Search Results (2,438)

Chinese forests, particularly the coniferous forest ecosystems represented by pines, play a crucial role in the global carbon cycle, significantly contributing to mitigating climate change, regulating regional climates, and maintaining ecological balance. However, pine wilt disease (PWD), caused by the pine wood nematode (PWN), has become a major threat to forest carbon stocks in China. This study evaluates the impact of PWN invasion on forest carbon stocks in China using multi-source data and an optimized MaxEnt model, and the study analyzes this invasion’s spread trends and potential risk areas. The results show that the high-suitability area for PWN has expanded from 68,000 km² in 2002 to 184,000 km² in 2021, with the spread of PWN accelerating, especially under warm and humid climate conditions and due to human activities. China’s forest carbon stocks increased from 111.34 billion tons of carbon (tC) to 168.05 billion tC, but the carbon risk due to PWN invasion also increased from 87 million tC to 99 million tC, highlighting the ongoing threat to the carbon storage capacity. The study further reveals significant differences in tree species’ sensitivity to PWN, with highly sensitive species such as Masson’s pine and black pine mainly concentrated in the southeastern coastal regions, while less sensitive species such as white pine and larch show stronger resistance in the northern and southwestern areas. This finding highlights the vulnerability of high-sensitivity tree species to PWN, especially in high-risk areas such as Guangdong, Guangxi, and Guizhou, where urgent and effective control measures are needed to reduce carbon stock losses. To address this challenge, the study recommends strengthening monitoring in high-risk areas and proposes specific measures to improve forest management and policy interventions, including promoting cross-regional joint control, enhancing early warning systems, and utilizing biological control measures, while encouraging local governments and communities to actively participate. By strengthening collaboration and implementing control measures, the health and sustainable development of forest ecosystems can be ensured, safeguarding the forests’ important role in climate regulation and carbon sequestration and contributing to global climate change mitigation. Full article

The estimation of water requirements constitutes a critical prerequisite for delineating water scarcity hotspots and mitigating intersectoral competition, particularly in endorheic basins in arid or semi-arid regions where hydrological closure exacerbates resource allocation conflicts. Under conditions of water scarcity, water supplied locally by precipitation and shallow groundwater bodies should be taken into account to estimate the net water requirements to be met with water conveyed from off-site sources. This concept is embodied in the distinction of blue ET (BET) and green ET (GET). In this study, the Budyko hypothesis (BH) method was optimized to partition the total ET into GET and BET during 2001–2018 in the Heihe River Basin. In this region, a better knowledge of net water requirements is even more important due to water allocation policies which reduced water supply to irrigated lands in the last 15 years. This study proposes a modified BH method based on a new vegetation-specific parameter (ωv) which was optimized for different vegetation types using precipitation and actual ET data obtained from remote sensing observations. The results show that the BH method partitioned GET and BET reasonably well, with a percent bias of 23.8% and 37.4% and a root mean square error of 84.8 mm/a and 113.6 mm/a, respectively, when compared with reported data, which are superior to that of the precipitation deficit and soil water balance methods. A sensitivity experiment showed that the BH method exhibits a low sensitivity to uncertainties of input data. The results documented differences in the contribution of GET and BET to total ET across different land cover types in the Heihe River Basin. As expected, rainfed forest and grassland ecosystems are predominantly governed by GET, with 81.3% and 87.2% of total ET, respectively. In contrast, croplands and shrublands are primarily regulated by BET, with contributions of 61.5% and 84.3% to total ET. The improved BH method developed in this study paves the way for further analyses of the net water requirements in arid and semi-arid regions. Full article

