Search Results (4,514)

Promoting social participation is a core objective of digital inclusive development. Drawing on rural household survey data from five provinces in China and the Digital Governance Index developed by Peking University, this study systematically examines the impact of digital governance on rural households’ social participation deprivation. The benchmark regression results show that the effect of digital governance on rural households’ social participation deprivation follows an inverted U-shape, characterized by an initial increase followed by a subsequent decline. A series of robustness and endogeneity tests confirms the stability of these findings. Further heterogeneity analyses reveal pronounced regional differences. In the western region, the impact of digital governance on farmers’ social participation deprivation follows a U-shaped pattern, with deprivation initially decreasing and then increasing as digital governance deepens. By contrast, in the central and eastern regions, the inflection point of the inverted U-shaped relationship shifts further to the right relative to the full sample. Furthermore, digital governance exerts a significantly stronger mitigating effect on social participation deprivation among households experiencing higher levels of deprivation. Mechanism analysis shows that digital governance reduces farmers’ social participation deprivation by enhancing their perceived access to public services and improving their psychological well-being. However, moderation analysis shows that household-level digital exclusion and relative poverty significantly weaken these beneficial effects. Full article

Households are the major contributor to food waste generation in the European Union according to the recently published data from Eurostat. Promoting food systems sustainability and aspiring to achieve the United Nations SDG 12.3 requires a better insight to the underlying drivers of the household food waste occurrence. The present study presents the combination of a well-established method of acquiring information, the questionnaire surveys, with a state-of-the-art technology for data imputation and interpretation using machine learning (ML). The Food Loss and Waste Prevention Unit (FLWPU) of the municipality of Halandri employed two surveys within the framework of the European funded projects Food Connections and FOODRUS. The first questionnaire was designed for rapid completion, to maximize response rates and minimize respondent burden, ensuring the collection of a consistent core dataset. A total of 154 replies were collected. The second questionnaire, associated with FOODRUS, was more detailed, enabling the participants to provide more in-depth information on their household food waste (HHFW) practices. In total, 43 responses were collected. ML algorithms were applied for data enhancement and data clustering. Specifically, ML and statistical techniques are applied for data imputations. An XGBoost algorithm was trained so as to capture complex relationships between variables. Behavioral intentions and effective strategies for reducing food waste at the community level are identified from the responses of both questionnaires, while a clustering of respondents in five groups emerged by using k-means, thus providing valuable insight into targeted HHFW prevention action plans. Full article

Household food waste remains a significant barrier to sustainable consumption goals. This study investigates the impact of sociodemographic characteristics influence self-reported food waste levels in Poland. A cross-sectional CAWI survey (N = 1000), based on the HFSSM, was conducted among adults responsible for purchasing household food. Associations between food waste and structural factors were analysed using χ² tests, Spearman rank correlations, and Mann–Whitney U tests. Age appears as the strongest determinant: younger respondents consistently report higher food waste, while older adults indicate markedly lower levels. Household composition is equally important—the presence and number of children significantly increase waste. Economic status is also relevant: pensioners and disability-benefit recipients report substantially less waste than employed and self-employed individuals. Net household income shows no significant effect. Education does not necessarily reduce food waste; in some comparisons, higher-educated respondents report slightly higher levels, suggesting that formal education does not automatically translate into effective food management routines. The findings highlight that food waste is primarily influenced by life stage, household structure, and daily habits rather than income or education. Interventions should focus on younger adults and families with children, emphasising practical skills such as meal planning, inventory management, appropriate storage, and the use of leftovers. 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

As agricultural labor shifted toward non-farm sectors and the farming population aged, innovative production arrangements became essential to sustain land productivity. While partial agricultural production chain management (PAPM) was widespread, the productivity impact of whole-process agricultural production chain management (WAPM)—a comprehensive model integrating all production stages—remained empirically underexplored. Using nationally representative panel data from the China Labor-force Dynamics Survey (CLDS, 2014–2018) for grain-producing households, this study estimates the differential impacts of WAPM and PAPM with a two-way fixed-effects (TWFE) model, supplemented by propensity score matching (PSM) as a robustness check. The results show that WAPM significantly enhanced land productivity. Notably, the effect size of WAPM (coefficient: 0.486) is substantially larger than that of PAPM (coefficient: 0.214), indicating that systematic integration of service chains offers superior efficiency gains over fragmented outsourcing. Mechanism analysis suggests that WAPM improves productivity primarily by alleviating labor constraints and mitigating the disadvantages of small-scale farming. Furthermore, heterogeneity analysis demonstrated that these benefits are amplified in major grain-producing regions and hilly areas. These findings support policies that facilitate a transition from single-link outsourcing toward whole-process integrated service provision. Full article

