Dynamic evolution characteristics and development trend of agricultural carbon emissions in Guangdong Province based on spatial and temporal perspective

To clarify the characteristics and influencing factors of agricultural carbon emissions in Guangdong Province, this study forecasted the trend of agricultural carbon emissions from 2023 to 2040 to provide a theoretical basis for formulating agricultural carbon emission reduction policies. Using the classical carbon emission calculation theory of the Intergovernmental Panel on Climate Change (IPCC), this study measured (1) the agricultural carbon emissions in Guangdong Province from 2000 to 2020 based on three main carbon sources: agricultural material input, farmland soil use, and livestock breeding; (2) analyzd its spatial and temporal characteristics and dynamic evolution trends further; (3) clarified inter-municipal differences; (4) used the LMDI model to carry out the analysis of influencing factors; and (5) used the gray prediction model GM (1,1) to forecast carbon emissions from 2023 to 2040. The results showed that: (1) from 2000 to 2020, the total amount and intensity of agricultural carbon emissions in Guangdong Province decreased year by year, and in 2020, the total amount of agricultural carbon emissions in Guangdong Province was 32.977 million tons, and the intensity of agricultural carbon emissions was 0.59 t∙(10⁴ ¥)⁻¹. Among them, agricultural soil use contributed the highest percentage of agricultural carbon emissions, followed by agricultural material inputs and livestock breeding. The average share of carbon emissions caused by late rice cultivation was the highest among agricultural soil use, reaching 41.06%, followed by carbon emissions caused by cattle rearing, chemical fertilizer, and early rice cultivation, and the sum of the four reaches 84.53% of the total agricultural carbon emissions in Guangdong Province. (2) The intensity and total amount of agricultural carbon emissions in Guangdong Province showed regional differences. The total amount and intensity of less economically developed areas were mainly high and second-highest, whereas economically developed areas were mainly second-low and low, showing an increasing trend from the center to the edge. From 2000 to 2020, there was a decreasing trend of agricultural carbon emission intensity in both the less economically developed and economically developed regions. (3) Agricultural production efficiency, regional industrial structure, and labor force size factors played a particular role in agricultural carbon emission reduction. In contrast, agricultural-industrial structure, regional economic development level, and urbanization were the main factors for increased agricultural carbon emissions. (4) The prediction results showed that agricultural carbon emissions in Guangdong Province will continue to decline after 2023. Among the 21 prefecture-level cities, agricultural carbon emissions in Maoming and Zhanjiang still have an increasing trend after 2023, whereas agricultural carbon emissions in other cities show a yearly decreasing trend. Based on these results, we proposed relevant policy recommendations such as strengthening scientific and technological innovation, improving the agricultural policy guarantee system, and increasing the penetration rate of green technology to provide theoretical references for agricultural carbon emission reduction planning in Guangdong Province.