Automated Extraction of Techno-Economic Data from 76,000 Energy System Studies

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Maxime Gorres, Jan Göpfert, Patrick Kuckertz, Noor Titan Putri Hartono, Heidi Heinrichs, Jochen Linßen, Iain Staffel, Jann Michael Weinand
• Venue: arXiv: cs.CL
• Published: 2026-07-21
• Collected: 2026-07-22
• URL: http://arxiv.org/abs/2607.19178v1
• PDF: https://arxiv.org/pdf/2607.19178v1
• DOI: UNKNOWN

Plain-Language Brief

Researchers developed a method to automatically extract quantitative information from 76,000 energy system studies published since 2010. They compiled over 3 million structured data points and 20 million metadata entries. The resulting database is designed to be FAIR (Findable, Accessible, Interoperable, Reusable) and can help analyze the energy systems literature. The method shows where academic assumptions diverge from empirical observations and how research priorities vary across technologies, regions, and time.

What the Document Says

• The researchers demonstrated an automated extraction of quantitative data from 76,000 energy system studies.
• They compiled 3.2 million structured data points and 20 million metadata entries.
• The resulting database is designed to be FAIR.
• The method shows where academic assumptions diverge from empirical observations.
• It reveals how research priorities vary across technologies, regions, and time.

Key Concepts Mentioned

• Energy system models
• Automated extraction
• Quantitative assumptions
• Transparency
• Modeling practices
• FAIR database
• Academic assumptions
• Empirical observations
• Research priorities

Claims Made by the Document

• Automated extraction can improve transparency and modeling practices.
• The resulting FAIR database makes the energy systems literature analysable.

Evidence Used

• The automated extraction of quantitative information from 76,000 studies.
• The creation of a FAIR database containing structured data points and metadata entries.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.