Search Results (1,980)

Chronic obstructive pulmonary disease (COPD) is strongly associated with the inhalation of harmful particulate matter in ambient air. This study examined 786,290 COPD-related hospital admissions among patients aged 45–64 in Istanbul from 2013 to 2015, using a Generalized Linear Model (GLM) with meteorological variables included as covariates and air pollutant effects evaluated across lag days 0–9. Daily mean concentrations of PM₁₀, PM_2.5, and NO₂ were used as air pollution indicators, while average temperature and relative humidity were considered as meteorological variables. Relative risk (RR) and excess relative risk (ERR) estimates were calculated for a 10 μg/m³ increase in pollutant concentrations across the lag period. Significant associations were found between air pollution and COPD-related hospital admissions in overall analyses as well as seasonal assessments, especially for temperature-related effects. A 10 μg/m³ increase in PM_2.5 was associated with an ERR of 1.26% in females and 1.07% in males at lag 1, while NO₂ exposure showed ERRs of 1.31% in males and 1.30% in females. The effects of PM₁₀ were comparatively smaller, peaking at about 1.13% ERR at lag 5. Stronger associations were observed in both summer and winter seasons. PM_2.5 demonstrated the highest overall impact, particularly among females, with an excess risk of 1.7%. Pollutant effects were more pronounced at ambient temperatures around 0 °C and 25 °C. Full article

Cropland fires are an important source of air pollution emissions and have a significant impact on regional air quality and human health. Although straw-burning ban policies have been implemented to mitigate emissions, the dynamics of PM_2.5 emissions from cropland fires under such stringent regulations are still not fully understood. This study utilizes PM_2.5 emission data from the Global Fire Assimilation System (GFAS), land-cover data from CLCD, and PM_2.5 concentration data from ChinaHighAirPollutants (CHAP) to examine the dynamic evolution of PM_2.5 emissions from cropland fires under straw-burning ban policies across China and to assess their environmental impacts. The results show that the 2013 Air Pollution Prevention and Control Action Plan initiated the development of provincial straw-burning ban policies. These policies resulted in a drastic reduction in PM_2.5 emissions from cropland fires in North China (NC), with a 65% decrease in 2022 compared to the 2012 peak. In contrast, a notable lagged effect was observed in Northeast China (NEC), where the increasing trend of PM_2.5 emissions was not reversed until 2017. By 2022, emissions in this region had declined by 53% and 45% compared to the 2015 peak and 2017 sub-peak, respectively. Moreover, significant regional differences were found in the environmental impacts of PM_2.5 emissions from cropland fires, with strong effects during summer in NC and during spring and autumn in NEC. This study provides empirical support for understanding the environmental impacts of cropland fires in key regions of China and offers critical insights to inform and refine related pollution control policies. Full article

Climate change can impact various facets of a region’s fire regime, such as the frequency and timing of fire ignitions. This study examines the temporal trends of monthly fire counts in the Northwest and Northeast Regions of Ontario, Canada, between 1960 and 2023. Fires ignited by human activities or lightning are analyzed separately. The significance of historical trends is investigated using the Cochrane–Orcutt method, which identifies decreasing trends in the number of human-caused fires for several months, including May through July. A complementary trend analysis of total area burned is also conducted. The forecasting of future months’ fire counts is explored using a Negative Binomial Autoregressive (NB-AR) model suitable for count time series data with overdispersion. In the NB-AR model, the use of climate teleconnections at a range of temporal lags as predictors is investigated, and their predictive skill is quantified through cross-validation estimates of Mean Absolute Error on a testing dataset. Considered teleconnections include the El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO). The study finds the use of teleconnection predictors promising, with a notable benefit for forecasting human-caused fire counts but mixed results for forecasting lightning-caused fire counts. Full article