This study investigates thermal stress and energy-related anxiety as lived, multidimensional manifestations of energy poverty in Western Macedonia, Greece, a coal phase-out region undergoing just transition. Using a 261-household survey, we construct a thermal stress index from four Likert-type items capturing seasonal thermal adequacy, energy anxiety, and restricted use of rooms. High thermal stress is defined as the upper quartile of the index. Descriptive results indicate that high thermal stress affects 27.2% of households, exceeding a 20% threshold, while energy-related anxiety and restricted room use are widespread. We then estimate logistic regression models to examine whether vulnerability characteristics (disability-related thermal/electric needs, single parenthood, dependent children, benefit receipt, elderly presence), financial stress indicators (arrears, energy debt, frequent forced reductions in consumption), and socio-economic controls (income, employment, tenure, age, gender) predict high thermal stress. Adjusted models show that vulnerability markers do not retain statistically independent associations once controls are included. In contrast, tenure and energy-related financial stress are significantly associated with the probability of high thermal stress. The findings highlight the importance of measurement choices and suggest that experiential indicators capture energy-poverty dynamics that are not reducible to income-based targeting, with implications for just-transition policy design and energy justice. Full article

Background: Adolescence is a formative period for lifelong dietary patterns, yet Korean adolescents show low fruit and vegetable intake, high sugar and sodium consumption, and rising obesity, highlighting the importance of multidimensional assessment that integrates behavioral, cultural, environmental, and competency-related factors. Methods: A total of 1010 adolescents aged 12–18 years completed an online cross-sectional survey assessing food intake, dietary and physical activity behaviors, dietary culture, and Food Literacy (FL) competencies. Standardized scores were used for hierarchical and K-means clustering to identify dietary practice patterns, and between-cluster differences were examined using ANOVA. Correlation and regression analyses were conducted to examine associations between Dietary Guideline Adherence (DGA) and FL. Results: Four clusters were identified—selective intake–low support (20.4%), regular habits–unbalanced intake (33.3%), high adherence (23.2%), and low adherence (23.1%)—with significant differences in DGA total and domain scores (p < 0.001). The high-adherence cluster showed balanced intake, regular routines, and strong household support, whereas the low-adherence cluster showed poor diet quality, irregular behaviors, and lower socioeconomic status. FL differed across clusters (p < 0.001) and correlated with DGA (r = 0.496, p < 0.01). Total FL predicted DGA (β = 0.496, p < 0.001), explaining 25% of its variance (R² = 0.246). Conclusions: Adolescent diet quality appears to be associated with behavioral, cultural, and competency-related factors. These findings suggest that cluster-specific strategies—such as fat–sugar–sodium reduction, promotion of low-sodium and diverse diets, and maintenance of balanced-dietary patterns—may support tailored school- and community-based nutrition programs and inform further longitudinal and intervention research. Full article

Objective: This study aims to investigate the dietary assessment and trends of preschoolers aged 3 to 5 years in Korea from 2012 to 2021 and to provide basic data for early childhood dietary education and policy development. Methods: Data from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2012 to 2021 were analyzed for 2510 children in the 3–5 age group. Dietary intake was assessed using a 24 h recall. Intakes of food groups, dishes, and nutrients were calculated, and trends across years were tested using generalized linear models adjusted for gender, age, household income, energy intake, mother’s age, and mother’s education. Results: Over the tenyear period, intakes of carbohydrates, phosphorus, iron, sodium, potassium, carotene, thiamine, niacin, and vitamin C, as well as the carbohydrate energy ratio, showed significant declines. Meanwhile, protein, fat, retinol, and riboflavin increased, as did the protein and fat energy ratios. Fruit intake decreased by approximately 42 g among food group intakes. Analysis of foods contributing to total food intake revealed that milk, white rice, apples, and eggs consistently accounted for a high proportion of total intake in all survey years. Average calcium intake was approximately 100 mg below the estimated average requirement. Conclusions: The results of this study showed that preschoolers exhibit insufficient intake of certain nutrients, such as calcium, and a decrease in fruit intake. Interventions are needed to establish regular meal patterns, promote plant food intake such as fruit, and improve calcium intake. These results provide valuable evidence for designing dietary education programs and dietary guidelines tailored to early childhood. Full article

