A Review of Research on Large Language Model-Assisted Identification of Green Bond Greenium

Main Article Content

Aili Sun
School of Finance, Zhongnan University of Economics and Law, Wuhan 430073, China

DOI: https://doi.org/10.70267/icrafm.2026177791

Keywords

green bonds, greenium, large language models, financial text analysis

Abstract

Global climate change has created large-scale demand for green investment and financing, and green bonds have become an important financing instrument through which capital markets support green and low-carbon projects. Greenium reflects the market pricing of the green attributes of bonds. Its identification is a core issue in green finance research and is related to bond pricing, investment decisions, and the improvement of regulatory standards. Existing studies mainly classify bonds into green and non-green categories according to external certification labels. This dichotomous method cannot depict continuous differences in the degree of greenness, and the estimation results also lack consensus because of different measurement criteria. The application of language models in financial text analysis has continued to deepen, but it has not yet entered the research on greenium identification. This study systematically reviews the literature on greenium research and language model applications from 2016 to 2026, and summarizes the formation mechanism of greenium, traditional identification methods, and the technological evolution of language models. On this basis, this study proposes a two-stage framework of text measurement and economic identification. Language models extract green attributes from bond texts, and econometric models estimate the price effect of green attributes after controlling for traditional pricing factors. The division of labor between the two is clear, and they cannot replace each other. This framework expands the measurement of greenium from binary classification to continuous measurement, and can provide methodological reference for empirical research and regulatory practice.

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Published
Oct 19, 2026
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Vol. 17 (2026): Proceedings of the International Conference on Research in Accounting, Finance and Management (ICRAFM 2026)
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How to Cite

Sun, Aili. “A Review of Research on Large Language Model-Assisted Identification of Green Bond Greenium”. Exploring Science Academic Conference Series, vol. 17, Oct. 2026, pp. 77-91, https://doi.org/10.70267/icrafm.2026177791.