Journal of Industrial Ecology最新文献

Although the literature indicates that auditing a city's metabolic flows is critical to enhancing the resilience of urban systems, the current knowledge is still unclear about how urban metabolism (UM) research could contribute to urban resilience (UR). Meanwhile, because cities compete for survival, sustainable resource management is already a common challenge. Given this background, this study asks whether UM research has contributed to UR by developing a conceptual review of UR in practice through an industrial ecology perspective.

This study selectively reviews 35 urban resilience strategies (URSs) from the 100 Resilient Cities (100RC), with dimensions of (1) UM terms, (2) UM framework, and (3) UM tools, used to reveal the contribution of UM research to UR, as well as their engagement with (4) socioeconomic factors. It demonstrates UM research's contribution in different patterns, finding that UM research has not been fully applied in contemporary UR practices; only 23% of URSs show a significant contribution, while 17% demonstrate no integration of UM research. Further investigation recognizes features of UM research's contribution: (1) adopting evidence-based analysis to enhance the analytical coherence and evidence-based structure of URSs, (2) providing precise benchmarks to urge cities to improve, (3) recognizing precise places to intervene, and (4) initiating possible cooperation among stakeholders. “Taking UM research seriously” is suggested as a relevant aspect of evaluating UR practices; yet, more work is needed to ensure that UM research will benefit UR. Policymakers and decision-makers should remain open-minded about adopting an UM lens to evaluate their practices and decisions consistently.

The notion of circular economy (CE) has been trending among policymakers, businesses, and academia for over a decade. Sometimes poorly understood and often misrepresented as a one-size-fits-all solution to environmental problems without economic trade-offs, the concept has recently drawn considerable criticism. According to its critics, the CE (1) rebrands existing concepts without clarity, (2) makes unrealistic environmental promises, (3) oversimplifies and overlooks critical factors, (4) clashes with societal values and norms, (5) fails in practical business applications, and (6) serves as a capitalist tool for Western interests. In this paper, we critically review these criticisms of the CE, many of which are not based on empirical realities, are obsolete, or originate in oversimplified interpretations of the circle metaphor. We argue that CE is an “umbrella” framing for existing concepts with a relatively concrete definition. Formerly corporate led, CE has matured into an academically dominated field, backed by substantial technical literature, new sub-fields led by social scientists, and an increasingly advanced and detailed understanding of previous simplifications. Empirical evidence is emerging that CE can be operationalized and scaled, provide considerable environmental benefits, and can align with societal values and priorities. While easy to criticize, the hope and momentum that the CE has sparked is creating tangible benefits over other sustainability-oriented concepts. Further research is required to establish its long-term advantages, drawbacks, and complementarities with alternative approaches.

This paper aims to analyze consumer behavior in laptop lithium-ion battery consumption throughout their life cycle. As the demand for battery-powered products continues to grow, helping consumers select batteries that align with their actual usage patterns is important for promoting sustainable consumption. However, there is limited research on whether consumers' choices of battery features, such as capacity, are compatible with their real consumption needs. To investigate this, a multinomial logistic regression model is developed to predict battery health status over time. The study uses a dataset of 719 records collected from student laptop users in Chicago, IL. The dataset includes technical specifications and usage metrics such as charging cycles, full and design capacities, and battery age. The findings show that each additional cycle increases the likelihood of degradation by 0.022. On the other hand, batteries with larger design capacities tend to be more durable, with each additional unit of capacity reducing the likelihood of degradation by 0.0011. Next, an optimized consumption scenario is suggested to demonstrate how aligning battery choice with real usage needs can lead to more sustainable outcomes. The results show a nearly 60% reduction in the likelihood of battery degradation, achieved by better matching battery capacity with consumer' actual needs. Finally, we discuss the sustainability benefits of the proposed scenario.

