Abstract
The success of the “net zero transition” relies on the acceleration of the clean technology development to increase renewable energy capacity and low-emission solutions, but also to improve energy efficiency and enable carbon capture. Tracking such technologies and their mineral requirements is becoming increasingly important, but has traditionally required expert knowledge. In this paper, we propose a framework using Large Language Models and question-answering tasks to monitor the novelty within the clean tech industry, but also the minerals on which these technologies rely. It demonstrates the benefits of using artificial intelligence, and more specifically NLP techniques, to reconstruct expert knowledge and track rapidly changing markets.
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