Search Results (333)

Food security is being experienced particularly deeply in vulnerable regions that are impacted by climate change. Therefore, this study aims to examine the impact of climate change and gross national income on food security in the Gulf Cooperation Council (GCC) countries. The study utilized cross-country panel data for GCC countries from 2000 to 2024, with food access acting as the dependent variable for food security. The annual meteorological temperature, energy-related carbon emissions, and gross national income are involved as independent variables representing the factors of climate change and economic growth, respectively. The Pedroni and Johansen–Fisher panel cointegration tests were implemented. Furthermore, the study employs Bayesian random-effects (BRE) and Bayesian mixed-effects (BME) models, estimated through Markov Chain Monte Carlo (MCMC) methods, for achieving posterior distributions of the model’s parameters. The results confirm the existence of a long-term cointegrating relationship among the selected variables. Gross national income has a positive impact on food security, whereas carbon emissions exert a negative effect. The findings reveal that food security is shaped by interconnected economic and climate factors, with notable differences between countries. These results underline the importance of regional cooperation and country-specific policies that focus on enhancing income, mitigating emissions, and investing in food systems. Full article

Agricultural biomass recycling efficiency is central to advancing the green and sustainable transition of agriculture. Drawing on panel data for 30 Chinese provinces from 2019 to 2023, this study measures recycling efficiency using a three-stage super-efficiency SBM model with undesirable output and examines its determinants with a panel Tobit model. The second-stage SFA indicates that the effects of external conditions on input slacks are input-specific. In particular, GDP is statistically significant only in the biomass-generation slack equation, whereas topographic relief and rural road network density do not show robust associations with any slack measure once controls are included. After removing the influence of environmental factors and random shocks, the overall national level of agricultural biomass recycling efficiency remains moderate. The national mean Stage 3 efficiency decreased from 0.586 in 2019 to 0.427 in 2022 and recovered to 0.543 in 2023. The five-year average was 0.510, which is close to the Stage 1 average of 0.503. Spatial analysis indicates weak global spatial autocorrelation, with only occasional local clustering. The efficiency centroid oscillated during the study period rather than following a one-way migration path, with a total displacement of 70.05 km. The determinant analysis indicates that the number of specialised agricultural machinery has the most stable positive association with recycling efficiency, while other policy, market, and human capital variables do not show robust significance in the short panel. These findings underline the need to align equipment deployment and collection systems with local terrain and transport conditions, expand machinery leasing and service provision, and strengthen capacity building in low-efficiency regions. Establishing a national information sharing and dispatch platform would facilitate cross-regional resource flows and more efficient allocation, while improving local service outlets would make participation more convenient for farmers and reduce transaction costs. Full article

Growing regulatory, investor, and societal pressures have heightened the importance of environmental, social, and governance (ESG) disclosure as a key mechanism for corporate transparency and accountability, particularly in emerging markets. This study examines the relationship between ethical leadership and ESG disclosure among publicly listed companies in Saudi Arabia within the context of the Vision 2030 reforms. Drawing on ethical leadership theory and stakeholder theory, ethical leadership is conceptualized as an internal behavioral governance mechanism shaping firms’ sustainability reporting practices. The empirical analysis is based on panel data of 147 non-financial firms listed on the Saudi Stock Exchange (Tadawul) from 2020 to 2024, yielding 735 firm-year observations. ESG disclosure was measured using Refinitiv ESG scores, whereas ethical leadership was captured using the CSRHub Ethical Leadership Index. Employing a random-effects panel regression model with firm-level clustered robust standard errors, the results reveal a positive and statistically significant association between ethical leadership and ESG disclosure. These findings indicate that leadership ethics play an important role in enhancing transparency and accountability in sustainability reporting, and offer relevant implications for corporate governance and ESG policy development in Saudi Arabia. Full article

