Life Cycle Inventory Model Research Articles

Mitigating Grand Challenges in Life Cycle Inventory Modeling through the Applications of Large Language Models.

The accuracy of life cycle assessment (LCA) studies is often questioned due to the two grand challenges of life cycle inventory (LCI) modeling: (1) missing foreground flow data and (2) inconsistency in background data matching. Traditional mechanistic methods (e.g., process simulation) and existing machine learning (ML) methods (e.g., similarity-based selection methods) are inadequate due to their limitations in scalability and generalizability. The large language models (LLMs) are well-positioned to address these challenges, given the massive and diverse knowledge learned through the pretraining step. Incorporating LLMs into LCI modeling can lead to the automation of inventory data curation from diverse data sources and to the implementation of a multimodal analytical capacity. In this article, we delineated the mechanisms and advantages of LLMs to addressing these two grand challenges. We also discussed the future research to enhance the use of LLMs for LCI modeling, which includes the key areas such as improving retrieval augmented generation (RAG), integration with knowledge graphs, developing prompt engineering strategies, and fine-tuning pretrained LLMs for LCI-specific tasks. The findings from our study serve as a foundation for future research on scalable and automated LCI modeling methods that can provide more appropriate data for LCA calculations.

PULPO: A framework for efficient integration of life cycle inventory models into life cycle product optimization

AbstractThis work presents the PULPO (Python‐based user‐defined lifecycle product optimization) framework, developed to efficiently integrate life cycle inventory (LCI) models into life cycle product optimization. Life cycle optimization (LCO), which has found interest in both the process systems engineering and life cycle assessment (LCA) communities, leverages LCA data to go beyond simple assessments of a limited number of alternatives and identify the best possible product systems configuration subject to a manifold of choices, constraints, and objectives. However, typically, aggregated inventories are used to build the optimization problems. Contrary to existing frameworks, PULPO integrates whole LCI databases and user inventories as a backbone for the optimization problem, considering economy‐wide feedback loops between fore‐ and background systems that would otherwise be omitted. The open‐source implementation combines functions from Brightway2 for the manipulation of inventory data and pyomo for the formulation and solution of the optimization problem. The advantages of this approach are demonstrated in a case study focusing on the design of optimal future global green methanol production systems from captured CO2 and electrolytic H2. It is shown that the approach can be used to assess sector‐coupling with multi‐functional processes and prospective background databases that would otherwise be impractical to approach from a standalone LCA perspective. The use of PULPO is particularly appealing when evaluating large‐scale decisions that have a strong impact on socioeconomic systems, resulting in changes in the technosphere on which the background system is based and which is often assumed constant in standard LCO approaches regardless of the decisions taken. This article met the requirements for a gold‐gold JIE data openness badge described at http://jie.click/badges.

Prospective Life Cycle Assessment of Hydrogen: A Systematic Review of Methodological Choices

This systematic review examines methodological choices in assessing hydrogen production and utilisation technologies using prospective life cycle assessments (LCA) between 2010 and 2022, following PRISMA guidelines. The review analysed 32 peer-reviewed articles identified through Scopus, Web of Science, and BASE. The study reveals a significant gap in the consistent application of prospective LCA methodologies for emerging hydrogen technologies. Most studies employed attributional approaches, often lacking prospective elements in life cycle inventory (LCI) modelling. Although some initiatives to integrate forward-looking components were noted, there was often lack of clarity in defining LCA objectives, technology readiness level (TRL), and upscaling methods. Of the 22 studies that focused on emerging hydrogen technologies, few detailed upscaling methods. Additionally, the review identified common issues, such as the limited use of prospective life cycle impact assessment (LCIA) methods, inadequate data quality evaluation, and insufficient sensitivity and uncertainty analysis. These findings highlight the substantial gaps in modelling low-TRL hydrogen technologies and the need for more robust, comprehensive approaches to assess uncertainties. The review also identified common practices and areas for improvement to enhance the reliability and relevance of hydrogen technology environmental assessments.

