Search Results (21)

The widening digital divide within the Global Critical Mineral Resource Supply Chain (GCMRS) 4.0 creates significant barriers to cross-border governance and operational efficiency. To quantify and address this disparity, this study identifies 20 Critical Success Factors (CSFs) through expert interviews with 15 industry specialists in South Korea. A hybrid multi-criteria decision-making framework integrating Fuzzy DEMATEL, Analytic Network Process (ANP), and the Choquet integral is developed to map causal relationships and determine factor weights. The empirical results reveal a distinct ‘technology-first’ dependency. Specifically, Scalable Technical Solutions and Cloud Computing Access emerge as the primary driving forces with the highest global weights, while Digital Investment Subsidies serve as the central hub for resource allocation. Unlike generic governance models, this study provides a quantifiable decision-making basis for policymakers. It demonstrates that bridging the hard infrastructure gap is a prerequisite for the effectiveness of soft collaborative mechanisms in the critical mineral sector. Full article

Megacity regions mark a transformative phase of urbanisation, in which interconnected cities undergo land-use and land-cover change (LUCC) that extends beyond administrative boundaries. However, the drivers of cross-boundary LUCC remain insufficiently examined, particularly before the top-down regional integration. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) provides a clear empirical case, having experienced cross-boundary LUCC prior to its formal designation as a megacity region in 2018. This study builds a Landsat-derived LUCC and driver dataset for the GBA. Global and local spatial autocorrelation (Moran’s I and LISA) are used to characterise spatial structure and clustering, and geographically weighted regression identifies the socio-economic and environmental determinants of built-up expansion over 1980–2018, spanning the pre-reform decade and the post-1990 land-transfer era. Findings reveal that: (1) LUCC in the GBA already exhibited a cross-border, spatially networked expansion pattern before formal regional integration policies at the national level, with built-up area growth extending beyond core cities into decentralised urban nodes. Two prominent cross-border cores and one cross-administrative core emerged, suggesting that regional integration was co-led by market forces and local governments before an institutional framework was established. (2) Although the GBA showed a clear trend towards integrated development, urban expansion was highly uneven. Such spatial disparities were mainly driven by varying socioeconomic and natural factors, including gross domestic product, population growth, real estate investment, water resource proximity, and infrastructure development. These findings enhance understanding of megacity-region dynamics and offer insights from the GBA for cross-border urbanisation and sustainable spatial governance. Full article

Europe’s energy transition beyond 2025 faces a resilience gap as reconfigured pipeline flows, stricter methane rules, and rising variable renewables increase the need for seasonal flexibility and system adequacy. This study examines how Ukraine’s gas transmission network and underground gas storage—among the largest in Europe—can serve as a “seasonal battery” for the EU. We integrate a policy and market review with quantitative scenarios for 2026–2030. Methods include security-of-supply indicators (the rule that the system must keep operating even if its largest single infrastructure element fails, peak-day coverage, and winter adequacy), estimates of market-accessible storage volumes and withdrawal rates for European market participants, and a techno-economic screening of hydrogen-readiness comparing repurposing with new-build options. Methane intensity constraints and compliance with monitoring, reporting, and verification and leak detection and repair requirements are applied. The results indicate that reallocating part of Europe’s seasonal balancing to Ukrainian underground gas storage can enhance resilience to extreme winter demand and liquefied natural gas price shocks, reduce price volatility and the curtailment of variable renewables, and enable phased, cost-effective hydrogen corridors via repurposable pipelines and compressors. We outline a policy roadmap specifying transparent access rules, interoperable gas quality and methane standards, and risk mitigation instruments needed to operationalise cross-border storage and hydrogen-ready investments without carbon lock-in. Full article

Cross-border greenfield investment in the financial services sector is increasingly understood not as isolated flows, but as a complex, dynamic global system. This systemic perspective is essential for understanding its holistic structure and evolution amidst globalisation and digital transformation. This paper utilises financial services greenfield investment projects from 100 major economies from 2003 to 2021 to construct the Global Financial Services Greenfield FDI Network (GFS-GFN). By combining Social Network Analysis (SNA) and Temporal Exponential Random Graph Models (TERGMs), we systematically investigate its dynamic evolutionary features and endogenous mechanisms. The findings reveal the following: (1) System-wide, the network exhibits persistent expansion, “small-world” properties, and a pronounced “rich club” effect among source countries. (2) Nodally, the structure has evolved from a US-UK “dual-core” to a multipolar configuration, as emerging hubs like China, the UAE, and Singapore rapidly approach the traditional centres. (3) Structurally, the network has fragmented from Euro-American dominance into five major communities, forming a diverse, complementary pattern. Network evolution is primarily driven by endogenous mechanisms. Investment relationships widely exhibit reciprocity, preferential attachment, transitive closure, and marked path dependence. Full article

