Brightcon 2025, hackathon & courses in Grenoble and online

In a context of mobility electrification, the number of lithium-ion (Li-ion) batteries is increasing rapidly. As Li-ion batteries depend on a variety of critical and strategical materials, it is essential to assess their environmental impacts. The literature has shown that there is a significant heterogeneity in the methods, software, indicators, functional units, scope, data used and definition of LCA studies. As the ISO 14040 [1] specifies, "the results of various life cycle analyses or inventories can only be compared when the assumptions and context of each study are the same". Furthermore, it has been shown that there is a lack of link regarding the battery use and its impact on battery lifetime. The variety of studies cannot be used to draw conclusions on how to make the batteries’ lifecycle more virtuous because of the lack of systemic approach. Moreover, the opacity of the data and methodologies used contradicts the fundament principle of ISO 14044, which “requires total transparency" [1]. This is an obstacle to the use of LCA as a decision-making tool.
This work presents the development of an open-source Life Cycle Assessment (LCA) tool for evaluating a battery’s sustainability from cradle to grave.
To address the lack of link between LCA and battery use, a battery model has been integrated. It is based on Vehlib [2] and has been translated to Python to make the project more accessible and freer. It is composed of an electrical model, a thermal model and an aging model. To automate the identification of the coefficients quantifying battery aging, a Python module was developed. This project, named Battery Aging Analysis and Estimation (BANANES), is used to simulate the battery aging depending on the conditions in which it is used (external temperature and use cycle). Indeed, the use case of the battery will have a significant impact on its lifetime and the energy required for its recharge. Those outputs are linked to an LCA model developed using Brightway 25 [3].
In order to have as much transparency as possible, the code is open-source and can be customized according to the user need. Key parameters such as functional unit, reference flux, database, battery under study, country and cycle of use and assessment method can be changed. The battery model can also be replaced to be more suitable to the user requirements.
As the ONU Sustainable Development Goals [5] specified, to make our planet a better place to live, it is also required to account for social and economic impacts. To assess the economic impacts of the battery, the Total Cost of Ownership (TCO) method has been used. Battery cost estimation is performed using the BatPac software [6], which takes into account battery chemistry and power.
Social impacts will be integrated using SOCA database [7]. This database will be implemented in Brightway as it is crucial to assess those impacts, as highlighted by Amnesty International, which has proved that more than 4,000 children are working in Cobalt mines in Democratic Republic of Congo (producing 75% of the Cobalt used worldwide) [8].
The ultimate objective of this work is to develop an open-source tool to assess the comprehensive sustainability of a battery based on its specific use case. This tool is available for the community for collaborative improvement: https://gitlab.in2p3.fr/morgane.gillet/vieillissement. A graphical interface has been developed and will be presented. Future developments could include the integration of battery models that account for more detailed physico-chemical phenomena. Additionally, further efforts are required to improve data accessibility in order to build more accurate life cycle inventories.
AKNOWLEDGMENTS
This work is part of project EDLB of the program CMA and received government funding managed by the « Caisse des dépots et Consigantions » and the Agence Nationale de la Recherche under the France 2030 program.
REFERENCES
[1] AFNOR, « NF EN ISO 14040 : Management environnemental, Analyse du cycle de vie », oct. 2006.
[2] B. Jeanneret, B. Kabalan, E. Redondo, R. Trigui, E. Vinot, « Vehlib », GitLab. Consulté le: 10 janvier 2025. [En ligne]. Disponible sur: https://gitlab.univ-eiffel.fr/eco7/vehlib
[3] « Brightway LCA Software Framework — Brightway documentation ». Consulté le: 20 janvier 2025. [En ligne]. Disponible sur: https://docs.brightway.dev/en/latest/
[4] « Database », ecoinvent. Consulté le: 20 janvier 2025. [En ligne]. Disponible sur: https://ecoinvent.org/database/
[5] « THE 17 GOALS | Sustainable Development ». Consulté le: 12 juillet 2024. [En ligne]. Disponible sur: https://sdgs.un.org/goals
[6] « BatPaC: Battery Manufacturing Cost Estimation », Argonne National Laboratory. Consulté le: 20 janvier 2025. [En ligne]. Disponible sur: https://www.anl.gov/partnerships/batpac-battery-manufacturing-cost-estimation
[7] « New SOCA V3.0 release – social add-on to ecoinvent | openLCA.org ». Consulté le: 17 juillet 2025. [En ligne]. Disponible sur: https://www.openlca.org/new-soca-v3-0-release-social-add-on-to-ecoinvent/
[8] « République démocratique du Congo : les petits forçats du cobalt », Amnesty France. Consulté le: 30 avril 2025. [En ligne]. Disponible sur: https://www.amnesty.fr/actualites/republique-democratique-du-congo-enfants-cobalt-face-cachee-de-nos-batterie

Green hydrogen technologies have emerged as promising technologies to reduce greenhouse gas emissions. Produced using renewable energy through water electrolysis, green hydrogen offers a carbon-free alternative for sectors like heavy industry and long-haul transport, and enhances energy system resilience by storing green electricity chemically. As an increasing number of technologies for converting electricity to hydrogen and vice versa are developed, accurately assessing their environmental impacts along their value chain is crucial for informed eco-design and technology selection.

Life Cycle Assessment (LCA) is a widely used and recognized methodology for evaluating the environmental impacts of technologies along their life cycle. Despite standardization under ISO 14040 and 14044, and sector-specific efforts like HyGuide and SH2E to improve LCA modeling for hydrogen technologies, current practices still face challenges in providing a harmonized and coherent framework for assessing emerging technologies such as electrolysers and fuel cells. Discrepancies in process trees, terminology, and parameters can lead to significant inconsistencies between studies. Moreover, a common ground to enhance communication and exchanges between technology developers and LCA practitioners needs to be further developed.

To address these challenges, we aim to develop a harmonized, open-source modular framework for consistently assessing green hydrogen technologies such as AEL, AEMEL, PEMEL, SOEL, PCCEL, AEMFC, PEMFC, and SOFC. Our tool features a single modular LCA process-tree structure with unified unit processes defined by a comprehensive set of key parameters (efficiency, sizing, geometry, …), ensuring consistency across all electrolysers and fuel cell technologies. It also includes standardized terminology mapping over 150 terms and synonyms to ease terminology issues.

Implemented in a user-friendly application, this tool generates clear and comparable LCA results and features ready-to-use databases tailored to green hydrogen technology, intended for community updates to keep pace with innovation advancements.

This work complements existing hydrogen LCA guidelines by enhancing communication between technology developers and LCA practitioners. It promotes robust and modular LCAs with appropriate detail and aggregation, improving comparability and identifying key environmental performance determinants. Ultimately, it facilitates more transparent assessments supporting informed decision-making and eco-design.