Greenhouse gas, water, and economic implications of permeable pavements: Quantification for pilot sponge cities in China

Climate change has exacerbated the occurrence of extreme storms, resulting in urban flooding. However, rainwater is a potential source of water for various end uses of municipal water. In this context, permeable pavements are promising for mitigating floods and alleviating water stress in cities. Herein, a process-based life-cycle inventory modeling approach was used to quantify the life-cycle greenhouse gas (GHG) and water footprints of different road pavements in 28 pilot sponge cities in China. Three asphalt pavements were considered in this study: permeable-surface, permeable-base, and conventional nonpermeable pavements. Moreover, life-cycle cost analyses were performed to reveal the cost-effectiveness of these pavements. Results showed that permeable-base pavements exhibited the highest levelized GHG emissions (5.9 kg CO2e/m2/yr), followed by nonpermeable (5.5 kg CO2e/m2/yr) and permeable-surface pavements (3.8 kg CO2e/m2/yr). In terms of levelized water footprint, permeable-base and permeable-surface pavements enabled water savings (433.8 and 304.1 kg/m2/yr, respectively), while nonpermeable pavements consumed 30.6 kg/m2/yr of water. The levelized life-cycle costs of permeable-surface, permeable-base, and nonpermeable pavements were 32.8, 35.4, and 40.5 yuan/m2/yr, respectively. With regard to transitioning from nonpermeable to permeable pavements in urban road construction, Chongqing and Shenzhen presented favorable potential for GHG mitigation (22 and 26 kt CO2e/yr, respectively) and water savings (196 and 187 Mt/yr, respectively). Contrarily, Hebi, Yuxi, Sanya, Qianan, Chizhou, and Xining were not cost-effective in terms of GHG emission reduction. Moreover, in this study, GHG–water–cost tradeoffs were identified among different pavements, highlighting directions for unlocking the green benefits of installing permeable asphalt pavements in China.

Coupling material and energy flow analysis with life cycle assessment to support circular strategies at the urban level

PurposeThis study aims to investigate how the coupling of Material and Energy Flow Analysis (MEFA) with Life Cycle Assessment (LCA) under an urban metabolism (UM) perspective (referred to as the UM-LCA approach) can support the design, evaluation, and monitoring of urban-level circular strategies. For this purpose, we apply the UM-LCA approach to the urban area of Umeå (Sweden) by expanding the goal and scope of a recent MEFA study conducted by the authors of the article.MethodsThe modeling combines MEFA with LCA and is performed both retrospectively and prospectively. The MEFA was performed in the prior study following a bottom-up approach to map and quantify material and energy flows in the urban system at the sectoral level. The quantified flows are used in the present study to construct the life cycle inventory (LCI) model of the urban system. The LCI model is first used as a basis of the retrospective LCA (rLCA), which is conducted to assess the environmental performance of the urban system with its sectors. The LCI model is then modified according to future scenarios and is used as a basis of the prospective LCA (pLCA), which is performed to assess the environmental implications of implementing circular strategies in the future.Results and discussionThe rLCA shows that the construction and household sectors are major drivers of environmental impacts in the urban system, with households being the largest contributors to 11 out of 12 analyzed impact categories. It also reveals the most impacting flows within these sectors, including food in households and steel in the construction sector. These findings indicate that the construction and household sectors and their most impacting flows should be prioritized in a circular strategy. Furthermore, the pLCA highlights that a future circular strategy promoting reductions in the material used in these two sectors could lead to higher reductions in all impact categories, ranging from 4.3 to 8.6%, than a strategy focused only on recycling, which could lead to reductions ranging from 0.2 to 1.2%.ConclusionsThis study concludes that the UM-LCA approach has great potential to comprehensively analyze, both retrospectively and prospectively, the environmental performance of an urban system providing valuable insights that could support policy makers in designing, assessing, and monitoring urban-level circular strategies. However, further research should address identified limitations of the UM-LCA approach (e.g., limited available data, inability to consider social and economic aspects) to facilitate its applicability and enhance its comprehensiveness.

Is lithium from geothermal brines the sustainable solution for Li-ion batteries?