Background: Contemporary models of parental burnout conceptualize it as an interplay between parental demands and insufficient resources. However, research and current models remain sparse in their understanding of these demands and dynamics within the context of managing a child’s sleep wellness and related problems, which constitute a fundamental aspect in early parenting. The present work addresses this gap by examining this issue comprehensively. Methods: 2291 mother–child dyads were recruited from two sources: a random population sample (n = 1409) and a clinical sample (n = 882) of mothers seeking consultation for their child’s sleep issues (0–5 years old). Mothers completed an extensive panel of validated instruments and survey questions covering burnout and psychopathologies, sleep parameters, psychosocial, organizational, and demographic variables. Inferential analyses, regression modeling, cluster analysis, and mediation models were applied. Results: Two distinct profiles of parental burnout emerged: one associated with child sleep disturbances and the other with general parenting stress. The strongest-weighted risk factors pertained to maladaptive beliefs and perceptions (e.g., shame, “I am a bad parent”, “My child cries because I do not meet his needs”), as well as additive stressors such as interparental tension and daytime child behavioral problems. The strongest protective factors involved resources that reduced parental demands or facilitated recovery, including couple satisfaction, a consistent bedtime routine, greater capacity to take breaks (e.g., additional caregivers, father nighttime involvement, parental cohabitation, and child screen time). Conclusion: The identification of two distinct burnout profiles highlights the importance of incorporating, or placing more centrally, the management of young children’s insomnia in contemporary theoretical models of parental burnout. This research highlights the need for interventions on healthy self-beliefs and perceptions, effective daytime parenting strategies, positive couple dynamics, consistency in bedtime routines, and equitable distribution of caregiving responsibilities between parents to reduce the risk of parental burnout. Full article

Smart cities require effective disaster management (like flooding, solar storms, sandstorms, or hurricanes), as it directly impacts people’s lives. The key challenges of disaster management are timely detection and effective notification during the crisis. This research presents a smart multi-layer framework for notification classification and management before and during flooding disasters. The framework includes an early detection module as the main phase in the alerting process. This step depends on an Ensemble Learning (EL) model based on a triad of the three best selected models (Deep Learning (DL), Random Forest (RF), and K-nearest Neighbor (KNN)) to analyze data collected continuously from the Internet of Things (IoT) layer. In the boosting phase, the framework utilizes Large Language Models (LLMs) with DL to analyze social textual crowdsourcing data. The results will enable the framework to identify the most affected areas during a flood. The framework adds a fog computing layer alongside a cloud layer to enable instantaneous processing of user responses and generate specialized alerts based on contextual factors such as location, time, risk level, alert type, and user characteristics. Through testing and implementation, the proposed algorithms demonstrated an accuracy rate of over 98% in detecting threats using a dataset of real, collected weather and flooding data. Additionally, the framework proposes a centralized control panel and a design of a smartphone application that offers essential services and facilitates communication among managed civil defense teams, citizens, and volunteers. Full article

Precisely identifying the key drivers of regional carbon emissions and their spatiotemporal heterogeneity is critical for formulating differentiated strategies under China’s “Dual Carbon” goals. To address the limitations of traditional models in variable screening and handling non-stationarity, this study constructs an analytical framework that integrates a Random Forest (RF) model for preliminary variable screening, Geographically and Temporally Weighted Regression (GTWR) for spatiotemporal quantification, and the CRITIC method for multidimensional evaluation. Based on panel data from 30 Chinese provinces spanning 2005 to 2023, this study investigates the spatiotemporal evolution of carbon emission drivers. The findings reveal significant regional disparities. In the eastern region, the emission-increasing effect driven by population continues to intensify. Although economic growth shows signs of decoupling from emissions, the emission reduction benefits of industrial upgrading are diminishing. Notably, provinces such as Jiangsu have even experienced a rebound in energy consumption, which is potentially linked to the expansion of digital infrastructure. In the central region, a “pollution haven” effect has emerged due to the relocation of energy-intensive industries. Furthermore, the impacts of population, urbanization, and energy consumption structure exhibit an inverted U-shaped trend, with green urbanization beginning to yield initial emission reductions. In the western region, the suppressive effect of energy intensity on emissions continues to strengthen, particularly around Shaanxi. For northern energy-rich areas, economic growth acts as a prominent driver, while the impact of population displays a clear “positive in the south, negative in the north” spatial pattern. Moreover, northern provinces have successfully leveraged agglomeration effects to achieve emission reductions. Ultimately, these findings provide robust empirical support for constructing a spatially differentiated governance system to facilitate carbon neutrality. Full article

