Life cycle and net environmental impact assessment of a shared e-bike service: application to the case of Madrid

From horticultural waste to feed: Circular economy potential of cucumber-straw silage in Mediterranean lamb production through integrated in vitro, in vivo, and life cycle assessment.

From buildings to district: A systematic review of life cycle assessment approaches for energy system planning and optimization

The construction industry produces a large amount of carbon emissions, with a significant portion attributed to embodied carbon (EC). Prefabrication has the potential to reduce EC through streamlined workflows and controlled production environments. However, inefficiencies such as rework, material waste, excessive labor input, and quality issues can offset these benefits, necessitating a flexible digital technology approach. This study proposes the integration of mixed reality (MR) into prefabrication workflows to mitigate these issues and reduce EC emissions. A hybrid input-output (I-O) life cycle assessment (LCA) model combined with Monte Carlo (MC) simulation was employed to evaluate EC emission under uncertainty. Four prefabrication scenarios representing increasing levels of MR integration, ranging from conventional methods to high-level digitalization, were analyzed across several life cycle stages. Results emphasize that EC emissions were reduced by 8.5%, 14.9%, and 21.2% in the Lower, Medium, and Higher MR scenarios, respectively, due to significant reductions in rework, rejections, labor hours, and energy use. MC simulation confirmed the robustness of the results, showing decreased variability and tighter confidence intervals with higher levels of MR integration. While the experimental context focuses on precast panel production, the proposed approach is generalizable to broader prefabrication systems with appropriate adaptations to the MR model. This study contributes to the body of knowledge by introducing a novel integration of MR with hybrid I-O LCA for EC accounting in prefabrication and by demonstrating how MC simulation enhances the reliability of environmental impact assessments under uncertainty. The proposed hybrid I-O LCA approach offers a replicable and data-driven method for sustainable decision-making in construction engineering and management.

Climate change poses the greatest threat to human health in the 21st century. The healthcare sector contributes approximately 5% of global greenhouse gas emissions and has a significant environmental impact. Although clinical trials are crucial for identifying effective and safe treatments and preventing disease, their environmental impact is poorly documented. Our study aimed to assess the environmental impact of a publicly funded, academic clinical trial by adapting life cycle assessment (LCA) methodology to clinical research. We performed a retrospective, simplified, full LCA using the EF 3.0 methodology on a prospective, double-blind, randomised controlled neurosurgery trial. The trial included 202 patients at 18 university hospitals throughout France. To identify hotspots of interest, 16 impact indicators and their combination into a single score were evaluated. The results showed that climate change (or greenhouse gas emissions) was the most important indicator, accounting for almost 30% of the single score. Greenhouse gas emissions were estimated at 31.6 t of carbon dioxide equivalent. The next most important were resource use of fossils (24%), resource use of minerals and metals (12%), and particulate matter emissions (8%). The main hotspots identified were patient transport and travel by clinical research assistants for source data verification. In conclusion, by using a full LCA approach, our study confirms that conducting a clinical trial has a substantial environmental impact, particularly with regard to greenhouse gas emissions. The main hotspots identified were related to patient transport and clinical research assistants' travel. Trial Registration: The SUCRE study (Treatment of Chronic Subdural Hematoma by Corticosteroids: A Prospective Randomised Study)-clinicaltrials.gov identifier: NCT02650609.

