The demand for life cycle assessments (LCA) is growing rapidly, which leads to an increasing demand of life cycle inventory (LCI) data. While the LCA community has made significant progress in developing LCI databases for diverse applications, challenges still need to be addressed. This perspective summarizes the current data gaps, transparency, and uncertainty aspects of existing LCI databases. Additionally, we survey and discuss novel techniques for LCI data generation, dissemination, and validation. We propose key future directions for LCI development efforts to address these challenges, including leveraging scientific and technical advances such as the Internet of Things (IoT), machine learning, and blockchain/cloud platforms. Adopting these advanced technologies can significantly improve the quality and accessibility of LCI data, thereby facilitating more accurate and reliable LCA studies.

While wild pollinators play a key role in global food production, their assessment is currently missing from the most commonly used environmental impact assessment method, Life Cycle Assessment (LCA). This is mainly due to constraints in data availability and compatibility with LCA inventories. To target this gap, relative pollinator abundance estimates were obtained with the use of a Delphi assessment, during which 25 experts, covering 16 nationalities and 45 countries of expertise, provided scores for low, typical, and high expected abundance associated with 24 land use categories. Based on these estimates, this study presents a set of globally generic characterization factors (CFs) that allows translating land use into relative impacts to wild pollinator abundance. The associated uncertainty of the CFs is presented along with an illustrative case to demonstrate the applicability in LCA studies. The CFs based on estimates that reached consensus during the Delphi assessment are recommended as readily applicable and allow key differences among land use types to be distinguished. The resulting CFs are proposed as the first step for incorporating pollinator impacts in LCA studies, exemplifying the use of expert elicitation methods as a useful tool to fill data gaps that constrain the characterization of key environmental impacts.

Life Cycle Inventory datasets on technologies for stone quarrying, cutting and finishing.

Pharmaceutical production substantially contributes to global greenhouse gas emissions. The application of Life Cycle Assessment (LCA) to evaluate these impacts is hindered by the limited Life Cycle Inventory (LCI) data. Existing LCI estimation methods often exclude key operations such as waste treatment and tablet formulation, relying on broad assumptions that lead to incomplete assessments. To address these gaps, this study developed a method to estimate industrial energy usage by upscaling laboratory-scale data. This method includes Active Pharmaceutical Ingredient (API) synthesis, tablet formulation, and auxiliary operations. Process Design Calculations (PDCs) derived in our method improve the energy estimation for unit operations. The application of this method to six pharmaceuticals resulted in total energy estimates exceeding those of the existing methods by over 102% for Lidocaine, Diclofenac, Paracetamol, and Ibuprofen. Higher estimated energy usage led to a 3% to 49% increase in carbon footprint, primarily because operations previously left out contributed over 17% to the total carbon footprint. The new method’s energy-based carbon footprint seems to align better with industrial reference data than other methods. We conclude that our method improves the estimation of industrial energy usage for pharmaceutical production and reduces the risk of impact underestimation. It enables LCA practitioners to conduct more reliable assessments, supporting sustainability decisions.

Purpose: Besides politics and the private sector, changes in the consumption pattern of individuals can significantly contribute to sustainable development. The recently published Life-LCA method adapts life cycle assessment to analyse human beings and quantifies their impacts. This method is applied for the first time in this case study to provide insights and remaining challenges. Methods: The environmental impacts caused by the life cycle of a middle-aged German man (“Dirk”) were determined by the Life-LCA method from his birth until his current age (0–49 years). To determine and quantify reduction options, a current 1-year period was analysed in detail by a baseline scenario of his current consumption and an optimized scenario after changing his consumption patterns. The environmental impact assessment included global warming (GWP), acidification (AP), eutrophication (EP), and photochemical ozone creation potentials (POCP). Results and discussion: Dirk has emitted 1,140 t CO 2 -eq., 4.48 t SO 2 -eq., 1.69 t PO 4 -eq., and 0.537 t C 2 H 4 -eq. emissions over his current lifetime. Transportation dominated all considered impact categories (40 up to 55%). Energy and water consumption is the second most significant product category for GWP (39%). Food products, with 10%, are the third biggest contributor to GWP, but contribute rather significantly to the impact categories AP (34%), EP (42%), and POCP (20%). The optimized scenario analysis revealed significant reductions for all studied impacts in the range of 60–65%. CO 2 -eq. emissions were reduced from 28 to 10 t/a. The remaining challenges include data collection for childhood, gaps and inconsistencies of existing data for consumer goods, the allocation between product users, and depreciation of long-living products. Conclusion: The first Life-LCA case study confirmed the applicability of the Life-LCA method. It showed that the Life-LCA approach allows for tracking individual consumption patterns of a human being. The impacts of behavioural changes were quantified, and significant reduction potentials of the environmental impacts were revealed. Additional case studies on people of different age, region, culture, and lifestyle are needed for further insights and methodological refinements.

