Search Results (1,145)

Against the backdrop of global sustainable development and the fast-growing digital economy, aligning corporate ESG practices with digital transformation is key for enterprises’ high-quality development, yet existing studies have not fully explored ESG’s directional impact on digital transformation. This study examines how corporate ESG performance drives digital transformation and the moderating roles of firm characteristics, industry types, and ownership structures, using 11,109 valid observations from Chinese A-share listed companies (2009–2022); it adopts the causal forest algorithm and supplements with OLS, quantile, and Poisson regressions for robustness tests. The results show that ESG significantly promotes digital transformation—with obvious positive effects from E and S dimensions, while G has no statistical impact—and further analysis reveals nonlinear moderation by firm characteristics and contextual differences: the positive effect is stronger in high-tech and private enterprises but weaker in traditional and state-owned enterprises (due to institutional constraints). These findings offer theoretical insights into ESG–digital synergies and practical guidance for targeted sustainability and digital strategies. Full article

Background: The global rise in atopic diseases, like atopic dermatitis and allergic rhinitis, may be linked to prenatal exposure to endocrine-disrupting chemicals like phthalates, with potential sex-specific effects. Methods: We analyzed 558 mother–child pairs from the PROGRESS birth cohort in Mexico City. Maternal urinary phthalate metabolites were measured during the 2nd and 3rd trimesters. Atopic dermatitis and allergic rhinitis symptoms were assessed at ages 4–6 and 6–8 years using the International Study of Asthma and Allergies in Childhood survey. Weighted Quantile Sum Regression (WQS) was used to assess sex-specific mixture associations. Individual sex-specific phthalate associations were examined using modified Poisson models with inclusion of product terms and stratification. Models were adjusted for maternal age, education, parity, pre-pregnancy body mass index, and prenatal tobacco exposure. Results: We found that child sex modified associations between the 2nd trimester phthalate mixture and current atopic dermatitis symptoms at both 4–6 years (WQS*sex OR: 1.23, 95% CI: 1.00–1.60) and 6–8 years (WQS*sex OR: 1.46, 95% CI: 1.01–2.10). Among males, higher phthalate concentrations were positively associated with symptoms at both ages (OR: 1.10, 95% CI: 0.92, 1.32; OR: 1.16, 95% CI: 0.92, 1.46), while associations were negative in females (OR: 0.87, 95% CI: 0.73, 1.04; OR: 0.79, 95% CI: 0.62, 1.02). No sex-specific associations were found for 3rd trimester exposures. Individual metabolite analyses also showed effect modification by sex for 2nd trimester exposures. Conclusions: Prenatal exposure to phthalates is associated with atopic dermatitis symptoms in childhood in a sex-specific manner. Full article

This study examines the effects of supply chain pressure, smart AI, and socio-economic fairness on long-term economic sustainability. To this end, this study uses quarterly data from 1999 Q1 through 2024 Q4 for the United States and employs the recently introduced Wavelet Cross-Quantile Regression (WCQR) to analyze this relationship. This study finds that smart AI, supply chain pressure (SC), and renewable energy consumption (REC) significantly drive U.S. economic growth, with the strongest short-term effects appearing when adoption and output are in the lower quantiles, reflecting threshold and diffusion dynamics. SC enhances growth once supply chain networks reach a critical level of connectivity, while REC generates substantial gains at low penetration levels, illustrating a “catch-up” effect. In contrast, economic inequality (EI) generally dampens growth, especially at moderate to high inequality levels; however, long-term reductions in EI yield positive returns in high-growth states by improving social cohesion and workforce productivity. Based on these findings, this study proposes funding low-adoption AI now, scaling to mid-adoption users mid-term, and entrenching long-term gains through economy-wide upskilling. Full article

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

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

Exploring the effects and mechanisms of spatial agglomeration of construction land resources on economic resilience across Chinese provinces will provide theoretical support for governments to optimize the allocation of productive forces and enhance economic resilience through rational distribution of construction land quotas. Based on the “Structure-Conduct-Performance (SCP)” analytical framework, this paper identifies spatial agglomeration through the share of the largest city and draws on the microeconomic concept of “elasticity” that reflects the relationships between variables to construct economic resilience with spatial relationship attributes. On this basis, it utilizes China’s provincial panel data gathered since 2000 and employs fixed-effects models, mediation models, moderation models, quantile regression, and subsample regression to examine the impact mechanisms of the spatial agglomeration of construction land on economic resilience. The research finds the following: the spatial agglomeration of construction land has a positive empowering effect on economic resilience; innovation and technical efficiency are important transmission paths for the spatial agglomeration of construction land to empower economic resilience; and further research shows that the empowering effect has an inverted U-shaped process, with the promoting effect being predominant. The empowering effect increases with rising quantiles and exhibits regional heterogeneity, showing an ascending gradient from eastern to western regions. The basic law in the western region is consistent with that of the whole country, and the scale of provincial construction land will strengthen the empowering effect. The research findings can provide decision-making references for the implementation and deepening of the main functional area strategy, as well as for strengthening the concentrated allocation of construction land quotas to advantageous regions. 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

