Enhancing Policy and Research: An Open-Access Tool for Advancing Green and Digital Transition.
Open Research objectives/practices
In our research on the twin transition (green and digital transition), we developed and provided open access to the TwinTransition Mapper, which uses large language models (LLMs) and machine learning algorithms to classify firms based on their contributions to green and AI products. Open access to the two algorithms enables other researchers and policymakers to explore, replicate, and extend the analysis in different regional or sectoral contexts. Furthermore, the availability of the algorithms allows for their application to other data sources, such as job postings, social media, and patent data.
We are the first researchers who share algorithms in our academic field (regional studies), and we hope we encourage other researchers to share their analytical tools to empower learners and researchers, and boost the further development of new insights into the twin transition and its role in regional innovation and policy development.
Introduction
The twin transition, combining green and digital transitions, has emerged as a critical focus in policy discussions, particularly considering the European Green Deal. As policymakers emphasize aligning these two transitions to reinforce one another, understanding the role of firms in driving this change is vital. However, identifying products that contribute to both transitions remains challenging due to a lack of accessible data. Our research addresses this gap by developing the TwinTransition Mapper. This machine learning tool uses web data to classify the products of 600,000 geolocated German firms, providing insights into their contributions to green and AI innovation. By offering open access to the two developed algorithms we enable further exploration and policy support.
Motivation
The motivation for this open research practice stems from the urgent need support the green and digital transitions, which are crucial for achieving sustainable development goals. As the European Commission underscores the importance of these transitions, policymakers face challenges in simultaneously assessing how firms contribute to both. The lack of accessible and up-to-date data on products complicates this task. By developing the TwinTransition Mapper, we bridged this gap and provide valuable insights into how firms drive eco-friendly and AI-based innovations. This tool will inform policy and enable future research on sustainable, data-driven regional development.
Lessons learned
One challenge in promoting the TwinTransition Mapper is the limited awareness of its potential. Information about the model does not easily diffuse across faculties and departments. To overcome this, workshops and collaborative sessions are essential to disseminate knowledge about the tool and demonstrate how it can be applied to research on green and digital transitions. Supporting factors include receiving recognition through the open research award, enhancing the tool’s visibility and credibility.
URLs, references and further information
| Last modified: | 13 November 2024 12.27 p.m. |