In recent decades, dams worldwide are increasingly constructed in a row along a single river or basin, thus forming reservoir cascades, and in turn producing cumulative ecological effects along these areas. The use of multimetric indices (MMI) based on fish assemblages to assess the ecological health status of rivers and lakes has also been extensively developed. However, to date, there are no studies that employ MMI for the identification of the cumulative effects of reservoir cascades. The aim of this study was to develop a new Fish-based Index of Biotic Integrity (F-IBI) that can effectively identify the cumulative effects of reservoir cascades on fish assemblages in two important habitats (the free-flowing reach between reservoirs and the transition zone in the reservoir). Fish assemblages from 12 sites were sampled along the cascade reservoirs in the Xijiang River, China. First, through screening for redundancy, precision, and responsiveness of the candidate metrics, a new F-IBI based on ecological trait information of fish species composition was developed to estimate the ecological status of all sites. F-IBI scores exhibited an obviously downward trend from upstream to downstream in a reservoir cascade, and the transition zones in the reservoir displayed significantly lower F-IBI scores than the free-flowing reaches between reservoirs. Secondly, using Random Forest models, it was shown that the F-IBI can effectively identify the cumulative effects of the reservoir cascades on fish assemblages. Furthermore, we also demonstrated metric-specific responses to different stressors, particularly the multiple reservoir cascades, which showed the following: (1) The F-IBI index system developed based on the Random Forest model can effectively identify the superimposed effects of cascade power stations on fish integrity changes, with the cumulative time effect and the GDP index of river segments playing a key role; (2) To effectively protect the fish resources in the main stream of the Xijiang River, where priority should be given to the habitat of the natural flowing river sections between the reservoirs. At the same time, environmental regulatory policies should be formulated accordingly based on the human development status of each river section. Full article

Currently, urban land use in China faces many challenges, such as irrational land use structure and inefficiency, which is especially obvious in resource-based cities. In order to improve this situation, this paper uses the super-efficient Slack-Based Measure (SBM) model to measure the green land use efficiency (GLUE) of 113 resource-based cities in China, analyzes its spatial-temporal evolution law, and identifies the formation law of heterogeneous GLUE in resource-based cities using the Tobit model. The research results show that: (1) GLUE in resource-based cities shows year-on-year growth and has certain stage characteristics, in which the eastern region is the best, followed by the western and central regions, and the northeastern region is the worst; regenerative cities are significantly better than mature, growth, and declining cities; oil and gas cities are better than non-metal, forest, metal, and coal cities in turn; (2) High-value resource-based cities are concentrated in the eastern and western regions, while low-value ones are concentrated in the central and northeastern regions. Moreover, the number of high-value resource-based cities is continuously increasing, while the number of low-value ones is significantly decreasing; (3) The level of economic development, industrial structure, level of technological input, number of green patents granted, government financial support, sewage treatment rate, and policy constraints all exhibit significant positive effects on the GLUE of resource-based cities. Furthermore, there is notable heterogeneity among resource-based cities in different regions, development stages, and resource types. In the future, policies should be implemented on a city-by-city basis, and a sound long-term mechanism for policy implementation should be established to enhance the long-term awareness of managers and land users so as to improve the GLUE in resource-based cities. Full article

Land use/land cover (LULC) change has greatly altered ecosystem carbon storage capacity and may eventually profoundly impact global climate change. Characterizing the LULC change and its impact on wetland ecosystem carbon stock provides useful data and insights that can guide decision-making procedures aimed at achieving sustainable development objectives. The Yellow River Delta (YRD) represents the most intact coastal wetland and is considered to be the most recent wetland ecosystem in China. It exhibits significant carbon stock capacity and ecological value. Based on the LULC data of the YRD in 2002, 2007, 2012, 2017, and 2022, this paper quantitatively evaluates the spatiotemporal changes in LULC and carbon stock in the region and analyzes the response characteristics of carbon stock to LULC change. The results show significant reductions in cropland and tidal flat wetland areas from 2002 to 2022, resulting in a decrease of 1,428,735.77 t and an increase of 139,856.58 t in carbon stock, respectively. The built-up land area expanded considerably, and carbon stock was lost by 1,467,915.82 t. Spatially, the carbon stock exhibited a pattern of “low along the coast, high inland; low in the center, high around the periphery”. In addition, protecting cropland, reducing building, facilitating the conversion of reservoirs and ponds to forest, and transforming tidal flat wetlands into reservoirs and ponds can increase the region’s carbon storage capacity. These findings provide valuable insights for regional carbon management strategies and ecological protection policies, supporting the sustainable development goals of the Yellow River Delta. Full article

