Search Results (640)

China’s dual carbon goals and rural revitalization strategy necessitate innovative models that integrate energy transition with ecological conservation. However, a critical disconnect persists between photovoltaic (PV) promotion and forest carbon sink projects, limiting their collective potential for coordinated urban–rural emission reduction and common prosperity. To bridge this gap, this study pioneers an integrated “cooperation-mutual assistance” framework that synergizes PV and carbon sinks. A system dynamics model encompassing economic, energy, and environmental subsystems is developed to simulate the long-term evolution (2025–2050) of this synergy under multiple policy scenarios. The simulation results demonstrate that this integrated model can achieve substantial co-benefits: It enables a cumulative carbon emission reduction of 17.5 Gt (gigatons of CO₂ equivalent) from 2025 to 2050, boosts regional GDP by 4.8% by 2050 compared to the baseline scenario, and narrows the urban–rural income gap by prioritizing rural resident income growth. The main contribution of this study is the novel integration of PV and carbon sinks into a unified analytical framework, quantitatively verifying its win–win potential. These findings provide a critical scientific basis for crafting integrated policies that combine carbon markets, green finance, and smart grid planning. Full article

Diabetes develops through a mix of clinical, metabolic, lifestyle, demographic, and environmental factors. Most current classification models focus on traditional biomedical indicators and do not include environmental exposure biomarkers. In this study, we develop and evaluate a supervised machine learning classification framework that integrates heterogeneous demographic, anthropometric, clinical, behavioral, and environmental exposure features to classify physician-diagnosed diabetes using data from the National Health and Nutrition Examination Survey (NHANES). We analyzed NHANES 2017–2018 data for adults aged ≥18 years, addressed missingness using Multiple Imputation by Chained Equations, and corrected class imbalance via the Synthetic Minority Oversampling Technique. Model performance was evaluated using stratified ten-fold cross-validation across eight supervised classifiers: logistic regression, random forest, XGBoost, support vector machine, multilayer perceptron neural network (artificial neural network), k-nearest neighbors, naïve Bayes, and classification tree. Random Forest and XGBoost performed best on the balanced dataset, with ROC AUC values of 0.891 and 0.885, respectively, after imputation and oversampling. Feature importance analysis indicated that age, household income, and waist circumference contributed most strongly to diabetes classification. To assess out-of-sample generalization, we conducted an independent 80/20 hold-out evaluation. XGBoost achieved the highest overall accuracy and F1-score, whereas random forest attained the greatest sensitivity, demonstrating stable performance beyond cross-validation. These results indicate that incorporating environmental exposure biomarkers alongside clinical and metabolic features yields improved classification performance for physician-diagnosed diabetes. The findings support the inclusion of chemical exposure variables in population-level diabetes classification and underscore the value of integrating heterogeneous feature sets in machine learning-based risk stratification. Full article

Maple syrup production has the potential to promote sustainable rural economic development in regions with suitable forest and climate conditions. Kentucky emerges as a promising candidate due to its extensive maple tree inventory and favorable seasonal patterns. However, the broader economy-wide implications of developing a maple syrup industry in the state remain underexplored. To fill this knowledge gap, this study employs a customized static single-region computable general equilibrium (CGE) modeling approach for Kentucky under nine scenarios based on production capacities and potential levels. The results consistently show positive impacts on net household income, social welfare (measured by equivalent variation), government revenues, and state GDP across all scenarios. Medium production capacities generate the most balanced and efficient outcomes, while high-potential scenarios, especially under small and large scales produce the largest absolute gains. These results underscore the viability of maple syrup production as an economic development strategy and highlight the role of production scale in maximizing benefits. Furthermore, expanding maple syrup production can enhance rural livelihoods by diversifying forest-based income and promoting long-term stewardship. As a non-timber forest product, maple syrup tapping provides economic incentives to maintain healthy forests, strengthening rural sustainability and resilience. Our findings indicate that developing this industry beyond traditional regions can generate meaningful economic benefits while encouraging sustainable resource use when appropriately scaled and managed. Full article

Urban floods frequently occur in Southeast Asian cities, causing extensive road disruptions and a significant decline in overall urban mobility. To effectively adapt to such conditions, it is crucial to understand how residents modify their travel behavior and daily activities during flood events. This study investigates these behavioral changes by comparing individual travel behaviors and activities under normal and flooding conditions, based on an Activity Diary Survey conducted in Kudus, Indonesia. The comparative analysis reveals that during floods, individuals tend to reduce non-essential activities and limit travel to essential purposes such as work and education. The findings from chi-square tests and applying the RF (random forest) model indicate that socioeconomic characteristics—particularly age, license, income, and level of flood—significantly influence the likelihood of behavioral change. These results highlight that flood-induced disruptions in mobility are not only physical but also socially differentiated, reflecting disparities in vulnerability and adaptive capacity. Full article