This study investigates the nonlinear, regime-dependent, and frequency-specific interdependencies between rare earth element (REE) markets and key global critical sectors, including artificial intelligence, semiconductors, clean energy, defense, and advanced manufacturing, under varying levels of geopolitical and financial uncertainty. The main objective is to assess how REE markets transmit and absorb systemic risks across these critical domains. Using a mixed-methods approach combining Quantile-on-Quantile Regression (QQR), Continuous Wavelet Transform (CWT), and Wavelet Transform Coherence (WTC), we examine the dynamic connections between two REE proxies, SOLLIT (Solactive Rare Earth Elements Total Return) and MVREMXTR (MVIS Global Rare Earth Metals Total Return), and major sectoral indices based on a dataset of daily observations from 2018 to 2025. Our results reveal strong evidence of asymmetric, regime-specific risk transmission, with REE markets acting as systemic amplifiers during periods of extreme uncertainty and as sensitive receptors under moderate or localized geopolitical stress. High co-volatility and persistent low-frequency coherence with critical sectors, especially defense, technology, and clean energy, indicate deeply embedded structural linkages and a heightened potential for cross-sectoral contagion. These findings confirm the systemic relevance of REEs and underscore the importance of integrating critical resource exposure into global supply chain risk strategies, sector-specific stress testing, and national security frameworks. This study offers relevant insights for policymakers, risk managers, and institutional investors aiming to anticipate disruptions and strengthen resilience in critical industries. 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

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

This study investigates the asymmetric responses of AI and ESG Exchange Traded Funds (ETFs) to geopolitical and financial uncertainty, with a focus on resilience across market regimes. The NASDAQ-100 and MSCI ESG Leaders indices are used as proxies for thematic ETFs, and their dynamic interlinkages are examined in relation to volatility indicators (VIX, GPR), alternative assets (Bitcoin, Ethereum, gold, oil, natural gas), and safe-haven currencies (CHF, JPY). A daily dataset spanning the 2016–2025 period is analyzed using Quantile-on-Quantile Regression (QQR) and Wavelet Coherence (WCO), enabling a granular assessment of nonlinear, regime-dependent behaviors across quantiles. Results reveal that ESG ETFs demonstrate stronger downside resilience under extreme uncertainty, maintaining stability even during periods of elevated geopolitical and financial risk. In contrast, AI-themed ETFs tend to outperform under moderate-risk conditions but exhibit greater vulnerability during systemic stress, reflecting differences in asset composition and investor risk perception. The findings contribute to the literature on ETF resilience and cross-asset contagion by highlighting differential behavior patterns under varying uncertainty regimes. Practical implications emerge for investors and policymakers seeking to enhance portfolio robustness through thematic diversification during market turbulence. 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

Estimating skewness and kurtosis from real-world data remains a long-standing challenge in actuarial science and financial risk management, where these higher-order moments are critical for capturing asymmetry and tail risk. Traditional moment-based estimators are known to be highly sensitive to outliers and often fail when the assumption of normality is violated. Despite numerous extensions—from robust moment-based methods to quantile-based measures—being proposed over the decades, no universally satisfactory solution has been reported, and many existing methods exhibit limited effectiveness, particularly under challenging distributional shapes. In this paper we propose a novel method that jointly estimates skewness and kurtosis based on a regression adaptation of the Cornish–Fisher expansion. By modeling the empirical quantiles as a cubic polynomial of the standard normal variable, the proposed approach produces a reliable and efficient estimator that better captures distributional shape without strong parametric assumptions. Our comprehensive simulation studies show that the proposed method performs much better than existing estimators across a wide range of distributions, especially when the data are skewed or heavy-tailed, as is typical in actuarial and financial applications. Full article

In many practical applications, data collected over time often exhibit autocorrelation, which, if unaccounted for, can lead to biased or misleading statistical inferences. To address this issue, we propose a varying-coefficient additive model for density-valued responses, incorporating a functional auto-regressive (FAR) error process to capture serial dependence. Our estimation procedure consists of three main steps, utilizing spline-based methods after mapping density functions into a linear space via the log-quantile density transformation. First, we obtain initial estimates of the bivariate varying-coefficient functions using a B-spline series approximation. Second, we estimate the error process from the residuals using spline smoothing techniques. Finally, we refine the estimates of the additive components by adjusting for the estimated error process. We establish theoretical properties of the proposed method, including convergence rates and asymptotic behavior. The effectiveness of our approach is further demonstrated through simulation studies and applications to real-world data. Full article