Mapping global innovation networks around clean energy technologies (Proposals Track)

Malte Toetzke (ETH Zurich); Francesco Re (ETH Zurich); Benedict Probst (ETH Zurich); Stefan Feuerriegel (LMU Munich); Laura Diaz Anadon (University of Cambridge); Volker Hoffmann (ETH Zurich)

Climate Finance & Economics Supply Chains Natural Language Processing


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.