The present study employs cluster analysis to segment Bulgarian domestic tourists based on microdata from a nationally representative survey (n = 1003) of summer holidaymakers on the Black Sea coast destinations. The primary objective is to identify homogeneous groups of tourists with similar demographic and behavioural characteristics, thereby enabling the development of more targeted tourism policies and marketing strategies. The methodological framework includes both hierarchical and non-hierarchical (k-means) clustering, applied to standardized variables such as age, household size, satisfaction with various aspects of the tourist experience, and behavioural intentions. The analysis reveals four distinct tourist profiles, each characterized by specific patterns of evaluation and travel behaviour—retirement age loyalists, middle-aged sceptics, younger moderate enthusiasts and young high loyalists. The findings reveal the heterogeneity of the domestic tourism market in Bulgaria and provide a data-driven foundation for enhancing the effectiveness of tourism management and promotional efforts. Full article

Access to ecologically grown, nutritious food remains limited in low-income U.S. communities due to cost, structural inequities, and the dominance of industrial food systems. Stone Barns Center’s Leading an Ecological and Accessible Food System (LEAF) program—developed through a community-based participatory partnership in the South Bronx—aims to address these challenges through biweekly distributions of regeneratively grown produce, seasonal gardening kits, and culturally responsive nutrition education. This study presents findings from the first two years (2023 and 2024) of a multi-timepoint repeated cross-sectional evaluation using six household-level surveys (n = 79–80 families per round). The surveys captured changes in fruit and vegetable consumption, gardening comfort, emotional well-being, participation in SNAP and WIC programs, food purchasing behaviors, and unmet needs. Statistically significant (p < 0.05) improvements were observed across key outcomes: mean fruit and vegetable intake increased from 3.8 to 4.5 (1–5 scale), comfort with growing food increased from 3.1 to 4.6, emotional response to gardening from 4.1 to 4.6. SNAP participation increased from 15% (12 of 79 households) to 33% (26 of 79 households), and purchasing shifted toward local access points. Notably, 99% (79 of 80 households) of Year 1 families returned for Year 2, reflecting strong engagement and trust. These results highlight the potential of integrated, community-partnered, and climate-aligned interventions to advance health equity, ecological literacy, and food justice. The LEAF program offers a replicable model that may support pathways towards more sustainable and community-aligned food systems in other under-resourced settings. Full article

Accurate monitoring of income inequality is critical for sustainable socioeconomic development and realizing the United Nations Sustainable Development Goals (SDGs). However, assessing inequality for counties continues to be challenging because of the high cost of household surveys and the limited accuracy of traditional nighttime light (NTL) proxies. To address this gap, we develop the Distribution Matching-based Individual Income Inequality Estimation Model (DM-I3EM), which integrates NTL data with household surveys. The model employs a three-stage workflow: logarithmic transformation of NTL data, estimation of Gini coefficients through Weibull distribution fitting, and selection of region-specific regression models, enabling high-resolution mapping and spatiotemporal analysis of county-level income inequality across China. Results show that DM-I3EM achieves superior performance, with an R² of 0.76 in China’s Eastern region (outperforming conventional NTL-based methods, R ≈ 0.5). By overcoming the spatiotemporal gaps of survey data, the model enables full-coverage estimation, revealing a regional divergence in income inequality across China from 2013 to 2022: inequality is intensifying in northern and western counties while stabilizing in the developed southern coastal regions. Furthermore, spatial agglomeration of inequality has strengthened, particularly in coastal urban clusters. These findings highlight emerging risks to socioeconomic sustainability. This study provides a robust, replicable framework for estimating inequality in data-scarce regions, offering policymakers actionable evidence to identify high-risk areas and design targeted strategies for advancing SDG 10 (Reduced Inequalities). Full article

Background/Objectives: The extent to which patients with cancer discontinue dental treatment for financial reasons remains unclear. This study compared the prevalence of financially driven dental treatment discontinuation between patients with cancer and without cancer (controls) and identified factors associated with discontinuation among patients with cancer. Methods: This cross-sectional, questionnaire-based survey conducted in November 2024 comprised 500 patients who had one of five common cancers in Japan and 500 non-cancer participants allocated to reflect the cancer group age distributions, sex, and household income. Only patients who received cancer treatment within the past 5 years and had a family dental clinic were included. Discontinuation was assessed from self-reported dental treatment cessation for economic reasons. Univariate analyses were employed for group comparisons (p < 0.05). Results: Dental treatment discontinuation for financial reasons occurred in 3.4% (95% CI 2.1–5.4%) of patients with cancer and 5.8% (95% CI 4.1–8.2%) of controls (p = 0.096). Among the patients with cancer, those who discontinued were younger (58.8 vs. 66.1 years, p = 0.010) and frequently reported physical or psychological barriers to dental care and discontinuation being financially driven (all p < 0.001). They also had poor oral health-related quality of life (Oral Health Impact Profile-14: 17.4 vs. 7.8, p < 0.001) and greater financial toxicity (Comprehensive Score for Financial Toxicity: 24.6 vs. 29.3, p = 0.001). Conclusions: Oral health-related quality of life was lower among participants reporting dental treatment discontinuation. Early identification of financial barriers and support may warrant further study. Full article

