A change in agricultural land management scale leads to the recombination and adjustment of production factors, which have an important impact on agricultural carbon emissions. There are few studies on the connection between the scale of land management and agricultural carbon emissions. In this study, we empirically examined the relationship between planting scale and agricultural carbon emissions using the threshold model, which allows the data to endogenously generate several regimes identified by the thresholds. The results showed that from 2003 to 2018, carbon emissions from planting first increased and then decreased, reaching their highest in 2015. Across the whole country in the main rice- and wheat-producing regions, the scale of planting land has a threshold effect on agricultural carbon emissions, showing an inverted “U” shape. Carbon sinks and natural disasters significantly affected planting carbon emissions in the above three regions. The amount of fiscal support for agriculture significantly affects planting carbon emissions in the national and main wheat-producing regions, while peasants’ per capita income significantly affects planting carbon emissions in the main rice- and wheat-producing regions. This study provides policy makers with new ideas, in that continuously expanding the scale of agricultural land management is conducive to reducing agricultural carbon emissions.

China’s industry is still in the middle of industrialization. Land use activities are crucial to the growth of carbon emissions. However, few scholars focus on the influence mechanism between industrial land use efficiency (ILUE) and industrial carbon emissions. In this paper, the threshold model and the spatial Durbin model are used to investigate the spatial threshold effect of industrial land use efficiency on industrial carbon emission from 2003 to 2018. The results show that ILUE of China’s provinces basically shows an improvement trend, with little difference in spatial distribution, showing a pattern of high in the eastern region and low in the western region. When economic development level (A) and technical level (T) are taken as the threshold variable, ILUE has a single threshold effect on industrial carbon emissions in the eastern region. In the central region, with a as the threshold variable, ILUE shows a double threshold effect on industrial carbon emission. Under the 0–1 geographical proximity weight matrix, the indirect spillover effect of ILUE on reducing regional carbon emissions is significant, and the indirect effect is even greater than that on regional carbon emissions. The spatial spillover effect is not significant in the eastern region. These findings have important practical significance for promoting regional industrial transformation and upgrading, optimizing land space and realizing high-quality economic development.