Search Results (8,436)

To effectively manage and conserve ecosystems, it is crucial to understand how vegetation changes over time and space and what drives these changes. The Normalized Difference Vegetation Index (NDVI) is a key measure of plant growth that is highly sensitive to climate variations. Despite its importance, there has been limited research on vegetation changes in the upper sections of the Irtysh River. In our study, we combined various datasets, including NDVI, temperature, precipitation, soil moisture, elevation, and land cover. We conducted several analyses, such as Theil–Sen median trend analysis, Mann–Kendall trend and mutation tests, partial correlation analysis, the geographical detector model, and wavelet analysis, to reveal the region’s pronounced warming and moistening trend in recent years, the response relationship between NDVI and the climate, and the primary drivers influencing NDVI variations. We also delved into the spatiotemporal evolution of NDVI and identified key factors driving these changes by analyzing atmospheric circulation patterns. Our main findings are as follows: (1) Between 1901 and 2022, the area’s temperature rose by 0.018 °C/a, with a noticeable increase in the rate of warming around 1990; precipitation increased by 0.292 mm/a. From 1950 to 2022, soil moisture exhibited a steady increase of 0.0002 m³ m⁻³/a. Spatial trend distributions indicated that increasing trends in temperature and precipitation were evident across the entire region, while trends in soil moisture showed significant spatial variation. (2) During 1982 to 2022, the vegetation greening trend was 0.002/10a, indicating a gradual improvement in vegetation growth in the study area. The spatial distribution of monthly average NDVI values revealed that the main growing season of vegetation spanned April to November, with peak NDVI values occurring in June–August. Combined with serial partial correlation and spatial partial correlation analysis, temperatures during April to May effectively promoted the germination and growth of vegetation, while soil moisture accumulation from June to August (or January to August) effectively met the water demand of vegetation during its growth process, with a significant promoting effect. Geographical detector results demonstrate that temperature exhibits the strongest explanatory power for NDVI variation, whereas land cover has the weakest. The synergistic promotional effect of multiple climatic factors is highly pronounced. (3) Wavelet analysis revealed that the periodic characteristics of NDVI and climate variables over a 2–15-year timescale may have been associated with the impacts of atmospheric circulation. Taking NDVI and climatic factors from June to August as an example, before 2000, temperature was the dominant influencing factor, followed by precipitation and soil moisture; after 2000, precipitation and soil moisture became the primary drivers. The North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were the primary atmospheric circulation patterns influencing vegetation variability in the region. Their effects were reflected in the inverse relationship observed between NAO/AO indices and NDVI, with typical phases of high and low NDVI closely corresponding to shifts in NAO and AO activity. This study helps us to understand how plants have been changing in the upper parts of the Irtysh River. These insights are critical for guiding efforts to develop the area in a way that is sustainable and beneficial for the environment. Full article

Municipalities aiming for climate neutrality and resilience must take a systemic approach to planning, implementing, and monitoring climate actions, to be able to mobilise the resources needed to achieve this ambitious goal. This involves complementing conventional top-down and technological measures with bottom-up and inclusive strategies that include not only citizen engagement but also the innovation of social practices. This study presents a comparative analysis of social innovation actions for climate neutrality planned by 53 cities from 21 countries participating in the Pilot Programme of the EU-funded project NetZeroCities. By identifying 445 actions across all cities’ pilot programmes and classifying them into 10 categories and 38 sub-categories, it is found that 53.71% of actions are linked with social innovation, offering timely insights into how social innovations are being designed in cities’ urban plans. The results reveal emerging patterns and geographical variations across Europe. With more than half of all social innovation interventions focused on stationary-energy and Scope-3-related emissions reduction, the analysis reveals that cities are increasingly relying on social innovation to foster the behavioural and socio-technical changes needed to shape sustainable energy use, consumption, and mobility patterns. These actions are based on co-creation, co-design, cross-sectoral partnerships, and public-sector capacity building, with regional differences. The comparative approach and analysis contribute to the transdisciplinary discourse on social innovation assessment in systemic innovation for transitions. Full article