The rising demand for Li, paramount for energy storage, necessitates expanded supply. As the supply is concentrated in a few countries, this poses supply chain risks for Li-ion battery makers. To diversify suppliers, alternative Li ore deposits such as geothermal brines are being explored. However, Li extraction from geothermal brines is challenging due to the unique chemistry and elevated temperatures. Since Li-extraction from geothermal brines is in its infancy, data availability and quality are still poor, hampering life cycle assessments. Hence, this study provides a parametrized life cycle inventory model of Li carbonate production from geothermal brines. The model accounts for site-specific environmental conditions and technological features. Life cycle impacts at the Salton Sea in the US (1686 cases) and the Upper Rhine Graben in Germany (1982 cases) are quantified. The high case numbers are chosen to mitigate the high uncertainties in input parameters. Specifically, the brine chemistry, adsorption yield, drilling required and energy inputs are varied. Climate change impacts of selected cases vary within 18–59 kg CO2eq/kg Li carbonate at the Salton Sea and within 5.3–46 kg CO2eq/kg Li carbonate at the Upper Rhine Graben, compared to 2.1–11 kg CO2eq/kg Li carbonate in existing ecoinvent data sets. The wide range of potential impacts underscore the necessity of early-stage assessments of the technologies. In case of high drilling demand and use of fossil energy, climate change impacts of Li-ion batteries using Li carbonate from geothermal brines can increase by 30–41 % compared to literature values.

Dissipation-based life cycle impact assessment of mineral resource use—a review, case study, and implications for the product environmental footprint

PurposeImpacts of mineral resource use on the availability of resources can be assessed using a broad range of methods. Until recently, life cycle inventory (LCI) and life cycle impact assessment (LCIA) models have been based on resource extraction. As extracted resources are not necessarily “lost” for future use, recent methodological developments have shifted the focus from resource extraction to resource dissipation. This paper aims at reviewing dissipation-based LCIA methods, testing them in a case study, analyzing potential implications for the product environmental footprint (PEF), and providing recommendations for future method development.MethodFive recently developed LCIA methods have been reviewed and compared based on 22 criteria, such as the forms and time horizons of dissipation considered, scientific publication, and number of characterization factors (CFs). Additionally, the abiotic depletion potential (ADP) method has been included to serve as a non-dissipation-based reference. All methods are tested in a case study on a theoretical product, designed solely for demonstration purposes, and consisting of 1 kg of the metals aluminum, cobalt, copper, molybdenum, nickel, and zinc. In addition to the absolute LCIA results, the contributions of metal production stages and individual resource extractions/emissions have been investigated. Finally, normalization and weighting have been carried out to analyze consequences of replacing ADP with the new dissipation-based methods in the context of PEF.Results and discussionMost recently developed LCIA methods take a long-term perspective, cover emissions of resources to the environment (and partly technosphere), and vary in the number of CFs and resources covered. The case study results obtained by ADP are dominated by the molybdenum dataset; the results of the dissipation-based LCIA methods are strongly influenced by the cobalt dataset. All results are strongly sensitive to the LCI database used (ecoinvent or GaBi). Normalization and weighting revealed that the mineral resource use impact result dominates the aggregated PEF score (57%), when using the currently recommended ADP model. Shifting from the resource extraction-based ADP to dissipation-based models can reduce the contribution to 23% or < 1% depending on the method.ConclusionThe development of methods addressing mineral resource use in LCIA has shifted from resource extraction to dissipation. The analyzed methods are applicable and lead to different findings than the extraction-based ADP. Using the newly developed methods in the context of PEF would significantly change the relevance of the mineral resource use impact category in comparison to other environmental impacts.

Regionalised modelling of recycled fertiliser P in agricultural fields: Development of the life cycle inventory model PLCI 2.0

Phosphorus (P) in societal waste streams can be recycled and used as recycled P fertilisers (RPFs). When developing new technologies for P recycling, life cycle assessment (LCA) can be used to assess their environmental impact and support environmentally friendly decisions. In LCA calculations, inventory factors describe, for example, the emissions, resource requirements and potential substitution of products. The Phosphorus Life Cycle Inventory (PLCI) model is a tool for estimating such inventory factors for the use of RPFs.The main objective of this study was to develop the PLCI model from its previous parameterisation for Denmark to make it applicable to all countries and regions of the EU. This was undertaken by considering parameters that describe regional conditions affecting the loss of P, crop P uptake and potential mineral fertiliser substitution. Such parameters include soil type, soil P status, soil erosion, groundwater leaching, crop types, productivity and crop P concentrations. Several RPFs were incorporated into the model, including coefficients describing the partitioning to the labile, plant-available P fraction.To illustrate the applicability of the model, a case study was performed. Applications of RPFs were modelled in the Copenhagen region in Denmark and the Piedmont region in Italy. The higher soil erosion rate in Piedmont resulted in over six times greater P loss than in Copenhagen. The difference in P fertilisation practice between the regions had a strong impact on the mineral fertiliser substitutions. The results point to the importance of defining the fertilisation regime of the region being modelled, and this is now possible with the new, more dynamic version of PLCI.In conclusion, the new PLCI 2.0 model provides a dynamic tool for LCA practitioners to estimate region-specific inventory factors for RPFs. The model is relatively easy to use and captures differences between fertilisers with varying P availability and between applications in different regions of the EU.

