Search Results (237)

To better capture ecological quality under aerosol pollution stress, an AOD-integrated Remote Sensing Ecological Index (ARSEI) was developed for the Shenyang Metropolitan Area (2000–2025). Using Google Earth Engine, multi-source MODIS products were compiled to generate an annual growing-season ARSEI through PCA, combining PC1 and PC2 by variance-weighted contributions. Long-term trends were assessed with Theil–Sen slope estimation and the Mann–Kendall test, future persistence with the Hurst index, and drivers with an optimal parameter geographical detector. ARSEI closely matched conventional RSEI in multi-year pixel means (R² = 0.98, p < 0.001) but identified larger “poor” (+0.4%) and “moderate” (+3.4%) areas from 2000 to 2025, indicating higher sensitivity to pollution-related stress. Ecological quality improved overall, with high grades in eastern mountainous forests and low grades in the central built-up core and surrounding croplands. Improvement was dominant (31.08% significant, 38.27% slight), while degradation was limited (4.27% significant, 13.92% slight) and concentrated in peri-urban expansion belts. Elevation was the strongest natural control, whereas land use and population were the leading socioeconomic drivers with increasing influence over time. Finally, we delineated differentiated management zones based on current status and projected trajectories to support targeted regional governance. Full article

Financial time series in turbulent markets exhibit complex long-memory dynamics and multifractal features that traditional deep learning models fail to capture due to inherent exponential forgetting mechanisms. To address this, we propose Frac-Attn-GL, a novel Fractional-order Spatiotemporal Attention-based GRU-LSTM framework. Grounded in the Fractal Market Hypothesis, the model embeds Grünwald–Letnikov fractional-order operators into a dual-channel architecture (FracLSTM and FracGRU) to characterize long-range memory with rigorous power-law decay priors. Furthermore, an extreme-aware asymmetric loss function is designed to drive a dynamic spatiotemporal routing mechanism, enabling adaptive shifts between long-term macro trends and short-term micro shocks. Empirical tests on major U.S. stock indices reveal three significant findings. First, the Frac-Attn-GL framework substantially reduces prediction errors, achieving up to a 93.1% RMSE reduction on the highly volatile NASDAQ index compared to standard baselines. Second, the adaptively learned fractional-order parameters exhibit a consistent quantitative alignment with the market’s empirical multifractal singularity spectrum, supporting the physical interpretability of the model’s endogenous memory mechanism. Finally, hybrid residual multifractal diagnostics indicate that the framework effectively captures deep long-range correlations, reducing the Hurst exponent of the prediction residuals from ~0.83 to approximately 0.50, a level consistent with the absence of significant long-range dependence. Full article

Drought is a complex natural hazard with severe impacts on ecosystems, agriculture, water resources, and socio-economic stability. Understanding its spatiotemporal evolution is critical for effective drought monitoring and prevention. This study analyzed drought characteristics in Henan province from 1961 to 2023 using the Standardized Precipitation Evapotranspiration Index (SPEI), calculated from daily meteorological data at 111 meteorological stations. Drought was examined at annual and seasonal scales across multiple time scales, including the 1-month time scale (SPEI1), 3-month time scale (SPEI3), and 12-month time scale (SPEI12), and future trends were assessed using Theil–Sen Median and Hurst exponent analyses. Key findings revealed the following: (1) Drought frequency showed a non-significant increasing trend overall, but drought intensity increased significantly, with severe and extreme droughts becoming more frequent. Most areas are projected to continue aridification. (2) Winter recorded the highest frequency and occurrence of droughts, followed by autumn and summer. Except for summer, moderate and severe droughts increased across all seasons. Extreme droughts increased significantly across all seasons, especially in spring and autumn. (3) High annual drought frequency was concentrated in the northwest, north, and east. Spatial patterns varied by drought severity: slight droughts were more common in the north, moderate droughts in the central–east, severe droughts in the west and south, and extreme droughts in the southwest and north. (4) Empirical Orthogonal Function (EOF) analysis revealed three main spatial modes: a uniform regional pattern, a southeast–northwest contrast, and a central–eastern opposition. Shorter time scales provided more detailed spatial patterns, while longer scales better reflected interannual characteristics of drought and flood variations. This study offers valuable insights for improving drought assessment and supporting risk management and policy decisions. Full article

