aiqbench analysis
This study leverages advanced natural language processing techniques to automate the extraction of quantitative data from 76,000 energy system studies, addressing critical bottlenecks in sourcing and auditing model assumptions. By compiling over 3 million structured data points into a FAIR database, the research significantly enhances transparency across diverse technologies and methodological approaches without relying on manual curation. The resulting infrastructure allows researchers to efficiently analyze divergences between academic projections and empirical observations at scale.
Automated Extraction of Techno-Economic Data from 76,000 Energy System Studies
arXiv:2607.19178v1 Announce Type: new Abstract: 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 structured quantitative data points together with 20 million associated metadata entries, spanning a broad spectrum of technologies, methodological approaches and system characteristics. Beyond providing input data for models, the resulting FAIR database make the energy systems literature itself analysable. We show where academic assumptions diverge from empirical observed data, and how research priorities vary at scale across technologies, regions and time. To facilitate broad use within the community, the database is provided through an interactive dashboard, enabling users to filter, analyse and download data according to their specific research needs.
Abstract: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 structured quantitative data points together with 20 million associated metadata entries, spanning a broad spectrum of technologies, methodological approaches and system characteristics. Beyond providing input data for models, the resulting FAIR database make the energy systems literature itself analysable. We show where academic assumptions diverge from empirical observed data, and how research priorities vary at scale across technologies, regions and time. To facilitate broad use within the community, the database is provided through an interactive dashboard, enabling users to filter, analyse and download data according to their specific research needs.
Computation and Language (cs.CL)
arXiv:2607.19178 [cs.CL]
(or arXiv:2607.19178v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.19178 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 261 words
