Search Results (261)

In the realm of financial management, optimizing the allocation of funds in banking companies is vital to their operational efficiency. Banks manage their funds by allocating them into reserve and credit funds as the main activities of banking. Optimizing these allocations ensures that all assets are effectively utilized. However, real-life optimization problems often involve uncertainty, making deterministic data assumptions insufficient. Robust Optimization is a methodology that addresses these uncertainties by incorporating computational tools to solve optimization problems with uncertain data. The uncertainty approach used in robust optimization is polyhedral sets. In the context of banking, uncertainties influencing the allocation of reserve and credit funds include financial risks and returns. These risks can be quantified using Conditional Value-at-Risk (CVaR), a suitable measure for banking fund allocation due to its ability to accommodate varying risk characteristics under different business conditions. This study focuses on developing an optimization model for reserve and credit fund allocation in banking companies using a Multi-objective Robust CVaR approach with lexicographic, informed by business risk data and credit instruments. The resulting optimization model yields optimal allocations for reserve and credit funds, ensuring efficient asset utilization to support banking operations. This approach offers new perspectives for banks to achieve fund allocations that are not only regulatory compliant but also optimal. The implications of such optimal allocations include mitigating risks associated with reserve fund imbalances and enhancing profitability through optimal credit returns. Full article

The agricultural economic policy in Mexico has inadequately addressed the integrated sustainability needs of the rural sector. This study adopts a sustainability perspective to examine economic policy distortions and market failures in the export-oriented rose cultivation sector, and evaluates their effects on the economic and social sustainability of producers in Tenancingo and Villa Guerrero, Mexico. A Policy Analysis Matrix (PAM) and CONEVAL poverty line metrics were used to evaluate private and social profitability as indicators of financial viability and resource use efficiency. Findings indicate that, despite being supported by distortionary policies, the rose export sector remains competitive and financially viable, constituting a key pillar of economic sustainability. Moreover, the social profitability of rose production exceeded its private profitability, suggesting a net positive socioeconomic benefit and a sustainable allocation of resources from a societal perspective. Furthermore, per capita income in the rose production unit (RPU) exceeded the poverty line established by CONEVAL, directly supporting social sustainability and strengthening livelihood resilience. The study concludes that current resource allocation mechanisms are inefficient for sustainability over the long term. It emphasizes the need for policy shifts toward greater innovation, more effective technology transfer, improved market access, and stronger human capital to strengthen the sustainability of the sector as a whole. Rose cultivation exhibited a significant positive multiplier effect on the regional economy, reinforcing its contribution to sustainable rural development. Full article

A large number of state-owned companies were privatized in the Czech Republic after the end of the communist regime, mostly through their transformation into joint-stock companies. The water management sector was no exception from this process. The ownership of infrastructure networks was transferred to individual municipalities, which are legally obliged to provide their inhabitants with water supply and sewerage disposal. Subsequently, the municipalities joined together in joint-stock companies to enhance their capacity to provide sufficient financial resources for the rehabilitation and development of water infrastructure and also to enable the implementation of sustainable water management strategies, which are key to environmental protection. Assets contributed to joint-stock companies in the form of non-monetary contributions serve as a basis for a proportionate allocation of shares, representing the shareholder’s share of participation in the company’s management. An analysis of the asset performance within these companies indicates the necessity of developing an optimized methodology for determining the number of shares allocated for such non-monetary contributions. This need arises from significant disparities in both profitability and cost-efficiency among municipalities, depending on factors such as population size (revenues) and the length and technical characteristics of the infrastructure networks (costs) contributed to the joint-stock companies. The authors of the article present the research project results, aimed at developing a methodological procedure for determining the price (value) of municipal infrastructure assets contributed as non-monetary capital to a joint-stock company that owns and operates water management networks, from which the secondary objective of determining the fair value of a municipality’s water management infrastructure assets based on the developed methodology is derived. The proposed methodological procedure is primarily based on establishing the ratio between the fixed and variable costs of the municipality. Full article

