西部陆海新通道综合运输效率及资源分配研究
Comprehensive transportation efficiency and resource allocation of the new international land-sea trade corridor
赵影 1周小琬 1朱江洪 1杨强2
作者信息
- 1. 重庆交通大学 交通运输学院,重庆 400074
- 2. 重庆交通大学 经济与管理学院,重庆 400074;重庆市人文社科重点研究基地 口岸物流与航运经济研究中心,重庆 400074
- 折叠
摘要
西部陆海新通道是连接中国西部和东南亚国家的贸易走廊,对该地区的交通、贸易和经济都有重要的影响.运用SBM-DDF模型和GML指数对西部陆海新通道沿线省份2017—2021年的综合运输效率进行测算,然后运用基于前沿变化的逆DEA模型对2025年发展规划目标进行合理的资源分配,最后结合综合运输效率和资源分配情况进行相关性分析.结果表明:2017—2021年,宁夏、广东、陕西、海南的综合运输效率较高,而云南、新疆、四川的效率较低;在2025年目标规划中,广东、海南几乎不需要增加投入就可以达到效率前沿,而对于其他省份则需要减小相应的投入,这在云南、广西、四川、重庆等地尤为明显;通过对综合运输效率和发展规划的研究可以看出,综合运输效率较高的省份,更容易达到效率前沿,但综合运输效率较低的省份则需要通过减小相应投入才能达到效率前沿.
Abstract
The new international land-sea trade corridor is a trade corridor connecting western China and Southeast Asian countries,bringing huge impacts on the transportation,trade,and economy of the region.In this paper,the SBM-DDF model and GML index are employed to calculate the comprehensive transportation efficiency of provinces along the western land sea new channel from 2017 to 2021.Then,the inverse DEA model based on frontier changes is adopted to allocate resources properly for the 2025 development planning goals.Finally,the correlation between comprehensive transportation efficiency and resource allocation is analyzed.Our results show:①from 2017 to 2021,the comprehensive transportation efficiency in Ningxia,Guangdong,Shaanxi,and Hainan is relatively high while that in Yunnan,Xinjiang,and Sichuan is relatively low;② In the 2025 target plan,Guangdong and Hainan do not have to increase investments to reach the efficiency frontier while other provinces need to cut investments,which is particularly evident in Yunnan,Guangxi,Sichuan and Chongqing;③ Provinces with higher comprehensive transportation efficiency are more likely to reach the efficiency frontier whereas provinces with lower comprehensive transportation efficiency need to reduce investments to achieve that.
关键词
西部陆海新通道/SBM方向距离函数/GML指数/逆DEA模型/前沿变化Key words
new international land-sea trade corridor/SBM direction distance function/GML index/inverse DEA model/frontal changes引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据