Search Results (203)

This paper applies the Multivariate Stochastic Volatility (MSV) model, alongside its extended DGC-t-MSV model, and integrates Bayesian methods with MCMC techniques to develop the DGC-t-MSV-BN model. This model is specifically designed to analyze the volatility spillover effects between stock and futures markets. Key findings are as follows: (1) Significant volatility spillover effects exist from futures market to stock market. Notably, the spillover effects among the Chinese carbon futures market and both the Chinese and international stock markets are stronger than those within the Chinese carbon futures market, as well as the international gold and crude oil futures markets. (2) A notable negative volatility spillover effect is observed between Chinese carbon futures market and the international stock market. Conversely, a significant positive volatility spillover effect exists in the Chinese carbon futures market and the Chinese stock market. (3) The Chinese carbon futures market, as an emerging sector, displays high volatility and immaturity, yet it is developing at a rapid pace. Full article

This study examines the impact of geopolitical risk (GPR) on the connectedness dynamics among the sovereign bonds of the emerging seven (E7) and the Group of Seven (G7) countries. Initially, a quantile-based vector-autoregressive (Q-VAR) connectedness approach is used to calculate the total connectedness index (TCI) among sovereign bonds under different market states. Then, the impact of GPR on the TCI at the median and tails is estimated to examine if GPR affects the TCI among sovereign bonds. Using daily yields from 30 January 2012, to 17 June 2024, the findings show that the GPR is one of the significant determinants of the TCI among sovereign bonds during normal and extreme market conditions. Other determinants of the TCI include yields on Treasury bills (T-bills), the exchange rate, and the financial market volatility index. The impact of GPR on the TCI varies significantly during different GPR episodes and bond market conditions. The effect of GPR on the TCI among sovereign bonds yields is higher during war times and when bond yields are average. These findings can be utilized by investors seeking to achieve international diversification and policymakers aiming to mitigate the effects of heightened geopolitical risk on financial stability. Furthermore, GPR can be used as an early signal tool for systematic tail risk spillovers among sovereign bonds. Full article

Climate change, resource scarcity, and ecological degradation have become critical bottlenecks constraining socio-economic development. Basin cities serve as key nodes in China’s ecological security pattern, playing indispensable roles in ecological civilization construction. This study established an evaluation index system spanning five dimensions to assess the effectiveness of ecological civilization construction. This study employs the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Back-Propagation (BP) neural network methods to evaluate the level of ecological civilization construction in the Yellow River Basin from 2010 to 2022, to analyze its indicator weights, and to explore the spatio-temporal evolution characteristics of each city. The results demonstrate the following: (1) Although the ecological civilization construction level of cities in the Yellow River Basin shows a steady improvement, significant regional development disparities persist. (2) The upper reaches are primarily constrained by ecological fragility and economic underdevelopment. The middle reaches exhibit significant internal divergence, with provincial capitals leading yet demonstrating limited spillover effects on neighboring areas. The lower reaches face intense anthropogenic pressures, necessitating greater economic–ecological coordination. (3) Among the dimensions considered, Territorial Space and Eco-environmental Protection emerged as the two most influential dimensions contributing to performance differences. According to the ecological civilization construction performance and changing characteristics of the 48 cities, this study proposes differentiated optimization measures and coordinated development pathways to advance the implementation of the national strategy for ecological protection and high-quality development in the Yellow River Basin. Full article

Mitigating traffic-related carbon emissions is pivotal for achieving carbon peaking targets and advancing sustainable urban development. This study employs spatial autocorrelation and high-low clustering analyses to analyze the spatial correlation and clustering patterns of urban road traffic carbon emissions in Shaanxi Province. The spatiotemporal evolution and structural impacts of emissions are quantified through a systematic framework, while the GTWR (Geographically Weighted Temporal Regression) model uncovers the multidimensional and heterogeneous driving mechanisms underlying carbon emissions. Findings reveal that road traffic CO₂ emissions in Shaanxi exhibit an upward trajectory, with a temporal evolution marked by distinct phases: “stable growth—rapid increase—gradual decline”. Emission dynamics vary significantly across transport modes: private vehicles emerge as the primary emission source, taxi/motorcycle emissions remain relatively stable, and bus/electric vehicle emissions persist at low levels. Spatially, the province demonstrates a pronounced high-carbon spillover effect, with persistent high-value clusters concentrated in central Shaanxi and the northern region of Yan’an City, exhibiting spillover effects on adjacent urban areas. Notably, the spatial distribution of CO₂ emissions has evolved significantly: a relatively balanced pattern across cities in 2010 transitioned to a pronounced “M”-shaped gradient along the north–south axis by 2015, stabilizing by 2020. The central urban cluster (Yan’an, Tongchuan, Xianyang, Baoji) initially formed a secondary low-carbon core, which later integrated into the regional emission gradient. By focusing on the micro-level dynamics of urban road traffic and its internal structural complexities—while incorporating built environment factors such as network layout, travel behavior, and infrastructure endowments—this study contributes novel insights to the transportation carbon emission literature, offering a robust framework for regional emission mitigation strategies. Full article