Eco-anxiety has emerged as a significant psychological response to the climate crisis. Yet its relationship with pro-environmental behavior remains far from settled, with findings ranging from behavioral paralysis to active engagement and seemingly contradictory evidence accumulating across studies. To clarify both the magnitude of this association and the conditions under which it holds, we conducted a systematic review and three-level random-effects meta-analysis. We systematically searched five databases (ProQuest, APA PsycArticles, PubMed, among others) through April 2025, identifying 20 independent studies that contributed 60 effect sizes (N = 34,206). The pooled results revealed a significant, small-to-moderate positive association between eco-anxiety and pro-environmental behavior (r = 0.24, 95% CI [0.15, 0.32], p < 0.001). So far, fairly straightforward. The complication emerged when examining heterogeneity: we observed substantial variation across studies (I² = 95.4%), with a 95% prediction interval ranging from −0.22 to 0.61. What this tells us is that eco-anxiety does not uniformly predict action across contexts; the variability is considerable and meaningful. Moderator analyses offered important clarification. The association proved significantly stronger for public and collective behaviors, such as activism and advocacy (r = 0.36), compared to private sphere actions (r = 0.22). Beyond this, effects were more robust in adult samples (r = 0.30) than among adolescents (r = 0.18). These findings suggest something worth emphasizing: eco-anxiety appears to function not merely as a pathological burden but as an adaptive, context-sensitive correlate of collective engagement. Put differently, the distress people experience in response to climate change may channel productively into systemic action, particularly when social and collective pathways are available. What this means for practice is significant. Future interventions, in this perspective, should focus on channeling climate distress toward collective, structural engagement rather than defaulting to individual behavioral prescriptions alone. Full article

African vegetation responses to extreme drought represent a key challenge for global change research and sustainable water–land resource management. Satellite remote sensing provides long-term observations of vegetation dynamics, yet conventional analyses focus on vegetation structural, greenness, or productivity changes, lacking of understanding on physiological adaptation. This study applies a multi-model framework integrating high-temporal-resolution (4-day) and multi-spectral satellite data with machine learning to disentangle structural and physiological responses across Central and Western Africa. Three key indicators were used: evapotranspiration (ET), relative solar-induced chlorophyll fluorescence (SIF_rel), and the ratio of midday to midnight vegetation optical depth (VOD_ratio), which respectively, represent water flux, photosynthetic activity, and water regulation. A random forest model, combined with SHapley Additive exPlanations (SHAP) analysis, was used to separate vegetation anomaly signals and identify key climatic controls. The results reveal pronounced differences in vegetation responses between arid and humid climatic regions. In arid regions, near-infrared reflectance of vegetation (NIRv) and solar-induced chlorophyll fluorescence (SIF) exhibited clear negative anomalies and significant pre-drought declines, accompanied by marked changes in vegetation optical depth (VOD), indicating canopy structural damage and reduced photosynthetic activity. In contrast, trend analysis revealed that although SIF and NIRv in humid regions showed relatively strong responses during the pre-drought phase, they did not exhibit significant trends after the drought peak, and changes in VOD were comparatively small, suggesting that higher water availability partially buffered the prolonged impacts of drought on vegetation structure and function. Process analysis showed that three months before and after drought peaks, physiological indicators exhibited strong anomalies that closely tracked drought duration. SIF_rel, ET signals peaked earlier than water-content anomalies (VOD_ratio), suggesting a two-phase regulation strategy: early stomatal closure followed by delayed deep-root water uptake. Physiological anomalies accounted for over 88% of total vegetation anomalies during drought peaks, highlighting their dominant role in early-stage drought response. Precipitation and temperature emerged as primary drivers, explaining 76.8% of photosynthetic variation, 60.3% of ET variation, and 53.9% of water-content variation in the development. The recovery is influenced by the duration of drought and the regrowth of vegetation. By explicitly decoupling physiological and structural vegetation responses, this study provides refined, process-based insights into African ecosystem adaptation to water stress. These findings contribute to more accurate drought monitoring, water availability assessment, and climate adaptation strategies, directly supporting sustainable water and land management goals. Full article

