Search Results (1,629)

The impact of invasive alien species (IAS) is one of the direct factors causing global biodiversity decline and economic losses, and predicting the potential invasion risks of invasive species is crucial for developing prevention and control strategies. In recent years, an increasing number of studies have shown that invasive species undergo rapid shifts in climate niche in invaded areas. Accurately quantifying the dynamic shifts in the climate niche of invasive species in invaded areas is crucial for developing a more accurate framework for early warning of invasive species risks. Pomacea canaliculata is a freshwater snail found in South America and has become one of the most aggressive aquatic species in the world. Since its introduction to China in 1981, it has rapidly spread and caused multiple serious damages to agriculture, ecology, and public health. Therefore, based on multi-source distribution data of P. canaliculata, this study calculated the climate niche overlap by Schoener’ s D, quantified the niche shifts between the P. canaliculata in native and invaded areas (China) via the COUE scheme (a unified terminology representing niche centroid shift, overlap, unfilling, and expansion), and analyzed their changes on a time scale. The results revealed that there have been significant climate niche shifts (Schoener’s D < 0.2, niche similarity tests p > 0.01, niche equivalence tests p < 0.01) between the native and invaded areas (China) of P. canaliculata, which does not support the climate niche conservation hypothesis. The minimum temperature of the coldest month (Bio 6) and precipitation seasonality (Bio 15) were the key climate variables driving the climatic niche shift, and P. canaliculata can survive in colder and more arid regions than their native counterparts. The changes in the niche shifts in P. canaliculata on a time scale show significant temporal heterogeneity, and its invasion behavior in China presents a discontinuous and phased expansion pattern, with strong adaptability to new environments. The results are of great significance for the future development of more accurate ecological niche model (ENM), the formulation of more targeted prevention and control strategies, and the study of adaptive evolution mechanisms of invasive species. Full article

Mountain glaciers are the most direct and sensitive indicators of climate change. In the context of global warming, monitoring changes in glacier elevation has become a crucial issue in modern cryosphere research. The Global Ecosystem Dynamics Investigation (GEDI) is a full-waveform laser altimeter with a multi-beam that provides unprecedented measurements of the Earth’s surface. Many studies have investigated its applications in assessing the vertical structure of various forests. However, few studies have assessed GEDI’s performance in detecting variations in glacier elevation in land ice in high-mountain Asia. To address this limitation, we selected the Southeastern Tibetan Plateau (SETP), one of the most sensitive areas to climate change, as a test area to assess the feasibility of using GEDI to monitor glacier elevation changes by comparing it with ICESat-2 ATL06 and the reference TanDEM-X DEM products. Moreover, this study further analyzes the influence of environmental factors (e.g., terrain slope and aspect, and altitude distribution) and glacier attributes (e.g., glacier area and debris cover) on changes in glacier elevation. The results show the following: (1) Compared to ICESat-2, in most cases, GEDI overestimated glacier thinning (i.e., elevation reduction) to some extent from 2019 to 2021, with an average overestimation value of about −0.29 m, while the annual average rate of elevation change was relatively close, at −0.70 ± 0.12 m/yr versus −0.62 ± 0.08 m/yr, respectively. (2) In terms of time, GEDI reflected glacier elevation changes at interannual and seasonal scales, and the trend of change was consistent with that found with ICESat-2. The results indicate that glacier accumulation mainly occurred in spring and winter, while the melting rate accelerated in summer and autumn. (3) GEDI effectively monitored and revealed the characteristics and patterns of glacier elevation changes with different terrain features, glacier area grades, etc.; however, as the slope increased, the accuracy of the reported changes in glacier elevation gradually decreased. Nonetheless, GEDI still provided reasonable estimates for changes in mountain glacier elevation. (4) The spatial distribution of GEDI footprints was uneven, directly affecting the accuracy of the monitoring results. Thus, to improve analyses of changes in glacier elevation, terrain factors should be comprehensively considered in further research. Overall, these promising results have the potential to be used as a basic dataset for further investigations of glacier mass and global climate change research. Full article

