Search Results (2,616)

Avoidable household food waste can arise at multiple stages of household food management, including storage, preparation, cooking, consumption, and post-cooking storage. It may also be associated with upstream food access and purchasing conditions. Using a purpose-designed 2017 survey of 204 rural households in Shandong, China, this study combines three-day household tracking with direct weighing of avoidable edible food waste. A food-purchase transport motorization index (TMI) is constructed from food-category-specific transport modes weighted by household consumption shares. Its association with observed food waste is examined using Tobit models, interval shape tests, and sensitivity analyses. TMI exhibits a nonlinear statistical association with total food waste: fitted waste increases at lower motorization levels and tends to decrease at higher levels, although formal interval tests do not confirm an inverted-U relationship for total waste. The nonlinear pattern is clearest for storage waste, whereas plate waste shows a positive linear association. TMI is significantly associated with waste occurrence, but its association with the amount wasted among households with positive waste is not statistically significant. Purchase frequency strengthens the association between TMI and waste occurrence, whereas dietary diversity shows no comparable moderating role. Associations are more pronounced for non-perishable and plant-based foods than for perishable or animal-based foods. These findings provide short-term micro-level evidence from a rural transport-transition setting. Full article

This study employs the sustainable livelihoods framework to examine the achievements and challenges of the community-based natural resource management (CBNRM) program, with an emphasis on wildlife-based tourism development in Botswana. The article analyses whether the CBNRM program in its 30-year history has achieved its main goal of improving rural livelihoods and promoting conservation in Botswana. The article used secondary data, supplemented by primary data collected through personal observations and interactions with key people in the tourism and conservation sectors. The paper argues that the proper implementation of CBNRM can enhance rural livelihoods and contribute to the conservation of biodiversity. In Botswana, wildlife-based tourism generates economic benefits, including employment opportunities and income, which in turn fosters positive attitudes among residents towards conservation and tourism. CBNRM also faces difficulties; reviews show that wildlife-based tourism employment provides low wages, and after reaching a certain age, residents become impoverished because they cannot participate in the formal labor force and have no alternative means of support because of their locations, where other livelihood options, such as agriculture, which is a legacy in most of Botswana, are illegal or unfavorable. Benefits from wildlife-based tourism are inequitably distributed among age groups. Employment monopolizes benefits. This situation occurs because most community-based organizations (CBOs) fail to allocate benefits from CBNRM ventures directly to households, where the costs of coexisting with wildlife are incurred; instead, benefits are disbursed to villages. The results also indicate that in Botswana, most of the successful and sustainable CBNRM CBOs benefiting from wildlife-based tourism through joint ventures were initially established during the introduction of CBNRM in the 1990s. CBOs that registered later are struggling to venture into wildlife-based tourism because of insufficient managerial and entrepreneurial competencies within the tourism industry, insufficient training and capacity development in CBNRM, and other factors limiting communities from benefiting from wildlife-based tourism. These village community members continue to regard wildlife as state property because they lack projects that give them a sense of ownership over wildlife species, raising questions about whether CBNRM is meeting its aim of improving livelihoods and conservation. This evidence reveals that Botswana has not utilized most of its wildlife-based tourism potential. Full article

