碳约束下省域物流能源效率空间关联效应及其影响因素

The Spatial Correlation Effect and Influencing Factors of Provincial Logistics Energy Efficiency under Carbon Constraint

摘要:

关注区域间物流能源效率空间关系，以我国30个省份为对象，从全要素角度考虑碳约束下物流能源效率，将物流碳排放与物流资本、能耗和劳动力联系起来，利用其2005－2019年的面板数据，采用Super-SBM模型测算其能源效率；并从整体空间关联角度出发，运用社会网络分析方法分析其能源效率的时空关联演变特征，构建二次指派程序模型实证探索其影响因素。结果表明：30个省份物流能源效率整体网络结构基本稳定，省域之间已形成了良好的可达性，2005－2019年间物流能源网络效率均在0.648～0.717之间浮动，网络层级从2005年的0.475下降到2019年的0.437；北京、上海、江苏、天津及浙江处于网络的中心并起着主导性作用；整体空间网络可划分为双向溢出、净受益、净溢出和经纪人四大功能板块，其中大部分地处偏远地区的省份均担任着净溢出的角色；经济发展、城镇化率、科研发展投入以及环境建设投资与物流能源效率空间关联呈正相关，而产业结构对空间关联影响不大。基于研究结论，提出大力发展低碳经济、发挥发达地区的资本和技术辐射作用和加强区域间物流能效协同管理等政策建议。

关键词:物流业;Super-SBM;社会网络分析;空间关联;影响因素

Abstract:

Focusing on the spatial relationship between regional logistics energy efficiency, this paper takes 30 provinces as the object, considers the logistics energy efficiency under the carbon constraint from the perspective of total factors, links logistics carbon emissions with logistics capital, energy consumption and labor, and uses the panel data from 2005 to 2019 to measure the energy efficiency using the Super-SBM model. From the perspective of global spatial correlation, social network analysis was used to analyze the spatio-temporal correlation evolution characteristics of energy efficiency, and a secondary assignment program model was constructed to empirically explore its influencing factors. The results show that the overall structure of logistics energy efficiency network in 30 provinces is basically stable, and good accessibility has been formed among provinces. From 2005 to 2019, the logistics energy efficiency network fluctuates between 0.648 and 0.717, and the network level decreases from 0.475 in 2005 to 0.437 in 2019. Beijing, Shanghai, Jiangsu, Tianjin and Zhejiang are in the center of the network and play a leading role. The overall spatial network can be divided into four functional areas: two-way spillover block, net benefits block, net spillovers block and brokers block. Most of the provinces located in remote areas play the role of net spillovers block. Economic development, urbanization rate, investment in scientific research and environmental construction are positively correlated with the spatial correlation of logistics energy efficiency, while the industrial structure has little effect on the spatial correlation. Based on the research conclusions, the paper puts forward some policy suggestions, such as vigorously developing low-carbon economy, giving full play to the radiating role of capital and technology in developed regions, and strengthening the coordinated management of logistics energy efficiency between regions.

Key words:Logistics industry; Super-SBM; social network analysis; spatial correlation; influencing factors