Journal of Industrial Ecology最新文献

The transportation sector is undergoing a transformative shift, driven by advancements like autonomous and electric vehicle technologies. In this research, we investigate employment, carbon emissions, and total cost of ownership of autonomy and electrification in the US trucking industry. We utilize life cycle assessment and multi-regional input-output modeling to develop a comprehensive life cycle sustainability assessment approach. According to the results, while enhanced fuel economy due to autonomous systems can lead up to a 18% and 41% reduction in emissions and costs, electrification of diesel trucks shows remarkable potential, achieving up to a 40% decline in emissions and a 12% saving in life cycle costs. Autonomy and electrification combined could lead to a 50% decrease in emissions and 46% savings in life cycle costs. On the other hand, autonomy, while enhancing fuel efficiency and reducing costs, causes job losses due to improved efficiency and the elimination of driver positions. Introducing autonomy to diesel trucks results in a 27% decrease in jobs within the US trucking sector, attributed to improved fuel efficiency and subsequent job losses. Transition to autonomy and electrification requires a deliberate balance between environmental, social, and economic aspects. Managerial strategies should consider the use of the proposed composite indicators when setting emission reduction, cost cutting, and managing employment implications. Flexible re-skilling and training programs should be developed to adapt to the changing skill requirements due to electrification and automation.

It is commonly assumed that data volume and network energy consumption are directly proportional, a notion perpetuated by numerous studies and media coverage. This paper challenges this assumption, offering a comprehensive examination of network operations to explain why the relationship between energy consumption and data volume is nonlinear. The power model approach is explored as an alternative methodology for calculating network energy consumption providing a more reliable representation of network energy use. The power model demonstrates that simple energy intensity calculations, expressed as kilowatt hours per gigabyte of data, are insufficient for accurately estimating real-world network energy consumption.

Decarbonization of steelmaking has stagnated while it has a considerable share of global greenhouse gas emissions and a growing demand. Digitalization is seen as a viable option to reduce emissions and costs of the sector in the near term and life cycle assessment (LCA) as a comprehensive framework to evaluate changes in production practices. In this study, we analyze the potential impact of using optimization algorithms to improve the operation of a steelmaking plant in Spain. Specifically, we study the potential effects of optimizing the sequence in which steel is produced to minimize losses during casting. The global warming (GW) impacts and economic costs are quantified using a dynamic LCA model, considering uncertainty and temporal variability using an open-source LCA framework. The results indicate, on average, modest savings in costs and are inconclusive regarding GW emissions. Most of the cost savings come from a reduction in the use of additives and electricity, which are wasted when the steel is scrapped during casting. The methodological framework has proven useful in quantifying and interpreting the potential effects of digitalization. The implemented solution, tested in an industrial setting, allows an automated evaluation of production at the plant using the LCA model, facilitating the use of sustainability criteria in decision-making.

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.

The intensive generation of industrial solid waste (ISW) in industrial parks poses a severe threat to the water and soil environment. To address the lack of system-level guidance strategies for ISW management (ISWM), the paper proposes a systematic ISWM framework within the context of the circular economy principle and applies it to a prominent eco-industrial park in China. This framework is founded on the metabolic characteristics of the entire ISW process, supported by multi-stakeholder cooperation, and guaranteed by a shareable information platform and infrastructure. A holistic ISW flow analytical process is introduced to facilitate the implementation of this framework. In the case study, a high-resolution inventory of ISW is established by 157 enterprises in 2021, covering 116 types of general ISW and 61 types of hazardous waste (HW). By integrating material flow analysis, questionnaires, and interviews, this study quantitatively reveals the underperformance of mini-scale HW recycling, with a recycling rate of 5.6% in 2021, well below the park-wide rate of 50.8%. Collaborative inboundary and transboundary disposal emerges as a crucial direction for ISWM, with over 80% of ISW being disposed of outside the park. Following the implementation of circular economy strategies guided by the management framework, notable improvements are observed, including a 26.5% increase in the recycling rate of mini-scale HW and a 13.5% increase in the proportion of disposal outside Beijing. To foster sustainable ISWM, this study recommends enhancing the decentralized infrastructure of collection–storage–transportation integration for mini-scale ISW in industrial parks. The developed management framework provides valuable insights and guidance for park-specific ISWM strategies.

In the United States, polyethylene terephthalate (PET) bottle collection rates have not increased in a decade. Recycling rates remain abysmal while industry commitments and policy targets escalate the demand for recycled plastics. We investigate the PET bottle recycling system, where collection is a critical bottleneck and recycled PET supply is not meeting the expected demand. We characterize demand for recycled PET (R-PET), analyze scenarios of expanding deposit return systems (DRS), and quantify cost barriers to improving PET bottle recycling. We find that a nation-wide DRS can increase PET bottle recycling rates from 24% to 82%, supplying approximately 2700 kt of recycled PET annually. With stability in demand, we estimate that this PET bottle recycling system can achieve 65% bottle-to-bottle circularity, at a net cost of 360 USD/tonne of PET recycled. We also discuss environmental impacts, stakeholder implications, producer responsibility, and complimentary policies toward an efficient and effective recycling system.

