Search Results (212)

Clarifying the dynamic changes in vegetation cover and the driving mechanisms under the combined influence of the natural environment and human activities is a crucial foundation for understanding the evolutionary processes of ecosystems in arid and semi-arid regions and for improving the effectiveness of ecological restoration. Taking the Kuye River Basin, a typical resource exploitation zone in the middle reaches of the Yellow River, as the research area, this study retrieved 30 m resolution annual maximum NDVI datasets from 1986 to 2020 to calculate the Fractional Vegetation Cover (FVC). Utilizing methods such as Theil–Sen slope analysis, Mann–Kendall significance test, Hurst exponent, stability analysis, geographical detector, and sensitivity index, this study systematically revealed the spatiotemporal patterns of vegetation change, future evolution trends, and the response mechanisms of FVC dynamics to multiple factors including climate, topography, and land use. The results indicated that from 1986 to 2020, FVC in the study area exhibited an overall increasing trend (0.0105 a⁻¹), with the average FVC rising from 0.21 to 0.61. Regions with very low and low vegetation coverage continued to decrease, while areas with high and very high vegetation coverage showed significant increases, particularly in the very high vegetation coverage category, which experienced the largest growth (CV = 179.32%). The regions with moderate vegetation coverage demonstrated the highest stability (CV = 48.42%). Analysis of the driving mechanisms revealed that precipitation and land use types were the primary factors influencing changes in FVC, with land use demonstrating a more stable explanatory power (CV = 3.63%). Furthermore, the interaction between these two factors significantly enhanced the explanatory power related to vegetation changes. Sensitivity analysis indicated that the increase in forest and grassland effectively mitigated the negative impact of cropland on moderate to high coverage areas; industrial and mining land had a notable impact on very low coverage areas. It can be inferred that the Grain for Green program and the expansion of industrial and mining lands might generate differentiated impacts across diverse vegetation coverage classes. Future projections indicate that 91.19% of the region exhibits potential for FVC improvement in the future. However, a risk of sustained vegetation degradation exists in densely populated areas and regions with concentrated industrial and mining land. The study demonstrates that under the combined influences of climate change and land use adjustments, optimizing land use structures and coordinating ecological restoration with resource development are critical approaches to enhancing the stability of ecosystems in arid and semi-arid regions, as well as promoting sustainable regional ecological development. Full article

To reveal the spatiotemporal evolution of PM_2.5 concentrations in East China from 2000 to 2023 and quantify the multiscale driving effects of natural and socioeconomic factors, we integrated satellite-derived and ground-based monitoring data to construct a multi-source dataset. XGBoost-SHAP screened key factors; Theil–Sen trend, Hurst index, and Moran’s I characterized spatiotemporal patterns; and the geographic detector and MGWR quantified driving mechanisms. PM_2.5 declined significantly (Sen slope: −1.29 $\mathsf{\mu }\mathrm{g}\cdot {\mathrm{m}}^{-3}\cdot {\mathrm{a}}^{-1}$ to −0.03 $\mathsf{\mu }\mathrm{g}\cdot {\mathrm{m}}^{-3}\cdot {\mathrm{a}}^{-1}$), with accelerated decrease after 2013. The Hurst index indicated sustainable improvement in the Yangtze River Delta core but reversal risk in southwestern Zhejiang and northern Fujian. Spatially, the north–south gradient intensified: high concentrations persisted in industrial regions (northern Jiangsu, northern Anhui, western Shandong), whereas low concentrations remained in ecological zones (southwestern Zhejiang, northern Fujian). Natural factors dominated spatial variation; temperature (q = 0.804) and precipitation (q = 0.724) showed the strongest explanatory power. MGWR further revealed the stronger negative effects of temperature and precipitation in the north than in the south, and a continuous spatial gradient of per capita GDP from coastal industrial clusters to inland ecological zones. These findings underscore the need for region-specific emission reduction strategies that account for climatic heterogeneity. Full article

