Life cycle assessments (LCAs) are essential for understanding the environmental impacts of material production. However, gaps in life cycle inventory (LCI) data for material and chemical inputs present a key challenge for LCA practitioners, especially in the early design stages. Strategies for filling in these gaps require additional time and expertise, which can hinder the LCA's completion. This study combined automatic material classification and probabilistic under-specification to create a time-efficient method to fill material LCI data gaps. To illustrate the proposed method, proxy environmental impact distributions were generated using publicly available material LCI data classified into the ChemOnt chemical taxonomy using the open-source chemical classification software ClassyFire. Input materials with data gaps were then classified into the same taxonomy, where proxy environmental impact values could be selected from the available distributions to quickly fill in any data gaps. Although these methods were applied to classify material production processes available in the Federal LCA Commons and Ecoinvent databases, they can be applied to any LCA database. This study shows that classifying materials by their chemical structure produces taxonomies with increased granularity relative to industrial classification, improving the ability of under-specified proxy data to be used for differentiating the environmental impacts of competing designs.

Dataset from the life cycle assessment and techno-economic analysis of net and longline nearshore Saccharina latissima cultivation systems.

Ensuring both worker safety and environmental sustainability remains a critical, often overlooked challenge within circular economy practices. While recycling reduces external environmental burdens, it frequently concentrates hazardous exposures within the workplace. This study developed a combined Occupational Health-Life Cycle Assessment (OH-LCA) framework to evaluate a formal steel coil recycling facility in Thailand. The methodology employed Industrial Hygiene (IH) Principles-specifically real-time Internet of Things (iOT) monitoring to independently validate the fine particulate matter formation impact modeled in the LCA. Importantly, Workplace Environment Characterization Factors (WE-CFs) were not applied; instead, the IoT exposure data served as empirical cross validation of the LCA's Human Health endpoint rather than direct inputs to the Life Cycle Inventory. Sequential operations were assessed using a semi-quantitative Hazard Identification and Risk Assessment (HIRA), while the IH “Evaluation” phase utilized IoT sensors to validate theoretical impacts modeled in OpenLCA using the ReCiPe 2016 Endpoint method. The “Cutting” stage was identified as the critical hotspot. Real-time sensors detected acute PM2.5 concentrations peaking at approximately 1398 μg/m 3 , empirically corroborating the high Human Health damage modeled in the LCA, which totaled 4.69 DALYs/month. The LCA further revealed a significant resource depletion cost of 11,368 USD/month. Eco-efficiency analysis demonstrated a critical trade-off: while the process was highly efficient for ecosystem preservation (25,871 ton/species·yr), it showed comparatively lower efficiency for human health protection and resource conservation. A techno-economic analysis further indicated that implementing IoT-based engineering controls is economically viable, offsetting initial investments through reduced energy consumption and mitigated long-term health liabilities. These findings highlight that formal ISO certifications alone do not guarantee worker safety during high-energy unit operations. The study concludes that integrating real-time IH monitoring with LCA provides a proactive, scalable pathway for decarbonizing the waste sector while ensuring worker well-being in the “Next Normal” industrial landscape. • Introduces a combined environmental LCA and occupational health assessment framework with IoT-based cross validation for steel coil recycling. • The cutting stage shows PM 2.5 levels (∼1398 μg/m 3 ), resulting in the health impacts (4.69 DALY). • Energy use and emissions were the main drivers of health damage and resource depletion, while impacts on ecosystems were relatively small. • IoT-based monitoring makes recycling both safer for workers and more sustainable.

