Effective Resource Management Research Articles

The Impact Of Financial Compensation On The Performance Of Marketing Employees

To achieve this goal, companies need to pay attention to employees in all aspects of their work because this becomes an incentive for employees to carry out their work activities and achieve organizational goals. For this reason, it is natural to pay attention to the whereabouts of employees from the start. Employees who are company assets need to know their needs and desires so that they are expected to be able to produce job satisfaction, good performance and high loyalty to the company so as to achieve company goals. Effective human resource management requires managers to find the best way to employ employees to achieve goals. company. There are many things that can be done to empower good human resources, including efforts that include level education, training, and creating a conducive work situation or environment so that employees feel at home carrying out their work. Compensation is an important aspect in determining employee performance. Company attention is important because employees try to achieve the performance determined by the company. Realizing the importance of compensation for company performance, compensation management should be important. In providing compensation, the timeliness of providing compensation and the amount of compensation given by the company to employees affects employee morale, motivation, work performance and performance. An employee's absolute income level will determine the scale of his life and relative income shows his status and dignity. Therefore, if employees perceive that the compensation they receive is inadequate, this will cause employee morale, motivation, work performance and performance to decline drastically. Motivation is a condition that drives employees who are directed or focused on achieving company goals. Achieving this goal means also achieving the personal goals of the members concerned.

Improving soil moisture prediction with deep learning and machine learning models

Reliable soil moisture (SM) data is critical for effective water resources management, yet its accurate measurement and prediction remain challenging. This study was conducted to develop a deep learning regression network for sub-hourly SM prediction and compare its performance with traditional machine learning models, including the eXtreme gradient boosting (XGB), light gradient-boosting (LGB), cat boosting (CB), random forest (RF), k-nearest neighbors (kNN), and long short-term memory (LSTM) models. Sub-hourly SM, electrical conductivity (EC), soil temperature (ST), and weather parameters were collected during research experiments conducted for two years (2020–2021 and 2021–2022) at the Tropical Research and Education Center (TREC), University of Florida. A network of SM sensors and a weather station were installed at the experimental site with 24 plots of green beans and sweet corn under full and three deficit irrigation treatments with three replications. Model performance metrics such as coefficient of determination (r2) and global performance indicator (GPI) were used to evaluate the performance of the models. Results showed that all MLs and DL models performed more than satisfactorily in simulating SM of green beans and sweet corn plots. The testing average r2 and GPI of MLs were 0.83 and 0.02 (green beans) and 0.85 and 0.02 (sweet corn). However, XGB and LGB models outperformed the remaining ML and DL models. The testing r2 and GPI of XGB were 0.86 and 0.014 for green beans, whereas 0.88 and 0.015 for sweet corn. The r2 and GPI values for the LGB were 0.85 and 0.014 for green beans, while 0.88 and 0.015 for sweet corn. Even though DL model took longer and resources to be trained, its performance was not as accurate as that of XGB and LGB models. However, the performance of DL was better than the LSTM model. The r2 and RMSE of the LSTM model were 0.68 and 0.02cm 3 cm-3 for green beans and 0.75 and 0.02cm 3 cm-3 for sweet corn, respectively. Whereas the r2 and RMSE of DL were 0.84 and 0.015cm 3 cm-3 (green beans) and 0.85 and 0.02 cm 3 cm-3 (sweet corn). The ML and DL models performed better in simulating SM of sweet corn plots than green beans. Overall, these results confirmed that the ML and DL models could be alternative tools for SM prediction for agricultural fields, with potential applications for irrigation scheduling and water resources management.

