ABSTRACT

Applied a non-radical non-oriented Slack Based Measure (SBM) model of Data Envelopment Analysis (DEA) to measure the linkage efficiency between the two sectors from 2009 to 2016. The paper analyzes the current situation of manufacturing and logistics efficiency, presents a way to improve the linkage efficiency of manufacturing and logistics industry, and using Tobit regression to analyze the environmental factors that affect the linkage efficiency. The results show that: Totality, the two industries in the three northeast provinces show a steady development trend in the period from 2009 to 2016, but the development of the two industries in northeast China is still insufficient. Unexpected pollutant output is the main reason for the decline of manufacturing efficiency in the regions. The redundancy of input factors is the main reason that affects the efficiency decline of the logistics industry. The results of Tobit regression analysis show that the investment of science and technology and opening to the outside world have a positive influence on the efficiency of the two industries, and government consumption has a negative effect on the efficiency of the two industries. This is to correctly understand and grasp the status of two industry linkage development in three northeast provinces. And it provides a certain basis for the development policy of the two-industry linkage development.

Implications: Due to the availability of data, reference to the relevant achievements of the research on the linkage between manufacturing industry and logistics industry. The transportation industry, warehousing, and postal and telecommunications industry data are generally used as logistics industry data. Manufacturing data replaced by industrial data. The manufacturing industry is the core industry in the industrial system. Its output value accounts for more than 90% of industrial output value, so it can reflect the development trend of the manufacturing industry in general.

摘要

应用数据包络分析 (DEA) 的非激进非定向 Slack Based Measure (SBM) 模型来衡量 2009 年至 2016 年两个行业之间的联动效率。本文分析了制造业和物流效率的现状，提出了一个提高制造业与物流业联动效率的方法，并利用Tobit回归分析影响联动效率的环境因素。结果表明：总体来看，2009-2016年东北三省两大产业呈现平稳发展态势，但东北三省两大产业发展仍存在不足。非预期的污染物排放是各地区制造效率​​下降的主要原因。投入要素的冗余是影响物流业效率下降的主要原因。Tobit回归分析结果表明，科技投资和对外开放对两个行业的效率有正向影响，政府消费对两个行业的效率有负向影响。这是为了正确认识和把握东北三省两产业联动发展的现状。为两产联动发展的发展政策提供了一定的依据。政府消费对两个行业的效率有负面影响。这是为了正确认识和把握东北三省两产业联动发展的现状。为两产联动发展的发展政策提供了一定的依据。政府消费对两个行业的效率有负面影响。这是为了正确认识和把握东北三省两产业联动发展的现状。为两产联动发展的发展政策提供了一定的依据。

启示：由于数据的可得性，可参考制造业与物流业联动关系研究的相关成果。交通运输业、仓储业、邮电业数据一般用作物流业数据。制造业数据被工业数据取代。制造业是产业体系中的核心产业。其产值占工业总产值的90%以上，能总体反映制造业的发展趋势。