Against the background of global warming, snow cover, as an extremely sensitive and active component of the cryosphere, plays an indispensable role in regulating regional water circulation, energy balance mechanisms and the climate system. To explore the dynamic variation characteristics and driving mechanisms of snow depth in southeastern Xizang, this study took Chamdo City as the research area. Based on multi-source datasets including snow depth, meteorology, vegetation, topography, and population density from 2000 to 2020, methods such as the coefficient of variation, Theil–Sen trend analysis, Mann–Kendall test, Hurst index, and geographical detector were adopted to systematically analyze the spatiotemporal patterns of snow depth variations and their influencing factors. The results indicate that, temporally, the overall snow depth in Chamdo City showed a fluctuating increasing trend over the past 20 years, with an annual growth rate of 0.03 cm. It exhibited distinct characteristics across three stages: snow depth increased at a rate of 0.12 cm·a⁻¹ from 2000 to 2005, decreased at 0.05 cm·a⁻¹ during 2005–2015, and rose rapidly from 2015 to 2020 at a growth rate of 0.52 cm·a⁻¹. Spatially, the distribution of snow depth varied significantly. The extremely shallow snow cover area (≤2 cm) accounted for 51.71% of the total area, primarily concentrated in low-altitude regions with intensive human activities. In contrast, the relatively deep (6–10 cm) and extremely deep (>10 cm) snow cover areas together constituted 14.34% of the total, mainly distributed in high-altitude regions with sparse populations. Hurst index analysis revealed that 61.71% of the study area exhibited persistent changes in snow depth, with a trend toward deepening snow cover in the future. The results from the geographical detector show that air temperature (X9, q = 0.90) was the core driving factor dominating the static spatial differentiation of multi-year average snow depth. Furthermore, the interactions between slope (X4) and air temperature (X9), vegetation type (X6) and air temperature (X9), and population density (X5) and aspect (X8) all demonstrated bivariate enhancement effects, with explanatory power significantly higher than that of individual factors. This study provides a scientific reference for water resource management, snowmelt runoff prediction and snow disaster prevention in Chamdo City. Full article

Macroeconomic systems are increasingly characterized by fractal structures, entropy-generating processes, and chaotic dynamics that challenge the assumptions of traditional regression methods. The presence of self-similarity, fractal structure, and sensitivity to initial conditions suggests that macroeconomic variables evolve through complex interactions that cannot be adequately explained within an equilibrium-based method. Motivated by this perspective, this paper tested the relationships between oil prices and macroeconomic variables in the United States over the period of 1960–2024 using a suggested fractal regression approach. The analysis proceeds in two stages. In the first stage, fractal, entropy, and chaotic structures of the variables were analyzed by employing entropy measures, Lyapunov exponents, attractor diagnostics by including Lorenz and Julia structures, and tests for fractal dimension: d parameter (GPH) and d parameter (Phillips), and long range dependendeceLo’s Modified R/S, and Hurst–Mandelbrot R/S. Our results explored evidence of fractal structure, complexity, and chaotic behavior within the selected macroeconomic series by indicating the presence of nonlinear dynamics and sensitivity to initial conditions. In the second stage, a proposed chaotic–fractal-based regression model is employed to explore the transmission mechanism of oil price to economic growth, inflation, and unemployment. By directly incorporating Lyapunov and fractal-based measures into the regression method, the model captured nonlinear interactions that are overlooked by traditional methods. The results revealed that oil price shocks generate chaotic and fractal effects across macroeconomic variables and that these effects vary according to the degree of chaotic divergence embedded in the system. Overall, the results suggested the interconnected roles of fractality, entropy, and chaos in shaping macroeconomic dynamics and showed the importance of chaos- and fractal-based modeling methods for understanding the economic consequences of energy shocks and their policy implications. Full article

