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Leveraging large language models to monitor climate technology innovation

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Probst, Benedict 
Feuerriegel, Stefan 


To achieve net-zero emissions, public policy needs to foster rapid innovation of climate technologies. However, there is a scarcity of comprehensive and up-to-date evidence to guide policymaking by monitoring climate innovation systems. This is notable, especially at the center of the innovation process, where nascent inventions transition into profitable and scalable market solutions. Here, we discuss the potential of large language models (LLMs) to monitor climate technology innovation. By analyzing large pools of unstructured text data sources, such as company reports and social media, LLMs can automate information retrieval processes and thereby improve existing monitoring in terms of cost-effectiveness, timeliness, and comprehensiveness. In this perspective, we show how LLMs can play a crucial role in informing innovation policy for the energy transition by highlighting promising use cases and prevailing challenges for research and policy.



machine learning, large language models, climate technologies, innovation

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IOP Publishing
Bundesamt für Energie (1-2020)
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (186932)