This study analyzes how a solid material with non-uniform thermal conductivity behaves under thermoelastic stress when subjected to a magnetic field and varying reference temperatures. The mathematical formulation is developed within the advanced framework of the refined three-phase-lag Green–Naghdi type III theory, which provides a robust mechanism for modeling generalized thermoelastic interactions. An analytical solution to the governing equations is achieved through the application of the normal mode technique coupled with an eigenvalue approach. This methodology facilitates the development of precise analytical solutions for key quantities, including the distributions of temperature, displacement, and stress. The material considered as an isotropic symmetrical thermoelastic medium has applications in engineering, geophysics, aircrafts, etc. The corresponding numerical results were obtained and plotted employing MATLAB R2013a, and are presented graphically to elucidate the impacts of the critical parameters. This study conclusively establishes the magnetic field, reference temperature, and variable thermal conductivity as dominant parameters that dictate the behavior and distribution of the physical fields, thereby fundamentally shaping the medium’s thermoelastic response. Full article

Despite its rich cultural heritage, the Yellow River Basin (YRB) faces challenges of ecological fragility and unbalanced development that constrain residents’ welfare improvement. Cultural–tourism integration (CTI)—aimed at creating employment, optimizing industrial structure, and improving public services—is increasingly promoted as a pathway to enhance people’s livelihood and well-being (PLW). Grounded in industrial integration theory and welfare economics, this study examined the impact effects, transmission mechanisms, and spatial spillovers of CTI on PLW. Panel data from 75 prefecture-level cities in the YRB, spanning 2011 to 2023, were utilized, and multi-dimensional indices were constructed for both CTI and PLW. Impact effects, mediating mechanisms, and spatial spillovers were examined through kernel density estimation, a dynamic system generalized-method-of-moments (SYS-GMM) model, mediation analysis, and a spatial Durbin model (SDM). The results showed that CTI and PLW both improved over time and displayed a spatial pattern of “midstream and downstream leading, upstream lagging”. CTI significantly promoted PLW, after controlling for dynamics and endogeneity (SYS-GMM coefficient = 0.130, p < 0.01). Industrial structure upgrading acted as a positive mediator, whereas digital infrastructure exhibited a short-term suppressing (negative mediating) effect, implying a phased mismatch between CTI investment priorities and digital input. Spatial estimates further indicated that CTI generated positive spillovers, improving PLW in neighboring cities, in addition to local gains. These findings suggest that basin-wide coordination and better alignment between CTI projects and digital infrastructure are essential for inclusive and sustainable well-being improvements, supporting regional progress toward the Sustainable Development Goals. Full article

Online visual object tracking is a critical component of remote sensing-based aerial vehicle physical tracking, enabling applications such as environmental monitoring, target surveillance, and disaster response. In real-world remote sensing scenarios, the inherent processing delay of tracking algorithms results in the tracker’s output lagging behind the actual state of the observed scene. This latency not only degrades the accuracy of visual tracking in dynamic remote sensing environments but also impairs the reliability of UAV physical tracking control systems. Although predictive trackers have shown promise in mitigating latency impacts by forecasting target future states, existing methods face two key challenges in remote sensing applications: weak correlation between trackers and predictors, where predictions rely solely on motion information without leveraging rich remote sensing visual features; and inadequate modeling of continuous historical memory from discrete remote sensing data, limiting adaptability to complex spatiotemporal changes. To address these issues, we propose TPC-Tracker, a Tracker-Predictor Correlation Framework tailored for latency compensation in remote sensing-based aerial tracking. A Visual Motion Decoder (VMD) is designed to fuse high-dimensional visual features from remote sensing imagery with motion information, strengthening the tracker-predictor connection. Additionally, the Visual Memory Module (VMM) and Motion Memory Module (M3) model discrete historical remote sensing data into continuous spatiotemporal memory, enhancing predictive robustness. Compared with state-of-the-art predictive trackers, TPC-Tracker reduces the Mean Squared Error (MSE) by up to 38.95% in remote sensing-oriented physical tracking simulations. Deployed on VTOL drones, it achieves stable tracking of remote sensing targets at 80 m altitude and 20 m/s speed. Extensive experiments on public UAV remote sensing datasets and real-world remote sensing tasks validate the framework’s superiority in handling latency-induced challenges in aerial remote sensing scenarios. Full article