Pineapple chips are a snack from sliced pineapples that are deep-fried and contain permitted food additives. The production activities of pineapple chip agroindustry have the potential to cause environmental impacts. Therefore, a comprehensive assessment of emissions generated throughout the life cycle of pineapple chip production is necessary using the Life Cycle Assessment (LCA) approach. This study aims to calculate the environmental emission potentials from the life cycle of pineapple chips, covering stages from cultivation to distribution. The research stages include goal and scope definition, inventory analysis using the mass balance method, impact assessment using the Open LCA software, and interpretation. The life cycle of the pineapple chips begins with land preparation for cultivation, including nursery, maintenance, and harvesting. The harvested pineapples are then transported to the processing facility, where they are transformed into packaged pineapple chips and distributed to souvenir centres. The results of the environmental impact assessment for a functional unit of 100 grams of pineapple chips show emissions of 7.04E-01 kg CO₂-eq for global warming potential (GWP), 2.29E-03 kg SO₂-eq for acidification potential (AP), 1.42E-02 kg PO₄-eq for eutrophication potential (EP), and 1.08E-08 kg CFC-11 eq for ozone depletion potential (ODP). The life cycle analysis reveals that the production stage is the main contributor to emissions, with eutrophication potential (EP) identified as the hotspot. To reduce the environmental burden, three improvement scenarios are proposed: substitution of chemical fertilizers with compost, conversion of gasoline to gas fuel, and replacement of palm oil with coconut oil. Keywords: environmental emissions, life cycle assessment, pineapple chips

Biostimulant-based nutrient management for energy-efficient and low-carbon mustard cultivation: a life cycle assessment approach for sustainable development.

Livestock farming is a significant source of global greenhouse gas (GHG) emissions. As a major livestock province in China, Henan's low-carbon livestock development plays an important role in achieving the national "dual carbon" goals. This study quantified GHG emissions from the livestock farming sector across 18 prefecture-level cities in Henan Province during 2000-2022 using a life cycle assessment (LCA) approach. Global and local Moran's I were employed to examine spatial autocorrelation and identify significant clusters and spatial outliers. The spatiotemporal heterogeneity of driving factors was further analyzed using a geographically and temporally weighted regression (GTWR) model. The results indicated that (1) total emissions declined from 4414.18 × 104 t carbon dioxide equivalent (CO2-eq) in 2000 to 3724.12 × 104 t CO2-eq in 2022, with livestock enteric fermentation (EEF), manure management (EMM), and feed crop cultivation (EFC) as the primary emission sources; (2) emissions exhibited significant positive spatial autocorrelation, forming a persistent south-north gradient pattern, with high-emission clusters concentrated in Southern Henan; (3) the GTWR results indicate that livestock farming industrial structure had a significant positive effect on emissions, whereas livestock labor mobility rate, economic development level, and urbanization process generally exerted inhibitory effects, with evident spatial heterogeneity in coefficient magnitudes. These findings suggest that differentiated livestock development strategies tailored to regional characteristics and driving mechanisms may help promote low-carbon transformation and enhance coordinated regional mitigation efforts.

Genetic selection for feed efficiency in dairy cattle is a promising strategy to mitigate environmental emissions reduce the environmental footprint of dairy production. In this study, genetic selection for residual feed intake (RFI) using the EcoFeed index developed by STgenetics was evaluated as a tool to improve feed efficiency and reduce GHG emissions. A life cycle assessment approach was used to quantify emissions from feed production, enteric fermentation, and manure management under 3 RFI selection scenarios: baseline (average genomic RFI [gRFI]), a 1-SD improvement in the genomic breeding value for RFI in heifers and cows (gRFIheifer and gRFIcow), and fa 3-SD improvement in gRFIheifer and gRFIcow. As expected, selection for improved gRFI led to enhanced feed efficiency. Animals with a 1-SD improvement in gRFI consumed 2.73% less feed over their lifetime, whereas those with a 3-SD improvement consumed 8.2% less, with no impact on productivity. These improvements in feed efficiency translated into a 2.42% reduction in lifetime CO2 equivalent (CO2e) emissions (35,769 vs. 34,902 kg CO2e) in the 1-SD group, and a 7.31% reduction (35,769 vs. 33,153 kg CO2e) in the 3-SD group. Enteric CH4 emissions were the largest contributor to the lifetime carbon footprint, accounting for 38.9% of total emissions in the baseline scenario, highlighting the importance of genetic selection for methane mitigation. Feed production and manure management accounted for 17.51% and 32.53% of total emissions, respectively. These findings suggest that genetic selection for RFI using the EcoFeed index significantly reduces the carbon intensity of milk production through improved lifetime feed efficiency and subsequently reduced feed intake per unit of milk production, establishing it as a key strategy for reducing GHG emissions the dairy sector.

