Journal of Industrial Ecology最新文献

The manufactured capital, usually denoted as material stocks from an industrial ecology perspective, has thus far received wide attention in sustainability and circularity science. Sustainable resource management should be rooted in detailed knowledge of manufactured capital accumulation in society at a high spatial resolution. Previous studies demonstrated that night-time light (NTL) data provide a great opportunity for monitoring material stocks dynamics at a higher spatial resolution on the regional and global scale. However, the potential of historical–geographical refined material stocks has not been fully analyzed and explored because of the inconsistency of NTL images detected by the different satellites. In this study, based on a new set of material stocks data in China and harmonized NTL images (1992–2018), we map the national stocks of 13 bulk materials (including cement, gravel, wood, brick, sand, asphalt, glass, lime, plastic, rubber, copper, aluminum, and steel) at a 1 × 1 km resolution from 1992 to 2018. The results find that the total material stocks increased from 190,000 to 460,000 t/km² between 1992 and 2018. Among the five end-use sectors, buildings have the highest density of 430,000 t/km², while domestic appliances have the lowest density of 140 t/km². Four manufactured capital clusters, including the Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei, and Chengdu–Chongqing agglomerations, possess 38% of the national total stocks in 2018, revealing an unbalanced distributed pattern of manufactured capital across China. Our results provide valuable support for policymakers and business decision-makers on efficient resource management and urban mining.

The building sector in China is responsible for 40% of total energy-related CO₂ emissions, driven by its large population, continuous economic growth, and construction boom. In addition to greenhouse gas (GHG) emissions from energy use, buildings drive significant emissions for construction activities and production of energy-intensive materials, such as steel and cement. While supply-side energy strategies have been extensively explored, a demand-side perspective that considers stock dynamics and circularity improvements is essential to assess sustainable pathways for the buildings sector. Here, we explore a set of decarbonization scenarios for the building sector in China considering a range of circular strategies and their interplay with different climate policies. The strategies include lifetime extension of buildings, switch to wood-based construction, reduction of per-capita floorspace, and a combination of all three strategies. We use the building sector model MESSAGEix-Buildings soft linked to the integrated assessment model (IAM) MESSAGEix-GLOBIOM and prospective life cycle assessment (LCA) to assess the effects of these circular strategies on building material and energy demands, and operational and embodied emissions. We find that the three strategies could reduce building material demand up to 60% on mass basis by 2060 compared to a reference scenario with continuation of current policies. This translates into a reduction of embodied and total GHG emissions of 62% and 24%, respectively, significantly contributing to achieving decarbonization targets. Integrating industrial ecology methods in IAMs, as demonstrated in this study, can provide valuable insights to inform national policy decisions on mitigation strategies accounting for both demand and supply sides.

Rapid advancements in artificial intelligence (AI) are driving transformative changes in many areas, with significant environmental implications. Yet, environmental assessments for specific applications are scarce. This study presents an in-depth life cycle assessment of “Foodforecast,” a machine learning (ML) cloud service designed to reduce food waste in bakeries by optimizing sales forecasting. It covers four impact categories: global warming, abiotic resource depletion, cumulative energy demand, and freshwater eutrophication. The assessment includes both the direct environmental impacts of the ML model and the underlying system hardware, as well as the indirect benefits of avoided bakery returns compared to traditional ordering methods, using real-world case study data. In 2022, “Foodforecast” led to an average 30% reduction in bakery returns, primarily bread and rolls, according to sales reports. The associated environmental benefits significantly outweighed the system's direct impacts by one to three orders of magnitude across impact categories and return utilization scenarios. The study identifies support activities such as service maintenance during deployment as major direct impact factors, surpassing those from cloud compute for ML operations. Data processing and inference dominate the latter, while the much-discussed ML training plays a minor role. The environmental consequences of AI are complex and dual sided. This case study demonstrates that AI might provide environmental benefits in certain contexts, yet results are constrained by methodological challenges and data uncertainties. There remains a need for further holistic LCAs across different ML applications to inform decision-making processes and ultimately guide the responsible use of AI.

Important expectations are placed globally on the private sector to take part in co-governing sustainability challenges. With increasing recognition that business organizations depend and impact on natural capital, biodiversity, and related ecosystem services, it has become pivotal for companies to be able to appraise and manage such issues. This calls for developing avenues through which biodiversity and ecosystem services can be incorporated in sustainability accounting (as well as reporting and response practices) by individual organizations, without neglecting a value chain-wide perspective, and aligning that with existing efforts for public accounting of natural capital, against the overall framework of national and global sustainability goal and targets. This article addresses such a call by elaborating on the contributions of ecosystem services to business organizations and illustrating the two main challenges related to feeding private sector data into national accounting of natural capital and ecosystem services; and to understand how the two challenges identified in the previous point can be addressed by companies, we provide an overview of the fragmented landscape of management systems, approaches, methods, and initiatives dedicated to monitoring, reporting on, and responding to biodiversity and ecosystem services issues at the organizational and value chain level. We conclude by offering reflections on how to foster a shift from one-off assessments at the company level to more systematic and comprehensive ones along the value chain.

