基于三阶段DEA-Malmquist模型的黄河流域低碳物流效率

曹德浩，张静怡，刘华琼^*

山东交通学院交通与物流工程学院，山东 济南  250357

摘要：为探究黄河流域低碳物流效率的时空演化特征，基于黄河流域9个省区2013—2023年的面板数据，采用三阶段数据包络分析(data envelopment analysis，DEA)模型测算黄河流域低碳物流效率；通过构建包含环境变量的随机前沿分析（stochastic frontier analysis，SFA）模型，剥离环境因素及随机误差影响，结合Malmquist指数模型，从静态与动态维度分析低碳物流效率。结果表明：1）地区人均生产总值、物流业财政支出占比、节能环保财政支出占比等外部环境变量对低碳物流效率影响显著，剥离环境干扰后，综合技术效率均值由0.899降至0.838，纯技术效率均值由0.977增至0.986，规模效率均值由0.922降至0.850，9个省区效率波动更趋真实，更能准确反映低碳物流效率水平。2）9个省区低碳物流效率呈明显区域分异，内蒙古、河南、山东、山西剥离环境干扰前、后均处于效率前沿面，受外部环境影响较小；宁夏、陕西、甘肃剥离环境干扰后效率下降；四川、青海调整后效率提高。3）2013—2023年黄河流域物流业全要素生产率指数整体波动上升，综合技术效率指数的提高幅度大于技术进步指数，综合技术效率的改善主要源于规模效率的贡献，纯技术效率指数年均增长幅度较小。建议内蒙古、河南、山东等强化资源整合与技术创新，四川、甘肃、陕西、山西等分类优化投入结构，青海着力扩大有效规模并强化政策支持。

关键词：黄河流域；三阶段DEA模型；Malmquist指数；低碳物流效率

Low-carbon logistics efficiency in the Yellow River Basin based on a three-stage DEA-Malmquist model

CAO Dehao, ZHANG Jingyi, LIU Huaqiong^*

School of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan 250357, China

Abstract: To examine the spatiotemporal evolution of low carbon logistics efficiency in the Yellow River Basin, panel data for nine provinces and autonomous regions from 2013 to 2023 are employed. A three stage data envelopment analysis (DEA) model is used to measure low carbon logistics efficiency. A stochastic frontier analysis (SFA) model that incorporates environmental variables is constructed to remove the influence of external environmental factors and statistical noise, and the Malmquist index is applied to analyze efficiency from both static and dynamic perspectives. The results show that: 1) external environmental variables such as regional GDP per capita, the share of fiscal expenditure on the logistics industry, and the share of fiscal expenditure on energy conservation and environmental protection significantly affect low carbon logistics efficiency. After adjustment, the mean overall technical efficiency decreases from 0.899 to 0.838, the meanpure technical efficiency increases from 0.977 to 0.986, and the mean scale efficiency decreases from 0.922 to 0.850. With environmental interference removed, efficiency fluctuations across provinces become more realistic and better reflect true efficiency levels. 2) Low carbon logistics efficiency exhibits marked spatial differentiation. Inner Mongolia, Henan, Shandong, and Shanxi lie on the efficiency frontier both before and after adjustment, indicating limited susceptibility to external environmental factors; Ningxia, Shaanxi, and Gansu experience declines in efficiency after adjustment; Sichuan and Qinghai show increases after adjustment. 3) From 2013 to 2023, the total factor productivity index of the logistics sector in the Yellow River Basin shows an overall upward fluctuation. The increase in overall technical efficiency exceeds that of technological progress, with improvements in overall technical efficiency primarily driven by scale efficiency; the average annual growth of the pure technical efficiency index is relatively small. Based on these findings, Inner Mongolia, Henan, and Shandong should strengthen resource integration and technological innovation; Sichuan, Gansu, Shaanxi, and Shanxi should optimize input structures by category; Qinghai should focus on expanding effective scale and enhancing policy support.

Keywords: Yellow River Basin; three stage DEA; Malmquist index; low carbon logistics efficiency