Ștefan Ionescu

Territorial development in the Republic of Moldova is spatially uneven, but district typologies based on absolute volumes risk reproducing differences in population size rather than identifying substantive territorial profiles. This study constructs a size-adjusted district-level typology linking socio-economic development intensity, infrastructure provision, environmental and service-related pressure, and environmental expenditure intensity. The analysis uses the latest available mixed-year cross-section data covering 34 territorial units, while Chișinău is examined separately because of its structurally exceptional profile. Population is used as a denominator and contextual variable rather than as an active clustering indicator. Absolute measures are converted into per-capita or rate-based indicators, positively skewed variables are logarithmically transformed, and all active indicators are standardized. Principal Component Analysis (PCA) is used to summarize the correlation structure, while Fuzzy C-Means (FCM) is applied to the first three descriptive component scores. A two-cluster resolution is reported as a binary benchmark, whereas three clusters are retained as the preferred substantive typology after jointly considering fuzzy validity, membership clarity, interpretability and robustness. The results distinguish between (i) development pressure intensive districts with higher environmental expenditure intensity; (ii) intermediate development districts with lower observed pressure; and (iii) peripheral lower-intensity districts with lower environmental expenditure. GIS mapping and fuzzy membership diagnostics show that several districts occupy transitional positions rather than belonging unambiguously to a single territorial type. The findings provide a size-adjusted account of Moldova’sinternal core–periphery structure and support differentiated, place-based territorial and environmental interventions. The mixed reference years and the proxy character of waste collection and environmental expenditure as indicators are explicitly acknowledged as limitations.

In the face of accelerating digitalization and growing systemic vulnerabilities, the ability to make accurate, real-time economic decisions has become a critical capability for financial and institutional stability. This study investigates how edge computing infrastructures influence decision-making accuracy, responsiveness, and risk containment in economic systems, particularly under the threat of financial contagion. A synthetic dataset simulating the interaction between economic indicators and edge performance metrics was constructed to emulate real-time decision environments. Composite indicators were developed to quantify key dynamics, and a range of machine learning models, including XGBoost, Random Forest, and Neural Networks, were applied to classify economic decision outcomes. The results indicate that low latency, efficient resource use, and balanced workload distribution are significantly associated with higher decision quality. XGBoost outperformed all other models, achieving 97% accuracy and a ROC-AUC of 0.997. The findings suggest that edge computing performance metrics can act as predictive signals for systemic fragility and may be integrated into early warning systems for financial risk management. This study contributes to the literature by offering a novel framework for modeling the economic implications of edge intelligence and provides policy insights for designing resilient, real-time financial infrastructures.

This study aims to provide a comprehensive bibliometric analysis covering the common areas between artificial intelligence (AI) applications and research focused on local or regional contexts. The analysis covers the period between the year 2002 and the year 2023, utilizing data sourced from the Web of Science database. Employing the Bibliometrix package within RStudio and VOSviewer software, the study identifies a significant increase in AI-related publications, with an annual growth rate of 22.67%. Notably, key journals such as Remote Sensing, PLOS ONE, and Sustainability rank among the top contributing sources. From the perspective of prominent contributing affiliations, institutions like Duy Tan University, Ton Duc Thang University, and the Chinese Academy of Sciences emerge as leading contributors, with Vietnam, Portugal, and China being the countries with the highest citation counts. Furthermore, a word cloud analysis is able to highlight the recurring keywords, including “model”, “classification”, “prediction”, “logistic regression”, “innovation”, “performance”, “random forest”, “impact”, “machine learning”, “artificial intelligence”, and “deep learning”. The co-occurrence network analysis reveals five clusters, amongst them being “artificial neural network”, “regional development”, “climate change”, “regional economy”, “management”, “technology”, “risk”, and “fuzzy inference system”. Our findings support the fact that AI is increasingly employed to address complex regional challenges, such as resource management and urban planning. AI applications, including machine learning algorithms and neural networks, have become essential for optimizing processes and decision-making at the local level. The study concludes with the fact that while AI holds vast potential for transforming local and regional research, ongoing international collaboration and the development of adaptable AI models are essential for maximizing the benefits of these technologies. Such efforts will ensure the effective implementation of AI in diverse contexts, thereby supporting sustainable regional development.