As Guangdong is a pivotal province in China’s national forest city initiative, examining the spatiotemporal evolution and key drivers of eco-environmental quality (EEQ) in Guangdong is essential for advancing regional sustainable development. To address the complexity of EEQ assessments in areas that are characterized by dense hydrological networks, extensive vegetation cover, and rapid urban expansion, the Google Earth Engine platform was utilized in this study, and remote sensing indices with heightened sensitivity to vegetation and moisture dynamics—namely, the kernel normalized difference vegetation index and the kernel normalized difference moisture index—were introduced to develop an improved water benefit-based ecological index (ImWBEI). Through an integrated analytical framework incorporating Theil–Sen trend analysis, Mann–Kendall significance testing, Hurst exponent analysis, an optimal parameter-based geographical detector, and a coupled coordination degree model, this research systematically evaluated the spatiotemporal patterns, future trends, driving mechanisms, and coordination with urbanization of the EEQ in Guangdong from 2000 to 2021. The results demonstrated that the ImWBEI enhanced the detailed characterization of complex underlying surfaces, such as urban built-up areas and land–water transition zones. Throughout the study period, the EEQ in Guangdong displayed a stable spatial distribution characterized by higher values in the north and lower values in the south. Concurrently, the EEQ significantly improved at a rate of 0.0092 per year. Hurst index analysis indicated that this trajectory would likely persist, with the future trend dominated by a pattern of weak persistent improvement. The comprehensive urbanization index was identified as the most critical factor influencing the spatial differentiation of the EEQ in Guangdong. Although notable north–south disparities were observed in the coordination between the EEQ and comprehensive urbanization, the provincial-level coupled coordination consistently improved. Consequently, this work yielded actionable insights and a replicable framework for ecological monitoring and coordinated development in similar water–forest integrated urban regions. It was particularly relevant for informing ecological restoration prioritization and development restriction decisions in critical land–water transition zones—areas where the ImWBEI demonstrated enhanced sensitivity. Full article

Accurate gold price forecasting is crucial for economic stability and investment decision-making. In order to improve the accuracy of gold price prediction and quantify the uncertainty of gold price fluctuation, this paper proposes a hybrid model (CNN-QRLSTM) that integrates convolutional neural network (CNN) and quantile regression long- and short-term memory network (QRLSTM) and innovatively introduces news text data to quantify the media sentiment. We combine EEMD with the Hurst index to remove white noise from the original signal, and the processed data is used as the input layer of the prediction model. Furthermore, to demonstrate the impact of news sentiment on gold prices, this paper employs entropy measurement methods based on information theory to quantify the uncertainty and information content embedded within processed gold price sequences and derived sentiment indicators. The mutual information (MI) algorithm, based on information entropy, captures the nonlinear correlations between financial keywords and market sentiment. It constructs a financial sentiment lexicon (covering keywords such as economic policies and geopolitical conflicts), combines semantic rules with context-weighted strategies, calculates sentiment scores for news texts, and generates daily aggregated media sentiment indicators. This entropy-based perception method not only enhances the interpretability of emotion-driven fluctuations but also provides a theoretical foundation for reducing prediction uncertainty through multi-source data fusion. The experiment uses 2022–2025 daily London gold spot price data, Shanghai Gold Exchange gold price data, and the same period of Gold Investment Network gold market news to carry out the study. The empirical study shows that the synergy of multi-source data fusion and the quantile regression mechanism can improve the accuracy of gold price prediction and the new paradigm of risk interpretation while providing theoretical support for the formulation of quantitative investment strategies. Full article

Clarifying the complex interaction between climate risks and the new energy industry chain is of key significance to advancing the energy transition and strengthening industrial chain robustness. This research pairwise-matches the climate physical risk and the climate transition risk with the entire range of the new energy industry chain segments, comprehensively examining the pairwise interactive relationships. By applying the MF-ADCCA series of methods, it was revealed that there are prevalent asymmetric cross-correlated multifractal characteristics between climate risks and the new energy industry. The long-term memory under the upward trend of the market is distinctly stronger than that under the downward trend. Given that this correlation can indirectly reflect market efficiency differences, this paper constructs the Hurst Volatility Sensitivity Index (HVI) and the Hurst Asymmetry Index (HAI) and further proposes the Unified Market Efficiency Index (UMEI). Its innovative advantage resides in the balanced integration of volatility efficiency and structural symmetry, in turn enabling a comprehensive assessment of the new energy market efficiency under climate risk perturbations. Static analysis reveals that the overall market efficiency of the new energy industry under the climate transition risk is generally higher than that under the climate physical risk, and the market efficiency of mature upstream and midstream new energy segments is significantly superior to that of the downstream. Dynamic evolution characteristics indicate that market efficiency has typical time-varying traits, the evolution of which is often driven by significant policies or extreme events. The climate transition risk tends to trigger aperiodic structural adjustments, while the climate physical risk mostly induces periodic efficiency fluctuations. This study furnishes solid evidence for the new energy market in coping with climate risks. Full article

