Green Finance Intelligence and Sustainable Capital Allocation: Artificial Intelligence Driven ESG Signal Processing and Capital Market Efficiency

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a quantitative framework linking AI, ESG processing, and market efficiency, offering a basis for further empirical work.

🏢実務担当者:Demonstrates how AI tools can be leveraged for ESG data analysis to improve investment decisions and reduce greenwashing risk.

🏛政策担当者:Empty string

The rapid expansion of sustainable investment markets has intensified the need for accurate Environmental, Social, and Governance (ESG) data analysis to support effective capital allocation. However, traditional analytical approaches often struggle to interpret the growing volume of heterogeneous sustainability information, leading to information asymmetry and suboptimal investment outcomes. This study examined the role of artificial intelligence-driven ESG signal processing in enhancing green finance intelligence and improving sustainable capital allocation and capital market efficiency. A quantitative research design was employed, utilizing primary data collected from 312 financial professionals working in banking, asset management, and financial technology sectors. Descriptive statistical analysis revealed strong agreement regarding the importance of AI adoption in sustainable finance, with artificial intelligence adoption recording the highest mean value (M = 4.14, SD = 0.69), followed by green finance intelligence (M = 4.09, SD = 0.68) and ESG signal processing capability (M = 4.07, SD = 0.72). Regression analysis demonstrated that artificial intelligence adoption significantly influenced green finance intelligence (β = 0.41, t = 7.86, p < 0.001), while ESG signal processing capability significantly improved sustainable capital allocation (β = 0.38, t = 6.94, p < 0.001). Additionally, green finance intelligence positively affected capital market efficiency (β = 0.35, t = 6.21, p < 0.001). The findings indicated that artificial intelligence significantly enhances ESG information processing, reduces information asymmetry, and supports data-driven sustainable investment strategies. 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