Against the backdrop of the “dual circulation” development pattern and the in-depth advancement of the Regional Comprehensive Economic Partnership (RCEP), the interconnection between China and global financial markets has significantly intensified. The spatio-temporal correlation risks faced in cross-border investment activities have become highly complex, posing a severe challenge to traditional investment risk prediction methods. Existing research has three limitations: first, traditional analytical tools struggle to capture the dynamic spatio-temporal correlations among financial markets; second, mainstream deep learning models lack the ability to directly output interpretable economic parameters; third, the uncertainty of model prediction results has not been systematically quantified for a long time, leading to a lack of credibility assessment in practical applications. To address these issues, this study constructs a spatio-temporal graph convolutional neural network panel regression model (STGCN-PDR) that incorporates uncertainty quantification. This model innovatively designs a hybrid architecture of “one layer of spatial graph convolution + two layers of temporal convolution”, modeling the spatial dependencies among global stock markets through graph networks and capturing the dynamic evolution patterns of market fluctuations with temporal convolutional networks. It particularly embeds an interpretable regression layer, enabling the model to directly output regression coefficients with economic significance, significantly enhancing the decision-making reference value of risk prediction. By designing multi-round random initialization perturbation experiments and introducing the coefficient of variation index to quantify the stability of model parameters, it achieves a systematic assessment of prediction uncertainty. Empirical results based on stock index data from 20 countries show that compared with the benchmark models, STGCN-PDR demonstrates significant advantages in both spatio-temporal feature extraction efficiency and risk prediction accuracy, providing a more interpretable and reliable quantitative analysis tool for cross-border investment decisions in complex market environments. Full article

The remote sensing monitoring of land use changes and future scenario simulation hold crucial significance for accurately characterizing urban expansion patterns, optimizing urban land use configurations, and thereby promoting coordinated regional development. Through the integration of multi-source data, this study systematically analyzes the spatiotemporal trajectories and driving forces of land use changes in the Pearl River Delta urban agglomeration (PRD) from 1990 to 2020 and further simulates the spatial patterns of urban land use under diverse development scenarios from 2025 to 2035. The results indicate the following: (1) During 1990–2020, urban expansion in the Pearl River Delta urban agglomeration exhibited a “stepwise growth” pattern, with an annual expansion rate of 3.7%. Regional land use remained dominated by forest (accounting for over 50%), while construction land surged from 6.5% to 21.8% of total land cover. The gravity center trajectory shifted southeastward. Concurrently, cropland fragmentation has intensified, accompanied by deteriorating connectivity of ecological lands. (2) Urban expansion in the PRD arises from synergistic interactions between natural and socioeconomic drivers. The Geographically and Temporally Weighted Regression (GTWR) model revealed that natural constraints—elevation (regression coefficients ranging −0.35 to −0.05) and river network density (−0.47 to −0.15)—exhibited significant spatial heterogeneity. Socioeconomic drivers dominated by year-end paved road area (0.26–0.28) and foreign direct investment (0.03–0.11) emerged as core expansion catalysts. Geographic detector analysis demonstrated pronounced interaction effects: all factor pairs exhibited either two-factor enhancement or nonlinear enhancement effects, with interaction explanatory power surpassing individual factors. (3) Validation of the Patch-generating Land Use Simulation (PLUS) model showed high reliability (Kappa coefficient = 0.9205, overall accuracy = 95.9%). Under the Natural Development Scenario, construction land would exceed the ecological security baseline, causing 408.60 km² of ecological space loss; Under the Ecological Protection Scenario, mandatory control boundaries could reduce cropland and forest loss by 3.04%, albeit with unused land development intensity rising to 24.09%; Under the Economic Development Scenario, cross-city contiguous development zones along the Pearl River Estuary would emerge, with land development intensity peaking in Guangzhou–Foshan and Shenzhen–Dongguan border areas. This study deciphers the spatiotemporal dynamics, driving mechanisms, and scenario outcomes of urban agglomeration expansion, providing critical insights for formulating regionally differentiated policies. Full article