Each financial market carries a unique “fractal signature” with its own distinct risk and return pattern. This study comparatively deciphers these fractal signatures of the leading stock market indices of the Emerging Seven (E7) countries (Turkey, India, Brazil, Mexico, Russia, China, Indonesia), using Multifractal Detrended Fluctuation Analysis (MFDFA) with data covering the 2021–2025 period. The findings reveal that all examined markets deviate from the classical random walk model and exhibit distinct multifractal characteristics. However, significant differences were observed among these signatures: in contrast to Russia’s chaotic structure, which showed extreme fragility to geopolitical shocks, the Chinese and Mexican markets presented a more stable and homogeneous risk profile. In all indices, it was found that small-scale fluctuations carry a strong long-memory effect (stable trends), while large-scale fluctuations assume a more random character (sudden shocks). This asymmetric behavior confirms the heterogeneous nature of investor expectations. For example, the generalized Hurst exponents H(q) ranged from 0.22 (RTS, Russia) to 0.73 (BIST100, Turkey), and the spectrum width Δα varied between 0.10 (Mexico) and 0.45 (Russia), confirming significant heterogeneity in market complexity. Turkey’s BIST100 index, with its structure encompassing both predictable and sudden-shock-prone dynamics, occupies a balanced position within this spectrum. Consequently, the study confirms that understanding these unique fractal signatures of emerging markets is a fundamental prerequisite for formulating effective risk management strategies and achieving global portfolio diversification. Full article

A new fractional Brownian vector field (FBVF) is created for modeling multidimensional and multivariate fractal data. It is shown that the FBVF is a multidimensional and multivariate generalization of the classical Kolmogorov–Wiener process, allowing the distribution of field increments to be defined solely through fractal Euclidean distances between observation points. Conditions are established under which the family of field distributions satisfies the Kolmogorov consistency theorem. Maximum likelihood and variogram-based methods are developed to analytically estimate the mean and covariance of the FBVF, while the Hurst parameter is computed using an one-variable optimization algorithm. A kriging method is constructed for solving prediction problems using observations of fractal data. For computer simulation of field realizations, recursive and kriging-based algorithms are applied. A computational Monte Carlo experiment confirms the reliability of the proposed methods, particularly in accurately estimating the Hurst parameter. Applications to heavy metal concentrations in soil and climate data analysis demonstrate the effectiveness of the model in representing and analyzing multifractal, multidimensional processes. Full article

Vegetation is a key component of ecosystems and a core indicator for monitoring terrestrial ecosystem changes. Studying its spatio-temporal dynamics and natural drivers is essential for ecological restoration and management in the Loess Plateau, a region with fragile ecology and complex human-land interactions. Using data from 2000 to 2024, this study systematically investigated the spatio-temporal evolution patterns, future trends, and primary influencing factors of Fractional Vegetation Cover (FVC) by integrating the Dimidiate Pixel Model, trend analysis, Hurst index, and optimal parameter geographic detector methods. The results show that: (1) Over the 25 years, FVC on the Loess Plateau showed an overall fluctuating upward trend, with a spatial distribution pattern characterized as “low in the northwest and high in the southeast”, and notable variations across different land use types. (2) The FVC change trend was dominated by extremely significant and significant increases, accounting for 67.92% of the total area, while areas with no significant change accounted for 31.27%. Spatially, the central region exhibited strong persistence in its increasing trend, whereas the northwestern and southeastern margins tended to remain stable. (3) Precipitation was the most important single factor affecting FVC (explanatory power q = 0.4199). The interactive explanatory power of factors was higher than that of single factors, with precipitation and elevation having the strongest interaction (q = 0.5124). Land use type, as an anthropogenic proxy, also plays a significant regulatory role in FVC patterns. Using conventional remote sensing methods (dimidiate pixel model, trend analysis, Hurst index, and optimal parameter geographic detector), this study primarily contributes by extending the analysis period to 2024 and providing a focused assessment of post-2020 vegetation dynamics. This study systematically analyzes the spatio-temporal evolution patterns of FVC and quantifies the explanatory power of natural factors and land use as a human activity proxy on the Loess Plateau, providing a scientific basis for assessing regional ecological restoration effectiveness and optimizing ecological management strategies. Full article

