Search Results (96)

Despite the continuous development of wind power forecasting methods, forecasting errors remain unavoidable, especially during extreme weather events. However, current research on quantifying these errors is quite limited. This paper proposes an adaptive error risk early warning method that can directly predict the magnitude of forecast errors and classify and warn of risks, thereby achieving proactive risk management. This method comprises three core designs. First, mechanism-based feature engineering captures the driving factors of error generation, including numerical weather prediction bias, atmospheric instability, and meteorological dynamics, all of which are key factors leading to forecast bias. Second, a stacked ensemble method integrates quantile regression, random forest, and gradient booster, utilizing complementary learning capabilities to handle high-dimensional non-stationary error patterns. Third, K-means clustering establishes a dynamic risk threshold that adapts to changes in seasonal error distribution, overcoming the limitations of fixed thresholds. Validation using actual wind farm operation data demonstrates significant improvements: the proposed ensemble model reduces the Root Mean Square Error (RMSE) by 2.5% compared to the best single model, and the dynamic threshold mechanism increases the High-Risk Recall rate from 89.7% to 96.9%. These results confirm that the method can effectively warn of high-error events and provide timely and actionable decision support to enhance grid stability and security. Full article

University students face rising mental health pressures, making restorative environmental perception (REP) in campus forests critical for psychological recovery. While environmental factors are recognized contributors, Socio-Ecological Systems (SES) theory emphasizes that environmental and social processes are interdependent. Within this context, informal social interaction (ISI)—low-effort encounters such as greetings or small talk—represent a key social dimension that may complement environmental restoration by fostering comfort and embedded affordances. However, most studies examine these factors separately, often using coarse measures that overlook heterogeneity in restorative mechanisms. This study investigates how environmental-exposure and social–environmental context dimensions jointly shape REP in campus forests, focusing on distributional patterns beyond average effects. Using a Public Participation Geographic Information Systems (PPGIS) approach, 30 students photographed 1294 tree-dominant scenes on a forest-rich campus. Environmental features were quantified via semantic segmentation, and ISI was rated alongside REP. Quantile regression estimated effects across the REP distribution. Three distributional patterns emerged. First, blue exposure and ISI acted as reliable resources, consistently enhancing REP with distinct profiles. Second, green exposure functioned as a threshold-dependent resource, with mid-quantile attenuation but amplified contributions in highly restorative scenes. Third, anthropogenic and demographic factors created conditional barriers with distribution-specific effects. Findings demonstrate that campus forest restoration operates through differentiated socio-ecological mechanisms rather than uniform pathways, informing strategies for equitable, restoration-optimized management. More broadly, the distributional framework offers transferable insights for urban forests as socio-ecological infrastructures supporting both human well-being and ecological resilience. Full article

Background: Comprehensive data on the economic burden of cervical cancer treatment remain scarce in China’s less developed regions, necessitating this study on hospitalization costs and expenditure trends in these areas. Methods: Employing a multi-stage stratified cluster sampling approach, this study enrolled 10,070 cervical cancer inpatients from 72 healthcare facilities in Gansu Province. Clinical and expenditure data were extracted from hospital information systems. Rank sum tests and Spearman correlation analyses were performed for univariate assessment, while quantile regression and random forest models were applied to identify determinant factors. Results: From 2019 to 2023, the average hospitalization duration for cervical cancer patients in Gansu Province was 16.12 days, with an average hospitalization cost of USD 3862.08 (2023 constant prices, converted from CNY at 1:7.0467). During these five years, the average inpatient costs per hospitalization increased from USD 3473.45 to USD 4202.57, and the average daily hospitalization cost rose from USD 230.53 to USD 241.77. The average drug cost decreased from USD 769.06 to USD 640.16. The main factors influencing hospitalization costs included the length of hospital stay, whether cervical cancer surgery was performed, hospital type, hospital level, and the proportion of medications. Conclusions: Our findings indicate that cervical cancer is a considerable economic burden on both families and society. This highlights the need to control the length of hospital stay and optimize the allocation of medical resources, in addition to strengthening cervical cancer screening and HPV vaccination in underdeveloped areas, in order to enhance the efficiency of prevention and treatment and ensure medical equity. Full article