Sustainable management of ecosystem services (ESs) is critical for balancing human well-being with conservation goals in biosphere reserves. This study examined the spatial and socio-demographic variation in the use and perceived importance of provisioning, regulating, supporting, and cultural ESs across different vegetation types and land use zones in the Vhembe Biosphere Reserve (VBR), South Africa. Household surveys were administered to 447 randomly selected households in six rural communities. Descriptive statistics, Chi-square tests, Kruskal–Wallis tests, and Friedman mean ranking analysis were employed. Results revealed significant differences (p < 0.05) in ES distribution and value across vegetation types, land use categories, and household characteristics, including income, education, age, and gender. Provisioning services, particularly fuelwood, wild fruits, and wild vegetables, were most intensively utilized in Mountain Woodland Moist and Ironwood Forest areas due to accessibility and limited livelihood alternatives. Regulating and supporting services, including water purification, erosion control, and habitat provision, were associated with forested and traditionally protected areas. Cultural services reflected strong socio-cultural ties, especially in sacred and tourism-associated landscapes. Overall, the study highlights the multifunctional importance of forested and agroforestry systems in rural livelihoods, emphasizing the need for integrated, culturally informed, and ecologically sound land use planning to support sustainable development in the VBR. Full article

Gender-based violence (GBV) in urban contexts is a complex, multifactorial phenomenon shaped by socioeconomic, territorial, and contextual factors. This study aims to develop a predictive model for GBV-related crimes in Valencia (Spain), using open geospatial data and advanced machine learning techniques to support the identification of high-risk areas and guide targeted interventions. A 25 m grid was generated to homogenize crime data and independent variables, including socioeconomic indicators, urban services, real estate information, and traffic intensity. Multiple models were tested—Multiple Linear Regression (MLR), Decision Tree (DT), and Random Forest (RF). Linear models were found to be insufficient for explaining GBV patterns (R² ≈ 0.45), while RF and DT achieved high predictive accuracy (R² ≈ 0.97 and 0.95, respectively. The variables with the greatest influence were traffic intensity, average monthly income, unemployment rate, and proximity to nightlife venues. To enhance the interpretability of the most accurate models, we applied SHAP (SHapley Additive exPlanations) to quantify the contribution of each predictor and elucidate the direction and magnitude of their effects on model predictions. These findings demonstrate the utility of geospatial ML techniques in understanding the spatial dynamics of GBV and in supporting urban safety policies. While the current model focuses on static spatial predictors and does not explicitly model temporal dynamics or spatial autocorrelation, future research will integrate these aspects, along with participatory data, and test the model’s applicability in other cities to enhance its robustness and generalizability. Full article

Ecosystems provide essential services to local communities, which in turn offer incentives for the preservation of natural resources, as these resources are crucial to the sustainability and evolution of human societies. So, this study examined the demand for ecosystem services among communities surrounding the Luki Biosphere Reserve in the Democratic Republic of Congo. Data were collected through semi-structured interviews with 361 randomly selected individuals and focus group discussions in 18 villages, complemented by field observations on local resource use (agriculture, charcoal production, wood harvesting, and tree felling). The services provided by the reserve were identified according to citation frequency, perceived usefulness, and level of agreement among respondents. Results indicate that agricultural products (28.5%), charcoal (19.1%), non-timber forest products (17.5%), and firewood (10%) are the most requested. The Chi-square test showed significant associations between dependence on ecosystem services and socio-economic variables such as gender (p = 0.014 < 0.05), education level (p = 0.033 < 0.05), and annual income (p = 0.000 < 0.05), while age was not significant (p = 0.504 > 0.05). Poverty and rapid demographic growth were identified as key drivers of demand and factors contributing to growing pressure on natural resources. The study emphasizes feedback loops between changes in ecosystem service supply and community responses, as well as trade-offs between services and actors. It recommends integrating ecosystem values into agricultural and forestry policies, while raising awareness and educating local communities to promote sustainable resource management. Full article

In Kazakhstan’s Akmola Region, rural households face heightened vulnerability from climate change, driven by reliance on weather-dependent resources and amplified risks of extreme precipitation events, prolonged dry spells, and progressive soil degradation—further intensified by limited adaptive capacity and inequities affecting women-led or ethnic minority families. This study conducted stratified household surveys across four agricultural districts, developed a tailored Livelihood Vulnerability Index (LVI) incorporating shelterbelt presence, condition, and perceived effects, alongside readiness for hydrological surface recovery (contour–strip organisation, swales/valokany, and tree–shrub planting). Results revealed an average LVI of 0.45–0.55, which was higher (+10–15%) in marginalized groups; testing pathways showed correlations (r = 0.65, p < 0.05) with water security, soil condition, income stability, and hazard reduction, with potential LVI reductions of 15–25% through integrated measures. District-specific recommendations include implementing the Potapenko–Lukin method on slopes <5% with valokany (width 80 cm, depth 1.5 m, spacing 100–500 m), endemic plantings, and biomaterial, supported by subsidies (488,028 tenge/ha/year) and GIS monitoring, to enhance resilience and equity in steppe and forest–steppe farming. Full article