This paper revisits the international finance trilemma by analyzing how different monetary policy objectives and exchange rate regimes shape the transmission of global risk shocks. Using a structural vector autoregressive model with exogenous variables (SVARX), we examine the monetary policy responses and exchange rate fluctuations of ASEAN+4 economies—China, Japan, Korea, and Hong Kong—to external shocks including U.S. monetary policy changes, oil price fluctuations, global policy uncertainty, and financial risk during 2010–2022. Economies are grouped according to their trilemma configurations: floating exchange rates with free capital flows, fixed exchange rates, and capital control regimes. Our findings broadly support the trilemma hypothesis: fixed-rate economies align with U.S. interest rate movements, capital control economies retain greater monetary autonomy, and open, floating regimes show partial responsiveness. More importantly, monetary responses vary by global shock type: U.S. monetary policy drives the most synchronized policy reactions, while oil price and uncertainty shocks produce more heterogeneous outcomes. Robustness checks include alternative model specifications, where global shocks are treated as endogenous, and extensions, such as using Japan’s monetary base as a proxy for unconventional monetary policy. These results refine the empirical understanding of the trilemma by showing that its dynamics depend not only on institutional arrangements but also on the nature of global shocks—underscoring the need for more tailored and, where possible, regionally coordinated monetary policy strategies. Full article

Using the OLS model with different quantiles of GPR, we aim to examine the impact of GPR, EPU, and VIX on monthly international crude oil prices, including WTI, BRENT, and DUBAI prices, while differentiating the impact on different levels of risks. Afterwards, we use the GARCH and MGARCH models to assess the impact of these metrics on the volatility of oil prices, and the spillover effects between oil prices with these three metrics as exogenous shocks. Our result indicates (i) global oil price is negatively affected by GPRT at a moderate level of risks in longer time intervals; (ii) GPR, EPU, and VIX affect oil price’s volatility, and (iii) there exists a stronger long-persistent spillover effect between BRENT and DUBAI, with these metrics as exogenous shocks, while WTI is not affected. Full article

Current research has made significant progress in exploring the sustainability efforts made by domestic and foreign firms, respectively, but paid insufficient attention to the interaction of these firms in the form of sustainability practice spillovers. This paper aims to fill this gap by discussing the spillovers of ‘best practices’ of corporate environmental sustainability (CES) from multinational enterprises (MNEs) that have made increasing investment in green production in recent decades to local forms in host developing countries where environmental protection is relatively weak. In line with internalization theory, we contend that MNEs have to internalize CES ‘best practices’ in their affiliates across the globe, and such practices can spill over to local firms in host developing countries with poorer CES practices. The level of development of press freedom in the host developing country conditions the CES practice spillovers. This study tests hypotheses against firm-level data from a large-scale survey and finds robust evidence to support our argument. This study takes a quantitative approach to unveil the existence and boundary conditions of CES practice spillovers and suggests that policymakers need to facilitate the spillovers of such practices and that scholars need to further advance research in this area. Full article

The diffusion of green energy technological innovation based on international green energy cooperation is a critical pathway to achieving global low-carbon emission reductions. However, few studies have considered the innovation diffusion pathways of green energy technologies under bilateral policy uncertainties. This paper constructs an evolutionary game model for the diffusion of green energy technological innovation in a complex network environment, with a focus on analyzing the impacts of key parameters such as policy spillover effects, technological heterogeneity, technical leakage risks, and free-riding risks on the equilibrium outcomes of evolutionary strategies. The results of the study are as follows: (1) Technological synergy and technological heterogeneity have a significant role in promoting the diffusion of green energy technological innovation, but when technological heterogeneity is too high, it is difficult for the two parties to find more common interests and areas of technological interaction, and the cooperative innovation will be turned into an empty shell that has a name but no reality. (2) Policy uncertainty has a significant impact on the diffusion of green energy technology innovation, and the specific impact depends on the type of policy, policy intensity, policy spillover effects, and other key parameters. (3) The risk of technological obsolescence has prompted countries to deeply participate in green energy international cooperation to realize the “curved road overtaking” of green energy technology based on technological locking and latecomer advantages; due to the existence of the phenomenon of “free-riding”, the logic of value creation based on win–win cooperation is replaced by the opportunism of “enjoying the benefits”, and cooperative innovation may be turned into a one-time “handshake agreement”. The existence of the risk of technology leakage can turn collaborative innovation into a “witch hunt” by the underdog against the overdog, and the diffusion process of green energy technology innovation is led in the wrong direction. Full article

