Search Results (122)

Monacha cartusiana (O. F. Müller, 1774), native to the Mediterranean region and Europe, is a terrestrial gastropod recognized as a highly destructive agricultural pest that causes significant damage to crop plants, fruit trees, vegetables, ornamentals, and natural ecosystems. Despite its broad geographic distribution, the evolutionary history and phylogeographic relationships of M. cartusiana populations remain globally unexplored. This study reports the first molecularly confirmed record of M. cartusiana in Pakistan and investigates its genetic diversity and phylogeographic structure within a global context using mitochondrial markers. After morphological identification, genomic DNA was extracted from collected specimens using the CTAB method, followed by amplification and sequencing of the mitochondrial COI and 16S rRNA genes. The resulting sequences were subsequently analyzed using DnaSP and PopART software to estimate genetic diversity, perform neutrality tests, and construct haplotype networks. Published sequences of M. cartusiana retrieved from GenBank were incorporated to provide a global comparative framework. The COI dataset (555 bp) revealed 52 haplotypes, whereas the 16S rRNA dataset (269 bp) identified 14 haplotypes across global populations. High haplotype diversity (Hd = 0.946 for COI; Hd = 0.831 for 16S rRNA) and moderate nucleotide diversity (π = 0.010 for COI; π = 0.01253 for 16S rRNA) indicated substantial genetic variability within the species. Neutrality tests produced negative and insignificant values for Tajima’s D for COI and significant values for 16S rRNA (−1.428 for COI; −0.20586 for 16S rRNA) and Fu’s Fs (−29.776 for COI; −1.263 for 16S rRNA), suggesting historical population expansion. Phylogenetic reconstruction and haplotype network analyses identified two major clades (Clade A and Clade B), reflecting genetic relationships among populations from different geographic regions. AMOVA based on COI and 16S rRNA sequences revealed significant population structuring, with 29.98–51.30% of the total genetic variation occurring among populations and high fixation indices (FST = 0.299–0.51398, p = 0.001), indicating pronounced genetic differentiation and restricted gene flow. Pairwise FST analyses indicated that the Pakistani population is most closely related to populations from Italy and Central Europe, suggesting a closer genetic affinity with Southern or Central European populations. However, FST alone does not allow definitive inference of introduction directionality, and additional analyses would be required to robustly identify the source population. Overall, this study provides the first comprehensive molecular and phylogeographic assessment of the M. cartusiana species from Pakistan within a global context. These findings contribute important baseline data for understanding the evolutionary dynamics, dispersal history, and population connectivity of this economically important pest species. The pronounced genetic differentiation among populations and the suggested genetic affinity of the Pakistani population with European lineages have direct implications for biosecurity monitoring, invasion pathway tracing, and targeted pest management strategies. Future research integrating nuclear markers with the mitochondrial data presented here will be essential for a more complete understanding of gene flow and local adaptation in this species. Full article

The transition from cash to digital payment instruments is reshaping retail commerce across Europe unevenly, with Central and Eastern European (CEE) countries exhibiting both some of the fastest growth and some of the lowest baseline levels in online shopping participation. This study examines whether the development of digital payment infrastructure proxied by the share of individuals using internet banking (NetBank) is associated with e-commerce adoption across eleven CEE EU member states over the period 2014–2023, yielding a balanced panel of 110 country-year observations. Drawing on harmonised data from Eurostat, the World Bank, and the ITU, we estimate a two-way fixed-effects model with kernel-robust standard errors and a dynamic specification with a lagged dependent variable. The results indicate that a one-standard-deviation improvement in internet banking penetration is associated with a 6.2 percentage point increase in the share of online shoppers once country and year fixed effects are controlled for, a finding that is precisely estimated under kernel standard errors (p < 0.001). Income-group heterogeneity analysis suggests that this association may be substantially larger in lower-income CEE countries (β = 6.9, p = 0.006) compared to higher-income ones (β = 2.3, p = 0.554), consistent with the hypothesis that payment infrastructure improvements generate the highest marginal returns where baseline access is lowest. Romania, despite recording the steepest absolute growth in online shopping in the EU over the sample period (+33 percentage points), remains persistently below the CEE median, illustrating how payment infrastructure constraints can slow convergence even during periods of rapid digitisation. The findings should be interpreted as robust conditional associations rather than causal effects, given the limitations of macro-panel identification. Full article