The state budget system in Vietnam functions within a cohesive structure that allocates financial resources between central and local governments; nevertheless, substantial disparities in socioeconomic conditions among provinces have resulted in increasing discrepancies in local budget revenue. This study, therefore, examines the impacts of fiscal decentralization policy, land utilization, urbanization, provincial competitiveness index, and human capital on local government revenue. The analysis utilizes quantitative panel-data techniques on a dataset encompassing all 63 Vietnamese provinces and municipalities from 2017 to 2022, totaling 378 observations. Econometric estimation employs pooled ordinary least squares, fixed-effects, random-effects, and viable generalized least squares models, along with diagnostic and robustness checks to mitigate unobserved heterogeneity and error dependence. The findings demonstrate statistically significant correlations between local budget revenue and five studied determinants. However, fiscal decentralization policy exerts the most significant influence on the revenue of the local government budget. The results suggest that enhancing municipal fiscal performance needs more than merely modifying revenue-sharing ratios, with significant ramifications. Full article

Background/Objectives: Advanced glycation end products (AGEs) and oxidative stress increase with aging and are implicated in Alzheimer’s disease (AD). We developed an anti-glycation blend using LC-MS-based screening and assessed its effects on oxidative and glycation-related biomarkers in humans. Methods: Twelve candidate compounds were screened in a BSA–glucose model using LC-MS peptide mapping to quantify lysine glycation and rank inhibitory activity. The top candidates were combined into a three-compound blend (quercetin, rutin, genistein). In a randomized, double-blind, placebo-controlled 3-month trial, older healthy adults (n = 30) and individuals with AD (n = 30) received anti-AGE blend (n = 15 in older group and n = 15 in AD group) or placebo (n = 15 in older group and n = 15 in AD group). Serum malondialdehyde and urinary Nε-(carboxymethyl)lysine were measured pre–post intervention. Pre/post and between-arm comparisons within each population were performed using REML ANOVA with Tukey post hoc tests. Serum MDA (malondialdehyde) and urinary CML (Nε-(carboxymethyl)lysine) were prespecified biomarker outcomes and are reported here as co-primary biomarker endpoints. No formal a priori sample size calculation was performed; the study size was feasibility-based. Results: LC-MS screening identified genistein, quercetin, and rutin as the most consistent inhibitors of glucose-driven BSA glycation. In older healthy adults, serum MDA decreased after anti-AGE supplementation (p < 0.001) and differed from the placebo (p < 0.01), while no change was observed within the placebo group (ns). In the AD cohort, MDA did not change significantly from baseline within either arm (ns), but post-intervention MDA was lower in anti-AGE than in the placebo (p < 0.05). Urinary CML was unchanged in older healthy adults (ns in both arms), whereas in AD, it decreased after anti-AGE supplementation (p < 0.01) and differed from the placebo (p < 0.05). Conclusions: A screening-guided anti-glycation blend supplementation was associated with changes in selected biomarkers in humans: MDA decreased across cohorts, while CML decreased selectively in AD. Larger trials with extended biomarker panels and LC–MS/MS confirmation are warranted. Full article

This paper examines the relationship between corporate carbon-reduction pledges and the subsequent adoption of renewable energy by pledging firms, and whether this relationship depends on the institutional conditions in which they operate. We propose a pressure-capacity model, highlighting two different institutional dimensions, (1) environmental policy stringency (institutional pressure) and (2) renewable energy infrastructure (institutional capacity), that may shape when firms’ symbolic pledges lead to observable change in their energy procurement behavior. We estimate random-effects logistic regression models with panel data on 552 publicly listed firms in South Korea, China, and Japan from 2002 to 2017. We find that the relationship between carbon-reduction pledges and renewable energy adoption is strengthened by both the stringency of environmental policy and the availability of renewable energy infrastructure. The marginal effects analysis indicates that the pledge effect is close to zero when institutional capacity is low. However, it increases to about 13 percentage points when policy stringency is high and 9 percentage points when renewable supply is high. The country-specific subsample analysis further uncovers that the conditional effect of institutional capacity is particularly pronounced among Japanese companies. The analysis of correlated random effects shows that these patterns remain robust even after controlling for between-firm confounding. Overall, our findings indicate that the extent to which voluntary corporate climate change commitments translate into actual green implementation depends on the regulatory and infrastructural environment in which firms operate. Full article