Toward a transparent life cycle assessment of photovoltaic systems: Addressing regulatory and methodological challenges

Polymers are fundamental components of modern pharmaceutical manufacturing, serving critical roles as excipients, binders, coatings, and matrices for controlled drug delivery systems. However, the conventional production of pharmaceutical polymers relies heavily on petrochemical feedstocks, energy-intensive processes, and hazardous solvents, leading to significant environmental and economic burdens. In recent years, increasing regulatory pressure, environmental awareness, and sustainability goals have driven the pharmaceutical industry toward greener manufacturing strategies. This review critically examines sustainable green polymer production for pharmaceutical applications, with a focus on both environmental and economic impacts. The review discusses the role of polymers in pharmaceutical manufacturing, outlines the limitations of conventional polymer synthesis, and highlights the relevance of green chemistry principles in addressing these challenges. Key green polymer synthesis techniques, including biopolymer production, enzymatic polymerization, microwave-assisted synthesis, supercritical CO2 processing, and the use of ionic liquids and deep eutectic solvents, are systematically evaluated. Additionally, life-cycle assessment (LCA) approaches are explored to assess the environmental performance of green polymer processes in comparison with traditional methods. Beyond environmental sustainability, this review emphasizes the importance of pharmacoeconomic evaluation in determining the feasibility of adopting green polymers at an industrial scale. Cost-benefit analyses, manufacturing cost comparisons, long-term economic advantages, and health-economic outcomes are discussed in the context of pharmaceutical supply chains. Regulatory perspectives, industrial implementation challenges, and future directions are also addressed. Overall, this review highlights sustainable polymer innovation as a critical pathway toward environmentally responsible, economically viable, and future-ready pharmaceutical manufacturing.

Abstract The healthcare sector is a substantial contributor to global greenhouse gas emissions, with surgical services accounting for a significant proportion due to the use of single-use consumables, volatile anaesthetic agents, and energy-intensive infrastructure, including ventilation, lighting & climate control systems. This study quantified the carbon footprint of commonly performed arterial vascular procedures and identified modifiable drivers to reduce their environmental impact. A prospective observational study was carried out at a single vascular surgery centre, focusing on four procedure types: simple endovascular aneurysm repair (EVAR), complex EVAR, percutaneous lower limb revascularisation, and hybrid lower limb revascularisation. Real-time data were collected to capture devices, consumables, and waste associated with each intervention. A life-cycle assessment (LCA) approach quantified emissions across the product pathway, including raw material extraction, manufacturing, packaging, transportation, and disposal. Carbon emissions varied significantly between procedure types (Kruskal–Wallis test, H = 11.53, P < 0.05), with complex EVAR associated with the highest median emissions, reflecting greater resource intensity and procedural complexity. Median emissions for complex EVAR were equivalent to driving approximately 418 miles in a standard petrol vehicle. A strong positive correlation was seen between the number of theatre personnel and the volume of single-use wearable items (Spearman’s ρ = 0.878, P < 0.001), suggesting staffing levels contribute meaningfully to procedural waste. Opportunities to reduce emissions were identified, including use of percutaneous techniques, reusable surgical textiles, sustainable packaging strategies and imaging optimisation. This pilot study represents the first observational quantification of the carbon footprint associated with common arterial vascular procedures and identifies targets for sustainability interventions within vascular surgery.

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.

This study develops and assesses sustainable polyvinyl chloride (PVC) composites reinforced with agro-industrial waste fillers, integrating an ontology-based lifecycle assessment (LCA) framework to enhance sustainability evaluation. Agro-waste reinforcements, including rice husk ash (RHA), coir, bamboo fibre, and wood flour, were examined for their capacity to improve the mechanical and environmental performance of PVC and to advance circular economy objectives. Empirical data from UK PVC window manufacturing were integrated with Granta EduPack, Eco Design, Eco Audit, OpenLCA, and Protégé within a multi-layered semantic pipeline that links materials, processes, and environmental indicators. The agro-filler composites exhibited lower embodied energy and CO2 emissions than glass fibre systems, with the PVC + 30% wood flour formulation achieving the highest efficiency. The ontology framework, comprising 25 classes, 7 object properties, 26 individuals, 16 data properties, and 218 axioms (generated automatically by Protégé's metrics feature and verified with the Pellet reasoner), ensured semantic interoperability and consistent validation across datasets, enabling transparent and traceable sustainability analysis. Overall, coupling industrial data with digital LCA and ontology reasoning provides a reproducible pathway toward net zero-aligned, sustainable PVC composite manufacturing.

