China is the largest carbon emitter in the world, with agricultural carbon emissions accounting for 17% of China’s total carbon emissions. Agricultural carbon emission reduction has become the key to achieving the “Double Carbon” goal. At the same time, the role of the digital economy in achieving the “dual carbon” goal cannot be ignored as an important engine to boost the high-quality development of China’s economy. Therefore, this paper uses the panel data of 30 provinces in mainland China from 2011 to 2019 to construct a spatial Durbin model and a mediation effect model to explore the impact of the digital economy on agricultural carbon intensity and the mediating role of agricultural technological progress. The research results show that: (1) China’s agricultural carbon intensity fluctuated and declined during the study period, but the current agricultural carbon intensity is still at a high level; (2) The inhibitory effect of the digital economy on agricultural carbon intensity is achieved by promoting agricultural technological progress, and the intermediary role of agricultural technological progress has been verified; (3) The digital economy can significantly reduce the carbon intensity of agriculture, and this inhibition has a positive spatial spillover effect. According to the research conclusions, the government should speed up the development of internet technology and digital inclusive finance, support agricultural technology research and improve farmers’ human capital, and strengthen regional cooperation to release the contribution of digital economy space.

Alleviating cropland misallocation is helpful for the sustainable development of agriculture. Does off-farm employment inevitably result in cropland misallocation (e.g., cropland abandonment) and threaten the sustainable development of agriculture? This study differs from prior studies in its view that off-farm employment does not necessarily result in cropland abandonment. Specifically, the study employs survey data from 8031 peasant households from 27 provinces in rural China and spatial statistics to analyze the distribution of off-farm employment and cropland abandonment. Empirical models (i.e., IV-Probit and IV-Tobit) are used to examine the quantitative relation between off-farm employment and cropland abandonment. The results are as follows. (1) The spatial distribution of off-farm employment or cropland abandonment differs among regions. Regions with a higher rate of off-farm employment show more cropland abandonment but a lower average area of cropland abandonment. (2) Off-farm employment has a significant and positive correlation with cropland abandonment. However, its square has a significant and negative correlation with cropland abandonment; i.e., there is an inverted U-shaped relationship between off-farm employment and cropland abandonment, with the turning point occurring at 46.00% off-farm employment. (3) Off-farm employment has a significant and positive correlation with the area of cropland abandonment. However, its square has a significant and negative correlation with the area; i.e., there is an inverted U-shaped relationship between off-farm employment and area, with the turning point occurring at 44.50% off-farm employment. This study reveals the relationship between off-farm employment and cropland abandonment and provides policymakers with references for use in developing sustainable agriculture.

Cropland, as the largest land use type in the human landscape, contributes to not only biodiversity but also global food security. However, cropland abandonment not only is harmful to agricultural cultural landscapes but also threatens food security. Prior studies have suggested that changes in the social environment drive farmers to abandon cropland. In contrast, this study reveals that factors of the natural environment (e.g., landslides) have steadily and significantly affected cropland abandonment after controlling the factors of the social environment. More specifically, based on the survey data of a large sample of 4850 mountainous households in 24 provinces of China and following the theoretical framework of “environment → land use”, this study quantitatively identified the impacts of landslides on cropland abandonment in mountainous areas using the Probit and Tobit models. The results show that: (1) There is a similar spatial agglomeration trend between landslides and cropland abandonment. Namely, an area that has a high incidence of landslides also has a high incidence of cropland abandonment. (2) There is a significant and positive correlation between landslides and cropland abandonment. Namely, compared with peasants who have not suffered from landslides, the probability that peasants suffering from landslides will abandon cropland and the area abandoned increase by 6.8% and 0.064 mu, respectively. (3) Elderly farmers (over 64 years old) and the development of urbanization help curb cropland abandonment in the mountains. The results of this study may provide reference for the government to implement effective policies for managing landslides and revitalizing unused cropland resources.