Crypto-asset markets have been rapidly evolving during the past years, being under the spotlight of a diverse set of actors in the financial ecosystem, including investors, financial institutions, regulators and academics. Their potential interconnections with the traditional financial markets are important, and identifying them can provide useful insight in a diversity of areas such as risk contagion and mitigation, price formation, portfolio management and regulatory framework design. In order to identify such interconnections, various lines of research are followed. Specifically, the correlation between prominent stock market indices and crypto-assets from 2018 to 2025 is examined, while their volatility is also evaluated. Furthermore, the relevant effect of news, events and announcements is explored. The results are based on both daily and high-frequency datasets, with the use of the latter focusing on intra-day variation. The analysis of the results identifies existing interconnections between 2020 and 2025, as well as the important respective impact of news and announcements. An additional generic outcome is the usefulness of high-frequency datasets in the crypto-asset context. The conclusions are useful for all actors in the financial ecosystem. Future work can focus on the extension of the research to additional markets or crypto-assets. Full article

The European Union’s energy transition framework presents significant challenges for the maritime sector, requiring technical, organizational, and market-based compliance measures. The FuelEU Maritime Regulation (FuelEU) introduces flexible mechanisms such as banking, borrowing, and pooling that broaden compliance options for shipowners. This study examines the role of pooling in European shipping, focusing on its operational models, enabling conditions, and economic implications. Pool formation demands extensive information exchange and effective coordination, creating a need for specialized intermediaries and supporting the emergence of a market in which pooling prices are shaped by supply and demand. The research employs a qualitative methodology combining stakeholder and exploratory market analysis, assessment of regulatory and market drivers, case studies, and comparative analysis, complemented by expert interviews and a targeted survey. The findings highlight pooling as a pivotal compliance instrument, particularly as a fallback option during the reporting period, and show that it currently represents the most cost-efficient means of achieving FuelEU compliance. Intermediary service providers, especially digital platforms, play a central role by facilitating coordination, improving transparency, and reducing compliance costs. The study further identifies key factors influencing shipowners’ costs and revenues in pools, emphasizing the critical importance of alternative fuel availability and prices. Full article

This study examines the evolution, heterogeneity, and short-term prospects of export performance in seven Central and Eastern European (CEE) economies—Croatia, Czech Republic, Hungary, Poland, Romania, Bulgaria, and Slovakia—over the period 1995–2024. Using annual World Bank data, exports are modeled as a share of GDP to ensure cross-country comparability and to capture differences in trade dependence. The analysis combines descriptive and inferential statistics with Augmented Dickey–Fuller tests, non-parametric comparisons, Granger causality analysis, and country-specific ARIMA models to investigate export dynamics, the role of foreign direct investment (FDI), and future export trajectories. The results reveal a common long-term upward trend in export intensity across all countries, driven by European integration and structural transformation, but with pronounced cross-country differences in export dependence and volatility. Highly open economies such as Slovakia, Hungary, and the Czech Republic exhibit strong export performance alongside greater exposure to external shocks, while larger domestic markets such as Poland and Romania display lower export intensity and greater stabilization. Granger causality tests indicate that FDI contributes to export growth in several economies, often with multi-year lags, highlighting the importance of absorptive capacity and institutional quality in translating investment inflows into export competitiveness. ARIMA-based forecasts for 2025–2027 suggest continued export expansion and relative stabilization despite recent global disruptions. This study’s primary contribution lies in integrating comparative export analysis, causality testing, and short-term forecasting within a unified econometric framework, offering policy-relevant insights into export-led growth and economic convergence in post-transition European economies. Full article

