Search Results (1,947)

This study pioneers an investigation into how artificial intelligence (AI)—shaped by financial development and knowledge production—is transforming the energy transition across BRICS economies and paving the way for a digitally enabled, sustainable future. Using panel data for 2005–2020, the findings confirm that AI is the primary driver of both explicit (EET) and implicit (IET) energy transitions in the BRICS nations, while economic growth, human capital, and financial globalization play comparatively smaller roles. We further find that AI’s effect on the explicit transition is fully mediated by efficiency gains. Financial development weakens—whereas knowledge production strengthens—AI’s green impact. Robustness checks across alternative models support these results, and spillover analyses indicate that cross-border AI advances, economic growth, human capital, and innovation flows shape each BRICS country’s energy-transition path. Based on these findings, the study proposes coordinated policy packages to harness AI for the energy transition while managing distributional and cross-border effects. Full article

Achieving carbon reduction is essential in advancing China’s dual carbon goals and promoting a green transformation in the transportation sector. Changes in energy structure and intensity constitute key drivers for sustainable and low-carbon development in this field. To explore the spatial spillover effects of the energy structure and intensity on the green transition of transportation, this study constructs a panel dataset of 30 Chinese provinces from 2007 to 2020. It employs a super-efficiency SBM model, non-parametric kernel density estimation, and a spatial Markov chain to verify and quantify the spatial spillover effects of green total factor productivity (GTFP) in the transportation sector. A dynamic spatial Durbin model is then used for empirical estimation. The main findings are as follows: (1) GTFP in China’s transportation sector exhibits a distinct spatial pattern of “high in the east, low in the west”, with an evident path dependence and structural divergence in its evolution; (2) GTFP displays spatial clustering characteristics, with “high–high” and “low–low” agglomeration patterns, and the spatial Markov chain confirms that the GTFP levels of neighboring regions significantly influence local transitions; (3) the optimization of the energy structure significantly promotes both local and neighboring GTFP in the short term, although the effect weakens over the long term; (4) a reduction in energy intensity also exerts a significant positive effect on GTFP, but with clear regional heterogeneity: the effects are more pronounced in the eastern and central regions, whereas the western and northeastern regions face risks of negative spillovers. Drawing on the empirical findings, several policy recommendations are proposed, including implementing regionally differentiated strategies for energy structure adjustment, enhancing transportation’s energy efficiency, strengthening cross-regional policy coordination, and establishing green development incentive mechanisms, with the aim of supporting the green and low-carbon transformation of the transportation sector both theoretically and practically. Full article

Urban carbon reduction is instrumental in enabling cities to realize their developmental sustainability objectives. However, regional disparities in economic development pose significant challenges to low-carbon transitions. This study utilizes panel data from 282 cities in China spanning 2006–2021, considering the energy saving and emission reduction (ESER) fiscal policy as an external shock. Using a multi-period difference-in-differences approach, we assess how ESER impacts urban carbon emissions. Our findings indicate that ESER significantly reduces municipal carbon emissions by an average of 23.3% compared to non-pilot cities. Mechanism analyses suggest that this effect operates through reduced energy consumption, improved industrial structure, and enhanced green innovation. ESER’s impact exhibits heterogeneity across cities with different levels of economic development, population size, innovation capacity, and industrial composition. Moreover, we find evidence of spatial spillover effects, as ESER benefits extend to neighboring regions. These results confirm the effectiveness of ESER in promoting low-carbon development and offer practical implications for enhancing environmental governance through green fiscal instruments. Full article

The registration system reform (RSR) represents a landmark innovation in China’s IPO system, aiming to promote a more transparent, competitive, and sustainable market. Exploiting the staggered implementation of RSR as a quasi-natural experiment, we employ a difference-in-differences (DID) model using a sample of Chinese A-share IPO firms from 2016 to 2022 to investigate its impact on corporate sustainability, as proxied by environmental, social, and governance (ESG) performance. Our findings indicate that RSR significantly enhances corporate ESG performance, especially the governance (G) performance. Mechanism analysis suggests that market competition, investor rationality, and sponsor reputation are potential channels through which the reform facilitates corporate sustainability. Furthermore, the above relationship is more pronounced in regions with a higher degree of marketization, among non-state-owned enterprises, and those with weaker profitability. Moreover, the reform not only exhibits long-term effects but also demonstrates positive spillover effects on peer firms originally listed under the approval-based system. Overall, our study extends the understanding of how capital market institutional reforms promote corporate sustainability in the era of the digital economy and provides valuable insights for regulators to standardize and enhance RSR, thereby establishing a resilient and sustainable financial ecosystem. Full article

