Search Results (94)

Climate fluctuations are expected to drive a decline in the growth of many conifer and broadleaf species, especially in the Mediterranean region, where these species grow at or very near the southern limits of their distribution. Such trends have important implications not only for forest productivity but also for plant diversity, as shifts in species performance may alter competitive interactions and long-term community composition. Using tree-ring data sourced from two Abies cephalonica stands with different elevation in Mount Parnassus in Central Greece, we evaluate the growth responses of the species to climatic variability employing a dendroecological approach. We hypothesize that radial growth at higher elevations is more strongly influenced by climate variability than at lower elevations. Despite the moderate to relatively good common signal indicated by the expressed population signal (EPS: 0.645 for the high-altitude stand and 0.782 for the low-altitude stand), the chronologies for both sites preserve crucial stand-level growth patterns, providing an important basis for ecological insights. The calculation of the Average Tree-Ring Width Index (ARWI) for both sites revealed that fir in both altitudes exhibited a decline in growth rates from the late 1980s to the early 1990s, followed by a general recovery and increase throughout the late 1990s. They also both experienced a significant decline in growth between approximately 2018 and 2022. The best-fit model for annual ring-width variation at lower elevations was a simple autoregressive model of order one (AR1), where growth was driven exclusively by the previous year’s growth (p < 0.001). At the higher elevation, a more complex model emerged: while previous year’s growth remained significant (p < 0.001), other variables such as maximum growing season temperature (p = 0.041), annual temperature (inverse effect, p = 0.039), annual precipitation (p = 0.017), and evapotranspiration (p = 0.039) also had a statistically significant impact on tree growth. Our results emphasize the prominent role of carry-over effects in shaping their annual growth patterns. Full article

Short-term photovoltaic (PV) power forecasting is pivotal for grid stability and high renewable-energy integration, yet existing hybrid deep-learning models face three unresolved challenges: they fail to balance accuracy, computational efficiency, and interpretability; cannot mitigate iTransformer’s inherent weakness in local feature capture (critical for transient events like minute-level cloud shading); and rely on linear concatenation that mismatches the nonlinear correlations between global multivariate trends and local fluctuations in PV sequences. To address these gaps, this study proposes a novel lightweight hybrid framework—DSC-Chebyshev KAN-iTransformer—for 15-min short-term PV power forecasting. The core novelty lies in the synergistic integration of Depthwise Separable Convolution (DSC) for low-redundancy local temporal pattern extraction, Chebyshev Kolmogorov–Arnold Network (Chebyshev KAN) for adaptive nonlinear fusion and global nonlinear modeling, and iTransformer for efficient capture of cross-variable global dependencies. This design not only compensates for iTransformer’s local feature deficiency but also resolves the linear fusion mismatch issue of traditional hybrid models. Experimental results on real-world PV datasets demonstrate that the proposed model achieves an $R^2$ of 0.996, with root mean square error (RMSE) and mean absolute error (MAE) reduced by 19.6–62.1% compared to state-of-the-art baselines (including iTransformer, BiLSTM, and DSC-CBAM-BiLSTM), while maintaining lightweight characteristics (2.04M parameters, 3.90 GFLOPs) for urban edge deployment. Moreover, Chebyshev polynomial weight visualization enables quantitative interpretation of variable contributions (e.g., solar irradiance dominates via low-order polynomials), enhancing model transparency for engineering applications. This research provides a lightweight, accurate, and interpretable forecasting solution, offering policymakers a data-driven tool to optimize urban PV-infrastructure integration and improve grid resilience amid the global energy transition. Full article

