Disentangling the web: towards a better characterization of the ecoinvent life cycle assessment technosphere database
Abstract
We present a network-based approach to life cycle assessment (LCA) using Python and containerized software for scalable data processing. First, we characterize the Ecoinvent technosphere using GDP heatmaps, degree distribution, and centrality to reveal key patterns in supply chains. Interactive tools enable real-time filtering and exploration, as well as a shift to smaller case studies. Findings highlight the importance of geospatial distribution in LCA and suggest the ability to find key sub-networks earlier in the process. Ongoing work will refine visualizations, improve performance, and enhance integration of dynamic local data, advancing LCA interpretability and usability.
Primary Faculty Mentor Name
Lampros Svolos
Status
Graduate
Student College
College of Engineering and Mathematical Sciences
Program/Major
Data Science
Primary Research Category
Engineering and Math Science
Disentangling the web: towards a better characterization of the ecoinvent life cycle assessment technosphere database
We present a network-based approach to life cycle assessment (LCA) using Python and containerized software for scalable data processing. First, we characterize the Ecoinvent technosphere using GDP heatmaps, degree distribution, and centrality to reveal key patterns in supply chains. Interactive tools enable real-time filtering and exploration, as well as a shift to smaller case studies. Findings highlight the importance of geospatial distribution in LCA and suggest the ability to find key sub-networks earlier in the process. Ongoing work will refine visualizations, improve performance, and enhance integration of dynamic local data, advancing LCA interpretability and usability.