Urban areas globally face the critical challenge of meeting growing energy demands while maintaining environmental sustainability. However, existing research provides limited and often inconsistent evidence on how green finance affects urban energy efficiency, largely due to heterogeneous measurement systems, methodological constraints, and insufficient identification of underlying mechanisms. To address these research gaps, this study investigates two core questions: Does green finance significantly improve urban energy efficiency? If so, what are the specific transmission mechanisms driving this impact? Methodologically, this exploration employs a Double Machine Learning (DML) approach to analyze panel data from 210 Chinese cities between 2006 and 2022. The analysis demonstrates a significant and positive impact of green finance on urban energy efficiency, with an estimated coefficient of 0.1910. Further analysis identifies three constructive mechanisms, including environmental regulations, industrial structures, and green technological innovation, which enhance resource allocation and energy utilization efficiency. Moreover, green finance shows a stronger positive impact in non-resource-dependent cities, regions outside traditional industrial bases, and financially developed areas. These findings recommend establishing standardized green finance frameworks, increasing targeted financial support for key regions, and integrating green innovation with industrial restructuring. These measures help consolidate China’s green finance system and improve regional energy efficiency through market expansion, energy transition, and technological advancement. Full article

Health financing is a core determinant of the resilience and equity of health systems. Using WHO’s three-pillar framework as an orienting reference—rather than a prescriptive template—this article analyzes the evolution, structural shortcomings, and policy dilemmas of the Greek health financing model, within a comparative European context. While many EU countries have strengthened public financing to ensure universal access, Greece maintains a hybrid, fragmented model in which out-of-pocket payments play a disproportionately large role. Despite recurrent reform attempts, Greece has not developed a cohesive public system with a clear commitment to social solidarity. Instead, the system has silently shifted into a de facto semi-privatized two-tier model that exacerbates social inequities, limits access and undermines efficiency. Drawing on international experience and documented policy lessons, the article proposes a strategic redesign of the health financing architecture. The proposal is conceptual and does not enter implementation specifics. Its central axis is the establishment of two national single purchasers of health services by level of care, with a clear allocation of responsibilities and authority, the Ministry of Health for hospital care, and the National Organization for Healthcare Services Provision (EOPYY) for primary, outpatient, and post-acute/rehabilitation care, to strengthen prevention, equitable access, and chronic care management while easing pressure on hospitals. The proposed model includes targeted investments in human resources and infrastructure, the enhancement of prospective payment mechanisms, the strengthening of primary care networks, and the leveraging of innovation. At the same time, it provides for reforms in governance, digital transformation of the system, and reallocation of resources based on principles of equity and efficiency. The proposed overall restructuring aims to strengthen financial protection, reduce inequities in access, and improve health outcomes through a publicly oriented, socially responsive, and strategically governed system. Full article

The shift towards renewable energy demands decision-making tools that unite economic performance with environmental stewardship and social equity. The conventional evaluation methods fail to consider these interconnected factors, which results in substandard investment results. The paper establishes a sustainability accounting system that uses the Elimination and Choice Expressing Reality (ELECTRE) method to optimize investment distribution between solar power, wind power, and bioenergy systems. The evaluation framework uses six performance indicators, which include cost efficiency and return on investment, together with CO₂ emissions intensity, job creation, energy output, and financial sustainability indicators, like Net Present Value (NPV) and payback period. The barrier optimization algorithm solved the model in 10 iterations, which took 0.10 s to achieve an optimal objective value of 1.6929. The wind energy source demonstrated superior performance in every evaluation criterion because it achieved the highest concordance scores, lowest discordance levels, best payback period, and strongest NPV. The maximum allocation went to wind at 53.3%, while bioenergy received 31.0%, and solar received 16.7%. The optimized portfolio reached a total sustainability index (SI) of 1.70, which validates the method’s strength. The research shows that using ELECTRE with sustainability accounting creates an exact and open system for renewable energy investment planning. The framework reveals wind as the core alternative yet demonstrates how bioenergy and solar work together to support sustainable development across environmental and economic and social dimensions. Full article

In recent decades, sustainable development has become a strategic priority for both national and EU economic policies, reflecting the need to integrate economic progress, social cohesion, and environmental protection. Public and private investments—particularly those directed toward infrastructure, human capital, and technological advancement—play a decisive role in supporting this transition. This study examines how the allocation of investments across institutional sectors in Romania influences the country’s sustainable development trajectory, with the underlying assumption that an efficient distribution of resources contributes to balanced regional growth, technological progress, and the strengthening of human capital. Using official national and European datasets, the research employs descriptive statistics, correlation analysis, sectoral comparisons, and complementary regression models to evaluate investment patterns over the period 2008–2023. The empirical findings indicate significant disparities in investment intensity among institutional sectors, which are reflected in uneven regional development and persistent gaps in innovation capacity. The results also show strong associations between targeted investments—especially those made by non-financial corporations and public institutions—and improvements in technological advancement, productivity, and human resource retention. Overall, the study concludes that a more coherent and strategically coordinated investment policy is essential for enhancing Romania’s sustainable development outcomes. Strengthening the alignment between investment flows and long-term development priorities would increase economic resilience, stimulate innovation, and support a more equitable and sustainable growth model. Full article