This paper examines the European Central Bank’s institutional capacity and readiness to introduce a digital euro in the context of accelerating digitalization, geopolitical uncertainty, and growing competition in the global monetary system. Rather than treating the digital euro primarily as a technological innovation, the study conceptualizes it as a multidimensional institutional project shaped by regulatory mandates, governance choices, stakeholder expectations and risk considerations. Drawing on institutional theory and stakeholder theory, the analysis adopts a qualitative research design combining semi-structured expert interviews with systematic document analysis of ECB and EU policy material. The findings indicate that while the ECB has developed a structured roadmap encompassing investigation, preparation and potential issuance phases, significant challenges remain across regulative, normative and cognitive dimensions of readiness. These challenges include tensions between privacy and compliance requirements, cybersecurity and interoperability risks, potential effects on financial stability, and the management of public trust and stakeholder acceptance. The paper argues that the success of a digital euro will depend not only on technical feasibility, but on the ECB’s ability to align design and implementation choices with institutional legitimacy and behavioral expectations. By integrating institutional readiness and risk analysis, the study contributes to the literature on central bank digital currencies and offers insights relevant to policymakers concerned with monetary sovereignty and financial resilience in the digital age. Full article

The triple shocks of the financial crisis, sovereign debt crisis, and COVID-19 pandemic have exerted significant impact on the financial markets in the Eurozone. Since the 2008 recession, the European Central Bank (ECB) has implemented an array of unconventional monetary policies (UMPs). These policies aim to address issues such as financing constraints and low inflation rates that the traditional monetary policy framework could not handle. The data indicated that when the ECB implemented its quantitative easing (QE) programs (e.g., the pandemic emergency purchase program), inflation in the Eurozone bounced back. It went up from −0.3% in August 2020 to 5% by December 2021. These measures prevented the pandemic from pushing the economy into a long-lasting deflation pressure. As the world’s second-largest economy, China’s monetary policy decisions play a crucial role in maintaining economic stability and fostering sustainable growth. This study examines ECB’S major unconventional monetary policy measures, evaluates their effects, and explores how these align with China’s monetary policy formulation and reforms. This research can provide useful insights for shaping monetary policy in the Eurozone and emerging economies such as China, especially during times of economic uncertainty. Full article

This paper investigates the systemic macroeconomic determinants of Pillar 2 Requirements (P2R) imposed by the European Central Bank (ECB) under the Single Supervisory Mechanism (SSM). While P2R is formally calibrated at the individual bank level through the Supervisory Review and Evaluation Process (SREP), we explore the extent to which common macro-financial shocks influence supervisory capital expectations across banks. Using a panel dataset covering euro area banks between 2021 and 2025, we match bank-level P2R data with country-level macroeconomic indicators. Those variables include real GDP growth, HICP inflation and index levels, government fiscal balance, euro yield curve spreads, net turnover, FDI inflows, construction and industrial production indices, the price-to-income ratio in real estate, and trade balance measures. We apply Principal Component Analysis (PCA) to extract latent variables related to the macroeconomic factors from a broad set of variables, which are then introduced into a Bayesian Vector Autoregression (BVAR) model to assess their dynamic impact on P2R. Our results identify three principal components that capture general macroeconomic cycles, sector-specific real activity, and financial/external imbalances. The impulse response analysis shows that sectoral and external shocks have a more immediate and statistically significant influence on P2R adjustments than broader macroeconomic trends. These findings clearly support the use of systemic macro-financial conditions in supervisory decision-making and support the integration of anticipating macro-prudential analysis into capital requirement frameworks. Full article

Small and medium-sized enterprises (SMEs) are critical contributors to economic growth, innovation, and employment. However, they often struggle in securing external financing. This financial gap mainly arises from perceived risks and information asymmetries creating barriers between SMEs and potential investors. To address this issue, our study proposes a machine learning (ML) framework for predicting the investment readiness (IR) of SMEs. All the models involved in this study are trained using data provided by the European Central Bank’s Survey on Access to Finance of Enterprises (SAFE). We train, evaluate, and compare the predictive performance of nine (9) machine learning algorithms and various ensemble methods. The results provide evidence on the ability of ML algorithms in identifying investment-ready SMEs in a heavily imbalanced and noisy dataset. In particular, the Gradient Boosting algorithm achieves a balanced accuracy of 75.4% and the highest ROC AUC score at 0.815. Employing a relevant cost function economically enhances these results. The approach can offer specific inference to policymakers seeking to design targeted interventions and can provide investors with data-driven methods for identifying promising SMEs. Full article

