An Analysis of the Impact of Green Bond Information Disclosure Quality on Financing Costs

Abstract

The impact of the quality of information disclosure on green bonds on financing costs is a cutting-edge topic in green finance research. Through a systematic literature review that integrates information asymmetry theory with signaling theory, this paper systematically synthesizes the conceptual connotations of information disclosure quality, the evolution of its measurement methodologies, and the mechanisms—along with empirical evidence—through which it influences green bond financing costs. Research has found that the quality of information disclosure is a key variable explaining the heterogeneity of green premiums, and high-quality disclosure can significantly reduce financing costs. The methods for measuring the quality of information disclosure have evolved through three stages: from manual scoring to dictionary-based methods (LDA), and then to deep learning and semantic analysis. NLP technology provides a new path for large-scale, refined measurement. The findings indicate that while information disclosure quality generally exerts a positive influence on financing costs, this effect is also moderated by boundary conditions such as market structure and investor type. This paper constructs a four-dimensional analytical framework of “substance, verifiability, transparency, and professionalism” to provide theoretical tools and methodological support for subsequent research.