Portfolio optimization is fundamental to modern finance, enabling investors to construct allocations that balance risk and return while satisfying practical constraints. When transaction costs and cardinality limits are incorporated, the problem becomes a computationally demanding mixed-integer quadratic program. This work demonstrates how principles from biomimetics—specifically, the computational strategies employed by biological neural systems—can inspire efficient algorithms for complex optimization problems. We demonstrate that this problem can be reformulated as a constrained quadratic program and solved using dynamics inspired by spiking neural networks. Building on recent theoretical work showing that leaky integrate-and-fire dynamics naturally implement projected gradient descent for convex optimization, we develop a solver that alternates between continuous gradient flow and discrete constraint projections. By mimicking the event-driven, energy-efficient computation observed in biological neurons, our approach offers a biomimetic pathway to solving computationally intensive financial optimization problems. We implement the approach in Python and evaluate it on portfolios of 5 to 50 assets using five years of market data, comparing solution quality against mixed-integer solvers (ECOS_BB), convex relaxations (OSQP), and particle swarm optimization. Experimental results demonstrate that the SNN solver achieves the highest expected return (0.261% daily) among all evaluated methods on the 50-asset portfolio, outperforming exact MIQP (0.225%) and PSO (0.092%), with runtimes ranging from 0.5 s for small portfolios to 8.4 s for high-quality schedules on large portfolios. While current Python runtimes are comparable to existing approaches, the key contribution is establishing a path to neuromorphic hardware deployment: specialized SNN processors could execute these dynamics orders of magnitude faster than conventional architectures, enabling real-time portfolio rebalancing at institutional scale. Full article

In the area of the European Union (EU) energy policy, among the entities involved in the process of financing investments in renewable energy sources (RESs), the European Investment Bank (EIB) plays a particularly important role. Therefore, the aim of the research was to identify the relationship between the EIB’s financing of RES projects and the level of energy transition, measured by the share of RES in gross final energy consumption (RE). The goal was achieved using quantitative methods and a two-way fixed-effects panel model FE (country and year), based on data from EIB, Eurostat, World Bank, OECD, EDGAR, and Our World in Data for 2012–2023. As a result of the research, it was determined that the scale of EIB financing alone does not translate into short-term growth of the RE in the examined sample (EU countries). Indeed, the effectiveness of funding depends on the regulatory and institutional context; the grid’s ability to absorb new capacities (throughput, storage, demand flexibility); and from the time horizon (delayed materialization of effects). Increasing the efficiency of converting euros into RE percentage points requires better targeting (power + grid), simplification of procedures and good financial assembly with the right allocation of risks. Full article

Peru faces critical challenges in the development and maintenance of its national road infrastructure, comprising over 32,000 km, of which only 26% are classified as being in good condition. This infrastructural deficit significantly elevates logistics costs and undermines national competitiveness, particularly in key sectors such as agriculture and mining. In this context, improving the accuracy and reliability of cost estimation in road infrastructure projects is imperative to optimize resource allocation and mitigate the risk of cost overruns. This study proposes a stochastic cost estimation framework that integrates Monte Carlo simulation with correlation matrices, enabling the modeling of uncertainty and the complex interdependencies among critical cost drivers. The methodology was applied to the Oyon Ambo highway in Peru. Historical input cost databases were analyzed to define probabilistic distributions, and correlation coefficients were employed to represent the dependencies between variables such as material prices, labor productivity, and equipment efficiency. The stochastic model produced probabilistic cost forecasts with associated confidence intervals and quantified risk exposure. The findings demonstrate that the proposed integrated approach significantly enhances the precision and robustness of cost estimates, providing project managers and decision-makers with a rigorous, data-driven tool for risk-informed budgeting and strategic financial planning in complex infrastructure projects. Full article

