Search Results (5,255)

The natural regeneration of planted forests in karst landscapes is severely constrained by extreme substrate heterogeneity and fragile edaphic conditions. However, the relative importance and interaction pathways of environmental versus stand structural drivers remain poorly quantified. In this study, 54 plots (10 m × 10 m) were surveyed across Cupressus funebris plantations in the karst landscape of the Lijiang River Basin, southern China. To identify the key factors and causal pathways governing regeneration, redundancy analysis (RDA), variation partitioning, partial least squares structural equation modeling (PLS-SEM), and threshold analyses were applied. Regeneration exhibited pronounced spatial heterogeneity, with 42.6% of plots showing complete recruitment failure and a characteristic inverted J-shaped size class distribution. The analysis identified soil rock fragment content (as a negative constraint) and canopy gap area (as a positive driver) as the two dominant predictors. PLS-SEM revealed that environmental factors influence regeneration primarily through an indirect pathway mediated by stand structure ($R^2=0.683$) rather than through direct effects. Threshold analyses identified quantitative benchmarks for key drivers, including a gap area breakpoint of approximately 10 ${\mathrm{m}}^2$ and a presence–absence effect of soil rock fragments. These findings contribute to a more sophisticated mechanistic understanding of forest regeneration in karst ecosystems and provide an empirical foundation for silvicultural management that aims to encourage natural regeneration and ecological restoration of degraded karst plantations. Full article

The Loess Plateau has experienced persistent vegetation greening over the past two decades, yet this recovery has occurred under a concurrent intensification of atmospheric evaporative demand and drying. This raises a key land–atmosphere question: which hydroclimatic processes most strongly constrain greening, and where do vegetation responses shift across environmental regimes? To address this issue, we integrated spatiotemporal trend analysis, Geographical Convergent Cross Mapping (GCCM)-based directional attribution, and an interpretable machine-learning framework combining Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) to diagnose the dominant controls and threshold-like response patterns of vegetation activity. Using 1 km kernel Normalized Difference Vegetation Index (kNDVI) and eight hydroclimatic variables during 2000–2019, we found that regionally averaged kNDVI increased from 0.099 in 2000 to 0.164 in 2019, with a significant trend of 0.003 year⁻¹, and greening trends covered 65.503% of the Loess Plateau. Over the same period, Vapor Pressure Deficit (VPD) increased from 0.142 to 0.275 kPa (+0.133 kPa), indicating that vegetation recovery did not occur under a more humid atmospheric background. GCCM results consistently showed stronger directional influence from hydroclimatic drivers to kNDVI than the reverse, with evaporation and thermal conditions, especially Tmin, emerging as the dominant constraints, followed by Tmax, VPD, and wind speed, whereas precipitation showed comparatively weaker recoverable influence. The tuned XGBoost model achieved strong out-of-sample performance (R² = 0.9611, RMSE = 0.0188, MAE = 0.0131), and SHAP revealed clear nonlinear thresholds: evaporation and Tmin shifted into persistently positive contribution regimes beyond 302 mm and −17.6 °C, respectively; Tmax became predominantly inhibitory beyond −1.9 °C, and Palmer Drought Severity Index (PDSI) exhibited a multi-stage non-monotonic transition around −0.7. These results provide a coherent evidence chain linking directional influence, relative contribution, and threshold boundaries, offering quantitative support for identifying climate-sensitive zones and restoration risk regimes under continued warming and rising atmospheric dryness. Full article

This study investigates the dynamic and causal effects of climate stress and Artificial Intelligence-enabled agricultural monitoring on cultivated land quality, productivity, and sustainability in Saudi Arabia. Using a balanced panel of region–crop observations covering 13 administrative regions and six major crops over the period 2010–2024, the analysis integrates high-resolution climate variables with remote sensing-based indicators, including the Normalized Difference Vegetation Index, Enhanced Vegetation Index, Net Primary Productivity, Water-Use Efficiency, and crop water productivity. A comprehensive econometric framework combining the System Generalized Method of Moments, Difference-in-Differences, and event-study approaches is employed to address persistence, endogeneity, and causal identification. The results show that water availability—captured by soil moisture and precipitation—significantly enhances cultivated land outcomes (coefficients ≈ 0.05–0.11), while heat stress and wind speed exert strong negative effects (coefficients ≈ −0.04 to −0.12), highlighting the vulnerability of arid agricultural systems. Artificial Intelligence-enabled monitoring and smart irrigation adoption consistently improve land quality and productivity, with the largest gains observed in water-use efficiency and crop water productivity. Artificial Intelligence adoption increases water-use efficiency and crop water productivity by approximately 8–10%, while heat stress reduces vegetation indicators by about 9–12%. Event-study evidence confirms that these effects emerge after adoption and persist over time, supporting a causal interpretation. Overall, the findings demonstrate that AI technologies mitigate climate stress primarily through improved water management and adaptive decision-making. The study provides policy-relevant insights aligned with Saudi Vision 2030, emphasizing digital agriculture as a key instrument for sustainable cultivated land governance, climate adaptation, and food security in water-scarce environments. Full article