Heavy Mineral Sands (HMS) extraction is energy-intensive and generates significant Greenhouse Gas (GHG) emissions, making it essential to identify key sources of Global Warming Potential (GWP) and energy use to develop sustainable reduction strategies. This study aims to characterize the main LCA indicators of Heavy Mineral Concentrate (HMC) production in two representative Australian wet and dry mining operations. The cradle-to-gate LCA was conducted for two operations to systematically evaluate their environmental impacts of production. The Life Cycle Inventory (LCI) was developed using real operational data, ensuring high data fidelity and compliance with standard framework. The environmental impacts were assessed using the ReCiPe midpoint impact assessment method, while the Cumulative Energy Demand (CED) method was applied to quantify the total amount of direct energy and embodied energy consumed. This structured methodology enables quantification of inputs/outputs, and associated environmental impacts, facilitating hotspot analysis and providing a mechanistic understanding of the processes with the most significant environmental burdens. It was found that LCA impact indicators for energy consumption, GWP, water consumption, and waste generation (ECWW footprints) indicators are significantly lower in the dry mining operation compared to the wet mining one per unit of HMC produced. The dry mining route consumes 2,068.24 MJ/t HMC primary energy, which is more than 3.5 times less than the wet mining route at 7,298.46 MJ/t HMC, and contributes 141.66 kg CO₂-e/t HMC to GWP, nearly 4 times less than the wet mining route at 537.82 kg CO₂-e/t HMC. The dry operation requires 3 times less water and generates more than 8 times less waste. The data also showed that regardless of the adopted mining technique, the energy demand and GHG emissions are highly dependent on the ore grade and the geological conditions of the ore reserve. It was also concluded that the environmental impact indicators are highly affected by the energy source, hence there is an imperative demand for green sources of energy for the operations going forward.

While sustainability is set as a goal by a broad range of international organizations, its definition varies, and there is still a lack of practical criteria for product designers to evaluate the degree of (un)sustainability in the design phase. Life cycle assessment (LCA) can allow quantification of the environmental impacts of a product but is often carried out post-design, when the manufacturing process is already settled. Finally, while significant advances have been made towards standardizing LCA calculations by providing product category rules, large uncertainties remain in the calculation results due to a lack of transparency regarding the choices of databases, system boundaries, allocation, cut-off rules, and level of data granularity. A practical way to improve in those areas is to share with the semiconductor community a parametrizable life cycle inventory (LCI) model based on a target device to (1) identify knowledge gaps in LCA methods for such products, (2) identify the main process variables, and (3) provide a starting point for LCA calculations by the designers themselves. With this aim, a parametrizable cradle-to-gate manufacturing LCI model was developed based on the peer-reviewed process flow of a trench field-stop silicon insulated gate bipolar transistor (IGBT) semiconductor power device. The model allows computation of the environmental impacts of the IGBT manufacturing process based on different tunable parameters such as die size, wafer diameter, manufacturing yield, abatement efficiency, wafer fab throughput, wafer fab location, and associated electricity mix. Embedding a high level of data granularity, it helps identify, at elementary process levels, key environmental hotspots and associated technical levers for their reduction. Analysis of the IGBT manufacturing process tends to demonstrate the importance of an impact assessment approach considering multiple environmental categories, going beyond the sole focus on greenhouse gas emissions and accounting for potential transfers of impact. With an open-source mindset and in a continuous improvement prospective, the manufacturing inventory model and its associated tools are freely available from a public GitHub repository and open for comments and consolidation from users.

With increasing requirements for environmental accountability, Life Cycle Assessment (LCA) is becoming key for sustainability reporting. Nevertheless, significant challenges remain regarding data availability, especially for emerging and low-carbon energy technologies, for which Life Cycle Inventory (LCI) data are usually scarce and spread across studies and reports. Common LCI challenges concern the exploitation of available, smaller or larger, LCA datasets and the collection of LCA data from various sources when these are not found in LCA databases. This study explores machine learning (ML), natural language processing, and large language models (LLM) applications to tackle such challenges and estimate missing LCI data. A thorough review of suggested ML and artificial intelligence (AI) applications for LCI compilation is performed, complemented by case studies investigating ML and generative LLM methods to impute or gather missing LCI data for car-driving, power plants, and geothermal energy systems. ML methods can provide more reliable estimations than simple linear regression even for small datasets, while generative LLMs are found to effectively identify and extract LCI information from scientific papers. The potential of ML and AI methods to facilitate LCI compilation and enhance data reliability and availability for the LCA of emerging energy technologies is large, highlighting the crucial role such methods can play for decarbonization. Nevertheless, relevant applications remain in their infancy. More research is needed to construct robust frameworks for large-scale deployment to complement traditional LCI methods, and ensure correct usage of ML algorithms towards automated, accurate, and interpretable AI-assisted LCA. • Data-driven methods can help fill LCI data gaps. • Different algorithms are applicable for different data sizes and structures. • Language models can extract useful information from documents. • Text-based matching can speed database selection. • Combined AI approaches can complement traditional LCI practices.

