Search Results (598)

Unemployment and weak economic productivity are significant global issues, particularly in West Africa. Recently, through diverse mechanisms, remittances and foreign direct investment (FDI) have been sources of foreign capital flow that have positively influenced many less developed economies, including ECOWAS (ECOWAS stands for Economic Community of West African States). Nevertheless, these financial flows have exhibited significant inconsistencies, primarily resulting from economic downturns in migrants’ destination countries, with remarkable implications for beneficiary economies. This study, therefore, examines the effect of remittances and FDI on employment in ECOWAS. Specifically, the study assesses the effects of the inflow of remittances and FDI on employment using panel dynamic ordinary least squares (PDOLS) and also investigates the shock effects of remittances and FDI by employing Panel Vector Error Correction (PVECM), which involves variance decomposition. The results show that foreign direct investment (FDI) positively and significantly affects employment. Other variables that show a significant relationship with employment are wage rate, education expenditure, and interest rate. The variance decomposition result revealed that external shocks on remittances and FDI have short- and long-term effects on employment. The above findings imply that foreign direct investment has a far-reaching positive impact on the economy-wide management of the West African sub-region and thus calls for relevant policy options. Full article

A deterministic Mixed-Integer Nonlinear Programming (MINLP) model for the Integrated Generation and Transmission Expansion Planning (IGTEP) problem is presented. The proposed framework is distinguished by its foundation on the complete AC power flow formulation, which is solved to global optimality using BARON, a deterministic MINLP solver, which ensures the identification of truly optimal expansion strategies, overcoming the limitations of heuristic approaches that may converge to local optima. This approach is employed to establish a definitive, high-fidelity economic and technical benchmark, addressing the limitations of commonly used DC approximations and metaheuristic methods that often fail to capture the nonlinearities and interdependencies inherent in power system planning. The co-optimization model is formulated to simultaneously minimize the total annualized costs, which include investment in new generation and transmission assets, the operating costs of the entire generator fleet, and the cost of unsupplied energy. The model’s effectiveness is demonstrated on the IEEE 14-bus system under various dynamic load growth scenarios and planning horizons. A key finding is the model’s ability to identify the most economic expansion pathway; for shorter horizons, the optimal solution prioritizes strategic transmission reinforcements to unlock existing generation capacity, thereby deferring capital-intensive generation investments. However, over longer horizons with higher demand growth, the model correctly identifies the necessity for combined investments in both significant new generation capacity and further network expansion. These results underscore the value of an integrated, AC-based approach, demonstrating its capacity to reveal non-intuitive, economically superior expansion strategies that would be missed by decoupled or simplified models. The framework thus provides a crucial, high-fidelity benchmark for the validation of more scalable planning tools. Full article

Ensuring a sustainable and healthy human environment in university dormitories is essential for students’ learning, living, and overall health and well-being. To address this need, we carried out a series of systematic field measurements of the noise levels at 30 dormitories in three representative major urban universities in a major provincial capital city in China and designed and implemented a comprehensive questionnaire and surveyed 1005 students about their perceptions of their acoustic environment. We proposed and applied a sustainability–health-oriented, multidimensional assessment framework to assess the acoustic environment of the dormitories and student responses to natural sound, technological sounds, and human-made sounds. Using the Structural Equation Modeling (SEM) approach combined with the field measurements and student surveys, we identified three categories and six factors on student health and well-being for assessing the acoustic environment of university dormitories. The field data indicated that noise levels at most of the measurement points exceeded the recommended or regulatory thresholds. Higher noise impacts were observed in early mornings and evenings, primarily due to traffic noise and indoor activities. Natural sounds (e.g., wind, birdsong, water flow) were highly valued by students for their positive effect on the students’ pleasantness and satisfaction. Conversely, human and technological sounds (traffic noise, construction noise, and indoor noise from student activities) were deemed highly disturbing. Gender differences were evident in the assessment of the acoustic environment, with male students generally reporting higher levels of the pleasantness and preference for natural sounds compared to female students. Educational backgrounds showed no significant influence on sound perceptions. The findings highlight the need for providing actionable guidelines for dormitory ecological design, such as integrating vertical greening in dormitory design, water features, and biodiversity planting to introduce natural soundscapes, in parallel with developing campus activity standards and lifestyle during noise-sensitive periods. The multidimensional assessment framework will drive a sustainable human–ecology–sound symbiosis in university dormitories, and the category and factor scales to be employed and actions to improve the level of student health and well-being, thus, providing a reference for both research and practice for sustainable cities and communities. Full article

