Journal of Industrial Ecology最新文献

Transitioning to a circular economy (CE) can contribute to the achievement of long-term climate targets. A comprehensive evaluation of CE strategies requires a macro-level approach that integrates environmental, social, and economic goals. Policy support, behavioral changes, innovation, and new business models play key roles in this transition. Additionally, understanding the potential synergies, benefits, and trade-offs associated with these strategies is essential for their effective implementation. This paper proposes a novel approach, using macroeconomic modeling, industrial ecology, and behavioral concepts to evaluate the impacts of CE strategies on socioeconomic and climate systems. As an application, the paper investigates the environmental and economic effects in Japan of increasing the extended producer responsibility (EPR) fee on energy-using durable goods. We show that increasing the fee makes households substitute conventional goods for circular services. Also, while increasing EPR fees for energy-using durable goods may decrease the materials needed to produce and operate these goods, the overall impact on sustainability goals is rather low.

This study estimates building attributes—floor area and age—using image-based machine learning. Building age and floor area are key inputs to the studies of urban metabolism, material stocks and flows, and embodied greenhouse gases (GHGs) in the built environment. However, these data are challenging to generate and maintain using traditional survey methods, their availability is uneven and often, even when available, very uncertain. Improving our understanding and future management of built environment resource flows and associated environmental impacts requires more complete access to building age and floor area data. The study formulates area prediction as a regression problem and age prediction as a classification problem over six historical periods, achieving a mean absolute percentage error of 19.42% for area prediction and an accuracy of 70.27% for age prediction in Toronto. These results are obtained using an EfficientNetV2 module for feature extraction from Google Street View images, followed by fully connected layers for estimating the two building attributes. The performance of the Toronto-trained model in five other Canadian cities is also reported, highlighting the model's varying effectiveness in different urban contexts and the benefit of local training. Our findings demonstrate the feasibility of using machine learning for building attribute estimation from street-view images, offering a basis for future automated large-scale material flow and stock analysis.

Symbiotic relationships between enterprises help mitigate resource and environmental impacts of industrial activities via exchanging waste or by-products as material inputs among each other. However, the emergence of such symbiotic relationships under complex driving factors across different geographical scales remains hitherto not well understood. Here, we provide an analytic framework including a random forest model and Shannon index, to systematically describe and explain the scale effects of driving factors underlying the symbiotic relationships. Based on a questionnaire survey for 324 enterprises in Chun'an, a typical industrial city in eastern China, we applied this analytical framework. The results show that, first, the quantity of symbiotic relationships exhibits an inversely proportional function across various geographical scales. Second, there exist significant differences in the dominant factors at different scales. Finally, the diversity of importance of factors and the emergence of symbiotic relationships exhibit a consistent trend of fluctuation, providing evidence for the explanatory potential of our proposed analytical framework for the driving mechanisms of emergence. We find that when enterprises are simultaneously affected by multiple driving factors with potent forces (referred to as the diversity of importance), symbiotic behaviors are more likely to occur. Moreover, our results suggest that fostering symbiotic relationships necessitates considering the variations in driving factors across different scales comprehensively and formulating targeted promotional measures tailored to the specific driving factors of different enterprise types. Our proposed framework would help to maximize industrial symbiosis potentials in a specific region.

Integrated assessment models (IAMs) are popular tools used to predict the evolution of human society, a challenging question that science has long tried to address. The World3 model is a popular IAM, designed in the seventies by several scientists convened by the Club of Rome and mostly known for its usage to analyze the so-called limits to growth. The recent Earth for all (E4A) model has been initiated by one of the major co-authors of the World3 model, Jørgen Randers. It is substantially more complicated than the relatively simple World3 model, and it has been used to compare two different and opposite world development scenarios: the too little too late scenario, in which current policies are assumed to continue, and the giant leap (GL) scenario, in which 21 policies related to five turnarounds are identified to produce significant improvements in six indicators of human well-being. By using global and local sensitivity analyses of the E4A model, we suggest that the evolution of the six indicators in the GL scenario can be approximately reached by focusing on just six policies and three turnarounds (namely, the energy, the inequality, and the poverty turnarounds). The evolution of the six indicators can be even improved by investing “reasonably” more on three of these six policies and by keeping unchanged the remaining three. From a methodological point of view, we exploit both global (Sobol) and local sensitivity analyses to identify the policies that most influence the six indicators, and we subsequently execute a scenario analysis of the identified policies to confirm that they can produce a similar (or even a better) evolution of the indicators themselves.

Information communication and technology (ICT) services and solutions can improve resource efficiency in a variety of sector, but also result in direct environmental impacts. This study assesses the direct environmental impacts of an ICT service that improves vehicle fuel efficiency using a cradle-to-grave life cycle assessment (LCA). This is one of the first studies to examine the entire life cycle of an ICT service from development to use and maintenance, with a focus on software—an aspect that is typically neglected in previous studies. The results suggest that software development and maintenance and the use of in-vehicle communicators for data transmission have the largest environmental impacts across multiple categories. Deployed across a fleet of 150,000 vehicles over 5 years, we estimate that the ICT service is responsible for 174 tCO₂e. However, this is negligible compared with the total emissions of the fleet and the potential savings from the service, given a single diesel vehicle in this fleet emits around 130 tCO₂e over the same period. We explore several scenarios to reduce the footprint of the ICT service. The largest potential reduction of around one-third is achieved by replacing in-house servers with cloud computing in a data center located in a region with low-carbon electricity. The study demonstrates how LCA can be used to assess the environmental impacts of ICT services and the importance of considering software in these assessments.