Farmers’ income growth is a central issue in consolidating the achievements of poverty alleviation in China and advancing common prosperity. It is also crucial for addressing the imbalance between urban and rural development and promoting agricultural and rural modernization. Based on theoretical analysis and mathematical modeling, this study develops a series of research hypotheses. A two-way fixed-effects model is employed to examine the impact of the land trusteeship interest linkage mechanism on farmers’ income growth. Furthermore, heterogeneity analyses are conducted from the perspectives of farmer characteristics, income levels, and crop types, followed by a series of robustness tests. The main findings are as follows: (1) The land trusteeship interest linkage mechanism has a significant positive effect on farmers’ income, and this conclusion remains robust after a series of robustness and endogeneity tests. Among the three core components, namely value cocreation, profit sharing, and risk division, all are found to effectively increase farmers’ income, with their marginal effects decreasing in sequence. (2) In terms of heterogeneity analysis, from the perspective of farmer types, the interest linkage mechanism has the most significant income-enhancing effect on large-scale specialized farmers, followed by small-scale full-time farmers and part-time farmers. From the perspective of income structure, the effect is mainly reflected in the increase in agricultural operating income, followed by wage income and transfer income. Regarding crop types, the income growth effect is more pronounced for vegetable producers than for wheat and maize producers. Based on these findings, efforts should focus on strengthening the interest linkage mechanism through enhanced value cocreation while implementing targeted and differentiated support measures for small-scale full-time farming households and vegetable growers. Particular attention should be paid to improving their operating income in order to maximize the income growth effects for farmers. This study provides important insights for improving mechanisms through which agricultural enterprises link with and benefit farmers, thereby contributing to the realization of China’s strategic goal of common prosperity. Full article

Based on survey data from the National Ecological Civilization Pilot Zone (Jiangxi), this study divides rural residents’ self-reported policy compliance in household waste classification (PC) into habit-based policy compliance in waste classification (HPC) and decision-based policy compliance in waste classification (DPC). The integration of a random forest model with Shapley Additive Explanations (SHAP) is utilized to identify the key predictors of each compliance type and to reveal the non-linear predictive contributions and marginal effects through which these factors respectively affect HPC and DPC. Furthermore, fuzzy-set qualitative comparative analysis (fsQCA) is used to uncover the multiple configurational pathways driving each type of compliance. Research indicates the following: (1) The key predictive factors of HPC and DPC are different. (2) Subjective norms (SUN), information interaction (II), and social norms (SON) exhibit significant nonlinear predictive contributions to HPC; policy identification (PI), SON, and procedural fairness (PF) show nonlinear trends in their contributions to the predictive probability of DPC. (3) There are five configuration paths for HPC and four for DPC. This study not only deepens the understanding of rural residents’ HPC and DPC but also provides practical guidance for governments to formulate differentiated and efficient measures to encourage compliance with waste classification policies. Full article

While drone delivery has gained significant scholarly and industrial interest, rural residents’ acceptance of these systems remains underexplored, despite the region’s acute logistics challenges and service inequities. Using survey data from rural U.S. residents, this study first estimates an ordered logistic regression (OLR) model to identify factors associated with five-level drone delivery acceptance. The study then compares OLR, multinomial logistic regression (MNL), Random Forest (RF), XGBoost, and LightGBM under matched feature sets to evaluate whether nonlinear machine-learning models improve prediction beyond the interpretable statistical baseline. Open-ended responses are coded into LLM-derived sentiment labels and added as supplementary predictors to test whether unstructured feedback improves acceptance prediction. Results show that willingness to pay is the strongest predictor of acceptance, while equitable same-day delivery demand and post-pandemic attitude adjustment are also positively associated with higher acceptance. Household disability status and urban accessibility are not significant after adjustment. In the five-level analysis, OLR provides a strong ordinal baseline, while XGBoost and other tree-based models improve selected class-level prediction metrics. In the binary high-acceptance analysis, machine-learning models show stronger predictive performance, especially when structured predictors are combined with sentiment features. This study contributes to rural drone-delivery literature by linking service equity, perceived value, and LLM-derived sentiment within a comparable statistical and machine-learning framework for rural service design. Full article

