Search Results (70)

The vulnerability of the global financial system to systemic risk-related adverse events has become more evident in recent years, as shown by the 2008 financial crisis and the global pandemic. This study examines systemic risk and its contributing factors using network analysis to understand how contagion occurs. To achieve this, a bibliometric analysis was conducted using a cluster analysis of publications from 2020 to 2025. The bibliometric analysis covered 1642 papers related to systemic risk and financial transmission networks. The CiteSpace software was used to identify seven thematic clusters. The results show the relevance of topological analysis in explaining the connection between institutions and the spread of risk. There is also a clear tradition in the literature of applying the DY spillover index, which captures the temporal dynamics of systemic connectivity. Multilayer networks stand out as a trend in recent studies, as they have the potential to represent different types of relationships simultaneously between nodes. Finally, the literature pays attention to systemic connectivity problems during crises, which can amplify volatility and generate forced asset sales, highlighting the need to use advanced VAR-type models to anticipate risk transmission and guide macroprudential management. Full article

In the context of promoting sustainable and low-carbon agricultural development, this study investigates the effects of rural labor migration (RLM) on agricultural ecological efficiency from the perspective of green total factor productivity (GTFP). Using panel data from 30 Chinese provinces (autonomous regions, municipalities) over 2011–2022, agricultural GTFP is calculated via the SBM–Global Malmquist–Luenberger (SBM–GML) index. Baseline regressions and the spatial Durbin model (SDM) are employed to examine the impacts of labor migration. The research results show that: (1) Agricultural ecological efficiency exhibits significant spatial clustering, demonstrating “high–high” and “low–low” aggregation patterns. (2) RLM significantly enhances local agricultural ecological efficiency while also generating a positive spatial spillover effect. (3) The effects are heterogeneous: northern regions and highly urbanized areas experience stronger positive impacts, whereas southern regions and less urbanized areas show weaker effects. The findings highlight the pivotal role of RLM in promoting agricultural modernization and provide insights for enhancing regional coordination and ecological efficiency. Full article

This study investigates how digital infrastructure contributes to smart city performance in emerging economic contexts and whether its impact is shaped by governance models. We estimate the effect of a Digital Technology Index on a composite Smart City Index, employing a generalized least squares (GLS) random-effects model to address heteroskedasticity and serial correlation. The analysis reveals a robust and statistically significant relationship: a one-standard-deviation increase in digital infrastructure corresponds to a 0.7-standard-deviation rise in smart city performance. The relationship is piecewise-linear, stagnating in the early stage before rising sharply after a threshold. To interpret these results, we draw on a qualitative case study of Wang Chan Valley (WCV), a science and innovation hub in Thailand’s Eastern Economic Corridor. WCV exemplifies how early-stage digital investment can amplify smart development outcomes and generate spillover effects across the broader urban region. The case reinforces the hypothesis that digital infrastructure embedded within participatory innovation ecosystems yields greater and more sustainable smart-city gains than technology investment alone. Taken together, the findings contribute to the understanding of how governance mediates the effectiveness of digital infrastructure in driving smart urban transformation within emerging economies. Full article

This study investigates the interaction between digital–intelligent integration and carbon productivity in 23 prefecture-level cities across the middle reaches of the Yellow River from 2013 to 2022, focusing on a resource-dependent region transitioning towards low-carbon development. The aim is to examine how digital technologies contribute to improving carbon productivity and reducing environmental pollution. An entropy-weighted index system was used to assess digital–intelligent transformation and carbon productivity. A coupling coordination model was applied to measure their joint performance, with spatial autocorrelation and spillover analyses used to detect regional patterns and intercity linkages. Data were sourced from official yearbooks, environmental bulletins, and urban big-data platforms. The results show a steady improvement in coordination between digital–intelligent integration and carbon productivity, with significant progress in 2018 and 2020 following national policy initiatives. Core cities showed higher coordination and generated positive spillovers, while peripheral cities lagged, resulting in noticeable spatial agglomeration. These findings highlight the growing coupling between digital–intelligent development and carbon productivity, reinforced by policy initiatives but accompanied by regional disparities. This study suggests that policies should focus on enhancing data infrastructure in core cities, improving regional cooperation, and bridging gaps in peripheral areas. It offers insights into the role of digital technologies in achieving low-carbon development in resource-dependent urban regions. Full article