This study describes a comprehensive bibliometric analysis of shadow banking and financial contagion dynamics from 1996 to 2022. Through a holistic approach, our study focuses on quantifying the impact and uncovering significant trends in scientific research related to these interconnected fields. Using advanced bibliometric methods, we explored the global network of publications, identifying key works, influential authors, and the evolution of research over time. The results of the bibliometric analysis have highlighted an annual growth rate of 22.05% in publications related to the topics of shadow banking and financial contagion, illustrating researchers’ interest and the dynamic nature of publications over time. Additionally, significant increases in scientific production have been recorded in recent years, reaching a total of 178 articles published in 2022. The most predominant keywords used in research include “systemic risks”, “risk assessment”, and “measuring systemic risk”. The thematic evolution has revealed that over time, the focus on fundamental concepts used in analyzing these two topics has shifted, considering technological advancements and disruptive events that have impacted the economic and financial system. Our findings provide a detailed insight into the progress, gaps, and future directions in understanding the complex interplay of shadow banking and financial contagion. Our study represents a valuable asset for researchers, practitioners, and policymakers with a keen interest in understanding the dynamics of these critical components within the global financial system.

This study explored the complex interplay and potential risk of financial contagion across major financial indices, focusing on the Bucharest Exchange Trading Investment Funds Index (BET-FI), along with global indices like the S&P 500, Nasdaq Composite (IXIC), and Dow Jones Industrial Average (DJIA). Our analysis covered an extensive period from 2012 to 2023, with a particular emphasis on Romania’s financial market. We employed Autoregressive Distributed Lag (ARDL) modeling to examine the interrelations among these indices, treating the BET-FI index as our primary variable. Our research also integrated Exponential Curve Fitting (EXCF) and Generalized Supremum Augmented Dickey–Fuller (GSADF) models to identify and scrutinize potential price bubbles in these indices. We analyzed moments of high volatility and deviations from typical market trends, influenced by diverse factors like government policies, presidential elections, tech sector performance, the COVID-19 pandemic, and geopolitical tensions, specifically the Russia–Ukraine conflict. The ARDL model revealed a stable long-term relationship among the variables, indicating their interconnectedness. Our study also highlights the significance of short-term market shifts leading to long-term equilibrium, as shown in the Error Correction Model (ECM). This suggests the existence of contagion effects, where small, short-term incidents can trigger long-term, domino-like impacts on the financial markets. Furthermore, our variance decomposition examined the evolving contributions of different factors over time, shedding light on their changing interactions and impact. The Cholesky factors demonstrated the interdependence between indices, essential for understanding financial contagion effects. Our research thus uncovered the nuanced dynamics of financial contagion, offering insights into market variations, the effectiveness of our models, and strategies for detecting financial bubbles. This study contributes valuable knowledge to the academic field and offers practical insights for investors in turbulent financial environments.

This research provides a comprehensive analysis of the dynamic interplay between agent-based modeling (ABM) and artificial intelligence (AI) through a meticulous bibliometric study. This study reveals a substantial increase in scholarly interest, particularly post-2006, peaking in 2021 and 2022, indicating a contemporary surge in research on the synergy between AI and ABM. Temporal trends and fluctuations prompt questions about influencing factors, potentially linked to technological advancements or shifts in research focus. The sustained increase in citations per document per year underscores the field’s impact, with the 2021 peak suggesting cumulative influence. Reference Publication Year Spectroscopy (RPYS) reveals historical patterns, and the recent decline prompts exploration into shifts in research focus. Lotka’s law is reflected in the author’s contributions, supported by Pareto analysis. Journal diversity signals extensive exploration of AI applications in ABM. Identifying impactful journals and clustering them per Bradford’s Law provides insights for researchers. Global scientific production dominance and regional collaboration maps emphasize the worldwide landscape. Despite acknowledging limitations, such as citation lag and interdisciplinary challenges, our study offers a global perspective with implications for future research and as a resource in the evolving AI and ABM landscape.

The uncertainty of the environment, the complexity of economic systems, both at the national and global economy levels, and the digital age and artificial intelligence draw attention to the existence or appearance of systemic, disruptive phenomena that can appear and propagate in different forms, producing effects that can turn into economic crises. These phenomena can be transmitted like a domino effect, and they are referred to as the contagion effect in the scientific literature. In this research, one of the four forms of financial contagion, known as residual contagion, is studied on the mortgage loan market in Romania using agent-based modeling. By considering the economic crisis of 2007–2009, also supported by the mortgage crisis, in the present paper, we aim to study the Romanian mortgage market in 2022 through the use of machine learning techniques and agent-based modeling. The purpose of this research is to capture the potential systemic risks that can outline a residual financial contagion effect. The simulation results highlight the fact that the degree of connectivity between the commercial banks in Romania and the way in which they are interconnected have a major importance in the emergence and propagation of contagion effects. The proposed approach and the obtained results can offer more insight to policymakers on how the contagion effect takes place within the banking sector.