Off-grid photovoltaic systems with battery storage are essential for sustainable rural electrification, yet national programs such as Ethiopia’s NEP 2.0 lack frameworks that support decentralized alternatives. This study introduces a novel swarm electrification model, in which higher-tier solar systems trade surplus energy to support lower-tier households, forming a peer-to-peer solar-sharing network. A comparative assessment of solar resources using models, predictions, and satellite databases showed stable annual irradiance in Ethiopia, ranging from 4.22 to 6.54 kWh/m²/day across two predictive models and two satellite datasets. Long-term PVGIS data (13-year average) recorded the highest annual value at 7.30 kWh/m²/day. Statistically, the artificial neural network yielded the lowest error margins, while the Allen Regression model offered the lowest bias. Based on these data, Tier 2 and Tier 3 PV systems were designed and simulated at 85% efficiency with three-day battery autonomy. A 400 Wp PV array paired with a 2 × 250 Ah battery bank was designed to meet the Tier 3 daily demand of 1.7 kWh, generating over 60% energy surplus. Peak consumption occurs during evening hours (17:00–19:00). Lithium-iron-phosphate batteries proved economically superior for Tier 3 loads exceeding 1.5 kWh/day over a 10–15-year lifecycle, requiring zero replacements and offering lower overall costs. The hierarchical tier-based model enables strategic cross-subsidization, where Tier 3 households support Tier 1 and Tier 2 users. A comparative cable topology analysis recommends the radial T3 2@12V configuration for linear households within 10 m, and the ring T3 topology for longer linear layouts of 15–25 m requiring moderate fault tolerance. All configurations maintain voltage drop below the critical 5% threshold. Overall, this study demonstrates that optimized off-grid PV systems with appropriate topology and battery selection offer a sustainable and scalable pathway for rural electrification in Ethiopia. Full article

Background: Influenza poses a serious threat to the health of older adults, and vaccination is a key strategy for prevention. As an economically developed city, Suzhou has a rapidly aging population, yet the influenza vaccination rate among older adults remains substantially lower than that in developed countries. There is an urgent need to understand the current vaccination status and its associated factors to inform future strategies for improving vaccination coverage. Objectives: To compare influenza- and vaccine-related knowledge, attitudes, practices, and information access channels between vaccinated and unvaccinated older adults (≥60 years) in Suzhou, and to identify factors associated with vaccination behavior in economically developed areas. Methods: A comparative cross-sectional survey was conducted from April to August 2024 across six districts or county-level cities randomly selected from Suzhou’s ten county-level administrative divisions. Participants were divided into vaccinated and unvaccinated groups based on their influenza vaccination status during the 2023–2024 influenza season. A two-stage sampling method was employed: the six districts were selected as primary sampling units; within each district, eligible older adults (aged ≥60 years) were randomly selected from two independent sampling frames—the vaccination information system for the vaccinated group and the basic public health service system for the unvaccinated group. Face-to-face structured interviews were conducted using a questionnaire consisting of four sections; data were entered using EpiData 13.1 and analyzed using SPSS 27.0. The χ² test, t-test, and multivariable logistic regression were used for statistical analysis. Results: Among 4622 valid older adults (vaccinated: n = 2007; unvaccinated: n = 2615), the vaccinated group scored significantly higher on influenza-related knowledge (4.06 ± 2.21 vs. 3.16 ± 2.33, p < 0.001), vaccine-related knowledge (2.10 ± 1.18 vs. 0.67 ± 1.07, p < 0.001), and perceived influenza risk scores (2.72 ± 1.42 vs. 2.26 ± 1.64, p < 0.001). Vaccine safety and efficacy endorsement were also markedly higher among the vaccinated group (70.3% vs. 22.0%; 49.7% vs. 9.3%). Regarding information sources, the vaccinated group relied more on healthcare institutions (54.56% vs. 46.92%), whereas the unvaccinated group relied more on television (60.34% vs. 51.17%). Multivariable logistic regression revealed that awareness of the influenza vaccine (aOR = 9.151, 95%CI: 6.32–13.25), having a planned vaccination site (aOR = 2.66, 95%CI: 2.01–3.54), and perceiving the vaccine as safe (aOR = 1.81, 95%CI: 1.31–2.49) were positively associated with vaccination, while rural household registration (aOR = 0.76, 95%CI: 0.63–0.93), full-time employment (aOR = 0.41, 95%CI: 0.28–0.60), and hypertension (aOR = 0.79, 95%CI: 0.65–0.94) were inversely associated. Conclusions: Compared with the unvaccinated group, the vaccinated group demonstrated significantly better influenza- and vaccine-related knowledge, risk perception, and recognition of vaccine safety and efficacy, and relied more on healthcare institutions for information. Multivariable regression analysis identified awareness of the influenza vaccine as the strongest associated factor. These findings suggest that vaccination coverage could be improved by strengthening vaccine knowledge promotion through healthcare professionals and increasing awareness of vaccination policies that integrate medical insurance reimbursement. Full article