Accurate air pollution monitoring is critical to understand and mitigate the impacts of air pollution on human health and ecosystems. Due to the limited number and geographical coverage of advanced, highly accurate sensors monitoring air pollutants, many low-cost and low-accuracy sensors have been deployed. Calibrating low-cost sensors is essential to fill the geographical gap in sensor coverage. We systematically examined how different machine learning (ML) models and open-source packages could help improve the accuracy of particulate matter (PM) 2.5 data collected by Purple Air sensors. Eleven ML models and five packages were examined. This systematic study found that both models and packages impacted accuracy, while the random training/testing split ratio (e.g., 80/20 vs. 70/30) had minimal impact (0.745% difference for R²). Long Short-Term Memory (LSTM) models trained in RStudio and TensorFlow excelled, with high R² scores of 0.856 and 0.857 and low Root Mean Squared Errors (RMSEs) of 4.25 µg/m³ and 4.26 µg/m³, respectively. However, LSTM models may be too slow (1.5 h) or computation-intensive for applications with fast response requirements. Tree-boosted models including XGBoost (0.7612, 5.377 µg/m³) in RStudio and Random Forest (RF) (0.7632, 5.366 µg/m³) in TensorFlow offered good performance with shorter training times (<1 min) and may be suitable for such applications. These findings suggest that AI/ML models, particularly LSTM models, can effectively calibrate low-cost sensors to produce precise, localized air quality data. This research is among the most comprehensive studies on AI/ML for air pollutant calibration. We also discussed limitations, applicability to other sensors, and the explanations for good model performances. This research can be adapted to enhance air quality monitoring for public health risk assessments, support broader environmental health initiatives, and inform policy decisions. Full article

Populus euphratica is the only dominant tree species of desert riparian forest in the Tarim River Basin and faces a great threat of drought. Policy-based artificial water delivery projects are an effective engineering method to mitigate drought and reduce the degradation of desert riparian forests. Forest gaps have been shown to be the primary mode of forest regeneration. However, little is known about growth status of P. euphratica in various arid zone habitats, particularly in light of the complex and diverse microenvironmental alterations in the understory. This study quantified the effects of forest gaps and flooded areas on microenvironmental changes in the understory. The relationships between the microenvironmental changes, soil physicochemical properties, and physiological characteristics of P. euphratica were investigated through a cross-experiment that compared whether the water delivery process was flooded and whether forest gaps existed. The results revealed that the forest gap increased the diversity of light conditions on the ground; floods decreased the temperature of the forest gap by 1.94 °C while they increased the air humidity by 8.19%. Flooding improved the vertical distribution of soil physicochemical properties within the forest gap while also altering the content of soil indicators in different directions. In the research area, only the peroxidase activity (POD) exhibited significant differences (p < 0.05) in drought indicators between the forest gaps and understory of P. euphratica, while all of the drought indicators improved after flooding. Changes in the microenvironments and soil physicochemical features together play an important ecological role in mitigating the drought of P. euphratica. These results provide an actionable theoretical basis for the efficient management of riparian forests and a research basis for sustainable forest development in arid zones. Full article

Forests are crucial for human well-being and the health of our planet, particularly due to their role in carbon storage and climate mitigation. Mediterranean forests, in particular, are a vital natural resource for the region. They help absorb anthropogenic carbon emissions, reduce erosion, and provide essential habitats for various species, which in turn increases genetic diversity and species richness. This study combines Random Forest and Markov chain models to propose a highly accurate method for predicting land use. This approach offers substantial scientific support for sustainable land management policies. The methods used demonstrated excellent classification performance over time, allowing for an examination of the evolution of Mediterranean forests in the Aspromonte region. This study also provides a foundation for estimating carbon stored above and below ground using remote sensing images. The model achieved an impressive accuracy of 98.88%, making it a reliable tool for predicting the dynamics of Mediterranean forests. The results of this study have significant implications for urban planning and climate change mitigation efforts. Full article

