This paper attempts to reveal the impact and mechanisms of digital inclusive finance (DIF) on agricultural carbon emission performance (ACEP). Specifically, based on the provincial panel data in China from 2011 to 2020, a super slacks-based measure (Super SBM) model is applied to measure ACEP. The panel regression model and spatial regression model are used to empirically analyze the impact of DIF on ACEP and its mechanism. The results show that: (1) during the study period, China’s ACEP exhibited a continuous growth trend, and began to accelerate after 2017. The high-value agglomeration areas of ACEP shifted from the Huang-Huai-Hai plain and the Pearl River Delta to the coastal regions and the Yellow River basin, the provincial differences displayed an increasing trend from 2011 to 2020. (2) DIF was found to have a significant positive impact on ACEP. The main manifestation is that the development of the coverage breadth and depth of use of DIF helps to improve the ACEP. (3) The positive impact of DIF on ACEP had a significant spatial spillover effect, that is, it had a positive effect on the improvement of ACEP in the surrounding provinces. These empirical results can help policymakers better understand the contribution of DIF to low-carbon agriculture, and provide them with valuable information for the formulation of supportive policies.

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Farmland has important ecological service value. Underestimating the added value is one of the reasons of extensive land use. From the view of ecological protection and sustainable development, this paper discusses the ecological function of farmland and assesses its ecological value by using statistical methods. Taking Shaoxing county of Zhejiang province as an example, the study analyzes the necessity of ecological compensation based on the investigation of land uses current situation and land acquisition compensation level. The compensation system in land acquisition should be improved in the aspects of institution, concept and policy.

The farmland loss caused by urban–rural land development has exacerbated China’s challenges of using limited farmland to feed more than 1.4 billion people. Earlier studies shed light on the impacts of urban sprawl and rural settlement expansion, separately. However, there is little quantitative understanding of which one has more severe impacts on farmland and its net primary productivity (NPP). Thus, this study used spatially explicit satellite data including land-use maps and estimated NPP data, as well as spatiotemporal analysis methods to conduct a comparative analysis of farmland loss due to urban sprawl and rural settlement expansion at different scales from 2000 to 2020 in China. The results show that during the study period, urban sprawl resulted in a loss of 49,086.6 km2 of farmland area and 8.34 TgC of farmland NPP, while the loss of farmland area and farmland NPP due to rural settlement expansion reached 18,006.8 km2 and 3.88 TgC. The largest gap between the total area of farmland loss due to urban sprawl and the total loss area due to rural settlement expansion was 12,983.3 km2 in Eastern China, while the smallest gap was 1291.1 km2 in Northeastern China. The largest gap between the loss of farmland NPP due to urban sprawl and the total loss due to rural settlement expansion occurred in Eastern China at 1.97 TgC. Spatially, the total loss of farmland and its NPP due to urban sprawl and rural settlement expansion occurred mainly in the eastern and central regions of China; the areas of farmland loss by urban sprawl were more concentrated than that by rural settlement expansion. The negative impacts of urban sprawl on farmland area and its NPP were greater in southern China than that of rural settlement expansion. Noticeably, the loss of NPP per unit of farmland due to rural settlement expansion was higher than that by urban sprawl, especially in the Yangtze River Delta and Beijing–Tianjin–Hebei region. The results highlight the non-negligible impacts of rural settlement expansion on farmland in China. It is necessary to improve farmland protection policies by optimizing the spatial allocation of urban and rural construction land.

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