Vegetation cover characteristics underpin the understanding of regional ecosystem status and guide sustainable development. While extensive research has documented long-term vegetation dynamics in Qinghai Province, critical gaps remain in identifying driving factors, quantifying their thresholds, and uncovering nonlinear relationships governing vegetation cover. In view of this, based on the MOD13Q1V6 dataset from the Google Earth Engine (GEE) platform, this study constructed a kernel normalized difference vegetation index (kNDVI) dataset for Qinghai Province spanning the period 2001–2023. Furthermore, the spatiotemporal characteristics and future evolution trends of vegetation cover were revealed by employing methods including the Theil–Sen–Mann–Kendall (Theil–Sen–MK) trend test, Hurst exponent, and centroid migration model. At a grid scale of 5 km × 5 km, based on the combined model of Extreme Gradient Boosting and SHapley Additive exPlanations (XGBoost-SHAP), this study integrated 10 multi-source remote sensing variables related to natural conditions, socioeconomic factors, and geographical accessibility to reveal the nonlinear effects between driving factors and kNDVI and identify the key threshold inflection points. The results showed the following: (1) From 2001 to 2023, the kNDVI of Qinghai Province exhibited a fluctuating growth trend with an annual growth rate of 0.0016 per year, presenting a spatial pattern of being higher in the southeast and lower in the northwest. Specifically, the kNDVI of unused land achieved the highest growth rate (65.96%), which was significantly higher than that of other land use types. (2) The kNDVI in Qinghai Province was dominated by stable areas, accounting for 52.75%. Future trend analysis indicated that the region was primarily characterized by sustainable improvement zones (39.91%), while areas with uncertain future trends accounted for 39.70%. (3) The XGBoost-SHAP model revealed that the annual mean precipitation (AMP) (47.26%) and Digital Elevation Model (DEM) (20.40%) exerted substantial impacts on the kNDVI. Marginal effect curves identified distinct threshold inflection points for the major characteristic factors: AMP = 363.2 mm (95%CI: 361.2–365.2 mm), DEM = 4463.9 m (95%CI: 4446.0–4481.1 m), grazing intensity = 1.8 SU (Stocking Unit)·ha⁻¹ (95%CI: 1.8–1.9 SU·ha⁻¹), and slope = 2.8° (95%CI: 2.7–3.0°) and 19.0° (95%CI: 18.8–19.3°). The interaction combinations of AMP × DEM and DEM × distance to construction land exerted a strong positive effect on the kNDVI in the study area, which was conducive to enhancing vegetation cover. These findings verified the effectiveness of ecological projects implemented in Qinghai Province to a certain extent and provided data support for subsequent differentiated restoration and management. Full article

The Yellow River Basin is among the regions in China most severely affected by soil erosion. Elucidating the evolution trend of its ecological environment quality and identifying the key driving factors can provide a theoretical basis for the management and protection of the ecological environment in the Yellow River Basin. In this study, an improved remote sensing ecological index (ARSEI) was constructed by incorporating the soil erosion factor (A) into the original remote sensing ecological index (RSEI). Subsequently, the Theil–Sen slope estimator, Mann–Kendall trend test, coefficient of variation, Hurst index and Geodetector were employed to analyze the spatiotemporal evolution trend and driving factors of the ecological environment quality in the basin from 2002 to 2022. The results were as follows: (1) During the study period, the mean ARSEI of the basin increased from 0.518 to 0.568, representing an increase of 9.65%, with a spatial pattern of “poor in the north and excellent in the south.” (2) 62.12% of the basin exhibited improved ecological quality, 75.74% of the area showed medium or lower fluctuation levels, and 35.12% of the region is projected to be at risk of degradation in the future. (3) Annual precipitation was identified as the dominant factor influencing the spatial variation in ARSEI (q = 0.428), followed by land use type (q = 0.299). All interactions between factors exhibited either nonlinear enhancement or bi-factor enhancement. Specifically, the interaction between annual precipitation and land use type was the strongest, with a maximum q-value of 0.693. This study provides a novel approach for assessing the ecological environment quality in regions severely affected by soil erosion. Full article

