"双碳"背景下长三角农产品低碳物流效率及影响因素
The Efficiency and Influencing Factors of Low-Carbon Logistics of Agricultural Products in the Yangtze River Delta Under the Background of"Double Carbon"
翟胜韬 1王建民1
作者信息
- 1. 安徽理工大学 经济与管理学院,安徽 淮南 232000
- 折叠
摘要
针对长三角农产品低碳物流效率问题,选取2013-2022 年长三角三省一市的农产品物流数据,利用非期望产出Super-SBM模型及GML指数,分别从静态与动态两个层面对长三角地区农产品低碳物流效率进行测算.结果显示:长三角农产品低碳物流效率平均水平仅 2022 年达到有效状态,各区域也具有较大的差异、且发展不平衡,效率从高到低依次为上海、安徽、浙江、江苏;GML指数均值呈递增趋势,技术进步是其增长的关键因素.通过Tobit回归模型分析该区域农产品低碳物流效率的影响因素,结果表明:物流效率与物流资源利用率和载货汽车数量呈显著正相关关系,与信息化水平呈显著负相关关系;政府支持未能通过显著性检验.
Abstract
In view of the low-carbon logistics efficiency of agricultural products in the Yangtze River Delta,the logistics data of agricultural products in three provinces and one city in the Yangtze River Delta from 2013 to 2022 were selected,and the low-carbon logistics efficiency of agricultural products in the Yangtze River Delta region was measured from the static and dy-namic levels by using the undesirable output Super-SBM model and GML index,respectively,and the results showed that:the average level of low-carbon logistics efficiency of agricultural products in the Yangtze River Delta only reached an effective state in 2022,and there were large differences and unbalanced development in each region,the efficiency values from high to low Shanghai,Anhui,Zhejiang,and Jiangsu;the average value of the GML index showed an increasing trend,and techno-logical progress is the key factor for its growth.The Tobit regression model was used to analyze the influencing factors of low-carbon logistics efficiency of agricultural products in the region,and the results showed that the logistics efficiency was signifi-cantly positively correlated with the utilization rate of logistics resources and the number of trucks,and negatively correlated with the level of informatization.Government support failed the significance test.
关键词
双碳/长三角/农产品低碳物流/非期望产出Super-SBM模型/GML指数/Tobit回归模型Key words
double carbon/Yangtze River Delta/low-carbon logistics of agricultural products/Super-SBM model of undes-ired output/GML index/Tobit regression model引用本文复制引用
基金项目
安徽省高校优秀科研创新团队项目(2023AH010026)
出版年
2024
NETL
NSTL
万方数据