Highlights / Discussion Points

• TianGong starts from building a Chinese LCA database and extends toward broader LCA infrastructure.
• The infrastructure connects database development with data structuring, review, calculation, interoperability, and reuse.
• AI is used within controlled, auditable workflows to support data curation and quality governance.
• This approach can help LCA databases work with wider tool, data, and collaboration ecosystems.

Abstract / Concise Description
The TianGong Initiative is building a Chinese LCA database while extending database development into a broader AI-enabled LCA infrastructure. The database remains the foundation, but its long-term value depends on the surrounding system that supports data production, structuring, review, calculation, interoperability, and reuse.

This presentation introduces TianGong’s experience in developing LCA data from standardized unit processes toward more connected representations of industrial systems. It discusses how TIDAS-based data structuring, human-machine collaborative review, structured ingestion, calculation support, and platform services can form an end-to-end workflow for scalable database development.

Rather than using AI as a standalone generator of LCA data, TianGong embeds AI into controlled and auditable workflows where extraction, verification, correction, and expert review remain traceable. The presentation uses TianGong as a concrete case to discuss how LCA databases can evolve into infrastructure that connects with wider toolchains, data systems, and international collaboration efforts, while improving transparency, scalability, quality governance, and reuse.

Context. A growing number of national green-building councils publish open EPD databases that provide pre-characterised GWP values per EN 15978 lifecycle module — the NZGBC Embodied Carbon Database in New Zealand, EPiC in Australia, ICE in the UK, ÖKOBAUDAT in Germany. These databases are explicitly designed to be open to any tool provider, yet they sit outside the elementary-flow paradigm on which Brightway and ecoinvent are built. For researchers without ecoinvent licensing — a common condition for self-funded doctoral candidates, public agencies, and consultancies in smaller markets — the practical question is whether these national databases can be combined with the open-source Python LCA ecosystem to support parametric, time-explicit assessment.
Approach. This presentation reports on a working bridge between the NZGBC API and lcpy (Gkousis & Katsou, 2025), a BSD-3-licensed Python package for parametric LCA and LCC. A connector module ingests the NZGBC REST API, normalises records to GWP-fossil/biogenic/luluc/stored sub-components, and feeds lcpy's external-unit-impact mode as direct dictionary inputs. The workflow currently runs a 99-cell parametric sweep — three pre-code NZ commercial-office archetypes × three climate zones × three grid-decarbonisation scenarios × interventions × HVAC sub-cases — against synthetic material inventories in under two minutes, producing dual-path (dynamic vs static grid EF) cumulative-carbon trajectories. Non-inversion is preserved as a first-class result. A live Jupyter notebook walks through the connector, the parametric loop, and the output structure. All code is open and reproducible.
Brightcon spirit. The session frames this work as a community discussion: whether bridging modules between national pre-characterised EPD databases and the Brightway ecosystem should be standardised, where they could live, and what interoperability with bw_timex's elementary-flow pipeline would require. The presentation makes the tooling, the limitations, and the open questions transparent.

The ICT sector still struggles to fully understand its environmental footprint. LCA can help in assessing it, but is currently hindered by the lack of open, trustworthy, and quality data, which can be explained by two elements: 1) a complex supply chain, and 2) business secrecy in a competitive environment.

In this presentation, the objective is first to present naKnow: a collaborative initiative to tackle this issue by gathering both academic and industrial stakeholders. A specific emphasis will be placed on the governance model, which ensures data transparency and reliability, and on how to enable collaboration in the context of such an opaque and complex industry.

The second half of the presentation will be dedicated to describing the technical approach for generating missing trustworthy data. The data production pipeline is initiated by collaboratively gathering information on processes and materiality of the subject of the study, via literature review and expert interviews. This knowledge is gathered to build a parametric tool, in Python, able to model advanced processes and complex supply chains. We will present an overview of the various technical challenges and solutions associated with it, enabling the bridge between traditional and parametrical LCA through Brightway. A special emphasis will be put on how to ensure interoperability with background databases. Finally, the tool models are mathematically corrected thanks to material analyses to further build confidence. This approach is being validated on the use cases of wafer, PCBs, screens, HDDs, and optical sensors, and is aimed to be extended via the naKnow initiative.

We will conclude by demonstrating how our tool helps us in fostering collaboration among experts, without requiring a deep technical background in software development and LCA.

The use of plastics inevitably leads to (micro)plastics entering and accumulating in the natural environment, affecting biodiversity, food security and human health. Biodegradable polymers have emerged as a potential solution to mitigate plastic pollution and the environmental accumulation of polymers, offering stable performance during use while leading to shorter residence times in natural environments. However, do we fully understand the effect of these novel polymers on microplastic accumulation in the natural environment?
We developed a comprehensive and universally applicable method to quantify microplastic accumulation in the natural environment. This method includes an integrated biodegradation model that enables the examination and comparison of microplastic formation and accumulation across different polymer types in diverse natural environments. The model uses experimental mineralisation curves of polymers to predict microplastic accumulation. It fits these curves to a state space model and thereby generates a mass balance of substances formed during the biodegradation process. This polymer–environment specific mass balance is then used to predict the concentrations and residence times of microplastics in the natural environment.
The modelling results can be used directly to assess and compare the accumulation of different polymers and can also serve as input for life cycle assessment (LCA). We applied this methodology to develop an open-access database on the biodegradation behaviour of a wide range of polymers across various environmental compartments. This database is intended to support LCA practitioners by enabling the integration of this behaviour, specifically the resulting quantities and residence times of polymers in specific environments, into their assessments.
Ultimately, the proposed methodology identifies the biodegradation behaviour required to prevent the accumulation of microplastics from specific plastic products in the natural environment. This will facilitate the transition to a system in which products are designed based on their intended functionality and anticipated end-of-life scenario.

We present TRAILS, an open-source Python framework for time-explicit life cycle assessment (LCA) that enables deep temporalization of product systems. Rather than representing the technosphere as a collection of year-specific, independent two-dimensional matrices, TRAILS treats it as an interconnected temporal structure (i.e., a three-dimensional tensor) in which processes and exchanges can be distributed over time and linked consistently across scenario years (i.e., background databases). This allows temporal differentiation not only in foreground activities, but throughout the supply chain, so that background transformations in energy, materials, infrastructure, and other intermediate flows can be represented as part of a connected temporal system.

From a methodological point of view, TRAILS combines temporal routing of exchanges with sequential, year-by-year life-cycle inventory calculations. This approach makes it possible to construct scenario-consistent time series of inventories and impacts while avoiding a memory-straining single solve across the entire temporal system. The framework also supports coupling with climate emulators, allowing temporally distributed greenhouse gas emissions and removals to be translated into climate-relevant outputs such as radiative forcing and temperature response.

TRAILS is designed to be modular and transparent for researchers and practitioners working on prospective and dynamic LCA. Its open implementation supports reproducibility, facilitates integration with scenario-based background LCA databases, and enables users to study systems for which timing is likely to affect overall environmental performance substantially.

During the presentation, we will demonstrate the use of TRAILS live with the audience through a Jupyter notebook. This hands-on demonstration will show how: (i) deeply temporalised systems are constructed, (ii) prospective background scenarios are connected into a temporally explicit technosphere, and (iii) resulting inventories, impacts, and climate responses can be calculated and explored in a reproducible workflow.

Highlights

• AI-assisted workflow for extracting life cycle inventory data from
  secondary resources
• Use of open-source LLMs and fine-tuned
  classification models for document screening and information
  extraction
• Harmonization of heterogeneous inventories for emerging
  technologies with low technology readiness levels
• Transparent and reproducible workflow implemented in Python using
  open datasets and shared code repositories

Description

Emerging technologies often lack primary inventory data, making Life Cycle Assessment (LCA) particularly challenging during early-stage development. As a result, practitioners frequently rely on scientific literature and technical reports to construct Life Cycle Inventories (LCIs). Although relevant information is available across published studies, extracting and structuring inventory data remains a manual and time-intensive task requiring substantial domain expertise. In addition, inventories reported in literature often differ in system boundaries, assumptions, process conditions, and level of detail, complicating their direct reuse and comparison.

This work presents an AI-assisted workflow for automated extraction and harmonization of LCI data from scientific literature for emerging technologies. The proposed framework combines machine learning techniques and open-source Large Language Models (LLMs) to support literature screening, information extraction, and inventory harmonization. The workflow consists of five stages: (1) automated literature collection using APIs and curated keyword sets; (2) identification of relevant publications using a fine-tuned classification model; (3) domain adaptation through pretraining on selected sustainability and LCA-related texts; (4) extraction of inventory data using fine-tuned LLM-based models; and (5) harmonization of heterogeneous inventories into a consolidated inventory suitable for subsequent LCA modeling.

The workflow is implemented in Python using open-source models and datasets to ensure transparency and reproducibility. Codes, metadata, and workflows are intended to be shared through institutional repositories to support reuse and community collaboration. The presentation discusses methodological challenges related to heterogeneous literature-derived inventories and highlights how AI-assisted workflows can support scalable and reproducible inventory generation for emerging technologies.

Highlights
1. AI integration in LCA introduces both new uncertainties and undetectable errors that differ fundamentally from traditional LCA uncertainty
2. A clear distinction is drawn between AI-induced uncertainty (stochastic, plausible outputs) and AI-induced error (hallucinated, factually wrong outputs)
3. A stage-based framework is proposed mapping AI-induced uncertainty and errors onto all four LCA phases
4. Unlike traditional LCA errors, AI-induced errors accumulate across stages, making fully automated pipelines inherently prone to error propagation
5. The framework provides a foundation for developing verification and quality control standards in AI-assisted LCA

Context
Life Cycle Assessment (LCA) involves uncertainty across four phases: goal and scope definition, life cycle inventory (LCI), life cycle impact assessment (LCIA), and interpretation. Uncertainty in LCA is commonly classified into model uncertainty, parameter uncertainty, and scenario uncertainty. Various methods have been developed to characterize and propagate these uncertainties, including pedigree matrix for data quality assessment and Monte Carlo simulation for uncertainty propagation.

While integrating artificial intelligence (AI) into the LCA process can reduce certain human-related uncertainties and enhance transparency, it also introduces new challenges in uncertainty characterization. The effect of AI integration depends on how AI-supported LCA systems are designed and applied. In this context, two distinct issues must be differentiated: uncertainty, where AI fills knowledge gaps and produces different but equally plausible models due to its stochastic nature; and error, where AI generates outputs that are simply wrong, such as hallucinated emission factors or fabricated characterization factors, yet remain undetectable without independent verification. Instead of introducing new types of uncertainty, AI can amplify the existing uncertainty in LCA, or introduce errors, such as generating fabricated emission factors that inflate data uncertainty. Unlike traditional uncertainties in LCA, which are often traceable and manageable, AI-induced uncertainty and errors are more difficult to detect due to the nature of AI algorithms. In addition, the traditional errors in LCA are detectable through expert review, AI-induced errors accumulate across four LCA stages, making fully automated LCA pipelines inherently prone to error propagation. Therefore, there is a need to systematically identify and define the AI-induced uncertainty and errors within the LCA context.

Approach
This study proposes a stage-based framework characterizing both AI-induced uncertainty and errors in AI-assisted LCA, mapped onto the four stages of LCA. In goal and scope definition, AI introduces uncertainty through misinterpretation of research intent, ambiguous functional unit definition, and opaque system boundary decisions. In life cycle inventory analysis, primary uncertainty sources include hallucinated inventory data, erroneous foreground system construction, and incorrect background database linkage. In life cycle impact assessment, uncertainty arises from the misassignment of impact categories and the selection of unverifiable characterization factors. In the interpretation stage, AI-generated conclusions lack traceable reasoning, compromising the reliability of hotspot identification and the overall credibility of the assessment.