Observational evidence links gut microbiota (GM) dysbiosis to HIV infection; however, the causal relationship between them has not been established. Mendelian randomization (MR) and 16S rRNA gene sequencing analyses were performed to identify gut microbial taxa associated with HIV infection risk. MR analysis results identified 18 gut microbial taxa associated with HIV infection (p values < 0.05), of which 16 taxa were detected in the 16S rRNA gene sequencing data. Following the exclusion of seven taxa with low relative abundance, eight taxa with potential relationships with HIV infection were detected in the 16S rRNA gene sequencing data. Four taxa (Clostridia class, Erysipelotrichales order, Paraprevotella genus, and Parabacteroides distasonis species) showed negative associations and four others (Proteobacteria phylum, Coriobacteriaceae family, Subdoligranulum genus, and Bacteroides ovatus species) showed positive associations with HIV infection risk. The eight taxa effectively distinguished between healthy controls (HCs) and people with HIV (PWH) (p values < 0.05). The area under the curve (AUC) values for the ROC curve analysis ranged from 0.62 to 0.87 for differentiating the HC and PWH groups. Furthermore, the effect of Ruminococcus callidus on HIV infection was partially mediated by hypoxanthine, exhibiting a mediated effect β of 0.17 (p = 0.042). These findings highlight the important role of the GM in HIV infection risk, facilitating future studies exploring better GM regulation strategies against HIV infection risk. Full article

Reconciling economic growth with environmental protection continues to represent a central global challenge. As one of the world’s largest developing economies, China has advanced an ecological civilization strategy that offers a unique opportunity to evaluate how national policy can shape sustainable development trajectories. This study assesses whether China’s ecological civilization construction enhances urban green total factor productivity (GTFP). Using panel data for 283 Chinese cities (2006–2019), this study identifies ecological civilization pilot cities through a standardized and reproducible protocol, measures urban GTFP using the Global Malmquist–Luenberger (GML) index and estimates policy effects with a multi-period difference-in-differences (DID) design that accounts for staggered implementation and overlapping policies. The results indicate that urban GTFP exhibited an overall upward but fluctuating trend during the study period, with regional growth rates ranking East > Central > West and a tendency toward convergence in recent years. The analysis further indicates that national ecological civilization construction policies exert a statistically significant and positive effect on urban GTFP, with the findings remaining robust to parallel trend tests and multiple robustness checks. The promotion effect displays marked regional heterogeneity, being strongest in western cities, followed by eastern and central regions, and remains positive across different urban contexts, including resource-based and non-resource-based cities as well as cities within and outside the Yangtze River Economic Belt. Mechanism analysis further reveals that the policy effect operates primarily through industrial upgrading and green technological innovation, whereas the industrial structure rationalization channel is not statistically significant. Overall, this study provides a transparent and reproducible framework for pilot city identification and causal evaluation, offering policy-relevant insights for differentiated and region-specific ecological governance aimed at balanced regional development, industrial upgrading, and green technological innovation. Full article

The transformation of the labor market driven by digital technology has profoundly affected workers’ income. Based on data from the China Family Panel Studies (CFPS) 2014–2022 and the China Labor-force Dynamic Survey (CLDS) 2012–2018, this paper systematically examines the causal effects of standard employment, traditional non-standard employment (labor dispatch), and new non-standard employment (non-contract employment) on income within a unified framework. This study adopts a progressive identification strategy combining the two-way fixed-effects model, individual fixed-effects model, and event study methodology. The findings are as follows: First, new non-standard employment exhibits a significant “income penalty” effect, with its wage level being 14–15% lower than that of standard employment. This effect remains robust after controlling for individual heterogeneity. Second, dynamic analysis shows that transitioning from standard employment to new non-standard employment leads to sustained income loss, with a decline of nearly 10.8% after four years. Third, mechanism testing reveals that workers increase part-time work to compensate for income loss, but job satisfaction significantly declines, leading to a dual dilemma of “exchanging time for income” and “welfare discount.” Fourth, heterogeneity analysis shows that less educated and rural workers suffer greater shocks. The study concludes that new non-standard employment has inherent income suppression characteristics, and its effects are persistent and heterogeneous. It calls for the improvement of a labor rights protection system that adapts to new forms of employment, as well as the implementation of targeted support policies for vulnerable groups, in order to build a more equitable and secure labor market. Full article

