Search Results (1,183)

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

Sustainability performance varies significantly across countries, yet global assessments overlook the underlying structural trends. This study bridges this gap using machine learning to uncover meaningful clustering in global sustainability outcomes based on the 2025 Sustainable Development Goals (SDG) Index. We applied K-Means clustering to group 166 countries into five standardized indicators: SDG score, spillover effects, regional score, population size, and recent progress. The five-cluster solution was confirmed by the Elbow and Silhouette procedures, with ANOVA and MANOVA tests subsequently indicating statistically significant cluster differences. For the validation and interpretation of the results, six supervised learning algorithms were employed. Random Forest, SVM, and ANN performed best in classification accuracy (97.7%) with perfect ROC-AUC scores (AUC = 1.0). Feature importance analysis showed that SDG and regional scores were most predictive of cluster membership, while population size was the least. This supervised–unsupervised hybrid approach offers a reproducible blueprint for cross-country benchmarking of sustainability. It also offers actionable insights for tailoring policy to groups of countries, whether high-income OECD nations, emerging markets, or resource-scarce countries. Our findings demonstrate that machine learning is a useful tool for revealing structural disparities in sustainability and informing cluster-specific policy interventions toward the 2030 Agenda. Full article

Business financing involves supplying funds or capital to initiate, expand, or maintain a business. This study investigates entrepreneurial funding in South Africa, emphasizing microloans, venture capital, and gender-sensitive grants as tools to facilitate inclusive business growth. Using a qualitative desktop research methodology, this study relies on policy documents, institutional reports, and peer-reviewed studies to assess how these funding strategies tackle access barriers for marginalized populations, specifically women, youth, and rural entrepreneurs. Guided by Access to Finance Theory, Gender Finance Theory, and Innovation Ecosystems Theory, this study indicates that microloans offer immediate funding for informal businesses but show minimal long-term effects without additional assistance. Venture capital promotes rapid innovation, yet it is predominantly based in urban regions and unattainable for underrepresented populations. Grants that address gender issues foster equity but are obstructed by institutional fragmentation and insufficient scale. The results highlight the necessity for unified financing frameworks that merge financial and non-financial assistance, facilitating scalable and inclusive business ventures. Policy suggestions involve aligning public financing tools with the National Integrated Small Enterprise Development Masterplan, integrating gender-sensitive budgeting frameworks, and utilizing digital financial platforms to enhance access. Future studies should utilize mixed-methods or longitudinal approaches to assess the ongoing developmental effects of coordinated financing models within the South African setting. Full article

Public–Private Partnership (PPP) procurement is a relatively new approach in Nigeria’s housing sector. This study introduces a Fuzzy Risk Allocation Decision Model (FRADM) designed to address the complex and subjective nature of risk allocation in PPP-procured Mass Housing Projects (MHPs). A structured quantitative approach involving 40 purposively selected PPP housing experts was employed. Using a fuzzy synthetic evaluation (FSE) technique, critical risk factors were assessed based on partners’ risk management capabilities and allocation criteria. Constants (Ci) normalized the risk-carrying capacity indices (RCCIs) of both public and private sectors. Results show that risk attitude ranks highest among nine allocation criteria (MIS = 6.21), with the private sector demonstrating higher overall risk management capability. For instance, the availability of finance risk is optimally shared 53.48% to the private and 46.52% to the public sector. The FRADM was validated as reliable, practical, and replicable. Implications point to enhanced transparency, equitable risk-sharing, and support for SDG 11. The model is a strategic tool for decision-makers in PPP housing delivery in Nigeria and can inform similar efforts in other emerging economies. Further research should examine applications across other infrastructure sectors. Full article

As carbon taxes gain traction in climate policy, public support remains limited. The purpose of this study was to investigate how different mineral oil tax designs, particularly those combining rebates and earmarking, affect public acceptance, and whether the effects are monotone. The data were based on an online survey that was conducted in 2022 in Austria (n = 1216). It was found that a tax increase of EUR 25-cents per liter is politically feasible if revenues are earmarked for public transport or climate protection and paired with moderate rebates. Other uses of revenue, especially the general budget, fail to achieve majority support, regardless of tax level or compensation. To capture non-monotonic and heterogeneous preferences, an adaptive-choice-based-conjoint experiment with hierarchical Bayesian estimation was employed. Rebates were modeled as a stand-alone attribute, allowing for the identification of non-monotonicities for this attribute. The findings show deviations from widespread monotonicity assumptions: a moderate tax increase (EUR 10-cent/liter) was preferred over no increase, even in the absence of earmarking. Similarly, larger annual rebates (EUR 200–300) reduced support compared to a EUR 100 rebate, which was most popular. Full article