Brightcon Spirit
This session invites the Brightway and broader LCA community to critically examine the reliability of AI-assisted LCA workflows. By presenting an open framework for uncertainty and error characterization, we aim to stimulate discussion on community standards for verification, quality control, and responsible AI integration in LCA practice.

Large Language Models (LLMs) are increasingly used in professional services, including sustainability assessment. However, their reliability in Life Cycle Assessment (LCA) remains largely unexplored, particularly in the interpretation of results to provide mitigation advice and the treatment of circular value chains.

This short study benchmarks at least 3 LLMs (e.g., ChatGPT and Llama) as sustainability consultant chatbots. Three use cases are considered: (i) interpretation of LCA results for an NMC battery, including hotspot identification and (ii) mitigation strategies, and (iii) application and explanation of the Circular Footprint Formula (CFF) in the context of critical raw material recovery through recycling.

Evaluation criteria include accuracy of hotspot identification, quality and feasibility of mitigation options, correctness in applying CFF principles, clarity for non-experts, consistency of responses, and robustness to prompt design. Particular attention is given to inconsistencies, oversimplifications, and risks of misleading recommendations.

The benchmarking is implemented in Python using the OpenAI library, with a reproducible notebook provided. Different configurations and prompts are systematically tested for each LLM. The results offer insights into the capabilities and limitations of LLMs in supporting LCA and circular economy assessments, helping users and developers more aware of if and how such tools can complement human expert judgement.

Highlights / Discussion Points

• A global physical uncertainty-aware oil Supply-Use Table (SUT) compiled through an AI-assisted scientific workflow.
• Human input was limited to prompting, review, and modelling judgement; AI generated the code.
• Demonstrates R/targets as a reproducible backbone for agentic data engineering.
• Discusses strengths, failure modes, memory management, and assumption tracking in AI-assisted database construction.
• Invites discussion on transparent, auditable AI-assisted sustainability data infrastructure.

Concise Description

LCA and sustainability modelling increasingly depend on complex, multi-source data pipelines. These pipelines require not only domain expertise, but also extensive software engineering: source adapters, mapping between classifications, validation routines, uncertainty handling, documentation, and reproducible execution. This presentation uses the compilation of a global physical oil supply-use table as a case study in agentic AI-assisted data engineering.

The underlying data product represents the 2022 global oil supply chain at country level, covering extraction, refining, bilateral trade, and sectoral end use in physical units. It includes detailed crude and refined-product categories, uncertainty metadata, validation checks, and a Bayesian balancing step. The product is relevant to LCA because petroleum flows are often embedded in background databases, transport systems, petrochemical supply chains, energy scenarios, and product carbon footprints, yet the underlying physical data and reconciliation choices are rarely transparent.

The workflow was implemented in R using targets and reproducible environment management. However, the distinctive feature is the process: code was produced through agentic AI, while the researcher acted as modeller, reviewer, and prompt designer. The talk will examine where AI was highly effective, including rapid scaffolding, repetitive data-wrangling code, schema consistency, automated reporting, and debugging support. It will also discuss limitations, including source misinterpretation, overconfident assumptions, increasing forgetfulness and laziness, fragile edge cases, and the continuing need for expert validation. Particular attention will be given to AI memory management across long workflows, including how central modelling decisions, assumptions, and unresolved issues were documented to keep the process auditable.

In the Brightcon spirit, the session treats AI-assisted compilation as something that must be auditable rather than magical. It will argue that agentic AI can accelerate open sustainability data work, but only when embedded in reproducible workflows with explicit assumptions, decision logs, testable outputs, version control, and community review.

Artificial intelligence (AI) is increasingly embedded in life cycle assessment (LCA) workflows, supporting inventory completion, literature synthesis, scenario analysis, and interpretation. Yet while AI is often framed as an efficiency gain for sustainability assessment, the environmental impacts of AI workflows themselves remain largely unexamined. Current AI-enabled LCA approaches tend to prioritize accuracy, scale, or speed, with limited attention to energy use, carbon intensity of inference, model size, prompt design, or iteration frequency. This creates a disconnect between sustainability goals and computational practice.

This presentation addresses the absence of LCA-aware design principles for AI workflows. We discuss how AI can be treated as an explicit system component within collaborative LCA practice, rather than as a neutral tool operating in isolation. Drawing on recent applications of lightweight, open-source language models and modular machine-learning pipelines, we explore how design choices in AI-enabled LCA tasks can influence both insight generation and computational burden. Rather than focusing solely on model performance, we highlight practical tradeoffs associated with task-specific model selection, constrained prompting, limited inference passes, shared infrastructure, and transparent reporting of proxy metrics such as runtime, token use, and model scale.

Using a small, reproducible case study using Brightway for life cycle inventory tasks, we present an emerging framework for designing low-carbon AI workflows that balance decision relevance with computational cost. Open-source code (Jupyter, Python) will accompany this presentation for adoption by others. By reframing AI adoption as a collective, tool-sharing design challenge, this work encourages reproducible, efficient, and environmentally coherent collaboration across researchers, consultants, and software developers using AI in LCA.

The LCA community has invested heavily in domain-specific AI: curated ontologies, hand-crafted matching algorithms, and specialised models trained on ecoinvent. We believe this approach, while well-intentioned, is following a familiar trajectory. Across field after field, the same pattern has emerged: domain-specific AI, carefully engineered with expert knowledge, is eventually overtaken by general-purpose models equipped with scale and better reasoning. We argue LCA is not immune to this dynamic. The bottleneck is rarely model architecture; it is whether the model has access to the right context at inference time. The central challenge for AI in LCA is therefore a context engineering problem: how to supply a general-purpose LLM with the right knowledge, compressed at the right granularity, to sustain reliable reasoning across a full LCA workflow.

This contribution presents an open-source Python workflow for variance-based global sensitivity analysis of upcycling life cycle assessment inventories, built on Brightway2 and the SALib library. The workflow enables practitioners to identify which foreground parameters drive result variability before committing to a full comparative study, addressing a common but under-supported step in LCA practice.
The workflow is demonstrated across six industrial upcycling cases from Danish SMEs, covering metal ceiling panels, concrete outdoor furniture, ceramic household products, acrylic serving trays, and paper-based office products. For each case, foreground inventories were constructed in Brightway and linked to ecoinvent. Sobol first-order and total-order sensitivity indices were computed across climate change and cumulative energy demand impact categories.
Results reveal both cross-case recurring sensitivity drivers and case-specific patterns tied to material system characteristics. The full workflow, including parameterised inventories, SALib configuration, and visualisation scripts, is shared as a reusable, openly licensed repository.

A Python Package for the Simultaneous Global Sensitivity Analysis of Foreground and Background Parameters in Life Cycle Assessment

Highlights/Discussion Points

• Existing Python packages can perform global sensitivity analysis (GSA) of foreground or background parameters, but not both at once.
• We propose a package based on Python and Brightway that simultaneously performs GSA on foreground parameters (flow amounts, scenarios) and background parameters (impact scores of background processes, fit between foreground and background).
• The package uses an estimator for the Sobol indices, which works from a simple Monte Carlo simulation, thus significantly reducing the compute time required to perform GSA with a lot of parameters
• We show how this package is used to iteratively model the comparative LCA of an environmental sensor.

Concise Description

Context

The reliability of a life cycle assessment depends on the quality of the data used to model the system under study. However, collecting high-quality data requires a lot of time and resources. Furthermore, not all processes and parameters are equally important with respect to the LCA results. Global sensitivity analysis (GSA) techniques such as the variance-based Sobol indices indicate the contribution of input parameters to the overall output variance. There exist Python packages such as lca_algebraic, gsa_framework, and SALib that compute the Sobol indices and require a specific sampling scheme. These methods require a number of samples that scales linearly with the number of parameters under study, which is prohibitive for LCAs that can contain up to hundreds of parameters.

Furthermore, the existing packages do not allow simultaneous evaluation of foreground and background contributions. It is important for the LCA practitioner to consider the contribution from both categories, because their associated uncertainty can be addressed with different actions.

Approach

We have developed the lca_toolbox (temporary name) Python package to perform global sensitivity analysis of foreground and background contributions concurrently. It operates as a layer on top of Brightway 2.5. It estimates Sobol indices based on a Monte Carlo simulation, which can also be used for uncertainty analysis. This reduces the computation load, especially when the number of parameters is high. In this presentation, we will demonstrate the use of our package for the iterative modelling of the comparative LCA of an environmental sensor.

BrightCon Spirit

The presentation is structured as a live demonstration using a Jupyter Notebook. Our package and the demonstration notebook are available on GitHub. We wish to use this opportunity to gauge the interest of the community in our work and discuss further collaborations as well as applications beyond LCA.

Highlights

• A coupling framework featuring a dual presampling strategy paired with a preaggregation step is developed to efficiently combine stochastic inputs with background stochasticity in LCA.
• The pelicun_2_brightway2 library is introduced to bridge pelicun and brightway2, open-source tools to perform asset performance assessment for the built environment and LCA, respectively.
• The case studies underline the ability of the coupling framework in generating large numbers of stochastic LCA results, without enforcing simplifications to the inputs or the LCA itself.

Description

LCA is increasingly leveraged as part of a series of simulation-intensive assessments, as in asset performance assessments. Asset performance assessments strive to estimate natural hazard effects on the sustainability of the built environment but require large numbers of stochastic samples to characterize the resulting environmental impacts associated with repair activities and collateral downtime effects. General-purpose LCA software struggle with interoperability issues when provided with large input samples, prompting for simplifications and limiting LCA uncertainty propagation. This project proposes a novel coupling framework that leverages a dual presampling strategy paired with a preaggregation step on background environmental datasets, expediting in minutes the computation of hundreds of thousands of stochastic LCA results. The Python implementation of the coupling framework bridges the open-source software pelicun and brightway2 via the GitHub-available library pelicun_2_brightway2. The coupling framework is investigated through two case studies exploring the influence of hurricane winds on the LCA of an individual building and a portfolio of buildings in Halifax, Canada (also available on GitHub). The case studies underline how the coupling framework can assist engineering-level and urban-scale decision-making in developing sustainable and resilient assets. The coupling framework and its underlying methodology aim to foster new developments in LCA for simulation-intensive assessments, a necessary feature in optimizing the mitigation and adaptation strategies of a changing climate.

Highlights/Discussion Points:
- Introduction of the problem
- How to go about assessing this issue with BW
- Results: Uncertainty due to price variance in economic allocation. Comparison to other sources of uncertainty in LCA.

Concise Description
Economic allocation is widely used in life cycle assessment (LCA) to distribute inputs and emissions among co-products, yet it introduces a less visible source of uncertainty: prices (of co-products) can change over time, across markets, and trading relationships. This means that, unlike physical allocation factors, economic allocation factors are not fixed parameters but stochastic quantities that influence the impact results. While critics argue that economic allocation is therefore unsuitable, proponents see it as best reflecting the economic drivers of the impact and tout its ability to capture complex attributes of product or service quality.

Various works discuss the different types of allocation and the uncertainty arising from the choice between them. However, to the author’s best knowledge, no quantitative study of the uncertainty from price variance in economic allocation has been conducted. Therefore, in this work, I investigate the uncertainty arising specifically from economic allocation in a Brightway-based Monte Carlo workflow that propagates price variability through co-product allocation and downstream impact assessment.

