In the context of the implementation of the rural revitalization strategy, the industrialization of agriculture and rural areas is rapidly increasing, and more and more capable people in rural management are actively leasing land to develop modern agriculture, broaden diversified livelihood channels, and pursue sustainable development. We used leased-in farmland households as an entry point, focusing on the development of rural industries and the choice of livelihood strategies to fill a gap in the existing literature. Based on sustainable livelihood theory, we constructed a sustainable livelihood analysis framework for leased-in farmland households using the survey data of 283 leased-in farmland households in Yudu County. A livelihood capital indicator system was constructed, and the entropy value method and other measurement methods were used. Firstly, we analyzed and comprehensively evaluated the livelihood capital and livelihood strategies of leased-in farmland households in Yudu County, Jiangxi Province. We then used binary logistic regression models and polynomial logistic regression models to analyze the influence of the differentiation in leased-in farmland households’ livelihood capital on the choice of livelihood strategies and their transformation and conducted robustness tests on the relevant findings. Different types of livelihood capital have different effects on agriculture-led livelihood strategies. Natural capital and physical capital have significant positive effects on agriculture-led livelihood strategies, but financial capital, human capital, and social capital have negative effects. Natural capital has a significant negative effect on the pluriactivity strategy, while physical, human, and social capital have insignificant effects on it. For off-farm livelihood strategies, natural capital and financial capital have insignificant effects, physical capital has a significant negative effect, and human capital and social capital have a significant positive contribution.

With the full penetration of the Internet, the Internet has had a great impact on the production and life of rural residents. The article takes a rural residents’ group as its research object to explore the impact mechanism. Leveraging data from the Chinese Family Panel Studies (CFPS) in 2016, we employ a multivariate ordered logit model as an empirical approach to delve into the various dimensions of Internet usage. By examining different facets of Internet utilization, we aim to assess the effects of these distinct uses on the well-being of rural residents. Furthermore, we investigate the mediating role of social capital in understanding the collective well-being of this demographic. The findings of this study are as follows: (1) The utilization of the Internet yields a positive impact on the well-being of rural residents. (2) Social networks exhibit a significant positive influence on the well-being of rural residents; social solidarity demonstrates a significant negative impact on the well-being of rural residents. Additionally, social trust is found to have a significant negative effect on the well-being of rural residents. (3) Regarding mobile phone access, we identify a positive and significant effect on the well-being of rural residents when mediated by social networks and social trust. In contrast, social mutual aid does not exhibit a significant mediating effect. Among the patterns of mobile phone usage, social network and social mutual aid within the realm of social capital emerge as intermediate variables that affect the well-being of rural residents through Internet usage. However, it is worth noting that social trust does not have a significant effect in this regard. These results contribute to our understanding of how Internet usage and social capital interact to shape the well-being of rural communities.

We analyze the impact of smartphone usage on multidimensional poverty reduction. Utilizing the A-F approach, we compute a multidimensional poverty index based on five dimensions: education, health, income, living standard, and labor force. This index categorizes multidimensional poverty into three levels: Vulnerable Multidimensional Poverty Index (VMPI), General Multidimensional Poverty Index (GMPI), and Extreme Multidimensional Poverty Index (EMPI), following MPI criteria. Furthermore, we investigate the mediating role of social capital in the smartphone-multidimensional poverty relationship through a mediating effects analysis. We used the survey data of 382 sample out-of-poverty rural households in Jiangxi, China, in 2020. Our results indicated that: (1) Education (37.80%), labor force (29.7%), and health (20.40%) were identified as the primary contributors to multidimensional poverty. (2) Increasing deprivation categories correlated with declining multidimensional poverty index, following an inverted U-shaped pattern. (3) Smartphone usage significantly reduced VMPI (57.6%), GMPI (52.6%), and EMPI (5%). (4) Social capital fully mediated EMPI reduction through smartphones (91.67%), and partially mediated VMPI (14.09%) and GMPI (20.84%) reduction. These insights inform targeted policy formulation for rural multidimensional poverty reduction.

The construction and development of digital infrastructure have emerged as a crucial indicator of national competitiveness, which holds significant importance in driving the sustained growth of the national economy and the comprehensive advancement of society. This paper explores the impact of digital infrastructure on farm households’ scale management, aiming to reveal the role and potential of digital technology in agricultural modernization. Additionally, it seeks to offer a scientific foundation for the government to formulate agricultural policies and advance agricultural modernization. Using the OLS (Ordinary Least Squares) model, moderating effect model, and other methods, this study investigates how digital infrastructure affects farm households’ scale management based on micro-level research data of 2510 farm households from the CRRS (China Rural Revitalization Survey). The following conclusions are drawn: Firstly, the enhancement of digital infrastructure can motivate farm households to expand the land management area and increase the unit output of land. Secondly, farm households’ digital literacy positively moderates the effect of digital infrastructure on their land unit output; moreover, digital skills training for farm households exhibits a positive moderating effect on the influence of digital infrastructure on their management area. Finally, there is a heterogeneity in the impact of digital infrastructure on farm households’ scale management. Specifically, the promotion of farm households’ scale management is stronger in plain areas and weaker in hilly and mountainous areas; stronger for middle-aged and older and small-scale farm households; and weaker for youth groups and large-scale farm households. Based on this, this paper suggests increasing the investment in digital infrastructure construction, improving farm households’ digital literacy, carrying out digital skills training, and formulating differentiated regional policies for reference.

This study uses field survey data from 382 families in the year 2020 in Jiangxi province, China, to explore the effects of land lease and labor migration, a well-known occurrence in China, on rural households’ multidimensional poverty status. We used the A-F method to measure the household’s multidimensional poverty in terms of health, education, income, living standard, and social relations. The Bootstrap Test approach, which worked well with our data, was used to build our mediating effect models while taking into account the influence mechanisms of land leasing and multidimensional poverty. According to our findings, 76.70% of sample homes experience multidimensional poverty at the threshold of 0.33. The reduction of multidimensional poverty is significantly aided by both leasing in and leasing out land. Land lease out and land lease in, however, have asymmetrical effects on multidimensional poverty alleviation, with the proportion of land lease out being 1.147 without control variables and the proportion of land lease in being 0.969 without control variables. Land lease in and lease out have positive effects on the multifaceted alleviation of poverty due to the mediating effect of labor migration. For policymakers to develop and put into action more relevant policies to help multidimensional poverty alleviation, our analysis offers critical insights.