Abstract Metal Additive Manufacturing (MAM) enables resource efficient production however poses sustainability challenges due to high energy demand and inert gas usage. This study experimentally quantifies subsystem-level energy consumption and greenhouse gas emissions during Directed Energy Deposition with laser and powder feed (DED-LB/p) using Inconel 718. Real-time power measurements were conducted on a 3-axis gantry DED system to characterize the operational profiles of the fiber laser, motion control system, powder-gas feeder, and the cooling unit under idle and active states. Argon consumption was also quantified, and all data were integrated into a cradle-to-gate Life Cycle Inventory (LCI) framework. Findings show that the fiber laser operation is the dominant contributor to process energy, while argon emissions contribute only marginally to the environmental footprint when powered by renewable electricity mix. Additionally, build strategy particularly layer count and interpass idle duration strongly influences overall efficiency. The sustainability impact is primarily driven by the embodied energy of powder production, with recycled powder yielding significant reductions in both energy demand and CO2-equivalent emissions. This study provides new experimental insight into subsystem-level contributions within DED-LB/p and identifies process optimization and recycled powder utilisation as strategies for advancing energy and resource efficient metal additive manufacturing.

Global biodiversity loss is accelerating, threatening ecosystems and our society. Because biodiversity underpins all economic activities, organisations need robust methods to understand both their impacts and dependencies on nature. We applied a Life Cycle Assessment (LCA) framework to conduct a screening-level analysis of biodiversity impacts associated with the operations of the National Trust, the largest conservation charity in Europe. The organisation's carbon account data were integrated into the Life Cycle Inventory (LCI), distinguishing between economic and non-economic components. Biodiversity impacts were quantified using ReCiPe2016 endpoint indicators, expressed as the 'potentially disappeared fraction of species'. Land use dominates the Trust's biodiversity footprint, accounting for 68-69% of total impacts across the 2020-2022. This was followed by global warming impacts on terrestrial ecosystems (15-16%) and terrestrial acidification (8-9%). Tenant agricultural activities were the principal driver, contributing approximately 84% of total biodiversity impacts in the reference year (2022), largely associated with livestock grazing and feed production. Construction-related activities within purchased goods and services also represent a substantial upstream contribution. Our findings demonstrate the value of applying LCA at organisation scale to identifying biodiversity hotspots and inform targeted mitigation strategies. We discuss methodological constrains, including data availability, spatial resolution, and the treatment of positive biodiversity outcomes. Despite these limitations, organisational LCA provides a transparent and practical starting point for organisations seeking to assess and reduce their nature-related impacts on their journey to becoming nature positive.

Comparative climate change impacts of different strawberry cultivation systems in Southeastern Europe: A study across open-field and protected cultivation systems towards sustainable production models

