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Evaluating and Analyzing the Efficiency and Influencing Factors of Cold Chain Logistics in China’s Major Urban Agglomerations under Carbon Constraints

Author

Listed:
  • Meiling He

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Mei Yang

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Xiaohui Wu

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Jun Pu

    (School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Kazuhiro Izui

    (Department of Micro Engineering, Kyoto University, Kyoto 615-8540, Japan)

Abstract

With environmental degradation and energy shortages, green and low-carbon development has become an industry trend, especially in regards to cold chain logistics (CCL), where energy consumption and emissions are substantial. In this context, determining how to scientifically evaluate the cold chain logistics efficiency (CCLE) under carbon emission constraints is of great significance for achieving sustainable development. This study uses the three-stage data envelopment analysis (DEA) and the Malmquist index model to analyze the overall level and regional differences regarding CCLE in China’s four major urban agglomerations, under carbon constraints, from 2010 to 2020. Then, the influencing factors of CCLE are identified through Tobit regression. The results reveal that: (1) the CCLE in the four urban agglomerations is overestimated when carbon constraints are not considered; (2) the CCLE in the four urban agglomerations shows an upward trend from 2010 to 2020, with an average annual growth rate of 1.25% in regards to total factor productivity. However, there are significant spatial and temporal variations, with low-scale efficiency being the primary constraint. (3) Different influencing factors have different directions and exert different effects on CCLE in different urban agglomerations, and the improvement of economic development levels positively affects all regions.

Suggested Citation

  • Meiling He & Mei Yang & Xiaohui Wu & Jun Pu & Kazuhiro Izui, 2024. "Evaluating and Analyzing the Efficiency and Influencing Factors of Cold Chain Logistics in China’s Major Urban Agglomerations under Carbon Constraints," Sustainability, MDPI, vol. 16(5), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:5:p:1997-:d:1348021
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    References listed on IDEAS

    as
    1. Yi Zhang & Guowei Hua & T. C. E. Cheng & Juliang Zhang, 2020. "Cold chain distribution: How to deal with node and arc time windows?," Annals of Operations Research, Springer, vol. 291(1), pages 1127-1151, August.
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    5. Xiaohong Jiang & Jianxiao Ma & Huizhe Zhu & Xiucheng Guo & Zhaoguo Huang, 2020. "Evaluating the Carbon Emissions Efficiency of the Logistics Industry Based on a Super-SBM Model and the Malmquist Index from a Strong Transportation Strategy Perspective in China," IJERPH, MDPI, vol. 17(22), pages 1-19, November.
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