PCA-DEA-tobit regression assessment with carbon emission constraints of China’s logistics industry

Abstract

China’s logistics industry has developed rapidly recently, but it also faces problems such as high costs, low efficiency and excessive carbon emissions, which has caused a heavy burden on the environment. However, there are few studies on the consideration of carbon emission factors in logistics performance evaluation. To this end, this study developed a comprehensive evaluation index system to assess the performance of China’s logistics. Principal Component Analysis (PCA) was applied to reduce the indicator dimensions and then a Slacks-Based Measure-Data Envelopment Analysis (SBM-DEA) was employed to measure and evaluate the logistics performance with and without carbon emissions constraints of 30 provinces/municipalities in China and analyze the overall level and spatial characteristics of China’s logistics industry efficiency. Regression analyses using the Tobit model were then conducted to identify the driving factors. The results show that: (1) There are large regional differences in China’s logistics efficiency, showing a gradual decline from east to west regions; (2) Low scale efficiency is an important factor restricting the logistics development; (3) In terms of influencing factors, regional economic and logistics development are positively related to the logistics efficiency, and energy structure and government influence are negatively related to the logistics efficiency.