The Sichuan–Yunnan region, a primary seismic-prone zone on the Qinghai–Tibet Plateau, has experienced heightened seismic exposure due to rapid urbanisation. In order to address the issue of disaster risks and to promote sustainable urban development, this study establishes an integrated urban seismic resilience evaluation framework based on the DPSIR (Driving–Pressure–State–Impact–Response) model. The CRITIC–AHP combined weighting method was utilised to determine indicator weights, and data from 37 prefecture-level cities (2010, 2015, 2020) were analysed to reveal spatial–temporal evolution patterns and correlations. The results demonstrate a consistent improvement in regional seismic resilience, with the overall index increasing from 0.501 in 2010 to 0.526 in 2020. Sichuan exhibited a “decline-then-rise” trend (0.570 to 0.566 to 0.585), while Yunnan demonstrated continuous growth (0.517 to 0.557). The spatial pattern underwent an evolution from “west–low, central–eastern–high” to “south–high, north–low”, with over half of the cities attaining relatively high resilience by 2020. Chengdu and Kunming have been identified as dual high-resilience cores, diffusing resilience outward to neighbouring regions. In contrast, mountainous areas such as Garze and Aba have been found to exhibit low resilience levels, primarily due to high seismic stress and limited socioeconomic capacity. Subsystem analysis has revealed divergent resilience pathways across provinces, while spatial autocorrelation has demonstrated fluctuating global Moran’s I values and temporary local clustering. This research provides a scientific foundation for seismic disaster mitigation and offers a transferable analytical framework for enhancing urban resilience in earthquake-prone regions globally. Full article

As a critical ecological barrier in the arid and semi-arid regions of northwestern China, the spatio-temporal evolution of vegetation carbon sequestration in the Hexi Corridor is of great significance to the ecological security of this region. Based on multi-source remote sensing and meteorological data, this study integrated second-order partial correlation analysis, ridge regression, and other methods to reveal the spatio-temporal evolution patterns of Gross Primary Productivity (GPP) in the Hexi Corridor from 2003 to 2022, as well as the response characteristics of GPP to air temperature, precipitation, and Vapor Pressure Deficit (VPD). From 2003 to 2022, GPP in the Hexi Corridor showed an overall increasing trend, the spatial distribution of GPP showed a pattern of being higher in the east and lower in the west. In the central oasis region, intensive irrigation agriculture supported consistently high GPP values with sustained growth. Elevated air temperatures extended the growing season, further promoting GPP growth. Due to irrigation and sufficient soil moisture, the contributions of precipitation and VPD were relatively low. In contrast, desert and high-altitude permafrost areas, constrained by water and heat limitations, exhibited consistently low GPP values, which further declined due to climate fluctuations. In desert regions, high air temperatures intensified evaporation, suppressing GPP, while precipitation and VPD played more significant roles. This study provides a detailed analysis of the spatio-temporal change patterns of GPP in the Hexi Corridor and its response to climatic factors. In the future, the Hexi Corridor needs to adopt dual approaches of natural restoration and precise regulation, coordinate ecological security, food security, and economic development, and provide a scientific paradigm for carbon neutrality and ecological barrier construction in arid areas of Northwest China. Full article

One of the main goals of the Compressed Baryonic Matter (CBM) experiment at the Facility for Antiproton and Ion Research (FAIR) is to investigate the properties of strongly interacting matter under high baryon densities and explore the QCD phase diagram. Fluctuations of conserved quantities like baryon number, electric charge, and strangeness are key probes for phase transitions and critical behavior, as are connected to thermodynamic susceptibilities predicted by lattice QCD calculations. In this paper, we report on up-to-the-fourth-order cumulants of (net-)proton number distributions in gold–gold ion collisions at the nucleon–nucleon center of mass energies $\sqrt{s_{NN}}$ = 3.5–19.6 GeV using the Parton–Hadron-Quantum-Molecular Dynamics (PHQMD) model. Protons and anti-protons are selected at midrapidity ($\left|y\right|$ < 0.5) within a transverse momentum range 0.4 $<p_T<$ 2.0 GeV/c of STAR experiment and 1.08 $<y<$ 2.08 and 0.4 $<p_T<$ 2.0 GeV/c of CBM acceptances. The results obtained from the PHQMD model are compared with the existing experimental data to undersatand potential signatures of critical behavior and to probe the vicinity of the critical end point in the CBM energy range. The results obtained here with the PHQMD calculations for $\kappa {\sigma }^2$ (the distribution kurtosis times variance squared) are consistent with the overall trend of the measurement results for the most central (0–5% centrality) collisions, although the calculations somewhat overestimate the experimental values. Full article