The Calakmul Biosphere Reserve, in southeastern Mexico, is a major conservation area known for its tropical forests, emblematic wildlife species, and long history of Maya occupation. Established in 1989 as a federal Natural Protected Area, it was incorporated into UNESCO’s Man and the Biosphere Program in 1993 and designated a mixed World Heritage Site in 2014. Its socioecological trajectory is distinctive: conservation efforts advanced alongside the contemporary rural settlement resulting from agrarian reform and subsequent development and welfare policies. This article examines the persistent imbalance between ecological conservation and socioeconomic development surrounding the Calakmul Biosphere Reserve, focusing on water vulnerability in adjacent communities. The study integrates environmental history with household-level survey data on water access and vulnerability among 200 households in eight communities in the Biosphere Reserve’s transition zone, complemented by interviews with key water-management stakeholders. We document the consolidation of conservation through management plans, advisory councils, payments for ecosystem services, scientific research, and expanding voluntary conservation areas. Yet these advances contrast sharply with everyday socioeconomic realities: 68% of households face prolonged water scarcity, with an average of more than 30 days annually without water. Calakmul’s case highlights structural mismatch between conservation and local human well-being in Natural Protected Areas contexts. Full article

Climate change brings considerable danger to India’s economic progress, with the agricultural sector and farmers’ livelihoods being particularly vulnerable. Himachal Pradesh is especially susceptible owing to its reliance on climate-sensitive economic activities and limited capacity to adapt to climate variability. Strengthening adaptation strategies in Himachal Pradesh is crucial for fortifying the resilience of communities reliant on environmental resources for their sustenance and economic well-being. This study examines the extent of adoption of Climate-Smart Agricultural Practices (CSAPs), identifies the factors influencing their uptake, and assesses their impact on the livelihood vulnerability of farm households in the temperate region of Himachal Pradesh. Using a multistage random sampling framework, data were collected from 432 farm households through primary surveys and secondary sources. The analysis employs descriptive statistics, a composite livelihood vulnerability index, and Ordinal Logistic and Multiple Linear Regression models. Results show higher adoption of low-cost practices such as composting, fruit-based agroforestry, crop–livestock integration, and mulching, while capital-intensive practices like micro-irrigation were limited due to financial constraints. Adoption is positively influenced by education, extension access, farming experience, financial resources, and climate information exposure. Importantly, CSAPs adoption is found to significantly reduce livelihood vulnerability, indicating enhanced resilience and reduced exposure to climate-induced risks among farm households. The findings highlight climate-smart agriculture as an effective adaptation strategy and underscore the need for policies that strengthen extension services, improve access to credit, and promote affordable climate-smart technologies to enhance resilience in vulnerable hill regions. Full article

Coastal ecological regulation plays a crucial role in coordinating the human–environment system and promotes sustainable development, yet it often imposes constraints on the livelihoods of local farmers. Drawing on questionnaire survey data from Chinese coastal farmers, this study quantifies farmers’ opportunity loss through the expectation function and entropy method. Subsequently, a Multinomial Logit model and Generalized Structural Equation Modeling (GSEM) are employed to systematically investigate the mechanisms through which ecological regulation-induced opportunity loss influences coastal farmers’ livelihood transition between 2013 and 2023. The findings reveal that greater opportunity loss significantly inhibits the fishing households’ livelihood transition, exhibiting a ‘livelihood stickiness’ effect. This inhibitory effect is partially mediated by a narrowing of farmers’ psychological distance from environmental issues. Specifically, social distance, reflecting community attachment and identity, plays a dominant mediating role. Furthermore, regulation intensity significantly amplifies this inhibitory effect. Notably, in the absence of substantive compensation or alternative livelihood support, greater policy publicity further reinforces this inhibitory impact. These findings underscore the need for policy interventions that provide compensation and alternative livelihood support commensurate with farmers’ opportunity loss. Enhancing community participation is also crucial to better reconcile coastal conservation objectives with the sustainable livelihoods of local communities. Full article