Population aging poses significant challenges to the sustainability of pension systems. This study presents an integrated methodological approach that uniquely combines actuarial life-cycle modeling with agent-based simulation to assess the potential of Reverse Mortgage Loans (RMLs) as a dual lever for enhancing retiree welfare and supporting pension system resilience under demographic and financial uncertainty. We explore Reverse Mortgage Loans (RMLs) as a potential financial instrument to support retirees while alleviating pressure on public pensions. Unlike prior research that treats individual decisions or policy outcomes in isolation, our hybrid model explicitly captures feedback loops between household-level behavior and system-wide financial stability. To test our hypothesis that RMLs can improve individual consumption outcomes and bolster systemic solvency, we develop a hybrid model combining actuarial techniques and agent-based simulations, incorporating stochastic housing prices, longevity risk, regulatory capital requirements, and demographic shifts. This dual-framework enables a structured investigation of how micro-level financial decisions propagate through market dynamics, influencing solvency, pricing, and adoption trends. Our central hypothesis is that reverse mortgages, when actuarially calibrated and macroprudentially regulated, enhance individual financial well-being while preserving long-run solvency at the system level. Simulation results indicate that RMLs can improve consumption smoothing, raise expected utility for retirees, and contribute to long-term fiscal sustainability. Moreover, we introduce a dynamic regulatory mechanism that adjusts capital buffers based on evolving market and demographic conditions, enhancing system resilience. Our simulation design supports multi-scenario testing of financial robustness and policy outcomes, providing a transparent tool for stress-testing RML adoption at scale. These findings suggest that, when well-regulated, RMLs can serve as a viable supplement to traditional retirement financing. Rather than offering prescriptive guidance, this framework provides insights to policymakers, financial institutions, and regulators seeking to integrate RMLs into broader pension strategies. Full article

The adoption of digital technologies, such as the Internet of Things (IoT), has emerged as a key strategy to improve efficiency, sustainability, and productivity in the agricultural sector, especially in contexts of modernization and digital transformation in developing regions. This study analyzes the key factors influencing the adoption of IoT technologies by farmers in the province of Guayas, Ecuador, and their impact on agricultural yields. The research is grounded in innovation diffusion theory and technology acceptance models, which emphasize the role of perception, usability, training, and economic viability in digital adoption. A total of 250 surveys were administered, with 232 valid responses (92.8% response rate), reflecting strong interest from the agricultural sector in digital transformation and precision agriculture. Using structural equation modeling (SEM), the results confirm that general perception of IoT (β = 0.514), practical functionality (β = 0.488), and technical training (β = 0.523) positively influence adoption, while high implementation costs negatively affect it (β = −0.651), all of which are statistically significant (p < 0.001). Furthermore, adoption has a strong positive effect on agricultural yield (β = 0.795). The model explained a high percentage of variance in both adoption (R² = 0.771) and performance (R² = 0.706), supporting its predictive capacity. These findings underscore the need for public and private institutions to implement targeted training and financing strategies to overcome economic barriers and foster the sustainable integration of IoT technologies in Ecuadorian agriculture. Full article

This paper presents a comprehensive analysis comparing the implementation of electric and diesel buses for public transport services in the low-density area of the City of Jastrebarsko in Croatia. It utilizes a multidimensional approach and incorporates direct and indirect costs, such as vehicle acquisition, operation, charging, maintenance, and environmental impact costs during the lifecycle of the buses. The results show that, despite the higher initial investment in electric buses, these vehicles offer savings, especially when coupled with significantly reduced emissions of pollutants, which decreases indirect costs. However, local contexts differ, leading to a need to revise whether or not a municipality can finance the procurement and operations of such a fleet. The paper utilizes a robust methodological framework, integrating a proposal based on real-world data and demand and combining it with predictive analytics to forecast long-term benefits. The findings of the paper support the introduction of buses as a sustainable solution for Jastrebarsko, which provides insights for public transport planners, urban planners, and policymakers, with a discussion about the specific issues regarding the introduction, procurement, and operations of buses of different propulsion in a low-density area. Full article