Extreme heatwaves threaten public health and economies. Using multi-source data from 1964–2023 and the Excess Heat Factor (EHF), we identified heatwaves and, with a generalized linear mixed model and Hurst-based intensity forecasting, assessed drivers and future trends across Belt and Road Initiative (BRI) regions. (1) Duration, frequency, and the number of events increased by 18.7 days, 21.2 days, and 5.5 events, respectively. During the 2004–2023 period, HWD, HWF, and HWN accelerated, expanding from South Asia/Middle East to Central Asia, the Caucasus, and North Asia. In 1994–2023, centroids shifted west/south: frequency 2.54° W, 1.83° S; and intensity 1.17° W, 2.79° S. (2) Between 2000 and 2019, interaction effects exceeded single effects; dominant drivers shifted from SPEI and wind speed to shortwave radiation and NDVI. (3) Future intensification peaks in East Asia, the Iranian Plateau, and China’s east coast; with H ≥ 0.7, enhanced areas exceed 33% (max 37%), concentrated in Central and western West Asia. Full article

Vegetation net primary productivity (NPP) is a key indicator of terrestrial carbon sequestration and ecological restoration effectiveness. The karst mountainous region of Southwest China is characterized by fragmented terrain and high ecological vulnerability, making quantification of NPP dynamics and drivers essential for regional management. Using MOD17A3 NPP data (2000–2020), this study applied trend analysis, Hurst exponent analysis, partial correlation analysis, residual trend analysis, and Geodetector to investigate NPP spatiotemporal patterns and driving mechanisms in Guizhou Province. Results show a significant increasing trend in NPP (3.653 gC·m⁻²·a⁻¹, p < 0.01), with 78.61% of the area exhibiting growth and a spatial pattern of higher values in the south and lower values in the north. NPP shows persistence, indicating a continued increasing tendency. Along elevation gradients, NPP exhibits a unimodal pattern, peaking at 1000–1200 m, while growth rates increase with elevation and slope, with greater variability at higher altitudes. Temperature exerts a stronger and more extensive influence on NPP than precipitation, with significant correlations over 34.35% and 10.16% of the study area, respectively (p < 0.05). Residual trend analysis indicates that non-climatic factors accounted for a larger share of NPP variation (64.49%) than climatic factors (35.51%), with ecological restoration likely the leading non-climatic driver. Geomorphological type is the primary driver of spatial heterogeneity (q = 0.220), followed by precipitation, temperature, and land use, with interaction effects mainly showing nonlinear enhancement. These findings provide insights for ecological restoration and vegetation management in karst regions. Full article

Understanding complex systems requires analytical tools capable of covering nonlinear dynamics, structural complexity, and informational uncertainty simultaneously. In this context, chaos theory, fractal analysis, and entropy measures provide complementary perspectives for examining any irregular behavior in natural and socio-economic systems. This paper examined the relation between the Geopolitical Risk Index and the World Uncertainty Index to the volatility of West Texas Intermediate crude oil, gold, and Bitcoin over the period October 2010–February 2026. The analysis was motivated by the recent intensification of geopolitical tensions, particularly conflicts involving Iran, the United States, and Israel, which have significantly heightened uncertainty in global energy and financial markets. The empirical analysis first investigated the underlying complexity of the variables using entropy, chaos, and fractionality measures. Results from the Shannon, R-T entropy, Kolmogorov–Sinai complexity, Hurst, H-M and Lo’s R/S statistics, Phillips, and GPH fractionality tests consistently indicate entropy, fractal persistence, and long-range dependence across the series. In addition, the largest Lyapunov exponents and Hurst coefficients confirmed the presence of chaotic dynamics. The results reveal strong regime heterogeneity with geopolitical shocks exerting significantly stronger effects during high-uncertainty periods. Forecast comparisons show that regime-switching models outperform linear specifications, highlighting the importance of fractal and nonlinear dynamics in understanding financial market responses to geopolitical risk. Full article