Human activities undoubtedly increase greenhouse gases (GHG), negatively influencing global climate change. The building and construction sector uses at least 40% of the total energy consumption and produces the same percentage of GHG emissions. Therefore, the development of sustainable building materials is a crucial key factor for environmental protection. The study contributes to the development of bio-based façade materials using available raw biomass like wheat straw, grey alder, and softwood (a mix of spruce and pine), to promote reduced emissions of CO₂. Two technologies were used to produce high-density particleboards based on (1) steam explosion treatment and (2) the addition of bio-based suberinic acids as a binder. In addition to the biomass species and board type, the influence of conventional and mold hot-pressing was investigated on produced board properties: density, thickness swelling, modules of rupture and elasticity in bending, and internal bonding. The obtained particleboards demonstrate significant differences in terms of the tested properties depending on all variable factors. The best performance, in terms of physical–mechanical properties, was achieved by the conventionally hot-pressed board of steam-exploded grey alder particles, being influenced by the highest density (1380 kg/m³). Mold hot-pressing in most cases resulted in decreased performance of obtained boards. Full article

Diospyros kaki Thunb. ‘Taishuu’ is novel fruit cultivar known for its excellent mouthfeel properties and high economic value. This study aimed to identify the ecological adaptability and potential suitable cultivating regions of this persimmon in China. In addition, key ecological factors influencing fruit mouthfeel were also investigated. Differences between key metabolites and mouthfeel properties of 35 persimmon samples from 13 provinces were compared. Subsequently, ecological factors were evaluated to explore interactions among dominant ecological factors, habitat suitability, and fruit quality. An adaptive segmentation map was ultimately created to highlight variations in mouthfeel properties of the persimmon. The findings were summarized as follows: The core ecological suitability zones encompass most warm, temperate and typically subtropical regions of China, spanning 116,200 square kilometers. Habitat suitability influences fruit size but does not affect mouthfeel properties. Key factors affecting mouthfeel properties of D. kaki ‘Taishuu’ include precipitation during the growing period, high temperature during the fruit ripening stage, and low temperatures during dormancy. Persimmons from coastal areas and Yunnan province were characterized by a lusciously sweeter and richer taste, a satisfying crisp texture, and an overall distinctly superior mouthfeel. In contrast, samples from central cultivation areas exhibited higher density, greater firmness, reduced crispness, and inferior flavor quality Based on zoning results, extensive regions show significant potential for high-quality production, making them highly promising for D. kaki ‘Taishuu’ cultivation. For marginally suitable habitats, appropriate cultivation measures should be implemented to mitigate limiting factors such as temperature and soil moisture. Full article

Studying the effects of environmental factors on microbial community assemblies is crucial for understanding microbial biodiversity and ecosystem processes. Although numerous studies have explored the spatial patterns of microbial communities in surface soils, bacterial community distributions in subsurface layers remain poorly understood. We investigated multiple community metrics of soil bacteria in arid and semi-arid grasslands in China, and the V4 region of 16S rDNA was analyzed using soil property measurements, fluorescent PCR, and high-throughput sequencing techniques. Specifically, copiotrophic taxa dominate the topsoil, whereas oligotrophic taxa are prevalent in nutrient-limited subsoil. Bacterial diversity decreases from the topsoil to subsoil, and bacterial distribution and ecological community composition exhibit a strong dependence on environmental factors. Moreover, microbial interaction networks demonstrated a progressive simplification with increasing soil depth: topsoil communities displayed higher modularity and a greater prevalence of positive interactions, whereas subsoil networks were significantly less complex. Null model analyses evidenced assembly mechanisms: deterministic processes (particularly homogeneous selection) dominated the bacterial community assembly, but their influence weakened with depth, whereas stochastic processes (e.g., dispersal limitation) increased progressively from the topsoil to subsoil. The PLS-PM analysis demonstrated that the relative influence of abiotic factors (e.g., climatic conditions and nutrient availability), biotic factors (interspecific interactions), along with drift and dispersal limitations on fungal community assembly exhibited depth-dependent patterns. This study provides novel insights into the vertical stratification of bacterial community in arid and semi-arid grasslands, and advances our understanding of pedogenic process under climate change and microbial adaptive strategies in heterogeneous soil environments. Full article