Understanding the relationship between regional vegetation dynamics and water resources is essential for improving integrated vegetation–water management, enhancing ecosystem services, and advancing the sustainable development of ecological–economic–social systems. As China’s second largest river basin, the Yellow River Basin (YRB) is ecologically fragile and experiences severe water scarcity. Vegetation changes further intensify conflicts between water supply and demand. To investigate the evolution and interaction mechanisms between vegetation and water resources in the YRB, this study uses the InVEST model to simulate annual water yield (Wyield) from 1982 to 2020 and applies the Dimidiate Pixel Model (DPM) to estimate fractional vegetation cover (FVC). The Theil–Sen method is applied to quantify the spatiotemporal trends of Wyield and FVC. A pixel-based second-order partial correlation analysis is performed to clarify the intrinsic relationship between FVC and Wyield at the grid scale. The main conclusions are as follows: (1) During the statistical period (1982–2020), the multi-year average annual Wyield in the YRB was 73.15 mm. Interannual Wyield showed a clear fluctuating trend, with an initial decline followed by a subsequent increase. Wyield showed marked spatial heterogeneity, with high values in the southern upper reaches and low values in the Longzhong Loess Plateau and Hetao Plain. During the same period, about 68.74% of the basin experienced increasing Wyield, while declines were concentrated in the upper reaches. (2) The average FVC across the basin was 0.51, showing a significant increasing trend during the statistical period. The long-term average FVC showed significant spatial heterogeneity, with high values in the Fenwei Plain, Shanxi Basin, and Taihang Mountains, and low values in the Loess Plateau and Hetao Plain. Spatially, 68.74% of the basin exhibited significant increases in FVC, mainly in the middle and lower reaches, while decreases were mostly in the upper reaches. (3) Areas with significant FVC–Wyield correlations covered a small portion of the basin: trade-off regions made up 10.35% (mainly in the southern upper reaches), and synergistic areas accounted for 5.26% (mostly in the Hetao Plain and central Loess Plateau), both dominated by grasslands and croplands. Mechanistic analysis revealed spatiotemporal heterogeneity in FVC–Wyield relationships across the basin, influenced by both natural drivers and anthropogenic activities. This study systematically explores the patterns and interaction mechanisms of FVC and Wyield in the YRB, offering a theoretical basis for regional water management, ecological protection, and sustainable development. Full article

Amid accelerating urbanization and intensifying climate variability, the Chengdu–Chongqing region faces acute tensions between high-quality agricultural development and water resource sustainability. This study constructs a multidimensional evaluation framework to analyze the spatiotemporal interaction between the Agricultural Quality Index (AQI) and the Water Resource Carrying Capacity Index (WCI) from 2013 to 2022 across 16 municipalities. Employing the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) model, obstacle degree analysis, standard deviational ellipse, and grey prediction modeling, the study finds that AQI exhibits a sustained upward trend—doubling in over half of the region’s cities—while WCI shows fluctuating growth, constrained by climatic extremes and uneven water distribution. Spatial analysis reveals persistent heterogeneity: cities such as Ya’an maintain superior WCI due to natural endowments, whereas Ziyang and Zigong lag due to infrastructural and environmental limitations. From 2013–2016, disparities between AQI and WCI widened, with the spatial coefficient of variation (sCoV) peaking due to resource misallocation and industrial imbalance. However, targeted policies since 2016—e.g., integrated water infrastructure, model agricultural zones, and adaptive land-use planning—have significantly improved regional coordination and narrowed these disparities. The study forecasts AQI to reach 2.0 by 2026, with Chongqing potentially exceeding 3.0, driven by technological modernization and resource integration. Policy recommendations include: (1) cross-regional water reallocation; (2) specialty agricultural clusters anchored by core cities; and (3) climate-resilient cropping systems. This research provides a scalable governance framework for reconciling resource constraints and agricultural modernization, offering practical insights for inland economic zones globally. Full article