Evaluation of hydrological models for streamflow prediction: a case study of the Mille River, Lower Awash Basin, Ethiopia

ABSTRACT Accurate prediction of stream flow is essential for effective water resource management, particularly in regions with complex hydrological dynamics. The Mille River, which flows through this basin and has a watershed area of 4,862.3 km², plays a crucial role in the region's water resources. However, selecting the most suitable hydrological model for this task remains a challenge due to varying model performances. This study addresses the problem of identifying the most effective hydrological model for predicting stream flow in the Mille River by evaluating three models: Soil and Water Assessment Tool (SWAT), Hydrologiska Byråns Vattenbalansavdelning (HBV-IHMS), and Artificial Neural Network (ANN). The objectives are to compare these models’ abilities to simulate daily stream flow and to determine their accuracy. The significance of this evaluation lies to guide water management decisions by identifying which model provides the most reliable predictions. The methodology involves using performance metrics, Nash-Sutcliffe Efficiency (NSE) values, to assess the accuracy of each model. The results show that SWAT achieved the highest NSE values of 0.81 and 0.82, indicating the best performance in simulating stream flow. ANN followed with NSE values of 0.79 and 0.81, while HBV-IHMS had lower NSE values of 0.73 and 0.74. Although all models demonstrated potential, SWAT and ANN outperformed HBV-IHMS in capturing the complex hydrological processes of the Mille River. The findings from this study indicate that the choice of model significantly impacts the accuracy and reliability of streamflow predictions. The SWAT and ANN models show strong potential for use in operational streamflow forecasting in the Mille River and similar basins. In conclusion, further research is recommended to refine model calibration and validation processes. Incorporating additional data sources, such as remote sensing and climate projections, could further enhance model accuracy and provide a more comprehensive understanding of the region's hydrological dynamics.

Spatiotemporal evolution and driving mechanism of Dongting Lake based on 2005–2020 multi-source remote sensing data

As one of the largest inland lakes in China, Dongting Lake has attracted widespread attention owing to its rich natural resources, unique geographical landscape, and important ecological functions. Recently, Dongting Lake has experienced phenomena such as an early dry season and backflow during the flood season. Multi-source remote sensing data and the normalised difference water index (NDWI) threshold method were used to systematically analyse the water area of the lake from 2005 to 2020. Additionally, it employed a centre of gravity migration model and a geographic detector model to investigate the lake's evolution patterns and driving mechanisms. The research identified notable fluctuations in Dongting Lake's water area during this period, with a particularly sharp decline in 2006—from 1509.74 km2 to 815 km2, marking a decrease of 694.74 km2 and a shrinkage rate of 46.01 %. Spatial analysis indicated that the centre of gravity of these water areas changed primarily between Nandashan Town, the Dongting Lake Management Committee, Wanzihu Township, and Qingtan Township, underscoring their significant influence on lake dynamics, including runoff, surface water availability, sediment deposition, and precipitation, all of which displayed strong positive correlations (Pearson coefficients of 0.57, 0.68, and 0.63, respectively), whereas population density showed a negative correlation (Pearson coefficient of −0.56). Furthermore, the study highlighted the substantial impact of the Digital Elevation Model (DEM) and its interaction with slope and aspect on Dongting Lake's evolution, with Q values of 0.537 and 0.543, respectively, emphasising their critical roles in shaping lake area changes and providing a crucial scientific basis for enhancing the understanding and effective management of water resources in the Dongting Lake Basin through comprehensive analysis of its spatiotemporal evolution and driving mechanisms.

Agility and Resilience in Supply Chains: Investigating Their Roles in Enhancing Financial Performance

Business sectors face disruptive challenges such as cash flow problems in finance and material flow problems in supply chain and logistics processes in today’s rapidly evolving and uncertain environment. Given these challenges, effective management of resource and material flows by managers has become increasingly complex. Supply chain management is crucial for businesses to sustain competitive market positioning. This study distinctively explores the interplay between supply chain management and the financial performance of manufacturing companies, highlighting the increasingly dynamic and competitive global markets. It scrutinizes the moderating roles of supply chain agility and flexibility in this relationship, offering diverse analytical perspectives. The research methodology involved surveying white-collar employees within these companies. Factor analysis was employed to affirm the scale’s validity, and the Hayes model 3 method was utilized to test hypotheses. Our research uncovered intricate interactions between supply chain management, agility, and resilience, underscoring their collective impact on financial performance. The thesis that supply chain management has a substantial impact on financial performance was corroborated by the study’s results. The study also emphasizes the moderating impact of supply chain agility in the relationship between financial performance and supply chain management. The results of the study that supply chain resilience moderates the moderating effect of supply chain agility indicate that the interaction between supply chain resilience and supply chain agility may affect the relationship between supply chain management and financial performance if supply chain resilience enhances the resilience of organizations to external challenges. These insights suggest organizations must integrate agility, management, and resilience considerations in their supply chains to optimize performance. This study contributes a novel viewpoint to the literature, providing strategic guidance for managerial decision making.