Primary forests harbor extraordinary biodiversity, but conversion from primary forests to secondary forests often leads to biodiversity loss and diminished ecosystem functioning. While much of the existing research has focused on plants and vertebrates, soil fauna—particularly Collembola—remain underexplored in this context. To address this gap, we conducted a comprehensive assessment of the Collembola diversity and community composition in primary and secondary forests across two regions in northeastern China. Among 5587 Collembola individuals, 69 morphospecies were identified. The Collembola abundance and Shannon–Wiener index were significantly higher in primary forests, although the species richness did not differ significantly between the forest types. In contrast, the community composition differed markedly, with several taxa found exclusively in primary forests. Notably, environmental factors exerted stronger influences on Collembola communities in primary forests, suggesting that these ecosystems may be more vulnerable to climate change and external disturbances. These findings demonstrate that primary forests play a crucial role in protecting soil fauna diversity and emphasize that future conservation efforts should focus on the strict protection of primary forests. Full article

This study examines the intersection of environmental, social, and governance (ESG) disclosure (operationalized through sustainability reporting), corporate financial performance, and the behavioral dynamics of herding in capital structure decisions among non-financial firms in five ASEAN countries. As ESG and sustainability finance gain prominence in addressing climate change and climate risk, understanding the behavioral factors that relate to ESG adoption is crucial. Employing a quantitative approach, this research utilizes a purposive sample of 125 non-financial firms from Indonesia, Malaysia, the Philippines, Singapore, and Thailand, gathered from the Bloomberg Terminal spanning 2018–2023. Managerial Herding Ratio (MHR) is used to assess herding behavior, while Sustainability Report Disclosure Index (SRDI) measures ESG disclosure. Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multigroup Analysis (MGA) were applied for data analysis. This research finds that while sustainability reporting enhances return on assets (ROA) and Tobin’s Q, it does not significantly relate to net profit margin (NPM). The findings also confirm that herding behavior—where companies mimic the financial structures of peers—moderates the relationship between sustainability reporting and performance outcomes, with leader firms gaining more from transparency efforts. This highlights the double-edged nature of herding: while it can accelerate ESG adoption, it may dilute the strategic depth of climate action if firms merely follow rather than lead. The study provides actionable insights for regulators and corporate strategists seeking to strengthen ESG finance as a driver for climate resilience and long-term stakeholder value. Full article

Optimizing agricultural structure serves as a crucial pathway to promote sustainable rural economic development. This study focuses on a representative village in the mountainous region of North China, where agricultural production is constrained by perennial low-temperature conditions, resulting in widespread adoption of single-cropping systems. There exists an urgent need to enhance both economic returns and risk resilience of limited arable land through refined cultivation planning. However, traditional planting strategies face difficulties in synergistically optimizing long-term benefits from multi-crop combinations, while remaining vulnerable to climate fluctuations, market volatility, and complex inter-crop relationships. These limitations lead to constrained land productivity and inadequate economic resilience. To address these challenges, we propose an integrated decision-making approach combining stochastic programming, robust optimization, and data-driven modeling. The methodology unfolds in three phases: First, we construct a stochastic programming model targeting seven-year total profit maximization, which quantitatively analyzes relationships between decision variables (crop planting areas) and stochastic variables (climate/market factors), with optimal planting solutions derived through robust optimization algorithms. Second, to address natural uncertainties, we develop an integer programming model for ideal scenarios, obtaining deterministic optimization solutions via genetic algorithms. Furthermore, this study conducts correlation analyses between expected sales volumes and cost/unit price for three crop categories (staples, vegetables, and edible fungi), establishing both linear and nonlinear regression models to quantify how crop complementarity–substitution effects influence profitability. Experimental results demonstrate that the optimized strategy significantly improves land-use efficiency, achieving a 16.93% increase in projected total revenue. Moreover, the multi-scenario collaborative optimization enhances production system resilience, effectively mitigating market and environmental risks. Our proposal provides a replicable decision-making framework for sustainable intensification of agriculture in cold-region rural areas. Full article