This paper analyzes the macroeconomic evolution of the European Union member states that have adopted the Euro, compared to those that continue to use national currencies, with a specific focus on the Central and Eastern European countries during the period 2018–2024. Using a quantitative and exploratory approach and data provided by Eurostat, the European Central Bank, and the International Monetary Fund, we examined a series of key indicators: interest rates, inflation, GDP per capita, public debt, and foreign direct investment. The results highlight several macroeconomic advantages for Eurozone countries, including lower interest rate volatility and a quicker recovery from inflation, largely due to access to monetary tools such as PEPP and TPI. Non-Euro countries have experienced more severe inflationary episodes and higher financing costs, which have negatively impacted FDI inflows. Although some of these countries, such as Romania and Poland, have recorded solid GDP growth, they remain exposed to structural vulnerabilities and political and economic uncertainties. Correlation analyses confirm significant negative relationships between interest rates, inflation, and FDI levels. Full article

This study examines the role of supervisory frameworks in shaping the risk management behavior of Greece’s four systemic banks during the period of 2015–2024. It explores how regulatory reforms under Capital Requirements Regulation II, Basel III, and European Central Bank oversight influenced capital adequacy, asset quality, and liquidity metrics. Employing a quantitative methodology, this study analyzes secondary data from Pillar III disclosures, annual financial reports, and supervisory statements. Key risk indicators (capital adequacy ratio, non-performing exposure ratio, liquidity coverage ratio, and risk-weighted assets) are evaluated in conjunction with regulatory interventions, such as International Financial Reporting Standards 9 transitional relief, the Hercules Asset Protection Scheme, and European Central Bank liquidity measures. The findings reveal that enhanced supervision contributed to improved resilience and regulatory compliance. International Financial Reporting Standards 9 transitional arrangements were pivotal in maintaining capital thresholds during stress periods. Supervisory flexibility and extraordinary European Central Bank support measures helped banks absorb shocks and improve risk governance. Differences across banks highlight the impact of institutional strategy on regulatory performance. This study offers a rare longitudinal assessment of supervisory influence on bank risk behavior in a high-volatility Eurozone context. Covering an entire decade (2015–2024), it uniquely links institutional strategies with evolving regulatory frameworks, including crisis-specific interventions such as International Financial Reporting Standards 9 relief and asset protection schemes. The results provide insights for policymakers and regulators on how targeted supervisory interventions and transitional mechanisms can enhance banking sector resilience during protracted crises. Full article

Effective analysis of central bank communications is critical for anticipating monetary policy changes and guiding market expectations. However, traditional natural language processing models face significant challenges in processing lengthy and nuanced policy documents, which often exceed tens of thousands of tokens. This study addresses these challenges by proposing a novel integrated deep learning framework based on Hyena Hierarchy architectures, which utilize sub-quadratic convolution mechanisms to efficiently process ultra-long sequences. The framework employs Delta-LoRA (low-rank adaptation) for parameter-efficient fine-tuning, updating less than 1% of the total parameters without additional inference overhead. To ensure robust performance across institutions and policy cycles, domain-adversarial neural networks are incorporated to learn domain-invariant representations, and a multi-task learning approach integrates auxiliary hawkish/dovish sentiment signals. Evaluations conducted on a comprehensive dataset comprising Federal Open Market Committee statements and European Central Bank speeches from 1977 to 2024 demonstrate state-of-the-art performance, achieving over 6% improvement in macro-F1 score compared to baseline models while significantly reducing inference latency by 65%. This work offers a powerful and efficient new paradigm for handling ultra-long financial policy texts and demonstrates the effectiveness of integrating advanced sequence modeling, efficient fine-tuning, and domain adaptation techniques for extracting timely economic signals, with the aim to open new avenues for quantitative policy analysis and financial market forecasting. Full article