Background: The COVID-19 pandemic led to an unprecedented global health and economic crisis, and vaccination emerged as a critical intervention to control the spread of the virus and mitigate its impact on health systems and economies. Despite the rapid development and deployment of vaccines, the financial commitments required for these vaccination programs are substantial, necessitating a comprehensive understanding of the associated costs to inform future public health strategies and resource allocation. Method: This analysis estimates the global, regional, and national economic costs of COVID-19 vaccination across 234 countries and regions in the period 2020–2023, consisting of vaccine procurement costs and administration costs. Result: As of 31 December 2023, the global costs of COVID-19 vaccination programs were estimated at USD 246.2 billion, with vaccine procurement accounting for approximately USD 140.2 billion and administration costs totaling USD 96.4 billion. Globally, a cumulative total of 136.9 billion doses of COVID-19 vaccines had been administered. Factoring in an estimated wastage rate of 10%, it is projected that approximately 150.6 billion doses were used. On a global scale, the average number of vaccine doses administered per capita was estimated at 1.73. The mean cost per capita was USD 17.70 (95% CI: USD 15.84–19.56) for vaccine procurement and USD 12.16 (95% CI: USD 10.29–14.02) for administration, resulting in a total average cost of USD 29.85 (95% CI: USD 26.33–33.37) per capita. Significant disparities in costs were observed across income groups and regions. High-income countries incurred a notably higher average cost per capita of USD 76.90 (95% CI: USD 72.38–81.41) in contrast to low-income countries, where the per capita cost was USD 7.20 (95% CI: USD 5.38–9.02). For middle-income countries, the average per capita costs were USD 15.02 (95% CI: USD 10.64–19.40) in lower-middle-income countries and USD 28.21 (95% CI: USD 23.60–32.83) in upper-middle-income countries. Regionally, the Americas (AMR) reported the highest total cost at USD 70.8 billion, with an average per capita cost of USD 65.23 (95% CI: USD 56.18–74.28). The Western Pacific Region (WPR) followed with a total cost of USD 63.9 billion and an average per capita cost of USD 31.93 (95% CI: USD 20.35–43.51). Conversely, the African Region (AFR) had the lowest total spending at USD 10.8 billion and a per capita cost of USD 8.85 (95% CI: USD 5.34–12.37), reflecting both lower vaccine procurement and administration costs. The European Region (EUR) recorded a high average per capita cost of USD 53.36 (95% CI: USD 46.79–59.94), with procurement costs at USD 31.28 (95% CI: USD 27.41–35.14) and administration costs of USD 22.09 (95% CI: USD 19.31–24.87). Conclusions: The global rollout of COVID-19 vaccination revealed substantial variation in cost structures across income groups. Procurement costs imposed greater burdens on low- and lower-middle-income countries, whereas delivery and administration costs dominated in higher-income settings. These disparities highlight persistent fiscal inequities and emphasize the need for stronger international coordination and cost transparency to enhance equity, efficiency, and preparedness in future vaccination efforts. Full article

While the inclusiveness of digital finance is widely acknowledged, existing research predominantly focuses on its developmental level, with limited attention to its spatial correlation network and structural characteristics. A city’s centrality within this network governs the flow and allocation of digital financial resources, thereby influencing interregional and urban-rural efficiency in resource allocation and income distribution, which ultimately shapes the trajectory of common prosperity. Based on panel data from 280 Chinese cities (2011–2021), this study employs social network analysis to measure urban centrality in the digital financial spatial correlation network and empirically investigates its impact and mechanisms on common prosperity. The main findings are as follows: (1) Benchmark regressions confirm that overall network centrality and its three dimensions—degree, betweenness, and closeness centrality—significantly promote common prosperity, specifically by enhancing the “wealth” dimension and reducing regional development disparities, with the growth effect currently surpassing the inclusion effect. (2) Robustness checks, including instrumental variable approaches addressing endogeneity, affirm the reliability of the core findings. (3) Heterogeneity analysis reveals that the positive effect is more pronounced in cities that are less developed or have weaker financial foundations, such as those in Western China, non-financial centers, cities with no presence of formal financial institutions in antiquity, fifth-tier cities, and small and medium-sized cities, suggesting that network centrality serves as a catalytic tool for urban catch-up strategies. (4) Mechanism analysis identifies that fostering entrepreneurship, particularly among self-employed individuals and wholesale/retail enterprises characterized by decentralized operations and abundant transaction data, is the primary channel through which centrality advances common prosperity. This study provides insights into promoting balanced regional development and common prosperity by optimizing the spatial structure of digital finance. Full article

