학술논문
On the Efficiency of International Logistics in the Border Cities of Northeast China: A Comparative Study with a DEA Model and the Malmquist Index
이용수 28
- 영문명
- 발행기관
- 한국무역연구원
- 저자명
- Xiu-jing Jiang Jae-Bong Kim Yong-Sik Oh
- 간행물 정보
- 『무역연구』제16권 제5호, 245~256쪽, 전체 12쪽
- 주제분류
- 경제경영 > 무역학
- 파일형태
- 발행일자
- 2020.10.30
국문 초록
영문 초록
Purpose - With the acceleration of the process of globalization, the border area of China plays an increasingly important role, especially in northeast region. The Purpose of this research is to analyze the efficiency of international logistics of six border cities in northeast China to promote the sustainable development of the logistics industry of border cities.
Design/Methodology/Approach - This study focuses on cities bordering on neighboring countries. Taking the border cities with the most frequent trade transactions between northeast China and neighboring countries as an example, this paper compares the efficiency of six major border cities of northeast China, and find differences, competitive advantages, and improvement points. This research use DEA model and the Malmquist Production Index method to study the efficiency of international logistics and analyzes the efficiency evaluations and change trends of the logistics industry in six major border cities in northeast China.
Findings - The result proves and emphasizes on the need to increase the productivity and improve the scale efficiency, especially in Yanbian, Heihe and Jiamusi. The result suggest that improve the scale efficiency by establishing a railway network to achieve the optimization of production scale, to realize the scale economy.
Research Implications - There are few studies on the border cities which are the core areas of the economy in border district of northeast China. So that in this study, the research focus on border cities rather than the central area which was dealt a lot in previous studies, to seek for the opportunity of new development in this area.
목차
Ⅰ. Introduction
Ⅱ. Prior Study
Ⅲ. The DEA and Malmquist Models
Ⅳ. Data Collection
Ⅴ. Empirical Analysis
Ⅵ. Conclusion
References
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