Kim, A., Mutel, C., & Hellweg, S. (2025). Global sensitivity analysis of correlated uncertainties in life cycle assessment. Journal of Industrial Ecology, 29, 1090–1104. https://doi.org/10.1111/jiec.70036

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This study conducts one of the first future-oriented assessments that privileges prospective life cycle assessment (LCA) and scenario-based social LCA to estimate the impacts of innovations, particularly those aimed at improving user experience and product appeal. The assessment examines various levels of environmental and social challenges while considering multiple technology implementation pathways, offering a comprehensive understanding of the implications of emerging technologies. The findings support the development of actionable strategies to manage these impacts effectively and provide stakeholders with critical information. By doing so, decision-makers are better equipped to determine whether the added value of an innovation justifies its additional impacts. Since the added value of such innovations is usually excluded when defining the functional unit in LCA, we advocate for decision-making processes aligned with sustainability goals—whether at the corporate, national, or international level. To demonstrate this approach, photochromic fabrics are used as a case study. While these fabrics are estimated to cause +10% to+20% climate change impacts compared to conventional ones, these impacts can be reduced through strategies such as extending product lifespan, using recycled materials in production (−10%), and reducing the amount of photochromic dye required for functionality (−12%). Ultimately, the decision to commercialize such innovations should depend on their alignment with sustainability targets.

The circular economy has gained momentum as a sustainability paradigm that aims to decouple economic growth from environmental degradation by promoting resource efficiency and waste minimization. However, mounting evidence points to a critical challenge: circular economy rebound (CER). This phenomenon occurs when efficiency improvements intended to reduce resource use provoke systemic, market, or behavioral responses that diminish or even reverse the anticipated environmental gains. This is particularly problematic within growth-oriented economies, where resource efficiency often reinforces rather than reduces consumption. This paper draws on insights from a roundtable discussion with leading experts—Trevor Zink, David Font Vivanco, and Andrea Genovese—to critically revisit the conceptual foundations of CER, assess the latest empirical evidence, and examine the policy and market dynamics shaping its occurrence. Rather than simply summarizing the discussion, the paper integrates these expert perspectives into a broader academic framework, offering an in-depth analysis of unresolved questions such as the role of imperfect substitution, the systemic drivers of rebound, and the extent to which alternative economic models might mitigate its effects. Addressing CER, we argue, necessitates an interdisciplinary approach, combining insights from industrial ecology, behavioral economics, and political economy. Beyond technical solutions, it requires a reassessment of the broader economic structures underpinning growth-oriented policies. The paper concludes by proposing a research and policy agenda that prioritizes robust quantification methods, interdisciplinary collaboration, and strategic interventions aimed at aligning circular economy initiatives with genuine sustainability goals.

Bodh Gaya, a small agrarian town in Bihar, India, is renowned as the cradle of Buddhism. The Mahabodhi Temple in Bodh Gaya was designated as a UNESCO World Heritage Site in 2002. Every year, millions of visitors come here to quench their spiritual thirst. Consequently, the town is experiencing significant strain on its water resource capacity due to tourism and haphazard urbanization. Water metabolism of such small touristic and religious towns, along with the impact of tourists' water demand on urban water flows and infrastructure, remains largely unexplored. This study aims to evaluate water flows pre- and post-the designation of World Heritage status and examine tourism's impact on water resources and socio-economic inequalities in access to water supply infrastructure by using a political industrial ecology (PIE) approach. A complementary PIE approach, integrating the water mass balance equation with urban political ecology, is employed through methods such as document analysis and semi-structured interviews. The urban water metabolism of Bodh Gaya underwent a drastic transformation between 2001 and 2019 (pre- and post-the World Heritage designation). Direct tourist consumption increased approximately eightfold, rising from 92 to 730 million liters (ML), and groundwater extraction increased from 1586 to 4271 ML over the same period. The political ecology analysis reveals that residents struggle to access piped water due to precarious infrastructure, fragmented governance, and persistent social inequalities. To address water scarcity, the town has paradoxically shifted to a distant water source rather than leveraging available water in the urban system, such as stormwater and wastewater, to meet demand. Lastly, we recommend integrating tourists' water demands into broader urban planning for sustainable water management.