Life cycle assessment of kerbside waste material for an open-looped and closed-loop production– towards circular economy designs

Urbanization growth has increased the generation of municipal solid waste (MSW) and has the potential for recycling and reuse. However, it is frequently limited to a linear lifecycle mode which end up in landfills. A novel attributional lifecycle inventory model for lifecycle assessment based on value retention process (VRP) model of circular economy was developed to quantify the lifecycle inventory and measure the environmental impacts of multiple lifecycle stages, from in-community separation to the end-of-use/life stage and subsequent lifecycles. This investigation focused on assessing the environmental impacts of two distinct in-community waste glass separation methods - separate kerbside glass recycling bin (SKGRB) and mixed kerbside recycling bin (MKRB) - in combination with two recycling approaches - open-loop (asphalt) and closed-loop (glass container). The goal of the study was to make a comparative evaluation of the environmental impacts of these methods. Results showed that the SKGRB method had better environmental performance (40–60% reduction compared to the MKRB method) for both materials. Closed-loop recycling of glass container production had higher environmental impacts due to higher energy consumption in production in one lifecycle, while the open-loop recycling method of asphalt had higher environmental impacts despite fewer circulations, due to higher production volume in 21 years. The results of the sensitivity/uncertainty analysis showed that environmental impacts decreased as the allocation coefficient decreased, reaching stability when the coefficient reached the waste materials percentage in the new product's mixed design.

Levelized cost of electricity and greenhouse gas emissions of Ce- and V-based redox flow batteries

Inexpensive energy storage technologies are critical to meeting rising renewable electricity demand and maintaining grid stability, but there is insufficient storage capacity to meet this demand. Redox flow batteries are a promising long duration (>10 h) storage method because they scale efficiently with delivery time compared to non-flow batteries. Because of the connection between energy storage and intermittent renewables, the greenhouse gas emissions associated with using flow batteries are important to consider. Here, we investigate the all-vanadium redox flow battery and a Ce–V battery, where the V4+/V5+ couple is replaced by Ce3+/Ce4+. We use performance metrics from the literature and our previous work to model the batteries' operation. We develop technoeconomic assessment and life cycle inventory models and determine that the Ce–V RFB minimum levelized cost of electricity is lower and the two RFBs’ levelized GHG emissions are similar, suggesting Ce should be considered further in RFB applications. Ultimately, the production of the RFBs results in minor emissions compared to use, except in scenarios where 100% renewables are used to generate electricity. The redox potential and exchange current density are highly influential to the Ce–V RFB cost and emissions, motivating further work into the phenomena that control thermodynamics and kinetics.

Reusable unit process life cycle inventory for manufacturing: high speed laser directed energy deposition

The unit process life cycle inventory is a modeling approach to estimate the energy demand and resource requirements of a unit process. Thus, a model of the unit process life cycle inventory for a specific manufacturing process can be used for quantifying the environmental impacts of specific products manufactured by that process. Within the approach, reusable models are developed for specific manufacturing processes. In this paper, the development and validation of a unit process life cycle inventory model for high-speed laser directed energy deposition is presented. This additive manufacturing process offers great potential for the industry due to its fast process speed. However, high-speed laser directed energy deposition has a high energy consumption and resource demand. Assessing the energy demand for individual manufactured products is a time-intensive process that requires expert knowledge. Thus, the development of an adaptable unit process life cycle inventory model enables more convenient assessment and improvement of its energy and resource efficiencies for producing different products. For the development of the model, the subsystems of a high-speed laser directed energy deposition machine are analyzed separately, e. g. the laser generator, the trajectory system, the powder feeder, and the suction system. Afterward, the energy and material demand of these subsystems are described in mathematical models. Finally, the model is validated by comparing the energy demands of three demonstration parts, measured by experiments, and predicted by the model.