This abstract is an essential description of the work going on within the "Evaluation of policies for enhancing sustainable wheat production in Italy"
(ECOWHEATALY) research project. The ECOWHEATALY project focuses on the power of policies in providing economic incentives for farms switching from less to more sustainable wheat production techniques.
To address real-world complexity, ECOWHEATALY developed an agent-based model for the wheat production system in Italy. This model is interfaced with an existing tool that can handle international wheat markets, as well as a module to measure the sustainability of the system.
The three key components of the model framework are: a) the ABM, fed by wheat farm data and green policy actions; b) the Global Economic Model (GEM), which outputs wheat prices at major international markets starting from produced and demanded quantity at the global level; and c) the LCA module, which measures the environmental impact deriving from individual farm-level decisions. The software to run the three modules is developed using the Repast Suite (https://repast.github.io) for the ABM and the GEM, and Brightway (https://docs.brightway.dev/en/latest/) for the LCA.
Regarding the LCA, we encountered several issues during project development, particularly because the project aims to produce and utilize open resources as much as possible.
Among the software tools for performing LCA analysis, we have chosen Brightway due to its open-source nature and the possibility of using it programmatically.
One of the project's objectives is to raise awareness among agents about the sustainability of their production choices, enabling them to adopt more sustainable practices. To this aim, we implemented the LCA for a very simple wheat production process. This is mainly to keep computation lightweight because our agent-based model has about 200000 farms (which is the number of wheat producers in Italy, according to the 2020 census). In our model, farmers use the following four inputs to produce wheat: 1) Tractor power, 2) Nitrogen as fertilizer, 3) 2,4-D salt as herbicide active principle, and 4) Deltamethrin as insecticide active principle.
The inventory phase is based on open resources. The tractor power activity was sourced from the "University of Washington Design for Environment Laboratory/Field Crop Production” database, available at the USDA’s LCA Commons. We load the process's XML file with the SingleOutputEcospold1Importer function.
The Nitrogen activity was digitized from the appendix of a scientific paper (Brentrup et al. 2004) and read into Python as a csv file. Due to the lack of an open inventory analysis for 2,4-D salt and Deltamethrin, we add them directly as outputs to the wheat production process.
Unfortunately, we have no access to Ecoinvent database. Therefore, we use the biosphere3 database provided with the Brightway2 version. The matching between the tractor power and nitrogen processes and the biosphere3 database was done by fuzzy string matching.
Concerning the LCIA phase, we chose the ReCiPe 2016 methodology. After installing the bw-recipe-2016, our main effort was to update the characterization factors of the original methods with those provided for Italy, where possible, i.e., Terrestrial Acidification, Particulate Matter Formation, Ozone Formation, and Freshwater Eutrophication.
We wrote several Python scripts to perform the operations described above. They will be made available on GitHub shortly. In any case, we will be happy to share them with interested people.
The main problem we encountered when integrating Brightway2 with our Repast4py agent-based model was the failure registered during the parallel execution of simulations. The failure seems to be caused by Brightway’s need to read data from databases.
When a considerable number of agents (farmers) perform LCA simultaneously, the database is overloaded with queries. Some queries find the database busy (locked), and the simulation stalls.
Our solution was to equip agents with the necessary matrices to perform the calculation, allow them to customize these matrices, and perform the computation without querying the database. We used Brightway2 to write the Activity, Biosphere, and Characterization matrices to a file that is imported by every farmer of our agent-based model to perform the relevant matrix calculations.
We would be delighted to present our work at Brightcom 2025 and discuss potential improvements during the conference.
We also took note of our experience in learning Brightway in a 22-page document titled "A basic introduction to LCA with open resources". Currently, it is a LaTeX document, but we are willing to provide it as an IPython file if it will be judged helpful to Brightway beginners.

Rising environmental pressure from anthropogenic systems asserts the need to evaluate anthropogenic systems designs in terms of environmental sustainability. Absolute environmental sustainability assessment enables such an evaluation by comparing the anthropogenic system’s environmental impacts against system-specific thresholds for environmental sustainability. However, this approach depends on the availability of system-specific sustainability thresholds as well as characterisation factors to determine the environmental impact of the anthropogenic system’s elementary flows.
To address this gap, we present a Python package that enables a planetary-boundary-based absolute environmental sustainability assessment (PB-AESA). The Python package enables the calculation of allocation factors from time-explicit multi-regional input-output tables to determine planetary-boundary-based environmental sustainability thresholds. Additionally, the Python package provides characterisation factors for the 2684 elementary flows within standard and prospective ecoinvent v3.10.1 databases, enabling the assessment of the elementary flows’ environmental impact on all global planetary boundary categories except novel entities, and imports the characterisation factors into Brightway. We thus provide a Python package enabling PB-AESA for a broad scope of anthropogenic systems and takes steps towards standardisation of PB-AESA.

Semiconductors are essential to the development of information and communication technologies (ICT). This industry is characterized by rapid innovation and industrial secrecy, with state-of-the-art manufacturing processes kept confidential and proprietary. From an environmental perspective, the semiconductor industry is also a resource- and energy-intensive sector, with a significant footprint due to the use of ultrapure materials and high energy consumption. As a result, the integrated circuits (ICs) that contain semiconductors emerge as environmental hotspots in ICT life cycle assessments (LCAs), making it increasingly urgent to understand and mitigate their impacts. Still, LCA practitioners face significant challenges in modelling ICs due to limited access to data.

One critical example is the estimation of semiconductor die area in ICs, which accounts for most of the embodied impacts of ICT products. However, this parameter is hidden from practitioners due to a lack of transparency in IC datasheets. To access it, practitioners must rely on destructive and time-intensive reverse engineering techniques (such as chemical decapping or X-ray imaging), which are rarely available in applied settings. As a result, they often fall back on heuristics or rules of thumb to quickly estimate this sensitive variable, introducing uncertainty in LCA results.

To address this issue, we have developed the Open Integrated Circuits Database (OICDB), an open-source, open-contribution dataset designed to support more accurate estimation of die area in ICs, especially for practitioners with limited means. Using this dataset, we apply advanced predictive models that estimate die area based on limited IC features, enabling practitioners to balance accuracy with time and resource constraints.

In this contribution, we will discuss:

How does industrial secrecy in the semiconductor industry limit the development of accurate, representative LCA models?

The practical advantages and limitations of using OICDB, with a focus on the trade-off between uncertainty and modelling effort in ICT LCAs.

Mitigating climate change requires the development of new chemical processes (Shukla et al., 2022). Confirming their climate benefits requires life cycle assessment (LCA). Still, in practice, LCAs of chemical processes are often conducted by manually extracting data from process simulation software and transferring it to LCA tools (Köck et al., 2023). This manual approach is time-consuming, susceptible to human error (Azzaro-Pantel et al., 2022), and may overlook process flows that appear negligible but can substantially affect the environmental impacts (Rosental et al., 2020).
Here, we present the tool ALCHEMIA (Automated Life Cycle extraction from cHEMical process models Into Assessments) that addresses these challenges by automatically connecting chemical process simulations with the Brightway environment (Mutel, 2017). A graphical user interface facilitates mapping process streams to life cycle inventories, while data handling is performed in the background.
We demonstrate the tool based on bio-based and $\text{CO}_2$-based process studies available in the literature. These studies are implemented in several flow sheeting software packages, including Aspen Plus, Aspen HYSYS, and AVEVA Process Simulation. We show the rapid generation of LCA results based on existing process simulation studies and highlight the importance of holistic LCA to account for all relevant streams.
Overall, ALCHEMIA streamlines data handling in LCA of chemical process simulations by unlocking automated data transfer to the LCA software environment Brightway.