This work investigates the moderate deviation principle for a class of two-time-scale Caputo fractional stochastic differential equations. The driving noise of the slow variable is fractional Brownian motion with Hurst index $H\in ({\displaystyle \frac{1}{2}},1)$. The driving noise of the fast variable is standard Brownian motion. The fractional derivative operator of the slow variable is defined by Caputo, and the derivative of the fast variable is of the integer order. The proof process is mainly based on the weak convergence method of fractional Brownian motion variational representation. We first establish the moderate deviation principle by proving the weak convergence of the single-time-scale controlled version. Subsequently, we combine Khasminskii time discretization technology to extend the theoretical framework to two-time-scale systems. Finally, a concrete computational case is offered to demonstrate the applicability of the theoretical framework. Full article

Xinjiang is an arid and semi-arid region where ecosystems are fragile, and monitoring how its ecology changes over time is critical for its sustainable development. In this study, a Remote Sensing Ecological Index (RSEI) was established for Xinjiang from 2000 to 2025. To understand temporal and spatial changes in ecological quality, we conducted spatial autocorrelation analysis, Theil–Sen median trend analysis, a Mann–Kendall trend test, and Hurst exponent analysis. We also used Geodetector to determine which factors affect the RSEI. The main results were as follows: (1) The RSEI in Xinjiang remained low, with a mean value between 0.285 and 0.336. Mountainous areas had higher values, basins had lower values, and spatial clustering was strong (Moran’s I index: 0.81–0.86). (2) H-H clusters expanded and then shrank, while L-L clusters grew after 2015. Areas with excellent ecological grades increased, but so did areas with poor grades, indicating that improvement and degradation both exist. (3) Most areas were stable, but 19.13% showed persistent degradation, indicating that these areas need more attention. (4) Land surface temperature (q = 0.624) and land cover (q = 0.576) were the main driving factors, and factor interactions showed enhanced effects. The results of this study could provide a scientific basis for ecosystem protection and restoration in Xinjiang. Full article

Accurately quantifying the ecological functions of small and micro green spaces in high density urban environments supports urban ecological planning and management. This study assessed 271 pocket parks in the main urban area of Fuzhou, China, using multi-source remote sensing data from the growing seasons of 2019 to 2024. Six indicators were derived, including NDVI, NPP, WET, NDBSI, ISI, and LST. A composite Eco-environmental Index (EEI) was constructed using the entropy weight method. We combined the coefficient of variation, Theil–Sen slope estimation, the Mann–Kendall test, and the Hurst exponent to quantify spatial heterogeneity, interannual stability, and short-term persistence. We also examined climatic associations using correlation analysis. Pocket parks consistently outperformed their surrounding 500 m buffers across all indicators, and park buffer contrasts increased for most indicators. The mean EEI significantly increased from 0.563 in 2019 to 0.650 in 2024, with a pronounced step increase around 2022. At the site level, 261 of 271 parks (96.3%) exhibited an upward trend in EEI, indicating widespread ecological improvement. Specifically, park vegetation greenness (NDVI) rose from 0.413 to 0.578, widening the gap with surrounding areas. Parks consistently maintained a lower land surface temperature (LST) than their buffers, with a cooling magnitude ranging from 3.5 °C to 4.6 °C. Precipitation was positively associated with NDVI and NPP, while LST was positively associated with air temperature and negatively associated with precipitation. These findings support the planning and adaptive management of pocket parks to strengthen urban ecological resilience. Full article

To clarify the spatial–temporal evolution patterns and climate-driven mechanisms of carbon sinks of forest ecosystems under climate change, we calculated the net ecosystem productivity (NEP) of forests in the Guangxi region using remote sensing and meteorological data from 2000 to 2023. By employing trend analysis, spatial clustering, the Hurst index, and climate contribution evaluation, we analyzed the spatial and temporal changes, sustainability, and the relative contribution of climate impacts on forest carbon sinks. The results are as follows: The carbon sink capacity of forests in Guangxi increased continuously from 2000 to 2023, at a rate of 3.57 g C·m⁻²·a⁻¹, reaching 39.19% higher in 2023 than in 2000. The carbon sink capacity was higher in the southwest and lower in the northeast, with hotspots mainly located in evergreen/deciduous broad-leaved forest areas. The Hurst index indicates that 84.44% of regions are likely to maintain this increasing trend, suggesting stability in forest carbon sink function. The climate contribution rate to forest carbon sinks was moderate, with significant temporal fluctuations. Temperature governed annual variation in forest carbon sinks, influencing up to 36.37% of the area. The annual average contribution rate of climate change to forest carbon sinks was 30.28%, but there were temporal fluctuations and spatial heterogeneity. Over time, climate contributions had a positive driving impact; however, extreme climate events tended to produce a negative effect. The pattern of forest carbon sinks in Guangxi showed a “heat sink-coupling” phenomenon, with 16.23% of the hotspots of forest carbon sinks coinciding with temperature control zones, highlighting the enhancing effect of temperature rise on carbon sinks against a background of water and heat synergy. This study provides a scientific basis for the assessment of forest carbon sink potential and climate suitability management in Guangxi. Full article