Besides their indisputable positive health effects, pharmaceutical residues in the environment are identified to also have potential adverse effects on wildlife and human beings. They may enter environmental compartments (e.g. surface water bodies) through different pathways, such as excretion and a subsequent insufficient waste water treatment. Manufacturing predominantly in low-cost countries with inadequate environmental regulations and an increased use of pharmaceuticals on a global scale further aggravates the environmental relevance of the pharmaceutical sector. To comprehensively identify potential environmental impacts of pharmaceuticals and to establish measures to effectively reduce them, a life cycle perspective is imperative. For this purpose, life cycle assessment (LCA) is the predominant methodology since it is internationally standardized and widely applied among different sectors. Due to its broad use, however, individual methodological specifications are also necessary for particular product groups which can be formulated as Product Category Rules (PCR) according to ISO 14025 and ISO/TS 14027. For pharmaceuticals, such harmonized specifications do not exist which leads to a high level of methodological inconsistency between existing LCA studies. Moreover, case studies from the pharmaceutical sector often focus on manufacturing processes, whereas the use and end-of-life (EoL) stage are excluded from the assessment. The goal of this thesis is therefore to develop a scientifically robust, comprehensive and yet applicable methodological framework to guide LCAs of pharmaceutical products and processes and, in the long term, to harmonize and thus facilitate the future application of the LCA methodology in the pharmaceutical sector. To this end, two research questions are formulated: How should a LCA framework for pharmaceutical products be outlined to provide methodological guidance on sector-specific questions and challenges (RQ.1) and how can life cycle stages beyond the manufacturing stage of pharmaceuticals be modeled (RQ.2)? First, a review on existing generic LCA standards and guidelines on PCR development, sector-specific LCA guidelines, PCRs and LCA case studies on pharmaceutical products is conducted to identify methodological differences, similarities and open gaps. Furthermore, the review provides a structural basis for the framework development. Based on this, either new rules are drafted (e.g. a classification scheme of pharmaceutical products based on their functionality, the definition of product system, system boundaries and functional unit (FU), guidance on impact assessment) or existing methodological specifications are adopted if there is already a high consensus on these rules among literature (e.g. regarding general data quality requirements). As one major gap in existing studies, the exclusion of the use and EoL stage is identified which is of particular importance since most of the pharmaceutical emissions are expected to occur here. Therefore, a life cycle inventory model is developed to estimate emissions of Active Pharmaceutical Ingredients (API) during and after use of a pharmaceutical. To this end, API flows and emissions for different galenic formulations are compiled and quantification approaches as well as potential data sources are presented. All results are finally applied in a case study on an ibuprofen analgesic from cradle to grave. The LCA study reveals that the manufacturing stage is the largest contributor to all environmental impacts, whereas the share of the use and EoL stage to the overall environmental impacts is insignificant. Even though a systematic review of the framework´s applicability and completeness are beyond the scope of the case study, it discloses some methodological and practical challenges, such as the general comparability of pharmaceuticals, how positive effects of pharmaceuticals could be integrated into the damage-oriented LCA, the expansion of system boundaries to include Research and Development (R&D) activities and other processes along the healthcare pathway or the transferability of the rules to veterinary medicine. The most limiting factor is indubitably the availability of consistent data. This affects not only the life cycle inventory but also calculations on an impact assessment level. Therefore, future research should focus on both, the further development of the framework as well as provision of comprehensive data. Yet, the methodological framework presented in this thesis significantly refines the LCA methodology for pharmaceuticals and allows a more comprehensive environmental assessment from cradle to grave with only few data which are usually publicly available. Hence, current environmental assessment approaches for pharmaceuticals are expanded by a more holistic perspective.

