Search Results (719)

Against the backdrop of China’s Rural Revitalization Strategy, rural industrial integration is regarded as a critical pathway to boosting farmers’ income, yet its specific impact and heterogeneous characteristics remain to be explored. Using biennial panel data from the 2021 and 2023 “Hundred Villages and Thousand Households” survey in Jiangxi Province, this study employs two-way fixed effects models, the instrumental variable method, and quantile regression to investigate the effect of farmers’ participation in rural industrial integration on their income. The findings show that participation in industrial integration significantly increases household income by an average of 28.6%, with causal relationships confirmed by instrumental variable analysis. Among different integration modes, industrial chain extension has the most significant effect, followed by functional expansion and internal multi-format integration, while technology penetration shows no significant effect; overlapping multiple modes exhibits a negative interactive effect. Additionally, high-standard farmland construction amplifies the income-increasing effect, and the effect is more pronounced for low-income farmers, those in mountainous areas, and farmers in the Central Jiangxi region. This study provides micro-level empirical evidence for optimizing industrial integration policies and advancing rural revitalization in central and western agricultural provinces. Full article

The vocal repertoire of the domestic pig (Sus scrofa domesticus) was examined in this study under conventional housing conditions. Therefore, direct behavior-associated vocalizations of fattening pigs were recorded and assigned to behavioral categories. Subsequently, a mathematical analysis of the recorded vocalizations was conducted using the frequency-based parameters of 25%, 50% and 75% quantiles of the frequency spectrum and the time-based parameters of variance of the time signal, mean level of the individual amplitude modulation and cumulative amplitude modulation. Most vocalizations were positively/neutrally assessed vocalizations constituting 59.7%, of which grunting was by far the most frequent vocalization. Negatively assessed vocalizations accounted for 37.8% of all vocalizations. Data analysis based on the six parameters resulted in a distinguishability of vocalizations related to negatively valenced behavior from those related to positively/neutrally valenced behavior. The study illustrates the relationship between auditory sensory perception and the underlying mathematical signals. It shows how pig vocalizations assessed by observations, for example, as positive or negative, are distinguishable using mathematical parameters but also which ambiguities arise when objective mathematical features widely overlap. In this way, the study encourages the use of more complex algorithms in the future to solve this challenging, multidimensional problem, forming the basis for future automatic detection of negative pig vocalizations. Full article

Background/Objectives: There is a ubiquitous presence of plastics worldwide, and recent data highlight the continuous growth in their production and usage—a trend paralleled by the rise in chronic diseases like diabetes. The multifactorial nature of these diseases suggests that environmental exposure, notably to bisphenol A (BPA), could be a contributing factor. This study investigates the potential correlation between emerging BPA substitutes, bisphenol S and F (BPS and BPF), and diabetes in a cohort of the general adult population. Methods: A retrospective cohort study was conducted using data from the U.S. National Health and Nutrition Examination Survey (NHANES) 2013–2014 and 2015–2016 cycles. Basic comparative analyses and Pearson correlation tests were performed, followed by logistic regression models. Advanced statistical approaches, including Weighted Quantile Sum (WQS) regression and quantile g-computation, were subsequently applied to evaluate the combined effects of bisphenol exposures. Results: Findings reveal a positive association between combined bisphenols (BPs) and glycated hemoglobin (HbA1c), with binomial logistic regression demonstrating an odds ratio (OR) of 1.103 (1.002–1.214) between BP levels corrected for creatinine (crucial due to glomerular filtration variations) and diabetes. weighted quantile sum (WQS) and quantile G-computation analyses showed a combined positive effect on diabetes, glucose levels, and HbA1c. Individual effect analysis identifies BPS as a significant monomer warranting attention in future diabetes-related research. Conclusions: Replacing BPA with new molecules like BPS or BPF may pose a greater risk in the context of diabetes. Full article

The global market for fog computing is expected to reach USD 6385 million by 2032. Modern enterprises rely on fog computing since it offers computational resources at edge devices through decentralized computation mechanisms. One of the crucial components of fog computing is the proper placement of applications on fog nodes (edge devices, Internet of Things (IoT)) for servicing. Large-scale, geographically distributed fog networks and heterogeneity of fog nodes make application placement a challenging task. Quantile Temporal Difference Learning (QTDL) is a promising distributed form of a reinforcement learning algorithm. It is superior compared to traditional reinforcement learning as it learns the act of prediction based on the full distribution of returns. QTDL is enriched by a small language model (SLM), which results in low inference latency, reduced costs of operation, and also enhanced rates of learning. The SLM, being a lightweight model, has policy-shaping capability, which makes it an ideal choice for the resource-constrained environment of edge devices. The data-driven quantiles of temporal difference learning are blended with the informed heuristics of the SLM to prevent quantile loss and over- or underestimation of the policies. In this paper, a novel SLM-guided QTDL framework is proposed to perform task scheduling among fog nodes. The proposed framework is implemented using the iFogSim simulator by considering both certain and uncertain fog computing environments. Further, the results obtained are validated using expected value analysis. The performance of the proposed framework is found to be satisfactory with respect of the following performance metrics: energy consumption, makespan time violations, budget violations, and load imbalance ratio. Full article

