Data quality means different things for different users, and that’s okay. At ecoinvent, we pride ourselves on the quality of our data. But what does high-quality life cycle assessment (LCA) data mean in practice?
Data quality means different things for different users, and that’s okay. At ecoinvent, we pride ourselves on the quality of our data. But what does high-quality life cycle assessment (LCA) data mean in practice?
To a regulator, quality often means getting the same number every year, because implementing regulation at a societal level needs that kind of stability. For academia, quality means having a modular database to use. But for a company monitoring the life cycle impacts of its products each year, or reviewing its CO₂ reduction goals, quality is something more dynamic. It means seeing annual updates on supply chains, with enough granularity to understand and justify changes in the results.
So, what does quality mean for us? We see quality as reaching far beyond the attributes of individual data points. For us, it’s a managed, end-to-end process that covers the entire life of a dataset: creation, review, calculation, publication, support, and maintenance. It runs from the moment the numbers are lined up in the dataset until the moment the dataset is replaced by something else, and is rooted in tranparency
Let me run you though this process in ecoinvent:
1. Building and maintaining the infrastructure: Our software team creates and maintains the tools and infrastructure that serve each dataset throughout its life. This team also keeps us as compliant and interoperable as possible with data schemas and nomenclature, and ingestion and publication formats.
2. Creating the data: Our sector experts create datasets and parametric models covering the different sectors. They also collaborate with external stakeholders, such as industry associations and researchers, who create data or contribute to models. We support these collaborators with guidance and access to our tools and infrastructure.
3. Reviewing the data: We create data as undefined unit processes (UPR), that is, transparent unit processes that are not yet allocated or linked. External third-party reviewers check these for plausibility, and our internal team reviews them for methodological compliance. From this same set of UPRs, we then calculate several versions of the database, one per system model. Each system model applies its own allocation and linking rules, reflecting different methodological rules, and producing different inventory results and impact scores, which we also review internally and externally. Finally, we review database-level scores before each annual release.
4. Supporting our users: Our technical team supports users with questions throughout the life of a dataset, so they can use the database properly.
5. Maintaining the database: We have released a new version of the database for the past 13 years. Each version contains new and updated data, transparently documented, including known limitations.
6. We keep innovating: That is an important part of our identity overall—we keep on moving. In addition to our annual v3 updates, we have recently been developing the next generation of the database, the ecoinvent v4, which you can read more about here.
Coming back to where we started: a regulator needs stable numbers, and a company needs up-to-date granularity. To learn more about how ecoinvent can support you, contact our team.