This paper proposes a Bayesian estimation method for spatial lagged panel quantile models. The proposed model simultaneously considers spatial lag effects of the dependent variable and the quantile regression framework, enabling effective capture of spatial dependence and conditional distribution heterogeneity. The research constructs a Bayesian estimation framework based on the asymmetric Laplace distribution by decomposing the random disturbance term into a combination of normal and exponential distributions, successfully developing a probabilistic model with both thick tail robustness and computational efficiency. On this basis, the study derives the full conditional posterior probability distributions of model parameters and designs a hybrid Markov Chain Monte Carlo (MCMC) sampling algorithm integrating Gibbs sampling and Metropolis–Hastings algorithm for parameter estimation. Numerical simulation experiments demonstrate that, compared with traditional estimation methods, the proposed Bayesian estimation approach exhibits superior estimation accuracy and robustness across different quantiles, with particularly pronounced advantages in small sample and heavy-tailed distribution scenarios. This methodology provides a more reliable theoretical tool for analyzing panel data with spatial dependencies. This method can not only accurately quantify the spatial spillover effect, but also identify the different effects of the same influencing factor at different emission levels, which provides a strong methodological support for formulating differentiated and precise emission reduction policies. Full article

Problem behaviors among rural adolescents remain a significant public health concern, yet the temporal roles of key psychosocial resources are not well understood. Grounded in Conservation of Resources theory and Problem Behavior Theory, this study examined the longitudinal associations between psychological capital, perceived social support, and problem behaviors among rural Chinese adolescents. A three-wave, one-year longitudinal design was conducted with 770 adolescents (49.86% male, Mage = 16.36, SD = 1.57). Random-intercept cross-lagged panel models were applied to disentangle stable between-person differences from within-person processes. At the between-person level, adolescents with higher overall psychological capital and perceived social support reported lower levels of problem behavior. At the within-person level, psychological capital showed a time-specific protective effect, with short-term increases predicting subsequent reductions in problem behavior, whereas problem behavior did not predict later psychological capital. In contrast, perceived social support demonstrated reciprocal associations with problem behavior: higher support predicted later decreases in problem behavior, while elevated problem behavior predicted subsequent declines in perceived support. These findings indicate that psychological capital and perceived social support operate through distinct temporal mechanisms and highlight the importance of early internal resource development and sustained relational support in rural adolescent populations. Full article

This study investigates how environmental, social, and governance (ESG) performance influences firm-level financial outcomes using a panel of approximately 24,500 firm-year observations from 2015 to 2024, based on Refinitiv ESG scores across 12 industries and multiple European countries. To capture institutional heterogeneity, the analysis separates Nordic and non-Nordic firms and applies fixed-effects models for the latter and random-effects models for the former, as supported by Hausman diagnostics. The results reveal that ESG performance is positively associated with firm value, while its effects on short-run accounting returns differ across regions. Specifically, ESG scores are associated with a negative and statistically significant impact on ROA and ROE in the non-Nordic subsample, suggesting transitional adjustment costs and delayed financial realization. For financing outcomes, the study shows that ESG engagement reduces the Weighted Average Cost of Capital (WACC) in both samples, though mechanisms differ. In Nordic markets, a 10-point increase in ESG score corresponds to an estimated 4.2-basis-point reduction in WACC, reflecting the benefits of mature disclosure systems. In contrast, governance emerges as the only ESG pillar capable of reducing financing costs in non-Nordic countries. These region-specific patterns confirm that institutional maturity and investor orientation shape the financial materiality of ESG practices. The novelty of this study lies in jointly modeling (i) positive valuation effects, (ii) negative short-run profitability adjustments, and (iii) financing-cost reductions within a unified ESG framework while explicitly distinguishing governance regimes across Europe. The findings offer new evidence on how disclosure quality and governance structures moderate ESG’s economic impact and suggest that strengthening governance transparency can help firms in less mature ESG environments realize capital-cost advantages. Full article