Utilizing data from 282 prefecture-level cities in China from 2005 to 2021, this study constructs an evaluation index system for high-quality economic development across the following five dimensions: innovation, coordination, green, openness, and sharing. A continuous difference-in-differences approach is employed for regression analysis to empirically examine the impact of high-speed rail on high-quality economic development, further exploring its mechanisms and spatial spillover effects. The findings reveal that (1) HSR significantly promotes high-quality economic development; (2) with the development of HSR, from 2005 to 2021, China’s high-quality economic development showed an evolutionary trend of overall improvement, with a gradual optimization of spatial patterns; (3) it facilitates high-quality economic development by enhancing capital and labor mobility, strengthening industrial chain resilience, and advancing industrial structure upgrading; (4) high-speed rail development in neighboring regions generates positive spatial spillover effects on local urban economic quality; and (5) the impact of high-speed rail on high-quality economic development exhibits significant heterogeneity across cities with different regions, tiers, scales, and resource endowments. These results confirm the positive role of high-speed rail in fostering high-quality economic development. Full article

As the relationship between cryptocurrency mining activities and electricity consumption becomes increasingly close, the risk spillover effect is steadily drawing a lot of attention to the energy and cryptocurrency markets. For the purpose of studying the risk contagion between the cryptocurrency and energy market, this paper constructs a risk contagion network between cryptocurrency and China’s energy market using complex network methods. The tail risk spillover effects under various time and frequency domains were captured by the spillover index, which was assessed by the leptokurtic quantile vector autoregression (QVAR) model. Considering the spatial heterogeneity of energy companies, the spatial Durbin model was used to explore the impact mechanism of risk spillovers. The research showed that the framework of this paper more accurately reflects the tail risk spillover effect between China’s energy market and cryptocurrency market under various shock scales, with the extreme state experiencing a much higher spillover effect than the normal state. Furthermore, this study found that the tail risk contagion between cryptocurrency and China’s energy market exhibits notable dynamic variation and cyclical features, and the long-term risk spillover effect is primarily responsible for the total spillover. At the same time, the study found that the company with the most significant spillover effect does not necessarily have the largest company size, and other factors, such as geographical location and business composition, need to be considered. Moreover, there are spatial spillover effects among listed energy companies, and the connectedness between cryptocurrency and the energy market network generates an obvious impact on risk spillover effects. The research conclusions have an important role in preventing cross-contagion of risks between cryptocurrency and the energy market. Full article

This study investigates the coupling coordination between carbon emission efficiency (CEE) and carbon balance (CB) in the Yellow River Basin (YRB), aiming to support high-quality regional development and the realization of China’s “dual carbon” goals. Based on panel data from 74 cities in the YRB between 2006 and 2022, the Super-SBM model, Ecological Support Coefficient (ESC), and coupling coordination degree (CCD) model are applied to evaluate the synergy between CEE and CB. Spatiotemporal patterns and driving mechanisms are analyzed using kernel density estimation, Moran’s I index, the Dagum Gini coefficient, Markov chains, and the XGBoost algorithm. The results reveal a generally low and declining level of CCD, with the upstream and midstream regions performing better than the downstream. Spatial clustering is evident, characterized by significant positive autocorrelation and high-high or low-low clusters. Although regional disparities in CCD have narrowed slightly over time, interregional differences remain the primary source of variation. The likelihood of leapfrog development in CCD is limited, and high-CCD regions exhibit weak spillover effects. Forest coverage is identified as the most critical driver, significantly promoting CCD. Conversely, population density, urbanization, energy structure, and energy intensity negatively affect coordination. Economic development demonstrates a U-shaped relationship with CCD. Moreover, nonlinear interactions among forest coverage, population density, energy structure, and industrial enterprise scale further intensify the complexity of CCD. These findings provide important implications for enhancing regional carbon governance and achieving balanced ecological-economic development in the YRB. Full article

This paper examines the firm-level carbon intensity of 83 constituent stocks in the Hang Seng Index, constructs two distinct indexes from the 20 firms with the highest and lowest carbon intensities, and analyzes the connectedness of their annualized daily volatilities with four key external factors over the past 15 years. Our findings reveal that low-carbon stocks—often represented by high-tech and financial firms—tend to exhibit higher volatility, reflecting their more dynamic business environments and greater sensitivity to changes in revenue and profitability. In contrast, high-carbon companies, such as those in the utilities and energy sectors, display more stable demand patterns and are generally less exposed to abrupt market shocks. We also find that oil price shocks result in greater volatility spillovers for low-carbon stocks. Among external influences, the U.S. stock market and Treasury yield exert the most significant spillover effects, while crude oil prices and the U.S. dollar–Chinese yuan exchange rate act as net volatility recipients. Full article