Evolution and Analysis of Water Yield under the Change of Land Use and Climate Change Based on the PLUS-InVEST Model: A Case Study of the Yellow River Basin in Henan Province

Understanding the interrelationships between land use, climate change, and regional water yield is critical for effective water resource management and ecosystem protection. However, comprehensive insights into how water yield evolves under different land use scenarios and climate change remain elusive. This study employs the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models, Patch-generating Land Use Simulation (PLUS) model, and Geodetector within a unified framework to evaluate the dynamics of land use, water yield, and their relationships with various factors (meteorological, social, economic, etc.). To forecast the land use/cover change (LUCC) pattern of the Yellow River Basin by 2030, three scenarios were considered: economic development priority (Scenario 1), ecological development priority (Scenario 2), and cropland development priority (Scenario 3). Climate change scenarios were constructed using CMIP6 data, representing low-stress (SSP119), medium-stress (SSP245), and high-stress (SSP585) conditions. The results show the following: (1) from 2000 to 2020, cropland was predominant in the Yellow River Basin, Henan Province, with significant land conversion to impervious land (construction land) and forest land; (2) water yield changes during this period were primarily influenced by meteorological factors, with land use changes having negligible impact; (3) by 2030, the water yield of Scenario 1 is highest among different land use scenarios, marginally surpassing Scenario 2 by 1.60 × 108 m3; (4) climate scenarios reveal significant disparities, with SSP126 yielding 54.95 × 108 m3 higher water yield than SSP245, driven predominantly by precipitation; (5) Geodetector analysis identifies precipitation as the most influential single factor, with significant interactions among meteorological and socio-economic factors. These findings offer valuable insights for policymakers and researchers in formulating land use and water resource management strategies.

Comparative Analysis of Machine Learning Techniques for Water Consumption Prediction: A Case Study from Kocaeli Province.

This study presents a comparative analysis of various Machine Learning (ML) techniques for predicting water consumption using a comprehensive dataset from Kocaeli Province, Turkey. Accurate prediction of water consumption is crucial for effective water resource management and planning, especially considering the significant impact of the COVID-19 pandemic on water usage patterns. A total of four ML models, Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machines (SVM), and Gradient Boosting Machines (GBM), were evaluated. Additionally, optimization techniques such as Particle Swarm Optimization (PSO) and the Second-Order Optimization (SOO) Levenberg-Marquardt (LM) algorithm were employed to enhance the performance of the ML models. These models incorporate historical data from previous months to enhance model accuracy and generalizability, allowing for robust predictions that account for both short-term fluctuations and long-term trends. The performance of each model was assessed using cross-validation. The R2 and correlation values obtained in this study for the best-performing models are highlighted in the results section. For instance, the GBM model achieved an R2 value of 0.881, indicating a strong capability in capturing the underlying patterns in the data. This study is one of the first to conduct a comprehensive analysis of water consumption prediction using machine learning algorithms on a large-scale dataset of 5000 subscribers, including the unique conditions imposed by the COVID-19 pandemic. The results highlight the strengths and limitations of each technique, providing insights into their applicability for water consumption prediction. This study aims to enhance the understanding of ML applications in water management and offers practical recommendations for future research and implementation.