Methane (CH4) from livestock farming is a significant environmental hotspot, accounting for a substantial share of global anthropogenic emissions. However, LCA studies often rely on generic or model-based emission data that may lack accuracy. This study aims to develop and validate an innovative UAV-based methodology for direct, on-site measurement of enteric and manure-related CH4 emissions from dairy cattle, to improve LCA emission inventories. Methane emissions from three Italian dairy farms were quantified using a mass balance approach with an open-path TDLAS sensor mounted on unmanned aerial vehicles (UAVs). Uncertainty analysis evaluated correlations with wind speed, animal number, wind direction variability, temperature, and time since last feeding. Data quality was assessed using a Data Quality Rating (DQR) following the ISO 14040 and ISO 14044 standards, considering technological, geographical, and temporal representativeness as well as methodological consistency. Daily enteric emissions per animal unit (AU) were measured, ranging from 0.18 to 0.24 kg CH4/AU/day. Based on an average live weight of 650 kg per cow (1 AU = 500 kg), this corresponds to approximately 0.23 to 0.31 kg CH4 per head per day, or 84 to 113 kg CH4 per head per year. Results showed that uncertainty decreased with higher wind speeds and larger herds but increased with variability in wind direction and temperature. The UAV-based measurements showed good agreement with IPCC model estimates (93–97% across sites), demonstrating reliability. Direct UAV-based CH4 measurements demonstrated significantly higher data quality (DQR = 1.2) compared to IPCC Tier 2 estimates (DQR = 1.6) and Ecoinvent data (DQR = 2.6), highlighting the added value of high-resolution, site-specific monitoring in agricultural emission assessments. A comparative LCA of a model dairy farm using both UAV-measured and IPCC emission factors demonstrated that direct measurements improve the accuracy and site-specificity of environmental assessments, underscoring the value of primary data for robust, context-specific life cycle inventories. UAV-based methane measurements resulted in a climate change impact 5.2% higher than IPCC Tier 2 estimates, and 11.2% higher than assessments using generic Ecoinvent emission factors, highlighting their greater sensitivity to real-world emission dynamics. This difference was primarily driven by CH4, highlighting its pivotal role in farm-level LCA precision. The UAV-based methodology provides a low-cost, innovative tool for direct, site-specific CH4 emission measurement from dairy cattle, improving the reliability of LCA inventories. Its integration supports more accurate environmental assessments and sustainable decision-making in livestock farming.

Current life cycle impact assessment (LCIA) practice for metals assumes full availability for partitioning in water, without accounting for the dissolution behavior of the emitted parent compound. This can overestimate freshwater ecotoxic impacts of poorly soluble metal forms. We propose a comparative ecotoxicity impact characterization framework that incorporates time-dependent release of metal ions into environmental fate and bioavailability modelling. The framework combines (1) the USEtox model for environmental fate modeling, (2) PHREEQC 3 for dissolution and liquid-phase speciation, and (3) the Free Ion Activity Model (FIAM) for updating effect factors based on global recommendations under the UNEP GLAM project. We demonstrate the application of the proposed framework for bivalent copper and zinc oxides (CuO, ZnO) across particle sizes from 10nm to 100μm emitted in seven EU freshwater archetypes of varying chemistry. For the USEtox default archetype (EU5), the characterization results ranged from 28 to 2.2∙105 and 270 to 2.5∙105 PAF·m3·day/kg for CuO and ZnO, respectively. The influence of the dissolution kinetics was significant for particles exceeding 1μm for CuO and 10μm for ZnO, with values one to four orders of magnitude lower than those currently used in LCIA for unspecified Zn(II) and Cu(II) forms. These findings highlight the need to consider dissolution kinetics in assessing toxic impact of metals in freshwater systems. We propose two pathways for practical implementation focusing on establishing consistent link with current and future life cycle inventories, where chemical forms are typically not specified.

Stone Matrix Asphalt (SMA) has emerged as a premier high-performance paving solution for critical infrastructure applications. Its distinctive skeleton structure, composed of coarse aggregates bound by a fiber-stabilized bituminous mastic, delivers exceptional mechanical performance, including superior resistance to rutting (≤3 mm after 106 load cycles) and fatigue cracking (>500,000 cycles to failure). While proven in demanding service environments, research has increasingly focused on enhancing the sustainability of SMA through key innovations: (1) the incorporation of recycled materials, such as 30-40% Reclaimed Asphalt Pavement (RAP) and 0.3-0.5% waste tire textile fibers (WTTF); (2) the development of bio-based binders derived from renewable sources; and (3) the adoption of Warm-Mix Asphalt (WMA) technologies that reduce production temperatures by 20-30 °C. These advancements yield significant environmental benefits, including approximately 25% lower CO2 emissions and 15-20% reduced energy consumption compared to conventional SMA production. It is important to distinguish between these quantitatively demonstrated benefits, primarily from Life Cycle Assessment (LCA) studies of technologies like WMA and RAP, and the more qualitative sustainability claims associated with emerging materials like nanomaterials or novel bio-additives, which often lack comprehensive lifecycle inventories. Nevertheless, challenges persist, notably moisture susceptibility (manifesting as a 10-15% strength reduction after saturation) and uncertainties regarding the long-term performance of modified mixes. This review consequently identifies critical research priorities: optimizing mix designs with locally available materials to minimize transport emissions, employing nano-scale modifiers to enhance moisture resistance, and developing standardized lifecycle assessment protocols. Addressing these challenges is paramount to establishing SMA as a model sustainable pavement technology that robustly meets both structural performance benchmarks and ecological sustainability goals.

