Research on the Efficiency Evaluation of Green Logistics in East China—Based on SBM Super-Efficiency and ML Index
Year: 2025
Type: article
Abstract: 基于华东六省一市2010~2022年的面板数据，本研究测度了考虑能源消耗与碳排放约束下的区域绿色物流效率。超效率SBM模型用于评估静态效率水平及其时空分异特征，ML指数则用于分解全要素生产率变动，探究效率动态演进的内在动力。结果表明：静态效率呈现显著梯度分化，上海长期处于领先地位，江苏、安徽构成第二梯队，福建、浙江、山东、江西效率相对较低，且驱动模式各异。动态演进呈现三阶段特征：2010~2015年波动调整期、2016~2019年技术转型期、2020~2022年韧性重构期。技术进步(TC)是推动华东地区绿色物流效率增长的核心驱动力，普遍高于技术效率(EC)的贡献。疫情冲击下虽导致技术效率显著下滑，但技术进步逆势增长，成为支撑效率快速恢复的关键韧性因素。Based on the panel data of six provinces and one municipality in East China from 2010 to 2022, this study measures the regional green logistics efficiency under the constraints of energy consumption and carbon emissions. The super-efficiency SBM model is used to evaluate the static efficiency level and its spatio-temporal differentiation characteristics, while the ML index is employed to decompose the changes in total factor productivity and explore the internal driving forces of efficiency dynamic evolution. The results show that the static efficiency presents a significant gradient differentiation. Shanghai has been in an absolute leading position for a long time, Jiangsu and Anhui form the second tier, and Fujian, Zhejiang, Shandong, and Jiangxi have relatively lower efficiency, with different driving modes. The dynamic evolution shows three-stage characteristics: the fluctuation adjustment period from 2010 to 2015, the technology dividend period from 2016 to 2019, and the resilience reconstruction period from 2020 to 2022. Technological progress (TC) is the core driving force for the growth of green logistics efficiency in East China, generally contributing more than technical efficiency (EC). Although the technological efficiency significantly declined under the impact of the epidemic, technological progress increased against the trend, becoming a key resilient factor supporting the rapid recovery of efficiency.
Source: Sustainable Development
Author: 中瑞 赵
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4412884819