Search Results (8,913)

Studying climate change’s impact on vegetation canopy growth and senescence is significant for understanding and predicting vegetation dynamics. However, there is a lack of adequate research on canopy changes across the lifecycles of different vegetation types. Using GLASS LAI (leaf area index) data (2001–2020), we investigated canopy development (April–June), maturity (July–August), and senescence (September–October) rates in Northeast China, focusing on their responses to preseason climatic factors. We identified that early stages saw canopy development acceleration, with over 71% of areas experiencing such acceleration in April and May. As the vegetation grew, the accelerating canopy development slowed down, and the canopy reached its maturation earlier. By analyzing the partial correlation between canopy growth and preseason climatic factors, it was identified that changes in canopy growth were most significantly affected by preseason air temperature. A positive correlation was observed in the early stages, which shifted to a negative correlation during canopy maturation and senescence. Notably, the transition timing varied among different vegetation types, with grasslands (June) occurring earlier than forests (July) and farmlands (August). Additionally, grassland canopy growth showed a stronger response to precipitation than forests and farmlands, with a lagged effect of 2.50 months. Our findings improve understanding of vegetation canopy growth across different stages, holding significant importance for ecological environmental monitoring, land-use planning, and sustainable development. Full article

The northern permafrost regions are increasingly experiencing frequent and intense extreme events, with a rise in the occurrence of compound extreme events. Many climate-related hazards in these areas are driven by such compound events, significantly affecting the stability and functionality of vegetation ecosystems. However, the cumulative and lagged effects of compound extreme events on vegetation remain unclear, which may lead to an underestimation of their actual impacts. This study provides a comprehensive analysis of the spatiotemporal variations in compound extreme events and the vegetation response to these events in the northern permafrost regions from 1982 to 2022. The primary focus of this study is on examining the cumulative and lagged effects of compound extreme climate events on the Kernel Normalized Difference Vegetation Index (kNDVI) during the growing seasons. The results indicate that in high-latitude regions, the frequency of extreme high temperature–precipitation compound events and high temperature–drought compound events have increased in 58.0% and 67.0% of the areas, respectively. Conversely, the frequency of extreme low temperature–drought compound events and extreme low temperature–precipitation compound events has decreased in 70.6% and 57.2% of the areas, with the high temperature–drought compound events showing the fastest increase. The temporal effects of compound extreme events on kNDVI vary with vegetation type; they produce more cumulative and lagged effects compared with single extreme high-temperature events and fewer effects compared with single extreme precipitation events, with compound events significantly affecting forest and grassland ecosystems. Notably, extreme high temperature–precipitation compound events exhibit the strongest cumulative and lagged effects on vegetation, while extreme low temperature–drought compound events influence wetland and shrubland areas within the same month. This study underscores the importance of a multivariable perspective in understanding vegetation dynamics in permafrost regions. Full article

Due to the influences of climate change and human activities, the resources and environments of the “One Belt and One Road” initiative are facing severe challenges. Using drought indicators, this study aimed to analyze the spatiotemporal characteristics of the drought environment and the response of vegetation cover in the area to drought conditions. The Gravity Recovery and Climate Experiment (GRACE) drought severity index (GRACE-DSI), GRACE water storage deficit index (GRACE-WSDI) and standardized precipitation index (SPI) were calculated to measure hydrological drought. Additionally, based on GRACE and Global Land Data Assimilation System (GLDAS) data, groundwater data in Central Asia was retrieved to calculate the groundwater drought index using the GRACE Standardized Groundwater Level Index (GRACE-SGI). The findings indicate that, from 2000, Central Asia’s annual precipitation decreased at a rate of 1.80 mm/year (p < 0.1), and its annual temperature increased slightly, at a rate of 0.008 °C/year (p = 0.62). Water storage decreased significantly at a rate of −3.53 mm/year (p < 0.001) and showed an increase-decrease-increase-decrease pattern. During the study period, the aridity in Central Asia deteriorated, especially on the eastern coast of the Caspian Sea and the Aral Sea basin. After 2020, most of Central Asia experienced droughts at both the hydrological and groundwater droughts levels and of varying lengths and severity. During the growing season, there was a substantial positive association between the Normalized Difference Vegetation Index (NDVI) and drought indicators such as GRACE-DSI and GRACE-WSDI. Nonetheless, the NDVI of cultivated land and grassland distribution areas in Central Asia displayed a strong negative correlation with GRACE-SGI. This study concludes that the arid environment in Central Asia affected the growth of vegetation. The ecological system in Central Asia may be put under additional stress if drought conditions continue to worsen. This paper explores the drought characteristics in Central Asia, especially those of groundwater drought, and analyzes the response of vegetation, which is very important for the ecological and environmental protection of the region. Full article