The US has undertaken both unconventional and conventional monetary policy stances in response to the COVID-19 pandemic and the Ukraine–Russia conflict, and there has been much debate on the effects of these various monetary policies on global financial markets. Our study considers the debate in the context of South Africa and uses the quantile–frequency connectedness approach to examine static and dynamic systemic spillover between the US shadow short rate (SSR) and South African equity, bond and currency markets between 1 December 2019 and 2 March 2023. The findings from the static analysis reveal that systemic connectedness is concentrated at their tail-end quantile distributions and US monetary policy plays a dominant role in transmitting these systemic shocks, albeit these shocks are mainly high frequency with very short cycles. However, the dynamic estimates further reveal that US monetary policy exerts longer-lasting spillover shocks to South African financial markets during periods corresponding to FOMC announcements of quantitative ‘easing’ or ‘tapering’ policies. Overall, these findings are useful for evaluating the effectiveness of the Reserve Bank’s macroprudential policies in ensuring market efficiency, as well as for enhancing investor decisions, portfolio allocation and risk management. Full article

As manufacturing and logistics-oriented supply chains continue to expand in scale and complexity, and the coupling between their physical execution layers and information–decision layers deepens, the resulting high interdependence within the system significantly increases overall fragility. Driven by key technological barriers, economies of scale, and the trend toward resource centralization, supply chains have increasingly evolved into centralized structures, with critical functions such as decision-making highly concentrated in a few focal firms. While this configuration may enhance coordination under normal conditions, it also significantly increases dependency on focal nodes. Once a focal node is disrupted, the intense task, information, and risk loads it carries cannot be effectively dispersed across the network, thereby amplifying load spillovers, coordination imbalances, and information delays, and ultimately triggering large-scale cascading failures. To capture this phenomenon, this study develops a coupled network model comprising a Physical Network and an Information and Decision Risk Network. The Physical Network incorporates a tri-load coordination mechanism that distinguishes among theoretical operational load (capacity), actual production load (production output), and actual delivery load (order fulfillment), using a load sensitivity coefficient to describe the asymmetric propagation among them. The Information and Decision Risk Network is further divided into a communication subnetwork, which represents transmission efficiency and delay, and a decision risk subnetwork, which reflects the diffusion of uncertainty and risk contagion caused by information delays. A discrete-event simulation approach is employed to evaluate system resilience under various failure modes and parametric conditions. The results reveal the following: (1) under a centralized structure, poorly allocated redundancy can worsen local imbalances and amplify disruptions; (2) the failure of a focal firm is more likely to cause a full network collapse; and (3) node failures in the Communication System Network have a greater destabilizing effect than those in the Physical Network. Full article

This research evaluates the suitability of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) for improving financial return predictions across 15 major worldwide stock indices. The proposed method uses graph modeling to represent financial index relationships which enables the detection of symmetric market dependencies including mutual spillover effects and bidirectional influence patterns. The symmetric network structures become most important during financial instability because market interdependencies strengthen at such times. The evaluation process compares these models against XGBoost and Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) traditional forecasting approaches. The results of 30 time-series cross-validation experiments show that GNN models produce lower RMSE and MAE values, especially during financial crises and recovery phases and volatile market periods. The models show reduced advantages when markets remain stable. The research demonstrates that graph-based forecasting models which incorporate symmetry effectively detect complex financial relationships which leads to important implications for investment strategies and financial risk management and global economic forecasting. Full article

To advance global climate governance, this study investigates the carbon emission efficiency (CEE) of 110 Chinese resource-based cities (RBCs) using a super-efficiency SBM-GML model combined with kernel density estimation and spatial analysis (2006–2022). Spatial Durbin model (SDM) and geographically and temporally weighted regression (GTWR) further elucidate the driving mechanisms. The results show that (1) RBCs achieved modest CEE growth (3.8% annual average), driven primarily by regenerative cities (4.8% growth). Regional disparities persisted due to decoupling between technological efficiency and technological progress, causing fluctuating growth rates; (2) CEE exhibited high-value clustering in the northeastern and eastern regions, contrasting with low-value continuity in the central and western areas. Regional convergence emerged through technology diffusion, narrowing spatial disparities; (3) energy intensity and government intervention directly hinder CEE improvement, while rigid industrial structures and expanded production cause negative spatial spillovers, increasing regional carbon lock-in risks. Conversely, trade openness and innovation level promote cross-regional emission reductions; (4) the influencing factors exhibit strong spatiotemporal heterogeneity, with varying magnitudes and directions across regions and development stages. The findings provide a spatial governance framework to facilitate improvements in CEE in RBCs, emphasizing industrial structure optimization, inter-regional technological alliances, and policy coordination to accelerate low-carbon transitions. Full article