Energy security has become a strategic priority for ensuring sustainable economic development, particularly for European Union (EU) countries characterized by high external energy dependence. This study investigates the key drivers of energy security risks in selected EU countries over the period 1995–2018, focusing on economic growth, tourism expenditures, technological innovation, renewable energy consumption, and urbanization. The empirical analysis employs panel vector autoregression and a panel error correction model to examine short- and long-run causal dynamics, while the augmented mean group estimator captures cross-country heterogeneity. The findings indicate that economic growth is the primary short-run determinant of energy security risk, whereas all variables exert significant long-run effects. Country-level results reveal common patterns for growth, renewable energy consumption, and urbanization, but heterogeneous impacts for tourism and technological innovation. These results suggest that strengthening renewable energy adoption, promoting innovation, and supporting sustainable urban development can enhance long-term energy resilience. Overall, this study provides policy-relevant insights for designing sustainability-oriented energy strategies aligned with the European Union’s climate transition goals. Full article

To improve the punctuality of flight schedules, causal inference methods are introduced to model the potential causal structure and intervention effects among ground support operations of flights. The effectiveness of these methods in improving flight punctuality is verified under experimental conditions. When the causal relationship of Flight Ground Support (FGS) is determined, the research initiates from the perspective of FGS. A time-constrained strategy based on the Q-learning causal optimal strategy algorithm is proposed to transform causal effects into causal strategies. Initially, the influencing factors of FGS operations are classified into intervention groups. The causal effects of these influencing factors on their target support operations are calculated, and the influence degrees of the causes on the results within the causal relationship are investigated. Subsequently, the time constraint of the FGS process is characterized as a Markov decision process. The experimental results indicate that, compared with the traditional probability strategy, the causal strategy that considers the causal relationship enables over 51% of the flight plans to depart on time, with an average increase of 2.79%. The proposed method is not restricted to a specific airport or a single ground handling process configuration. Under the condition that ground handling operations are observable and sufficient historical operational data are available, it provides an interpretable optimization framework for time-constraint decision-making in flight ground handling operations across airports of different scales. Full article

In the European Union, decarbonization has progressed unevenly across sectors and member states. This study examines sectoral CO₂ trajectories in the EU-27 during 2000–2022 using a harmonized annual panel built primarily from the European Commission’s Energy Statistical Country Datasheets and complemented with EDGAR/JRC sectoral emissions data. The empirical strategy combines descriptive analysis with OLS, fixed-effects, log-linear, and exploratory difference-in-differences specifications to assess conditional associations among per capita CO₂ emissions, the renewable energy share, GDP per capita, and the carbon price. EU-wide CO₂ emissions declined by 26.4% over the study period, with the largest contraction in the energy sector, while transport emissions remained comparatively stable. Across specifications, renewable energy share is consistently associated with lower emissions, although its magnitude weakens after controlling for time-invariant country heterogeneity. Carbon price is negatively associated with emissions in the baseline and log-linear models. In contrast, the exploratory DiD interaction is not statistically informative in the main treatment specification and yields negligible effect sizes in regional split models. The sign reversal in GDP between the pooled and within-country models indicates that cross-country differences and within-country dynamics should not be treated as equivalent. Overall, the findings support a heterogeneous and multi-speed decarbonization pattern and suggest that carbon pricing is better understood as part of a broader policy mix rather than as a stand-alone causal driver. Full article

Low Earth Orbit (LEO) satellite signals of opportunity (SOP) provide a viable positioning alternative in GNSS (Global Navigation Satellite System)-denied environments, yet their accuracy is fundamentally constrained by the low-quality orbital data typically available, such as SGP4 (Simplified General Perturbations model 4) predictions derived from Two-Line Elements (TLEs). To address this limitation, this paper proposes a dynamics-informed non-causal deep learning framework that enhances low-quality orbital data into high-fidelity trajectories for accurate SOP positioning. The proposed Non-Causal Dynamics-Informed Representation Temporal Convolutional Network (Non-Causal DIR-TCN) integrates phase space reconstruction and a Temporal Convolutional Network to explicitly model the chaotic dynamics inherent in LEO orbits, while relaxing the causality constraints of standard temporal convolutions to utilize both past and future context from the available SGP4 stream. Experimental results demonstrate that the framework significantly reduces orbit estimation errors and accelerates model convergence. When applied to LEO-SOP positioning, it achieves approximately 20% improvement in 2D positioning accuracy compared to conventional SGP4-based methods. This work effectively bridges the gap between accessible low-precision orbital data and high-accuracy state estimation, advancing the practical deployment of opportunistic signals for resilient positioning in challenging environments. Full article

