Artificial Intelligence-Powered Green Finance and Environmental, Social and Governance Tracking in Emerging Markets: A Systematic Review

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

🔬研究者:This review provides a comprehensive mapping of AI techniques and barriers in ESG and green finance, offering a foundation for future research on AI in climate disclosure.

🏢実務担当者:Corporate sustainability teams in emerging markets can identify AI tools and data challenges to enhance ESG reporting credibility.

🏛政策担当者:Regulators can use this evidence to design policies that foster AI integration and data standardization for sustainable finance.

Artificial Intelligence (AI) has been adopted to significantly enhance the quality (credibility, timeliness and comparability) of green finance and environmental, social and governance (ESG) reporting. Nevertheless, in emerging economies, fragmented data infrastructures and irregular regulatory structures complicate its adoption process. This review synthesises empirical evidence (2015 to 2025) on the AI methods and tools applied to green finance and ESG reporting analytics in emerging markets, identifying key methods, outcomes, barriers, drivers and quality of reporting. Following the PRISMA design, Scopus records (n = 49) were screened meticulously against the inclusion criteria to 27 studies for actual full-text extraction. We coded AI technique, ESG domain, data sources, outcomes, barriers and drivers. We also applied a hybrid quality appraisal (MMAT domains and AI-specific rubric for transparency, validation, provenance, emerging market relevance and reproducibility) to ensure quality conclusions. The research outcomes show that AI applications are dominated by machine learning (ML), natural language processing (NLP). Also, frequent tasks include carbon-based and climate-related disclosure analytics, ESG scoring, green-finance prediction and green bond verification. Furthermore, Asia accounts for the largest share (52%), followed by Africa (22%) and Latin America (19%). In addition, Key AI application barriers include data quality and coverage issues, standardisation gaps, knowledge and technical skills limitations and cost constraints. Furthermore, drivers for its application include AI integration and development, alignment with International Financial Reporting Standards (IFRS) (S1&S2), Taskforce on Climate-related Financial Disclosures (TCFD) and a free data accessibility ecosystem. Also, quality scores cluster at a high range (mean 4.2/5; SD = 0.5), with recurrent limitations to data provenance and reproducibility. The study concludes that AI shows significant prospects to improve ESG credibility and sustainable finance in emerging economies, but strongly depends on strong disclosure taxonomies, quality datasets, transparency and validated AI frameworks.

• semanticscholar https://doi.org/10.32479/irmm.22852first seen 2026-05-15 16:57:31