Due to uncertain business climate, fierce competition, environmental challenges, regulatory requirements, and the need for responsible business operations, organizations are forced to implement sustainable supply chains. This necessitates the use of proper data analytics methods and tools to monitor economic, environmental, and social performance, as well as to manage and optimize supply chain operations. This paper discusses issues, challenges, and the state of the art approaches in supply chain analytics and gives a systematic literature review of big data developments associated with supply chain management (SCM). Even though big data technologies promise many benefits and advantages, the prospective applications of big data technologies in sustainable SCM are still not achieved to a full extent. This necessitates work on several segments like research, the design of new models, architectures, services, and tools for big data analytics. The goal of the paper is to introduce a methodology covering the whole Business Intelligence (BI) lifecycle and a unified model for advanced supply chain big data analytics (BDA). The model is multi-layered, cloud-based, and adaptive in terms of specific big data scenarios. It comprises business process modeling, data ingestion, storage, processing, machine learning, and end-user intelligence and visualization. It enables the creation of next-generation BDA systems that improve supply chain performance and enable sustainable SCM. The proposed supply chain BDA methodology and the model have been successfully applied in practice for the purpose of supplier quality management. The solution based on the real-world dataset and the illustrative supply chain case are presented and discussed. The results demonstrate the effectiveness and applicability of the big data model for intelligent and insight-driven decision making and sustainable supply chain management. Full article

The challenges posed by climate change have had a crucial impact on global food security, with crop yields negatively affected by abiotic and biotic stresses. Consequently, the identification of abiotic stress-responsive genes (SRGs) in crops is essential for augmenting their resilience. This study presents a computational model utilizing machine learning techniques to predict genes in Chinese cabbage that respond to four abiotic stresses: cold, heat, drought, and salt. To construct this model, data from relevant studies regarding responses to these abiotic stresses were compiled, and the protein sequences encoded by abiotic SRGs were converted into numerical representations for subsequent analysis. For the selected feature set, six distinct machine learning binary classification algorithms were employed. The results demonstrate that the constructed models can effectively predict SRGs associated with the four types of abiotic stresses, with the area under the receiver operating characteristic curve (auROC) for the models being 81.42%, 87.92%, 80.85%, and 88.87%, respectively. For each type of stress, a distinct number of stress-resistant genes was predicted, and the ten genes with the highest scores were selected for further analysis. To facilitate the implementation of the proposed strategy by users, an online prediction server, has been developed. This study provides new insights into computational approaches to the identification of abiotic SRGs in Chinese cabbage as well as in other plants. Full article

Vegetation, as a fundamental component of terrestrial ecosystems, plays a pivotal role in the flux of water, heat, and nutrients between the lithosphere, biosphere, and atmosphere. Assessing the impacts of climate change and human activities on vegetation dynamics is essential for maintaining the health and stability of fragile ecosystems, such as the Yarlung Zangbo River (YZR) basin of the Tibetan Plateau, the highest-elevation river basin in the world. Vegetation responses to climate change are inherently asymmetric, characterized by distinct temporal effects. However, these temporal effects remain poorly understood, particularly in high-altitude ecosystems. Here, we examine the spatiotemporal changes in leaf area index (LAI) and four climatic factors—air temperature, precipitation, potential evapotranspiration, and solar radiation—in the YZR basin over the period 2000–2019. We further explore the time-lag and time-accumulation impacts of these climatic factors on LAI dynamics and apply an enhanced residual trend analysis to disentangle the relative contributions of climate change and human activities. Results indicated that (1) a modest increase in annual LAI at a rate of 0.02 m² m⁻² dec⁻¹ was detected across the YZR basin. Spatially, LAI increased in 66% of vegetated areas, with significant increases (p < 0.05) in 10% of the basin. (2) Temperature, precipitation, and potential evapotranspiration exhibited minimal time-lag (<0.5 months) but pronounced notable time-accumulation effects on LAI variations, with accumulation periods ranging from 1 to 2 months. In contrast, solar radiation demonstrated significant time-lag impacts, with an average lag period of 2.4 months, while its accumulation effects were relatively weaker. (3) Climate change and human activities contributed 0.023 ± 0.092 and –0.005 ± 0.109 m² m⁻² dec⁻¹ to LAI changes, respectively, accounting for 60% and 40% on the observed variability. Spatially, climate change accounted for 85% of the changes in LAI in the upper YZR basin, while vegetation dynamics in the lower basin was primarily driven by human activities, contributing 63%. In the middle basin, vegetation dynamics were influenced by the combined effects of climate change and human activities. Our findings deepen insights into the drivers of vegetation dynamics and provide critical guidance for formulating adaptive management strategies in alpine ecosystems. Full article