Peritoneal dialysis (PD) is a life-sustaining treatment for end-stage kidney disease but contributes significantly to environmental degradation due to its reliance on single-use plastics, energy-intensive manufacturing and high-volume transport. Redesigning PD products for sustainability is increasingly important as healthcare systems seek to reduce their carbon footprint. In this study, ten high-use peritoneal dialysis (PD) products were redesigned using life cycle thinking. Interventions included low-carbon transport (electric vans), renewable energy and improved waste treatment (pyrolysis). Life cycle inventories (LCIs) were modelled in Open Life Cycle Assessment (OpenLCA)and modelled using cradle-to-gate carbon footprints (kg CO₂-eq) to compare redesigned and conventional versions. All redesigned products achieved carbon footprint reductions, with eight showing decreases greater than 40%. The automated PD set and 2 L dialysate bag saw reductions of 63% and 54%, respectively (saving 1.15 and 0.86 kg CO2-eq per item). The APD machine achieved the largest percentage reduction at 87%, primarily driven by the elimination of printed packaging and the use of renewable electricity. Key contributors to emissions savings across products included lower-impact transport, sustainable packaging materials and circular waste strategies. Redesigning PD products using sustainable materials and processes can deliver substantial environmental benefits without compromising functionality. These findings support evidence-based pathways for reducing emissions in kidney care through product innovation and procurement reform.

Parametrization of biowaste composting system for life cycle assessment.

International audience

ABSTRACT This work investigates the sustainability of electro-assisted sheet metal forming using electric pulses, a technology approaching a manufacturing readiness level of 7, in comparison with conventional heat-assisted processing. A comparative Life Cycle Assessment is presented to evaluate the environmental performance of a novel electro-pulsed treatment (EPT) as a replacement for traditional heat treatment (HT) applied prior to Incremental Sheet Forming (ISF) and Deep Drawing (DD) of Ti6Al4V alloy. Four different scenarios were modeled, and Life Cycle Inventory (LCI) data were obtained from both literature sources and a dedicated experimental campaign to ensure high accuracy. The results demonstrate that EPT provides significant environmental advantages, reducing Global Warming Potential by 8–10% compared to HT. Moreover, Cumulative Energy Demand is drastically reduced, by 94.45% for ISF and 93.9% for DD. Overall, the adoption of EPT represents a more sustainable alternative, significantly lowering energy consumption and environmental impacts while improving the formability of Ti6Al4V alloy.

NdFeB magnets are critical to many clean energy applications, such as electric vehicles and wind turbines. However, neodymium and other rare earth (RE) elements used in NdFeB magnets carry considerable environmental burdens (including carbon footprint) owing to direct emissions as well as intense energy and chemical consumptions in all major process steps. The environmental impacts of REEs and NdFeB magnet production have recently been discussed in many lifecycle assessment (LCA) studies. These LCA studies are primarily static in nature, i.e., the lifecycle inventory (LCI) is compiled using the material and energy flows of existing processes and facilities. On the one hand, the RE industry is implementing many initiatives to reduce the environmental footprint; on the other hand, increasing demand has pushed the industry to keep exploring low-grade or unconventional ores that require higher energy and material utilization for processing. As manufacturers of clean energy technologies strive to build a green supply chain, predicting the environmental impacts of NdFeB magnets under different scenarios has become an essential task. The present study explores a dynamic LCA (DLCA) with a focus on the carbon footprint given the declining ore grade, best available (control) technology, recycling rate, RE ore type/origin/processing pathways, and environmental policy implementations. We used the Python-based tool Brightway2/Temporalis for the DLCA. The dynamic LCI was compiled from the critical material LCA tool as well as plans and policies from a Chinese REE company, various national governments, and potential REE mining projects worldwide. The scenario analysis is considered sustainable, moderate, and business as usual. The results show that 90% of the contributions to greenhouse gas emissions were from the RE metals used and that the environmental burden could be significantly decreased by implementing a higher magnet recycling rate and utilizing RE metals from low-carbon pathways, e.g., Mount Weld in Australia. The DLCA of the NdFeB magnet production process reveals trends regarding the environmental impacts of RE magnets, thereby providing a clear policy strategy to achieve the sustainability goals for stakeholders.