The rapid expansion of economic globalization and trade has led to a sharp increase in the shipping investment risks currently faced by cities around the world. This study aims to explore the risk warning mechanism of shipping input under public health events to establish an effective risk warning method. This would enable the rapid identification of potential risk inputs and the implementation of targeted prevention and control measures to ensure public health and safety. This study investigates the mechanisms of both intra-regional and cross-border risk transmission within shipping networks. It establishes a transmission dynamics model (termed the SEIR-SEI model) incorporating climatic, economic, and health factors to analyze the potential inherent risks of regional shipping nodes. It uses the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) model, modified by the entropy weight method, to calculate the importance of nodes in the shipping network. This comprehensive approach considers the network’s clustering coefficient, betweenness centrality, closeness centrality, degree centrality, and eigenvector centrality. To validate the practicality of the model, this study selects shipping data with Shanghai, China, as the destination node to conduct simulation computations of different risk propagation chains. The findings demonstrate that overall risk transmission is determined by the joint influence of a node’s inherent risks and propagation probabilities. This study not only clarifies the process of cross-border transmission of public health events through the shipping network between cities of different countries, but also provides insights for the application of shipping input risk assessment systems, enriching the academic research on logistics network propagation. Full article

Although the impact of integrating solar and wind sources into the power system has been studied in the past, the chaos caused by wind and solar energy generation has not yet had broader mitigation solutions notwithstanding their rapid deployment. Many research efforts in using prediction models have developed real-time monitoring of variability and machine learning predictive algorithms in contrast to the conventional methods of studying variability. This study focused on the causes and types of variability, challenges, and mitigation strategies used to minimize variability in grids worldwide. A summary of the top ten cases of countries that have successfully managed variability in their electrical power grids has been presented. Review shows that most of the success cases embraced advanced energy storage, grid upgrading, and flexible energy mix as key technological and economic strategies. A seven-point conceptual framework involving all energy stakeholders for managing variability in power system networks and increasing variable renewable energy (VRE)-grid integration has been proposed. Long-duration energy storage, virtual power plants (VPPs), smart grid infrastructure, cross-border interconnection, power-to-X, and grid flexibility are the key takeaways in achieving a reliable, resilient, and stable grid. This review provides a useful summary of up-to-date research information for researchers and industries investing in a renewable energy-intensive grid. Full article

The One Health approach integrates human, animal, and environmental health to address complex challenges like emerging zoonotic diseases and antimicrobial resistance (AMR). Singapore’s dense urban environment, biodiversity, and role as a global hub make it vulnerable to these health threats, necessitating a robust and coordinated One Health framework. This paper reviews Singapore’s One Health strategy, focusing on governance, surveillance, cross-sector partnerships, and public health infrastructure. A structured literature review, including peer-reviewed articles and grey literature, identified key strengths and gaps. Strengths include interagency coordination through the One Health Coordinating Committee, advanced surveillance systems like CDLENS and SIDPIC, and key institutions such as the National Centre for Infectious Diseases (NCID) and the National Public Health Laboratory (NPHL). However, gaps remain in multi-sector engagement, data-sharing mechanisms, and public awareness of One Health principles. To address these challenges, this paper recommends enhancing multi-sector collaboration, strengthening data-sharing networks, and increasing public education on One Health. Investments in preventive medicine, cross-border capacity-building, and leveraging artificial intelligence for predictive analytics are essential for bolstering Singapore’s health security. By addressing these gaps, Singapore can enhance its preparedness and serve as a global leader in One Health implementation. Full article

In this paper, we investigate whether the VC institutions’ position in the network affects their investment strategies and performance. Our results show that network location has a negative correlation with geographic concentration index, industry concentration index, and stage concentration index, which indicates that the higher the network position of the cross-border VC institutions, the higher the degree of geographical diversification, the wider the industry diversification, and the higher the investment stage diversification of their investments. In addition, our results show that the relationship between network position and the VC’s performance is not simply linear but rather an inverted U-shaped correlation. When the network location is lower than the critical value, higher network location positively improves the VC’s investment performance; however, when the network location is higher than the critical value, then the relationship is reversed. Full article

This paper employs social network analysis to investigate the characteristics and determinants of co-movement in the volatility of net cross-border capital flows. We have identified a significant “small-world” pattern in the co-movement network. Economies with highly positive or negative co-movement of volatility in net capital flows display regional differences. Furthermore, economies with high trade intensity, large interest-rate spreads, membership in the same economic organization, and geographical adjacency exhibit significantly increased co-movement of volatility in net private capital flows and net other investment flows. Economies with large differences in economic growth present less co-movement of volatility in net portfolio flows. Full article