This study examines the relationship between stock prices, energy prices, and climate policy uncertainty using 11 sectoral stocks in the U.S. market. The evidence confirms that rising prices of energy commodities positively affect not only the energy and oil sector stocks but also create spillover effects across other sectors. Notably, all sectoral stocks, except Real Estate sector, show resilience to increases in crude oil and gasoline, suggesting potential hedging benefits. In addition, the findings reveal that sectoral stock returns are generally negatively affected by several types of uncertainty, including climate policy uncertainty, economic policy uncertainty, oil price uncertainty, as well as energy and environmental regulation-induced equity market volatility and the energy uncertainty index. These adverse effects are present across sectors, with few exceptions. The evidence reveals that the feedback effect between changes in climate policy uncertainty and changes in oil prices has an adverse impact on stock returns. Omitting these uncertainty factors from analyses could lead to biased estimates in the relationship between stock prices and energy prices. Full article

With the increasingly urgent demand for the localization of the United Nations’ sustainable development goals (SDGs), the construction of an evaluation system and the practice paths of counties, as important spatial units of China’s sustainable development, urgently need to be deepened. Based on the articulation of the SDGs and China’s national conditions, this study innovatively designed an indicator delivery framework covering the United Nations level to the county level; constructed a county-level sustainable development evaluation indicator system that includes three dimensions, including economic development, social culture, and ecological environment; adopted the entropy weight method to determine the weights of indicators; and introduced a dynamic evaluation and analysis model utilizing three analytical methods, namely coupling coordination analysis, obstacle analysis, and Dagum decomposition, to evaluate the level of sustainable development of 76 counties in the 2010–2021 period considering both time and space. The results show that (1) the national county sustainable development index (CSDI) was significantly improved, regional differences were narrowed, the central region has the best overall performance, and the western region has the fastest growth rate; (2) economic development has become the main driving force, and the economic gap between regions has gradually narrowed, but the spatial heterogeneity of the environmental and social dimensions is still prominent; (3) the eastern region has generated positive spillover effects on the central and western regions through industrial transfer and technology diffusion, while the northeastern region develops relatively slowly due to the lagging industrial transformation; and (4) the degree of coupling coordination rises as a whole, but the differences in synergistic ability between regions are obvious. This study provides a scientific basis for the formulation of differentiated sustainable development policies for counties and emphasizes the key role of regional synergy mechanisms in narrowing the development gap. Full article

This study aims to analyze the spatial effects triggered by dollar liquidity by constructing a multidimensional spatial matrix that modifies the traditional monetary spatial framework. We utilized a three-level spatial econometric model (Spatial Lag, Durbin, and Generalized Nested Space) to measure Gross Domestic Product (GDP), Consumer Price Index (CPI), and Asset Price Bubbles (BBL) through five spillover channels (geography, linguistics, politics, war, and economy). Our aim is to establish a systematic relationship between the conduction mechanism, means, economic indicators, and dollar externalities to examine liquidity spillover effects at varying distances in the global monetary space. We find that the spatial effects induced by the global circulation of the US dollar behave significantly differently in a single matrix space compared to in a multidimensional space. While the model verifies the existence of a positive correlation between the complexity of a single space and the spillover effect from a conduction mechanism perspective, the measure of the multidimensional matrix shows that the significance of the spillover effect weakens with an increase in abstraction level from a conduction means perspective. It suggests that spatial matrices of different dimensions reflect different economic realities. The former shows hierarchical multivariate details in independent matrices, while the variation in the level of abstraction of matrices of different dimensions in the latter enhances their interactivity and complexity. Full article

