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Article

Smart Money, Greener Future: AI-Enhanced English Financial Text Processing for ESG Investment Decisions

1
School of Foreign Languages, Guangzhou College of Commerce, Guangzhou 511363, China
2
Business School, Aalborg University, DK-9220 Aalborg, Denmark
3
Business School, Beijing Technology and Business University, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(15), 6971; https://doi.org/10.3390/su17156971 (registering DOI)
Submission received: 2 July 2025 / Revised: 25 July 2025 / Accepted: 29 July 2025 / Published: 31 July 2025

Abstract

Emerging markets face growing pressures to integrate sustainable English business practices while maintaining economic growth, particularly in addressing environmental challenges and achieving carbon neutrality goals. English Financial information extraction becomes crucial for supporting green finance initiatives, Environmental, Social, and Governance (ESG) compliance, and sustainable investment decisions in these markets. This paper presents FinATG, an AI-driven autoregressive framework for extracting sustainability-related English financial information from English texts, specifically designed to support emerging markets in their transition toward sustainable development. The framework addresses the complex challenges of processing ESG reports, green bond disclosures, carbon footprint assessments, and sustainable investment documentation prevalent in emerging economies. FinATG introduces a domain-adaptive span representation method fine-tuned on sustainability-focused English financial corpora, implements constrained decoding mechanisms based on green finance regulations, and integrates FinBERT with autoregressive generation for end-to-end extraction of environmental and governance information. While achieving competitive performance on standard benchmarks, FinATG’s primary contribution lies in its architecture, which prioritizes correctness and compliance for the high-stakes financial domain. Experimental validation demonstrates FinATG’s effectiveness with entity F1 scores of 88.5 and REL F1 scores of 80.2 on standard English datasets, while achieving superior performance (85.7–86.0 entity F1, 73.1–74.0 REL+ F1) on sustainability-focused financial datasets. The framework particularly excels in extracting carbon emission data, green investment relationships, and ESG compliance indicators, achieving average AUC and RGR scores of 0.93 and 0.89 respectively. By automating the extraction of sustainability metrics from complex English financial documents, FinATG supports emerging markets in meeting international ESG standards, facilitating green finance flows, and enhancing transparency in sustainable business practices, ultimately contributing to their sustainable development goals and climate action commitments.
Keywords: sustainable development; digital finance; ESG reporting; sustainable investment; English financial information extraction; English language processing; constrained decoding sustainable development; digital finance; ESG reporting; sustainable investment; English financial information extraction; English language processing; constrained decoding

Share and Cite

MDPI and ACS Style

Fan, J.; Wang, D.; Zheng, Y. Smart Money, Greener Future: AI-Enhanced English Financial Text Processing for ESG Investment Decisions. Sustainability 2025, 17, 6971. https://doi.org/10.3390/su17156971

AMA Style

Fan J, Wang D, Zheng Y. Smart Money, Greener Future: AI-Enhanced English Financial Text Processing for ESG Investment Decisions. Sustainability. 2025; 17(15):6971. https://doi.org/10.3390/su17156971

Chicago/Turabian Style

Fan, Junying, Daojuan Wang, and Yuhua Zheng. 2025. "Smart Money, Greener Future: AI-Enhanced English Financial Text Processing for ESG Investment Decisions" Sustainability 17, no. 15: 6971. https://doi.org/10.3390/su17156971

APA Style

Fan, J., Wang, D., & Zheng, Y. (2025). Smart Money, Greener Future: AI-Enhanced English Financial Text Processing for ESG Investment Decisions. Sustainability, 17(15), 6971. https://doi.org/10.3390/su17156971

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