Journal of Industrial Ecology最新文献

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.

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.

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.

This work presents the PULPO (Python-based user-defined lifecycle product optimization) 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 CO₂ and electrolytic H₂. 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 JIE data openness badge described at http://jie.click/badges.

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.

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.

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, narrow, slow, and close, 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 m² for residential and 70-120 billion m² 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 m² per capita leads to global savings of 800-2500 megatons (Mt) of cement, 300-1000 Mt of steel, and 3-10 Gt CO₂-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 CO₂-eq of system-wide GHGE. CE reduces 2020-2050 cumulative GHGE by up to 44%, where the highest contribution comes from the narrow 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 JIE 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 (MISO2), 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 JIE data openness badge described at http://jie.click/badges.

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.

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.