis at the sectoral level by using consumption-based indicators from the multi-regional input-output model Exiobase. Our analysis shows that for Austria, the currently popular pursuit of material efficiency appears to run the risk of coinciding with higher emissions, suggesting that the opportunities to achieve both decarbonization and dematerialization are limited. The Austrian service sectors could contribute to a reduction of the CO footprint via material efficiency improvements, but strong economic growth foils this possibility coming to fruition. The Austrian economy would do well to either curb demand for goods and services driving global CO emissions or to produce imported goods and services domestically in an environmentally more benign manner.

This study proposes methods to improve data mining workflows for modeling chemical manufacturing life cycle inventory. Secondary data sources can provide valuable information about environmental releases during chemical manufacturing. However, the often facility-level nature of the data challenges their utility for modeling specific processes and can impact the quality of the resulting inventory. First, a thorough data source analysis is performed to establish data quality scoring and create filtering rules to resolve data selection issues when source and species overlaps arise. A method is then introduced to develop context-based filter rules that leverage process metadata within data sources to improve how facility air releases are attributed to specific processes and increase the technological correlation and completeness of the inventory. Finally, a sanitization method is demonstrated to improve data quality by minimizing the exclusion of confidential business information (CBI). The viability of the methods is explored using case studies of cumene and sodium hydroxide production in the United States. The attribution of air releases using process context enables more sophisticated filtering to remove unnecessary flows from the inventory. The ability to sanitize and incorporate CBI is promising because it increases the sample size, and therefore representativeness, when constructing geographically averaged inventories. Future work will focus on expanding the application of context-based data filtering to other types and sources of environmental data.

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 input-output (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.

A large part of the world population is exposed to noise levels that are unhealthy. Yet noise is often neglected when impact assessment studies are conducted and when policy interventions are designed. In this study, we provide a way to calculate the noise footprint of citizens directly determined by their use of private and public transport on land. The study combines the results of the large transport simulation model MATSim applied to Switzerland, with a noise characterization model, N-LCA, developed in the context of life cycle assessment. MATSim results allow tracking the use of private and public transportation by agents in the model. The results after characterization provide a consumption-based noise footprint, thus the total noise and impacts that are caused by the private mobility demand of the citizens of Switzerland. Our results confirm that road transportation is the largest contributor to the total noise footprint of land-based mobility. We also included a scenario with a full transition to an electrified car fleet, which showed the potential for the reduction of impacts, particularly in urban areas, by about 55% as compared to the modeled regime with combustion engines.

Life cycle interpretation is the fourth and last phase of life cycle assessment (LCA). Being a "pivot" phase linking all other phases and the conclusions and recommendations from an LCA study, it represents a challenging task for practitioners, who miss harmonized guidelines that are sufficiently complete, detailed, and practical to conduct its different steps effectively. Here, we aim to bridge this gap. We review available literature describing the life cycle interpretation phase, including standards, LCA books, technical reports, and relevant scientific literature. On this basis, we evaluate and clarify the definition and purposes of the interpretation phase and propose an array of methods supporting its conduct in LCA practice. The five steps of interpretation defined in ISO 14040-44 are proposed to be reorganized around a framework that offers a more pragmatic approach to interpretation. It orders the steps as follows: (i) completeness check, (ii) consistency check, (iii) sensitivity check, (iv) identification of significant issues, and (v) conclusions, limitations, and recommendations. We provide toolboxes, consisting of methods and procedures supporting the analyses, computations, points to evaluate or check, and reflective processes for each of these steps. All methods are succinctly discussed with relevant referencing for further details of their applications. This proposed framework, substantiated with the large variety of methods, is envisioned to help LCA practitioners increase the relevance of their interpretation and the soundness of their conclusions and recommendations. It is a first step toward a more comprehensive and harmonized LCA practice to improve the reliability and credibility of LCA studies.