Understanding the temporal dynamics and spatial distribution of key soil properties is essential for sustainable land management and informed decision-making. This study assessed the spatial variability and decadal changes (2013–2023) of topsoil properties in Córdoba, central Argentina, using digital soil mapping (DSM) and machine learning (ML) algorithms. Three ML methods—Quantile Regression Forest (QRF), Cubist, and Support Vector Machine (SVM)—were compared to predict soil organic matter (SOM), extractable phosphorus (P), and pH at 0–20 cm depth, based on environmental covariates related to site climate, vegetation, and topography. QRF consistently outperformed the other models in prediction accuracy and uncertainty, confirming its suitability for DSM in heterogeneous landscapes. Prediction uncertainty was higher in marginal mountainous areas than in intensively managed plains. Over ten years, SOM, P, and pH exhibited changes across land-use classes (cropland, pasture, and forest). Extractable P declined by 15–35%, with the sharpest reduction in croplands (−35.4%). SOM decreased in croplands (−6.7%) and pastures (−3.1%) but remained stable in forests. pH trends varied, with slight decreases in croplands and forests and a small increase in pastures. By integrating high-resolution mapping and temporal assessment, this study advances DSM applications and supports regional soil monitoring and sustainable land-use planning. Full article

Against the backdrop of global sustainable development and the fast-growing digital economy, aligning corporate ESG practices with digital transformation is key for enterprises’ high-quality development, yet existing studies have not fully explored ESG’s directional impact on digital transformation. This study examines how corporate ESG performance drives digital transformation and the moderating roles of firm characteristics, industry types, and ownership structures, using 11,109 valid observations from Chinese A-share listed companies (2009–2022); it adopts the causal forest algorithm and supplements with OLS, quantile, and Poisson regressions for robustness tests. The results show that ESG significantly promotes digital transformation—with obvious positive effects from E and S dimensions, while G has no statistical impact—and further analysis reveals nonlinear moderation by firm characteristics and contextual differences: the positive effect is stronger in high-tech and private enterprises but weaker in traditional and state-owned enterprises (due to institutional constraints). These findings offer theoretical insights into ESG–digital synergies and practical guidance for targeted sustainability and digital strategies. Full article

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

The value realization of forest ecological products (VRF) is crucial for rural revitalization, while the rural digital economy (RDE) plays a central role in enhancing farmers’ income (FI). This study constructs index systems to evaluate the RDE and VRF using the entropy weight method and the input–output model. Based on panel data from 31 Chinese provinces (2011–2021), we employ a comprehensive analytical framework that includes spatiotemporal evolution analysis, benchmark regression models, mediation effect analysis, and heterogeneity analysis. The results of the benchmark regression models show that the RDE significantly boosts FI, with each unit of increase in the RDE leading to a 2579-unit rise in income. Spatiotemporal evolution analysis reveals that the positive effect of the RDE weakens from the Eastern coastal regions to the less developed Western regions. Furthermore, mediation effect analysis indicates that VRF mediates the relationship between the RDE and FI. Heterogeneity analysis demonstrates that the impact of the RDE varies across regions and income levels. These findings provide strong evidence of the role of the RDE in promoting FI and highlight VRF as a mediating mechanism, offering policy insights for integrating digital and ecological strategies to foster inclusive rural growth. Full article

Unlike traditional yield mapping, which is conducted using costly yield sensors mounted on combine harvesters to collect post-harvest data, yield potential prediction using remote sensing data is considered a low-cost alternative. In this study, an effort was made to address the research gap concerning the effectiveness of phenological modeling in crop yield potential prediction using machine learning. Combinations of seven vegetation indices from Sentinel-2 imagery and seven phenology metrics were evaluated for the prediction of maize and soybean yield potential. Ground truth yield data were provided by the Quantile Loss Domain Adversarial Neural Network (QDANN) database, with 1000 samples randomly selected per year from 2019 to 2022 for Iowa and Illinois. Four machine learning algorithms were tested: random forest (RF), support vector machine regression (SVM), multivariate adaptive regression splines (MARS), and Bayesian regularized neural networks (BRNNs). Across all evaluations, RF was found to outperform the other models in both cross-validation and final model accuracy metrics. Vegetation index values at peak of season (POS) and phenological timing, expressed as the day of year (DOY) of phenological events, were identified as the most influential covariates for predicting yield potential in particular years for both maize and soybean. Full article