Moray (Cusco, Peru) represents one of the most sophisticated examples of Inca agricultural engineering, where architecture, environmental management, and constructive systems converge to generate controlled microclimates for agricultural experimentation. Recognized as an important archaeological heritage site, Moray provides valuable insight into ancestral Andean strategies for adapting agriculture to complex high-altitude environments. However, the site is increasingly exposed to environmental pressures associated with climatic variability, soil erosion, structural collapses, and tourism intensity. This study aims to analyze the relationship between microclimates, geometric design, and constructive sustainability of the Moray archaeological complex through integrated spatial, functional, and constructive analyses, supported by digital tools such as Google Earth Pro, AutoCAD 2023, SketchUp 2023, and environmental simulations developed by Andrew Marsh. The research examines the geometric configuration of the circular terraces, which present radii between 45 and 65 m, heights ranging from 3 to 5 m, and slope variations between 14% and 48%, generating temperature gradients of 12–15 °C between upper and lower levels. These conditions enabled the Incas to experiment with and adapt diverse ecological species across different thermal zones. The study also evaluates the irrigation and infiltration systems composed of gravel, sand, and stone layers that ensured soil stability and moisture regulation. Climate data from SENAMHI (2019–2024) indicate that Moray is located in a semi-arid meso-Andean environment, reinforcing its interpretation as an ancestral environmental laboratory. The results demonstrate Inca mastery in integrating environmental design, hydrological engineering, and agricultural experimentation while also identifying current conservation challenges related to erosion processes, structural deterioration, and tourism pressure. This research contributes to understanding Moray as a climate-sensitive heritage system, offering insights relevant to contemporary strategies for sustainable agriculture, climate adaptation, and heritage conservation in Andean regions. Full article

Based on a previously developed empirical model of global solar irradiance (EMGSI) at the Dome C station under all-sky conditions, and on good simulations of global solar radiation and its losses in the atmosphere caused by absorption and scattering components, as well as albedos at the top of the atmosphere (TOA) and the surface (TOAsur) during 2006–2016, similar estimations for the above parameters during 2018–2021 and 2006–2021 were computed by further application of this empirical model, and reliable calculations were also obtained, as in 2006–2016. The long-term variations in the above variables were thoroughly investigated during 2006–2021. For annual averages over 2006–2021, the calculated and observed global solar radiation decreased, and the absorption and scattering losses increased, well associated with increases in absorption and scattering atmospheric substances. Air temperature increased by 0.99 °C, showing regional climate warming. The mechanisms of air temperature increase were fully studied, and the basic mechanism reported previously was further confirmed. Additionally, the mechanisms of air temperature change vary with gases, liquids, and particles (GLPs) and with sites. Therefore, a proposal is recommended that, to reduce climate warming, all forms of direct emissions of GLPs and the secondary formation of new GLPs in the atmosphere produced by these directly emitted GLPs via chemical and photochemical reactions (CPRs) should be controlled. The estimated and satellite-derived albedos during 2006–2021 decreased at the TOAsur. An integrated understanding of solar radiation transfer in the atmosphere and of energy balance at the TOAsur is necessary. Full article

Urbanization and climate change have intensified the need for advanced methods to simulate building energy performance within realistic urban environmental contexts. This study presents a microclimate-informed framework for developing representative building energy prototypes that enable the estimation of energy use for buildings sharing similar microclimatic conditions and building-level characteristics. The framework is demonstrated using Des Moines, Iowa, as a case study. The framework combines high-resolution microclimate modeling with geospatial analysis to quantify the influence of urban form and vegetation on building energy use. Localized weather files were generated using the Weather Research and Forecasting (WRF) model to capture spatial variations in microclimate across the city. Detailed three-dimensional models of buildings and trees were developed from Light Detection and Ranging (LiDAR) point cloud data and integrated with building attributes, including construction materials and heating and cooling systems, to generate representative building typologies use them to build a similarity-based lookup table. Urban energy simulations were conducted using the Urban Modeling Interface (UMI). To demonstrate the effectiveness of the framework, simulations were conducted for two building prototypes according to the framework. Results show that monthly energy use intensity (EUI) of a representative cluster compared to randomly selected buildings differs by 10% to 19%, with both positive and negative deviations observed depending on building template and month. Thus, the proposed framework shows great promise to capture comparable energy performance trends across buildings with similar construction characteristics and urban context and minimize computational demands for doing so. While evapotranspiration effects are not explicitly modeled in the current framework, they are recognized as an important microclimatic process and will be incorporated in future work. This study demonstrates that the proposed framework provides a scalable and computationally efficient approach for urban-scale energy analysis and can support data driven decision making for climate-responsive urban planning. Full article