Life Cycle Assessment (LCA) is widely used to quantify environmental impacts but often faces data gaps, heterogeneous practices, and limited timeliness. This review examines how machine learning (ML) can strengthen LCA across all four phases—goal & scope, life cycle inventory (LCI), life cycle impact assessment (LCIA), and interpretation—while providing a reproducible bibliometric map of recent research. We performed a bibliometric search and keyword co-occurrence visualization (VOSviewer) and organized the literature by LCA phases. We highlight actionable opportunities: NLP-assisted scope definition, probabilistic imputation and uncertainty quantification for LCI, surrogate and hybrid models for LCIA, and calibrated, decision-oriented interpretation. Compared with prior reviews, we (i) deliver phase-specific guidance instead of generic lists, (ii) extend coverage to recent work with reproducible bibliometrics, and (iii) foreground early-phase opportunities that remain under-explored. These insights—together with open materials for reuse—aim to make LCA more data-robust, transparent, and actionable in research and practice.

Life cycle assessment (LCA) has grown rapidly and is now well established within the electronics industry. The growing number of journal publications, conferences, and special issues is a proof for the same. A number of literature reviews have been published till now in this area focusing on different aspects. This study has identified 134 significant journal articles to conduct a systematic and narrative literature review. This review covers a wide range of product categories and analyzes the usefulness of LCA as a decision-making tool within the electronics industry which has not been explored fully in previous reviews conducted in this area of research. For this purpose, we organized LCA studies into 10 main product categories. A narrative review was employed to summarize the significant findings from the LCA studies. Although the central objective of all the studies was to evaluate the environmental impact created by the product, the focus and methods employed differed. A systematic review was used to categorize the overall frameworks used in the studies. The studies were classified based on their research purpose, types of approach, LCIA methods used, system boundaries involved, data collection methods, and data analysis levels. Within the subcategory of research purpose, three research domains were identified and the studies were classified accordingly. Generally it has been revealed that use phase, end of life, and production phase are the dominant phases in that order. However discrepancies occur owing to functional units, data usage, and assumptions made. All these and more make benchmarking difficult. Finally we identified gaps that merit attention in future research. It is also hoped that this review is a good resource for anyone interested in doing research on LCA of electronic products, helping them identify current research trends, provide suggestions for future research, and stimulate interest in creating new research directions.

The high expectations placed on hydrogen as a clean fuel have led to a growing amount of life-cycle assessment (LCA) studies of hydrogen-related systems. The multiple methodological choices and diverse technical characteristics contribute to a broad set of practices, resulting in significant variability even among similar systems. This study sets the basis for the development of harmonised guidelines for LCA of fuel cells and hydrogen (FCH) systems by analysing current LCA practices. The reviewed literature suggests that previous efforts on harmonisation of LCA methodological choices have led to common practices for certain choices, e.g. functional unit. However, an incomplete definition of some parameters hinders the interpretability of LCA results and hides the potential sources of variability in terms of LCA estimates. In this work, in addition to a systematic literature review of LCA of FCH systems to identify current practices and gaps, sources of variability were investigated for the life-cycle greenhouse gas emissions of hydrogen production systems through a meta-regression analysis (MRA). The MRA results show that the variability of LCA estimates in the literature can be explained by a limited set of qualitative (e.g. implementation of CO2 capture, among other technological choices) and quantitative (e.g. electricity consumption for hydrogen processing) variables. Although a certain progress towards common methodological choices in LCA of FCH systems was identified, further work is still needed to harmonise practices, as well as to extend the application of the proposed MRA approach to other life-cycle indicators for both identification of main drivers and harmonisation of LCA impact scores.

Lithium (Li) is essential for decarbonization strategies, such as electric vehicles and renewable energy storage, which experiences the largest growth rates among metals required for low-carbon technologies. To meet this demand, the raw materials sector must increase current capacities and develop new capacities at untapped deposits. Understanding life cycle impacts is crucial to avoid severe environmental burden shifts in the future. Although site-specific life cycle inventories exist, they do not allow for a comprehensive global assessment of the Li sector, particularly in capturing technological developments. To address this, our study presents a life cycle inventory model for brines that maintains essential site-specific parameters while providing a global perspective. We define core parameters for site-specific modeling of Li carbonate (Li2CO3) production and develop a systematic approach to addressing data gaps. Our model employs a class-based structure for 30 mapped processes from the literature and quantifies environmental and technical flows. Overall, we cover 25 sites, representing 300 kilotonnes (90%) of current Li2CO3 production from brines and an additional 315 kilotonnes of potential future production. One key finding is that sites using direct Li extraction have 7-fold higher climate change impacts than sites using conventional technologies on average, while water scarcity impacts are doubled on average. The difference is a result of the larger brine mass required to be treated due to lower Li grades. Furthermore, our model allows the implications for Li-ion battery production to be analyzed.