Under the dual pressures of increasing fragmentation of farmland and profound changes in the agricultural labor force, the problem of abandoned farmland in rural China has become increasingly prominent, posing a serious threat to food security. Previous studies have mainly focused on the impact of socioeconomic factors on farmland abandonment behavior; however, there remains a lack of systematic investigations into the underlying mechanisms through which farmland fragmentation influences farmers’ land-use decisions. Therefore, this study applies behavioral theory to construct a “fragmentation–mechanization–abandonment” analytical framework to analyze the potential mechanisms of land-use decision-making. Using micro-level household survey data from multiple provinces across China, we conduct empirical tests through Probit models and Karlson Holm and Breen (KHB) mediation effect analysis. Our findings include: (1) Farmland fragmentation significantly increases the probability of farmland abandonment, which remains robust across multiple tests. (2) Farmland fragmentation indirectly influences abandonment behavior through the mediating variable of agricultural mechanization level, but this mechanism plays only a partial mediating role. (3) Heterogeneity tests indicate that the positive effect of farmland fragmentation is significantly stronger among households with low access to finance than among those with high access to finance. This influence is also stronger among households with high farmland accessibility. This study indicates that abandonment governance requires moving beyond a singular focus on land consolidation and shifting toward a synergistic approach that combines differentiated regional strategies with targeted support for smallholders. From a micro-mechanism perspective, this study provides empirical evidence for targeted implementation of land consolidation and livelihood support for smallholders, offering important policy implications for achieving agricultural modernization. Full article

Climate risks and rural labor outflow are jointly challenging the sustainability of farm households in disaster-prone agricultural regions. Agricultural insurance, as an important risk management tool, may influence not only farm income but also farmers’ production and labor decisions. While previous studies on agricultural insurance have primarily focused on staple crops and income stabilization, the effects of labor allocation in facility-based intensive agriculture remain underexplored. This study addresses this gap by examining how participation in agricultural insurance affects farm household labor allocation in the context of greenhouse vegetable production. Using survey data collected from July to August 2025 from 247 greenhouse vegetable farmers in ShouGuang, China’s largest vegetable production base, we employ Probit and Two-Stage Least Squares (2SLS) models to address endogeneity and mediation analysis to explore the underlying mechanisms. The findings show that: (1) Agricultural insurance participation significantly increases the probability of farm households increasing agricultural labor input in the next planting season. (2) Agricultural insurance reduces farmers’ likelihood of exiting agricultural production through increased investment in agricultural technology, but this effect is constrained by the availability of investment capital. (3) The impact of agricultural insurance on labor allocation varies across different farmer groups, and is more pronounced among middle-aged and younger farmers with relatively small farm sizes, lower disaster risk, and higher levels of education. Several insurance-related policy recommendations are proposed to stabilize the agricultural labor force and mitigate rural labor decline—key dimensions of sustainable rural development. Given the single-region focus and sample size, the generalizability of these findings requires further validation in other agricultural contexts. Full article

