Open access
Date
2023-09Type
- Journal Article
ETH Bibliography
yes
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Abstract
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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000633479Publication status
publishedExternal links
Journal / series
Environmental Research LettersVolume
Pages / Article No.
Publisher
IOP PublishingSubject
innovation; large language models; machine learning; climate technologiesOrganisational unit
03695 - Hoffmann, Volker / Hoffmann, Volker
Funding
186932 - Data-driven health management (SNF)
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ETH Bibliography
yes
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