Using macroeconomic data to derive reasonable variations in co-product prices, this study quantifies allocation uncertainty in activity-level impacts in the ecoinvent database. I show that economic allocation uncertainty can be substantial and is not uniform across activities. Furthermore, I will compare the uncertainty due to price variance in economic allocation with traditional parameter uncertainty and other sources of uncertainty, such as the truncation error in product LCA.

The full assessment pipeline is, or will be made, fully available in a GitHub repository. The assessment is fully based on open source software, and the presentation will highlight how to go about this.

Life Cycle Assessment (LCA) models are increasingly complex and interdisciplinary. Challenges related to transparency, reproducibility, and data interoperability are becoming more critical for both research and practice. Integrating diverse datasets, combining complex modelling approaches, and ensuring methodological consistency across disciplines require not only technical competence but also shared understanding of documentation practices, assumptions, and data management. At the same time there is a need for practical and engaging educational approaches that make these challenges tangible.
We propose a 45 minute workshop structured with a 25 minute hands on exercise and a 20-minute plenary discussion connecting the exercise to reproducibility challenges in LCA research and practice. LEGO is used as media for hands-on exercises on model construction, documentation, and reproduction. The exercise will serve as a starting point for reflection on ambiguity, missing information, interpretation differences, and undocumented assumptions.
The exercise is designed to parallel key challenges in LCA practice, including goal and scope definition, functional unit interpretation, modeling choices, methodological transparency, and data quality issues. In the plenary discussion the participants will reflect on the balance between transparency and practicality, the limitations of peer review, and the implications of irreproducible studies for scientific credibility and policy support. The workshop aims to identify best practices for interdisciplinary collaboration, improving reproducibility in LCA, and fostering more transparent and reusable modelling approaches across LCA research

This hands-on workshop introduces a Python-based modelling framework to support consequential life cycle assessment of biobased products using openly available bioeconomy data. Participants will actively work with Jupyter notebooks and an open-source repository to install, set up, and run the model.
Through guided exercises, attendees will learn how to construct a bioeconomy matrix from publicly available datasets (e.g., JRC data), map biomass flows across sectors, and apply optimization algorithms to identify marginal suppliers under biomass constraints. The workshop will also demonstrate how these outputs can be directly used to build life cycle inventories for biobased product systems.
The session is designed for participants interested in LCA modeling of emerging biobased products. It builds on existing Python-based tools and introduces how these can be extended for consequential modelling of biomass competition.
In addition to step-by-step coding exercises, participants will work in small groups to apply the model to a simple case study, explore scenario assumptions, and discuss results. The workshop will conclude with a plenary discussion focused on practical applications, limitations, and opportunities to further improve the codebase and documentation.
Participants will leave with (i) a working local setup of the model, (ii) example notebooks they can adapt to their own studies, and (iii) a clear understanding of how to incorporate biomass constraints into consequential LCA practice.

Highlights

1. Showcases MyLCA, a Python modelling framework for generic, parametric and modular LCA with Brightway25
2. Enables reusable, composable, parametric LCA models through grouped containers and activities, and associated parameters
3. Demonstrates a reproducible workflow to rebuild and compare published battery LCA studies
4. Identifies and quantifies drivers of impact differences
5. Takeaway: a parametric and modular approach to explain discrepancies in complex LCA models

Concise Description

In Life Cycle Assessment (LCA), practitioners typically model systems as fixed process graphs, where exchanges are explicitly defined between datasets. While some parametrization is possible, these models often remain static and tightly coupled to specific background data, limiting genericity, modularity and reusability. At the same time, the growing need to compare complex systems is hindered by the difficulty of explaining differences between studies that rely on varying assumptions and structures.

This session introduces MyLCA, a Python library built on top of Brightway25, designed to enable generic, parametric, and modular LCA modelling. The approach extends traditional activity representations with “ports” that abstract the activities that will be used as inputs during the calculation. Processes can be encapsulated in “containers”, enabling sub-modelling, encapsulation of alternatives delivering the same products, templating, and reuse across the model. A hierarchical parameter system (namespaces, inheritance, and parameter transfer) combined with a built-in solver enables efficient and consistent model evaluation. Evaluated models are then converted into Brightway objects for LCA calculation, including sensitivity and uncertainty analyses across foreground and background systems.

The capabilities of MyLCA are illustrated through a battery LCA use case. Several published studies are reconstructed within a unified parametric model using the new flexible descriptions enabled by MyLCA. This allows controlled comparison from differences in product design (e.g., cell chemistry, performance assumptions), supply chain configurations (e.g., energy mix, sourcing), or methodological choices such as functional units, system boundaries, and databases. By systematically varying parameters (design, supply chain, and methodological choices), causal drivers of impact differences can be identified more easily and natively.

The session will include: (1) presentation of core concepts, (2) integration with Brightway25, (3) application to comparison of battery LCA and (4) discussion to gather feedback and assess the community interest.

In the perspective of an open-source release of MyLCA, inputs regarding the development, potential overlapping or integration with other tools will be very-much appreciated.

Heat pump deployment is widely assumed to reduce building-sector greenhouse gas emissions. This assumption holds in countries with low-carbon electricity grids but has rarely been tested for carbon-intensive grid contexts using open LCA tools and data. This contribution presents a Brightway2-based case study that computes the operational global warming potential (GWP) of a residential air-source heat pump (COP = 3.0, R410A) across multiple regions using Ecoinvent 3.12 cutoff electricity market activities. Results show that Morocco's grid carbon intensity, computed at approximately 1120 g CO₂-eq/kWh from Ecoinvent, yields an ASHP operational GWP of 115.5 g CO₂-eq/MJ — exceeding the equivalent gas boiler benchmark. This contrasts sharply with France or Switzerland, where the same system achieves substantially lower impacts. The workflow, implemented as a reproducible Python script with parameterized activity matching and LCIA computation, illustrates how open tools and open data can challenge policy assumptions that are implicitly calibrated to European grid conditions. The presentation will share the notebook and discuss how regional grid data availability in Ecoinvent shapes LCA conclusions for underrepresented regions such as North Africa and the MENA area.

The decarbonization of maritime transport requires scalable fuel pathways that achieve both economic viability and low life cycle emissions. This study presents a prospective life cycle assessment (LCA) of Power-to-Liquid (PtL) e-diesel production within the FLEET 4.0 framework, targeting maritime applications in Saudi Arabia. The work integrates detailed process simulation with system-level environmental assessment to quantify cradle-to-gate and well-to-wake impacts across multiple energy and feedstock scenarios.

An Aspen Plus-based model was developed to simulate the full e-diesel production chain, including hydrogen production via electrolysis, reverse water-gas shift (RWGS), Fischer–Tropsch (FT) synthesis, and product separation. Both pilot-scale (~100 t/year) and commercial-scale (~100 t/day) systems were evaluated. Four scenarios were defined based on hydrogen and CO₂ sourcing: (S1) renewable H₂ with point-source CO₂, (S2) grid-based H₂ with point-source CO₂, (S3) renewable H₂ with low-cost industrial CO₂, and (S4) renewable H₂ with direct air capture (DAC) CO₂ .

The LCA was implemented in Brightway 2.5 using the Activity Browser, enabling the construction of foreground systems and their integration with background inventories. A prospective framework was applied to capture evolving electricity carbon intensities in Saudi Arabia. Environmental impacts were assessed using GWP100, with sensitivity analyses on electricity mix, hydrogen production, and CO₂ sourcing.

Results demonstrate that electricity carbon intensity is the dominant driver of life cycle emissions across all scenarios, exceeding the influence of process configuration or CO₂ sourcing. Scenario S3 achieves the lowest carbon intensity due to the combined benefits of renewable electricity and low-burden CO₂ supply, highlighting the role of industrial symbiosis. In contrast, S2 results in the highest emissions, with grid-based hydrogen production offsetting any carbon utilization benefits and potentially exceeding fossil diesel benchmarks. While S4 enables near-complete carbon circularity, its high energy demand introduces a significant upstream burden, limiting its near-term environmental advantage.

Overall, switching to low-carbon electricity yields greater emission reductions than changes in CO₂ sourcing strategy, underscoring the critical importance of electricity decarbonization for PtL systems. At commercial scale, the best-performing configurations demonstrate strong potential to meet future maritime decarbonization targets.

This study highlights the need for integrated process–LCA frameworks to support decision-making and shows that system-level factors, particularly electricity supply, ultimately determine the environmental viability of e-fuels. The methodology is transferable and provides a robust basis for scaling PtL technologies under Saudi Vision 2030 and global net-zero pathways.

Highlights
• The SALCA models transitioned from an Excel tool to a Python implementation.
• A modular structure separates parameters, tables, models, and calculation levels.
• The long-term and large collaborative effort required coordination and harmonization.
• The open source aims to facilitate reproducibility, reuse, and external collaboration.

Description
Agriculture is a complex sector which delivers numerous products and is a major driver of environmental impacts. It requires efficient and flexible tools that can estimate such impacts for different crop and animal production systems. The Swiss Agricultural Life Cycle Assessment (SALCA) models apply to Swiss and, more generally, Central European conditions and have been developed over more than two decades. By now, they cover several types of direct emissions, from carbon dioxide to heavy metals, as well as biodiversity and soil quality. While initially implemented in Excel, the increasing complexity of the models, connections between some of the models, large input data requirements, the need for continuous updates to integrate new scientific findings, the gradual expansion of the models to capture additional impact categories, and the interest from external researchers in using such models for their own studies in and outside of Switzerland motivated a transition to a Python implementation and eventually its open-source publication.
The Python implementation follows a modular structure to enable flexible application in different types of research projects, as well as easy adaptations and extensions of the models. The development of the SALCA models has been a long-term process and large collaborative effort among researchers across multiple generations and with diverse disciplinary backgrounds and different experience levels and styles in Python programming. This collaborative effort was needed to ensure a high quality of the models necessitating different specialist expertise. To facilitate reuse by and collaboration with external researchers while also making the software development process more sustainable and ensuring continuity despite potential changes within the research team, we defined guidelines and a short-term time plan for cleaning our codebase and outlined future plans to further improve documentation and refactor the code for increased reproducibility and efficiency, alongside further developments of individual modules.

Highlights / Discussion Points
- Why and how it was built, its architecture and what it allows today.
- Demo of an inventory/impact calculation, hotspot analysis and a what-if substitution scenario
- How it was used for Ecobalyse
- Openings on the real needs of the LCA sector : data

Concise Description
For fun and continuous learning, I wrote a brand new LCA software from scratch using AI. Volca is early and still read-only but already has all the buzzwords embedded: API-first, Cloud-native, AI-native, scriptable, small, fast, stable, open-source. Agribalyse 4.0 will be used to showcase an inventory, impact and hotspot calculation, and instant result on a substitution scenario.

It was used to generate hundreds of transformed ingredients for the french brightway-based Environmental labeling platform Ecobalyse and to reverse-engineer Agribalyse 4.0. It offers an alternative comparison point for LCI and LCIA results.

Following the AI revolution, the hard part is not writing software anymore: the pain point is DATA : collaboration, collection, sharing, aggregation, traceability, compatibility. This is where the real trailhead begins.

Discussion Points
• Modeling needs in the electronics LCA community.
• Introduction of the open-source framework developed at UCLouvain.
• How does this framework facilitate data sharing and reuse?
• Demonstration with a dense printed circuit board with various integrated circuits.

