Forecasting carbon market with large language models

Purpose This paper aims to develop an accurate carbon price forecasting framework using large language models (LLMs) to overcome limitations of conventional structured data methods. Design/methodology/approach This paper details the comprehensive LLM framework that integrates historical prices with market drivers and news texts through advanced prompt engineering, utilizing zero-shot inference with the Chinese carbon market as the case study. Findings This paper highlights the superior performance achievements, including enhanced accuracy in price and directional predictions, improved forecasting capabilities through news integration and increased market transparency. Originality/value This paper emphasizes the novel contributions, specifically the first comprehensive LLM framework for carbon market forecasting that combines structured and unstructured data and the innovative zero-shot inference methodology that eliminates the need for prior training examples.