物流企业的碳排放效率评价及驱动因素分析

doi:10.16097/j.cnki.1009-6744.2023.02.002

交通运输系统工程与信息 ›› 2023, Vol. 23 ›› Issue (2): 11-21.DOI: 10.16097/j.cnki.1009-6744.2023.02.002

• 综合交通运输体系论坛 • 上一篇下一篇

物流企业的碳排放效率评价及驱动因素分析

郑琰^*，贲宇姝，王康得，姜晓红

南京林业大学，汽车与交通工程学院，南京 210037

收稿日期:2022-11-17 修回日期:2022-12-29 接受日期:2023-01-10 出版日期:2023-04-25 发布日期:2023-04-18
作者简介:郑琰(1983- )，女，河北唐山人，副教授，博士
基金资助:
国家自然科学基金 (71701099，72271116)

Carbon Emission Efficiency Evaluation and Driving Factors Analysis of Logistics Enterprises

ZHENG Yan^*, BEN Yu-shu, WANG Kang-de, JIANG Xiao-hong

College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China

Received:2022-11-17 Revised:2022-12-29 Accepted:2023-01-10 Online:2023-04-25 Published:2023-04-18
Supported by:
National Natural Science Foundation of China (71701099，72271116)

摘要/Abstract

摘要： 近年来，物流业在促进经济增长的同时，能源消耗量也在逐年递增，物流企业作为主体在运输过程中所造成的环境污染问题不容忽视。因此，为促进物流企业节能低碳化发展，采用“自上而下”法测算陆运和空运两种移动碳源的排放量。本文选取能源消耗、资本存量及劳动力作为投入变量，营业收入和碳排放量作为产出变量，建立 Super-SBM (Super-efficiency Slack-based Measurement)模型分析碳排放效率。此外，采用对数平均迪氏指数法(Logarithmic Mean Divisia Index, LMDI)将碳排放的驱动因素分解为4种，并逐个分析其对物流企业碳排放量的影响。本文以顺丰速运企业为例，根据该企业2016—2021年间的营运数据，评价其碳排放效率。结果表明：综合技术效率值持续增长，并在2019年后稳定在1以上；纯技术效率值在1.1上下范围波动；规模效率值均处于1以下，但持续增长。说明顺丰能够在原有规模上进行技术创新，争取以最少的投入获得最高的产出，使资源得到合理配置。另外，经济发展水平和人口规模是促进碳排放的因素，前者作用最为显著，而能源效率则在很大程度上抑制碳排放，能源消费结构对碳排放量增加的拉动作用比较有限。实验结果验证了本文所提出测度方法的有效性，利用Super-SBM和LMDI 模型能够对物流企业的碳排放效率进行有效评价及驱动因素分析。

关键词: 物流工程, 碳排放效率, Super-SBM模型, 物流企业, 驱动因素

Abstract: In recent years, while the logistics industry is promoting economic growth, its energy consumption is also increasing year by year. The environmental pollution caused by logistics enterprises in the transportation process cannot be ignored. To facilitate the energy-saving and low-carbon development of logistics enterprises, the "top-down" method is used to calculate the mobile carbon emissions of land transportation and air transportation modes. This paper selects energy consumption, capital stock and labor as input variables, and business income and carbon emissions as output variables to develop a Super-efficiency Slack-based Measurement (Super-SBM) model, which is used to analyze the efficiency of carbon emission. In addition, the Logarithmic Mean Divisia Index (LMDI) is used to decompose the driving factors of carbon emissions into four categories. Their impact on carbon emissions of logistics enterprises is analyzed. This paper takes Shunfeng Express Enterprise as an example, and evaluates its efficiency of carbon emission according to its operating data from 2016 to 2021. The results show that the comprehensive technical efficiency value continues to grow and remain above 1.0 after 2019. The pure technical efficiency value fluctuates in the range of 1.1. The scale efficiency values are all below 1.0 but continue to grow. The results show that Shunfeng can carry out technological innovation on the original scale, and strive to obtain the highest output with the least input. Thus their resources can be reasonably allocated. Moreover, the level of economic development and population size both are the influencing factors that related to carbon emissions. The energy efficiency largely inhibits the increase of carbon emissions. The influence of energy consumption structure in promoting carbon emissions is relatively limited. The experimental results verified the effectiveness of the proposed measurement method. The Super- SBM and LMDI models can effectively evaluate the efficiency of carbon emission of logistics enterprises and analyze the influence of driving factors on carbon emission.

Key words: logistics engineering, carbon emission efficiency, Super-SBM model, logistics enterprises, driving factors

中图分类号:

郑琰, 贲宇姝, 王康得, 姜晓红. 物流企业的碳排放效率评价及驱动因素分析[J]. 交通运输系统工程与信息, 2023, 23(2): 11-21.

ZHENG Yan, BEN Yu-shu, WANG Kang-de, JIANG Xiao-hong. Carbon Emission Efficiency Evaluation and Driving Factors Analysis of Logistics Enterprises[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(2): 11-21.

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http://www.tseit.org.cn/CN/Y2023/V23/I2/11

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物流企业的碳排放效率评价及驱动因素分析

Carbon Emission Efficiency Evaluation and Driving Factors Analysis of Logistics Enterprises

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