Temporal and spatial dynamics of agricultural carbon emissions in Yunnan Province from 2013 to 2022

Agricultural activities are an important source of greenhouse gases (GHG) emissions that significantly impact global climate change. However, the spatiotemporal agricultural carbon emission patterns at the provincial level are not fully understood, especially in regions with diverse agricultural production modes, such as Yunnan Province, China. This study systematically investigated the spatiotemporal characteristics, driving factors, and spatial spillover effects of agricultural carbon emissions in Yunnan Province from 2013 to 2022 to provide a scientific basis for formulating low-carbon agricultural policies and achieving sustainable agricultural development. This study used statistical data from 2013 to 2022, combined with grey relational analysis, geographic detector method, spatial autocorrelation test, standard deviation ellipse analysis, and spatial econometric modeling, to quantitatively analyze the total amount, intensity, and spatiotemporal distribution of agricultural carbon emissions in Yunnan Province. The research results indicate that 1) total carbon emissions from agriculture showed a trend of first increasing and then decreasing. They increased from 12.413 4 Mt in 2013 to 13.109 5 Mt in 2016, but then decreased year by year to 10.502 0 Mt in 2022, which was a cumulative decrease of 19.9% compared to that in 2016. The carbon emission intensity declined from 0.406 t∙(10⁴ ¥)⁻¹ in 2013 to 0.159 t∙(10⁴ ¥)⁻¹ in 2022, and the cumulative decrease was 60.8%. 2) In terms of spatial distribution, agricultural carbon emissions showed a pattern of high in the east and low in the northwest. Qujing, Kunming, and Honghe were the main high emission areas, whereas northwestern regions (e.g. Nujiang and Diqing) showed lower agricultural carbon emissions. The agricultural carbon emission intensity was characterized by low in the middle and high in the surrounding areas, and Lijiang and Honghe showed higher agricultural carbon emission intensity. 3) Nitrogen fertilizer, agricultural film, compound fertilizer, and pesticide were the main sources of agricultural carbon emissions, and agricultural output value, rural population, and GDP were the key factors driving the spatial heterogeneity associated with carbon emissions. 4) The spatial spillover effect was significant. The agricultural carbon emissions from adjacent areas increased by 0.009% and 0.013% for every 1% increase in agricultural output value and rural population, respectively. The growth in local GDP had a suppressive effect on agricultural carbon emissions in adjacent areas, with every 1% increase in local GDP leading to a 0.001% reduction in agricultural carbon emissions in adjacent areas. This study used spatial econometric model and geographical detector to reveal the spatiotemporal differentiation patterns and cross-regional linkage mechanisms driving agricultural carbon emissions in Yunnan Province. It also incorporated spatial spillover effects into the provincial-scale carbon emission research framework. The results provide a scientific basis that can be used by Yunnan Province to formulate differentiated low-carbon agricultural policies, especially for high-emission areas such as Qujing, Kunming, and Honghe. The precision fertilization should be promoted, there should be a reduction in the use of agricultural films, and clean energy technologies should be adopted. For low-emission areas such as Nujiang, the low emission mode should continue to be maintained to reduce dependence on fertilizers and pesticides.