Energy raw materials are the basis of the economic system. From this emerges the need to examine in more detail how various uncertainty indices interact with the dynamic of spillover connectedness among energy markets. The TVP-VAR model is used to investigate connectedness among US, European, and Indian oil and gas markets and the S&P carbon allowances Eua index. Following this, the wavelet decomposition technique is used to capture the dynamic correlations between uncertainty indices (GPR and VIX) and connectedness indices. First, the results indicate that energy market spillovers are time-varying and crisis-sensitive. Second, the time–frequency dependence among uncertainty indices and connectedness indices is more marked and can change with the occurrence of unexpected events and geopolitical conflicts. The VIX index shows a positive dependence on total dynamic connectedness in the mid-long-term, while the GPR index has a long-term effect only after 2020. The analysis of the interdependence among the connectedness of each market and the uncertainty indices is more heterogeneous. Political tensions and geopolitical risks are, therefore, causal factors of energy prices. Given their strategic and economic importance, policy makers and investors should establish a risk warning mechanism and try to avoid the transmission of spillovers as much as possible. Full article

With the advancement of global climate governance, public attention—an emerging form of social capital—has played an increasingly important role in the carbon emission effects of green technological innovation. Based on panel data from 267 prefecture-level cities in China from 2012 to 2022, this study employed a two-way fixed-effects model to identify the nonlinear relationship between green innovation and carbon emissions, incorporated interaction terms to examine the moderating effect of public attention, and applied a spatial Durbin model to analyze the spatial spillover effects of green innovation. The results reveal an inverted U-shaped relationship between green innovation and carbon emissions, with the inflection point corresponding to 8.58 authorized green patents per 10,000 people—a threshold that most cities have yet to reach. Public attention significantly altered the shape of the carbon effect curve by making it steeper; in cities with a higher share of secondary industry, it delayed the inflection point, whereas in cities dominated by the tertiary industry, the turning point appeared earlier. In addition, green innovation had significant spatial spillover effects, and its impact on carbon emissions in neighboring cities displayed a U-shaped pattern. This paper proposes an analytical framework of “socially empowered innovation” to reveal the nonlinear moderating mechanism through which public attention influences the carbon effects of green innovation. The findings offer important policy implications: efforts should focus on long-term innovation, promote regional coordination, guide rational public participation, and avoid short-sighted and unsustainable mitigation practices. Full article

This study develops an integrated analytical framework to examine the interplay of urbanization, energy consumption, and CO₂ emissions at the city level in China. Utilizing the Entropy-TOPSIS method for multidimensional urbanization measurement, the GM_Combo model for spatial spillover analysis, and Random Forest for identifying emission drivers, we analyze data from 282 Chinese cities from 2006 to 2020. Results reveal significant hierarchical differences in urbanization, with K-means clustering identifying high, medium, and low urbanization groups reflecting diverse regional development pathways. Energy consumption increasingly drives emissions, while urbanization’s influence declines, indicating partial decoupling. Strong spatial spillovers highlight the need for regional coordination. Ecological assets provide moderate mitigation effects. These findings contribute to the literature by introducing a multidimensional urbanization index, uncovering nonlinear energy–emissions dynamics, and quantifying intercity spillovers, offering empirical support for tailored low-carbon policies and sustainable urban governance. Full article

