Temporal-spatial evolution analysis of carbon emission efficiency in the logistics industry of coastal provinces in China based on the super-efficiency SBM model
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View abstract on PubMed
Summary
This summary is machine-generated.Improving China
Area Of Science
- Environmental Science
- Economics
- Logistics Management
Background
- China's logistics industry is vital for national economic development, particularly in coastal provinces.
- Rapid logistics growth in these areas leads to high carbon emissions, hindering dual carbon goals.
- Understanding spatial dynamics of logistics carbon emission efficiency (LCEE) is crucial for sustainable development.
Purpose Of The Study
- To analyze the temporal and spatial evolution characteristics of LCEE in China's coastal provinces.
- To investigate the influence of neighboring provinces' LCEE on local LCEE using a spatial Markov chain model.
- To identify key factors affecting LCEE and propose targeted strategies for improvement.
Main Methods
- Introduction of the spatial lag factor into the Markov chain (MC) to form a spatial Markov chain (SMC).
- Analysis of LCEE data for China's eleven coastal provinces from 2007 to 2022.
- Examination of temporal-spatial evolution patterns and spatial clustering features of LCEE.
Main Results
- LCEE values were generally below one, indicating untapped emission reduction potential and challenges in low-carbon development.
- Low technical efficiency was the primary barrier to improving LCEE.
- Significant spatial clustering (high-high) was observed, suggesting regional coordinated development, with evident spatial spillover effects.
Conclusions
- Significant LCEE disparities exist among China's coastal provinces.
- Regional cooperation, technological investment, and targeted policies are essential for improving LCEE.
- Enhancing LCEE is critical for the sustainable development of the logistics industry and achieving China's carbon reduction targets.
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