The low efficiency of photovoltaic (PV) systems arises from their nonlinear current-voltage characteristics, necessitating the use of maximum power point tracking (MPPT) techniques. Conventional MPPT methods are popular for their simplicity and low cost but exhibit poor performance under rapidly changing atmospheric conditions, leading to considerable energy losses. Under uniform solar irradiation, these traditional approaches can locate the maximum power Point (MPP), yet their reliance on small, fixed step sizes causes oscillations and output ripple. In dynamic environmental conditions, they often fail to accurately track the true MPP. To address these challenges, this paper proposes an MPPT strategy based on the artificial Gorilla Troops Optimizer (GTO) to enhance PV performance under partial shading conditions (PSCs) and fast climatic variations. An enhanced version of the algorithm (EnGTO) was developed to further improve MPPT efficiency. Comparative simulations with the perturb and observe (P&O) method and the classic GTO demonstrate that the proposed approach achieves rapid response to environmental changes and higher accuracy and lower oscillations under PSCs, reaching efficiencies of up to 99.96% (STCs) and 99.81% (PSCs). Full article

The transition to renewable energy, mainly via the use of PV (photovoltaic) systems, is essential for addressing global concerns related to climate change, energy security, and sustainability. Conventional Maximum Power Point Tracking (MPPT) techniques, particularly Perturb and Observe (P&O) and Incremental Conductance methods, rely on fixed-step gradient-based control, which leads to steady-state oscillations around the maximum power point, slow convergence during rapid irradiance and temperature variations, and inaccurate tracking under partial shading conditions. These technical limitations often cause the controller to deviate from the global maximum power point, resulting in reduced dynamic efficiency, increased power losses, and degraded power quality in practical PV applications. To overcome these limitations, this research proposes a hybrid optimization model that incorporates ACO (Ant Colony Optimization) and an ANN (Artificial Neural Network) to enrich the effectiveness of MPPT in PV systems. The proposed model is designed to dynamically adapt to variations in solar irradiance and temperature, effectively addressing the inadequacies present in the conventional techniques and also improving the MPPT efficiency in PV systems. By leveraging the unique strengths of both ACO and ANN, the model significantly improves energy extraction and also ensures robustness against environmental fluctuations. Simulation results demonstrate that the proposed ACO–ANN MPPT framework achieves a total harmonic distortion (THD) of 1.39%, representing a reduction of approximately 34–70% compared to conventional and recent AI-based MPPT techniques, while simultaneously delivering higher voltage stability, faster convergence, and increased maximum power extraction. This contribution is vital in paving the way for future advancements in renewable energy systems and provides a more reliable approach to solar power optimization, which can greatly aid in achieving sustainable energy goals. Full article