Automated fish counting system based on instance segmentation in aquaculture

Non-invasive methods of fish counting are crucial for the effective management of aquaculture and aquatic resources. However, accurate and reliable estimation of fish populations in real-time is a significant challenge due to severe occlusion, body deformation, and rapid movement of fish. Recent advancements in computer vision and deep learning algorithms offer substantial potential to revolutionize fish counting methodologies, thereby enhancing conservation efforts and promoting the sustainable management of aquatic resources. This paper presents a comprehensive dataset of 256,580 fish instances annotated with mask information at the pixel level. To address the challenges associated with geometric variations in fish size, pose, perspective, and body shape, we propose a lightweight instance segmentation model based on YOLOv8, which integrates the CSP bottleneck with dual convolutions and the deformable convolutional networks (DConv). In addition, an efficient feature fusion network incorporating multi-scale information was introduced to improve segmentation accuracy by enhancing the representation of feature information, and a decoupled head with attention mechanisms was designed to detect and segment fish bodies in real time. Our proposed approach achieved an outstanding mAP50 of 98.1 % at 60.6 FPS, with model parameters and floating point operations per second (FLOPS) amounting to 25.4 M and 96.9 G, respectively. The instance segmentation-based counting system exhibited impressive performance metrics, with Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R-Square (R2) values of 1.19, 1.65, and 0.992, respectively. The proposed method demonstrates strong potential for practical application as an intelligent solution for biomass estimation in recirculating aquaculture systems.

ENCODING RESTRICTIONS AS AN OBJECT OF CADASTRAL SYSTEMS IN UKRAINE

Transparency and uniformity of registration of land use restrictions are interdependent and complementary aspects critical for ensuring effective management of land resources. The interaction of transparency and uniformity creates conditions for accessibility, comprehensibility, and better perception of data. The study analyses land use restrictions identification during their registration in the State Land and Urban Planning Cadastres to determine the uniformity of the registration. The analysis mainly focuses on the main attributes of the land use restrictions, such as code and name. Based on the research results, we established that the classification of land use restrictions for registration in various state cadastral systems, particularly State Land and Urban Planning Cadastres, is mostly not identical and requires coordination. There is an unjustified duplication of some land use restrictions and ambiguity in assigning them to specific categories. A particular problem is coding land use restrictions as registration objects without considering the hierarchical spatial organisation of restriction zones and the principle of logical construction. There is also an inexplicable duplication of some categories of land use restrictions, as in: the protected archaeological territory (codes 06.06.3 and 16.04), the historic and cultural protected territory (codes 06.06.5 and 16.03), the historical and cultural reserve (codes 06.06.4 and 16.02), the historical area of ​​the inhabited place (codes 06.06.2 and 16.07). In addition, there are minor differences in the names. It is worth noting that correctly identifying land use restrictions is essential for developing local urban planning documents due to the requirements of integrating information from various sources and updating them regularly. Transparency and uniformity of registration of land use restrictions in the State Land Cadastre and Urban Planning Cadastre are the main factors contributing to the effective management of land resources, ensuring legal certainty, attracting investments, and protecting the rights of land owners. They also contribute to reducing corruption, increasing trust in public authorities, and protecting the environment. Keywords: restrictions, code, state land cadastre, urban planning cadastre.

Factors and circumstances affecting the business performance of firms based on new technologies

New technology-based firms (NTBFs) invent, develop, and commercialize original technologies whose research, development, and commercial success is highly uncertain and risky. Their growth is lengthy and often insufficient, but investors’ growth expectations are high. The study aims to determine factors and circumstances that affect the growth of new technology-based firms. The source of knowledge is a personal experience from a guided interview with the founder, which is recorded in the questionnaire. The research sample includes 67 NTBFs doing business in Slovakia. The respondents’ statements were analyzed using critical discourse analysis (CDA) with the support of artificial intelligence. The results are the factors that influence the business performance of the investigated companies. The performance of companies that show a long-term loss is affected by very limited access to financial resources, restrictive regulations, and a lack of qualified employees. The performance of companies that show a long-term profit is influenced by favorable access to external capital, effective management of human resources, optimization of internal processes, and improvement of marketing. The order of the identified factors expresses their importance in the thematic group. The secondary results are the accompanying circumstances of the growth of the investigated companies, namely the reasons for the establishment of NTBF and entry into business, commercialization of new technology, and cooperation with investors and banks. The combination of limited access to finance and a lack of qualified human resources creates a complex set of obstacles to the business performance of NTBFs. AcknowledgmentThis work was supported by the Scientific Grant Agency of the Ministry of Education of the Slovak Republic and Slovak Academy of Sciences (VEGA) under the grant “Accelerating the growth of innovative enterprises - scaling up scale-ups and new technology-based firms (NTBFs)”, number 1/0006/22.