Dominik, N., Lauk, C., Haas, W., Singh, S. J., Petridis, P., & Wiedenhofer, D. (2022). The sociometabolic transition of a small Greek island: Assessing stock dynamics, resource flows, and material circularity from 1929 to 2019. Journal of Industrial Ecology, 26(2), 577–591. https://doi.org/10.1111/jiec.13206.

INTRODUCTION

In the article titled “The sociometabolic transition of a small Greek island: Assessing stock dynamics, resource flows, and material circularity from 1929 to 2019” published in Journal of Industrial Ecology, 26(2), 577–591, 2022, we identified the following errors that require correction. We would like to emphasize that these corrections do not alter the overall results, interpretations, or conclusions presented in the article, and the core message of the study remains unchanged. Supporting data file has been updated and all deviations from the originally submitted file have been highlighted in yellow (S2).

CORRECTIONS

FIGURE 3d: DEC per capita increased from 62.1 GJ/cap/year in 1929 to 245.16 GJ/cap/year in 2019.

FIGURE 4b: DPO emissions increased from 4.5 kt/year in 1929 to a maximum of 23.8 kt/year in 2003, declining to 15.3 kt/year until 2019. Domestic open combustion of MSW resulted in average emissions of 0.7 kt/year between 2010 and 2013. Today, technical biomass combustion for heating is responsible for 38%, fossil fuel combustion for 10%, and livestock and human respiration and composting for 52% of total emissions.

FIGURE 5: Corrections concern the following indicators: Exports (1970–2019); DMC, PM (2013–2019); eUSE (1970–2019); IntOut (1929–2019); DPO emissions (2013–2019); water vapor (2013–2019); total input/output materials (1971–2019). The corresponding numbers have been corrected in this figure. The complete data set can be downloaded in the supporting information (S2).

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.

Limited insights have been obtained regarding the flows and stocks of thulium, the scarcest rare earth element. Thus, this study quantifies the thulium material flow from 2011 to 2020 in China, the largest thulium producer and reserve holder. Dynamic material flow analysis method is used and the demand and supply pattern of China's thulium is clarified. Results reveal that the final demand for thulium grows at an annual rate of 6.2%, reaching from 121 tonnes in 2011 to 208 tonnes in 2020, driven mainly by laser-related end-use sectors. Accumulative 1506 tonnes (80.7%) of domestically produced thulium in China originates from illegal mining during 2011–2020. This, combined with imported thulium compounds, bridges the significant gap between final demand and domestic legal supply. Moreover, 1422 tonnes of thulium are stocked in end-use sectors by 2020, with 30.8% in fiber-optic communication sector. A supply–demand gap has emerged since 2016 with thulium demand surpassing its domestic supply, which is projected to further widen in the future. Measures like promoting recycling and recovery, optimizing the trade patterns, and exploring material and technology substitution are proposed to mitigate such a gap.

This paper calculates the future levelized cost of storage (LCOS) and conducts a prospective life cycle assessment (PLCA) for second-life batteries (SLB) in Flanders, Belgium. A cradle-to-grave approach is chosen for climate change (CC) and economic impacts of SLB. Impacts of processes related to the first and the second life are allocated according to the delivered electricity. Furthermore, impacts are determined according to their temporal occurrence. For LCOS, activities are time adjusted by discounting. For PLCA, new background databases are generated and changed based on the activities occurrence in time. Additionally, future CC impact of three Belgian energy paths is modeled, introducing user-defined scenarios of PLCA. To conceptualize impacts, three use cases are defined: (a) residential, (b) industrial and (c) utility use case (UTI). The residential and industrial use cases (INDs) represent photovoltaic (PV) installations with battery storage, the UTI is a large-scale battery participating in the Belgian secondary reserve market. Lowest LCOS of the SLB in 2050 are found in the IND, namely 39.66 €/MWh, and are below the benchmark batteries. CC impact of SLB in the residential is 58.7 $��{\text{gCO}}_2�\mathrm{eq}�$/kWh and below the benchmark batteries. The CC impact of SLB is 75.2 and 78.5 $��{\text{gCO}}_2�\mathrm{eq}�$/kWh in the industrial and UTI and thus higher than the benchmark batteries. Crucial for both assessments are increased dismantling and repurposing facility throughput, fair charging tariffs, the manufacturing, the charging electricity generated by PV installations, and power electronics. On the contrary, changing the background does not lead to major changes in CC.

Environmental impacts resulting from plastic food packaging, made from both fossil-based and bio-based polymers, are increasingly analyzed in life cycle assessment (LCA) studies. However, the literature reveals significant variations in results for the same polymer within the same scope. To enhance the reliability of these assessments, data quality assessment (DQA) plays a relevant role. However, despite most of the LCA studies employing aggregated life cycle inventory (LCI) datasets, in the literature, DQA methods for aggregated processes are not available. To fill this gap, in this paper, a DQA for aggregated LCI datasets is proposed and demonstrated through its application to 101 aggregated LCI datasets, extracted from Ecoinvent and GaBi databases. The DQA method has been developed by adapting and integrating the pedigree matrix and the data quality ranking proposed by the recently published EC Plastic LCA method. The three data quality indicators (DQIs) used are technological, geographical, and time-related representativeness. The application of this method exhibits an overall positive evaluation of the selected datasets with differences among the three DQIs. Moreover, it highlights the role of metadata structure in adequately supporting a robust DQA. Indeed, in the absence of a common framework that defines, assesses, and provides access to data quality information, transparency must be assured by the operator in the metadata interpretation and related assumptions along the DQA process. Finally, although the proposed DQA method was developed for the plastic sector, its application can be extended to LCI aggregated datasets relevant to other sectors, materials, and products.