Exploring the spatiotemporal evolution and spatial correlation network characteristics of cultivated land carbon emissions in China

As a predominantly agrarian nation, cutting emissions from agriculture is essential to achieving its “dual carbon” targets. The primary source of agricultural production, cultivated land, has a major impact on the sector’s total carbon emissions. However, the spatial correlation network features have received little attention in previous investigations, which have mostly concentrated on driving variables and emission measurements. Examining the spatial network characteristics of cultivated land carbon emissions (CLCE) provides a basis for promoting coordinated regional emission reduction. This study integrates the carbon emission coefficient method, standard deviational ellipse, ESDA, a modified gravity model, and social network analysis to reveal the spatiotemporal evolution and spatial network structure of CLCE in China from 2000 to 2023. The main findings of this study are as follows: (1) China’s CLCE increased overall, with fluctuations, from 6028.83 × 104 t in 2000 to a peak of 9111.60 × 104 t in 2015, before decreasing to 7555.91 × 104 t in 2023. (2) Spatially, the center of gravity of CLCE has continuously shifted northwestward, exhibiting a significant positive spatial autocorrelation characterized mainly by “high-high” clustering. (3) CLCE demonstrates a clear networked spatial structure, with increasing overall spatial connection strength, good connectivity, and stability, though the overall network compactness remains to be improved. (4) The spatial network of CLCE exhibits a “core–periphery” structure, with Shandong, Jiangsu, Henan, Hebei, Anhui, Hubei, and Sichuan serving as long-term core provinces, while the northwest and northeast regions are mostly located at the periphery. The research offers theoretical support for developing low-carbon agricultural strategies and promoting coordinated regional emission reduction policies in China.