Building Resilient Campuses: Harnessing Self-Efficacy to Tackle Human Resources Management Obstacles

This study is based on the importance of effective human resource management in achieving the goals of higher education institutions, including producing quality graduates, improving academic reputation, and contributing to community development through self-efficacy. This study aims to analyze the role of self-efficacy in improving the performance and productivity of lecturers in higher education environments. This study uses a qualitative case study approach, with data collection techniques in the form of in-depth interviews, observation, and documentation. The findings show that lecturers and staff with high self-efficacy tend to be more confident in carrying out academic and administrative tasks, and are better able to adopt new technologies in the teaching and research process. These findings also indicate that high self-efficacy contributes to more appropriate and efficient decision making, reducing errors, and increasing the effectiveness of human resource management. The results of this study are expected to provide practical insights for higher education institutions to improve lecturer self-efficacy through appropriate training and management support, so that they can achieve better education, research, and service goals.

ACTUAL ISSUES OF REPRODUCTION OF FIXED ASSETS OF DOMESTIC ENTERPRISES

The article touches upon topical issues of reproduction of fixed assets of domestic enterprises in terms of their accounting, audit and strategic aspects of management. The study emphasises the necessity of constant renewal of fixed assets to ensure competitiveness, increase of labour productivity and quality of products. The main purpose of the study is to identify the key problems of managing the reproduction of fixed assets and formulate ways to solve them. To achieve the research objective, the methods of analysis and synthesis, comparative analysis and empirical methods were used. The paper emphasises the importance of improving accounting and auditing, considers the concepts of physical and moral wear and tear, as well as depreciation of fixed assets. It is determined that improving the depreciation policy is an important factor in increasing the investment activity of business entities. It is substantiated that internal audit plays an important role in providing reliable information on the process of reproduction of fixed assets, which contributes to the effective management of enterprise resources. The author defines the tasks of internal audit, including verification of the correctness of recognition and assessment of fixed assets restoration costs, reliability of information on depreciation charges, depreciation and residual value of fixed assets, control over accounting of lease transactions and transactions with investment property. The publication also discusses the strategic aspects of fixed assets reproduction, emphasising the importance of an innovative approach to improving the competitiveness of enterprises. It is noted that the implementation of innovative reproduction requires an integrated approach, including the construction of a mechanism that ensures the orientation of the market and the macroeconomic model of management to innovative forms of reproduction of fixed capital. The article pays special attention to the problems of financing innovative projects and cooperative technological chains. The article concludes by emphasising the need to form a comprehensive mechanism at the macro level, focused on the priority reproduction of fixed asset systems using the latest technologies. The paper emphasises the importance of creating a unified system of innovation and investment activities of enterprises, taking into account the interests of all participants. It stresses the expediency of using both market and non-market incentives for the reproduction of fixed assets, which will facilitate the production of competitive products and ensure the sustainable development of enterprises in the market.

Modelling Water Availability in Livestock Ponds by Remote Sensing: Enhancing Management in Iberian Agrosilvopastoral Systems

