Temporal-spatial evolution analysis of carbon emission efficiency in the logistics industry of coastal provinces in China based on the super-efficiency SBM model

This study conducted an in-depth analysis of the temporal-spatial evolution characteristics of LCEE in China's coastal provinces. There are significant differences in LCEE among these provinces. Each province needs to reduce the carbon dioxide emissions of the logistics industry and improve the LCEE through regional cooperation, technological investment, and targeted policies, so as to promote the sustainable development of the logistics industry in China's coastal provinces.

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Publication Details

Journal
Carbon Balance and Management
Published
2025-05-14
DOI
https://doi.org/10.1186/s13021-025-00299-z
Citations
4
Primary Topic
Efficiency Analysis Using DEA
Type
article
Field-Weighted Citation Impact
8.43

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article

Temporal-spatial evolution analysis of carbon emission efficiency in the logistics industry of coastal provinces in China based on the super-efficiency SBM model

Beilei Wang, Meiling Liu, Shan Gao
4 citations
Carbon Balance and Management
Efficiency Analysis Using DEA
8.43
article

Temporal-spatial evolution analysis of carbon emission efficiency in the logistics industry of coastal provinces in China based on the super-efficiency SBM model

Beilei Wang, Meiling Liu, Shan Gao
article en
4 citations

Abstract

This study conducted an in-depth analysis of the temporal-spatial evolution characteristics of LCEE in China's coastal provinces. There are significant differences in LCEE among these provinces. Each province needs to reduce the carbon dioxide emissions of the logistics industry and improve the LCEE through regional cooperation, technological investment, and targeted policies, so as to promote the sustainable development of the logistics industry in China's coastal provinces.

Carbon Balance and ManagementVol. 20(1)
Northeast Forestry University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 3%
Efficiency Analysis Using DEA
8.43
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