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ORIGINAL RESEARCH article
Front. Sustain. Food Syst.
Sec. Agricultural and Food Economics
Volume 9 - 2025 | doi: 10.3389/fsufs.2025.1508492
Structural characteristics and influencing factors of agricultural carbon emissions spatial correlation network: evidence from Shandong Province
Provisionally accepted- 1 Shandong University of Science and Technology, Qingdao, China
- 2 Ordos Yingpanhao Coal, Inner Mongolia, China
- 3 School of Creative Science and Engineering, Waseda University, Tokyo, Japan
With the development of agricultural industry clustering and scale expansion, agricultural carbon emissions (ACE) 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. 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. The findings show that: (1) Overall, ACE in Shandong Province have 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 the spatial associations should be fully utilized to enhance crossregional 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.
Keywords: Agricultural carbon emissions, social network analysis, Spatial correlation network, QAP, Shandong province
Received: 09 Oct 2024; Accepted: 17 Mar 2025.
Copyright: © 2025 Shan, Ji, Jin, Li, Fang and Ji. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Min Ji, Shandong University of Science and Technology, Qingdao, China
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