This study provides a comprehensive synthesis of climate risk and financial stability literature through a systematic review and bibliometric analysis of 174 Scopus-indexed publications from 1988 to 2024. Publications increased by 500% from 1988 to 2019, indicating growing research interest following the 2015 Paris Agreement. It explores how physical and transition climate risks affect financial markets, asset pricing, financial regulation, and long-term sustainability. Common themes include macroprudential policy, climate disclosures, and environmental risk integration in financial management. Influential authors and key journals are identified, with keyword analysis showing strong links between “climate change”, “financial stability”, and “climate risk”. Various methodologies are used, including econometric modeling, panel data analysis, and policy review. The main finding indicates a shift toward integrated, risk-based financial frameworks and rising concern over systemic climate threats. Policy implications include the need for harmonized disclosures, ESG integration, and strengthened adaptation finance mechanisms. Full article

This study evaluates the impact of digital systems and IT audit frameworks on tax performance and integrity within tax administrations. Using international data from organizations like the World Bank, OECD (Organisation for Economic Co-operation and Development), and IMF (International Monetary Fund), the research examines the relationship between tax revenue as a percentage of GDP, digital infrastructure, corruption perception, e-government development, and cybersecurity readiness. Quantitative analysis, including correlation, regression, and clustering methods, reveals a strong positive relationship between digital maturity, e-governance, and tax performance. Countries with advanced digital governance systems and robust IT audit frameworks, such as COBIT, tend to show higher tax revenues and lower corruption levels. The study finds that e-government development and anti-corruption measures explain over 40% of the variance in tax performance. Cluster analysis distinguishes between digitally advanced, high-compliance countries and those lagging in IT adoption. The findings suggest that digital transformation strengthens fiscal integrity by automating compliance and reducing human contact, which in turn mitigates bribery risks and enhances fraud detection. The study highlights the need for adopting international best practices to guide the digitalization of tax administrations, improving efficiency, transparency, and trust in public finance. Full article

The intersection of green finance and artificial intelligence (AI) represents a rapidly emerging and high-impact research domain with the potential to reshape sustainable economic systems. This study presents a comprehensive bibliometric and network analysis aimed at mapping the scientific landscape, identifying research hotspots, and highlighting methodological trends at this nexus. A dataset of 268 peer-reviewed publications (2014–June 2025) was retrieved from the Web of Science Core Collection, filtered by the Business Economics category. Analytical techniques employed include Bibliometrix in R, VOSviewer, and science mapping tools such as thematic mapping, trend topic analysis, co-citation networks, and co-occurrence clustering. Results indicate an annual growth rate of 53.31%, with China leading in both productivity and impact, followed by Vietnam and the United Kingdom. The most prolific affiliations and authors, primarily based in China, underscore a concentrated regional research output. The most relevant journals include Energy Economics and Finance Research Letters. Network visualizations identified 17 clusters, with focused analysis on the top three: (1) Emission, Health, and Environmental Risk, (2) Institutional and Technological Infrastructure, and (3) Green Innovation and Sustainable Urban Development. The methodological landscape is equally diverse, with top techniques including blockchain technology, large language models, convolutional neural networks, sentiment analysis, and structural equation modeling, demonstrating a blend of traditional econometrics and advanced AI. This study not only uncovers intellectual structures and thematic evolution but also identifies underdeveloped areas and proposes future research directions. These include dynamic topic modeling, regional case studies, and ethical frameworks for AI in sustainable finance. The findings provide a strategic foundation for advancing interdisciplinary collaboration and policy innovation in green AI–finance ecosystems. Full article

