Analysis of spatial and temporal characteristics and drivers of agricultural carbon emissions in China

Against the background of global warming, China has implemented numerous emission reduction measures. In-depth discussions of the sources, structure, drivers, and emission-reduction strategies of agricultural carbon emissions are of great significance for promoting the low-carbon transformation of China’s agricultural industry. To explore the spatial and temporal distribution characteristics and driving factors of China’s agricultural carbon emissions, this study used statistical yearbook data from 31 provinces (autonomous regions and municipalities, excluding Hong Kong, Macao, and Taiwan, and same as below) in China from 2000 to 2021. This study investigated the carbon emissions of water use, land use, and energy consumption from 2000 to 2021 using Intergovernmental Panel on Climate Change (IPCC) carbon emission factors and selected the carbon sources of chemical fertilizers, pesticides, agricultural films, diesel oil, irrigation, and plowing. Three subsystem-related variables were used to calculate the total annual agricultural carbon emissions of each province (autonomous regions and municipalities). Then, we analyzed the results of carbon emissions from agriculture in terms of uncertainty using Monte Carlo simulation. The spatiotemporal evolution trend and spatial correlation characteristics of agricultural carbon emissions were analyzed by combining them with Moran’s Index. The main driving factors of agricultural carbon emissions were analyzed using the Logarithmic Mean Divisia Index. The results show that: 1) From the perspective of time-ordered change, the overall trend of agricultural carbon emissions is inverted “V”-shaped. 2) The provinces with large carbon emissions are mainly concentrated in the Huang-Huai-Hai Region and the central plain, whereas the western region and municipalities have lower agricultural carbon emissions. From the perspective of agricultural carbon emission sources, carbon emissions from chemical fertilizer accounted for the highest proportion. Areas with high-quality land and water resources have high agricultural carbon emissions. Changes in areas with high carbon emissions tended to expand northward. Henan, Anhui, Shandong, and other provinces (autonomous regions and municipalities) show significantly high-high clustering effects, whereas Beijing, Tianjin, Qinghai, and other provinces (autonomous regions and municipalities) show a significantly low-low clustering effect. 3) The economic output factor of agricultural water resources and agricultural labor intensity factor are positive driving factors, whereas the economic output factor of agricultural water resources is the most important influencing factor for the increase in agricultural carbon emissions in China. The agricultural productivity factor, labor scale factor, and agricultural water-land matching factor are the negative driving factors of carbon emissions. Among these factors, the agricultural productivity factor has the highest contribution to carbon emissions reduction and is the most important driving factor for reducing agricultural carbon emissions in China. The findings of this study provide recommendations for China’s decision-making on agricultural emissions reduction. The government should increase investment in low-carbon agriculture, support the research and development of new fertilizers and agricultural machinery, improve the efficiency of land and water resources, and enhance the quality of labor. Simultaneously, it is necessary to take advantage of the agglomeration effect of agricultural carbon emissions to promote concentrated agricultural development and interregional cooperation and cultivate new agricultural talent.