Accurate modeling of height–diameter (h–d) relationships is critical for forest inventory and management, particularly in complex forest ecosystems such as natural and pure Crimean juniper (Juniperus excelsa Bieb.) stands. This study evaluates both traditional parametric and modern machine learning (ML) approaches to develop reliable h–d models based on 2135 sample trees measured in southern Türkiye. The modeling approaches include fixed-effects (FE), mixed-effects (ME), three quantile regression (QR) models based on three, five, and nine quantile levels, and non-parametric ML methods: shallow multilayer perceptron (S_MLP), extreme gradient boost (XGBoost), and random forest (RF). According to the assessment metrics for the fitting and test datasets, the XGBoost modeling approach achieved the most accurate performance. For the fitting dataset, it achieved root mean square error values of 1.11 m and 1.21 m. For the test dataset, the corresponding error values were 1.16 m and 1.24 m, resulting in the highest accuracy among all models, closely followed by the RF and S_MLP models. A key practical advantage of ML approaches is that they do not depend on calibration scenarios, meaning they can operate without the need for preliminary parameter configuration. In contrast, the ME model showed the highest accuracy among the parametric methods when calibration was applied. In this case, when applying ME models, the study recommends calibrating the model by measuring four randomly selected trees per plot to balance prediction accuracy and field sampling effort. Full article

Quantifying changes in soil properties greatly benefits our understanding of soil management and sustainable land use, especially in the context of strong anthropogenic activities and climate change. This study investigated the effects of long-term reclamation on soil properties in an artificial oasis region with a cultivation history of more than 50 years. Critical soil properties were measured at 77 sites, and a total of 462 soil samples were collected down to a depth of 1 m, which captures both surface and subsurface processes that are critical for long-term cultivation effects. Thirteen critical soil properties were analyzed, among which four properties—soil organic carbon (SOC), total phosphorus (TP), pH, and ammonium nitrogen (NH₄⁺)—were selected for detailed analysis due to their ecological significance and low intercorrelation. By comparing cultivated soils with nearby desert soils, this study found that semicentennial cultivation led to significant improvements in soil properties, including increased concentrations of SOC, NH₄⁺, and TP, as well as reduced pH throughout the soil profile, indicating improved fertility and reduced alkalinity. Further analysis suggested that environmental factors—including temperature, clay content, evaporation differences between surface and subsurface layers, sparse vegetation cover, cotton root distribution, as well as prolonged irrigation and fertilization—collectively contributed to the enhancement of SOC decomposition and the reduction of soil alkalinity. Furthermore, three-dimensional digital soil mapping was performed to investigate the effects of long-term cultivation on the distributions of soil properties at unvisited sites. The soil depth functions were separately fitted to model the vertical variation in the soil properties, including the exponential function, power function, logarithmic function, and cubic polynomial function, and the parameters were extrapolated to unvisited sites via the quantile regression forest (QRF), boosted regression tree, and multiple linear regression techniques. The QRF technique yielded the best performance for SOC (R² = 0.78 and RMSE = 0.62), TP (R² = 0.79 and RMSE = 0.12), pH (R² = 0.78 and RMSE = 0.10), and NH₄⁺ (R² = 0.71 and RMSE = 0.38). The results showed that depth function coupled with machine learning methods can predict the spatial distribution of soil properties in arid areas efficiently and accurately. These research conclusions will lead to more effective targeted measures and guarantees for local agricultural development and food security. Full article

The rapid development of lithium-ion (Li-ion) batteries has raised requirements for cycle life prediction to ensure safety and reliability. However, the intricate and nonlinear behavior of the battery degradation process poses significant challenges in accurately predicting its cycle life at an early stage. This work addresses the battery lifetime prediction problem by leveraging machine learning (ML) methods. First, we apply quantile transformation (QT) to both features and battery cycle lives to improve their correlation. Next, we adopt a centered isotonic regression (CIR) method in our work, a variant of isotonic regression (IR) that centers the feature values and then reduces overfitting to improve model performance. Finally, we perform convex regression (CR) to capture specific patterns in the battery dataset where monotonicity is absent and achieve an overall prediction for battery cycle lives. To validate our proposed method, we have done a comprehensive comparison among several different benchmarks, including elastic net, gradient boosting regression tree, decision tree, support vector machine, random forest, and Gaussian process regression. In contrast to existing methods, our CIR model has shown the best performance, with an average percentage error of 9.8% and a root mean square error of 149 cycles. These experiment results demonstrate the capability and potential of our proposed CIR model in the problem of battery lifetime prediction. Full article