Based on the statistical data of Wuhan from 2000 to 2022, this paper constructs an evaluation system for the coordinated development of new urbanization and the economy. It uses the entropy weight method and the coupling coordination degree model to comprehensively measure the relationship between the two. Then, the temporal variation characteristics of the coupling coordination degree between them are analyzed. Finally, Geodetector is employed to analyze the driving factors. The results show that (1) during the study period, the overall urbanization indices of population, economy, society, and space in Wuhan showed an upward trend, while the development trends of the subsystems of the economy were different. Among them, the economic structure developed relatively steadily; the economic environment showed an overall upward trend. The economic scale grew steadily from 2000 to 2019 but significantly decreased after 2019 due to the impact of the COVID-19 pandemic. (2) The comprehensive evaluation index of new urbanization and the economy in Wuhan showed a fluctuating upward trend. The levels of urbanization and economic development were constantly improving. Urbanization lagged behind economic growth from 2000 to 2008, developed synchronously during 2009–2019, and surpassed economic development between 2020 and 2022. (3) The coupling coordination degree has changed from severe dissonance to quality coordination severe dissonance to quality coordination. Its development process is affected by policy and the social environment. (4) According to the analysis of the geographic detector, the following indicators have a high impact: the share of the urban population, the consumer price index, and the proportion of the employed population in secondary and tertiary industries. Based on the analysis results, corresponding countermeasures and suggestions are proposed from three aspects in order to provide references for the coordinated development of urbanization and the economy: urbanization rate, employment, and consumption levels. Full article

Rapid economic development has exacerbated environmental degradation, particularly because of carbon dioxide emissions. To address these issues, China has proposed economic transformation from high-speed to high-quality development to achieve carbon peak and neutrality. Regional collaborative carbon emission reduction is critical for sustainability. Therefore, measuring regional carbon emission reduction capacity and the collaborative development level is imperative. This study employed provincial- and city-level data (2014–2023) from the Yangtze River Delta to assess regional collaborative carbon emission reduction capacity. Evaluation model of carbon emission reduction capacity was constructed based on five perspectives: economic development, carbon emission, carbon transfer, carbon sink, and industrial development. The entropy weighting method was employed to assign index weights, which was then integrated with a composite system synergy degree model. The subsystem order parameters and the composite system’s order degree were utilized to assess carbon emission reduction and collaborative trends. Results revealed that (1) overall carbon emission reduction capacity in the Yangtze River Delta constantly improved; (2) provincial economic development strengthened while carbon emissions declined; (3) carbon transfer fluctuations decreased; (4) technology and carbon sinks improved; (5) industrial development fluctuated or declined; and (6) interregional carbon emission reduction cooperation remained stable and improved. This research offers a theoretical and scientific reference for formulating low-carbon development strategies in similar regions. Full article

Developed countries with animal husbandry are confronted with the pressing issues of ensuring stable livestock product supplies while maintaining ecological sustainability. Additional research is required to ascertain whether environmental regulation can effectively facilitate the green transformation of animal husbandry and establish a harmonious equilibrium between environmental protection and economic growth. It is essential for the empirical development of environmental policies in animal husbandry, as it evaluates the impact of regulatory measures on this sector’s ecological efficiency and precisely investigates the underlying mechanisms of these effects. This paper evaluates the nonlinear impact of environmental regulation policies on the ecological efficiency of animal husbandry using the super-efficiency EBM model, spatial Durbin model, and panel threshold model, which are based on panel data from 31 Chinese provinces (2010–2022). The findings indicated that: (1) The ecological efficiency and environmental regulation intensity of animal husbandry in China exhibited a fluctuating upward trend. The environmental regulation is ranked from high to low in the following order: Northeast, West, Central, and Eastern regions. Conversely, the regions with high ecological efficiency are concentrated in the Northeast and Western regions. (2) The impacts of environmental regulation on the ecological efficiency of animal husbandry were N-type nonlinear, with the extreme points being 6.322 and 9.456. Environmental regulation also produced an “inverted N” type spatial spillover effect on the ecological efficiency of animal husbandry in adjacent areas, with extreme values of 5.330 and 7.670. (3) Environmental regulation considerably enhanced the ecological efficiency of animal husbandry in the Eastern and Central regions in terms of location characteristics. The influence on the Western and Northeastern regions exhibited N-type nonlinear characteristics. (4) From 2017 to 2022, ER had an N-type nonlinear effect on animal husbandry ecological efficiency in terms of temporal heterogeneity. However, the effect was not significant from 2010 to 2016. Full article