Accurate calculation of the soiling loss ratio (SLR) is essential for photovoltaic power prediction and cleaning optimization. While theoretical power-based methods perform reliably under stable, clear-sky conditions, their accuracy in fluctuating, cloudy weather remains uncertain. This study evaluates the impact of cloudy conditions on SLR calculation through a 20-day comparative experiment using a 10 kW PV system. The power difference between cleaned and soiled arrays defined the benchmark soiling loss ratio (SLR_bm), against which theoretical power-derived soiling loss ratio (SLR_tpd) was compared. Results show strong agreement between SLR_tpd and SLR_bm under sunny days but significant fluctuations (mean daily dispersion ratio: 17.8) and frequent anomalous negative values under cloudy conditions. These findings indicate that rapid irradiance changes and MPPT lag in cloudy weather amplify inherent model errors, highlighting the need for revised models adapted to complex meteorological conditions. Full article

Against the escalating challenges of global climate change and intensifying resource-environment constraints, exploring the green effects of industrial spatial organization has become crucial. Utilizing panel data from the Yangtze River Delta cities spanning 2011–2023, this study empirically examines the nonlinear impact of manufacturing-producer services co-agglomeration on urban green efficiency. The results reveal a significant U-shaped relationship: co-agglomeration initially suppresses efficiency due to coordination costs and congestion effects, but after crossing a specific threshold, the resulting scale economies and knowledge spillovers dominate and begin to promote green enhancement. Mechanism tests indicate that industrial upgrading serves as a direct mediating channel, while the mediating effect of green technological innovation exhibits a time lag. Further heterogeneity analysis shows that this U-shaped pattern is particularly pronounced in cities with low agglomeration levels, those not designated as low-carbon pilots, and non-resource-based cities. This study uncovers the nonlinear dynamics and key boundary conditions of the green effects arising from industrial co-agglomeration, providing an empirical basis for implementing differentiated regional spatial coordination policies. Full article

This study investigates the valuation dynamics of 30 publicly listed fintech firms across six Asian economies from January 2021 to December 2025. It examines how intrinsic firm-level scale (market capitalization) and extrinsic macroeconomic conditions (GDP growth) jointly influence fintech valuation ratios, as reflected in price-to-earnings (P/E), price-to-book (P/B), and price-to-sales (P/S) measures. It also identifies significant structural heterogeneity and distributional asymmetries in valuation outcomes by implementing a multi-method empirical strategy that includes a Panel Autoregressive Distributed Lag (ARDL) framework, two-way fixed-effects models with interaction terms, and quantile regression. The findings reveal a robust, positive long-run relationship between market capitalization and valuation multiples across all ratios, confirming that firm-level scale as reflected in market capitalization is the primary driver of market value. Critically, the analysis identifies a dual-regime landscape in the Asian fintech sector: developed markets (South Korea, Japan, and Singapore) are fundamentally firm-scale driven, where intrinsic scale is the superior predictor of valuation. In contrast, developing markets (China, India, and Indonesia) are primarily macro-growth driven, exhibiting high sensitivity to GDP growth as a macroeconomic indicator of market expansion. The quantile regression results demonstrate a winner-takes-all effect, where the impact of scale on valuation is significantly more pronounced for highly valued firms in the 75th percentile. These results challenge the efficacy of universal valuation models and provide a context-dependent navigational framework for investors, analysts, and policymakers to distinguish between structural scale and cyclical growth in the rapidly evolving Asian fintech ecosystem. Full article