The hydrosphere is an element of the climate system and changes in the latter are reasonably projected over the river outflow. Climatic changes, however, are unevenly distributed over the Earth, and understanding their regional imprint on the hydrosphere is of great importance. In this study, we have conducted a statistical analysis of the monthly maximum and minimum river discharge recorded in 22 hydrological stations located on 19 of the Bulgarian rivers during the period 1993–2022. We have found that in half of the river basins, the trend of the spring maximum discharge is significantly positive (α = 0.05). In the other half of the stations, the trend is neutral. The stations with a positive trend are not randomly distributed but grouped, forming a pattern crossing the country from northwest to southeast. This pattern of trend distribution raises questions about the causes of the irregular hydrological response to the rising global near-surface temperatures. A comparison of hydrological data with some climatic variables (i.e., temperature, precipitation, and ozone at 70 hPa), combined with neural network analysis results, suggests ozone as a possible reason for the heterogeneous hydrological response. Its effect could be explained by an imposed episodic warming of the near-surface temperature due to fluctuations in the ozone density near the tropopause, which in turn favours the faster melting of ice and snow in the corresponding river basins. Full article

Rift Valley fever (RVF) is a devastating zoonotic mosquito-borne viral hemorrhagic fever disease that threats human and animal health and biodiversity in Africa, including in Rwanda. RVF is increasingly outbreaking in Africa, leading to devastating impacts on health, socioeconomic stability and growth, and food insecurity in the region, particularly among livestock-dependent communi-ties. This systematic review synthesizes existing evidence on RVF’s epidemiology, transmission dynamics, and the prevention and control measures implemented in Rwanda. Our findings high-light the rapidly increasing prevalence of RVF and the expansion of its geographical distribution and host range in Rwanda. Furthermore, the review reveals gaps in local evidence, including the existence of competent vectors of RVFV and the risk factors associated with the emergence and spread of RVF in the country. This underscores the urgent need for prospective research to inform evidence-based health policymaking, strategic planning, and the development and implementation of cost-effective preventive and control measures, including diagnosis and surveillance for early detection and response. It also calls for the institutionalization of a cost-effective, multisectoral, and transdisciplinary One Health strategy for reducing the burden and risk of climate climate-sensitive and zoonotic diseases, including RVF, in the country. We recommend exploring cost-effective human and/or animal vaccination mechanisms for RVF, integrating AI-powered drones into dis-ease vectors surveillance and control, and the routine implementation of genomics-enhanced xenosurveillance to monitor changes in pathogens and vectors dynamics in order to inform poli-cymaking and guide the control interventions. Full article

Energy is often described as the lifeblood of a nation’s economy, and the world energy trilemma calls for collaboration and innovative solutions at the national level. This is where Education for Sustainable Development (ESD) plays a crucial role, helping integrate the achievement of the United Nations Sustainable Development Goals (SDGs) while addressing the challenges posed by the energy trilemma. Europe’s strong commitment to transitioning to sustainable energy is evident in its response to geopolitical changes and climate targets. Notably, the Baltic States have taken decisive action in response to the war in Ukraine, choosing to completely halt electricity imports from Russia and Belarus. This shift was supported by increased energy imports via interconnectors from Finland, Sweden, and Poland, with electricity imports rising to 13,053 GWh—an increase of 2.6% in 2023 compared to the previous year. Latvia, which holds the highest green energy potential in the Baltic Sea region, has nevertheless lagged behind its Baltic counterparts in terms of implementation. In 2021, Latvia ranked third among European Union (EU) countries for renewable energy share in final energy consumption, with 42.1%, significantly higher than the EU average of 21.8%. However, further progress is needed to meet Latvia’s 2030 target of 14% renewable energy use in transport. The Baltic States aim to produce 98–100% of their electricity from renewable sources by 2050. The Baltic States should be regarded as a unified energy system, with a coordinated strategy for achieving sustainable energy development through collaboration and joint planning. This analysis highlights the complexities of managing energy markets amidst global and regional challenges, emphasizing the importance of well-designed public interventions to secure long-term benefits. The study concludes with a call for enhanced interagency cooperation to reform ESD and create a new interdisciplinary sector dedicated to “Sustainable Development”. Full article