Industrial symbiosis (IS) facilitates the transition toward a circular built environment. Following IS principles, multiple buildings can be symbiotically linked via closed-loop material flows beyond the boundaries of individual projects. However, there are few IS matchmaking methods that support the identification of IS opportunities among multiple deconstruction and construction projects. This research develops an agent-based model to fill this gap. The agent architecture is designed based on the concept of shearing layers. Circularity hubs are proposed to support IS matchmaking by allowing larger transportation ranges and keeping IS requests active for longer periods. The model's applicability is demonstrated through an industrial–urban symbiosis case in Enschede, the Netherlands. The model simulates the spatial–temporal dynamics of IS matchmaking as an emergent phenomenon under future scenarios. The results show operational evidence of IS matchmaking via the strategic implementation of circularity hubs. Overall, this research provides a new methodological perspective to explore the circularity in the built environment at scale.

With the circular economy (CE) gaining more traction worldwide, local authorities are engaging in efforts to develop circular strategies at the urban level. Developing and monitoring such strategies require detailed quantitative information on material and energy flows, which can be obtained through an urban metabolism (UM) analysis. This study demonstrates a bottom-up approach to analyze UM at the sectoral level based on material and energy flow analysis (MEFA), aiming to examine its utility within the context of the CE. The analysis is performed for Umeå urban area (Sweden) with a 5-year timeframe (2017–2021). The application of MEFA provides a detailed quantitative description of material and energy flows per sector, indicating the critical sectors in terms of resource consumption and waste generation and the most significant flows. More specifically, it reveals that the construction sector and households are key sectors within Umeå’s UM and that construction materials, food products, fossil fuels, and drinking water are significant metabolic flows. Furthermore, the application of MEFA with a multi-year timeframe uncovers trends in consumption rates of materials and generation rates of waste and emissions, revealing, for example, the correlation of material consumption and waste generation with the level of construction activity. Overall, by illustrating the potential of MEFA to provide a detailed quantitative analysis of material and energy flows, this study emphasizes its utility in supporting the design and monitoring of circular strategies at the urban level. At the same time, it highlights limitations of the method and suggests areas for future research.

This study combined the World Input–Output Database and Asian Development Bank's Multiregional Input–Output database to investigate Canada's embodied CO₂ emissions in exports (EEE) for the period of 2000–2018. We examined the key drivers and paths through structural decomposition analysis and structural path analysis. First, the results showed that embodied emissions in the intermediate exports were the major contributor to Canada's EEE, and emission paths involving more than three countries were on the rise, indicating that the expansion of the global industrial supply chains has complicated the paths of Canada's EEE. Second, the factors such as emission intensity of sectors, export structure, and export scale, had varying influences on Canada's EEE over time. For several sectors, the benefit from reduced emission intensity was largely offset by the additional emissions from the increased export scale. Hence, the design of emission regulations should consider the heterogeneity of industrial sectors in order to mitigate emissions for the diverse industries in Canada. Third, energy and resource industries (e.g., electricity, petroleum, wood, metals, and so on) played an essential role in Canada's exports. A significant amount of embodied emissions was transferred from these sectors to the downstream sectors along the supply chain, indicating that abatement measures should be adopted from the whole life cycle perspective of a product/service through an integrated governance of the supply chain. This article met the requirements for a Gold–Silver JIE data openness badge described at http://jie.click/badges.

The construction industry has been criticized for its negative environmental impacts, leading industry experts to advocate for a shift toward a circular economy (CE) model. However, there is a lack of research on stakeholder opinions regarding that. This research paper examines stakeholders’ perspectives on implementing CE principles in the construction industry by conducting artificial intelligence-powered natural language processing (NLP) through online sources. It answers three questions: What themes and concepts are associated with the CE in construction? How do opinions on the CE vary across different online platforms? And what factors shape positive attitudes toward the CE? The data obtained from various platforms showed that 57% of sentiments were positive, 28% were neutral, and 15% were negative. This research provides critical knowledge on the analysis of CE representation on social media in construction. Moreover, a webpage tool has been created that can assess any input opinion on the scale (positive, neutral, or negative) for further use (https://ce-sentiment.streamlit.app/). This NLP-based research of social media discourse in the construction sector can directly influence policy decisions by offering real-time insights into public sentiment and preferences, shaping regulations that align with societal needs. It also provides industry professionals with data-driven guidance, enabling them to identify growth opportunities and innovation pathways within the CE, ultimately fostering a more sustainable and prosperous future.