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In 2021, The People's Republic of China proposed goals for peaking carbon dioxide emissions before 2030 and carbon neutrality before 2060, in the 15 counties (districts) of the Main Stream Area of the Yellow River Basin in Gansu that plays an important role in ecological protection and green development. Next the CO2 equivalents were converted according to the IPCC2 standard, the total agricultural GHG emissions was calculated, the relationship with the agricultural output value was analyzed, and the discretization of the space was analyzed by the coefficient of variation and standard deviation. Firstly, the total agricultural GHG emissions in 15 counties (districts) of the Main Stream Area of the Yellow River Basin increased 55.54% in 2000–2019, and 2.35% annually, roughly divided into three stages: the rapid growth period (2000–2008), the slow decline period (2009–2014) and the rapid decline period (2015–2019). The economic efficiency is significantly improved, with an average annual decline of 6.49%, roughly divided into three stages: the slow-descent stage (2000–2004), the period of slow-growth stage (2005–2008) and the period of fast-decline (2009–2019). Secondly, based on the characteristics of the total GHG emissions, Maqu County has the largest GHG emissions increase, from 26.8842 kt in 2000 to 38.9603 kt, in 2019, an increase of 44.92%, while the smallest GHG emissions, in Anning District, decreased 87.33% from 111 t in 2000 to 14.1 t in 2019; In the rate of increase in the total GHG emissions, Dongxiang County had the largest rate of increase from 2000 to 2019, an increase of 160.28% and an average annual increase of 4.90%. The smallest rate of decrease in GHG emissions was seen in Chengguan District, where they decreased 92.11% from 2000 to 2019, an average annual decrease of 11.93%. The characteristics of agricultural GHG emissions intensity is a significant declining trending and agricultural production efficiency has been significantly improved. Finally, to provide a basis for the formulation of differentiated agricultural energy conservation and emissions reduction policies, reduce agricultural GHG emissions intensity and reduce the use efficiency of resources by formulating differentiated emission targets, tasks and incentive measures. [ABSTRACT FROM AUTHOR]

• Carbon emission accounting method at county level. • Spatially explicit method of multi-source carbon emission data. • Method for optimum spatial resolution of carbon emissions. The county is the basic economic spatial unit in China, and is also an integrated spatial carbon emitter. Focusing on a county, especially the developed counties in the Yangtze River Delta, it is of typical significance and enlightenment to conduct an in-depth and detailed dissection. The spatialization of a county's carbon emissions is crucial for exploring the spatial pattern of carbon emissions, which is fundamental information needed to reduce carbon emissions through the optimization or reorganization of the spatial pattern. This study systematically calculated the carbon emissions of Changxing county from four aspects (including energy; industrial processes and product use; agriculture, forestry, and other land use; and wastes), based on a combination of bottom-up and top-down approaches. Subsequently, we constructed ten carbon emission spatial datasets with a resolution of 50–500 meters (m). According to landscape metrics and the information loss evaluation model, we analyzed the scale effect of carbon emissions at different resolutions and determined the optimal resolution of the spatial carbon emissions at the county scale to be a 200 m × 200 m grid. A reasonable scale of spatial carbon emission data provides support for low-carbon spatial planning and emission reduction policymaking. [Display omitted] [ABSTRACT FROM AUTHOR]