References
P. R. Shukla, et al., 2022, Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC, Cambridge University Press, Cambridge, UK and New York, NY, USA)
B. Köck, et al., 2023, Sustainability 15(6), 5531
C. Azzaro-Pantel, et al., 2022, Food and Bioproducts Processing 131, 40–59
M. Rosental, et al., 2020, Front. Clim. 2, 586199
C. Mutel, 2017, JOSS 2(12), 236

In sustainability research, and especially in Life Cycle Assessment (LCA), practitioners face two recurring challenges when working with chemical products in inventories:

1. Finding the exact chemical – Large datasets such as ecoinvent contain inconsistent naming, multiple synonyms, and sometimes missing identifiers, making it difficult to locate the right entry.
2. Finding a suitable proxy – When the exact chemical is absent, a replacement with similar properties must be chosen. This selection is often subjective and error-prone when relying only on textual information.

Chemistry is inherently visual—structural diagrams reveal patterns, functional groups, and similarities that text alone cannot. Yet most LCA workflows present chemicals only as names or formulae, limiting practitioners’ ability to quickly assess analogues.

To address these issues, I developed an open-source workflow that connects LCA databases to open chemical datasets. Built with Brightway, Python, and a lightweight web platform, it works with ecoinvent 3.10 but is easily adaptable. It:

• Extracts and enriches data – CAS numbers are pulled from ecoinvent and supplemented with metadata from open chemical resources.
• Generates annotated diagrams – RDKit produces SVG structures with names, formulae, and molecular weights.
• Enables visual browsing – An online gallery allows side-by-side comparison of structures, making it easier to identify exact matches or structurally similar proxies, with direct links to public databases for deeper research.

By pairing visual chemical navigation with existing data harmonisation and classification efforts, this approach combines interoperability with molecule-level insight—reducing errors, speeding up proxy selection, and improving transparency in open-data-driven LCA practice.

Links:
- Interactive gallery: http://scmcd.ch/projects/ecoinvent-chemical-structures/gallery-ecoinvent-chemical-structures.html
- Codebase: https://github.com/Stew-McD/brightway-scripts/tree/main/ecoinvent-chemical_structures

The accuracy and reliability of LCA results heavily depend on LCA databases. However, missing data and the cumbersome process of searching and comparing datasets makes LCA modelling manual inefficient, and error prone. The project aims to integrate Graph Retrieval-augmented Generation (GraphRAG) with Ecoinvent to make inventory search process simpler, and reliable. Retrieval-Augmented Generation (RAG) is a technique used to enhance the performance of LLMs. It combines LLMs with external knowledge sources during inference. However, traditional RAG approaches often rely on flat document retrieval, which limits their capacity to capture complex relationships inherent in structured datasets.

To address this limitation, GraphRAG has been introduced, it combines graph-based retrieval with large language models (LLMs) to improve contextual understanding and enable more semantically rich querying.
In this project, we utilize Brightway to import the Ecoinvent database and transform its activity and exchange data into a graph structure. Based on this graph representation, we implement the GraphRAG approach to reason over interconnected activities, exchanges, and metadata, improving both the relevance and depth of retrieved information. The framework is deployed as an interactive web interface that allows users to query specific activities, receive suggestions for similar processes, and explore associated exchanges

Highlights/Discussion points:
1. Exploring the role of AI in LCA databases
2. Graph-based data structures for LCA databases

LCA is already hard enough and our tools are sometimes a challenge to use. What can OpenLCA and Activity Browser learn from each other and what should we both improve? We want to learn from our users, so come and discuss with us!

In this talk, we present a project that transforms automatically a parametric inventory, built with lca_algebraic, into an interactive web application, ready to be published.

Traditional Life Cycle Impact Assessment (LCIA) methods assign characterization factors (CFs) uniformly to flows (nodes), neglecting the contextual richness of each exchange in a product system. In this session, we introduce edges, an open-source Python library that expands the Brightway2/25 LCA framework by shifting impact characterization from nodes to edges—the exchanges between processes.

This novel approach allows CFs to reflect critical contextual attributes, such as the geographic locations of suppliers and consumers, the magnitude of exchanged flows, and scenario-based parameters (e.g., atmospheric CO₂ concentration).

Built for flexibility and scalability, edges integrates seamlessly into existing Brightway workflows. It supports regionally differentiated CFs for over 340 regions, composite and relative region resolution (e.g., RoW, RER), and symbolic CF expressions that respond to user-defined scenarios. A wide range of matching criteria allows fine-grained mapping of exchanges to CF rules, enabling some control in prospective and context-sensitive impact modeling. edges already includes regionalized indicators for AWARE, ImpactWorld+, and GeoPolRisk.

This 45-minute interactive session will combine a technical introduction with hands-on exercises using Jupyter notebooks. Participants will:

• Explore the concepts and capabilities of edge-based LCIA.
• Learn how to load and apply dynamic CFs based on scenario variables.
• Perform impact assessment using regionally and temporally adaptive CFs.
• Experiment with a simplified, user-editable LCIA method to model custom scenarios.

By the end of the session, attendees will understand how edges empowers researchers and practitioners to perform transparent, parameter-driven, and geographically explicit LCIA, pushing the boundaries of open-source sustainability modeling in Brightway.

Life cycle assessment (LCA) is an iterative process per se. However, most tools today used by LCA practitioners require using multiple applications and migrate data between tools to successfully draw conclusions of an environmental assessment. Life cycle impact assessment scores (LCIA Scores) is a tool designed to simplify and improve LCA workflows for non-experienced practitioners, with a particular focus on the inventory phase — one of the most time-consuming stages of LCA . Build upon the Brightway, the Scores tool is designed to take profit of LCA common database formats and nomenclatures to organize life cycle inventory and impacts assessment results. In particular, the tool consists of a set of excel spreadsheets with several built-in macros that do not require programming knowledge and allow to: (i) easily organize primary data and build life cycle inventories in a standardized manner, enabling to uniquely search and identify technosphere and biosphere processes; (ii) model multiple foreground scenarios by altering input data and building multiple inventories at once; (iii) export inventories into standard brightway-excel sheets and CSV Simapro files to ease data interoperability within common LCA tools (Brightway, Simapro). Moreover, the Scores tool allows to calculate impact assessment results, producing a set of LCIA scores for first tier contribution assessments. Thus, automatized visuals can be programmed for an easy reporting. Overall, we aim to accelerate LCA workflows and enhance interoperability between LCA software tools and practitioners with diverse modelling approaches, making research more cumulative, timely and relevant.