This study employs the panel quantile regression (QR) technique to evaluate whether globalization threshold conditions are essential for achieving effective economic growth, utilizing data from 47 Sub-Saharan African (SSA) countries for the period from 2000 to 2021. The bootstrap simultaneous conditional QR analysis was conducted using the fixed-effects panel QR approach. The study findings revealed that the globalization thresholds at which the total effect of globalization as a percentage of global integration changes from negative to positive are 3.82% and 4.36%, respectively. Furthermore, the critical mass of FDI and trade thresholds at which the total effects of FDI and trade, as a percentage of knowledge spillovers, change from negative to positive is 4.66% and 2.19%, respectively. Conversely, these results revealed an asymmetric relationship between globalization and growth among SSA countries. Therefore, these triggers and globalization thresholds serve as essential conditions and catalysts that will foster economic development in SSA economies. The results also indicate significant effects of globalization thresholds on economic growth among the SSA countries. Regarding policy relevance, these findings are also crucial for policymakers when they are developing strategies that will promote equal opportunity and balance development in the region through knowledge spillovers and improvements in global integration. Full article

Poor rural households still face vulnerability of the sustainable livelihood capacity caused by multiple risk disturbances even after they are lifted out of poverty, and become vulnerable poverty-eradicated households. However, quantifying the spatiotemporal heterogeneity of the impact of rural household livelihood vulnerability on resilience from a multi-risk perspective remains a challenge. This study integrates the theoretical connotations of livelihood vulnerability and resilience to develop a systematic analysis framework of sustainable livelihood-vulnerability-resilience for rural households from the perspective of multi-risk disturbance, and reveals the dynamic interaction process and mechanism of the three. On this basis, the VEP model for forward-looking and multi-risk perspectives, which embeds multiple risk factors as feature vectors, and the cloud-based fuzzy integrated evaluation method are employed to measure rural households’ livelihood vulnerability and resilience, respectively. Subsequently, based on clarifying the correlation between the two, we use the quantile regression method and factor contribution model to reveal the spatiotemporal impact mechanism of multi-level and multi-risk dominated vulnerability of rural households on resilience. These methods collectively enable us to quantify the spatiotemporal heterogeneity of vulnerability and resilience impacts from a risk perspective, taking a step forward and broadening the analytical perspective in the field of sustainable livelihoods research. The case study in Fugong County of China shows that, both rural households’ livelihood vulnerability and resilience exhibit spatiotemporal heterogeneity, and the negative correlation between the two gradually increases over time; as the level of livelihood vulnerability increases, the internal main contributing factors of livelihood resilience and their degree of contribution change accordingly; as the types of risks that dominate vulnerability change, the impact of vulnerability on the overall livelihood resilience and its internal dimensions also varies, where the change in resilience is greatest when the vulnerability is dominated by social risks, while the least change occurred when vulnerability is dominated by labor and income risks. This study provides a feasible methodological reference and a technical foundation for decision-making aimed at guiding rural households out of poverty sustainably and achieving sustainable livelihood. It can effectively enhance the predictive and post-event coping capacity of vulnerable rural households when subjected to multi-risk disturbances. Full article

The aim of this research is to provide a 25-year multi-model analysis of gender dynamics in economics articles that include at least one Romanian-affiliated author, published in Web of Science journals between 2000 and 2025 (2025 records current as of 15 May 2025). Drawing on 4030 papers, we map the bibliometric gender gap by examining first-author status, collaboration patterns, research topics and citation impact. The results show that the female-to-male first-author ratio for Romanian-affiliated publications is close to parity, in sharp contrast to the pronounced under-representation of women among foreign-affiliated first authors. Combining negative binomial, journal fixed-effects Poisson, quantile regressions with a text-based topic analysis, we find no systematic or robust gender penalty in citations once structural and topical factors are controlled for. The initial gender gap largely reflects men’s over-representation in higher-impact journals rather than an intrinsic bias against women’s work. Team size consistently emerges as the strongest predictor of citations, and, by extension, scientific visibility. Our findings offer valuable insights into gender dynamics in a semi-peripheral scientific system, highlighting the nuanced interplay between institutional context, research practices, legislation and academic recognition. 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