Against the backdrop of global climate change, remote subtropical mountainous power grids face severe operational challenges due to their fragile infrastructure and complex climatic conditions. However, existing research has insufficiently addressed load forecasting in data-sparse regions, particularly lacking systematic analysis of the “meteorology–load–failure” coupling mechanism. To address this gap, this study focused on 10 kV distribution lines in a typical subtropical monsoon region of southern China. Based on hourly load and meteorological data from 2016 to 2025, we propose a two-stage hybrid model combining “Random Forest (RF) feature selection + Long Short-Term Memory (LSTM) time series forecasting”. Through deep feature engineering, composite, lagged, and interactive features were constructed. Using the RF algorithm, we quantitatively identified the core drivers of load variation across different time scales: at the hourly scale, variations are dominated by historical inertia (with weights of 0.5915 and 0.3757 for 1-h and 24-h lagged loads, respectively); at the daily scale, the logic shifts to meteorological triggering and cumulative effects, where the composite feature load_lag1_hi_product emerged as the most critical driver (weight of 0.8044). Experimental results demonstrate that the hybrid model significantly improved forecasting accuracy compared to the full-feature LSTM benchmark: on a daily scale, RMSE decreased by 13.29% and MAE by 16.67%, with R² reaching 0.8654; on an hourly scale, R² reached 0.9687. Furthermore, correlation analysis with failure data revealed that most grid faults occurred during intervals of extremely low load variation (0–5%), suggesting that “chronic stress” from environmental exposure in hot and humid conditions is the primary cause, with lightning identified as the leading external threat (26.90%). The interpretable forecasting framework proposed in this study transcends regional limitations. It provides a strategic “low-cost, high-resilience” prototype applicable to power systems in humid-subtropical zones worldwide, particularly for developing regions facing the dual challenges of data sparsity and climate vulnerability. Full article

Under the fast development of urbanization, PM_2.5 pollution has become a prominent issue affecting the urban ecological environment and residents’ health. To investigate the impact of urban landscape patterns on PM_2.5 concentrations, this study applies the Local Climate Zone (LCZ) classification to Shanghai using the World Urban Database and Access Portal Tools (WUDAPT). LCZ-derived landscape metrics are adopted as predictor variables to focus on how urban form and spatial configuration affect PM_2.5 distribution and to identify the key landscape categories and types influencing PM_2.5 levels. The results reveal notable seasonal and spatial differences in the effects of different LCZ types and landscape metrics on PM_2.5 concentrations; on average, over 69% of the spatial variation in PM_2.5 across the four seasons can be explained by the Multi-scale Geographically Weighted Regression (MGWR) model. This research demonstrates that the LCZ framework effectively uncovers the seasonal and spatial mechanisms by which urban landscape patterns influence PM_2.5 concentrations in Shanghai. It offers a novel perspective for understanding the interplay between urban landscape and atmospheric pollution, and provides scientific guidance for sustainable urban planning and precise air pollution control strategies in other cities. Full article

In 2023–2025, a research study named “Application of nuclear, seismic and infrasound methods for assessing climate change and mitigating the effects of climate change” was conducted in Kazakhstan under the Targeted Funding Program. The main task of the study was to create an observation network for processes occurring in the glaciers of the high Tien Shan. Seismic and infrasound methods were used for signal recording, and meteorological data was additionally used for the analysis. A network of seismic, infrasound and meteorological stations has been installed near the large glaciers of Tien Shan in Kazakhstan. This paper presents the results of the recorded data in terms of seismic and infrasound noise levels, daily variations, and the relationship between noise and changes in temperature and wind speed. The threshold of the expected minimal magnitude and energy classes of glacial earthquakes for day and night was assessed. Seismic and infrasound monitoring has proven to be a reliable all-season and all-weather tool for monitoring the dynamics of glacial processes. Among the large number of recorded glacial events, more than 4000 have been located, and a seismic bulletin that includes information on the location, magnitude, and energy class of each has been compiled. Full article