The e-Hydrogen Cost Optimizer is a Python-based user-defined application that combines techno-economic optimization and environmental modeling to evaluate the feasibility and sustainability of green hydrogen production systems. Built with an accessible GUI and modular codebase, the e-Hydrogen Cost Optimizer integrates key components such as photovoltaic (PV) and wind turbine generation, electrolyzer, energy and hydrogen storage. The application allows users to define system configurations and boundary conditions, and it includes a location-based retrieval of solar and wind resource data to estimate renewable electricity availability. Once an optimized system layout is determined (minimizing the levelized cost of hydrogen (LCOH)) its technical specifications (e.g., installed capacity, system size, electricity profiles) are used as direct inputs to a life cycle assessment. The tool is designed to interoperate with Brightway, enabling transparent, scenario-based environmental evaluations. By linking system design with environmental assessment, the e-Hydrogen Cost Optimizer is currently being used to support sustainable energy planning at multiple scales, including its application in strategic urban projects such as The Line by NEOM in Saudi Arabia, where green hydrogen integration plays a key role in shaping future zero-carbon cities.
Join us to see how this application is built!

GitHub link: here

This study applies a combination of prospective and time-explicit life cycle assessment (LCA) to evaluate Ocean Alkalinity Enhancement (OAE) via Bipolar Membrane Electrodialysis (BPMED), an emerging carbon dioxide removal (CDR) technology. By combining prospective and time-explicit LCA, we developed a flexible, time- and location oriented LCA framework which captures both evolving background systems and the delayed, time-distributed nature of atmospheric CO₂ uptake, a key to accurately reflecting the environmental performance of OAE over time.
The methodological workflow integrates Brightway for core LCA modeling, Premise to generate future background scenarios based on the SSP2-RCP2.6 integrated assessment pathway, and a combination of Temporalis and Timex to structure foreground system changes and conduct time-explicit impact calculations. The cradle-to-grave analysis quantifies the environmental impacts of 1 tonne of CO₂-equivalent removal across ten global locations, incorporating a 2.5% annual learning rate for BPMED electricity consumption and location-specific decarbonization trajectories.
Results indicate that overall impact significantly decreases over time, reflecting the projected decarbonization of energy systems and background processes as projected by integrated assessment models (IAMs). Key carbon hotspots like precipitation softening remain dominant contributors throughout the period, as their chemical processes are mostly not addressed in IAMs. The integration of spatial analysis with prospective LCA reveals that regions like China undergo substantial reductions in climate intensity over time, while consistently low-carbon regions such as the Nordics exhibit smaller relative changes but maintain the lowest overall impacts throughout the study period.
This open-source setup provides a reproducible, interoperable framework aligned with industrial ecology principles, enabling detailed exploration of how both technological and systemic changes shape future environmental outcomes.

Highlights:

1. CAIRN (CairnOpen) demo from CEA: brief demonstration of core
   capabilities, modular technology components, scenario management,
   MILP constraints for sizing/operation, multiple solver back-ends,
   time-series I/O, and reproducible exports.
2. BW2 to CAIRN integration via GUI: a graphical interface links
   Brightway2/Activity Browser activities to CAIRN, enabling direct
   import of foreground systems into the MILP model without custom glue
   code.
3. Hybrid GA+MILP for explicit trade-offs: a genetic algorithm explores
   system configurations while CAIRN’s MILP computes per-individual
   optima; Pareto dominance and hypervolume metrics reveal and
   stabilize the non-dominated frontier.

Short proposal:

Designing sustainable energy systems requires methods that address environmental impacts, economic costs, and technology evolution. We present a hybrid multi-objective framework that couples prospective life-cycle inventories with deterministic and evolutionary optimization, applied to a green hydrogen supply chain in Mallorca (GreenHysland, 2025). The workflow integrates: (i) premise to adapt ecoinvent under forward-looking European energy scenarios; (ii) Brightway2 to compute impacts under EF 3.1 midpoint (16 categories); and (iii) CAIRN, an open-source engine that solves mixed-integer linear programs (MILP) for component sizing and operation.

Three implementation advances are central. First, a graphical interface connects Brightway2 (and Activity Browser) with CAIRN, importing previously defined activities and foreground systems as parameterized components that can be optimized directly, improving transparency and reusability.

Second, the ENTSO-E API provides hourly electricity mixes from which hourly impact factors are derived; these time-resolved factors are injected into the MILP, yielding dispatch and sizing decisions consistent with real grid variability rather than average values.

Third, the optimization is hybrid, a genetic algorithm generates a population of candidate configurations. For each individual, CAIRN’s MILP computes the cost/impact-optimal system, the GA evaluates Pareto dominance and evolves toward a robust non-dominated frontier.

The study assesses portfolios combining PV generation, electrolyzers, storage, and hydrogen distribution (trucks versus pipelines), including partial substitution in public transport. Results show meaningful trade-offs across categories: designs minimizing GHGs can increase mineral resource use. The workflow, and github of CAIRN will be shared to facilitate reuse across regions, scenarios, and impact methods.

Topic
Hydrogen-based decentralized energy systems (H2-DES) are gaining prominence as potential solutions for local decarbonization of heat and power supply. We realized the life cycle assessment (LCA) of a H2-DES based on a combined heat and power internal combustion engine (ICE-CHP), the LCA being grounded on a techno-economic analysis (TEA) model implemented in OpenModelica. The study addresses the relative underexploration of H2-ICE-CHP technologies in the LCA literature, offering a comprehensive analysis based on real hourly demand data of buildings in Offenburg (Germany), a municipality involved in the Franco-German Interreg CO2InnO project. Several scenarios (28 divided into 4 families) were designed with varying configurations and dimensioning — PV, wind, battery, H2 storage. Results show that impacts are strongly dependent of the scenario family and the system boundaries, highlighting the necessity of careful system design, systematic scenario and boundary comparison in policy and environmental reporting. This work required the results of the OpenModelica TEA model to be interfaced with the ActivityBrowser LCA software through custom Python-based routines and manual implementations. Despite clear limitations, the routines could be used as building blocks for future automation efforts. This presentation thus aims to share a situated feedback on the approach, achievements and difficulties of code-curious users who wish to methodologically contribute towards the development of an open-source TEA-LCA workflow.

Highlights:
1. Scenarios of H2-ICE-CHP decentralized systems assessed using real data buildings demand in a TEA-LCA approach
2. Open-source framework with limited TEA-LCA integration explicitly discussed and propositions of ways forward.

Concise description:
Situated feedback on the approach, achievements and difficulties of code-curious users who wish to methodologically contribute towards the development of an open-source TEA-LCA workflow, through a case study on Hydrogen-based decentralized energy systems.