In the context of rural construction, studying particle diffusion alongside residents’ satisfaction with rural external heated kangs provides important support for upgrading heating facilities and improving domestic infrastructure in the northwest arid regions of China. This study takes a single rural household equipped with an external fire kang in Baoji as the research object. From the perspectives of operational and service management in human settlements, the spatio-temporal diffusion characteristics of particulate matter in the outdoor combustion port, living room, and bedroom during the winter heating period are continuously monitored, and data on resident satisfaction are collected through a questionnaire. The results show that, during the external kang’s heating period, the spatial concentrations of particulate matter decrease in the following order: combustion port > living room > bedroom. Concentrated morning and evening kang heating creates a typical bimodal distribution of outdoor particulate matter concentration, with peaks at 8:00 am and 8:00 pm. During the ignition period, coarse particles (PM₁₀) are mostly released, while the proportion of fine particles (PM_2.5, PM_1.0) increases during the stable combustion period. During the sealing period, the concentration of fine particles doubles. After extinguishing, PM₁₀ decays rapidly due to gravity deposition, while PM_1.0 shows a longer environmental retention time due to its slow diffusion. The core diffusion process of the particulate matter mainly occurs within a distance of 5–15 m from the combustion port. The resident satisfaction survey shows moderate comprehensive satisfaction with external heated kangs, at 3.10 points on a 5-point scale. Addressing existing problems and proposing relevant strategies, this study provides a theoretical basis and technical support for the rational transformation of external heated kangs in the arid areas of Northwest China. Full article

Agricultural E-commerce alters farmers’ production and marketing decisions, thereby shaping land-use intensity and livelihood stability. In the context of rapid digital transformation, enhancing farmers’ livelihood resilience is critical to sustainable rural development and resilience to socio-economic and environmental shocks. Using survey data from 542 rural communities in China, this study examines the relationship between agricultural E-commerce participation and farmers’ livelihood resilience across its multidimensional components. The empirical results show that participation in agricultural E-commerce is positively associated with farmers’ overall livelihood resilience, with notable positive associations with buffering capacity, learning capacity, and self-organization capacity. Among these, learning capacity exhibits the strongest association, followed by buffering capacity and self-organization capacity. Farmers’ digital capability positively moderates the relationship between E-commerce participation and livelihood resilience by facilitating access to and effective use of digital information, thereby lowering information costs and contributing to returns from online market participation. Heterogeneity analysis further indicates that the positive association between agricultural E-commerce participation and livelihood resilience is stronger among cooperative members, households with adequate production capital, non-agricultural households, and families with sufficient labor resources. By identifying the role of agricultural E-commerce in building livelihood resilience, this study contributes to the empirical understanding of how digital engagement may support inclusive and sustainable rural development under similar institutional conditions. Full article

Alleviating energy poverty is fundamental to enhancing societal well-being and advancing sustainable development. This paper examines the relationship between adult children’s education and parental energy poverty alleviation. We draw on microdata from the China Health and Retirement Longitudinal Study (CHARLS) and exploit the Compulsory Education Law reforms implemented across China around 1986 as a source of exogenous variation in schooling years. Our findings reveal that each additional year of offspring schooling significantly reduces the likelihood of parental energy poverty. We identify two potential mechanisms: promoting internet adoption and improving access to modern energy equipment. Heterogeneity analysis further reveals that the effect is more pronounced in rural households and among families with male children than among those with female children. These findings offer actionable insights for addressing energy poverty and advancing intergenerational sustainability. Full article

Background/Objectives: In rural settings, work, family, and environmental conditions are closely intertwined, requiring Primary Health Care to recognize care needs arising from productive work and everyday family responsibilities. This study aimed to analyze overall and domain-specific levels of perceived workload related to agriculture/fishing and family involvement in the household among rural and small-scale fishing workers, examine associated factors, and discuss analytical implications for nursing care organization in Primary Health Care. Methods: This cross-sectional study was conducted in three island territories in Rio Grande, southern Brazil, following the STROBE guidelines. Data were collected using a structured questionnaire on sociodemographic, family, and occupational characteristics and the NASA Task Load Index (NASA-TLX), applied separately to assess workload related to agriculture/fishing and family involvement in the household. Results: The sample included 146 rural and small-scale fishing workers. Perceived workload could suggest high in both dimensions, with higher levels in agriculture/fishing (mean 84.0 ± 16.5; 89.7% high/very high) than in family involvement in the household (mean 76.1 ± 24.5; 75.4% high/very high). Agriculture/fishing workload perceived could suggest highest scores for overall effort level, temporal demand, and physical demand, whereas family involvement could suggest highest scores for performance-related workload and overall effort level. In the adjusted model, agriculture/fishing perceived workload may be associated with longer daily working time (b = 1.69; 95% CI: 0.82 to 2.56; p < 0.001) and shorter rest time during work (b = −0.05; 95% CI: −0.08 to −0.01; p = 0.011). Perceived workload related to family involvement in the household may be associated with monthly income up to two minimum wages (b = 19.90; 95% CI: 7.78 to 32.10; p = 0.001). The adjusted R² values were 17.3% and 6.7%, respectively. Conclusions: Perceived workload appears to be related to high levels in both productive and family household contexts. The findings might contribute to providing analytical support for future discussions on nursing care organization in Primary Health Care, potentially by considering working conditions, family responsibilities, and territorial context when discussing care needs among rural and small-scale fishing workers. Full article