Climate impact of bioenergy with or without carbon dioxide removal: influence of functional unit and parameter variability

PurposeBioenergy with carbon dioxide removal (CDR) is increasingly proposed as an efficient way to mitigate climate change. This study examined the circumstances and methodological choices in which two CDR bioenergy systems were preferable to a reference bioenergy system from a climate change mitigation perspective. The CDR systems were also compared.MethodsThree systems were modelled: two CDR systems (Biochar, bioenergy with carbon capture and storage (BECCS)), with a combined heat and power (CHP) system as reference. A parameterised life cycle inventory (LCI) model was developed and computed for all systems and four different functional units (FUs), resulting in different distributions of climate impacts. Contribution analysis was performed, followed by pair-wise comparison of all scenarios to establish their ranking. First-order Sobol indices were computed to assess the contribution of each parameter to total variance. When ranking of scenarios was strongly dependent on parameter values, decision tree analysis was applied.Results and discussionThe CDR systems had a lower climate impact than CHP in most computations, across all FUs. On comparing the two CDR systems, the preferable system changed with FU. With heat or carbon sequestration as FU the Biochar system was preferable in general, while with electricity or biomass use as FU, the BECCS system had the lowest climate impact in most computations. For most system configurations, energy substitutions had a large influence and contributed most to the variance in results. The system ranking also depended on the reference activities in the background energy system.ConclusionsThe Biochar and BECCS systems were generally preferable to the reference CHP system from a climate mitigation perspective, particularly when the reference energy systems had a relatively low climate impact. However, FU and parameters affected the system ranking. For comparing BECCS and biochar, case-specific climate impacts will be decisive, but not always conclusive, as the choice of FU has such large impact on the results.RecommendationsWhen conducting LCA of multi-functional systems, the use of several FUs, parameterised LCI, and contribution analysis allows for deeper investigation than conventional sensitivity analyses. When analysing the climate impact of bioenergy with or without carbon removal, it is especially important to perform sensitivity analysis on the energy background system, since it strongly affects the results.

Social footprint of European food production and consumption

The transition towards more sustainable food systems is one of the great challenges at global level. While environmental considerations have been widely explored, the social sustainability of agri-food systems have been scarcely addressed in literature, especially regarding the externalization of impacts due to international trade. In this study, we apply Social Life Cycle Assessment (S-LCA) to address social sustainability considerations of food production and consumption, supporting the monitoring of progress towards the Sustainable Development Goals (SDGs). Building on the model of the Consumption Footprint developed by the EC-JRC to address environmental sustainability aspects, this paper presents a macro-scale assessment of social risk associated with the EU food consumption. In the context of operationalizing value chain assessment within SDGs, the analysis combines social indicators with production, trade and consumption data, allowing the evaluation of the social footprint of European food production and consumption. Selected social indicators and impact subcategories from those available in the Product Social Impact Life Cycle Assessment (PSILCA) database were employed to assess a process-based Life Cycle Inventory model of EU food consumption by means of 44 representative products selected for the EU Consumption Footprint indicators. The assessment included an evaluation of SDGs coverage by S-LCA indicators and a sensitivity analysis based on two different weighting schemes, which unveiled the need of a harmonized weighting approach. Unlike the environmental impacts, social hotspots emerge mainly in rice and fruit & vegetables categories. Hotspots are concentrated in India, Argentina, and other extra EU countries. These results suggest that trade-offs could emerge between the different sustainability pillars and that fair trade and responsible sourcing approaches should be guaranteed in order to promote sustainable food systems.

