Structural characteristics and influencing factors of agricultural carbon emissions spatial correlation network: evidence from Shandong Province

Introduction With the development of agricultural industry clustering and scale expansion, agricultural carbon emissions (ACEs) have gradually formed a spatial association network. Clarifying the agricultural carbon emissions spatial correlation network (ACESCN) and its influencing factors in Shandong Province is crucial for advancing low-carbon agricultural development. Methods Based on ACE in 16 cities of Shandong Province, this study uses Social Network Analysis (SNA) and Quadratic Assignment Procedure (QAP) to investigate the spatial spillover effects and driving factors of ACESCN in Shandong Province from 2010 to 2022. Results and discussion The findings show that the following: (1) overall, ACE in Shandong Province has shown a trend of initially increasing and then decreasing. (2) the ACESCN in Shandong Province has gradually improved in both connectivity and robustness, forming a network structure centered around Weifang, Jinan, and Tai’an. However, the degree of network connectivity remains relatively loose, indicating that the network structure needs optimization. Within the network, there are significant spatial spillover and spatial agglomeration effects. (3) Geographical proximity, economic level, industrial structure, and the opening-up degree have a significant impact on spatial correlation. Therefore, this study suggests that spatial associations should be fully utilized to enhance cross-regional agricultural production interactions and cooperation. This approach will help form a rational agricultural industry agglomeration structure, providing a scientific basis for Shandong Province to achieve low-carbon agricultural development and regional coordinated emission reductions.