Temperature variability and weather-related fluctuations significantly affect the energy, agricultural, and industrial sectors that are highly sensitive to meteorological changes. These conditions may lead to financial losses caused by demand fluctuations and operational disruptions. This study aims to develop a fractional weather-derivative pricing model based on temperature dynamics by integrating the Ornstein–Uhlenbeck (OU) process, the classical Black–Scholes model (BSM), and the fractional Black–Scholes model (fBSM). Daily temperature data from 2016 to 2025 obtained from the Bandung Geophysical Station, West Java, Indonesia, were used as the basis of analysis. Temperature dynamics were modeled using an OU process, and parameter estimation was conducted using Ordinary Least Squares (OLS). The strike price was determined using Historical Burn Analysis (HBA), whereas weather-derivative pricing was performed using call and put option approaches under both the BSM and fBSM frameworks, incorporating the Hurst parameter to capture long-term memory effects. The results indicate that the fractional Black–Scholes model analytical solution is obtained using the Daftardar–Gejji Aboodh method. Furthermore, the OU process successfully captured daily temperature dynamics, yielding a Mean Absolute Percentage Error (MAPE) of 4.344% and a Root Mean Square Error (RMSE) of 1.396 °C, indicating high predictive accuracy across both relative and absolute error measures. In addition, the fBSM consistently generated higher option values than the classical BSM, particularly under higher observed temperatures during the study period and at higher strike prices. These findings demonstrate that long-term memory significantly influences effective volatility and option valuation. This study is expected to contribute to the development of weather derivative models that more realistically represent temperature dynamics and to serve as a reference for weather derivative pricing, hedging, and decision-making, as well as for more measurable, systematic, and sustainable climate-related financial analysis using derivative pricing frameworks. Full article

We investigate dynamic weighted fractional information-theoretic measures for linear stochastic differential equations driven by fractional Brownian motion with Hurst parameter $H\in (1/2,1)$. Motivated by recent constructions of fractional Deng entropy and building upon explicit Gaussian solutions and closed-form fractional moments derived in previous work, we establish fully analytical expressions for the Shannon entropy, Rényi entropy, Tsallis entropy, extropy, and a continuous weighted fractional entropy $\mathbb{E}\left[X_t^p(-\log p_{X_t}\left(X_t\right))\right]$ for $p\ge 0$, expressed directly in terms of known fractional moments without density estimation. All derived measures share a universal asymptotic scaling law growing as $H\log t$, establishing a precise quantitative link between long-memory effects and information dynamics. The weighted fractional entropy further reveals remarkable structural properties as a function of the weighting order p, exposing a dual role of long memory on the system’s informational content. As a concrete application, we characterize anomalous diffusion in aging soft materials through an explicit critical time linking maximal uncertainty to the memory exponent H and the macroscopic aging rate. All results are validated through extensive Monte-Carlo simulations, demonstrating excellent agreement with the closed-form expressions across a wide range of Hurst exponents H and weighting orders p. Full article

Evapotranspiration (ET) is a key component of the terrestrial water and energy cycle, and its long-term dynamics are essential for regional hydrological assessment in subtropical China. In this study, two widely used satellite-based ET products, MOD16 and PML-V2, were selected for intercomparison because they provide consistent spatial (500 m) and temporal (8-day) resolutions. Validation against flux observations showed that PML-V2 performed better than MOD16 and was therefore used for subsequent analysis. Based on the 500 m, 8-day PML-V2 dataset, the spatiotemporal variation in ET in subtropical China during 2001–2020 was examined using the Theil–Sen slope estimator, Mann–Kendall test, and Hurst exponent. To identify the most relevant controls on ET variation, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) were used to screen environmental factors and rank their relative importance. Multiple linear regression (MLR) was then applied only to the selected dominant factors to quantify their contributions. Residual analysis was used to distinguish climate–vegetation effects from residual influences, which could arise from human activities and unmodeled natural processes. The results showed that annual ET averaged 669 mm and increased significantly at a rate of 2.03 mm yr⁻¹ from 2001 to 2020, with an accelerated increase after 2010. Spatially, ET exhibited clear gradients from south to north and from coastal to inland regions. Downward shortwave radiation (SWDown) and leaf area index (LAI) were the dominant drivers over most of the study area, although their controls varied geographically, with northern subregions being more energy-limited and southern subregions being jointly influenced by vegetation and temperature. Residual ET trends largely coincide with cropland and urbanising areas, indicating a partial influence of human activities, while in subregions such as XM, complex terrain and hydrological heterogeneity suggest that unmodeled natural processes may dominate. These findings enhance understanding of ET dynamics in subtropical China and demonstrate the value of high-resolution remote sensing products for regional hydrological monitoring and driver attribution. Full article

