Building a shared Life Cycle Inventory database for energy and process systems

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:LCA研究者は、データ共有の実践的課題とBrightwayコミュニティの活用方法を学べる。

🏢実務担当者:サステナビリティ担当者は、LCAデータの標準化と共有によるScope 3算定の効率化に活用できる。

🏛政策担当者:政策担当者は、LCAデータ基盤の整備が排出量算定の信頼性向上に寄与する点を認識すべき。

Despite the increasing importance of FAIR data (Findable, Accessible, Interoperable, and Reusable) in academic research, structured data management remains an often-overlooked challenge in the life cycle assessment (LCA)-community. Most researchers work independently on separate projects and tools, which leads to isolated LCA models, and little exchange of reusable data. In our LCA-focused research group, this has resulted in and repeated modelling of similar processes with inhomogeneous data quality and unnecessary time investment. To address this issue, we are developing a shared library of Life Cycle Inventories for energy and process systems LCA processes based on Brightway. The goal is to collect and organize existing models from different projects, publications and literature in a central cloud database that is easy to use and accessibleintuitive, well-documented and accessible for to all group members of the research group. The workflow for building and using the database consists of the following steps: (1) Structured data submission: Researchers submit process data into a structured Excel template. (2) Automated data integration: This Submitted data is automatically processed and imported into a cloud-based Brightway database. (3) Metadata and documentation: Each entry is stored together with metadata and full documentation. (4) Search and export functionality: Users can search the database for specific processes and export them either as Brightway activities or in Excel format, ready to be used in new projects. Furthermore, our approach raises important discussion pointsKey points of discussion for this approach are in LCA collaboration such as how to reduce redundancy, ensure data quality in modelling, and how to enable more efficient collaboration. Special attention is given to academic needs: proper authorship attribution, data confidentiality for varying project types (research, teaching, industry), and accessibility for users without prior Brightway experience.Further features, which could be integrated in the future, include version control, variable parametrization, and the integration of automated standard analysis tools (e.g., contribution analysis or uncertainty calculationsanalysis). The setup of the database should also consider possible interfaces to external modelling software (e.g. chemical process models). We propose this contribution to share our approach, discuss practical challenges in academic data collaboration, and explore how the Brightway community can support research workflows beyond individual projects.

• openalex https://doi.org/10.5281/zenodo.17800355first seen 2026-08-02 19:01:25