Strengthening circular economy in Sub-Saharan agriculture through intercropped mixed feedstock biorefineries: A techno-economic and life cycle assessment approach

Environmental sustainability of digestate-derived Rhamnolipids: Life cycle assessment approach

Transitions to sustainable societies require assessments of future environmental impacts at the macro-level. We examined how prospective process-based Life Cycle Assessment (LCA) is used to model environmental impacts at national to global scales. Our research objectives were to (i) provide an overview of modelling approaches in prospective macro-level LCA; (ii) identify common pitfalls and best practices; and (iii) highlight key challenges and suggest priorities for future research. We conducted a systematic literature review. An initial search in Web of Science, complemented by studies reviewed by Bisinella et al. (2021), yielded 925 studies. After screening based on predefined inclusion criteria and adding 33 additional articles through citation tracking, a final set of 86 peer-reviewed articles was analysed. We reviewed these studies with a primary focus on how system scaling, temporal evolution, and temporal distribution were addressed in the inventory analysis phase. In addition, we assessed elements from the other three LCA phases, including research objectives, temporal scope, system boundaries, and the treatment of sensitivity and uncertainty. We also examined terminology use and transparency. We classified the reviewed approaches by how system scaling is treated in the foreground system: (i) coupling with Dynamic Stock Models, which captures stock dynamics but overlooks socioeconomic aspects; (ii) coupling with Energy System Models, which provides detailed insights but is limited to the energy sector; (iii) coupling with Integrated Assessment Models, which offers broader socioeconomic coverage but operates at coarse resolution and typically requires collaboration with model developers; and (iv) uncoupled approaches, which allow flexibility but risk oversimplification. Methodological choices in the reviewed literature often appear guided by methods rather than research questions. We identify twelve key pitfalls, including simplified treatments of system scaling, temporal dynamics, and distribution; a narrow climate focus; limited scenario diversity; and weak internal consistency. We also highlight several best practices. Our review reveals a diverse field with inconsistent terminology, assumptions, and modelling practices. To strengthen the field, we recommend improving the representation of the complexity of sustainability transitions; strengthening policy relevance; facilitating transparency and adopting consistent terminology; and developing methodological guidance. Such guidance should clarify which approaches are suited to which types of research questions. Addressing these priorities will improve the robustness of prospective macro-level LCA and advance understanding of sustainability transitions.

Abstract To quantitatively identify the environmental mitigation potential of crystalline silicon photovoltaic (PV) systems under the “dual-carbon” targets, this study applies a life-cycle assessment (LCA) approach to a 1 kWp multi-crystalline silicon grid-connected PV system. A cradle-to-end-of-life system boundary is established, covering metallurgical-grade silicon, solar-grade polysilicon, wafers, cells, modules, balance of system (BOS), and end-of-life recycling and disposal. Based on the ecoinvent database and the IMPACT 2002+ method, multiple environmental impact categories are selected to compare the production, use, and end-of-life stages. The results show that module production is the dominant source of environmental burdens, typically contributing 80%–90% of the total life-cycle impacts, while BOS components contribute around 10%. Under material-recycling scenarios, the end-oflife stage can reduce key indicators such as greenhouse gas emissions and fossil energy consumption by approximately 3%–7%. The study suggests that optimizing upstream energy-intensive processes, decarbonizing the electricity mix, and improving module recycling rates are key to further enhancing the life-cycle environmental performance of PV systems.

Balancing performance and environmental impacts of crumb rubber-modified asphalt binders using a multi-objective lifecycle optimization process

Life carbon assessment of embodied and operational carbon with view assessment across facade systems in Japanese climate zones