Extensive coal mining activities can exert substantial negative impacts on surface ecosystems. Vegetation indices are widely recognized as effective indicators of land ecological conditions and provide valuable insights into long-term ecological changes in mining areas. In this study, the Zhaogu mining area of the Jiaozuo Coalfield was selected as the study site. Using the Google Earth Engine (GEE) platform, the Kernel Normalized Difference Vegetation Index (kNDVI) was constructed to generate a vegetation dataset covering the period from 2010 to 2024. The temporal dynamics and future trends of vegetation coverage were analyzed using Theil–Sen median trend analysis, the Mann–Kendall test, the Hurst index, and residual analysis. Furthermore, the relative contributions of climatic factors and human activities to vegetation changes were quantitatively assessed. The results indicate that: (1) vegetation coverage in the Zhaogu mining area exhibits an overall improving trend, affecting approximately 77.1% of the study area, while slight degradation is mainly concentrated in the southeastern region, accounting for about 15.2%; (2) vegetation dynamics are predominantly characterized by low and relatively low fluctuations, covering approximately 78.5% of the region, whereas areas with high fluctuations are limited and mainly distributed in zones with intensive mining activities; although the current vegetation trend is generally increasing, future projections suggest a potential decline in approximately 55.8% of the area; and (3) vegetation changes in the Zhaogu mining area are jointly influenced by climatic factors and human activities, with climatic factors promoting vegetation growth in approximately 70.6% of the study area, while human activities exert inhibitory effects in about 24.2%, particularly in regions affected by mining operations and urban expansion. Full article

Ecological quality (EQ) in the Yangtze River Economic Belt (YREB) has been profoundly reshaped by rapid urbanization and intensive ecological restoration over the past two decades. This study aimed to reveal the long-term spatiotemporal patterns of EQ and their driving forces at the basin scale. We constructed a 1 km, 25-year (2000–2024) Remote Sensing Ecological Index (RSEI) series using MODIS data and applied Sen’s slope, the Mann–Kendall and Hurst tests, and Geographically Weighted Ridge Regression (GWRR) to quantify trends, persistence, and spatially non-stationary driver effects. Results showed a significant overall improvement: by 2024, 69.6% of the YREB is classified as Good or Excellent EQ, with 34.6% of land showing continuous improvement and 6.4% faced persistent degradation risks. Forest and grassland cover exerted stable positive effects, while built-up expansion, population density, and GDP increasingly contribute to EQ decline, and the area dominated by urbanization-related negative coefficients expanded to 84.6% of the middle and lower reaches. The GWRR model achieved high average local $R^2$ (>0.92) and revealed pronounced spatial heterogeneity and multicollinearity-robust driver estimates. This study illustrates the potential of GWRR-based EQ diagnosis to support differentiated ecological governance strategies tailored to the upper, middle, and lower reaches of the YREB. Full article

River discharge scaling is fundamental to the global hydrological cycle and to water resource assessment. This study investigates the existence of multiple scaling regimes and introduces a novel framework for clustering river discharge using multiscale fractal characteristics. We analyzed daily discharge data from 38 stations across continental Canada over an 80-year period. Multifractal characterization was performed at decadal and long-term scales using three key parameters: the singularity exponent (${\alpha }_0$), multifractal strength ($\alpha$), and asymmetry index (r). K-means clustering in the $\alpha -r$, ${\alpha }_0-r$, and $\alpha -{\alpha }_0$ planes revealed distinct clusters, with the asymmetric parameter (r) emerging as the strongest distinguishing factor. These clusters represent groups of rivers with similar dynamical structures: the $\alpha -r$ clusters categorize discharge based on scaling strength and fluctuation influence. Analysis of the generalized Hurst exponent revealed anti-persistent behavior at most stations, with exceptions at five specific locations. This multifractal clustering approach provides a powerful method for classifying river regimes based on intrinsic characteristics and identifying the physical drivers of discharge fluctuations. Full article