Extensive livestock farming plays a crucial role in the economy of agrosilvopastoral systems of the southwestern Iberian Peninsula (known as dehesas and montados in Spanish and Portuguese, respectively) as well as providing essential ecosystem services. The existence of livestock in these areas heavily relies on the effective management of natural resources (annual pastures and water stored in ponds built ad hoc). The present work aims to assess the water availability in these ponds by developing equations to estimate the water volume based on the surface area, which can be quantified by means of remote sensing techniques. For this purpose, field surveys were carried out in September 2021, 2022 and 2023 at ponds located in representative farms, using unmanned aerial vehicles (UAVs) equipped with RGB sensors and survey-grade global navigation satellite systems and inertial measurement units (GNSS-IMU). These datasets were used to produce high-resolution 3D models by means of Structure-from-Motion and Multi-View Stereo photogrammetry, facilitating the estimation of the stored water volume within a Geographic Information System (GIS). The Volume–Area–Height relationships were calibrated to allow conversions between these parameters. Regression analyses were performed using the maximum volume and area data to derive mathematical models (power and quadratic functions) that resulted in significant statistical relationships (r2 > 0.90, p < 0.0001). The root mean square error (RMSE) varied from 1.59 to 17.06 m3 and 0.16 to 3.93 m3 for the power and quadratic function, respectively. Both obtained equations (i.e., power and quadratic general functions) were applied to the estimated water storage in similar water bodies using available aerial or satellite imagery for the period from 1984 to 2021.

Propagation Dynamics from Meteorological to Agricultural Drought in Northwestern China: Key Influencing Factors

Meteorological and agricultural droughts are inherently correlated, whereas the propagation mechanism between them remains unclear in Northwestern China. Investigating the linkages between these drought types and identifying the potential influencing factors is crucial for effective water resource management and drought mitigation. This study adopted the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Soil Moisture Index (SSMI) to characterize the meteorological and agricultural droughts from 1960 to 2018. The propagation time between these droughts was detected using the Pearson correlation analysis, and the cross-wavelet transform and wavelet cross-correlation were utilized to describe their linkages across the time–frequency scales. The grey relational analysis was applied to explore the potential factors influencing the propagation time. The results revealed that the agricultural drought typically lagged behind the meteorological drought by an average of 6 months in Northwestern China, with distinct seasonal and regional characteristics. The shortest propagation time occurred in the summer (3 months), followed by the autumn (4 months), and the propagation time was longer in the winter (8 months) and spring (9 months). Additionally, the average propagation time was longer in the plateau climate zone (8 months) than in the southeastern climate zone (6 months) and the westerly climate zone (4 months). There was a multi-timescale response between the meteorological and agricultural droughts, with a relatively stable and significant positive correlation over long timescales, whereas the correlation was less clear over short timescales. The key factors influencing the propagation time were soil moisture, elevation, precipitation, and potential evapotranspiration. Furthermore, the wavelet cross-correlation between agricultural and meteorological droughts was relatively high, with a lag of 0 to 3 months; as the timescale increased, the fluctuation period of their cross-correlation also increased.

Talent management: Preparing future leaders

Effective human resource management is an important factor in preparing future leaders for a company. The process encompasses various strategic stages, including selective recruitment, continuous development, and solid employee retention. In addition, the utilisation of advanced technology and the implementation of diversity and inclusion values play a major role in creating a dynamic and performance-oriented work environment. In practical terms, companies that successfully integrate all of these elements can attract and retain the best talent, and create clear development paths for high-potential employees. This ensures that future leaders have the skills, experience and motivation needed to lead the company to sustainable success. A holistic approach to human resource management also ensures that every individual in the company feels valued and can make maximum contributions. By placing diversity and inclusion as a priority, companies can capitalise on multiple perspectives and diverse talents, which in turn increases levels of innovation and creativity. Advanced technologies, such as predictive analytics and collaboration tools, play an important role in supporting this strategy. Ultimately, companies committed to holistic human capital management not only prepare future leaders but also create a work environment that empowers every employee to thrive and fulfil their full potential.