Beautiful Leisure Tourism Villages (BLTVs) represent an effective pathway for advancing high-quality rural industrial development and promoting comprehensive rural revitalization. They are of great significance to enriching new rural business formats and new functions. The analysis is interpreted within an integrated location–distance attenuation framework. Based on the methods of spatial clustering analysis, geographical linkage rate and geographical weighted regression, the spatio-temporal evolution of 1982 BLTVs in China up to 2023 was examined to uncover the underlying driving mechanisms. Findings indicated that (1) a staged expansion in the number of villages across China, with the most pronounced growth occurring between 2014 and 2018, averaged 124 new villages per year; their stage characteristics showed an obvious “unipolar core-bipolar multi-core-bipolar network” development model; (2) the barycenters of villages were all located in Nanyang City of Henan Province; they migrated from east to west, and formed a push and pull migration trend from east to west and then east; (3) the spatial distribution of villages was highly aggregated and demonstrated marked regional heterogeneity, following a south–north and east–west gradient, with the highest concentration in Jiangzhe and the lowest in Ningxia Hui Autonomous Region; and (4) natural ecology, hydrological and climatic conditions, socioeconomic context, transportation accessibility, and resource endowment collectively shaped the spatial layout of villages, exhibiting pronounced spatial variation in the intensity of these driving factors. On the whole, topography, social economy, traffic condition and precipitation condition had greater influences on the spatial distribution of villages in the western than in the eastern part of China. In contrast, the effects of resource endowment and temperature on the spatial distribution of BLTVs were stronger in eastern China than in western China. These findings enhance the theoretical understanding of tourism-oriented rural development by integrating spatio-temporal evolution with a location–distance attenuation perspective and provide differentiated guidance for the sustainable development of BLTVs across regions. Full article

The Lake Sevan basin is particularly sensitive to climate change due to its continental climate and mountainous terrain, which collectively amplify climatic impacts. This study aimed to assess the influence of climate change on the thermal dynamics of the basin by analyzing both historical and projected temperature variations. Over the past three decades, the region has experienced a marked rise in air temperatures. Seasonal variability revealed distinct contrasts between winter and summer, with winter exhibiting greater fluctuations, ranging from 1.67 to 2.41 °C, compared to the more stable summer range of 0.81 to 1.41 °C. An analysis of heat inflow and outflow patterns demonstrated a moderating effect of Lake Sevan on temperature extremes. Stations, located near the lake, recorded lower levels of heat inflow and outflow, indicating that the lake’s thermal inertia helps buffer seasonal temperature extremes. In contrast, stations situated farther from the lake exhibited more pronounced fluctuations, reflecting the absence of this stabilizing influence. These results underscore the lake’s critical role in modulating the local climate by dampening extreme thermal variations. Additionally, comparative analysis of air and water temperature trends revealed that, while both exhibit warming, air temperatures show greater interannual variability. In contrast, water temperatures remained more stable, particularly during winter, due to the lake’s thermal inertia. Future climate projections for the Lake Sevan region, based on CMIP6 (Coupled Model Intercomparison Project phase 6) ensemble outputs under four Shared Socioeconomic Pathways (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5), suggest a persistent warming trend throughout the 21st century. We project that the most significant increases are expected during summer months, with an anticipated mean annual temperature rise of up to 6 °C by the end of the century under the high-emission scenario (SSP5–8.5). Full article

Global change represents one of the most pressing threats to ecosystems, profoundly influencing habitats and redefining management and conservation priorities. Rising temperatures, altered precipitation regimes, invasive species and the increasing frequency of extreme events, such as prolonged droughts and wildfires, are modifying the composition, structure, and resilience of forests. Often, these changes result in habitat fragmentation, which isolates populations and diminishes their ability to adapt. This situation calls for an urgent reassessment of traditional protected area management practices. In response to climate change, it is essential to prioritize conservation strategies that focus on adaptation and maintaining biodiversity, while combating the spread of invasive species. For this reason, this study aims to analyze the impact of global changes on forest vegetation within protected areas, using Aspromonte National Park, a highly biodiverse region, as a case study. It evaluates the transformations in habitat cover and fragmentation over twenty years by comparing the 2001 vegetation map of Aspromonte National Park with the Map of Nature of the Calabria region, to quantify spatial and temporal habitat variations using QGIS 3.42.3 software. Morphological Spatial Pattern Analysis (MSPA) and FRAGSTATS v4.2 were employed to evaluate habitat fragmentation. The results indicate that most forest habitats have experienced a slight increase in area over the past 20 years. However, the area occupied by Pinus nigra subsp. laricio forests (Habitat 42.65) has decreased significantly, most likely due to repeated fires in previous years. In conclusion, this study establishes a scientific foundation for guiding conservation policies in the protected area and promoting the resilience of native plant communities against global change. This is essential for ensuring their survival for future generations while mitigating both habitat fragmentation and the introduction and spread of non-native species. Full article