Context
Electronic products are complex and heterogeneous. As a result, their LCA modeling is data- and time-intensive. Although modeling guidelines exist, they are hard for a single practitioner to apply because each component requires distinct expertise.

In the LCA community for electronics, practitioners would benefit from a framework that (1) facilitates applying existing modeling guidelines, (2) helps cope with granularity limitations of background databases, and (3) enables easy reuse of foreground data. Hence, we have developed a framework to address these needs while leveraging Brightway and LCA Algebraic's capabilities. Specifically, this framework introduces a complementary layer that provides:
(i) guidance to match foreground with background data through built-in functions,
(ii) a library of custom datasets for common electronic components, and
(iii) a versatile architecture for foreground data collection.

Functions in (i) implement guidelines and prediction models that map features of electronic components to one or more background datasets. The custom dataset library in (ii) gathers electronics components for which manufacturer data exist online in the form of full material declarations. Foreground data in (iii) is specified using YAML files, which provide structured data with human-readable syntax, support input validation schemas to prevent runtime errors, and enable efficient parsing.

Separating (i), (ii), and (iii) facilitates foreground data reuse and sharing because the data is not embedded within the functions.

Approach
The session will include a 15-minute presentation, followed by a 5-minute live demonstration using a Notebook.

The "Brightcon Spirit"
The framework was developed based on the needs of LCA practitioners. We position it as a structured approach to using Brightway, not as a replacement for existing tools. The functions described in point (ii) are based on established literature and designed for easy extension to other electronic components. The framework is open source, and we plan to create tutorials.

Industrial Ecology increasingly aims at connecting complementary assessment frameworks rather than limiting analysis to conventional Life Cycle Assessment (LCA). In this context, we propose a framework to assess what we define as intrinsic circularity, i.e., the direct circulation of physical flows through and across product systems (1).

The framework questions the conventional definition of product system boundaries in LCA. Product systems frequently exclude so-called cut-off product flows, either due to cut-off criteria application (2) or multifunctionality resolution strategies (3). We argue that these flows may nevertheless represent important carriers of physical content entering or leaving the studied system, and therefore should be accounted for when evaluating circularity.

To address this issue, we developed a workflow based on the Python library Brightway. Two Python modules were implemented to identify and extract cut-off product flows from LCA databases. A second module (circularity_calculator.py) derives circularity indicators directly from the Life Cycle Inventory (LCI). The approach requires translating heterogeneous physical units (e.g., kg, MJ, kWh, m3) into common equivalent metrics such as kg-eq or MJ-eq. To achieve this, the framework uses recorded flow properties available in databases such as ecoinvent v3 (e.g., densities or calorific values).

For the presentation, the proposed framework is illustrated through a simple Lithium-ion battery storage system example. A multifunctional battery use process generates both an energy storage service and an end-of-life battery flow. Under traditional cut-off modelling, the end-of-life flow remains outside the circularity accounting framework. We extend the intervention matrix to include cut-off product flows as resource flows contributing to circularity measurements. The resulting framework allows the derivation of circularity and inefficiency indicators. In the example case study, the proposed method captures circular flows ignored by the traditional LCI formulation, including the circulation of stored energy and end-of-life batteries. The approach also highlights the importance of assigning physical properties to flows: without conversion factors such as water density or fuel calorific value, physically meaningful aggregation becomes impossible.

The framework was further applied to 1000 randomly selected product systems from the ecoinvent v3.12 cut-off system model. Figure 1 presents inefficiency indicators expressed in both mass-equivalent and energy-equivalent units. Preliminary observations suggest that inefficiency values expressed in MJ-eq remain systematically low, likely because heat dissipation and thermal losses are insufficiently recorded in current LCA databases. This highlights both the potential and the present limitations of database structures for intrinsic circularity assessment.

More broadly, this work opens a discussion on the allocation choice to maintain physical balance, and of the utilitarian nature of LCA and questions the centrality of the functional unit when analysing the circulation of physical content across interconnected product systems.

Figure 1 (4): Inefficiency indicators derived from 1000 randomly selected product systems from the ecoinvent v3.12 cut-off system model, expressed in kg-eq and MJ-eq. Results suggest that inefficiency values expressed in MJ-eq remain systematically low, likely due to insufficient recording of heat dissipation and thermal losses in current LCA databases.

Post scriptum
The attached abstract includes the simple numerical example detailing the proposed methodology. The corresponding calculations and implementation are available in the accompanying GitHub repository.

References:
(1) Michael Saidani et al. “A taxonomy of circular economy indicators”. In: Journal of Cleaner Pro-duction 207 (2019), pp. 542–559. ISSN: 09596526. DOI: 10.1016/J.JCLEPRO.2018.10.014. arXiv: 1901.02709.

(2) ISO. Environmental management — Life cycle assessment — Principles and framework. ICS : 13.020.10 13.020.60. Geneva, Switzerland, July 2006. URL: https://www.iso.org /standard/37456.html.

(3) Reinout Heijungs and Sangwon Suh. The Computational Structure of Life Cycle Assessment. Vol. 11. ISBN: 978-90-481-6041-9. Dordrecht: Springer Netherlands, 2002. DOI: 10.1007/978- 94-015-9900-9. URL: http://link.springer.com/10.1007/978-94-015-9900-9
(visited on 09/04/2024).

(4) Figure 1: https://github.com/LouisFreboeuf/circularity_lca/blob/main/results/plots/CI-kg_CI-mj/pc_dup_bw-25_ecoinvent-3.11-cutoff_fm-fe_1000uprs/eta-_hybrid_scale_by_section.svg

HIGHLIGHTS
• Calculating new material-related metrics: amount of manufactured material
• Automatic calculation based only on Life Cycle Inventories data through the implementation of an LCIA method in the Brightway framework
• Comparison with existing tools Relics and Edges
• Demonstration with simple examples and exploration of the ecoinvent database through this new lens
• Industrial-Academic collaboration for open-source tools development😊

CONCISE DESCRIPTION

1. Need, goal and specifications
Impact indicators related to materials depletion, criticality, or circularity are complex to understand. An industrial company expressed the need for a complementary material-related indicator based on simple physical flows: the quantity of manufactured materials (eg. copper, aluminium or steel). The objective is to extract this physical metric directly from Life Cycle Inventories (LCI) data, similarly to how the Python library Edges does. Contrary to Edges, the new method is implemented as a Brightway Life Cycle Impact Assessment (LCIA) method to benefit from the full range of functionalities of LCIA methods within the Brightway framework.

2. Material method” implementation
The “material method” has been developed in Python language and implemented in the Brightway framework. It was tested with ecoinvent 3.11-cutoff & attributional.
The main principles are explained with a copper-related example. A new placeholder biosphere flow “copper material” with an amount of “1kg” is added to ecoinvent activities that produce 1kg of a reference product called “copper, cathode”. An algorithm also avoids double accounting of copper at the production stage and market stage. An LCIA indicator associated with one characterization factor (1kg/1kg of the new “copper material” pseudo biosphere flow) is also created.

3. Results
The method was applied to several ecoinvent activities for different materials to demonstrate that it accounts for:
• the material used in the foreground of the LCI
• the upstream material used to produce this material
• the material used for all other non-copper-related activities
The results were compared with results obtained with the Python libraries Edges and Relics.
This method was also applied to ecoinvent activities for copper, steel, and aluminium materials to explore the complex industrial value chain modeled by ecoinvent database through this new lens.

4. Conclusion
This method offers a new material-related lens to explore LCI through new metrics. It can be easily implemented for any reference product, not necessarily material- related.

Disclaimer
This work has been conducted through a collaboration with the French Transmission System Operator RTE and Mines Paris – PSL university.

Highlights

1. The development of 'time-explicit LCA' and indeed 'prospective LCA' requires further conceptual development to specify what such an LCA represents, with the development of a common understanding and vocabulary with which to characterise a given approach. We propose such conceptual contributions.
2. A product system in LCA represents some interconnected aspects of society's metabolism, and must thereby reckon with complexity and dynamism. Developments in time-explicit LCA can allow us to reach beyond the static, steady-state assumptions conventional to LCA.
3. A reproducible notebook (based on the bw_timex package for Brightway) illustrates how time-explicit LCA can be coupled with a dynamic system while maintaining the conceptual integrity of both tools. Such coupling invites a broader integration between LCA and other Industrial Ecology data and tools.

Concise description
There is a tension between the heterogeneous development of tools to understand and represent society's metabolism on one hand and on the other hand to provide LCA practitioners with clear guidelines, streamlined where possible based on justifiable heuristics. With the development of time-explicit LCA, there is a promise of systematically breaking from the static, steady-state assumptions conventional to LCA by capturing time-dependent change within a system. Yet in doing so, this approach requires considerably more data and, while its computational structure has been robustly described, this does not extend to a shared understanding of what it means to apply time-explicit LCA in practice.

This presentation will demonstrate an approach to modelling a time-explicit product system. By largely relying on the existing features of the bw_timex package for Brightway, a product system can be modelled to represent a functional unit which is not limited to a lifecycle as reflecting a single object, but as something that occurs and changes over time, and captures the function of many objects over time. An illustrative dynamic stock model will be used to demonstrate this point in an open-source, reproducible notebook. Furthermore, the principles demonstrated can be applied in conjunction with other dynamic models.

By formulating and sharing this illustrative case study, we hope to contribute to the ongoing effort of developing what it means to conduct prospective LCA in support of a particular decision context: in terms of its goal and scope as well as in terms of its robust and transparent computational execution. This effort can only be fruitful when it engages diverse segments of the LCA community.

Highlights

• Integration of the edges Python library into the BONSAI open-source
  framework for spatialized LCIA.
• Demonstrates how exchange-based LCIA
  enables geographically explicit impact assessment beyond global
  averages.
• Case study: quantifying how French consumption drives
  water scarcity in producer regions worldwide. Fully reproducible,
  open-source workflow supporting transparency and collaborative
  development.
• Provides a pathway to identify supply chain hotspots
  and policy-relevant environmental pressures using a fully open
  workflow.
• Concise Description

Concise Description

The growing need for geographically explicit Life Cycle Assessment (LCA) has highlighted the limitations of conventional impact assessment methods based on global average characterization factors. This study addresses these challenges by integrating the exchange-based Life Cycle Impact Assessment (LCIA) methodology (Sacchi et al., 2025) into the BONSAI framework, an open-source environmentally extended input-output database.

The approach is implemented using the Python library “edges”, enabling spatially explicit, context-sensitive, and scenario-based environmental assessments. Unlike traditional LCIA approaches, the exchange-based method accounts for the geographic origin of traded goods and intermediate exchanges, allowing the calculation of region-specific characterization factors that incorporate local environmental conditions (e.g., water scarcity) and supply chain dependencies.

The framework is applied to a case study of French consumption, focusing on water scarcity impacts embodied in global supply chains. By combining BONSAI consumption inventories, based on input–output modelling, which provides a robust representation of international trade flows and country-level demand, with edges-based LCIA methods, the study identifies critical regions and sectors contributing to water stress. This approach enables a more nuanced and geographically explicit understanding of environmental pressures linked to consumption.

This session demonstrates a reproducible workflow based on open-source tools (Python, BONSAI, edges), fostering transparency and enabling participants to extend the case study to other countries within their own practice. The work reflects the Brightcon spirit by promoting openness, methodological transparency, and collaborative development of LCA tools. Therefore also a notebook will be provided where participants can follow along and try themselves to start using edges with BONSAI data.