To facilitate the upscaling of LCA studies, so-called Product Category Template (PCT) data sets are developed and the photovoltaics (PV) product group is taken as an applicable example. We will present the contents and the workflows for PCTs, which can simplify life cycle modeling and increase the comparability and compliance of its results. The open-source file of only a few megabytes in size can be directly imported in compatible LCA tools and is available in ILCD format, OLCA (JSON-LD) format, or as an immutable openLCA library.
Each PCT includes the needed biosphere (e.g. from ecoinvent), impact assessment methods (e.g. the Environmental Footprint 3.1 method), automated calculations of additional indicators for the product group of interest, (e.g. the energy payback time for photovoltaics PCT), modular life cycle stages following EN15804, linked and pre-defined unit processes that support compliance with different standards (e.g. EN15804 compliance in general and IEA-PVPS 2020 Guideline compliance for photovoltaics in particular), customizable core parameters for calculations and to fulfill the correct declared or functional unit of the product category (e.g. photovoltaics module lifetime, efficiency, irradiation), background EPD results from publicly available sources that support the product category, as well as selected links to background data sets from commercial databases (e.g. from ecoinvent) to facilitate the connection of the foreground.
We will show that modeling efforts are drastically reduced and LCA practitioners can fully focus on their section of interest, which might be very specific parts of the product group (e.g. the solar cell absorber production). In cases where no primary data is available, background data sets (e.g. from ecoinvent) can be very quickly selected from the pre-defined provider lists, which avoids additional modeling requirements. However, the practitioner can also link own custom models to the fixed foreground system.
We want to make these PCTs available open-source, so that development and update routines can be tracked via a versioning system (e.g. the Collaboration Server or GitHub), which allows improvements and corrections of the PCT over time. Using the PCT avoids modeling from scratch, it can facilitate the modeling compliance with given standards, it can increase the upscaling of available LCA data for similar products, and it consists of pre-defined structures that allows for easier review and verification of third-parties. The concept is like a simplified tool inside a data file.
The presentation will focus on the concept of the PCT, the structure of the LCA data behind and will show practical use cases, as well as practical calculation examples from the LCA modeling inside the PCT. This work is done within the framework of the Horizon Europe Project Hi-BITS (hi-bits.eu). The concept for so-called Product Category Templates (PCTs) can be generally applied to other product categories of interest and is currently developed for a PCT on hydrogen storage systems.

Using primary data for life cycle assessments is the common and preferred way to conduct life cycle assessments. But collecting data is time consuming, can be incomplete and only reflects a static and specific result. In order to generate reliable inventory data, the additional use of external software, like flowsheet simulation is necessary. Flowsheet simulation software is based on thermodynamic models, fluid property databases, specialised numerical solvers and more tools for design and optimization of processes. Such rigorous simulations open up new perspectives for life cycle assessment: They enable transparent scenario analyses, the quantification of uncertainties and the investigation of technological alternatives.
Since there is currently no available interface between LCA software and process simulation tools, data exchange is mainly carried out manually in practice. Although approaches to semi-automated data transfer have already been published (TEPET), a generic open interface is still lacking. Such an interface is necessary for the consistent, efficient and reusable implementation of simulation models into the workflow of LCA studies.
In this contribution the work on the python package SiModIn is presented, offering an open interface of external simulation models to brightway25. The package includes a data schema for an interface to brightway25, and offers abstract classes for linking external simulation models. Built-in unit transformation and validation ensure a straightforward implementation of simulation models as data source in LCA. This enables a linking of multiple modelling frameworks with brightway25 and promotes the possibility to make models available for others in a transparent, consistent and reusable way.
The functionality of SiModIn is demonstrated using a process heat model created with the python package TESPy (Thermal Engineering Systems in Python). The example model depicts the specific process heat demand linked to steam generation and distribution and enables the creation of detailed, site-specific LCA datasets. Process relevant parameters such as the required steam temperature and other plant specific parameters are taken into account.

This work develops an end-to-end, low-code workflow that automatically converts heterogeneous documents—reports, theses and open-data PDFs—into high-quality, machine-readable Life Cycle Assessment (LCA) inventories. Built on the open-source orchestrator n8n, the pipeline (i) prunes non-informative pages with custom Python APIs, (ii) extracts text while preserving tables through the free LLMWhisperer API, (iii) enriches content via Deepseek LLM nodes for domain-specific entity recognition, (iv) normalises and annotates data with JavaScript routines to yield standards-compliant JSON-LD, and (v) uploads the output to a MySQL-backed Mexican LCA web platform. A supervised-validation layer—combining rule-based checks and expert review—assigns data-quality scores, ensuring transparent provenance before database ingestion. Tested on twenty academic theses, the system cut manual curation time by 80 % and produced consistent inventories in under five minutes per document; metadata enrichment improved downstream query performance by ~30 % compared with hand-curated entries. By eliminating repetitive tasks, enforcing schema uniformity and providing a direct bridge from source document to live database, the workflow accelerates the population of national and international LCA repositories and supports rapid creation of efficient datasets and databases. Key challenges include processing poorly scanned files, harmonising domain-specific nomenclature and scaling the validation module, while opportunities lie in multilingual expansion, uncertainty quantification and broader integration with circular-economy datasets. Overall, coupling open-source automation, advanced language models and supervised quality assurance offers a replicable blueprint for reliable, rapid LCA data generation that lowers barriers for researchers and strengthens global open-data infrastructure.

Despite the increasing importance of FAIR data (Findable, Accessible, Interoperable, and Re-usable) in academic research, structured data management remains an often-overlooked challenge in the life cycle assessment (LCA)-community. Most researchers work independently on separate projects and tools, which leads to isolated LCA models, little exchange of reusable data and repeated modelling of similar processes with inhomogeneous data quality.

To address this issue, we are developing a shared library of Life Cycle Inventories for energy and process systems based on Brightway. The goal is to collect and organize existing models from different projects, publications and literature in a central cloud database that is intuitive, well-documented and accessible to all members of the research group. The workflow for building and using the database consists of the following steps:
(1) Structured data submission: Researchers submit process data into a structured Excel template.
(2) Automated data integration: Submitted data is automatically processed and imported into a cloud-based Brightway database.
(3) Metadata and documentation: Each entry is stored together with metadata and full documentation.
(4) Search and export functionality: Users can search the database for specific processes and export them either as Brightway activities or in Excel format, ready to be used in new projects.

Furthermore, our approach raises important discussion points in LCA collaboration such as how to reduce redundancy, ensure data quality in modelling, and how to enable more efficient collaboration. Special attention is given to academic needs: proper authorship attribution, data confidentiality for varying project types (research, teaching, industry), and accessibility for users without prior Brightway experience. Further features, which could be integrated in the future, include version control, variable parametrization, and the integration of automated standard analysis tools (e.g. contribution or uncertainty analysis). The setup of the database should also consider possible interfaces to external modelling software (e.g. chemical process models).

We propose this contribution to share our approach, discuss practical challenges in academic data collaboration, and explore how the Brightway community can support research workflows beyond individual projects.

Highlights:

1. Flexible, modular TEA-LCA framework enabling integration of parametric process models.
2. Framework applied to a simulated Haber-Bosch process using electrolytic hydrogen.

Integrated environmental assessments in chemical process design are often constrained by current tools. Standard process simulators typically report only direct and energy-related $\mathrm{CO_2}$ emissions (Scope 1 and 2) [1], lacking the broader ecological scope provided by life cycle assessment (LCA) tools such as Brightway. Custom tools coupling process simulation to Brightway typically produce fixed life cycle inventories from individual simulations, often without support for parameter variation or scenario exploration [2, 3].

We present a Python-based framework enabling holistic, parametric assessment of both operational and investment-related impacts through integrated techno-economic analysis (TEA) and LCA. Users structure data from parameterized or interface-linked process models into an abstraction layer composed of resource, equipment, and process classes. These classes are aggregated, linked, and passed to the TEA and
LCA modules within a consistent system boundary. The framework is demonstrated using a simulation-based Haber-Bosch process.

This unified approach allows engineers to systematically analyze economic and environmental trade-offs, capture parameter dependencies, and explore design alternatives in the early stages of process development.

