The geography of digital and green (twin) firms in Germany
(2025) In Regional Studies, Regional Science 12(1). p.513-516- Abstract
The twin transition, which combines green and digital innovation in economic activities, is increasingly central to policy agendas and is also receiving growing attention in regional research. However, accurately mapping green, digital and twin (both green and digital) economic activities across regions remains challenging, particularly due to data constraints. In this study, we advance this research frontier and present a geographic analysis of digital, green and twin economic activities in Germany, using a web-mined dataset of website texts from 678,381 firms, collected through web scraping in 2023. By processing over 44 million text paragraphs from these websites and applying a cosine similarity filter with green and AI-related... (More)
The twin transition, which combines green and digital innovation in economic activities, is increasingly central to policy agendas and is also receiving growing attention in regional research. However, accurately mapping green, digital and twin (both green and digital) economic activities across regions remains challenging, particularly due to data constraints. In this study, we advance this research frontier and present a geographic analysis of digital, green and twin economic activities in Germany, using a web-mined dataset of website texts from 678,381 firms, collected through web scraping in 2023. By processing over 44 million text paragraphs from these websites and applying a cosine similarity filter with green and AI-related terms, we filtered firms that are likely engaged in green, digital and twin activities. Based on this subset, 1437 text paragraphs were manually annotated to fine-tune two transformer models within a SetFit framework, accurately classifying firms as green, digital or both. We aggregate this firm-level data into hexagonal cells to reveal the geographic concentration of the twin transition in Germany. The final map shows a higher number of firms involved in green activities, widely spread across Germany, while AI activities are concentrated in urban centres. We identify 23,819 firms engaged in both green and digital activities, with major hubs like Berlin and Munich leading, and peripheral regions potentially being left behind. Our findings offer critical insights into the geography of the twin transition and highlight the need for policies that address potentially induced spatial inequalities.
(Less)
- author
- Kriesch, Lukas ; Abbasiharofteh, Milad and Losacker, Sebastian LU
- organization
- publishing date
- 2025
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- digital transition, Germany, green transition, natural language processing, Twin transition, web-mining
- in
- Regional Studies, Regional Science
- volume
- 12
- issue
- 1
- pages
- 4 pages
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:105009458913
- ISSN
- 2168-1376
- DOI
- 10.1080/21681376.2025.2510679
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
- id
- 3ca6138c-cdd4-43d5-acf9-3f342d709e25
- date added to LUP
- 2026-01-22 14:51:59
- date last changed
- 2026-01-22 14:52:39
@article{3ca6138c-cdd4-43d5-acf9-3f342d709e25,
abstract = {{<p>The twin transition, which combines green and digital innovation in economic activities, is increasingly central to policy agendas and is also receiving growing attention in regional research. However, accurately mapping green, digital and twin (both green and digital) economic activities across regions remains challenging, particularly due to data constraints. In this study, we advance this research frontier and present a geographic analysis of digital, green and twin economic activities in Germany, using a web-mined dataset of website texts from 678,381 firms, collected through web scraping in 2023. By processing over 44 million text paragraphs from these websites and applying a cosine similarity filter with green and AI-related terms, we filtered firms that are likely engaged in green, digital and twin activities. Based on this subset, 1437 text paragraphs were manually annotated to fine-tune two transformer models within a SetFit framework, accurately classifying firms as green, digital or both. We aggregate this firm-level data into hexagonal cells to reveal the geographic concentration of the twin transition in Germany. The final map shows a higher number of firms involved in green activities, widely spread across Germany, while AI activities are concentrated in urban centres. We identify 23,819 firms engaged in both green and digital activities, with major hubs like Berlin and Munich leading, and peripheral regions potentially being left behind. Our findings offer critical insights into the geography of the twin transition and highlight the need for policies that address potentially induced spatial inequalities.</p>}},
author = {{Kriesch, Lukas and Abbasiharofteh, Milad and Losacker, Sebastian}},
issn = {{2168-1376}},
keywords = {{digital transition; Germany; green transition; natural language processing; Twin transition; web-mining}},
language = {{eng}},
number = {{1}},
pages = {{513--516}},
publisher = {{Taylor & Francis}},
series = {{Regional Studies, Regional Science}},
title = {{The geography of digital and green (twin) firms in Germany}},
url = {{http://dx.doi.org/10.1080/21681376.2025.2510679}},
doi = {{10.1080/21681376.2025.2510679}},
volume = {{12}},
year = {{2025}},
}