This paper considers the impact of Central Bank Digital Currencies (CBDCs) on the world’s financial systems with a special emphasis on G20 economies. Using quarterly macro-financial data for the period of 2000 to 2024, collected from the IMF, BIS, World Bank, and Atlantic Council, a Global Vector Autoregression (GVAR) model is applied to 20 G20 countries. The results reveal significant heterogeneity across economies: CBDC shocks intensify emerging market financial instability (e.g., India, Brazil), while more digitally advanced countries (e.g., UK, Japan) experience stabilization. Retail CBDCs increase disintermediation risks in more fragile banking systems, while wholesale CBDCs improve cross-border liquidity. This article contributes to the literature by providing the first GVAR-based estimation of CBDC spillovers globally. Full article

As global climate change intensifies and the transition to clean energy accelerates, lithium-ion batteries—critical components of electric vehicles—are becoming increasingly vital in international trade networks. This study investigates the structural evolution and determinants of the electric vehicle lithium-ion battery trade network among European Union (EU) member states from 2012 to 2023, employing social network analysis and the multiple regression quadratic assignment procedure method. The findings demonstrate the transformation of the network from a centralized and loosely connected structure, with Germany as the dominant hub, to a more interconnected and decentralized system in which Poland and Hungary emerge as the leading players. Key network metrics, such as the density, clustering coefficients, and average path lengths, reveal increased regional trade connectivity and enhanced supply chain efficiency. The analysis identifies geographic and economic proximity, logistics performance, labor cost differentials, energy resource availability, and venture capital investment as significant drivers of trade flows, highlighting the interaction among spatial, economic, and infrastructural factors in shaping the network. Based on these findings, this study underscores the need for targeted policy measures to support Central and Eastern European countries, including investment in logistics infrastructure, technological innovation, and regional cooperation initiatives, to strengthen their integration into the supply chain and bolster their export capacity. Furthermore, fostering balanced inter-regional collaborations is essential in building a resilient trade network. Continued investment in transportation infrastructure and innovation is recommended to sustain the EU’s competitive advantage in the global electric vehicle lithium-ion battery supply chain. Full article

Hydrogen is increasingly recognized as a key element of the transition to a low-carbon energy system, leading to a growing interest in accurate and sustainable assessment of its economic viability. Levelized Cost of Hydrogen (LCOH) is one of the most widely used metrics for comparing hydrogen production technologies and informing investment decisions. However, traditional LCOH calculation methods apply a single discount rate to all cash flows without distinguishing between the risks associated with outflows and inflows. This approach may yield a systematic overestimation of costs, especially in capital-intensive projects. In this study, we adapt a binary cash flow discounting model, previously proposed in the finance literature, for hydrogen energy systems. The model employs two distinct discount rates, one for costs and one for revenues, with a rate structure based on the required return and the risk-free rate, thereby ensuring that arbitrage conditions are not present. Our approach allows the range of possible LCOH values to be determined, eliminating the methodological errors inherent in traditional formulas. A numerical analysis is performed to assess the impact of a change in the general rate of return on the final LCOH value. The method is tested on five typical hydrogen production technologies with fixed productivity and cost parameters. The results show that the traditional approach consistently overestimates costs, whereas the binary model provides a more balanced and risk-adjusted representation of costs, particularly for projects with high capital expenditures. These findings may be useful for investors, policymakers, and researchers developing tools to support and evaluate hydrogen energy projects. Full article

Water Supply and Distribution Networks (WSDNs) offer underexplored potential for energy recovery. While many studies confirm their technical feasibility, few assess the long-term operational compatibility and economic viability of such solutions. This study evaluates the energy recovery potential of the Brussels Capital Region’s WSDN using four years (2019–2022) of operational data. Rather than focusing on available technologies, the analysis examines whether the real behavior of the network supports sustainable energy extraction. The approach includes network topology identification, theoretical power modeling, and detailed flow and pressure analysis. The Brussels system, composed of a Water Supply Network (WSN) and a Water Distribution Network (WDN), reveals strong disparities: the WSN offers localized opportunities for energy recovery, while the WDN presents significant operational constraints that limit economic viability. Our findings suggest that day-ahead electricity markets provide more suitable valorization pathways than flexibility markets. Most importantly, the study highlights the necessity of long-term behavioral analysis to avoid misleading conclusions based on short-term data and to support informed investment decisions in the urban water–energy nexus. Full article