Unit manufacturing processes often involve conflicting objectives: achieving high throughput and productivity while minimizing environmental impacts, reducing costs, and ensuring product quality. In this context, computational methods are instrumental in deriving quantitative indicators from process data and supporting data-driven decision-making. Computational, including optimization-based, methods are commonly employed to quantify and compare the performance of alternative processes. However, most existing studies focus on specific use cases, resulting in fragmented insights. Therefore, this paper surveys state-of-the-art data- and model-driven methods used to quantify sustainability performance in unit manufacturing processes. Specifically, we review sustainability evaluation methods applied to three major categories of unit manufacturing processes: additive, subtractive, and formative. We highlight that life cycle assessment (LCA) is effective for quantitative environmental system assessment; however, compiling reliable life cycle inventories is time-consuming and often requires proprietary software tools. In contrast, bottom-up parametric modeling approaches, which describe system behavior based on individual process parameters, have proved effective in evaluating complex systems and enabling flexible what-if analyses. The paper further investigates the integration of these modeling methods with data-driven analytical approaches such as multi-criteria decision-making and artificial intelligence–based algorithms for extracting actionable information. Based on this review, we propose a holistic data-driven framework for resource-informed assessment of manufacturing sustainability.

In product-oriented Life Cycle Assessment (LCA) studies, obtaining reliable and accurate data during the Life Cycle Inventory (LCI) analysis is challenging due to security concerns of industrial enterprises. In this study, a question catalogue inventory was intended to be developed to obtain accurate data in LCI processes and to overcome this challenge. The LCA process consists of four main stages: "Raw Material – Production – Use – Disposal." Within this process flow, the LCI inquiry focuses on system boundaries, energy, and transportation at each stage of the LCA process. While deriving the questions, the categories of "Definition – Raw Material – Production – Point of Sale and Distribution – Consumption – Recycling – Disposal" were taken as the basis. A total of 50 questions were developed in the study. This study aims to enhance the environmental sustainability of newly developed technological products by applying the LCI inquiry filter during the project phase, in the context of global industrial competition. Additionally, it will serve as a guiding light for those who are new to conducting LCA studies.

Olive cultivation is a major agroecosystem in the Mediterranean basin, yet the environmental performance of its production systems remains poorly quantified, particularly in North Africa where life cycle inventory (LCI) data are limited. This study applied a comparative Life Cycle Assessment (LCA) to eight representative olive production systems (traditional, integrated, and intensive). Primary data were obtained from field surveys and farm records, while secondary data from the Ecoinvent database were used for background processes. Environmental impacts were evaluated per hectare and per ton of olives for global warming potential, acidification, eutrophication and water consumption . Fertilization and soil management emerged as dominant hotspots across all assessed impact categories, with synthetic inputs contributing up to 576 kg CO 2 -eq/ha to global warming potential and driving nutrient-related burdens. Water consumption ranged from 0.98 to 1767 m 3 /ha, primarily influenced by irrigation intensity. Overall global warming potential varied from 617 to 2583 kg CO 2 -eq/ha, reflecting substantial differences in input levels and resource-use efficiency among systems. The results demonstrate that environmental performance is strongly shaped by fertilizer regimes, irrigation practices, and soil management. Precision nutrient management, optimized irrigation, reduced tillage and agroecological interventions could substantially reduce impacts. This study provides one of the first structured LCAs for Tunisian olive systems and offers essential evidence to support the development of robust regional LCI datasets for Mediterranean olive production. • Soil management and fertilization dominate environmental impacts in olive systems • Fertilization contributes up to 576 kg CO 2 eq per hectare in intensive cultivation • Irrigation drives water consumption ranging from 0.96 to 1767 m 3 per hectare • Precision agriculture offers significant potential for environmental improvement