AI and green finance join forces to build climate-resilient economies

The integration of artificial intelligence (AI) with green finance is rapidly redefining how financial systems support global sustainability goals. A new bibliometric study brings into light critical research trends, emerging technologies, and underexplored areas at this intersection. The analysis reveals that AI is increasingly embedded in green finance mechanisms, boosting efficiency, transparency, and impact across environmental and economic domains.

Published in the Journal of Risk and Financial Management, the study "The Nexus Between Green Finance and Artificial Intelligence: A Systemic Bibliometric Analysis Based on Web of Science Database" provides the first comprehensive mapping of research linking AI with green financial instruments. Using advanced bibliometric techniques, the authors analyzed 268 peer-reviewed publications spanning 2014 to June 2025, uncovering how this field has evolved, where it stands, and where it is heading.

What are the current trends in AI and green finance research?

The research highlights a surge in scientific activity on the AI–green finance nexus, with annual publication growth exceeding 53%. This expansion underscores the strategic importance of integrating digital innovation with environmental finance to address climate challenges. The analysis shows that China dominates both productivity and citation impact, followed by Vietnam and the United Kingdom. Chinese institutions such as Guizhou University and Jilin University lead in research output, while the most relevant journals include Energy Economics and Finance Research Letters.

In terms of authorship, Chinese researchers, particularly Su X., Li X., and Wang J., stand out as the most prolific contributors, shaping the intellectual foundation of this domain. Collaboration is also increasing, with nearly 40% of publications involving international co-authorships, reflecting a growing recognition that climate-related financial innovation demands global cooperation.

The study identifies three main thematic clusters driving research:

• Emission, Health, and Environmental Risk – AI is deployed to monitor air quality, track carbon emissions, and model public health impacts, highlighting its role in environmental risk mitigation.

• Institutional and Technological Infrastructure – Research focuses on how AI-powered fintech, blockchain applications, and digital governance frameworks strengthen green financial systems.

• Green Innovation and Urban Sustainability – AI is enabling cleaner technologies, industrial upgrading, and sustainable urban planning, aligning economic growth with ecological goals.

These clusters illustrate a shift from isolated studies to interdisciplinary, impact-oriented research that blends environmental science, finance, and digital technology.

How are methods and technologies shaping the AI–green finance nexus?

The study reveals that technological innovation is not just a topic of analysis but also a driver of methodological diversity. The authors report widespread adoption of advanced AI techniques alongside traditional econometric methods. Blockchain technology emerges as the most cited tool, reflecting its transformative role in enhancing transparency and traceability in green financial flows. Applications include blockchain-based smart contracts for green bonds, tokenization of carbon credits, and ESG data tracking systems.

Convolutional neural networks (CNNs), large language models (LLMs), and sentiment analysis are increasingly used to extract insights from complex datasets, ranging from satellite imagery for environmental monitoring to investor sentiment analysis in green markets. Traditional statistical methods like structural equation modeling, panel quantile regression, and genetic algorithms remain relevant, often integrated into hybrid models that combine computational intelligence with rigorous statistical evaluation.

Validation tools such as mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) demonstrate a growing emphasis on predictive accuracy. The inclusion of generative AI, deep learning, and computer vision further indicates that researchers are moving toward cutting-edge solutions capable of handling high-dimensional, real-time environmental and financial data.

This convergence of AI and finance extends beyond methodology. It fundamentally transforms how climate risks, investment opportunities, and regulatory frameworks are assessed. For instance, AI-driven ESG portfolio optimization, automated climate risk assessments, and predictive carbon tracking tools illustrate how digital innovations amplify the impact of green finance initiatives.

What are the gaps and future directions in this emerging field?

While the research landscape is expanding rapidly, the study identifies several critical gaps. Geographic diversity remains limited, with most publications originating from Asia and Europe. Contributions from Africa, Latin America, and parts of North America are underrepresented, which risks overlooking context-specific challenges and solutions in climate-vulnerable regions. To bridge this gap, the authors call for multi-database approaches that include sources beyond Web of Science and encourage broader regional case studies.

The study also underscores the importance of ethical frameworks for AI in green finance. Algorithmic transparency, fairness, and accountability remain underexplored areas. Without proper safeguards, AI-driven systems for ESG scoring, carbon credit allocation, or investment screening could inadvertently reinforce inequalities, particularly for small organizations or underdeveloped regions. Moreover, issues around data privacy, consent, and security demand urgent attention as AI applications increasingly rely on large-scale environmental datasets.

Future research directions proposed by the authors include dynamic topic modeling to monitor thematic evolution over time, comparative studies across diverse financial systems, and interdisciplinary investigations into the legal and ethical dimensions of AI in sustainable finance. The study suggests that the success of this field will depend not only on technological advancements but also on regulatory oversight, stakeholder engagement, and inclusive innovation.