[1] AVEVA Group Limited, AVEVA Process Simulation, 2025.
[2] L. Spiekermann et al., Automatic Life Cycle Assessment from Chemical Process Simulations, ESCAPE-34, 2024.
[3] N. Bertoldo, Aspen-x-bw: Integrating Aspen Plus Simulations with Brightway 2.5 for Sustainable Process Design, LCIC 2024,
2024.

The use of light-weight materials for automotive components (ecodesign strategy commonly known as lightweighting) can induce environmental benefits due to weight-induced energy savings in internal combustion engine vehicles. However, for battery electric vehicles, the effectiveness of lightweighting depends on the electricity grid mix powering the vehicle during its use phase. As Europe transitions toward renewable energy sources, the environmental impacts of electricity grids are decreasing annually. This ongoing evolution creates a need for dynamically assessing the use phase impacts of automotive components to understand how evolving electricity grid mixes influence the ecodesign process. This study compares retrospective and prospective life cycle assessment (LCA) approaches for a case study of a prototype automobile interior door handle (Technology Readiness Level 3-4, laboratory-tested) made from a novel polypropylene composite that incorporates recycled materials and eucalyptus fiber. The prototype is compared with material alternatives currently used for automotive components, including a polypropylene composite with 40 % hemp fibers, a polyamide 6 composite reinforced with 40 % glass fibers, and a polyamide 6-6 composite reinforced with 20 % carbon fibers. The influence of weight-induced energy consumption during the use phase is analyzed for a battery electric vehicle, comparing two electricity grid mixes (Poland and Portugal), using two approaches: (i) retrospective, based on the market electricity generation datasets in the ecoinvent 3.11 database, which use average data from 2021. (ii) prospective, based on forecasted market electricity generation scenarios mapped and processed to align with the electricity generation datasets in ecoinvent using Brightway25. The preliminary results reveal significant differences between retrospective and prospective approaches, particularly for the use phase in Poland, due to its current fossil fuel dependence and forecasted renewable energy transition, resulting in environmental trade-offs between the alternatives for the automobile interior door handle.

This study explores the parametric environmental assessment and optimization of locally manufactured SWTs. The studied system is a horizontal-axis wind turbine designed and built by the community of self-builders. The goal of this work is to support design decisions based on environmental performance by identifying trade-offs between key geometric parameters: mast height and rotor diameter.
To achieve this, we developed a Python tool that integrates OpenAFPM for generator modeling, Brightway 2 for LCA, and Noload for non-linear optimization. A parametric inventory was built using the lca.algebraic library, enabling flexible modeling of system components as functions of design variables. The environmental impacts were assessed using the PEF methodology. Results of the sensitivity analysis show that ADP(MR) is mainly driven by rotor diameter due to copper usage while GWP is primarily influenced by mast height due to the presence of steel and concrete.
A constrained optimization was performed to minimize the environmental score per kWh produced under different site conditions, using a logarithmic wind profile to model wind speed as a function of height. The analysis reveals that optimal configurations vary depending on site characteristics and that a trade-off exists between maximizing energy production and minimizing environmental impacts. Thus, while the rotor diameter consistently reaches its upper bound, indicating no internal
optimum, the mast height exhibits an optimal value that balances material usage with energy gains. Therefore, a large rotor on a short mast seems to be preferable from an environmental point of view rather than the opposite. This work demonstrates the value of combining parametric LCA and optimization to inform environmentally conscious design of energy systems.

The Finnish Environment Institute (Syke) has been exploring the application of the Activity Browser and Brightway framework in life cycle assessment (LCA) research, recognizing their methodological flexibility and transparency compared to conventional LCA software. This study outlines testing of Activity Browser and Brightway as a part of our research workflows, with a particular emphasis on their potential to perform the prospective LCA modeling. We reflect on the barriers encountered during adoption, including technical complexity and limited user guidance, and seek insights from the broader research community to accelerate our learning process.

As part of the Clean Energy System Transition (REPower-CEST) we are conducting a case study on the environmental impacts of offshore wind energy systems in Finland. Using Activity Browser and Brightway, we will model the life cycle of offshore wind turbines, from construction through energy production and eventually end of life. Furthermore, the study incorporates the geographical boundaries and specific environmental conditions of the Finnish maritime region. Research results will show (i) an assessment of the environmental impacts for developing wind power technologies, with a focus on offshore systems, and (ii) demonstrate the applicability of these LCA tools in assessing renewable energy technologies within a regional context and (iii) demonstrate how prospective LCA results can inform technology development and energy system planning.

The work was supported by the European Union (number 151, P5C1I2, NextGenerationEU, REPower-CEST project).

This session presents the final results of the Getting the Data Right project: BONSAI, a unique and open life-cycle assessment (LCA) database. This database is generated using an open-source codebase and built entirely on open data. BONSAI comprehensively quantifies the climate impacts associated with all inputs and outputs of the global economy. We will demonstrate how to import BONSAI into the open-source LCA framework Brightway and explore its key features, highlighting its utility for researchers and practitioners.

BONSAI is an automated LCI background database. It takes open data that is collected via APIs or scraped from websites, cleans and harmonizes it, and runs it through a multi-step workflow to produce an open LCI dataset. First, we build a multi-national monetary supply and use table (MSUT). Using production volumes and other auxiliary data, we disaggregate the data and add physical layers. This is then used to construct and balance a hybrid input-output model, from which we derive LCI coefficients.

All of this is open source and open data, and available at bonsai.uno. The goal is not just to produce usable LCI data for LCA practitioners, but also to provide intermediate outputs that may be relevant for other communities working with open data and sustainability.

In this talk, I want to give an insight into how we’ve approached the development of BONSAI over the past five years: what worked well, what didn’t, and what we’d do differently if we were to start again. I’ll briefly outline our tech stack (Python/Django, Airflow, PostgreSQL) and then focus on the challenges that may be relevant to others: how we approached full traceability, how we managed modeling decisions under uncertainty, and what lessons we learned about collaboration and development workflows. Hopefully, this talk can help others avoid similar pitfalls, spark ideas, or even lead to future collaborations.

The BONSAI project is still evolving, and many parts of the pipeline could benefit from community input, whether in the form of data contributions, technical development, or modeling discussions. One of our goals is to lower the entry barrier and make it easier for new contributors to join, especially those who bring expertise from related open data or sustainability projects. We’d like to share our experience not just to inform, but to start a conversation on how to collaboratively build open and reliable infrastructure for sustainability research.

A university lab and two private companies have come together to form the Cornerstone Sustainability Data Initiative [1] with the primary goal of developing an open global MRIO model that builds off the USEEIO [2] and openCEDA [3] models. In this presentation I will share the initial direction and ambitions of the Cornerstone Initiative, covering technical details of the proposed initial model(s). I will also discuss the logistical issues of moving forward with existing open source codebases, a core team including authors and experts of existing models, and engaging the broad community of industrial ecologists working in the same general domain. I hope to generate interest and/or invoke reactions that will lead to a productive discussion on topics like how this effort is distinct from other like efforts and how others might be able to contribute or build off Cornerstone work.
[1] https://www.globenewswire.com/news-release/2025/08/08/3130038/0/en/Cornerstone-Initiative-Launches-to-Preserve-and-Expand-Access-to-Foundational-Sustainability-Data.html
[2] https://www.epa.gov/land-research/us-environmentally-extended-input-output-useeio-models
[3] https://watershed.com/blog/open-ceda

This presentation explores Ecobalyse, France's public platform for environmental impact assessment, examining its strategic positioning and technical implementation. As a cornerstone of France's environmental labeling policy, Ecobalyse democratizes LCA methodology for businesses of all sizes.