Energy security has emerged as a critical concern amid intensifying climate risks and surging energy demand driven by economic growth. This study examines the impact of climate risk on energy security by constructing a panel dataset covering 30 Chinese provinces from 2006 to 2022. Using the instrumental variable generalized method of moments (IV-GMM) model, we estimate the marginal impact of climate risk on energy security and further investigate its asymmetric, direct, and indirect relationships via panel quantile regression and mediation analysis. Our key findings are as follows: (1) Climate risk exerts a significant negative impact on energy security, indicating an inverse relationship. (2) The effect of climate risk is asymmetric, with a stronger adverse impact in regions with lower levels of energy security. (3) Climate risk undermines energy security by reducing energy accessibility, affordability, sustainability, and technological efficiency. (4) Energy transition and energy efficiency serve as critical mediators in the relationship between climate risk and energy security, offering insights into potential mitigation pathways. Unlike previous studies that primarily examine energy security in isolation or focus on single dimensions, this research integrates a multidimensional indicator system and advanced econometric techniques to uncover both direct and mediated pathways, thereby filling a key gap in understanding the climate–energy nexus at the provincial level in China. Based on these findings, we propose targeted policy recommendations to enhance energy security by improving climate resilience, accelerating the deployment of renewable energy, and optimizing energy infrastructure investments. Full article

Many scholars are interested in modeling complex data in an effort to create novel probability distributions. This article proposes a novel class of distributions based on the inverse of the exponentiated Weibull hazard rate function. A particular member of this class, the Weibull–Rayleigh distribution (WR), is presented with focus. The WR features diverse probability density functions, including symmetric, right-skewed, left-skewed, and the inverse J-shaped distribution which is flexible in modeling lifetime and systems data. Several significant statistical features of the suggested WR are examined, covering the quantile, moments, characteristic function, probability weighted moment, order statistics, and entropy measures. The model accuracy was verified through Monte Carlo simulations of five different statistical estimation methods. The significance of WR is demonstrated with three real-world data sets, revealing a higher goodness of fit compared to other competing models. Additionally, the change point for the WR model is illustrated using the modified information criterion (MIC) to identify changes in the structures of these data. The MIC and curve analysis captured a potential change point, supporting and proving the effectiveness of WR distribution in describing transitions. Full article

Frequent grassland fires have severely affected regional ecosystems as well as the production and living conditions of local residents. Grassland fire prevention capabilities constitute an integral part of the disaster prevention and mitigation system and play an important role in improving grassroots governance. To gain a deeper understanding of the practical foundation and influencing mechanisms of grassland fire prevention capabilities, establish an evaluation index system for prevention capabilities covering the four dimensions of disaster prevention, disaster resistance, disaster relief, and recovery. Combining micro-level survey data, a quantile regression model is used to analyze the influencing factors. The research findings indicate that (1) disaster resistance (0.49) plays a prominent role in grassland fire prevention capabilities, with economic foundations and individual disaster relief capabilities being particularly critical for overall improvement. Although residents have strong fire prevention awareness, their organizational collaboration capabilities are relatively weak, and there are significant differences in prevention capabilities across regions, necessitating tailored, precise enhancements. (2) There are significant differences in prevention capabilities among residents of different agricultural and pastoral production types, with semi-agricultural and semi-pastoral areas having the strongest comprehensive capabilities and pastoral areas relatively weaker. (3) A significant analysis of factors influencing grassland fire prevention capabilities: effective and diverse risk communication is a prerequisite for enhancing residents’ prevention capabilities; the level of panic regarding grassland fires and road infrastructure are important influencing factors, but residents’ understanding of climate change and grassroots organizations’ capacity for mechanism construction have insignificant impacts. Therefore, in future grassland fire disaster prevention and mitigation efforts, it is essential to strengthen risk communication, improve infrastructure, monitor environmental changes and the spatiotemporal patterns of grassland fires, enhance residents’ understanding of climate change, reinforce the emergency response capabilities of grassroots organizations, and stimulate public participation awareness to collectively build a multi-tiered grassland fire prevention system. Full article