Abstract: The province of Sankuru, located within the Democratic Republic of Congo, is distinguished by its extensive rubber plantations, which have a long history in the region. These plantations have had a considerable impact on the region’s agrarian landscape over time. In addition to the exploitation of latex, for which the conditions are currently very limited, these plantations provide goods and services to the local population and are dominated by rural communities that are highly dependent on these natural resources. This study aimed to characterize the socio-demographic and agrarian profile of historical rubber plantations while assessing the occurrence of the ecosystem services (ESs) they provide. Particular attention will be paid to the farmers’ perceptions of these services, an essential element for the rational management of natural resources. This study used a mixed methodological approach, integrating semi-structured interviews, focus groups, and statistical analyses including chi-square testing and multiple correspondence factorial analysis (MCAFA) to obtain and analyze the data comprehensively. The results indicate that historical rubber plantations in Sankuru provide 21 ESs, which are grouped into four categories: eleven provisioning services, four regulating services, four cultural services, and two supporting services. It has been observed that local communities attach significant importance to the provision of services including the provision of firewood (96.67%) and the utilization of forest resources for traditional pharmacopoeia (91.33%). These plantations have come to be regarded as valuable cultural heritage by local communities over time. The younger generation evinces a greater interest in utility services than the older generation, which displays a preference for cultural services. However, older people demonstrate a more profound understanding of cultural and regulatory services. By emphasizing the species that contribute to ESs and recognizing plantations as cultural heritage, the study enhances the comprehension of the significance of local ecosystems. These findings provide a crucial foundation for directing local policy toward integrated management of historic rubber plantations in Sankuru. By considering the perceptions of local people, the study contributes to the sustainable conservation of these plantations for the present and future generations. Full article

Urban expansion reshapes spatial patterns over time, leading to complex challenges such as environmental degradation, resource scarcity, and socio-economic inequality. It is critical to anticipate these transformations in order to devise proactive urban policies and implement sustainable planning practices that minimize negative impacts on ecosystems and human livelihoods. This study investigates LULC changes in the rapidly urbanizing Manisa metropolitan area of Turkey using Sentinel-2 satellite imagery and advanced machine learning algorithms. High-accuracy LULC maps were generated for 2018, 2021, and 2024 using Random Forest, Support Vector Machine, k-Nearest Neighbors, and Classification and Regression Trees algorithms. Among these, the Random Forest algorithm demonstrated superior accuracy and consistency in distinguishing complex land-cover classes. Future LULC scenarios for 2027 and 2030 were simulated using the Cellular Automata–Artificial Neural Network model and the QGIS MOLUSCE plugin. The results indicate significant urban growth, with built-up areas projected to increase by 23.67% between 2024 and 2030, accompanied by declines in natural resources such as bare land and water bodies. This study highlights the implications of urban expansion regarding ecological balance and demonstrates the importance of integrating machine learning and simulation models to forecast land use changes, enabling sustainable urban planning and resource management. Overall, effective policies must be developed to manage the negative environmental impacts of urbanization and conduct land use planning in a balanced manner. Full article

Background: The rapid adoption of telehealth services for youth mental health care necessitates a comprehensive evaluation of its effectiveness. This study aimed to analyze the impact of telehealth on youth mental health outcomes using artificial intelligence techniques applied to large-scale public health data. Methods: We conducted an AI-driven analysis of data from the National Survey on Drug Use and Health (NSDUH) and other SAMHSA datasets. Machine learning techniques, including random forest models, K-means clustering, and time series analysis, were employed to evaluate telehealth adoption patterns, predictors of effectiveness, and comparative outcomes with traditional in-person care. Natural language processing was used to analyze sentiment in user feedback. Results: Telehealth adoption among youth increased significantly, with usage rising from 2.3 sessions per year in 2019 to 8.7 in 2022. Telehealth showed comparable effectiveness to in-person care for depressive disorders and superior effectiveness for anxiety disorders. Session frequency, age, and prior diagnosis were identified as key predictors of telehealth effectiveness. Four distinct user clusters were identified, with socioeconomic status and home environment strongly associated with positive outcomes. States with favorable reimbursement policies saw a 15% greater increase in youth telehealth utilization and 7% greater improvement in mental health outcomes. Conclusions: Telehealth demonstrates significant potential in improving access to and effectiveness of mental health services for youth. However, addressing technological barriers and socioeconomic disparities is crucial to maximize its benefits. Full article

