Characteristics of agricultural carbon emissions and carbon peak analysis in Hunan Province

Understanding the characteristics of and factors influencing agricultural carbon emissions in Hunan Province can provide a scientific basis for the development of green and low-carbon agriculture in Hunan Province. By reviewing the Hunan Statistical Yearbook and the statistical yearbooks of various cities and regions, we integrated the data on crop area, agricultural inputs, and livestock and poultry production, and calculated the agricultural carbon emissions of Hunan Province from 2007 to 2020 using the classical carbon emission calculation theory of the Intergovernmental Panel on Climate Change (IPCC). Taking 2007 as the base year, the Kaya carbon emission formula and Logarithmic Mean Divisia Index (LMDI) were used to analyze the influencing factors, whereas the grey prediction model GM (1, 1) was introduced to predict the carbon emissions of Hunan Province during the period of 2021–2040. The calculation results showed that the carbon emissions in Hunan Province were 6.15 × 10⁷ t in 2020, and the carbon emission intensity was 1.01 t·(×10⁴¥)⁻¹, with the peak reached in 2015. Agricultural carbon emissions in Hunan Province showed a three-stage change. Due to the severe impact of the 2008 snow and ice disaster on agriculture, agricultural carbon emissions showed a decreasing trend during 2007–2008, a steady increase during 2009–2015, a peak in 2015, and an overall decreasing trend during 2015–2020. At the same time, there were obvious differences among different cities: Changsha, Xiangtan, Hengyang, Shaoyang, Yueyang, Changde, and Yiyang reached their peak carbon emissions around 2015, whereas the other cities failed to reach their peaks before 2030. Agricultural carbon emission intensity in all cities and towns in Hunan Province showed decreasing trend; the larger the agricultural carbon emission intensity in the base year, the larger the decrease in agricultural carbon emission intensity in the following years. The average coefficient of variation of agricultural carbon emissions in each city between 2007 and 2020 was 42%, whereas the average coefficient of variation of agricultural carbon intensity was 20%. This indicates that the difference in agricultural carbon emissions between cities was much larger than the difference in agricultural carbon intensity. The proportion of agricultural carbon emission sources followed the order of farmland utilization > livestock and poultry production > agricultural inputs. The level of regional economic development, labor force level, and total rural electricity consumption play major roles in increasing agricultural carbon emissions; the level of regional economic development and total rural electricity consumption are the main influencing factors, and agricultural production efficiency, agricultural industrial structure, regional industrial structure, and the reciprocal of the per capita electricity consumption of rural residents play important roles in the process of decreasing agricultural carbon emissions. The study shows that agricultural carbon emissions in Hunan Province peaked in 2015. In order to achieve the goal of carbon neutrality and provide reference for the decision-making of agricultural carbon emission reduction in Hunan Province, this paper puts forward suggestions such as optimizing the industrial structure, promoting green innovation according to local conditions, and strengthening government functions.