Under the guidance of high-quality development goals, the energy industry should not only pay attention to the development level but also to the coordination effect among multiple elements. In the process of low-carbon development, natural gas plays an important transitional role as a clean fossil energy. In this study, by introducing the theoretical perspective of energy trilemma, a comprehensive measurement system of the three-dimensional development level of the regional natural gas industry was constructed. Then, in order to overcome the limitation that the coordination effect is weakened due to the concentration of function values, an improved coupling coordination model was established based on the redefined coupling degree distribution function. Next, based on actual data from Beijing from 2006 to 2022, the safety–economy–environmental protection development level of the natural gas industry was empirically analyzed, and the coupling coordination degree of multi-dimensional factors was deeply investigated. The empirical results reveal the following: (1) Beijing is one of the largest natural gas consumption markets in China, so the economy level of its natural gas industry was relatively high. However, the safety level and environmental protection level needed to be improved. This is mainly due to the scarce resource endowment, and the dependence of economic growth on fossil energy. (2) The coupling coordination degree showed a fluctuating upward trend. The coordination degree of safety and environmental protection was the best, mainly because they coexisted and promoted each other at the policy level. The coordination degree of safety and economy was also relatively high, mainly because supply security could provide resource support for market expansion and stabilize price levels. Meanwhile, a prosperous market would stimulate energy exploration and infrastructure extension. This study will help to provide a high-quality development plan for the natural gas industry for solving the regional energy trilemma. Full article

By employing electrochemical impedance spectroscopy, we performed an impedance analysis of three commercial Li-ion Panasonic NCR18650B cells in order to investigate the direct effects of their internal impedance on the operating voltage, rate capability, and efficiency and their practical capacity. We aimed to assess their performance, safety, and longevity when distinct fast charge/discharge rates were applied. By maintaining a constant fast discharge rate of 2C, we monitored the degradation speed and the influence of the C-rates on the LIBs by applying distinct charge rates, namely, 1C, 1.5C, and 2C. In order to understand how their performance correlates with usage conditions, an SoH evolution analysis, together with a Q–Q₀ total charge and energy consumption examination, was performed, taking into account that cycling monitoring is vital for ensuring their longevity and/or safety. Increasing the I_charge from 1C to 1.5C reduces the battery lifetime by ~50%, while in the case of fast charge/discharge rates of 2C, the lifetime performance decrease is almost ~70% due to a capacity loss that accelerates quickly when the charge rates increase. Moreover, for the latter cell, the last discharge rate can only go up to ~80% SoH, as the battery charge rate can no longer support faster degradation. In agreement with these results, the fluctuations in the Q–Q₀ total charge become more pronounced, clearly affecting LIB efficiency. High charge rates add an additional high voltage that increases the batteries’ stress, leading to a shorter lifetime. Energy consumption data follow the same trend, in which efficiency decreases dramatically when losses appear because the internal resistance causes more and more heat to be produced during both fast charging and discharging. Full article

Complexity evaluation of hydro-climatic datasets is a challenging but essential pre-requisite for accurate modeling and subsequent planning. Changes in climate and anthropogenic interventions amplify the complexity of hydro-climatic time-series. Understanding persistence and fractal features may help us to develop new and robust modeling frameworks which can work well under non-stationary and non-linear environments. Classical fractal hydrology, rooted in statistical physics, has been developed since the 1980s and the modern alternatives based on de-trending, complex network, and time–frequency principles have been developed since 2002. More specifically, this review presents the procedures of Multifractal Detrended Fluctuation Analysis (MFDFA) and Arbitrary Order Hilbert Spectral Analysis (AOHSA), along with their applications in the field of hydro-climatology. Moreover, this study proposes a complex network-based fractal analysis (CNFA) framework for the multifractal analysis of daily streamflows as an alternative. The case study proves the efficacy of CNMFA and shows that it has the flexibility to be applied in visibility and inverted visibility schemes, which is effective in complex datasets comprising both high- and low-amplitude fluctuations. The comprehensive review showed that more than 75% of the literature focuses on characteristic analysis of the time-series using MFDFA rather than modeling. Among the variables, about 70% of studies focused on analyzing fine-resolution streamflow and rainfall datasets. This study recommends the use of CNMF in hydro-climatology and advocates the necessity of knowledge integration from multiple fields to enhance the multifractal modeling applications. This study further asserts that transforming the characterization into operational hydrology is highly warranted. Full article

