A new architecture for scaling and sharing LCA data

Linked Sessions:

Poster Number:  31 

Main Presenter:    João Gonçalves 

Co-Authors:   Chris Mutel                                               

Traditional unit processes have a number of attributes that reduce their suitability for LCA at scale. They need to be complete, so the barriers for data entry and consistency are high. They can only include relatively awkward expressions of underlying causality, without tests or proper software engineering, so their logic is not useful. They have a fixed temporal, spatial, and product scope, and so are difficult to adapt. There are also no real standards or widely adopted tools which allow these disparate publicly available datasets to be linked into broader databases.

We present an alternative path to construct and share data. To avoid the chaos of inconsistent or non-standard names, we start with a glossary of terms based on international standards. These terms can be supplemented in an open and public process, maximizing compatibility and community buy-in.

We then cleanly separate logic from data. The logic can be encapsulated in computational models of arbitrary complexity. Such models can compete to best reflect reality, just as machine learning models are evaluated and scored against each other. Data can be simple and incomplete – the search function will find the necessary data for the context of the model evaluation, and use gap-filling or interpolation if necessary. Both the models and the input data can be used to construct validation checks, and data lineage and quality can be assessed throughout system operation.

Our approach is still theoretical, and will need refinement and broad community participation to be successful. However, it addresses most of the barriers faced by current LCA data sharing and scaling, and we intend to try to develop the code, metadata, services, and platforms needed for such an approach freely and openly. We invite the community to come along for the ride.

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