Veterinary vaccines are essential tools for controlling infectious and zoonotic diseases, safeguarding animal welfare, and ensuring global food security. However, conventional vaccines are hindered by cold-chain dependence, thermal instability, and logistical challenges, particularly in low- and middle-income countries (LMICs). This review explores next-generation veterinary vaccines, emphasizing innovations in thermostability and delivery platforms to overcome these barriers. Recent advances in vaccine drying technologies, such as lyophilization and spray drying, have improved antigen stability and storage resilience, facilitating effective immunization in remote settings. Additionally, novel delivery systems, including nanoparticle-based formulations, microneedles, and mucosal routes (intranasal, aerosol, and oral), enhance vaccine efficacy, targeting immune responses at mucosal surfaces while minimizing invasiveness and cost. These approaches reduce reliance on cold-chain logistics, improve vaccine uptake, and enable large-scale deployment in field conditions. The integration of thermostable formulations with innovative delivery technologies offers scalable solutions to immunize livestock and aquatic species against major pathogens. Moreover, these strategies contribute significantly to One Health objectives by mitigating zoonotic spillovers, reducing antibiotic reliance, and supporting sustainable development through improved animal productivity. The emerging role of artificial intelligence (AI) in vaccine design—facilitating epitope prediction, formulation optimization, and rapid diagnostics—further accelerates vaccine innovation, particularly in resource-constrained environments. Collectively, the convergence of thermostability, advanced delivery systems, and AI-driven tools represents a transformative shift in veterinary vaccinology, with profound implications for public health, food systems, and global pandemic preparedness. Full article

Sustainability performance varies significantly across countries, yet global assessments overlook the underlying structural trends. This study bridges this gap using machine learning to uncover meaningful clustering in global sustainability outcomes based on the 2025 Sustainable Development Goals (SDG) Index. We applied K-Means clustering to group 166 countries into five standardized indicators: SDG score, spillover effects, regional score, population size, and recent progress. The five-cluster solution was confirmed by the Elbow and Silhouette procedures, with ANOVA and MANOVA tests subsequently indicating statistically significant cluster differences. For the validation and interpretation of the results, six supervised learning algorithms were employed. Random Forest, SVM, and ANN performed best in classification accuracy (97.7%) with perfect ROC-AUC scores (AUC = 1.0). Feature importance analysis showed that SDG and regional scores were most predictive of cluster membership, while population size was the least. This supervised–unsupervised hybrid approach offers a reproducible blueprint for cross-country benchmarking of sustainability. It also offers actionable insights for tailoring policy to groups of countries, whether high-income OECD nations, emerging markets, or resource-scarce countries. Our findings demonstrate that machine learning is a useful tool for revealing structural disparities in sustainability and informing cluster-specific policy interventions toward the 2030 Agenda. Full article

Global water scarcity has emerged as a critical barrier to sustainable socio-economic development, stimulating water rights trading to serve as a policy instrument designed to enhance water use efficiency. This study systematically evaluates the impact of water rights trading (WRT) on agricultural water use efficiency (AWE) using panel data from 30 provinces (2011–2022) and a difference-in-difference (DID) model, while thoroughly investigating the underlying mechanisms and spatial spillover effects. The following are primary conclusions: (1) WRT significantly improves efficiency, reducing water consumption per unit of agricultural output by 4.5% in pilot regions, with robustness checks confirming reliability; (2) the policy’s effects on agricultural water use efficiency vary across regions; (3) mechanism analysis suggests that efficiency improvements are primarily driven by optimized crop planting patterns, adoption of water-saving irrigation technologies, advancements in agricultural mechanization, and strengthened environmental regulations; and (4) the policy exhibits notable spatial spillover effects. These findings contribute to the evaluation of WRT policy and offer practical insights for market-based water allocation reforms, suggesting further expansion of WRT with an emphasis on regional coordination and cross-regional cooperation mechanisms. Full article

Work–life balance (WLB) is central to sustainable social and economic development, as reflected in the UN Sustainable Development Goals 3, 5, and 8. The purpose of this article is to identify and examine the key organisational and personal factors influencing the perceived work–life balance of employees in rural areas. The theoretical framework is grounded in three complementary approaches: the job demands–resources (JD-R) model, spillover theory, and boundary theory. Together, they offer a comprehensive perspective on role dynamics in the context of limited resources, technostress, and family-related tensions. The study was conducted on a sample of 700 rural employees in Poland, predominantly women (60.6%), with the majority aged 35–55 years (53.0%). Data were collected via a structured questionnaire and analysed using an exploratory approach based on regression trees (CART), which are effective in identifying latent and multidimensional relationships. The findings highlight the mechanisms underlying WLB disruptions in rural contexts and pinpoint areas for intervention through public and organisational policies aimed at supporting employee well-being. The most influential factors were workplace comfort, work flexibility, time autonomy, and employee age. Notably, younger employees require better working conditions than older ones to achieve similar WLB levels. The CART analysis also indicates that some disadvantages, such as low workplace comfort, can be mitigated by more flexible work schedules. Employers should therefore provide multidimensional support through complementary measures, monitor job demands, and educate employees on the effective use of available resources. Full article