To examine the impact of green finance (GF) on urban land green use efficiency (LGUE), we treat the Green Finance Reform and Innovation Pilot Zone (GFRIPZ) policy, implemented in 2017, as quasi-natural experiment. The results from a multi-period difference-in-difference model show that GF contributes to improving urban LGUE. This conclusion is validated further by a generalized random forest model. The mechanism analysis demonstrates that GF enhances LGUE through the effects of green technological innovation, industrial upgrading, and public green behavior. The moderation analysis further reveals that artificial intelligence can amplify the positive impact of GF on LGUE. The heterogeneity results show that the positive relationship between GF and LGUE is more pronounced in midwestern cities, non-resource-based cities, and cities with a high level of financial development. Therefore, it is essential to expand the GF pilot program in a structured manner and establish a coordinated mechanism to promote LGUE improvement through GF in different regions, thereby enhancing financial service efficiency for the real economy. Full article

Accurate crop yield prediction and modeling are essential for ensuring food security, optimizing resource allocation, and guiding policy decisions in agriculture, ultimately benefiting society at large. With the increasing threat of weather change, it is important to understand the impacts of weather dynamics on agricultural productivity, particularly for crucial crops like soybeans. This study considers the study area of the Greater Mississippi River Basin, where most soybeans are typically planted, with a large variety of weather across from the North to the South in the US. Leveraging the greenness and density measured by the normalized difference vegetation index (NDVI) from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images, along with weather variables including mean precipitation, minimum temperature, and maximum temperature, we aim to uncover the relationships between these variables and soybean yield for different geographical and weather regions. Our analysis focuses on the four weather regions within the US: Very Cold, Cold, Mixed Humid, and Hot Humid, where most soybeans are planted in the Mississippi River Basin. The findings reveal that soybean yield in the Cold and Very Cold regions is positively correlated with minimum temperatures, whereas in the Mixed Humid and Hot Humid regions, negative correlations between maximum temperatures and yields are found. We identify a significant positive correlation between precipitation and soybean yield across all regions. In addition, the NDVI shows significant positive correlations with the soybean yield. Both linear and nonlinear regression models, including support vector machine and random forest models, are trained with remotely sensed data and weather data, showing a reliable and improved crop yield prediction. The findings of this study contribute to a better understanding of how soybean yield responds to climatic variations and will help the national agricultural management system in better monitoring and predicting crop yield when facing the increasing challenge of weather dynamics. Full article

This study developed and evaluated machine learning models (MLMs) for predicting the drivers of green sukuk investment interest (GSII) in Nigeria, adopting the planks of hypothesised determinants adapted from variants of the planned behavioural model and behavioural finance theory. Of the seven models leveraged in the prediction, random forest, which had the highest level of accuracy (82.35% for testing and 90.37% for training datasets), with a good R² value (0.774), afforded the optimal choice for prediction. The random forest model ultimately classified 10 of the hypothesised predictors of GSII, which underpinned constructs such as risk, perceived behavioural control, information availability, and growth, as highly important; 21, which were inclusive of all of the hypothesised constructs in measurement, as moderately important; and the remaining 15 as low in importance. The feature importance determined by the random forest model afforded an indicator-specific value, which can help green sukuk (GS) issuers to prioritise the most important drivers of investment interest, suggest important contexts for ethical investment policy enhancement, and inform insights about optimal resource allocation and pragmatic recommendations for stakeholders with respect to the funding of climate change mitigation projects in Nigeria. Full article

Under complex conditions, the collaborative control capability of UAV swarms is considered to be the key to ensuring the stability and safety of swarm flights. However, in complex environments such as forest firefighting, traditional swarm control methods struggle to meet the differentiated needs of UAVs with differences in behavior characteristics and mutually coupled constraints, which gives rise to the problem that adjustments and feedback to the control policy during training are prone to erroneous judgments, leading to decision-making dissonance. This study proposed a swarm control method for complementary collaboration of UAVs under complex conditions. The method first generates training data through the interaction between UAV swarms and the environment; then it captures the potential patterns of UAV behaviors, extracts their differentiated behavior characteristics, and explores diversified behavior combination scenarios with complementary advantages; accordingly, dynamic behavior allocations are made according to the differences in perception accuracy and action capability to achieve collaborative cooperation; and finally, it optimizes the neural network parameters through behavior learning to improve the decision-making policy. According to the experimental results, the UAV swarm control method proposed in this study demonstrates high formation stability and integrity when dealing with the collaborative missions of multiple types of UAVs. Full article