China’s Cultural Ecological Protection Zones (CEPZs) are a distinctive policy instrument intended to safeguard intangible cultural heritage through the “integrated” protection of cultural practices and their supporting socioecological environments. However, there remains limited robust causal evidence on whether CEPZs generate measurable ecological co-benefits and whether such benefits come with landscape-structure trade-offs. Using a county-level panel covering 454 counties from 2006 to 2023, we evaluate CEPZs’ impacts on ecosystem quality and landscape patterns through a multi-period DID design with a rich set of socio-environmental controls. Ecosystem quality is proxied by a satellite-derived NDVI, NDWI, and NPP, while landscape pattern outcomes are captured by a composite landscape pattern connectivity index (LPCI) derived from multi-metric landscape configuration indicators. We further test mechanisms using mediators constructed from Morphological Spatial Pattern Analysis (MSPA) (core habitat proportion) and built-up land proportion, and examine heterogeneity by local governance capacity and region. The results show that CEPZ designation significantly increases the NDVI (β = 0.002, p < 0.01) but significantly reduces the LPCI (β = −0.004, p < 0.01). The average effects on the NDWI and NPP are statistically insignificant. Mechanism tests reveal two countervailing pathways: CEPZs increase the share of core habitat (β = 0.009, p < 0.01), which is positively associated with the NDVI, while simultaneously expanding built-up land (β = 0.012, p < 0.01), which offsets greening and drives fragmentation. Heterogeneity analyses suggest that ecological gains are amplified where independent CEPZ management agencies exist and are stronger in western/central China. These findings provide causal evidence that biocultural governance can yield “greening” co-benefits but may undermine landscape integrity unless development pressures are spatially regulated and local institutional capacity is strengthened. Full article

This study examined the ‘too central to fail’ concept in the South African equity sector. We employed the Granger causality framework and PageRank algorithm to generate the centrality scores of the sectors on the Johannesburg Stock Exchange under extreme market conditions. Using the realized volatilities of sectoral returns for the full sample period (3 January 2006–31 December 2021), as well as during the global financial crisis (GFC), European debt crisis (EDC), COVID-19 pandemic, and US–China trade war sub-periods, we analyzed the sectors’ interconnections and calculated each sector’s centrality score across the entire sample and under different extreme market conditions. This allowed us to rank sectors relative to their centrality scores. The results indicate that, in the full sample, the insurance sector has the highest PageRank centrality score, suggesting it is too central to fail. This implies that the insurance sector acts as a systemic receiver of risks and provides stability within the network of sectors. However, the sub-period analyses reveal that General Industrial and Automobiles emerged as the key sectors with the highest PageRank centrality scores, and shocks from other sectors can disproportionately affect these industries during crisis periods. Underperformance in these sectors could have destabilizing effects on the South African economy. The findings have significant implications for regulators and policymakers, portfolio and fund managers, local and international investors, and researchers in the field of finance. Full article

Background: Posterior spinal fusion (PSF) for adolescent idiopathic scoliosis causes significant postoperative pain and high opioid requirements. Methadone, with dual μ- and κ-opioid agonism and NMDA antagonism, provides long-acting analgesia and may reduce perioperative opioid use. This study evaluated whether perioperative low-dose methadone (0.1 mg/kg) improves postoperative pain and opioid outcomes after pediatric PSF. Methods: In this single-center retrospective cohort study (January 2019–June 2023), pediatric patients <23 years old undergoing PSF were categorized by perioperative methadone exposure (intraoperative and/or postoperative) versus no methadone. The primary outcome was total postoperative opioid consumption (morphine milligram equivalents per kilogram, MME/kg) over postoperative days (POD) 0–3. Secondary outcomes were average daily pain scores and hospital length of stay (LOS). Inverse probability weighting (IPW) adjusted for age, sex, and protocol period. Results: A total of 339 patients (51% no methadone, 49% methadone; mean age 14.6 ± 2.5 years; 76% female) were analyzed. Methadone patients had longer anesthesia (392 vs. 372 min, p = 0.042) and surgery times (287 vs. 266 min, p = 0.01). IPW-adjusted associations show postoperative opioid use was significantly higher in the methadone group on POD 0 (median 2.5 vs. 2.1 MME/kg in no methadone group; p = 0.005). No significant differences were found in postoperative average pain scores (e.g., mean NRS: 2.3 vs. 2.5 on POD 0, p = 0.12) and LOS (3.3 vs. 3.1 days, p = 0.38) between methadone group and no methadone group. Discussion: Perioperative methadone provided similar analgesia for pain management and recovery without prolonging hospitalization, despite higher early opioid use on POD 0. Retrospective design limits causal inference, and residual confounding may persist despite propensity score-based adjustments. Further prospective trials are required to establish safety and dosing. Conclusions: In this retrospective cohort, perioperative low-dose methadone was associated with higher early postoperative opioid use but no significant differences in pain scores or length of stay compared with standard regimens. Methadone did not demonstrate an opioid-sparing effect in this real-world setting. Prospective studies are needed to better define its role and safety in pediatric posterior spinal fusion. Full article