In its response to the economic and financial crises of 2008, the sovereign debt and euro crisis of 2010–2015, and the COVID-19 pandemic of 2020–2023, the European Central Bank (ECB) implemented an unconventional monetary policy aimed at providing liquidity for more than a decade, through a complex set of tools and operations that make up the so-called quantitative easing. The results of all of them are being analyzed from different perspectives. This paper studies the relationship between a large base money, characterized by a voluminous concentration of liquidity in the form of excess reserves, and broad money (the broad M3 aggregate). Our econometric work shows a low elasticity of broad money with respect to base money, concluding the existence of a weak relationship between both monetary magnitudes, with a sharp decline in the money multiplier. The demand for money has remained stable relative to its determining variables, interest rates and income. At the same time, some practices related to the handling of excess liquidity by European banks through deposit facilities deserve consideration. We propose strict control by the monetary authority over the nature and origin of the funds that constitute the excess liquidity derived from the ECB’s unconventional operations, and over its management. Full article

I show that house prices can be modeled using machine learning (kNN and tree-bagging) and a small dataset composed of macroeconomic factors (MEF), including an inflation metric (CPI), US Treasury rates (10-yr), Gross Domestic Product (GDP), and portfolio size of central banks (ECB, FED). This set of parameters covers all the parties involved in a transaction (buyer, seller, and financing facility) while ignoring the intrinsic properties of each asset and encompassing local (inflation) and liquidity issues that may impede each transaction composing a market. The model here takes the point of view of a real estate trader who is interested in both the financing and the price of the transaction. Machine learning allows for the discrimination of two periods within the dataset. First, and up to 2015, I show that, although the US Treasury rates level is the most critical parameter to explain the change of house-price indices, other macroeconomic factors (e.g., consumer price indices) are essential to include in the modeling because they highlight the degree of openness of an economy and the contribution of the economic context to price changes. Second, and for the period from 2015 to today, I show that, to explain the most recent price evolution, it is necessary to include the datasets of the European Central Bank programs, which were designed to support the economy since the beginning of the 2010s. Indeed, unconventional policies of central banks may have allowed some institutional investors to arbitrage between real estate returns and other bond markets (sovereign and corporate). Finally, to assess the models’ relative performances, I performed various sensitivity tests, which tend to constrain the possibilities of each approach for each need. I also show that some models can predict the evolution of prices over the next 4 quarters with uncertainties that outperform existing index uncertainties. Full article

Space heating consumption prediction is critical for energy management and efficiency, directly impacting sustainability and efforts to reduce greenhouse gas emissions. Accurate models enable better demand forecasting, promote the use of green energy, and support decarbonization goals. However, existing models often lack precision due to limited feature sets, suboptimal algorithm choices, and limited access to weather data, which reduces generalizability. This study addresses these gaps by evaluating various Machine Learning and Deep Learning models, including K-Nearest Neighbors, Support Vector Regression, Decision Trees, Linear Regression, XGBoost, Random Forest, Gradient Boosting, AdaBoost, Long Short-Term Memory, and Gated Recurrent Units. We utilized space heating consumption data from the European Central Bank Headquarters office as a case study. We employed a methodology that involved splitting the features into three categories based on the correlation and evaluating model performance using Mean Squared Error, Mean Absolute Error, Root Mean Squared Error, and R-squared metrics. Results indicate that XGBoost consistently outperformed other models, particularly when utilizing all available features, achieving an R² value of 0.966 using the weather data from the building weather station. This model’s superior performance underscores the importance of comprehensive feature sets for accurate predictions. The significance of this study lies in its contribution to sustainable energy management practices. By improving the accuracy of space heating consumption forecasts, our approach supports the efficient use of green energy resources, aiding in the global efforts towards decarbonization and reducing carbon footprints in urban environments. Full article