Greenhouse gas emissions of meat products in China: A provincial-level quantification

Driven by a continuous rise in household living standards, China's meat consumption keeps growing, increasing the need for greenhouse gas (GHG) emissions mitigation. This study employed a process-based life-cycle inventory model to quantify the province-level GHG emissions of meat products in China. Four major livestock products (beef, mutton, chicken, and pork) at four farming scales (cage-free, small, medium, and large-scale farming) were considered. The GHG emissions of the Chinese livestock sector were 429 million tons (MT) CO2e in 2018. Beef had the highest GHG intensity (19.6 kg CO2e/kg), followed by mutton (10.0 kg CO2e/kg), chicken (4.4 kg CO2e/kg), and pork (3.8 kg CO2e/kg). Spatially, northwestern China had a high GHG intensity 5.58 g CO2e/kcal), while the eastern and central regions presented low-intensity values (1.89 and 1.71 g CO2e/kcal, respectively). At the province level, Shandong (38.2 MT CO2e) was the top emitter, while Shanghai (0.5 MT CO2e) had the lowest emissions. This study identified the GHG emission hotspots along the meat product production chain and revealed the spatial pattern of meat-related GHG emissions in China, advancing the low-carbon transition of China's livestock sector.

Improving automated Life Cycle Assessment with Life Cycle Inventory model constructs

Global warming is a global and critical challenge and car manufacturers pledge to reduce their carbon footprint. It is mandatory to assess the carbon footprint of current and upcoming vehicles to achieve these self-set and quantified targets. Life Cycle Assessment (LCA) is a standardized and scientifically acknowledged method to quantify a product's or company's environmental impact. Due to the complexity of a vehicle's cradle-to-gate phase, automation of LCAs is a suitable simplification strategy by importing product information and automatically assign them to pre-defined life cycle inventory (LCI) models. The data acquisition is a recognized issue, but the LCI modelling is hardly discussed in the literature for automated LCAs even though it has a significant influence on their accuracy. This paper presents an approach to elaborate an LCI model construct that enables consistent and efficient automated LCA while maintaining defined accuracy and applicability in commercial LCA software. Such an LCI model construct and its derivation are presented in this paper for automotive aluminium components considering sheet components, casting components and extrusion profiles. It is mandatory for a precise calculation to take the most influencing parameters into consideration. Grid points for each of these parameters are elaborated dependent on the characteristic if the parameter is continuously or discretely variable. The resulting LCI model construct makes this most influencing parameters customizable in an automated LCA. A generic method for the design of such an LCI model construct based on the findings of the use case was derived in this paper. The application of this methodology is not limited for vehicle's LCAs and carbon footprint, but other complex products and impact categories to enable efficient, consistent, and accurate LCA results for complex products.

A review of inventory modeling methods for missing data in life cycle assessment

AbstractMissing data is the key challenge facing life cycle inventory (LCI) modeling. The collection of missing data can be cost‐prohibitive and infeasible in many circumstances. Major strategies to address this issue include proxy selection (i.e., selecting a surrogate dataset to represent the missing data) and data creation (e.g., through empirical equations or mechanistic models). Within these two strategies, we identified three approaches that are widely used for LCI modeling: Data‐driven, mechanistic, and future (e.g., 2050) inventory modeling. We critically reviewed the 12 common methods of these three approaches by focusing on their features, scope of application, underlying assumptions, and limitations. These methods were characterized based on the following criteria: “domain knowledge requirement” (both as a method developer and a user), “post‐treatment requirement,” “challenge in assessing data quality uncertainty,” “challenge in generalizability,” and “challenge in automation.” These criteria can be used by LCA practitioners to select the suitable method(s) to bridge the data gap in LCI modeling, based on the goal and scope of the intended study. We also identified several aspects for future improvement for these reviewed methods.

Life‐cycle energy and environmental emissions of cargo ships

AbstractMaritime shipping is under increasing pressure to alleviate its environmental impact. To this end, life‐cycle footprint accounting provides a foundation for taking targeted measures in the green transition of shipping. This study used the process‐based hybrid life‐cycle inventory (LCI) modeling approach to estimate the “cradle‐to‐propeller” footprint of ships, including primary energy consumption, carbon dioxide emissions, and sulfur dioxide emissions. We used the input–output LCI model to calculate the embodied energy and emissions associated with the material and fuel use of ship manufacturing. We used a bottom‐up emission model and global marine traffic data to estimate the operational footprint of different types of ships. Based on 382 cargo ships (including bulk carriers, container ships, and general cargo ships) constructed in mainland China between 2011 and 2015, we estimated that the embodied footprint accounted for &lt;10% of the cradle‐to‐propeller footprint under the pre‐2019 policy scenario. In terms of life‐cycle energy intensity (MJ/nm/1000 deadweight tonnage [DWT]), the large bulk carrier (&gt;100,000 DWT) establishes the lowest value (46), followed by the small (0–100,000 DWT) bulk carrier (96), the large container ship (133), the small container ship (196), and the small general cargo (238). The bulk carrier was identified as the most energy efficient among the three ship types, and ships with larger capacities (i.e., DWT) had higher energy efficiencies than ships with lower capacities. Our study provides a comprehensive understanding of the life‐cycle footprints of cargo ships, thus enabling better evidence‐based policymaking to transition the global marine‐shipping industry to a future of greener energy.