Using blockchain technology to achieve sustainability in the hospitality industry by reducing food waste

The hospitality industry generates significant food waste, leading to adverse environmental and economic impacts, social and ethical concerns, legal and regulatory issues, and operational challenges. The lack of traceability and trust among stakeholders in the hospitality industry exacerbates the expensive and difficult nature of food waste management. This work proposes a blockchain-based solution that aims to minimize food waste generated in hospitality outlets, thereby fostering a sustainable food ecosystem. Within this context, the term’sustainable food ecosystem’ refers to the interconnected network of stakeholders involved in the responsible management of food resources within the hospitality industry. In this context, we categorize these stakeholders under the umbrella of’food resource management entities,’ encompassing both food donation organizations and recycling companies, recognizing their distinct roles in the effective management of surplus food resources. With our proposed solution, businesses can track and verify the origin, movement, and disposal of food items in real-time, facilitating streamlined inventory planning, waste reduction, and heightened operational efficiency. Additionally, the proposed system ensures transparency and accountability, guaranteeing the proper and efficient disposal of food waste. Using a permissioned Ethereum network, our solution establishes seamless connectivity between food and beverage outlets and various food resource management entities. We present a generalized system for secure information sharing, integrating advanced algorithms tailored to capture and enhance trust among participating stakeholders. Furthermore, our thorough security analysis underscores the robustness of our proposed solution, effectively addressing various security vulnerabilities. Lastly, the proposed blockchain-based solution is commercially viable, enabling the hospitality industry to operate with enhanced efficiency while providing improved information connectivity for various stakeholders in the food supply chain.

Emergence of Forest Environmental Kuznets Curve in Africa: Some New Evidences

ABSTRACT: The African continent, renowned for its diverse landscapes and abundant biodiversity, grapples with a critical environmental challenge – deforestation. The swift depletion of forests, serving as the primary habitat for 80% of the world’s terrestrial animals and plants, poses a severe threat in Africa. Human activities, including logging, agricultural expansion, and urbanization, have jeopardized numerous ecosystems, exacerbating the overarching global climate crisis. Beyond serving as mere collections of trees, forests are intricate ecosystems supporting a staggering diversity of flora and fauna. The repercussions of forest loss in Africa are profound, resulting in a significant decline in biodiversity. Numerous species, some exclusive to these ecosystems and found nowhere else on the planet, now teeter on the brink of extinction. The predicament of deforestation in Africa is intimately intertwined with the broader global challenge of climate change. Urgency is paramount in addressing this crisis, as the loss of forests impacts not only local ecosystems but also exacerbates the overarching climate emergency. Reforestation emerges as a pivotal solution, offering an opportunity to reverse the damage inflicted on these vital ecosystems. This study examines whether forest natural resources can explain the environmental Kuznets curve hypothesis in African countries. The empirical strategy is based on the second-generation cointegration approach, cross-sectional dependency autoregressive distributed lag (CS-ARDL) and Augmented Mean Group (AMG) estimator. The findings show a potential long-run relationship between the forest rent and the environment. Further analysis highlights the importance of effective management of natural forest resources for improving environmental quality in Africa. We recommend that policymakers prioritize the restoration of ecosystems that have suffered carbon depletion due to human activities. Restoration efforts should strategically aim to enhance carbon sequestration capacity and rebuild biodiversity. Additionally, the establishment of robust monitoring and reporting systems is imperative to track current carbon emissions from forests. This entails regular assessments of forest carbon stocks, land-use changes, and the effectiveness of ongoing conservation and restoration initiatives.

Optimizing sustainable development in arid river basins: A multi-objective approach to balancing water, energy, economy, carbon and ecology nexus