As China’s regional development enters a critical stage of population redistribution and urban transformation, the impact of population mobility on regional demographic structures and urban functional restructuring has become increasingly prominent. Against this backdrop of accelerating Beijing–Tianjin–Hebei integration, cities of varying types have been confronted with growing complexity in demographic transitions and socioeconomic stratification during their processes of absorbing or exporting populations. This study employed microdata from the National Bureau of Statistics to construct migration flow matrices and utilized a decomposition quantification approach to explore the impact of internal migration on the population structure and quality in 13 cities within the Beijing–Tianjin–Hebei region. The findings indicated that, while Beijing has achieved some success in population control, it overall exhibits a “large inflow and large outflow” pattern. Langfang has attracted a large number of highly educated individuals and male migrants under the spillover effect from Beijing. Shijiazhuang’s population attractiveness has increased, yet the outflow of highly educated individuals remains unmitigated. Meanwhile, the internal migrant structures in industrial cities such as Tianjin and Tangshan have undergone significant changes following the industrial transformation. Cities in the passive outflow area and agricultural transformation area have experienced siphoning effects, but they face different potential risks due to their unique characteristics. This study further enriches the understanding of the impact mechanisms underlying domestic population mobility and provides differentiated policy references for cities to reconcile their development objectives with demographic realities. Full article

Grounded in the theory of new economic geography, this research develops a comprehensive theoretical framework to examine the spatial interaction mechanisms between the Green Finance Index and carbon emissions. Employing a range of econometric techniques—including three-dimensional kernel density estimation, spatial quantile regression, bivariate spatial autocorrelation analysis, and the spatial linkage equation model—the dynamic evolution, spatial pattern shifts, and mutual influences of green finance and carbon emissions in the middle and lower reaches of the Yellow River from 2003 to 2022 are systematically assessed. The findings indicate that (1) both carbon emissions and the Green Finance Index have experienced a trajectory of continuous growth, phased decline, and structural optimization, accompanied by a gradual shift in the regional center of gravity from coastal economic zones towards resource-intensive and traditional industry-concentrated areas; (2) significant spatial clustering is evident for both green finance and carbon emissions, demonstrating a strong spatial correlation and regional synergy effects; (3) a persistent negative spatial correlation exists between green finance and carbon emissions; and (4) green finance exerts a stable negative spatial spillover effect on carbon emissions, suggesting that the influence of green finance extends beyond localities to adjacent regions through spatial externalities, manifesting pronounced spatial transmission and linkage characteristics. By unveiling the bidirectional spatial association between green finance and carbon emissions, this study highlights the pivotal role of green finance in driving regional low-carbon transitions. The results provide theoretical insights for optimizing green finance policies within the Yellow River Basin and offer valuable international references for similar regional low-carbon development initiatives. Full article

As a progressive and systematic initiative that necessitates the collective participation of society, achieving the goals of carbon peaking and carbon neutrality has had a significant and positive impact on the transformation of the energy structure, the development of the new energy industry, the enhancement of economic efficiency and environmental quality, and the deepening of international cooperation across multiple dimensions. This study examines how the digital economy affects carbon reductions in the context of China’s pursuit of carbon peak and carbon neutrality targets. To thoroughly examine how regional digital economy development influences agricultural carbon emissions and uncover its underlying mechanism, this study uses regression analysis models using panel data from 31 Chinese provinces (not including Hong Kong, Macau, and Taiwan) from 2013 to 2022. In the meantime, the study investigates the spatial effects of the digital economy on agricultural carbon emissions. The results show that the rapid development of the digital economy plays a significant role in reducing agricultural carbon emissions. In particular, every 1 unit increase in the level of digital economy development is associated with a 0.125-unit reduction in agricultural carbon emissions. Second, the expansion of the digital economy allows regional labor transfer, which indirectly influences its suppressive effect on agricultural carbon emissions through this channel. Third, the expansion of the digital economy in one area has significant spatial spillover effects, leading to agricultural carbon emissions in other provinces and cities. Fourth, these spatial spillover effects vary depending on the topography and economic production. In particular, flat regions and high-yield agricultural areas see greater carbon reduction spillover effects from the digital economy compared to steep regions and low-yield agricultural areas. Therefore, research on the impact of the digital economy on agricultural carbon emissions can help to reveal the path of the digital-technology-driven green transformation of agriculture and provide a scientific basis for optimizing agricultural carbon-emission-reduction policies and achieving sustainable agricultural development. Full article