ESG (Environmental, Social, and Governance) considerations are increasingly influencing REIT (real estate investment trust) investment decisions; however, empirical evidence on the ESG–financial performance nexus in infrastructure REITs remains scarce. Given China’s nascent highway REIT market, this exploratory study proposes a hybrid modeling framework that integrates static econometric analysis with dynamic system simulation to examine how ESG factors affect investment risk. Using VaR (Value at Risk) analysis and an ESG-adjusted CAPM (Capital Asset Pricing Model) on 10 Chinese highway REITs (2021Q2–2025Q2), we constructed a composite ESG indicator via a weighted proxy approach. We identified three key findings testing hypotheses linked to ESG finance theory; these findings support H1 (non-monotonic VaR reduction) and partially confirm H2 (inverted-U path with lag): (1) the ESG-adjusted weighted average cost of capital (WACC) exhibits an inverted U-shaped trajectory with post-peak oscillations and an overall 20-month implementation lag (derived from system dynamics simulations) to efficiency realization; (2) the results suggest initial evidence showing that an ESG investment intensity (IEP ≈ 0.40, representing moderate ESG resource allocation) may indicate potential outperformance over both under-investment (−5.0% deviation in risk-adjusted returns) and over-investment (−8.0% deviation in risk-adjusted returns), though with uncertainty in static estimates; and (3) system dynamics validation suggests potential predictive accuracy. These preliminary findings challenge linear ESG–performance assumptions and offer dynamic risk assessment tools; nevertheless, as an exploratory study, they warrant replication in larger and more diverse samples. Thus, the results should be regarded as preliminary guidance rather than conclusive evidence, with further validation needed to confirm generalizability. Full article

Digital finance, representing the deep integration of finance and technology, has become a critical enabler of sustainable industrial transformation. Focusing on resource-based enterprises (RBEs)—key actors in transitioning towards sustainable practices—this study investigates how digital finance development fosters new quality productive forces (NQPFs), a core driver of high-quality, sustainable development. Utilizing panel data from Chinese A-share listed RBEs (2008–2022), we measure NQPF using the entropy method and gauge regional digital finance development with the Peking University Digital Financial Inclusion Index (DFII). Empirical analysis employing two-way fixed effects and panel threshold regression models provides robust evidence that digital finance significantly enhances NQPFs within RBEs. Crucially, mechanism analysis identifies three fundamental pathways underpinning sustainability: (1) mitigating financial constraints; (2) facilitating technological innovation and transformation; (3) strengthening green transition awareness. Furthermore, the impact of digital finance exhibits synergistic enhancement alongside increasing environmental regulation intensity and improved financial resource allocation efficiency. Heterogeneity analysis reveals that the effect is more pronounced in regions with lower marketization, within state-owned enterprises, and among RBEs in recession stages. Collectively, these findings offer significant implications for policymakers and industry practitioners aiming to strategically leverage digital finance to accelerate the sustainable transformation of resource-intensive industries, thereby contributing directly to environmentally sustainable and resilient economic development. Full article

SThe green finance structure refers to the configuration of financial instruments within the green finance system, the optimization of which is crucial for efficient resource allocation and corporate green transformation. Using panel data from Chinese A-share listed companies from 2014 to 2021, this study empirically examines the relationship between green finance structure and corporate green transformation. The results reveal a significant inverted U-shaped relationship, indicating that a coordinated balance between market-based and bank-based instruments most effectively promotes green transformation. This relationship is influenced by technological and institutional environments: in high-tech industries and regions with weaker environmental regulations, a more market-oriented green finance structure is associated with stronger transformation performance. Further analysis identifies a significant synergistic effect between green credit and green bonds, showing that their complementarity can further enhance corporate green transformation and varies across different technological and institutional contexts. Heterogeneity analysis indicates that the inverted U-shaped pattern is more pronounced in western regions and among firms with stronger internal control systems, while eastern and central regions exhibit a more linear positive relationship. Overall, this study introduces a structural perspective to explain the role of green finance in supporting corporate sustainability transitions and provides new empirical evidence for optimizing the green financial system. Full article