Analysis on agricultural carbon emissions transfer of inter-provincial trade in China
Authors: Zhang Xidong , Zhang Juan , Fu Wenhao , Pei Ziting , Zhang Bin , Zhuang Mufan 
Published: November 2024

The "carbon peaking and carbon neutrality goals" has put forward new requirements for China’s agricultural carbon emission reduction. It is easy to ignore the carbon emission transfer caused by agricultural trade if the reduction responsibility of carbon emission is merely defined from the supply side. Therefore, it is necessary to conduct in-depth research on agricultural carbon transfer for reasonably dividing the responsibility of agricultural carbon reduction in different provinces. In this study, the cross-section data of 31 provincial-level administrative regions in China in 2015, 2018 and 2021 were used to calculate the agricultural carbon emissions of each province from the production side, and the agricultural carbon transfer model was applied to study the spatial transfer characteristics of agricultural carbon emissions. The results show that: (1) In 2015, 2018, and 2021, the net carbon transfer in Chinese agriculture was 125.76 million tons, 132.49 million tons, and 133.02 million tons, respectively, accounting for 11.97%, 13.31%, and 13.61% of agricultural carbon emissions respectively. (2) The net input area of agricultural carbon emissions formed a spatial distribution pattern of four major regions which are concentrated in the southeast coastal areas, and the gap of net input of emissions was narrowing among the regions. Shanghai, Zhejiang, and Fujian are the regions with the largest net agricultural carbon input among the net input regions. The net agricultural carbon input increased from 43.00 million tons in 2015 to 52.71 million tons in 2021. In Guangdong and Guangxi, agricultural carbon emissions decreased from 41.34 million tons in 2015 to 35.61 million tons in 2021. In Sichuan, Chongqing, and Guizhou, agricultural carbon emissions decreased from 22.98 million tons in 2015 to 14.20 million tons in 2021. Beijing and Tianjin are the regions with the smallest net agricultural carbon input among the four net input regions, with the net agricultural carbon input increasing from 12.53 million tons in 2015 to 13.92 million tons in 2021. (3) The net output area of agricultural carbon emissions also formed a spatial distribution pattern of four major regions, and they were concentrated in the north of China with the center of gravity of net output shifting to the north. In 2015, Heilongjiang and Jilin were the regions with the largest net carbon output among the four net output regions. The net agricultural carbon output increased from 38.45 million tons in 2015 to 39.44 million tons in 2021. In Xinjiang and Gansu, the net agricultural carbon output increased from 15.87 million tons in 2015 to 23.37 million tons in 2021. In Inner Mongolia, the net agricultural carbon output increased from 17.03 million tons in 2015 to 23.05 million tons in 2021. Henan and Anhui have consistently maintained a high level of net agricultural carbon output, the net agricultural carbon output decreased from 35.54 million tons in 2015 to 25.68 million tons in 2021. On the whole, the spatial transfer of agricultural carbon emissions in China shows the characteristics of "north carbon transport to south" bounded by the Yangtze River. This paper believes that agricultural policies of carbon emission reduction should be formulated at both ends of agricultural supply and demand due to the spatial transfer of agricultural carbon emissions, which is not only conducive to stabilizing the production enthusiasm of major agricultural production provinces, but also conducive to controlling carbon emissions in output and input regions. For this purpose, the study puts forward countermeasures and suggestions to promote the reduction of agricultural carbon emission in different provinces, so as to better leverage the green and low-carbon development in the agricultural field under the guidance of the "carbon peaking and carbon neutrality goals".