According to the Plan for Rural Development of Digital Agriculture (2019–2025), accelerated integration of digital technologies and agriculture is crucial to promoting high-quality agriculture in China. The application of DIT in agricultural activities will not only help improve the efficiency of agricultural production, but also promote the green development of agriculture and the achievement of the Dual Carbon Target (DCT). In order to further clarify the comprehensive effects of the application of DIT in agricultural systems and provide routes for government decision-makers to assist in reducing agricultural emissions by DIT, this paper adopts the logical deductive method and starts with the application status to draw out the specific paths of low-carbon transformation in DIT-driven agriculture, while further discussing the potential issues in the process and corresponding solutions. DIT is a double-edged sword. It can promote the green and low-carbon transformation of agriculture by implementing precision operation, environmental monitoring, optimizing carbon emission accounting, and supervising the carbon market. However, at the same time, it may face problems such as unbalanced rural development and excessive financialization of the carbon market. Therefore, we should be optimistic but cautious about the application of DIT in reducing agricultural emissions. We can address potential problems by strengthening government-led investment, broadening channels for capital investment, strengthening skills training for farmers, and enhancing the regulation of trading in carbon sink markets.

(1) Studying the driving factors and spatiotemporal characteristics of China’s regional animal husbandry emissions is highly relevant to policy formulation. (2) Methods: We calculated the total CO₂ equivalent emissions of animal husbandry across the country and each province separately, and then used the Logarithmic Mean Divisia Index (LMDI) to analyze how the driving forces of animal husbandry emissions changed across the country and in different provinces from 2001 to 2019. (3) Results: ① During the period 2001–2019, national animal husbandry carbon emissions showed an overall downward trend. Economic growth and population contributed positively to the emissions (which means more CO₂), while technological advancement, structural change in agriculture, and change in the national industrial structure had negative effects (which means less CO₂). ② Using aspects of provincial animal husbandry, we categorized 31 provinces into four types: fluctuating rising, fast falling, slow falling, and steadily falling. Then, according to the magnitude of the different driving forces in different provinces, we classified 31 provinces into three types: economic structure adjustment-driven, technological progress-driven, and economic growth-driven. ③ The driving effects of agricultural structural change and population in some provinces are not consistent with the effects shown at the national level.