---
title: "Automated Extraction of Techno-Economic Data from 76,000 Energy System Studies"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.19178v1"
permalink: "https://feed.astralor.com/items/itm_e14b589eddf46cc0280be767"
published: "2026-07-21T15:14:32.000Z"
score: 71
channel: "tech"
tags: ["arXiv","FAIR Database","Interactive Dashboard","Meta-Analysis","Energy System Modeling","Automated Data Extraction"]
---

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

## AI commentary

> This is a model work applying NLP techniques to scientific literature mining, offering important insights for both energy modeling and AI information extraction. It is especially valuable for researchers in energy system studies, techno-economic analysis, or scientific meta-analysis, showing how massive literature can be turned into structured knowledge bases.

## At a glance

This paper presents a highly accurate automated extraction method that extracts 3.2 million structured quantitative data points and 20 million associated metadata entries from 76,000 energy system studies published since 2010, covering a broad spectrum of technologies, methodological approaches, and system characteristics. The resulting FAIR database not only provides input data for models but also makes the energy system literature itself analyzable, revealing divergences between academic assumptions and empirical data, as well as how research priorities vary across technologies, regions, and time. An interactive dashboard enables community users to filter, analyze, and download data according to their specific research needs.

## Key points

- Automated extraction of quantitative data from 76,000 energy system studies
- Construction of a FAIR database with 3.2 million data points and 20 million metadata entries
- Revealing divergences between academic assumptions and empirical data
- Providing an interactive dashboard for community use

## Full text

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.

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