尚杰, 吉雪强, 石锐, 朱美荣. 中国农业碳排放效率空间关联网络结构及驱动因素研究[J]. 中国生态农业学报 (中英文), 2022, 30(4): 543−557. DOI: 10.12357/cjea.20210607
引用本文: 尚杰, 吉雪强, 石锐, 朱美荣. 中国农业碳排放效率空间关联网络结构及驱动因素研究[J]. 中国生态农业学报 (中英文), 2022, 30(4): 543−557. DOI: 10.12357/cjea.20210607
SHANG J, JI X Q, SHI R, ZHU M R. Structure and driving factors of spatial correlation network of agricultural carbon emission efficiency in China[J]. Chinese Journal of Eco-Agriculture, 2022, 30(4): 543−557. DOI: 10.12357/cjea.20210607
Citation: SHANG J, JI X Q, SHI R, ZHU M R. Structure and driving factors of spatial correlation network of agricultural carbon emission efficiency in China[J]. Chinese Journal of Eco-Agriculture, 2022, 30(4): 543−557. DOI: 10.12357/cjea.20210607

中国农业碳排放效率空间关联网络结构及驱动因素研究

Structure and driving factors of spatial correlation network of agricultural carbon emission efficiency in China

  • 摘要: 农业碳排放效率研究对于农业碳达峰、碳中和目标的实现具有重要意义, 现有研究缺乏基于关系数据和网络视角进行的农业碳排放效率研究, 制约了区域农业协同减排活动的开展。本研究基于关系数据和网络视角, 以2010—2019年中国大陆31个省(市、自治区)农业碳排放效率为研究对象, 以非期望产出的SBM模型测度其农业碳排放效率, 利用修改的引力模型构建农业碳排放效率空间关联网络引力矩阵, 应用社会网络分析法就空间关联网络的结构特征进行分析, 最后通过QAP模型就其驱动因素进行探索。研究表明: 1)在研究期间, 中国31省(市、自治区)农业碳排放效率提升较快, 由0.400增长至0.756, 增长88.8%, 但仍有一定改进空间, 且各省(市、自治区)间存在较大差距; 此外, 其空间效应在全国范围呈现空间关联网络特征。2)在研究期间内, 中国31省(市、自治区)农业碳排放效率空间关联网络的网络关联性增强, 网络内部森严的等级关系逐渐松散, 网络结构的稳定性得到较大提升; 且该空间关联网络形成了多个网络中心, 对空间关联网络的形成发挥了重要作用, 并对各省(市、自治区)农业碳排放效率产生影响和控制; 东部沿海地区是该空间关联网络空间溢出的主要目的地。3)交通运输水平差异和第一产业产值差异有利于推动空间关联网络的形成; 相似的居民人均收入和信息化水平以及相近的空间距离能够促进空间关联网络形成。为此, 中国农业碳排放效率具有空间关联网络特征, 相关政策措施应当考虑其空间关联网络结构及动因。

     

    Abstract: The study of agricultural carbon emission efficiency is important for the realization of agricultural carbon peak and carbon neutrality goals. There is a lack of studies on agricultural carbon emission efficiency based on relational data and network perspectives. These limitations restrict the development of regional agricultural collaborative emissions reduction activities. Therefore, based on relational data and network perspective, taking the development of the agricultural carbon emission efficiency of 31 provinces (cities and autonomous regions) from 2010 to 2019 as the research subject, the study used the SBM-Undesirable model to measure the efficiency of agricultural carbon emissions, constructed a modified gravity matrix of spatial correlation network of agricultural carbon emission efficiency, analyzed the structural characteristics of the spatial correlation network by applying the social network analysis method, and finally explored the driving factors through a quadratic assignment procedure (QAP) model. There are several main findings. First, despite the wide disparity across the 31 provinces (cities, autonomous regions) in China, agricultural carbon emission efficiency increased rapidly, from 0.400 to 0.756, increasing 88.8% with a creation room for improvement. Second, the network relevance of agricultural carbon emission efficiency in the provinces (cities, autonomous regions) was enhanced. For the spatial correlation networks of agricultural carbon emission efficiency in the 31 provinces (cities, autonomous regions) of China, the number of network relations increased from 121 to 211, and the network density increased from 0.130 to 0.227, while network ranking declined from 0.458 to 0.293, followed by network efficiency, which declined from 0.837 to 0.692. In addition, the spatial correlation network of agricultural carbon emission efficiency among the 31 provinces (cities, autonomous regions) had formed multiple network centers that played an important role in controlling agricultural carbon emission efficiency. Overall, the eastern coastal areas were the main destinations for cyberspace space-related spillover of agricultural carbon emission efficiency in 31 provinces (cities and autonomous regions) in China. Third, the transport-level difference, resident income difference, difference in the output value of the first industry and information-level difference had an important impact on the formation of a spatial correlation network of agricultural carbon emission efficiency in China. Finally, the study findings demonstrated that the differences in transportation level and the output value of the primary industry significantly promoted spatial correlation network development. Similarly, it was found that per capita income, information level, and spatial distance also emphasized spatial correlation network formation. Based on the research conclusions, we proposed some suggestions for enhancing the spatial correlation of agricultural carbon emission efficiency, such as emphasizing the development of inter-regional coordinated emission reduction activities and differences of various provinces (cities and autonomous regions) in spatially related networks, making full use of driving factors strengthening the connection between the agricultural product market and organizations, and enhancing the information and transportation network support.

     

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