Abstract: The effective implementation of emission reduction strategies through economic structural transformation and technological progress has become an inevitable choice for achieving low-carbon economic and social transformation and development in China. Considering that the disposability assumption of the shadow price calculation method is inconsistent with the realistic theory in the existing literature, a new calculation method for the carbon shadow price based on the data envelopment analysis (DEA) framework modeling and satisfying the principles of economic theory and material conservation was applied, in which the expected output production sub-technology and the undesired output production sub-technology are linked according to the generation relationship of pollutants. Improved By-production technology was used to accurately measure the agricultural carbon shadow price in 31 provinces from 1997 to 2020, and kernel density was used to analyze the dynamic evolution characteristics of agricultural carbon shadow prices. A feasible generalized least squares (FGLS) model was used to examine the impact of structural transformation and technological progress choices on the shadow price of agricultural carbon emissions. The results showed that: 1) the shadow price of agricultural carbon price showed an increasing trend, which was 7759.69 ¥∙t⁻¹ in the east region, 4192.35 ¥∙t⁻¹ in the central region, and 3997.51 ¥∙t⁻¹ in the west region, and the rising rates decreased in that order. 2) Kernel density analysis revealed that the kernel density value of the agricultural carbon shadow price had an increasing trend. The kernel density curve in the east region was right-shifted, in the central region left-right shifted with increasing regional differences, and in the west region it was downward and widened. 3) The overall regression analysis showed that the shadow price of agricultural carbon was significantly increased by structural transformation and labor-saving technological progress but that this increase was inhibited by capital deepening. Meanwhile, the level of economic development, the scale of agricultural operations, urbanization, and the level of opening up all played important roles in the agricultural carbon shadow price. 4) The regional regression analysis results highlighted that the influencing factors of agricultural carbon shadow prices differed in different regions. Structural transformation significantly increased the carbon shadow price in the east region but significantly inhibited it in the west region. Labor-saving technological progress reduced the carbon shadow price in the east region while significantly increasing it in the west region. Therefore, it is necessary to promote the transformation of the industrial structure, formulate differentiated green and coordinated development policies, and establish an agricultural carbon emissions trading market to promote low-carbon, green, and high-quality agricultural development.