The structure of the network among firms participating in global value chains is an important factor in understanding the changes in China’s carbon emissions. This paper focuses on the interdependence between firms and the interconnected networks to which they belong, utilizing an inter-country input–output model that distinguishes between domestic-owned enterprises and foreign-invested enterprises for measurement purposes. By distinguishing between domestic and cross-border global value chains, we illustrate the carbon emission effects of relational global value chains and their network structures, thereby contributing a Chinese perspective on relational global value chains and carbon emission reduction. This study reveals that (1) relational global value chain activities have emerged as a significant contributor to China’s carbon emissions, constituting approximately 26.8%, with its growth mainly stemming from the expansion of domestic global value chain emissions. At the sectoral level, relational global value chain activities lead to higher carbon emissions from the service sector than from the manufacturing sector. (2) Domestic global value chain relationship activities are more likely to have favorable economic and environmental trade-offs, as evidenced by the lower carbon intensity of the domestic global value chain than the cross-border global value chain. The circle-structured relationship activities between domestic-owned enterprises and foreign-invested enterprises are associated with more sustainable carbon emission growth and greater potential for emission reduction than the chain structure. (3) Structural decomposition analysis indicates that the impact of cross-border global value chain emissions on China’s carbon emission growth has been decreasing since 2012, while the influence of the domestic global value chain is on the rise and surpasses that of the cross-border global value chain by the end of the period. Full article

In the context of the Chinese market, foreign cross-border venture capitalists have devised specific strategies to mitigate the challenges associated with the liabilities of foreignness, such as risks and information asymmetry. They have strategically leveraged social capital to not only decrease investment risk but also to influence their investment preferences and behaviors. To investigate the influence of different types of social capital on the investment decisions of cross-border venture capitalists, hypotheses are proposed and tested using regression analysis. Our research reveals several key findings in this regard. Firstly, cross-border venture capitalists with a robust structural social capital network exhibit a greater propensity to invest in early-stage companies. This suggests that well-established connections and partnerships within the Chinese entrepreneurial ecosystem provide a level of comfort and confidence when investing in ventures at their infancy. Interestingly, relational and cognitive social capital, though undoubtedly valuable, do not significantly impact the decision to make early-stage investments. Furthermore, we have observed that venture capitalists with higher levels of structural and cognitive social capital are more inclined to form syndications. Collaborative partnerships and shared knowledge networks seem to be crucial factors that drive syndication decisions. Lastly, venture capitalists endowed with substantial structural and relational social capital tend to allocate larger investment amounts, signifying the influence of business or personal relationships and network connections on the scale of their investments. Full article

Analyzing the efficiency of transport infrastructure connectivity and trade in the Regional Comprehensive Economic Partnership (RCEP) is very important for regional integration for international trade in the RCEP. This study aims to significantly measure the efficiency of the connectivity of infrastructure in the RCEP for improving the performance of infrastructure connection and suggest the way to improve the connection of infrastructure. Therefore, the input and output variables of infrastructure connectivity have been inserted to achieve this objective. The inputs are: the number of ports, rail range, and road networks, the number of land borders, the number of maritime borders, number of cross border points, railway linkage with other countries, number of ports connected with railways, and the number of ports connected with road base on the “intermodal and multimodal concept”. On the other hand, the output factors most related to trade and economics are GDP, transport, import, and export volume. The paper applied DEA (Data Envelopment Analysis) model by using DEAP software to analyze the data. The result reveals that the efficiency of infrastructures connectivity and international trade in 10 countries were efficient and 5 countries were inefficient. The research study presents ways of development to improve the connectivity by investing in the basic infrastructures, such as increasing the logistics connection points and driving forward for international trade in the RCEP. Full article

Property development around transit stations has been viewed by many governments as a considerable way of financing public transportation. However, despite mounting evidence of the positive relationship between transport investment and proximate land value, the stakeholder relationship in enabling complex property–transit development has received relatively scarce attention. In this study, we analyze the railway financing strategies in two cities (Shenzhen and Hong Kong) connected by the first cross-border high-speed rail (HSR) network in China. Using a holistic power approach, this study presents power direction, power strength, and power mechanism as the critical factors for each case. The results reveal that different stakeholder relations arising from different social and institutional contexts have led to varying land value capture practices. The findings of this study contribute to sustainable railway financing in three phases: First, it unravels the relationship between railway financing and property development under the context of an intercity railway program, with the intervention of state power. Second, it sorts out critical elements in the implementation of the land value capture mechanism, especially institutional factors such as the role of the transit agency. Third, it directs a flexible development of the land value capture theory to cope with foreseeable problems such as land resource scarcity, institutional complexity, and interest divergence. Full article