Exploring the law and evolution mechanism of coupling and coordination between agricultural carbon emission efficiency (ACE) and agricultural economic growth (AEG) can provide a reference basis for agricultural low-carbon transformation. This study takes 11 cities in Jiangxi Province as the research object; measures the level of ACE based on the panel data from 2008 to 2022; and analyzes the development and influencing factors of the coupling and coordination between ACE and AEG by using the coupling coordination degree model, the Dagum Gini coefficient decomposition method, and the Tobit regression model. The results reveal the following: (1) The overall ACE in Jiangxi Province displays a significant upward trend, with the average efficiency value increasing from 0.172 to 0.624, reflecting an average annual growth rate of 72.43%. Nonetheless, there remains clear regional heterogeneity, characterized by lower efficiencies in Central and Southern Jiangxi compared to the higher efficiencies found in Northern and Western Jiangxi. (2) Despite gradual improvements in regional coordination, the Central and Southern Jiangxi regions still lag Northern and Western Jiangxi in terms of the linked coordination between ACE and AEG, symptoms of which had been previously misaligned. (3) The results of Dagum’s Gini coefficient decomposition show that inter-regional disparities are the main source of overall disparities, with a contribution of 37.43%, which is higher than the synergistic effect of intra-regional disparities and hyper-variable densities, corroborating the core contradiction of uneven development across regions. (4) The Tobit model reveals that government investment, industrial structure optimization, urbanization, and educational attainment exert a significant positive influence on promoting coupling coordination. To establish a scientific basis for achieving a low-carbon agricultural transformation and equitable AEG in Jiangxi Province, this research recommends bolstering regional cooperation, fostering innovations in agricultural science and technology, optimizing the industrial structure, and enhancing farmers’ awareness of low-carbon practices. This study expands the theoretical system of agricultural low-carbon transition in terms of research methods and scales to provide a scientific basis for agricultural provinces to realize agricultural low-carbon transition and balanced economic development.

Global warming is increasing the frequency of extreme weather events and threatening agricultural production and food security. As land is a core input in grain production, adjustments in land allocation represent a key adaptation pathway. Using national fixed-point survey data from China’s Ministry of Agriculture and Rural Affairs and multi-dimensional fixed-effects framework, we examine how extreme weather shapes grain farmers’ land transfer (rental) decisions. Linear probability models (LPMs) are employed for binary transfer decisions, and standard linear models for transfer area, both controlling for year fixed effects, regional fixed effects and their interactive effects. We find that extreme weather discourages grain farmers from renting land and encourages them to rent out land. Farmers with stronger resource endowments attenuate this response—showing smaller reductions in land renting and smaller increases in land renting-out—consistent with factor substitution, asset specificity, and risk-buffering effects. Market and policy factors both moderate the extreme weather–land transfer relationship but in opposite directions: market factors primarily ease labor mobility constraints, whereas policy factors focus on stabilizing agricultural production. The effects also vary substantially by gender, income, and region.

Introduction: Socialized services can facilitate the organic integration of smallholder farmers with modern agriculture, playing a significant role in accelerating the advancement of Chinese-style agricultural modernization and building China into a leading agricultural nation. Methods: This study uses the 2020 Chinese Academy of Social Sciences rural revitalization research data, applies factor analysis and principal component analysis to measure the digital literacy level of farmers, and after clarifying the internal logic of digital literacy affecting the service scale operation of farmers, applies a linear regression model, mediation effect model, and other methods to explore the specific impact of the digital literacy level of farmers on the service scale operation of farmers and further verify the intrinsic mechanism between the two. Results: The results of the benchmark regression show that the impact of farmers’ digital literacy level on service scale operation shows a nonlinear U-shaped relationship that shrinks first and then expands, and the U-shaped relationship still holds after the robustness test and endogeneity test. The results of the mechanism test show that the improvement of farmers’ digital literacy level promotes the development of agricultural service scale operation by promoting the fourfold mechanism of farmers’ natural capital accumulation, social capital accumulation, financial capital accumulation, and human capital accumulation. The results of the sub-dimension test show that: in the dimension of digital access, digital media literacy will hinder the scale operation of agricultural service; in the dimension of digital application, digital information literacy shows a U-shape relationship with the scale operation of agricultural service; digital social literacy shows a U-shape relationship with the width of agricultural service, and negatively affects the depth of service; digital business literacy has a positive and significant effect on the scale operation of agricultural service; digital problem-solving literacy has a positive and significant effect on the width of agricultural service; and digital problem-solving literacy has a positive and significant effect on the scale operation of agricultural service; positive and significant effect on farmers’ service width and negative effect on service depth. The results of heterogeneity analysis show that there are urban-rural and regional differences in the influence of farmers’ digital literacy level on service-scale operation behavior. Among them, the service scale operation behavior of farmers in remote areas is affected by the U-shaped influence of the digital literacy level, and the service scale operation behavior of farmers in the suburbs of the city is not affected by it; the influence of the level of digital literacy of farmers in the eastern region shows a U-shaped relationship, the influence on the western region shows a linear positive correlation, and the influence on the central region is not significant. Discussion: In order to better play the role of digital literacy level on agricultural service-scale operation, it is necessary to further strengthen the construction of rural digital infrastructure; encourage social capital and financial capital to invest in agriculture; promote the accumulation of human capital of farmers; and improve the agricultural socialized service system.