Like in many countries, the transfer of increased land value created by public actions without landowner contributions back to the public is under debate in Türkiye. Although various Land Value Capture (LVC) mechanisms are employed worldwide to finance infrastructure investments, no comprehensive system has been established in Türkiye for this purpose. In this study, an improved LVC model that integrates land value and development rights is proposed. This model, termed Hybrid Land Readjustment (hLR), is designed to ensure that land value increases triggered by public investments are returned to the public. To this end, existing Turkish value capture instruments with potential are examined. Under the proposed hLR framework, equal basic development rights are granted to cadastral parcels, parcel and building-block value maps are utilized, basic rights are adjusted according to land-value changes, and a portion of additional development rights is transferred to the public. A practical application scenario is provided to illustrate the model’s operation. The system is configured for seamless integration into Türkiye’s existing legal and planning framework, offering a sustainable mechanism for financing infrastructure and implementing zoning plans. Full article

This study analyses the conditions under which co-opetition improves the supply of healthcare services in Benin. Using non-centralised administrative data from a sample of public and private health centres, we apply network theory and negative binomial regression to assess the extent to which competition affects collaboration between public and private healthcare providers. We found that competition reduces the degree of collaboration between private and public health providers. However, the COVID-19 pandemic significantly mitigated this effect, highlighting the potential for competition within the healthcare system without compromising social welfare. Notwithstanding that, we show that these benefits are not sustained over time. These findings have policy implications for the sustainability of health system financing in Africa, particularly by promoting sustainable financial mechanisms for the private sector and more inclusive governance structures. Full article

Artificial intelligence (AI) agents are increasingly shaping vital sectors of society, including healthcare, education, supply chains, and finance. As their influence grows, AI alignment research plays a pivotal role in ensuring these systems are trustworthy, transparent, and aligned with human values. Leveraging blockchain technology, proven over the past decade in enabling transparent, tamper-resistant distributed systems, offers significant potential to strengthen AI alignment. However, despite its potential, the current AI alignment literature has yet to systematically explore the effectiveness of blockchain in facilitating secure and ethical behavior in AI agents. While existing systematic literature reviews (SLRs) in AI alignment address various aspects of AI safety and AI alignment, this SLR specifically examines the gap at the intersection of AI alignment, blockchain, and ethics. To address this gap, this SLR explores how blockchain technology can overcome the limitations of existing AI alignment approaches. We searched for studies containing keywords from AI, blockchain, and ethics domains in the Scopus database, identifying 7110 initial records on 28 May 2024. We excluded studies which did not answer our research questions and did not discuss the thematic intersection between AI, blockchain, and ethics to a sufficient extent. The quality of the selected studies was assessed on the basis of their methodology, clarity, completeness, and transparency, resulting in a final number of 46 included studies, the majority of which were journal articles. Results were synthesized through quantitative topic analysis and qualitative analysis to identify key themes and patterns. The contributions of this paper include the following: (i) presentation of the results of an SLR conducted to identify, extract, evaluate, and synthesize studies on the symbiosis of AI alignment, blockchain, and ethics; (ii) summary and categorization of the existing benefits and challenges in incorporating blockchain for AI alignment within the context of ethics; (iii) development of a framework that will facilitate new research activities; and (iv) establishment of the state of evidence with in-depth assessment. The proposed blockchain-based AI alignment framework in this study demonstrates that integrating blockchain with AI alignment can substantially enhance robustness, promote public trust, and facilitate ethical compliance in AI systems. Full article

This study aims to map the scientific knowledge of bank profitability and its determinants. It identifies trends and gaps in existing research through a bibliometric analysis. To this end, 634 documents published in the Web of Science database over the last 54 years were analyzed using the bibliometric package. The results indicate an increase in the volume of publications following the 2008 financial crisis, focusing on analyzing the factors influencing bank profitability and economic growth. The Journal of Banking and Finance is the preeminent publication in this field. The literature reviewed shows that bank profitability depends on internal factors (size, credit risk, liquidity, efficiency, and management) and external factors (such as GDP, inflation, interest rates, and unemployment). In addition to the traditional determinants, the recent literature highlights the importance of innovation and technological factors such as digitalization, mobile banking, and electronic payments as relevant to bank profitability. ESG (environmental, social, and governance) and governance indicators, which are still emerging but have been extensively researched in companies, indicate a need for evidence in this area. This paper also provides relevant insights for the formulation of monetary policy and the strategic formulation of banks, helping managers and owners to improve bank performance. It also provides directions for future empirical studies and research collaborations in this field. Full article