| Title |
Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods |
| ID_Doc |
19293 |
| Authors |
Yao, X; Cheng, YY; Zhou, L; Song, ML |
| Title |
Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods |
| Year |
2022 |
| Published |
Annals Of Operations Research, 308.0, 1-2 |
| DOI |
10.1007/s10479-020-03763-w |
| Abstract |
This paper aims to analyze the green efficiency performance of the logistics industry in China's 30 provinces from 2008 to 2017. We first evaluate the green efficiency of the logistics industry through the non-directional distance function method. Then, we use the functional clustering method funHDDC, which is one of the popular machine learning methods, to divide 30 provinces into 4 clusters and analyze the similarities and differences in green efficiency performance patterns among different groups. Further, we explore the driving factors of dynamic changes in green efficiency through the decomposition method. The main conclusions of this paper are as follows: (1) in general, the level of green efficiency is closely related to the geographical location. From the clustering results, we can find that most of the eastern regions belong to the cluster with higher green efficiency, while most of the western regions belong to the cluster with lower green efficiency. However, the green efficiency performance in several regions with high economic levels, such as Beijing and Shanghai, is not satisfactory. (2) Based on the analysis of decomposition results, the innovation effect of China's logistics industry is the most obvious, but the efficiency change still needs to be improved, and technical leadership should be strengthened. Based on these conclusions, we further propose some policy recommendations for the green development of the logistics industry in China. |
| Author Keywords |
Logistics industry; Green efficiency; Non-directional distance function; Functional clustering; Machine learning |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000563623500001 |
| WoS Category |
Operations Research & Management Science |
| Research Area |
Operations Research & Management Science |
| PDF |
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