The abrupt COVID-19 lockdown in early 2020 offered a unique natural experiment to examine vegetation productivity responses to sudden declines in human activity. Although vegetation often responds to environmental changes with time lags, how such lags operate under short-term, intensive disturbances remains unclear. This study combined multi-source environmental data with an interpretable machine learning framework (XGBoost-SHAP) to analyze spatiotemporal variations in net primary productivity (NPP) across the Beijing-Tianjin-Hebei region during the strict lockdown (March–May) and recovery (June–August) periods, using 2017–2019 as a baseline. Results indicate that: (1) NPP showed a significant increase during lockdown, with 88.4% of pixels showing positive changes, especially in central urban areas. During recovery, vegetation responses weakened (65.31% positive) and became more spatially heterogeneous. (2) Integrating lagged environmental variables improved model performance (R² increased by an average of 0.071). SHAP analysis identified climatic factors (temperature, precipitation, radiation) as dominant drivers of NPP, while aerosol optical depth (AOD) and nighttime light (NTL) had minimal influence and weak lagged effects. Importantly, under lockdown, vegetation exhibited stronger immediate responses to concurrent temperature, precipitation, and radiation (SHAP contribution increased by approximately 7.05% compared to the baseline), whereas lagged effects seen in baseline conditions were substantially reduced. Compared to the lockdown period, anthropogenic disturbances during the recovery phase showed a direct weakening of their impact (decreasing by 6.01%). However, the air quality improvements resulting from the spring lockdown exhibited a significant cross-seasonal lag effect. (3) Spatially, NPP response times showed an “urban-immediate, mountainous-delayed” pattern, reflecting both the ecological memory of mountain systems and the rapid adjustment capacity of urban vegetation. These findings demonstrate that short-term removal of anthropogenic disturbances shifted vegetation responses toward greater immediacy and sensitivity to environmental conditions. This offers new insights into a “green window period” for ecological management and supports evidence-based, adaptive regional climate and ecosystem policies. Full article

With the increasing frequency of precipitation events under global warming, understanding rainfall-induced disruptions to urban mobility has become increasingly important. While prior studies primarily focus on road traffic, the lagged and threshold effects of rainfall on urban rail transit (URT) passenger flow remain insufficiently explored. This study analyzes 109 days of automatic fare collection data from Tianhe District, Guangzhou, in combination with hourly meteorological records and station-level built environment attributes. A rainfall threshold-aware gradient boosting framework is proposed to capture nonlinear response regimes, and an explainable learning approach is used to quantify the relative importance of rainfall, temporal factors, and built environment characteristics. The proposed framework outperforms the baseline model, with the root mean squared error (RMSE) and mean absolute error (MAE) reduced by over 5.38% and 5.93%, respectively. Results further indicate that lagged rainfall intensity exerts the strongest influence on passenger flow variation, with impact magnitudes varying systematically across station types. These findings enhance understanding of the nonlinear, time-dependent effects of rainfall on URT demand and provide practical guidance for passenger flow management and operational planning under rainfall conditions. Full article

In the context of the global pursuit of the ‘carbon neutrality’ objective, Chinese enterprises are proactively advancing green development and low-carbon transformation. Among these efforts, climate transition risks have emerged as a crucial factor affecting strategic enterprise decisions and long-term competitiveness. This study utilizes a sample comprising Chinese A-share listed enterprises over the period from 2012 to 2024 to construct an enterprise climate transition risk index using text analysis methods. It empirically investigates this index’s impact on enterprise green innovation by adopting panel data analysis method to construct a fixed effects model and further examines the moderating roles of institutional investors’ shareholding and enterprise environmental uncertainties in response to climate transition risks. The research findings indicate the following: First, climate transition risks significantly enhance enterprise green innovation. The validity of this conclusion persists following a series of robustness and endogeneity tests, including replacing the explained variable, lagging the explanatory variable, controlling for city-level fixed effects, and applying instrumental variable methods. Second, both institutional investors’ shareholding and enterprise environmental uncertainties exert a significant positive regulatory effect on the relationship between climate transition risk and green innovation, indicating that external monitoring and heightened risk perception jointly enhance enterprises’ responsiveness in driving green innovation. Thirdly, heterogeneity analysis indicates that the positive impact of climate transition risks on green innovation is notably amplified within non-state-owned enterprises and manufacturing enterprises. By examining the dual regulatory mechanisms of ‘external monitoring’ and ‘risk perception’, this study broadens the study framework on the relationship between climate risks and enterprise green innovation, offering new empirical evidence supporting the applicability of the ‘Porter Hypothesis’ within the context of climate-related challenges. Furthermore, it provides valuable implications for policymakers in refining climate information disclosure policies and assists enterprises in developing forward-looking green innovation strategies. Full article