South Africa has implemented an extensive social grant system to improve the wellbeing of vulnerable households. However, evidence remains limited regarding the pathways through which social grants are associated with household food insecurity and whether these relationships vary across geographical contexts of South Africa. Understanding these mechanisms is essential for designing more effective and spatially targeted social protection policies. This study examined the relationship between social grant receipt and household food insecurity, hypothesizing that household income serves as the principal pathway linking social grants to food insecurity. This study used nationally representative data from the 2024 General Household Survey. A latent food insecurity index was constructed using a two-parameter logistic Item Response Theory model, and a moderated-mediation structural equation model was employed to decompose the association between social grant receipt and food insecurity into direct and indirect components while accounting for geographic heterogeneity. The results indicate that social grants are strongly targeted toward households with higher levels of food insecurity and lower incomes, confirming that grant recipients are among the most vulnerable households in South Africa. Household income was negatively associated with food insecurity and mediated 55.4% of the total association between social grant receipt and household food insecurity. This underscores income as the main transmission pathway. High geographic heterogeneity was observed. The indirect income-mediated association was strongest in commercial farming areas, followed by urban and rural areas administered by local leaders. The direct association was significant in urban and rural areas administered by local leaders. These results show that the effectiveness of income transfers depends on the household’s demographic and socioeconomic conditions. The findings suggest that although social grants play an important role in reducing household vulnerability, cash support alone is insufficient to address household food insecurity among disadvantaged households in South Africa. Therefore, strengthening income support provided through social grants should be complemented by location-specific interventions that expand employment opportunities, improve market access, promote productive asset accumulation, and strengthen local food systems, particularly in rural areas where constraints continue to limit household food security. Full article

The rapid expansion of artificial intelligence (AI) is accelerating data center construction and creating downstream implications for households. This study examines how AI-era data center costs (comprising construction, energy, water, grid upgrades, cooling, and lifecycle management) move through service supply chains and affect household prices in healthcare, transportation, education, banking, and commerce. It also considers the productivity and welfare benefits that AI may transmit. This study identifies four pass-through channels: utility-rate socialization of energy costs, cloud-platform pricing, sectoral pass-through from AI-adopting industries, and indirect effects through supply chains and labor markets. It introduces the AI-inflated net good basket, defined as transmitted cost minus transmitted benefit, to show how AI reshapes the overall net cost of household consumption rather than simply inflating individual prices. The study develops the AI Infrastructure Net Cost Pass-Through Model (AI-NCPM), a four-layer conceptual framework tracing net cost flows from data center investment to sectoral allocation and household outcomes. The model’s parameters are analytically specified but not empirically calibrated; numerical examples are illustrative rather than representing estimated effects. Its main contribution is an integrative framework linking cost pass-through, infrastructure cost socialization, two-sided platform allocation, environmental externalities, and household expenditure incidence within a single net-cost account. Because these effects originate in the design, construction, energy and cooling systems, and lifecycle operation of data centers, the analysis connects AI infrastructure economics to the built environment. The framework suggests that low-income, minority, rural, older adult, and disability-affected households may face disproportionate net burdens, as costs fall heavily on essential services while benefits accrue more readily to affluent and digitally connected households. Full article