The geography of digital and green (twin) firms in Germany
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 reg...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Regional Studies, Regional Science |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/21681376.2025.2510679 |
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| author | Lukas Kriesch Milad Abbasiharofteh Sebastian Losacker |
| author_facet | Lukas Kriesch Milad Abbasiharofteh Sebastian Losacker |
| author_sort | Lukas Kriesch |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-722678e5887f4bd4b3f71e3ea073f4b0 |
| institution | OA Journals |
| issn | 2168-1376 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Regional Studies, Regional Science |
| spelling | doaj-art-722678e5887f4bd4b3f71e3ea073f4b02025-08-20T02:23:48ZengTaylor & Francis GroupRegional Studies, Regional Science2168-13762025-12-0112151351610.1080/21681376.2025.2510679The geography of digital and green (twin) firms in GermanyLukas Kriesch0Milad Abbasiharofteh1Sebastian Losacker2Department of Geography, University of Giessen, GermanyDepartment of Economic Geography, Faculty of Spatial Sciences, University of Groningen, the NetherlandsDepartment of Geography, University of Giessen, GermanyThe 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.https://www.tandfonline.com/doi/10.1080/21681376.2025.2510679Twin transitionweb-mininggreen transitiondigital transitionnatural language processingGermany |
| spellingShingle | Lukas Kriesch Milad Abbasiharofteh Sebastian Losacker The geography of digital and green (twin) firms in Germany Regional Studies, Regional Science Twin transition web-mining green transition digital transition natural language processing Germany |
| title | The geography of digital and green (twin) firms in Germany |
| title_full | The geography of digital and green (twin) firms in Germany |
| title_fullStr | The geography of digital and green (twin) firms in Germany |
| title_full_unstemmed | The geography of digital and green (twin) firms in Germany |
| title_short | The geography of digital and green (twin) firms in Germany |
| title_sort | geography of digital and green twin firms in germany |
| topic | Twin transition web-mining green transition digital transition natural language processing Germany |
| url | https://www.tandfonline.com/doi/10.1080/21681376.2025.2510679 |
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