Understanding the dynamics and drivers of groundwater storage (GWS) is crucial for sustainable resource management. Most studies attribute GWS changes to climate change or human activities, often neglecting external hydrological influences. In this study, we categorize the driving factors influencing GWS changes into three groups: climate change, human activity, and regional hydrological pressure. We emphasize that the coupling effects and potential disturbances from adjacent hydrological systems may significantly affect local groundwater evolution. This perspective differs from conventional approaches that focus solely on local factors. This study analyzes the spatiotemporal evolution of GWS in Shanxi Province, located in the eastern Loess Plateau, from 2003 to 2023 using GRACE and GLDAS data. We examine the linkage between GWS in Shanxi and the North China Plain through correlation analysis, Engle–Granger cointegration tests, and Granger causality tests. The results show that GWS in Shanxi showed an average annual reduction of −17.27 ± 1.4 mm/yr, with the most severe depletion occurring in the southeastern region, which is geographically adjacent to the North China Plain. The results of the Engle–Granger cointegration test and Granger causality analysis reveal a bidirectional causal relationship between GWS changes in the two regions, indicating that changes in GWS in either region may have a significant impact on the other. The results of the contribution analysis indicate that the North China Plain’s groundwater decline contributes approximately −53.89% to the reduction of GWS in Shanxi, while human activities and external hydrological influences together explain over 98% of the change. This result suggests that relying solely on climatic and human activity factors to explain groundwater changes may lead to significant biases, as ignoring interregional hydrological linkages can amplify or obscure the attribution of local groundwater variations, resulting in distorted conclusions. These findings highlight the value of remote sensing in capturing regional hydrological interactions and underscore the need to integrate interregional groundwater connectivity into policy design for sustainable groundwater governance. Full article

The Amazon River Basin (ARB) experienced an extreme drought from summer 2023 to spring 2024, driven by complex interactions among multiple climatic and environmental factors. A detailed investigation into this drought is crucial in understanding the entire process of the drought. Here, we employ drought indices derived from the Gravity Recovery and Climate Experiment (GRACE), GRACE Follow-On (GFO), and Swarm missions to reconstruct the drought’s progression, combined with reanalysis datasets and extreme-climate indices to analyze atmospheric and hydrological mechanisms. Our findings reveal a six-month drought from September 2023, reaching a drought peak of −1.29 and a drought severity of −5.62, with its epicenter migrating systematically from the northwestern to southeastern basin, spatially mirroring the 2015–2016 extreme drought pattern. Reduced precipitation and abnormal warming were the direct causes, which were closely linked to the 2023 El Niño event. This event disrupted atmospheric vertical movements. These changes led to abnormally strong sinking motions over the basin, which interacted synergistically with anomalies in land cover types caused by deforestation, triggering this extreme drought. This study provides spatiotemporal drought diagnostics valuable for hydrological forecasting and climate adaptation planning. Full article

Given the crucial role of land use in shaping coupled water–carbon processes in ecosystems, it is essential to assess carbon use efficiency (CUE) and water use efficiency (WUE) across different land use types. This study established an analytical framework incorporating trend analysis, partial correlation, and relative contribution methods to evaluate how WUE and CUE in the Yellow River Basin (YRB) responded to key climatic variables between 2001 and 2023. It also identified the dominant climatic drivers across different land use types during 2001–2022. The principal findings were as follows: (1) from 2001 to 2023, the mean WUE and CUE were 0.73 g C m⁻² mm⁻¹ and 0.60, respectively. (2) Wetlands and croplands had higher WUE, while grasslands and shrublands showed higher CUE. (3) MAT was negatively correlated with WUE and CUE across 89% and 74% of the YRB, respectively, while MAP and SR showed spatially variable effects. (4) MAT was the dominant factor driving WUE variation across all land use types (accounting for approximately 40%). Except for shrublands, CUE was mainly influenced by MAP. These results highlight differentiated climate impacts across land use types, providing important insights for ecosystem restoration and spatial governance. Full article

Understanding the spatial dynamics and drivers of armed conflict is crucial for anticipating risk and informing targeted interventions. However, current research rarely considers the spatio-temporal clustering characteristics of armed conflicts. Here, we assess the distribution dynamics and driving factors of armed conflict from the perspective of armed conflict clusters, employing complex network dynamic community detection methods and interpretable machine learning approaches. The results show that conflict clusters vary in terms of regional distribution. Sub-Saharan Africa boasts the highest number of conflict clusters, accounting for 37.9% of the global total and covering 40.4% of the total cluster area. In contrast, South Asia and Afghanistan, despite having a smaller proportion of clusters at 12.1%, hold the second-largest cluster area, which is 18.1% of the total. The characteristics of different conflict networks are influenced by different factors. Historical exposure, socio-economic deprivation, and spatial structure are the primary determinants of conflict patterns, while climatic variables contribute less prominently as part of a broader system of environmental vulnerability. Moreover, the influence of driving factors shows spatial heterogeneity. By integrating cluster-level analysis with interpretable machine learning, this study offers a novel perspective for understanding the multidimensional characteristics of armed conflicts. Full article