The bioeconomy is promoted as a concept that enables sustainable green growth by enabling the substitution of fossil resources with regenerative ones. The term encompasses several different visions. Depending on whom one asks, the bioeconomy promises either regional autarky, sufficiency within planetary boundaries, or climate-neutral global supply chains. With this work we aim to make those different visions tangible and assess their environmental implications. We answer the research question: Which environmental impacts result from different future visions of the bioeconomy?
We will present early results of answering this research question for the chemistry and transport sector in Germany. The results are achieved based on the following three methodological steps.
First, linking bioeconomy visions to the biomass-use optimization model BenOpt (Jordan et al. 2023). In previous work the model scope has been extended to additionally include material uses of biomass. In this work we will present three bioeconomy scenarios representing three different visions: a regional, a sufficiency-based and a global bioeconomy. Second, we soft-couple the biomass utilization pathways to life cycle inventories derived from literature and databases. This enables us to present life cycle assessment results of those bioeconomy scenarios. Third, we will conduct an absolute environmental sustainability assessment to determine not only which of the expressed scenarios and visons might be the most sustainable. We will also answer the question which vision is sustainable enough, in order to remain within biophysical limits (Bjørn et al. 2020).
The studies novelty consists out of the integration of bioeconomy scenarios for multiple sectors and their environmental assessment against planetary boundaries. With this the presented work will assess the bioeconomy’s capability to deliver goods and services within biophysical limits, lays the ground work to extend the assessment to further sectors and discuss necessary methodological adaptions. Discussed future potential additions include social and regional assessment, as well as integration of optimization within biophysical limits.

References
Bjørn A, Chandrakumar C, Boulay A-M, Doka G, Fang K, Gondran N, Hauschild MZ, Kerkhof A, King H, Margni M, McLaren S, Mueller C, Owsianiak M, Peters G, Roos S, Sala S, Sandin G, Sim S, Vargas-Gonzalez M, Ryberg M (2020) Review of life-cycle based methods for absolute environmental sustainability assessment and their applications. Environ. Res. Lett. 15(8): 83001. https://doi.org/10.1088/1748-9326/ab89d7
Jordan M, Meisel K, Dotzauer M, Schröder J, Cyffka K-F, Dögnitz N, Schmid C, Lenz V, Naumann K, Daniel-Gromke J, Paiva GC de, Schindler H, Aliabadi DE, Szarka N, Thrän D (2023) The controversial role of energy crops in the future German energy system: The trade offs of a phase-out and allocation priorities of the remaining biomass residues. Energy Reports 10: 3848–3858. https://doi.org/10.1016/j.egyr.2023.10.055

Authors: Timen M. Boeve and Søren Løkke
presenting author

Effective data management and data interpretation are essential prerequisites for conducting robust and decision-relevant Life Cycle Assessment (LCA) studies, particularly within large and complex organisations. This presentation draws on experiences from a PhD project in which an organisation-wide consequential LCA was conducted for Aalborg University, assessing the university’s complete climate impact (See Boeve, 2025).
The study involved extensive collaboration with internal stakeholders and external data providers, where effective data governance and stakeholder engagement proved critical for obtaining accurate and contextually meaningful data. Tailored communication strategies were developed to support data providers in understanding both the required data formats and the role of their contributions within the LCA.
A central finding of the project was that data management in decentralised organisations without a unified data infrastructure is highly dependent on individuals possessing tacit organisational knowledge regarding data accessibility and interpretation. This dependency became particularly evident when a key staff member retired during the project, resulting in the loss of access to previously available data streams. Consequently, a revised data collection framework anchored within the university’s central administration was established to ensure continuity and reproducibility of the assessment process.
The study was conducted over a four-year period during which institutional data management systems and reporting structures changed continuously. These developments created significant challenges for maintaining temporal consistency and comparability across annual LCA assessments. To address these challenges, two complementary approaches were implemented. First, capacity-building activities were conducted with data providers to improve understanding of LCA data requirements and strengthen data quality. Second, a collaborative network of stakeholders and data providers was established to facilitate interpretation of evolving data structures and support alignment between organisational reporting practices and LCA requirements.
The experiences from this work demonstrate the importance of institutional anchoring of data governance, continuous stakeholder engagement, and collaborative data interpretation in enabling reliable organisational LCA studies. The presentation would likely conclude with practical recommendations for strengthening data resilience, transparency, and comparability in sustainability assessments within complex organisations.

Boeve, T.M., 2025. CLIMATE IMPACT-ACCOUNTING FOR AALBORG UNIVERSITY. Aalborg University. https://doi.org/10.54337/aau819586888

Assessing the environmental impacts of emerging products and using life cycle assessment (LCA) to support sustainable design requires close collaboration across supply chains and alignment of assessment methodologies across life cycle stages. Even when typical early-stage LCA challenges, such as limited data availability and low supply-chain transparency, are addressed, inconsistencies in modelling approaches, data formats, and underlying assumptions often hinder the development of a coherent cradle-to-grave assessment. These issues are further exacerbated when practitioners rely on proprietary software and isolated workflows, resulting in fragmented and incompatible LCAs.

This contribution presents a real-world case study from a large European research project on bio-based composites (SSUCHY-Next), demonstrating how open-source LCA tools can improve transparency, interoperability, and reproducibility across complex supply chains. The study applies Brightway and Activity Browser to assess environmental impacts along the full composite value chain, combining primary data for raw materials such as hemp fibres and bio-based resins with data provided by designers and engineers on composite products, application scenarios, and end-of-life options.

Key challenges addressed include integrating existing LCA models, such as SimaPro exports, into a shared Brightway-based workflow, consistently handling heterogeneous data sources, and using open visualisation packages to communicate results in formats that are meaningful for non-LCA experts. Particular emphasis is placed on translating LCA outcomes into actionable insights that directly support sustainable design decisions.

By sharing both the application of existing frameworks and the lessons learned, this case study illustrates how open tools are can be used in multi-actor LCA projects, and which challenges need to be overcome for embedding LCA more effectively into collaborative innovation processes.

Power-to-Gas technologies that convert CO₂ into renewable biomethane are attracting increasing interest as routes for long-term energy storage, carbon valorisation, and decarbonisation of the gas grid. Among them, electromethanogenesis, where electrotrophic methanogenic archaea reduce CO₂ to CH₄ at a biocathode driven by renewable electricity, is a promising but highly coupled bioelectrochemical process. Its development requires modelling tools able to connect electrochemical, biological, mass-transfer, thermodynamic, and energy-related phenomena across scales, from microbial biofilm behaviour to industrial deployment.

Within the Fuels-C Horizon Europe project, we have developed a modular digital twin for an electromethanogenesis bioelectrochemical system. The tool integrates coupled sub-models describing electrochemical polarisation, anodic biofilm growth, cathodic methanogenesis, gas–liquid mass transfer, carbonate speciation, and energy balance within a unified dynamic simulation framework. The model was calibrated and validated against experimental time-series data obtained under two different CO₂ feeding conditions, showing close agreement with measured electrochemical and biological responses across both scenarios.

A distinctive feature of the tool is its industrial scale-up module, which translates cell-level experimental performance into production targets, infrastructure requirements, and preliminary cost indicators. This functionality makes key bottlenecks visible and quantitative, supporting the prioritisation of future experiments and longer-term deployment strategies. In addition, the model outputs and parameter sets can be connected with Life Cycle Assessment workflows to evaluate prospective environmental impacts under realistic scale-up scenarios.

Beyond the specific electromethanogenesis case study, this work illustrates the value of open, modular digital twins within the sustainability engineering ecosystem. By making model structure, assumptions, calibration data, and scale-up logic explicit and reusable, the approach enables collaborative refinement across institutions, establishes traceable links between laboratory observations and real-world projections, and reduces duplicated modelling effort. This aligns directly with the BrightCon 2026 focus on connecting tools, data, and people to accelerate transparent, reproducible, and actionable sustainability assessment.

Highlights and Discussion Points
- Facilitating a model-based integration of Life Cycle Assessments into early product development
- Supporting the transferability of the LCA method based on an open-source, Brightway-based software tool
- Incorporating uncertainties caused by lack of data and low system knowledge to prevent unaware design choices
- Providing a comprehensible way of dynamically visualising results for non LCA-practitioners by utilisation of visual analytics

Description
Embedding environmental considerations into early product development instead of retrofitting previously designed products to meet environmental requirements is, in itself, the preferable approach. However, this introduces significant uncertainties into the environmental assessment due to a low system knowledge and lack of reliable and verified data. The composition of internal primary data, information from potential suppliers, and supplementary datasets from generic databases causes a high variance in the obtained results, decreasing the precision of derived decision support. To ensure that product developers can use the information gained from preliminary Life Cycle Assessments (LCAs) for an expansion of the comparison of design variants and general decision-making to the environmental perspective, uncertainties not only have to be included in the results but their presentation needs to be carried out in a reasonable and appropriate way.
We propose a framework that tackles the handling of uncertainties at three stages: Firstly, life cycle inventory data, design parameters, and their respective uncertainties are harmonized within the Brightway LCA model. Secondly, foreground and background uncertainties are propagated through the model and quantified during the impact assessment. Lastly, visualisation tools are used to properly communicate the results to decision-makers, enabling, e.g., the identification of environmental hotspots under uncertainties and the consideration of impact distributions without the extensive background knowledge necessary to perform the LCA itself.
A case study on a car’s underbody component illustrates how this approach not only streamlines the generation of coherent inventory data but also enables a straightforward and flexible evaluation of environmental impacts under uncertainty. The combination of parametrisation and visual analytics ensures transparent, data-driven, and reproducible decisions in product design as means to lower the threshold for integrating LCAs at an early stage of the product development process.

Modern sustainability assessments increasingly require dynamic integration of life cyclce assessment (LCA) results with engineering simulations for systems optimization, real-time decision support, and multi-physics modeling. While Brightway-based LCA makes this integration more feasible than end-to-end software stacks (e.g., SimaPro, OpenLCA), coupling with external simulation environments remains challenging due to incompatible interfaces and computational overhead. The Functional Mockup Interface (FMI) standard offers a solution by enabling seamless co-simulation between heterogeneous modeling tools.

This presentation demonstrates a complete workflow for converting Brightway2 LCA models into standardized FMI 2.0-compliant Functional Mockup Units (FMUs). Using Python, Brightway2, and the pythonfmu library, we showcase automated extraction of emission factors from ecoinvent 3.12 databases for both climate change impacts (IPCC 2021 GWP100) and endpoint damages (ReCiPe 2016). The workflow transforms JSON inventories into production-ready FMUs with standardized input/output interfaces (energy consumption as input and single-score environmental impacts as output). These FMUs are ready for co-simulation with dynamic multi-domain system simulation tools, such as Modelica. A live demonstration includes FMU generation for various energy storage systems (e.g., sand battery), followed by validation. All code, data, and FMUs are provided as reproducible examples with complete automation scripts.

With a focus on advancing the sustainable design and operation of energy systems, this presentation advocates for fully open-source, reproducible tools that democratize access to advanced LCA-simulation coupling. By standardizing LCA interfaces through FMI compliance, we foster cross-platform collaboration between LCA practitioners and model-based systems engineers, advancing transparent and interoperable sustainability assessment workflows.