Based on the panel data of 30 provinces in China from 2013 to 2022, this paper constructs a measurement index system for the symbiotic level of digital innovation ecosystems from three dimensions: the symbiosis of digital innovation subjects, the digital innovation environment, and digital innovation interaction. This paper applies the entropy weight TOPSIS method, Dagum Gini coefficient decomposition, and spatial convergence analysis to empirically examine the symbiotic levels, regional disparities, and spatial convergence of China’s digital innovation ecosystem. The results are as follows: (i) At the national level, the symbiotic level of China’s digital innovation ecosystem has generally increased, creating a spatial distribution pattern that is “high in the east, flat in the middle, and low in the west”. (ii) From a regional perspective, the major disparities between regions are the primary factors contributing to the overall difference in the symbiotic level of China’s digital innovation ecosystem. (iii) From the perspective of σ convergence, regional disparities in the symbiotic level of the digital innovation ecosystem are constantly expanding, and uneven regional development is intensifying. (iv) From the perspective of absolute β convergence, regions with lower levels of symbiosis in the digital innovation ecosystem have a faster growth rate of symbiosis than regions with higher levels of symbiosis, and there is a certain spatial spillover effect. (v) From the perspective of conditional β convergence, economic structure and innovation application can accelerate the spatial convergence of China’s digital innovation ecosystem symbiosis to a certain extent. Full article

Amid comprehensive reforms, artificial intelligence (AI) has emerged as a vital force in solving people’s problems and enhancing quality of life. Yet, theoretical inquiries into the mechanisms by which AI influences social well-being remain limited. Drawing upon cultural lag theory, this study constructs a social well-being index system based on the Gini coefficient objective weighting method. By integrating a moderated mediation model with a spatial econometric model, it examines the mechanisms and impacts of artificial intelligence on social well-being. The findings reveal that AI induces multiple cultural lags and exerts a U-shaped impact on social well-being. AI enhances well-being through the channels of employment opportunities, human capital, and green innovation, while digital inclusion and foreign direct investment (FDI) further reinforce this relationship. Additionally, AI generates spatial spillover effects on social well-being, and the region’s well-being landscape exhibits convergence. However, both digital inclusion and FDI negatively moderate the convergence process, slowing its overall pace. These insights provide substantial practical guidance for crafting informed policies aimed at elevating public well-being. Full article

In the context of a low-carbon economic transition and escalating uncertainties in financial markets, understanding the relationship between the long-term benefits of ESG (Environmental, Social, and Governance) investments and the stability of China’s financial markets emerges as a critical issue. This paper analyzes the risk contagion mechanisms within China’s financial system from the perspective of volatility spillovers associated with ESG investments. Initially, the study employs the Time-Varying Parameter Vector Autoregression (TVP-VAR) model to calculate the variance decomposition spillover index, contrasting the dynamics and risk transmission mechanisms of market volatility between portfolios composed of ESG and conventional stocks. Building upon the analysis of risk spillover relations among financial sub-markets, the study utilizes the generalized forecast error variance decomposition method to construct a complex network of financial system risk spillovers, investigating the risk contagion characteristics within both financial systems through network topology. Empirical findings indicate a significant reduction in the risk and net spillover effects of China’s financial system when ESG stock indices replace conventional stock indices, with a notable mutation in the volatility spillover network structure during extreme risk events and even more substantial changes during the COVID-19 pandemic. Furthermore, based on volatility spillover analysis, the study computes optimal weights and hedging strategies for portfolios incorporating the ESG volatility index and other market volatility indices. The conclusions of this research are instrumental for regulatory authorities in establishing early warning mechanisms and for investors in avoiding financial investment risks. Full article

Education is the cornerstone of rural revitalization. This study aims to comprehensively evaluate the development of rural education in China from 2006 to 2020. From a systemic perspective, this study established a multidimensional evaluation index system for rural education and used the weight-TOPSIS method for measurement. Additionally, geographic information system and spatial econometric methods were employed to explore spatial–temporal differentiation and influencing factors. The results show that (1) rural education levels in China have generally improved in recent years, with higher development in northern, northeastern, and eastern regions and lower levels in central and southwestern regions. (2) In terms of spatial differentiation, rural education development among provinces has significant spatial agglomeration. The provinces around Beijing are hot spots, while remote southwestern provinces are cold spots. (3) Regarding dynamic evolution, the disparity in rural education development among provinces has widened, with a few provinces significantly ahead. There are club convergence features, and the hierarchy of rural education development between provinces is relatively stable, with less likelihood of lagging provinces catching up. (4) Economy, finance, industry, population, and urbanization are key factors influencing rural education, with spatial spillover effects on neighboring provinces. The study provides empirical support and policy insights for advancing balanced and high-quality rural education development. Full article