Automated Extraction of Techno-Economic Data from 76,000 Energy System Studies
Energy system models guide societally important decisions, but their credibility rests on quantitative assumptions that are difficult to source and audit. Meta-analyses can improve transparency and modeling practices, but the rapid growth of publications makes manual information extraction increasingly impractical. Consequently, databases are updated infrequently and efforts are often duplicated across research groups. Here, we demonstrate the highly accurate automated extraction of quantitative information from 76,000 energy system studies published since 2010. We compile 3.2 million structur
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 47%Bluecore Energy →
- PossiblePossibly related (embedding) · 46%Recovering Climate’s History with Artificial Intelligence - The University of Chicago Press: Journals →
- LinkedLinked via arxiv author · 85%Maxime Gorres →
“Automated Extraction of Techno-Economic Data from 76,000 Energy System Studies”
- LinkedLinked via arxiv author · 85%Jan Göpfert →
“Automated Extraction of Techno-Economic Data from 76,000 Energy System Studies”
- LinkedLinked via arxiv author · 85%Patrick Kuckertz →
“Automated Extraction of Techno-Economic Data from 76,000 Energy System Studies”
- LinkedLinked via arxiv author · 85%Noor Titan Putri Hartono →
“Automated Extraction of Techno-Economic Data from 76,000 Energy System Studies”
- LinkedLinked via arxiv author · 85%Heidi Heinrichs →
“Automated Extraction of Techno-Economic Data from 76,000 Energy System Studies”
- LinkedLinked via arxiv author · 85%Jochen Linßen →
“Automated Extraction of Techno-Economic Data from 76,000 Energy System Studies”