In response to climate change, the reduction of carbon emissions during agricultural production has garnered increasing global focus. This study takes high-standard farmland construction (HSFC) implemented in 2011 as the standard natural experiment and adopts the continuous differences-in-differences (DID) model to explore the impact and internal mechanism of HSFC on agricultural carbon emissions based on a panel data of 31 provinces, municipalities, and autonomous regions in China from 2003 to 2021. The results show that HSFC can effectively reduce the carbon emissions in agricultural production, and the average annual reduction can reach 53.8%. The effects of HSFC on agriculture carbon emissions could be associated with reducing agricultural fossil energy consumption and reducing agricultural chemical use. Further, the heterogeneity study shows that the carbon reduction effect of HSFC was mainly reflected in non-major grain-producing areas, while there was no significant impact in major grain-producing areas. Policymakers should unswervingly continue to promote HSFC, considering their own economic and geographical conditions. This study can provide valuable information and references for developing countries similar to China to formulate policies on agricultural carbon reduction. Full article

The understanding of species distribution in Peru is limited, in part due to cartographic representations that traditionally use political rather than biogeographical boundaries. The objective of this study was to determine the distribution of Arecaceae species in the department of Amazonas by representing them in biogeographical regions. To this end, geographic information systems and global databases were used to map and analyze the species in four categories: Ecosystems Map, Ecoregions Map, Peru Climate Classification Map, and Protected Natural Areas Map. Subsequently, diversity metrics were estimated, revealing high diversity in Amazonas, with 22 genera and 90 species of Arecaceae representing 51.16% and 41.28% of the records in Peru, respectively. In addition, predominant genera and species were identified, and diversity was evaluated in biogeographic units. Of a total of 336,029 records, 45 genera were found, with Geonoma and Bactris being the most representative, and of the 218 species found in total, the records that stood out the most varied according to biogeographical regions. For each Biogeographic unit by category, different responses were obtained, for example, for Index Margalef, between 0.000 (low in Agricultural Area), 7.2489 (medium in Eastern Cordillera Real Montane Forests), and 13.2636 (high in Non-protected Areas). Similarly, for the Shannon–Wiener diversity index ($\overline{H}$), where values were obtained between 0.000 (low in Jalca (Andean High Grasslands), (medium in Reserved Zonez) and 3.7054 (high in Non-protected Areas). The results suggest high under-recording, evidencing gaps in knowledge and information, as analyses based on detailed studies of diversity in specific biogeographic categories in these other families, as well as future research to determine, for example, genomes and Hill numbers, will be carried out. The conclusions highlight the high correlation between the diversity metrics analyzed, confirm the theoretical validity, and allow us to recommend species richness and the Margalef Index as useful and relevant metrics due to their applicability and ease of interpretation. This study offers key information for decision makers in policies for the conservation of Arecaceae diversity and motivates us to project research of this type in other families in Peru. Full article

The carbon storage in forest ecosystems is closely linked to biomass, and its dynamic changes are of significant importance for assessing forest structure and function, as well as their response to global climate change. Recently, the research on the influencing mechanism of forest carbon storage has been a hotpot in the field of forest ecology. However, it remains unclear on the relationships among stand structure, stand stability, and carbon storage. The issues needed to be answered are as follows: How are tree density, tree species diversity, stand structural characteristics, stand stability, and carbon storage correlated? Is there a direct or indirect effect between tree density, tree species diversity, stand structural characteristics, stand stability, and carbon storage? Do these factors have an impact on stand stability, and, subsequently, carbon storage? What is the crucial factor in the mechanism that influences carbon storage? Here, the natural Quercus mongolica forests in the Xiaolong Mountains were taken as the research object. Several methods, including Pearson’s correlation, the best-fitting SEM, and multiple regression, were used to examine the relationships among tree density, tree species diversity, stand structural characteristics, stand stability, and carbon storage. Our results show that there were correlations between tree density, tree species diversity, stand structural characteristics, stand stability, and carbon storage. Tree density not only directly affects stand stability but also indirectly influences it through the mediation of tree species diversity and stand structural characteristics. Meanwhile, tree density also indirectly influences carbon storage through the mediation of tree species diversity, stand structural characteristics, and stand stability. Crown volume exerts the greatest influence on stand stability, while carbon storage is mostly impacted by stand stability. Overall, the combination of tree density, tree species diversity, stand structural characteristics, and stand stability influences carbon storage (66.4%). Therefore, it is important to consider stand stability when assessing carbon sequestration potentials; furthermore, the importance of tree density, tree species composition, and stand structural characteristics should be emphasized. Our research provides a scientific basis for conservation and management decision-making in natural forests and offers novel insights as well as a scientific reference for future large-scale carbon storage investigations. Full article

