The integration of Artificial Intelligence (AI), including Machine Learning (ML) and Natural Language Processing (NLP), into Environmental, Social, and Governance (ESG) frameworks and sustainable finance has gained significant academic and industry attention. This study presents a bibliometric analysis of research at the intersection of AI, ESG, and sustainable finance, using Web of Science data from 2004 to 2025. Our analysis maps the evolution of research trends, identifies key authors, influential publications, and collaboration networks, and explores the intellectual structure of the field. Using co-citation analysis, keyword co-occurrence mapping, and thematic clustering, we reveal how AI-driven methodologies have shaped ESG assessments, sustainable investment strategies, and financial decision-making. Findings highlight a sharp rise in AI applications in ESG post-2015, particularly in ESG risk modelling, climate analytics, and AI-driven reporting. This study provides a comprehensive overview of the evolving academic discourse, identifying emerging research directions and challenges related to data standardization, transparency, and ethical AI applications in sustainability.

The informal economy, often characterized by unregulated and untaxed economic activities, has significant yet understudied implications for sustainable development and inclusivity. This paper explores the influence of the size of the shadow economy and undeclared work patterns (envelope wage, without a formal contract and bogus self-employed) on the Leave No One Behind Index and the Sustainable Development Index developed by the United Nations across European countries from 2000 to 2021. Employing panel data regression models, the research investigates the differential impacts of informality on these indices, revealing a negative correlation with the informal economy and a positive association with various forms of undeclared work. The analysis, comprising 18 models segmented by region – Central and Eastern Europe, Western Europe, and Europe – demonstrates that the adverse effects of the informal economy are more pronounced, whereas the positive contributions of undeclared work patterns are subtler, yet significant. The study’s findings suggest a complex interaction between informal economic activities and sustainable development goals, challenging the conventional perspective on informality and its role in inclusive development. The results indicate that while the shadow economy may hinder broad development efforts, informal labor arrangements support vulnerable populations, underscoring the need for nuanced policy interventions that recognize the multifaceted nature of work and its impacts on development.

This paper explores the responsibility of European countries in adopting artificial intelligence (AI) through a cluster analysis approach. Using hierarchical clustering (Ward’s method) and K-means clustering, distinct groupings of nations are identified based on their level of AI adoption. The analysis reveals key performance poles, highlighting countries leading to AI adoption and those facing significant challenges due to digital infrastructure, public trust, and policy frameworks. The study relies on data from Eurobarometer 95.2 (2021), incorporating variables related to digital literacy, AI perception, internet usage, and technological infrastructure. Results show clear regional disparities: Northern and Western European countries demonstrate higher AI adoption responsibility, benefiting from strong policies and digital ecosystems. In contrast, Southern and Eastern European nations face obstacles such as limited infrastructure and weaker regulatory frameworks. The findings contribute to a deeper understanding of AI adoption dynamics, revealing structural differences between clusters and offering insights into the key drivers of AI integration. By applying unsupervised machine learning techniques, this research underscores the need for tailored policy interventions to bridge the AI adoption gap. The results provide a foundation for designing targeted strategies that promote AI integration across different national contexts, ensuring more inclusive and sustainable technological development.

The research investigates the underground economy in Romania, an essential phenomenon that affects tax revenue, competitive conditions in the marketplace, and economic stability. This empirical research estimates the informal economy using the most recent machine learning algorithms, with a focus on support vector regression (SVR) In our application, support vector regression (SVR) provided estimates of the coefficients needed for estimating the currency demand approach (CDA) and measuring the size of unobserved economic activities. The literature has examined different forms of measurement of the informal economy: direct approaches, indirect approaches, and statistical approaches such as the CDA and multiple-indicator multiple-cause (MIMIC) model. The classical methods have a number of issues, such as the numerous assumptions and poor data availability. Artificial intelligence (AI) or Machine learning (ML) can offer even more potentially accurate measures than what the classical methods have offered in the past. This paper integrates measures of economic indicators, such as cash in circulation, tax revenues, inflation and unemployment rates, with measures of digitalisation including numbers of cards, ATMs and POS terminals. In addition to SVR, other machine learning algorithms, such as Random Forests and Gradient Boosting, were estimated in order to evaluate which type of model produces the best predictive models of liquidity demand. Overall, the evidence establishes that SVR is a clear improvement over standard methodologies because it models complex and nonlinear relationships in the data. The research illustrates the acute fluctuations registered by Romania’s underground economy from 2000 to 2023, thus illustrating how economic crises and changes in fiscal policies dictate the amount of informal economic activity. The paper suggests improved calculation methods resulting in better estimates of natural economic activity and provides ongoing information for economic forecasters and policymakers wishing to maintain the level of informal activities at acceptable levels.

The investigation and exploration of the general fiscal field with emphasis on “direct taxation” through the analysis of the published scientific literature, looking at the conceptual, intellectual, and collaborative structure while highlighting the future research directions is aiming to visualize the prevailing knowledge structure in order to help the future researchers and research clusters. A sample of 557 documents was extract from the Web of Science platform for the 1954 - 2022-time range and a bibliometric analysis was applied as a quantitative technique to investigate the scientific literature. The main questions for which this study could offer an answer are: Are there emerging subfields or topics within direct taxations research? Which authors have made significant contributions to the study of direct taxation? But what are the most influential and frequently cited publications? How has the research on direct taxations evolved over time in terms of publication trends? Is there any geographical concentration of research in this area? Which collaborative partnerships are standing out?