This study proposes a geographically weighted (GW) quantile machine learning (GWQML) framework for soil moisture (SM) prediction, integrating spatial kernel functions with quantile-based prediction and uncertainty quantification. The framework incorporates satellite radar backscatter, meteorological re-analysis, and topographic variables, applied across 15 SM stations and six land use systems at the North Wyke Farm Platform, southwest England, UK. GWQML was implemented using Gaussian and Tricube spatial kernels across a range of kernel bandwidths (500–1500 m). Model performance was evaluated using both in-sample and Leave-One-Land-Use-Out validation schemes, and a global quantile machine learning model (QML) without spatial weighting served as the benchmark. GWQML achieved R² values up to 0.85 and prediction interval coverage probabilities up to 0.9, with intermediate kernel bandwidths (750–1250 m) offering the best balance between accuracy and uncertainty calibration. Spatial autocorrelation analysis using Moran’s I revealed a lower residual clustering under GWQML relative to the benchmark model, which suggests improved handling of local spatial variation. This study represents one of the first applications of geographically weighted kernel functions in a quantile machine learning framework for daily soil moisture prediction. The approach implicitly captures spatially varying relationships while delivering calibrated uncertainty estimates for scalable SM monitoring across heterogenous agricultural landscapes. Full article

This study investigates whether green assets can serve as safe havens for dirty assets in the context of carbon and energy futures markets. Using daily data from April 2021 to June 2025, the analysis focuses on four key instruments: carbon emissions futures and crude oil futures, EUA futures, and natural gas futures. The study applies two main approaches—a conditional value-at-risk (CVaR)-based relative risk ratio (RRR) analysis and dynamic conditional correlation (DCC-GARCH) modeling—to assess tail risk mitigation and time-varying correlations. The results show that while green assets do not consistently act as safe havens during extreme market downturns, they can reduce the portfolio tail risk beyond certain allocation thresholds. Natural gas futures demonstrate significant volatility but offer diversification benefits when their portfolio weight exceeds 40%. EUA futures, although highly correlated with carbon emissions futures, show limited safe haven behavior. The findings challenge the assumption that green assets inherently provide downside protection and highlight the importance of strategic allocation. This research contributes to the literature by extending safe haven theory to environmental futures and offering empirical insights into the risk dynamics between green and dirty assets. Full article

Against the backdrop of accelerated global integration and China’s pursuit of new type urbanization pathways, the role of trade openness—moderated by industrial upgrading—represents a critical yet underexplored nexus for emerging economies. Using provincial panel data from 31 Chinese provinces spanning from 2008 to 2022, this study empirically examines the impact of trade openness on urbanization. It further examines the moderating role of industrial structure upgrading in this relationship. To address endogeneity and distributional heterogeneity, we employ economic distance as an instrumental variable and apply quantile regression methods, thereby providing a robust quantification of the dynamic effects of trade openness on urbanization. The study demonstrates that trade openness contributes to the advancement of China’s new type urbanization and that the upgrading of industrial structures positively reinforces this effect through trade openness. Further heterogeneity analysis reveals that the eastern region, which is more economically developed and more globally integrated, exhibits a stronger awareness of and responsiveness to the impact of trade openness on urbanization. This article provides a theoretical framework for the sustainable development of China’s new type urbanization, encouraging stakeholders to actively engage in the urbanization process and to promote balanced economic, social, and environmental development. This study offers actionable insights for policymakers to align trade openness with new type urbanization pathways. Full article

The digital creative industries have emerged as a critical driver of regional economic transformation, upgrading, and sustainable development. While previous research has primarily focused on creative industry layout and agglomeration in urban areas, with the integration of digital technology and the creative industry, existing research has an insufficient explanation for the digital creative industry. Specifically, few people have studied the spatial distribution and diffusion of digital creative industries in emerging economies from the macro-regional level. To address this gap, this study analyzes the spatial diffusion mode and regional spatial response law of digital creative industries in the Yangtze River Delta during three critical time windows (2016, 2019, and 2022) in the context of national strategy implementation. A range of spatial analysis technologies is utilized to process the full sample of big data from digital creative industries. This study utilizes OLS and a quantile regression model to determine the dominant factors that affect spatial diffusion and response in the digital creative industries. The results demonstrate that, against the backdrop of regional development strategies, digital creative industries exhibit a variety of diffusion modes, including contagious, hierarchical, corridor, and jump diffusion. The response of industries to regional strategies has different rules in terms of regional space, urban development, and sub-industries. Furthermore, the comprehensive influence of institutional environment, urban economy, development and innovation significantly impacts industrial spatial diffusion and regional response. Among them, government investment in science and technology and the number of universities have consistently been important influencing factors, and policy exhibits nonlinear effects and asymmetric characteristics on industry agglomeration and diffusion. This study enhances the understanding of digital creative industry development in the YRD and offers a theoretical basis for optimizing regional industrial spatial structure and promoting the sustainable development of digital industries. Full article