Rural revitalization has emerged as a core agenda in the global pursuit of sustainable development, with its success fundamentally hinging on enhancing the resilience of rural households to withstand shocks and restore their livelihoods. In contrast to mainstream research that primarily examines whether Medical Financial Assistance (MFA) reduces medical burden, this paper focuses on MFA as ex-post cash compensation and investigates whether and how it affects the sustainable livelihood recovery of low-income rural households following health shocks, thereby providing empirical evidence for understanding the foundational role of health security in rural revitalization. A quasi-natural experiment is constructed by leveraging the institutional feature that MFA eligibility is activated by exogenous health shocks. Using two-wave balanced panel data (2021–2022) from a nationally designated deep poverty-stricken county in Hebei Province, China, the Propensity Score Matching–Difference-in-Differences (PSM-DID) method and mediation models are employed for causal identification and mechanism testing. The findings indicate that (1) MFA significantly promotes household income recovery. It enables recipient households to recover per capita net income by an average of approximately 13.2% (p < 0.01), demonstrating a protective recovery effect, and simultaneously recovers per capita non-farm labor income by an average of approximately 13.8% (p < 0.05), revealing a developmental recovery effect. The latter is partially mediated by the non-farm labor participation rate (mediation ratio 51.7%, Sobel Z = 2.10). This finding validates the “time release effect,” demonstrating that MFA stimulates endogenous dynamics by restoring health capital and releasing labor previously constrained by family care responsibilities. It thereby extends the application of health capital theory from the individual to the household level. (2) Mechanism analysis shows that the protective recovery effect is fully mediated by the amount of MFA received (mediation ratio 326.7%, Sobel Z = 12.85), providing empirical evidence for precautionary saving theory in the context of targeted social assistance and revealing the potential productive attributes of the social safety net. (3) Heterogeneity analysis reveals clear group targeting and shock thresholds. The protective effect is concentrated among elderly households, while the developmental effect is primarily evident in middle-aged households. Both recovery effects manifest significantly only for households experiencing major disease shocks, confirming the theoretical expectation of “conditional effectiveness,” namely that policy effects are systematically moderated by household life-cycle characteristics and the severity of health shocks. This study demonstrates that MFA serves both as a safety net and an empowerment tool, but its effectiveness is highly contingent upon household characteristics and shock severity. By uncovering the foundational mechanisms through which health security contributes to rural household resilience, this study provides empirical evidence from China for building sustainable poverty prevention systems in the global process of rural revitalization. Full article

Accurate time-domain photovoltaic (PV) models are needed to evaluate performance under outdoor variability beyond STC datasheet conditions. This paper presents a traceable modeling workflow based on the standard single-diode formulation, implemented in MATLAB/Simulink (R2023a) as a modular white-box architecture that explicitly resolves photocurrent generation and loss mechanisms (diode recombination, shunt leakage, and series resistance effects) with temperature-consistent propagation through V_T(T) and saturation-current terms. The method couples optical boundary conditions to the electrical model by embedding plane-of-array (POA) excitation via the incidence angle Θ(t) and roof albedo directly into the photocurrent source term, preserving the causal chain from mounting geometry to electrical response. Calibration is separated from prediction by initializing key parameters using the standard Simulink PV block and then freezing them for time-domain evaluation. The workflow is validated on a 395 W rooftop prototype using 1 min resolved POA irradiance (ISO 9060:2018 Class A radiometric chain) and module temperature (IEC 60751 Class A Pt100), synchronized with electrical measurements. Over a multi-week campaign, the model exhibits high fidelity, with a worst-case relative current error of ~1.1% and a consistently low bias and dispersion, quantified by ME, MAE, RMSE, σ_e, and thresholded MAPE. Full article