Material flow analyses (MFAs) are powerful tools for highlighting resource efficiency opportunities in supply chains. MFAs are often represented as directed graphs, with nodes denoting processes and edges representing mass flows. However, network structure uncertainty—uncertainty in the presence or absence of flows between nodes—is common and can compromise flow predictions. While collection of more MFA data can reduce network structure uncertainty, an intelligent data acquisition strategy is crucial to optimize the resources (person-hours and money spent on collecting and purchasing data) invested in constructing an MFA. In this study, we apply Bayesian optimal experimental design, based on the Kullback–Leibler divergence, to efficiently target high-utility MFA data—data that minimizes network structure uncertainty. We introduce a new method with reduced bias for estimating expected utility, demonstrating its superior accuracy over traditional approaches. We illustrate these advances with a case study on the US steel sector MFA, where the expected utility of collecting specific single pieces of steel mass flow data aligns with the actual reduction in network structure uncertainty achieved by collecting said data from the United States Geological Survey and the World Steel Association. The results highlight that the optimal MFA data to collect depends on the total amount of data being gathered, making it sensitive to the scale of the data collection effort. Overall, our methods support intelligent data acquisition strategies, accelerating uncertainty reduction in MFAs and enhancing their utility for impact quantification and informed decision-making.

Standardized life cyclestudy assessment (LCA) commonly treats buildings as static systems operating under fixed conditions, thereby neglecting the inherently dynamic nature of their long life cycles. In response, dynamic life cycle assessment (DLCA) has emerged as an approach that incorporates potential future changes by addressing the absence of temporal, spatial, and other context-specific variables. Although various forms of dynamism have been identified within DLCA, their application in the context of material reuse remains largely underexplored. To bridge this gap, the present study introduces a DLCA framework specifically designed to account for the dynamics of material reuse. Emphasizing embodied and end-of-life (EoL) impacts, the approach incorporates temporal dynamics related to advancements in production technologies and EoL treatment. The methodology is applied to a Swiss case study featuring four comparative scenarios with different amounts of reuse. A comparison between the dynamic and standardized LCA approaches is conducted to assess the influence of incorporating temporal variability. The results highlight that reuse consistently outperforms new material use, even when accounting for expected advances in production and EoL treatment technologies. Furthermore, improvements in production processes exert a greater influence on environmental outcomes compared to advancements in EoL treatment. By capturing evolving contexts, the implementation of DLCA enables a more accurate assessment of reuse-related environmental benefits, thereby supporting informed policy-making and promoting resilient, sustainable building practices.

Wind energy is expanding rapidly in Europe and plays a crucial role in the energy transition, yet existing life cycle inventory databases are outdated and lack the flexibility to accommodate continuously growing sizes of wind turbines. Here, we introduce WindTrace, an open-source parametric model built on Brightway that generates customized life cycle inventories for onshore wind turbines and parks. Fed by up-to-date data from literature and industry reports, the model uses 20 user-defined parameters, covering both turbine characteristics (e.g., hub height and power capacity) and wind park attributes (e.g., number of turbines and coordinates). Such parameters serve to unveil the influence of onshore wind turbines' design on their respective environmental impacts.

In this work, we first demonstrate WindTrace's advantages by comparing the differences in life cycle inventories and environmental impacts of 800 kW, 2 MW, and 4.5 MW wind turbines with their Ecoinvent counterparts. This is particularly true for 4.5 MW turbines, where differences in tower design, land use, and end-of-life assumptions cause 16× higher freshwater ecotoxicity, 2.2× higher climate change, and 1.6× lower land use impacts in Ecoinvent. By testing model parameters, we highlight that scaling up from 1990s turbines (700 kW; 60 m) to current average sizes (4.5 MW; 100 m) has reduced the turbines’ climate change intensity by 38%. Furthermore, transitioning to future cleaner steel production could cut climate change impacts by 28%. Finally, increasing the European capacity factor from 24% to 35%, as suggested by WindEurope, reduces climate change impacts per kWh by 31.4%.