Greenhouse gas (GHG) emissions datasets are often incomplete due to inconsistent reporting and poor transparency. Filling the gaps in these datasets allows for more accurate targeting of strategies aiming to accelerate the reduction of GHG emissions. This study evaluates the potential of machine learning methods to automate the completion of GHG datasets. We use three datasets of increasing complexity with 18 different gap-filling methods and provide a guide to which methods are useful in which circumstances. If few dataset features are available, or the gap consists only of a missing time step in a record, then simple interpolation is often the most accurate method and complex models should be avoided. However, if more features are available and the gap involves non-reporting emitters, then machine learning methods can be more accurate than simple extrapolation. Furthermore, the secondary output of feature importance from complex models allows for data collection prioritization to accelerate the improvement of datasets. Graph-based methods are particularly scalable due to the ease of updating predictions given new data and incorporating multimodal data sources. This study can serve as a guide to the community upon which to base ever more integrated frameworks for automated detailed GHG emissions estimations, and implementation guidance is available at https://hackmd.io/@luke-scot/ML-for-GHG-database-completion and https://doi.org/10.5281/zenodo.10463104. This article met the requirements for a gold-gold JIE data openness badge described at http://jie.click/badges.

In industrial ecology, approaches have been developed to analyze the contribution of specific sectors to environmental impacts within supply chains. In economics, a range of methods addresses the forward linkage (use of output) and backward linkage (dependency on inputs) of sectors, and the analysis of key sectors. This article offers a formal investigation of the relationship between these. It shows that both the analysis of supply chain impacts and of intersectoral linkages can be seen as special cases of a more general hypothetical extraction method (HEM). In HEM, sectors' role is assessed as the effect of their removal on the input–output model's solution. HEM also allows for the (partial) extraction of individual transactions. HEM thus offers a flexible approach to assessing the contribution of one or several sectors, or transactions, or parts thereof, to value added or footprint of any final demand. It can be applied to study the environmental footprints of companies or intermediate products, the contribution of certain inputs to sectors, or the potential impact of disruptions of supply chains on producers and consumers. In this article, the price model for HEM is introduced to identify the contribution of the extracted (target) sectors to the price or unit footprint of a commodity.

The low-carbon energy transition requires significant increases in production for many mineral commodities. Understanding demand, technological requirements, and prices associated with this production increase requires understanding the supply chain dynamics of many minerals simultaneously, and via a consistent framework. A generalized economics-informed material flow method, global materials modeling using Bayesian optimization, captures the market dynamics of key mineral commodities. The method relies only on a limited set of widely available historical data as input, enabling quantification of economic relationships (elasticities) for supply chain components where data are sparse, and relationships cannot be obtained via traditional statistical approaches. Building upon established material flow analysis (MFA) and economic modeling techniques, Bayesian optimization was applied to fit an economics-informed MFA model to global historical demand, supply, and price for aluminum, copper, gold, lead, nickel, silver, iron, tin, and zinc. This approach enables estimates for the evolution of ore grades, mine costs, refining charges, sector-specific demand, and scrap collection for each commodity. Economic relationships were quantified and compared with a database compiled from the literature, including 1333 values from 213 analyses across 65 publications. Discrepancies in methods and limited coverage make use of these parameters in modeling efforts difficult. This work provides a single, homogeneous, probabilistic approach to identifying economic relationships across mineral supply chains, with uncertainty quantification, a literature database for comparison, and a modeling framework in which to use them. This article met the requirements for a Gold-Gold JIE data openness badge described at http://jie.click/badges.

In this paper, we take an axiomatic approach to the design of ecological footprint indices. Our focus is put on the heterogeneity of land with respect to types and regions, at the core of an inherent aggregation problem. We propose an axiomatic characterization of the ecological footprint index with two fundamentally new axioms, symmetry and independence, which can resolve the problem of land heterogeneity. It is shown that a unique index, up to an affine transformation, exists meeting the axiom system. This index simplifies the aggregation procedure considerably and avoids the need for a synthetic unit of measurement, like global hectares, as well as complex transformations of variables by means of weighting schemes. Our findings reveal differences with the Global Footprint Network (GFN) index, in particular with regard to the treatment of land heterogeneity. Finally, the axiomatic methodology employed may open up perspectives for the development of ecological measures in general, and especially of measures for sustainability and tipping points.

Emergy analysis (EmA) is the quantitative sustainability method that analyses materials, energy resources, goods, or services under a common unit of solar emergy (seJ). Meanwhile, life cycle assessment (LCA) is one of the popular tools to evaluate environmental impacts. Both methods have been widely used in various applications, hence, the idea of co-jointing these two methods has been increasing to optimize the assessment's inclusivity. However, the existing integrated LCA–EmA methods are applied segmentally with minimal data integration, which has limited the potential of co-benefits in sustainability accounting. This research proposes a more comprehensive integration between LCA and EmA, known as life cycle emergy analysis (LCEmA). The process data is fully integrated, and the detailed methodological approach is presented to demonstrate the assessment process. Diaper manufacturing is selected as a case study to validate the functionality of the proposed LCEmA approach. The inclusion of foreground and background data offered in LCEmA approach considers backend processes such as electricity generation for manufacturing activities. This has shifted the sustainability hotspot from raw material extraction to the manufacturing process. This research demonstrates that the LCEmA approach can perform comprehensive analysis as a promising alternative to the existing integrated LCA–EmA methods.

The assessment of energy consumption of data traffic for Internet services usually relies on energy intensity figures (in Wh/GB). In this paper, we argue against using these indicators for evaluating the evolution of energy consumption of data transmission induced by changes in Internet usage. We describe a model that estimates global impacts for different scenarios of Internet usages and technological hypothesises, and show that it can overcome some limitations of intensity indicators. We experiment the model on four use-cases: basic usage, video streaming, large downloads, and video conferencing. Results show that increasing the resolution of videos does increase the total energy consumption while misleadingly decreasing the power intensity indicator at the same time. In other words, a more efficient network does not necessarily mean less energy consumption.