物流业碳排放区域差异特征及效率解析

基于Theil指数探究2015—2020年中国物流业碳排放的区域差异性。构建三阶段SBM模型与GML指数模型，分别从静态和动态视角对效率进行分析，借助Moran’I揭示其时空演化特征。结果表明：(1)中国物流业碳排放的区域差异性明显，表现出组内趋同且组间趋异的趋同特征。(2)调整后的效率提升明显，东部、北部和南部沿海地区效率优势明显，东北、西南地区效率处于劣势。基于外部环境因素影响的分析发现，加强政府支持和发挥区位优势是减少资源冗余、促进效率提升的优势因素。(3)全要素生产率呈“M”形不稳定波动趋势，技术进步是影响全要素生产率的主因；南部沿海、黄河中游、北部沿海、东部沿海、西南和西北地区的全要素生产率处于增长态势，东北和长江中游地区的全要素生产率则呈下降趋势。(4)时序上，中国物流业效率具有显著的空间正相关关系，且经历了离散-集聚-离散的“倒N”形演变过程；空间上，基本形成了效率高水平的东部、北部沿海地区趋同和效率低水平的西南、东北地区趋同的空间集聚格局。

Analysis of Regional Differences and Efficiency of Carbon Emissions in Logistics Industry

Based on Theil index, the regional differences in carbon emissions of China’s logistics industry from 2015 to 2020 are explored. The three-stage SBM model and GML index model are constructed to analyze the efficiency from the static and dynamic perspectives respectively. Meanwhile, Moran ’I is used to reveal its temporal and spatial evolution characteristics. The results show that: there is a significant regional difference in carbon emissions from China’s logistics industry, showing a convergence characteristic of intra group convergence and inter group divergence. After adjustment, the efficiency is obviously improved, the eastern, northern and southern coastal areas have obvious efficiency advantages, while the northeast and southwest areas are in a disadvantageous position. Based on the analysis of external environmental factors, it is found that strengthening government support and leveraging location advantages are advantageous factors to reduce resource redundancy and promote efficiency. The total factor productivity shows an “M” type unstable fluctuation trend, and technological progress is the main factor affecting the total factor productivity. The total factor productivity of the southern coastal area, the middle reaches of the Yellow River, the north coastal area, the eastern coastal area, the southwest and the northwest areas is in a growing state, while the northeast area and the middle reaches of the Yangtze River are in a declining state. In terms of time series, the efficiency of China’s logistics industry has a significant spatial positive correlation, and has experienced an “inverse N” type evolution process of discreteness-agglomeration-discreteness. Spatially, it has basically formed a spatial agglomeration pattern of convergence in the eastern and northern coastal areas with high efficiency and convergence in the southwest and northeast areas with low efficiency.