Ecobalyse emerged from a critical policy imperative: scaling up environmental labeling across French industries. ADEME and the ministry were facing two main challenges :
- Limitation of PEF and LCA indicators : it was necessary to define a new metric, based on LCA/PEF but also dealing with its current limitation (ex: local biodiversity). A tool was necessary to build the “French environmental price”.
- Scalability and transparency: traditional LCA tools present barriers—complexity, cost, and accessibility challenges that prevent non-LCA experts and SME’s from participating to the methodological testing, discussion and eventually disclosing ecolabelling.

Ecobalyse addresses this by providing a simplified, open, government-backed "Level 1" assessment tool enabling rapid environmental impact calculations based on easily accessible parameters.

By lowering entry barriers, Ecobalyse catalyses market transformation, encouraging businesses to engage with environmental assessment as a first step toward more comprehensive LCA practices.

Ecobalyse's technical architecture demonstrates innovative approaches to LCA data management and computational efficiency. The platform leverages multiple data sources, including ecoinvent for generic processes, AGRIBALYSE for agricultural products and some custom datasets for textile manufacturing, ensuring coverage and quality. By relying partly on ecoinvent private data, we ensure data coverage and quality; but it comes at a price of following ecoinvent licence conditions and IP rights. Public policies would highly benefit of broader opendata availability, which is raising the question of funding mechanisms.

Integration with Brightway framework enables the platform to automate calculation and adjust data in an agile manner; while maintaining our data pipeline resilient. For instance we can create “extrapolated processes” : Brazilian chicken from French chicken; by changing the feed. We adjust pesticide use, by removing forbidden molecules from the inventory (ex: acetamiprid) etc… While Brightway plateform has been a powerful tool, it also came with quite a few challenges and we identify several improvement possibilities.

This case study demonstrates how strategic policy objectives can drive technical innovation in LCA methodology, creating public goods that advance environmental transparency while contributing to the open-source ecosystem.

Correctly modelling the relationships between correlated, uncertain input data is crucial for producing accurate uncertainty estimates of model results. This requires both uncertainty propagation that accounts for correlations and the appropriate communication of the results, so that other analysts can correctly interpret the reported uncertainties. A common case in Industrial Ecology, in which correlations are important but often ignored, is data disaggregation. One reason for this is that dealing with correlated uncertainties is a complex undertaking, particularly when data is only partially available. MaxEnt_disaggregation is a Python (and R) package that makes it a breeze to propagate correlated uncertainties when disaggregating data points. It is based on the Maximum Entropy Principle, ensuring maximally unbiased results. It aims to help improve the accuracy of uncertainty quantification in Material Flow Analysis (e.g. where allocation coefficients split flows to sectors), Input Output Analysis (e.g. where aggregated environmental impact data has to be disaggregated to detailed economic sectors), and some instances in Life Cycle Assessment (e.g. where market shares are uncertain).

Dynamic Material Flow Analysis (MFA) is a cornerstone method in industrial ecology, enabling systematic, time-dependent tracking of materials through their life cycles and quantifying stock accumulation. The open-source tool ODYM has provided a generalized framework for custom MFA modelling, and is widely used in the community. flodym introduces a re-implementation with significantly enhanced capabilities in flexibility, usability, and performance.

Built on the principles of ODYM, flodym simplifies multi-dimensional array operations through the FlodymArray class, which automates dimension management and enables intuitive slicing, manipulation, and computation. This allows users to write cleaner, more adaptable code, minimizing errors and easing future modifications. The library tightly integrates multi-dimensional dynamic stock models – handling inflows, outflows, and stock values over arbitrary time steps – with the overall MFA structure, achieving both conceptual clarity and computational efficiency.

flodym leverages modern Python tools such as pandas for flexible data import/export and pydantic for robust data validation. It includes built-in visualization routines using Matplotlib and Plotly.

flodym is comprehensively documented, featuring how-tos, practical examples, and a complete API reference, making it easy to learn and use. Developed with modern software engineering best practices, it is structured for clarity, maintainability, and scalability. Contributions and collaborative enhancements are highly encouraged and warmly welcomed.

The presentation will begin with an introduction to the core tools provided by the flodym library, demonstrating how to construct and compute MFA systems, as well as how to effectively use FlodymArray objects. These demonstrations will draw on examples from the official documentation. The talk will conclude with a showcase of REMIND-MFA, a large-scale, open application built using flodym, highlighting the library’s capabilities in real-world research contexts.

Activity Browser 3 has been in development for over a year now and we would love to show what we've been up to. In this demonstration we will cover all that's old and new in the latest version of the Activity Browser.

Most notable changes:

- It’s been built on top of Brightway25
- It has out of the box support for multifunctionality
- It sports a fully redesigned interface

Join us to find out how these features look in practice, and how you can start using them in your LCA work as soon as possible.

The Reverse Engineering Lab (ReLab) of Leiden University CML is developing an open-source platform for collaborative data collection on product composition through disassembly documentation. Through citizen science approaches, the platform aims at filling data gaps in circular economy research through physical observation of the products currently on the market.

Workshop participants will document material composition, and other properties, while disassembling simple products (e.g. handheld radios) using our platform. The session aims to show how community-driven approaches can contribute to building open datasets for industrial ecology and testing the platform and approaches with industrial ecology expert users.

Discussion points:
- Interoperability: How can we connect practical tools like this data collection platform to existing industrial ecology databases and ontologies?
- Community Engagement: How can the sustainability community involve the broader public in data collection?

Workshop structure:
- Introduction (10 min): Data collection challenges and platform overview
- Hands-on Activity (35 min): Groups of 3-5 people disassemble provided products using our app to document components and materials
- Wrap-up (15 min): Group reflection on the data collection process, potential improvements and collaborations

Climate change constitutes a global challenge affecting across all regions of the world, producing significant risks to human health and natural ecosystems. Allocating global greenhouse gas emissions is necessary to identify effective mitigation strategies.
The methodology of environmentally extended multiregional input-output (EEMRIO) analysis enables the calculation of environmental impacts by assigning emissions and other environmental burdens to the locations where they are produced. This methodology is based on input-output matrix, an economic tool that models the interdependencies between different sectors of the economy, usually on an annual basis. Adding environmental data, is possible to determine the environmental footprint of goods and services across the supply chain.
However, one of the main challenges of applying EEMRIO lies in the complexity and size of the available databases, such as EXIOBASE, EORA or WIOD. These datasets are rich in information, but it could be difficult to integrate into life cycle assessment (LCA) tools, due to their structure and scale, hindering the implementation of this methodology.
This study addresses this limitation by developing a routine to import EXIOBASE (hybrid version) into Brightway 2.5. Two approaches are explored: creating a datapackage, and generating a database. Once the data is successfully integrated, it is possible to use conventional Brightway 2.5 to carry out a LCA of products. The datapackage approach presents some advantages, such as the reduced runtime when executing the code, while the database approach offers the advantage of requiring only a single import, facilitating easier reuse in future analyses.
The code imports emission data not only for quantifying climate change impacts but also for almost all categories of Environmental Footprint 3.1 (EF) method, and it can likewise be extended to evaluate social impacts within the social footprint method. Future lines of this work include adding the database created to other LCA software, e.g. Activity Browser.