Rapid industrialization and the indiscriminate use of fossil fuels have generated an impact that is affecting the climate worldwide. Among the substances that are causing climate change are several gases such as carbon dioxide (CO₂), methane (CH₄), nitrous oxide (N₂O) and sulphur hexafluoride (SF₆), among others. Particularly, carbon dioxide is one of the substances that has attracted the most attention from researchers, as it is responsible for more than three quarters of greenhouse gases. Because of this, many efforts have been directed towards the capture of CO₂, its separation, adsorption and transformation into products that are less harmful to the environment or that even have added value in the industry. For this purpose, the use of different types of macrocycles has been explored mainly in the last 5 years. This review seeks to present the advances that have occurred in recent years in the capture and transformation of CO₂ by different methods, to finally focus on the capture and transformation through macrocycle systems such as azacompounds, heterometallic macrocycles, calixpyrrols, modified cyclodextrins and metallic porphyrins, among others. Full article

In arid regions, climatic fluctuations significantly affect vegetation structure and function. Sun-induced chlorophyll fluorescence (SIF) can quantify certain physiological parameters of vegetation but has limitations in characterizing responses to climate change. This study analyzed the spatiotemporal differences in response to climate change across various ecological regions and vegetation types from 2000 to 2020 in Xinjiang. According to China’s ecological zoning, R1 (Altai Mountains-Western Junggar Mountains forest-steppe) and R5 (Pamir-Kunlun Mountains-Altyn Tagh high-altitude desert grasslands) represent two ecological extremes, while R2–R4 span desert and forest-steppe ecosystems. We employed the standardized precipitation evapotranspiration index (SPEI) at different timescales to represent drought intensity and frequency in conjunction with global OCO-2 SIF products (GOSIF) and the normalized difference vegetation index (NDVI) to assess vegetation growth conditions. The results show that (1) between 2000 and 2020, the overall drought severity in Xinjiang exhibited a slight deterioration, particularly in northern regions (R1 and R2), with a gradual transition from short-term to long-term drought conditions. The R4 and R5 ecological regions in southern Xinjiang also displayed a slight deterioration trend; however, R5 remained relatively stable on the SPEI24 timescale. (2) The NDVI and SIF values across Xinjiang exhibited an upward trend. However, in densely vegetated areas (R1–R3), both NDVI and SIF declined, with a more pronounced decrease in SIF observed in natural forests. (3) Vegetation in northern Xinjiang showed a significantly stronger response to climate change than that in southern Xinjiang, with physiological parameters (SIF) being more sensitive than structural parameters (NDVI). The R1, R2, and R3 ecological regions were primarily influenced by long-term climate change, whereas the R4 and R5 regions were more affected by short-term climate change. Natural grasslands showed a significantly stronger response than forests, particularly in areas with lower vegetation cover that are more structurally impacted. This study provides an important scientific basis for ecological management and climate adaptation in Xinjiang, emphasizing the need for differentiated strategies across ecological regions to support sustainable development. Full article

Stock–recruitment relationships depend on the total abundance of females, their fecundity, and patterns of their maturation. However, the effects of climatic conditions on the abundance, biomass, and mean weight of female red king crabs, Paralithodes camtschaticus, from the introduced population (Barents Sea) have not yet been studied. For this reason, we analyzed long-term fluctuations in stock indices and the average weight of an individual crab in a small bay of the Barents Sea and related these parameters to the dynamics of temperature conditions (temperature in January–December, mean yearly temperature, and temperature anomaly) in the sea. The average weight of a crab at age 6–9 had strong negative correlations with water temperature at lags 8 and 9, indicating faster female maturation in warm periods. Positive relationships were registered between temperature and stock indices for 15–19-year-old females at lag 4 and for 10–14-year-old females at lag 10, supporting the idea of higher survival rates of juveniles and their rapid development being a response to a pool of warm waters. Both redundancy and correlation analyses revealed seawater temperatures in June–August being the most important predictors of female abundance and biomass, indicating that favorable temperature conditions in the first 3 months of crab benthic life result in high survivorship rates for red king crabs. Full article