Abstract
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.
Highlights
IntroductionAgricultural production is the world’s second-largest greenhouse gas (collectively referred to in this article as GHG) emissions source [1]
Agricultural production is the world’s second-largest greenhouse gas emissions source [1]
This paper aims to research the GHG emissions and GHG emissions intensity in the Main Stream Area of the Yellow River Basin in Gansu, which includes CH4, CO2, and N2O emissions
Summary
Agricultural production is the world’s second-largest greenhouse gas (collectively referred to in this article as GHG) emissions source [1]. The temporal and spatial characteristics of agricultural carbon emissions across regions are constantly explored, such as The Yangtze River Economic Belt [14] or the factors of China using the Kaya identity and LMDI index decomposition method [15], research on emission estimation for China’s Fujian Province [16], and the characteristics of agricultural source GHG emissions in Anhui Province [17], Empirical study on the influence factors of carbon emissions transaction price [18] and research on carbon emissions from the planting industry [19] in Hubei Province, GHG emissions and SOC stocks of the crop-farm animal production system in Heilongjiang Land Reclamation Area [20], comprehensive evaluation of ecological environment on the anniversary of wheat-corn double-cooked farmland in Henan Province [21], study on the potential estimation of GHG emissions reduction in the “breeding-biogas” ecological model in Shanxi Province [22], research on agricultural GHG emissions reduction in Xinjiang Uygur Autonomous Region and Sichuan Province [23]. Agricultural carbon emissions are mainly studied from a relatively macro perspective, and there are relatively few studies on county-level agricultural carbon emissions
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