With the continuous integration of globalization and financial markets, the linkage of global financial risks has increased significantly. This study examines the risk spillover effects and transmission dynamics among the financial markets in G20 countries, which together represent over 80% of global GDP. With increasing globalization and the interconnectedness of financial markets, understanding risk transmission mechanisms has become critical for effective risk management. Previous research has primarily focused on price volatility to measure financial risks, often overlooking other critical dimensions such as liquidity, credit, and operational risks. This paper addresses this gap by utilizing the vector autoregressive (VAR) model to explore the spillover effects and the temporal and spatial characteristics of risk transmission. Specifically, we employ global and local Moran indices to analyze spatial dependencies across markets. Our findings reveal that the risk linkages among the G20 financial markets exhibit significant time-varying characteristics, with spatial risk distribution showing weaker dispersion. By constructing a comprehensive financial risk index system and applying a network-based spillover analysis, this study enhances the measurement of financial market risk and uncovers the complex transmission pathways between sub-markets and countries. These results not only deepen our understanding of global financial market dynamics but also provide valuable insights for the design of effective cross-border financial regulatory policies. The study’s contributions lie in enriching the empirical literature on multi-dimensional financial risks, advancing policy formulation by identifying key risk transmission channels, and supporting international risk management strategies through the detection and mitigation of potential contagion effects. Full article

Solid waste governance actions are important to achieve sustainable urban development. This study uses the “zero-waste city” pilot policy as a natural experiment to evaluate the impact of solid waste governance actions on corporate environmental, social, and governance (ESG) performance. The research shows that solid waste governance actions improve corporate ESG performance by enhancing government environmental concerns, public environmental concerns, and corporate green innovation. The analysis of spillover effects indicates that solid waste governance exerts positive spatial spillover effects. Heterogeneity tests reveal that the positive effect of solid waste governance actions on corporate ESG performance is more pronounced in enterprises characterized by higher-quality information disclosure and stronger internal governance, industries with greater solid waste output and more advanced technology, regions with a closer government–market relationship, and in central–eastern regions. These findings contribute to understanding the micro-level effects of solid waste governance actions and the determinants of corporate ESG performance, providing valuable insights for other developing countries to govern solid waste and improve corporate ESG performance. Full article

The world is currently experiencing an unprecedented period of disruption. Traditional theories of comparative advantage can no longer serve as the sole drivers for enhancing the international competitiveness of China’s manufacturing industry. In this new era, the future development of China’s manufacturing industry has become a pressing issue that demands immediate attention. With the rapid advancement of next-generation digital technologies and information and communication technologies, global digital trade has surged, emerging as a key engine of economic growth for countries worldwide. This trend undoubtedly presents new opportunities and platforms for strengthening the international competitiveness of China’s manufacturing industry. How China’s manufacturing industry can effectively leverage digital trade to secure a competitive advantage amid intensifying global competition has become a critical and urgent area of research. Using panel data from 31 provinces, autonomous regions, and municipalities in China spanning from 2012 to 2022, this study develops a comprehensive evaluation framework for digital trade and manufacturing competitiveness. It empirically investigates the impact and mechanisms through benchmark regression models, mediation effect models, and spatial econometric models. The findings reveal that digital trade has a significant positive impact on the international competitiveness of China’s manufacturing industry. The effect of digital trade on competitiveness is most pronounced in the eastern region and least evident in the western region. Additionally, foreign direct investment and technological research and development capabilities are found to indirectly enhance the international competitiveness of the manufacturing industry. Furthermore, digital trade exhibits spatial spillover effects, wherein improvements in manufacturing competitiveness within one province positively influence neighboring provinces. This study offers valuable theoretical and policy implications for evaluating the impact of digital trade on the international competitiveness of manufacturing and strategies for enhancing it. Full article

Sustainable property development in developing economies requires a careful balance between attracting foreign capital and maintaining housing affordability for local residents. While foreign direct investment (FDI) serves as a crucial engine for economic growth by enhancing productive capacity and international competitiveness, its effects on local housing markets remain inadequately understood in policy frameworks. This study examines how economic development strategies can be designed to harness FDI benefits while preventing residential market distortions in rapidly industrializing regions. Using Malaysia’s Kulim Hi-Tech Park and Batu Kawan Industrial Park as empirical cases, we analyze the relationship between foreign capital inflows and residential property prices from 2000 to 2022 through time-series regression analysis supplemented by stakeholder consultations. Our findings reveal that FDI significantly influences housing price dynamics in industrial zones, with both positive economic spillovers and challenges for housing affordability. The results demonstrate that targeted policy interventions—including affordable housing mandates, developer incentives, and strategic land use planning—can effectively moderate price appreciation while maintaining investment attractiveness. This research contributes to evidence-based policymaking by identifying integrated mechanisms that promote sustainable and inclusive growth in emerging economies seeking to balance industrial advancement with equitable housing access. The Malaysian experience offers valuable practical insights for policymakers in developing nations navigating the complex relationship between international investment, housing markets, and social welfare. Full article