The dataset is used as a foundation for the scientific article Footprint cohesion and prevalence of environmental impact categories in blue mussel aquaculture life cycle assessments. Conducting a comprehensive assessment of environmental footprint is essential for defining any foods environmental impact. However, concentrating on too few environmental metrics may inadvertently lead to burden shifting and involuntary create greater harm. This dataset is a collection of all the existing literature related to the environmental footprint and Life Cycle Assessments of farmed blue mussel to determine its environmental footprint. This dataset is connected to the interdisciplinary research project SECURE (Novel Marine Resources for Food Security and Food Safety) which investigate the potential of low-trophic marine species. The overall goal of the project is to develop knowledge that enables sustainable food security. More information is available through https://en.uit.no/project/secure Highlights of the study • Carbon-, eutrophication- and acidification emissions was most commonly assessed • Discrepancies in evaluation of bioremediation and carbon sequestration • Several impact categories are sparsely used and unevenly included across studies • Most footprints were higher than previously assumed • Dominating impacting inputs varied from electricity and diesel to capital goods Abstract Aquaculture is promoted as a solution for strengthening food security. Non-fed organisms like blue mussels have gained interest as feed is a frequent hotspot in aquaculture. In this literature review, all published studies on environmental footprint evaluations with life cycle assessments (LCA) on blue mussel aquaculture, was assessed. Through harmonisation, the studies were enabled numerical comparison of the environmental footprints. It was found that blue mussel aquaculture LCA most frequently study some impact categories, resulting in an average global warming potential of 263 ± 179 (range 9.52 - 627) kg CO2 eq.; eutrophication potential of 0.13 ± 0.33 (range -0.89 – 0.44) kg PO4 eq.; and acidification potential of 2.072 ± 1.641 (range 0.71 – 6.5) kg SO2 eq. per ton whole mussel. Consequently, significant gaps exist in several other impact categories, with some impact estimates varying by factor of thousand between the highest and lowest. Some aspects were found to deviate between the studies like how to handle carbon sequestration in the shell and bioremediation of nitrogen and phosphorous. The most analysed production method was variations of longlines; the most used life cycle impact assessment method was CML; and the most evaluated species was M. galloprovincialis. Many footprints were higher than previously assumed, but is still lower than many alternative food products. Future research should focus on establishing category rules or sector-wide agreements to address specific challenges, such as remediation of nutrients and carbon. Additionally, expanding the range of impact categories evaluated will help distinguish differences across case studies.

This study demonstrates how machine learning techniques can bridge data gaps in the ecotoxicological hazard assessment of chemical pollutants and illustrates how the results can be used in practice. The innovation herein consists of the prediction of the sensitivity of all species that were tested for at least one chemical for all chemicals based on all available data. As proof of concept, pairwise learning was applied to 3295 × 1267 (chemical,species) pairs of Observed LC50 data, where only 0.5% of the pairs have experimental data. This yielded more than four million Predicted LC50s for separate exposure durations. These were used to create (1) a novel Hazard Heatmap of Predicted LC50s, (2) Species Sensitivity Distributions (SSD) for all chemicals based on 1267 species each, as well as (3) for taxonomic groups separately, and (4) newly defined Chemical Hazard Distributions (CHD) for all species based on 3295 chemicals each. Validation results and graphical examples illustrate the utility of the results and highlight species and compound selection biases in the input data. The results are broadly applicable, ranging from Safe and Sustainable by Design (SSbD) assessments and setting protective standards to Life Cycle Assessment of products and assessing and mitigating impacts of chemical pollution on biodiversity.

Electrifying the global economy is accepted as a main decarbonization lever to reach the Paris Agreement targets. The IEA’s 2050 Net Zero transition pathways all involve some degree of nuclear power, highlighting its potential as a low-carbon electricity source. Greenhouse gas emissions of nuclear power reported in the life cycle assessment literature vary widely, from a few grams of CO2 equivalents to more than 100 g/kWh, globally. The reasons for such a variation are often misunderstood when reported and used by policymakers. To fill this gap, one can make LCA models explicit, exploring the role of the most significant parameters, and develop simplified models for the scientific community, policymakers, and the public. We developed a parametric cradle-to-grave life cycle model with 20 potentially significant variables: ore grade, extraction technique, enrichment technique, and power plant construction requirements, among others. Average GHG emissions of global nuclear power in 2020 are found to be 6.1 g CO2 equiv/kWh, whereas pessimistic and optimistic scenarios provide extreme values of 5.4–122 g CO2 equiv/kWh. We also provide simplified models, one per environmental impact indicator, which can be used to estimate environmental impacts of electricity generated by a pressurized water reactor without running the full-scale model.