Hybrid LCA utilizes Input Output data to account for the broader range of impacts occurring due to indirect effects of the increased demand for products or services. Performing Hybrid LCA requires the use of Input Output tables stored in databases such as EXIOBASE. Specific packages for parsing the database, such as MARIO, are available; but the linkage with the LCA foreground system is not directly possible. Brightway allows to link the foreground system with the background system stored in an LCA database, such as Ecoinvent and Bonsai. However, it is not easy to link it with EXIOBASE and perform uncertainty analysis. Therefore, a library called “bamboo” was developed to support the import of Input-Output tables (specific for EXIOBASE), link them with the foreground system, and perform uncertainty analysis. It’s an open-source library available on GitHub (https://github.com/Annedrew/bamboo).

The library works with Brightway 2.5 and adapts the input output data to generate datapackages directly so that it can be used to perform static LCA. It's faster than creating the database and then structuring the matrices. In addition, it also allows adding uncertainty analysis in different ways, faster, and easier: uniform, columnwise and itemwise based on the user’s preferences and user’s inputs. The input is extracted and structured into uncertainty arrays and then passed to the datapackages, allowing Brightway to use it within uncertainty analysis.

The Digital Product Passports (DPP) from the Ecodesign for Sustainable Products Regulation (ESPR) aims to establish an interoperable and potentially distributed network of product and manufacturing information to promote traceability of products and materials, together with their circularity and sustainability performance. However, while standardization efforts are ongoing (e.g., DaCapo and CIRPASS-2 projecs), there is currently no widely adopted implementation for DPP data exchange and formatting, and the space is still dominated by commercial solutions. This limits expandability, transparency in the system implementation and the standardization of DPP creation. In this work, we present our early stage and ongoing work on the architecture development for an open data exchange protocol for DPPs. We propose a structured clustering of the information needed for the DPP, distinguishing between required and optional type. In addition, we show how enterprise information systems and third-party software such as LCA tools may interact with DPPs as data holder and consumer. We further discuss implications of such protocol from the perspective of governing product sustainability and circularity data, while fulfilling enterprise requirements.

Carbon Capture, Utilisation, and Storage (CCUS) is expected to play a key role in reaching climate neutrality targets. However, the evaluation of CCUS technologies is often inconsistent due to fragmented data, diverse modelling assumptions, and varying methodological boundaries. To address this, the TRANSMIT COST Action is developing an open-access database platform designed to support harmonised assessment across Life Cycle Assessment (LCA), Techno-Economic Assessment (TEA), and process modelling.
The platform provides a searchable interface for users to access structured and reviewed data relevant to CCUS systems. It highlights typical modelling parameters, system configurations, and boundary conditions commonly used in assessment studies. To ensure consistency, all entries follow a uniform format that allows for cross-case comparisons and benchmarking.
Users may also submit their own calculated datasets for inclusion, subject to peer review. This ensures quality while encouraging knowledge sharing and community engagement. Open access to the platform strengthens transparency, supports broader participation in CCUS research, and enables evidence-based decision-making across academic, policy, and industrial contexts.

Evaluating the sustainability of novel products and technologies, especially those at lower Technology Readiness Levels (TRL), presents unique challenges due to the inherent uncertainties and future developments. Environmental assessment like life cycle assessment (LCA) must do more than analyze today’s lab and pilot plants but also quantify the potential impacts of tomorrow’s full-scale production. This work demonstrates how the Brightway can be used to perform a dynamic prospective LCA that incorporates time-dependent changes in the future background systems. By integrating scenario-based future background inventories derived from Integrated Assessment Models (IAMs). Moreover, we aim to extend prospective LCA to incorporate spatial analysis to see how this changes the results of the LCA. Learnings and challenges encountered in carrying out this LCA will be shared and will discussed during the conference.

Within the EU Horizon Europe project SAGELi, we are developing an interactive platform to visualise results of multicriteria ecodesign assessments. The platform aims to provide early-stage feedback to technology developers on how specific design choice – e.g., electrolyte or electrodes’ composition – affect key sustainability dimensions. These dimensions are critical raw material use, safety, recyclability, economics, and environmental impacts estimated using streamlined LCA. As the technologies are in development, results are highly uncertain and the key value of the platform is to highlight environmental hotspots and demonstrate sensitivities of different design alternatives.

To this end, we are developing an interactive dashboard built with plotly/dash. In the background, an LCA database should be connected, either directly or using brightway2.
Two key questions are i) the design of a smart interactive solution to sensitivity studies for ecodesign and ii) the feasibility of creating a method to assess the critical raw material use leveraging open industrial ecology software.

In this presentation, we will showcase our current ideas for those two key questions hoping to stimulate valuable discussions. Regarding i), we will present a mock-up created with Figma upon implementation of user’s feedback. Regarding ii), we will present a very naïve approach using ecoinvent resource flows and demonstrate its shortcomings.

Reliable LCA background databases are essential for conducting robust environmental impact assessments. Brightway is one of the most flexible tools for performing LCA, yet it primarily relies on the ecoinvent database, making compatibility with other commercial datasets challenging. Consequently, the transparent integration of multiple background databases—such as ecoinvent, Agribalyse, or the World Food Database—has not been systematically addressed. In this work we present a Python-based workflow that streamlines the import and harmonization of LCA databases from SimaPro CSV files into the Brightway framework. The workflow was tested with three commercial food databases—Agribalyse, Agri-footprint, and the World Food Database—demonstrating two key benefits that remained unsolved up to now: (i) the incorporation of biosphere flows and its characterization factors that hindered environmental impacts of many agri-food products, and (ii) the consistent combination of different databases to ensure that the latest versions of ecoinvent datasets are used seamlessly. By expanding modeling capabilities in Brightway, improving data consistency, and enabling transparent database integration, this workflow enhances the reliability of LCA analyses and supports more robust environmental impact assessments in the food sector and beyond.

Contribution Analysis in LCA is not a single thing, but there are many different approaches to finding interesting results. In this talk I will discuss different approaches to Contribution Analysis and explain them based on a recent paper The talk will focus on the approaches and will be independent of software.

ILCD is a large and complex data format, designed to allow for publication of unit processes, aggregated processes, and impact assessment data. This broad scope produced a complex format, with a typical dataset having over 100 fields spread across multiple files.

This complexity has significant downsides: cognitive overload, duplicated information, and non-compliant publication.

In this talk we discuss a survey of over 10 thousand real-world ILCD published processes, and derive a backwards-compatible and significantly simplified minimum viable ILCD format variant for publishing inventory data.