This article examines the statistical relationships between ENUS, defined as per capita energy use, and Environmental, Social, and Governance variables, with particular emphasis on the Environmental dimension and its connections with national energy systems. The study investigates whether systematic associations exist between ESG indicators and the cross-country and temporal variation in ENUS as per capita energy use, and to what extent machine learning methods can contribute to the description and interpretation of these relationships in comparison with panel econometric models. The analysis is based on a large World Bank dataset covering approximately 161 countries over the period 2004–2023 and follows a three-step methodological strategy. First, fixed-effects, random-effects, and Weighted Least Squares panel models are estimated to explore the statistical associations between a broad set of ESG variables and ENUS as per capita energy use, while controlling for unobserved country-level heterogeneity. Second, clustering techniques are applied to identify groups of countries with similar joint patterns in multidimensional variables related to energy systems, emissions, climate conditions, and natural resource use. Third, several machine learning models are implemented, with particular attention to the performance of the K-Nearest Neighbors algorithm evaluated through normalized measures of predictive accuracy and goodness of fit. Model interpretability is enhanced using dropout loss and additive explanation methods to assess the contribution of ESG variables to the prediction of ENUS as per capita energy use. Overall, the results reveal a rich and multidimensional structure of relationships between ESG indicators and ENUS expressed as per capita energy use. In particular, the evidence indicates a close association between ENUS and key environmental variables such as emissions intensity, energy intensity as a control variable, and the use of natural resources, together with Social and Governance factors related to development, institutional quality, and economic structure. These findings suggest that cross-country differences in ENUS as per capita energy use correspond to distinct environmental, social, and governance profiles within the ESG framework. Full article

This study examines the determinants of renewable energy consumption among BRICS countries (Brazil, Russia, India, China, South Africa, Saudi Arabia, Egypt, the United Arab Emirates, Ethiopia, Iran, and Indonesia) between 2000 and 2022. Using static (Fixed and Random Effects) and dynamic (First-Difference GMM) panel data models, the research investigates how economic, institutional, and social factors influence renewable energy transition. The results reveal structural heterogeneity within the bloc. Among the founding members, renewable energy consumption is positively associated with governance quality and trade openness, while GDP per capita exhibits a negative relationship, consistent with the Environmental Kuznets Curve hypothesis. In contrast, the new members show strong energy dependence and limited institutional capacity, with dynamic models confirming high persistence in energy consumption and weak responsiveness to economic and policy changes. Variables such as education and life expectancy were omitted in the dynamic specification due to limited temporal variation, without compromising model consistency. Diagnostic tests (Hansen, Sargan, and AR(2)) confirm the robustness of the estimates. Overall, the findings highlight the importance of strengthening institutional governance, technological innovation, and intra-bloc cooperation to advance energy transition and achieve sustainable development across the BRICS economies. Full article

This study investigates the nexus between sustainable development indicators and economic growth across seven strategic emerging economies: China, Turkey, Brazil, Malaysia, Iran, Egypt, and Argentina, from the period 2002 to 2023. Utilizing panel data regression analysis, the Random Effects model was identified as the most appropriate estimation method based on rigorous statistical criteria. The empirical results reveal that R&D expenditures, health expenditures, the renewable energy share, and CO₂ emissions exert a positive and significant influence on GDP. In contrast, education expenditures were found to have a negative and statistically insignificant effect on growth. This study emphasizes the necessity of supporting vital sectors, such as agriculture and industry, while simultaneously adopting effective environmental policies to reduce emissions and ensure long-term sustainable development goals in the analyzed countries. Full article