城市建设用地碳排放效率空间关联网络特征及影响因素分析——以山东省为例

Analysis on spatial correlation network characteristics and influencing factors of the construction land carbon emission efficiency——A case study of Shandong Province

探讨碳排放约束下城市建设用地碳排放效率的空间关联特征及其影响因素,有助于协同提升区域间的建设用地碳排放效率,进而提升土地资源配置效率、推动绿色低碳发展。采用SBM模型、引力矩阵模型与社会网络分析法等方法探究2003—2019年山东省建设用地碳排放效率的空间关联网络特征及其影响因素。结果表明:(1)山东省建设用地碳排放效率逐年提升,空间上由西向东逐渐优化。(2)山东省建设用地碳排放效率空间网络密度值相对较低,呈现“扁平化”的等级结构特征,网络结构稳定性逐渐增强。(3)东营、济南、青岛等城市对资金、人才和技术等资源要素的吸引力较强,滨州、聊城、德州等城市同时处于地理和网络位置边缘,缺乏效率受益和溢出渠道。(4)板块之间联系更为紧密且互动趋势明显,呈现出受益和溢出板块城市成员少而经纪板块城市成员多的非均衡特征。(5)空间关联网络的形成是地理邻近、经济发展、产业结构、土地利用强度、土地利用结构和城市化等影响因素综合作用的结果。

Exploring the spatial correlation characteristics and influencing factors of carbon emission efficiency of urban construction land under carbon emission constraints can help synergistically improve the carbon emission efficiency of construction land between regions,thereby improving the efficiency of land resource allocation and promoting green and low-carbon development.Using methods such as SBM model,gravity matrix model,and social network analysis,this paper explored the spatial correlation network characteristics and influencing factors of carbon emission efficiency of construction land in Shandong Province from 2003 to 2019.The results showed that:①The carbon emission efficiency of construction land in Shandong Province has been increasing year by year,and the spatial optimization was gradually optimizing from west to east.②The spatial network density value of carbon emission efficiency of construction land in Shandong Province was relatively low,showing a"flattened"hierarchical structure feature,and the stability of the network structure was gradually increasing.③Cities such as Dongying,Jinan,and Qingdao had a strong attraction for resource elements such as funds,talent,and technology,while cities such as Binzhou,Liaocheng,and Dezhou were at the edge of both geographical and network locations,lacking efficiency benefits and spillover channels.④The connections between sectors were closer and the trend of interaction was obvious,showing an imbalanced feature of fewer members in the benefiting and spillover sectors and more members in the brokerage sectors The formation of spatial correlation networks was the result of a comprehensive effect of factors such as geographical proximity,economic development,industrial structure,land use intensity,land use structure,and urbanization.