The utilization of thermal energy from co-produced water during natural gas production offers a promising pathway to enhance energy efficiency and reduce carbon emissions. This study proposes a techno-economic evaluation model to assess the feasibility and profitability of geothermal energy recovery from co-produced water in marginal gas wells. A wellbore fluid flow and heat transfer model is developed and validated against field data, with deviations in calculated wellhead temperature and pressure within 10%, demonstrating the model’s reliability. Sensitivity analyses are conducted to investigate the influence of key technical and economic parameters on project performance. The results show that electricity price, heat price, and especially government one-off subsidies have a significant impact on the net present value (NPV), whereas the effects of insulation length and annular fluid thermal conductivity are comparatively limited. Under optimal conditions—including 2048 m of insulated tubing, annular protection fluid with a thermal conductivity of 0.4 W/(m·°C), a 30% increase in heat and electricity prices, and a 30% government capital subsidy—the project breaks even in the 14th year, with the 50-year NPV reaching 0.896 M$. This study provides a practical framework for evaluating and optimizing geothermal energy recovery from co-produced water, offering guidance for future sustainable energy development. Full article

Residential segregation remains a persistent challenge in European urban environments and is an increasing focal point in urban policy debates. This study investigates the changing geographies of ethnic diversity and residential segregation in Riga, the capital city of Latvia. The research addresses the complex dynamics of ethnic residential patterns within the distinctive context of post-socialist urban transformation, examining how historical legacies of ethnic diversity interact with contemporary migration flows to reshape neighborhood ethnic composition. Using geo-referenced data from 2000, 2011, and 2021 census rounds, we examined changes in the spatial distribution of five major ethnic groups. Our analysis employs the Dissimilarity Index to measure ethnic residential segregation and the Location Quotient to identify the residential concentration of ethnic groups across the city. The findings reveal that Riga’s ethnic landscape is undergoing a gradual yet impactful transformation. The spatial distribution of ethnic groups is shifting, with the increasing segregation of certain groups, particularly traditional ethnic minorities, coupled with a growing concentration of Europeans and non-Europeans in the inner city. The findings reveal distinctive patterns of ethnic diversification and demographic change, wherein long-term trends intersect with contemporary migration dynamics to produce unique trajectories of ethnic residential segregation, which differ from those observed in Western European contexts. However, the specific dynamics in Riga, particularly the persistence of traditional ethnic minority communities and the emergence of new ethnic groups, highlight the unique context of post-socialist urban landscapes. Full article

Understanding flood scenes is essential for effective disaster response. Previous research has primarily focused on computer vision-based approaches for analyzing flood scenes, capitalizing on their ability to rapidly and accurately cover affected regions. However, most existing methods emphasize static image analysis, with limited attention given to dynamic video analysis. Compared to image-based approaches, video analysis in flood scenarios offers significant advantages, including real-time monitoring, flow estimation, object tracking, change detection, and behavior recognition. To address this gap, this study proposes a computer vision-based multi-object tracking (MOT) framework for intelligent flood scene understanding. The proposed method integrates an optical-flow-based module for short-term undetected mask estimation and a deep re-identification (ReID) module to handle long-term occlusions. Experimental results demonstrate that the proposed method achieves state-of-the-art performance across key metrics, with a HOTA of 69.57%, DetA of 67.32%, AssA of 73.21%, and IDF1 of 89.82%. Field tests further confirm its improved accuracy, robustness, and generalization. This study not only addresses key practical challenges but also offers methodological insights, supporting the application of intelligent technologies in disaster response and humanitarian aid. Full article

In regions where transportation and the economy are closely integrated, optimizing network structure and enhancing synergy are vital for regional integration. This paper constructs a dual-factor linkage network using enterprise investment and liner shipping data to analyze linkage strength and synergy effects among cities in the Greater Bay Area. The findings reveal that (1) a core-periphery structure exists, with core cities dominating resource flows while secondary cities remain weak. The logistics network is led by Hong Kong and Shenzhen, while the capital flow network showcases the dominance of Hong Kong, Shenzhen, and Guangzhou. (2) From 2016 to 2021, interactions between transportation and the economy deepened, showing strong correlations in logistics and capital flows among core cities and between core and edge cities, but weaker correlations with sub-core and edge cities. Core cities stabilize regional transportation and economy, fostering agglomeration, while sub-core cities are more reliant on them, indicating a need for better resource balance. (3) The spatio-temporal coupling analysis reveals significant heterogeneity in flows among cities, with many exhibiting antagonistic couplings outside core areas. This study enhances understanding of synergy mechanisms in transportation and economic networks, offering insights for optimizing layouts and improving capital flow efficiency. Full article