Life Cycle Assessment (LCA), both in theory and practice, has traditionally relied on a strict distinction between the environment and the economy. Weidema et al. (2018) proposed a conceptual shift by relaxing this boundary and unifying economic activities and environmental mechanisms within an extended techno-biosphere matrix. In this formulation, environmental feedbacks can be represented in a mathematically consistent manner as input and output flows between environmental mechanisms and economic activities. Economic activities emit elementary flows that trigger environmental mechanisms, which in turn generate feedback flows to the technosphere, thereby closing the loop.
While this work opened the door to explicitly modeling feedback loops in LCA and brought the field closer to ecosystem services modeling, it has not yet been operationalized. A key limitation lies in the absence of guidance on how to quantify the flows returning from the environment to the economy, leaving the framework largely theoretical.
This contribution presents a brightway-based package, together with an extended conceptual and mathematical framework, designed to operationalize environmental feedbacks in LCA. A central feature of the approach is the explicit treatment of time, recognizing that environmental feedbacks unfold over extended temporal horizons. The implementation therefore builds on bw_timex (Müller et al., 2025) and premise (Sacchi et al., 2022) to propagate feedback mechanisms dynamically and prospectively. By representing the economy and environment as a single interacting system, the framework enables the construction of time-resolved characterized inventories, where elementary flows act as initial perturbations, analogous to functional unit demands in conventional LCA.
The framework provides a general structure to model feedbacks based on a set of inputs: (i) a feedback driver time pattern describing how an environmental variable evolves following an emission, (ii) a response pattern capturing how technosphere activities respond to this driver, and (iii) exogenous trajectories for both the driver and the demand of affected activities. For instance, feedbacks between CO₂ emissions and agricultural production via global temperature can be modeled using prospective and dynamic Global Temperature Potential (Barbosa Watanabe & Cherubini, 2026) to describe the temperature response, and global gridded crop models (Rosenzweig et al., 2014) to derive yield responses. An initial emission induces a temperature trajectory modification, which affects crop yields over time; when combined with projected demands for the affected crops (e.g., FAO scenarios), this results in additional emissions attributable to the initial perturbation. These new emissions induce a new temperature modification, leading to next iteration. The feedback is then iteratively propagated across time, with consistent alignment between prospective technosphere and biosphere dynamics.
A critical aspect of the framework is the separation of endogenous effects, i.e., those attributable to the initial emission, from exogenous influences driven by broader system dynamics. This distinction is rigorously implemented using partial derivatives within an adapted matrix-based LCA formulation, enabling consistent interaction among multiple feedback loops.
Overall, this work proposes a unified and operational framework for feedback modeling in LCA, bridging Life Cycle Inventory (LCI) and Life Cycle Impact Assessment (LCIA) within a dynamic and prospective perspective. By integrating feedbacks, temporal dynamics, and uncertainty propagation, it lays the foundation for a new research direction at the intersection of these domains. The modular design of the package supports detailed uncertainty analysis and scenario exploration. The open-source implementation is currently being tested on climate–agriculture feedbacks and interactions between particulate matter emissions and the healthcare sector.

Barbosa Watanabe, M. D., & Cherubini, F. (2026). Prospective Characterization Factors for Assessing Climate Change Impacts in Life Cycle Assessments. Environmental Science & Technology, acs.est.5c12391. https://doi.org/10.1021/acs.est.5c12391
Müller, A., Diepers, T., Jakobs, A., Cardellini, G., Von Der Assen, N., Guinée, J., & Steubing, B. (2025). Time-explicit life cycle assessment : A flexible framework for coherent consideration of temporal dynamics. The International Journal of Life Cycle Assessment. https://doi.org/10.1007/s11367-025-02539-3
Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C., Arneth, A., Boote, K. J., Folberth, C., Glotter, M., Khabarov, N., Neumann, K., Piontek, F., Pugh, T. A. M., Schmid, E., Stehfest, E., Yang, H., & Jones, J. W. (2014). Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proceedings of the National Academy of Sciences, 111(9), 3268‑3273. https://doi.org/10.1073/pnas.1222463110
Sacchi, R., Terlouw, T., Siala, K., Dirnaichner, A., Bauer, C., Cox, B., Mutel, C., Daioglou, V., & Luderer, G. (2022). PRospective EnvironMental Impact asSEment ( premise ) : A streamlined approach to producing databases for prospective life cycle assessment using integrated assessment models IMAGE. Renewable and Sustainable Energy Reviews, 160(April 2021), 112311. https://doi.org/10.1016/j.rser.2022.112311
Weidema, B. P., Schmidt, J., Fantke, P., & Pauliuk, S. (2018). On the boundary between economy and environment in life cycle assessment. The International Journal of Life Cycle Assessment, 23(9), 1839‑1846. https://doi.org/10.1007/s11367-017-1398-4

Regioinvent is a novel, open-source Python framework designed to work seamlessly with Brightway to unlock the full potential of regionalization within the ecoinvent database. The tool systematically generates thousands of contextualized activity datasets by adapting key local parameters, such as electricity mixes, heat production, and waste treatment, to specific national contexts. This allows for the automated creation of localized processes, such as country-specific "steel production" for every geography globally.

To further refine the granularity of these supply chains, Regioinvent integrates ecoinvent with the BACI database (which tracks international bilateral trade flows) along with production data from various sources. This connection enables the automated construction of consumption markets for all ecoinvent commodities across all relevant countries (e.g., a "consumption market for passenger cars" specifically describing the origin of cars purchased in Denmark). These markets are dynamically linked to the previously created local production processes, such as car manufacturing in Denmark, utilize the corresponding regionalized inputs, e.g., the Danish consumption market for steel or the Danish consumption market for tires.

Finally, the tool spatializes all relevant elementary flows, connecting them to three comprehensive, fully regionalized LCIA methods: IMPACT World+, ReCiPe 2016, and Environmental Footprint (EF).

By replacing generic global averages within ecoinvent with high-resolution, trade-linked supply chains, as well as consistently relying on regionalized characterization factors, Regioinvent provides a more realistic representation of global production systems and their potential environmental impacts, significantly enhancing the accuracy and reliability of LCA results.

Energy System Models (ESMs) are powerful tools for designing and assessing energy transition scenarios, but their environmental scope is typically restricted to direct carbon emissions during the operation phase. This narrow framing overlooks the full life-cycle burden of energy infrastructure — from manufacturing to end-of-life — as well as a broad range of impact categories beyond climate change, including human toxicity, water use, land use, and mineral resource depletion. Failing to account for these dimensions risks shifting environmental burdens rather than eliminating them, and misguides decision-making in low-carbon transitions.
mescal is an open-source Python package (MIT license) built as a specialized component of the Brightway Software Framework. It automates the coupling between any bottom-up ESM and LCA, relying on bw2calc and wurst to perform systematic LCI database transformations and LCIA computations. Starting from a user-defined mapping between ESM technologies/resources and ecoinvent (or premise-generated) datasets, mescal performs foreground regionalization, double-counting removal, and parameter harmonization (efficiency, lifetime, capacity factor, physical units) before pre-computing normalized LCIA scores ready for integration in the ESM. After the ESM run, mescal propagates energy system results back into the LCI database, enabling iterative feedback loops between the two models.
A key design principle is model-agnosticism: mescal provides data formats compatible with any bottom-up ESM (TIMES, MESSAGEix, Calliope, EnergyScope, etc.), and the ESM-LCA coupling equations it proposes can support both single-objective and multi-objective optimization. An illustrative case study shows that ESMs restricted to direct CO₂ emissions capture only a small fraction of total environmental impacts in low-carbon scenarios (e.g., 2-10% in our case study), underscoring the necessity of a full life-cycle perspective.
mescal is designed for energy modelers who are not LCA experts, as well as for LCA practitioners wishing to integrate ESM projections into prospective assessments. Its methodology is documented in a peer-reviewed article (Souttre et al., 2026, Journal of Industrial Ecology), and the package is available on PyPI, GitHub, and Zenodo with full documentation and example notebooks.

An LCA Data Package Manager (LDPM)

Highlights

• Open-source LCA data is increasingly being published in smaller collections than whole background DBs
• The classic software-package-manager pattern might be usefully adapted to LCA data
• Prototyping of an LCA Data Package Manager (LDPM) was initiated to support knitting U.S. FLCAC data into a unified DB

Description

The U.S. Federal LCA Commons (FLCAC) is a federation of data packages (DPKG) independently developed and maintained by various U.S. federal agencies. This separation ensures clear ownership and attribution but also partially constrains the development of a single, combined database (DB). Data object links (i.e., a "Ref" in the olca-schema) across DPKGs are not well supported by the FLCAC's backend LCA Collaboration Server instance, and even if they were, independent DPKG releases pose a series of pair-wise version control dilemmas. These inter-DPKG links have to-date been managed via bridge processes and libraries, with the former requiring users to manually update Exchange.defaultProvider selections.

To overcome these challenges and begin developing an all-in-one FLCAC DB, ERG began prototyping an LCA Data Package Manager (LDPM), with design principles and manifest format inspired by Rust's Cargo package manager. The LDPM should be fast and deterministic: it must yield a reproducible output from a given manifest and set of additional configuration files. Users should be able to easily read, write, reuse, and extend manifests, and the resulting background DB easily rebuilt on any machine. Additionally, the latest LDPM prototype requires configuration files containing instructions for two main features: data object de-duplication and inter-DPKG object linking. The de-duplication routine amends previously necessary patterns on the FLCAC; later on, this could be condensed into a collection of tests. Linking, however, is a core feature, as it enables cross-DPKG Exchange.defaultProvider and Flow selections. Rather than trying to nest Ref links in a referent object, we index links by pairs of versioned DPKG identifiers (e.g., [DPKG_A-version, DPKG_B-version]) as top-level keys. Maintaining DPKGs and link sets separately offers certain version control and dependency resolution advantages.

Broadening our horizons further, the steady emergence and improvement of national LCI DBs writ large means that an LDPM-like tool could offer a common LCA-stack component and language (via the manifest format) to define and mint background DBs that encompass global supply chains. For example, an LDPM-assembled background DB could serve as the foundation upon which experts build a PEF or EPD template. Those experts, relevant regulators, and regulated parties alike could then each propose refinements to that template's background DB by way of changes to the LDPM manifest and/or configuration files. Much like the FLCAC, national LCI DBs and other open-source industrial ecology datasets can be collectively viewed and managed as a federation of DPKGs, all waiting to be reproducibly woven together by a dedicated "data loom"—the LDPM.

Battery lifecycle assessment (LCA) increasingly depends on heterogeneous and rapidly evolving datasets originating from manufacturing, operation, second-life use, and recycling contexts. However, these datasets are often encoded in incompatible formats, making reuse, interoperability, and transparent data exchange difficult across projects and organizations. At the same time, sustainability practitioners are seeking more reproducible and machine-readable workflows that can support collaborative modeling, traceability, and future AI-assisted analysis.

This workshop introduces a practical semantic-web workflow for organizing and querying battery lifecycle information using interoperable ontologies and knowledge graphs. Participants will explore a populated battery knowledge graph built from four connected ontologies: the Battery Production Ontology (BPO), the Battery Use Ontolgy (BUO), the Digital Battery Passport ontology (DBP), and BattReLife for battery circularity and end-of-life modeling. The workshop uses RDF, OWL, SHACL, SPARQL, Python notebooks, and a hosted GraphDB environment to demonstrate how lifecycle data can be transformed into reusable, queryable knowledge structures.

The session combines a short conceptual introduction with guided hands-on activities. Participants will execute and modify SPARQL queries on a benchmark dataset containing populated battery-pack instances. The exercises demonstrate provenance-aware querying, cross-ontology interoperability, and lifecycle-spanning analyses linking production, use-phase, and end-of-life information. No prior ontology experience is required.

This workshop is designed to encourage discussion, experimentation, and community feedback on how semantic technologies can support more transparent and collaborative sustainability assessment workflows.