Forests play a crucial role in storing excess carbon released into the atmosphere. By mitigating climate change, forest carbon stocks play a vital role in achieving green transitions. However, limited information is available regarding the factors that affect forest carbon stocks. The primary objective of this analysis is to investigate the impact of green agricultural technologies and bioenergy on forest carbon stocks. The empirical investigation was conducted using the method of moments quantile regression (MMQR) technique. Results using the MMQR approach indicate that bioenergy is beneficial in augmenting forest carbon stores at all levels. A 1% increase in bioenergy is associated with an increase in forest carbon stocks ranging from 3.100 at the 10th quantile to 1.599 at the 90th quantile. In the context of developing economies, similar findings are observed; however, in developed economies, bioenergy only fosters forest carbon stocks at lower and middle quantiles. In contrast, green agricultural technologies have an adverse effect on forest carbon stocks. Green agricultural technologies have a significant negative impact on forest carbon stocks, particularly between the 10th and 80th quantiles, with their influence declining in magnitude from −2.398 to −0.619. This negative connection is observed in both developed and developing countries at most quantiles, except for higher quantiles in developed economies. Gross domestic product (GDP) has an adverse effect on forest carbon stores only in developing countries, whereas human capital diminishes forest carbon stocks in both developed and developing nations. Governments should provide support for the creators of bioenergy and agroforestry technologies so that forest carbon stocks can be increased. Full article

Under the global context of climate governance and sustainable development, low-carbon energy transition has become a strategic imperative. As a critical force in resource allocation, the financial system’s impact on energy transition has attracted extensive academic attention. This paper presents the first comprehensive literature review on energy transition research in the context of financial development. We develop a “Financial Functions-Energy Transition Dynamics” analytical framework to comprehensively examine the theoretical and empirical evidence regarding the relationship between financial development (covering both traditional finance and emerging finance) and energy transition. The understanding of financial development’s impact on energy transition has progressed from linear to nonlinear perspectives. Early research identified a simple linear promoting effect, whereas current studies reveal distinctly nonlinear and multidimensional effects, dynamically driven by three fundamental factors: economy, technology, and resources. Emerging finance has become a crucial driver of transition through technological innovation, risk diversification, and improved capital allocation efficiency. Notable disagreements persist in the existing literature on conceptual frameworks, measurement approaches, and empirical findings. By synthesizing cutting-edge empirical evidence, we identify three critical future research directions: (1) dynamic coupling mechanisms, (2) heterogeneity of financial instruments, and (3) stage-dependent evolutionary pathways. Our study provides a theoretical foundation for understanding the complex finance-energy transition relationship and informs policy-making and interdisciplinary research. Full article

The Tibetan-Himalayan region, recognized as a global biodiversity hotspot, is increasingly threatened by the dual pressures of climate change and human activities. Understanding the vulnerability of plant species to these forces is crucial for effective ecological conservation in this region. This study employed an improved Climate Niche Factor Analysis (CNFA) framework to assess the vulnerability of six representative alpine endemic herbaceous plants in this ecologically sensitive region under future climate changes. Our results show distinct spatial vulnerability patterns for the six species, with higher vulnerability in the western regions of the Tibetan-Himalayan region and lower vulnerability in the eastern areas. Particularly under high-emission scenarios (SSP5-8.5), climate change is projected to substantially intensify threats to these plant species, reinforcing the imperative for targeted conservation strategies. Additionally, we found that the current coverage of protected areas (PAs) within the species’ habitats was severely insufficient, with less than 25% coverage overall, and it was even lower (<7%) in highly vulnerable regions. Human activity hotspots, such as the regions around Lhasa and Chengdu, further exacerbate species vulnerability. Notably, some species currently classified as least concern (e.g., Stipa purpurea (S. purpurea)) according to the IUCN Red List exhibit higher vulnerability than species listed as near threatened (e.g., Cyananthus microphyllus (C. microphylla)) under future climate change. These findings suggest that existing biodiversity assessments, such as the IUCN Red List, may not adequately account for future climate risks, highlighting the importance of incorporating climate change projections into conservation planning. Our study calls for expanding and optimizing PAs, improving management, and enhancing climate resilience to mitigate biodiversity loss in the face of climate change and human pressures. Full article