The ongoing water crisis poses significant threats to the socioeconomic sustainability and ecological security of arid and semi-arid river basins. Achieving Sustainable Development Goals (SDGs) within a complex socio-ecological nexus requires effective and balanced resource management. However, due to the intricate interactions between human societies and environmental systems, the tradeoffs and synergies of different SDGs remain unclear, posing a substantial challenge for collaborative management of natural resources. Here we introduce a gray fractional multi-objective optimization (GFMOP) model to balance multi-dimensional SDGs through a novel water–energy–economy–carbon–ecology nexus perspective. The model was applied to a typical arid river basin in Northwest China, where thirty-two scenarios were explored, considering factors such as shared socioeconomic pathways, carbon removal rates, water conveyance efficiencies, and ecological requirements. The results reveal a strong tradeoff between marginal benefit and carbon emission intensity, indicating that improving the economic efficiency of water use can simultaneously reduce emissions and protect the environment. Given the immense power generation potential, wind power development should be prioritized in the future, with its share in the energy structure projected to increase to 23.3% by 2060. Furthermore, promoting carbon capture technologies and expanding grassland coverage are recommended to achieve regional carbon neutrality, contributing 39.5% and 49.1% to carbon absorption during 2021–2060, respectively. Compared with traditional single-objective models, GFMOP demonstrates a superiority in uncovering interrelationships among multiple SDGs and identifying compromised alternatives within the compound socio-ecological nexus. The model also provides detailed strategies for resource allocation and pollutant control, offering valuable guidance to policymakers and stakeholders in pursuing sustainable and harmonious watershed management.

Understanding the Source and Evolution of Precipitation Stable Isotope Composition across O‘ahu, Hawai‘i

Abstract Pacific Islands present unique challenges for water resource management due to their environmental vulnerability, dynamic climates, and heavy reliance on groundwater. Quantifying connections between meteoric, ground, and surface waters is critical for effective water resource management. Analyses of the stable isotopes of oxygen and hydrogen in the hydrosphere can help illuminate such connections. This study investigates the stable isotope composition of rainfall on O‘ahu in the Hawaiian Islands, with a particular focus on how altitude impacts stable isotope composition. Rainfall was sampled at 20 locations from March 2018 to August 2021. The new precipitation stable isotope data were integrated with previously published data to create the most spatially and topographically diverse precipitation collector network on O‘ahu to date. Results show that δ18O and δ2H values in precipitation displayed distinct isotopic signatures influenced by geographical location, season, and precipitation source. Altitude and isotopic compositions were strongly correlated along certain elevation transects, but these relationships could not be extrapolated to larger regions due to microclimate influences. Altitude and deuterium excess were strongly correlated across the study region, suggesting that deuterium excess may be a reliable proxy for precipitation elevation in local water tracer studies. Analysis of spring, rainfall, and fog stable isotope composition from Mount Ka‘ala suggests that fog may contribute up to 45% of total groundwater recharge at the summit. These findings highlight the strong influence of microclimates on the stable isotope composition of rainfall, underscore the need for further investigation into fog’s role in the water budget, and demonstrate the importance of stable isotope analysis for comprehending hydrologic dynamics in environmentally sensitive regions.

Spatiotemporal Distribution and Dispersal Pattern of Early Life Stages of the Small Yellow Croaker (Larimichthys Polyactis) in the Southern Yellow Sea

Nursery habitats play a significant role in completing fish life cycles, and they are now recognized as essential habitats. Monthly variations in nursery ground distributions of Larimichthys polyactis were investigated in the southern Yellow Sea in 2019. Bayesian hierarchical models with integrated nested Laplace approximation were utilized to model the preferential nursery habitats of L. polyactis larvae. The study analyzed the spatial and temporal distributions of the larvae and juveniles based on three environmental variables: sea surface temperature, sea surface salinity, and depth. Additionally, this study examined the utilization of habitats by different fish life stages and ontogenetic shifts. A total of 3240 individuals were collected from April to June, with the peak occurring in May (0.05 ind./m3), and the distribution areas varied between different months. The prediction of the model reveals the ecological adaptability of L. polyactis to temperature variations. The optimal temperature for L. polyactis density ranges from 12.5 °C to 16.5 °C in April and 16.5 °C to 17.5 °C in May, demonstrating a broad temperature tolerance for L. polyactis survival. In addition, there are variations in distribution patterns among different developmental stages. Larimichthys polyactis spawn in the inshore and nearshore waters, and after hatching, larvae in the pre-flexion stage tend to remain aggregated near the spawning beds. However, larvae in the advanced development stage (post-flexion) and juveniles move towards the sandy ridge habitats along the coast and start to migrate offshore in June. This study provides valuable insights for the effective management of fishery resources in the area and can be utilized to identify marine areas with specific habitat features that require conservation.