Farmers’ participation in sustainable forest management plays a significant role in increasing their income and contributing to the comprehensive advancing of the rural revitalization strategy. This study focuses on farmers living near existing national forest health bases in Inner Mongolia. Using the endogenous switching regression model (ESRM), we empirically examine the income effects of farmers’ participation in sustainable forest management through employment and land leasing. The robustness of the model estimation is tested through various methods, including replacing the dependent variable. Furthermore, heterogeneity analysis is conducted using quantile regression. The results show the following: (1) Participation in sustainable forest management through employment (p < 0.001) and land leasing (p < 0.001) significantly increases annual household income by 4.28% and 1.44%, respectively. The income effect for farmers participating through employment is 2.84% higher than for those participating through land leasing. (2) For farmers who did not participate in sustainable forest management, the counterfactual scenario indicates a reduction in annual household income by 5.87% and 2.55%, respectively, highlighting a greater potential income improvement for non-participating farmers if they were to engage in sustainable forest management. (3) Heterogeneity analysis reveals that the income effects of the two participation forms vary across income levels. Employment participation in forest health bases has a more significant impact on low-income (QR_10) farmers, while land leasing participation has a greater impact on high-income (QR_90) farmers. Full article

The complexity and resource-sharing nature of traffic within multi-airport regions present significant challenges for air traffic management. This paper aims to develop a mesoscopic traffic model for exploring the traffic dynamics under coupled operations, and thus to conduct capacity decoupling analysis. We propose an integrated surface–airspace model. In the surface model, we utilize linear regression and random forest regression to model unimpeded taxiing time and taxiway network delays due to sparsity of ground traffic. In the airspace model, a dualized queuing network topology is constructed including a runway system, where the $G(t)/GI/s(t)$ fluid queuing model is applied, and an inter-node traffic flow transmission mechanism is introduced to simulate airspace network traffic. Based on the hybrid and efficient model, we employ a Monte Carlo approach and use a quantile regression envelope model for capacity decoupling analysis. Using the Shanghai multi-airport region as a case study, the model’s performance is validated from the perspectives of operation time and traffic throughput. The results show that our model accurately represents traffic dynamics and estimates delays within an acceptable margin of error. The capacity decoupling analysis effectively captures the interdependence in traffic flow caused by resource sharing, both within a single airport and between airports. Full article

Forests serve as the lungs of our planet, yet their mismanagement causes environmental problems and threatens global sustainability. Global forest footprints continue to increase, requiring studies to investigate and provide solutions. This study aims to establish how forest rents and government effectiveness shape forest footprints in China. Specifically, it assesses the impact of forest rents (FRs), fossil fuel consumption (FFC), foreign direct investment (FDI), economic growth (GDP), population (POP), and ecological footprints (EFFs) while considering the moderating role of government effectiveness (GEFF). This study used quantile regression, ordinary least squares, and Granger causality tests for a comparative analysis. This study found that forest rents significantly increase ecological footprints, but the impact diminishes at higher quantities, an indication that environmental policies can mitigate their adverse effects. Moreover, GEFF plays a crucial role in reducing EFFs across all quantiles, signifying the relevance of effective governance in achieving sustainability. Again, while FFC and FDI contribute to environmental sustainability, economic growth exacerbates ecological degradation, particularly at higher quantiles. The Granger causality test further indicates that forest rents and government effectiveness drive ecological changes, while population growth exerts a bidirectional influence on sustainability. These findings provide critical insights for policymakers and emphasize the need for robust governance, sustainable forest management, and eco-friendly economic strategies. Full article

With the transformation of the rural economy and the increasing national emphasis on forest resources, forestry management plays an increasingly important role in promoting household income growth and sustainable rural development. This study, based on a field survey of 1043 micro-level household data collected in Guizhou Province, China, empirically analyzes the impact of participation in forestry management on household income, income structure, and income inequality, as well as its underlying mechanisms. Using endogenous switching models, quantile regression models, and mediation effect models, the study reveals the following findings: First, participation in forestry management significantly enhances household income. Second, the impact of participation in forestry management on income structure varies, significantly increasing both forestry and non-forestry income, with the effect on forestry income being particularly pronounced. Third, participation in forestry management significantly alleviates income inequality, especially for low-income households. Fourth, forestry management indirectly increases household income and non-forestry income by promoting forest-based employment. Forest-based employment acts as a partial mediator in the effect of forestry management on household income and a full mediator in the increase in non-forestry income. The contribution of this study lies in its multidimensional approach to revealing the comprehensive impact of participation in forestry management on rural household income, providing important policy insights for increasing household income and achieving sustainable rural development. Full article