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数据要素是我国具有重要战略地位的基本生产要素，数据要素的价值需要依托有效的配置来实现，数据要素优化配置能够最大程度的降低数据交易费用、释放数据要素潜力。随着人们交易需求的不断增加，物流产业的运营方式和服务种类日益多样化，数据要素在现代物流运行过程中被广泛使用。文章从理论上探讨了数据要素配置对物流效率的影响机制，人力资本和技术创新赋能数据要素提升物流效率的机制与路径，以及不同效率和不同区域数据要素配置对于物流效率的影响差异。
首先，本文以我国30个省（除西藏及港澳台地区）2013-2021年的面板数据为研究对象，从数据研发管理、数据传播共享、数据应用环境和数字化推广度四个方面建立数据要素配置评价指标体系，运用熵值法测得数据要素配置水平。从投入、产出和非期望产出三个角度选取物流效率测度指标，运用DEA-CCR模型测得物流效率水平。采用固定面板回归模型分析和检验数据要素配置水平对物流效率的直接影响结果。采用主成分分析法测算数据要素配置水平、调整样本期、滞后一期解释变量等三种方法再次回归，证明了回归结果的稳健性。其次，将人力资本和技术创新作为中介变量，回归分析了数据要素配置如何通过人力资本和技术创新影响物流效率。最后，从效率和地区两个角度划分样本，分析了不同效率和不同地区数据要素配置对物流效率的影响效果。
本文研究发现：第一，我国数据要素配置水平整体提升较快，但地区差异显著，缩小数据要素配置地域差异刻不容缓。第二，物流效率水平整体呈上升趋势，大部分地区效率水平较高，市场潜力巨大。第三，数据要素合理配置可以显著提升物流效率。第四，数据要素合理配置能够提升人力资本价值和技术创新并产生中介效应，共同促进物流效率提升。第五，数据要素配置对不同物流效率和不同地区的影响程度具有差异性，数据要素配置对于物流效率较低的地区和西部地区的提升幅度大。最后针对研究结果，提出了落实大数据产业发展政策、完善物流基础设施、创新产业发展模式、培养综合创新型人才以及推进区域协调发展等五个方面的对策建议。

Data elements are fundamental production factors with important strategic positions in China, and the value of data elements needs to be realized through effective allocation. Optimizing the allocation of data elements can minimize data transaction costs and unleash the potential of data elements. With the continuous improvement of people's transaction needs, the operation methods and types of the logistics industry are becoming increasingly diversified, and data elements are widely used in the operation process of the logistics industry.The article theoretically explores the effect of data elements empowering the logistics industry, the mechanism and path of human capital promoting the efficiency improvement of the logistics industry, and the impact of different efficiency and regional data elements on the efficiency of the logistics industry.
Firstly,this article takes panel data from 30 provinces in China from 2013 to 2021 as the research object, and establishes an evaluation index system for data element configuration from four aspects: digital R&D management, data application environment, digital dissemination and sharing, and digital promotion degree. The entropy method is used to measure the level of data element configuration. Establish a logistics efficiency index system from the perspectives of input, output, and unexpected output, and use the DEA-CCR model to measure the efficiency level of the logistics industry.Using a fixed panel regression model to analyze and test the direct impact of data element allocation level on logistics industry efficiency. Using principal component analysis to measure the level of data element allocation, adjust sample period, and lagged explanatory variables by one period, the robustness of the regression results was further demonstrated through regression analysis. Secondly, human capital and technological innovation are taken as mediating variables, and the regression analysis analyzes how the allocation of data elements affects logistics efficiency through human capital and technological innovation. Finally, the samples are divided from the perspectives of efficiency and region, and the impact of data element allocation on logistics efficiency in different efficiencies and regions is analyzed.
The results of this paper show that: first, the overall level of data element allocation in China has improved rapidly, but the regional differences are significant, and it is urgent to reduce the regional differences in data element allocation. Second, the efficiency level of the logistics industry is on the rise, with a high level of efficiency in most regions and huge market potential. Third, the rational allocation of data elements can significantly improve logistics efficiency. Fourth, the rational allocation of data elements can enhance the value of human capital and technological innovation, and create synergies, and jointly promote the improvement of logistics efficiency.Fifth, the impact of data element allocation on different logistics efficiency and different regions is different, and the impact of data element allocation on regions with low logistics efficiency and western region is large. Finally, according to the research results, five countermeasures and suggestions are put forward, including the implementation of big data industry development policy, the improvement of logistics infrastructure, the innovation of industrial development model, the cultivation of comprehensive innovative talents, and the promotion of regional coordinated development.