This study investigated the interrelationships among Lifestyles of Health and Sustainability (LOHASs), recreational benefits, and environmentally responsible behaviors (ERBs) of visitors to the Xitou Forest Recreation Area in Taiwan and estimated the conservation value of its forest recreation resources using the contingent valuation method. The structural equation modeling analysis supported six of eight hypotheses. Three LOHAS factors (environmental awareness, internal health, and external health) indirectly promoted ERB through recreational benefits, including environmental education, psychological, physiological, and social benefits. Higher income, stronger perceived recreational benefits, and recognition of ecological or facility value significantly increased visitors’ willingness to pay (WTP). Among the three identified lifestyle clusters, the health-conscious LOHAS group consistently exhibited the highest WTP at NTD$263, with a confidence interval of NTD$255–271, surpassing both the eco-friendly group (NTD$193–209) and socially engaged group (NTD$184–200), demonstrating a stronger commitment to ecological and environmental protection and recreational facility maintenance. Forest recreation managers can target different LOHAS segments and emphasize the holistic benefits of forest recreation. Implementing flexible pricing alongside environmental education can increase WTP, supporting sustainable conservation funding and improved visitor experiences. Full article

Background: Global immunization against Haemophilus influenzae type b (Hib) has expanded with Gavi support. We estimated health, economic benefits, equity and cost-effectiveness in 159 countries (1990–2021), and projected effects of future introduction in China. Methods: We used a random forest model to simulate counterfactual scenarios without Hib vaccine introduction in 159 countries (1990–2021) and to project effects of Hib vaccine introduction in China over the next decade. Ten variables were sourced from the World Bank and WHO; Hib disease burden estimates were from the Global Burden of Disease Study 2021. We compared counterfactual and actual results to quantify benefits, equity, and cost-effectiveness. Extensive uncertainty analyses were performed. Results: Between 1990 and 2021, Hib immunization averted an estimated 1,321,123 (95% uncertainty interval [UI] 32,034–2,723,304) deaths and 90,973,504 (95% UI 3,573,718–197,099,799) disability-adjusted life-years globally. Greatest health and economic gains occurred in Africa and low- and middle-income countries (LMICs). Deaths averted decreased with later vaccine introduction (Pearson’s r = −0.56). Vaccination did not improve health equity, and access remains limited in Africa and LMICs. Hib immunization was cost-saving in all countries. In China, introduction at any point in the next decade would provide health and economic benefits and be cost-effective, with earlier introduction yielding greater gains. Conclusions: Hib immunization provide substantial, cost-effective health and economic benefits globally. Persistent inequities in vaccine access for LMICs require targeted solutions. Policymakers in China should consider these findings for future vaccine introduction. Full article

In recent years, as Chinese cities have entered a stage of high-quality transformation, enhancing livability and achieving refined governance within existing urban spaces has become a central issue in urban planning and management. The establishment of the Urban Physical Examination mechanism has provided a scientific framework for evaluating urban performance. However, most existing studies focus primarily on objective indicators, paying insufficient attention to residents’ subjective perceptions and their spatial variations. As a result, the multi-scale mechanisms underlying human settlement satisfaction remain poorly understood. Using Xiamen City as a case, this study draws on data from the 2025 Urban Physical Examination Resident Survey and constructs a Geographically Random Forest (GRF) model to examine how block, community, housing, and personal attributes jointly shape human settlement satisfaction (HSS) and its spatial heterogeneity. The results show that (1) overall, block’ business vitality is the most influential factor affecting HSS, followed by community management and housing safety, highlighting the dominant roles of the built environment and grassroots management in shaping residential experience; (2) management and safety issues at the community level are more prominent in suburban areas, old neighborhoods, and zones surrounding tourist attractions, reflecting a mismatch between service provision and urban expansion; (3) housing-scale factors display significant spatial variation, with tenure and housing affordability emerging as key determinants of satisfaction among residents in newly developed districts; and (4) at the personal characteristic, age, residential duration, occupational prestige, and household income exhibit marked spatial heterogeneity, revealing satisfaction patterns jointly shaped by social mobility and urban growth. The study concludes that multi-scale spatial identification and resident perception feedback mechanisms should be strengthened within the Urban Physical Examination framework. Such efforts can promote a shift from static indicator monitoring to dynamic spatial governance, providing theoretical and methodological support for refined urban management and the improvement of human settlement environments. Full article