The influence of degradation effects in proton exchange membrane fuel cells on life cycle assessment modelling and environmental impact indicators

Although proton exchange membrane fuel cell (PEMFC) systems are expected to have lower environmental impacts in the operational phase, compared to conventional energy conversion systems, there are still certain economic, operational, and environmental setbacks. Durability under a wide range of operating conditions presents a challenge because degradation processes affect the PEMFC efficiency. Typically, life cycle assessment (LCA) of PEMFC systems do not include performance degradation. Thus, a novel semi-empirical PEMFC model is developed, which includes degradation effects caused by different operational regimes (dynamic and steady-state). The model is integrated into LCA through life cycle inventory (LCI) to achieve a more realistic and accurate evaluation of environmental impacts. Verification of the model clearly showed that the use of existing LCI models underestimates the environmental impacts. This is especially evident when green hydrogen is used in PEMFC operational phase, where manufacturing phase and maintenance (stack replacements) become more influential. Input parameters of the model can be modified to reflect technological improvements (e.g. platinum loading or durability) and evaluate the effects of future scenarios.

Integration of future water scarcity and electricity supply into prospective LCA: Application to the assessment of water desalination for the steel industry

AbstractThe urgency of tackling global environmental issues calls for radical technological and behavioral changes. New prospective (or ex ante) methods are needed to assess the impacts of these changes. Prospective life cycle assessment (LCA) can contribute by detailed analysis of environmental consequences. A new stream of research has taken up the challenge to create prospective life cycle inventory (LCI) databases, building on projections of integrated assessment models to describe future changes in technology use and their underlying environmental performance. The present work extends on this by addressing the research question on how to project life cycle impact assessment methods for water scarcity consistent with prospective LCI modeling. Water scarcity characterization factors are projected from 2010–2050 using the AWARE method, based on SSP‐RCP scenario results of the integrated assessment model IMAGE. This work is coupled with prospective LCI databases, where electricity datasets are adapted based on the energy component of IMAGE for the same scenario. Based on this, an LCA case study of water desalination for the steel industry in Spain is presented. The resulting regional characterization factors show that some regions (i.e., the Iberian Peninsula) could experience an increase in water scarcity in the future. Results of the case study show how this can lead to trade‐offs between climate change and water scarcity impacts and how disregarding such trends could lead to biased assessments. The relevance and limitations are finally discussed, highlighting further research needs, such as the temporalization of the impacts.

An Empirical Benchmark for Resource Use in Fused Deposition Modelling 3D Printing of Isovolumetric Mechanical Components

The lack of detailed process information such as part shape, process setups, and process efficiencies limit the accuracy of life cycle inventory models that are used for estimating environmental impacts of manufacturing processes. Such limitations are particularly true for processes such as additive manufacturing, where part shape significantly influences the resulting process characteristics. To address the above knowledge gap, this paper investigates the influence of part shape on process inventory of fused deposition modeling 3D printing. To this end, we experimentally measured the resource consumption for printing 68 different isovolumetric mechanical components with identical print settings. These results are presented in a structured form, titled “Resource Use for 3D Printing Isovolumetric Mechanical Components (r3DiM) Benchmark”, that includes our experimental data, 3D model files, NC code used for printing the models, and layer-wise energy consumption of the heaters of the bed & extruder, material use, and other printing peripherals of each object. It is our hope that the r3DiM benchmark will serve as a reference of future researchers interested in creating shape-aware, predictive life cycle inventory models for fused deposition modeling 3D printing processes. The r3DiM benchmark can be accessed from the following url: https://tinyurl.com/r3DiMBenchmark.