Liu Jia-Bao, Liu Bei-Ran, Lee Chien-Chiang
School of Mathematics and Physics, Anhui Jianzhu University, Hefei 230601, China.
School of Economics and Management, Nanchang University, Nanchang, China; Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon; Research Center of the Central China for Economic and Social Development, Nanchang University, Nanchang, China.
Sci Total Environ. 2024 Apr 20;922:171031. doi: 10.1016/j.scitotenv.2024.171031. Epub 2024 Feb 23.
China's transportation sector is a vital link between production and consumption, but it also has issues with low efficiency, high carbon emissions, and technological bottlenecks. To improve efficiency and provide actionable recommendations and strategies, this study first constructs a comprehensive evaluation index system to gauge the transportation sector's inputs using panel data from different Chinese provinces from 2007 to 2021. Within the assessment system, the principal component analysis (PCA) method is used to reduce the dimension of the indexes, thereby yielding a set of adjusted inputs. Subsequently, the transportation system efficiency (TSE) is evaluated using the super-efficiency SBM-DEA model, which includes unexpected outputs such as carbon emissions, and three-stage DEA modifies the efficiency. Then, we calculate the Malmquist-Luenberger index (TML) and its components: technological change (TTC) and technological efficiency change (TEC). Lastly, the influential factors impacting TSE are analyzed via a truncated regression Tobit model. The following are the conclusions: (1) The transportation industry in China exhibits inefficiency, and the average TSE in Stage I and III is 0.91 and 0.93, respectively. TSE is underestimated due to the influence of external environmental factors and inefficiencies in management in Stage I. (2) TSE in the eastern area also produces significant carbon emissions that surpass the national average. At the same time, other regions face efficiency limitations due to geographical constraints and management obstacles. (3) Insufficient technical capacity is a major cause of inefficiency in the transport sector and is prevalent in the northeast, west, and central regions. (4) Population growth and income per capita advancements foster transportation industry development, while increased GDP, fiscal revenues, and traffic accidents contribute to declining efficiency. The study above findings serve as a foundation for regional and national management initiatives and policies to enhance transportation effectiveness.
中国交通运输业是生产与消费之间的重要纽带，但也存在效率低下、碳排放高和技术瓶颈等问题。为提高效率并提供可行的建议和策略，本研究首先构建了一个综合评价指标体系，利用2007年至2021年中国不同省份的面板数据来衡量交通运输业的投入。在评估体系中，采用主成分分析（PCA）方法对指标进行降维，从而得出一组调整后的投入。随后，使用包含碳排放等非期望产出的超效率SBM-DEA模型评估运输系统效率（TSE），并通过三阶段DEA对效率进行修正。然后，计算Malmquist-Luenberger指数（TML）及其组成部分：技术变化（TTC）和技术效率变化（TEC）。最后，通过截断回归Tobit模型分析影响TSE的影响因素。研究得出以下结论：（1）中国交通运输业效率低下，第一阶段和第三阶段的平均TSE分别为0.91和0.93。由于外部环境因素的影响和第一阶段管理效率低下，TSE被低估。（2）东部地区的TSE也产生了显著超过全国平均水平的碳排放。与此同时，其他地区由于地理限制和管理障碍面临效率限制。（3）技术能力不足是运输部门效率低下的主要原因，在东北、西部和中部地区普遍存在。（4）人口增长和人均收入的提高促进了交通运输业的发展，而GDP、财政收入的增加和交通事故则导致效率下降。上述研究结果为加强交通运输有效性的区域和国家管理举措及政策奠定了基础。