Soil erosion is a serious problem threatening regional ecological security, particularly in the Loess Plateau of China. This study focuses on the Wuding River Basin on the Loess Plateau. Based on multi-source data from 2001 to 2024, the RUSLE model was used to estimate the soil erosion modulus. We used comprehensive methods, such as trend analysis, multiple regression, scenario simulation, partial least squares structural equation modeling (PLS-SEM), hot spot analysis, and Hurst exponent, to systematically analyze the spatiotemporal evolution characteristics of soil erosion, the contributions of driving factors, and the sustainability of trends. The results showed that over the 24-year period, the soil erosion modulus in the basin generally showed a decreasing trend, suggesting an improvement in soil erosion conditions. The area of mild and above erosion grades continued to shrink. Among the RUSLE factors, the vegetation cover factor (C) showed a significant downward trend (R² = 0.7721), with the decreasing area accounting for 95.8%; the rainfall erosivity factor (R) showed a slight upward trend, with the increasing area accounting for 92.7%; and the erosion control practice factor (P) remained stable in most areas (96.8%). Relative contribution analysis indicated that the R-factor dominated the largest area (46.85%), while absolute contribution analysis showed that the C-factor contributed most significantly to erosion reduction. PLS-SEM demonstrated that the influence pathways of natural factors and human activities on soil erosion differed significantly across spatial and temporal scales. On the temporal scale, the R-factor had the strongest direct positive effect on erosion; on the spatial scale, the topography factor (LS) had the strongest positive effect on erosion. Furthermore, we found that the disturbance of vegetation by human activities is being weakened with the continuous implementation of soil and water conservation projects. The cold and hot spots of erosion trends were concentrated in the southeastern part of the basin. Based on trend sustainability, the basin was divided into successfully treated areas (57.6%), potential rebound risk areas (29.4%), emergency treatment areas (11.2%), and monitoring priority areas (1.8%). Overall, this study advances the understanding of soil erosion evolution under long-term ecological restoration and provides a scientific basis for optimizing sustainable soil and water conservation management in the Wuding River Basin. Full article

The long-term stability of rock–concrete composites largely depends on the mechanical properties and durability of the rock–concrete interface. This study investigated the coupling effect of interfacial roughness and acid sulfate corrosion on sandstone–concrete composites by using uniaxial compression tests combined with acoustic emission (AE) monitoring. The results showed that corrosion continuously reduces the mechanical properties of the specimens with peak strength and elastic modulus, exhibiting a two-stage evolution: rapid degradation in the early stage followed by a slow decline in the later stage. After 60 days of corrosion, the peak strength for composites with JRC = 5, JRC = 10, and JRC = 15 interfaces decreased by 46.59%, 44.34%, and 50.43%, respectively. The elastic modulus exhibited the same pattern of variation, and the decreasing rate was 68.90%, 66.96%, and 76.46% for the JRC = 5, JRC = 10, and JRC = 15 groups. Acoustic emission activities appeared earlier and were more significant after corrosion. With the effect of corrosion, the fracture mode evolved from tensile-dominated cracks to mixed tensile–shear cracks with a stronger shear component. Fractal analysis of AE energy revealed that the Hurst exponent decreased from 0.842–0.864 in the natural state to 0.503–0.567 after 60 days of immersion, whereas the fractal dimension increased from 1.136–1.182 to 1.433–1.497, indicating a decrease in the persistence and increase in complexity of the acoustic emission energy release process. Overall, the moderately rough interface (JRC = 10) achieved a better balance between initial strengthening and long-term corrosion resistance. These findings provide experimental support for evaluating the durability of sandstone–concrete composites in acidic sulfate environments. Full article