Highlights / Discussion Points:
Primary takeaway: How can lab based product disassembly and characterization data reliably feed LCA and policy relevant open databases?
We hope to co-create the following takeaways:
A shared understanding of minimum requirements for reliability and usability of product characterization data in LCA databases
Community input on priority gaps and barriers to integration
Initial ideas and collaborations for scaling open, networked lab based data infrastructures within the Brightcon community
Initial ideas for a data validation and review approaches

Description
There is an urgent need for high quality, transparent data on the material composition, design characteristics, and circularity potential of common goods to support policy instruments such as Safe and Sustainable by Design, Digital Product Passports, and Product Environmental Footprint.
Across the world, many laboratories are already generating this kind of detailed data through, for example, product disassembly and waste characterization. However, much of this data remains fragmented, difficult to integrate into life cycle inventories, and under utilized in LCA and MFA studies and databases.
This 60 minute plenum session aims to bring together data users, database developers, and practitioners to explore how laboratory, field, and waste characterization data can be more effectively digitized, harmonized, and integrated into open source databases which can be accessed by a network of labs and practitioners alike.
Using the CML Re.Lab pilot citizen science disassembly platform as an example, the session will combine inputs with interactive polling and structured group discussion to address the following questions:

• What tools, standards, and reporting practices are needed to harmonize disassembly and product characterization data across labs?
• What are practitioners’ experiences (the good, the bad, and the ugly) when integrating lab or field based data into LCA, MFA, and policy studies?
• How can data quality, reliability indicators, and critical review processes be designed to support both scientific rigor and openness?
• What would it take to scale pilots such as the CML platform into a federated network of labs, researchers, and citizen scientists?
In the sprit of Brightcon, this plenum is intended as a community building and agenda setting conversation, helping align technical, methodological, and governance needs around product data to better support open source LCA and evidence based sustainability policy.

Highlights / Discussion Points

• Introducing MODASTI, a proposed open-source platform for modular and composable LCIA configurations
• Exploring whether there is a community need for transparent, interoperable, and collaboratively maintained LCIA infrastructure
• Discussing modular schema design, provenance, versioning, and persistent identifiers for reproducible LCIA compositions
• Examining technical, institutional, and social barriers to collaborative LCIA development and adoption
• Collaborative prototype discussion and hands-on exploration of implementation concepts using Python- and Brightway-compatible structures

Concise Description

Modern life cycle impact assessment (LCIA) frameworks are typically assembled from methods, datasets, and modeling approaches developed across multiple research groups and domains. However, many existing frameworks remain difficult to inspect, adapt, or incrementally improve at the level of individual methodological components. Smaller contributions such as updated fate factors, revised regionalization approaches, mappings, normalization references, or background datasets are often embedded within larger institutional frameworks and can become difficult to trace, reuse, or openly maintain. At the same time, practitioners and method developers increasingly work in contexts where selective adaptation of existing LCIA approaches may be scientifically useful or regionally necessary.

This workshop introduces MODASTI, a proposed open-source platform concept for constructing, modifying, and sharing composable LCIA configurations from interoperable modules with explicit provenance and persistent identifiers. The concept spans multiple levels of modularity, ranging from combining existing midpoint indicators across frameworks to modifying submodules such as fate or effect models, and ultimately adapting underlying spatial aggregations or background data. Rather than proposing another integrated LCIA framework, MODASTI aims to explore whether a community-driven infrastructure for transparent and reproducible LCIA composition would be valuable and feasible.

The session will combine a short conceptual introduction and prototype demonstration with collaborative discussion and hands-on exploration of implementation concepts. Topics will include modular schema design, metadata requirements, interoperability, governance, and barriers to adoption. The prototype will be based on open-source Python tooling with an emphasis on interoperability with Brightway and exportable data structures.

By emphasizing transparent compositions, reproducible identifiers, open contribution pathways, and collaborative infrastructure development, the workshop aims to foster discussion around more accessible and community-maintained LCIA ecosystems.

Brightway’s inventory calculations usually rely on direct sparse solvers such as PARDISO or UMFPACK. These are robust and fast for many applications, but they can become a major bottleneck when technosphere systems grow very large. In particular, the matrix factorisation step can require far more memory than the sparse matrix itself, making calculations on large or dense systems painfully slow or even impractical on standard laptops.

In this talk, I present a recent addition to bw2calc: an alternative iterative solver based on a Jacobi preconditioner and the GMRES Krylov method. Instead of performing a full direct factorisation, this approach solves the system iteratively to a specified residual tolerance, greatly reducing memory pressure while still delivering results accurate enough for practical LCA work.

The presentation will centre on a short Jupyter notebook demo. Using technosphere systems of different sizes and sparsities, I will show where the iterative approach changes the game. In one benchmark, a 250k × 250k regionalised technosphere system was solved in about 4 seconds instead of 8 minutes. In another synthetic test, a 40k × 40k system with 0.1% density dropped from 1540 seconds with a direct solver to 0.02 seconds with the iterative approach. These results suggest that iterative methods could enable Brightway to handle much larger systems, including cases currently limited by memory rather than by the LCA framework itself.

Beyond the speed-up, I will discuss the main trade-off: this is no longer an exact direct solve, but an approximate one controlled by convergence criteria. For many applications, however, this approximation appears to be more than sufficient, while making previously unwieldy systems tractable. The talk aims to show both the practical implementation now available in bw2calc and the potential to scale open-source LCA to much larger problem sizes.

Land-based carbon sequestration strategies require linking soil carbon dynamics with supply chain responses, accounting for land-use effects and biomass valorization. This work develops a parametric modelling workflow in Brightway 2.5 for consequential LCA of soil carbon sequestration strategies (SCS) in French croplands. Life cycle inventories integrate soil organic carbon (SOC) trajectories (C-TOOL and AMG soil models), tree growth modelling, and system expansion to represent multifunctionality and substitution pathways. Global sensitivity analysis is implemented through a modular Python toolkit with multiprocessing, enabling systematic perturbation of key parameters across both agronomic and supply-chain processes.
The analysis focuses on hedgerow integration and biochar-to-soil systems. In the hedgerow system, black locust hedges are introduced into conventional cropping systems, with biomass valorized through material substitution, including MDF replacing PVC and timber products substituting bricks. The model accounts for nitrogen-related, yield compensation, and iLUC. Climate benefits are driven by avoided materials, while both freshwater and marine eutrophication, as well as water use, are exacerbated by yield-compensation following cropland surface reduction from hedge planting. In the biochar system, pine plantations supply feedstock for pyrolysis, with biochar applied to soil and carbon storage assessed over 100 years. Agronomic side effects, including changes in yield, irrigation, nitrogen emissions, and albedo, are included. Results show that biochar offers substantial climate change mitigation from SOC sequestration, but iLUC drives increases in five of six assessed impact categories. Notably, oil recovery for energy is only beneficial if it substitutes fossil fuels; under a renewable energy matrix, this credit is lost.
Monte Carlo results show substantial overlap in impact distributions, indicating that variability across agronomic, SOC, and supply-chain parameters can offset the expected benefits of carbon sequestration.
This workflow enables transparent evaluation of how agronomic processes and supply-chain mechanisms interact in consequential LCA, supporting reproducible assessment of soil carbon mitigation strategies.

Life-cycle assessments are rarely applied as often to the operational decision-making in production as to the planning stage, despite the significant cumulative environmental impacts of high-volume processes. The bottleneck here is that an LCA is too complex for daily use, and decision-makers in production are not trained to interpret the results.

For the ENCIRCLE project, parameterised LCAs are conducted, using as parameters operational variables. The parameters are varied and used to train a surrogate model with the output, i.e. the life-cycle impacts. To determine the stopping criterion for the calculations, the CO2e footprint from the LCA calculations is compared with the optimisation potential derived from more accurate LCI results.

The approach is being tested in two industrial use cases: galvanising and aluminium recycling. As the surrogate model's output is integrated into an Reinforcement Learning AI agent as an optimization objective, it must aggregate all impacts into a single normalized value to form its reward function, a requirement present both in the simulation environment where the agent trains, and the live production line in which it will imminently be deployed.
To this end, the values in the damage categories of ReCiPe 2016 are normalised so that a standard use case corresponds to 100%.

An interactive notebook is used to demonstrate how the surrogate model is generated and ported in Brightway 2.5, using the joblib package.

Digital Product Passports (DPPs) will soon become mandatory for many products sold in the European Union. These digital records of product information are an important development for supply chain transparency and support of conscious consumer choices. Companies see the need for compliance, but clear guidance on data requirements is missing.
To support data collection and data sharing across supply chains, consumers and service operators, an efficient data structure is needed. Previous research has mainly focused on data exchange protocols, while paying less attention to data collection and structured storage. We developed a web-application and relational database for DPP data which enable structured data entry via a User Interface and API. Several functions streamline data collection and completion. Environmental indicator values are determined by Brightway-based calculations, which use process and product information. Both the front-end and back-end will be shown in a live demonstration.
Furthermore, this contribution discusses the challenges and opportunities of sustainability data sharing through DPPs, with particular attention to data quality, representativeness, and confidentiality. It also examines the implications for Life Cycle Assessment practice arising from the widespread availability of machine-readable product information.

Highlights
1. Most AI-assisted LCA research focuses on data gap filling, automated foreground system construction remains underexplored.
2. A user-driven pipeline is proposed: users input a rough system description, and LLMs automatically construct the foreground system with system boundaries and key inventory parameters.
3. The framework will be implemented in Python and integrated with Brightway for LCA calculation.
4. The AI-constructed foreground system will be evaluated against traditional manual LCA approaches in terms of efficiency and consistency.
5. The pipeline aims to lower the barrier for LCA practitioners without deep modeling expertise.

Context
AI applications in Life Cycle Assessment (LCA) automation have grown rapidly in recent years. Existing studies have explored two main directions: using machine learning or large language models to fill data gaps in the life cycle inventory (LCI) stage and developing AI-driven pipelines to automate the overall LCA workflow. However, the automated construction and calculation of foreground systems specifically remain underexplored.

Approach
This study proposes an AI-driven pipeline in which users provide a rough description of their system, and the tool automatically constructs the foreground system. Users can then review and adjust the outputs before proceeding to calculation. This approach significantly reduces the manual effort required in conventional LCA practice.

The framework will be implemented in Python with Brightway as the core LCA engine. The AI-constructed foreground system will be compared against traditionally built models to evaluate consistency and time efficiency, and its ability to meet user-defined requirements will be assessed.

Brightcon Spirit
The proposed pipeline will be developed as an open-source Python tool built on Brightway, ensuring full transparency and reproducibility. Although the codebase is currently under development, the session will present the framework design, preliminary results, and invite community feedback on both the methodology and implementation.

Highlights / Discussion Points
• ISAT (IPM Self-Assessment Tool) is the first freely accessible, web-based platform integrating the PLEU with GHG emissions and cost indicators for field-level crop protection scenario comparison: http://synops.julius-kuehn.de/support.
• New harmonized pesticide risk indicator (PLEU) covering 41 hazard metrics across environmental fate, ecotoxicity, and human health for 516 active ingredients — including over 60 biopesticides.
• Live demonstration of the tool using a winter wheat case study (Germany), showing how IPM scenarios can reduce pesticide risk by up to 75% while revealing trade-offs with GHG emissions and costs.
• Open dataset and reproducible methodology: PLEU scores for all 516 active ingredients are publicly available; the tool runs on open, documented data sources (PPDB, BPDB, Ecoinvent, KTBL).
• Key takeaway: Transparent, multi-indicator tools like ISAT can bridge the gap between IPM research and real-world adoption by farmers and advisors.

