Skip to yearly menu bar Skip to main content


Poster
in
Workshop: Tackling Climate Change with Machine Learning: Global Perspectives and Local Challenges

Mapping global innovation networks around clean energy technologies

Malte Toetzke · Francesco Re · Benedict Probst · Stefan Feuerriegel · Laura Diaz Anadon · Volker Hoffmann

Keywords: [ Supply chains ] [ natural language processing ] [ Climate finance and economics ]


Abstract:

Reaching net zero emissions requires rapid innovation and scale-up of clean tech. In this context, clean tech innovation networks (CTINs) can play a crucial role by pooling necessary resources and competences and enabling knowledge transfers between different actors. However, existing evidence on CTINs is limited due to a lack of comprehensive data. Here, we develop a machine learning framework to identify CTINs from announcements on social media to map the global CTIN landscape. Specifically, we classify the social media announcements regarding the type of technology (e.g., hydrogen, solar), interaction type (e.g., equity investment, R\&D collaboration), and status (e.g., commencement, update). We then extract referenced organizations via entity recognition. Thereby, we generate a large-scale dataset of CTINs across different technologies, countries, and over time. This allows us to compare characteristics of CTINs, such as the geographic proximity of actors, and to investigate the association between network evolution and technology innovation and diffusion. As a direct implication, our work helps policy makers to promote CTINs by identifying current barriers and needs.

Chat is not available.