Accurate permeability predictions remain pivotal for understanding fluid flow in porous media, influencing crucial operations across petroleum engineering, hydrogeology, and related fields. Traditional approaches, while robust, often grapple with the inherent heterogeneity of reservoir rocks. With the advent of deep learning, convolutional neural networks (CNNs) have emerged as potent tools in image-based permeability estimation, capitalizing on micro-CT scans and digital rock imagery. This paper introduces a novel paradigm, employing vision transformers (ViTs)—a recent advancement in computer vision—for this crucial task. ViTs, which segment images into fixed-sized patches and process them through transformer architectures, present a promising alternative to CNNs. We present a methodology for implementing ViTs for permeability prediction, its results on diverse rock samples, and a comparison against conventional CNNs. The prediction results suggest that, with adequate training data, ViTs can match or surpass the predictive accuracy of CNNs, especially in rocks exhibiting significant heterogeneity. This study underscores the potential of ViTs as an innovative tool in permeability prediction, paving the way for further research and integration into mainstream reservoir characterization workflows. Full article

Exchange rate volatility poses significant challenges for emerging markets, influencing trade balances, inflation, and capital flows. South Africa’s Rand is particularly vulnerable to global risk sentiment, gold price fluctuations, and interest rate differentials, yet prior studies often analyse these factors in isolation. This study integrates them within an autoregressive distributed lag framework, using monthly data from 2005 to 2023 to capture both short-term fluctuations and long-term equilibrium effects. The findings confirm that higher global risk sentiment triggers immediate Rand depreciation, driven by capital outflows to safe-haven assets. Conversely, rising gold prices and favourable interest rate differentials stabilise the Rand, strengthening trade balances and attracting capital inflows. These results underscore the interconnected nature of global financial conditions and exchange rate movements. This study highlights the importance of economic diversification, foreign reserve accumulation, and proactive monetary policies in mitigating currency instability in emerging markets. Full article

Amid the rapid growth of social media and live streaming platforms, streamers, who serve as a crucial link between products and users, have garnered significant attention from both academia and industry. This study explores the impact of the streamer’s social capital (S) on user stickiness (R), as well as the mediating roles of perceived value and flow experience (O) in light of the Stimuli-Organism-Response (SOR) framework and social capital theory. A total of 322 valid samples were analyzed through Structural Equation Modeling (SEM) and Fuzzy-set Qualitative Comparative Analysis (fsQCA). The results from the SEM indicate that the structural capital, cognitive capital, and relational capital of streamers in e-commerce live streaming significantly influence users’ perceived value, while structural capital and relational capital substantially impact users’ flow experience. Furthermore, both perceived value and flow experience are found to have a significant effect on user stickiness, with chained mediating effects observed between perceived value and flow experience. The fsQCA results further identify three configurational paths influencing user stickiness: the perceived value-oriented path, the flow experience-oriented path, and a hybrid path. This study offers valuable insights and practical implications for e-commerce merchants and companies involved in live streaming activities. Full article

The Pacific Alliance (PA), established in 2011, aims to foster economic integration among its member states—Peru, Chile, Colombia, and Mexico—by promoting trade liberalization and economic cooperation. However, recent political shifts within these countries have influenced trade policies, affecting intra-bloc commerce and relations with external markets, particularly China and the United States. This study explores how the political environment within the PA has shaped sustainable trade, considering economic policies, trade agreements, and shifts in regional priorities. Using a qualitative and descriptive approach, this research is based on a documentary review of academic literature, official reports, and international trade data. Content analysis was applied to assess the impact of political decisions on PA trade sustainability, including the examination of tariff structures, trade flows, and capital movements. The findings indicate that intra-regional trade within the PA remains limited, with an intraregional trade index below 4%. Mexico continues to prioritize exports to the U.S., while Peru and Chile strengthen ties with China. Although PA member states have maintained liberal economic policies, disparities in trade liberalization levels hinder integration. Furthermore, despite the reduction of tariffs and the implementation of digital trade facilitation measures, political instability and differences in economic strategies among member states challenge the PA’s long-term sustainability. Strengthening institutional frameworks and increasing investments in research and development are crucial for enhancing economic integration and ensuring trade resilience within the bloc. Full article