Many developing countries are highly dependent on agriculture and land expansion, which leads to the loss of forests and other natural habitats. Both features pose a challenge for green rural transformation. By applying a “land” Kaya identity, growth in agricultural land use for developing countries is at least partially attributed to four factors: growth in income (GDP) per capita, population, the agricultural value-added share of GDP, and the land intensity of agricultural value added. The results show that, across 122 developing countries from 2010 to 2021, both the agricultural value-added share of GDP and land expansion are negatively correlated with GDP per capita. However, there is little association between income per person and land intensity. From 2000 to 2021, the agricultural value-added share of GDP and land intensity declined in developing countries, but not sufficiently to offset the pressure on agricultural land expansion from the growth of GDP per capita and population. Decoupling land use expansion from economic growth will require substantial reductions in land intensity and agricultural dependency through policies that raise agricultural land productivity, improve efficiency and equity, and limit unnecessary agricultural land expansion. Future research should focus on applying geospatial data and sub-national analysis to analyze these trends. Full article

Background: Diarrheal disease (DD) remains a major public health challenge and is the leading cause of malnutrition and the second leading cause of death among children under five globally. Although DD can be caused by a wide range of pathogens, its primary drivers are often linked to unimproved sanitation, limited access to clean drinking water, and poor hygiene practices. Low- and middle-income countries, particularly those in South Asia, experience the highest burden. These regions are also increasingly vulnerable to climate change and land use/cover changes, which may further exacerbate DD risk. However, the relative influence of environmental and social drivers at localized scales is not well understood. This gap presents a critical opportunity to identify scalable, data-informed interventions that address environmental determinants of health in the context of a changing climate. Methods: To investigate these dynamics, we analyzed 21,779 records from the Demographic and Health Surveys (DHS) for Bangladesh, integrating them with remotely sensed data on forest cover change, temperature, and rainfall. Using Random Forest machine learning models, we assessed the relative importance of both environmental and socio-demographic variables at household and regional (village) levels. Results: The results show that DD risk varies across scales: household-level outcomes are primarily associated with socio-demographic characteristics, while regional-level outcomes are more strongly influenced by environmental and geographic features, including precipitation, elevation, and proximity to water bodies. Conclusions: These findings underscore the importance of scale-sensitive approaches when assessing environmental health risks and developing climate-adaptive public health strategies. Full article

Soil salinity, soil moisture, and nutrient loss significantly reduce agricultural productivity and economic benefits in the semi-arid regions of Ethiopia. However, knowledge of how to mitigate these risks remains limited. This study examined the combined effects of salinity (EC), soil moisture (Sm), and nitrate (N) on the yield and profitability of banana, cotton, and maize using field-based and satellite data with seven machine learning algorithms. Our results showed that a higher EC level reduced crop yields, whereas sufficient Sm and N improved productivity and income. Among the models, Random Forest (RF) performed the best, achieving high accuracy (e.g., R² = 0.998 for cotton, 0.869 for banana, and 0.793 for maize). SHapley Additive exPlanations (SHAP) analysis further identified EC as the most critical determinant, highlighting the priority of salinity mitigation, alongside water and nutrient management. These findings provide farmers and decision-makers with practical insights into how to sustain crop productivity, improve livelihoods, and strengthen food security in semi-arid regions. Full article

Introduction: Cardiovascular disease (CVD) remains a primary cause of mortality worldwide, with persistent geographic disparities driven by a complex interplay of risk factors. Continual updates of localized variations in CVD mortality are essential to develop targeted interventions for optimizing disease and healthcare management. Methods: This study investigated associations between CVD mortality and a comprehensive set of biological, environmental, behavioral, and socioeconomic factors across all U.S. counties, employing correlation, geospatial visualization, stepwise multiple regression, and machine learning models to evaluate the importance of risk associations. Results: Significant disparities in CVD mortality trend were observed across race, age, sex, and region, with elevated rates among older adults, men, and Blacks, particularly in southeastern states exhibiting severe social vulnerability. Correlation analysis identified disease management (e.g., COPD, hypertension, medication non-adherence), environmental factors (PM2.5), lifestyle behaviors (e.g., smoking, sleep duration), and socioeconomic status (e.g., poverty, single-parent households, education) as important contributors to CVD mortality. Conversely, higher household income, physical activity, and cardiac rehabilitation participation were strong protectors. Multiple regression explained 66.9% variance in CVD mortality, recognizing PM2.5, smoking, and medication non-adherence as top associated factors. Random Forest models underscored COPD’s predictive dominance, followed by medication non-adherence, smoking, and sleep duration. Conclusions: The findings highlight the geospatial connection of risk factors to CVD mortality disparities across U.S. counties. They emphasize the critical importance of data-driven strategies targeting air quality, tobacco control, social inequities, and chronic disease management to mitigate CVD burden and promote health equity. Full article