Concise Description
Context: Despite a wealth of pesticide risk indicators and decision support systems, farmers and advisors across Europe still lack a user-friendly, openly accessible tool that integrates harmonized risk assessment with practical crop protection scenario comparison. Current EU monitoring relies heavily on sales data and the Harmonised Risk Indicator 1 (HRI1), both of which have well-documented limitations in scientific validity and transparency. This gap directly constrains the uptake of Integrated Pest Management (IPM) — a priority of the EU Sustainable Use of Pesticides Directive.
Approach: This presentation introduces the IPM Self-Assessment Tool (ISAT), a freely accessible web application developed within the EU Horizon Europe SUPPORT project. At its core is the Pesticide Load for Europe (PLEU), a newly developed, harmonized indicator covering 41 hazard metrics across three sub-indicators (Environmental Fate, Ecotoxicity, Human Health), computed for 452 synthetic and 64 biopesticide active ingredients. ISAT further integrates life-cycle-based GHG emission calculations (via MiLA/Ecoinvent) and cost estimates (via KTBL), enabling comparison of multiple management scenarios for the same crop and field. The tool was validated with data from ten European countries and eight crops. The presentation will include a live demonstration using a winter wheat case study from Germany, showing how the tool visualizes risk-driving active ingredients and quantifies trade-offs between pesticide risk, emissions, and cost across contrasting IPM scenarios.
ISAT is built on open data (PPDB, BPDB, Ecoinvent), produces exportable CSV outputs, and its underlying PLEU methodology and dataset are fully open (https://doi.org/10.5281/zenodo.19705986). We welcome community feedback on extending the indicator, integrating additional crop systems, and co-developing pathways toward EU-wide harmonization.

Highlights/Dicussion Points:
- Present the brightway libraries used in EcoAgent
- Share jupyter code for using AI-RAG based database search and open source repository I made
- Future objective to balance self sustaining business model and open code share

Concise Description
EcoAgent is an AI assistant for LCA. It relies on a few Brightway libraries. In this talk, the workings of EcoAgent with Brightway and LLM models will be presented. Code and repository to replicate database search capabilities of EcoAgent will be shared. This will be helpful for LCA practitioners to implement AI in their Brightway workflows. The objective of EcoAgent is to balance self sustaining business model with open code sharing.

Achieving net-zero targets necessitates large quantities of newly installed renewable energy capacity and sustainable hydrogen production. Since the projected demand of hydrogen and its derivatives in various end use sectors in Europe cannot be produced locally economically, imports from regions with surpluses of renewable energy are likely. New trade routes will emerge, transporting hydrogen and its derivatives across the oceans and requiring end-use specific distribution chains. Ammonia is one promising option as a hydrogen transport vector, shipping fuel and as a product itself.

To date, the python package for prospective Life Cycle Assessment (premise) models hydrogen distribution in a simplified way, with few datasets for pipeline, ship and trailer transport. This contribution implements improved transport models in premise, and presents new life cycle inventory (LCI) datasets for international hydrogen transport with a focus on ammonia shipping and suggested end-use specific distribution. LCI’s are developed individually and are designed to be modular, supporting scenario-dependent parametrization so that transport-related burdens can evolve consistently according to scenario results from various Integrated Assessment Models (IAMs).

A case study demonstrates the differences between environmental impacts quantified with the current generic premise representation of hydrogen transport and the LCIA scores based on the expanded implementation, showcasing the practical relevance of the improved data for evaluation of future international hydrogen value chains.

Highlights:

• Introduction of Lighthouse, a Python package integrating Brightway and premise
• Flexible building modelling framework
• Automated generation of the solution space of phased renovation scenarios
• Time-resolved life cycle assessment with dynamic life cycle inventory
• Advanced decision-support capabilities
• Open-source, scalable, and interoperable design

Abstract

The urgent need to decarbonise the European building stock, as highlighted by the EU Renovation Wave, requires robust and transparent tools to support large-scale renovation decision-making. A significant share of existing buildings must undergo deep energy renovation in the coming decades, raising complex questions about optimal renovation strategies. However, current building LCA practices largely represent renovation as a single event, failing to capture the evolutive nature of buildings, which is shaped by financial, technological and regulatory constraints.
This context led to the development of Lighthouse, a Python package designed to systematically generate and evaluate building renovation scenarios. Lighthouse allows users to model a building by selecting an archetype and specifying its dimensions, material composition, systems, and renovation history. Based on a flexible set of renovation measures and constraints, the tool automatically generates all compatible renovation scenarios.
For each scenario, Lighthouse performs a comprehensive, time-resolved life cycle assessment, including dynamic updates to the whole life cycle inventory through integration with premise and the Brightway ecosystem. The tool additionally provides multiple analytical tools to support decision-making, including scenario comparison, sensitivity analysis, and Pareto front exploration based on characteristics occurrences.
Lighthouse directly addresses key limitations in current LCA practices by providing a way to navigate the uncertainty related to the evolutive nature of buildings. The tool will soon be released as an open-source Python package. Developed with scalability and interoperability in mind, it supports applications from single buildings to urban-scale analyses and integration of external tools such as EnergyPlus. Its presentation in the “Open Tools and Development” session highlights its contribution to transparency, reproducibility, and community-driven development.

Many life cycle inventories are technically public but practically unusable: they are buried in PDFs, supplementary spreadsheets, or tables with weak metadata, unclear licensing, and no stable version history. Sharing data with interested parties is often hindered by differences in LCA software and data formats, background database versions, and other incompatibilities. CLIC (Curated Life cycle Inventory Collection) is a human-powered platform built to close that gap by treating LCI data as a managed lifecycle, from original data to a usable, published version, rather than a one-off file upload.

In CLIC, contributors create dataset drafts by uploading inventories exported from various LCA software (Brightway, Simapro, and OpenLCA). Before review, submissions are first turned into a harmonised format, and then machine checked for required metadata, file validity, rights and license declarations, and controlled classifications. Community reviewers and editors then curate datasets through defined states from draft to submitted, under review, accepted, and published. Published versions are immutable, semantically versioned, and exposed through stable public pages, metadata search with a classification tree, a read-only API, and generating exports tailored for specific LCA software (i.e., Brightway, OpenLCA, and SimaPro) and background database (e.g. ecoinvent) version.

This talk presents CLIC as a proof-of-concept case study for building pragmatic digital infrastructure for an open environmental modelling data-sharing platform using a lightweight Django/PostgreSQL architecture. The core argument is that accessibility and interoperability do not come solely from releasing files; they require validation, version-aware publication, reviewable curation, keeping datasets up to date and relevant, and explicit export pathways usable in common LCA software. By structuring inventories this way, the platform creates a reliable foundation for downstream LCA automation, including background-database relinking, duplicate detection, and automated data cleaning and harmonisation. The aim is not only to host open inventories, but to make them discoverable, trustworthy, and genuinely reusable in research and practice.

Life Cycle Assessments (LCA) of novel, low-TRL technologies are often constrained by low availability of inventory data. The assessment of input materials based on patent information one feasible approach to overcome the lack of data, e.g., for less-common chemical substances used as input materials for novel electrolytes.

This presentation highlights the approach applied in the SAGELi project to convert patent information into life cycle inventories. It combines the open-source process simulation tool DWSIM with Brightway. A python-based implementation of the process in DWSIM allows to estimate energy consumption, solvent use, and material needs for the synthesis of several substances in the project based on publicly available information in patents. The output from the process simulation in DWSIM is directly linked to Brightway to serve as life cycle inventory for LCA. The approach is exemplified for at least one case study on a component of the SAGELi project.

Overall, this work demonstrates how by combining the open tools DWSIM and Brightway, gaps in data can be filled and the robustness of LCA evaluations in early design stages improved.

Highlights:
• LCA from patent information
• Integration of process simulation (DWSIM) and Brightway for LCA

Context
At Brightcon 2025, Départ de Sentier highlighted three fundamental life cycle assessment (LCA) data-related challenges (findability, maintenance, and flexibility) and decided to focus on three solutions: vocabulary, models, and data [1]. Although the community recognizes the need for FAIR (findable, accessible, interoperable and reusable) principles in LCA data, the landscape remains fragmented regarding these principles [2,3]. To the best of our knowledge there exists no systematic review of LCA data formats (i.e., schemas and serialization formats). This gap creates a dual risk: it leaves LCA practitioners without a roadmap to navigate software and data format silos, and it prevents developers from leveraging historical design choices to inform he development of future data formats. Furthermore, most of the studies discussing LCA data formats seem Western-centric, potentially overlooking significant data format developments from East Asia and the Global South.
Approach
This study provides the first systematic review of LCA data formats. We include 100+ studies in the review to summarize the benefits, limitations, and silos of current data formats. The raw data and resulting visual representations will be available on GitHub. This allows the review to be updated dynamically, transitioning from a static study to a functional resource for the community.
The Brightcon Spirit
We aim to make this work collaborative. Through open-source collaborative public repository, we invite the LCA community to expand the review beyond Western-centric sources. This ensures that the findings remain relevant as new LCA methodologies, databases and software emerge globally, directly supporting the goal of identifying and overcoming data silos in sustainability practices.

[1] Mutel C. (2025). Keynote: Départ de Sentier and friends: The next 12 months. Available at https://youtu.be/RyxDtk_J1OU?si=8wwxq5BvFDYjgYW_ (Accessed 23/04/2026)
[2] Ghose, A. (2024). Can LCA be FAIR? Assessing the status quo and opportunities for FAIR data sharing. The International Journal of Life Cycle Assessment, 29(4), 733-744.
[3] Xu, C., Jia, T., Qi, J., Cai, Z., Zhang, R., Xiong, R., ... & Xu, M. (2025). Addressing critical challenges towards a robust data system for life cycle assessment. Nature Reviews Clean Technology, 1(11), 788-800.

Switzerland's environmental agency BAFU published an "ecoinvent alike" open LCA Database in December 2025. It is planned to have a yearly regular update of the database. It has a focus on Switzerland but includes “RoE”, “RER”, “RoW” and “GLO” datasets as well. So far the official versions are published in ecospold1 and OpenLCA's zolca format. The OpenLCA version includes characterisation factors for IPCC 2021 GWP100, Ecological Scarcity and EF 3.1. The database is published under non-standard, BAFU specific terms of use (which are in parts similar to CC BY-(NC)-SA). Overall it is a convenient starting point for an open LCA background database and allows for community collaboration to increase its quality and coverage (sectors, technologies, geography).

This discussion session shall identify possible ways for the community to join forces for these efforts. Things to be discussed with the audience are:

• What tools are suited to help in the data centred collaboration (e.g. DVC, lakeFS)?
• Is github with issues and tickets the best platform to allow for the organization of the community?
• How to ensure that the community efforts can be included in official BAFU updates (license aspects, formats, ...)?
• What collaboration efforts do already exist? Do they happen openly? How to join them? How to include them?
• What topics are suited and beneficial for collaboration?

Topics which could be suited and beneficial for collaboration:

• Identify priority datasets in BAFU:2025+ for improvement
• Collaborative workflows for dataset updates and new additions
• Integration of other impact assessment methods
• Elementary nomenclature systems (BAFU specific, openLCA, brightway, EF 3.1, future EF 4.0, future UNEP GLAD elementaries)
• Ensuring transparency and reproducibility in contributions
• Legal and license aspects