The interplay between government economic policy uncertainty (EPU) and bank risk remains a key concern in the financial stability literature. This study advances the field by examining the dynamic, time-varying impact of EPU on bank risk, explicitly differentiating between short- and long-term effects. We posit a dual hypothesis: heightened EPU increases short-run bank risk by raising borrower default probabilities while decreasing long-run risk as banks adopt more conservative lending strategies, given the option value of waiting under high uncertainty. Analyzing bank-level data across 22 countries from 1998 to 2017, we find robust empirical support: EPU exerts an immediate positive effect on bank risk and a significant negative effect with a lag of two to four years. These findings are robust to endogeneity and multiple sensitivity checks. Our results explicitly demonstrate the dual role of policy uncertainty in shaping bank risk-taking and offer timely guidance for the design of regulatory and macroprudential frameworks. Full article

In a worldwide context marked by increasing attention to financial literacy as a factor of financial inclusion, Morocco take part of this dynamic, seeking to improve the financial skills of its population. This article does not measure financial literacy directly but aims to explore the structural conditions that enable its diffusion in Morocco, using macroeconomic indicators such as income, employability, and education, along with financial infrastructure. Adopting a mixed methodology, this study combines both qualitative and quantitative analysis of the national context, including an overview of public policies, socioeconomic characteristics, and financial literacy initiatives, with a quantitative analysis based on an Autoregressive Distributed Lag (ARDL) econometric model. Bank branch density is employed as an indirect proxy for financial infrastructure, reflecting access to formal financial services in the absence of time-series literacy data. The results show that gross national income (GNI) per capita, the labor forces, and elementary school enrolment rates influence banking density, though without producing statistically significant effects in the long term. In the short term, only GNI has a temporary but not very robust impact. These results highlight the limitations of macroeconomic indicators alone in explaining financial literacy diffusion and underscore the potential role of structural factors such as digital innovation, governance, or inclusion of youth and female indicators. Full article

Intensified water consumption has driven rapid groundwater depletion globally, threatening economic and environmental sustainability. Understanding large-scale groundwater dynamics has been constrained by the scarcity of long-term, high-resolution records. This study uses multi-decadal, high-density groundwater level monitoring data from the Southern Hebei Plain (SHP) to analyze the evolution of the groundwater flow field and depression cones from 1959 to 2020. We quantitatively characterize trends over six decades and assess the impact of the South-to-North Water Diversion Project (SNWD). The regional flow field shifted from a natural topographic-driven pattern (foothills to coast) in the 1960s to localized systems centered on depression cones by the 1980s. Subsequent management policies and the SNWD have progressively reduced the extent of these cones, facilitating a partial recovery of the regional flow pattern towards its original direction. Shallow aquifer levels declined steeply from the 1980s until 2016, particularly along the Taihang Mountains’ alluvial fan margins, with cumulative drawdown of 20–60 m. After SNWD implementation, levels stabilized and began recovering in piedmont urban areas. Deep aquifer levels generally declined from the 1980s to 2016, with the most significant drawdown (40–90 m) occurring in the central–eastern plain. The recovery of deep aquifers lagged behind shallow ones. These results provide critical insights for supporting sustainable groundwater management and depression cone recovery in the Hebei Plain. Full article