Separate DC-DC converters for each energy source are typically configured in fuel-cell hybrid vehicles. This results in a complex control structure of the powertrain system, low energy density of the converter, and high cost due to the large number of components. Conducting research on DC-DC converters with good energy flow management and high integration is a trend to solve such problems. Based on the analysis of the basic functional structure of the converter, this paper designs a buffering unit circuit with energy collection and distribution functions and appropriately connects it with the pulse unit circuit of the converter. Through device optimization reuse and power transmission path integration, a class of non-isolated four-port DC-DC converters is constructed, which consists of an auxiliary energy charging module, input energy source control module, braking energy feedback module and forward bootstrap boost circuit. This converter has two bi-directional ports, a uni-directional input and a bi-directional output, for separate connection to the power batteries, supercapacitors, fuel cells and DC bus. It can adapt to the fluctuation of the vehicle’s driving condition while achieving dynamic and flexible regulation of power flow and can flexibly allocate power according to the load current and voltage level of energy. It can realize a total of 14 operation modes, including six output power supply operation modes, five auxiliary power charging operation modes, and three braking energy regeneration operation modes. Furthermore, the mathematical model of this converter is constructed using the state-average method and the small-signal modeling method in order to achieve the responsiveness and stability of switching multiple operating modalities. The PI control parameters are optimized using the particle swarm optimization algorithm to achieve optimized control of the converter. The simulation system is set up using MATLAB R2024a to verify that the proposed converter topology and algorithm can dynamically allocate appropriate current paths to manipulate the power flow under various operating conditions, effectively improving the utilization rate and efficiency of energy. The converter has the characteristics of high gain and high power density, which is suitable for three-energy fuel cell hybrid electric vehicles. Full article

The rapid growth of the global population, the acceleration of the urbanization process, and the demands of economic development, place enormous pressure on scarce land resources. Cultivated land use presents a series of problems, hindering its socioeconomic and ecological sustainability. The sustainable intensification of cultivated land use (SICLU) is a development model designed to maximize land use efficiency, while minimizing environmental pollution. It is considered to be an efficient method to achieve three aspects of sustainable goals, namely in regard to society, the economy, and ecology, simultaneously. This approach has significant theoretical and practical implications for China’s food security and ecological safety. This study incorporates the “agricultural carbon emissions” indicator into the indicator evaluation system. Using the super-efficiency SBM model, we estimate the SICLU levels in China from 2001 to 2020. ArcGIS and the Dagum Gini coefficient decomposition model are employed to explore the temporal and spatial evolution characteristics and non-equilibrium spatial dynamics of SICLU in China. Finally, the Tobit regression model is used to reveal the driving factors. The results show the following: (1) Since 2003, China’s SICLU levels demonstrate an overall ascent amid fluctuations, sustaining a relatively high average annual level of 0.945. (2) In terms of spatial evolution patterns, China’s SICLU levels demonstrate significant spatial disparities, with distinct differences among the four major regions. Regions with similar SICLU levels show a certain degree of spatial adjacency. (3) There are significant regional disparities in China’s SICLU levels, which overall exhibit a declining trend. The differences between regions are the primary source of spatial variation, followed by hypervariable density and intra-regional disparities. (4) The regional industrial structure, the level of agricultural modernization, the agricultural cropping structure, and the per capita sown area, positively influence the enhancement of SICLU levels in China. Throughout the study period, the SICLU levels in China continuously improved and the overall regional disparities diminished. However, significant inter-regional imbalances persist, necessitating tailored optimization measures, based on local conditions. Establishing a coordinated mechanism for orderly and synergistic regional development is crucial, in order to provide references to decision-makers to promote the rational use of arable land in China. Full article